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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Zhiwei Meng, Yucong Fu, Kexue Wang, Shuo Song, Zhijian Academy for Engineering and Technology Fudan University Shanghai200433 China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai200032 China Shandong Computer Science Center National Supercomputer Center in Jinan China

ISBN: (纸本)157735897X

Weakly Supervised Semantic Segmentation (WSSS) with image-level labels typically uses Class Activation Maps (CAM) to achieve dense predictions. Recently, Vision Transformer (ViT) has provided an alternative to generate localization maps from class-patch attention. However, due to insufficient constraints on modeling such attention, we observe that the Localization Attention Maps (LAM) often struggle with the artifact issue, i.e., patch regions with minimal semantic relevance are falsely activated by class tokens. In this work, we propose MoRe to address this issue and further explore the potential of LAM. Our findings suggest that imposing additional regularization on class-patch attention is necessary. To this end, we first view the attention as a novel directed graph and propose the Graph Category Representation module to implicitly regularize the interaction among class-patch entities. It ensures that class tokens dynamically condense the related patch information and suppress unrelated artifacts at a graph level. Second, motivated by the observation that CAM from classification weights maintains smooth localization of objects, we devise the Localization-informed Regularization module to explicitly regularize the class-patch attention. It directly mines the token relations from CAM and further supervises the consistency between class and patch tokens in a learnable manner. Extensive experiments on PASCAL VOC and MS COCO validate that MoRe effectively addresses the artifact issue and achieves state-of-the-art performance, surpassing recent single-stage and even multi-stage methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Flow graphs

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Control of Magnetization Precession Frequency Using Current Density of Spin-Orbit Torque Applied to Local Area

Control of Magnetization Precession Frequency Using Current ...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Koda, Tetsunori Nakagawa, Ao Muroga, Sho Endo, Yasushi Oshima College National Institute of Technology Department of Electronic Mechanical Engineering Suo-Oshima742-2193 Japan Akita University Graduate School of Engineering Science Department of Mathematical Science and Electrical-Electronic-Computer Engineering Akita010-8502 Japan Department of Electrical Engineering Graduate School of Engineering Tohoku University Sendai980-8579 Japan Center for Spintronics Research Network Tohoku University Sendai980-8577 Japan Organization for Advanced Studies Tohoku University Sendai980-8577 Japan

ISBN: (纸本)9798350338362

We numerically study current density dependence of magnetization dynamics in a local area induced by the spin-orbit torque as a source of spin waves. The precession frequency of magnetization dynamics shows large change at a critical current density, which controls the wave length of spin waves. The origin of the change is due to the increase of exchange field caused by the magnetization torsion at the interface between SOT-applied and non-applied regions. Our new finding gives a way to manipulate spin wave's properties by controlling the magnetization direction in a local area. © 2023 IEEE.

关键词： Spin waves

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Detection of Magnetite Rust Using Circular Microstrip Patch Antenna-based Chipless RFID Sensor

Detection of Magnetite Rust Using Circular Microstrip Patch ...

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International Electrical engineering Congress (iEECON)

作者： Sirisak Nongpromma Suwan Janin Kamol Kaemarungsi Akkarat Boonpoonga Nonchanutt Chudpooti Lakkhana Bannawat Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Engineering and Technology Faculty of Engineering Rajamangala University of Technology Lanna Lampang Lampang Thailand National Science and Technology Development Agency National Electronics and Computer Technology Center Pathumthani Thailand Department of Electrical and Computer Engineering Faculty of Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Computer Engineering Faculty of Engineering Rajamangala University of Technology Rattanakosin Nakhon Pathom Thailand

In this paper, detection of the magnetite rust is presented by using a chipless RFID sensor based on a circular microstrip patch antenna (CMPA). The sensor is designed at the resonant frequency of 4.89 GHz by using a single-layer PCB with its dielectric constant of 4.3. Simulations were conducted to verify that the designed sensor can detect the magnetite rust through shifted resonant frequency. In simulations, the sensor was mounted on the iron with its magnetite rust. The width and depth of iron rust were varied in order to investigate the effect of iron rust on the resonant frequency. Simulation results have been shown that the proposed chipless RFID sensor and RCS’ can be utilized to detect the effect of the iron rust.

关键词： Radar cross-sections Patch antennas Simulation Magnetic resonance Microstrip antennas Radar antennas Iron

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FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning

arXiv

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arXiv 2024年

作者： He, Jialuo Chen, Wei Zhang, Xiaojin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China School of Software Engineering Huazhong University of Science and Technology Wuhan430074 China

Federated Learning (FL) has emerged as a promising approach for privacy-preserving model training across decentralized devices. However, it faces challenges such as statistical heterogeneity and susceptibility to adversarial attacks, which can impact model robustness and fairness. Personalized FL attempts to provide some relief by customizing models for individual clients. However, it falls short in addressing server-side aggregation vulnerabilities. We introduce a novel method called FedAA, which optimizes client contributions via Adaptive Aggregation to enhance model robustness against malicious clients and ensure fairness across participants in non-identically distributed settings. To achieve this goal, we propose an approach involving a Deep Deterministic Policy Gradient-based algorithm for continuous control of aggregation weights, an innovative client selection method based on model parameter distances, and a reward mechanism guided by validation set performance. Empirically, extensive experiments demonstrate that, in terms of robustness, FedAA outperforms the state-of-the-art methods, while maintaining comparable levels of fairness, offering a promising solution to build resilient and fair federated systems. Our code is available at https://***/Gp1g/FedAA. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Effects of Low-Geometrical-Precision PCB Manufacturing on mmWave Passive Metasurfaces

Effects of Low-Geometrical-Precision PCB Manufacturing on mm...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Panithan La-aiddee Paramin Sangwongngam Pornanong Pongpaibool Datchakorn Tancharoen Lunchakorn Wuttisittikulkij Pisit Vanichchanunt Department of Electrical Engineering Wireless Communication Ecosystem Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Pakkred Nonthaburi Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

As millimeter-wave (mmWave) signals suffer from high path loss and blocking, passive metasurface reflectors that can be fabricated with conventional PCB manufacturing become a promising cost-effective candidate for enhancing mmWave mobile service coverage. In this work, we show how PCB fabrication geometrical precision affects the performance of passive reflectors that are used in 5G and beyond mmWave networks. With the broad beamwidth of PCB-based metasurfaces, the dimension tolerance of PCB manufacturing can be traded off between cost benefits and key communications performance. From our numerical results based on full-wave simulations, the received power at the design anomalous angle is not significantly affected by the geometrical precision while high errors can cause higher interference at specular and mirror angles. However, the fabrication error in unit cell shape rotation does not influence the received power at the design anomalous, specular and mirror angles.

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Special issue on the 100^(th) anniversary of Xiamen University

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Light(Science & Applications) 2021年第10期10卷 1812-1813页

作者： Junyong Kang Minghui Hong Zhongqun Tian Engineering Research Center of Micro-nano Optoelectronic Materials and Devices Ministry of EducationFujian Key Laboratory of Semiconductor Materials and ApplicationsCI center for OSEDCollege of Physical Science and Technology361005 XiamenChina Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3117576 SingaporeSingapore State Key Laboratory of Physical Chemistry of Solid Surfaces Collaborative Innovation Center of Chemistry for Energy MaterialsCollege of Chemistry and Chemical EngineeringXiamen University361005 XiamenChina

This special issue is devoted to the celebration of the century anniversary of Xiamen University(XMU)(6 April 2021)and the establishment of the LSA Editorial Office in Xiamen(3 July 2021),a collection to highlight the recent exciting research works performed in XMU or by XMU alumni,from all aspects of optics and photonics,including basic,applied and engineering research and *** guest editors are three XMU alumni who are active researchers in these areas:Professor Minghui Hong from national University of Singapore,Professor Zhongqun Tian and Professor Junyong Kang from XMU.

关键词： Xiamen anniversary devoted

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A UNIFIED FRAMEWORK FOR SOFT THRESHOLD PRUNING

arXiv

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arXiv 2023年

作者： Chen, Yanqi Ma, Zhengyu Fang, Wei Zheng, Xiawu Yu, Zhaofei Tian, Yonghong National Engineering Research Center of Visual Technology School of Computer Science Peking University China Institute for Artificial Intelligence Peking University China Peng Cheng Laboratory China

Soft threshold pruning is among the cutting-edge pruning methods with state-of-the-art performance1. However, previous methods either perform aimless searching on the threshold scheduler or simply set the threshold trainable, lacking theoretical explanation from a unified perspective. In this work, we reformulate soft threshold pruning as an implicit optimization problem solved using the Iterative Shrinkage-Thresholding Algorithm (ISTA), a classic method from the fields of sparse recovery and compressed sensing. Under this theoretical framework, all threshold tuning strategies proposed in previous studies of soft threshold pruning are concluded as different styles of tuning L1-regularization term. We further derive an optimal threshold scheduler through an in-depth study of threshold scheduling based on our framework. This scheduler keeps L1-regularization coefficient stable, implying a time-invariant objective function from the perspective of optimization. In principle, the derived pruning algorithm could sparsify any mathematical model trained via SGD. We conduct extensive experiments and verify its state-of-the-art performance on both Artificial Neural Networks (ResNet-50 and MobileNet-V1) and Spiking Neural Networks (SEW ResNet-18) on ImageNet datasets. On the basis of this framework, we derive a family of pruning methods, including sparsify-during-training, early pruning, and pruning at initialization. The code is available at https://***/Yanqi-Chen/LATS. Copyright © 2023, The Authors. All rights reserved.

关键词： Neural networks

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Wavenumber Domain Sparse Channel Estimation in Holographic MIMO

Wavenumber Domain Sparse Channel Estimation in Holographic M...

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IEEE International Conference on Communications (ICC)

作者： Xufeng Guo Yuanbin Chen Ying Wang Zhaocheng Wang Zhu Han State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, we investigate the sparse channel estimation in holographic multiple-input multiple-output (HMIMO) systems. The conventional angular-domain representation fails to capture the continuous angular power spectrum characterized by the spatially -stationary electromagnetic random field, thus leading to the ambiguous detection of the significant angular power, which is referred to as the power leakage. To tackle this challenge, the HMIMO channel is represented in the wavenumber domain for exploring its cluster-dominated sparsity. Specifically, a finite set of Fourier harmonics acts as a series of sampling probes to encapsulate the integral of the power spectrum over specific angular regions. This technique effectively eliminates power leakage resulting from power mismatches induced by the use of discrete angular-domain probes. Next, the channel estimation problem is recast as a sparse recovery of the significant angular power spectrum over the continuous integration region. We then propose an accompanying graph-cut-based swap expansion (GCSE) algorithm to extract beneficial sparsity inherent in HMIMO channels. Numerical results demonstrate that this wavenumber-domain-based GCSE approach achieves robust performance with rapid convergence.

关键词： Computational modeling Simulation Channel estimation Clustering algorithms Power system harmonics Reflector antennas Sensors

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A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

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Sensors 2023年第16期23卷 7233页

作者： Hamzei, Marzieh Khandagh, Saeed Jafari Navimipour, Nima Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz51376-53515 Iran Electrical Engineering Department Tabriz Branch University of Applied Sciences and Technology Tabriz51376-53515 Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul34083 Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan

The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%. © 2023 by

关键词： Heuristic algorithms

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Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recognition

Sensor Data Augmentation from Skeleton Pose Sequences for Im...

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Activity and Behavior Computing (ABC), International Conference on

作者： Parham Zolfaghari Vitor Fortes Rey Lala Ray Hyun Kim Sungho Suh Paul Lukowicz Department of Computer Science Saarland University Saarbrucken Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany Department of Computer Science RPTU Kaiserslautern-Landau Germany Department of Electrical and Information Engineering Seoul National University of Science and Technology Korea

ISBN: (数字)9798350375503

ISBN: (纸本)9798350375510

The proliferation of deep learning has significantly advanced various fields, yet Human Activity Recognition (HAR) has not fully capitalized on these developments, primarily due to the scarcity of labeled datasets. Despite the integration of advanced Inertial Measurement Units (IMUs) in ubiquitous wearable devices like smartwatches and fitness trackers, which offer self-labeled activity data from users, the volume of labeled data remains insufficient compared to domains where deep learning has achieved remarkable success. Addressing this gap, in this paper, we propose a novel approach to improve wearable sensor-based HAR by introducing a pose-to-sensor network model that generates sensor data directly from 3D skeleton pose sequences. our method simultaneously trains the pose-to-sensor network and a human activity classifier, optimizing both data reconstruction and activity recognition. Our contributions include the integration of simultaneous training, direct pose-to-sensor generation, and a comprehensive evaluation on the MM-Fit dataset. Experimental results demonstrate the superiority of our framework with significant performance improvements over baseline methods.

关键词： Training Deep learning Solid modeling Three-dimensional displays Data models Skeleton Human activity recognition

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