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data Compression Conference (DCC)

作者： Wei Wang Baohua Zhang Tao Cui Yimeng Chai Yue Li College of Computer Science KLMDASR Trusted AI System Lab Nankai University Tianjin China College of Computer Science Tianjin Key Lab of Network and Data Security Technology Nankai University Tianjin China NCMIS LSEC Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

The compression of Generative Adversarial networks (GANs) has been an emerging study in recent years. However, conventional compression methods can hardly be applied to GANs due to the training process and optimization target of GANs are different from the traditional classification detection network. Inspired by the recent success of knowledge distillation, we condense the recent researches on them applied to particular GANs, i.e. WGAN and CGAN for special tasks into two strategies: Soft Target Only Strategy (STOS) and Inherited Hard Target Strategy (IHTS), and propose an novel Random Hard Target Strategy, namely RHTS. In RHTS, we take the student network generator loss as the hard target of knowledge distillation when the discriminator is fixed, and the output of the teacher network generator as the soft target to distill GANs. The experiments on image datasets (MNIST, CIFAR-10), and structured-data datasets (Australian Credit Approval dataset, Credit Approval data Set) show that STOS and IHTS are effective, and RHTS is the best. Such results are further proved on WGAN, WGAN-GP and LSGAN in order to demonstrate the better generalization ability of the strategies. Besides, the RHTS improve the stability of the GAN training process, where the performance and stability confirm the rationality of our design.

关键词： Training Knowledge engineering data compression Generative adversarial networks Generators Task analysis Optimization

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Better Pseudo-label: Joint Domain-aware label and Dual-classifier for Semi-supervised Domain Generalization

arXiv

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arXiv 2021年

作者： Wang, Ruiqi Qi, Lei Shi, Yinghuan Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China School of Computer Science and Engineering Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

With the goal of directly generalizing trained model to unseen target domains, domain generalization (DG), a newly proposed learning paradigm, has attracted considerable attention. Previous DG models usually require a sufficient quantity of annotated samples from observed source domains during training. In this paper, we relax this requirement about full annotation and investigate semisupervised domain generalization (SSDG) where only one source domain is fully annotated along with the other domains totally unlabeled in the training process. With the challenges of tackling the domain gap between observed source domains and predicting unseen target domains, we propose a novel deep framework via joint domain-aware labels and dual-classifier to produce high-quality pseudo-labels. Concretely, to predict accurate pseudo-labels under domain shift, a domain-aware pseudo-labeling module is developed. Also, considering inconsistent goals between generalization and pseudo-labeling: former prevents overfitting on all source domains while latter might overfit the unlabeled source domains for high accuracy, we employ a dual-classifier to independently perform pseudo-labeling and domain generalization in the training process. When accurate pseudo-labels are generated for unlabeled source domains, the domain mixup operation is applied to augment new domains between labeled and unlabeled domains, which is beneficial for boosting the generalization capability of the model. Extensive results on publicly available DG benchmark datasets show the efficacy of our proposed SSDG method. Copyright © 2021, The Authors. All rights reserved.

关键词： Image recognition

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Learning Hybrid Negative Probability Model for Weakly-Supervised Whole Slide Image Recognition

Learning Hybrid Negative Probability Model for Weakly-Superv...

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International Conference on Acoustics, Speech, and Signal Processing (IcasSP)

作者： Yining Qiu Yuxi Li Jiafu Wu Zhenye Gan Mingmin Chi Yabiao Wang Chengjie Wang Pei Wang School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China Tencent Youtu Lab China Zhongshan PoolNet Technology Co. Ltd Zhongshan Fudan Joint Innovation Center China Zhejiang University China NAOC CAS China

Classifying an entire Whole Slide Image (WSI) in a single forward pass is challenging due to its vast resolution. Consequently, current effort on WSI classification resorts to multiple instance learning (MIL), using patch-wise instances to predict categories under image-wise supervision. However, recent MIL methods usually follow implicit instance selection strategy and ignore the effect from inherent patch category imbalances. In a statistical sense, negative patches dominate in WSIs and provide sufficient samples for accurate density estimation. Therefore, in this paper, we learn from anomaly detection and propose a deep MIL framework which learns a hybrid negative probability model to bootstrap discovery of potential positive lesion. We associate attention-based MIL approach with a regularization loss function to explicitly improve patch selection process in positive images. Experiments conducted on benchmarks of WSI recognition demonstrate that our method brings significant improvement to classic attention-based MIL baseline and achieves state-of-the-art performance.

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WiOpen: A Robust Wi-Fi-based Open-set Gesture Recognition Framework

arXiv

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arXiv 2024年

作者： Zhang, Xiang Huang, Jingyang Yan, Huan Zhao, Peng Zhuang, Guohang Liu, Zhi Liu, Bin CAS Key Laboratory of Electromagnetic Space Information University of Science and Technology of China Hefei230026 China School of Computer and Information Hefei University of Technology Hefei230601 China School of Big Data and Computer Science Guizhou Normal University Guiyang550025 China Department of Computer and Network Engineering The University of Electro-Communications Tokyo1828585 Japan

Recent years have witnessed a growing interest in Wi-Fi-based gesture recognition. However, existing works have predominantly focused on closed-set paradigms, where all testing gestures are predefined during training. This poses a significant challenge in real-world applications, as unseen gestures might be misclassified as known classes during testing. To address this issue, we propose WiOpen, a robust Wi-Fi-based Open-Set Gesture Recognition (OSGR) framework. Implementing OSGR requires addressing challenges caused by the unique uncertainty in Wi-Fi sensing. This uncertainty, resulting from noise and domains, leads to widely scattered and irregular data distributions in collected Wi-Fi sensing data. Consequently, data ambiguity between classes and challenges in defining appropriate decision boundaries to identify unknowns arise. To tackle these challenges, WiOpen adopts a two-fold approach to eliminate uncertainty and define precise decision boundaries. Initially, it addresses uncertainty induced by noise during data preprocessing by utilizing the CSI ratio. Next, it designs the OSGR network based on an uncertainty quantification method. Throughout the learning process, this network effectively mitigates uncertainty stemming from domains. Ultimately, the network leverages relationships among samples’ neighbors to dynamically define open-set decision boundaries, successfully realizing OSGR. Comprehensive experiments on publicly accessible datasets confirm WiOpen’s effectiveness. Notably, WiOpen also demonstrates superiority in cross-domain tasks when compared to state-of-the-art approaches. © 2024, CC BY-SA.

关键词： Learning systems

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A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict

arXiv

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arXiv 2021年

作者： Liu, Yiyi Wang, Yequan Sun, Aixin Meng, Xuying Li, Jing Guo, Jiafeng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Beijing Academy of Artificial Intelligence Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute of Computing Technology Chinese Academy of Sciences Beijing China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

Sarcasm employs ambivalence, where one says something positive but actually means negative, and vice versa. The essence of sarcasm, which is also a sufficient and necessary condition, is the conflict between literal and implied sentiments expressed in one sentence. However, it is difficult to recognize such sentiment conflict because the sentiments are mixed or even implicit. As a result, the recognition of sophisticated and obscure sentiment brings in a great challenge to sarcasm detection. In this paper, we propose a Dual-Channel Framework by modeling both literal and implied sentiments separately. Based on this dual-channel framework, we design the Dual-Channel network (DC-Net) to recognize sentiment conflict. Experiments on political debates (i.e., IAC-V1 and IAC-V2) and Twitter datasets show that our proposed DC-Net achieves state-of-the-art performance on sarcasm recognition. Our code is released to support research https://***/yiyi-ict/dual-channel-for-sarcasm. © 2021, CC BY.

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Safety Guaranteed Power-Delivered-to-Load Maximization for Magnetic Wireless Power Transfer

Safety Guaranteed Power-Delivered-to-Load Maximization for M...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Wangqiu Zhou Xinyu Wang Hao Zhou Shenyao Jiang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

关键词： Performance evaluation Monte Carlo methods System performance Inductive charging Prototypes Wireless power transfer Parallel processing

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LAORA: Location-Aware Orientation Adjustment for MIMO Magnetic Wireless Charging System

LAORA: Location-Aware Orientation Adjustment for MIMO Magnet...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and networks (SECON)

作者： Lingchang Kong Xinyu Wang Hao Zhou Fengyu Zhou Shenyao Jiang Peide Zhu Qi Song Zhi Liu LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan School of Mathematics and Statistics Fujian Normal University Fuzhou China Department of Computer Science City University of Hong Kong Hong Kong China

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186 % while reducing the mechanical rotations by 83.3 %.

关键词： Location awareness Coils Transmitters Magnetic resonance imaging Prototypes Reinforcement learning Wireless power transfer Transforms Sensors Impedance

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A Novel Configurable High-precision and Low-cost Circuit Design of Sigmoid and Tanh Activation Function

A Novel Configurable High-precision and Low-cost Circuit Des...

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IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)

作者： Feng Liu Bowen Zhang Gang Chen Guoliang Gong Huaxiang Lu Wenchang Li Institute of Semiconductors Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Lab Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Shanghai China

ISBN: (纸本)9781665417488

This paper proposes a configurable Sigmoid and Tanh activation function circuit design by second-order approximation and deviation compensation. The proposed circuit is synthesized in TSMC 180nm technology within a 7218 um 2 area at 500MHz, it is also deployed on Xilinx ZC706 FPGA by 158 LUTs and 46 FFs at 333MHz. It achieves almost 4.68× better precision and 20.2% less area than previous works. The experimental results show that our method has almost no accuracy loss on LSTM Chinese speech recognition tasks and the YOLOv3 image recognition post-processing task.

关键词： Integrated circuit technology Image recognition Costs Conferences Speech recognition Hardware Circuit synthesis

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Detector development at the Back-n white neutron source

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Radiation Detection technology and Methods 2023年第2期7卷 171-191页

作者： Fan Ruirui Li Qiang Bao Jie Li Yang Liu Rong Jiang Wei Ren Jie Zhang Qiwei Cao Ping Gu Minhao Ren Zhizhou Yi Han Tang Jingyu An Qi Bai Haofan Bai Jiangbo Chen Qiping Chen Yonghao Chen Zhen Cui Zengqi Fan Anchuan Feng Changqing Feng Fanzhen Gao Keqing Han Changcai Han Zijie He Guozhu He Yongcheng Hong Yang Hu Yiwei Huang Hanxiong Jia Weihua Jiang Haoyu Jiang Zhijie Jin Zhengyao Kang Ling Li Bo Li Chao Li Gong Li Jiawen Li Xiao Liu Jie Liu Shubin Luan Guangyuan Ning Changjun Qi Binbin Ruan Xichao Song Zhaohui Sun Kang Tan Zhixin Tang Shengda Wang Pengcheng Wang Zhaohui Wen Zhongwei Wu Xiaoguang Wu Xuan Xie Likun Yang Yiwei Yu Yongji Zhang Guohui Zhang Linhao Zhang Mohan Zhang Xianpeng Zhang Yuliang Zhang Yue Zhang Zhiyong Zhao Maoyuan Zhou Luping Zhou Zhihao Zhu Kejun The CSNS Back-n Collaboration Institute of High Energy Physics CASBeijing 100049People’s Republic of China Spallation Neutron Source Science Center Dongguan 523803People’s Republic of China State Key Laboratory of Particle Detection and Electronics Beijing 100049People’s Republic of China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413People’s Republic of China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900People’s Republic of China Department of Modern Physics University of Science and Technology of ChinaHefei 230026People’s Republic of China State Key Laboratory of Particle Detection and Electronics Hefei 230026People’s Republic of China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871People’s Republic of China USTC archaeometry lab University of Science and Technology of ChinaHefei 230026People’s Republic of China Northwest Institute of Nuclear Technology Xi’an 710024People’s Republic of China University of Chinese Academy of Sciences Beijing 100049People’s Republic of China 不详

Purpose Back-n is a white neutron beamline at China spallation neutron source,which was established in the year of *** is a powerful facility for nuclear data measurement,neutron detector calibration,and radiation effect *** A series of detectors were built for different experiments,including beam monitoring,beam profile measurement,neutron induced secondaries(fission fragments,light charged particles and gamma)cross section measurement,and neutron resonance radiography,etc.A common digitization electronics and a cluster-based DAQ were developed for these detector *** detectors have been employed at Back-n and serviced for experiments from the beginning of the beamline *** and conclusion As an overview of detectors of Back-n,the details of the detector design and the experiment performing are described in this *** developing systems,e.g.,MTPC and B-MCP,are also included.

关键词： White neutron source Beam diagnostics Fission fragment Charged particle Gamma Neutron resonance radiography

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Target-Oriented WiFi Sensing for Respiratory Healthcare: from Indiscriminate Perception to In-Area Sensing

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IEEE network 2024年

作者： Wang, Meng Huang, Jinyang Zhang, Xiang Liu, Zhi Li, Meng Zhao, Peng Yan, Huan Sun, Xiao Dong, Mianxiong Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligence Machine School of Computer and Information Hefei230601 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China Muroran Institute of Technology Department of Sciences and Informatics 0508585 Japan

Driven by the vision of integrated sensing and communication (ISAC) toward 6G technology, the WiFi-based respiration sensing approach has emerged as a highly competitive candidate for advanced healthcare services. Nevertheless, the indiscriminate perception of all the moving objects within the sensing area raises challenges for system stability and robustness for real-world deployment, especially for the susceptibility to motion interference from other people. Meanwhile, thanks to the emerging in-area wireless sensing technologies, i.e., beamforming, the dynamic environment tolerance of these technologies gives crucial opportunities for target-oriented WiFi-based healthcare services. This article discusses the compelling intersection of in-area WiFi sensing and the future of target-oriented intelligent respiratory healthcare network services. We first present a novel WiFi-based healthcare-assisting framework tailored for inter-user motion interference-tolerable (IMIT) sensing. The detailed components of low-level signal preprocessing, target-direction signal extraction, and healthcare-related services are discussed. By utilizing CSI beamforming technology, which adjusts a directional beam toward the desired direction and adaptively places beam nulls in noisy directions, we can mitigate motion interference and achieve target-oriented healthcare sensing. Finally, we discuss open challenges and potential solutions for target-oriented WiFi healthcare sensing. © 1986-2012 IEEE.

关键词： Wireless local area networks (WLAN)

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