检索结果-内蒙古大学图书馆

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

学校读者我要写书评

暂无评论

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

Complexity analysis vs. Engineering design in CSP algorithms: Contravening conventional wisdom again 23

学校读者我要写书评

暂无评论

Complexity analysis vs. Engineering design in CSP algorithms...

23rd RCRA International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, RCRA 2016

作者： Wallace, Richard J. Insight Centre for Data Analytics Department of Computer Science University College Cork Cork Ireland

In this work we compare simple and 'advanced' algorithms that have been used to perform various functions in connection with constraint satisfaction problems (CSPs). These include algorithms for arc consistency, singleton arc consistency, arc consistency used with MAC, maxRPC algorithms, and algorithms for simple table reduction with non-binary constraints. In each case tested so far, we find tradeoffs between efficient implementations of basic operations like constraint checking and elaborations that allow a reduction in the number of these basic operations but add bookkeeping expenses. It is argued that such tradeoffs must be given more thorough consideration. This also suggests that there are interesting theoretical problems in this connection that have not yet received the attention they merit. Finally, the possibility that certain psychological biases have affected the analysis of algorithms in this area is considered.

关键词： Constraint satisfaction problems

OPTIMAL EXPERIMENTAL DESIGN FOR CONSTRAINED INVERSE PROBLEMS

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Ruthotto, Lars Chung, Julianne Chung, Matthias Department of Mathematics and Computer Science Emory University AtlantaGA United States Department of Mathematics Computational Modeling and Data Analytics Division Academy of Integrated Science Virginia Tech BlacksburgVA United States

In this paper, we address the challenging problem of optimal experimental design (OED) of constrained inverse problems. We consider two OED formulations that allow to reduce the experimental costs by minimizing the number of measurements. The first formulation assumes a fine discretization of the design parameter space and uses sparsity promoting regularization to obtain an efficient design. The second formulation parameterizes the design and seeks optimal placement for these measurements by solving a small-dimensional optimization problem. We consider both problems in a Bayes risk as well as an empirical Bayes risk minimization framework. For the unconstrained inverse state problem, we exploit the closed form solution for the inner problem to efficiently compute derivatives for the outer OED problem. The empirical formulation does not require an explicit solution of the inverse problem and therefore allows to integrate constraints efficiently. A key contribution is an efficient optimization method for solving the resulting, typically high-dimensional, bilevel optimization problem using derivative-based methods. To overcome the lack of non-differentiability in active set methods for inequality constraints problems, we use a relaxed interior point method. To address the growing computational complexity of empirical Bayes OED, we parallelize the computation over the training models. Numerical examples and illustrations from tomographic reconstruction, for various data sets and under different constraints, demonstrate the impact of constraints on the optimal design and highlight the importance of OED for constrained problems.62K05, 65F22, 80M50 Copyright © 2017, The Authors. All rights reserved.

关键词： Constrained optimization

Interpretable human action recognition in compressed domain

学校读者我要写书评

暂无评论

Interpretable human action recognition in compressed domain

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Vignesh Srinivasan Sebastian Lapuschkin Cornelius Hellge Klaus-Robert Müller Wojciech Samek Berlin Big Data Center Berlin Germany Department of Video Coding & Analytics Fraunhofer Heinrich Hertz Institute Berlin Germany Department of Computer Science Technische Universität Berlin Germany

ISBN: (纸本)9781509041183

Compressed domain human action recognition algorithms are extremely efficient, because they only require a partial decoding of the video bit stream. However, the question what exactly makes these algorithms decide for a particular action is still a mystery. In this paper, we present a general method, Layer-wise Relevance Propagation (LRP), to understand and interpret action recognition algorithms and apply it to a state-of-the-art compressed domain method based on Fisher vector encoding and SVM classification. By using LRP, the classifiers decisions are propagated back every step in the action recognition pipeline until the input is reached. This methodology allows to identify where and when the important (from the classifier's perspective) action happens in the video. To our knowledge, this is the first work to interpret a compressed domain action recognition algorithm. We evaluate our method on the HMDB51 dataset and show that in many cases a few significant frames contribute most towards the prediction of the video to a particular class.

关键词： Support vector machines Computational modeling Histograms Heating systems Classification algorithms Visualization Kernel

Local nutrient addition drives plant diversity losses but not biotic homogenization in global grasslands

学校读者我要写书评

暂无评论

Nature communications 2025年第1期16卷 4903页

作者： Qingqing Chen Shane A Blowes W Stanley Harpole Emma Ladouceur Elizabeth T Borer Andrew MacDougall Jason P Martina Jonathan D Bakker Pedro M Tognetti Eric W Seabloom Pedro Daleo Sally Power Christiane Roscher Peter B Adler Ian Donohue George Wheeler Carly Stevens G F Ciska Veen Anita C Risch Glenda M Wardle Yann Hautier Catalina Estrada Erika Hersch-Green Yujie Niu Pablo L Peri Anu Eskelinen Daniel S Gruner Harry Olde Venterink Carla D'Antonio Marc W Cadotte Sylvia Haider Nico Eisenhauer Jane Catford Risto Virtanen John W Morgan Michelle Tedder Sumanta Bagchi Maria C Caldeira Miguel N Bugalho Johannes M H Knops Chris R Dickman Nicole Hagenah Anke Jentsch Petr Macek Brooke B Osborne Lauri Laanisto Jonathan M Chase German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Puschstraße 4 04103 Leipzig Germany. chqq365@***. Senckenberg Museum for Natural History Görlitz D-02826 Görlitz Germany. chqq365@***. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Puschstraße 4 04103 Leipzig Germany. Department of Computer Science Martin Luther University Halle-Wittenberg Halle (Saale) Germany. Helmholtz Center for Environmental Research-UFZ Department of Physiological Diversity Permoserstrasse 15 04318 Leipzig Germany. Martin Luther University Halle-Wittenberg Am Kirchtor 1 06108 Halle (Saale) Germany. Department of Biology University of Prince Edward Island Charlottetown PE Canada. Canadian Centre for Climate Change and Adaptation University of Prince Edward Island St. Peter's Bay Charlottetown PE C0A 2A0 Canada. School of Climate Change and Adaptation University of Prince Edward Island Charlottetown PE C1A 4P3 Canada. Department of Ecology Evolution and Behavior University of Minnesota St. Paul MN 55108 USA. Department of Integrative Biology University of Guelph Guelph ON N1G2W1 Canada. Department of Biology Texas State University San Marcos TX 78666 USA. School of Environmental and Forest Sciences University of Washington Seattle WA USA. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a La Agricultura (IFEVA) Facultad de Agronomía Universidad de Buenos Aires and CONICET Buenos Aires Argentina. Instituto de Investigaciones Marinas y Costeras (IIMyC) UNMdP-CONICET CC 1260 Correo Central B7600WAG Mar Del Plata Argentina. Hawkesbury Institute for the Environment Locked Bag 1797 Penrith NSW 2751 Australia. Department of Wildland Resources and the Ecology Center Utah State University Logan UT 84322 USA. Zoology School of Natural Sciences Trinity College Dublin Dublin Ireland. University of Nebraska-Lincoln Lincoln NE USA. Lancaster Environment Centre Lancaster University Lancaster LA1

Nutrient enrichment typically causes local plant diversity declines. A common but untested expectation is that nutrient enrichment also reduces variation in nutrient conditions among localities and selects for a smaller pool of species, causing greater diversity declines at larger than local scales and thus biotic homogenization. Here we apply a framework that links changes in species richness across scales to changes in the numbers of spatially restricted and widespread species for a standardized nutrient addition experiment across 72 grasslands on six continents. Overall, we find proportionally similar species loss at local and larger scales, suggesting similar declines of spatially restricted and widespread species, and no biotic homogenization after 4 years and up to 14 years of treatment. These patterns of diversity changes are generally consistent across species groups. Thus, nutrient enrichment poses threats to plant diversity, including for widespread species that are often critical for ecosystem functions.

关键词：

Structure-based Fitness Prediction for the Variable-structure DANNA Neuromorphic Architecture

学校读者我要写书评

暂无评论

Structure-based Fitness Prediction for the Variable-structur...

International Joint Conference on Neural Networks

作者： Aleksander Klibisz Grant Bruer James S. Plank Catherine D. Schuman Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee USA 37996 Computational Data Analytics Oak Ridge National Laboratory Oak Ridge Tennessee USA 37831

ISBN: (纸本)9781509061839

In recent years, research on neuromporphic computing platforms has focused on variable-structure, spiking network models. An important methodology for programming these networks is evoluationary optimization (EO), where thousands of networks are generated and then evaluated by determining fitness scores on specific tasks. Fitness scores guide the generation of new networks until a target fitness is achieved. One source of performance overhead during EO is the simulation of the task on each network to determing its fitness. To mitigate this source of overhead, we formulate the Static Fitness Prediction Task (SFPT), for predicting a network's fitness without direct simulation. Our hypothesis is that we can use SFPT to predict a network's fitness sufficiently accurately to reject a significant portion of networks during EO without having to simulate them, thereby making the EO more efficient. We propose a data-driven approach to the SFPT on the neuromorphic model DANNA [1]. Our approach transforms networks into directed graphs and extracts structural features to train an ancillary model for predicting the fitness of new networks. We analyze the extracted features and evaluate several predictive models to predict the fitness of networks for five tasks. Our results demonstrate a predictive capacity in these features and models. Our primary contribution is to demonstrate the utility of graph-level features extracted from variable-structure networks to predict network fitness and circumvent expensive simulations.

关键词： Predictive models Feature extraction Computational modeling Neurons Training Reservoirs Genetic algorithms

On the Complexity of Robust Stable Marriage

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Genc, Begum Siala, Mohamed Simonin, Gilles O'Sullivan, Barry Insight Centre for Data Analytics Department of Computer Science University College Cork Ireland TASC Institut Mines Telecom Atlantique LS2N UMR 6004 Nantes France

Robust Stable Marriage (RSM) is a variant of the classical Stable Marriage problem, where the robustness of a given stable matching is measured by the number of modifications required for repairing it in case an unforeseen event occurs. We focus on the complexity of finding an (a,b)-supermatch. An (a,b)-supermatch is defined as a stable matching in which if any a (non-fixed) men/women break up it is possible to find another stable matching by changing the partners of those a men/women and also the partners of at most b others. In order to show deciding if there exists an (a,b)-supermatch is NP-complete, we first introduce a SAT formulation that is NP-complete by using Schaefer's Dichotomy Theorem. Then, we show the equivalence between the SAT formulation and finding a (1,1)-supermatch on a specific family of instances. Copyright © 2017, The Authors. All rights reserved.

关键词： Game theory

Finding robust solutions to stable marriage

学校读者我要写书评

暂无评论

arXiv 2017年

We study the notion of robustness in stable matching problems. We first define robustness by introducing (a,b)-supermatches. An (a, b)-supermatch is a stable matching in which if any a pairs break up it is possible to find another stable matching by changing the partners of those a pairs and the partners of at most b other pairs. In this context, we define the most robust stable matching as a (1, b)-supermatch where b is minimum. We first show that checking whether a given stable matching is a (1, b)-supermatch can be done in polynomial time. Next, we use this procedure to design a constraint programming model, a local search approach, and a genetic algorithm to find the most robust stable matching. Our empirical evaluation on large instances shows that local search outperforms the other approaches. Copyright © 2017, The Authors. All rights reserved.

关键词： Polynomial approximation

Declarative statistics

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Rossi, Roberto Akgün, Özgür Prestwich, Steven Tarim, S. Armagan Business School University of Edinburgh United Kingdom Department of Computer Science University of Saint Andrews United Kingdom Insight Centre for Data Analytics University College Cork Ireland Department of Management Cankaya University Turkey

In this work we introduce declarative statistics, a suite of declarative modelling tools for statistical analysis. Statistical constraints represent the key building block of declarative statistics. First, we introduce a range of relevant counting and matrix constraints and associated decompositions, some of which novel, that are instrumental in the design of statistical constraints. Second, we introduce a selection of novel statistical constraints and associated decompositions, which constitute a selfcontained toolbox that can be used to tackle a wide range of problems typically encountered by statisticians. Finally, we deploy these statistical constraints to a wide range of application areas drawn from classical statistics and we contrast our framework against established practices. Copyright © 2017, The Authors. All rights reserved.

关键词： Statistics