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Application-adaptive resource scheduling in a computational grid

Journal of Zhejiang University-science A(Applied Physics & engineering) 2006年第10期7卷 1634-1641页

作者： LUAN Cui-ju SONG Guang-hua ZHENG Yao School of Computer Science and Center for Engineering and Scientific Computation Zhejiang University Hangzhou 310027 China College of lnformatton Engmeermg Shanghai Maritime University Shanghai 200135 China

Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid. Resource scheduling should consider the specific characteristics of the application, and decide the metrics to be used accordingly. This paper presents a distributed resource scheduling framework mainly consisting of a job scheduler and a local scheduler. In order to meet the requirements of different applications, we adopt HGSA, a Heuristic-based Greedy Scheduling Algorithm, to schedule jobs in the grid, where the heuristic knowledge is the metric weights of the computing resources and the metric workload impact factors. The metric weight is used to control the effect of the metric on the application. For different applications, only metric weights and the metric workload impact factors need to be changed, while the scheduling algorithm remains the same. Experimental results are presented to demonstrate the adaptability of the HGSA.

关键词： Grid Resource scheduling Heuristic knowledge Greedy scheduling algorithm

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Embedding heterogeneous data using statistical models

Embedding heterogeneous data using statistical models

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21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06

作者： Globerson, Amir Chechik, Gal Pereira, Fernando Tishby, Naftali Computer Science and Artificial Intelligence Laboratory MIT Cambridge MA 02139 Computer Science Department Stanford University Stanford CA 94305 United States Department of Computer and Information Science University of Pennsylvania Philadelphia PA 19104 United States School of Computer Science and Engineering Interdisciplinary Center for Neural Computation Hebrew University Jerusalem 91904 Israel

ISBN: (纸本)1577352815

Embedding algorithms are a method for revealing low dimensional structure in complex data. Most embedding algorithms are designed to handle objects of a single type for which pairwise distances are specified. Here we describe a method for embedding objects of different types (such as authors and terms) into a single common Euclidean space based on their co-occurrence statistics. The joint distributions of the heterogenous objects are modeled as exponentials of squared Euclidean distances in a low-dimensional embedding space. This construction links the problem to convex optimization over positive semidefinite matrices. 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. Copyright © 2006, American Association for Artificial Intelligence (***). All rights reserved.

关键词： Data reduction

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Application-adaptive Resource Scheduling in a computational Grid

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Journal of Zhejiang University-science B(Biomedicine & Biotechnology) 2006年第10期7卷 1634-1641页

作者： Cui-Ju Luan Guang-Hua Song Yao Zheng School of Computer Science and Center for Engineering and Scientific Computation Zhejiang University Hangzhou 310027 China

关键词： Grid Resource scheduling Heuristic knowledge Greedy scheduling algorithm

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Online multiclass learning by interclass hypothesis sharing 06

Online multiclass learning by interclass hypothesis sharing

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ICML 2006: 23rd International Conference on Machine Learning

作者： Fink, Michael Shalev-Shwartz, Shai Singer, Yoram Ullman, Shimon Center for Neural Computation Hebrew University Jerusalem 91904 Israel School of Computer Science and Engineering Hebrew University Jerusalem 91904 Israel Google Inc. 1600 Amphitheatre Parkway Mountain View CA 94043 United States Weizmann Institute Rehovot 76100 Israel

ISBN: (纸本)1595933832

We describe a general framework for online multiclass learning based on the notion of hypothesis sharing. In our framework sets of classes are associated with hypotheses. Thus, all classes within a given set share the same hypothesis. This framework includes as special cases commonly used constructions for multiclass categorization such as allocating a unique hypothesis for each class and allocating a single common hypothesis for all classes. We generalize the multiclass Perception to our framework and derive a unifying mistake bound analysis. Our construction naturally extends to settings where the number of classes is not known in advance but, rather, is revealed along the online learning process. We demonstrate the merits of our approach by comparing it to previous methods on both synthetic and natural datasets.

关键词： Online systems

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Information bottleneck for non co-occurrence data 06

Information bottleneck for non co-occurrence data

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Proceedings of the 20th International Conference on neural Information Processing Systems

作者： Yevgeny Seldin Noam Slonim Naftali Tishby School of Computer Science and Engineering The Hebrew University of Jerusalem The Lewis-Sigler Institute for Integrative Genomics Princeton University School of Computer Science and Engineering The Hebrew University of Jerusalem and Interdisciplinary Center for Neural Computation The Hebrew University of Jerusalem

We present a general model-independent approach to the analysis of data in cases when these data do not appear in the form of co-occurrence of two variables X, Y, but rather as a sample of values of an unknown (stochastic) function Z(X, Y). For example, in gene expression data, the expression level Z is a function of gene X and condition Y; or in movie ratings data the rating Z is a function of viewer X and movie Y. The approach represents a consistent extension of the Information Bottleneck method that has previously relied on the availability of co-occurrence statistics. By altering the relevance variable we eliminate the need in the sample of joint distribution of all input variables. This new formulation also enables simple MDL-like model complexity control and prediction of missing values of Z. The approach is analyzed and shown to be on a par with the best known clustering algorithms for a wide range of domains. For the prediction of missing values (collaborative filtering) it improves the currently best known results.

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Efficient representation as a design principle for neural coding and computation

Efficient representation as a design principle for neural co...

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IEEE International Symposium on Information Theory

作者： William Bialek Rob R. De Ruyter Van Steveninck Naftali Tishby Joseph Henry Laboratories of Physics Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton New Jersey USA Department of Physics Indiana University Bloomington Indiana USA School of Computer Science and Engineering Interdisciplinary Center for Neural Computation Hebrew University Jerusalem Israel

ISBN: (纸本)142440505X

Does the brain construct an efficient representation of the sensory world? We review progress on this question, focusing on a series of experiments in the last decade which use fly vision as a model system in which theory and experiment can confront each other. Although the idea of efficient representation has been productive, clearly it is incomplete since it doesn't tell us which bits of sensory information are most valuable to the organism. We argue that, in fact, an organism which maximizes the (biologically meaningful) adaptive value of its actions given fixed resources must have internal representations of the outside world that are optimal in a very specific information theoretic sense: they maximize the information about the future of sensory inputs at a fixed value of the information about their past. This principle contains as special cases computations which the brain seems to carry out, and it should be possible to test this optimization directly. We return to the fly visual system and report the results of preliminary experiments that are in very suggestive agreement with theory

关键词： Neurons Visual system Physics computing Design engineering Organisms System testing Laboratories Genomics Bioinformatics computer science

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Hierarchical chunking during learning of visuomotor sequences

Hierarchical chunking during learning of visuomotor sequence...

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International Joint Conference on neural Networks 2006, IJCNN '06

作者： Miyapuram, Krishna P. Bapi, Raju S. Pammi, Chandrasekhar V. S. Ahmed Doya, Kenji Department of Computer and Information Sciences University of Hyderabad Hyderabad 500046 India Department of Physiology Development and Neuroscience Downing Site Cambridge CB2 3DY United Kingdom Department of Computer and Information Sciences Center for Cognitive Science University of Hyderabad Hyderabad 500046 India Department of Psychiatry and Behavioral Sciences Emory University School of Medicine Atlanta GA 30322 United States Computational Intelligence Laboratory Department of Computer and Information Sciences University of Hyderabad Hyderabad 500046 India Department of Computational Neurobiology Computational Neuroscience Laboratories ATR International Kyoto Japan Neural Computation Unit Okinawa Institute of Science and Technology Gushikawa Okinawa Japan

ISBN: (纸本)0780394909

It is well known that learning a sequential skill involves chaining a number of primitive actions together into chunks. We describe three different experiments using an explicit visuomotor sequence learning paradigm called the m×n task. The m×n task enables hierarchical learning of sequences by presenting m elements of the sequence at a time (called the set). The entire sequence to be learned is composed of n such sets and is called a hyperset. In the first experiment, we showed the chunking phenomenon while learning a sequence as opposed to following randomly generated visual cues. We further explored the nature of chunking across sets using complex sequences in the second experiment. Finally, we investigated effector dependence of the chunking patterns in the third experiment. Our results point out the facilitating factors for chunk formation in visuomotor sequence learning. © 2006 IEEE.

关键词： Hierarchical systems

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Noninvasive Study of the Human Heart using Independent Component Analysis

Noninvasive Study of the Human Heart using Independent Compo...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yi Zhu Tong Lee Chen Wanping Zhang Tzyy-ping Jung Jeng-ren Duann Scott Makeig Chung-kuan Cheng Department of Computer Science and Engineering University of California San Diego La Jolla CA USA Intuit Mountain View CA USA SoftMax Inc. San Diego CA USA Swartz Center for Computational Neuroscience Institute for Neural Computation University of California San Diego La Jolla CA USA

We have developed a new approach to studying human heart activity using independent component analysis. The electrocardiogram (ECG) is an important tool in diagnosis of heart disease. However, the normal 12-lead ECG can only record limited aspects of heart's electrical signals and mostly their interpretation relies on trained and experienced medical doctors. We have performed experiments in which heart signals were recorded in high spatial resolution. Independent component analysis was applied to the recorded signals to separate distinct temporal components of the recorded signals. The separated components were further analyzed by back-projecting their activities to the surface montage to examine each component's property. Experimental results show this to be a promising approach that can be extended to build more detailed heart activity simulations

关键词： Humans Heart Independent component analysis Electrocardiography Biomedical measurements Pollution measurement Cardiac disease Rhythm Signal analysis Blind source separation

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Efficient learning of relational object class models

Efficient learning of relational object class models

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Proceedings - 10th IEEE International Conference on computer Vision, ICCV 2005

作者： Hillel, Aharon Bar Hertz, Tomer Weinshall, Daphna School of Computer Science and Engineering Center for Neural Computation Hebrew University of Jerusalem 91904 Israel

ISBN: (纸本)076952334X

We present an efficient method for learning part-based object class models. The models include location and scale relations between parts, as well as part appearance. Models are learnt from raw object and background images, represented as an unordered set of features extracted using an interest point detector. The object class is generatively modeled using a simple Bayesian network with a central hidden node containing location and scale information, and nodes describing object parts. The model's parameters, however, are optimized to reduce a loss function which reflects training error, as in discriminative methods. Specifically, the optimization is done using a boosting-like technique with complexity linear in the number of parts and the number of features per image. This efficiency allows our method to learn relational models with many parts and features, and leads to improved results when compared with other methods. Extensive experimental results are described, using some common bench-mark datasets and three sets of newly collected data, showing the relative advantage of our method. © 2005 IEEE.

关键词： Learning systems

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Generalization in clustering with unobserved features

Generalization in clustering with unobserved features

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2005 Annual Conference on neural Information Processing Systems, NIPS 2005

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

ISBN: (纸本)9780262232531

We argue that when objects are characterized by many attributes, clustering them on the basis of a relatively small random subset of these attributes can capture information on the unobserved attributes as well. Moreover, we show that under mild technical conditions, clustering the objects on the basis of such a random subset performs almost as well as clustering with the full attribute set. We prove a finite sample generalization theorems for this novel learning scheme that extends analogous results from the supervised learning setting. The scheme is demonstrated for collaborative filtering of users with movies rating as attributes.

关键词： Collaborative filtering

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