Lessons Learned from Managing a Petabyte
The amount of data collected and stored by the average business doubles each year. Many commercial databases are already approaching hundreds of terabytes, and at this rate, will soon be managing petabytes. More data enables new functionality and capability, but the larger scale reveals new problems and issues hidden in ''smaller'' terascale environments. This paper presents some of these new problems along with implemented solutions in the framework of a petabyte dataset for a large High Energy Physics experiment. Through experience with two persistence technologies, a commercial database and a file-based approach, we expose format-independent concepts and issues prevalent at this new scale of computing.

title: Server-Side Parallel Data Reduction and Analysis
abstract: 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: 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: 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: Stability analysis of biological systems with real solution classification
abstract: This paper presents a new and general approach for analyzing the stability of a large class of biological networks, modeled as autonomous systems of differential equations, using real solving and solution classification. The proposed approach, based on the classical technique of linearization from the qualitative theory of ordinary differential equations yet with exact symbolic computation, is applied to analyzing the local stability of the Cdc2-cyclin B/Wee1 system and the Mos/MEK/p42 MAPK cascade, two well-known models for cell and protein signaling that have been studied extensively in the literature. We provide rigorous proofs and generalizations for some of the previous results established experimentally and report our new findings.

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: A neuro-fuzzy approach for diagnosis of antibody deficiency syndrome
abstract: This paper presents a neuro-fuzzy approach for diagnosis of antibody deficiency syndrome, where a new neuro-fuzzy network with fuzzy activation functions (FAFs) at hidden layer is used. The FAFs capturing some essential information on pattern distributions, can be adaptively constructed using training examples. To improve the generalization capability and reduce the model complexity, a heuristic method for feature selection is proposed by measuring the size of non-overlapped areas of the FAFs. The effectiveness of our proposed techniques is investigated by an immunology clinical data set collected from the University of California, Irvine (UCI) immunology laboratory.

title: Anticipation Model for Sequential Learning of Complex Sequences
abstract: One of the fundamental aspects of human intelligence is the ability to process temporal information (Lashley, 1951). Learning and reproducing temporal sequences are closely associated with our ability to perceive and generate body movements, speech and language, music, etc. A considerable body of neural network literature is devoted to temporal pattern generation (see Wang, 2001, for a recent review). These models generally treat a temporal pattern as a sequence of discrete patterns, called a temporal sequence. Most of the models are based on either multilayer perceptrons with backpropagation training or the Hopfield model of associative recall. The basic idea for the former class of models is to view a temporal sequence as a set of associations between consecutive components, and learn these associations as input-output transformations (Jordan, 1986; Elman, 1990; Mozer, 1993). To deal with temporal dependencies beyond consecutive components, part of the input layer is used to keep a trace of history, behaving as short-term memory (STM). Similarly, for temporal recall based on the Hopfield associative memory, a temporal sequence is viewed as associations between consecutive components. These associations are stored in extended versions of the Hopfield model that includes some time delays (Sompolinsky & Kanter, 1986; Buhmann & Schulten, 1987; Heskes & Gielen, 1992). To deal with longer temporal dependencies, high-order networks have been proposed (Guyon et al., 1988).

title: Singing Voice Separation from Monaural Recordings
abstract: Separating singing voice from music accompaniment has wide applications in areas such as automatic lyrics recognition and alignment, singer identification, and music information retrieval. Compared to the extensive studies of speech separation, singing voice separation has been little explored. We propose a system to separate singing voice from music accompaniment from monaural recordings. The system has three stages. The singing voice detection stage partitions and classifies an input into vocal and non-vocal portions. Then the predominant pitch detection stage detects the pitch contour of the singing voice for vocal portions. Finally the separation stage uses the detected pitch contour to group the time-frequency segments of the singing voice. Quantitative results show that the system performs well in singing voice separation.