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arXiv 2022年

作者： Liu, Huikang Zhang, Jiaojiao So, Anthony Man-Cho Ling, Qing The Research Institute for Interdisciplinary Sciences School of Information Management and Engineering Shanghai University of Finance and Economics China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Sweden The Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong The School of Computer Science and Engineering Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University China The Pazhou Lab China

In this paper, we consider a strongly convex finite-sum minimization problem over a decentralized network and propose a communication-efficient decentralized Newton's method for solving it. The main challenges in designing such an algorithm come from three aspects: (i) mismatch between local gradients/ Hessians and the global ones;(ii) cost of sharing second-order information;(iii) tradeoff among computation and communication. To handle these challenges, we first apply dynamic average consensus (DAC) so that each node is able to use a local gradient approximation and a local Hessian approximation to track the global gradient and Hessian, respectively. Second, since exchanging Hessian approximations is far from communication-efficient, we require the nodes to exchange the compressed ones instead and then apply an error compensation mechanism to correct for the compression noise. Third, we introduce multistep consensus for exchanging local variables and local gradient approximations to balance between computation and communication. With novel analysis, we establish the globally linear (resp., asymptotically super-linear) convergence rate of the proposed method when m is constant (resp., tends to infinity), where m ≥ 1 is the number of consensus inner steps. To the best of our knowledge, this is the first super-linear convergence result for a communication-efficient decentralized Newton's method. Moreover, the rate we establish is provably faster than those of first-order methods. Our numerical results on various applications corroborate the theoretical findings. Copyright © 2022, The Authors. All rights reserved.

关键词： Newton-Raphson method

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Detection method of nickel foam foreign matter based on double-branch lightweight network

Detection method of nickel foam foreign matter based on doub...

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Chinese Automation Congress (CAC)

作者： Jianqi Li Yincong Liang Huiting Liu Binfang Cao Wei Yan Rui Du Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraft Hunan University of Arts and Sciences Changde China College of Information Science and Engineering Jishou University Changde China College of Computer and Electrical Engineering Hunan University of Arts and Sciences Changde China College of of Mathematics and Science Hunan University of Arts and Sciences Changde China College of Automation and Electronic Information Xiangtan University Xiangtan China

Real-time, accurate and rapid detection of defects in nickel foam is a challenging task and plays a crucial role in the quality control of nickel foam production. Currently, research on deep learning-based industrial defect detection usually uses large neural network models to pursue high accuracy, ignoring model complexity and hardware requirements. In order to reduce the application cost, the Detection method of nickel foam foreign matter based on double-branch lightweight network is proposed. First, a lightweight backbone convolutional network is designed to extract features, which reduces computational complexity while effectively extracting features. Then, atrous convolution is used to further optimize the model accuracy, increasing the receptive field of the convolution without losing information, and ensuring the quality of the extracted features. Finally, we use a dual-branch structure for segmentation and classification to dynamically query the detection results of defects. The experimental results on the nickel foam defect dataset show that our model is able to achieve high detection accuracy and single-sheet detection rate, and has strong competitive accuracy and lower detection rate compared with state-of-the-art detectors amount of calculation.

关键词： Convolution Computational modeling Production Quality control Object detection Feature extraction Nickel

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Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding

arXiv

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arXiv 2022年

作者： Liu, Xuejing Li, Liang Wang, Shuhui Zha, Zheng-Jun Li, Zechao Tian, Qi Huang, Qingming Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518066 China The School of Information Science and Technology University of Science and Technology of China Hefei230027 China The School of Computer Science Nanjing University of Science and Technology Nanjing210094 China Huawei Cloud & AI Shenzhen518129 China The School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100190 China The Institute of Computing Technology CAS Beijing100190 China

Weakly supervised Referring Expression Grounding (REG) aims to ground a particular target in an image described by a language expression while lacking the correspondence between target and expression. Two main problems exist in weakly supervised REG. First, the lack of region-level annotations introduces ambiguities between proposals and queries. Second, most previous weakly supervised REG methods ignore the discriminative location and context of the referent, causing difficulties in distinguishing the target from other same-category objects. To address the above challenges, we design an entity-enhanced adaptive reconstruction network (EARN). Specifically, EARN includes three modules: entity enhancement, adaptive grounding, and collaborative reconstruction. In entity enhancement, we calculate semantic similarity as supervision to select the candidate proposals. Adaptive grounding calculates the ranking score of candidate proposals upon subject, location and context with hierarchical attention. Collaborative reconstruction measures the ranking result from three perspectives: adaptive reconstruction, language reconstruction and attribute classification. The adaptive mechanism helps to alleviate the variance of different referring expressions. Experiments on five datasets show EARN outperforms existing state-of-the-art methods. Qualitative results demonstrate that the proposed EARN can better handle the situation where multiple objects of a particular category are situated together. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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A Complete Axiomatisation for Divergence Preserving Branching Congruence of Finite-State Behaviours

A Complete Axiomatisation for Divergence Preserving Branchin...

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Annual Symposium on Logic in computer Science

作者： Xinxin Liu Tingting Yu State key laboratory of Computer Science ISCAS University of Chinese Academy of Sciences RISE School of Computer and Information Science South West University China Beijing Sunwise information Technology Ltd Beijing Institute of Control Engineering

We present an equational inference system for finite-state expressions, and prove that the system is sound and complete with respect to divergence preserving branching congruence, closing a problem that has been open since 1993. The inference system refines Rob van Glabbeek's simple and elegant complete axiomatisation for branching bisimulation congruence of finite-state behaviours by joining four simple axioms after dropping one axiom which is unsound under the more refined divergence sensitive semantics.

关键词： computer science Semantics

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A Survey for Large Language Models in Biomedicine

arXiv

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arXiv 2024年

作者： Wang, Chong Li, Mengyao He, Junjun Wang, Zhongruo Darzi, Erfan Chen, Zan Ye, Jin Li, Tianbin Su, Yanzhou Ke, Jing Qu, Kaili Li, Shuxin Yu, Yi Liò, Pietro Wang, Tianyun Wang, Yu Guang Shen, Yiqing School of Medical Engineering Xinxiang Medical University Xinxiang China Engineering Technology Research Center of Neurosense and Control of Henan Province Xinxiang China Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design Xinxiang China Shanghai AI Laboratory Shanghai China Amazon Palo AltoCA United States Boston Children's Hospital MA United States Harvard Medical School Harvard University MA United States Toursun Synbio Shanghai China Department of Data Science & AI Faculty of IT Monash University Melbourne Australia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom School of Basic Medical Sciences Xinxiang Medical University Xinxiang China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Department of Computer Science Johns Hopkins University MD United States

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of Science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs. As this field of LLM rapidly evolves, continued research and development are essential to fully harness the capabilities of LLMs in biomedicine while ensuring their respon

关键词： Federated learning

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A complete axiomatisation for divergence preserving branching congruence of finite-state behaviours

arXiv

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arXiv 2021年

作者： Liu, Xinxin Yu, Tingting State Key Laboratory of Computer Science ISCAS University of Chinese Academy of Sciences RISE School of Computer and Information Science South West University China Beijing Sunwise Information Technology Ltd. Beijing Institute of Control Engineering

We present an equational inference system for finite-state expressions, and prove that the system is sound and complete with respect to divergence preserving branching congruence, closing a problem that has been open since 1993. The inference system refines Rob van Glabbeek’s simple and elegant complete axiomatisation for branching bisimulation congruence of finite-state behaviours by joining four simple axioms after dropping one axiom which is unsound under the more refined divergence sensitive semantics. © 2021, CC BY-NC-SA.

关键词： Semantics

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Community Structure Recovery and Interaction Probability Estimation for Gossip Opinion Dynamics

arXiv

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arXiv 2021年

作者： Xing, Yu He, Xingkang Fang, Haitao Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China

We study how to jointly recover the community structure and estimate the interaction probabilities of gossip opinion dynamics. In this process, agents randomly interact pairwise, and there are stubborn agents never changing their states. Such a model illustrates how disagreement and opinion fluctuation arise in a social network. It is assumed that each agent is assigned with one of two community labels, and the agents interact with probabilities depending on their labels. The considered problem is to jointly recover the community labels of the agents and estimate interaction probabilities between the agents, based on a single trajectory of the model. We first study stability and limit theorems of the model, and then propose a joint recovery and estimation algorithm based on a trajectory. It is verified that the community recovery can be achieved in finite time, and the interaction estimator converges almost surely. We derive a sample-complexity result for the recovery, and analyze the estimator’s convergence rate. Simulations are presented for illustration of the performance of the proposed algorithm. © 2021, CC BY.

关键词： Recovery

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Low-Cost Approach for Improving Video Transmission Efficiency in WVSN

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Journal of Shanghai Jiaotong university(Science) 2020年第5期25卷 600-605页

作者： LIU Min DENG Bin TANG Ying WU Minghu WANG Juan Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System.Hubei University of Technology Wuhan 430068.China Department of Electrical and Computer Engineering.Rowan University GlassboroNJ 08028USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Smart Education Qingdao Academy of Intelligent IndustriesQingdao 266041ShandongChina

The wireless visual sensor network(WVSN)as a new emerged intelligent visual system,has been applied in many video monitoring ***,there is still great challenge because of the limited wireless network *** resolve the problem,we propose a real-time dynamic texture approach which can detect and reduce the temporal redundancy during many successive image ***,an adaptively learning background model is improved to discover successive similar image frames from the inputting video ***,the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and background element ***,a background discarding strategy based on visual motion coherence is proposed to determine whether each image frame is streamed or *** evaluate the trade-off performance of the proposed method,it is tested on the CDW-2014 dataset,which can accurately detect the first foreground frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes,and the misdetection rate of the undetected foreground frames is near to *** to the original stream,it can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art methods.

关键词： wireless visual sensor network(WVSN) temporal redundancy foreground background dynamic texture

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State Estimation for Integrated Energy Systems: Motivations, Advances, and Future Work

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IEEE Transactions on Power Systems 2024年

作者： Zhao, Junbo Srivastava, Ankur Guo, Ye Cetenovic, Dragan Lin, Yuzhang Levi, Victor Yin, Guanxiong Huang, Manyun Zhang, Tingting Li, Zhigang Chen, Yanbo Exposito, Antonio G. Zhang, Wen Zheng, Weiye Chakrabarti, Saikat Li, Yang Sharma, Ankush Kamwa, Innocent Guo, Qinglai Terzija, Vladimir Mili, Lamine University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States Tsinghua University Tsinghua-Berkeley Shenzhen Institute Shenzhen518055 China The University of Manchester Department of Eee ManchesterM13 9PL United Kingdom University of Kragujevac Faculty of Technical Sciences Čačak Čačak32000 Serbia New York University Department of Electrical and Computer Engineering NY11201 United States Tsinghua University Department of Electrical Engineering Beijing100084 China Hohai University College of Energy and Electrical Engineering Nanjing210098 China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan250061 China The Chinese University of Hong Kong-Shenzhen School of Science and Engineering Shenzhen518172 China North China Electric Power University School of Electrical and Electronic Engineering Beijing102206 China University of Sevilla Spain Sevilla41092 Spain South China University of Technology School of Electrical Power Engineering Guangzhou510641 China Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India Northeast Electric Power University School of Electrical Engineering Jilin132012 China Laval University Department of Electrical and Computer Engineering QuebecQCG1V 0A6 Canada Tsinghua University State Key Laboratory of Power Systems Department of Electrical Engineering Beijing100084 China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering Falls ChurchVA22043 United States

This paper summarizes the technical activities of a three-year-long IEEE Task Force (TF) on State Estimation (SE) for Integrated Energy Systems (IES). It presents the formal definition and characteristics of IES, along with the comprehensive discussion on Electric Power Systems (EPS) model, and static and dynamic models associated with heating and natural gas systems. The paper also identifies the barriers of SE for IES, such as estimation modeling, observability analysis, and measurement requirements, together with addressing multi-scale dynamics. An extensive comparative analysis between Integrated Energy Systems-State Estimation (IES-SE) and more established Electric Power System-State Estimation (EPS-SE) is presented. The paper also provides future research needs and directions related to IES-SE. © 1969-2012 IEEE.

关键词： Power system analysis

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A global to local double embedding method for multi-person pose estimation

arXiv

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arXiv 2021年

作者： Xu, Yiming Li, Jiaxin Peng, Yiheng Ding, Yan Wei, Hualiang Yingcai Honors College University of Electronic Science and Technology of China Chengdu China Key Laboratory of Dynamics and Control of Flight Vehicle Ministry of Education School of Aerospace Engineering Beijing Institute of Technology Beijing100081 China School of Computer Science and Technology Donghua University Shanghai China Department of Automatic Control and Systems Engineering University of Shefield ShefieldS1 3JD United Kingdom

Multi-person pose estimation is a fundamental and challenging problem to many computer vision tasks. Most existing methods can be broadly categorized into two classes: top-down and bottom-up methods. Both of the two types of methods involve two stages, namely, person detection and joints detection. Conventionally, the two stages are implemented separately without considering their interactions between them, and this may inevitably cause some issue intrinsically. In this paper, we present a novel method to simplify the pipeline by implementing person detection and joints detection simultaneously. We propose a Double Embedding (DE) method to complete the multi-person pose estimation task in a global-to-local way. DE consists of Global Embedding (GE) and Local Embedding (LE). GE encodes different person instances and processes information covering the whole image and LE encodes the local limbs information. GE functions for the person detection in top-down strategy while LE connects the rest joints sequentially which functions for joint grouping and information processing in A bottom-up strategy. Based on LE, we design the Mutual Refine Machine (MRM) to reduce the prediction difficulty in complex scenarios. MRM can effectively realize the information communicating between keypoints and further improve the accuracy. We achieve the competitive results on benchmarks MSCOCO, MPII and CrowdPose, demonstrating the effectiveness and generalization ability of our method. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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