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arXiv 2024年

作者： Liu, Yiqun Zhang, Ke Zhu, Yin School of Information Technology and Media Hexi University Zhangye734000 China Transportation Research Center Tsinghua University China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China

Ground reaction force (GRF) is the force exerted by the ground on a body in contact with it. GRF-based automatic disease detection (ADD) has become an emerging medical diagnosis method, which aims to learn and identify disease patterns corresponding to different gait pressures based on deep learning methods. Although existing ADD methods can save doctors time in making diagnoses, training deep models still struggles with the cost caused by the labeling engineering for a large number of gait diagnostic data for subjects. On the other hand, the accuracy of the deep model under the unified benchmark GRF dataset and the generalization ability on scalable gait datasets need to be further improved. To address these issues, we propose MA2, a GRF-based self-supervised and motion augmenting autoencoder, which innovatively models the ADD task as an encoder-decoder paradigm. In the encoder, we introduce an embedding block including the 3-layer 1D convolution for extracting the token and a mask generator to randomly mask out the sequence of tokens to maximize the model’s potential to capture high-level, discriminative, intrinsic representations. whereafter, the decoder utilizes this information to reconstruct the pixel sequence of the origin input and calculate the reconstruction loss to optimize the network. Moreover, the backbone of an autoencoder is multi-head self-attention that can consider the global information of the token from the input, not just the local neighborhood. This allows the model to capture generalized contextual information. Extensive experiments demonstrate MA2 has SOTA performance of 90.91% accuracy on 1% limited pathological GRF samples with labels, and good generalization ability of 78.57% accuracy on scalable Parkinson’s disease dataset. See supplementary materials for source code, and two pre-processed datasets are downloadable at https://***/EchoItLiu/MA2-PyTorch. © 2024, CC BY-NC-ND.

关键词： Cost engineering

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Fourier-Reflexive Partitions Induced by Poset Metric

Fourier-Reflexive Partitions Induced by Poset Metric

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作者： Xu, Yang Kan, Haibin Han, Guangyue Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Fudan-Zhongan Joint Laboratory of Blockchain and Information Security Shanghai Engineering Research Center of Blockchain Fudan University Shanghai200433 China Department of Mathematics Faculty of Science The University of Hong Kong Hong Kong

Let H be the cartesian product of a family of finite abelian groups indexed by a finite set Ω. A given poset (i.e., partially ordered set) P = (Ω, ≤P) gives rise to a poset metric on H, which further leads to a partition Q(H, P) of H. We prove that if Q(H, P) is Fourier-reflexive, then its dual partition Λ coincides with the partition of Ĥ induced by P, the dual poset of P, and moreover, P is necessarily hierarchical. This result establishes a conjecture proposed by Gluesing-Luerssen in Gluesing-Luerssen, 2015. We also show that with some other assumptions, Λ is finer than the partition of Ĥ induced by P. In addition, we give some necessary and sufficient conditions for P to be hierarchical, and for the case that P is hierarchical, we give an explicit criterion for determining whether two codewords in Ĥ belong to the same block of Λ. We prove these results by relating the involved partitions with certain family of polynomials, a generalized version of which is also proposed and studied to generalize the aforementioned results © 1963-2012 IEEE.

关键词： Group theory

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Revealing Performance Issues in Server-side WebAssembly Runtimes via Differential Testing

arXiv

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arXiv 2023年

作者： Jiang, Shuyao Zeng, Ruiying Rao, Zihao Gu, Jiazhen Zhou, Yangfan Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

WebAssembly (Wasm) is a bytecode format originally serving as a compilation target for Web applications. It has recently been used increasingly on the server side, e.g., providing a safer, faster, and more portable alternative to Linux containers. With the popularity of server-side Wasm applications, it is essential to study performance issues (i.e., abnormal latency) in Wasm runtimes, as they may cause a significant impact on server-side applications. However, there is still a lack of attention to performance issues in server-side Wasm runtimes. In this paper, we design a novel differential testing approach WarpDiff to identify performance issues in server-side Wasm runtimes. The key insight is that in normal cases, the execution time of the same test case on different Wasm runtimes should follow an oracle ratio. We identify abnormal cases where the execution time ratio significantly deviates from the oracle ratio and subsequently locate the Wasm runtimes that cause the performance issues. We apply WarpDiff to test five popular server-side Wasm runtimes using 123 test cases from the LLVM test suite and demonstrate the top 10 abnormal cases we identified. We further conduct an in-depth analysis of these abnormal cases and summarize seven performance issues, all of which have been confirmed by the developers. We hope our work can inspire future investigation on improving Wasm runtime implementation and thus promoting the development of server-side Wasm applications. Copyright © 2023, The Authors. All rights reserved.

关键词： computer operating systems

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UNRAVELING THE ENIGMA OF DOUBLE DESCENT: AN IN-DEPTH ANALYSIS THROUGH THE LENS OF LEARNED FEATURE SPACE

arXiv

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arXiv 2023年

作者： Gu, Yufei Zheng, Xiaoqing Aste, Tomaso School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China Department of Computer Science University College London United Kingdom

Double descent presents a counter-intuitive aspect within the machine learning domain, and researchers have observed its manifestation in various models and tasks. While some theoretical explanations have been proposed for this phenomenon in specific contexts, an accepted theory for its occurring mechanism in deep learning remains yet to be established. In this study, we revisit the phenomenon of double descent and demonstrate that the presence of noisy data strongly influences its occurrence. By comprehensively analysing the feature space of learned representations, we unveil that double descent arises in imperfect models trained with noisy data. We argue that while small and intermediate models before the interpolation threshold follow the traditional bias-variance trade-off, over-parameterized models interpolate noisy samples among robust data thus acquiring the capability to separate the information from the noise. The source code is available at https://***/Yufei-Gu-451/double_descent_***. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

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Newborn emergency transport based on the fifth-generation wireless networks and blockchain

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World Journal of Pediatrics 2022年第7期18卷 520-524页

作者： Jin-Wu Fang Wei-Jia Fu Rui Feng Hong-Tao Ni Yun Cao Cheng-Jie Ye Ying Gu Xiao-Ling Ge Fan Zhang Long-Quan Jiang Juan-Wei Xing Liang-Feng Tang Ying-Ying Zhang Jian-Guo Zhou Ru-Yi Zheng Hong Xu Xiao-Bo Zhang Department of Epidemiology School of Public HealthFudan UniversityShanghaiChina Children's Hospital of Fudan University ShanghaiChina Shanghai Key Laboratory of Intelligent Information.Processing School of Computer ScienceFudan UniversityShanghaiChina China Telecom(Shanghai)Co. LidShanghaiChina Department of Respiratory Children's Hospital of Fudan UniversityNo.399 Wanyuan RoadShanghai 201102China Department of Nephrology Children's Hospital of Fudan UniversityNo.399 Wanyuan RoadShanghai 201102China

Neonatal care is of great significance to health *** neonatal intensive care units(NICUs)started to be established in the late 1980s in China,neonatal mortal-ity has decreased each year to reach 3.4‰in 2020[1,2].However,the improvement for neonatal care is still on the *** than three-fifths of deaths included critically ill neonates occur in their first 28 days[3].Critically ill neo-nates usually have lower gestational age and body weight,leading to high mortality or requiring specific treatment and monitoring[4].It is a common practice to transfer a critically ill newborn patient to a tertiary children's hospi-tal with an excellent ***,it is crucial to pro-mote patients'safety by improving the neonatal transport network services[5].Owing to the complexity and rapid changes in neonatal conditions,especially for those requir-ing multi-disciplinary treatment,there is a high reliance on a specialized newborn transport team[6,7].

关键词： mortality transport treatment

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Meta-ZSDETR: Zero-shot DETR with Meta-learning

Meta-ZSDETR: Zero-shot DETR with Meta-learning

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International Conference on computer Vision (ICCV)

作者： Lu Zhang Chenbo Zhang Jiajia Zhao Jihong Guan Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China Department of Computer Science & Technology Tongji University China

Zero-shot object detection aims to localize and recognize objects of unseen classes. Most of existing works face two problems: the low recall of RPN in unseen classes and the confusion of unseen classes with background. In this paper, we present the first method that combines DETR and meta-learning to perform zero-shot object detection, named Meta-ZSDETR, where model training is formalized as an individual episode based meta-learning task. Different from Faster R-CNN based methods that firstly generate class-agnostic proposals, and then classify them with visual-semantic alignment module, Meta-ZSDETR directly predict class-specific boxes with class-specific queries and further filter them with the predicted accuracy from classification head. The model is optimized with meta-contrastive learning, which contains a regression head to generate the coordinates of class-specific boxes, a classification head to predict the accuracy of generated boxes, and a contrastive head that utilizes the proposed contrastive-reconstruction loss to further separate different classes in visual space. We conduct extensive experiments on two benchmark datasets MS COCO and PASCAL VOC. Experimental results show that our method outperforms the existing ZSD methods by a large margin.

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Soccer match broadcast video analysis method based on detection and tracking

Soccer match broadcast video analysis method based on detect...

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作者： Li, Hongyu Yang, Meng Yang, Chao Kang, Jianglang Suo, Xiang Meng, Weiliang Li, Zhen Mao, Lijuan Sheng, Bin Qi, Jun School of Information science and Technology Beijing Forestry University Beijing China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing China School of Athletic Performance Shanghai University of Sport Shanghai China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong Univerity Shanghai China Department of Computing Xi'an JiaoTong-Liverpool University Suzhou China

We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two-dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high-speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non-trivial. To mitigate this, we curate a large-scale, high-precision soccer ball detection dataset and devise a robust detection model, which achieved the (Formula presented.) of 80.9%. Additionally, we develop a high-speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real-time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations. © 2024 John Wiley & Sons Ltd.

关键词： Visualization

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Meta-ZSDETR: Zero-shot DETR with Meta-learning

arXiv

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arXiv 2023年

作者： Zhang, Lu Zhang, Chenbo Zhao, Jiajia Guan, Jihong Zhou, Shuigeng Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China Department of Computer Science & Technology Tongji University China

关键词： Object detection

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RARN: A Real-Time Skeleton-based Action Recognition Network for Auxiliary Rehabilitation Therapy

RARN: A Real-Time Skeleton-based Action Recognition Network ...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Mengqi Shen Hong Lu Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

ISBN: (数字)9781665484855

ISBN: (纸本)9781665484862

Rehabilitation gymnastic training is an effective therapy for degeneration of spine disease in Traditional Chinese Medicine (TCM). In this paper, we propose a lightweight real-time Rehabilitation Action Recognition Network (RARN) using skeleton sequence obtained through 2d pose estimation and build an intelligent auxiliary rehabilitation therapy system. We design a set of training exercises consisting of 8 actions, and construct a dataset called Rehabilitation Action for Degenerative Spine Diseases (RDSD), containing 1012 skeleton sequences. We describe a demo application to conduct real-time action evaluation for rehabilitation therapy. The experimental results on RDSD shows that our system achieves high accuracy while still working under 6ms per frame averagely and the inference costs only about 0. 2ms per frame.

关键词： Training Costs Circuits and systems Pose estimation Medical treatment Real-time systems Skeleton

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TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities

arXiv

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arXiv 2024年

作者： Zhang, Ming Huang, Caishuang Wu, Yilong Liu, Shichun Zheng, Huiyuan Dong, Yurui Shen, Yujiong Dou, Shihan Zhao, Jun Ye, Junjie Zhang, Qi Gui, Tao Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Institute of Modern Languages and Linguistics Fudan University China

Task-oriented dialogue (TOD) systems aim to efficiently handle task-oriented conversations, including information collection. How to utilize TOD accurately, efficiently and effectively for information collection has always been a critical and challenging task. Recent studies have demonstrated that Large Language Models (LLMs) excel in dialogue, instruction generation, and reasoning, and can significantly enhance the performance of TOD through fine-tuning. However, current datasets primarily cater to user-led systems and are limited to predefined specific scenarios and slots, thereby necessitating improvements in the proactiveness, diversity, and capabilities of TOD. In this study, we present a detailed multi-domain task-oriented data construction process for conversations, and a Chinese dialogue dataset generated based on this process, TransferTOD, which authentically simulates human-computer dialogues in 30 popular life service scenarios. Leveraging this dataset, we trained a model using full-parameter fine-tuning called TransferTOD-7B, showcasing notable abilities in slot filling and questioning. Our work has demonstrated its strong generalization capabilities in various downstream scenarios, significantly enhancing both data utilization efficiency and system performance. The data is released in https://***/KongLongGeFDU/TransferTOD. Copyright © 2024, The Authors. All rights reserved.

关键词： Human computer interaction

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