hysts-bot commited on
Commit
3f66277
·
verified ·
1 Parent(s): 6d354f5

Upload folder using huggingface_hub

Browse files
0.codes.pt CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:2e4fed7fe8e07e157259be656559662b8b8c10519c3dc29867f387f8171d7c2f
3
- size 4663196
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d2b3f985ff1c581ec5a07469ba241950a6eca3e80307d0759667096cc4e990a8
3
+ size 4665244
0.metadata.json CHANGED
@@ -1,6 +1,6 @@
1
  {
2
  "passage_offset": 0,
3
- "num_passages": 6805,
4
- "num_embeddings": 1165514,
5
  "embedding_offset": 0
6
  }
 
1
  {
2
  "passage_offset": 0,
3
+ "num_passages": 6808,
4
+ "num_embeddings": 1166029,
5
  "embedding_offset": 0
6
  }
0.residuals.pt CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:c536a55abadf54ccc5db520151bffa5df27a3acbb21c3688b1b02f010073749f
3
- size 74594096
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7865b9b2145537892ac43efca0a14b221e040b628468f4b9d20de9ddcfefa0b6
3
+ size 74627056
avg_residual.pt CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:80b7ae7abe6adf86703295325b8db006bcd4d5c9ae0f21f43e3eb2eedac641c1
3
  size 1205
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f0b28fbbe93aebb32bffa9b8e546de9f215a1a2520699892025a5bbd43ae4352
3
  size 1205
buckets.pt CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:82e0da8df0ffb80ab3b063b603b4e65b41fe912409ae736834241326ae239157
3
  size 1432
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:bf0bdbb9ea71bd0416bf329b1085d71ff933f1e9d0de53cc590081606ba10062
3
  size 1432
centroids.pt CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:81ac94749da951eee5c157b291a2cabba64441763e6ec9ddaffa9b2d5979a3b0
3
  size 4195494
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9479d1be973411be625c58259268cd9d78dff4e09aac167f87f2fe60b8b5573e
3
  size 4195494
collection.json CHANGED
@@ -6803,5 +6803,8 @@
6803
  "We propose a Generator-And-Sampler Transformer (GS-Former) to generate discriminative queries and eliminate uninformative representations from unevenly distributed geometric signals. Finally, GeoX benefits from visual instruction tuning, empowering it to take geometric images and questions as input and generate verifiable solutions. Experiments show that GeoX outperforms both generalists and geometric specialists on publicly recognized benchmarks, such as GeoQA, UniGeo, Geometry3K, and PGPS9k.",
6804
  "Synthesizing novel views from in-the-wild monocular videos is challenging due to scene dynamics and the lack of multi-view cues. To address this, we propose SplineGS, a COLMAP-free dynamic 3D Gaussian Splatting (3DGS) framework for high-quality reconstruction and fast rendering from monocular videos. At its core is a novel Motion-Adaptive Spline (MAS) method, which represents continuous dynamic 3D Gaussian trajectories using cubic Hermite splines with a small number of control points. For MAS, we introduce a Motion-Adaptive Control points Pruning (MACP) method to model the deformation of each dynamic 3D Gaussian across varying motions, progressively pruning control points while maintaining dynamic modeling integrity. Additionally, we present a joint optimization strategy for camera parameter estimation and 3D Gaussian attributes, leveraging photometric and geometric consistency. This eliminates the need for Structure-from-Motion preprocessing and enhances SplineGS's robustness in real-world conditions. Experiments show that SplineGS significantly outperforms state-of-the-art methods in novel view synthesis quality for dynamic scenes from monocular videos, achieving thousands times faster rendering speed.",
6805
  "Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, their performance heavily depends on the quality of their training data. In this work, we propose Reinforcement Learning Distilled Generalists (RLDG), a method that leverages reinforcement learning to generate high-quality training data for finetuning generalist policies. Through extensive real-world experiments on precise manipulation tasks like connector insertion and assembly, we demonstrate that generalist policies trained with RL-generated data consistently outperform those trained with human demonstrations, achieving up to 40% higher success rates while generalizing better to new tasks. We also provide a detailed analysis that reveals this performance gain stems from both optimized action distributions and improved state coverage. Our results suggest that combining task-specific RL with generalist policy distillation offers a promising approach for developing more capable and efficient robotic manipulation systems that maintain the flexibility of foundation models while achieving the performance of specialized controllers. Videos and code can be found on our project website https://generalist-distillation.github.io",
6806
- "Recent advancements in diffusion models revolutionize image generation but pose risks of misuse, such as replicating artworks or generating deepfakes. Existing image protection methods, though effective, struggle to balance protection efficacy, invisibility, and latency, thus limiting practical use. We introduce perturbation pre-training to reduce latency and propose a mixture-of-perturbations approach that dynamically adapts to input images to minimize performance degradation. Our novel training strategy computes protection loss across multiple VAE feature spaces, while adaptive targeted protection at inference enhances robustness and invisibility. Experiments show comparable protection performance with improved invisibility and drastically reduced inference time. The code and demo are available at https://webtoon.github.io/impasto"
 
 
 
6807
  ]
 
6803
  "We propose a Generator-And-Sampler Transformer (GS-Former) to generate discriminative queries and eliminate uninformative representations from unevenly distributed geometric signals. Finally, GeoX benefits from visual instruction tuning, empowering it to take geometric images and questions as input and generate verifiable solutions. Experiments show that GeoX outperforms both generalists and geometric specialists on publicly recognized benchmarks, such as GeoQA, UniGeo, Geometry3K, and PGPS9k.",
6804
  "Synthesizing novel views from in-the-wild monocular videos is challenging due to scene dynamics and the lack of multi-view cues. To address this, we propose SplineGS, a COLMAP-free dynamic 3D Gaussian Splatting (3DGS) framework for high-quality reconstruction and fast rendering from monocular videos. At its core is a novel Motion-Adaptive Spline (MAS) method, which represents continuous dynamic 3D Gaussian trajectories using cubic Hermite splines with a small number of control points. For MAS, we introduce a Motion-Adaptive Control points Pruning (MACP) method to model the deformation of each dynamic 3D Gaussian across varying motions, progressively pruning control points while maintaining dynamic modeling integrity. Additionally, we present a joint optimization strategy for camera parameter estimation and 3D Gaussian attributes, leveraging photometric and geometric consistency. This eliminates the need for Structure-from-Motion preprocessing and enhances SplineGS's robustness in real-world conditions. Experiments show that SplineGS significantly outperforms state-of-the-art methods in novel view synthesis quality for dynamic scenes from monocular videos, achieving thousands times faster rendering speed.",
6805
  "Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, their performance heavily depends on the quality of their training data. In this work, we propose Reinforcement Learning Distilled Generalists (RLDG), a method that leverages reinforcement learning to generate high-quality training data for finetuning generalist policies. Through extensive real-world experiments on precise manipulation tasks like connector insertion and assembly, we demonstrate that generalist policies trained with RL-generated data consistently outperform those trained with human demonstrations, achieving up to 40% higher success rates while generalizing better to new tasks. We also provide a detailed analysis that reveals this performance gain stems from both optimized action distributions and improved state coverage. Our results suggest that combining task-specific RL with generalist policy distillation offers a promising approach for developing more capable and efficient robotic manipulation systems that maintain the flexibility of foundation models while achieving the performance of specialized controllers. Videos and code can be found on our project website https://generalist-distillation.github.io",
6806
+ "Recent advancements in diffusion models revolutionize image generation but pose risks of misuse, such as replicating artworks or generating deepfakes. Existing image protection methods, though effective, struggle to balance protection efficacy, invisibility, and latency, thus limiting practical use. We introduce perturbation pre-training to reduce latency and propose a mixture-of-perturbations approach that dynamically adapts to input images to minimize performance degradation. Our novel training strategy computes protection loss across multiple VAE feature spaces, while adaptive targeted protection at inference enhances robustness and invisibility. Experiments show comparable protection performance with improved invisibility and drastically reduced inference time. The code and demo are available at https://webtoon.github.io/impasto",
6807
+ "The rapid advancement of Large Language Models (LLMs) has demonstrated remarkable progress in complex reasoning tasks. However, a significant discrepancy persists between benchmark performances and real-world applications. We identify this gap as primarily stemming from current evaluation protocols and metrics, which inadequately capture the full spectrum of LLM capabilities, particularly in complex reasoning tasks where both accuracy and consistency are crucial. This work makes two key contributions. First, we introduce G-Pass@k, a novel evaluation metric that provides a continuous assessment of model performance across multiple sampling attempts, quantifying both the model's peak performance potential and its stability. Second, we present LiveMathBench, a dynamic benchmark comprising challenging, contemporary mathematical problems designed to minimize data leakage risks during evaluation. Through extensive experiments using G-Pass@k on state-of-the-art LLMs with LiveMathBench, we provide comprehensive insights into both their maximum capabilities and operational consistency. Our findings reveal substantial room for improvement in LLMs' \"realistic\" reasoning capabilities, highlighting the need for more robust evaluation methods. The benchmark and detailed results are available at: https://github.com/open-compass/GPassK.",
6808
+ "The rapidly developing field of large multimodal models (LMMs) has led to the emergence of diverse models with remarkable capabilities. However, existing benchmarks fail to comprehensively, objectively and accurately evaluate whether LMMs align with the diverse needs of humans in real-world scenarios. To bridge this gap, we propose the Multi-Dimensional Insights (MDI) benchmark, which includes over 500 images covering six common scenarios of human life. Notably, the MDI-Benchmark offers two significant advantages over existing evaluations: (1) Each image is accompanied by two types of questions: simple questions to assess the model's understanding of the image, and complex questions to evaluate the model's ability to analyze and reason beyond basic content. (2) Recognizing that people of different age groups have varying needs and perspectives when faced with the same scenario, our benchmark stratifies questions into three age categories: young people, middle-aged people, and older people. This design allows for a detailed assessment of LMMs' capabilities in meeting the preferences and needs of different age groups.",
6809
+ "This design allows for a detailed assessment of LMMs' capabilities in meeting the preferences and needs of different age groups. With MDI-Benchmark, the strong model like GPT-4o achieve 79% accuracy on age-related tasks, indicating that existing LMMs still have considerable room for improvement in addressing real-world applications. Looking ahead, we anticipate that the MDI-Benchmark will open new pathways for aligning real-world personalization in LMMs. The MDI-Benchmark data and evaluation code are available at https://mdi-benchmark.github.io/"
6810
  ]
doclens.0.json CHANGED
@@ -1 +1 @@
1
- [206,104,226,67,200,185,221,212,206,222,88,210,228,174,155,205,172,218,148,132,212,91,163,184,205,132,213,190,212,198,230,227,159,198,122,216,175,197,118,217,219,224,69,220,197,72,204,92,169,191,191,155,175,111,218,77,207,36,195,178,123,170,231,91,209,177,146,205,151,221,217,95,199,89,153,216,154,202,167,213,104,184,176,226,200,232,53,223,73,202,220,158,174,189,165,222,158,221,211,78,205,72,214,212,215,232,175,219,110,205,192,226,34,176,209,158,222,132,179,208,98,126,205,126,172,167,227,100,224,182,208,117,217,199,195,191,169,178,158,217,152,157,163,163,204,209,146,217,150,194,217,23,125,200,221,200,212,41,176,223,207,95,178,232,68,182,173,205,198,210,139,152,147,215,196,223,83,199,123,197,119,230,223,65,216,85,210,52,210,204,179,111,138,215,83,177,219,69,212,77,182,226,99,178,207,197,87,190,222,216,85,208,211,66,220,226,221,134,175,190,170,166,157,216,135,211,132,200,67,227,195,178,221,179,205,168,186,208,127,207,139,205,67,209,223,117,195,190,202,64,218,77,199,221,189,125,216,80,148,214,143,181,98,221,194,112,219,212,138,216,87,127,187,219,122,220,97,223,221,164,205,220,100,227,101,171,188,223,89,213,137,175,172,219,77,102,234,109,197,114,205,184,221,138,175,152,163,133,227,73,224,181,174,147,144,178,147,224,211,121,123,211,219,209,210,204,80,182,215,152,118,224,216,154,220,163,204,187,133,151,162,221,94,184,224,213,72,187,148,223,195,110,182,117,224,88,202,162,220,154,151,197,227,197,74,221,205,97,221,219,103,206,184,72,191,201,192,230,164,217,170,214,118,221,218,118,215,181,224,78,183,154,190,206,102,202,74,195,96,225,228,221,99,224,104,123,203,184,211,90,209,173,199,203,99,201,176,160,207,132,231,96,195,213,173,101,190,206,61,225,232,215,178,211,227,192,148,195,213,114,212,127,162,214,213,129,220,70,176,224,209,137,232,116,228,212,75,182,207,71,111,173,189,207,96,172,213,119,222,93,195,204,211,76,179,200,184,216,107,135,202,71,118,219,123,202,210,146,225,76,227,62,214,226,98,158,202,219,169,83,156,223,125,211,213,102,219,223,72,224,178,190,204,110,228,64,214,200,193,205,213,160,201,216,134,195,128,186,213,219,187,199,216,84,202,150,211,127,230,206,204,180,183,183,167,213,224,72,209,79,195,205,153,228,133,203,210,78,190,207,195,133,225,192,197,190,201,187,222,109,138,227,103,223,209,164,190,73,226,200,205,191,189,100,226,135,198,129,185,207,229,148,209,216,58,222,117,219,92,221,147,222,219,63,216,228,165,181,157,213,197,218,158,191,186,223,204,109,172,229,91,231,204,102,192,217,204,144,222,173,209,209,95,186,195,60,212,219,209,162,183,227,216,111,193,182,222,71,183,222,122,178,219,130,228,82,225,223,69,198,232,63,193,207,216,139,223,74,164,198,224,80,148,227,138,227,199,225,86,148,230,169,210,92,166,187,202,165,196,78,202,74,229,173,216,215,213,164,205,102,206,91,212,173,198,109,183,94,206,115,224,126,229,222,57,225,191,221,218,215,44,177,217,214,218,191,221,80,198,109,196,95,192,181,214,214,123,208,191,225,219,218,108,204,150,199,206,90,227,71,207,123,199,106,131,213,93,178,208,60,132,217,149,204,80,227,218,202,94,232,175,213,115,217,114,222,200,173,184,217,222,178,226,205,192,185,197,180,193,205,211,136,221,62,184,213,89,225,71,169,216,135,198,98,214,204,88,191,154,207,213,227,93,230,133,216,189,122,224,108,229,237,218,209,214,66,153,203,219,204,222,218,107,174,214,148,169,188,210,98,203,142,167,174,224,103,223,220,148,216,199,188,175,173,130,156,97,220,111,216,115,216,70,221,210,77,173,162,216,164,216,69,203,175,172,166,199,156,184,131,201,210,98,220,133,211,116,221,82,213,88,213,128,227,219,177,190,216,180,237,217,64,215,134,221,172,220,221,70,169,221,189,226,74,229,197,209,128,190,213,106,221,198,162,184,182,219,86,201,143,229,205,146,228,106,154,209,60,216,222,74,221,215,141,168,202,82,194,213,80,217,214,204,164,199,220,149,114,140,215,150,218,108,109,211,170,206,125,183,148,193,229,210,124,188,143,160,153,199,178,75,225,105,199,83,222,112,205,75,222,205,70,104,194,91,158,220,99,215,209,219,65,124,215,158,223,234,208,101,218,200,66,188,222,149,201,189,211,51,179,208,198,160,182,152,195,141,216,112,205,207,170,128,219,118,188,223,224,80,177,218,127,196,151,159,117,186,113,216,212,134,204,90,200,231,184,222,68,146,151,158,202,173,144,206,171,215,68,188,222,89,215,117,223,80,191,209,204,105,228,67,174,210,74,208,105,151,159,183,142,163,202,214,41,147,222,227,188,208,200,157,132,195,94,191,185,214,132,201,155,166,195,220,147,219,156,214,165,220,105,223,217,100,176,225,117,207,134,166,202,218,172,200,157,183,217,87,214,181,229,217,76,231,199,139,154,213,208,208,169,218,82,219,191,139,207,222,49,122,154,176,219,110,185,112,137,231,80,208,136,189,192,208,190,192,187,180,220,186,225,218,208,102,190,124,152,83,219,199,95,198,133,235,214,96,213,217,148,168,200,32,145,207,222,198,171,208,218,104,178,230,77,214,129,218,136,139,212,233,93,221,153,221,95,155,151,211,225,60,81,217,201,108,208,234,172,177,172,193,208,72,216,224,211,44,205,82,221,224,199,198,221,137,208,81,183,105,182,106,222,180,184,146,180,205,187,192,163,219,213,164,229,218,190,147,221,193,213,182,126,228,231,97,173,218,116,161,156,207,122,193,198,155,207,103,223,192,92,153,223,170,206,177,225,40,224,106,203,208,116,203,105,197,152,221,211,84,226,111,215,154,224,221,65,215,142,169,212,184,135,207,85,209,213,76,225,229,130,203,217,186,229,140,177,116,216,219,62,209,105,164,150,207,214,211,114,148,230,118,201,134,197,124,209,163,184,164,186,189,219,106,221,220,95,141,217,87,200,224,114,220,168,212,112,229,192,106,189,183,209,212,85,187,202,219,222,223,223,212,226,79,193,128,222,209,206,197,178,152,114,222,111,164,183,189,179,214,130,149,150,212,122,160,195,118,115,204,144,163,159,140,211,208,120,222,220,125,195,174,220,80,173,204,87,149,212,82,221,119,213,139,186,227,97,215,213,136,215,68,229,218,63,224,182,221,218,94,213,90,168,202,85,168,222,121,215,213,93,221,83,220,112,197,213,213,92,210,76,216,79,179,220,94,194,99,186,181,197,214,194,138,221,91,218,172,159,219,74,153,221,201,108,213,131,217,112,214,107,209,35,224,188,219,108,191,105,157,211,126,206,148,220,126,217,164,203,129,201,211,119,204,165,209,75,205,130,204,215,206,119,178,211,218,137,193,167,199,220,182,115,207,184,189,217,178,186,159,208,230,140,194,75,173,205,64,213,167,189,110,220,195,136,137,196,134,208,120,198,182,189,176,162,183,172,172,214,200,95,212,189,193,184,195,116,167,199,110,180,217,126,193,226,142,204,66,140,222,226,218,144,218,214,140,206,66,196,224,143,121,166,221,101,154,204,216,201,221,152,225,69,226,226,180,200,181,212,128,193,206,136,219,141,208,128,202,209,105,168,178,204,211,110,145,163,193,211,69,220,147,194,223,165,212,218,124,197,229,226,132,220,156,211,80,203,140,192,94,208,102,180,215,134,152,218,106,191,204,102,211,108,182,204,206,208,90,165,197,105,186,115,213,208,186,165,186,174,213,211,164,210,62,129,201,217,170,169,217,193,167,222,174,217,167,128,182,113,159,208,85,172,219,89,219,126,218,190,65,227,208,52,225,125,160,205,135,168,186,213,212,69,124,205,67,204,110,151,180,220,205,193,106,223,80,124,166,218,194,138,148,205,88,179,183,163,227,221,198,213,87,190,200,239,81,202,211,213,115,204,105,216,83,170,201,210,95,219,219,180,216,222,228,86,231,83,227,116,223,84,207,216,115,204,109,194,171,198,183,197,86,216,144,209,125,226,221,57,215,117,199,185,152,230,174,220,61,209,99,163,218,218,134,183,212,85,208,92,190,214,81,146,220,181,224,214,212,79,171,209,217,209,100,213,72,220,223,193,72,130,203,111,214,192,197,222,201,229,204,189,162,124,221,157,218,34,204,88,170,205,76,168,210,46,196,223,73,180,85,118,200,85,221,68,160,202,201,219,181,148,207,210,208,69,210,101,215,76,217,104,188,180,224,71,199,166,203,106,216,196,182,84,183,125,128,224,89,206,88,212,212,152,205,92,210,65,166,171,200,120,228,213,83,163,131,153,180,215,228,99,179,210,82,218,95,209,94,215,206,196,110,236,219,209,193,95,172,177,218,218,140,203,228,102,160,215,102,204,192,89,168,222,56,223,155,189,213,208,220,78,204,213,168,202,169,230,191,218,203,73,222,127,208,143,221,217,217,100,160,212,208,156,223,197,205,201,223,220,144,209,90,209,139,182,218,22,221,183,219,79,216,194,224,86,208,225,190,102,200,212,162,211,83,221,106,215,113,161,197,111,216,210,92,219,124,208,185,152,203,220,230,106,182,193,185,221,79,197,79,231,192,202,113,166,211,142,195,170,179,226,222,208,228,152,133,200,89,197,180,155,215,97,201,139,205,145,217,163,213,85,223,209,200,139,217,123,160,183,201,136,210,206,105,208,132,206,208,101,134,174,112,205,181,221,215,221,213,225,116,217,80,162,186,120,183,122,214,201,97,188,209,174,229,205,120,194,203,130,228,134,228,83,201,119,218,210,55,225,64,171,174,227,107,226,95,139,190,154,171,206,139,154,219,157,224,213,206,212,79,135,186,156,217,186,128,118,184,187,213,221,86,183,217,218,210,216,155,195,188,134,172,223,125,176,167,85,187,214,217,94,213,128,217,133,192,217,60,189,122,217,117,224,95,199,74,206,89,221,205,158,196,77,228,114,188,105,210,70,209,83,188,169,214,225,184,205,127,197,115,182,174,181,238,222,223,59,229,219,112,205,115,190,128,196,210,222,176,215,194,187,112,212,90,183,207,86,212,85,178,187,138,217,80,203,96,221,198,82,207,96,164,227,208,76,214,179,209,186,207,205,221,224,143,224,178,228,189,229,132,213,182,222,218,67,229,186,201,169,211,88,210,48,219,152,225,207,62,200,210,220,68,216,44,137,155,181,221,83,214,103,222,183,90,215,86,179,205,128,219,179,164,202,130,224,148,221,146,175,184,213,117,190,201,142,216,82,231,230,92,172,218,207,189,223,150,226,46,216,209,221,216,111,168,219,92,197,228,204,204,101,223,65,211,220,203,122,206,108,224,74,181,142,216,189,190,180,212,107,223,120,191,232,56,209,214,66,209,222,116,172,222,101,179,177,185,168,193,163,194,209,193,105,170,211,212,105,191,117,90,226,90,190,121,216,60,207,67,161,171,220,219,89,184,221,144,182,194,208,214,214,226,196,133,188,202,204,68,194,216,214,77,208,92,215,63,204,178,197,77,115,190,152,227,181,207,102,199,131,208,216,177,116,202,220,121,223,128,196,175,211,219,207,74,218,215,221,93,177,214,183,192,143,139,196,217,226,83,102,119,211,100,212,172,183,180,204,219,226,96,194,47,149,186,143,160,221,224,61,226,147,214,60,198,207,164,146,200,140,182,207,196,208,180,186,215,111,181,221,214,161,216,169,208,136,168,178,112,189,196,124,234,119,202,67,201,74,163,172,148,179,211,143,124,191,202,192,175,218,80,209,72,190,156,217,117,223,122,180,193,182,186,210,219,201,202,224,64,138,192,114,199,208,190,109,218,149,208,97,213,189,87,232,131,152,188,205,219,196,225,94,197,164,210,173,191,222,185,172,189,205,163,208,73,196,201,73,224,206,110,191,122,221,136,165,221,185,156,210,69,210,211,210,102,213,86,170,209,221,197,207,50,154,74,96,212,180,231,158,208,139,150,166,127,213,216,202,95,201,155,217,54,206,214,208,179,140,191,136,228,191,91,232,45,145,220,162,220,74,209,147,155,160,182,217,209,160,174,180,227,219,221,182,100,212,67,213,148,208,51,188,209,82,211,210,71,218,82,176,88,174,117,186,210,160,157,212,135,165,201,162,170,136,176,220,189,167,219,49,212,190,220,73,152,150,176,204,221,124,220,209,117,175,213,228,173,195,159,194,218,57,201,214,164,175,229,79,176,116,206,137,203,152,173,206,78,227,209,154,175,190,89,225,113,222,219,214,50,219,66,202,90,188,214,80,195,74,212,60,130,206,186,157,204,122,198,73,194,230,108,196,205,213,83,192,104,207,117,216,171,126,222,99,229,221,79,214,79,217,144,217,197,206,207,110,160,206,172,197,183,207,217,207,113,210,221,71,161,221,164,227,214,142,177,185,180,103,130,198,123,205,74,216,102,219,160,217,75,204,114,192,213,166,188,118,222,227,92,195,219,161,200,221,69,203,143,198,198,217,198,66,212,50,208,116,199,125,210,207,167,225,116,207,97,184,99,220,184,203,184,219,177,167,202,214,55,207,161,197,122,212,226,187,96,216,201,188,135,224,207,139,225,230,220,121,221,107,212,66,170,169,210,199,102,220,94,159,184,207,92,207,231,214,125,227,220,205,58,193,203,215,223,229,78,196,170,185,196,162,234,56,201,123,171,231,196,86,162,199,213,220,68,200,68,205,88,225,135,220,82,182,215,222,79,152,230,62,162,218,184,224,67,206,99,189,124,214,197,73,204,105,221,179,102,218,232,80,214,181,170,204,165,216,207,217,212,195,176,215,106,192,160,221,182,217,57,211,88,198,233,113,171,204,138,193,209,225,59,176,184,134,223,151,193,200,217,100,225,79,180,142,190,123,222,80,232,216,133,216,148,211,110,198,96,187,224,95,208,112,178,227,94,171,96,181,209,170,225,196,206,94,216,87,217,171,191,82,218,127,227,176,219,207,230,79,214,203,105,213,143,174,188,125,193,220,60,215,172,214,101,211,110,161,117,187,180,125,218,220,62,208,203,217,87,198,156,216,226,161,161,223,224,72,178,198,213,195,219,208,140,175,217,74,201,201,66,186,154,229,89,226,169,204,87,184,85,161,133,201,80,176,188,114,224,77,207,126,202,83,219,200,125,172,169,190,216,80,88,221,68,218,133,216,117,217,157,217,170,190,124,214,210,156,231,84,207,204,113,200,70,222,162,208,227,92,223,136,167,195,221,221,77,173,213,109,214,117,211,217,89,217,91,210,152,194,206,202,110,216,177,190,207,227,185,172,230,172,207,171,199,234,207,149,194,192,179,212,209,210,101,198,225,85,164,211,110,194,182,211,224,65,228,218,79,224,81,122,208,154,129,206,92,193,171,148,188,221,80,220,161,165,166,161,214,99,210,64,174,224,221,105,200,122,230,216,94,223,128,225,161,219,126,187,137,191,222,214,148,151,198,218,210,110,208,228,184,211,35,202,218,195,216,115,212,95,177,199,101,184,208,202,212,134,193,129,192,81,182,223,70,226,230,134,167,183,198,222,227,227,226,63,213,109,187,177,219,223,203,144,179,209,103,177,181,158,221,90,222,166,207,175,230,207,99,205,234,210,210,168,223,143,210,187,209,204,150,209,213,208,193,221,214,77,215,199,81,197,82,177,190,210,231,79,179,221,64,182,199,82,204,204,95,172,187,178,209,86,222,220,118,192,223,88,220,77,174,104,224,137,182,186,96,207,198,74,152,196,217,206,79,214,208,204,180,94,215,81,177,160,201,164,173,205,76,199,220,228,91,215,155,226,79,133,181,136,182,226,96,221,109,209,223,71,202,95,217,87,202,204,183,210,187,212,81,226,184,224,88,170,214,198,226,142,212,81,209,189,172,192,221,216,123,221,126,204,218,222,76,205,73,225,221,73,204,108,201,88,174,197,136,223,90,189,56,207,147,206,212,73,201,83,204,112,137,227,67,208,137,219,225,65,200,186,99,214,97,215,74,203,65,199,216,108,216,80,206,219,104,226,180,225,199,186,197,226,157,102,177,107,231,156,141,226,70,220,216,223,64,214,66,201,174,170,207,46,202,131,173,218,125,217,157,234,192,159,174,209,95,196,224,59,220,69,211,130,203,222,88,208,86,198,127,219,228,75,218,170,168,198,128,215,54,211,167,186,117,211,162,221,219,105,223,99,223,127,202,218,213,143,194,181,200,180,230,224,97,181,132,173,202,221,57,151,220,77,220,160,206,188,101,197,72,213,95,193,212,189,105,226,100,205,201,56,211,93,178,212,88,208,83,213,165,219,183,236,121,220,210,94,212,171,186,218,137,212,129,175,203,223,134,194,95,193,191,105,229,208,102,196,120,191,221,217,65,206,200,74,168,180,199,217,119,223,68,211,125,204,105,180,164,215,227,128,211,166,218,86,185,74,214,57,200,171,111,185,73,199,220,213,192,216,107,211,115,219,227,192,221,101,203,65,211,51,216,84,193,121,214,86,195,115,179,229,90,215,92,207,63,179,212,38,202,104,182,125,179,99,147,184,210,166,227,232,164,120,218,169,203,154,192,224,217,122,160,205,206,221,80,191,217,166,202,78,206,147,202,155,195,76,204,136,191,112,195,160,147,226,91,224,216,212,177,188,165,174,130,203,221,220,133,209,147,216,69,159,155,143,213,94,227,139,209,163,183,199,112,217,213,98,217,96,185,158,173,229,51,209,195,227,214,161,213,83,168,229,209,118,221,224,59,179,161,220,209,193,199,199,212,107,226,219,204,117,166,223,122,166,181,163,176,223,176,130,223,221,202,89,188,147,160,143,218,223,206,151,201,161,130,176,175,138,126,209,112,230,94,211,17,103,218,73,218,131,210,104,214,63,222,38,135,140,215,143,215,191,185,223,207,215,203,46,219,207,93,177,85,213,191,223,56,181,209,82,210,221,66,210,195,223,184,138,217,48,194,73,150,199,220,183,209,60,194,103,218,103,211,216,124,197,217,185,106,185,207,174,165,204,138,220,68,218,151,202,68,214,155,183,221,66,216,61,218,122,214,178,202,178,217,142,215,126,187,148,219,98,180,222,217,80,210,203,43,208,154,220,101,167,206,211,212,208,72,147,225,139,174,207,36,200,234,205,211,180,205,202,126,159,186,116,211,154,192,155,194,168,198,160,218,220,202,153,222,215,66,174,128,211,104,136,171,235,219,112,156,209,109,203,132,192,181,215,112,205,68,215,82,213,117,189,221,186,211,171,208,136,189,128,210,96,199,107,195,232,74,223,132,193,198,46,220,73,181,112,224,133,221,144,224,83,232,217,131,186,53,214,225,95,203,70,102,217,106,224,79,210,113,177,150,228,220,102,225,80,221,170,206,105,223,112,210,46,201,89,197,207,128,235,111,212,161,144,221,182,200,77,213,229,90,134,223,179,212,204,125,197,215,80,233,218,44,226,53,152,184,220,113,219,216,110,214,206,151,215,224,216,163,144,190,133,223,195,216,203,67,95,169,191,131,208,78,104,176,179,148,207,172,220,98,202,118,218,204,120,213,92,213,93,210,203,219,75,212,227,212,188,187,201,100,206,151,200,96,197,215,157,210,70,207,182,205,205,101,212,117,230,86,163,143,167,189,215,168,216,194,98,218,128,219,94,149,188,217,48,172,174,131,131,182,171,200,115,220,217,91,200,80,178,188,226,49,192,205,222,127,194,134,175,214,115,212,214,82,193,106,217,48,197,114,204,114,201,221,190,174,214,168,223,86,217,212,214,181,204,96,190,189,216,91,204,118,226,110,198,224,158,195,117,189,200,150,231,206,91,209,207,118,223,183,232,146,158,207,213,122,204,118,200,103,200,227,76,179,195,73,215,93,214,170,215,232,41,210,107,138,202,204,125,198,134,225,80,117,164,185,197,106,232,74,139,216,207,209,57,207,94,200,229,190,192,140,112,208,155,191,177,216,203,142,192,103,195,219,91,179,228,187,115,213,217,192,193,215,141,218,70,186,37,225,190,84,178,177,162,218,210,185,176,195,97,218,63,218,176,227,215,149,224,221,115,208,214,133,182,188,205,163,207,58,199,217,129,208,184,214,206,133,228,200,80,224,179,229,152,208,95,194,170,224,178,196,181,94,199,90,203,75,216,153,169,213,86,225,67,192,194,65,189,201,103,188,153,163,213,226,142,185,133,226,106,181,215,199,209,211,56,204,114,181,163,171,228,228,73,74,198,203,186,178,185,125,229,221,204,41,165,189,126,205,173,116,179,198,159,216,129,209,222,174,183,229,95,212,68,177,152,217,138,156,135,105,204,193,188,127,209,150,133,163,208,80,218,232,211,77,230,37,198,224,165,213,81,220,207,195,173,174,212,102,206,117,196,178,222,134,205,216,203,86,151,184,157,217,222,123,213,159,195,121,207,159,220,120,212,176,144,217,67,173,216,105,206,90,220,121,217,59,184,219,156,213,149,143,216,221,140,225,182,115,209,115,209,45,223,172,227,133,187,165,199,168,224,91,206,115,153,202,197,62,169,210,134,215,167,203,214,141,200,213,182,90,214,170,206,199,219,54,167,154,72,194,122,181,197,129,214,105,153,209,137,202,227,72,207,61,178,127,181,210,200,46,188,210,214,67,189,216,51,209,125,190,127,208,110,191,219,137,213,76,206,120,186,121,201,222,113,195,194,68,183,179,184,223,61,180,220,197,133,208,226,136,217,200,93,178,220,113,197,198,172,141,225,102,159,149,213,196,100,220,196,176,232,182,187,171,165,182,101,175,169,191,224,110,200,128,200,129,114,179,188,165,198,216,184,174,216,67,229,198,220,32,232,219,72,219,203,127,88,212,81,142,223,210,166,97,145,209,77,216,227,196,83,202,137,214,82,223,114,205,177,183,196,214,129,196,122,223,157,232,99,180,188,203,132,229,223,186,115,209,191,218,50,192,184,220,102,207,87,196,162,219,92,221,140,217,139,169,213,79,211,99,205,104,200,86,210,90,157,151,227,228,53,205,72,195,75,226,89,226,74,218,145,228,224,208,171,215,153,140,208,182,161,228,107,209,220,217,207,125,181,195,212,220,95,202,95,191,233,74,201,184,221,81,231,181,120,227,119,139,121,179,199,203,216,154,210,144,195,129,153,213,103,209,219,212,125,216,229,219,108,223,65,212,92,221,197,162,211,147,210,197,178,221,162,192,172,215,84,194,52,204,70,175,187,187,194,186,235,185,177,170,216,213,64,212,102,191,112,143,204,96,164,226,218,107,182,116,224,157,223,171,194,104,228,114,218,40,207,54,204,220,108,199,214,195,81,158,130,133,116,118,203,215,215,146,219,210,69,216,121,225,59,210,65,217,202,79,209,76,156,152,178,193,86,228,50,217,94,220,71,156,206,84,202,88,113,211,215,65,168,221,195,219,213,148,204,84,212,217,184,218,228,118,222,76,222,226,205,126,218,224,216,77,122,218,74,213,81,220,66,190,132,212,213,200,216,63,204,105,169,166,173,193,142,201,168,213,96,200,210,93,210,217,214,182,204,199,172,159,200,216,162,198,168,191,200,99,214,227,230,87,171,204,195,176,210,165,158,221,118,195,182,217,225,217,191,210,120,164,100,222,195,227,81,229,174,207,212,92,191,204,206,61,209,143,230,113,218,224,188,223,95,216,71,154,212,107,150,171,192,204,66,216,218,113,218,206,155,187,185,209,180,111,230,66,206,89,225,62,218,183,201,229,215,172,196,114,199,211,54,203,198,211,110,223,85,219,133,133,221,154,155,186,205,77,36,213,221,196,174,180,135,217,54,181,219,215,194,206,154,216,101,209,162,184,192,147,224,84,183,223,68,197,155,205,174,97,199,177,211,221,68,204,103,221,163,203,109,186,216,216,152,228,67,218,100,140,222,113,202,70,202,183,125,126,203,226,175,165,160,182,121,172,231,204,185,159,198,219,227,150,194,108,204,72,215,119,212,190,223,213,63,234,116,234,180,218,182,189,175,205,110,226,213,184,191,88,182,162,187,163,193,198,49,214,148,159,214,162,154,227,187,123,185,167,184,127,229,228,147,150,187,222,104,217,153,229,211,112,179,198,211,124,222,166,207,99,152,175,160,214,176,134,213,74,201,93,171,202,183,212,91,208,60,221,164,207,72,181,167,210,170,226,50,147,206,80,209,136,210,108,180,221,218,105,213,118,188,216,219,94,216,162,219,99,209,207,206,217,221,221,222,56,182,222,220,229,226,209,111,231,144,229,126,210,140,187,155,214,138,226,128,213,193,99,159,115,218,98,219,96,152,221,224,136,220,215,224,71,201,96,190,212,102,229,133,216,62,217,87,187,200,198,213,93,201,108,217,210,101,198,71,192,178,197,221,232,107,220,81,169,220,210,201,82,222,219,195,128,225,147,221,79,202,159,182,100,214,229,221,80,206,128,182,216,103,200,127,205,90,193,133,168,213,72,217,122,210,91,207,117,220,147,211,98,199,150,203,159,217,63,203,62,154,211,222,227,180,161,169,227,86,212,157,177,220,140,216,125,229,228,212,100,222,193,107,186,176,212,187,224,144,171,225,69,217,102,183,222,129,219,71,220,206,91,158,212,114,172,151,200,207,175,177,168,177,210,224,77,201,74,205,206,218,123,216,94,190,87,221,225,220,210,227,52,209,110,177,218,97,220,225,93,159,212,102,201,83,178,219,212,189,136,224,221,189,81,224,140,226,202,206,101,218,46,199,101,209,106,182,220,195,205,221,144,191,210,220,95,191,220,177,203,222,69,203,111,214,70,223,162,228,230,143,221,84,210,81,203,195,220,70,170,120,179,183,148,209,149,207,187,224,151,208,136,208,98,223,126,216,220,205,109,224,45,171,206,98,194,176,131,158,221,213,169,115,197,97,149,204,197,221,96,203,199,161,209,126,207,215,213,143,208,78,223,61,156,190,223,67,186,217,85,217,200,115,225,217,112,187,88,216,147,214,228,198,211,124,221,103,174,199,68,214,192,193,201,197,96,212,72,226,177,225,117,214,119,200,123,209,202,210,200,79,211,214,91,215,120,213,187,208,79,218,144,203,223,223,99,218,72,178,218,211,215,108,230,224,103,197,218,113,215,97,198,106,173,230,138,224,229,229,62,191,219,207,78,184,185,229,67,215,113,181,71,193,208,187,118,204,209,73,202,130,176,215,203,63,227,68,222,74,198,220,172,224,208,217,145,197,94,172,117,224,47,220,229,73,196,100,214,198,212,230,121,216,219,181,205,79,108,202,211,179,165,228,109,177,203,120,200,90,208,220,72,142,217,214,84,224,233,140,167,199,111,184,208,93,223,69,218,205,221,82,203,213,156,236,92,210,151,206,225,82,227,215,106,206,61,184,132,228,59,187,185,182,213,230,202,104,215,52,190,177,165,133,178,167,225,221,114,219,107,189,217,179,212,109,201,223,197,216,181,185,193,110,151,180,168,227,216,60,185,195,208,102,206,153,136,216,73,223,206,223,72,203,109,173,219,211,180,223,105,224,172,184,171,218,83,209,38,144,194,95,211,105,224,231,209,176,177,138,217,81,192,218,169,219,103,171,221,59,191,98,118,185,217,160,211,181,147,208,225,189,190,119,215,125,160,209,76,208,232,84,208,198,205,119,206,110,196,180,187,169,154,212,161,202,117,203,106,210,119,206,206,135,195,199,107,188,201,217,150,199,134,190,219,168,173,225,194,221,152,173,205,100,219,81,181,139,189,216,53,198,75,207,214,192,200,65,193,196,184,220,222,204,183,188,195,222,214,67,194,102,222,192,77,214,110,198,158,222,223,76,213,219,94,226,53,223,230,185,201,132,93,193,179,224,115,228,181,192,105,213,218,71,219,137,197,148,199,149,162,196,207,74,218,125,147,224,118,209,59,190,193,221,77,124,151,223,157,208,203,62,226,114,215,182,196,184,165,127,200,211,222,155,203,129,177,194,134,188,179,205,72,163,216,205,160,214,165,195,153,151,213,40,221,215,226,124,212,160,217,113,200,192,218,194,92,220,86,208,121,212,130,225,174,221,224,62,162,221,218,61,229,166,141,208,98,222,108,125,207,24,196,197,191,155,156,230,165,211,211,99,167,214,214,234,185,160,211,94,204,87,222,221,108,227,107,207,220,71,218,218,74,156,226,223,79,207,109,215,59,202,111,162,202,104,189,131,209,77,212,115,228,75,213,121,218,92,182,208,203,161,225,94,206,203,189,67,220,111,132,204,147,195,210,103,204,38,199,214,216,93,216,119,194,219,214,67,208,164,152,208,149,206,124,224,213,85,208,88,210,213,121,193,215,111,208,165,217,92,137,218,137,201,119,181,163,205,221,182,210,95,152,202,178,187,96,211,114,210,223,203,185,215,132,220,210,204,181,210,222,162,121,209,225,82,203,234,214,126,224,223,102,213,125,190,219,203,204,95,134,204,220,82,171,198,120,200,88,223,222,66,205,105,162,201,86,223,98,211,189,109,211,216,168,125,182,218,173,217,152,175,212,191,218,99,187,83,212,189,133,163,114,193,209,185,210,197,77,219,195,149,200,148,209,117,217,220,213,146,221,43,212,93,109,227,84,223,218,81,218,115,219,210,83,212,76,202,58,172,221,221,103,196,227,216,184,106,197,118,210,211,170,153,218,199,111,194,218,138,221,220,94,173,203,69,229,48,221,79,176,223,213,103,216,217,166,214,225,93,217,106,221,198,111,226,77,197,201,180,96,140,219,117,209,124,215,63,161,203,225,226,154,224,84,204,227,208,99,212,128,206,156,195,206,168,209,42,218,181,202,172,198,104,138,206,100,216,110,232,113,200,99,223,96,213,72,210,194,160,176,133,209,97,196,169,161,190,203,232,49,158,171,222,188,226,184,166,166,147,222,79,195,97,218,115,222,218,191,120,190,187,218,43,181,157,148,207,61,222,55,220,219,95,232,163,194,181,166,218,150,223,117,216,200,110,194,191,129,206,204,99,227,198,195,177,159,173,221,63,196,113,203,125,185,182,184,183,170,219,199,114,216,125,217,85,221,112,187,224,78,205,224,136,143,222,115,190,229,225,200,84,206,168,176,198,228,71,99,203,107,218,157,223,179,225,63,197,106,210,188,170,185,215,119,222,64,211,110,192,40,220,142,220,215,167,215,170,225,183,132,228,197,106,211,219,217,172,207,113,223,60,194,221,72,204,193,223,205,178,178,110,216,201,197,141,80,200,227,228,76,207,215,95,222,210,144]
 
1
+ [206,104,226,67,200,185,221,212,206,222,88,210,228,174,155,205,172,218,148,132,212,91,163,184,205,132,213,190,212,198,230,227,159,198,122,216,175,197,118,217,219,224,69,220,197,72,204,92,169,191,191,155,175,111,218,77,207,36,195,178,123,170,231,91,209,177,146,205,151,221,217,95,199,89,153,216,154,202,167,213,104,184,176,226,200,232,53,223,73,202,220,158,174,189,165,222,158,221,211,78,205,72,214,212,215,232,175,219,110,205,192,226,34,176,209,158,222,132,179,208,98,126,205,126,172,167,227,100,224,182,208,117,217,199,195,191,169,178,158,217,152,157,163,163,204,209,146,217,150,194,217,23,125,200,221,200,212,41,176,223,207,95,178,232,68,182,173,205,198,210,139,152,147,215,196,223,83,199,123,197,119,230,223,65,216,85,210,52,210,204,179,111,138,215,83,177,219,69,212,77,182,226,99,178,207,197,87,190,222,216,85,208,211,66,220,226,221,134,175,190,170,166,157,216,135,211,132,200,67,227,195,178,221,179,205,168,186,208,127,207,139,205,67,209,223,117,195,190,202,64,218,77,199,221,189,125,216,80,148,214,143,181,98,221,194,112,219,212,138,216,87,127,187,219,122,220,97,223,221,164,205,220,100,227,101,171,188,223,89,213,137,175,172,219,77,102,234,109,197,114,205,184,221,138,175,152,163,133,227,73,224,181,174,147,144,178,147,224,211,121,123,211,219,209,210,204,80,182,215,152,118,224,216,154,220,163,204,187,133,151,162,221,94,184,224,213,72,187,148,223,195,110,182,117,224,88,202,162,220,154,151,197,227,197,74,221,205,97,221,219,103,206,184,72,191,201,192,230,164,217,170,214,118,221,218,118,215,181,224,78,183,154,190,206,102,202,74,195,96,225,228,221,99,224,104,123,203,184,211,90,209,173,199,203,99,201,176,160,207,132,231,96,195,213,173,101,190,206,61,225,232,215,178,211,227,192,148,195,213,114,212,127,162,214,213,129,220,70,176,224,209,137,232,116,228,212,75,182,207,71,111,173,189,207,96,172,213,119,222,93,195,204,211,76,179,200,184,216,107,135,202,71,118,219,123,202,210,146,225,76,227,62,214,226,98,158,202,219,169,83,156,223,125,211,213,102,219,223,72,224,178,190,204,110,228,64,214,200,193,205,213,160,201,216,134,195,128,186,213,219,187,199,216,84,202,150,211,127,230,206,204,180,183,183,167,213,224,72,209,79,195,205,153,228,133,203,210,78,190,207,195,133,225,192,197,190,201,187,222,109,138,227,103,223,209,164,190,73,226,200,205,191,189,100,226,135,198,129,185,207,229,148,209,216,58,222,117,219,92,221,147,222,219,63,216,228,165,181,157,213,197,218,158,191,186,223,204,109,172,229,91,231,204,102,192,217,204,144,222,173,209,209,95,186,195,60,212,219,209,162,183,227,216,111,193,182,222,71,183,222,122,178,219,130,228,82,225,223,69,198,232,63,193,207,216,139,223,74,164,198,224,80,148,227,138,227,199,225,86,148,230,169,210,92,166,187,202,165,196,78,202,74,229,173,216,215,213,164,205,102,206,91,212,173,198,109,183,94,206,115,224,126,229,222,57,225,191,221,218,215,44,177,217,214,218,191,221,80,198,109,196,95,192,181,214,214,123,208,191,225,219,218,108,204,150,199,206,90,227,71,207,123,199,106,131,213,93,178,208,60,132,217,149,204,80,227,218,202,94,232,175,213,115,217,114,222,200,173,184,217,222,178,226,205,192,185,197,180,193,205,211,136,221,62,184,213,89,225,71,169,216,135,198,98,214,204,88,191,154,207,213,227,93,230,133,216,189,122,224,108,229,237,218,209,214,66,153,203,219,204,222,218,107,174,214,148,169,188,210,98,203,142,167,174,224,103,223,220,148,216,199,188,175,173,130,156,97,220,111,216,115,216,70,221,210,77,173,162,216,164,216,69,203,175,172,166,199,156,184,131,201,210,98,220,133,211,116,221,82,213,88,213,128,227,219,177,190,216,180,237,217,64,215,134,221,172,220,221,70,169,221,189,226,74,229,197,209,128,190,213,106,221,198,162,184,182,219,86,201,143,229,205,146,228,106,154,209,60,216,222,74,221,215,141,168,202,82,194,213,80,217,214,204,164,199,220,149,114,140,215,150,218,108,109,211,170,206,125,183,148,193,229,210,124,188,143,160,153,199,178,75,225,105,199,83,222,112,205,75,222,205,70,104,194,91,158,220,99,215,209,219,65,124,215,158,223,234,208,101,218,200,66,188,222,149,201,189,211,51,179,208,198,160,182,152,195,141,216,112,205,207,170,128,219,118,188,223,224,80,177,218,127,196,151,159,117,186,113,216,212,134,204,90,200,231,184,222,68,146,151,158,202,173,144,206,171,215,68,188,222,89,215,117,223,80,191,209,204,105,228,67,174,210,74,208,105,151,159,183,142,163,202,214,41,147,222,227,188,208,200,157,132,195,94,191,185,214,132,201,155,166,195,220,147,219,156,214,165,220,105,223,217,100,176,225,117,207,134,166,202,218,172,200,157,183,217,87,214,181,229,217,76,231,199,139,154,213,208,208,169,218,82,219,191,139,207,222,49,122,154,176,219,110,185,112,137,231,80,208,136,189,192,208,190,192,187,180,220,186,225,218,208,102,190,124,152,83,219,199,95,198,133,235,214,96,213,217,148,168,200,32,145,207,222,198,171,208,218,104,178,230,77,214,129,218,136,139,212,233,93,221,153,221,95,155,151,211,225,60,81,217,201,108,208,234,172,177,172,193,208,72,216,224,211,44,205,82,221,224,199,198,221,137,208,81,183,105,182,106,222,180,184,146,180,205,187,192,163,219,213,164,229,218,190,147,221,193,213,182,126,228,231,97,173,218,116,161,156,207,122,193,198,155,207,103,223,192,92,153,223,170,206,177,225,40,224,106,203,208,116,203,105,197,152,221,211,84,226,111,215,154,224,221,65,215,142,169,212,184,135,207,85,209,213,76,225,229,130,203,217,186,229,140,177,116,216,219,62,209,105,164,150,207,214,211,114,148,230,118,201,134,197,124,209,163,184,164,186,189,219,106,221,220,95,141,217,87,200,224,114,220,168,212,112,229,192,106,189,183,209,212,85,187,202,219,222,223,223,212,226,79,193,128,222,209,206,197,178,152,114,222,111,164,183,189,179,214,130,149,150,212,122,160,195,118,115,204,144,163,159,140,211,208,120,222,220,125,195,174,220,80,173,204,87,149,212,82,221,119,213,139,186,227,97,215,213,136,215,68,229,218,63,224,182,221,218,94,213,90,168,202,85,168,222,121,215,213,93,221,83,220,112,197,213,213,92,210,76,216,79,179,220,94,194,99,186,181,197,214,194,138,221,91,218,172,159,219,74,153,221,201,108,213,131,217,112,214,107,209,35,224,188,219,108,191,105,157,211,126,206,148,220,126,217,164,203,129,201,211,119,204,165,209,75,205,130,204,215,206,119,178,211,218,137,193,167,199,220,182,115,207,184,189,217,178,186,159,208,230,140,194,75,173,205,64,213,167,189,110,220,195,136,137,196,134,208,120,198,182,189,176,162,183,172,172,214,200,95,212,189,193,184,195,116,167,199,110,180,217,126,193,226,142,204,66,140,222,226,218,144,218,214,140,206,66,196,224,143,121,166,221,101,154,204,216,201,221,152,225,69,226,226,180,200,181,212,128,193,206,136,219,141,208,128,202,209,105,168,178,204,211,110,145,163,193,211,69,220,147,194,223,165,212,218,124,197,229,226,132,220,156,211,80,203,140,192,94,208,102,180,215,134,152,218,106,191,204,102,211,108,182,204,206,208,90,165,197,105,186,115,213,208,186,165,186,174,213,211,164,210,62,129,201,217,170,169,217,193,167,222,174,217,167,128,182,113,159,208,85,172,219,89,219,126,218,190,65,227,208,52,225,125,160,205,135,168,186,213,212,69,124,205,67,204,110,151,180,220,205,193,106,223,80,124,166,218,194,138,148,205,88,179,183,163,227,221,198,213,87,190,200,239,81,202,211,213,115,204,105,216,83,170,201,210,95,219,219,180,216,222,228,86,231,83,227,116,223,84,207,216,115,204,109,194,171,198,183,197,86,216,144,209,125,226,221,57,215,117,199,185,152,230,174,220,61,209,99,163,218,218,134,183,212,85,208,92,190,214,81,146,220,181,224,214,212,79,171,209,217,209,100,213,72,220,223,193,72,130,203,111,214,192,197,222,201,229,204,189,162,124,221,157,218,34,204,88,170,205,76,168,210,46,196,223,73,180,85,118,200,85,221,68,160,202,201,219,181,148,207,210,208,69,210,101,215,76,217,104,188,180,224,71,199,166,203,106,216,196,182,84,183,125,128,224,89,206,88,212,212,152,205,92,210,65,166,171,200,120,228,213,83,163,131,153,180,215,228,99,179,210,82,218,95,209,94,215,206,196,110,236,219,209,193,95,172,177,218,218,140,203,228,102,160,215,102,204,192,89,168,222,56,223,155,189,213,208,220,78,204,213,168,202,169,230,191,218,203,73,222,127,208,143,221,217,217,100,160,212,208,156,223,197,205,201,223,220,144,209,90,209,139,182,218,22,221,183,219,79,216,194,224,86,208,225,190,102,200,212,162,211,83,221,106,215,113,161,197,111,216,210,92,219,124,208,185,152,203,220,230,106,182,193,185,221,79,197,79,231,192,202,113,166,211,142,195,170,179,226,222,208,228,152,133,200,89,197,180,155,215,97,201,139,205,145,217,163,213,85,223,209,200,139,217,123,160,183,201,136,210,206,105,208,132,206,208,101,134,174,112,205,181,221,215,221,213,225,116,217,80,162,186,120,183,122,214,201,97,188,209,174,229,205,120,194,203,130,228,134,228,83,201,119,218,210,55,225,64,171,174,227,107,226,95,139,190,154,171,206,139,154,219,157,224,213,206,212,79,135,186,156,217,186,128,118,184,187,213,221,86,183,217,218,210,216,155,195,188,134,172,223,125,176,167,85,187,214,217,94,213,128,217,133,192,217,60,189,122,217,117,224,95,199,74,206,89,221,205,158,196,77,228,114,188,105,210,70,209,83,188,169,214,225,184,205,127,197,115,182,174,181,238,222,223,59,229,219,112,205,115,190,128,196,210,222,176,215,194,187,112,212,90,183,207,86,212,85,178,187,138,217,80,203,96,221,198,82,207,96,164,227,208,76,214,179,209,186,207,205,221,224,143,224,178,228,189,229,132,213,182,222,218,67,229,186,201,169,211,88,210,48,219,152,225,207,62,200,210,220,68,216,44,137,155,181,221,83,214,103,222,183,90,215,86,179,205,128,219,179,164,202,130,224,148,221,146,175,184,213,117,190,201,142,216,82,231,230,92,172,218,207,189,223,150,226,46,216,209,221,216,111,168,219,92,197,228,204,204,101,223,65,211,220,203,122,206,108,224,74,181,142,216,189,190,180,212,107,223,120,191,232,56,209,214,66,209,222,116,172,222,101,179,177,185,168,193,163,194,209,193,105,170,211,212,105,191,117,90,226,90,190,121,216,60,207,67,161,171,220,219,89,184,221,144,182,194,208,214,214,226,196,133,188,202,204,68,194,216,214,77,208,92,215,63,204,178,197,77,115,190,152,227,181,207,102,199,131,208,216,177,116,202,220,121,223,128,196,175,211,219,207,74,218,215,221,93,177,214,183,192,143,139,196,217,226,83,102,119,211,100,212,172,183,180,204,219,226,96,194,47,149,186,143,160,221,224,61,226,147,214,60,198,207,164,146,200,140,182,207,196,208,180,186,215,111,181,221,214,161,216,169,208,136,168,178,112,189,196,124,234,119,202,67,201,74,163,172,148,179,211,143,124,191,202,192,175,218,80,209,72,190,156,217,117,223,122,180,193,182,186,210,219,201,202,224,64,138,192,114,199,208,190,109,218,149,208,97,213,189,87,232,131,152,188,205,219,196,225,94,197,164,210,173,191,222,185,172,189,205,163,208,73,196,201,73,224,206,110,191,122,221,136,165,221,185,156,210,69,210,211,210,102,213,86,170,209,221,197,207,50,154,74,96,212,180,231,158,208,139,150,166,127,213,216,202,95,201,155,217,54,206,214,208,179,140,191,136,228,191,91,232,45,145,220,162,220,74,209,147,155,160,182,217,209,160,174,180,227,219,221,182,100,212,67,213,148,208,51,188,209,82,211,210,71,218,82,176,88,174,117,186,210,160,157,212,135,165,201,162,170,136,176,220,189,167,219,49,212,190,220,73,152,150,176,204,221,124,220,209,117,175,213,228,173,195,159,194,218,57,201,214,164,175,229,79,176,116,206,137,203,152,173,206,78,227,209,154,175,190,89,225,113,222,219,214,50,219,66,202,90,188,214,80,195,74,212,60,130,206,186,157,204,122,198,73,194,230,108,196,205,213,83,192,104,207,117,216,171,126,222,99,229,221,79,214,79,217,144,217,197,206,207,110,160,206,172,197,183,207,217,207,113,210,221,71,161,221,164,227,214,142,177,185,180,103,130,198,123,205,74,216,102,219,160,217,75,204,114,192,213,166,188,118,222,227,92,195,219,161,200,221,69,203,143,198,198,217,198,66,212,50,208,116,199,125,210,207,167,225,116,207,97,184,99,220,184,203,184,219,177,167,202,214,55,207,161,197,122,212,226,187,96,216,201,188,135,224,207,139,225,230,220,121,221,107,212,66,170,169,210,199,102,220,94,159,184,207,92,207,231,214,125,227,220,205,58,193,203,215,223,229,78,196,170,185,196,162,234,56,201,123,171,231,196,86,162,199,213,220,68,200,68,205,88,225,135,220,82,182,215,222,79,152,230,62,162,218,184,224,67,206,99,189,124,214,197,73,204,105,221,179,102,218,232,80,214,181,170,204,165,216,207,217,212,195,176,215,106,192,160,221,182,217,57,211,88,198,233,113,171,204,138,193,209,225,59,176,184,134,223,151,193,200,217,100,225,79,180,142,190,123,222,80,232,216,133,216,148,211,110,198,96,187,224,95,208,112,178,227,94,171,96,181,209,170,225,196,206,94,216,87,217,171,191,82,218,127,227,176,219,207,230,79,214,203,105,213,143,174,188,125,193,220,60,215,172,214,101,211,110,161,117,187,180,125,218,220,62,208,203,217,87,198,156,216,226,161,161,223,224,72,178,198,213,195,219,208,140,175,217,74,201,201,66,186,154,229,89,226,169,204,87,184,85,161,133,201,80,176,188,114,224,77,207,126,202,83,219,200,125,172,169,190,216,80,88,221,68,218,133,216,117,217,157,217,170,190,124,214,210,156,231,84,207,204,113,200,70,222,162,208,227,92,223,136,167,195,221,221,77,173,213,109,214,117,211,217,89,217,91,210,152,194,206,202,110,216,177,190,207,227,185,172,230,172,207,171,199,234,207,149,194,192,179,212,209,210,101,198,225,85,164,211,110,194,182,211,224,65,228,218,79,224,81,122,208,154,129,206,92,193,171,148,188,221,80,220,161,165,166,161,214,99,210,64,174,224,221,105,200,122,230,216,94,223,128,225,161,219,126,187,137,191,222,214,148,151,198,218,210,110,208,228,184,211,35,202,218,195,216,115,212,95,177,199,101,184,208,202,212,134,193,129,192,81,182,223,70,226,230,134,167,183,198,222,227,227,226,63,213,109,187,177,219,223,203,144,179,209,103,177,181,158,221,90,222,166,207,175,230,207,99,205,234,210,210,168,223,143,210,187,209,204,150,209,213,208,193,221,214,77,215,199,81,197,82,177,190,210,231,79,179,221,64,182,199,82,204,204,95,172,187,178,209,86,222,220,118,192,223,88,220,77,174,104,224,137,182,186,96,207,198,74,152,196,217,206,79,214,208,204,180,94,215,81,177,160,201,164,173,205,76,199,220,228,91,215,155,226,79,133,181,136,182,226,96,221,109,209,223,71,202,95,217,87,202,204,183,210,187,212,81,226,184,224,88,170,214,198,226,142,212,81,209,189,172,192,221,216,123,221,126,204,218,222,76,205,73,225,221,73,204,108,201,88,174,197,136,223,90,189,56,207,147,206,212,73,201,83,204,112,137,227,67,208,137,219,225,65,200,186,99,214,97,215,74,203,65,199,216,108,216,80,206,219,104,226,180,225,199,186,197,226,157,102,177,107,231,156,141,226,70,220,216,223,64,214,66,201,174,170,207,46,202,131,173,218,125,217,157,234,192,159,174,209,95,196,224,59,220,69,211,130,203,222,88,208,86,198,127,219,228,75,218,170,168,198,128,215,54,211,167,186,117,211,162,221,219,105,223,99,223,127,202,218,213,143,194,181,200,180,230,224,97,181,132,173,202,221,57,151,220,77,220,160,206,188,101,197,72,213,95,193,212,189,105,226,100,205,201,56,211,93,178,212,88,208,83,213,165,219,183,236,121,220,210,94,212,171,186,218,137,212,129,175,203,223,134,194,95,193,191,105,229,208,102,196,120,191,221,217,65,206,200,74,168,180,199,217,119,223,68,211,125,204,105,180,164,215,227,128,211,166,218,86,185,74,214,57,200,171,111,185,73,199,220,213,192,216,107,211,115,219,227,192,221,101,203,65,211,51,216,84,193,121,214,86,195,115,179,229,90,215,92,207,63,179,212,38,202,104,182,125,179,99,147,184,210,166,227,232,164,120,218,169,203,154,192,224,217,122,160,205,206,221,80,191,217,166,202,78,206,147,202,155,195,76,204,136,191,112,195,160,147,226,91,224,216,212,177,188,165,174,130,203,221,220,133,209,147,216,69,159,155,143,213,94,227,139,209,163,183,199,112,217,213,98,217,96,185,158,173,229,51,209,195,227,214,161,213,83,168,229,209,118,221,224,59,179,161,220,209,193,199,199,212,107,226,219,204,117,166,223,122,166,181,163,176,223,176,130,223,221,202,89,188,147,160,143,218,223,206,151,201,161,130,176,175,138,126,209,112,230,94,211,17,103,218,73,218,131,210,104,214,63,222,38,135,140,215,143,215,191,185,223,207,215,203,46,219,207,93,177,85,213,191,223,56,181,209,82,210,221,66,210,195,223,184,138,217,48,194,73,150,199,220,183,209,60,194,103,218,103,211,216,124,197,217,185,106,185,207,174,165,204,138,220,68,218,151,202,68,214,155,183,221,66,216,61,218,122,214,178,202,178,217,142,215,126,187,148,219,98,180,222,217,80,210,203,43,208,154,220,101,167,206,211,212,208,72,147,225,139,174,207,36,200,234,205,211,180,205,202,126,159,186,116,211,154,192,155,194,168,198,160,218,220,202,153,222,215,66,174,128,211,104,136,171,235,219,112,156,209,109,203,132,192,181,215,112,205,68,215,82,213,117,189,221,186,211,171,208,136,189,128,210,96,199,107,195,232,74,223,132,193,198,46,220,73,181,112,224,133,221,144,224,83,232,217,131,186,53,214,225,95,203,70,102,217,106,224,79,210,113,177,150,228,220,102,225,80,221,170,206,105,223,112,210,46,201,89,197,207,128,235,111,212,161,144,221,182,200,77,213,229,90,134,223,179,212,204,125,197,215,80,233,218,44,226,53,152,184,220,113,219,216,110,214,206,151,215,224,216,163,144,190,133,223,195,216,203,67,95,169,191,131,208,78,104,176,179,148,207,172,220,98,202,118,218,204,120,213,92,213,93,210,203,219,75,212,227,212,188,187,201,100,206,151,200,96,197,215,157,210,70,207,182,205,205,101,212,117,230,86,163,143,167,189,215,168,216,194,98,218,128,219,94,149,188,217,48,172,174,131,131,182,171,200,115,220,217,91,200,80,178,188,226,49,192,205,222,127,194,134,175,214,115,212,214,82,193,106,217,48,197,114,204,114,201,221,190,174,214,168,223,86,217,212,214,181,204,96,190,189,216,91,204,118,226,110,198,224,158,195,117,189,200,150,231,206,91,209,207,118,223,183,232,146,158,207,213,122,204,118,200,103,200,227,76,179,195,73,215,93,214,170,215,232,41,210,107,138,202,204,125,198,134,225,80,117,164,185,197,106,232,74,139,216,207,209,57,207,94,200,229,190,192,140,112,208,155,191,177,216,203,142,192,103,195,219,91,179,228,187,115,213,217,192,193,215,141,218,70,186,37,225,190,84,178,177,162,218,210,185,176,195,97,218,63,218,176,227,215,149,224,221,115,208,214,133,182,188,205,163,207,58,199,217,129,208,184,214,206,133,228,200,80,224,179,229,152,208,95,194,170,224,178,196,181,94,199,90,203,75,216,153,169,213,86,225,67,192,194,65,189,201,103,188,153,163,213,226,142,185,133,226,106,181,215,199,209,211,56,204,114,181,163,171,228,228,73,74,198,203,186,178,185,125,229,221,204,41,165,189,126,205,173,116,179,198,159,216,129,209,222,174,183,229,95,212,68,177,152,217,138,156,135,105,204,193,188,127,209,150,133,163,208,80,218,232,211,77,230,37,198,224,165,213,81,220,207,195,173,174,212,102,206,117,196,178,222,134,205,216,203,86,151,184,157,217,222,123,213,159,195,121,207,159,220,120,212,176,144,217,67,173,216,105,206,90,220,121,217,59,184,219,156,213,149,143,216,221,140,225,182,115,209,115,209,45,223,172,227,133,187,165,199,168,224,91,206,115,153,202,197,62,169,210,134,215,167,203,214,141,200,213,182,90,214,170,206,199,219,54,167,154,72,194,122,181,197,129,214,105,153,209,137,202,227,72,207,61,178,127,181,210,200,46,188,210,214,67,189,216,51,209,125,190,127,208,110,191,219,137,213,76,206,120,186,121,201,222,113,195,194,68,183,179,184,223,61,180,220,197,133,208,226,136,217,200,93,178,220,113,197,198,172,141,225,102,159,149,213,196,100,220,196,176,232,182,187,171,165,182,101,175,169,191,224,110,200,128,200,129,114,179,188,165,198,216,184,174,216,67,229,198,220,32,232,219,72,219,203,127,88,212,81,142,223,210,166,97,145,209,77,216,227,196,83,202,137,214,82,223,114,205,177,183,196,214,129,196,122,223,157,232,99,180,188,203,132,229,223,186,115,209,191,218,50,192,184,220,102,207,87,196,162,219,92,221,140,217,139,169,213,79,211,99,205,104,200,86,210,90,157,151,227,228,53,205,72,195,75,226,89,226,74,218,145,228,224,208,171,215,153,140,208,182,161,228,107,209,220,217,207,125,181,195,212,220,95,202,95,191,233,74,201,184,221,81,231,181,120,227,119,139,121,179,199,203,216,154,210,144,195,129,153,213,103,209,219,212,125,216,229,219,108,223,65,212,92,221,197,162,211,147,210,197,178,221,162,192,172,215,84,194,52,204,70,175,187,187,194,186,235,185,177,170,216,213,64,212,102,191,112,143,204,96,164,226,218,107,182,116,224,157,223,171,194,104,228,114,218,40,207,54,204,220,108,199,214,195,81,158,130,133,116,118,203,215,215,146,219,210,69,216,121,225,59,210,65,217,202,79,209,76,156,152,178,193,86,228,50,217,94,220,71,156,206,84,202,88,113,211,215,65,168,221,195,219,213,148,204,84,212,217,184,218,228,118,222,76,222,226,205,126,218,224,216,77,122,218,74,213,81,220,66,190,132,212,213,200,216,63,204,105,169,166,173,193,142,201,168,213,96,200,210,93,210,217,214,182,204,199,172,159,200,216,162,198,168,191,200,99,214,227,230,87,171,204,195,176,210,165,158,221,118,195,182,217,225,217,191,210,120,164,100,222,195,227,81,229,174,207,212,92,191,204,206,61,209,143,230,113,218,224,188,223,95,216,71,154,212,107,150,171,192,204,66,216,218,113,218,206,155,187,185,209,180,111,230,66,206,89,225,62,218,183,201,229,215,172,196,114,199,211,54,203,198,211,110,223,85,219,133,133,221,154,155,186,205,77,36,213,221,196,174,180,135,217,54,181,219,215,194,206,154,216,101,209,162,184,192,147,224,84,183,223,68,197,155,205,174,97,199,177,211,221,68,204,103,221,163,203,109,186,216,216,152,228,67,218,100,140,222,113,202,70,202,183,125,126,203,226,175,165,160,182,121,172,231,204,185,159,198,219,227,150,194,108,204,72,215,119,212,190,223,213,63,234,116,234,180,218,182,189,175,205,110,226,213,184,191,88,182,162,187,163,193,198,49,214,148,159,214,162,154,227,187,123,185,167,184,127,229,228,147,150,187,222,104,217,153,229,211,112,179,198,211,124,222,166,207,99,152,175,160,214,176,134,213,74,201,93,171,202,183,212,91,208,60,221,164,207,72,181,167,210,170,226,50,147,206,80,209,136,210,108,180,221,218,105,213,118,188,216,219,94,216,162,219,99,209,207,206,217,221,221,222,56,182,222,220,229,226,209,111,231,144,229,126,210,140,187,155,214,138,226,128,213,193,99,159,115,218,98,219,96,152,221,224,136,220,215,224,71,201,96,190,212,102,229,133,216,62,217,87,187,200,198,213,93,201,108,217,210,101,198,71,192,178,197,221,232,107,220,81,169,220,210,201,82,222,219,195,128,225,147,221,79,202,159,182,100,214,229,221,80,206,128,182,216,103,200,127,205,90,193,133,168,213,72,217,122,210,91,207,117,220,147,211,98,199,150,203,159,217,63,203,62,154,211,222,227,180,161,169,227,86,212,157,177,220,140,216,125,229,228,212,100,222,193,107,186,176,212,187,224,144,171,225,69,217,102,183,222,129,219,71,220,206,91,158,212,114,172,151,200,207,175,177,168,177,210,224,77,201,74,205,206,218,123,216,94,190,87,221,225,220,210,227,52,209,110,177,218,97,220,225,93,159,212,102,201,83,178,219,212,189,136,224,221,189,81,224,140,226,202,206,101,218,46,199,101,209,106,182,220,195,205,221,144,191,210,220,95,191,220,177,203,222,69,203,111,214,70,223,162,228,230,143,221,84,210,81,203,195,220,70,170,120,179,183,148,209,149,207,187,224,151,208,136,208,98,223,126,216,220,205,109,224,45,171,206,98,194,176,131,158,221,213,169,115,197,97,149,204,197,221,96,203,199,161,209,126,207,215,213,143,208,78,223,61,156,190,223,67,186,217,85,217,200,115,225,217,112,187,88,216,147,214,228,198,211,124,221,103,174,199,68,214,192,193,201,197,96,212,72,226,177,225,117,214,119,200,123,209,202,210,200,79,211,214,91,215,120,213,187,208,79,218,144,203,223,223,99,218,72,178,218,211,215,108,230,224,103,197,218,113,215,97,198,106,173,230,138,224,229,229,62,191,219,207,78,184,185,229,67,215,113,181,71,193,208,187,118,204,209,73,202,130,176,215,203,63,227,68,222,74,198,220,172,224,208,217,145,197,94,172,117,224,47,220,229,73,196,100,214,198,212,230,121,216,219,181,205,79,108,202,211,179,165,228,109,177,203,120,200,90,208,220,72,142,217,214,84,224,233,140,167,199,111,184,208,93,223,69,218,205,221,82,203,213,156,236,92,210,151,206,225,82,227,215,106,206,61,184,132,228,59,187,185,182,213,230,202,104,215,52,190,177,165,133,178,167,225,221,114,219,107,189,217,179,212,109,201,223,197,216,181,185,193,110,151,180,168,227,216,60,185,195,208,102,206,153,136,216,73,223,206,223,72,203,109,173,219,211,180,223,105,224,172,184,171,218,83,209,38,144,194,95,211,105,224,231,209,176,177,138,217,81,192,218,169,219,103,171,221,59,191,98,118,185,217,160,211,181,147,208,225,189,190,119,215,125,160,209,76,208,232,84,208,198,205,119,206,110,196,180,187,169,154,212,161,202,117,203,106,210,119,206,206,135,195,199,107,188,201,217,150,199,134,190,219,168,173,225,194,221,152,173,205,100,219,81,181,139,189,216,53,198,75,207,214,192,200,65,193,196,184,220,222,204,183,188,195,222,214,67,194,102,222,192,77,214,110,198,158,222,223,76,213,219,94,226,53,223,230,185,201,132,93,193,179,224,115,228,181,192,105,213,218,71,219,137,197,148,199,149,162,196,207,74,218,125,147,224,118,209,59,190,193,221,77,124,151,223,157,208,203,62,226,114,215,182,196,184,165,127,200,211,222,155,203,129,177,194,134,188,179,205,72,163,216,205,160,214,165,195,153,151,213,40,221,215,226,124,212,160,217,113,200,192,218,194,92,220,86,208,121,212,130,225,174,221,224,62,162,221,218,61,229,166,141,208,98,222,108,125,207,24,196,197,191,155,156,230,165,211,211,99,167,214,214,234,185,160,211,94,204,87,222,221,108,227,107,207,220,71,218,218,74,156,226,223,79,207,109,215,59,202,111,162,202,104,189,131,209,77,212,115,228,75,213,121,218,92,182,208,203,161,225,94,206,203,189,67,220,111,132,204,147,195,210,103,204,38,199,214,216,93,216,119,194,219,214,67,208,164,152,208,149,206,124,224,213,85,208,88,210,213,121,193,215,111,208,165,217,92,137,218,137,201,119,181,163,205,221,182,210,95,152,202,178,187,96,211,114,210,223,203,185,215,132,220,210,204,181,210,222,162,121,209,225,82,203,234,214,126,224,223,102,213,125,190,219,203,204,95,134,204,220,82,171,198,120,200,88,223,222,66,205,105,162,201,86,223,98,211,189,109,211,216,168,125,182,218,173,217,152,175,212,191,218,99,187,83,212,189,133,163,114,193,209,185,210,197,77,219,195,149,200,148,209,117,217,220,213,146,221,43,212,93,109,227,84,223,218,81,218,115,219,210,83,212,76,202,58,172,221,221,103,196,227,216,184,106,197,118,210,211,170,153,218,199,111,194,218,138,221,220,94,173,203,69,229,48,221,79,176,223,213,103,216,217,166,214,225,93,217,106,221,198,111,226,77,197,201,180,96,140,219,117,209,124,215,63,161,203,225,226,154,224,84,204,227,208,99,212,128,206,156,195,206,168,209,42,218,181,202,172,198,104,138,206,100,216,110,232,113,200,99,223,96,213,72,210,194,160,176,133,209,97,196,169,161,190,203,232,49,158,171,222,188,226,184,166,166,147,222,79,195,97,218,115,222,218,191,120,190,187,218,43,181,157,148,207,61,222,55,220,219,95,232,163,194,181,166,218,150,223,117,216,200,110,194,191,129,206,204,99,227,198,195,177,159,173,221,63,196,113,203,125,185,182,184,183,170,219,199,114,216,125,217,85,221,112,187,224,78,205,224,136,143,222,115,190,229,225,200,84,206,168,176,198,228,71,99,203,107,218,157,223,179,225,63,197,106,210,188,170,185,215,119,222,64,211,110,192,40,220,142,220,215,167,215,170,225,183,132,228,197,106,211,219,217,172,207,113,223,60,194,221,72,204,193,223,205,178,178,110,216,201,197,141,80,200,227,228,76,207,215,95,222,210,144,213,192,110]
ivf.pid.pt CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:890653e68f6b688c0e6bdd0a3f6d4e1acaf771fa9e5d1b81f4ac05729073637b
3
- size 3186648
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2034933babebb706aadab76c74c3f4952dbcfc65837aeef740e6b2e99144bf7d
3
+ size 3202520
metadata.json CHANGED
@@ -37,7 +37,7 @@
37
  "checkpoint":"colbert-ir/colbertv2.0",
38
  "triples":"/future/u/okhattab/root/unit/experiments/2021.10/downstream.distillation.round2.2_score/round2.nway6.cosine.ib/examples.64.json",
39
  "collection":[
40
- "list with 6805 elements starting with...",
41
  [
42
  "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.",
43
  "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 @@
50
  "root":".ragatouille/",
51
  "experiment":"colbert",
52
  "index_root":null,
53
- "name":"2024-12/18/03.54.58",
54
  "rank":0,
55
  "nranks":1,
56
  "amp":true,
@@ -59,8 +59,8 @@
59
  },
60
  "num_chunks":1,
61
  "num_partitions":16384,
62
- "num_embeddings":1165514,
63
- "avg_doclen":171.2731814842,
64
  "RAGatouille":{
65
  "index_config":{
66
  "index_type":"PLAID",
 
37
  "checkpoint":"colbert-ir/colbertv2.0",
38
  "triples":"/future/u/okhattab/root/unit/experiments/2021.10/downstream.distillation.round2.2_score/round2.nway6.cosine.ib/examples.64.json",
39
  "collection":[
40
+ "list with 6808 elements starting with...",
41
  [
42
  "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.",
43
  "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
  "root":".ragatouille/",
51
  "experiment":"colbert",
52
  "index_root":null,
53
+ "name":"2024-12/18/04.54.10",
54
  "rank":0,
55
  "nranks":1,
56
  "amp":true,
 
59
  },
60
  "num_chunks":1,
61
  "num_partitions":16384,
62
+ "num_embeddings":1166029,
63
+ "avg_doclen":171.2733548766,
64
  "RAGatouille":{
65
  "index_config":{
66
  "index_type":"PLAID",
pid_docid_map.json CHANGED
@@ -6803,5 +6803,8 @@
6803
  "6801":"2412.11863",
6804
  "6802":"2412.09982",
6805
  "6803":"2412.09858",
6806
- "6804":"2412.11423"
 
 
 
6807
  }
 
6803
  "6801":"2412.11863",
6804
  "6802":"2412.09982",
6805
  "6803":"2412.09858",
6806
+ "6804":"2412.11423",
6807
+ "6805":"2412.13147",
6808
+ "6806":"2412.12606",
6809
+ "6807":"2412.12606"
6810
  }
plan.json CHANGED
@@ -37,7 +37,7 @@
37
  "checkpoint": "colbert-ir\/colbertv2.0",
38
  "triples": "\/future\/u\/okhattab\/root\/unit\/experiments\/2021.10\/downstream.distillation.round2.2_score\/round2.nway6.cosine.ib\/examples.64.json",
39
  "collection": [
40
- "list with 6805 elements starting with...",
41
  [
42
  "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.",
43
  "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 @@
50
  "root": ".ragatouille\/",
51
  "experiment": "colbert",
52
  "index_root": null,
53
- "name": "2024-12\/18\/03.54.58",
54
  "rank": 0,
55
  "nranks": 1,
56
  "amp": true,
@@ -59,6 +59,6 @@
59
  },
60
  "num_chunks": 1,
61
  "num_partitions": 16384,
62
- "num_embeddings_est": 1165513.9769744873,
63
- "avg_doclen_est": 171.27317810058594
64
  }
 
37
  "checkpoint": "colbert-ir\/colbertv2.0",
38
  "triples": "\/future\/u\/okhattab\/root\/unit\/experiments\/2021.10\/downstream.distillation.round2.2_score\/round2.nway6.cosine.ib\/examples.64.json",
39
  "collection": [
40
+ "list with 6808 elements starting with...",
41
  [
42
  "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.",
43
  "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
  "root": ".ragatouille\/",
51
  "experiment": "colbert",
52
  "index_root": null,
53
+ "name": "2024-12\/18\/04.54.10",
54
  "rank": 0,
55
  "nranks": 1,
56
  "amp": true,
 
59
  },
60
  "num_chunks": 1,
61
  "num_partitions": 16384,
62
+ "num_embeddings_est": 1166029.0430908203,
63
+ "avg_doclen_est": 171.2733612060547
64
  }