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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 3007卷 331-340页

作者： Huang, Changqin Chen, Deren Zheng, Yao Hu, Hualiang College of Computer Science Zhejiang University Hangzhou 310027 China Center for Engineering and Scientific Computation Zhejiang University Hangzhou 310027 China

To gain higher performance under many constraints, effective scheduling is a key concern in data-intensive grid computing. Based on a Dual-Component and Dual-Queue Distributed Schedule Model (DCDQDSM), we present task and data co-scheduling algorithms, by which the waiting time to access datasets for the scheduled task will reduce. Firstly data replication and elimination schedule are processed by an independent approach. Secondly, if a task is divisible, the task and its dataset are divided into subtasks and their necessary data subsets. Task scheduling adopts a general approach. Finally, when a scheduled task/subtask doesn't hit its dataset, associated data transferring is bound to this task. On the basis of relation between task execution and data access, data replication and computing may proceed concurrently in one scheduled task with divisible dataset or between scheduled tasks. Corresponding theoretic analysis and experimental results suggest that the scheduling algorithms improve execution performance and resource utilization. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Scheduling algorithms

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Application-level checkpointing for shared memory programs

Application-level checkpointing for shared memory programs

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作者： Bronevetsky, Greg Marques, Daniel Pingali, Keshav Szwed, Peter Schulz, Martin Department of Computer Science Cornell University Ithaca NY 14853 School of Electrical and Computer Engineering Cornell University Ithaca NY 14853 Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA 94551

Trends in high-performance computing are making it necessary for long-running applications to tolerate hardware faults. The most commonly used approach is checkpoint and restart (CPR) - the state of the computation is saved periodically on disk, and when a failure occurs, the computation is restarted from the last saved state. At present, it is the responsibility of the programmer to instrument applications for CPR. Our group is investigating the use of compiler technology to instrument codes to make them self-checkpointing and self-restarting, thereby providing an automatic solution to the problem of making long-running scientific applications resilient to hardware faults. Our previous work focused on message-passing programs. In this paper, we describe such a system for shared-memory programs running on symmetric multiprocessors. This system has two components: (i) a pre-compiler for source-to-source modification of applications, and (ii) a runtime system that implements a protocol for coordinating CPR among the threads of the parallel application. For the sake of concreteness, we focus on a non-trivial subset of OpenMP that includes barriers and locks. One of the advantages of this approach is that the ability to tolerate faults becomes embedded within the application itself, so applications become self-checkpointing and self-restarting on any platform. We demonstrate this by showing that our transformed benchmarks can checkpoint and restart on three different platforms (Windows/x86, Linux/x86, and Tru64/Alpha). Our experiments show that the overhead introduced by this approach is usually quite small;they also suggest ways in which the current implementation can be tuned to reduced overheads further. Copyright 2004 ACM.

关键词： computer systems programming

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Learning distance functions for image retrieval

Learning distance functions for image retrieval

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： T. Hertz A. Bar-Hillel D. Weinshall School of Computer Science and Engineering and the Center for Neural Computation Hebrew University of Jerusalem Jerusalem Israel

Image retrieval critically relies on the distance function used to compare a query image to images in the database. We suggest learning such distance functions by training binary classifiers with margins, where the classifiers are defined over the product space of pairs of images. The classifiers are trained to distinguish between pairs in which the images are from the same class and pairs, which contain images from different classes. The signed margin is used as a distance function. We explore several variants of this idea, based on using SVM and boosting algorithms as product space classifiers. Our main contribution is a distance learning method, which combines boosting hypotheses over the product space with a weak learner based on partitioning the original feature space. The weak learner used is a Gaussian mixture model computed using a constrained EM algorithm, where the constraints are equivalence constraints on pairs of data points. This approach allows us to incorporate unlabeled data into the training process. Using some benchmark databases from the UCI repository, we show that our margin based methods significantly outperform existing metric learning methods, which are based an learning a Mahalanobis distance. We then show comparative results of image retrieval in a distributed learning paradigm, using two databases: a large database of facial images (YaleB), and a database of natural images taken from a commercial CD. In both cases our GMM based boosting method outperforms all other methods, and its generalization to unseen classes is superior.

关键词： Image retrieval Image databases Boosting Distributed databases Information retrieval Partitioning algorithms Support vector machines Support vector machine classification computer aided instruction Spatial databases

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The Healthgrid White Paper 3

The Healthgrid White Paper

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3rd Annual Conference of the Healthgrid Association, Healthgrid 2005

作者： Breton, Vincent Dean, Kevin Solomonides, Tony Aloisio, Giovanni Benkner, Siegfried Bilofsky, Howard Blanquer, Ignacio Brady, Michael Cannataro, Mario Chouvarda, Ioanna Claerhout, Brecht De Moor, Georges De Neve, Wilfried De Wagter, Carlos Fiore, Sandro Hassan, Kinda Heeren, Germaine Hernández, Vicente Herveg, Jean A.M. Hofmann, Martin Jones, Chris Koutkias, Vassilios Lloyd, Sharon Lonsdale, Guy Alonso, Victoria L. Maglaveras, Nicos Maigne, Lydia Malousi, Andigoni Martín-Sánchez, Fernando McClatchey, Richard Medico, Enzo Miguet, Serge Mirto, Maria Montagnat, Johan Nørager1, Sofie Nozaki, Kazunori Oliveira, Ilídio Castro Pennec, Xavier Poullet, Yves Merino, Juan Pedro Sánchez Strizh, Irina G. Taillet, Michel Tristram, Clive Tverdokhlebov, Nikolay Veltri, Pierangelo Ziegler, René CNRS-IN2P3 LPC Campus des Cézeaux AubiereCedex63177 France Internet Business Solutions Group Cisco Systems 9 New Square Bedfont Lakes Middlesex FelthamTW14 8HA United Kingdom University of the West of England Bristol CEMS Coldharbour Lane BristolBS6 6TH United Kingdom Center for Advanced Computational Technologies ISUFI Dept. of Innovation Engineering University of Lecce Via per Monteroni Lecce73100 Italy Institute of Scientific Computing University of Vienna Nordbergstrasse 15 ViennaA-1090 Austria University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Center for Bioinformatics 1416 Blockley Hall 423 Guardian Drive PhiladelphiaPA19104-6021 United States Universidad Politecnica de Valencia Camino de Vera/n Valencia46022 Spain FRS FREng Dept. of Engineering Science Oxford University Parks Road OxfordOX1 3PJ United Kingdom Magna Graecia University of Catanzaro School of Bioinformatics and Biomedical Engineering Campus di Germaneto Viale Europa Germaneto Catanzaro88100 Italy Aristotle University The Medical School Lab of Medical Informatics Box 323 Thessaloniki54124 Greece Custodix NV Verlorenbroodstraat 120 Bus 14 MerelbekeB-9820 Belgium Research in Advanced Medical Informatics and Telematics UZ Gent 5K3 De Pintelaan 185 GentB9000 Belgium Department of Radiotherapy Ghent University Hospital Building P7 De Pintelaan 185 GentB-9000 Belgium Laboratoire LIRIS Université Lumière Lyon 2 Campus Porte des Alpes 5 avenue Pierre Mendès France Bron69676 France av. E.Mounierlaan 83 Brussels1200 Belgium Faculté de Droit de Namur - FUNDP Centre de Recherches Informatiques & Droit 5 rempart de la Vierge Namur5000 Belgium Schloss Birlinghoven Sankt Augustin53754 Germany CERN Geneva 231211 Switzerland Oxford University Computing Laboratory Wolfson Building OxfordOX1 3QD United Kingdom C&C Research Laboratories NEC Europe Ltd. Rathausallee 10 Sankt AugustinD-53757 Germany Medic

ISBN: (纸本)158603510X

Over the last four years, a community of researchers working on Grid and High Performance Computing technologies started discussing the barriers and opportunities that grid technologies must face and exploit for the development of health-related applications. This interest lead to the first Healthgrid conference, held in Lyon, France, on January 16th–17th, 2003, with the focus of creating increased awareness about the possibilities and advantages linked to the deployment of grid technologies in health, ultimately targeting the creation of a European/international grid infrastructure for health. The topics of this conference converged with the position of the eHealth division of the European Commission, whose mandate from the Lisbon Meeting was "To develop an intelligent environment that enables ubiquitous management of citizens’ health status, and to assist health professionals in coping with some major challenges, risk management and the integration into clinical practice of advances in health knowledge." In this context "Health" involves not only clinical procedures but covers the whole range of information from molecular level (genetic and proteomic information) over cells and tissues, to the individual and finally the population level (social healthcare). Grid technology offers the opportunity to create a common working backbone for all different members of this large "health family" and will hopefully lead to an increased awareness and interoperability among disciplines. The first HealthGrid conference led to the creation of the Healthgrid association, a non-profit research association legally incorporated in France but formed from the broad community of European researchers and institutions sharing expertise in health grids. After the second Healthgrid conference, held in Clermont-Ferrand on January 29th–30th, 2004, the need for a "white paper" on the current status and prospective of health grids was raised. Over fifty experts from different areas of grid tec

关键词： Health risks

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Learning From a Small Number of Training Examples by Exploiting Object Categories

Learning From a Small Number of Training Examples by Exploit...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： K. Levi M. Fink Y. Weiss School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel Center for Neural Computation Hebrew University of Jerusalem Jerusalem Israel

In the last few years, object detection techniques have progressed immensely. Impressive detection results have been achieved for many objects such as faces [11, 14, 9] and cars [11]. The robustness of these systems emerges from a training stage utilizing thousands of positive examples. One approach to enable learning from a small set of training examples is to find an efficient set of features that accurately represent the target object. Unfortunately, automatically selecting such a feature set is a difficult task in itself. In this paper we present a novel feature selection method that is based on the notion of object categories. We assume that when learning to recognize a new object (like an apple) we also know a category it belongs to (fruit). We further assume that features that are useful for learning other objects in the same category (e.g. pear or orange) will also be useful for learning the novel object. This leads to a simple criterion for selecting features and building classifiers. We show that our method gives significant improvement in detection performance in challenging domains.

关键词： Object detection Face detection computer science Robustness Humans computer vision computer displays Switches computer Society Pattern recognition

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A temporal kernel-based model for tracking hand-movements from neural activities 04

A temporal kernel-based model for tracking hand-movements fr...

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Proceedings of the 18th International Conference on Neural Information Processing Systems

作者： Lavi Shpigelman Koby Crammer Rony Paz Eilon Vaadia Yoram Singer School of computer Science and Engineering The Hebrew University Jerusalem Israel and Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem Israel School of computer Science and Engineering The Hebrew University Jerusalem Israel Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem Israel and Dept. of Physiology Hadassah Medical School The Hebrew University Jerusalem Israel

We devise and experiment with a dynamical kernel-based system for tracking hand movements from neural activity. The state of the system corresponds to the hand location, velocity, and acceleration, while the system's input are the instantaneous spike rates. The system's state dynamics is defined as a combination of a linear mapping from the previous estimated state and a kernel-based mapping tailored for modeling neural activities. In contrast to generative models, the activity-to-state mapping is learned using discriminative methods by minimizing a noise-robust loss function. We use this approach to predict hand trajectories on the basis of neural activity in motor cortex of behaving monkeys and find that the proposed approach is more accurate than both a static approach based on support vector regression and the Kalman filter.

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Euclidean embedding of co-occurrence data 04

Euclidean embedding of co-occurrence data

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Proceedings of the 18th International Conference on Neural Information Processing Systems

作者： Amir Globerson Gal Chechik Fernando Pereira Naftali Tishby School of computer Science and Engineering Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem Israel Computer Science Department Stanford University Stanford CA Department of Computer and Information Science University of Pennsylvania Philadelphia PA

Embedding algorithms search for low dimensional structure in complex data, but most algorithms only handle objects of a single type for which pairwise distances are specified. This paper describes a method for embedding objects of different types, such as images and text, into a single common Euclidean space based on their co-occurrence statistics. The joint distributions are modeled as exponentials of Euclidean distances in the low-dimensional embedding space, which links the problem to convex optimization over positive semidefinite matrices. The local structure of our embedding corresponds to the statistical correlations via random walks in the Euclidean space. We quantify the performance of our method on two text datasets, and show that it consistently and significantly outperforms standard methods of statistical correspondence modeling, such as multidimensional scaling and correspondence analysis.

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Spike sorting: Bayesian clustering of non-stationary data 04

Spike sorting: Bayesian clustering of non-stationary data

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Proceedings of the 17th International Conference on Neural Information Processing Systems

作者： Aharon Bar-Hillel Adam Spiro Eran Stark Neural Computation Center The Hebrew University of Jerusalem School of Computer Science and Engineering The Hebrew University of Jerusalem Department of Physiology The Hebrew University of Jerusalem

Spike sorting involves clustering spike trains recorded by a micro-electrode according to the source neuron. It is a complicated problem, which requires a lot of human labor, partly due to the non-stationary nature of the data. We propose an automated technique for the clustering of non-stationary Gaussian sources in a Bayesian framework. At a first search stage, data is divided into short time frames and candidate descriptions of the data as a mixture of Gaussians are computed for each frame. At a second stage transition probabilities between candidate mixtures are computed, and a globally optimal clustering is found as the MAP solution of the resulting probabilistic model. Transition probabilities are computed using local stationarity assumptions and are based on a Gaussian version of the Jensen-Shannon divergence. The method was applied to several recordings. The performance appeared almost indistinguishable from humans in a wide range of scenarios, including movement, merges, and splits of clusters.

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Margin analysis of the LVQ algorithm 15

Margin analysis of the LVQ algorithm

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16th Annual Neural Information Processing Systems Conference, NIPS 2002

作者： Crammer, Koby Gilad-Bachrach, Ran Navot, Amir Tishby, Naftali School of Computer Science and Engineering Interdisciplinary Center for Neural Computation Hebrew University Jerusalem Israel

ISBN: (纸本)0262025507

Prototypes based algorithms are commonly used to reduce the computational complexity of Nearest-Neighbour (NN) classifiers. In this paper we discuss theoretical and algorithmical aspects of such algorithms. On the theory side, we present margin based generalization bounds that suggest that these kinds of classifiers can be more accurate then the 1-NN rule. Furthermore, we derived a training algorithm that selects a good set of prototypes using large margin principles. We also show that the 20 years old Learning Vector Quantization (LVQ) algorithm emerges naturally from our framework.

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Extracting relevant structures with side information 15

Extracting relevant structures with side information

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16th Annual Neural Information Processing Systems Conference, NIPS 2002

作者： Chechik, Gal Tishby, Naftali School of Computer Science and Engineering Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem 91904 Israel

ISBN: (纸本)0262025507

The problem of extracting the relevant aspects of data, in face of multiple conflicting structures, is inherent to modeling of complex data. Extracting structure in one random variable that is relevant for another variable has been principally addressed recently via the information bottleneck method [15]. However, such auxiliary variables often contain more information than is actually required due to structures that are irrelevant for the task. In many other cases it is in fact easier to specify what is irrelevant than what is, for the task at hand. Identifying the relevant structures, however, can thus be considerably improved by also minimizing the information about another, irrelevant, variable. In this paper we give a general formulation of this problem and derive its formal, as well as algorithmic, solution. Its operation is demonstrated in a synthetic example and in two real world problems in the context of text categorization and face images. While the original information bottleneck problem is related to rate distortion theory, with the distortion measure replaced by the relevant information, extracting relevant features while removing irrelevant ones is related to rate distortion with side information.

关键词： Signal distortion

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