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arXiv 2018年

作者： Chen, Yao Xia, Weiguo Cao, Ming Lü, Jinhu Department of Computer Science Southwestern University of Finance and Economics Chengdu611130 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China Faculty of Science and Engineering ENTEG University of Groningen Groningen9747 AG Netherlands School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed coordination algorithms (DCA) carry out information processing processes among a group of networked agents without centralized information fusion. Though it is well known that DCA characterized by an SIA (stochastic, indecomposable, aperiodic) matrix generate consensus asymptotically via synchronous iterations, the dynamics of DCA with asynchronous iterations have not been studied extensively, especially when viewed as stochastic processes. This paper aims to show that for any given irreducible stochastic matrix, even non-SIA, the corresponding DCA lead to consensus successfully via random asynchronous iterations under a wide range of conditions on the transition probability. Particularly, the transition probability is neither required to be independent and identically distributed, nor characterized by a Markov chain. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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DABC-Net for robust pneumonia segmentation and prediction of COVID-19 progression on chest CT scans

Research Square

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Research Square 2020年

作者： Zhang, Xiao-Yong Yu, Ziqi Han, Xiaoyang Zhao, Botao Zhuo, Yaoyao Ren, Yan Xue, Xiangyang Lamm, Lorenz Feng, Jianfeng Marr, Carsten Shan, Fei Peng, Tingying Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai200433 China Department of Radiology Shanghai Public Health Clinical Center Fudan University Shanghai201508 China Department of Radiology Huashan Hospital Fudan University Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Institute of Computational Biology Helmholtz Zentrum München Ingolstädter Landstraße 1 D-85764 Germany Helmholtz AI Helmholtz Zentrum München Ingolstädter Landstraße 1 NeuherbergD-85764 Germany

Currently, reliable, robust and ready-to-use CT-based tools for prediction of COVID-19 progression are still lacking. To address this problem, we present DABC-Net, a novel deep learning (DL) tool that combines a 2D U-net for intra-slice spatial information processing, and a recurrent LSTM network to leverage inter-slice context, for automatic volumetric segmentation of lung and pneumonia lesions. We evaluate DABC-Net on more than 10,000 radiologists-labeled CT slices from four different cohorts. Compared to state-of-the-art segmentation tools, DABC-Net is much faster, more robust, and able to estimate segmentation uncertainty. Based only on the first two CT scans within 3 days after admission from 656 longitudinal CT scans, the AUC of our DBAC-Net for disease progression prediction reaches 93%. We release our tool as a GUI for patient-specific prediction of pneumonia progression, to provide clinicians with additional assistance to triage patients at early days after the diagnosis and to optimize the assignment of limited medical resources, which is of particular importance in current critical COVID-19 pandemic. © 2020, CC BY.

关键词： COVID-19

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Gut microbiota links to TRANK1 overexpression in bipolar depression

Gut microbiota links to TRANK1 overexpression in bipolar dep...

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2020年浙江省医学会精神病学学术大会浙江省医师协会精神科医师分会第十三届年会

作者： Jianbo Lai Ang Li Xiuxia Yuan Jiajun Jiang Peifen Zhang Caixi Xi Yanli Du Yifan Li Tingting Huang Lingling Wu Ming Li Huijuan Li Xin Cai Zheng Wang Jingkai Chen Jing Lu Shaojia Lu Tingting Mou Hetong Zhou Dandan Wang Manli Huang Fengqin Dong Peng Zheng Ming D.Li Yi Xu Xueqin Song Shaohua Hu Department of Psychiatry The First Affiliated HospitalZhejiang University School of Medicine The Key Laboratory of Mental Disorder's Management in Zhejiang Province Brain Research Institute of Zhejiang University Gene Hospital of Henan Province Precision Medicine CenterThe First Affiliated Hospital of Zhengzhou University Department of Psychiatry First Affiliated Hospital of Zhengzhou University Department of Neurology The First Affiliated Hospital of Chongqing Medical University NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases The First Affiliated Hospital of Chongqing Medical University Hangzhou Geriatric Hospital Hangzhou First People's Hospital Group Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province Kunming Institute of ZoologyChinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Department of Endocrinology and Metabolism The First Affiliated HospitalZhejiang University School of Medicine State Key Laboratory for Diagnosis and Treatment of Infectious Diseases Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases The First Affiliated HospitalZhejiang University School of Medicine

Accumulating evidences have shown gut microbial dysbiosis in patients with bipolar disorder(BD),but little is known how the microbiota-gut-brain axis operates in *** this study,we aimed to characterize the gut microbiota with metagenomic sequencing and explored its linkage to tetratricopeptide repeat and ankyrin repeat containing 1(TRANK1),a risk gene of *** samples were obtained from 60 healthy individuals and 62 patients in acute depressive episodes,who received over one-month quetiapine treatment *** analysis of BD microbiota revealed specific alterations in microbial diversity and compositions and the impacts of quetiapine treatment on microbial *** neural oscillation in hippocampus was positively associated with the abundance of Clostridium *** transplanted with feces from depressed patients presented depression-like *** mRNA expressions of inflammatory cytokines,brain-derived neurotrophic factor,kynurenine aminotransferase Ⅱ,and TRANK1 were detected in mice hippocampus and prefrontal ***3.1(+) vector encoding human TRANK 1 was constructed to explore the impact of TRANK1 overexpression on neuronal morphology,which manifested as decreased dendritic spine density in primary cortical *** findings add new evidences to microbiota-gut-brain regulation in BD,indicating that microbiota is possibly involved in the neuropathogenesis of BD by modulating the expression of TRANK1.

关键词： bipolar disorder gut microbiota quetiapine metagenomics tetratricopeptide repeat and ankyrin repeat containing 1(TRANK1)

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From EEG to Eye Movements: Cross-modal Emotion Recognition Using Constrained Adversarial Network with Dual Attention

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IEEE Transactions on Affective Computing 2024年

作者： Wang, Yiting Liu, Jia-Wen Lu, Bao-Liang Zheng, Wei-Long Karolinska Institutet Department of Clinical Neuroscience Stockholm171 77 Sweden Shanghai Jiao Tong University China Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China Shanghai Jiao Tong University School of Medicine RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai200020 China Shanghai Emotionhelper Technology Co. Ltd. Shanghai China

Emotion recognition is a fundamental part of affective computing, obtaining performance gain from multimodal methods. Electroencephalography (EEG) and eye movements are extensively used as they contain complementary information. However, the inconvenient acquisition of EEG is hindering the extensive adoption of multimodal emotion recognition in daily applications while eye movements are more convenient to collect but with lower performance. To tackle this issue, we propose a Constrained Adversarial Network with Dual Attention (CANDA), exploiting the complementary information from multiple modalities during training to improve the test-time performance of single easily acquired modality, i.e., transferring knowledge from a stronger modality to a weaker modality. During training, a common joint space is learned to diminish the distribution discrepancy among different modalities and incorporate the multimodal representations. During test, single modality is converted to the common space achieving comparable performance to multiple modalities. Extensive experiments demonstrate that our model achieves the state-of-the-art performance for cross-modal emotion recognition. Specifically, the mean accuracy increases around 15% on SEED, 15% on SEED-IV, and 2% on SEED-V compared to the latest baseline for emotion recognition. Visualization of features in the joint space illustrates that the distribution of different modalities aligns together with the discriminative ability regarding various emotions. © 2010-2012 IEEE.

关键词： Electroencephalography

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Efficient Low-Resolution Face Recognition via Bridge Distillation

arXiv

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arXiv 2024年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Zhang, Yu Li, Jia The Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China SenseTime Group Limited 100084 China The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The Peng Cheng Laboratory Shenzhen518055 China

Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability. In this paper, we propose a bridge distillation approach to turn a complex face model pretrained on private high-resolution faces into a light-weight one for low-resolution face recognition. In our approach, such a cross-dataset resolution-adapted knowledge transfer problem is solved via two-step distillation. In the first step, we conduct cross-dataset distillation to transfer the prior knowledge from private high-resolution faces to public high-resolution faces and generate compact and discriminative features. In the second step, the resolution-adapted distillation is conducted to further transfer the prior knowledge to synthetic low-resolution faces via multi-task learning. By learning low-resolution face representations and mimicking the adapted high-resolution knowledge, a light-weight student model can be constructed with high efficiency and promising accuracy in recognizing low-resolution faces. Experimental results show that the student model performs impressively in recognizing low-resolution faces with only 0.21M parameters and 0.057MB memory. Meanwhile, its speed reaches up to 14,705, 934 and 763 faces per second on GPU, CPU and mobile phone, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi-task learning

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Hierarchical taxonomy-aware and attentional graph capsule RCNNs for large-scale multi-label text classification

arXiv

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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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