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arXiv 2018年

作者： Huggins, Jonathan H. Kasprzak, Mikolaj Campbell, Trevor Broderick, Tamara Department of Biostatistics Harvard T. H. Chan School of Public Health Department of Statistics University of Oxford Department of Statistics University of British Columbia Department of Electrical Engineering and Computer Science MIT

Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is to output high-accuracy posterior mean and uncertainty estimates. Classical Monte Carlo methods, particularly Markov Chain Monte Carlo, remain the gold standard for approximate Bayesian inference because they have a robust finite-sample theory and reliable convergence diagnostics. However, alternative methods, which are more scalable or apply to problems where Markov Chain Monte Carlo cannot be used, lack the same finite-data approximation theory and tools for evaluating their accuracy. In this work, we develop a flexible new approach to bounding the error of mean and uncertainty estimates of scalable inference algorithms. Our strategy is to control the estimation errors in terms of Wasserstein distance, then bound the Wasserstein distance via a generalized notion of Fisher distance. Unlike computing the Wasserstein distance, which requires access to the normalized posterior distribution, the Fisher distance is tractable to compute because it requires access only to the gradient of the log posterior density. We demonstrate the usefulness of our Fisher distance approach by deriving bounds on the Wasserstein error of the Laplace approximation and Hilbert coresets. We anticipate that our approach will be applicable to many other approximate inference methods such as the integrated Laplace approximation, variational inference, and approximate Bayesian computation. Copyright © 2018, The Authors. All rights reserved.

关键词： Errors

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National identity predicts public health support during a global pandemic (vol 13, 517, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 517-517页

作者： Van Bavel, Jay J. Cichocka, Aleksandra Capraro, Valerio Sjastad, Hallgeir Nezlek, John B. Pavlovic, Tomislav Alfano, Mark Gelfand, Michele J. Azevedo, Flavio Birtel, Michele D. Cislak, Aleksandra Lockwood, Patricia L. Ross, Robert Malcolm Abts, Koen Agadullina, Elena Aruta, John Jamir Benzon Besharati, Sahba Nomvula Bor, Alexander Choma, Becky L. Crabtree, Charles David Cunningham, William A. De, Koustav Ejaz, Waqas Elbaek, Christian T. Findor, Andrej Flichtentrei, Daniel Franc, Renata Gjoneska, Biljana Gruber, June Gualda, Estrella Horiuchi, Yusaku Huynh, Toan Luu Duc Ibanez, Agustin Imran, Mostak Ahamed Israelashvili, Jacob Jasko, Katarzyna Kantorowicz, Jaroslaw Kantorowicz-Reznichenko, Elena Krouwel, Andre Laakasuo, Michael Lamm, Claus Leygue, Caroline Lin, Ming-Jen Mansoor, Mohammad Sabbir Marie, Antoine Mayiwar, Lewend Mazepus, Honorata McHugh, Cillian Minda, John Paul Mitkidis, Panagiotis Olsson, Andreas Otterbring, Tobias Packer, Dominic J. Perry, Anat Petersen, Michael Bang Puthillam, Arathy Riano-Moreno, Julian C. Rothmund, Tobias Santamaria-Garcia, Hernando Schmid, Petra C. Stoyanov, Drozdstoy Tewari, Shruti Todosijevic, Bojan Tsakiris, Manos Tung, Hans H. Umbres, Radu G. Vanags, Edmunds Vlasceanu, Madalina Vonasch, Andrew Yucel, Meltem Zhang, Yucheng Abad, Mohcine Adler, Eli Akrawi, Narin Mdarhri, Hamza Alaoui Amara, Hanane Amodio, David M. Antazo, Benedict G. Apps, Matthew Ay, F. Ceren Ba, Mouhamadou Hady Barbosa, Sergio Bastian, Brock Berg, Anton Bernal-Zarate, Maria P. Bernstein, Michael Bialek, Michal Bilancini, Ennio Bogatyreva, Natalia Boncinelli, Leonardo Booth, Jonathan E. Borau, Sylvie Buchel, Ondrej Cameron, C. Daryl Carvalho, Chrissie F. Celadin, Tatiana Cerami, Chiara Chalise, Hom Nath Cheng, Xiaojun Cian, Luca Cockcroft, Kate Conway, Jane Cordoba-Delgado, Mateo Andres Crespi, Chiara Crouzevialle, Marie Cutler, Jo Cypryanska, Marzena Dabrowska, Justyna Daniels, Michael A. Davis, Victoria H. Dayley, Pamala N. Delouvee, Sylvain Denkovski, Ognjan Dezecache, Guillaume Dhaliwal, Nathan A. Diato, Department of Psychology and Neural Science New York University New York NY USA School of Psychology University of Kent Canterbury England Department of Economics Middlesex University London London England Department of Strategy and Management Norwegian School of Economics Bergen Norway SWPS University of Social Sciences and Humanities Poznań Poland Department of Psychological Sciences College of William and Mary Williamsburg VA USA Institute of Social Sciences Ivo Pilar Zagreb Croatia Department of Philosophy Macquarie University Sydney NSW Australia Stanford Graduate School of Business Stanford University Stanford CA USA Institute of Communication Science Friedrich-Schiller University Jena Jena Germany School of Human Sciences Institute for Lifecourse Development University of Greenwich London England Department of Experimental Psychology University of Oxford Oxford England Center for Human Brain Health School of Psychology University of Birmingham Birmingham England Department of Psychology Macquarie University Sydney NSW Australia KU Leuven Leuven Belgium National Research University Higher School of Economics (HSE) Moscow Russia De La Salle University Manila Philippines Department of Psychology University of the Witwatersrand Johannesburg South Africa Department of Political Science Aarhus University Aarhus Denmark X University Toronto Canada Department of Government Dartmouth College Hanover NH USA Department of Psychology University of Toronto Toronto ON Canada Gatton College of Business and Economics University of Kentucky Lexington KY USA Department of Mass Communication National University of Science and Technology (NUST) Islamabad Pakistan Department of Management Aarhus University Aarhus Denmark Faculty of Social and Economic Sciences Comenius University Bratislava Slovakia IntraMed Buenos Aires Argentina Macedonian Academy of Sciences and Arts North Macedonia Republic of North Macedonia University of Colorado Boulder Bould

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics. Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic.

关键词： Viral infection Human behaviour

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Comparative performance between C4.5 and Naive Bayes classifiers in predicting student academic performance in a Virtual Learning Environment

Comparative performance between C4.5 and Naive Bayes classif...

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International Conference on Education and Technology (ICET)

作者： Erwina Nurul Azizah Utomo Pujianto Eki Nugraha Darusalam Department of Electrical Engineering Universitas Negeri Malang Malang Indonesia Department of Computer Science Universitas Pendidikan Indonesia Bandung Indonesia Faculty of Technology Policy and Management Delft University of Technology Delft Netherlands

ISBN: (纸本)9781538674260;9781538674253

People now trying to maximizing function of Virtual Learning Environment. Virtual Learning Environment, not only as a place to help learning system but now has become a place of learning itself. But with the change of the learning system, teacher now have difficulty to monitor the activity of the student and the learning material. Although there is data that is considered capable become a benchmark for students and the interaction with Virtual Learning Activity. This paper will make a data prediction using Naïve bayes and C4.5 Algorithm using the Web History data and the sum of webpage interaction of the students in Virtual Learning Environment.

关键词： Education Classification algorithms Prediction algorithms Web pages Data mining Learning systems computer science

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Unamplified and sensitive DNA sensor for MRSA detection by capacitive sensing and low voltage AC dielectrophoresis

Unamplified and sensitive DNA sensor for MRSA detection by c...

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Annual Meeting of the American Electrophoresis Society 2017, AES 2017 - Topical Conference at the 2017 AIChE Annual Meeting

作者： Oueslati, Rania Chen, Jiangang Wu, Jayne Department of Electrical Engineering and Computer Science University of Tennessee Knoxville United States Department of Public Health University of Tennessee Knoxville United States

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Pandemics are catalysts of scientific novelty: Evidence from COVID-19

arXiv

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arXiv 2020年

作者： Liu, Meijun Bu, Yi Chen, Chongyan Xu, Jian Li, Daifeng Leng, Yan Freeman, Richard Barry Meyer, Eric Yoon, Wonjin Sung, Mujeen Jeong, Minbyul Lee, Jinhyuk Kang, Jaewoo Min, Chao Song, Min Zhai, Yujia Ding, Ying Institute for Global Public Policy Fudan University Shanghai China Department of Information Management Peking University Beijing China School of Information University of Texas at Austin AustinTX United States School of Information Management Sun Yat-Sen University Guangzhou China McCombs School of Business University of Texas at Austin AustinTX United States Department of Economics Harvard University CambridgeMA United States CambridgeMA United States Department of Computer Science and Engineering Korea University Seoul Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Korea Republic of School of Information Management Nanjing University Nanjing China Department of Library and Information Science Yonsei University Seoul Korea Republic of Department of Information Resource Management School of Management Tianjin Normal University Tianjin China School of Information Management Wuhan University Wuhan China Dell Medical School University of Texas at Austin AustinTX United States

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pre-trained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Introduction to the Issue on Photonics for Sensing

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IEEE Journal of Selected Topics in Quantum Electronics 2017年第2期23卷 5-7页

作者： Xiao, H. Arregui, F.J. Bao, X. Fan, X. Rao, Y. Wysocki, G. Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29631 United States Department of Electrical and Computer Engineering and Bioengineering Public University of Navarre Pampalona31006 Spain Department of Physics University of Ottawa OttawaONK1N 6N5 Canada Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Key Lab of Optical Fiber Sensing and Communications University of Electronics Science and Technology of China Chengdu611731 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

The papers in this special section address the use of photonics for sensing applications. Light has long been used for sensing, metrology, imaging, and measurement in human history. The inventions of semiconductor sources and detectors, optical fibers and integrated optical waveguides in the last century have led to the emergence of photonics. The papers cover a wide variety of topics in the design, characterization, packaging and demonstration of new photonic devices, structures, functional materials, fabrication methods, and signal processing principles for sensing various physical, chemical and biological parameters. The sensing scales range from nanometers based on tip enhanced Raman scattering and quantum dots to kilometers based on distributed Raman or Brillouin scattering in optical fibers. The electromagnetic spectra cover microwave, terahertzrange, deep ultraviolet (UV), visible, and mid-infrared (IR).

关键词： Special issues and sections Photonics Sensing Remote sensing Image processing Metrology Electromagnetics

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Making Cubesats and space science more accessible through educational outreach 69

Making Cubesats and space science more accessible through ed...

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69th International Astronautical Congress: #InvolvingEveryone, IAC 2018

作者： Roy, Aimee S. Ferrari, Ruth E. Damkjar, Stefan Sarai, Simran Fairgrieve-Park, Logan Lissina, Callie Kale, Andy Mann, Ian R. Elliott, Duncan Department of Elementary Education University of Alberta EdmontonABT6G 2G5 Canada Edmonton Public School Board 1 Kingsway NW EdmontonABT5H 4G9 Canada Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada Department of Physics University of Alberta EdmontonABT6G 2E1 Canada Department of Mechanical Engineering University of Alberta EdmontonABT6G 2G8 Canada Department of Physics University of Alberta EdmontonABT6G 2E1 Canada Department of Physics University of Alberta EdmontonABT6G 2E1 Canada Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada

CubeSatellites offer a more accessible option for the study of space science while also providing an outlet for a variety of educational outreach programs. AlbertaSat, a student group at the University of Alberta which focuses on CubeSatellite design, has created such a program for primary students, students in kindergarten through grade six, and secondary students, students in grades seven to twelve (7-12). Created in 2014, the program recognized that since an understanding of CubeSatellites is not embedded in the Alberta school's curriculum, many teachers did not have the content knowledge for this area, while we lacked the pedagogical knowledge to teach this. Our program has become very collaborative as we have relied heavily on people from an educational background as well as Science, Technology, engineering, and Mathematics (STEM) disciplines working together. In the last year, the program has undergone a great deal of growth and change. Since 2014, this program has grown from one successful presentation to eight different program options for teachers. We have developed five new sessions for primary and secondary school. We now have four sessions for primary and four for secondary. This is up from three sessions in the 2016/2017 academic year, two for primary and one for secondary. Our program covers a variety of STEM topics which focus on space science and project design. We have also recently completed our first program booklet;a resource for teachers interested in teaching our sessions. In this booklet, we outline our sessions using common teaching strategies such as scaffolding and step-by-step instruction to encourage the use of our sessions in the classroom, reduce cognitive overload, and provide a useful resource for teachers. This was done to make space science more accessible. Our program has been well-received and highly successful. Between July 2016 and March 2018, we will have reached approximately 1600 primary and secondary students. We hope to rea

关键词： Students

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A Pipeline Extraction on Forward-Looking Sonar Images Using the Self-Organizing Map

A Pipeline Extraction on Forward-Looking Sonar Images Using ...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Siwakorn Tulsook Teerasit Kasetkasem Yodyium Tipsuwan Nobuhiko Sugino Thitiporn Chanwimaluang Phakhachon Hoonsuwan Department of Electrical Engineering Kasetsart University Bangkok Thailand Department of Computer Engineering Kasetsart University Bangkok Thailand Department of Information processing Tokyo Institute of Technology Tokyo Japan National Electronics and Computer Technology Center (NECTEC) Pathumthani Thailand PTT Exploration and Production Public Company Limited (PTTEP) Bangkok Thailand

ISBN: (纸本)9781538635568;9781538635551

The underwater pipeline surveillance and inspection using an autonomous underwater vehicle (AUV) is the future of the oil and gas industries since the inspection using a remotely operated underwater vehicle (ROV) is very expensive. However, the successful deployment of AUVs relies on the accurate pipeline path extraction that can automatically navigate along its direction in real-time. Due to the limited visibility of the underwater environment, we proposed to use the forward-looking sonar (FLS) to overcome environment dependencies. However, the sonar images are very noisy from a nature of acoustic imaging itself. Thus, this paper mainly contributes a real-time pipeline extraction to overcome speckle noise and intensity inhomogeneity in FLS by utilizing cell-averaging constant false alarm rate (CACFAR) and the self-organizing map (SOM). In our experiment, the pipeline images data obtained from the Gulf of Thailand were used for the performance analysis where our method can accurately extract more than 89% of the dataset.

关键词： Pipelines Neurons Sonar Self-organizing feature maps Sonar navigation Inspection Data mining

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AI for the public sector: Opportunities and challenges of cross-sector collaboration

arXiv

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arXiv 2018年

作者： Mikhaylov, Slava Jankin Esteve, Marc Campion, Averill Institute for Analytics and Data Science School of Computer Science Electronic Engineering Department of Government University of Essex School of Public Policy University College London Department of Strategy and General Management ESADE Ramon Llull University

public sector organisations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high uncertainty environments. The long-term success of data science and AI in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities and challenges from AI for public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations. Copyright © 2018, The Authors. All rights reserved.

关键词： Artificial intelligence

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New method for the stability analysis of neutral systems with time-varying structured uncertainties

New method for the stability analysis of neutral systems wit...

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第37届中国控制会议

作者： Liming Ding Dajiang He Xianwu Mi Jun Shu Leiping Chen Fazhong Hou Dengfeng Xiao College of Electrical and Information Engineering Huaihua University Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Teaching and Research Section for Computer in Department of Public Courses Huaihua University of Medicine School of Information and Electrical Engineering Hunan University of Science and Technology

This study investigates the delay-dependent stability problem for neutral systems with mixed *** from previous works,this paper employs the free-weighting matrix(FWM) approach and integral inequality approach to estimate the derivative of LKF ***,a new integral inequality based the Wirtinger inequality is *** improved inequality is right used to cope with the terms ∫x(s)ds which are generated by using the free-weighting matrix ***,by integrating the newly integral inequality with free-weighting matrix approach,some less conservative stability criteria are *** examples demonstrate the effectiveness of the method and its advantage over existing methods.

关键词： free-weighting matrix（FWM） integral inequality Stabilization Linear matrix inequality（LMI） neutral systems

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