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Operational Research: Methods and Applications

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arXiv 2023年

作者： Petropoulos, Fotios Laporte, Gilbert Aktas, Emel Alumur, Sibel A. Archetti, Claudia Ayhan, Hayriye Battarra, Maria Bennell, Julia A. Bourjolly, Jean-Marie Boylan, John E. Breton, Michèle Canca, David Charlin, Laurent Chen, Bo Cicek, Cihan Tugrul Cox, Louis Anthony Currie, Christine S.M. Demeulemeester, Erik Ding, Li Disney, Stephen M. Ehrgott, Matthias Eppler, Martin J. Erdogan, Güneş Fortz, Bernard Franco, L. Alberto Frische, Jens Greco, Salvatore Gregory, Amanda J. Hämäläinen, Raimo P. Herroelen, Willy Hewitt, Mike Holmström, Jan Hooker, John N. Işık, Tugçe Johnes, Jill Kara, Bahar Y. Karsu, Özlem Kent, Katherine Köhler, Charlotte Kunc, Martin Kuo, Yong-Hong Letchford, Adam N. Leung, Janny Li, Dong Li, Haitao Lienert, Judit Ljubić, Ivana Lodi, Andrea Lozano, Sebastián Lurkin, Virginie Martello, Silvano McHale, Ian G. Midgley, Gerald Morecroft, John D.W. Mutha, Akshay Oguz, Ceyda Petrovic, Sanja Pferschy, Ulrich Psaraftis, Harilaos N. Rose, Sam Saarinen, Lauri Salhi, Said Song, Jing-Sheng Sotiros, Dimitrios Stecke, Kathryn E. Strauss, Arne K. Tarhan, Istenç Thielen, Clemens Toth, Paolo Van Woensel, Tom Vanden Berghe, Greet Vasilakis, Christos Vaze, Vikrant Vigo, Daniele Virtanen, Kai Wang, Xun Weron, Rafal White, Leroy Yearworth, Mike Yıldırım, E. Alper Zaccour, Georges Zhao, Xuying School of Management University of Bath Bath United Kingdom Makridakis Open Forecasting Center University of Nicosia Nicosia Cyprus Department of Decision Sciences HEC Montréal Montreal Canada Molde University College Molde Norway Cranfield School of Management Cranfield University Cranfield United Kingdom Department of Management Sciences University of Waterloo Waterloo Canada ESSEC Business School in Paris Department of Information Systems Decision Sciences and Statistics Cergy France H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta United States Centre for Decision Research Leeds University Business School University of Leeds Leeds United Kingdom Université du Québec à Montréal Montreal Canada Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University Lancaster United Kingdom School of Engineering Department of Industrial Engineering and Management Science I University of Seville Seville Spain Mila-Quebec AI Institute Montreal Canada Warwick Business School University of Warwick Coventry United Kingdom Department of Industrial Engineering Atilim University Ankara Turkey Department of Business Analytics University of Colorado School of Business Denver United States Cox Associates Denver United States Mathematical Sciences University of Southampton Southampton United Kingdom Faculty of Economics and Business Research Center for Operations Management KU Leuven Leuven Belgium Durham University Business School Durham University Durham United Kingdom Center for Simulation Analytics and Modelling University of Exeter Business School Exeter United Kingdom Department of Management Science Lancaster University Management School Lancaster University Lancaster United Kingdom University of St. Gallen St. Gallen Switzerland HEC - Management School of the University of Liège Liège Belgium Département d'Informatique Université Libre de Bruxelles Bruss

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes. © 2023, CC BY.

关键词： Decision making

ON THE OCCUPANCY PROBLEM FOR A REGIME SWITCHING MODEL

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arXiv 2018年

作者： Grabchak, Michael Paris, Quentin Kelbert, Mark University of North Carolina Charlotte Department of Mathematics and Statistics CharlotteNC United States Faculty of Economics Department of Statistics and Data Analysis Moscow Russia Faculty of Computer Science School of Data Analysis and Artificial Intelligence & HDI LAB Moscow Russia

This article studies the expected occupancy probabilities on an alphabet. Unlike the standard situation, where observations are assumed to be independent and identically distributed (iid), we assume that they follow a regime switching Markov chain. For this model, we 1) give finite sample bounds on the occupancy probabilities, and 2) provide detailed asymptotics in the case where the underlying distribution is regularly varying. We find that, in the regularly varying case, the finite sample bounds are rate optimal and have, up to a constant, the same rate of decay as the asymptotic result. Copyright © 2018, The Authors. All rights reserved.

关键词： Sampling

Using compressed suffix-arrays for a compact representation of temporal-graphs

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arXiv 2018年

作者： Brisaboa, Nieves R. Caro, Diego Fariña, Antonio Andrea Rodriguez, M. Data Science Institute Faculty of Engineering Universidad del Desarrollo Chile Telefónica I+D Fellow Chile Database Laboratory University of A Coruña Spain Department of Computer Science University of Concepción Chile

Temporal graphs represent binary relationships that change along time. They can model the dynamism of, for example, social and communication networks. Temporal graphs are defined as sets of contacts that are edges tagged with the temporal intervals when they are active. This work explores the use of the Compressed Suffix Array (CSA), a well-known compact and self-indexed data structure in the area of text indexing, to represent large temporal graphs. The new structure, called Temporal Graph CSA (TGCSA), is experimentally compared with the most competitive compact data structures in the state-of-the-art, namely, EdgeLog and CET. The experimental results show that TGCSA obtains a good space-time trade-off. It uses a reasonable space and is efficient for solving complex temporal queries. Furthermore, TGCSA has wider expressive capabilities than EdgeLog and CET, because it is able to represent temporal graphs where contacts on an edge can temporally overlap. Copyright © 2018, The Authors. All rights reserved.

关键词： data structures

Region-wise generative adversarial image inpainting for large missing areas

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arXiv 2019年

作者： Ma, Yuqing Liu, Xianglong Bai, Shihao Wang, Lei Liu, Aishan Tao, Dacheng Hancock, Edwin State Key Lab of Software Development Environment Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine UBTECH Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney DarlingtonNSW2008 Australia Department of Computer Science University of York York United Kingdom

Recently deep neutral networks have achieved promising performance for filling large missing regions in image inpainting tasks. They usually adopted the standard convolutional architecture over the corrupted image, leading to meaningless contents, such as color discrepancy, blur and artifacts. Moreover, most inpainting approaches cannot well handle the large contiguous missing area cases. To address these problems, we propose a generic inpainting framework capable of handling with incomplete images on both contiguous and discontiguous large missing areas, in an adversarial manner. From which, region-wise convolution is deployed in both generator and discriminator to separately handle with the different regions, namely existing regions and missing ones. Moreover, a correlation loss is introduced to capture the non-local correlations between different patches, and thus guides the generator to obtain more information during inference. With the help of our proposed framework, we can restore semantically reasonable and visually realistic images. Extensive experiments on three widely-used datasets for image inpainting tasks have been conducted, and both qualitative and quantitative experimental results demonstrate that the proposed model significantly outperforms the state-of-the-art approaches, both on the large contiguous and discontiguous missing areas. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

A Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree

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A Novel Personalized Recommendation Algorithm Based on Trust...

IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Weimin Li Heng Zhu Xiaokang Zhou Shohei Shimizu Mingjun Xin Qun Jin School of Computer Engineering and Technology Shanghai University Shanghai China Faculty of Data Science Shiga University Hikone Japan Software Evaluating and Testing Shanghai Key Laboratory of Computer Shanghai China Faculty of Human Sciences Waseda University Japan

ISBN: (纸本)9781538675199

The rapid development of the Internet and ecommerce has brought a lot of convenience to people's life. Personalized recommendation technology provides users with services that they may be interested according to users' information such as personal characteristics and historical behaviors. The research of personalized recommendation has been a hot point of data mining and social networks. In this paper, we focus on resolving the problem of data sparsity based on users' rating data and social network information, introduce a set of new measures for social trust and propose a novel personalized recommendation algorithm based on matrix factorization combining trust relevancy. Our experiments were performed on the Dianping datasets. The results show that our algorithm outperforms traditional approaches in terms of accuracy and stability.

关键词： Social network services Collaboration Filtering Prediction algorithms Training data science Internet

Partition-wise recurrent neural networks for point-based AIS trajectory classification 25

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Partition-wise recurrent neural networks for point-based AIS...

25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017

作者： Jiang, Xiang De Souza, Erico N. Liu, Xuan Soleimani, Behrouz Haji Wang, Xiaoguang Silver, Daniel L. Matwin, Stan Faculty of Computer Science Dalhousie University HalifaxNS Canada Institute for Big Data Analytics Dalhousie University HalifaxNS Canada Institute of Computer Science Polish Academy of Sciences Warsaw Poland

ISBN: (纸本)9782875870391

We present Partition-wise Recurrent Neural Networks (pRNNs) for point-based trajectory classification to detect fishing activities in the ocean. This method partitions each feature and uses region-specific parameters for distinct partitions, which can greatly improve the expressive power of deep recurrent neural networks on low-dimensional yet heterogeneous trajectory data. We show that our approach outperforms the state-of-the-art systems. © ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.

关键词： Recurrent neural networks

Search for the rare decay B^(0)→J/ψФ

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Chinese Physics C 2021年第4期45卷 47-60页

作者： R.Aaij C.Abellin Beteta T.Ackernley B.Adeva M.Adinolfi H.Afsharnial C.A.Aidala S.Aiola Z.Ajaltouni S.Akar J.Albrecht F.Alessio M.Alexander A.Alfonso Albero Z.Aliouche G.Alkhazov P.Alvarez Cartelle S.Amato Y.Amhis L.An L.Anderlini A.Andreianov M.Andreotti F.Archilli A.Artamonov M.Artuso K.Arzymatov E.Aslanides M.Atzeni B.Audurier S.Bachmann M.Bachmaver J.J.Back S.Baker P.Baladron Rodriguez V.Balagura W.Baldinit J.Baptista Leite R.J.Barlow S.Barsuk W.Barter M.Bartolini F.Baryshnikov J.M.Basels G.Bassi B.Batsukh A.Battig A.Bay M.Becker F.Bedeschis I.Bediaga A.Beiter V.Belavin S.Belin V.Bellee K.Belous I.Belov I.Belyaev G.Bencivenni E.Ben-Haim A.Berezhnoy *** D.Berninghoff H.C.Bernstein C.Bertella E.Bertholet A.Bertolin C.Betancourt F.Betti M.O.Bettler Ia.Bezshyiko S.Bhasin J.Bhom L.Bian M.S.Bieker S.Bifani P.Billoir M.Birch F.C.R.Bishop A.Bizzeti M.Biorn M.P.Blago T.Blake F.Blanc S.Blusk D.Bobulska J.A.Boelhauve O.Boente Garcia T.Boettcher A.Boldyrev A.Bondar N.Bondar S.Borghi M.Borisyak M.Borsato J.T.Borsuk S.A.Bouchiba T.J.V.Bowcock A.Boyer C.Bozzi M.J.Bradley S.Braun A.Brea Rodriguezs M.Brodski J.Brodzicka A.Brossa Gonzalo D.Brundu A.Buonaura C.Burr A.Burschee A.Butkevich J.S.Butter J.Buytaert W.Byczynski S.Cadeddu H.Cai R.Calabrese L.Calefice L.Calero Diaz S.Cali R.Calladine M.Calvi M.Calvo Gomez P.Camargo Magalhaes A.Camboni P.Campana D.H.Campora Perez A.F.Campoverde Quezada S.Capelli L.Capriotti A.Carbonel G.Carboni R.Cardinale A.Cardini I.Carli P.Carniti L.Carus K.Carvalho Akiba A.Casais Vidal G.Casse M.Cattaneo G.Cavallero S.Celani J.Cerasoli A.J.Chadwick M.G.Chapman M.Charles Ph.Charpentier G.Chatzikonstantinidis C.A.Chavez Barajas M.Chefdeville C.Chen S.Chen A.Chernov S.-G.Chitic V.Chobanova S.Cholak M.Chrzaszcz A.Chubykin V.Chulikov P.Ciambrone M.F.Cicala X.Cid Vidal G.Ciezarek P.E.L.Clarke M.Clemencic H.V.Cliff J.Closier J.L.Cobbledick V.Coco J.A.B.Coelho J.Cogan E.Cogneras L.Cojocariu P.Collins T.Colombo L.Congedo A.Contu N.Cooke G.Coombs G.Corti C.M.Costa Sobral B.Couturier D.C.Craik J.Crkovska M.Cruz Centro Brasileiro de Pesquisas Fisicas(CBPF) Riode JaneiroBrazil Universidade Federal do Riode Janeiro(UFRJ) Riode JaneiroBrazil Center for High Energy Physics Tsinghua UniversityBeijingChina School of Physics State Key Laboratory of Nuclear Physics and Technology Peking UniversityBeijingChina University of Chinese Academy of Sciences BeijingChina Institute Of High Energy Physics(IHEP) BejingChina Institute of Particle Physics Central China Normal UniversityWuhanHubeiChina Univ.Grenoble Alpes Univ.Savoie Mont BlancCNRSIN2P3-LAPPAnnecyFrance Universite Clermont Auvergne CNRS/IN2P3LPCClermont-FerrandFrance Aix Marseille Univ CNRS/IN2P3CPPMMarseilleFrance Universitq Paris-Saclay CNRS/IN2P3IJCLabOrsayFrance LPNHE Sorbonne UniversiteParis Diderot Sorbonne Paris CiteCNRS/IN2P3ParisFrance I.Physikalisches Institut RWTH Aachen UniversityAachenGermany Fakultat Physik Technische Universitat DortmundDortmundGermany Max-Planck-Institut fir Kernphysik(MPIK) HeidelbergGermany Physikalisches Institut Ruprecht-Karls-Universitat HeidelbergHeidelbergGermany School of Physics University College DublinDublinIreland INFN Sezione di Bari BariItaly INFN Sezione di Bologna BolognaItaly INFN Sezione di Ferrara FerraraItaly INFN Sezione di Firenze FirenzeItaly INFN Laboratori Nazionali di Frascati Frascatiltaly INFN Sezione di Genova GenovaItaly INFN Sezione di Milano Bicocca Milanolaly INFN Sezione di Milano MilanoItaly INFN Sezione di Cagliari Monserratoltaly Universita degli Studi di Padova Universita e INFNPadovaPadovaItaly INFN Sezione di Pisa PisaItaly INFN Sezione di Roma Tor Vergata RomaItaly INFN Sezione di Roma La Sapienza RomaItaly Nikhef National Institute for Subatomic Physics AmsterdamNetherlands Nikhef National Institute for Subatomic Physics and VU University Amsterdam AmsterdamNetherlands Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences KrakowPoland AGH-University of Science and Technology Faculty of Physics and Applied Computer Science

A search for the rare decay B^(0)→J/ψФis performed using Pp collision data collected with the LHCb dete-ctor at centre-of-mass energies of 7,8 and 13 TeV,corresponding to an integrated luminosity of9 *** significant signal of the decay is observed and an upper limitof 1.1x 10^(-7)at 90%confidence level is set on the branching fraction.

关键词： B physics flavour physics rare decay ω-Фmixing branching fraction

Fostering innovation and advancement in evidence-based practice and guidelines within the field of pediatrics: The 2024 BRIGHT declaration

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Pediatric Discovery

Pediatric Discovery 2024年第4期2卷 e2513-e2513页

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

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

关键词： declaration evidence-based medicine guidelines pediartic

OS12.7.A Characterization of intra-tumoral heterogeneity and differential immune activation during malignant progression of meningiomas on single cell level

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Neuro-Oncology 2021年第SUPPLEMENT_2期23卷 ii15-ii16页

作者： Blume, C Dogan, H Schweizer, L Wick, W Weller, M Mann, M Kalamarides, M von Deimling, A Schlesner, M Sahm, F CCU Neuropathology German Cancer Research Centre (DKFZ) Heidelberg Germany Dept. of Neuropathology University Hospital Heidelberg Heidelberg Germany Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany German Cancer Research Centre (DKFZ) Heidelberg Germany Department of Neurology and Neurooncology Program National Center for Tumor Diseases Heidelberg University Hospital Heidelberg Germany Department of Neurology Clinical Neuroscience Center University Hospital and University of Zürich Zürich Switzerland NNF Center for Protein Research Faculty of Health Sciences University of Copenhagen Copenhagen Denmark Sorbonne Université and Department of Neurosurgery Pitié Salpêtrière Hospital Paris France Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany