SCIENTIFIC COMPUTING AND IMAGING INSTITUTE
at the University of Utah

An internationally recognized leader in visualization, scientific computing, and image analysis

SCI Publications

2007


J. Cates, P.T. Fletcher, M. Styner, M. Shenton, R.T. Whitaker. “Shape Modeling and Analysis with Entropy-Based Particle Systems,” In Proceedings of Information Processing in Medical Imaging (IPMI) 2007, LNCS 4584, pp. 333--345. 2007.


D. Chisnall, M. Chen, C.D. Hansen. “Ray-Driven Dynamic Working Set Rendering for Complex Volume Scene Graphs Involving Large Point Clouds,” In The Visual Computer, Vol. 23, No. 3, pp. 167--179. 2007.


S. Curtis, R.M. Kirby, J.K. Ryan, C.-W. Shu. “Post-Processing for the Discontinuous Galerkin Method Over Non-Uniform Meshes,” In SIAM Journal of Scientific Computing, Vol. 30, No. 1, pp. 272--289. 2007.


K. Damevski, K. Zhang, S.G. Parker. “Practical Parallel Remote Method Invocation for the Babel Compiler,” In Proceedings of the 2007 Symposium on Component and Framework Technology in High-Performance and Scientific Computing, pp. 131--140. 2007.


K. Damevski, A. Swaminathan, S.G. Parker. “Highly Scalable Distributed Component Framework for Scientific Computing,” In Proceedings of the 3rd International Conference on High Performance Computing and Communication (HPCC 2007), 2007.


K. Damevski, A. Swaminathan, S.G. Parker. “CCALoop: Scalable Design of a Distributed Component Framework,” In Proceedings of the 16th International High Performance Distributed Computing Symposium (HPDC 2007), pp. 213--214. 2007.


J.D. Daniels, L. Ha, T. Ochotta, C.T. Silva. “Robust Smooth Feature Extraction from Point Clouds,” In Proceedings of the 2007 International Conference on Shape Modeling and Applications, Lyon, France, 2007.


B. Davis, P.T. Fletcher, E. Bullitt, S. Joshi. “Population Shape Regression From Random Design Data,” In Proceedings of the Eleventh IEEE International Conference on Computer Vision (ICCV '07), pp. 1-7. 2007.


S. Davidson, S. Cohen-Boulakia, A. Eyal, B. Ludascher, T. McPhillips, S. Bowers, J. Freire. “Provenance in Scientific Workflow Systems,” In IEEE Data Engineering Bulletin, Vol. 32, No. 4, pp. 44--50. 2007.


C.C. Douglas, M.J. Cole, P. Dostert, Y. Efendiev, R.E. Ewing, G. Haase, J. Hatcher, M. Iskandarani, C.R. Johnson, R.A. Lodder. “Dynamically identifying and tracking contaminants in water bodies,” In Proceedings of the 7th International Conference on Computational Science (ICCS) 2007, Part I, Beijing, China, Lecture Notes in Computer Science (LNCS), Vol. 4887, Edited by Y. Shi and G.D. van Albada and P.M.A. Sloot and J.J. Dongarra, Springer-Verlag, Berlin Heidelberg, pp. 1002--1009. May, 2007.


C.C. Douglas, D. Bansal, J.D. Beezley, L.S. Bennethum, S. Chakraborty, J.L. Coen, Y. Efendiev, R.E. Ewing, J. Hatcher, M. Iskandarani, C.R. Johnson, M. Kim, D. Li, R.A. Lodder, J. Mandel, G. Qin, A. Vodacek. “Dynamic data-driven application systems for empty houses, contaminat tracking, and wildland fireline prediction,” In Grid-Based Problem Solving Environments, IFIP series, Edited by P.W. Gaffney and J.C.T. Pool, Springer-Verlag, Berlin, pp. 255-272. 2007.
DOI: 10.1007/978-0-387-73659-4_14


P.T. Fletcher, S. Joshi. “Riemannian Geometry for the Statistical Analysis of Diffusion Tensor Data,” In Signal Processing, Vol. 87, No. 2, pp. 250--262. February, 2007.


P.T. Fletcher, R. Tao, W.-K. Jeong, R.T. Whitaker. “A Volumetric Approach to Quantifying Region-to-Region White Matter Connectivity in Diffusion Tensor MRI,” In Information Processing in Medical Imaging, Vol. 4584/2007, pp. 346--358. 2007.


P.T. Fletcher, S. Powell, N.L. Foster, S. Joshi. “Quantifying Metabolic Asymmetry Modulo Structure in Alzheimer's Disease,” In Lecture Notes in Computer Science, Springer, pp. 446--457. 2007.
DOI: 10.1007/978-3-540-73273-0_37
PubMed ID: 17633720


H. Friedrich, I. Wald, P. Slusallek. “Interactive Iso-Surface Ray Tracing of Massive Volumetric Data Sets,” In Proceedings of the 2007 Eurographics Symposium on Parallel Graphics and Visualization, pp. 109--116. 2007.


C. Garth, G. Li, X. Tricoche, C.D. Hansen, H. Hagen. “Visualization of Coherent Structures in Transient 2D Flows,” In Proceedings of the 2007 Workshop on Topology-Based Method in Visualization (TopoInVis), Grimma, Germany, March, 2007.


C. Garth, F. Gerhardt, X. Tricoche, H. Hagen. “Efficient Computation and Visualization of Coherent Structures in Fluid Flow Applications,” In Proceeding of IEEE Visualization 2007, pp. 1464--1471. 2007.


C. Garth, B. Laramee, X. Tricoche, H. Hauser, J. Schneider. “Extraction and Visualization of Swirl and Tumble Motion from Engine Simulation Data,” In Topology-based Methods in Visualization, Mathematics and Visualization, Springer Berlin Heidelberg, pp. 121--135. 2007.
ISBN: 978-3-540-70822-3
DOI: 10.1007/978-3-540-70823-0_9

ABSTRACT

An optimal combustion process within an engine block is central to the performance of many motorized vehicles. Associated with this process are two important patterns of flow: swirl and tumble motion, which optimize the mixing of fluid within each of an engine's cylinders. The simulation data associated with in-cylinder tumble motion within a gas engine, given on an unstructured, timevarying and adaptive resolution CFD grid, demands robust visualization methods that apply to unsteady flow. Good visualizations are necessary to analyze the simulation data of these in-cylinder flows. We present a range of methods including integral, feature-based, and image-based schemes with the goal of extracting and visualizing these two important patterns of motion. We place a strong emphasis on automatic and semi-automatic methods, including topological analysis, that require little or no user input.We make effective use of animation to visualize the time-dependent simulation data. We also describe the challenges and implementation measures necessary in order to apply the presented methods to time-varying, volumetric grids.


S.E. Geneser, R.M. Kirby, D. Xiu, F.B. Sachse. “Stochastic Markovian Modeling of Electrophysiology of Ion Channels: Reconstruction of Standard Deviations in Macroscopic Currents,” In Journal of Theoretical Biology, Vol. 245, No. 4, pp. 627--637. 2007.
DOI: 10.1016/j.jtbi.2006.10.016

ABSTRACT

Markovian models of ion channels have proven useful in the reconstruction of experimental data and prediction of cellular electrophysiology. We present the stochastic Galerkin method as an alternative to Monte Carlo and other stochastic methods for assessing the impact of uncertain rate coefficients on the predictions of Markovian ion channel models. We extend and study two different ion channel models: a simple model with only a single open and a closed state and a detailed model of the cardiac rapidly activating delayed rectifier potassium current. We demonstrate the efficacy of stochastic Galerkin methods for computing solutions to systems with random model parameters. Our studies illustrate the characteristic changes in distributions of state transitions and electrical currents through ion channels due to random rate coefficients. Furthermore, the studies indicate the applicability of the stochastic Galerkin technique for uncertainty and sensitivity analysis of bio-mathematical models.

Keywords: Stochastic Galerkin, Polynomial chaos, Stochastic processes, Markov modeling, Ion channels


S. Gerber, T. Tasdizen, R.T. Whitaker. “Robust Non-linear Dimensionality Reduction using Successive 1-Dimensional Laplacian Eigenmaps,” In Proceedings of the 2007 International Conference on Machine Learning (ICML), pp. 281--288. 2007.