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arXiv 2022年

作者： Wang, Yuntao Su, Zhou Benslimane, Abderrahim Xu, Qichao Dai, Minghui Li, Ruidong School of Cyber Science and Engineering Xi’an Jiaotong University China Laboratory of Computer Sciences Avignon University France School of Mechatronic Engineering and Automation Shanghai University China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Department of Electrical and Computer Engineering Kanazawa University Japan

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but it also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is regarded as a promising lightweight defense to actively protect mobile Internet of things, especially UAV networks. Existing works primarily focused on honeypot design and attack pattern recognition, the incentive issue for motivating UAVs’ participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks is still under-explored. This paper proposes a novel game-based collaborative defense approach to address optimal, fair, and feasible incentive mechanism design, in the presence of network dynamics and UAVs’ multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs under both partial and complete information asymmetry scenarios. We then devise a contract-theoretic method to solve the optimal VDD-reward contract design problem with partial information asymmetry, while ensuring truthfulness, fairness, and computational efficiency. Furthermore, under complete information asymmetry, we devise a reinforcement learning based distributed method to dynamically design optimal contracts for distinct types of UAVs in the fast-changing network. Experimental simulations show that the proposed scheme can motivate UAV’s collaboration in VDD sharing and enhance defensive effectiveness, compared with existing solutions. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Spectral evidence for local-moment ferromagnetism in the van der Waals metals Fe3GaTe2 and Fe3GeTe2

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Physical Review B 2024年第10期109卷 104410-104410页

作者： Han Wu Chaowei Hu Yaofeng Xie Bo Gyu Jang Jianwei Huang Yucheng Guo Shan Wu Cheng Hu Ziqin Yue Yue Shi Rourav Basak Zheng Ren T. Yilmaz Elio Vescovo Chris Jozwiak Aaron Bostwick Eli Rotenberg Alexei Fedorov Jonathan D. Denlinger Christoph Klewe Padraic Shafer Donghui Lu Makoto Hashimoto Junichiro Kono Alex Frano Robert J. Birgeneau Xiaodong Xu Jian-Xin Zhu Pengcheng Dai Jiun-Haw Chu Ming Yi Department of Physics and Astronomy and Rice Center for Quantum Materials Rice University Houston Texas 77005 USA Department of Physics University of Washington Seattle Washington 98195 USA Department of Materials Science and Engineering University of Washington Seattle Washington 98195 USA Theoretical Division and Center for Integrated Nanotechnologies Los Alamos National Laboratory Los Alamos New Mexico USA Department of Advanced Materials Engineering for Information and Electronics Kyung Hee University Yongin 17104 Republic of Korea Department of Physics University of California at Berkeley Berkeley California 94720 USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics University of California San Diego La Jolla California 92093USA National Synchrotron Light Source II Brookhaven National Lab Upton New York 11973 USA Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park California 94025 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Department of Material Science and NanoEngineering Rice University Houston Texas 77005 USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Materials Science and Engineering University of California Berkeley CaliforniaUSA

Magnetism in two-dimensional (2D) materials has attracted considerable attention recently for both fundamental understanding of magnetism and its tunability towards device applications. The isostructural Fe3GeTe2 and Fe3GaTe2 are two members of the Fe-based van der Waals (vdW) ferromagnet family, but exhibit very different Curie temperatures (TC) of 210 and 360 K, respectively. Here, by using angle-resolved photoemission spectroscopy and density functional theory, we systematically compare the electronic structures of the two compounds. Qualitative similarities in the Fermi surface can be found between the two compounds, with expanded hole pockets in Fe3GaTe2 suggesting additional hole carriers compared to Fe3GeTe2. Interestingly, we observe almost no band shift in Fe3GaTe2 across its TC of 360 K, compared to a small shift in Fe3GeTe2 across its TC of 210 K. The weak temperature-dependent evolution strongly deviates from the expectations of an itinerant Stoner mechanism. Our results suggest that itinerant electrons have minimal contributions to the enhancement of TC in Fe3GaTe2 compared to Fe3GeTe2, and that the nature of ferromagnetism in these Fe-based vdW ferromagnets must be understood with considerations of the electron correlations.

关键词： Magnetism Van der Waals systems Angle-resolved photoemission spectroscopy

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Seamless Interaction Design with Coexistence and Cooperation Modes for Robust Human-Robot Collaboration

arXiv

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arXiv 2022年

作者： Huang, Zhe Mun, Ye-Ji Li, Xiang Xie, Yiqing Zhong, Ninghan Liang, Weihang Geng, Junyi Chen, Tan Driggs-Campbell, Katherine The Department of Electrical and Computer Engineering The University of Illinois Urbana-Champaign United States The Department of Computer Science The University of Illinois Urbana-Champaign United States The Robotics Institute Carnegie Mellon University United States The Coordinated Science Laboratory The University of Illinois Urbana-Champaign United States

A robot needs multiple interaction modes to robustly collaborate with a human in complicated industrial tasks. We develop a Coexistence-and-Cooperation (CoCo) human-robot collaboration system. Coexistence mode enables the robot to work with the human on different sub-tasks independently in a shared space. Cooperation mode enables the robot to follow human guidance and recover failures. A human intention tracking algorithm takes in both human and robot motion measurements as input and provides a switch on the interaction modes. We demonstrate the effectiveness of CoCo system in a use case analogous to a real world multi-step assembly task. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine design

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A general survey on medical image super-resolution via deep learning

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computers in Biology and Medicine 2025年 193卷 110345-110345页

作者： Yu, Miao Xu, Zhenghua Lukasiewicz, Thomas Public Health Sciences and Engineering College Tianjin University of Traditional Chinese Medicine Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin China Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health School of Health Sciences and Biomedical Engineering Hebei University of Technology China Institute of Logic and Computation TU Wien Vienna Austria Department of Computer Science University of Oxford Oxford United Kingdom

Medical image super-resolution (SR) is a classic regression task in low-level vision. Limited by hardware limitations, acquisition time, low radiation dose, and other factors, the spatial resolution of some medical images is not sufficient. To address this problem, many different SR methods have been proposed. Especially in recent years, medical image SR networks based on deep learning have been vigorously developed. This survey provides a modular and detailed introduction to the key components of medical image SR technology based on deep learning. In this paper, we first introduce the background concepts of deep learning and medical image SR task. Subsequently, we present a comprehensive analysis of the key components from the perspectives of effective architecture, upsampling module, learning strategy, and image quality assessment of medical image SR networks. Furthermore, we focus on the urgent problems that need to be addressed in the medical image SR task based on deep learning. And finally we summarize the trends and challenges of future development. © 2025 Elsevier Ltd

关键词： Damage detection

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A Hybrid Swarm Intelligence Model for Efficient Query Optimization in Distributed Databases Using Genetic and Ant Colony Algorithms

A Hybrid Swarm Intelligence Model for Efficient Query Optimi...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Jayasri, Nemala Santosh Kumar, B. Singh, Navdeep Singh, Ajeet Al-Rubaye, Taqi Mohammed Khattab Rao, Mallikarjuna Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India New Horizon College of Engineering Department of Computer Science Engineering Bangalore India Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India College of Medical Technologies The Islamic University Department of Medical Laboratory Technology Najaf Iraq Griet Department of Cse Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

Optimisation of queries in distributed databases is more imperative to enhance the processing rate and use of resources with limited computational abilities. This work presents a novel GA-ACO hybrid swarm intelligence model to address the query optimization problem. The optimization effort of the proposed model starts with GA for an initial population of probable paths for query execution which uses the concept of evolution to select diverse solutions. After that, ACO strengthens this set by the application of pheromone-based reinforcement that allows adjusting of a query paths toward the best solution or its approximation with the following number values. In this way the enhancement of the convergence rates and the scalability of the proposed solution when distributed databases are used can be explained. The model was tested with artificial query logs with DEAP being used for the implementation of the genetic algorithm while ant colony operations were implemented using Python built ACO libraries. Performance analysis shows the success of the developed model in decreasing query response time as follows: This essentially complex hybrid mechanism shows potential for future use in other areas with large data sets and efficient query handling. © 2025 IEEE.

关键词： Ant colony optimization

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Detection of White Matter Hyperintensities in Magnetic Resonance Imaging by Hyperspectral Subpixel Detection

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IEEE Access 2024年 12卷 111992-112007页

作者： Yung-Chieh Chang Chein-I Chang Yen-Chieh Ouyang Jyh-Wen Chai Wen-Hsien Chen Kuan-Jung Pan Hsin-Che Wang Clayton Chi-Chang Chen Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging and Radiological Sciences Central Taiwan University of Science and Technology Taichung Taiwan Department of Computer Science and Electrical Engineering Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD USA Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan College of Medicine China Medical University Taichung Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan Department of Medical Research Center for Quantitative Imaging in Medicine (CQUIM) Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging China Medical University Hospital Taichung Taiwan

White matter hyperintensities (WMHs) are lesion in brain magnetic resonance images generally associated with Alzheimer’s disease (AD) and cognitive decline. Finding WMHs of AD poses a great challenge for diagnosis. This paper interprets a brain MR image as a hyperspectral image so that a well stablished hyperspectral subpixel detection algorithm, constrained energy minimization (CEM), is applicable to solving the WMHs detection problem at mixed pixel and subpixel level. To resolve nonlinear mixing in detecting WMHs nearby boundaries, a nonlinear CEM, called kernel CEM (KCEM) is also developed. Since CEM is a hyperspectral technique without taking spatial correlation into account, CEM was also extended to iterative CEM (ICEM) by including spatial filters to capture spatial information for WMHs detection. This paper combines ICEM and KCEM to derive a new WHMs detection algorithm, iterative KCEM (IKCEM) to improve ICEM and KCEM on WMHs detection. To evaluate the WMHs detection performance, two criteria, Dice similarity index (DSI) and 3D ROC analysis are used as evaluation tools. In order to show the superiority of IKCEM, two commonly used software packages, statistical parametric mapping (SPM)-based algorithms, SPM-lesion growth algorithm (SPM-LGA) and SPM-lesion prediction algorithm (SPM-LPA) are implemented for validation and comparison.

关键词： Hyperspectral imaging Magnetic resonance imaging Iterative methods Medical diagnostic imaging White matter Manuals Energy management Neuroimaging Brain modeling Alzheimer's disease Energy measurement

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Coded Residual Transform for Generalizable Deep Metric Learning

arXiv

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arXiv 2022年

作者： Kan, Shichao Liang, Yixiong Li, Min Cen, Yigang Wang, Jianxin He, Zhihai School of Computer Science and Engineering Central South University Hunan Changsha410083 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Pengcheng Lab Shenzhen518066 China

A fundamental challenge in deep metric learning is the generalization capability of the feature embedding network model since the embedding network learned on training classes need to be evaluated on new test classes. To address this challenge, in this paper, we introduce a new method called coded residual transform (CRT) for deep metric learning to significantly improve its generalization capability. Specifically, we learn a set of diversified prototype features, project the feature map onto each prototype, and then encode its features using their projection residuals weighted by their correlation coefficients with each prototype. The proposed CRT method has the following two unique characteristics. First, it represents and encodes the feature map from a set of complimentary perspectives based on projections onto diversified prototypes. Second, unlike existing transformer-based feature representation approaches which encode the original values of features based on global correlation analysis, the proposed coded residual transform encodes the relative differences between the original features and their projected prototypes. Embedding space density and spectral decay analysis show that this multi-perspective projection onto diversified prototypes and coded residual representation are able to achieve significantly improved generalization capability in metric learning. Finally, to further enhance the generalization performance, we propose to enforce the consistency on their feature similarity matrices between coded residual transforms with different sizes of projection prototypes and embedding dimensions. Our extensive experimental results and ablation studies demonstrate that the proposed CRT method outperform the state-of-the-art deep metric learning methods by large margins and improving upon the current best method by up to 4.28% on the CUB dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Infinite-Q guided modes radiate in the continuum

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Physical Review B 2023年第11期107卷 115415-115415页

作者： Kaili Sun Heng Wei Weijin Chen Yang Chen Yangjian Cai Cheng-Wei Qiu Zhanghua Han Shandong Provincial Key Laboratory of Optics and Photonic Devices Center of Light Manipulation and Applications School of Physics and Electronics Shandong Normal University Jinan 250358 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore 117583 Chinese Academy of Sciences Key Laboratory of Mechanical Behavior and Design of Materials Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei China

Guided modes in photonic structures, with broadband infinite-quality factors, are inaccessible from the far field due to the momentum mismatch. They become leaky modes in the continuum when the mismatch is compensated for by introducing periodic perturbations to form photonic structures. However, the quality factors (Q factors) of such leaky modes deteriorate significantly in most regions of the k-space except at a few discrete high-symmetry points. It is an intriguing question as to whether guided modes can hop above the light cone and yet maintain high Q. Here, we propose a double-band-folding strategy to achieve high-Q leaky modes in compound lattices, exemplified with a one-dimensional grating and a two-dimensional zigzag array of dielectric disks. The Q factor of those leaky modes can be made ultrahigh at arbitrarily any incident angles, showing that such modes do not originate from bound states in the continuum (BICs) above the light cone. Our findings provide unique insight for elucidating the relations between guided modes, BICs, quasi-BICs, and radiation. They further provide a generalized recipe for numerous optical applications such as all-dielectric sensing, lasing, and nonlinear generation with multiple inputs.

关键词： Light propagation, transmission & absorption Metasurfaces Nanophotonics Photonic crystals

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A2B: Anchor to Barycentric Coordinate for Robust Correspondence

arXiv

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arXiv 2023年

作者： Zhao, Weiyue Lu, Hao Cao, Zhiguo Li, Xin The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Lane Department of Computer Science and Electrical Engineering West Virginia University Commonwealth of Virginia Morgantown 15461 United States

There is a long-standing problem of repeated patterns in correspondence problems, where mismatches frequently occur because of inherent ambiguity. The unique position information associated with repeated patterns makes coordinate representations a useful supplement to appearance representations for improving feature correspondences. However, the issue of appropriate coordinate representation has remained unresolved. In this study, we demonstrate that geometric-invariant coordinate representations, such as barycentric coordinates, can significantly reduce mismatches between features. The first step is to establish a theoretical foundation for geometrically invariant coordinates. We present a seed matching and filtering network (SMFNet) that combines feature matching and consistency filtering with a coarse-to-fine matching strategy in order to acquire reliable sparse correspondences. We then introduce DEGREE, a novel anchor-to-barycentric (A2B) coordinate encoding approach, which generates multiple affine-invariant correspondence coordinates from paired images. DEGREE can be used as a plug-in with standard descriptors, feature matchers, and consistency filters to improve the matching quality. Extensive experiments in synthesized indoor and outdoor datasets demonstrate that DEGREE alleviates the problem of repeated patterns and helps achieve state-of-the-art performance. Furthermore, DEGREE also reports competitive performance in the third Image Matching Challenge at CVPR 2021. This approach offers a new perspective to alleviate the problem of repeated patterns and emphasizes the importance of choosing coordinate representations for feature correspondences. Copyright © 2023, The Authors. All rights reserved.

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Correction to "High-Throughput 3D-Printed Model of the Feto-Maternal Interface for the Discovery and Development of Preterm Birth Therapies" (ACS Applied Materials and Interfaces (2024) 16:32 (41892-41906) DOI: 10.1021/acsami.4c08731)

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ACS Applied Materials and Interfaces 2025年第20期17卷 30360-30361页

作者： Rahul Cherukuri Ananth Kumar Kammala Tilu Jain Thomas Leah Saylor Lauren Richardson Sungjin Kim Marc Ferrer Cristina Antich Min Jae Song Shayne Frebert Akhilesh K. Gaharwar Ramkumar Menon Arum Han Department of Electrical and Computer Engineering Texas A&M University College Station Texas 77840 United States Division of Basic Science and Translational Research Department of Obstetrics & Gynecology The University of Texas Medical Branch at Galveston Galveston Texas 77555 United States 3D Tissue Bioprinting Laboratory National Center for Advancing Translational Sciences National Institute of Sciences Bethesda Maryland 20892 United States Department of Biomedical Engineering Texas A&M University College Station Texas 77840 United States Department of Electrical and Computer Engineering Texas A&M University College Station Texas 77840 United States；Department of Biomedical Engineering Texas A&M University College Station Texas 77840 United States；Department of Chemical Engineering Texas A&M University College Station Texas 77840 United States

In our original paper, a new author, Shayne Frebert, needs added to the author list (affiliated with 3 3D Tissue Bioprinting laboratory, National Center for Advancing Translational sciences, National Institute of sciences, Bethesda, Maryland, 20892, United States). This new author is the tenth author. The reason for the addition is that Mr. Frebert provided key insights for the 3D printing process in this paper. Unfortunately, during the merger of different revisions of the manuscript, his name was overlooked and omitted. We understand the importance of accurate authorship and greatly appreciate the opportunity to correct this error. A name change is also required for one of the authors from "Cristina Acedo" to "Cristina Antich". Ms. Antich’s last name was inadvertently misspelled during the manuscript revision process. We sincerely apologize for the oversight and greatly appreciate the opportunity to correct this error. These changes do not effect the scientific conclusions of the paper. © 2025 American Chemical Society.

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