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Workshop on Mathematical Foundations of Scientific visualization, computer Graphics, and Massive data Exploration, 2004

作者： Bremer, Peer-Timo Pascucci, Valerio Hamann, Bernd Department of Computer Science University of Illinois Urbana-Champaign United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Institute for Data Analysis and Visualization Department of Computer Science University of California Davis United States

ISBN: (纸本)9783642543005

We present a highly adaptive hierarchical representation of the topology of functions defined over two-manifold domains. Guided by the theory of Morse–Smale complexes, we encode dependencies between cancellations of critical points using two independent structures: a traditional mesh hierarchy to store connectivity information and a new structure called cancellation trees to encode the configuration of critical points. Cancellation trees provide a powerful method to increase adaptivity while using a simple, easy-to-implement data structure. The resulting hierarchy is significantly more flexible than the one previously reported (IEEE Trans. Vis. Comput. Graph. 10(4):385–396, 2004). In particular, the resulting hierarchy is guaranteed to be of logarithmic height. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Trees (mathematics)

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Cataclysmic Variables and Other Compact Binaries in the Globular Cluster NGC 362: Candidates from Chandra and HST

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AIP Conference Proceedings 2010年第1期1314卷 163-168页

作者： B. Margon B. Beck‐Winchatz L. Homer D. Pooley C. G. Bassa S. F. Anderson W. H. G. Lewin F. Verbunt A. K. H. Kong R. M. Plotkin aDepartment of Astronomy & Astrophysics University of California Santa Cruz CA 95064 USA bScientific Data Analysis and Visualization Program DePaul University Chicago IL 60614 USA cDepartment of Astronomy University of Washington Box 351580 Seattle WA 98195 USA dEureka Scientific 5248 Valley View Road El Sobrante CA 94803 USA eJodrell Bank Centre for Astrophysics School of Physics and Astronomy University of Manchester Manchester M13 9PL UK fKavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology Cambridge MA 02139 USA gAstronomical Institute Utrecht University PO Box 80000 3508 TA Utrecht The Netherlands hInstitute of Astronomy and Department of Physics National Tsing Hua University Hsinchu 30013 Taiwan iAstronomical Institute “Anton Pannekoek ” University of Amsterdam Science Park 904 1098 XH Amsterdam The Netherlands

Highly sensitive and precise X‐ray imaging from Chandra, combined with the superb spatial resolution of HST optical images, dramatically enhances our empirical understanding of compact binaries such as cataclysmic variables and low mass X‐ray binaries, their progeny, and other stellar X‐ray source populations deep into the cores of globular clusters. Our Chandra X‐ray images of the globular cluster NGC 362 reveal 100 X‐ray sources, the bulk of which are likely cluster members. Using HST color‐magnitude and color‐color diagrams, we quantitatively consider the optical content of the NGC 362 Chandra X‐ray error circles, especially to assess and identify the compact binary population in this condensed‐core globular cluster. Despite residual significant crowding in both X‐rays and optical, we identify an excess population of Hα‐emitting objects that is statistically associated with the Chandra X‐ray sources. The X‐ray and optical characteristics suggest that these are mainly cataclysmic variables, but we also identify a candidate quiescent low mass X‐ray binary. A potentially interesting and largely unanticipated use of observations such as these may be to help constrain the macroscopic dynamic state of globular clusters.

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Compression and occlusion culling for fast isosurface extraction from massive datasets

Compression and occlusion culling for fast isosurface extrac...

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Workshop on Mathematical Foundations of Scientific visualization, computer Graphics, and Massive data Exploration, 2004

作者： Gregorski, Benjamin Senecal, Joshua Duchaineau, Mark Joy, Kenneth I. Center for Applied Scientific Computing Lawrence Livermore National Laboratory 7000 East Avenue LivermoreCA94551 United States Institute for Data Analysis and Visualization Department of Computer Science University of California Davis One Shields Avenue DavisCA95616 United States

ISBN: (纸本)9783642543005

We present two algorithms for data compression and occlusion culling that improve interactive, adaptive isosurface extraction from large volume datasets. Our algorithm, based on hierarchical tetrahedral meshes defined by longest edge bisection, allows arbitrary isosurfaces to be adaptively extracted at interactive rates from losslessly compressed volumes where the region of interest, determined at runtime by user interaction, is decompressed on-the-fly. For interactive applications, we exploit frame-to-frame coherence between consecutive views to simplify the mesh structure in occluded regions and eliminate occluded triangles significantly reducing the complexity of the visualized surface and the underlying multiresolution volume representation. We extend the use of hardware accelerated occlusion queries to adaptive isosurface extraction applications where the surface geometry and topology change with the level-of-detail and view-point and the user can select an arbitrary isovalue for visualization. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Extraction

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Automatic beam path analysis of laser wakefield particle acceleration data

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Computational science and Discovery 2009年第1期2卷

作者： Rübel, Oliver Geddes, Cameron G R. Cormier-Michel, Estelle Wu, Kesheng Prabhat Weber, Gunther H. Ushizima, Daniela M. Messmer, Peter Hagen, Hans Hamann, Bernd Bethel, Wes Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 One Cyclotron Road United States 'Visualization of Large and Unstructured Data Sets - Applic. in Geospatial Plan. Modeling and Eng. 'Technische Universität Kaiserslautern D-67653 Kaiserslautern Erwin-Schrödinger-Straße Germany Institute for Data Analysis and Visualization (IDAV) Department of Computer Science University of California Davis CA 95616 Davis One Shields Avenue United States LOASIS Program of Lawrence Berkeley National Laboratory Berkeley CA 94720 One Cyclotron Road United States Tech-X Corporation Boulder CO 80303 5621 Arapahoe Avenue United States

Numerical simulations of laser wakefield particle accelerators play a key role in the understanding of the complex acceleration process and in the design of expensive experimental facilities. As the size and complexity of simulation output grows, an increasingly acute challenge is the practical need for computational techniques that aid in scientific knowledge discovery. To that end, we present a set of data-understanding algorithms that work in concert in a pipeline fashion to automatically locate and analyze high-energy particle bunches undergoing acceleration in very large simulation datasets. These techniques work cooperatively by first identifying features of interest in individual timesteps, then integrating features across timesteps, and based on the information-derived perform analysis of temporally dynamic features. This combination of techniques supports accurate detection of particle beams enabling a deeper level of scientific understanding of physical phenomena than has been possible before. By combining efficient data analysis algorithms and state-of-the-art data management we enable high-performance analysis of extremely large particle datasets in 3D. We demonstrate the usefulness of our methods for a variety of 2D and 3D datasets and discuss the performance of our analysis pipeline. © 2009 IOP Publishing Ltd.

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Why interval arithmetic is so useful

Why interval arithmetic is so useful

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2nd Workshop of the DFG's International Research Training Group on visualization of Large and Unstructured data Sets - Applications in Geospatial Planning, Modeling, and Engineering, VLUDS 2007

作者： Hijazi, Younis Hagen, Hans Hansen, Charles Joy, Kenneth I. Department of Computer Sciences University of Kaiserslautern and IRTG 1131 D-67653 Kaiserslautern Germany SCI Institute University of Utah United States Computer Science Department Institute for Data Analysis and Visualization University of California Davis United States

ISBN: (纸本)9783885794417

Interval arithmetic was introduced by Ramon Moore [Moo66] in the 1960s as an approach to bound rounding errors in mathematical computation. The theory of interval analysis emerged considering the computation of both the exact solution and the error term as a single entity, i.e. the interval. Though a simple idea, it is a very powerful technique with numerous applications in mathematics, computer science, and engineering. In this survey we discuss the basic concepts of interval arithmetic and some of its extensions, and review successful applications of this theory in particular in computer science.

关键词： Computation theory

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Generation of accurate integral surfaces in time-dependent vector fields

Generation of accurate integral surfaces in time-dependent v...

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作者： Garth, Christoph Krishnan, Hari Tricoche, Xavier Bobach, Tom Joy, Kenneth I. Institute of Data Analysis and Visualization University of California Davis United States Computer Science Dept. Purdue University Geometric Algorithms Group University of Kaiserslautern

We present a novel approach for the direct computation of integral surfaces in time-dependent vector fields. As opposed to previous work, which we analyze in detail, our approach is based on a separation of integral surface computation into two stages: surface approximation and generation of a graphical representation. This allows us to overcome several limitations of existing techniques. We first describe an algorithm for surface integration that approximates a series of time lines using iterative refinement and computes a skeleton of the integral surface. In a second step, we generate a well-conditioned triangulation. Our approach allows a highly accurate treatment of very large time-varying vector fields in an efficient, streaming fashion. We examine the properties of the presented methods on several example datasets and perform a numerical study of its correctness and accuracy. Finally, we investigate some visualization aspects of integral surfaces. © 2008 IEEE.

关键词： Numerical methods

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PointCloudXplore 2: Visual exploration of 3D gene expression

PointCloudXplore 2: Visual exploration of 3D gene expression

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2nd Workshop of the DFG's International Research Training Group on visualization of Large and Unstructured data Sets - Applications in Geospatial Planning, Modeling, and Engineering, VLUDS 2007

作者： Rübel, O. Weber, G.H. Huang, M.-Y. Bethel, E.W. Keränen, S.V.E. Fowlkes, C.C. Hendriks, C.L. Luengo DePace, Angela H. Simirenko, L. Eisen, M.B. Biggin, M.D. Hagen, H. Malik, J. Knowles, D.W. Hamann, B. International Research Training Group University of Kaiserslautern Germany Institute for Data Analysis and Visualization University of California Davis United States Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Genomics Division Lawrence Berkeley National Laboratory Berkeley United States Computer Science Department University of California Irvine United States Computer Science Division University of California Berkeley United States Lawrence Berkeley National Laboratory Life Sciences Division Berkeley United States Department of Molecular and Cellular Biology Center for Integrative Genomics University of California Berkeley United States

ISBN: (纸本)9783885794417

To better understand how developmental regulatory networks are defined in the genome sequence, the Berkeley Drosophila Transcription Network Project (BDNTP ) has developed a suite of methods to describe 3D gene expression data, i.e., the output of the network at cellular resolution for multiple time points. To allow researchers to explore these novel data sets we have developed PointCloudXplore (PCX). In PCX we have linked physical and information visualization views via the concept of brushing (cell selection). For each view dedicated operations for performing selection of cells are available. In PCX, all cell selections are stored in a central management system. Cells selected in one view can in this way be highlighted in any view allowing further cell subset properties to be determined. Complex cell queries can be defined by combining different cell selections using logical operations such as AND, OR, and NOT. Here we are going to provide an overview of PointCloudXplore 2 (PCX2), the latest publicly available version of PCX. PCX2 has shown to be an effective tool for visual exploration of 3D gene expression data. We discuss (i) all views available in PCX2, (ii) different strategies to perform cell selection, (iii) the basic architecture of PCX2., and (iv) illustrate the usefulness of PCX2 using selected examples.

关键词： visualization

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Feature Identification and Extraction in Function Fields

Feature Identification and Extraction in Function Fields

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9th Joint Eurographics/IEEE VGTC Symposium on visualization, EuroVis 2007

作者： Anderson, John C. Gosink, Luke J. Duchaineau, Mark A. Joy, Kenneth I. Institute for Data Analysis and Visualization Department of Computer Science University of California Davis United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory

ISBN: (纸本)9783905673456

We present interactive techniques for identifying and extracting features in function fields. Function fields map points in n-dimensional Euclidean space to 1-dimensional scalar functions. Visual feature identification is accomplished by interactively rendering scalar distance fields, constructed by applying a function-space distance metric over the function field. Combining visual exploration with feature extraction queries, formulated as a set of function-space constraints, facilitates quantitative analysis and annotation. Numerous application domains give rise to function fields. We present results for two-dimensional hyperspectral images, and a simulated time-varying, three-dimensional air quality dataset. © The Eurographics Association 2007.

关键词： Air quality

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A Geometric Representation of Protein Sequences

A Geometric Representation of Protein Sequences

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shengyin Gu Olivier Poch Bernd Hamann Patrice Koehl Institute for Data Analysis and Visualization Department of Computer Science University of California Davis CA USA Laboratoire de Biologie Structurale IGBMC Strasbourg France Institute for Data Analysis and Visualization Department of Computer Science University of California Davis CA USA Genome Center University of California Davis CA USA

The amino acid sequence of a protein is the key to understanding its structure and ultimately its function in the cell. This paper addresses the fundamental issue of encoding amino acids in ways that the visualization of protein sequences facilitates the decoding of its information content. We show that a feature-based representation in a three-dimensional (3D) space derived from substitution matrices provides an adequate representation from which the domain content of a protein can be predicted. In addition, we show that each dimension of the feature space can be related to a physical property of the amino acids.

关键词： Amino acids Protein sequence Bioinformatics data visualization USA Councils Genomics Encoding data analysis computer science Biological information theory

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Quality assessment of Affymetrix GeneChip data using the EM algorithm and a naïve Bayes classifier

Quality assessment of Affymetrix GeneChip data using the EM ...

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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Howard, Brian E. Sick, Beate Perera, Imara Yang, Ju Im Winter-Sederoff, Heike Heber, Steffen Bioinformatics Research Center North Carolina State University Raleigh NC United States Institute of Data Analysis and Process Design Zurich University of Applied Science Winterthur Winterthur Switzerland Department of Plant Biology North Carolina State University Raleigh NC United States Department of Computer Science North Carolina State University Raleigh NC United States

ISBN: (纸本)1424415098

Recent research has demonstrated the utility of using supervised classification systems for automatic identification of low quality microarray data. However, this approach requires annotation of a large training set by a qualified expert. In this paper we demonstrate the utility of an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and naïve Bayes classification. On our test set, this system exhibits performance comparable to that of an analogous supervised learner constructed from the same training data. ©2007 IEEE.

关键词： Maximum principle

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