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Publisher Correction: A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images

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Scientific reports 2020年第1期10卷 8591页

作者： Zichao Guo Hong Liu Haomiao Ni Xiangdong Wang Mingming Su Wei Guo Kuansong Wang Taijiao Jiang Yueliang Qian Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China. Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China. hliu@***. Research Center for Big Data of Biomedical Sciences Institute of Basic Medical Sciences Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100005 China. Suzhou Institute of Systems Medicine Suzhou 215123 China. Graduate School of Peking Union Medical College Beijing 100005 China. Department of Pathology Xiangya Hospital Central South University Changsha Hunan 410013 China. Department of Pathology School of Basic Medical Sciences Central South University Changsha Hunan 410013 China. Department of Pathology Xiangya Hospital Central South University Changsha Hunan 410013 China. ***@***. Department of Pathology School of Basic Medical Sciences Central South University Changsha Hunan 410013 China. ***@***. Research Center for Big Data of Biomedical Sciences Institute of Basic Medical Sciences Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100005 China. taijiao@***. Suzhou Institute of Systems Medicine Suzhou 215123 China. taijiao@***.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Electricity Sales Prediction Model of Electricity Market of the Non-linear Regression Combination with the Optimal Weight

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Journal of Computers (Taiwan) 2019年第5期30卷 75-87S页

作者： Qu, Zhao-Yang Xie, Shu-Ya Feng, Rong-qiang Qu, Nan Lv, Hong-bo Chi, Zuo-Wei Qu, Chong Department of computer science and technology of the Institute of Information Engineering Northeast Electric Power University Jilin Jilin132012 China Intelligent Electric Power Big Data Processing Jilin Engineering Technology Research Center Jilin Jilin132012 China Maintenance Company of Jiangsu Power Company NanJing210000 China State Grid Jilin Electric Power Supply Company Jilin130000 China Fushun Power Supply Company of State Grid Liaoning Electric Power Supply Company LiaoNing113001 China Guizhou Power Grid Co. Ltd. Guiyang Baiyun Power Supply Bureau GuiYang550014 China

The model to estimate electric sales in electric market of the optimal weighting nonlinear regression combination is brought forth to improve the accuracy of electricity sales forecast. Firstly, the least square method is used to estimate the nonlinear regression equation in order to obtain the result sets of the prediction sequence. Secondly, the design method of minimizing the calculation of fitting variance and weighting com-bination to calculate the residual sequence and weighting of the Radial Basis Function (RBF) neural network and the least squares vector machine, finally the result sets of residual sequence concluded. At the end, a nonlinear decreasing strategy of inertia weight is introduced on the basis of cuckoo algorithm to optimize the extended search space of model parameters and the model solution. The simulation results show that the model can accurately and comprehensively reflect the law of electric sales and improve the accuracy of sales prediction. © 2019 Computer Society of the Republic of China. All rights reserved.

关键词： Sales

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A Vehicular Positioning Enhancement with Connected Vehicle Assistance Using Extended Kalman Filtering 1st

A Vehicular Positioning Enhancement with Connected Vehicle A...

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1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Tian, Daxin Liu, Wenhao Duan, Xuting Rong, Hui Guo, Peng Wang, Wenyang Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

In this paper, we consider the problem of vehicular positioning enhancement with emerging connected vehicles (CV) technologies. In order to actually describe the scenario, the Interacting Multiple Model (IMM) filter is used for depicting varies of observation models. A CV-enhanced IMM filtering approach is proposed to locate a vehicle by data fusion from both coarse GPS data and the Doppler frequency shifts (DFS) measured from dedicated short-range communications (DSRC) radio signals. Simulation results state the effectiveness of the proposed approach called CV-IMM-EKF. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Global positioning system

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REFF: REliable and Fast Forwarding in Vehicular Ad-hoc Network 1st

REFF: REliable and Fast Forwarding in Vehicular Ad-hoc Netwo...

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1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Tian, Daxin Dai, Ziyi Zheng, Kunxian Zhou, Jianshan Duan, Xuting Guo, Peng Rong, Hui Wang, Wenyang Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Vehicular Ad-Hoc Network (VANET) has emerged as an increasingly dominant technology for future connected vehicle and vehicular networks, where the focus of the development of VANET lies in the standardization of message transmission and dissemination via multi-hop broadcasting. However, the current communication protocols concerning VANET face many challenges, including data flooding and collision, transmission delay and other problems. Most of the challenges are closely related to next-hop selection. Therefore, this paper proposes a new routing protocol named REliable and Fast Forwarding (REFF) to optimize the selection of nodes in VANET. In this protocol, node filtering and node evaluation are two main steps. Distance between previous node and candidate node, relative velocity between previous node and candidate nodes, included angle between direction of target node’s velocity and candidate node’s velocity and transmission power of candidate node are adopted as indexes to help select a specific node as the next hop using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). By using this technique, the number of candidates as next-hop is largely reduced, avoiding the data flooding and resulting transmission relay. In addition, simulations based on experiments are done to verify the feasibility. The results show that message achieves a faster and more reliable transmission using REFF. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Vehicular ad hoc networks

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Modeling and Simulation on Cooperative Movement of Vehicle Group Based on the Behavior of Fish 1st

Modeling and Simulation on Cooperative Movement of Vehicle G...

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1st International Conference on 5G for Future Wireless Networks, 5GWN 2017

作者： Tian, Daxin Kang, Lu Zheng, Kunxian Duan, Xuting Rong, Hui Guo, Peng Wang, Wenyang Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No.37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No.68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No.30 Beijing100083 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University XueYuan Road No.37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Fatigue driving might affect the traffic safety when the vehicles are on the cruising state in highway. Trying to solve this problem, this paper uses the movement pattern of the fish to the vehicles fleet, and develops a model of vehicle group with realistic restrictions based on the existed fish algorithms, the mobile behavioral model and cluster behavior model, and then demonstrates the feasibility of applying the fish behavior to the cooperative movement of vehicle groups through analyzing the trajectory, velocity and spacing of vehicles. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Vehicles

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Radio detection of an elusive millisecond pulsar in the Globular Cluster NGC 6397

arXiv

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arXiv 2022年

作者： Zhang, Lei Ridolfi, Alessandro Blumer, Harsha Freire, Paulo C.C. Manchester, Richard N. McLaughlin, Maura Kremer, Kyle Cameron, Andrew D. Zhang, Zhiyu Behrend, Jan Burgay, Marta Buchner, Sarah Champion, David J. Chen, Weiwei Dai, Shi Feng, Yi Fu, Xiaoting Guo, Meng Hobbs, George Keane, Evan F. Kramer, Michael Levin, Lina Li, Xiangdong Ni, Mengmeng Pan, Jingshan Padmanabh, Prajwal V. Possenti, Andrea Ransom, Scott M. Tsai, Chao-Wei Krishnan, Vivek Venkatraman Wang, Pei Zhang, Jie Zhi, Qijun Zhang, Yongkun Li, Di National Astronomical Observatories Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia SelargiusI-09047 Italy Department of Physics and Astronomy West Virginia University MorgantownWV26506 United States Max-Planck Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany CSIRO Space and Astronomy Australia Telescope National Facility PO Box 76 EppingNSW1710 Australia TAPIR California Institute of Technology PasadenaCA91125 United States The Observatories of the Carnegie Institution for Science PasadenaCA91101 United States Swinburne University of Technology Mail H74 PO Box 218 VIC3122 Australia School of Astronomy and Space Science Nanjing University Nanjing210023 China Ministry of Education Nanjing210093 China South African Radio Astronomy Observatory Fir Street Black River Park Cape Town7925 South Africa School of Science Western Sydney University Locked Bag 1797 PenrithNSW2751 Australia Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China National Supercomputing Center in Jinan Qilu University of Technology 28666 East Jingshi Road Licheng Distrist Jinan250103 China Jinan Institute of Supercomputing Technology 28666 East Jingshi Road Licheng Distrist Jinan250103 China School of Physics Trinity College Dublin College Green Dublin Ireland Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy The University of Manchester ManchesterM13 9PL United Kingdom HannoverD-30167 Germany MonserratoI-09042 Italy NRAO 520 Edgemont Road CharlottesvilleVA22903 United States College of Physics and Electronic Engineering Qilu Normal University 2 Wenbo Road Zhangqiu District Jinan250200 China Guizhou Provincial Key Laboratory of Radio

We report the discovery of a new 5.78 ms-period millisecond pulsar (MSP), PSR J1740−5340B (NGC 6397B), in an eclipsing binary system discovered with the Parkes radio telescope (now also known as Murriyanga), Australia, and confirmed with the MeerKAT radio telescope in South Africa. The measured orbital period, 1.97 days, is the longest among all eclipsing binaries in globular clusters (GCs) and consistent with that of the coincident X-ray source U18, previously suggested to be a ‘hidden MSP’. Our XMM-Newton observations during NGC 6397B’s radio quiescent epochs detected no X-ray flares. NGC 6397B is either a transitional MSP or an eclipsing binary in its initial stage of mass transfer after the companion star left the main sequence. The discovery of NGC 6397B potentially reveals a subgroup of extremely faint and heavily obscured binary pulsars, thus providing a plausible explanation to the apparent dearth of binary neutron stars in core-collapsed GCs as well as a critical constraint on the evolution of GCs. Copyright © 2022, The Authors. All rights reserved.

关键词： Mass transfer

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Extension Analysis of Employee Management Based on Social Network Model

Extension Analysis of Employee Management Based on Social Ne...

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The 1st International Conference on Education, Management Science and Economics (ICEMSE 2016)

作者： Honghui Zhu Xingsen Li Ningbo Institute of Technology Zhejiang University Research Center on Intelligent Computing and Data Management Ningbo Institute of Technology Zhejiang University

This paper establishes a social network model to analyze the correlation between employees and reduce the turnover rate in the application of the *** model is combined with Extenics,which uses the matter-element"(physical existence),"affair-element"(events and actions) and "relation-element" to extend,analyzes and establish human resource basic element *** to the algorithm of Page Rank,and analyzing the loyalty of employees by the analytic hierarchy process(AHP),through the interconnection of Single-layer Network Model,a Multi-employee Social Network Model is ***,it can find out that who maybe leave with high probability and *** model combines social network with Extenics,mines the inherent law of the loss of staff and the potential needs of employees,provides a basis for reducing the company's brain *** the future,the model can predict the employee's turnover in human resource management.

关键词： social network model Extenics HR basic element base personal turnover rate

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Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

arXiv

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arXiv 2022年

作者： Srivastava, Aarohi Rastogi, Abhinav Rao, Abhishek Shoeb, Abu Awal Md Abid, Abubakar Fisch, Adam Brown, Adam R. Santoro, Adam Gupta, Aditya Garriga-Alonso, Adrià Kluska, Agnieszka Lewkowycz, Aitor Agarwal, Akshat Power, Alethea Ray, Alex Warstadt, Alex Kocurek, Alexander W. Safaya, Ali Tazarv, Ali Xiang, Alice Parrish, Alicia Nie, Allen Hussain, Aman Askell, Amanda Dsouza, Amanda Slone, Ambrose Rahane, Ameet Iyer, Anantharaman S. Andreassen, Anders Madotto, Andrea Santilli, Andrea Stuhlmüller, Andreas Dai, Andrew La, Andrew Lampinen, Andrew Zou, Andy Jiang, Angela Chen, Angelica Vuong, Anh Gupta, Animesh Gottardi, Anna Norelli, Antonio Venkatesh, Anu Gholamidavoodi, Arash Tabassum, Arfa Menezes, Arul Kirubarajan, Arun Mullokandov, Asher Sabharwal, Ashish Herrick, Austin Efrat, Avia Erdem, Aykut Karakaş, Ayla Ryan Roberts, B. Loe, Bao Sheng Zoph, Barret Bojanowski, Bartlomiej Özyurt, Batuhan Hedayatnia, Behnam Neyshabur, Behnam Inden, Benjamin Stein, Benno Ekmekci, Berk Lin, Bill Yuchen Howald, Blake Orinion, Bryan Diao, Cameron Dour, Cameron Stinson, Catherine Argueta, Cedrick Ramírez, César Ferri Singh, Chandan Rathkopf, Charles Meng, Chenlin Baral, Chitta Wu, Chiyu Callison-Burch, Chris Waites, Chris Voigt, Christian Manning, Christopher D. Potts, Christopher Ramirez, Cindy Rivera, Clara E. Siro, Clemencia Raffel, Colin Ashcraft, Courtney Garbacea, Cristina Sileo, Damien Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, Dan Freeman, Daniel Khashabi, Daniel Levy, Daniel González, Daniel Moseguí Perszyk, Danielle Hernandez, Danny Chen, Danqi Ippolito, Daphne Gilboa, Dar Dohan, David Drakard, David University of Notre Dame United States Google United States Rutgers University United States Stanford University United States MIT United States DeepMind United Kingdom University of Cambridge United Kingdom University of Amsterdam Netherlands Google Research Blueshift Team NeuralSpace New York University United States Cornell University United States Koç University Turkey University of California Irvine United States Duke Kunshan University China Anthropic United States Georgia Tech United States KNC Neural Calculus National Public School - HSR Layout HKUST Hong Kong Sapienza University of Rome Italy Ought Google Research United States UC Berkeley United States Thapar University India Amazon United States Microsoft United States University of Pennsylvania United States Imperial College London United Kingdom Allen Instituite for AI Tel Aviv University Yale University United States Wroclaw University of Science and Technology Poland Blueshift Max Planck Institute for Mathematics in the Sciences Germany Bauhaus-Universität Weimar Germany Thomson Reuters Special Services University of Southern California United States Rice University United States Queen's University Kingston Canada Princeton University United States Universitat Politècnica de València Spain Juelich Research Center Germany Arizona State University United States Carnegie Mellon University United States Karlsruhe Institute of Technology Germany UNC Chapel Hill United States University of Michigan United States KU Leuven Belgium Harvard University United States ML Collective University of Virginia United States University of Edinburgh United Kingdom Research Institutes of Sweden Sweden University of Washington United States Facebook AI Research United States Mila Canada Landskape AI Umeå University Sweden Brain Team OpenAI Brown University United States MIT Media Lab United States Hacettepe University Turkey Saarland University Germany National University of Singapore Singapore Strathmore University

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (big-bench). big-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. big-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on big-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance);performance is remarkably similar across model classes, though with benefits from sparsity;tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics;social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting. Copyright © 2022, The Authors. All rights reserved.

关键词： Software design

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SPT clusters with DES and HST weak lensing. I. Cluster lensing and Bayesian population modeling of multiwavelength cluster datasets

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Physical Review D 2024年第8期110卷 083509页

作者： S. Bocquet S. Grandis L. E. Bleem M. Klein J. J. Mohr M. Aguena A. Alarcon S. Allam S. W. Allen O. Alves A. Amon B. Ansarinejad D. Bacon M. Bayliss K. Bechtol M. R. Becker B. A. Benson G. M. Bernstein M. Brodwin D. Brooks A. Campos R. E. A. Canning J. E. Carlstrom A. Carnero Rosell M. Carrasco Kind J. Carretero R. Cawthon C. Chang R. Chen A. Choi J. Cordero M. Costanzi L. N. da Costa M. E. S. Pereira C. Davis J. DeRose S. Desai T. de Haan J. De Vicente H. T. Diehl S. Dodelson P. Doel C. Doux A. Drlica-Wagner K. Eckert J. Elvin-Poole S. Everett I. Ferrero A. Ferté A. M. Flores J. Frieman J. García-Bellido M. Gatti G. Giannini M. D. Gladders D. Gruen R. A. Gruendl I. Harrison W. G. Hartley K. Herner S. R. Hinton D. L. Hollowood W. L. Holzapfel K. Honscheid N. Huang E. M. Huff D. J. James M. Jarvis G. Khullar K. Kim R. Kraft K. Kuehn N. Kuropatkin F. Kéruzoré S. Lee P.-F. Leget N. MacCrann G. Mahler A. Mantz J. L. Marshall J. McCullough M. McDonald J. Mena-Fernández R. Miquel J. Myles A. Navarro-Alsina R. L. C. Ogando A. Palmese S. Pandey A. Pieres A. A. Plazas Malagón J. Prat M. Raveri C. L. Reichardt J. Roberson R. P. Rollins A. K. Romer C. Romero A. Roodman A. J. Ross E. S. Rykoff L. Salvati C. Sánchez E. Sanchez D. Sanchez Cid A. Saro T. Schrabback M. Schubnell L. F. Secco I. Sevilla-Noarbe K. Sharon E. Sheldon T. Shin M. Smith T. Somboonpanyakul B. Stalder A. A. Stark V. Strazzullo E. Suchyta M. E. C. Swanson G. Tarle C. To M. A. Troxel I. Tutusaus T. N. Varga A. von der Linden N. Weaverdyck J. Weller P. Wiseman B. Yanny B. Yin M. Young Y. Zhang J. Zuntz University Observatory Faculty of Physics Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 6020 Innsbruck Austria Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Max Planck Institute for Extraterrestrial Physics Gießenbachstr. 1 85748 Garching Germany Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 Rio de Janeiro RJ-20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall Stanford California 94305 USA Department of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 USA SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom School of Physics University of Melbourne Parkville Victoria 3010 Australia Institute of Cosmology and Gravitation University of Portsmouth Portsmouth PO1 3FX United Kingdom Department of Physics University of Cincinnati Cincinnati Ohio 45221 USA Physics Department 2320 Chamberlin Hall University of Wisconsin-Madison 1150 University Avenue Madison Wisconsin 53706-1390 USA Department of Astronomy and Astrophysics University of Chicago Chicago Illinois 60637 USA Fermi National Accelerator Laboratory Batavia Illinois 60510-0500 USA Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA Department of Physics and Astronomy University of Missouri 5110 Rockhill Road Kansas City Missouri 64110 USA Department of Physics and Astronomy University Coll

We present a Bayesian population modeling method to analyze the abundance of galaxy clusters identified by the South Pole Telescope (SPT) with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). We discuss and validate the modeling choices with a particular focus on a robust, weak-lensing-based mass calibration using DES data. For the DES Year 3 data, we report a systematic uncertainty in weak-lensing mass calibration that increases from 1% at z=0.25 to 10% at z=0.95, to which we add 2% in quadrature to account for uncertainties in the impact of baryonic effects. We implement an analysis pipeline that joins the cluster abundance likelihood with a multiobservable likelihood for the Sunyaev-Zel’dovich effect, optical richness, and weak-lensing measurements for each individual cluster. We validate that our analysis pipeline can recover unbiased cosmological constraints by analyzing mocks that closely resemble the cluster sample extracted from the SPT-SZ, SPTpol ECS, and SPTpol 500d surveys and the DES Year 3 and HST-39 weak-lensing datasets. This work represents a crucial prerequisite for the subsequent cosmological analysis of the real dataset.

关键词： Cosmological parameters Gravitational lenses Large scale structure of the Universe

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Unveiling latent behaviors of video viewers with cross-platform information 27

Unveiling latent behaviors of video viewers with cross-platf...

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27th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2017

作者： Gu, Xuhong Zhou, Yipeng Wu, Di Chan, Terence H. Chen, Min School of Data and Computer Science Sun Yat-sen University China Institute for Telecommunications Research University of South Australia Australia Guangdong Province Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University China Collaborative Innovation Center of High Performance Computing National University of Defense Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781450350037

The online video streaming service is of huge market values with billions of worldwide users. For online video providers, e.g., Net-flix, Youku, the crucial question is how to understand users' view behaviors and preferences because this knowledge is important for their business operation. Existing solutions mainly rely on analyzing users' historical view records, which however are not always available, especially for new videos and unprovided videos. Different from existing solutions, we propose to infer user behaviors and preferences by jointly analyzing data collected from multiple platforms (e.g., video streaming systems, video databases, etc.). In particular, we use the movie data crawled from a leading video streaming system (i.e., Youku), and a well-known video database in China (i.e, Douban) for this study. Our investigation points out that movie quality (evaluated in terms of Douban scores) and release date jointly influence viewers' preferences. In addition, we reveal a series of user behaviors, e.g., users are reluctant to post comments or ratings for movies they do not like, and user eyeballs are heavily captured by new movies. Understanding of these user behaviors covered by this study is essential for video recommendation and video popularity prediction which can benefit video procurement and advertisement campaign. © 2017 ACM.

关键词： Video streaming

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