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作者： Grosse, Roger Raina, Rajat Kwong, Helen Ng, Andrew Y. Computer Science Dept. Stanford University Stanford CA 94305 United States Symbolic Systems Program Stanford University Stanford CA 94305 United States

ISBN: (纸本)0974903930

Sparse coding is an unsupervised learning algorithm that learns a succinct high-level representation of the inputs given only unlabeled data;it represents each input as a sparse linear combination of a set of basis functions. Originally applied to modeling the human visual cortex, sparse coding has also been shown to be useful for self-taught learning, in which the goal is to solve a supervised classification task given access to additional unlabeled data drawn from different classes than that in the supervised learning problem. Shift-invariant sparse coding (SISC) is an extension of sparse coding which reconstructs a (usually time-series) input using all of the basis functions in all possible shifts. In this paper, we present an efficient algorithm for learning SISC bases. Our method is based on iteratively solving two large convex optimization problems: The first, which computes the linear coefficients, is an L1-regularized linear least squares problem with potentially hundreds of thousands of variables. Existing methods typically use a heuristic to select a small subset of the variables to optimize, but we present a way to efficiently compute the exact solution. The second, which solves for bases, is a constrained linear least squares problem. By optimizing over complex-valued variables in the Fourier domain, we reduce the coupling between the different variables, allowing the problem to be solved efficiently. We show that SISC's learned high-level representations of speech and music provide useful features for classification tasks within those domains. When applied to classification, under certain conditions the learned features outperform state of the art spectral and cep-stral features.

关键词： Classification (of information)

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Optimum sensor position for inverse load recovery on a composite plate

Optimum sensor position for inverse load recovery on a compo...

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1st International Conference on Advances and Trends in Engineering Materials and their Applications, AES-ATEMA'2007

作者： Coates, Cameron W. Hashemi, Ray R. Daniell, Darryl W. Engineering Studies Program Armstrong Atlantic State University Savannah GA 31419 United States Dept. of Computer Science Armstrong Atlantic State University Savannah GA 31419 United States

ISBN: (纸本)0978047907

An inverse method using strain values obtained at finite locations is used to predict coefficients of an unknown continuous static load function applied to a composite sandwich plate. The strain values obtained from a Finite Element Model (FEM) are used to simulate strain sensor readings. A Back-Propagation Neural Network (BPNN) is developed in order to predict optimum strain sensor locations necessary for accurate load function coefficient recovery using the inverse method. Coefficient values output from the inverse method, as well as material, and geometrical properties are used as input into the BPNN. The BPNN utilizes a back propagation algorithm that incorporates a linear transfer function. The BPNN developed predicts locations for sensor placement such that load function coefficients are recovered by the inverse method with good accuracy. © AES-Advanced Engineering Solutions.

关键词： Neural networks

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An effective snippet generation method using the pseudo relevance feedback technique

An effective snippet generation method using the pseudo rele...

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30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'07

作者： Ko, Youngjoong An, Hongkuk Seo, Jungyun Dept. of Computer Engineering Dong-A University 840 Hadan 2-dong Saha-gu Busan 604-714 Korea Republic of Computer Science and Program of Integrated Biotechnology Sogang Univ. Sinsu-dong Mapo-gu Seoul 121-742 Korea Republic of

ISBN: (纸本)1595935975

A (page or web) snippet is document excerpts allowing a user to understand if a document is indeed relevant without accessing it. This paper proposes an effective snippet generation method. The pseudo relevance feedback technique and text summarization techniques are applied to salient sentences extraction for generating good quality snippets. In the experimental results, the proposed method showed much better performance than other methods including Google and Naver.

关键词： Information retrieval systems

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Use mathematics and computer to predict Boat's drift for search and rescue

Use mathematics and computer to predict Boat's drift for sea...

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International Multi-Conference on Society, Cybernetics and Informatics, IMSCI 2007

作者： Han, Jim Su, Hui Fang Huang Su, Tsung-Chow Joe Dept. of Computer Engineering and Computer Science Florida Atlantic University Boca Raton FL 33431 United States Graduate Teacher Education Program Nova Southeastern University 1750 NE 167th St. North Miami Beach FL 33162 United States Dept. of Mechanical Engineering Florida Atlantic University Boca Raton FL 33431 United States

ISBN: (纸本)1934272256

Finding a boat in wind and current is an age old problem that puzzled many people throughout history. It is only until recently a simple theory is developed to allow efficient and accurate prediction of the boat's drift trajectory. Such theory could be developed using simple physics and algebra. In this paper, we will describe the derivation of the theory that would give the boat's trajectory in terms of different types of boat condition under given wind field and current field. We will show that the relationship is versatile, and could be used for practical application. We will further implement the results in computer animation setting so that the results can be obtained easily for a given application. The paper demonstrates the power of mathematics and technology to solve problem of boat drift of search and rescue.

关键词： Boats

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Comparing user simulation models for dialog strategy learning

Comparing user simulation models for dialog strategy learnin...

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2007 Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, NAACL-HLT 2007

作者： Ai, Hua Tetreault, Joel R. Litman, Diane J. University of Pittsburgh Intelligent Systems Program PittsburghPA15260 United States University of Pittsburgh LRDC PittsburghPA15260 United States University of Pittsburgh Dept. of Computer Science LRDC PittsburghPA15260 United States

This paper explores what kind of user simulation model is suitable for developing a training corpus for using Markov Decision Processes (MDPs) to automatically learn dialog strategies. Our results suggest that with sparse training data, a model that aims to randomly explore more dialog state spaces with certain constraints actually performs at the same or better than a more complex model that simulates realistic user behaviors in a statistical way. © 2007 Association for Computational Linguistics.

关键词： Markov processes

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Interdependency modeling and emergency response

Interdependency modeling and emergency response

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Summer computer Simulation Conference 2007, SCSC 2007, Part of the 2007 Summer Simulation Multiconference, SummerSim 2007

作者： Dudenhoeffer, Donald D. Permann, May R. Woolsey, Steven Timpany, Robert Miller, Chuck McDermott, Anthony Manic, Milos Idaho National Laboratory Idaho Falls ID United States Priority 5 Inc United States CS and ECE Program Center for Higher Education University of Idaho Idaho Falls United States Computer Science Dept. United States

ISBN: (纸本)9781622763580

In large-scale disaster events, infrastructure owners are faced with many challenges in deciding the allocation ofresources for preparation and response actions. This decision process involves building situation awareness, evaluating course of action, and effecting response. This paper describes a modeling and simulation system called CIMS© that presents a visual environment for assessing the causal effects of events and actions in complex environments. Specifically, CIMS© provides a framework for evaluating cascading effects associated with infrastructure interdependencies, thus providing greater situational awareness to infrastructure owners and decisionmakers. This paper first presents the area of interdependency analysis and then presents CIMS© as a network framework for simulating the interactions between multiple infrastructures. Also introduced is the integration of infrastructure simulation with a decision support systems.

关键词： Discrete event simulation

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Multivariate feature selection using random subspace classifiers for gene expression data

Multivariate feature selection using random subspace classif...

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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Kamath, Vidya P. Hall, Lawrence O. Yeatman, Timothy J. Eschrich, Steven A. Biomedical Engineering Program University of South Florida H. Lee Moffitt Cancer Center and Research Institute Tampa FL United States Dept. of Computer Science and Engineering University of South Florida Tampa FL United States Dept. Interdisciplinary Oncology H. Lee Moffitt Cancer Center and Research Institute Tampa FL United States

ISBN: (纸本)1424415098

Gene expression analysis techniques identify important genes that predict specified outcomes based on sample characteristics. Given the small sample sizes common to these studies and the large dimensionality of the data, feature selection methods are essential. In addition, cancer-related expression analysis often involves imbalanced datasets due to rare forms of disease. Popular methods of feature selection employ univariate techniques to identify the features most suitable for analysis. We propose a multivariate technique for selecting accurate subsets of features using an approach based on random subspaces. The random subspace method is used to explore random combinations of features and only subspaces that produce accurate classifiers are retained. The method is tested on two independent gene expression datasets and compared with a univariate approach. The multivariate feature selection method resulted in a 33% improvement in classification accuracy overall and 90% improvement in classification accuracy for the minority class. ©2007 IEEE.

关键词： Gene expression

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Comparing spoken dialog corpora collected with recruited subjects versus real users

Comparing spoken dialog corpora collected with recruited sub...

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8th SIGdial Workshop on Discourseand Dialogue

作者： Ai, Hua Raux, Antoine Bohus, Dan Eskenazi, Maxine Litman, Diane Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States Language Technologies Institute Carnegie Mellon University Pittsburgh PA 15213 United States Computer Science Department Carnegie Mellon University Pittsburgh PA 15213 United States Dept. of Computer Science LRDC University of Pittsburgh Pittsburgh PA 15260 United States Microsoft Research Redmond WA United States

ISBN: (纸本)9789074029322

Empirical spoken dialog research often involves the collection and analysis of a dialog corpus. However, it is not well understood whether and how a corpus of dialogs collected using recruited subjects differs from a corpus of dialogs obtained from real users. In this paper we use Let's Go Lab, a platform for experimenting with a deployed spoken dialog bus information system, to address this question. Our first corpus is collected by recruiting subjects to call Let's Go in a standard laboratory setting, while our second corpus consists of calls from real users calling Let's Go during its operating hours. We quantitatively characterize the two collected corpora using previously proposed measures from the spoken dialog literature, then discuss the statistically significant similarities and differences between the two corpora with respect to these measures. For example, we find that recruited subjects talk more and speak faster, while real users ask for more help and more frequently interrupt the system. In contrast, we find no difference with respect to dialog structure. © 2007 Association for Computational Linguistics.

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Personal digital collections

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Proceedings of the Association for Information science and Technology 2007年第1期43卷 1-7页

作者： Luz M. Quiroga Deborah Barreau William Jones Christine Borgman Cathy Marshall (Organizer) University of Hawaii. Information and Computer Science Dept. Library and Information Science Program School of Information and Library Science University of North Carolina at Chapel Hill Information Studies University of California Los Angeles The Information School University of Washington Microsoft and The Center for the Study of Digital Libraries at Texas A&M University

The vast growth in digital content generated by individuals marks a new social trend known as “Generation C” ( ***/trends/GENERATION_*** ). Personal content ranges from informal to formal, and includes scholarly papers, blogs, genealogical records, personal webpages, photo albums, family videos, music collections, power point presentations, bookmarks, personal correspondence, articles, computer programs, audio recordings (e.g. research interviews), spaces in collaborative systems, and personal digital libraries. Digital storage available for personal use continues to increase dramatically in capacity while declining in cost. Panelists will address challenges in the design of personal digital collections such as gathering, organizing, preserving, segmenting, accessing, and using digital content. Panelists are leaders in their field, representing an array of perspectives.

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Epileptic seizure detection

Epileptic seizure detection

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作者： Schuyler, Ronald White, Andrew Staley, Kevin Cios, Krzysztof J. Computational Bioscience Ph.D. Program UCDHSC United States University of Colorado United States American Epilepsy Society Society for Neuroscience United States Department of Neurology Massachusetts General Hospital Harvard Medical School United States Staley Lab. United States University of Colorado at Denver Health Sciences Center United States University of Colorado Bioenergetics Institute United States Data Mining and Bioinformatics Laboratory Turkey Polish Academy of Arts and Sciences Poland Dept. of Computer Science and Engineering University of Colorado at Denver Health Sciences Center P.O. Box 173364 Denver CO 80217-3364 United States

No abstract available

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