Analysis of discontinuous Galerkin methods using mesh-dependent norms and applications to problems with rough data

We prove the inf-sup stability of a discontinuous Galerkin scheme for second order elliptic operators in (unbalanced) mesh-dependent norms for quasi-uniform meshes for all spatial dimensions. This results in a priori error bounds in these norms. As an application we examine some problems with rough source term where the solution can not be characterised as a weak solution and show quasi-optimal error control.     

Distributed data mining on grids: services, tools, and applications

Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called Knowledge Grid. This paper describes the Knowledge Grid framework and presents the toolset provided by the Knowledge Grid for implementing distributed knowledge discovery. The paper discusses how to design and implement data mining applications by using the Knowledge Grid tools starting from searching grid resources, composing software and data components, and executing the resulting data mining process on a grid. Some performance results are also discussed.     

Dynamic multiobjective optimization problems: test cases, approximations, and applications

After demonstrating adequately the usefulness of evolutionary multiobjective optimization (EMO) algorithms in finding multiple Pareto-optimal solutions for static multiobjective optimization problems, there is now a growing need for solving dynamic multiobjective optimization problems in a similar manner. In this paper, we focus on addressing this issue by developing a number of test problems and by suggesting a baseline algorithm. Since in a dynamic multiobjective optimization problem, the resulting Pareto-optimal set is expected to change with time (or, iteration of the optimization process), a suite of five test problems offering different patterns of such changes and different difficulties in tracking the dynamic Pareto-optimal front by a multiobjective optimization algorithm is presented. Moreover, a simple example of a dynamic multiobjective optimization problem arising from a dynamic control loop is presented. An extension to a previously proposed direction-based search method is proposed for solving such problems and tested on the proposed test problems. The test problems introduced in this paper should encourage researchers interested in multiobjective optimization and dynamic optimization problems to develop more efficient algorithms in the near future.     

Data science on multimedia data: Challenges and applications

Multimedia Tools and Applications -     

The matrix ridge approximation: algorithms and applications

We are concerned with an approximation problem for a symmetric positive semidefinite matrix due to motivation from a class of nonlinear machine learning methods. We discuss an approximation approach that we call matrix ridge approximation. In particular, we define the matrix ridge approximation as an incomplete matrix factorization plus a ridge term. Moreover, we present probabilistic interpretations using a normal latent variable model and a Wishart model for this approximation approach. The idea behind the latent variable model in turn leads us to an efficient EM iterative method for handling the matrix ridge approximation problem. Finally, we illustrate the applications of the approximation approach in multivariate data analysis. Empirical studies in spectral clustering and Gaussian process regression show that the matrix ridge approximation with the EM iteration is potentially useful.     

Guest editorial: special issue on temporal data mining: theory,algorithms and applications

This special issue provides a leading forum for timely, in-depth presentation of recent advances in algorithms, theories and applications in temporal data mining. The selected papers underwent a rigorous refereeing and revision process.     

Equilibria problems on games: Complexity versus succinctness

We study the computational complexity of problems involving equilibria in strategic games and in perfect information extensive games when the number of players is large. We consider, among others, the problems of deciding the existence of a pure Nash equilibrium in strategic games or deciding the existence of a pure Nash or a subgame perfect Nash equilibrium with a given payoff in finite perfect information extensive games. We address the fundamental question of how can we represent a game with a large number of players? We propose three ways of representing a game with different degrees of succinctness for the components of the game. For perfect information extensive games we show that when the number of moves of each player is large and the input game is represented succinctly these problems are PSPACE-complete. In contraposition, when the game is described explicitly by means of its associated tree all these problems are decidable in polynomial time. For strategic games we show that the complexity of deciding the existence of a pure Nash equilibrium depends on the succinctness of the game representation and then on the size of the action sets. In particular we show that it is NP-complete, when the number of players is large and the number of actions for each player is constant, and that the problem is -complete when the number of players is a constant and the size of the action sets is exponential in the size of the game representation. Again when the game is described explicitly the problem is decidable in polynomial time.     

Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics

This paper develops an unsupervised discriminant projection (UDP) technique for dimensionality reduction of high-dimensional data in small sample size cases. UDP can be seen as a linear approximation of a multimanifolds-based learning framework which takes into account both the local and nonlocal quantities. UDP characterizes the local scatter as well as the nonlocal scatter, seeking to find a projection that simultaneously maximizes the nonlocal scatter and minimizes the local scatter. This characteristic makes UDP more intuitive and more powerful than the most up-to-date method, locality preserving projection (LPP), which considers only the local scatter for clustering or classification tasks. The proposed method is applied to face and palm biometrics and is examined using the Yale, FERET, and AR face image databases and the PolyU palmprint database. The experimental results show that UDP consistently outperforms LPP and PCA and outperforms LDA when the training sample size per class is small. This demonstrates that UDP is a good choice for real-world biometrics applications     

Dynamicity in distributed applications: issues, problems and the ASSIST approach

Dynamic reconfiguration refers to the ability of changing properties and structure of a distributed system, while it is running. It is essential when designing applications that need to adapt to unpredictable events, run on non-dedicated environments; for which configuration cannot be statically determined, or may change at runtime. Grid computing also gave new emphasis to the topic, being based on dynamic environments by definition. The first part of this work tries to give a definite organization to known literature and existing experiments, providing a general overview of the problem. Then we describe our approach to dynamicity in the context of the ASSIST programming environment and show how it is able to provide users with many of the required abstractions to develop adaptive, high-performance, distributed applications.     

浅析办公软件应用教学过程中的问题及解决办法

办公软件应用模块中级和高级操作员考试教学培训过程中的问题,有些是软件安装问题,有些是Office2003的环境设置问题。本文围绕全国计算机信息高新技术考试《试题汇编》与《试题解答》教材,重点分析教学培训过程中遇到的问题,提出解决办法。     

An array language for data parallelism: Definition,compilation, and applications

The programming of SIMD machines that strongly support data parallelism, such as the Connection Machine, ¹ presents new challenges for language, compiler, and algorithm designers. We propose an array language that captures many of the abstractions that are necessary for the effective programming of such machines, thereby liberating the user from having to specify low-level details. Consequently, this new language, ALP, allows for efficient compilation using state-of-the-art techniques, achieving hand-code quality. We demonstrate the effectiveness of our approach by two examples which show that despite being an array language, ALP does not restrict expressiveness to rigidly regular computational structures.Work supported in part by ONR grant number N00014-89-J-1906 while on sabbatical leave at the Department of Computer Science, Yale University.Work supported in part by NSF grant number DCR-8405478 while on sabbatical leave at the Department of Computer Science, Yale University.     

A matrix approach to data base exploration: Analysis of classifier results

Exploratory pattern analysis is an iterative process. In many instances, numerous iterations are required before a practical model or classification scheme can be concisely stated and adequately analyzed. There are at least three distinct functions that are required for solving the important and difficult pattern recognition problems. These are: (1) conceptualization of a classification model; (2) mathematical modeling and analyzing the practical and theoretical implications of the model; (3) testing the model on actual data. These tasks are interdependent and the investigation proceeds in what often appears to be an unsystematic approach to problem solving. This paper will address the third task and consequently, by association, hopefully affect the other two in a beneficial and constructive manner.The purpose of this article is to illustrate a general methodology, based on a matrix approach, that can be used in organizing, formatting and statistically analyzing classifier results. The discussion is intended for all individuals interested in analyzing pattern analysis and classification experiments, however, it should be of particular interest to those involved in designing interactive pattern recognition software packages. The discussion proceeds from a matrix algebra study of classifier results to techniques for statistical analysis using Cohen's kappa and Cochran's Q statistics. An example from nuclear medicine is used to illustrate the methodology.     

Mixed computation: potential applications and problems for study

Mixed computation is processing of an incomplete information. Its product are a partially processed information and a so-called residual program destined to complete in sequel the processing of the remaining information. Many kinds of practical work with programs are nothing more but obtaining a residual program. We demonstrate, as an example, the application of mixed computation to compilation. Under computational approach mixed computation generalizes the operational semantics of a language by inclusion of steps which generate residual program instructions. Under transformational approach the residual program is obtained as a result of a series of so-called basic transformations of the program text. We argue that the transformational approach is more fundamental, for it allows to describe mixed computation in all its variety and moreover, to relate mixed computation to other kinds of program manipulation: execution, optimization, macroprocessing, synthesis. Such an integrated approach leads us to a transformational machine concept.     

Optical correlators for space applications: prospects and problems

Demanding ever-increasing throughput and processing power, space applications push the outer limits of conventional pattern-recognition technology. Optical correlators offer a siren's song of potential advantages over all-electronic devices. Realizing these advantages, however, will be difficult because of limitations on current spatial light modulators, key elements in an optical correlator. We propose a promising architecture that may overcome these limitations     

分布式应用开发中的Java技术分析与比较

由于Java技术的面向对象、面向网络和数据库及跨平台等特性,使用Java技术来开发分布式应用系统就变得相当容易。现在应用比较广泛的3种主要的Java分布式计算技术,如RMI、EJB以及Jini具有各自不同的特点,对此进行了详细介绍,在此基础上,还对各自的异同点和使用范围进行了分析比较。     

Analysis of complexity indices for classification problems: Cancer gene expression data

Currently, cancer diagnosis at a molecular level has been made possible through the analysis of gene expression data. More specifically, one usually uses machine learning (ML) techniques to build, from cancer gene expression data, automatic diagnosis models (classifiers). Cancer gene expression data often present some characteristics that can have a negative impact in the generalization ability of the classifiers generated. Some of these properties are data sparsity and an unbalanced class distribution. We investigate the results of a set of indices able to extract the intrinsic complexity information from the data. Such measures can be used to analyze, among other things, which particular characteristics of cancer gene expression data mostly impact the prediction ability of support vector machine classifiers. In this context, we also show that, by applying a proper feature selection procedure to the data, one can reduce the influence of those characteristics in the error rates of the classifiers induced.