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Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

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Journal of medical Internet research 2021年第11期23卷 e34625页

作者： Griffin M Weber Harrison G Zhang Sehi L'Yi Clara-Lea Bonzel Chuan Hong Paul Avillach Alba Gutiérrez-Sacristán Nathan P Palmer Amelia Li Min Tan Xuan Wang William Yuan Nils Gehlenborg Anna Alloni Danilo F Amendola Antonio Bellasi Riccardo Bellazzi Michele Beraghi Mauro Bucalo Luca Chiovato Kelly Cho Arianna Dagliati Hossein Estiri Robert W Follett Noelia García Barrio David A Hanauer Darren W Henderson Yuk-Lam Ho John H Holmes Meghan R Hutch Ramakanth Kavuluru Katie Kirchoff Jeffrey G Klann Ashok K Krishnamurthy Trang T Le Molei Liu Ne Hooi Will Loh Sara Lozano-Zahonero Yuan Luo Sarah Maidlow Adeline Makoudjou Alberto Malovini Marcelo Roberto Martins Bertrand Moal Michele Morris Danielle L Mowery Shawn N Murphy Antoine Neuraz Kee Yuan Ngiam Marina P Okoshi Gilbert S Omenn Lav P Patel Miguel Pedrera Jiménez Robson A Prudente Malarkodi Jebathilagam Samayamuthu Fernando J Sanz Vidorreta Emily R Schriver Petra Schubert Pablo Serrano Balazote Byorn Wl Tan Suzana E Tanni Valentina Tibollo Shyam Visweswaran Kavishwar B Wagholikar Zongqi Xia Daniela Zöller Isaac S Kohane Tianxi Cai Andrew M South Gabriel A Brat Department of Biomedical Informatics Harvard Medical School Boston MA United States. BIOMERIS (BIOMedical Research Informatics Solutions) Pavia Italy. Clinical Research Unit Botucatu Medical School São Paulo State University Botucatu Brazil. Division of Nephrology Department of Medicine Ente Ospedaliero Cantonale Lugano Switzerland. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Information Technology Department Azienda Socio-Sanitaria Territoriale di Pavia Pavia Italy. Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Pavia Italy. Massachusetts Veterans Epidemiology Research and Information Center Veterans Affairs Boston Healthcare System Boston MA United States. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Department of Medicine Massachusetts General Hospital Boston MA United States. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States. Health Informatics Hospital Universitario 12 de Octubre Madrid Spain. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI United States. Department of Biomedical Informatics University of Kentucky Lexington KY United States. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Department of Preventive Medicine Northwestern University Chicago IL United States. Institute for Biomedical Informatics University of Kentucky Lexington KY United States. Medical University of South Carolina Charleston SC United States. Department of Computer Science Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill NC United States. Departm

[This corrects the article DOI: 10.2196/31400.].

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Permanent Magnet Synchronous Generator WECS based on a four-level double star converter 8

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Permanent Magnet Synchronous Generator WECS based on a four-...

8th IEEE International Symposium on Power Electronics for Distributed Generation systems, PEDG 2017

作者： Reusser, Carlos A. Kouro, Samir Llor, Ana Cardenas, Roberto Yaramasu, Venkata Dept. of Electronics Universidad Tecnica Federico Santa Maria Valparaiso Chile Dept. of Electrical Engineering Universidad de Chile Santiago Chile School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAR United States

ISBN: (纸本)9781509053384

The following work introduces the use of a modified four-level medium voltage converter in a back-to-back configuration for its application on wind energy conversion systems (WECS). The proposed converter topology is based on a three-level neutral-point clamped converter (3L-NPC) without clamping diods or power switches, so that dc-link capacitors are in a floating condition respect to the neutral point. The implementation of the back-to-back configuration enables the topology for its application in variable speed SM-Permanent Magnet Synchronous Generator WECS. © 2017 IEEE.

关键词： Topology

Decoding communities in networks

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arXiv 2017年

作者： Radicchi, Filippo Center for Complex Networks and Systems Research School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States

According to a recent information-theoretical proposal, the problem of defining and identifying communities in networks can be interpreted as a classical communication task over a noisy channel: memberships of nodes are information bits erased by the channel, edges and non-edges in the network are parity bits introduced by the encoder but degraded through the channel, and a community identification algorithm is a decoder. The interpretation is perfectly equivalent to the one at the basis of well-known statistical inference algorithms for community detection. The only difference in the interpretation is that a noisy channel replaces a stochastic network model. However, the different perspective gives the opportunity to take advantage of the rich set of tools of coding theory to generate novel insights on the problem of community detection. In this paper, we illustrate two main applications of standard coding-theoretical methods to community detection. First, we leverage a state-of-the-art decoding technique to generate a family of quasi-optimal community detection algorithms. Second and more important, we show that the Shannon’s noisy-channel coding theorem can be invoked to establish a lower bound, here named as decodability bound, for the maximum amount of noise tolerable by an ideal decoder to achieve perfect detection of communities. When computed for well-established synthetic benchmarks, the decodability bound explains accurately the performance achieved by the best community detection algorithms existing on the market, telling us that only little room for their improvement is still potentially left. Copyright © 2017, The Authors. All rights reserved.

关键词： Decoding

Non-parametric quality assessment of high-content screening assays 9

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Non-parametric quality assessment of high-content screening ...

9th International Conference on Bioinformatics and Computational Biology, BICOB 2017

作者： Trevino, Robert P. Faucon, Philippe C. Lamkin, Thomas J. Kawamoto, Steven A. Smith, Ross Liu, Huan School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Air Force Research Laboratory 711th HPW/RHXBC Wright Patterson Air Force Base DaytonOH45433 United States UES Inc. DaytonOH45432 United States Engility Corporation Aberdeen Proving GroundMD21005 United States

ISBN: (纸本)9781943436071

Quality control is a key component of high-content screening for novel drug discovery endeavors and phenotype-genotype mapping. Current methods such Z-factor and strictly standardized mean difference (SSMD) rely on a normal distribution assumption with control data. Unfortunately, this assumption often times is not accurate, leading to low quality scores on viable biological assessments (bioassays). This can result in lost resources, increased cost and wasted time in attempting to optimize a bioassay. We propose a novel non-parametric approach that is robust to noise and is capable of assessing the quality of bioassays where the control data may not follow a Gaussian distribution. We demonstrate that our method produces accurate results when assessing the quality of real-world siRNA and small-molecule screens. We test the proposed quality score on synthetic data using different distributions and demonstrate that our method provides a more accurate assessment of data separation on non-Gaussian datasets as well.

关键词： Bioassay

Feedback, Dynamics, and Optimal Control in Climate Economics⋆

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arXiv 2018年

作者： Kellett, Christopher M. Weller, Steven R. Faulwasser, Timm Grüne, Lars Semmler, Willi School of Electrical Engineering and Computing University of Newcastle CallaghanNSW2308 Australia Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany Mathematisches Institut Universität Bayreuth Bayreuth95440 Germany New School for Social Research New York United States University of Bielefeld Germany International Institute for Applied Systems Analysis Laxenburg Austria

For his work in the economics of climate change, Professor William Nordhaus was a co-recipient of the 2018 Nobel Memorial Prize for Economic Sciences. A core component of the work undertaken by Nordhaus is the Dynamic Integrated model of Climate and Economy, known as the DICE model. The DICE model is a discrete-time model with two control inputs and is primarily used in conjunction with a particular optimal control problem in order to estimate optimal pathways for reducing greenhouse gas emissions. In this paper, we provide a tutorial introduction to the DICE model and we indicate challenges and open problems of potential interest for the systems and control community. Copyright © 2018, The Authors. All rights reserved.

关键词： Economics

Forecasting: theory and practice

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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

Two birds, one stone: A fast, yet lightweight, indexing scheme for modern database systems 43

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Two birds, one stone: A fast, yet lightweight, indexing sche...

43rd International Conference on Very Large Data Bases, VLDB 2017

作者： Yu, Jia Sarwat, Mohamed School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S. Mill Avenue TempeAZ United States

Classic database indexes (e.g., B+-Tree), though speed up queries, suffer from two main drawbacks: (1) An index usually yields 5% to 15% additional storage overhead which results in non-ignorable dollar cost in big data scenarios especially when deployed on modern storage devices. (2) Maintaining an index incurs high latency because the DBMS has to locate and update those index pages affected by the underlying table changes. This paper proposes Hippo a fast, yet scalable, database indexing approach. It significantly shrinks the index storage and mitigates maintenance overhead without compromising much on the query execution performance. Hippo stores disk page ranges instead of tuple pointers in the indexed table to reduce the storage space occupied by the index. It maintains simplified histograms that represent the data distribution and adopts a page grouping technique that groups contiguous pages into page ranges based on the similarity of their index key attribute distributions. When a query is issued, Hippo leverages the page ranges and histogram-based page summaries to recognize those pages such that their tuples are guaranteed not to satisfy the query predicates and inspects the remaining pages. Experiments based on real and synthetic datasets show that Hippo occupies up to two orders of magnitude less storage space than that of the B+-Tree while still achieving comparable query execution performance to that of the B+-Tree for 0.1% - 1% selectivity factors. Also, the experiments show that Hippo outperforms BRIN (Block Range Index) in executing queries with various selectivity factors. Furthermore, Hippo achieves up to three orders of magnitude less maintenance overhead and up to an order of magnitude higher throughput (for hybrid query/update workloads) than its counterparts.

关键词： Big data

Finding K contingency list in power networks using a new model of dependency

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arXiv 2017年

作者： Banerjee, Joydeep Pal, Anamitra Basu, Kaustav Padhee, Malhar Sen, Arunabha School of Computing Informatics and Decision System Engineering Arizona State University TempeAZ United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ United States

Smart grid systems are composed of power and communication network components. The components in either network exhibit complex dependencies on components in its own as well as the other network to drive their functionality. Existing, models fail to capture these complex dependencies. In this paper, we restrict to the dependencies in the power network and propose the Multi-scale Implicative Interdependency Relation (MIIR) model that address the existing limitations. A formal description of the model along with its working dynamics and a brief validation with respect to the 2011 Southwest blackout are provided. Utilizing the MIIR model, the K Contingency List problem is proposed. For a given time instant, the problem solves for a set of K entities in a power network which when failed at that time instant would cause the maximum number of entities to fail eventually. Owing to the problem being NP-complete we devised a Mixed Integer Program (MIP) to obtain the optimal solution and a polynomial time sub-optimal heuristic. The efficacy of the heuristic with respect to the MIP is compared by using different bus system data. In general, the heuristic is shown to provide near optimal solution at a much faster time than the MIP. Copyright © 2017, The Authors. All rights reserved.

关键词： Electric power transmission networks

VC-bots: A vehicular cloud computing testbed with mobile robots 1

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VC-bots: A vehicular cloud computing testbed with mobile rob...

1st MobiHoc International Workshop on Internet of Vehicles and Vehicles of Internet, IoV-VoI 2016

作者： Lu, Duo Li, Zhichao Huang, Dijiang Lu, Xianglong Deng, Yuli Chowdhary, Ankur Li, Bing School of Computing Informatics and Decision Systems Arizona State University United States School of Electrical Computer and Energy Engineering Arizona State University United States

ISBN: (纸本)9781450343459

Smart vehicles with computing, sensing, and communication capabilities are gaining popularity. With various vehicular applications equipped, these smart vehicles not only improve driving safety, but also facilitate data collection and information sharing for traffic optimization, insurance estimation, and infotainment. However, developing and testing such cloud based vehicular application is challenging due to the high cost of running the application on actual cars in various traffic scenarios. For the same reason it is also difficult to understand and model the network protocol behavior among multiple vehicles. In this paper we proposed VC-bots, a vehicular cloud testbed using mobile robot vehicles, which can emulate different types of vehicles for testing vehicular network protocols and vehicular cloud applications in various scenarios, which can be easily reconfigured without any infrastructure assistance. To facilitate software integration, we also developed a message based service framework for applications running on the robot vehicle and in the cloud. © 2016 ACM.

关键词： Mobile robots