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arXiv 2020年

作者： Yan, Rui Duan, Xiaoming Shi, Zongying Zhong, Yisheng Marden, Jason R. Bullo, Francesco Department of Automation Tsinghua University Beijing100084 China Mechanical Engineering Department Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States Department of Electrical and Computer Engineering UC Santa Barbara CA93106-5070 United States

This paper introduces two metrics (cycle-based and memory-based metrics), grounded on a dynamical game-theoretic solution concept called sink equilibrium, for the evaluation, ranking, and computation of policies in multi-agent learning. We adopt strict best response dynamics (SBRD) to model selfish behaviors at a meta-level for multi-agent reinforcement learning. Our approach can deal with dynamical cyclical behaviors (unlike approaches based on Nash equilibria and Elo ratings), and is more compatible with single-agent reinforcement learning than α-rank which relies on weakly better responses. We first consider settings where the difference between largest and second largest underlying metric has a known lower bound. With this knowledge we propose a class of perturbed SBRD with the following property: only policies with maximum metric are observed with nonzero probability for a broad class of stochastic games with finite memory. We then consider settings where the lower bound for the difference is unknown. For this setting, we propose a class of perturbed SBRD such that the metrics of the policies observed with nonzero probability differ from the optimal by any given tolerance. The proposed perturbed SBRD addresses the opponent-induced non-stationarity by fixing the strategies of others for the learning agent, and uses empirical game-theoretic analysis to estimate payoffs for each strategy profile obtained due to the perturbation. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

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CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron

arXiv

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arXiv 2022年

作者： Liu, Wanli Li, Chen Xu, Ning Jiang, Tao Rahaman, Md Mamunur Sun, Hongzan Wu, Xiangchen Hu, Weiming Chen, Haoyuan Sun, Changhao Yao, Yudong Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Liaoning Petrochemical University Fushun China School of Control Engineering Chengdu University of Information Technology Chengdu China Shengjing Hospital China Medical University Shenyang China Suzhou Ruiqian Technology Company Ltd. Suzhou China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken United States Institute of Medical Informatics University of Luebeck Luebeck Germany

Cervical cancer is the seventh most common cancer among all the cancers worldwide and the fourth most common cancer among women. Cervical cytopathology image classification is an important method to diagnose cervical cancer. Manual screening of cytopathology images is time-consuming and error-prone. The emergence of the automatic computer-aided diagnosis system solves this problem. This paper proposes a framework called CVM-Cervix based on deep learning to perform cervical cell classification tasks. It can analyze pap slides quickly and accurately. CVM-Cervix first proposes a Convolutional Neural Network module and a Visual Transformer module for local and global feature extraction respectively, then a Multilayer Perceptron module is designed to fuse the local and global features for the final classification. Experimental results show the effectiveness and potential of the proposed CVM-Cervix in the field of cervical Pap smear image classification. In addition, according to the practical needs of clinical work, we perform a lightweight post-processing to compress the model. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

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Bayesian Neural Networks Uncertainty Quantification with Cubature Rules

Bayesian Neural Networks Uncertainty Quantification with Cub...

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International Joint Conference on Neural Networks (IJCNN)

作者： Peng Wang Nidhal C. Bouaynaya Lyudmila Mihaylova Jikai Wang Qibin Zhang Renke He Automatic Control & Systems Engineering Dept. The University of Sheffield Information and Navigation College Air Force Engineering University The 32nd Research Institute of CETC Department of Automation University of Science and Technology of China Department of Electrical and Computer Engineering Rowan University

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.

关键词： Neural networks Uncertainty Bayes methods Computational modeling Gaussian distribution Two dimensional displays Weather forecasting

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Decision making in multi-agent systems based on the evolutionary game with switching probabilities

Decision making in multi-agent systems based on the evolutio...

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Chinese Intelligent Systems Conference, CISC 2017

作者： Xu, Zimin Zhang, Jianlei Li, Qiaoyu Chen, Zengqiang Department of Automation College of Computer and Control Engineering Nankai University Tianjin300071 China

ISBN: (纸本)9789811064951

Much attention has been paid on exploring the solutions for cooperative dilemma in multi-agent systems. Thereinto, the evolutionary game theory which describes cooperative dilemma is seen as an effective approach. Notably, many of previous works are based on the ideal hypothesis that individuals can feasibly obtain their neighbours’ payoffs to update strategies. Considering the difficulty of getting the exact information about payoffs, we propose the switching probabilities between strategies which do not require the payoffs. Here the evolutionary dynamics driven by the switching probabilities in a three-strategy game model is established. Results show that the steady state of the gaming system is closely related with the switching probability matrix. These findings give a novel account about the decision making process in the gaming systems, when a strategy updating rule weakening the ideal assumption about payoffs is established. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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The Design of Logic Gate Based on Triplex Structures 14th

The Design of Logic Gate Based on Triplex Structures

引用

14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Hu, Yingxin Wang, Zhiyu Chen, Zhekun Liao, Sulin Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology WuhanHubei430074 China College of Information Science and Technology Shijiazhuang Tiedao University ShijiazhuangHebei050043 China School of Computer Science and Technology Huazhong University of Science and Technology WuhanHubei430074 China Department of Pathology Renmin Hospital of Wuhan University WuhanHubei430060 China

ISBN: (纸本)9789811534140

Molecular carch hotspots in the area of DNA nanotechnology due to its inherent large storage capacity, high parallelism, and energy efficiency. The assembled structures in the existing computing units mainly adopt a double-helix structure. However, besides duplex, DNA is known to adopt other helical forms such as triplex form. The triplex structure has introduced a novel paradigm into the DNA engineer owing to their unique characteristic sequence, structural and assembly requirements. Based on the assembly and disassembly of the triplex structure, a YES logic gate was constructed via strand displacement reaction. The experiment results showed that GC content and stem length would influence the displacement reaction. Then AND logic gate was also established through cooperative effect by two DNA strands. The construction of the cascading circuit indicated triplex-based logic gate could be integrated into larger circuits and enrich the toolbox of the computing devices. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Logic gates

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Domain-invariant similarity activation map contrastive learning for retrieval-based long-term visual localization

arXiv

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arXiv 2020年

作者： Hu, Hanjiang Wang, Hesheng Liu, Zhe Chen, Weidong Autonomous Robot Lab Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory of Robotics and System HIT The Department of Computer Science and Technology University of Cambridge United Kingdom

Visual localization is a crucial component in the application of mobile robot and autonomous driving. Image retrieval is an efficient and effective technique in image-based localization methods. Due to the drastic variability of environmental conditions, e.g. illumination, seasonal and weather changes, retrieval-based visual localization is severely affected and becomes a challenging problem. In this work, a general architecture is first formulated probabilistically to extract domain-invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. We also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised manner. The final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models without and with Grad-SAM loss. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons dataset. The strong generalization ability of our approach is verified on RobotCar dataset using models pre-trained on urban part of CMU-Seasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision, especially under the challenging environments with illumination variance, vegetation and night-time images. The code and pretrained models are available on https://***/HanjiangHu/DISAM. Copyright © 2020, The Authors. All rights reserved.

关键词： Image retrieval

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Reputation-based Distributed Filtering Over Sensor Networks Subject to Stochastic Nonlinearity and Network-Induced Quantization

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IEEE Transactions on Network Science and Engineering 2025年

作者： Jia, Chaoqing Wang, Zidong Hu, Jun Dong, Hongli Harbin University of Science and Technology School of Automation Harbin150080 China Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin150080 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Harbin University of Science and Technology Department of Applied Mathematics Harbin150080 China Northeast Petroleum University Artificial Intelligence Energy Research Institute Daqing163318 China Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Daqing163318 China

In sensor networks, due to inevitable sensor faults, malfunctions, or deliberate attacks, sensors may transmit erroneous, inaccurate, or misleading data, thereby degrading overall system performance. To address this issue, an effective approach is to assign reputation scores to sensors based on their trustworthiness, historical performance, or reliability. In this paper, the reputation-based distributed filtering (RBDF) problem is considered for a class of stochastic nonlinear systems over sensor networks with network-induced quantization. A reputation mechanism is employed to mitigate the adverse effects caused by noisy, faulty, or malicious sensors. Specifically, reputations are allocated by each sensor to the data received from its neighbors, ensuring that abnormal data are assigned smaller reputation values and may even be discarded. For the first time, a recursive RBDF algorithm is proposed, wherein an upper bound of the filtering error covariance (UBFEC) is derived by solving two matrix equations. Subsequently, the filter gain is determined by minimizing the trace of UBFEC at each step. Furthermore, a sufficient condition is presented to ensure the uniform boundedness of the filtering error dynamics. Finally, a simulation example is provided to verify the feasibility and validity of the developed RBDF algorithm. © 2013 IEEE.

关键词： Digital arithmetic

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Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization

arXiv

引用

arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Digital Futures Stockholm10044 Sweden The Department of Control Science and Engineering College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

This paper considers the distributed online convex optimization problem with time-varying constraints over a network of agents. This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions. At each round, each agent selects a decision from the decision set, and then only a portion of the loss function and a coordinate block of the constraint function at this round are privately revealed to this agent. The goal of the network is to minimize the network-wide loss accumulated over time. Two distributed online algorithms with full-information and bandit feedback are proposed. Both dynamic and static network regret bounds are analyzed for the proposed algorithms, and network cumulative constraint violation is used to measure constraint violation, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. In particular, we show that the proposed algorithms achieve O(T max{κ,1-κ}) static network regret and O(T 1-κ/2) network cumulative constraint violation, where T is the time horizon and κ ∈ (0, 1) is a user-defined trade-off parameter. Moreover, if the loss functions are strongly convex, then the static network regret bound can be reduced to O(T κ). Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

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An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

引用

Signal Transduction and Targeted Therapy 2021年第5期6卷 1585-1600页

作者： Yiyue Ge Tingzhong Tian Suling Huang Fangping Wan Jingxin Li Shuya Li Xiaoting Wang Hui Yang Lixiang Hong Nian Wu Enming Yuan Yunan Luo Lili Cheng Chengliang Hu Yipin Lei Hantao Shu Xiaolong Feng Ziyuan Jiang Yunfu Wu Ying Chi Xiling Guo Lunbiao Cui Liang Xiao Zeng Li Chunhao Yang Zehong Miao Ligong Chen Haitao Li Hainian Zeng Dan Zhao Fengcai Zhu Xiaokun Shen Jianyang Zeng Institute for Interdisciplinary Information Sciences Tsinghua UniversityBeijingChina NHC Key laboratory of Emeric Pathogenic Microbiology Jiangsu Provincial Center for Diseases Control and PreventionNanjingJiangsu ProvinceChina Shanghai Institute of Materia Medica Chinese Academy of SciencesShanghaiChina Silexon Al Technology Co. Ltd.NanjingJiangsu ProvinceChina Department of Computer Science University of Illinois at Urbana-ChampaignIllinoisILUSA School of Pharmaceutical Sciences Tsinghua UniversityBeijingChina School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhanHubei ProvinceChina Institute of Pathology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanHubei ProvinceChina Department of Automation Tsinghua UniversityBeijingChina Inner Mongolia Alashan League Organization Establishment Committee Office Electronic Support Center AlashanInner MongoliaChina Convalife(Shanghai)Co. Ltd.ShanghaiChina Advanced Innovation Center for Human Brain Protection Beijing Tiantan HospitalCapital Medical UniversityBeijingChina Beijing Advanced Innovation Center for Structural Biology Tsinghua-Peking Joint Center for Life SciencesDepartment of Basic Medical SciencesSchool of MedicineTsinghua UniversityBeijingChina Center for Global Health Nanjing Medical UniversityNanjingJiangsu ProvinceChina

The global spread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019(COVID-19).In this study,we developed an integrative drug repositioning framework,which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph,literature and transcriptome data to discover the potential drug candidates against *** in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1(PARP1)inhibitor,CVL218,currently in Phase I clinical trial,may be repurposed to treat *** in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic *** addition,we showed that CVL218 can interact with the nucleocapsid(N)protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.

关键词： prevention respiratory acute

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Reliability, fault tolerance and other critical components for survivability in information warfare 1

引用

14th International Joint Conference on e-Business and Telecommunications, ICETE 2017

作者： Stavroulakis, Peter Kolisnyk, Maryna Kharchenko, Vyacheslav Doukas, Nikolaos Markovskyi, Oleksandr P. Bardis, Nikolaos G. Chania Greece Department of Computer Systems and Networks National Aerospace University "KhAI" Kharkiv Ukraine Hellenic Army Academy Varis – Koropiou Avenue Vari 116 72 Greece Department of Computer Engineering National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kiev Ukraine Department of Automation and Control in Technical Systems National Technical University "KPI" Kharkiv Ukraine

ISBN: (数字)9783030110390

ISBN: (纸本)9783030110383

The information revolution has caused many aspects of human activity to critically depend on a wide variety of physically existing or virtual technological achievements such as electronic devices, computer systems, algorithms, cloud resources, artificial intelligence hardware and software entities etc. Many of these systems are used in highly sensitive contexts, such as military applications. This implies the existence of an increasing number of unintentional disturbances or malicious attacks. Successful operation requires qualities such as robustness, fault tolerance, reliability, availability and security. All these may be summarized by the title of survivability. Survivability of critical systems working for sensitive applications involves the ability to provide uninterrupted operation under severe disturbances, gracefully degrade when limiting conditions are reached and maintain the ability to resume normal service once the disturbances have been removed. Survivability is an important, even - though non – functional, lifecycle property of many engineering systems. Further desirable elements of survivability include the ability of systems to recognize and resist attacks or accidents, adapt in order to avoid them and modify their behavior in order to diminish the effects of similar future occurrences. This chapter presents a quantitative approach to assessing survivability and an account of survivability in military systems. A scheme for survivability via replica diversity in the implementation of the AES algorithm is then presented. Following that, an algorithm for adaptive attack aversion in user authentication systems is presented that is based on Boolean transformations. An approach for increased survivability in Internet of Things (IoT) systems is then presented. Finally, an algorithm for secure data storage in cloud resources is presented that allows attack detection and avoidance. © Springer Nature Switzerland AG 2019.

关键词： Fault tolerance

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