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arXiv 2021年

作者： Yao, Yuan Dong, Qingxiu Guan, Jian Cao, Boxi Zhang, Zhengyan Xiao, Chaojun Wang, Xiaozhi Qi, Fanchao Bao, Junwei Nie, Jinran Zeng, Zheni Gu, Yuxian Zhou, Kun Huang, Xuancheng Li, Wenhao Ren, Shuhuai Lu, Jinliang Xu, Chengqiang Wang, Huadong Zeng, Guoyang Zhou, Zile Zhang, Jiajun Li, Juanzi Huang, Minlie Yan, Rui He, Xiaodong Wan, Xiaojun Zhao, Xin Sun, Xu Liu, Yang Liu, Zhiyuan Han, Xianpei Yang, Erhong Sui, Zhifang Sun, Maosong Department of Computer Science and Technology Tsinghua University China MOE Key Lab of Computational Linguistics School of EECS Peking University China Institute of Software Chinese Academy of Sciences China JD AI Research Beijing China School of Information Science Beijing Language and Culture University China School of Information Renmin University of China China National Laboratory of Pattern Recognition Institute of Automation CAS China Gaoling School of Artificial Intelligence Renmin University of China China Wangxuan Institute of Computer Technology Peking University Beijing Academy of Artificial Intelligence China

Realizing general-purpose language intelligence has been a longstanding goal for natural language processing, where standard evaluation benchmarks play a fundamental and guiding role. We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic. To this end, we propose CUGE, a Chinese Language Understanding and Generation Evaluation benchmark with the following features: (1) Hierarchical benchmark framework, where datasets are principally selected and organized with a language capability-task-dataset hierarchy. (2) Multilevel scoring strategy, where different levels of model performance are provided based on the hierarchical framework. To facilitate CUGE, we provide a public leaderboard that can be customized to support flexible model judging criteria. Evaluation results on representative pre-trained language models indicate ample room for improvement towards general-purpose language intelligence. CUGE is publicly available at ***. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

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Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer’s Disease:A Data-Driven Meta-Analysis with N=3,118

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Neuroscience Bulletin 2024年第9期40卷 1274-1286页

作者： Xiaopeng Kang Dawei Wang Jiaji Lin Hongxiang Yao Kun Zhao Chengyuan Song Pindong Chen Yida Qu Hongwei Yang Zengqiang Zhang Bo Zhou Tong Han Zhengluan Liao Yan Chen Jie Lu Chunshui Yu Pan Wang Xinqing Zhang Ming Li Xi Zhang Tianzi Jiang Yuying Zhou Bing Liu Ying Han Yong Liu The Alzheimer’s Disease Neuroimaging Initiative The Multi-Center Alzheimer’s Disease Imaging(MCADI)Consortium School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100049China Brainnetome Center and National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing100190China Department of Radiology Qilu Hospital of Shandong UniversityJi’nan250063China Department of Neurology the Second Affiliated Hospital of Air Force Medical UniversityXi’an710032China Department of Radiology Chinese PLA General HospitalBeijing100853China Department of Radiology the Second Medical CentreNational Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijing100853China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing100191China Department of Neurology Qilu Hospital of Shandong UniversityJi’nan250063China Department of Radiology Xuanwu Hospital of Capital Medical UniversityBeijing100053China Branch of Chinese PLA General HospitalSanya572013China Department of Neurology the Second Medical CentreNational Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijing100853China Department of Radiology Tianjin Huanhu HospitalTianjin300222China Department of Psychiatry People’s Hospital of Hangzhou Medical CollegeZhejiang Provincial People’s HospitalHangzhou310014China Department of Radiology Tianjin Medical University General HospitalTianjin300070China Department of Neurology Tianjin Huanhu HospitalTianjin300222China Department of Neurology Xuanwu Hospital of Capital Medical UniversityBeijing100053China Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences&Yunnan Province Kunming Institute of ZoologyChinese Academy of SciencesKunming650201YunnanChina State Key Lab of Cognition Neuroscience&Learning Beijing Normal UniversityBeijing100875China National Clinical Research Center for Geriatric Disorders Beijing100053China Center of Alzheimer’s Disease Beijing Institute for Brain DisordersBeijing1000

The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer’s disease(AD).We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy *** we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in *** results showed that the hippocampus and amygdala exhibit the most severe atrophy,followed by the temporal,frontal,and occipital lobes in mild cognitive impairment(MCI)and *** extent of atrophy in MCI was less severe than that in AD.A series of biological processes related to the glutamate signaling pathway,cellular stress response,and synapse structure and function were investigated through gene set enrichment *** study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy,providing new insight for further clinical research on AD.

关键词： Alzheimer’s disease Structural magnetic resonance imaging Meta-analysis Brain atrophy Gene set enrichment analysis

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ChaLearn looking at people: IsoGD and ConGD large-scale RGB-D gesture recognition

arXiv

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arXiv 2019年

作者： Wan, Jun Lin, Chi Wen, Longyin Li, Yunan Miao, Qiguang Escalera, Sergio Anbarjafari, Gholamreza Guyon, Isabelle Guo, Guodong Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China JD Finance Mountain ViewCA United States University of Southern California Los AngelesCA90089-0911 United States School of Computer Science and Technology Xidian University & Xi'an Key Laboratory of Big Data and Intelligent Vision 2nd South Taibai Road Xi'an710071 China Universitat de Barcelona Computer Vision Center Spain iCV Lab Institute of Technology University of Tartu Estonia Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Institute of Digital Technologies Loughborough University London United Kingdom ChaLearn United States University Paris-Saclay France institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application China

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on pattern recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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Application of a genetic algorithm in triangulation of a 3-D object surface

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International Journal of Computers and Applications 2000年第2期22卷 73-77页

作者： Zhenyu, Chen Mbede, J.B. Yan, Zhou Dehua, Li Hanping, Hu State Commn. Res. Open Lab. Image P. Inst. Pattern Recog. Artif. Intell. Huazhong Univ. of Sci. and Technol. 430074 Wuhan China Intelligent Contr. and Robotics Lab. Dept. of Contr. Sci. and Engineering Huazhong Univ. of Sci. and Technol. 430074 Wuhan China Second Artillery Institute Huazhong Univ. of Sci. and Technol. Inst. Pattern Recog. Artif. Intell. Huazhong Univ. of Sci. and Technol. Second Artillery Institute China Wuhan University China Artificial Intelligence Department University of Edinburgh Inst. of AI and Pattern Recognition Huazhong Univ. of Sci. and Technol. China Cogn. Sci. Natl. Key Found. Res. P. Douala University Cameroon Tübingen University Germany Automatic Control Laboratory Darmstadt University Department of Service Cameroon Min. of National Education Robotics Laboratory Huazhong Univ. of Sci. and Technol. Wuhan China Assoc. Connectionnistes These-ACTH Rennes France IEEE Soc. of Robotics and Automation IEEE Society of Control Systems IASTED Assoc. Jeunes Chercheurs Robotique France Navy University of Engineering China Huazhong Univ. of Sci. and Technol. China Department of Computer Science Airforce Radar Acad. of Engineering China Inst. of P.R and AI Huazhong Univ. of Sci. and Technol.

This paper proposes a genetic-based algorithm for surface reconstruction of three-dimension (3-D) objects from a group of contours representing its section plane lines. The algorithm can optimize the triangulation of the surface of 3-D objects with a multi-objective optimization function to meet the needs of a wide range of applications. Further, a new crossover operator for triangulation and a new 3-D quadrilateral mutation operator are also introduced.

关键词： Object recognition

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