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arXiv 2017年

作者： McAvoy, Alex Fraiman, Nicolas Hauert, Christoph Wakeley, John Nowak, Martin A. Program for Evolutionary Dynamics Harvard University CambridgeMA02138 Department of Statistics and Operations Research University of North Carolina at Chapel Hill Chapel HillNC27599 Department of Mathematics University of British Columbia 1984 Mathematics Road VancouverBCV6T 1Z2 Canada Department of Organismic and Evolutionary Biology Harvard University CambridgeMA02138 Department of Mathematics Harvard University CambridgeMA02138

Many mathematical frameworks of evolutionary game dynamics assume that the total population size is constant and that selection affects only the relative frequency of strategies. Here, we consider evolutionary game dynamics in an extended Wright-Fisher process with variable population size. In such a scenario, it is possible that the entire population becomes extinct. Survival of the population may depend on which strategy prevails in the game dynamics. Studying cooperative dilemmas, it is a natural feature of such a model that cooperators enable survival, while defectors drive extinction. Although defectors are favored for any mixed population, random drift could lead to their elimination and the resulting pure-cooperator population could survive. On the other hand, if the defectors remain, then the population will quickly go extinct because the frequency of cooperators steadily declines and defectors alone cannot survive. In a mutation-selection model, we find that (i) a steady supply of cooperators can enable long-term population survival, provided selection is sufficiently strong, and (ii) selection can increase the abundance of cooperators but reduce their relative frequency. Thus, evolutionary game dynamics in populations with variable size generate a multifaceted notion of what constitutes a trait’s long-term success. Copyright © 2017, The Authors. All rights reserved.

关键词： Dynamics

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Monitoring the navy's strategic reserve of ozone-depleting substances

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NAVAL ENGINEERS JOURNAL 2002年第4期114卷 95-104页

作者： Smith, DE Breslin, DA Toms, GS Norton, P DENNIS E. SMITH PH.D. is currently a statistical consultant for GEO-CENTERS INC. He received a B.A. in Mathematics (magna cum laude)from Wabash College and an M.S. in mathematics an M.S. in statistics and a Ph.D. in statistics from the University of Wisconsin. From November 1973 until February 1999 he was president and principal statistician at Desmatics Inc. where he participated in a number of studies involving the application of statistical methods to various technical problems in the Navy's environmental-protection and auxiliary-machinery programs. Dr. Smith was instrumental in developing the statistical procedures underlying the Navy's ODS reserve monitoring plan. DAVID A. BRESLIN P.E. is the director of technical operations for the Naval Surface Warfare Center and chairs the ASNE/SNAME Joint Committee on Environmental Engineering. He received a master of science in aerospace engineering from Virginia Tech a master of engineering administration in industrial and systems engineering from Virginia Tech and a bachelor of engineering in mechanical engineering from Stevens Institute of Technology. From 1993 to 1997 he was the program manager of the Naval Sea Systems Command's CFC and Halon Elimination Program and played a critical role in establishing the Navy's strategic reserve of ozone-depleting substances. Because of his many efforts he received a Stratospheric Ozone Protection Award in 1995 from the U.S. Environmental Protection Agency in recognition of “exceptional contributions to global environmental protection”. GREGORY S. TOMS P.E. is the CFC &Halon Elimination Program Manager for the Naval Sea Systems Command (NAVSEA). He received a master of science degree in mechanical engineering from the University of Maryland and a B.S. degree in mechanical engineering from West Virginia University. From 1984 to 1997 he was employed at the Naval Surface Warfare Center Carderock Division Annapolis Detachment (formerly David Taylor Research Center)where he worked on the development of advanced cent

The domestic production of the most powerful ozone-depleting substances (ODSs) has permanently ceased and the abundant supplies of a number of refrigerants, fire-fighting agents, and solvents, once taken for granted, are now a thing of the past. The Navy has adopted a successful strategy for addressing the threat posed by the unavailability of ODSs. That strategy includes relying on a strategic reserve of ODSs. Considering that the strategic reserve will play a critical role in sustained Fleet operations into the middle of the century, it is important to record the history of the reserve and to document lessons learned in order to educate future generations that may face similar challenges. This is the third of three papers that cover all aspects of the Navy's strategic reserve of ODSs. Breslin (1999) documented the history of the Navy's effort to size, establish, and monitor a reserve designed to support Fleet operations until the middle of the century. Breslin, Smith, and Toms (2000) addressed in detail the process used to estimate the required size of the reserve, including the survey method, statistical analyses, and sizing algorithms. This paper discusses the Navy's plan for monitoring the Navy's strategic reserves of ODSs and describes the methods used in the monitoring process. In particular it discusses the underlying assumptions and statistical techniques associated with the Navy's prediction of the expected drawdown of the reserves. A comparison of actual data, collected since the strategic reserves began operations in 1995, with the predicted reserve drawdown, is included in the paper.

关键词： Marine engineering

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A statistical method for estimating the life expectancy of certain shipboard equipment

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NAVAL ENGINEERS JOURNAL 2002年第1期114卷 27-32页

作者： Smith, DE Breslin, DA Toms, GS DENNIS E. SMITH PH.D. is a statistical consultant for Geo-Centers Inc. He received a B.A. in mathe-matics (magna cum laude) from Wabash College and an M.S. in mathematics an M.S. in statistics and a Ph.D. in statistics from the University of Wisconsin. From November 1973 until February 1999 he was President and Principal Statistician at Desmatics Inc. where he participated in a number of studies involving the application of statistical methods to various technical problems in the Navy's environmental-protection and auxiliary-machinery programs. Dr. Smith was instrumental in developing the statistical procedures underlying the Navy's ODS reserve monitoring plan. DAVID A. BRESLIN P.E. is the Director of Technical Operations for the Naval Surface Warfare Center and is the chairperson of ASNE's Committee on Environmental Engineering. He received a master of science degree in aerospace engineering from Virginia Tech a master of engineering administration in indus-trial & systems engineering from Virginia Tech and a bachelor of engineering in mechanical engineering from Stevens Institute of Technology. From 1993 to 1997 he was the program manager of the Naval Sea Systems Command's CFC & Halon Elimination Program and played a critical role in establishing the Navy's strategic reserve of ozone-depleting substances. Because of his many efforts he received a Stratospheric Ozone Protection Award in 1995 from the U.S. Environmental Protection Agency in recogni-tion of “exceptional contributions to global environmental protection”. GREGORY S. TOMS P.E. is the CFC & Halon Elimination Program Manager for the Naval Sea Systems Command (NAVSEA). He received a master of science degree in mechanical engineering from the University of Maryland and a B.S. degree in mechanical engineering from West Virginia University. From 1984 to 1997 he was employed at the Naval Surface Warfare Center Carderock Division Annapolis Detachment (formerly David Taylor Research Center) where he worked on the development of advanced cent

This paper reviews an inexpensive statistical-sampling methodology that can be used to determine the attrition rates of certain types of shipboard equipment. The methodology relies on surveys that are conducted in conjunction with regularly scheduled ship visits over a period of time and a statistical analysis of the survey results. The statistical technique allows for reasonably accurate estimates to be made of attrition rates. The specific example cited in this paper is related to galley-type refrigeration equipment, such as salad chillers, beverage dispensers, et cetera. In the case of galley-type refrigeration equipment, the Navy needs to know whether the Fleet's inventory of equipment relying on Class I Ozone-Depleting Substances (ODSs) will be replaced through natural attrition by the end of fiscal year (FY) 2005, at which time supplies of ODS-based refrigerants will no longer be available for use by ship galleys. This paper outlines the process used to gather data, the analysis of the data, and concludes that the Navy's inventory of galley equipment using Class I ODSs will be replaced through natural attrition by the end of FY05. The methodology reviewed by this paper has wider applications. For various types of shipboard equipment, the methodology can also be used to estimate with reasonable accuracy life expectancies, configurations, conditions, and other items of interest.

关键词： Ship equipment

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Neural FIM for learning Fisher information metrics from point cloud data

arXiv

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arXiv 2023年

作者： Fasina, Oluwadamilola Huguet, Guillaume Tong, Alexander Zhang, Yanlei Wolf, Guy Nickel, Maximilian Adelstein, Ian Krishnaswamy, Smita Applied Math Program Yale University New HavenCT United States Mila - Quebec AI Institute MontrealQC Canada Department of Mathematics and Statistics Université de Montréal MontrealQC Canada Department of Computer Science and Operations Research Université de Montréal MontrealQC Canada FAIR Meta AI United States Department of Math Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States

Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the underlying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature. To this end, we propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data - allowing for a continuous manifold model for the data. Neural FIM creates an extensible metric space from discrete point cloud data such that information from the metric can inform us of manifold characteristics such as volume and geodesics. We demonstrate Neural FIM's utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells). Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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The Subtype-Free Average Causal Effect for Heterogeneous Disease Etiology

arXiv

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arXiv 2022年

作者： Sasson, Amit Wang, Molin Ogino, Shuji Nevo, Daniel Department of Statistics and Operations Research Tel Aviv University Tel Aviv Israel Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital Harvard Medical School BostonMA United States Broad Institute of MIT and Harvard CambridgeMA United States Program in MPE Molecular Pathological Epidemiology Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States

Studies have shown that the effect an exposure may have on a disease can vary for different subtypes of the same disease. However, existing approaches to estimate and compare these effects largely overlook causality. In this paper, we study the effect smoking may have on having colorectal cancer subtypes defined by a trait known as microsatellite instability (MSI). We use principal stratification to propose an alternative causal estimand, the Subtype-Free Average Causal Effect (SF-ACE). The SF-ACE is the causal effect of the exposure among those who would be free from other disease subtypes under any exposure level. We study non-parametric identification of the SF-ACE, and discuss different monotonicity assumptions, which are more nuanced than in the standard setting. As is often the case with principal stratum effects, the assumptions underlying the identification of the SF-ACE from the data are untestable and can be too strong. Therefore, we also develop sensitivity analysis methods that relax these assumptions. We present three different estimators, including a doubly-robust estimator, for the SF-ACE. We implement our methodology for data from two large cohorts to study the heterogeneity in the causal effect of smoking on colorectal cancer with respect to MSI subtypes. Copyright © 2022, The Authors. All rights reserved.

关键词： Diseases

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TRANSCRIPTION PROFILING OF ART NA(I)VE KAPOSI SARCOMA LESIONS IN PATIENTS FROM LILONGWE, MALAWI

TRANSCRIPTION PROFILING OF ART NA(I)VE KAPOSI SARCOMA LESION...

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17th International Workshop on Kaposi’s Sarcoma Associated Herpesvirus (KSHV))and Related Agents(第十七届卡波西肉瘤孢疹病毒(KSHV)及相关病毒国际年会)

作者： Mina Hosseinipour Kristen Sweet Jie Xiong Dan Namarika Albert Mwafongo Michael Nyirenda Loreen Chiwoko Deborah Kamwendo Irving Hoffman Jeanette Lee Sam Phiri Blossom Damania Dirk Dittmer Department of Microbiology and Immunology Curriculum in Genetics UNC Chapel Hill USA Department of Medicine UNC Chapel Hill USA University of Arkansas Center for Infectious Diseases UNC Chapel Hill USA University of Arkansas Center for Infectious Diseases UNC Chapel Hill USA Program in Global Oncology Lineberger Comprehensive Cancer Center and Center for AIDS Research (CfAR) Kamuzu Central Hospital UNC Project Lilongwe Malawi Lighthouse Trust Center for Infectious Diseases UNC Chapel Hill USA AIDS malignancy consortium (AMC) Center for Infectious Diseases UNC Chapel Hill USA Department of Medicine UNC Chapel Hill USA University of Arkansas AIDS malignancy consortium (AMC) UNC Project Lilongwe Malawi Department of Microbiology and Immunology Curriculum in Genetics UNC Chapel Hill USA Department of Statistics and Operations Research UNC Chapel Hill USA

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TECHNICAL DOCUMENTATION MANAGEMENT IN THE ERA OF THE PAPERLESS SHIP

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NAVAL ENGINEERS JOURNAL 1989年第3期101卷 61-72页

作者： CHICKERING, JE QUALLS, WB John E. Chickering:is a systems analyst with American Management Systems Inc. in Arlington Virginia. Mr. Chickering received his BS degree in marine engineering from the U.S. Merchant Marine Academy in Kings Point New York in 1981 and his MBA degree in operations research and statistics from the University of Maryland in 1985. He is a licensed third assistant engineer of steam and motor vessels and a member of the Naval Reserve. Mr. Chickering's work includes the design and specification of several management information systems for the U.S. Navy including one that will automate the Navy's engineering drawing management system. Most recently Mr. Chickering has helped develop a workstation for the Navy's Paperless Ship Initiative. William B. Quails:is a management consultant with American Management Systems Inc. in Arlington Virginia. Mr. Quails received his BA degree in English from the Tulane University in New Orleans Louisiana in 1977 and his MPA degree in management science from the University of Georgia in 1982. Mr. Quails participated in the design and development of the U.S. Navy's Shipboard Non-tactical ADP Program II (SNAP II). Recently Mr. Quails has participated in the design and development of several knowledge-based computer systems using artificial intelligence programming technologies. Mr. Quails is a member of Pi Alpha Alpha an honorary public affairs and administration society.

The U.S. Navy relies heavily on advanced technology to carry out its missions. As a consequence, today's sailors are spending increasing amounts of time maintaining and repairing complex systems and equipment. In performing these duties, sailors depend on supporting technical documentation. As the complexity of systems and equipment grow, the volume and complexity of this technical documentation also increases. As a result, technical information, one of the Navy's critical resources, is simultaneously becoming more vital and more difficult to use. The Navy is meeting this challenge by exploring innovative approaches to the management of technical documentation. One example is the Paperless Ship Initiative, which employs optical disk technology as the primary means for document storage. Optical disks can store large volumes of technical information in a small space. One way to take advantage of this auto mated access is to make technical information more readily accessible and easier to use. As an example, a technical manual can be organized on an optical disk into discrete segments of text and indexed for fast retrieval by section, subsection, or paragraph. Furthermore, mechanisms can be developed which allow computer-supported links between logically related segments of the text. This style of interface allows a user to interact directly with the textual passages and to establish new organizational and referential links between them. This style of interface falls under the general category of hypertext (also known as linked text ). The combination of optical disk storage and hypertext offers new possibilities for improving access to large volumes of technical documentation while maintaining all of the advantages of traditional paper-based documentation. This paper begins with a discussion of the need for better technical documentation support. Alternatives that can fulfill this need are reviewed and the role of hypertext is described. The paper illustrates a sample

关键词： Information Retrieval Systems

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Forecasting: theory and practice

arXiv

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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Erratum to: A survey of best practices for RNA-seq data analysis

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Genome biology 2016年第1期17卷 181页

作者： Ana Conesa Pedro Madrigal Sonia Tarazona David Gomez-Cabrero Alejandra Cervera Andrew McPherson Michal Wojciech Szcześniak Daniel J Gaffney Laura L Elo Xuegong Zhang Ali Mortazavi Institute for Food and Agricultural Sciences Department of Microbiology and Cell Science University of Florida Gainesville FL 32603 USA. aconesa@ufl.edu. Centro de Investigación Príncipe Felipe Genomics of Gene Expression Laboratory 46012 Valencia Spain. aconesa@ufl.edu. Wellcome Trust Sanger Institute Wellcome Trust Genome Campus Hinxton Cambridge CB10 1SA UK. pm12@sanger.ac.uk. Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute Anne McLaren Laboratory for Regenerative Medicine Department of Surgery University of Cambridge Cambridge CB2 0SZ UK. pm12@sanger.ac.uk. Centro de Investigación Príncipe Felipe Genomics of Gene Expression Laboratory 46012 Valencia Spain. Department of Applied Statistics Operations Research and Quality Universidad Politécnica de Valencia 46020 Valencia Spain. Unit of Computational Medicine Department of Medicine Karolinska Institutet Karolinska University Hospital 171 77 Stockholm Sweden. Center for Molecular Medicine Karolinska Institutet 17177 Stockholm Sweden. Unit of Clinical Epidemiology Department of Medicine Karolinska University Hospital L8 17176 Stockholm Sweden. Science for Life Laboratory 17121 Solna Sweden. Systems Biology Laboratory Institute of Biomedicine and Genome-Scale Biology Research Program University of Helsinki 00014 Helsinki Finland. School of Computing Science Simon Fraser University Burnaby V5A 1S6BC Canada. Department of Bioinformatics Institute of Molecular Biology and Biotechnology Adam Mickiewicz University in Poznań 61-614 Poznań Poland. Wellcome Trust Sanger Institute Wellcome Trust Genome Campus Hinxton Cambridge CB10 1SA UK. Turku Centre for Biotechnology University of Turku and Åbo Akademi University FI-20520 Turku Finland. Key Lab of Bioinformatics/Bioinformatics Division TNLIST and Department of Automation Tsinghua University Beijing 100084 China. School of Life Sciences Tsinghua University Beijing 100084 China. Department of Developmental and Cell Biolo

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