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arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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Emergent Ferromagnetism in CaRuO₃/CaMnO₃ (111)-Oriented Superlattices

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Nano Letters 2024年第8期24卷 2567-2573页

作者： Kane, Margaret Bhandari, Churna Holtz, Megan E. Balakrishnan, Purnima P. Grutter, Alexander J. Fitzsimmons, Michael Yang, Chao-Yao Satpathy, Sashi Paudyal, Durga Suzuki, Yuri Department of Materials Science and Engineering Stanford University Stanford 94305 CA United States Ames National Laboratory Iowa State University Ames 50011 IA United States Metallurgical and Materials Engineering Colorado School of Mines Golden 80401 CO United States NIST Center for Neutron Research National Institute of Standards and Technology Gaithersburg 20899 MD United States Neutron Scattering Division Oak Ridge National Laboratory Oak Ridge 37830 TN United States Department of Material Science and Engineering National Yang Ming Chiao Tung University Hsinchu City 30100 Taiwan Department of Physics & Astronomy University of Missouri Columbia 65211 MO United States Department of Applied Physics Stanford University Stanford 94305 CA United States Geballe Laboratory for Advanced Materials Stanford University Stanford 94305 CA United States Department of Electrical and Computer Engineering Iowa State University Ames 50011 IA United States

The boundary between CaRuO3 and CaMnO3 is an ideal test bed for emergent magnetic ground states stabilized through interfacial electron interactions. In this system, nominally antiferromagnetic and paramagnetic materi... 详细信息

The boundary between CaRuO₃ and CaMnO₃ is an ideal test bed for emergent magnetic ground states stabilized through interfacial electron interactions. In this system, nominally antiferromagnetic and paramagnetic materials combine to yield interfacial ferromagnetism in CaMnO₃ due to electron leakage across the interface. In this work, we show that the crystal symmetry at the surface is a critical factor determining the nature of the interfacial interactions. Specifically, by growing CaRuO₃/CaMnO₃ heterostructures along the (111) instead of the (001) crystallographic axis, we achieve a 3-fold enhancement of the magnetization and involve the CaRuO₃ layers in the ferromagnetism, which now spans both constituent materials. The stabilization of a net magnetic moment in CaRuO₃ through strain effects has been long-sought but never consistently achieved, and our observations demonstrate the importance of interface engineering in the development of new functional heterostructures. © 2024 American Chemical Society.

关键词： complex oxides emergent magnetism manganites ruthenatess superlattices

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6D Movable Antenna Enhanced Multi-Access Point Coordination via Position and Orientation Optimization

arXiv

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arXiv 2024年

作者： Pi, Xiangyu Zhu, Lipeng Mao, Haobin Xiao, Zhenyu Xia, Xiang-Gen Zhang, Rui School of Electronic and Information Engineering State Key Laboratory of CNS/ATM Beihang University Beijing100191 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

The effective utilization of unlicensed spectrum is regarded as an important direction to enable the massive access and broad coverage for next-generation wireless local area network (WLAN). Due to the crowded spectrum occupancy and dense user terminals (UTs), the conventional fixed antenna (FA)-based access points (APs) face huge challenges in realizing massive access and interference cancellation. To address this issue, in this paper we develop a six-dimensional movable antenna (6DMA) enhanced multi-AP coordination system for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP’s antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Moreover, to further reduce the overhead of antenna movement, we propose an offline solution for APV and AOM design based on statistical channel state information (CSI). In addition, we further extend the proposed scheme from uni-polarized to dual-polarized modes for all antennas. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and both of the online and offline 6DMA sch

关键词： Convex optimization

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Physical Safety and Cyber Security Analysis of Multi-Agent Systems:A Survey of Recent Advances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 319-333页

作者： Dan Zhang Gang Feng Yang Shi Dipti Srinivasan the Research Center of Automation and Artificial Intelligence Zhejiang University of TechnologyHangzhou310023 Key Laboratory of Advanced Control and Optimization for Chemical Processes(East China University of Science and Technology) Ministry of EducationShanghai 200237China the Department of Biomedical Engineering City University of Hong KongHong KongChina the Department of Mechanical Engineering University of VictoriaCanada the Department of Electrical and Computer Engineering National University of SingaporeSingapore

Multi-agent systems(MASs)are typically composed of multiple smart entities with independent sensing,communication,computing,and decision-making ***,MASs have a wide range of applications in smart grids,smart manufacturing,sensor networks,and intelligent transportation *** of the MASs are often coordinated through information interaction among agents,which is one of the most important factors affecting coordination and cooperation ***,unexpected physical faults and cyber attacks on a single agent may spread to other agents via information interaction very quickly,and thus could lead to severe degradation of the whole system performance and even destruction of *** paper is concerned with the safety/security analysis and synthesis of MASs arising from physical faults and cyber attacks,and our goal is to present a comprehensive survey on recent results on fault estimation,detection,diagnosis and fault-tolerant control of MASs,and cyber attack detection and secure control of MASs subject to two typical cyber ***,the paper concludes with some potential future research topics on the security issues of MASs.

关键词： Consensus deception attack deny-of-service(DoS)attack fault detection fault estimation fault tolerant control multiagent systems

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Using Aerial Videos for Natural Disaster Damage Assessment 7

Using Aerial Videos for Natural Disaster Damage Assessment

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7th International Youth Conference on Radio Electronics, electrical and Power engineering, REEPE 2025

作者： Krichen, Moez Abdalzaher, Mohamed S. Shaaban, Mostafa Aburukba, Raafat Al-Baha University Al-Baha Saudi Arabia ReD Cad Laboratory University of Sfax Sfax Tunisia American University of Sharjah Electrical Engineering Department Sharjah United Arab Emirates National Research Institute of Astronomy and Geophysics Seismology Department Cairo Egypt Energy Water and Sustainable Environment Research Center American University of Sharjah United Arab Emirates American University of Sharjah Computer Science and Engineering Department Sharjah United Arab Emirates

ISBN: (纸本)9798331531836

Rapid damage assessment is crucial in the aftermath of natural disasters, enabling emergency responders to efficiently allocate resources and facilitate recovery efforts. This paper investigates the potential of utilizing satellite imagery and drone (UAV) technology to enhance the speed and accuracy of disaster damage assessment. We explore the various applications of satellite imagery, from pre-disaster risk assessment to post-disaster situational awareness and reconstruction monitoring. The advantages of UAV-captured data for detailed, on-the-ground damage evaluation are also discussed. The paper then delves into the role of advanced analytics, such as convolutional neural networks (CNNs) and semantic segmentation, in automating the analysis of satellite and UAV imagery for rapid damage mapping and classification. Additionally, we highlight the value of integrating crowdsourced data with remote sensing inputs to further improve situational awareness and damage reporting. The paper also examines the use of 3- D reconstruction techniques that leverage multiple data sources to create detailed models for damage assessment and recovery planning. By synthesizing the latest advancements in satellite technology, drone capabilities, and artificial intelligence, this paper presents a comprehensive framework for leveraging these tools to enable more effective and efficient disaster response and recovery operations. The findings and recommendations outlined in this work can inform both academic research and practical applications in the field of disaster management. © 2025 IEEE.

关键词： Disaster Management Remote Sensing Satellite Imagery Unmanned Aerial Vehicles

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DisCOV: Distributed COVID-19 Detection on X-Ray Images with Edge-Cloud Collaboration

DisCOV: Distributed COVID-19 Detection on X-Ray Images with ...

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2022 IEEE World Congress on Services, SERVICES 2022

作者： Xu, Xiaolong Tian, Hao Zhang, Xuyun Qi, Lianyong He, Qiang Dou, Wanchun Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Macquarie University Department of Computing Sydney Australia Qufu Normal University School of Information Science and Engineering Rizhao China Swinburne University of Technology School of Software and Electrical Engineering Melbourne Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services computing DOI 10.1109/TSC.2022.3142265]

ISBN: (数字)9781665481311

ISBN: (纸本)9781665481311

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services computing DOI 10.1109/TSC.2022.3142265]

关键词： COVID-19 Image edge detection Collaboration X-ray imaging

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RSRP Measurement Based Channel Autocorrelation Estimation for IRS-Aided Wideband Communication

RSRP Measurement Based Channel Autocorrelation Estimation fo...

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IEEE Conference on Wireless Communications and Networking

作者： He Sun Lipeng Zhu Weidong Mei Rui Zhang Department of Electrical and Computer Engineering National University of Singapore Singapore National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China China School of Science and Engineering Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen Guangdong China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed significant challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To address these challenges, we propose a novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems. This framework relies only on the easily accessible reference signal received power (RSRP) measurements at users in existing wideband communication systems, without requiring additional pilot transmission. Based on the estimates of channel autocorrelation matrix, the passive reflection of IRS is optimized to maximize the average user received signal-to-noise ratio (SNR) over all subcarriers in the OFDM system. Numerical results verify that the proposed algorithm significantly outperforms existing power-measurement-based IRS reflection designs in wideband channels.

关键词： Power measurement Upper bound OFDM Simulation Channel estimation Estimation Reflection Autocorrelation Wideband Signal to noise ratio

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MR2CPPIS: Accurate prediction of protein–protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism

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computers in Biology and Medicine 2024年 176卷 108543-108543页

作者： Gong, Yinyin Li, Rui Liu, Yan Wang, Jilong Cao, Buwen Fu, Xiangzheng Li, Renfa Chen, Danny Z. College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Hunan University Changsha410082 China Peng Cheng Laboratory Shenzhen518066 China College of Information and Electronic Engineering Hunan City University Yiyang413002 China Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States

Proteins play a vital role in various biological processes and achieve their functions through protein–protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costly, labor-intensive, and time-consuming. The development of computational prediction methods for PPI sites offers promising alternatives. Most known deep learning (DL) methods employ layer-wise multi-scale CNNs to extract features from protein sequences. But, these methods usually neglect the spatial positions and hierarchical information embedded within protein sequences, which are actually crucial for PPI site prediction. In this paper, we propose MR2CPPIS, a novel sequence-based DL model that utilizes the multi-scale Res2Net with coordinate attention mechanism to exploit multi-scale features and enhance PPI site prediction capability. We leverage the multi-scale Res2Net to expand the receptive field for each network layer, thus capturing multi-scale information of protein sequences at a granular level. To further explore the local contextual features of each target residue, we employ a coordinate attention block to characterize the precise spatial position information, enabling the network to effectively extract long-range dependencies. We evaluate our MR2CPPIS on three public benchmark datasets (Dset 72, Dset 186, and PDBset 164), achieving state-of-the-art performance. The source codes are available at https://***/YyinGong/MR2CPPIS. © 2024 Elsevier Ltd

关键词： Forecasting

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QoS Improvement using Federated Learning-based Position Prediction for mobile IoT

QoS Improvement using Federated Learning-based Position Pred...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Saniya Zafar Mohammed S. Alshehri Syed Aziz Shah Jawad Ahmad Adnan Zafar Aizaz Ahmad Khattak Department of Electrical Engineering Institute of Space Technology Islamabad Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University KSA Research Centre for Intelligent Healthcare Coventry University UK Cybersecurity Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia School of Computing Engineering and the Built Environment Edinburgh Napier University UK

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The rapid advancement in mobile broadband technology has evolved wireless infrastructure influencing internet-of-things (IoTs). This evolution has made mobile IoTs (mIoTs) as centre of attention due to its potential of providing seamless connectivity to enable intelligent transportation systems with huge data demands of vehicular users. To address the ever-escalating data demands of vehicular mobile users, vehicular edge computing (VEC) and moving small cells (mSCs) play a pivotal role. mSCs together with VEC are capable to provide improved service quality for mIoTs. However, incorporating the mSCs in vehicles for mIoTs presents challenges while assigning the resources in mIoT-mSC network. This is due to the existence of time varying interference among mSCs. This work explores the distributed resource assignment mechanism for RSU assisted mIoT-mSC network formed in vehicular environment. In the proposed work, location prediction of city buses deployed with mSCs is done using federated learning (FL) which is subsequently utilized for efficient assignment of resources in mIoT-mSC network. Numerical results are provided to validate the proposed resource assignment mechanism.

关键词： Wireless communication Urban areas Interference Quality of service Predictive models Prediction algorithms Probabilistic logic Internet of Things Long short term memory Edge computing

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Nonparametric Sparse Online Learning of the Koopman Operator

arXiv

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arXiv 2024年

作者： Hou, Boya Sanjari, Sina Dahlin, Nathan Koppel, Alec Bose, Subhonmesh Carl R. Woese Institute for Genomic Biology University of Illinois Urbana-Champaign United States Department of Mathematics and Computer Science Royal Military College of Canada Canada Department of Electrical and Computer Engineering University at Albany SUNY United States Artificial Intelligence Research JP Morgan Chase & Co United States Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign United States

The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. Data-driven techniques to learn the Koopman operator typically assume that the chosen function space is closed under system dynamics. In this paper, we study the Koopman operator via its action on the reproducing kernel Hilbert space (RKHS), and explore the mis-specified scenario where the dynamics may escape the chosen function space. We relate the Koopman operator to the conditional mean embeddings (CME) operator and then present an operator stochastic approximation algorithm to learn the Koopman operator iteratively with control over the complexity of the representation. We provide both asymptotic and finite-time last-iterate guarantees of the online sparse learning algorithm with trajectory-based sampling with an analysis that is substantially more involved than that for finite-dimensional stochastic approximation. Numerical examples confirm the effectiveness of the proposed algorithm. © 2024, CC BY-NC-ND.

关键词： Dynamical systems

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