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arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Wang, Senzhang He, Lifang Li, Bo Wang, Lihong Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 State Key Laboratory of Software Development Environment Beihang University Beijing100083 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Biostatistics and Epidemiology University of Pennsylvania PhiladelphiaPA19104 United States National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics, but how to consider them both coherently is less studied. In addition, most existing methods treat output labels as independent medoids, but ignore the hierarchical relations among them, leading to useful semantic information loss. In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification. Specifically, we first propose to model each document as a word order preserved graph-of-words and normalize it as a corresponding words-matrix representation which preserves both the non-consecutive, long-distance and local sequential semantics. Then the words-matrix is input to the proposed attentional graph capsule recurrent CNNs for more effectively learning the semantic features. To leverage the hierarchical relations among the class labels, we propose a hierarchical taxonomy embedding method to learn their representations, and define a novel weighted margin loss by incorporating the label representation similarity. Extensive evaluations on three datasets show that our model significantly improves the performance of large-scale multi-label text classification by comparing with state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Correction to: AEAU-Net: An unsupervised end-to-end registration network by combining affine transformation and deformable medical image registration

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Medical & biological engineering & computing 2023年第11期61卷 2875页

作者： Wei Qiu Lianjin Xiong Ning Li Zhangrong Luo Yaobin Wang Yangsong Zhang School of Computer Science and Technology Laboratory for Brain Science and Medical Artificial Intelligence Southwest University of Science and Technology Mianyang 621010 China. School of Computer Science and Technology Laboratory for Brain Science and Medical Artificial Intelligence Southwest University of Science and Technology Mianyang 621010 China. zhangysacademy@***. NHC Key Laboratory of Nuclear Technology Medical Transformation (Mianyang Central Hospital) Mianyang 621010 China. zhangysacademy@***. Key Laboratory of Testing Technology for Manufacturing Process Ministry of Education Southwest University of Science and Technology Mianyang 621010 Sichuan China. zhangysacademy@***.

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Deep learning for polygenic score analysis for Alzheimer's disease risk prediction in the Chinese population

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2021年第Sup3期17卷 e056625-e056625页

作者： Xiaopu Zhou Yu Chen Fanny C. F. Ip Yuanbing JIANG Han Cao Huan Zhong Yuewen Chen Jiahang Chen Yulin Zhang Shuangshuang Ma Nicole Chit Hang Lai Ronnie M.N. Lo Kit Cheung Estella Pui-Sze Tong Kin Y Mok John Hardy Qihao Guo Vincent C.T. Mok Timothy CY Kwok Lei Chen Amy K.Y. Fu Nancy Y. Ip Division of Life Science State Key Laboratory of Molecular Neuroscience Molecular Neuroscience Center The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Guangdong Provincial Key Laboratory of Brain Science Disease and Drug Development HKUST Shenzhen Research Institute Shenzhen-Hong Kong Institute of Brain Science Shenzhen China Hong Kong Center for Neurodegenerative Diseases Hong Kong Science and Technology Parks Hong Kong Brain Cognition and Brain Disease Institute Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Neurodegenerative Disease UCL Institute of Neurology London United Kingdom Institute for Advanced Study The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Shanghai Jiao Tong University Affiliated Sixth People's HospitalShanghai China Gerald Choa Neuroscience Centre Lui Che Woo Institute of Innovative Medicine Therese Pei Fong Chow Research Centre for Prevention of Dementia Division of Neurology Department of Medicine and Therapeutics Chinese University of Hong Kong Hong Kong Therese Pei Fong Chow Research Centre for Prevention of Dementia Division of Geriatrics Department of Medicine and Therapeutics The Chinese University of Hong Kong Shatin Hong Kong Division of Life Science State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

BACKGROUND: Alzheimer's disease (AD) is a leading cause of mortality in the elderly. Genetics studies have identified variants associated with AD. Moreover, Polygenic score analysis can infer the risk of developing AD solely on the basis of genotype information. METHOD: Whole-genome sequencing was performed in 2 AD cohorts recruited from mainland China (n = 2,340) and Hong Kong (n = 1,009). Genotype and phenotype data were input into a neural network model that output a polygenic score. Regression analysis was subsequently conducted to examine the associations between the resultant polygenic scores and AD plasma biomarkers. RESULT: The resultant polygenic scores were associated with AD pathogenesis and the levels of specific AD plasma biomarkers in the Chinese population. CONCLUSION: The results suggest that deep-learning methods have utility for investigating the underlying mechanisms of AD and promote the possibility of developing a genotype-based strategy for AD risk prediction. © 2021 the Alzheimer's Association.

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Predicting extreme events from data using deep machine learning: When and where

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Physical Review Research 2022年第2期4卷 023028-023028页

作者： Junjie Jiang Zi-Gang Huang Celso Grebogi Ying-Cheng Lai The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi'an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Xi'an Shaanxi 710049 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University Tempe Arizona 85287 USA

We develop a framework based on the deep convolutional neural network (DCNN) for model-free prediction of the occurrence of extreme events both in time (“when”) and in space (“where”) in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the two-dimensional complex Ginzburg-Landau equation and empirical wind speed data from the North Atlantic Ocean to demonstrate and validate our machine-learning-based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme events on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world.

关键词： Machine learning Spatiotemporal chaos Artificial neural networks Extreme event statistics

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technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Generating large-scale dynamic optimization problem instances using the generalized moving peaks benchmark

arXiv

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arXiv 2021年

作者： Omidvar, Mohammad Nabi Yazdani, Danial Branke, Jürgen Li, Xiaodong Yang, Shengxiang Yao, Xin School of Computing University of Leeds Leeds University Business School Leeds United Kingdom Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Operational Research and Management Sciences Group Warwick Business School University of Warwick CoventryCV4 7AL United Kingdom RMIT University GPO Box 2476 Melbourne3001 Australia School of Computer Science and Informatics De Montfort University Leicester United Kingdom School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

This document describes the generalized moving peaks benchmark (GMPB) [1] and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems. It presents a set 15 benchmark problems, the relevant source code, and a performance indicator, designed for comparative studies and competitions in large-scale dynamic optimization. Although its primary purpose is to provide a coherent basis for running competitions, its generality allows the interested reader to use this document as a guide to design customized problem instances to investigate issues beyond the scope of the presented benchmark suite. To this end, we explain the modular structure of the GMPB and how its constituents can be assembled to form problem instances with a variety of controllable characteristics ranging from unimodal to highly multimodal, symmetric to highly asymmetric, smooth to highly irregular, and various degrees of variable interaction and ill-conditioning. © 2021, CC BY-NC-ND.

关键词： Optimization

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Neuroimaging epicenters as potential sites of onset of the neuroanatomical pathology in schizophrenia

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science Advances 2024年第24期10卷 eadk6063页

作者： Jiang, Yuchao Palaniyappan, Lena Luo, Cheng Chang, Xiao Zhang, Jie Tang, Yingying Zhang, Tianhong Li, Chunbo Zhou, Enpeng Yu, Xin Li, Wei An, Dongmei Zhou, Dong Huang, Chu-Chung Tsai, Shih-Jen Lin, Ching-Po Cheng, Jingliang Wang, Jijun Yao, Dezhong Cheng, Wei Feng, Jianfeng Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Ministry of Education Shanghai China Douglas Mental Health University Institute Department of Psychiatry McGill University MontréalQC Canada Robarts Research Institute University of Western Ontario LondonON Canada Lawson Health Research Institute LondonON Canada Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of life Science and Technology University of Electronic Science and Technology of China Chengdu China High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province Center for Information in Medicine University of Electronic Science and Technology of China Chengdu China Chinese Academy of Medical Sciences Chengdu China Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Shanghai200030 China Beijing China Department of Neurology West China Hospital Sichuan University Chengdu610041 China School of Psychology and Cognitive Science East China Normal University Shanghai China Shanghai Changning Mental Health Center Shanghai China Department of Psychiatry Taipei Veterans General Hospital Taipei Taiwan Institute of Neuroscience National Yang Ming Chiao Tung University Taipei Taiwan Department of MRI First Affiliated Hospital of Zhengzhou University Zhengzhou China Department of Neurology Huashan Hospital Fudan University Shanghai China Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence Zhejiang Normal University Jinhua China MOE Frontiers Center for Brain Science Fudan University Shanghai China Zhangjiang Fudan International Innovation Center Shanghai China School of Data Science Fudan University Shanghai China Department of Computer Science University of Warwick CoventryCV 4 7AL United Kingdom

Schizophrenia lacks a clear definition at the neuroanatomical level, capturing the sites of origin and progress of this disorder. Using a network-theory approach called epicenter mapping on cross-sectional magnetic resonance imaging from 1124 individuals with schizophrenia, we identified the most likely "source of origin" of the structural pathology. Our results suggest that the Broca’s area and adjacent frontoinsular cortex may be the epicenters of neuroanatomical pathophysiology in schizophrenia. These epicenters can predict an individual’s response to treatment for psychosis. In addition, cross-diagnostic similarities based on epicenter mapping over of 4000 individuals diagnosed with neurological, neurodevelopmental, or psychiatric disorders appear to be limited. When present, these similarities are restricted to bipolar disorder, major depressive disorder, and obsessive-compulsive disorder. We provide a comprehensive framework linking schizophrenia-specific epicenters to multiple levels of neurobiology, including cognitive processes, neurotransmitter receptors and transporters, and human brain gene expression. Epicenter mapping may be a reliable tool for identifying the potential onset sites of neural pathophysiology in schizophrenia. © 2024 the Authors, some rights reserved

关键词： Mapping

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Detecting out-of-distribution samples via variational auto-encoder with reliable uncertainty estimation

arXiv

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arXiv 2020年

作者： Ran, Xuming Xu, Mingkun Mei, Lingrui Xu, Qi Liu, Quanying Shenzhen Key Laboratory of Smart Healthcare Engineering Department of Biomedical Engineering Southern University of Science and Technology Shenzhen518055 China Center for Brain Inspired Computing Research Department of Precision Instrument Tsinghua University Beijing100084 China China Automotive Engineering Research Institute Chongqing401122 China School of Artifical Intelligence Electronic and Electrical Engineering School of Artifical Intelligence Dalian University of Technology Dalian116024 China College of Mathematics and Statistics Chongqing Jiaotong University Chongqing400074 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Variational autoencoders (VAEs) are influential generative models with rich representation capabilities from the deep neural network architecture and Bayesian method. However, VAE models have a weakness that assign a higher likelihood to out-of-distribution (OOD) inputs than in-distribution (ID) inputs. To address this problem, a reliable uncertainty estimation is considered to be critical for in-depth understanding of OOD inputs. In this study, we propose an improved noise contrastive prior (INCP) to be able to integrate into the encoder of VAEs, called INCPVAE. INCP is scalable, trainable and compatible with VAEs, and it also adopts the merits from the INCP for uncertainty estimation. Experiments on various datasets demonstrate that compared to the standard VAEs, our model is superior in uncertainty estimation for the OOD data and is robust in anomaly detection tasks. The INCPVAE model obtains reliable uncertainty estimation for OOD inputs and solves the OOD problem in VAE models. © 2020, CC BY.

关键词： Bayesian networks

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Networked Integrated Sensing and Communications for 6G Wireless Systems

arXiv

引用

arXiv 2024年

作者： Li, Jiapeng Shao, Xiaodan Chen, Feng Wan, Shaohua Liu, Chang Wei, Zhiqiang Ng, Derrick Wing Kwan The College of Artificial Intelligence Southwest University Chongqing400715 China The Brain-Inspired Computing and Intelligent Control Key Laboratory Southwest University Chongqing400715 China The Key Laboratory of Luminescence Analysis and Molecular Sensing Southwest University Chongqing400715 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China The School of Mathematics and Statistics Xi'an Jiaotong University Xi'An710049 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China The Department of Computer Science and Information Technology La Trobe University MelbourneVIC3150 Australia The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia

Integrated sensing and communication (ISAC) is envisioned as a key pillar for enabling the upcoming sixth generation (6G) communication systems, requiring not only reliable communication functionalities but also highly accurate environmental sensing capabilities. In this paper, we design a novel networked ISAC framework to explore the collaboration among multiple users for environmental sensing. Specifically, multiple users can serve as powerful sensors, capturing back scattered signals from a target at various angles to facilitate reliable computational imaging. Centralized sensing approaches are extremely sensitive to the capability of the leader node because it requires the leader node to process the signals sent by all the users. To this end, we propose a two-step distributed cooperative sensing algorithm that allows low-dimensional intermediate estimate exchange among neighboring users, thus eliminating the reliance on the centralized leader node and improving the robustness of sensing. This way, multiple users can cooperatively sense a target by exploiting the block-wise environment sparsity and the interference cancellation technique. Furthermore, we analyze the mean square error of the proposed distributed algorithm as a networked sensing performance metric and propose a beamforming design for the proposed network ISAC scheme to maximize the networked sensing accuracy and communication performance subject to a transmit power constraint. Simulation results validate the effectiveness of the proposed algorithm compared with the state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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ECG-DPM: Electrocardiogram Generation via a Spectrogram-based Diffusion Probabilistic Model

ECG-DPM: Electrocardiogram Generation via a Spectrogram-base...

引用

Smart World Congress (SWC), IEEE

作者： Lujundong Li Tong Xia Haojie Zhang Dongchen He Kun Qian Bin Hu Yoshiharu Yamamoto Björn W. Schuller Cecilia Mascolo Key Laboratory of Brain Health Intelligent Evaluation and Intervention Ministry of Education Beijing Institute of Technology Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Info Hub AI Thrust the Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Technology University of Cambridge UK School of Life Sciences Peking University Beijing China Guaduate School of Frontier Sciences Computational Biology and Medical Sciences The University of Tokyo Japan Educational Physiology Laboratory Graduate School of Education The University of Tokyo Japan GLAM – the Group on Language Audio & Music Imperial College London UK CHI – Chair of Health Informatics MRI Technical University of Munich Germany

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

An electrocardiogram (ECG) records the electrical signals from the heart to assess various cardiovascular conditions. Deep learning methods have been proposed to model ECGs, but the insufficient availability of ECG data and annotations often hinders their performance. To address this challenge, this paper explores the latest data synthesis technique, i.e., diffusion probabilistic models (DPMs), to enable the generation of an unlimited number of ECGs representing various cardiovascular conditions. In contrast to previous approaches that treat ECGs as time series data or convert them into power spectrograms, we introduce a novel multi-channel spectrogram-based diffusion framework. In our method, the diffusion model enhances generation diversity, while the multi-channel spectrogram preserves both magnitude and phase information, ensuring high fidelity in the reconstructed ECGs. Extensive experiments conducted on real-world ECG data demonstrate the superiority of our approach. Notably, our ECG-DPM outperforms the best baseline by a margin ranging from ${1 2. 5 \%}$ to ${6 2. 5 \%}$ when generating ECGs for 30 seconds.

关键词： Training Heart Biological system modeling Time series analysis Electrocardiography Diffusion models brain modeling Data models Distance measurement Spectrogram

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