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Physical Review Letters 2023年第26期130卷 266303-266303页

作者： Guoqiang Xu Xue Zhou Ying Li Qitao Cao Weijin Chen Yunfeng Xiao Lan Yang Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing 400067 China Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China International Joint Innovation Center Key Lab of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang The Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-optoelectronics School of Physics Peking University100871 Beijing China Department of Electrical and Systems Engineering Washington University St. Louis Missouri 63130 USA

Exceptional point (EP) has been captivated as a concept of interpreting eigenvalue degeneracy and eigenstate exchange in non-Hermitian physics. The chirality in the vicinity of EP is intrinsically preserved and usually immune to external bias or perturbation, resulting in the robustness of asymmetric backscattering and directional emission in classical wave fields. Despite recent progress in non-Hermitian thermal diffusion, all state-of-the-art approaches fail to exhibit chiral states or directional robustness in heat transport. Here we report the first discovery of chiral heat transport, which is manifested only in the vicinity of EP but suppressed at the EP of a thermal system. The chiral heat transport demonstrates significant robustness against drastically varying advections and thermal perturbations imposed. Our results reveal the chirality in heat transport process and provide a novel strategy for manipulating mass, charge, and diffusive light.

关键词： Diffusion Heat transfer Metamaterials Non-Hermitean systems

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Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration

arXiv

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arXiv 2024年

作者： Concha, David Molina Li, Jiping Yin, Haoran Park, Kyeonghyeon Lee, Hyun-Rok Lee, Taesik Sirohi, Dhruv Lee, Chi-Guhn The Dynamic Optimization and Reinforcement Learning Laboratory The Department of Mechanical & Industrial Engineering University of Toronto TorontoONM5S3G8 Canada The Department of Electrical & Computer Engineering University of Toronto TorontoONM5S3G8 Canada The Division of Engineering Science The Faculty of Applied Science and Engineering University of Toronto TorontoONM5S3G8 Canada The Complex System Design Laboratory The Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of The Department of Industrial Engineering Inha University Incheon22212 Korea Republic of

This study addresses the challenge of fleet design optimization in the context of heterogeneous multi-robot fleets, aiming to obtain feasible designs that balance performance and costs. In the domain of autonomous multi-robot exploration, reinforcement learning agents play a central role, offering adaptability to complex terrains and facilitating collaboration among robots. However, modifying the fleet composition results in changes in the learned behavior, and training multi-robot systems using multi-agent reinforcement learning is expensive. Therefore, an exhaustive evaluation of each potential fleet design is infeasible. To tackle these hurdles, we introduce Bayesian Optimization for Fleet Design (BOFD), a framework leveraging multiobjective Bayesian Optimization to explore fleets on the Pareto front of performance and cost while accounting for uncertainty in the design space. Moreover, we establish a sub-linear bound for cumulative regret, supporting BOFD’s robustness and efficacy. Extensive benchmark experiments in synthetic and simulated environments demonstrate the superiority of our framework over state-of-the-art methods, achieving efficient fleet designs with minimal fleet evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China The School of Computing Kent Interdisciplinary Research Centre in Cyber Security University of Kent CanterburyCT2 7NF United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The Intelligent Transportation Systems Lab Innopolis University Innopolis Russia The School of Computer Science and Technology Xidian University China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. © 2004-2012 IEEE. All rights reserved.

关键词： Graph neural networks

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Dominant Defect and Microstructure Transformation engineering for Highly Efficient Low-Bandgap Stannite Solar Cells

SSRN

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SSRN 2024年

作者： Yu, Zixuan Li, Shuoren Li, Chuanhao Huang, Sihao Wang, Yonghao Su, Zhenghua Liang, Guangxing Yan, Chang Guangdong Guangzhou511400 China Institute of Thin Film Physics and Applications Shenzhen Key Laboratory of Advanced Thin Films and Applications Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province State Key Laboratory of Radio Frequency Heterogeneous Integration College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Guangdong Guangzhou511400 China Guangdong-Hong Kong Joint Laboratory for Carbon Neutrality Jiangmen Laboratory of Carbon Science and Technology Guangdong Province Jiangmen529199 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong999077 Hong Kong

To meet the requirements of high-efficiency multi-junction tandem, it is imperative to seek cost-effective low-bandgap bottom subcell candidates. The bandgap of Cu2ZnSn(S,Se)4 (CZTSSe) can be effectively narrowed by Cd alloying with the lowest achievable bandgap of 0.87 eV after 60% Zn substitution by Cd. However, the low-bandgap Cu2CdZnSn(S,Se)4 (CCZTSSe) is not efficient, majorly owing to the poor crystallinity of thin films and severe non-radiative recombination. Herein, we report an effective approach to enhance the crystallinity of CCZTSSe via in situ Na-addition in the bottom precursor. Sodium attracts Se within the bottom layers, facilitating the grain growth. Electrical properties are promoted accordingly, with the reduced interface defect densities, modified grain boundaries and superior Ohmic contact at the back interface. The most significant improvement is the dominant defect transformation, from detrimental deep CuCd to benign shallow VCu. These advances enable non-radiative recombination to be greatly suppressed both at the interface and in the bulk. Eventually, an efficiency of 10.48% has been achieved, which is the highest reported efficiency for stannite solar cells. This is a significant step forward on the road to applying CCZTSSe in future tandem technologies. © 2024, The Authors. All rights reserved.

关键词： Grain boundaries

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Freeze-thaw electrical resistivity hysteresis response of frost susceptible clayey sands

Freeze-thaw electrical resistivity hysteresis response of fr...

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2021 International Foundations Congress and Equipment Expo: From Traditional to Emerging Geotechnics, IFCEE 2021

作者： Liu, Rui Offenbacker, Daniel Zhu, Cheng Schmalzel, John Mehta, Yusuf Barrowes, Benjamin Glaser, Danney Lein, Wade Center for Research and Education in Advanced Transportation Engineering Systems Dept. of Civil and Environmental Engineering Rowan Univ. GlassboroNJ United States Dept. of Electrical and Computer Engineering Rowan Univ. GlassboroNJ United States Center for Research and Education in Advanced Transportation Engineering Dept. of Civil and Environmental Engineering Rowan Univ. GlassboroNJ United States Cold Regions Research and Engineering Laboratory U.S. Army Engineer Research and Development Center HanoverNH United States

ISBN: (数字)9780784483428

In this study, we carry out lab-scale 1D electrical resistivity measurements of frost-susceptible clayey sands, focusing on several major influencing factors including initial water content, soil density, temperature, and freezing/thawing conditions. Soil columns are configured following a four-pole type Wenner electrode array and placed in an environmental chamber to reach target temperatures (-20°C to 0°C). We use a portable resistivity meter for temporal electrical resistivity measurements and thermocouples for temperature measurements. Experimental results highlight the significant dependence of soil electrical resistivity on temperature. Above the freezing point, higher initial liquid water content and larger bulk density decrease the electrical resistivity. Below the freezing point, soil resistivity increases with decreasing temperature. We observe a hysteresis effect on the evolution of electrical resistivity during the freeze thaw cycle. This study presents a relationship between the electrical resistivity of frozen soil and the key governing parameters. © ASCE.

关键词： Thermocouples

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Fault Analysis of Microgrids with Inverter Interfaced Resources in Grid-Connected and Islanded Modes

Fault Analysis of Microgrids with Inverter Interfaced Resour...

引用

On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Jigyesh Sharma Kushan Tharuka Lulbadda Anindita Golder Tarlochan Sidhu Sheldon S. Williamson Department of Electrical and Computer Engineering Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa ON Canada

The increasing penetration of inverter-based generation such as solar, wind and battery energy storage systems (BESS) has an impact on the fault currents in a microgrid. Also, if the microgrid is grid-connected or islanded affects the fault current. As such, it is important to conduct fault analysis of microgrids in grid-connected and islanded modes with different penetration levels of inverter-based generation sources to understand the impact of increasing levels of renewable energy on the fault currents. Different types of faults in a microgrid with inverter-based resources are simulated and the results are discussed and analyzed in this paper.

关键词： Wind energy generation Industrial electronics Renewable energy sources Wind energy Microgrids Inverters Batteries

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Efficient meta neural heuristic for multi-objective combinatorial optimization 23

Efficient meta neural heuristic for multi-objective combinat...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Jinbiao Chen Jiahai Wang Zizhen Zhang Zhiguang Cao Te Ye Siyuan Chen School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China School of Computing and Information Systems Singapore Management University Singapore

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta-model, so as to speed up the training; meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time.

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A Multiple Ant Colony System for the Electric Vehicle Routing Problem with Time Windows

A Multiple Ant Colony System for the Electric Vehicle Routin...

引用

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Michalis Mavrovouniotis Georgios Ellinas Changhe Li Marios Polycarpou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781665487696

Ant colony optimization (ACO) has been found to be useful on several vehicle routing problem variations. In this work, ACO is applied to the electric vehicle routing problem with time windows (E-VRPTW). The E-VRPTW has a hierarchical multiple objective function, which is to minimize the number of electric vehicles and the total distance traveled. A multiple ACO is applied to E-VRPTW in which two colonies cooperate to minimize the objectives in parallel. A local search is embedded in ACO to improve the quality of the output. The experimental results on a set of benchmark instances show that the multiple ACO is competitive with existing methods.

关键词： Ant colony optimization Vehicle routing Benchmark testing Electric vehicles Routing Search problems Linear programming

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Energy-Efficient Resource Allocation for MEC and Blockchain-Enabled IoT via CRL Approach

Energy-Efficient Resource Allocation for MEC and Blockchain-...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Meng Li Pan Pei F. Richard Yu Pengbo Si Ruizhe Yang Zhuwei Wang Faculty of Information Technology Beijing University of Technology Beijing P.R. China Beijing Laboratory of Advanced Information Networks Beijing P.R. China Department of Systems and Computer Engineering Carleton University Ottawa ON Canada

ISBN: (纸本)9781665435413

Driven by numerous emerging mobile devices and various quality of service requirements, mobile edge computing (MEC) has been recognized as a prospective paradigm to promote the computation capability of mobile devices, as well as reduce energy overhead and service latency of applications for the Internet of Things (IoT). However, there are still some open issues in the existing research works: 1) limited network and computing resource, 2) simple or non-intelligent resource management, 3) ignored security and reliability. In order to cope with these issues, in this article, 6G and blockchain technology are considered to improve network performance and ensure the authenticity of data sharing for the MEC-enabled IoT. Meanwhile, a novel intelligent optimization method named as collective reinforcement learning (CRL) is proposed and introduced, to realize intelligent resource allocation, meet distributed training results sharing and avoid excessive consumption of system resources. Based on the designed network model, a cloud-edge collaborative resource allocation framework is formulated. By joint optimizing the offloading decision, block interval and transmission power, it aims to minimize the consumption overheads of system energy and service latency. Then the formulated problem is designed as a Markov decision process, and the optimal strategy can be obtained by the CRL. Some evaluation results reveal that the system performance based on the proposed scheme outperforms other existing schemes obviously.

关键词： Training System performance Optimization methods Reinforcement learning Quality of service Mobile handsets Resource management

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Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary

arXiv

引用

arXiv 2022年

作者： Liu, Quande Chen, Cheng Dou, Qi Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

Domain generalization typically requires data from multiple source domains for model learning. However, such strong assumption may not always hold in practice, especially in medical field where the data sharing is highly concerned and sometimes prohibitive due to privacy issue. This paper studies the important yet challenging single domain generalization problem, in which a model is learned under the worst-case scenario with only one source domain to directly generalize to different unseen target domains. We present a novel approach to address this problem in medical image segmentation, which extracts and integrates the semantic shape prior information of segmentation that are invariant across domains and can be well-captured even from single domain data to facilitate segmentation under distribution shifts. Besides, a test-time adaptation strategy with dual-consistency regularization is further devised to promote dynamic incorporation of these shape priors under each unseen domain to improve model generalizability. Extensive experiments on two medical image segmentation tasks demonstrate the consistent improvements of our method across various unseen domains, as well as its superiority over state-of-the-art approaches in addressing domain generalization under the worst-case scenario. © 2022, CC BY.

关键词： Semantics

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