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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Abhinav Bhatele Andrew R. Titus Jayaraman J. Thiagarajan Nikhil Jain Todd Gamblin Peer-Timo Bremer Martin Schulz Laxmikant V. Kale Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA Lawrence Livermore National Laboratory Livermore CA US Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL

Network congestion is one of the primary causes of performance degradation, performance variability and poor scaling in communication-heavy parallel applications. However, the causes and mechanisms of network congestion on modern interconnection networks are not well understood. We need new approaches to analyze, model and predict this critical behaviour in order to improve the performance of large-scale parallel applications. This paper applies supervised learning algorithms, such as forests of extremely randomized trees and gradient boosted regression trees, to perform regression analysis on communication data and application execution time. Using data derived from multiple executions, we create models to predict the execution time of communication-heavy parallel applications. This analysis also identifies the features and associated hardware components that have the most impact on network congestion and intern, on execution time. The ideas presented in this paper have wide applicability: predicting the execution time on a different number of nodes, or different input datasets, or even for an unknown code, identifying the best configuration parameters for an application, and finding the root causes of network congestion on different architectures.

关键词： Hardware Predictive models Vegetation Data models Three-dimensional displays Prediction algorithms Regression tree analysis

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Models of reduced-noise, probabilistic linear amplifiers

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Physical Review A 2016年第5期93卷 052310-052310页

作者： Joshua Combes Nathan Walk A. P. Lund T. C. Ralph Carlton M. Caves Institute for Quantum Computing Department of Applied Mathematics University of Waterloo Waterloo Ontario Canada Perimeter Institute for Theoretical Physics 31 Caroline St. N Waterloo Ontario Canada N2L 2Y5 Centre for Engineered Quantum Systems School of Mathematics and Physics University of Queensland Brisbane Queensland 4072 Australia Centre for Quantum Dynamics Griffith University Nathan Queensland 4111 Australia Centre for Quantum Computation and Communication Technology School of Mathematics and Physics University of Queensland Brisbane Queensland 4072 Australia Department of Computer Science University of Oxford Wolfson Building Parks Road Oxford OX1 3QD United Kingdom Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131-0001 USA

We construct an amplifier that interpolates between a nondeterministic, immaculate linear amplifier and a deterministic, ideal linear amplifier and beyond to nonideal linear amplifiers. The construction involves cascading an immaculate linear amplifier that has amplitude gain g1 with a (possibly) nonideal linear amplifier that has gain g2. With respect to normally ordered moments, the device has output noise μ2(G2−1) where G=g1g2 is the overall amplitude gain and μ2 is a noise parameter. When μ2≥1, our devices realize ideal (μ2=1) and nonideal (μ2>1) linear amplifiers. When 0≤μ2<1, these devices work effectively only over a restricted region of phase space and with some subunity success probability p✓. We investigate the performance of our μ2 amplifiers in terms of a gain-corrected probability-fidelity product and the ratio of input to output signal-to-noise ratios corrected for success probability.

关键词： Optical parametric oscillators & amplifiers Quantum fluctuations & noise Quantum information architectures & platforms

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Data mining for AMD screening: A classification based approach

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International Journal of Simulation: Systems, science and Technology 2014年第2期15卷 57-69页

作者： Hijazi, Mohd Hanafi Ahmad Coenen, Frans Zheng, Yalin Applied Computing Group Universiti Malaysia Sabah Malaysia Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Eye and Vision Science University of Liverpool Liverpool United Kingdom

This paper investigates the use of three alternative approaches to classifying retinal images. The novelty of these approaches is that they are not founded on individual lesion segmentation for feature generation, instead use encodings focused on the entire image. Three different mechanisms for encoding retinal image data were considered: (i) time series, (ii) tabular and (iii) tree based representations. For the evaluation two publically available, retinal fundus image data sets were used. The evaluation was conducted in the context of Age-related Macular Degeneration (AMD) screening and according to statistical significance tests. Excellent results were produced: Sensitivity, specificity and accuracy rates of 99% and over were recorded, while the tree based approach has the best performance with a sensitivity of 99.5%. Further evaluation indicated that the results were statistically significant. The excellent results indicated that these classification systems are ideally suited to large scale AMD screening processes.

关键词： Data mining

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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

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Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Reports of the AAAI 2014 conference workshops

Reports of the AAAI 2014 conference workshops

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作者： Albrecht, Stefano V. Barreto, André M. S. Braziunas, Darius Buckeridge, David L. Cuayáhuitl, Heriberto Dethlefs, Nina Endres, Markus Farahmand, Amir-Massoud Fox, Mark Frommberger, Lutz Ganzfried, Sam Guillet, Sébastien Gil, Yolanda Hunter, Lawrence E. Jhala, Arnav Kersting, Kristian Konidaris, George Lecue, Freddy McIlraith, Sheila Natarajan, Sriraam Noorian, Zeinab Poole, David Ronfard, Rémi Saffiotti, Alessandro Shaban-Nejad, Arash Srivastava, Biplav Tesauro, Gerald Uceda-Sosa, Rosario Van Den Broeck, Guy Van Otterlo, Martijn Wallace, Byron C. Weng, Paul Wiens, Jenna Zhang, Jie School of Informatics University of Edinburgh United Kingdom Department of Applied and Computational Mathematics Brazilian National Laboratory Brazil Big Data Group Kobo Inc. Brazil Department of Epidemiology and Biostatistics McGill University Canada School of Mathematical and Computer Sciences Heriot-Watt University in Edinburgh United Kingdom University of Augsburg Germany Carnegie Mellon University United States University of Toronto Canada Cognitive Systems Group University of Bremen Germany LIARA UQAC Chicoutimi Canada Information Sciences Institute University of Southern California Computer Science Department United States Center for Computational Pharmacology University of Colorado School of Medicine United States Department of Computational Media University of California Santa Cruz United States Computer Science Department Technical University of Dortmund Germany MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States IBM Research - Smarter Cities Technology Centre Dubline Ireland School of Informatics and Computing Indiana University United States Department of Computer Science University of Saskatchewan Canada University of British Columbia Canada INRIA University of Grenoble France Orebro University Sweden McGill Clinical and Health Informatics Group McGill University Canada IBM Research United States Cognitive Computing Department IBM Research Yorktown HeightsNY United States Computer Science Department Katholieke Universiteit Leuven Belgium Cognitive Artificial Intelligence Department Radboud University Nijmegen Netherlands University of Texas Austin United States Pierre and Marie Curie University Paris France University of Michigan United States School of Computer Engineering Nanyang Technological University Singapore

The AAAI-14 Workshop program was held Sunday and Monday, July 27-28, 2014, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Artificial Intelligence and Robotics;Artificial Intelligence applied to Assistive Technologies and Smart Environments;Cognitive computing for Augmented Human Intelligence;computer Poker and Imperfect Information;Discovery Informatics;Incentives and Trust in Electronic Communities;Intelligent Cinematography and Editing;Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action, and Communication;Modern Artificial Intelligence for Health Analytics;Multiagent Interaction Without Prior Coordination;Multidisciplinary Workshop on Advances in Preference Handling;Semantic Cities - Beyond Open Data to Models, Standards, and Reasoning;Sequential Decision Making with Big Data;Statistical Relational AI;and the World Wide Web and Public Health Intelligence. This article presents short summaries of those events. © 2015, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Decision making

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Double Noise Filtering in CT: Pre- and Post-Reconstruction

Double Noise Filtering in CT: Pre- and Post-Reconstruction

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Brazilian Symposium on computer Graphics and Image Processing (SIBGRAPI)

作者： Vinicius C. Assis Denis H.P. Salvadeo Nelson D.A. Mascarenhas Alexandre L.M. Levada Department of Statistics Applied Mathematics and Computing State University of São Paulo Rio Claro SP Brazil Faculdade Campo Limpo Paulista Graduate Program in Computer Science SP Brazil Department of Computing Federal University of São Carlos São Carlos SP Brazil

Motivated by the ALARA (As Low As Reasonably Achievable) principle, this paper proposes to denoise Computed Tomography (CT) images by using a double-filtering approach. First, projection data were filtered using methods to filter Poisson noise (pre-filtering step). Then the filtered back projection (FBP) algorithm was applied to image reconstruction. After, the reconstructed images were denoised by using suitable methods for filtering Gaussian noise (post-filtering step). Finally, known metrics of image quality evaluation (such as SSIM and PSNR) were used to compare the filtered images with the ones considered ideal images in various combinations of filters. The results lead to the conclusion that a second filtering applied on image domain can improve the CT denoising quality from pre-filtering step. Thus, CT double-filtering strategy achieved a better balance between noise reduction and details preservation.

关键词： Noise Noise measurement Computed tomography Noise reduction Image reconstruction Correlation Standards

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Optimal resource allocation for survivable virtual infrastructures

Optimal resource allocation for survivable virtual infrastru...

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10th International Conference on Design of Reliable Communication Networks, DRCN 2014

作者： De S. Cavalcanti, Gustavo A. Obelheiro, Rafael R. Koslovski, Guilherme Graduate Program in Applied Computing Department of Computer Science Santa Catarina State University Brazil

ISBN: (纸本)9781479940097

Virtual infrastructures (VIs) consolidated the dynamic provisioning of computing and communication resources. A VI is a set of virtual machines interconnected by virtual links and switches/routers. Infrastructure providers (InPs) manage the physical substrates in which virtual resources requested by VIs (such as CPU, disk, memory, bandwidth) are reserved and allocated. Resource allocation is a complex problem that needs to satisfy different goals: users expect to run their applications on survivable VIs, while InPs aim to maximize profits, minimize costs and reduce substrate fragmentation. However, there is a dichotomy between minimizing substrate fragmentation, by co-locating VIs, and maximizing VI survivability, by sparsely allocating resources in order to decrease the impact of substrate failures. In this context, this paper discusses VI survivability and its impact on substrate fragmentation. We propose a mixed integer linear programming model to allocate resources considering the joint coordination of fragmentation and survivability. Experimental results suggest that it is possible to enhance VI survivability without significantly impacting substrate fragmentation. © 2014 IEEE.

关键词： Substrates

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Customer-oriented diagnosis of memory provisioning for IaaS clouds

Customer-oriented diagnosis of memory provisioning for IaaS ...

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Brazilian Symposium on computer System Engineering, SBESC 2013

作者： Pfitscher, Ricardo J. Pillon, Mauricio A. Obelheiro, Rafael R. Santa Catarina State University Graduate Program in Applied Computing Department of Computer Science Brazil

Infrastructure-as-a-service clouds enable customers to use computing resources in a flexible manner to satisfy their needs, and pay only for the allocated resources. One challenge for IaaS customers is the correct provisioning of their resources. Many users end up underprovisioning, hurting application performance, or overprovisioning, paying for resources that are not really necessary. Memory is an essential resource for any computing system, and is frequently a performance-limiting factor in cloud environments. In this work, we propose a model that enables cloud customers to determine whether the memory allocated to their virtual machines is correctly provisioned, underprovisioned, or overprovisioned. The model uses two metrics collected inside a VM, resident and committed memory, and defines thresholds for these metrics that characterize each provisioning level. Experimental results with Linux guests on Xen, running four benchmarks with different workloads and varying memory capacity, show that the model was able to accurately diagnose memory provisioning in 98% of the scenarios evaluated. Copyright 2013 by the Authors.

关键词： Customer satisfaction

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Charm++ and MPI: Combining the Best of Both Worlds 29

Charm++ and MPI: Combining the Best of Both Worlds

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29th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2015

作者： Jain, Nikhil Bhatele, Abhinav Yeom, Jae-Seung Adams, Mark F. Miniati, Francesco Mei, Chao Kale, Laxmikant V. Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94551 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institute for Astronomy ETH Zurich Zurich8093 Switzerland Google Inc. Mountain ViewCA94043 United States

ISBN: (纸本)9781479986484

Charm++ and MPI embody two distinct perspectives for writing parallel programs. While MPI provides a process-centric, user-driven model for developing parallel codes, Charm++ supports work-centric, system-driven parallel programming. One of them might be a better or more natural fit for individual modules that constitute a parallel application. In this paper, we present a framework that enables hybrid parallel programming with Charm++ and MPI, and allows programmers to develop different modules of a parallel application in these two languages while facilitating smooth interoperation. We describe the challenges in enabling interoperation between Charm++ and MPI, and present techniques for managing the control flow and resource sharing between the two. Finally, we demonstrate the benefits of interoperation between Charm++ and MPI through several case studies that use production applications and libraries, including CHARM/Chombo, EpiSimdemics, NAMD, FFTW, MPI-IO and ParMETIS. © 2015 IEEE.

关键词： computer programming

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Search for Heavy Higgs Bosons A/H Decaying to a Top Quark Pair in pp Collisions at s=8 TeV with the ATLAS Detector

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Physical Review Letters 2017年第19期119卷 191803-191803页

作者： M. Aaboud G. Aad B. Abbott O. Abdinov B. Abeloos S. H. Abidi O. S. AbouZeid N. L. Abraham H. Abramowicz H. Abreu R. Abreu Y. Abulaiti B. S. Acharya S. Adachi L. Adamczyk J. Adelman M. Adersberger T. Adye A. A. Affolder T. Agatonovic-Jovin C. Agheorghiesei J. A. Aguilar-Saavedra S. P. Ahlen F. Ahmadov G. Aielli S. Akatsuka H. Akerstedt T. P. A. Åkesson E. Akilli A. V. Akimov G. L. Alberghi J. Albert P. Albicocco M. J. Alconada Verzini S. C. Alderweireldt M. Aleksa I. N. Aleksandrov C. Alexa G. Alexander T. Alexopoulos M. Alhroob B. Ali M. Aliev G. Alimonti J. Alison S. P. Alkire B. M. M. Allbrooke B. W. Allen P. P. Allport A. Aloisio A. Alonso F. Alonso C. Alpigiani A. A. Alshehri M. I. Alstaty B. Alvarez Gonzalez D. Álvarez Piqueras M. G. Alviggi B. T. Amadio Y. Amaral Coutinho C. Amelung D. Amidei S. P. Amor Dos Santos S. Amoroso G. Amundsen C. Anastopoulos L. S. Ancu N. Andari T. Andeen C. F. Anders J. K. Anders K. J. Anderson A. Andreazza V. Andrei S. Angelidakis I. Angelozzi A. Angerami A. V. Anisenkov N. Anjos A. Annovi C. Antel M. Antonelli A. Antonov D. J. Antrim F. Anulli M. Aoki L. Aperio Bella G. Arabidze Y. Arai J. P. Araque V. Araujo Ferraz A. T. H. Arce R. E. Ardell F. A. Arduh J-F. Arguin S. Argyropoulos M. Arik A. J. Armbruster L. J. Armitage O. Arnaez H. Arnold M. Arratia O. Arslan A. Artamonov G. Artoni S. Artz S. Asai N. Asbah A. Ashkenazi L. Asquith K. Assamagan R. Astalos M. Atkinson N. B. Atlay K. Augsten G. Avolio B. Axen M. K. Ayoub G. Azuelos A. E. Baas M. J. Baca H. Bachacou K. Bachas M. Backes M. Backhaus P. Bagnaia M. Bahmani H. Bahrasemani J. T. Baines M. Bajic O. K. Baker E. M. Baldin P. Balek F. Balli W. K. Balunas E. Banas A. Bandyopadhyay Sw. Banerjee A. A. E. Bannoura L. Barak E. L. Barberio D. Barberis M. Barbero T. Barillari M-S Barisits J. T. Barkeloo T. Barklow N. Barlow S. L. Barnes B. M. Barnett R. M. Barnett Z. Barnovska-Blenessy A. Baroncelli G. Barone A. J. Barr L. Barranco Navarro F. Barreiro J. Barreiro Guimarães da Costa R. Bartoldus A. E. Barton P. Bartos A. Basalaev Faculté des Sciences Université Mohamed Premier and LPTPM Oujda Morocco CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman Oklahoma USA Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France Department of Physics University of Toronto Toronto Ontario Canada Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz California USA Department of Physics and Astronomy University of Sussex Brighton United Kingdom Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel Center for High Energy Physics University of Oregon Eugene Oregon USA Department of Physics Stockholm University Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy International Center for Elementary Particle Physics and Department of Physics The University of Tokyo Tokyo Japan AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Northern Illinois University DeKalb Illinois USA Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Institute of Physics University of Belgrade Belgrade Serbia Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Laboratório de Instrumentação e Física Experimental de Partículas - LIP Lisboa Portugal Departamento de Fisica Teorica y del Cosmos Universidad de Granada Granada Portugal Department of Physics Boston University Boston Massachusetts USA Joint Institute for Nuclear Research JINR Dubna Dubna Russia INFN Sezione di Roma Tor Vergata Italy Dipartimento di Fisica Un

A search for heavy pseudoscalar (A) and scalar (H) Higgs bosons decaying into a top quark pair (tt¯) has been performed with 20.3 fb−1 of proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider at a center-of-mass energy s=8 TeV. Interference effects between the signal process and standard model tt¯ production, which are expected to distort the signal shape from a single peak to a peak-dip structure, are taken into account. No significant deviation from the standard model prediction is observed in the tt¯ invariant mass spectrum in final states with an electron or muon, large missing transverse momentum, and at least four jets. The results are interpreted within the context of a type-II two-Higgs-doublet model. Exclusion limits on the signal strength are derived as a function of the mass mA/H and the ratio of the vacuum expectation values of the two Higgs fields, tanβ, for mA/H>500 GeV.

关键词： Extensions of Higgs sector Hypothetical particle physics models Hypothetical scalars Top quark Hadron colliders

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