检索结果-内蒙古大学图书馆

作者： Liu, Peng Yu, Jiyang Huang, Michael C. College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi214125 China Department of Shannon Lab. Huawei Technologies Co. Ltd. Hangzhou310051 China Department of Electrical and Computer Engineering University of Rochester RochesterNY14627-0231 United States

Most processors employ hardware data prefetching techniques to hide memory access latencies. However, the prefetching requests from different threads on a multicore processor can cause severe interference with prefetching and/or demand requests of others. The data prefetching can lead to significant performance degradation due to shared resource contention on shared memory multicore systems. This article proposes a thread-aware data prefetching mechanism based on low-overhead runtime information to tune prefetching modes and aggressiveness, mitigating the resource contention in the memory system. Our solution has three new components: (1) a self-tuning prefetcher that uses runtime feedback to dynamically adjust data prefetching modes and arguments of each thread, (2) a filtering mechanism that informs the hardware about which prefetching request can cause shared data invalidation and should be discarded, and (3) a limiter thread acceleration mechanism to estimate and accelerate the critical thread which has the longest completion time in the parallel region of execution. On a set of multithreaded parallel benchmarks, our thread-aware data prefetchingmechanism improves the overall performance of 64-core system by 13% over a multimode prefetch baseline system with two-level cache organization and conventional modified, exclusive, shared, and invalid-based directory coherence *** compare our approach with the feedback directed prefetching technique and find that it provides 9% performance improvement on multicore systems, while saving the memory bandwidth consumption.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Color Object Recognition via Cross-Domain Learning on RGB-D Images

Color Object Recognition via Cross-Domain Learning on RGB-D ...

引用

IEEE International Conference on Robotics and Automation

作者： Yawen Huang Fan Zhu Ling Shao Alejandro F. Frangi the Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) Department of Electronic and Electrical Engineering The University of Sheffield Sheffield United Kingdom the Multimedia and Visual Computing Lab Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi the Computer Vision and Artificial Intelligence Group Department of Computer Science and Digital Technologies Northumbria University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9781467380270

This paper addresses the object recognition problem using multiple-domain inputs. We present a novel approach that utilizes labeled RGB-D data in the training stage, where depth features are extracted for enhancing the discriminative capability of the original learning system that only relies on RGB images. The highly dissimilar source and target domain data are mapped into a unified feature space through transfer at both feature and classifier levels. In order to alleviate cross-domain discrepancy, we employ a state-of-the-art domain-adaptive dictionary learning algorithm that updates image representations in both domains and the classifier parameters simultaneously. The proposed method is trained on a RGB-D Object dataset and evaluated on the Caltech-256 dataset. Experimental results suggest that our approach can lead to significant performance gain over the state-of-the-art methods.

关键词： RGB-D enhancing discriminative capability

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of GPU-Based Convolutional Neural Networks

Performance Analysis of GPU-Based Convolutional Neural Netwo...

引用

International Conference on Parallel Processing (ICPP)

作者： Xiaqing Li Guangyan Zhang H. Howie Huang Zhufan Wang Weimin Zheng Tsinghua National Laboratory for Information Science and Technology State Key Lab of Mathematical Engineering and Advanced Computing Wuxi China Department of Computer Science and Technology Tsinghua University Department of Electrical and Computer Engineering George Washington University

ISBN: (纸本)9781509028245

As one of the most important deep learning models, convolutional neural networks (CNNs) have achieved great successes in a number of applications such as image classification, speech recognition and nature language understanding. Training CNNs on large data sets is computationally expensive, leading to a flurry of research and development of open-source parallel implementations on GPUs. However, few studies have been performed to evaluate the performance characteristics of those implementations. In this paper, we conduct a comprehensive comparison of these implementations over a wide range of parameter configurations, investigate potential performance bottlenecks and point out a number of opportunities for further optimization.

关键词： Convolution Graphics processing units Runtime Training Computational modeling computer architecture Kernel

来源：评论

学校读者我要写书评

暂无评论

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

引用

arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Development of a Carbon Fiber Knitted Capacitive Touch Sensor

Development of a Carbon Fiber Knitted Capacitive Touch Senso...

引用

作者： Vallett, Richard Young, Ryan Knittel, Chelsea Kim, Youngmoo Dion, Genevieve Shima Seiki Haute Tech Lab at ExCITe Drexel University PhiladelphiaPA United States ExCITe Center Drexel University PhiladelphiaPA United States Mechanical Engineering and Mechanics College of Engineering Drexel University PhiladelphiaPA United States Department of Computer Science College of Computing and Informatics Drexel University PhiladelphiaPA United States Materials Science and Engineering College of Engineering Drexel University PhiladelphiaPA United States Electrical and Computer Engineering College of Engineering Drexel University PhiladelphiaPA United States Department of Design Westphal College of Media Arts and Design Drexel University PhiladelphiaPA United States

Textiles, in combination with advances in materials and design, offer exciting new possibilities for human and environmental interaction, including biometric and touch-based sensing. Previous fabric-based or flexible touch sensors have generally required a large number of sensing electrodes positioned in a dense XY grid configuration and a multitude of wires. This paper investigates the design and manufacturing of a planar (two-dimensional, XY location) touch fabric sensor with only two electrodes (wires) to sense both planar touch and pressure, making it ideal for applications with limited space/complexity for wiring. The proposed knitted structure incorporates a supplementary method of sensing to detect human touch on the fabric surface, which offers advantages over previous methods of touch localization through an efficient use of wire connections and sensing materials. This structure is easily manufactured as a single component utilizing flatbed knitting techniques and electrically conductive yarns. The design requires no embedded electronics or solid components in the fabric, which allows the sensor to be flexible and resilient. This paper discusses the design, fabrication, sensing methods, and applications of the fabric sensor in robotics and human-machine interaction, smart garments, and wearables, as well as the highly transdisciplinary approach pursued in developing medical textiles and flexible embedded sensors. Copyright © Materials Research Society 2016.

关键词： Wire

来源：评论

学校读者我要写书评

暂无评论

Boundary aware navigation and mapping for a mobile automaton

Boundary aware navigation and mapping for a mobile automaton

引用

International Conference on Information and Automation (ICIA)

作者： Chaomin Luo Gene Eu Jan Jing Zhang Furao Shen Department of Electrical and Computer Engineering University of Detroit Mercy Michigan USA Department of Computer Science and Information Engineering National Taipei University Taiwan Beijing University of Chemical Technology China Robotic Intelligence & Neural Computing Lab Nanjing University Nanjing China

ISBN: (纸本)9781509041039

When autonomous mobile robots explore unknown terrain, map building is required for the vehicles to effectively and efficiently search the workspace with obstacle avoidance. In this paper, a boundary aware methodology is proposed for real-time autonomous robot navigation and mapping under unknown environments. An initial global trajectory is first created based on a re-planning technique before a LIDAR-based boundary aware local navigation approach is utilized to guide the robot locally with obstacle avoidance while building up its local map. Its effectiveness and efficiency of real-time simultaneous navigation and mapping for an autonomous robot were successfully validated by simulation and comparison studies.

关键词： Navigation Robot sensing systems Collision avoidance Mobile robots Heuristic algorithms Buildings

来源：评论

学校读者我要写书评

暂无评论

Proceedings of the 16th Annual UT-KBRIN Bioinformatics Summit 2016: bioinformatics

引用

BMC Bioinformatics 2017年第9期18卷 1-9页

作者： Eric C. Rouchka Juw Won Park Julia H. Chariker David A. Tieri Shreedharkumar Rajurkar Vikas Singh Nishchal K. Verma Yan Cui Mark Farman Bradford Condon Jolanta Jaromczyk Neil Moore Jerzy Jaromczyk Daniel Harris Patrick Calie Eun Kyong Shin Robert L. Davis Arash Shaban-Nejad Joshua M. Mitchell Hunter N. B. Moseley Robert M. Flight Qing Jun Wang Richard M. Higashi Teresa W-M Fan Andrew N. Lane Liangqun Lu Bernie J Daigle Jr. Xi Chen Andrey Smelter Jerzy W. Jaromczyk Bailey K. Phan Nathaniel J. Serpico Ethan G. Toney Li Chen Caroline E. Melton Jennifer R. Mandel Bernie J. Daigle Jr. Hao Chen Kazi I. Zaman Ramin Homayouni Patrick J. Trainor Andrew P. DeFilippis Samantha M. Carlisle Shesh N. Rai Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville KY 40292 USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core 522 East Gray Street Louisville KY 40292 USA Department of Psychological and Brain Sciences University of Louisville Louisville KY 40292 USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville KY 40292 USA Department of Electrical Engineering IIT Kanpur India Department of Microbiology Immunology and Biochemistry University of Tennessee Health Science Center Memphis TN 38103 USA Center for Integrative and Translational Genomics University of Tennessee Health Science Center Memphis TN 38103 USA Department of Plant Pathology University of Kentucky Lexington KY 40546 USA UK Healthcare Genomics University of Kentucky Lexington KY 40536 USA Department of Entomology & Plant Pathology University of Tennessee Knoxville TN 37996 USA Department of Forestry University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40508 USA Department of Biological Sciences Eastern Kentucky University Richmond KY 40475 USA University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics Department of Pediatrics Memphis TN 38103 USA Department of Molecular & Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Markey Cancer Center University of Kentucky Lexington KY 40536 USA Center for Environmental and Systems Biochemistry University of Kentucky Lexington KY 40536 USA Institute for Biomedical Informatics University of Kentucky Lexington KY 40536 USA Department of Molecular and Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Department of Statistics University of Kentucky Louisville KY

来源：评论

学校读者我要写书评

暂无评论

Learning with privileged information using Bayesian networks

引用

Frontiers of computer Science 2015年第2期9卷 185-199页

作者： Shangfei WANG Menghua HE Yachen ZHU Shan HE Yue LIU Qiang JI School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Key Lab of Computing and Communicating Software of Anhui Province Hefei 230027 China School of Mathematical Sciences University of Science and Technology of China Hefei 230027 China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY 12180-3590 USA

For many supervised learning applications, addi- tional information, besides the labels, is often available dur- ing training, but not available during testing. Such additional information, referred to the privileged information, can be exploited during training to construct a better classifier. In this paper, we propose a Bayesian network （BN） approach for learning with privileged information. We propose to in- corporate the privileged information through a three-node BN. We further mathematically evaluate different topologies of the three-node BN and identify those structures, through which the privileged information can benefit the classifica- tion. Experimental results on handwritten digit recognition, spontaneous versus posed expression recognition, and gender recognition demonstrate the effectiveness of our approach.

关键词： Bayesian network privileged information clas-sification maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

Making advanced scientific algorithms and big scientific data management more accessible 14

Making advanced scientific algorithms and big scientific dat...

引用

Computational Imaging XIV 2016

作者： Venkatakrishnan, S.V. Mohan, K. Aditya Beattie, Keith Correa, Joaquin Dart, Eli Deslippe, Jack R. Hexemer, Alexander Krishnan, Harinarayan MacDowell, Alastair A. Marchesini, Stefano Patton, Simon J. Perciano, Talita Sethian, James A. Stromsness, Rune Tierney, Brian L. Tull, Craig E. Ushizima, Daniela Parkinson, Dilworth Y. Center for Advanced Mathematics for Energy Research Applications Lawrence Berkeley National Lab BerkeleyCA94720 United States Advanced Light Source Lawrence Berkeley National Lab BerkeleyCA94720 United States School of Electrical and Computer Engineering Purdue University PurdueIN47907 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States National Energy Research Scientific Computing Center BerkeleyCA94720 United States Energy Sciences Network BerkeleyCA94720 United States Department of Mathematics UC Berkeley BerkeleyCA94720 United States

Synchrotrons such as the Advanced Light Source (ALS) at Lawrence Berkeley National laboratory are user facilities - they are sources of extremely bright X-ray beams, and scientists come from all over the world to perform experiments that require these beams. As the complexity of experiments has increased, and the size and rates of data sets has exploded, managing, analyzing and presenting the data collected at synchrotrons has been an increasing challenge. The ALS has partnered with high performance computing, fast networking, and applied mathematics groups to create a"super-facility", giving users simultaneous access to the experimental, computational, and algorithmic resources to overcome this challenge. This combination forms an efficient closed loop, where data despite its high rate and volume is transferred and processed, in many cases immediately and automatically, on appropriate compute resources, and results are extracted, visualized, and presented to users or to the experimental control system, both to provide immediate insight and to guide decisions about subsequent experiments during beam-time. In this paper, We will present work done on advanced tomographic reconstruction algorithms to support users of the 3D micron-scale imaging instrument (Beamline 8.3.2, hard X-ray micro-tomography). © 2016 Society for Imaging Science and Technology.

关键词： Tomography

来源：评论

学校读者我要写书评

暂无评论

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

引用

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：