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arXiv 2023年

作者： An, Zheng-Hua Antier, S. Bi, Xing-Zi Bu, Qing-Cui Cai, Ce Cao, Xue-Lei Camisasca, Anna-Elisa Chang, Zhi Chen, Gang Chen, Li Chen, Tian-Xiang Chen, Wen Chen, Yi-Bao Chen, Yong Chen, Yu-Peng Coughlin, Michael W. Cui, Wei-Wei Dai, Zi-Gao Hussenot-Desenonges, T. Du, Yan-Qi Du, Yuan-Yuan Du, Yun-Fei Fan, Cheng-Cheng Frontera, Filippo Gao, He Gao, Min Ge, Ming-Yu Gong, Ke Gu, Yu-Dong Guan, Ju Guo, Dong-Ya Guo, Zhi-Wei Guidorzi, Cristiano Han, Da-Wei He, Jian-Jian He, Jun-Wang Hou, Dong-Jie Huang, Yue Huo, Jia Ji, Zhen Jia, Shu-Mei Jiang, Wei-Chun Kann, David Alexander Klotz, A. Kong, Ling-Da Lan, Lin Li, An Li, Bing Li, Chao-Yang Li, Cheng-Kui Li, Gang Li, Mao-Shun Li, Ti-Pei Li, Wei Li, Xiao-Bo Li, Xin-Qiao Li, Xu-Fang Li, Yan-Guo Li, Zheng-Wei Liang, Jing Liang, Xiao-Hua Liao, Jin-Yuan Lin, Lin Liu, Cong-Zhan Liu, He-Xin Liu, Hong-Wei Liu, Jia-Cong Liu, Xiao-Jing Liu, Ya-Qing Liu, Yu-Rong Lu, Fang-Jun Lu, Hong Lu, Xue-Feng Luo, Qi Luo, Tao Ma, Bin-Yuan Ma, Fu-Li Ma, Rui-Can Ma, Xiang Maccary, Romain Mao, Ji-Rong Meng, Bin Nie, Jian-Yin Orlandini, Mauro Ou, Ge Peng, Jing-Qiang Peng, Wen-Xi Qiao, Rui Qu, Jin-Lu Ren, Xiao-Qin Shi, Jing-Yan Shi, Qi Song, Li-Ming Song, Xin-Ying Su, Ju Sun, Gong-Xing Sun, Liang Sun, Xi-Lei Tan, Wen-Jun Tan, Ying Tao, Lian Tuo, You-Li Turpin, Damien Wang, Jin-Zhou Wang, Chen Wang, Chen-Wei Wang, Hong-Jun Wang, Hui Wang, Jin Wang, Ling-Jun Wang, Peng-Ju Wang, Ping Wang, Wen-Shuai Wang, Xiang-Yu Wang, Xi-Lu Wang, Yu-Sa Wang, Yue Wen, Xiang-Yang Wu, Bo-Bing Wu, Bai-Yang Wu, Hong Xiao, Sheng-Hui Xiao, Shuo Xiao, Yun-Xiang Xie, Sheng-Lun Xiong, Shao-Lin Xiong, Sen-Lin Xu, Dong Xu, He Xu, Yan-Jun Xu, Yan-Bing Xu, Ying-Chen Xu, Yu-Peng Xue, Wang-Chen Yang, Sheng Yang, Yan-Ji Yang, Zi-Xu Ye, Wen-Tao Yi, Qi-Bin Yi, Shu-Xu Yin, Qian-Qing You, Yuan Yu, Yun-Wei Yu, Wei Yu, Wen-Hui Zeng, Ming Zhang, Bing Zhang, Bin-Bin Zhang, Da-Li Zhang, Fan Zhang, Hong-Mei Zhang, Juan Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences 19B Yuquan Road Beijing100049 China Institut für Astronomie und Astrophysik Kepler Center for Astro and Particle Physics Eberhard Karls Universität Sand 1 Tübingen72076 Germany College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Department of Physics and Earth Science University of Ferrara Via Saragat 1 Ferrara44122 Italy Department of Astronomy Beijing Normal University Beijing100875 China CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Hefei230026 China Department of Astronomy Tsinghua University Beijing100084 China Southwest Jiaotong University Chengdu610092 China INAF Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 101 Bologna40129 Italy University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China College of physics Sciences Technology Hebei University No. 180 Wusi Dong Road Lian Chi District Hebei Baoding071002 China INFN - Sezione di Ferrara Via Saragat 1 Ferrara44122 Italy Physics and Space Science College China West Normal University China Yunnan Observatories Chinese Academy of Sciences Yunnan Province Kunming650011 China Key Laboratory of Space Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China School of Astronomy and Space Science Nanjing University Nanjing210093 China School of Physics and Optoelectronics Xiangtan University Yuhu District Hunan Xiangtan411105 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China Institute of Astrophysics Central China Normal University Wuhan430079 China Nevada Center for Astrophysics Department of Physics and Astronomy University of

GRB 221009A is the brightest gamma-ray burst ever detected since the discovery of this kind of energetic explosions. However, an accurate measurement of the prompt emission properties of this burst is very challenging due to its exceptional brightness. With joint observations of Insight-HXMT and GECAM-C, we made an unprecedentedly accurate measurement of the emission during the first ∼1800 s of GRB 221009A, including its precursor, main emission (ME, which dominates the burst in flux), flaring emission and early afterglow, in the hard X-ray to soft gamma-ray band from ∼ 10 keV to ∼ 6 MeV. Based on the GECAM-C unsaturated data of the ME, we measure a record-breaking isotropic equivalent energy (Eiso) of ∼ 1.5 × 1055 erg, which is about eight times the total rest-mass energy of the Sun. The early afterglow data require a significant jet break between 650 s and 1100 s, most likely at ∼ 950 s from the afterglow starting time TAG, which corresponds to a jet opening angle of ∼ 0.7◦ (ηγn)1/8, where n is the ambient medium density in units of cm−3 and ηγ is the ratio between γ-ray energy and afterglow kinetic energy. The beaming-corrected total γ-ray energy Eγ is ∼ 1.15 × 1051 (ηγn)1/4 erg, which is typical for long GRBs. These results suggest that this GRB may have a special central engine, which could launch and collimate a very narrowly beamed jet with an ordinary energy budget, leading to exceptionally luminous gamma-ray radiation per unit solid angle. Alternatively, more GRBs might have such a narrow and bright beam, which are missed by an unfavorable viewing angle or have been detected without distance measurement. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

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Transferable Contrastive network for Generalized Zero-Shot Learning

Transferable Contrastive Network for Generalized Zero-Shot L...

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International Conference on Computer Vision (ICCV)

作者： Huajie Jiang Ruiping Wang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Shanghai Institute of Microsystem and Information Technology CAS Shanghai China School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Zero-shot learning (ZSL) is a challenging problem that aims to recognize the target categories without seen data, where semantic information is leveraged to transfer knowledge from some source classes. Although ZSL has made great progress in recent years, most existing approaches are easy to overfit the sources classes in generalized zero-shot learning (GZSL) task, which indicates that they learn little knowledge about target classes. To tackle such problem, we propose a novel Transferable Contrastive network (TCN) that explicitly transfers knowledge from the source classes to the target classes. It automatically contrasts one image with different classes to judge whether they are consistent or not. By exploiting the class similarities to make knowledge transfer from source images to similar target classes, our approach is more robust to recognize the target images. Experiments on five benchmark datasets show the superiority of our approach for GZSL.

关键词： Semantics Visualization Image recognition Robustness Target recognition Task analysis Fuses

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Scintillation Arc from FRB 20220912A

arXiv

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arXiv 2023年

作者： Wu, Zi-Wei Main, Robert A. Zhu, Wei-Wei Zhang, Bing Jiang, Peng Niu, Jia-Rui Han, Jin-Lin Li, Di Lee, Ke-Jia Li, Dong-Zi Yang, Yuan-Pei Wang, Fa-Yin Luo, Rui Wang, Pei Niu, Chen-Hui Xu, Heng Wang, Bo-Jun Wang, Wei-Yang Zhang, Yong-Kun Feng, Yi Zhou, De-Jiang Xu, Yong-Hua Deng, Can-Min Zhu, Yu-Hao National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany Institute for Frontier in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Nevada Center for Astrophysics Department of Physics and Astronomy University of Nevada Las VegasNV89154 United States CAS Key Laboratory of FAST National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Astronomy University of Chinese Academy of Sciences Beijing China NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-Natal Durban4000 South Africa Department of Astronomy Peking University Beijing100871 China Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Cahill Center for Astronomy and Astrophysics California Institute of Technology MC 249-17 PasadenaCA91125 United States South-Western Institute For Astronomy Research Yunnan University Yunnan650504 China School of Astronomy and Space Science Nanjing University Nanjing210093 China Ministry of Education China CSIRO Space and Astronomy PO Box 76 EppingNSW1710 Australia Department of Astronomy School of Physics and Materials Science Guangzhou University Guangzhou510006 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Yunnan Observatories Chinese Academy of Sciences Kunming650011 China Guangxi Key Laboratory for Relativistic Astrophysics Department of Physics Guangxi University Nanning530004 China

We present the interstellar scintillation analysis of fast radio burst (FRB) 20220912A during its extremely active episode in 2022 using data from the Five-hundred-meter Aperture Spherical Radio Telescope (FAST). We detect a scintillation arc in the FRB’s secondary spectrum, which describes the power in terms of the scattered FRB signals’ time delay and Doppler shift. The arc indicates that the scintillation is caused by a highly localized region. Our analysis favors a Milky Way origin of the ionized interstellar medium (IISM) for the localized scattering medium but cannot rule out a host galaxy origin. We present our method for detecting the scintillation arc, which can be applied generally to sources with irregularly spaced bursts or pulses. These methods could help shed light on the complex interstellar environment surrounding the FRBs and in our Galaxy. Copyright © 2023, The Authors. All rights reserved.

关键词： Scintillation

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Design and implementation of monitoring data storage and processing scheme based on distributed computing 3

Design and implementation of monitoring data storage and pro...

引用

3rd IEEE International Conference on Big data Analysis, ICBDA 2018

作者： Chi, Yaping Yang, Yintan Xu, Ping Li, Gefei Li, Shuhao Beijing Electronic Science and Technology Institute Key Laboratory of Network Assessment Technology Institute of Information Engineering CAS Beijing China Department of Communication Engineering Xidian University Beijing Electronic Science and Technology Institute Beijing China

ISBN: (纸本)9781538647936

The monitoring system in cloud computing environment will generate a large number of monitoring data in real time, how to achieve efficient storage and processing of monitoring data in the big data environment is particularly important. To solve this problem, a scheme of monitoring data storage and processing based on Hadoop is proposed. The scheme adopts the HBase database to store the timing monitoring data, and enhance the field method of wide table storage model improved HBase database to improve storage efficiency monitoring data;the traffic data, using MapReduce distributed computing to improve the processing efficiency. The experimental results show that the scheme is scientific and effective, and it can improve the processing speed of the monitoring system under the massive monitoring data, and solve the bottleneck problem of the monitoring data in the cloud computing environment. © 2018 IEEE.

关键词： Monitoring

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SUPER-RESOLVED IMAGE OF M87 OBSERVED WITH EAST ASIAN VLBI network

arXiv

引用

arXiv 2023年

作者： Tazaki, Fumie Cui, Yuzhu Hada, Kazuhiro Kino, Motoki Cho, Ilje Zhao, Guang-Yao Akiyama, Kazunori Mizuno, Yosuke Ro, Hyunwook Honma, Mareki Lu, Ru-Sen Shen, Zhi-Qiang Cui, Lang Yonekura, Yoshinori Tokyo Electron Technology Solutions Limited Iwate023-1101 Japan Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai201210 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Mizusawa VLBI Observatory National Astronomical Observatory of Japan Iwate023-0861 Japan Academic Support Center Kogakuin University of Technology and Engineering Tokyo192-0015 Japan Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Massachusetts Institute Technology Haystack Observatory WestfordMA01886 United States Black Hole Initiative Harvard University CambridgeMA02138 United States School of Physics & Astronomy Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Straße 1 Frankfurt am MainD-60438 Germany Department of Astronomy Yonsei University Seodaemun-gu Seoul03722 Korea Republic of Korea Astronomy and Space Science Institute Yuseong-gu Daejeon34055 Korea Republic of Department of Astronomical Science The Graduate University for Advanced Studies SOKENDAI Tokyo181-8588 Japan Institute of Astronomy The University of Tokyo Tokyo181-0015 Japan Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Radio Astronomy Chinese Academy of Sciences Nanjing210008 China Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Xinjiang Astronomical Observatory CAS 150 Science 1-Street Urumqi830011 China Center for Astronomy Ibaraki University Ibaraki310-8512 Japan

Obtaining high-resolution images at centimeter-or-longer wavelengths is vital for understanding the physics of jets. We reconstructed images from the M87 22 GHz data observed with the East Asian VLBI network (EAVN) by using the regularized maximum likelihood (RML) method, which is different from the conventional imaging method CLEAN. Consequently, a bright core and jet extending about 30 mas to the northwest were detected with a higher resolution than in the CLEAN image. The width of the jet was 0.5 mas at 0.3 mas from the core, consistent with the width measured in the 86 GHz image in the previous study. In addition, three ridges were able to be detected at around 8 mas from the core, even though the peak-to-peak separation was only 1.0 mas. This indicates that the RML image's spatial resolution is at least 30% higher than that of the CLEAN image. This study is an important step for future multi-frequency and high-cadence observations of the EAVN to discuss the more detailed structure of the jet and its time variability. © 2023, CC BY-SA.

关键词： Active Galactic Nuclei

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Generic detection-based error-mitigation using quantum autoencoders

arXiv

引用

arXiv 2020年

作者： Zhang, Xiao-Ming Kong, Weicheng Farooq, Muhammad Usman Yung, Man-Hong Guo, Guoping Wang, Xin Department of Physics City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong Origin Quantum Computing Company Limited Hefei Anhui230088 China Department of Mathematics City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong Shenzhen Institute for Quantum Science and Engineering Department of Physics Southern University of Science and Technology Shenzhen518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui230026 China City University of Hong Kong Shenzhen Research Institute Shenzhen Guangdong518057 China

Efficient error-mitigation techniques demanding minimal resources is key to quantum information processing. We propose a generic protocol to mitigate quantum errors using detection-based quantum autoencoders. In our protocol, the quantum data are compressed into a latent subspace while leaving errors outside, the latter of which is then removed by a measurement and post-selection. Compared to previously developed methods, our protocol on the one hand requires no extra qubits, and on the other hand has a near-optimal denoising power, in which under reasonable requirements all errors detected outside of the latent subspace can be removed, while those inside the subspace cannot be removed by any means. Our detection-based quantum autoencoders are therefore particularly useful for near-term quantum devices in which controllable qubits are limited while noise reduction is important. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Explicit Linear Dual-Multistep Methods Applied to ZNN Illustrated via Discrete Time-Dependent Linear and Nonlinear Inequalities System Solving

Explicit Linear Dual-Multistep Methods Applied to ZNN Illust...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Jinjin Guo Binbin Qiu Liangjie Ming Yunong Zhang School of Data and Computer Science Sun Yat-sen University Guangzhou China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this work, time-dependent linear and nonlinear inequalities system (TDLNIS) is studied and solved. First, using zeroing neural network (ZNN) method twice, a continuous time-dependent ZNN (CTDZNN) model is proposed to solve the continuous TDLNIS. Subsequently, explicit linear dual-multistep methods, i.e., explicit linear dual-4-step, dual-3-step, and dual-2-step methods, are presented and studied. Afterwards, by applying the explicit linear dual-4-step method to the proposed CTDZNN model, a 4-step discrete time-dependent ZNN (4S-DTDZNN) model is proposed to solve the discrete TDLNIS. For comparison, 3-step discrete time-dependent ZNN (3S-DTDZNN) and 2-step discrete time-dependent ZNN (2S-DTDZNN) models are also developed for solving the discrete TDLNIS. In addition, theoretical analyses and results indicate the effectiveness and superiority of the proposed 4S-DTDZNN model. Finally, numerical experimental results further substantiate the effectiveness and superiority of the proposed 4S-DTDZNN model.

关键词： Numerical models Mathematical model Trajectory Neural networks Analytical models Computational modeling Finite wordlength effects

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MFPR: A Personalized Ranking Recommendation with Multiple Feedback

引用

ACM Transactions on Social computing 2018年第2期1卷 1–22页

作者： Shi, Chuan Liu, Jian Zhang, Yiding Hu, Binbin Liu, Shenghua Yu, Philip S. Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications No. 10 Xi Tu Cheng Road Beijing China Cas Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Zhongguancun Beijing China Department of Computer Science University of Illinois at Chicago 851 South Morgan Street Chicago United States

Recently, recommender systems have played an important role in improving web user experiences and increasing profits. Recommender systems exploit users' behavioral history (i.e., feedback on items) to build models. The feedback usually includes explicit feedback (e.g., ratings) and implicit feedback (e.g., browsing history, click logs), which are both useful for improving recommendations. However, as far as we are concerned, no existing works have integrated both explicit and multiple implicit feedback simultaneously. Therefore, we propose a unified and flexible model, named Multiple Feedback-based Personalized Ranking (MFPR), to make full use of multiple feedback, which uses a personalized ranking framework. To train model MFPR, we design an algorithm to generate ordered item pairs as labeled data, with consideration of both rating scores and multiple implicit feedback. Extensive experiments on two real-world datasets validate the effectiveness of the MFPR model. With the integration of multiple feedback, MFPR significantly improves recommendation performance. © 2018 ACM.

关键词： Recommender systems

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Applications of Game Theory in Vehicular networks: A Survey

arXiv

引用

arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

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MODMA dataset: a Multi-modal Open dataset for Mental-disorder Analysis

arXiv

引用

arXiv 2020年

作者： Cai, Hanshu Gao, Yiwen Sun, Shuting Li, Na Tian, Fuze Xiao, Han Li, Jianxiu Yang, Zhengwu Li, Xiaowei Zhao, Qinglin Liu, Zhenyu Yao, Zhijun Yang, Minqiang Peng, Hong Zhu, Jing Zhang, Xiaowei Gao, Guoping Zheng, Fang Li, Rui Guo, Zhihua Ma, Rong Yang, Jing Zhang, Lan Hu, Xiping Li, Yumin Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Open Source Software and Real-Time System Lanzhou University Ministry of Education China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Lanzhou University Second Hospital China

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation;the 3-electrode EEG signals of 55 subjects were recorded in resting state;the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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