Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference, S. Palande, V. Jose, B. Zielinski, J. Anderson, P.T. Fletcher, B. Wang. In Connectomics in NeuroImaging, Springer International Publishing, pp. 98--107. 2017. DOI: 10.1007/978-3-319-67159-8_12 A large body of evidence relates autism with abnormal structural and functional brain connectivity. Structural covariance MRI (scMRI) is a technique that maps brain regions with covarying gray matter density across subjects. It provides a way to probe the anatomical structures underlying intrinsic connectivity networks (ICNs) through the analysis of the gray matter signal covariance. In this paper, we apply topological data analysis in conjunction with scMRI to explore network-specific differences in the gray matter structure in subjects with autism versus age-, gender- and IQ-matched controls. Specifically, we investigate topological differences in gray matter structures captured by structural covariance networks (SCNs) derived from three ICNs strongly implicated in autism, namely, the salience network (SN), the default mode network (DMN) and the executive control network (ECN). By combining topological data analysis with statistical inference, our results provide evidence of statistically significant network-specific structural abnormalities in autism, from SCNs derived from SN and ECN. These differences in brain architecture are consistent with direct structural analysis using scMRI (Zielinski et al. 2012). |
Visualization in Meteorology---A Survey of Techniques and Tools for Data Analysis Tasks M. Rautenhaus, M. Böttinger, S. Siemen, R. Hoffman, R.M. Kirby, M. Mirzargar, N. Rober, R. Westermann. In IEEE Transactions on Visualization and Computer Graphics, IEEE, pp. 1--1. 2017. DOI: 10.1109/tvcg.2017.2779501 This article surveys the history and current state of the art of visualization in meteorology, focusing on visualization techniques and tools used for meteorological data analysis. We examine characteristics of meteorological data and analysis tasks, describe the development of computer graphics methods for visualization in meteorology from the 1960s to today, and visit the state of the art of visualization techniques and tools in operational weather forecasting and atmospheric research. We approach the topic from both the visualization and the meteorological side, showing visualization techniques commonly used in meteorological practice, and surveying recent studies in visualization research aimed at meteorological applications. Our overview covers visualization techniques from the fields of display design, 3D visualization, flow dynamics, feature-based visualization, comparative visualization and data fusion, uncertainty and ensemble visualization, interactive visual analysis, efficient rendering, and scalability and reproducibility. We discuss demands and challenges for visualization research targeting meteorological data analysis, highlighting aspects in demonstration of benefit, interactive visual analysis, seamless visualization, ensemble visualization, 3D visualization, and technical issues. |
Reducing network congestion and synchronization overhead during aggregation of hierarchical data, S. Kumar, D. Hoang, S. Petruzza, J. Edwards, V. Pascucci. In 2017 IEEE 24th International Conference on High Performance Computing (HiPC), IEEE, Dec, 2017. DOI: 10.1109/hipc.2017.00034 Hierarchical data representations have been shown to be effective tools for coping with large-scale scientific data. Writing hierarchical data on supercomputers, however, is challenging as it often involves all-to-one communication during aggregation of low-resolution data which tends to span the entire network domain, resulting in several bottlenecks. We introduce the concept of indexing templates, which succinctly describe data organization and can be used to alter movement of data in beneficial ways. We present two techniques, domain partitioning and localized aggregation, that leverage indexing templates to alleviate congestion and synchronization overheads during data aggregation. We report experimental results that show significant I/O speedup using our proposed schemes on two of today's fastest supercomputers, Mira and Shaheen II, using the Uintah and S3D simulation frameworks. |
Interactive Visual Exploration And Refinement Of Cluster Assignments M. Kern, A. Lex, N. Gehlenborg, C. R. Johnson. In BMC Bioinformatics, Cold Spring Harbor Laboratory, April, 2017. DOI: 10.1101/123844 Background: |
Massively Parallel Simulations of Spread of Infectious Diseases over Realistic Social Networks A. Bhatele, J. Yeom, N. Jain, C. J. Kuhlman, Y. Livnat, K. R. Bisset, L. V. Kale, M. V. Marathe. In 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), May, 2017. DOI: 10.1109/ccgrid.2017.141 Controlling the spread of infectious diseases in large populations is an important societal challenge. Mathematically, the problem is best captured as a certain class of reaction-diffusion processes (referred to as contagion processes) over appropriate synthesized interaction networks. Agent-based models have been successfully used in the recent past to study such contagion processes. We describe EpiSimdemics, a highly scalable, parallel code written in Charm++ that uses agent-based modeling to simulate disease spreads over large, realistic, co-evolving interaction networks. We present a new parallel implementation of EpiSimdemics that achieves unprecedented strong and weak scaling on different architectures — Blue Waters, Cori and Mira. EpiSimdemics achieves five times greater speedup than the second fastest parallel code in this field. This unprecedented scaling is an important step to support the long term vision of real-time epidemic science. Finally, we demonstrate the capabilities of EpiSimdemics by simulating the spread of influenza over a realistic synthetic social contact network spanning the continental United States (∼280 million nodes and 5.8 billion social contacts). |
A Virtual Reality Visualization Tool for Neuron Tracing W. Usher, P. Klacansky, F. Federer, P. T. Bremer, A. Knoll, J. Yarch, A. Angelucci, V. Pascucci. In IEEE Transactions on Visualization and Computer Graphics, IEEE, 2017. ISSN: 1077-2626 DOI: 10.1109/TVCG.2017.2744079 Tracing neurons in large-scale microscopy data is crucial to establishing a wiring diagram of the brain, which is needed to understand how neural circuits in the brain process information and generate behavior. Automatic techniques often fail for large and complex datasets, and connectomics researchers may spend weeks or months manually tracing neurons using 2D image stacks. We present a design study of a new virtual reality (VR) system, developed in collaboration with trained neuroanatomists, to trace neurons in microscope scans of the visual cortex of primates. We hypothesize that using consumer-grade VR technology to interact with neurons directly in 3D will help neuroscientists better resolve complex cases and enable them to trace neurons faster and with less physical and mental strain. We discuss both the design process and technical challenges in developing an interactive system to navigate and manipulate terabyte-sized image volumes in VR. Using a number of different datasets, we demonstrate that, compared to widely used commercial software, consumer-grade VR presents a promising alternative for scientists. |
Progressive CPU Volume Rendering with Sample Accumulation W. Usher, J. Amstutz, C. Brownlee, A. Knoll, I. Wald . In Eurographics Symposium on Parallel Graphics and Visualization, Edited by Alexandru Telea and Janine Bennett, The Eurographics Association, 2017. ISBN: 978-3-03868-034-5 ISSN: 1727-348X DOI: 10.2312/pgv.20171090 We present a new method for progressive volume rendering by accumulating object-space samples over successively rendered frames. Existing methods for progressive refinement either use image space methods or average pixels over frames, which can blur features or integrate incorrectly with respect to depth. Our approach stores samples along each ray, accumulates new samples each frame into a buffer, and progressively interleaves and integrates these samples. Though this process requires additional memory, it ensures interactivity and is well suited for CPU architectures with large memory and cache. This approach also extends well to distributed rendering in cluster environments. We implement this technique in Intel's open source OSPRay CPU ray tracing framework and demonstrate that it is particularly useful for rendering volumetric data with costly sampling functions. |
FluoRender: joint freehand segmentation and visualization for many-channel fluorescence data analysis Y. Wan, H. Otsuna, H. A. Holman, B. Bagley, M. Ito, A. K. Lewis, M. Colasanto, G. Kardon, K. Ito, C. Hansen. In BMC Bioinformatics, Vol. 18, No. 1, Springer Nature, May, 2017. DOI: 10.1186/s12859-017-1694-9 Background: |
State of the Art in Transfer Functions for Direct Volume Rendering P. Ljung, J. Krüger, E. Gröller, M. Hadwiger, C. D. Hansen,, A. Ynnerman. In Computer Graphics Forum, Vol. 35, No. 3, Wiley-Blackwell, pp. 669--691. June, 2016. DOI: 10.1111/cgf.12934 A central topic in scientific visualization is the transfer function (TF) for volume rendering. The TF serves a fundamental role in translating scalar and multivariate data into color and opacity to express and reveal the relevant features present in the data studied. Beyond this core functionality, TFs also serve as a tool for encoding and utilizing domain knowledge and as an expression for visual design of material appearances. TFs also enable interactive volumetric exploration of complex data. The purpose of this state-of-the-art report (STAR) is to provide an overview of research into the various aspects of TFs, which lead to interpretation of the underlying data through the use of meaningful visual representations. The STAR classifies TF research into the following aspects: dimensionality, derived attributes, aggregated attributes, rendering aspects, automation, and user interfaces. The STAR concludes with some interesting research challenges that form the basis of an agenda for the development of next generation TF tools and methodologies. |
VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures K. Moreland, C. Sewell, W. Usher, L. Lo, J. Meredith, D. Pugmire, J. Kress, H. Schroots, K. Ma, H. Childs, M. Larsen, C. Chen, R. Maynard, B. Geveci. In IEEE Computer Graphics and Applications, Vol. 36, No. 3, pp. 48--58. May, 2016. ISSN: 0272-1716 DOI: 10.1109/MCG.2016.48 Traditional scientific visualization software approaches do not fare well in massively threaded environments. To address the needs of the high-performance computing community, the VTK-m framework fills the gaps in functionality by bringing together the most recent research. |