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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

Automated Detection of Epileptic Spikes and Seizures Incorpo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanyang Dong Shurong Sheng Xiongfei Wang Jia-Hong Gao Yi Sun Wanli Yang Kuntao Xiao Pengfei Teng Guoming Luan Zhao Lv School of Artificial Intelligence Anhui University Hefei China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Neurosurgery Beijing Key Laboratory of Epilepsy Sanbo Brain Hospital Capital Medical University Beijing China McGovern Institute for Brain Research Peking University Beijing China Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing China Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Peking University Beijing China Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal’s intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor’s spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset.

关键词： Convolution Magnetic sensors Neural networks Magnetoencephalography Manuals Feature extraction Electroencephalography Encoding Recording Magnetic fields

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Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

arXiv

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arXiv 2023年

作者： Liu, Jin Mao, Junbin Lin, Hanhe Kuang, Hulin Pan, Shirui Wu, Xusheng Xie, Shan Liu, Fei Pan, Yi Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830000 China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom School of Information and Communication Technology Griffith University Gold CoastQLD4215 Australia Shenzhen Health Development Research and Data Management Center Shenzhen518109 China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology. The source code will be available at https://***/yutian0315/MMKGL. © 2023, CC BY-NC-ND.

关键词： Graph embeddings

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An acupoint-originated human interstitial fluid circulatory network

引用

Chinese Medical Journal 2021年第19期134卷 2365-2369页

作者： Hong-Yi Li Fang Wang Min Chen Zhi-Jian Zheng Ya-Jun Yin Jun Hu Hua Li Andreas Sammer Georg Feigl Norbert Maurer Chao Ma Fu-Sui Ji Department of Cardiology Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of Radiology Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of Accupuncture Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijing 100730China Department of Engineering Mechanics Tsinghua UniversityBeijing 10084China Key Lab of Interfacial Physics and Technology Shanghai Institute of Applied PhysicsChinese Academy of SciencesShanghai 201210China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201210China Key Lab of Intelligent Information Processing Institute of Computing Technology and University of Chinese Academy of SciencesBeijing 100190China Ordination Dr.Sammer Dr.med.univ.Andreas SammerGrazAustria Institute of Anatomy and Clinical Morphology University of Witten/HerdeckeWittenGermany University Clinic for Physical Medicine Rehabilitation and Occupational MedicineMedical University of ViennaViennaAustria Institute of Basic Medical Sciences Chinese Academy of Medical SciencesDepartment of Human AnatomyHistology and EmbryologySchool of Basic MedicinePeking Union Medical CollegeBeijing 100005China Chinese Institute for Brain Research Beijing 100005China

To the Editor:Since 1027,when the Tian Sheng Bronze Statue showing acupoints and meridians was first introduced for medical practitioners,it has evolved to the currently used acupoints and meridians atlas in traditional Chinese medicine(TCM).[1]To date,the acupoints represent defined areas on the body surface relative to certain *** acupoints in the extremity endings would connect anatomically with diverse associated visceral organs or tissues,forming a Meridian and Collateral *** on the atlas,each main meridian is usually a virtual line connecting a group of adjacent acupoints that correlate with certain visceral *** modern medical science,the anatomical structures of main meridians remain largely unclarified.

关键词： organs originated connecting

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Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design

arXiv

引用

arXiv 2021年

作者： Huang, Chongwen Yang, Zhaohui Alexandropoulos, George C. Xiong, Kai Wei, Li Yuen, Chau Zhang, Zhaoyang Debbah, Mérouane The College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China The ZJU-UIUC Institute Zhejiang University Haining314400 China The Centre for Telecommunications Research Department of Engineering King's College London WC2R 2LS United Kingdom The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Panepistimiopolis Ilissia Athens15784 Greece School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China The Singapore University of Technology and Design Singapore487372 Singapore CentraleSupélec University Paris-Saclay Gif-sur-Yvette 91192 France The Lagrange Mathematical and Computing Research Center Paris75007 France

Wireless communication in the TeraHertz band (0.1-10 THz) is envisioned as one of the key enabling technologies for the future sixth generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology. However, very high propagation attenuations and molecular absorptions of THz frequencies often limit the signal transmission distance and coverage range. Benefited from the recent breakthrough on the reconfigurable intelligent surfaces (RIS) for realizing smart radio propagation environment, we propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies. Particularly, multiple passive and controllable RISs are deployed to assist the transmissions between the base station (BS) and multiple single-antenna users. We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss. To improve the convergence of the proposed DRL-based algorithm, two algorithms are then designed to initialize the digital beamforming and the analog beamforming matrices utilizing the alternating optimization technique. Simulation results show that our proposed scheme is able to improve 50% more coverage range of THz communications compared with the benchmarks. Furthermore, it is also shown that our proposed DRL-based method is a state-of-the-art method to solve the NP-hard beamforming problem, especially when the signals at RIS-assisted THz communication networks experience multiple hops. Copyright © 2021, The Authors. All rights reserved.

关键词： Beamforming

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Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

arXiv

引用

arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

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Sum Rate Maximization under AoI Constraints for RIS-Assisted mmWave Communications

arXiv

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arXiv 2023年

作者： Guo, Ziqi Niu, Yong Mao, Shiwen Zhang, Changming Wang, Ning Zhong, Zhangdui Ai, Bo The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Research Institute of Intelligent Networks Zhejiang Lab Hangzhou311121 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China

The concept of age of information (AoI) has been proposed to quantify information freshness, which is crucial for time-sensitive applications. However, in millimeter wave (mmWave) communication systems, the link blockage caused by obstacles and the severe path loss greatly impair the freshness of information received by the user equipments (UEs). In this paper, we focus on reconfigurable intelligent surface (RIS)-assisted mmWave communications, where beamforming is performed at transceivers to provide directional beam gain and a RIS is deployed to combat link blockage. We aim to maximize the system sum rate while satisfying the information freshness requirements of UEs by jointly optimizing the beamforming at transceivers, the discrete RIS reflection coefficients, and the UE scheduling strategy. To facilitate a practical solution, we decompose the problem into two subproblems. For the first per-UE data rate maximization problem, we further decompose it into a beamforming optimization subproblem and a RIS reflection coefficient optimization subproblem. Considering the difficulty of channel estimation, we utilize the hierarchical search method for the former and the local search method for the latter, and then adopt the block coordinate descent (BCD) method to alternately solve them. For the second scheduling strategy design problem, a low-complexity heuristic scheduling algorithm is designed. Simulation results show that the proposed algorithm can effectively improve the system sum rate while satisfying the information freshness requirements of all UEs. Copyright © 2023, The Authors. All rights reserved.

关键词： Scheduling algorithms

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Major strategies for improving the performance of perovskite solar cells

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iEnergy 2023年第3期2卷 172-199页

作者： Lixiu Zhang Hang Li Kai Zhang Wenzhe Li Chuantian Zuo George Omololu Odunmbaku Jingde Chen Cong Chen Luozheng Zhang Ru Li Yuping Gao Baomin Xu Jiangzhao Chen Yongsheng Liu Yang Wang Yanlin Song Jianxin Tang Feng Gao Qing Zhao Yong Peng Mingzhen Liu Lei Tao Yuelong Li Zhimin Fang Ming Cheng Kuan Sun Dewei Zhao Yixin Zhao Shihe Yang Chenyi Yi Liming Ding Center for Excellence in Nanoscience(CAS) Key Laboratory of Nanosystem and Hierarchical Fabrication(CAS)National Center for Nanoscience and TechnologyBeijing 100190China State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Shenzhen Graduate School Peking UniversityShenzhen 518055China School of Environmental Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China College of Materials Science and Engineering&Engineering Research Center of Alternative Energy Materials&Devices Sichuan UniversityChengdu 610065China School of Energy&Power Engineer-ing Chongqing UniversityChongqing 400044China Institute for Energy Research Jiangsu UniversityZhenjiang 212013China Institute of New Energy Technology Department of Electronic EngineeringCollege of Information Science and TechnologyJinan UniversityGuangzhou 510631China Institute of Functional Nano&Soft Materials(FUNSOM) Soochow UniversitySuzhou 215123China Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen 518055China College of Optoelectronic Engineering Chongqing UniversityChongqing 400044China College of Chemistry Nankai UniversityTianjin 300071China Institute of Chemistry Chinese Academy of SciencesBeijing 100190China State Key Lab for Mesoscopic Physics and Frontiers Science Center for Nano-optoelec-tronics School of PhysicsPeking UniversityBeijing 100871China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China School of Materials and Energy University of Electronic Science and Technology of ChinaChengdu 611731China Institute of Photoelectronic Thin Film Devices and Technology Nankai UniversityTianjin 300350China School of Materials Science and Engineering Shaanxi Normal UniversityXi’an 710119China

Metal halide perovskite solar cell(PSC)has successfully distinguished itself in optoelectronic field by virtue of the sharp rise in power conversion efficiency over the past *** remarkable efficiency breakthrough at such a fast speed can be mainly attributed to the comprehensive study on film deposition techniques,especially the effective management of surface and interfacial defects in recent ***,we summarized the current trends in performance enhancement for PSCs,with a focus on the generally applicable strategies in high-performance works,involving deposition methods,compositional engineering,additive engineering,crystallization manipulation,charge transport material selection,interfacial passivation,optical coupling effect and constructing tandem solar *** directions and perspectives are also provided.

关键词： Perovskite solar cells efficiency improvement strategies milestones

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Cross-Region Graph Convolutional Network with Periodicity Shift Adaptation for Wide-area SST Prediction

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ACM Transactions on intelligent Systems and Technology 1000年

作者： Han Peng Wengen Li Chang Jin Yichao Zhang Jihong Guan Hanchen Yang Shuigeng Zhou Department of Computer Science and Technology Tongji University China Department of Computer Science and Technology Tongji University China and Department of Computing The Hong Kong Polytechnic University China Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University China

Accurate prediction of sea surface temperature (SST) is of high importance in marine science, benefiting applications ranging from ecosystem protection to extreme weather forecasting and climate analysis. Wide-area SST usually shows diverse SST patterns in different sea areas due to the changes of temperature zones and the dynamics of ocean currents. However, existing studies on SST prediction often focus on small-area predictions and lack the consideration of diverse SST patterns. Furthermore, SST shows an annual periodicity, but the periodicity is not strictly adherent to an annual cycle. Existing SST prediction methods struggle to adapt to this non-strict periodicity. To address these two issues, we proposed the Cross-Region Graph Convolutional Network with Periodicity Shift Adaptation (RGCN-PSA) model which is equipped with the Cross-Region Graph Convolutional Network module and the Periodicity Shift Adaption module. The Cross-Region Graph Convolutional Network module enhances wide-area SST prediction by learning and incorporating diverse SST patterns. Meanwhile, the periodicity Shift Adaptation module accounts for the annual periodicity and enable the model to adapt to the possible temporal shift automatically. We conduct experiments on two real-world SST datasets, and the results demonstrate that our RGCN-PSA model obviously outperforms baseline models in terms of prediction accuracy. The code of RGCN-PSA model is available at https://***/ADMIS-TONGJI/RGCN-PSA/.

关键词： Wide-area SST prediction graph neural network diverse SST patterns periodicity shift adaptation

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