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arXiv 2019年

作者： Browne, Cameron Soemers, Dennis J.N.J. Piette, Éric Stephenson, Matthew Conrad, Michael Crist, Walter Depaulis, Thierry Duggan, Eddie Horn, Fred Kelk, Steven Lucas, Simon M. Neto, João Pedro Parlett, David Saffidine, Abdallah Schädler, Ulrich Silva, Jorge Nuno de Voogt, Alex Winands, Mark H.M. Department of Data Science and Knowledge Engineering Maastricht University Netherlands Kunsthistorisches Institut Universität Zürich Switzerland Department of Anthropology American Museum of Natural History United States Department of Science Technology and Engineering University of Suffolk United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom University of Lisbon Portugal School of Computer Science and Engineering University of New South Wales Australia Swiss Museum of Games Switzerland Economics & Business Department Drew University United States

Digital Archæoludology (DAL) is a new field of study involving the analysis and reconstruction of ancient games from incomplete descriptions and archaeological evidence using modern computational techniques. The aim is to provide digital tools and methods to help game historians and other researchers better understand traditional games, their development throughout recorded human history, and their relationship to the development of human culture and mathematical knowledge. This work is being explored in the ERC-funded Digital Ludeme Project. The aim of this inaugural international research meeting on DAL is to gather together leading experts in relevant disciplines – computer science, artificial intelligence, machine learning, computational phylogenetics, mathematics, history, archaeology, anthropology, etc. – to discuss the key themes and establish the foundations for this new field of research, so that it may continue beyond the lifetime of its initiating project. Copyright © 2019, The Authors. All rights reserved.

关键词： History

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Partitioning vectors into quadruples: Worst-case analysis of a matching-based algorithm

arXiv

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arXiv 2018年

作者： Ficker, Annette M.C. Erlebach, Thomas Mihalák, Matúš Spieksma, Frits C.R. Faculty of Economics and Business KU Leuven Leuven Belgium Department of Informatics University of Leicester Leicester United Kingdom Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands

Consider a problem where 4k given vectors need to be partitioned into k clusters of four vectors each. A cluster of four vectors is called a quad, and the cost of a quad is the sum of the component-wise maxima of the four vectors in the quad. The problem is to partition the given 4k vectors into k quads with minimum total cost. We analyze a straightforward matching-based algorithm, and prove that this algorithm is a 23-approximation algorithm for this problem. We further analyze the performance of this algorithm on a hierarchy of special cases of the problem, and prove that, in one particular case, the algorithm is a 54-approximation algorithm. Our analysis is tight in all cases except *** codes 68Q25 Copyright © 2018, The Authors. All rights reserved.

关键词： Vectors

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Towards customised visualisation of ontologies: State of the art and future applications for online polls analysis 17

Towards customised visualisation of ontologies: State of the...

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2017 Australasian Computer Science Week Multiconference, ACSW 2017

作者： Burgstaller, Felix Stabauer, Martin Morgan, Rebecca Department of Business Informatics - Data and Knowledge Engineering Johannes Kepler University Linz Austria Department of Data Processing in Social Sciences Economics and Business Johannes Kepler University Linz Austria Georg Grossmann School of IT and MS University of South Australia Adelaide Australia

ISBN: (纸本)9781450347686

Online polling is a popular tool to increase user involvement on all kinds of websites. Consumers are interested in sharing their opinion and so contribute to the website's content. Aggregated opinions, attitudes, and preferences convey a great deal of knowledge which is often unutilised as there exists no efficient method to explore them. An ongoing research project suggests methods, technologies, and processes to extract the knowledge that lies within the questions posed by website publishers and the answers given by users. This knowledge is saved in a triple store and enhanced by reasoning and other methods of the semantic web. One important technique is the visualisation of both the structure (entities and relations) and aggregated information of consumers in the knowledge base. Existing techniques often focus on only one of them although integration of both is required to explore the nature of the content and information about user groups such as size and intersections among them. The research described in this paper surveys current visualisation tools and libraries for the support of identified requirements in a case study. Based on the findings, an implementation of Agile Visualization for a specific polling system in specific and ontologies in general is proposed, which allows for a more customisable and exible visualisation. A reusable transformation process for the ontology's data is discussed, which makes it possible to use the aforementioned knowledge base as input for the agile visualization approach. © 2017 ACM.

关键词： data visualization

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Correction: Variability of temperature measurements recorded by a wearable device by biological sex

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Biology of sex differences 2023年第1期14卷 82页

作者： Lauryn Keeler Bruce Patrick Kasl Severine Soltani Varun K Viswanath Wendy Hartogensis Stephan Dilchert Frederick M Hecht Anoushka Chowdhary Claudine Anglo Leena Pandya Subhasis Dasgupta Ilkay Altintas Amarnath Gupta Ashley E Mason Benjamin L Smarr Department of Biomedical Informatics UC San Diego Health University of California San Diego San Diego CA USA. Gene Lay Department of Bioengineering University of California San Diego 9500 Gilman Dr La Jolla San Diego CA USA. Bioinformatics and Systems Biology University of California San Diego San Diego CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Osher Center for Integrative Health University of California San Francisco San Francisco CA USA. Department of Management Zicklin School of Business Baruch College The City University of New York New York NY USA. San Diego Supercomputer Center University of California San Diego San Diego CA USA. Halıcıoğlu Data Science Institute University of California San Diego San Diego CA USA. Gene Lay Department of Bioengineering University of California San Diego 9500 Gilman Dr La Jolla San Diego CA USA. bsmarr@eng.ucsd.edu. Halıcıoğlu Data Science Institute University of California San Diego San Diego CA USA. bsmarr@eng.ucsd.edu.

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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A Novel Texture Generation Super Resolution Model

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Procedia Computer Science 2019年 162卷 924-931页

作者： Biao Li Yong Shi Sujuan Li Bo Wang Zhiquan Qi Jiabin Liu School of Economics and Management University of Chinese Academy of Sciences Beijing 101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA Department of Otology the First Affiliated Hospital of Zhengzhou University No. 1 eastern Jianshe road Zhengzhou Henan 450052 China School of Information Technology and Management University of International Business and Economics Beijing 100029 China Department of Computer Science and Engineering Texas A&M University TX 77843 USA

Recently, super-resolution methods pursue visual pleasant details attract more attention in academic circle. Unlike former accurate driving models, they leverage new losses measured the difference of features extracting from a pre-trained deep learning model. Moreover, the popular GANs structure are introduced to approach better outputs. In general, these new visual feeling promoting losses are constrained by the learning ability of model and effected by the weakness of GANs. In order to address these problems, we propose a novel SR method to generating abundant details. Based on former SR methods and texture generated models, we combine new model constructure and integrated loss function to achieve our goal. The new constructure derives from the very thought that more information from the LR input leads to more accurate results. The new integrated loss try to decrease the weakness of each one by corresponding loss.. Experiments show that our model achieves higher quality generations than former methods.

关键词： Super Resolution Multi Scale Smooth Loss Visual Quality

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Corrigendum to: Multi-Swarm Cuckoo Search Algorithm withQ-Learning Model

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The Computer Journal 2022年第7期65卷 1938-1938页

作者： Li, Juan Xiao, Dan-dan Zhang, Ting Liu, Chun Li, Yuan-xiang Wang, Gai-ge School of Artificial Intelligence Wuhan Technology and Business University Wuhan 430065 China School of Artificial Intelligence Wuchang University of Technology Wuhan 430223 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University No. 2699 Qianjin Street Gaoxin District Changchun Jilin China 130012 School of Computer Wuhan University No. 299 Bayi Street Wuchang District Wuhan Hubei China 430072 Department of Computer Science and Technology Ocean University of China No. 238 Songling Street Laoshan District Qingdao Shandong China 266100 Institute of Algorithm and Big Data Analysis Northeast Normal University No. 5268 Renmin Street Nanguan District Changchun Jilin China 130117

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A review of possible effects of cognitive biases on interpretation of rule-based machine learning models

arXiv

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arXiv 2018年

作者： Kliegr, Tomáš Bahník, Štěpán Fürnkranz, Johannes Department of Information and Knowledge Engineering Prague University of Economics and Business Czech Republic The Prague College of Psychosocial Studies Czech Republic Johannes Kepler University Computational Data Analytics Group Linz Austria

While the interpretability of machine learning models is often equated with their mere syntactic comprehensibility, we think that interpretability goes beyond that, and that human interpretability should also be investigated from the point of view of cognitive science. In particular, the goal of this paper is to discuss to what extent cognitive biases may affect human understanding of interpretable machine learning models, in particular of logical rules discovered from data. Twenty cognitive biases are covered, as are possible debiasing techniques that can be adopted by designers of machine learning algorithms and software. Our review transfers results obtained in cognitive psychology to the domain of machine learning, aiming to bridge the current gap between these two areas. It needs to be followed by empirical studies specifically focused on the machine learning domain. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Rare SLC13A1 variants associate with intervertebral disc disorder highlighting role of sulfate in disc pathology (vol 13, 634, 2022)

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

作者： Bjornsdottir, Gyda Stefansdottir, Lilja Thorleifsson, Gudmar Sulem, Patrick Norland, Kristjan Ferkingstad, Egil Oddsson, Asmundur Zink, Florian Lund, Sigrun H. Nawaz, Muhammad S. Walters, G. Bragi Skuladottir, Astros Th. Gudjonsson, Sigurjon A. Einarsson, Gudmundur Halldorsson, Gisli H. Bjarnadottir, Valgerdur Sveinbjornsson, Gardar Helgadottir, Anna Styrkarsdottir, Unnur Gudmundsson, Larus J. Pedersen, Ole B. Hansen, Thomas Folkmann Werge, Thomas Banasik, Karina Troelsen, Anders Skou, Soren T. Thorner, Lise Wegner Erikstrup, Christian Nielsen, Kaspar Rene Mikkelsen, Susan Jonsdottir, Ingileif Bjornsson, Aron Olafsson, Ingvar H. Ulfarsson, Elfar Blondal, Josep Vikingsson, Arnor Brunak, Soren Ostrowski, Sisse R. Ullum, Henrik Thorsteinsdottir, Unnur Stefansson, Hreinn Gudbjartsson, Daniel F. Thorgeirsson, Thorgeir E. Stefansson, Kari deCODE Genetics/Amgen Inc. Reykjavik Iceland Faculty of Medicine School of Health Sciences University of Iceland Reykjavik Iceland School of Engineering and Natural Sciences University of Iceland Reykjavik Iceland Landspitali University Hospital Reykjavik Iceland Department of Clinical Immunology Zealand University Hospital Køge Denmark Department of Clinical Medicine Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Institute of Biological Psychiatry Mental Health Services Copenhagen University Hospital Copenhagen Denmark Lundbeck Foundation for GeoGenetics GLOBE Institute University of Copenhagen Copenhagen Denmark Department of Orthopaedic Surgery CAG ROAD—Research OsteoArthritis Denmark Copenhagen University Hospital Hvidovre Denmark Department of Clinical Immunology Copenhagen University Hospital Copenhagen Denmark Danish Headache Center Dept. Neurology Rigshospitalet-Glostrup Glostrup Denmark Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Research Unit for Musculoskeletal Function and Physiotherapy Department of Sports Science and Clinical Biomechanics University of Southern Denmark Odense Denmark The Research Unit PROgrez Department of Physiotherapy and Occupational Therapy Næstved-Slagelse-Ringsted Hospitals Næstved Denmark Department of Clinical Immunology Aarhus University Hospital Aarhus Denmark Department of Clinical Immunology Aalborg University Hospital Aalborg Denmark Department of Neurosurgery Landspitali University Hospital Reykjavik Iceland Health Care Institution of West Iceland Stykkisholmur Iceland Statens Serum Institut Copenhagen Copenhagen Denmark Department of Finance Copenhagen Business School Copenhagen Denmark Department of Clinical Neurophysiology University of Copenhagen Copenhagen Denmark Department of Biomedicine Aarhus University Aarhus Denmark Department of Clinical Immunology Odense Univ

Back pain is a common and debilitating disorder with largely unknown underlying biology. Here we report a genome-wide association study of back pain using diagnoses assigned in clinical practice; dorsalgia (119,100 cases, 909,847 controls) and intervertebral disc disorder (IDD) (58,854 cases, 922,958 controls). We identify 41 variants at 33 loci. The most significant association (ORIDD= 0.92,P= 1.6 × 10−39; ORdorsalgia= 0.92,P= 7.2 × 10−15) is with a 3’UTR variant (rs1871452-T) inCHST3, encoding a sulfotransferase enzyme expressed in intervertebral discs. The largest effects on IDD are conferred by rare (MAF = 0.07 − 0.32%) loss-of-function (LoF) variants inSLC13A1, encoding a sodium-sulfate co-transporter (LoF burden OR = 1.44,P= 3.1 × 10−11); variants that also associate with reduced serum sulfate. Genes implicated by this study are involved in cartilage and bone biology, as well as neurological and inflammatory processes.

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s-LWSR: Super lightweight super-resolution network

arXiv

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arXiv 2019年

作者： Li, Biao Liu, Jiabin Wang, Bo Qi, Zhiquan Shi, Yong School of Economics and Management University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Information Technology and Management University of International Business and Economics Beijing100029 China Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States College of Information Science and Technology University of Nebraska OmahaNE68182 United States

Deep learning (DL) architectures for super-resolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance. However, with the widespread use of mobile phones for taking and retouching photos, this character greatly hampers the deployment of DL-SR models on the mobile devices. To address this problem, in this paper, we propose a super lightweight SR network: s-LWSR. There are mainly three contributions in our work. Firstly, in order to efficiently abstract features from the low resolution image, we build an information pool to mix multi-level information from the first half part of the pipeline. Accordingly, the information pool feeds the second half part with the combination of hierarchical features from the previous layers. Secondly, we employ a compression module to further decrease the size of parameters. Intensive analysis confirms its capacity of trade-off between model complexity and accuracy. Thirdly, by revealing the specific role of activation in deep models, we remove several activation layers in our SR model to retain more information for performance improvement. Extensive experiments show that our s-LWSR, with limited parameters and operations, can achieve similar performance to other cumbersome DL-SR methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical activation

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