Server-Side Parallel Data Reduction and Analysis
Geoscience analysis is currently limited by cumbersome access and manipulation of large datasets from remote sources. Due to their data-heavy and compute-light nature, these analysis workloads represent a class of applications unsuited to a computational grid optimized for compute-intensive applications. We present the Script Workflow Analysis for MultiProcessing (SWAMP) system, which relocates data-intensive workflows from scientists' workstations to the hosting datacenters in order to reduce data transfer and exploit locality. Our colocation of computation and data leverages the typically reductive characteristics of these workflows, allowing SWAMP to complete workflows in a fraction of the time and with much less data transfer. We describe SWAMP's implementation and interface, which is designed to leverage scientists' existing script-based workflows. Tests with a production geoscience workflow show drastic improvements not only in overall execution time, but in computation time as well. SWAMP's workflow analysis capability allows it to detect dependencies, optimize I/O, and dynamically parallelize execution. Benchmarks quantify the drastic reduction in transfer time, computation time, and end-to-end execution time.

title: On the Verge of One Petabyte - the Story Behind the BaBar Database System
abstract: The BaBar database has pioneered the use of a commercial ODBMS within the HEP community. The unique object-oriented architecture of Objectivity/DB has made it possible to manage over 700 terabytes of production data generated since May'99, making the BaBar database the world's largest known database. The ongoing development includes new features, addressing the ever-increasing luminosity of the detector as well as other changing physics requirements. Significant efforts are focused on reducing space requirements and operational costs. The paper discusses our experience with developing a large scale database system, emphasizing universal aspects which may be applied to any large scale system, independently of underlying technology used.

title: Classification of compound images based on transform coefficient likelihood
abstract: Applications like distance learning and teleconferencing often require compression of images that contain both text and graphics. Because text and graphics have different properties, a compression scheme can benefit by treating the textual and graphical portions of such compound images separately. In this paper, we propose new methods, called transform coefficient likelihood (TCL) schemes, for separating the textual and graphical portions of a compound image. TCL schemes examine the DCT coefficient values of an 8/spl times/8 block. For each coefficient, they refer to stored histograms that give the likelihood that a certain value occurs in a text block, or in a graphics block. They then examine the differences in these two likelihoods over all the coefficients in the block to decide whether it contains text or graphics. Experimental results show that the best TCL methods significantly outperform previously proposed techniques.

title: Gene Selection Through Sensitivity Analysis of Support Vector Machines
abstract: We present a novel approach to gene selection for microarry data through the sensitivity analysis of support vector machines (SVMs). A new measurement (sensitivity) is defined to quantify the saliencies of individual features (genes) by analyzing the discriminative function in SVMs. Our feature selection strategy is first to select the features with higher sensitivities but meanwhile keep the remaining ones, and then refine the selected subset by tentatively substituting some part with fragments of the previously rejected features. The accuracy of our method is validated experimentally on the benchmark microarray datasets.

title: Model-based sequential organization in cochannel speech
abstract: A human listener has the ability to follow a speaker's voice while others are speaking simultaneously; in particular, the listener can organize the time-frequency energy of the same speaker across time into a single stream. In this paper, we focus on sequential organization in cochannel speech, or mixtures of two voices. We extract minimally corrupted segments, or usable speech, in cochannel speech using a robust multipitch tracking algorithm. The extracted usable speech is shown to capture speaker characteristics and improves speaker identification (SID) performance across various target-to-interferer ratios. To utilize speaker characteristics for sequential organization, we extend the traditional SID framework to cochannel speech and derive a joint objective for sequential grouping and SID, leading to a problem of search for the optimum hypothesis. Subsequently we propose a hypothesis pruning algorithm based on speaker models in order to make the search computationally efficient. Evaluation results show that the proposed system approaches the ceiling SID performance obtained with prior pitch information and yields significant improvement over alternative approaches to sequential organization.

title: Computer Aided Analysis and Derivation for Artificial Neural Systems
abstract: The theoretical analysis and derivation of artificial neural systems, which consists essentially of manipulating symbolic mathematical objects according to certain mathematical and biological knowledge, can be done more efficiently with computer assistance by using and extending methods and systems of symbolic computation. After presenting the mathematical characteristics of neural systems and a brief review on Lyapunov stability theory, the authors present some features and capabilities of existing systems and the extension for manipulating objects occurring in the analysis of neural systems. Some strategies and a toolkit developed in MACSYMA for computer-aided analysis and derivation are described. A concrete example is given to demonstrate the derivation of a hybrid neural system, i.e. a system which in its learning rule combines elements of supervised and unsupervised learning. Future work and research directions are indicated.

title: Automatic assume guarantee analysis for assertion-based formal verification

title: Decomposing Algebraic Varieties
abstract: An algebraic variety is a geometric figure defined by the zeros of a set of multivariate polynomials. This paper explains how to adapt two general zero decomposition methods for efficient decomposition of affine algebraic varieties into unmixed and irreducible components. Two devices based on Grobner bases are presented for computing the generators of the saturated ideals of triangular sets. We also discuss a few techniques and variants which, when properly used, may speed up the decomposition. Experiments for a set of examples are reported with comparison to show the performance and effectiveness of such techniques, variants and the whole decomposition methods. Several theoretical results are stated along with the description of algorithms. The paper ends with a brief mention of some applications of variety decomposition.