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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Wireless information and energy transfer for decode-and-forward relying mimo-ofdm networks

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ICIC Express Letters 2015年第7期9卷 1925-1932页

作者： Du, Guanyao Dong, Zhilong Xiong, Ke Qiu, Zhengding School of Computer and Information Technology Beijing Jiaotong University No. 3 Shangyuancun Haidian Dist Beijing China State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences No. 55 Zhongguancun East Road Haidian Dist Beijing China

This paper investigates the system achievable rate and optimization for the multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) system with an energy harvesting (EH) relay. Firstly we propose a time switchingbased relaying (TSR) protocol to enable the simultaneous information processing and energy harvesting at the relay. Then, we discuss its achievable rate performance theoretically and formulated an optimization problem to maximize the system achievable rate. As the problem is difficult to solve, we design an Augmented Lagrangian Penalty Function (ALPF) method for it. Extensive simulation results are provided to demonstrate the accuracy of the analytical results and the effectiveness of the ALPF method. © 2015, ISSN 1881-803X.

关键词： Energy harvesting

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

arXiv

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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

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Automatic Emotion Variation Detection using Multi-Scaled Sliding Window

Automatic Emotion Variation Detection using Multi-Scaled Sli...

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第十三届全国人机语音通讯学术会议(NCMMSC2015)

作者： Yuchao Fan Mingxing Xu Zhiyong Wu Lianhong Cai Key Laboratory of Pervasive Computing Ministry of Education Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Computer Science and Technology Tsinghua University Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Graduate School at Shenzhen Tsinghua University Tianjin University

Emotion recognition from speech plays an important role in developing affective and intelligent Human Computer Interaction. The goal of this work is to build an Automatic Emotion Variation Detection(AEVD) system to determine each emotional salient segment in continuous speech. We focus on emotion detection in angry-neutral speech, which is common in recent studies of AEVD. This work proposes a novel frame-work for AEVD using Multi-scaled Sliding Window(MSW-AEVD) to assign an emotion class to each window-shift by fusion decisions of all the sliding windows containing the shift. Firstly, sliding window with fixed-length is introduced as the basic procedure, in which several different fusion methods are investigated. Then multi-scaled sliding window is employed to support multi-classifiers with different timescale features, in which another two fusion strategies are provided. Finally, a post-processing is applied to refine the final outputs. Performance evaluation is carried out on the public Berlin database EMO-DB. Our experimental results show that proposed MSW-AEVD significantly outperforms the traditional HMM-based AEVD.

关键词： Automatic Emotion Variation Detection using Multi-Scaled Sliding Window

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A comprehensive view of ubiquitous learning context usage in context-aware learning system 2nd

A comprehensive view of ubiquitous learning context usage in...

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2nd International Conference on Context-Aware systems and Applications, ICCASA 2013

作者： Souabni, Raoudha Saadi, Ines Bayoudh Kinshuk Ghezala, Henda Ben RIADI Research Laboratory - ENSI Manouba University Manouba Tunisia School of Computing and Information Systems Athabasca University Athabasca Canada

ISBN: (纸本)9783319059389

Research related to context-aware u-learning system has proliferated during last decades. systems identified in literature are developed in ad-hoc manner to resolve a specific problem in a given context;however, u-learning systems developers need to have a clear and a general view of how their intended systems make use of the ubiquitous context. The paper details a general descriptive view of context usage within learning systems through three different view-points inspired from Dowson’s work [1]. Each view is capturing a particular aspect of context handling. Then a set of facets is associated to each given aspect in order to study, understand and appropriately describe it. This work would be useful for context-aware u-learning system developers in order to have a clear understanding of the context usage in such systems and to underline the requirements of the u-learning environment to respond. This research is also aimed to establish a structured baseline to help in comparing and evaluating context-aware u-learning systems according to the descriptive system views. © Springer International Publishing Switzerland 2014.

关键词： Learning systems

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An intentional model for pedagogical process guidance supported by an adaptive learning system

An intentional model for pedagogical process guidance suppor...

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23rd International Business information Management Association Conference, IBIMA 2014

作者： Bayounes, Walid Saâdi, Ines Bayoudh Kinshuk Ghézala, Henda Ben RIADI Research Laboratory ENSI Manouba University Manouba Tunisia School of Computing and Information Systems Athabasca University Athabasca Canada

ISBN: (纸本)9780986041921

Pedagogical process is used by different learning systems with aim to guide learners and help to make learning easier and more effective. However, issues still remain;in particular learning systems guide the pedagogical process without any dynamic orientation to the learning process. Many commercial systems allow the experts to build the pedagogical process, but it is currently not possible to orient the pedagogical process to the learning process of individual learners through the pedagogical process guidance in real-time. In order to solve the issues of a process guidance;this paper discusses the potential of intentional model for pedagogical process guidance. The latter couples the teacher's intention with the pedagogical strategies, and provides a multitude of paths between teacher intentions. In fact, based on the family of pedagogical strategies, the family of pedagogical method and the teaching style, we can adaptively support the teacher to achieve his intention through the selected strategy. The paper concludes by discussing the benefits and limitations of our model implementation and discusses future extensions to allow for the automatic adaptation of pedagogical process.

关键词： Learning systems

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Robust Scheduling of Scientific Workflows with Deadline and Budget Constraints in clouds

Robust Scheduling of Scientific Workflows with Deadline and ...

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IEEE International Conference on Advanced information Networking and Applications

作者： Deepak Poola Saurabh Kumar Garg Rajkumar Buyya Yun Yang Kotagiri Ramamohanarao Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computing and Information Systems The University of Melbourne Australia Department of Computing and Information Systems University of Tasmania Hobart Australia Faculty of SET Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9781479936311

Dynamic resource provisioning and the notion of seemingly unlimited resources are attracting scientific workflows rapidly into cloud computing. Existing works on workflow scheduling in the context of clouds are either on deadline or cost optimization, ignoring the necessity for robustness. Robust scheduling that handles performance variations of cloud resources and failures in the environment is essential in the context of clouds. In this paper, we present a robust scheduling algorithm with resource allocation policies that schedule workflow tasks on heterogeneous cloud resources while trying to minimize the total elapsed time (makespan) and the cost. Our results show that the proposed resource allocation policies provide robust and fault-tolerant schedule while minimizing makespan. The results also show that with the increase in budget, our policies increase the robustness of the schedule.

关键词： Workflows cloud Robustness Fault-Tolerance Scheduling

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The Use of cloud computing for Storing and Processing Instrumental Data from Seismological Observations Networks

The Use of Cloud Computing for Storing and Processing Instru...

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2015 International Conference on Computer Science and Environmental Engineering(CSEE 2015)

作者： A.A.Sorokin S.P.Korolev S.V.Makogonov N.V.Shestakov Information and Telecom Systems Laboratory Computing Center of the Far Eastern BranchRussian Academy of Sciences Department of Geodesy Land Management and CadastreSchool of EngineeringFar Eastern Federal University Institute of Applied Mathematics Far Eastern Branch of the Russian Academy of Sciences

The archives of seismological data used as an example to consider the problems of storing large scientific *** is an information system layout created for distributed storage of instrumentaldata from observations networks of the Far Eastern Branch of the Russian Academy of Sciences.A study shows the efficiency of the established system that is using cloud technology for distributed storage and processing of structured scientific data archives.

关键词： network of instrumental observations cloud computing structured scientific data sets archive automated information system

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Selecting good measurements via 1 relaxation: A convex approach for robust estimation over graphs

Selecting good measurements via 1 relaxation: A convex appro...

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2014 IEEE/RSJ International Conference on Intelligent Robots and systems, IROS 2014

作者： Carlone, Luca Censi, Andrea Dellaert, Frank School of Interactive Computing College of Computing Georgia Institute of Technology AtlantaGA United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9781479969340

Pose graph optimization is an elegant and efficient formulation for robot localization and mapping. Experimental evidence suggests that, in real problems, the set of measurements used to estimate robot poses is prone to contain outliers, due to perceptual aliasing and incorrect data association. While several related works deal with the rejection of outliers during pose estimation, the goal of this paper is to propose a grounded strategy for measurements selection, i.e., the output of our approach is a set of 'reliable' measurements, rather than pose estimates. Because the classification in inliers/outliers is not observable in general, we pose the problem as finding the maximal subset of the measurements that is internally coherent. In the linear case, we show that the selection of the maximal coherent set can be (conservatively) relaxed to obtain a linear programming problem with 1 objective. We show that this approach can be extended to (nonlinear) planar pose graph optimization using similar ideas as our previous work on linear approaches to pose graph optimization. We evaluate our method on standard datasets, and we show that it is robust to a large number of outliers and different outlier generation models, while entailing the advantages of linear programming (fast computation, scalability). © 2014 IEEE.

关键词： Linear programming

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Virtual Machine Image Content Aware I/O Optimization for Mobile Virtualization

Virtual Machine Image Content Aware I/O Optimization for Mob...

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IEEE International Symposium on Cyberspace Safety and Security

作者： Renhai Chen Yi Wang Jingtong Hu Duo Liu Zili Shao Yong Guan Embedded Systems and CPS Laboratory Department of Computing The Hong Kong Polytechnic University College of Computer Science and Software Engineering Shenzhen University School of Electrical and Computer Engineering Oklahoma State University College of Computer Science Chongqing University College of Computer and Information Management Capital Normal University

ISBN: (纸本)9781479989386

Mobile virtualization introduces extra layers in software stacks, which leads to performance degradation. Especially, each I/O operation has to pass through several software layers to reach the NAND-flash-based storage systems. This paper targets at optimizing I/O for mobile virtualization, since I/O becomes one of major performance bottlenecks that seriously affects the performance of mobile devices. Among all the I/O operations, a large percentage is updating metadata. Frequent updating metadata not only degrades overall I/O performance but also severely reduces flash memory lifetime. In this paper, we propose a novel I/O optimization technique to identify the metadata of a guest file system which is stored in a VM (Virtual Machine) image file and frequently updated. Then, these metadata are stored in a small additional NVM (Non-Volatile Memory) which is faster and more endurable to greatly improve flash memory's performance and lifetime. To the best of our knowledge, this is the first work to identify the file system metadata from regular data in a guest OS VM image file under mobile virtualization. The proposed scheme is evaluated on a real hardware embedded platform. The experimental results show that the proposed techniques can improve write performance to 45.21% in mobile devices with virtualization.

关键词： IO Virtualization Metadata Mobile devices virtual machines Cache Memory System degradation of performance Mobile Conference documents

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