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European Conference on Microwave

作者： Amirhossein Askarian Pascal Burasa Jianping Yao Zhenguo Lu Ke Wu Poly-Grames Research Center Polytechnique Montreal Canada School of Electrical Engineering and Computer Science University of Ottawa Canada Advanced Electronics and Photonics Research Center the National Research Council Canada (NRC) Canada

ISBN: (数字)9782874870774

ISBN: (纸本)9798350385892

We propose and investigate two methodologies for the design and realization of scalable low-footprint multi-band antenna arrays through the interleaving of electric and magnetic currents on mutually shared radiating surfaces. In our earlier approach, we successfully engineered and fabricated a highly isolated dual-wideband prototype operating over Ku-band, respectively showcasing impedance and gain bandwidth of approximately 42%. Furthermore, it demonstrates operability within Ka-band, respectively exhibiting impedance and gain bandwidth of 29% and 16.32%. Additionally, we have developed and fabricated a space-efficient 2×4 dual-band array designed to operate over both Ku- and Ka-bands. Our measurement results suggest that the proposed antenna array technique achieves impedance and gain bandwidth of 30% and 25.4% within Ku-band and 10.65% and 8.52% within Ka-band, respectively.

关键词： Antenna measurements Microwave measurement Surface impedance Current measurement Prototypes Bandwidth Microwave theory and techniques Transceivers Microwave antenna arrays Impedance

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Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning

arXiv

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arXiv 2023年

作者： Miao, Qing Wu, Xiaohe Xu, Chao Ji, Yanli Zuo, Wangmeng Guo, Yiwen Meng, Zhaopeng College of Intelligence and Computing Tianjin University Tianjin300072 China School of Computer Science and Technology Harbin Institute of Technology Harbin150006 China Center for Future Media School of Computer Science and Engineering UESTC Chengdu611731 China China

Learning with noisy labels (LNL) has been extensively studied, with existing approaches typically following a framework that alternates between clean sample selection and semi-supervised learning (SSL). However, this approach has a limitation: the clean set selected by the Deep Neural Network (DNN) classifier, trained through self-training, inevitably contains noisy samples. This mixture of clean and noisy samples leads to misguidance in DNN training during SSL, resulting in impaired generalization performance due to confirmation bias caused by error accumulation in sample selection. To address this issue, we propose a method called Collaborative Sample Selection (CSS), which leverages the large-scale pre-trained model CLIP. CSS aims to remove the mixed noisy samples from the identified clean set. We achieve this by training a 2-Dimensional Gaussian Mixture Model (2D-GMM) that combines the probabilities from CLIP with the predictions from the DNN classifier. To further enhance the adaptation of CLIP to LNL, we introduce a co-training mechanism with a contrastive loss in semi-supervised learning. This allows us to jointly train the prompt of CLIP and the DNN classifier, resulting in improved feature representation, boosted classification performance of DNNs, and reciprocal benefits to our Collaborative Sample Selection. By incorporating auxiliary information from CLIP and utilizing prompt fine-tuning, we effectively eliminate noisy samples from the clean set and mitigate confirmation bias during training. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed method in comparison with the state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Was Wuhan the early epicenter of the COVID-19 pandemic?——a critique

引用

National science Review 2023年第4期10卷 19-21页

作者： Yanan Cao Lingling Chen Hua Chen Yupeng Cun Xiaofeng Dai Hongli Du Feng Gao Fengbiao Guo Yalong Guo Pei Hao Shunmin He Shunping He XiongLei He Zheng Hu Boon-Peng Hoh Xin Jin Qian Jiang Qinghua Jiang Asifullah Khan Hong-Zhi Kong Jinchen Li Shuai Cheng Li Ying Li Qiang Lin Jianquan Liu Qi Liu Jian Lu Xuemei Lu Shujin Luo Qinghua Nie Zilong Qiu Tieliu Shi Xiaofeng Song Jianzhong Su Sheng-ce Tao Chaolong Wang Chuan-Chao Wang Guo-Dong Wang Jiguang Wang Qi Wu Shaoyuan Wu Shuhua Xu Yu Xue Wenjun Yang Zhaohui Yang Kai Ye Yuan-Nong Ye Li Yu Fangqing Zhao Yiqiang Zhao Weiwei Zhai Dandan Zhang Liye Zhang Houfeng Zheng Qi Zhou Tianqi Zhu Ya-ping Zhang Ruijin Hospital Shanghai Jiao Tong University College of Life Science and Technology Guangxi University Beijing Institute of Genomics Chinese Academy of Sciences Children's Hospital of Chongqing Medical University Wuxi School of Medicine Jiangnan University School of Biology and Biological Engineering South China University of Technology Department of Physics School of ScienceTianjin University School of Pharmaceutical Sciences Wuhan University Institute of Botany Chinese Academy of Sciences Institut Pasteur of Shanghai Chinese Academy of Sciences Institute of Biophysics Chinese Academy of Sciences Institute of Hydrobiology Chinese Academy of Sciences School of Life Sciences Sun Yat-sen University Institute of Synthetic Biology Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Faculty of Medicine and Health Sciences University College Sedaya International School of Medicine South China University of Technology Department of Medical Genetics Capital Institute of Pediatrics School of Life Science and Technology Harbin Institute of Technology Department of Biochemistry Abdul Wali Khan University Xiangya Hospital Central South University Department of Computer Science City University of Hong Kong College of Life Science and Technology Foshan University South China Sea Institute of Oceanology Chinese Academy of Sciences College of Ecology Lanzhou University School of Life Sciences and Technology Tongji University School of Life Sciences Peking University Kunming Institute of Zoology Chinese Academy of Sciences College of Animal Science South China Agricultural University Institute of Neuroscience Chinese Academy of Sciences School of Life Sciences East China Normal University Nanjing University of Aeronautics and Astronautics Wenzhou Institute University of Chinese Academy of Sciences Institute of Systems Biomedicine Shanghai Jiao Tong University Tongji Medical College Huazhong University of Science and Technology School of Life Sciences Xiamen University Division of Life Science and Department of

Recently, Worobey et al.(2022) published a report entitled ‘The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic' that succinctly summarizes their study [1]. A pre-print version of this study had earlier elicited a series of highprofile media coverages [2,3]. All these reports deliver a social-political message that the Huanan market is the epicenter of COVID-19.

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AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems

arXiv

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arXiv 2023年

作者： Liu, Ruixuan Hu, Ming Xia, Zeke Xia, Jun Zhang, Pengyu Huang, Yihao Liu, Yang Chen, Mingsong MoE Engineering Research Center of SW/HW Co-Design Tech. and App. East China Normal University China School of Computer Science and Engineering Nanyang Technological University Singapore Computer Science and Engineering University of Notre Dame United States

Federated Learning (FL) enables collaborative learning of large-scale distributed clients without data sharing. However, due to the disparity of computing resources among massive mobile computing devices, the performance of traditional homogeneous model-based Federated Learning (FL) is seriously limited. On the one hand, to achieve model training in all the diverse clients, mobile computing systems can only use small low-performance models for collaborative learning. On the other hand, devices with high computing resources cannot train a high-performance large model with their insufficient raw data. To address the resource-constrained problem in mobile computing systems, we present a novel heterogeneous FL approach named AdapterFL, which uses a model reassemble strategy to facilitate collaborative training of massive heterogeneous mobile devices adaptively. Specifically, we select multiple candidate heterogeneous models based on the computing performance of massive mobile devices and then divide each heterogeneous model into two partitions. By reassembling the partitions, we can generate models with varied sizes that are combined by the partial parameters of the large model with the partial parameters of the small model. Using these reassembled models for FL training, we can train the partial parameters of the large model using low-performance devices. In this way, we can alleviate performance degradation in large models due to resource constraints. The experimental results show that AdapterFL can achieve up to 12% accuracy improvement compared to the state-of-the-art heterogeneous federated learning methods in resource-constrained scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile computing

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DualConvNet: Enhancing CNN Inference Efficiency Through Compressed Convolutions and Reparameterization

DualConvNet: Enhancing CNN Inference Efficiency Through Comp...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Haipeng Du Muyan Jiao Jiageng Zhang Xin Lv Jie Zhang School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China Network Information Center Xi’an Jiaotong University Xi’an China School of Computer Science and Engineering Xi’an University of Technology Xi’an China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Convolutional Neural Networks (CNNs) have significantly advanced computer vision tasks, but their increasing complexity poses challenges for efficient inference, particularly on resource-constrained devices. We present DualConvNet, a novel CNN architecture that enhances inference efficiency through two key innovations: compressed convolutions and reparameterization. Compressed convolutions reduce computational complexity by selectively processing input channel subsets during both training and inference. For inference, we introduce a reparameterization technique that merges the multi-branch structure into a single, efficient operation, significantly improving speed. Experiments on CIFAR-10, CIFAR-100, and ImageNet-1k demonstrate DualConvNet’s effectiveness, consistently outperforming state-of-the-art models in both accuracy and inference speed. On the COCO dataset, DualConvNet shows competitive accuracy in object detection and instance segmentation tasks while substantially reducing GPU latency. Ablation studies validate the impact of our dual-strategy approach, revealing significant improvements in both accuracy and computational efficiency compared to alternative designs. These results demonstrate DualConvNet’s effectiveness in improving inference efficiency while maintaining high accuracy across various tasks and datasets, making it particularly suitable for real-time applications in resource-constrained scenarios.

关键词： Training Technological innovation Accuracy Image coding Computational modeling computer architecture Transformers Computational efficiency Convolutional neural networks Computational complexity

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Physique- Based Human Activity Recognition Using Deep Learning Approaches and Smartphone Sensors 8

Physique- Based Human Activity Recognition Using Deep Learni...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Pravesjit, Sakkayaphop Jantawong, Ponnipa Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Information Technology University of Phayao Phayao Thailand School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350396546

Understanding human actions via the analysis of sensor data captured by wearable sensors is the goal of the complex subject of study known as sensor-based human activity recognition (S-HAR). Human participants' characteristics are only periodically included in deep learning (DL) approaches to S-HAR. Recognizing people was challenging for these DL methods because of the variety of physical characteristics people have. To address this challenge, we introduce a physique-based S-HAR architecture that could support deep learning networks to achieve higher identification a ccuracies a nd F1-scores. The HARSense dataset, a publicly available benchmark S-HAR dataset that compiles raw sensor data acquired from smartphones, was employed to build and evaluate five DL networks. A ccording to the experiments, the five models' detection performance improves dramatically when given access to biological data. © 2023 IEEE.

关键词： Classification (of information)

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

Robin: A Novel Method to Produce Robust Interpreters for Dee...

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IEEE International Conference on Automated Software engineering (ASE)

作者： Zhen Li Ruqian Zhang Deqing Zou Ning Wang Yating Li Shouhuai Xu Chen Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Hubei Engineering Research Center on Big Data Security Department of Computer Science University of Colorado Colorado Springs USA Center for Research in Computer Vision University of Central Florida USA School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA.

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MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

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IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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Human-Centric Goal Reasoning with Ripple-Down Rules

arXiv

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arXiv 2024年

作者： Brameld, Kenji Sammut, Claude Castro, Germán Roberts, Mark Aha, David W. School of Computer Science and Engineering The University of New South Wales Australia Navy Center for Applied Research in AI Naval Research Laboratory WashingtonDC20375 United States

ActorSim is a goal reasoning framework developed at the Naval Research Laboratory. Originally, all goal reasoning rules were hand-crafted. This work extends ActorSim with the capability of learning by demonstration, that is, when a human trainer disagrees with a decision made by the system, the trainer can take over and show the system the correct decision. The learning component uses Ripple-Down Rules (RDR) to build new decision rules to correctly handle similar cases in the future. The system is demonstrated using the RoboCup Rescue Agent Simulation, which simulates a city-wide disaster, requiring emergency services, including fire, ambulance and police, to be dispatched to different sites to evacuate civilians from dangerous situations. The RDRs are implemented in a scripting language, FrameScript, which is used to mediate between ActorSim and the agent simulator. Using Ripple-Down Rules, ActorSim can scale to an order of magnitude more goals than the previous version. © 2024, CC BY.

关键词： Research laboratories

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Soft Constraint in Local Structure Approximation-PINN

Soft Constraint in Local Structure Approximation-PINN

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Artificial Intelligence (CAI), IEEE Conference on

作者： Jian Cheng Wong Pao-Hsiung Chiu Chinchun Ooi My Ha Dao Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore School of Computer Science and Engineering Nanyang Technological University (NTU) Singapore Center for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

We previously presented a novel loss formulation for efficient learning of complex dynamics from governing physics, typically described by partial differential equations (PDEs), using physics-informed neural networks (PINNs). In these experiments, the incorporation of a Boundary Connectivity (BCXN) loss function was shown to greatly improve physics-informed learning across many problems, especially those with complex geometries. However, this imposition may not always be ideal, as the previously presented BCXN-loss strongly enforces a linear local structure at the boundary. While this assumption helps facilitate faster learning with an order of magnitude sparser training samples, this can adversely impact convergence in other situations. Hence, we propose a modification of this BCXN-loss that reduces the imposed structure to a soft constraint, allowing for more flexible learning and convergence. We further demonstrate the potential for this method to improve the convergence and performance of LSA-PINN across additional numerical experiments, with much smaller errors than existing methods in terms of the standard L2-norm metric. In particular, we have applied this method to the modelling of flow past complex geometries such as airfoils, which serve as the basic building block for various applications in fluid dynamics and renewable energy (e.g., in wind and tidal turbine design).

关键词： Geometry Training Renewable energy sources Wind energy Fluid dynamics Wind turbines Wind forecasting

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