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Pediatric Discovery 2024年第4期2卷

作者： Xu Wang Hui Liu Janne Estill Fujian Song Akihiko Ozaki Juan V. A. Franco Ivan D. Florez Etienne Ngeh Nav Persaud Liliya Eugenevna Ziganshina Myeong Soo Lee Lu Zhang Yuan Chi Yuting Duan Enmei Liu Yaolong Chen Xiaodong Zhao National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders Chongqing Key Laboratory of Child Infection and Immunity China International Science and Technology Cooperation base of Child Development and Critical Disorders Chongqing China Chevidence Lab Child & Adolescent Health Department of Pediatric Research Institute Children's Hospital of Chongqing Medical University Chongqing China Evidence-based Medicine Center School of Basic Medical Sciences Lanzhou University Lanzhou Gansu China Institute of Global Health University of Geneva Geneva Switzerland Norwich Medical School University of East Anglia Norwich UK Breast and Thyroid Center Jyoban Hospital of Tokiwa Foundation Fukushima Japan Institute of General Practice Medical Faculty Heinrich-Heine-Universitat Dusseldorf Dusseldorf Nordrhein-Westfalen Germany Department of Pediatrics University of Antioquia Medellin Colombia School of Rehabilitation Science McMaster University Hamilton Ontario Canada Pediatric Intensive Care Unit Clinica Las Americas-AUNA Medellin Colombia School of Health and Well Being Sheffield Hallam University Sheffield UK Research Organisation for Health Education and Rehabilitation-Cameroon (ROHER-CAM) Bamenda Cameroon Department of Family Medicine University of Toronto Faculty of Medicine Toronto Ontario Canada Russian Medical Academy for Continuing Professional Education (RMANPO) Moscow Russian Federation The Peoples' Friendship University of Russia named after Patrice Lumumba (RUDN University) Moscow Russian Federation The Kazan State Medical University Kazan Russian Federation KM Science Research Division Korea Institute of Oriental Medicine Daejeon Republic of Korea Department of Computer Science Hong Kong Baptist University Hong Kong China Institute of Systems Medicine and Health Sciences Hong Kong Baptist University Hong Kong China Yealth Network Beijing Health Technology Co. Ltd Beijing Chin

The first Better evidence and RecommendatIons for the next Generation HealTh—BRIGHT symposium was held in Chongqing, China between June 21 and 23, 2024. The symposium did not only showcase the recent progress made by multidisciplinary teams in generating and translating evidence for children's healthcare, guideline development and evaluation, and the utilization of AI applications in pediatrics but also fostered transnational and transregional cooperation to promote the advancement of high-quality evidence and guidelines in this field. The symposium contributed significantly to the future development and transformation of research endeavors in pediatrics.

关键词： declaration evidence-based medicine guidelines pediartic

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Swift/UVOT discovery of Swift J221951-484240: a UV luminous ambiguous nuclear transient

arXiv

引用

arXiv 2023年

作者： Oates, S.R. Kuin, N.P.M. Nicholl, M. Marshall, F. Ridley, E. Boutsia, K. Breeveld, A.A. Buckley, D.A.H. Cenko, S.B. De Pasquale, M. Edwards, P.G. Gromadzki, M. Gupta, R. Laha, S. Morrell, N. Orio, M. Pandey, S.B. Page, M.J. Page, K.L. Parsotan, T. Rau, A. Schady, P. Stevens, J. Brown, P.J. Evans, P.A. Gronwall, C. Kennea, J.A. Klingler, N.J. Siegel, M.H. Tohuvavohu, A. Ambrosi, E. Barthelmy, S.D. Beardmore, A.P. Bernardini, M.G. Bonnerot, C. Campana, S. Caputo, R. Ciroi, S. Cusumano, G. D’Aì, A. D’Avanzo, P. D’Elia, V. Giommi, P. Hartmann, D.H. Krimm, H.A. Malesani, D.B. Melandri, A. Nousek, J.A. O’Brien, P.T. Osborne, J.P. Pagani, C. Palmer, D.M. Perri, M. Racusin, J.L. Sakamoto, T. Sbarufatti, B. Schlieder, J.E. Tagliaferri, G. Troja, E. Xu, D. School of Physics and Astronomy Institute for Gravitational Wave Astronomy University of Birmingham B15 2TT United Kingdom University College London Mullard Space Science Laboratory Holmbury St. Mary DorkingRH5 6NT United Kingdom Astrophysics Research Centre School of Mathematics and Physics Queens University Belfast BelfastBT7 1NN United Kingdom Astrophysics Science Division NASA Goddard Space Flight Center GreenbeltMD20771 United States Las Campanas Observatory Carnegie Observatories Colina El Pino Casilla 601 La Serena Chile South African Astronomical Observatory PO Box 9 Observatory Cape Town7935 South Africa Department of Astronomy University of Cape Town Private Bag X3 Rondebosch7701 South Africa Department of Physics University of the Free State PO Box 339 Bloemfontein9300 South Africa Joint Space-Science Institute University of Maryland Computer and Space Sciences Building College ParkMD20742 United States University of Messina MIFT Department Polo Papardo Viale F.S. D’Alcontres 31 Messina98166 Italy Australia Telescope National Facility CSIRO Space and Astronomy PO Box 76 EppingNSW1710 Australia Astronomical Observatory University of Warsaw Al. Ujazdowskie 4 Warszawa00-478 Poland Manora Peak Nainital263002 India Department of Physics Deen Dayal Upadhyaya Gorakhpur University Gorakhpur273009 India Center for Space Science and Technology University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Center for Research and Exploration in Space Science and Technology NASA/GSFC GreenbeltMD20771 United States Department of Astronomy University of Wisconsin 475 N. Charter Str. MadisonWI53704 United States INAF Osservatorio di Padova vicolo dell Osservatorio 5 PadovaI-35122 Italy School of Physics and Astronomy University of Leicester LE1 7RH United Kingdom Max-Planck-Institut für Extraterrestrische Physik Giessenbachstraße 1 Garching85748 Germany Department of Physics University of B

We report the discovery of Swift J221951-484240 (hereafter: J221951), a luminous slow-evolving blue transient that was detected by the Neil Gehrels Swift Observatory Ultra-violet/Optical Telescope (Swift/UVOT) during the follow-up of Gravitational Wave alert S190930t, to which it is unrelated. Swift/UVOT photometry shows the UV spectral energy distribution of the transient to be well modelled by a slowly shrinking black body with an approximately constant temperature of T ∼ 2.5 × 104 K. At a redshift z = 0.5205, J221951 had a peak absolute magnitude of Mu,AB = −23 mag, peak bolometric luminosity Lmax = 1.1 × 1045 erg s−1 and a total radiated energy of E > 2.6 × 1052 erg. The archival WISE IR photometry shows a slow rise prior to a peak near the discovery date. Spectroscopic UV observations display broad absorption lines in N V and O VI, pointing toward an outflow at coronal temperatures. The lack of emission in the higher H Lyman lines, N I and other neutral lines is consistent with a viewing angle close to the plane of the accretion or debris disc. The origin of J221951 can not be determined with certainty but has properties consistent with a tidal disruption event and the turn-on of an active galactic nucleus. © 2023, CC BY.

关键词： Gravity waves

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PEPS++: Towards extreme-scale simulations of strongly correlated quantum many-particle models on sunway tainhulight

arXiv

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arXiv 2018年

作者： He, Lixin An, Hong Yang, Chao Wang, Fei Chen, Junshi Wang, Chao Liang, Weihao Dong, Shaojun Sun, Qiao Han, Wenting Liu, Wenyuan Han, Yongjian Yao, Wenjun CAS Key Lab of Quantum Information University of Science and Technology of China Hefei China School of Computer Science & Technology University of Science and Technology of China Hefei China CCSE & CAPT School of Mathematical Sciences NELVT Peking University Beijing China Institute of Software & State Key Laboratory of Computer Science CAS Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

The study of strongly frustrated magnetic systems has drawn great attentions from both theoretical and experimental physics. Efficient simulations of these models are essential for understanding their exotic properties. Here we present PEPS++, a novel computational paradigm for simulating frustrated magnetic systems and other strongly correlated quantum many-body systems. PEPS++ can accurately solve these models at the extreme scale with low cost and high scalability on modern heterogeneous supercomputers. We implement PEPS++ on Sunway TaihuLight based on a carefully designed tensor computation library for manipulating high-rank tensors and optimize it by invoking various high-performance matrix and tensor operations. By solving a 2D strongly frustrated J1-J2 model with over ten million cores, PEPS++ demonstrates the capability of simulating strongly correlated quantum many-body problems at unprecedented scales with accuracy and time-to-solution far beyond the previous state of the art. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Dense matter with eXTP

引用

science China(Physics,Mechanics & Astronomy) 2019年第2期62卷 28-44页

作者： Anna L.Watts WenFei Yu Juri Poutanen Shu Zhang Sudip Bhattacharyya Slavko Bogdanov Long Ji Alessandro Patruno Thomas E.Riley Pavel Bakala Altan Baykal Federico Bernardini Ignazio Bombaci Edward Brown Yuri Cavecchi Deepto Chakrabarty Jér?me Chenevez Nathalie Degenaar Melania Del Santo Tiziana Di Salvo Victor Doroshenko Maurizio Falanga Robert D.Ferdman Marco Feroci Angelo F.Gambino MingYu Ge Svenja K.Greif Sebastien Guillot Can Gungor Dieter H.Hartmann Kai Hebeler Alexander Heger Jeroen Homan Rosario Iaria Jean in 't Zand Oleg Kargaltsev Aleksi KurkelaTheoretical Physics Department CERN XiaoYu Lai Ang Li XiangDong Li ZhaoSheng Li Manuel Linares FangJun Lu Simin Mahmoodifar Mariano Méndez M.Coleman Miller Sharon Morsink Joonas N?ttil? Andrea Possenti Chanda Prescod-Weinstein JinLu Qu Alessandro Riggio Tuomo Salmi Andrea Sanna Andrea Santangelo Hendrik Schatz Achim Schwenk LiMing Song Eva?rámková Benjamin Stappers Holger Stiele Tod Strohmayer Ingo Tews Laura Tolos Gabriel T?r?k David Tsang Martin Urbanec Andrea Vacchi RenXin Xu YuPeng Xu Silvia Zane GuoBao Zhang ShuangNan Zhang WenDa Zhang ShiJie Zheng Xia Zhou Anton Pannekoek Institute for Astronomy University of Amsterdam Shanghai Astronomical Observatory Tuorla Observatory Department of Physics and Astronomy University of Turku Nordita KTH Royal Institute of Technology and Stockholm University Institute of High Energy Physics Chinese Academy Sciences Tata Institute of Fundamental Research Columbia Astrophysics Laboratory Columbia University Institut für Astronomie und Astrophysik Tübingen Universit?t Tübingen Leiden Observatory Leiden University Research Center for Computational Physics and Data Processing Silesian University in Opava Physics Department Middle East Technical University INAF Osservatorio Astronomico di Roma New York University Abu Dhabi Dipartimento di Fisica Enrico Fermi University of Pisa INFN Italian National Institute for Nuclear Physics Department of Physics and Astronomy Michigan State University Department of Astrophysical Sciences Princeton University Mathematical Sciences and STAG Research Centre University of Southampton MIT Kavli Institute for Astrophysics and Space Research DTU Space Technical University of Denmark INAF/IASF Palermo via Ugo La Malfa 153 Universita degli Studi di Palermo Dipartimento di Fisica e Chimica International Space Science Institute (ISSI) Faculty of Science University of East Anglia INAF Istituto di Astrofisica e Planetologie Spaziali Institut für Kernphysik Technische Universit?t Darmstadt ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Instituto de Astrof'?sica Pontificia Universidad Católica de Chile Department of Physics & Astronomy Kinard Lab of Physics Clemson University School of Physics and Astronomy Monash University SRON Netherlands Institute for Space Research Department of Physics The George Washington University Faculty of Science and Technology University of Stavanger School of Physics and Mechanical & Electrical Engineering Hubei University of Education Department of Astronomy Xiamen University (Haiyun Campus) School of Astronomy and Space

In this White Paper we present the potential of the Enhanced X-ray Timing and Polarimetry(eXTP) mission for determining the nature of dense matter; neutron star cores host an extreme density regime which cannot be replicated in a terrestrial laboratory. The tightest statistical constraints on the dense matter equation of state will come from pulse profile modelling of accretion-powered pulsars, burst oscillation sources, and rotation-powered pulsars. Additional constraints will derive from spin measurements, burst spectra, and properties of the accretion flows in the vicinity of the neutron star. Under development by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of sciences, the eXTP mission is expected to be launched in the mid 2020 s.

关键词： neutron X-rays dense matter equation of state

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Optimal design of SWIPT-aware fog computing networks

arXiv

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arXiv 2019年

作者： Liu, Jingxian Xiong, Ke Fan, Pingyi Zhong, Zhangdui Letaief, Khaled Ben School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Hong Kong University of Science and Technology Hong Kong

This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing network, where a multiple antenna fog function integrated hybrid access point (F-HAP) transfers information and energy to multiple heterogeneous single-antenna sensors and also helps some of them fulfill computing tasks. By jointly optimizing energy and information beamforming designs at the F-HAP, the bandwidth allocation and the computation offloading distribution, an optimization problem is formulated to minimize the required energy under communication and computation requirements, as well as energy harvesting constraints. Two optimal designs, i.e., fixed offloading time (FOT) and optimized offloading time (OOT) designs, are proposed. As both designs get involved in solving non-convex problems, there are no known solutions to them. Therefore, for the FOT design, the semidefinite relaxation (SDR) is adopted to solve it. It is theoretically proved that the rank-one constraints are always satisfied, so the global optimal solution is guaranteed. For the OOT design, since its non-convexity is hard to deal with, a penalty dual decomposition (PDD)-based algorithm is proposed, which is able to achieve a suboptimal solution. The computational complexity for two designs are analyzed. Numerical results show that the partial offloading mode is superior to binary benchmark modes. It is also shown that if the system is with strong enough computing capability, the OOT design is suggested to achieve lower required energy;Otherwise, the FOT design is preferred to achieve a relatively low computation complexity. Copyright © 2019, The Authors. All rights reserved.

关键词： Fog computing

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Optimal online transmission policy in wireless powered networks with urgency-aware age of information

arXiv

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arXiv 2019年

作者： Lu, Yang Xiong, Ke Fan, Pingyi Zhong, Zhangdui Letaief, Khaled Ben School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Hong Kong University of Science and Technology Hong Kong

This paper investigates the age of information (AoI) for a radio frequency (RF) energy harvesting (EH) enabled network, where a sensor first scavenges energy from a wireless power station and then transmits the collected status update to a sink node. To capture the thirst for the fresh update becoming more and more urgent as time elapsing, urgency-aware AoI (U-AoI) is defined, which increases exponentially with time between two received updates. Due to EH, some waiting time is required at the sensor before transmitting the status update. To find the optimal transmission policy, an optimization problem is formulated to minimize the long-term average U-AoI under constraint of energy causality. As the problem is non-convex and with no known solution, a two-layer algorithm is presented to solve it, where the outer loop is designed based on Dinklebach's method, and in the inner loop, a semi-closed-form expression of the optimal waiting time policy is derived based on Karush-Kuhn-Tucker (KKT) optimality conditions. Numerical results shows that our proposed optimal transmission policy outperforms the the zero time waiting policy and equal time waiting policy in terms of long-term average U-AoI, especially when the networks are non-congested. It is also observed that in order to achieve the lower U-AoI, the sensor should transmit the next update without waiting when the network is congested while should wait a moment before transmitting the next update when the network is non-congested. Additionally, it also shows that the system U-AoI first decreases and then keep unchanged with the increments of EH circuit's saturation level and the energy outage probability. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy harvesting

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Towards Robust Neural Machine Translation

arXiv

引用

arXiv 2018年

作者： Cheng, Yong Tu, Zhaopeng Meng, Fandong Zhai, Junjie Liu, Yang Tencent AI Lab China State Key Laboratory of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing Advanced Innovation Center for Language Resources Beijing China

Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation (NMT) models. In this paper, we propose to improve the robustness of NMT models with adversarial stability training. The basic idea is to make both the encoder and decoder in NMT models robust against input perturbations by enabling them to behave similarly for the original input and its perturbed counterpart. Experimental results on Chinese-English, English-German and English-French translation tasks show that our approaches can not only achieve significant improvements over strong NMT systems but also improve the robustness of NMT models. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural machine translation

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WVCC: Weighted virtual congestion control for datacenter networks

WVCC: Weighted virtual congestion control for datacenter net...

引用

ACM SIGCOMM 2017 Conference

作者： Dong, Jiaqing Wang, Yi Tian, Chen Jin, Bo Yin, Hao Zhang, Gong Department of Computer Science Tsinghua University China Future Network Theory Lab Huawei China State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)9781450350570

Enforcing virtualized congestion control is a new trend for datacenter networks. Virtual Congestion Control (VCC) can also enforce differentiated Quality-of-Service (QoS) for flows. However, current flow differentiation algorithms only provide qualitative rather than quantitative bandwidth allocation. Weighted bandwidth allocation is critical to enforce administrator policy. In this work, we propose Weighted Virtual Congestion Control (WVCC) enforcement for datacenter networks. It is a novel per-flow differentiation mechanism capable of proportionally allocating bandwidth among flows. © 2017 ACM.

关键词： Virtualization

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Compare and Model Multi-level Stereoscopic 3D Visual Fatigue Based on EEG

Compare and Model Multi-level Stereoscopic 3D Visual Fatigue...

引用

SID Symposium, Seminar, and Exhibition 2017, Display Week 2017

作者： Yue, Kang Wang, Danli Hu, Haichen Yang, Xinpan Chiu, Steve C. State Key Laboratory of Computer Institute of Software Chinese Academy of Sciences Beijing China Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China College of Science and Engineering Idaho State University Pocatello United States

ISBN: (纸本)9781510848757

In this paper, we designed an experiment to observe changes for different fatigue level in human brain when watching random dot stereogram (RDS) using the method of EEG. The subjects were asked to watch 3D content for 30 minutes and the disparity of RDS changed every 10 seconds. During this watching task, subjects should rate their extent of fatigue during the experiment on the scale of one to five only when they felt worse or better. 30 channels electroencephalogram (EEG) signals were recorded through the experiment. Finally, the relationship between various fatigue levels and disparities which revealed by EEG signals were investigated. Our results show that the power of waveband, namely alpha increased with the fatigue level increasing from one to four during watching RDS, but decreased in level five. We have employed logistic regression to model fatigue level and achieved classification accuracy of 94.18%. Our research has important implications for the continuous monitoring of user's fatigue in the future. © 2017 SID.

关键词： Electroencephalography

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