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IEEE International Conference on Robotics and Automation (ICRA)

作者： Chen-Chen Fan Haiqun Xie Liang Peng Hongjun Yang Zhen-Liang Ni Guan’an Wang Yan-Jie Zhou Sheng Chen Zhijie Fang Shuyun Huang Zeng-Guang Hou State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China First People's Hospital of Foshan Foshan China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China

Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF- DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group- level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system.

关键词： Deep learning Neural networks Medical services Human factors Sensitivity and specificity Feature extraction Robot sensing systems

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Group feature learning and domain adversarial neural network for aMCI diagnosis system based on EEG

arXiv

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arXiv 2021年

作者： Fan, Chen-Chen Xie, Haiqun Peng, Liang Yang, Hongjun Ni, Zhen-Liang Wang, Guanan Zhou, Yan-Jie Chen, Sheng Fang, Zhijie Huang, Shuyun Hou, Zeng-Guang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China Department of Neurology First People’s Hospital of Foshan FoshanGuangdong528000 China

— Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer’s disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF-DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group-level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system. © 2021, CC BY.

关键词： Diagnosis

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Back Cover: PD-L1 Aptamer-Functionalized Metal–Organic Framework Nanoparticles for Robust Photo-Immunotherapy against Cancer with Enhanced Safety (Angew. Chem. Int. Ed. 5/2023)

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Angewandte Chemie International Edition 2023年第5期62卷

作者： Dr. Jingfang Zhang Dr. Wenzhe Li Dr. Yafei Qi Dr. Guorong Wang Prof. Lele Li Prof. Zhengyu Jin Prof. Jie Tian Prof. Yang Du Beijing Key Laboratory for Bioengineering and Sensing Technology School of Chemistry and Biological Engineering University of Science and Technology Beijing Beijing 100083 China CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology Beijing 100190 China State Key Laboratory of Natural and Biomimetic Drugs School of Pharmaceutical Sciences Peking University Beijing 100191 China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Radiology Peking Union Medical College Hospital Peking Union Medical College and Chinese Academy of Medical Sciences Beijing 100730 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine Beihang University Beijing 100191 China

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Rücktitelbild: PD-L1 Aptamer-Functionalized Metal–Organic Framework Nanoparticles for Robust Photo-Immunotherapy against Cancer with Enhanced Safety (Angew. Chem. 5/2023)

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Angewandte Chemie 2023年第5期135卷

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Can AI detect pain and express pain empathy? A review from emotion recognition and a human-centered AI perspective

arXiv

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arXiv 2021年

作者： Cao, Siqi Fu, Di Yang, Xu Wermter, Stefan Liu, Xun Wu, Haiyan CAS Key Laboratory of Behavioral Science Institute of Psychology Chinese Academy of Sciences Beijing China Department of Psychology University of Chinese Academy of Sciences Beijing China Department of Experimental Psychology University of Oxford Oxford United Kingdom State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Informatics University of Hamburg Hamburg Germany Centre for Cognitive and Brain Sciences Department of Psychology University of Macau Taipa China

Sensory and emotional experiences such as pain and empathy are essential for mental and physical health. Cognitive neuroscience has been working on revealing mechanisms underlying pain and empathy. Furthermore, as trending research areas, computational pain recognition and empathic artificial intelligence (AI) show progress and promise for healthcare or human-computer interaction. Although AI research has recently made it increasingly possible to create artificial systems with affective processing, most cognitive neuroscience and AI research do not jointly address the issues of empathy in AI and cognitive neuroscience. The main aim of this paper is to introduce key advances, cognitive challenges and technical barriers in computational pain recognition and the implementation of artificial empathy. Our discussion covers the following topics: How can AI recognize pain from unimodal and multimodal information? Is it crucial for AI to be empathic? What are the benefits and challenges of empathic AI? Despite some consensus on the importance of AI, including empathic recognition and responses, we also highlight future challenges for artificial empathy and possible paths from interdisciplinary perspectives. Furthermore, we discuss challenges for responsible evaluation of cognitive methods and computational techniques and show approaches to future work to contribute to affective assistants capable of empathy. © 2021, CC BY-NC-ND.

关键词： Cognitive systems

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First stirling-type cryocooler reaching lambda point of ~4He(2.17K) and its prospect in Chinese HUBS satellite project

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Science Bulletin 2019年第4期64卷 219-221页

作者： Jue Wang Changzhao Pan Tong Zhang Kaiqi Luo Xiaotong Xi Xianlin Wu Jianpeng Zheng Liubiao Chen Junjie Wang Yuan Zhou Hai Jin Wei Cui University of Chinese Academy of Sciences CAS Key Laboratory of Cryogenics Technical Institute of Physics and Chemistry Chinese Academy of Sciences Laboratoire National de Métrologie Et d'essais-Conservatoire National des Arts et Métiers (LNE-Cnam) State Key Laboratory of Control and Simulation of Power Systems and Generation Equipments Department of Electrical Engineering Tsinghua University Department of Physics and Center for Astrophysics Tsinghua University

The issue of "missing baryons” is always an unsolved mystery in galaxy cosmology, and an important reason for the great uncertainty of the galaxies' formation and evolution. To find the "missing" baryons, extra high-resolution spectral observation and imaging of the soft X-ray band (<1 kev) is required. However, the existing observation methods cannot meet this requirement. Therefore, Tsinghua University leads and proposes the Chinese "Hot Universe Baryon Surveyor (HUBS)" satellite program.

关键词： missing baryons HUBS missing

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The Performance Evaluation of SSVEP-BCI Actuated Wheelchair with Parameter Setting of Time-Window Length and Stimulation Layout

The Performance Evaluation of SSVEP-BCI Actuated Wheelchair ...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Jun Xie Xiaojun Wu Peng Fang Guanglin Li Guozhi Cao Tao Xue Science and Technology on Electrooptic Control Laboratory Luoyang China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology & Shenzhen Engineering Laboratory of Neural Rehabilitation Shenzhen China School of Mechanical Engineering Xi'an Jiaotong University Xi'an China

ISBN: (数字)9781728148922

ISBN: (纸本)9781728148939

Due to the advantages of prominent brain responses, less training duration, and robustness to artifacts, steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has been recently applied to intelligent wheelchair navigation. Recent researches mainly focused on the implementation of a specific recognition method to analyze the wheelchair navigation performance under fixed stimulation target number. The navigation performance evaluation under different recognition methods and different BCI target numbers is rarely compared. In this paper, the navigation performance comparison between different numbers of stimulation targets and different recognition methods was carried out. The multichannel integrated GT 2 circ and canonical correlation analysis (CCA) recognition methods were used to compare the wheelchair navigation performance under different time-window (TW) lengths. And the 3-target and 4-target arrangement layouts were adopted to compare the navigation performance between the “No feedback” and the “Feedback” conditions. Experimental results showed that the GT 2 circ recognition method with TW length of 2 s would promote the navigation efficiency and different stimulation target number could be tailored for different wheelchair application purposes, which would bring tutorial instructions to the applicable navigation of the BCI-actuated wheelchair.

关键词： brain-computer interface (BCI) steady-state visual evoked potential (SSVEP) wheelchair navigation time-window (TW) length stimulation layout Performance Evaluation Wheelchair parameter setting navigation Navigation efficiency Brain-Computer Interfaces Recognition (Psychology) Stimuli layouts

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Improving the Automated Coronal Jet Identification with U-NET

arXiv

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arXiv 2024年

作者： Liu, Jiajia Ji, Chunyu Wang, Yimin Soós, Szabolcs Jiang, Ye Erdélyi, Robertus Korsós, M.B. Wang, Yuming Deep Space Exploration Lab School of Earth and Space Sciences University of Science and Technology of China Hefei230026 China CAS Key Laboratory of Geospace Environment CAS Center for Excellence in Comparative Planetology Mengcheng National Geophysical Observatory University of Science and Technology of China Hefei230026 China School of Data Science Qingdao University of Science and Technology Qingdao266100 China Department of Astronomy Eötvös Loránd University Pázmány P. sétány 1/A BudapestH-1117 Hungary Petőfi tér 3. GyulaH-5700 Hungary School of Information Science and Technology Qingdao University of Science and Technology Qingdao266100 China School of Mathematics and Statistics The University of Sheffield SheffieldS3 7RH United Kingdom University of Sheffield Department of Automatic Control and Systems Engineering Amy Johnson Building Portabello Street SheffieldS1 3JD United Kingdom

Coronal jets are one of the most common eruptive activities in the solar atmosphere. They are related to rich physics processes, including but not limited to magnetic reconnection, flaring, instabilities, and plasma heating. Automated identification of off-limb coronal jets has been difficult due to their abundant nature, complex appearance, and relatively small size compared to other features in the corona. In this paper, we present an automated coronal jet identification algorithm (AJIA) that utilizes true and fake jets previously detected by a laborious semi-automated jet detection algorithm (SAJIA, Liu et al. 2023) as the input of an image segmentation neural network U-NET. It is found that AJIA could achieve a much higher (0.81) detecting precision than SAJIA (0.34), meanwhile giving the possibility of whether each pixel in an input image belongs to a jet. We demonstrate that with the aid of artificial neural networks, AJIA could enable fast, accurate, and real-time coronal jet identification from SDO/AIA 304 Å observations, which are essential in studying the collective and long-term behavior of coronal jets and their relation with the solar activity cycles. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Development of a propeller with undulating fins and its characteristics

Development of a propeller with undulating fins and its char...

引用

2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019

作者： Ma, Ruichen Wang, Yu Wang, Rui Wang, Shuo State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

ISBN: (纸本)9781728137261

This paper addresses some experiments about measurements of thrust and heave force generated by a modular designed biomimetic underwater propeller. The propeller has a ribbon fin that consists of twelve rays linked by a latex membrane. Different forms of waves can be generated by the propeller. A measurement platform is constructed to obtain the thrust and heave force generated by the waves along with the input power of the propeller. Three types of waves are performed on the platform, including sinusoidal waves, fusiform sinusoidal waves and inward counter-propagating sinusoidal waves. By changing the parameters of undulating waves, a total of three hundred series of experiments are implemented. The measurement results of three types of waves are analyzed and the differences among three types of waves are compared. © 2019 IEEE.

关键词： Fins (heat exchange)

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Author Correction: Host susceptibility and structural and immunological insight of S proteins of two SARS-CoV-2 closely related bat coronaviruses

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Cell discovery 2023年第1期9卷 102页

作者： Xiuyuan Ou Ge Xu Pei Li Yan Liu Fuwen Zan Pan Liu Jiaxin Hu Xing Lu Siwen Dong Yao Zhou Zhixia Mu Zhiqiang Wu Jianwei Wang Qi Jin Pinghuang Liu Jian Lu Xiangxi Wang Zhaohui Qian Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College) Ministry of Education Beijing China. NHC Key Laboratory of Systems Biology of Pathogens Institute of Pathogen Biology Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China. State Key Laboratory of Respiratory Health and Multimorbidity Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China. CAS Key Laboratory of Infection and Immunity National Laboratory of Macromolecules Institute of Biophysics Chinese Academy of Sciences Beijing China. Key Laboratory of Animal Epidemiology of the Ministry of Agriculture College of Veterinary Medicine China Agricultural University Beijing China. College of Life Sciences Peking University Beijing China. CAS Key Laboratory of Infection and Immunity National Laboratory of Macromolecules Institute of Biophysics Chinese Academy of Sciences Beijing China. xiangxi@***. Key Laboratory of Pathogen Infection Prevention and Control (Peking Union Medical College) Ministry of Education Beijing China. zqian2013@***. NHC Key Laboratory of Systems Biology of Pathogens Institute of Pathogen Biology Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China. zqian2013@***. State Key Laboratory of Respiratory Health and Multimorbidity Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China. zqian2013@***.

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