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Correction to: Equilibrium customer behavior in the M/M/1 retrial queue with working vacations and a constant retrial rate

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Operational Research 2019年第2期20卷 647-647页

作者： Do, Nam H. Van Do, Tien Melikov, Agassi Division of Knowledge and System Engineering for ICT (KSE-ICT) Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Vietnam Analysis Design and Development of ICT Systems (AddICT) Laboratory Department of Networked Systems and Services Budapest University of Technology and Economics Budapest Hungary Institute of Cybernetics National Academy of Sciences of Azerbaijan Baku Azerbaijan

In the originally published article, the order of the affiliations of the first and the corresponding author was incorrectly indicated.

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Diagnosis of epilepsy after first seizure. Introducing the SWISS FIRST study

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Clinical and Translational Neuroscience 2020年第2期4卷

作者： Baudouin Zongxin Jin Pia De Stefano Valentina Petroulia Christian Rummel Claus Kiefer Mauricio Reyes Kaspar Schindler Pieter van Mierlo Margitta Seeck Roland Wiest Support Center for Advanced Neuroimaging Inselspital University of Bern Bern SwitzerlandSleep-Wake-Epilepsy Center and Center for Experimental Neurology Department of Neurology Inselspital University Hospital University of Bern Bern Switzerland Electroencephalography and Epilepsy Unit Department of Neurology University Hospitals of Geneva Geneva Switzerland Support Center for Advanced Neuroimaging Inselspital University of Bern Bern Switzerland ARTORG Center for Biomedical Engineering University of Bern/Insel Data Science Center Inselspital Bern Switzerland Sleep-Wake-Epilepsy Center and Center for Experimental Neurology Department of Neurology Inselspital University Hospital University of Bern Bern Switzerland Department of Electronics and Information Systems Ghent University Ghent Belgium

Diagnosis of epilepsy after a first unprovoked seizure is possible according to the guidelines by the International League Against Epilepsy, if the risk recurrence of a second unprovoked seizure is exceeding 60%. However, this cutoff constitutes only a proxy depending on the patients’ history, magnetic resonance imaging (MRI), and electroencephalography (EEG) findings but nevertheless also from the treating neurologists’ individual experience. In a Switzerland-wide observational study, we aim to recruit patients that were admitted to the emergency department with the referral diagnosis of a first and unprovoked seizure. We make use of optimized MRI protocols to identify potential structural epileptogenic lesions, introduce new imaging-based markers of epileptogenecity, and use most recent postprocessing methods as automatic morphometry, spike map analysis, and functional connectivity. With these diagnostic tools, we aim to segregate patients that present with epileptic seizures versus mimicks and non-epileptic seizures and stratify for every finding in MRI and EEG its predictive value for a second unprovoked seizure. These findings shall support neurologists to calculate and not only estimate the seizure recurrence rate in future.

关键词： Epilepsy neuroimaging automatic morphometry functional connectivity spike maps

Dynamics of reverse annealing for the fully-connected p-spin model

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arXiv 2019年

作者： Yamashiro, Yu Ohkuwa, Masaki Nishimori, Hidetoshi Lidar, Daniel A. Department of Physics Tokyo Institute of Technology Nagatsuta-cho Midori-ku Yokohama226-8503 Japan Jij Inc. High tech Hongo Building 1F 5-25-18 Hongo Bunkyo Tokyo113-0033 Japan NTT DATA Mathematical Systems Inc. Shinanomachi Shinjuku-ku Tokyo160-0016 Japan Institute of Innovative Research Tokyo Institute of Technology Nagatsuta-cho Midori-ku Yokohama226-8503 Japan Graduate School of Information Sciences Tohoku University Sendai980-8579 Japan Departments of Electrical Engineering Chemistry and Physics & Astronomy University of Southern California Los AngelesCA90089 United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States

Reverse annealing is a relatively new variant of quantum annealing, in which one starts from a classical state and increases and then decreases the amplitude of the transverse field, in the hope of finding a better classical state than the initial state for a given optimization problem. We numerically study the unitary quantum dynamics of reverse annealing for the mean-field-type p-spin model and show that the results are consistent with the predictions of equilibrium statistical mechanics. In particular, we corroborate the equilibrium analysis prediction that reverse annealing provides an exponential speedup over conventional quantum annealing in terms of solving the p-spin model. This lends support to the expectation that equilibrium analyses are effective at revealing essential aspects of the dynamics of quantum annealing. We also compare the results of quantum dynamics with the corresponding classical dynamics, to reveal their similarities and differences. We distinguish between two reverse annealing protocols we call adiabatic and iterated reverse annealing. We further show that iterated reverse annealing, as has been realized in the D-Wave device, is ineffective in the case of the p-spin model, but note that a recently-introduced protocol ("h-gain"), which implements adiabatic reverse annealing, may lead to improved performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum theory

Centrality measures for graphons: Accounting for uncertainty in networks

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arXiv 2017年

作者： Avella-Medina, Marco Parise, Francesca Schaub, Michael T. Segarra, Santiago Department of Statistics Columbia University Laboratory for Information and Decision Systems MIT Institute for Data Systems and Society MIT Department of Engineering Science University of Oxford United Kingdom Department of Electrical and Computer Engineering Rice University

As relational datasets modeled as graphs keep increasing in size and their data-acquisition is permeated by uncertainty, graph-based analysis techniques can become computationally and conceptually challenging. In particular, node centrality measures rely on the assumption that the graph is perfectly known - a premise not necessarily fulfilled for large, uncertain networks. Accordingly, centrality measures may fail to faithfully extract the importance of nodes in the presence of uncertainty. To mitigate these problems, we suggest a statistical approach based on graphon theory: we introduce formal definitions of centrality measures for graphons and establish their connections to classical graph centrality measures. A key advantage of this approach is that centrality measures defined at the modeling level of graphons are inherently robust to stochastic variations of specific graph realizations. Using the theory of linear integral operators, we define degree, eigenvector, Katz and PageRank centrality functions for graphons and establish concentration inequalities demonstrating that graphon centrality functions arise naturally as limits of their counterparts defined on sequences of graphs of increasing size. The same concentration inequalities also provide high-probability bounds between the graphon centrality functions and the centrality measures on any sampled graph, thereby establishing a measure of uncertainty of the measured centrality score. Copyright © 2017, The Authors. All rights reserved.

关键词： Graphic methods

DOP20 Can lifestyle and psychosocial factors predict flares of IBD;an exploratory study using telemedicine

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Journal of Crohn's and Colitis 2020年第SUPPLEMENT_1期14卷 S059-S060页

作者： Lalisang, R C A MyIBDcoach Study Group Adriaans, G de Jong, M van der Meulen-de Jong, A Romberg-Camps, M Mahmmod, N Markus-de Kwaadsteniet, T Dijkstra, G Haans, J Stamm, C Vanwersch, R Jonkers, D Almeida, R J Pierik, M J Division of Gastroenterology and Hepatology Maastricht Universitair Medisch Centrum+ Pacmed Maastricht The Netherlands Division of Gastroenterology and Hepatology Maastricht Universitair Medisch Centrum+ Maastricht The Netherlands Haga Ziekenhuis Department of Gastroenterology Den Haag The Netherlands Department of Gastroenterology Leiden University Medical Center Leiden The Netherlands Department of Gastroenterology Zuyderland Medical Centre Sittard The Netherlands Department of Gastroenterology St. Antonius Ziekenhuis Nieuwegein The Netherlands Stichting mijnIBDcoach Board Mijdrecht The Netherlands Department of Gastroenterology Universitair Medisch Centrum Groningen Groningen The Netherlands Department of Health Care Pacmed Amsterdam The Netherlands Information Systems Faculty of Industrial Engineering Eindhoven University of Technology Eindhoven The Netherlands Department of Internal Medicine Division of Gastroenterology-Hepatology University Maastricht Maastricht The Netherlands Department of Quantitative Economics School of Business and Economics Maastricht University Maastricht The Netherlands Department of Data Analytics and Digitalisation Maastricht University Maastricht The Netherlands

Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

Highly accurate model for prediction of lung nodule malignancy with ct scans

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

Human behavior analysis for library and information science

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Library Hi Tech 2017年第4期35卷 442-444页

作者： Mu-Yen Chen Edwin David Lughofer Neil Y. Yen Chia-Chen Chen Department of Information Management National Taichung University of Science and Technology Taichung Taiwan Department of Knowledge-Based Mathematical Systems Johannes Kepler University Linz Linz Austria School of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan Department of Management Information Systems National Chung Hsing University Taichung City Taiwan

A structured view on weighted counting with relations to counting, quantum computation and applications

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arXiv 2017年

作者： de Campos, Cassio P. Stamoulis, Georgios Weyland, Dennis Queen’s University Belfast United Kingdom Department of Information and Computing Sciences Utrecht University Netherlands Department of Data Science & Knowledge Engineering Maastricht University Netherlands Google Zürich Switzerland

Weighted counting problems are a natural generalization of counting problems where a weight is associated with every computational path of polynomial-time non-deterministic Turing machines and the goal is to compute the sum of the weights of all paths (instead of just computing the number of accepting paths). Useful closure properties and plenty of applications make weighted counting problems interesting. The general definition of these problems captures even undecidable problems, but it turns out that obtaining an exponentially small additive approximation is just as hard as solving conventional counting problems. In many cases such an approximation is sufficient and working with weighted counting problems tends to be very convenient. We present a structured view on weighted counting by defining classes that depend on the range of the function that assigns weights to paths and by showing the relationships between these different classes. These classes constitute generalizations of the usual counting problems. Weighted counting allows us to easily cast a number of famous results of computational complexity in its terms, especially regarding counting and quantum computation. Moreover, these classes are flexible enough and capture the complexity of various problems in fields such as probabilistic graphical models and stochastic combinatorial optimization. Using the weighted counting terminology and our results, we are able to simplify and answer some open questions in those fields. Copyright © 2017, The Authors. All rights reserved.

关键词： Turing machines