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arXiv 2024年

作者： Pu, L.I. Xiaoyan, Y.U. Peng, Hao Xian, Yantuan Wang, Linqin Li, S.U.N. Zhang, Jingyun Philip, S.Y.U. Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Cyber Science and Technology Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent years, Graph Neural Network (GNN) based solutions have achieved state-of-the-art performance. However, GNN-based methods often struggle with missing and noisy edges between messages, affecting the quality of learned message embedding. Moreover, these methods statically initialize node embedding before training, which, in turn, limits the ability to learn from message texts and relations simultaneously. In this paper, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present RPLM (Relational prompt-based Pre-trained Language Models for Social Event Detection). We first propose a new pairwise message modeling strategy to construct social messages into message pairs with multi-relational sequences. Secondly, a new multi-relational prompt-based pairwise message learning mechanism is proposed to learn more comprehensive message representation from message pairs with multi-relational prompts using PLMs. Thirdly, we design a new clustering constraint to optimize the encoding process by enhancing intra-cluster compactness and inter-cluster dispersion, making the message representation more distinguishable. We evaluate the RPLM on three real-world datasets, demonstrating that the RPLM model achieves state-of-the-art performance in offline, online, low-resource, and long-tail distribution scenarios for social event detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Reduced Network Cumulative Constraint Violation for Distributed Bandit Convex Optimization under Slater’s Condition

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Ding, Jinliang Cao, Ming Johansson, Karl H. Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201800 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater’s condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal–dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes an O(T max{c,1−c})network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the total number of iterations and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds, the network cumulative constraint violation bound is reduced to O(T 1−c). In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced to O(T1−c), and the network cumulative constraint violation bound is O(T1−c/2) and O(T1−c) without and with Slater’s condition, respectively. For the case where strongly convex parameters are known, the network regret bound is further reduced to O)(log(T)), and the network cumulative constraint violation bound is reduced to O(log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater’s condition. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2

关键词： Convex optimization

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Multi-view hybrid embedding: A divide-and-conquer approach

arXiv

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arXiv 2018年

作者： Xu, Jiamiao Yu, Shujian You, Xinge Leng, Mengjun Jing, Xiao-Yuan Chen, C. L. Philip School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Science University of Houston HoustonTX77204 United States State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multi-view data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divideand-Conquer strategy, we propose Multi-view Hybrid Embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase discriminability in intra-view and interview samples respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate overwhelming advantages against the stateof-the-art MvSL based cross-view classification approaches in terms of classification accuracy and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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Grasp-Anything: Large-scale Grasp Dataset from Foundation Models

Grasp-Anything: Large-scale Grasp Dataset from Foundation Mo...

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IEEE International Conference on Robotics and automation (ICRA)

作者： A. D. Vuong M. N. Vu H. Le B. Huang H. T. T. Binh T. Vo A. Kugi A. Nguyen FPT Software AI Center Vietnam Automation & Control Institute TU Wien Vienna Austria AIT Austrian Institute of Technology Vienna Austria Imperial College London UK Hanoi University of Science and Technology Vietnam Faculty of Mathematics and Statistics Ton Duc Thang University Ho Chi Minh City Vietnam Department of Computer Science University of Liverpool UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Foundation models such as ChatGPT have made significant strides in robotic tasks due to their universal representation of real-world domains. In this paper, we leverage foundation models to tackle grasp detection, a persistent challenge in robotics with broad industrial applications. Despite numerous grasp datasets, their object diversity remains limited compared to real-world figures. Fortunately, foundation models possess an extensive repository of real-world knowledge, including objects we encounter in our daily lives. As a consequence, a promising solution to the limited representation in previous grasp datasets is to harness the universal knowledge embedded in these foundation models. We present Grasp-Anything, a new large-scale grasp dataset synthesized from foundation models to implement this solution. Grasp-Anything excels in diversity and magnitude, boasting 1M samples with text descriptions and more than 3M objects, surpassing prior datasets. Empirically, we show that Grasp-Anything successfully facilitates zero-shot grasp detection on vision-based tasks and real-world robotic experiments. Our dataset and code are available at https://***/grasp-anything/.

关键词： Codes Service robots Chatbots Task analysis

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Krasovskii and Shifted Passivity Based Output Consensus

arXiv

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arXiv 2022年

作者： Kawano, Yu Cucuzzella, Michele Feng, Shuai Scherpen, Jacquelien M.A. Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima739-8527 Japan Department of Electrical Computer and Biomedical Engineering University of Pavia PV Pavia27100 Italy School of Automation Nanjing University of Science and Technology Nanjing210094 China Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands

Motivated by current sharing in power networks, we consider a class of output consensus (also called agreement) problems for nonlinear systems, where the consensus value is determined by external disturbances, e.g., power demand. This output consensus problem is solved by a simple distributed output feedback controller if a system is either Krasovskii or shifted passive, which is the only essential requirement. The effectiveness of the proposed controller is shown in simulation on an islanded DC power network. Copyright © 2022, The Authors. All rights reserved.

关键词： Nonlinear systems

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Modal regression based structured low-rank matrix recovery for multi-view learning

arXiv

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arXiv 2020年

作者： Xu, Jiamiao Wang, Fangzhao Peng, Qinmu You, Xinge Wang, Shuo Jing, Xiao-Yuan Philip Chen, C.L. School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute China State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau 99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously, which thus leads to the performance degradation when there is a large discrepancy among multi-view data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such limitation, modal regression is elegantly incorporated into the framework of SLMR (term it MR-SLMR). Different from previous LMvSL based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to efficiently optimize MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Preconditioning for the integration of a spatiotemporal pharmacodynamic system

Preconditioning for the integration of a spatiotemporal phar...

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92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2022

作者： Tebbens, Jurjen Duintjer Lanzendörfer, Martin Matonoha, Ctirad Papáček, Štěpán Department of Computational Mathematics Institute of Computer Science of the Czech Academy of Sciences Pod Vodárenskou věží 271/2 Praha-Libeň 182 07 Czech Republic Department of Biophysics and Physical Chemistry Faculty of Pharmacy in Hradec Kralove Charles University Heyrovského 1203 Hradec Králové500 05 Czech Republic Institute of Hydrogeology Engi neering Geology and Applied Geophysics Faculty of Science Charles University Albertov 6 Praha 2 128 43 Czech Republic Department of Control Theory Institute of Information Theory and Automation of the Czech Academy of Sciences Pod Vodárenskou věží 4 Praha-Libeň 182 00 Czech Republic

We address efficient modeling of the intracellular action of a drug binding to the nuclear pregnane X receptor. The binded complex enters the cell nucleus and acts on DNA, resulting in enhanced production of an enzyme which metabolizes, among others, co-administered drugs. Pharmacodynamic modeling is traditionally based on a compartmental approach, which simplifies the complex processes in the human body through the definition of a small number of compartments representing organs, tissues, cells, cytoplasm or abstract units. Inside a compartment, the concentrations of all substances are assumed to be distributed homogeneously, i.e. there is no spatial dependence. They lead to a system of ODEs for the time-dependence of the concentrations of the active substances. We will present an extension of a model from the literature for the action of the tuberculose-drug Rifampicin. The extension consists of a first attempt to add spatial resolution for substances that are active in the cytoplasm. Spatial resolution can be benecifial to analyze important issues like, among others, local exceeding of toxic drug levels, delay of transport and drug-drug interactions. We address efficient solution of the linear systems arising when numerically integrating the resulting PDE’s and consider some preconditioning techniques based on properties of the underlying biochemical network. © 2023 Wiley-VCH GmbH.

关键词： Linear systems

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Optimizing Supervision and control for Industrial Furnaces: Predictive control Based Design

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IFAC Proceedings Volumes 2004年第19期37卷 17-28页

作者： Georgi M. Dimirovski A. Talha Dinibütün Miomir K. Vukobratovic J. Zhao SS Cyril and Methodius University Faculty of Electrical Engineering MK-1000 Skopje Republic of Macedonia Dogus University Computer Engineering Department Faculty of Engineering Acibadem Zeamet Sk. 21 Kadikoy TR-34722 Istanbul Turkey Institute “Mihailo Pupin” Centre of Robotics and Flexible Automation 11000 Belgrade Serbia and Montenegro Northeastern University School of Information Science and Engineering Institute of Control Theory Shenyang Liaoning 110004 P.R. of China

A systems re-engineering technique to integrated control and supervision for applications to industrial multi-zone furnaces has been elaborated by using known theories on generalized predictive control and nonlinear programming. This paper presents the derivation of optimizing equations and inequalities. The design is based on the use of general predictive control to provide optimized set-points under the presumption a well designed regulatory control was implemented at the executive level. Digital implementation of control functions are sought within standard computer process control platform for practical engineering and maintenance reasons.

关键词： Complex time-delay processes generalized predictive control integrated control and supervision optimization set-point control

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Enhancing Precision Agriculture Through Human-in-the-Loop Planning and control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

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IEEE International Conference on automation science and Engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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Delay-dependent stability of 2D state-delayed linear systems

Delay-dependent stability of 2D state-delayed linear systems

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： W. Paszke J. Lam K. Galkowski Shengyuan Xu E. Rogers A. Kummert Institute of Control and Computation Engineering University of Zielona Góra Zielona Gora Poland Department of Mechanical Engineering University of Hong Kong Hong Kong China Department of Automation Nanjing University of Science and Technology of PLA Nanjing China Faculty of Electrical Information and Media Engineering University of Wuppertal Wuppertal Germany School of Electronics and Computer Science University of Southampton Southampton UK

This paper addresses the problem of stability for two-dimensional systems with delays in the state. To solve this problem, the Lyapunov second method is used. The resulting condition is written in terms of linear matrix inequalities and it is dependent on the size of delays. This fact allows us to reduce the conservatism in the stability analysis of two-dimensional systems with state delays. A simulation example is given to illustrate the theoretical developments

关键词： Stability Linear systems Delay systems Symmetric matrices control systems Linear matrix inequalities Delay lines Automatic control control engineering computing Mechanical engineering

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