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Author Correction: Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

Nature communications 2020年第1期11卷 6292页

作者： Zhu Liu Philippe Ciais Zhu Deng Ruixue Lei Steven J Davis Sha Feng Bo Zheng Duo Cui Xinyu Dou Biqing Zhu Rui Guo Piyu Ke Taochun Sun Chenxi Lu Pan He Yuan Wang Xu Yue Yilong Wang Yadong Lei Hao Zhou Zhaonan Cai Yuhui Wu Runtao Guo Tingxuan Han Jinjun Xue Olivier Boucher Eulalie Boucher Frédéric Chevallier Katsumasa Tanaka Yiming Wei Haiwang Zhong Chongqing Kang Ning Zhang Bin Chen Fengming Xi Miaomiao Liu François-Marie Bréon Yonglong Lu Qiang Zhang Dabo Guan Peng Gong Daniel M Kammen Kebin He Hans Joachim Schellnhuber Department of Earth System Science Tsinghua University Beijing China. zhuliu@***. Laboratoire des Sciences du Climat et de l'Environnement LSCE CEA CNRS UVSQ Centre d'Etudes Orme de Merisiers Gif-sur-Yvette France. Department of Earth System Science Tsinghua University Beijing China. Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park PA USA. Department of Earth System Science University of California Irvine 3232Croul Hall Irvine CA USA. Division of Geological and Planetary Sciences California Institute of Technology Pasadena CA USA. Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science and Technology Nanjing China. Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China. Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. School of Environment Tsinghua University Beijing China. School of Mathematical School Tsinghua University Beijing China. Department of Mathematical Sciences Tsinghua University Beijing China. Center of Hubei Cooperative Innovation for Emissions Trading System Wuhan China. Faculty of Management and Economics Kunming University of Science and Technology 13 Kunming China. Economic Research Centre of Nagoya University Furo-cho Chikusa-ku Nagoya Japan. Institut Pierre-Simon Laplace Sorbonne Université/CNRS Paris France. Université Paris Dauphine Place du Maréchal de Lattre de Tassigny 75016 Paris France.

A Correction to this paper has been published: https://***/10.1038/s41467-020-20254-5.

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Deep learning for multi-messenger astrophysics a gateway for discovery in the big data era

arXiv

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arXiv 2019年

作者： Allen, Gabrielle Andreoni, Igor Bachelet, Etienne Bruce Berriman, G. Bianco, Federica B. Biswas, Rahul Kind, Matias Carrasco Chard, Kyle Cho, Minsik Cowperthwaite, Philip S. Etienne, Zachariah B. George, Daniel Gibbs, Tom Graham, Matthew Gropp, William Gupta, Anushri Haas, Roland Huerta, E.A. Jennings, Elise Katz, Daniel S. Khan, Asad Kindratenko, Volodymyr Kramer, William T.C. Liu, Xin Mahabal, Ashish McHenry, Kenton Miller, J.M. Neubauer, M.S. Oberlin, Steve Olivas, Alexander R. Rosofsky, Shawn Ruiz, Milton Saxton, Aaron Schutz, Bernard Schwing, Alex Seidel, Ed Shapiro, Stuart L. Shen, Hongyu Shen, Yue Sipocz, Brigitta M. Sun, Lunan Towns, John Tsokaros, Antonios Wei, Wei Wells, Jack Williams, Timothy J. Xiong, Jinjun Zhao, Zhizhen NCSA University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117 United States Caltech/IPAC-NExScI 1200 E California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Joseph R. Biden Jr . School of Public Policy and Administration University of Delaware NewarkDE19716 United States Data Science Institute University of Delaware NewarkDE19716 United States Center for Urban Science and Progress New York University 370 Jay St BrooklynNY11201 United States The Oskar Klein Centre for Cosmoparticle Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden University of Chicago ChicagoIL60605 United States IBM Systems AustinTX78717 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Department of Mathematics West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95050 United States Center for Data Driven Discovery Caltech PasadenaCA91125 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Argonne National Laboratory Leadership Computing Facility LemontIL60439 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 Unit

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics;(ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data;(iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups;(iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era;(v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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Real-time Gestural Interface for Navigation in Virtual Environment

Real-time Gestural Interface for Navigation in Virtual Envir...

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International Conference on Development and Application Systems

作者： Oana-Mihaela Vultur Stefan-Gheorghe Pentiuc Valeriu Lupu Stefan cel Mare University of Suceava Suceava Romania Faculty of Electrical Engineering and Computer Science MintViz Lab MANSiD Research Center Stefan cel Mare University of Suceava Romania Faculty of Economics and Public Administration Stefan cel Mare University of Suceava Suceava Romania

ISBN: (纸本)9781509019946

Human computer Interaction (HCI) is a challenging domain that has changed and proposed many user experience paradigms. In this paper we will try to present a innovative gestural interface that allows users to navigate in a virtual environment, representing the Rectorat building of the "Stefan cel Mare" University of Suceava, using dynamic gestures. In this fully natural interface the human body is the controller. We designed an interface for human gesture recognition based on the sets of the coordinates of human joints transmitted by a Microsoft Kinect sensor to the computer. The virtual reality system and the gestural recognition module are running on the computer. The human movements are supervised continuously and if a gesture is recognized the corresponding command is transmitted to the VR system. We also made a performance analysis of the gestural interface. The evaluation has been focused on the accuracy of the gesture recognition process and on the degree of user satisfaction after experimenting such interaction with VR system.

关键词： Human-computer Interaction (HCI) image processing computer Vision gesture recognition

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A New Logistic Map Based Chaotic Biogeography-based Optimization Approach for Cluster Analysis

A New Logistic Map Based Chaotic Biogeography-based Optimiza...

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang School of Computer Science and Technology Nanjing Normal University Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Department of Electrical Engineering The city college of New YorkCUNY Department of Mechanical and Control Engineering Kyushu Institute of Technology School of Information Engineering Yangzhou University School of Physics and Electronic Engineering Taizhou University School of Electronic Information Shanghai Dianji University School of Energy and Mechanical Engineering Nanjing Normal University Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization(CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： chaotic biogeography-based optimization cluster analysis centroid-based clustering simulation particle swarm optimization

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Examining uptake of online education on obstructive sleep apnoea in general practitioners: A randomised trial

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BMC Research Notes 2016年第1期9卷 1-6页

作者： Paul, Christine Rose, Shiho Hensley, Michael Pretto, Jeffrey Hardy, Margaret Henskens, Frans Clinton-Mcharg, Tara Carey, Mariko School of Medicine and Public Health University of Newcastle University Drive Callaghan 2308 NSW Australia Hunter Medical Research Institute New Lambton Heights 2308 NSW Australia Department of Respiratory and Sleep Medicine John Hunter Hospital Lookout Road New Lambton Heights 2305 NSW Australia Woolcock Institute of Medical Research 431 Glebe Point Rd Glebe 2307 NSW Australia GP Education Committee Australasian Sleep Association Blacktown Australia School of Electrical Engineering and Computer Science University of Newcastle University Drive Callaghan NSW Australia

Background: Obstructive sleep apnoea (OSA) affects up to 28 % of the adult population in Western countries. The detection and management of OSA by general practitioners (GPs) can be poor. The study aimed to examine what influence enhanced invitations had on uptake of on-line learning modules for OSA by GPs, and whether recent referrals of patients to sleep specialists influenced uptake. Methods: Practicing GPs in regional Australia were identified and randomised to receive either an enhanced or standard invitation letter to a new on-line education module for OSA. The enhanced letter included indication that the module was eligible for professional accreditation and described the prevalence and burden of sleep disorders. Some included extra emphasis if the GP had recently referred a patient for diagnostic investigation of OSA. Two reminder letters were sent. Results: Of 796 eligible GPs who received the letters, sixteen (2 %) accessed the website and four completed the modules over the four-month study period. GPs who received an enhanced invitation letter were not significantly more likely to access the website compared to GPs who received the standard invitation letter. Recent referral of a patient for diagnostic investigation was also not a significant factor in influencing use of the module. Conclusion: GP interest in on-line education about OSA appears low, and emphasis of relevant recent past patient(s) and the opportunity for professional education points was not successful in increasing engagement. There is a need to identify effective approaches to improving the detection and management of OSA in general practice. © 2016 The Author(s).

关键词： Australia Education General practice Obstructive Professional Sleep apnea Sleep disorders

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Developing Novel Machine Learning Algorithms to Improve Sedentary Assessment for Youth Health Enhancement

Developing Novel Machine Learning Algorithms to Improve Sede...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Gowtham Kumar Golla Jordan A. Carlson Jun Huan Jacqueline Kerr Tarrah Mitchell Kelsey Borner Department of Electrical Engineering and Computer Science University of Kansas Jayhawk Blvd Lawrence KS Center for Children's Healthy Lifestyles and Nutrition Children's Mercy Hospital Department of Family Medicine and Public Health University of California San Diego CA Department of Psychology University of Kansas Lawrence KS

ISBN: (纸本)9781509061181

Sedentary behavior of youth is an important determinant of health. However, better measures are needed to improve understanding of this relationship and the mechanisms at play, as well as to evaluate health promotion interventions. Wearable accelerometers are considered as the standard for assessing physical activity in research, but do not perform well for assessing posture (i.e., sitting vs. standing), a critical component of sedentary behavior. The machine learning algorithms that we propose for assessing sedentary behavior will allow us to re-examine existing accelerometer data to better understand the association between sedentary time and health in various populations. We collected two datasets, a laboratory-controlled dataset and a free-living dataset. We trained machine learning classifiers separately on each dataset and compared performance across datasets. The classifiers predict five postures: sit, stand, sit-stand, stand-sit, and stand\walk. We compared a manually constructed Hidden Markov model (HMM) with an automated HMM from existing software. The manually constructed HMM gave more F1-Macro score on both datasets.

关键词： Hidden Markov models Machine learning algorithms Accelerometers Algorithm design and analysis Standards Prediction algorithms Training

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A novel adaptive image zooming scheme via weighted least-squares estimation

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Frontiers of computer Science 2015年第5期9卷 703-712页

作者： Xuexia ZHONG Guorui FENG Jian WANG Wenfei WANG Wen SI Cyber Physical System Research and Development Center The Third Research Institute of Ministry of Public Security Shanghai 201204 China School of Communication and Information Engineering Shanghai University Shanghai 200444 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China Shanghai Chenrui Information Technology Company Shanghai 201204 China College of Information and Computer Science Shanghai Business School Shanghai 201400 China

A critical issue in image interpolation is preserving edge detail and texture information in images when zooming. In this paper, we propose a novel adaptive image zooming algorithm using weighted least-square estimation that can achieve arbitrary integer-ratio zoom （WLS-AIZ） For a given zooming ratio n, every pixel in a low-resolution （LR） image is associated with an n x n block of high-resolution （HR） pixels in the HR image. In WLS-AIZ, the LR image is interpolated using the bilinear method in advance. Model parameters of every n×n block are worked out through weighted least-square estimation. Subsequently, each pixel in the n × n block is substituted by a combination of its eight neighboring HR pixels using estimated parameters. Finally, a refinement strategy is adopted to obtain the ultimate HR pixel values. The proposed algorithm has significant adaptability to local image structure. Extensive experiments comparing WLS-AIZ with other state of the art image zooming methods demonstrate the superiority of WLS-AIZ. In terms of peak signal to noise ratio （PSNR）, structural similarity index （SSIM） and feature similarity index （FSIM）, WLS-AIZ produces better results than all other image integer-ratio zoom algorithms.

关键词： adaptive interpolation refinement strategy,weighted least-squares estimation arbitrary integer and WLS-AIZ scheme

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(OA32) Chronic Radiation-Associated Dysphagia in Oropharyngeal Cancer Survivors: Towards Age-Adjusted Dose Constraints for Deglutitive Muscles

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International Journal of Radiation Oncology*Biology*Physics 2018年第2期101卷 e14-e14页

作者： Kaitlin Christopherson Alokananda Ghosh Abdallah Sherif Radwan Mohamed Mona Jomaa G. Brandon Gunn Liz Marai Guadalupe Canahuate David Vock Timothy Dale Jayashree Kalpathy-Cramer Jay Messer Adam S. Garden Hesham Elhalawani Steven J. Frank Jan Lewin Stephen Y. Lai Beth M. Beadle William H. Morrison Clifton D. Fuller Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston TX USA Department of Biostatistics University of Texas School of Public Health Houston TX USA Department of Clinical Oncology University of Alexandria Alexandria Egypt Head and Neck Surgery The University of Texas MD Anderson Cancer Center Houston TX USA Department of Computer Science University of Pittsburgh Pittsburgh PA Thoracic & Head and Neck Oncology The University of Texas MD Anderson Cancer Center Houston TX USA Department of Electrical and Computer Engineering The University of Iowa Iowa City IA Division of Biostatistics School of Public Health University of Minnesota Minneapolis MN Baylor College of Medicine Houston TX USA Athinoula A. Martinos Center for Biomedical Imaging/Massachusetts General Hospital/Massachusetts Institute of Technology Charlestown MA USA Department of Radiation Oncology The University of Texas Medical Branch Galveston TX USA Department of Radiation Oncology Stanford University Palo Alto CA USA Medical Physics Program The University of Texas Graduate School of Biomedical Sciences Houston TX USA

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Image-based ear biometric smartphone app for patient identification in field settings 10

Image-based ear biometric smartphone app for patient identif...

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10th International Conference on computer Vision Theory and Applications, VISAPP 2015

作者： Bargal, Sarah Adel Welles, Alexander Chan, Cliff R. Howes, Samuel Sclaroff, Stan Ragan, Elizabeth Johnson, Courtney Gill, Christopher Computer Science Dept. Boston University BostonMA United States Computer Engineering Dept. Boston University BostonMA United States Electrical Engineering Dept. Boston University BostonMA United States School of Public Health Boston University BostonMA United States

ISBN: (纸本)9789897580918

We present a work in progress of a computer vision application that would directly impact the delivery of healthcare in underdeveloped countries. We describe the development of an image-based smartphone application prototype for ear biometrics. The application targets the public health problem of managing medical records at on-site medical clinics in less developed countries where many individuals do not hold IDs. The domain presents challenges for an ear biometric system, including varying scale, rotation, and illumination. It was not clear which feature descriptors would work best for the application, so a comparative study of three ear biometric extraction techniques was performed, one of which was used to develop an iOS application prototype to establish the identity of an individual using a smartphone camera image. A pilot study was then conducted on the developed application to test feasibility in naturalistic settings.

关键词： Smartphones

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ARIMA-based Demand Forecasting Method Considering Probabilistic Model of Electric Vehicles' Parking Lots

ARIMA-based Demand Forecasting Method Considering Probabilis...

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IEEE Power and Energy Society General Meeting

作者： M. H. Amini O. Karabasoglu Marija D. Ilic Kianoosh G. Boroojeni S. S. Iyengar Joint Institute of Engineering Department of Electrical and Computer Engineering Sun Yat-sen University-Carnegie Mellon University Departments of Electrical and Computer Engineering and Engineering and Public Policy Carnegie Mellon University School of Computing and Information Sciences Florida International University Florida International University's School of Computing and Information Sciences

ISBN: (纸本)9781467380416

Electric transportation is one of the key elements of the future power systems since conventional power networks are rapidly evolving towards smart grids. This transition creates the need for systematic utilization of electric vehicles (EV) in order to avoid unpredictable effects on the power systems. An accurate and efficient method for demand forecasting of EVs is needed to perform a feasible scheduling of resources in order to supply the predicted load sufficiently. This paper presents a method for electricity demand fore-casting considering EV parking lots' charging demand using historical load data. The method is based on auto-regressive integrated moving average (ARIMA) model for medium-term demand forecasting. The proposed approach improves the fore-casting accuracy. Probabilistic hierarchical EVs' parking lot demand modeling is used to estimate the expected load for each parking lots' daily charging demand. In order to evaluate the effectiveness of the proposed approach, it is implemented on PJM historical load data. The simulation results show the high accuracy of the proposed method for electricity demand forecasting.

关键词： Load modeling Predictive models Mathematical model Smart grids Probabilistic logic Demand forecasting

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