ISAVS: Interactive Scalable Analysis and Visualization System|
S. Petruzza, A. Venkat, A. Gyulassy, G. Scorzelli, F. Federer, A. Angelucci, V. Pascucci, P. T. Bremer. In ACM SIGGRAPH Asia 2017 Symposium on Visualization, ACM Press, 2017.
Modern science is inundated with ever increasing data sizes as computational capabilities and image acquisition techniques continue to improve. For example, simulations are tackling ever larger domains with higher fidelity, and high-throughput microscopy techniques generate larger data that are fundamental to gather biologically and medically relevant insights. As the image sizes exceed memory, and even sometimes local disk space, each step in a scientific workflow is impacted. Current software solutions enable data exploration with limited interactivity for visualization and analytic tasks. Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability. We present a software infrastructure that simplifies end-to-end visualization and analysis of massive data. First, a hierarchical streaming data access layer enables interactive exploration of remote data, with fast data fetching to test analytics on subsets of the data. Second, a library simplifies the process of developing new analytics algorithms, allowing users to rapidly prototype new approaches and deploy them in an HPC setting. Third, a scalable runtime system automates mapping analysis algorithms to whatever computational hardware is available, reducing the complexity of developing scaling algorithms. We demonstrate the usability and performance of our system using a use case from neuroscience: filtering, registration, and visualization of tera-scale microscopy data. We evaluate the performance of our system using a leadership-class supercomputer, Shaheen II.
CPU Volume Rendering of Adaptive Mesh Refinement Data|
I. Wald, C. Brownlee, W. Usher, A. Knoll. In ACM SIGGRAPH Asia 2017 Symposium on Visualization, ACM Press, 2017.
Adaptive Mesh Refinement (AMR) methods are widespread in scientific computing, and visualizing the resulting data with efficient and accurate rendering methods can be vital for enabling interactive data exploration. In this work, we detail a comprehensive solution for directly volume rendering block-structured (Berger-Colella) AMR data in the OSPRay interactive CPU ray tracing framework. In particular, we contribute a general method for representing and traversing AMR data using a kd-tree structure, and four different reconstruction options, one of which in particular (the basis function approach) is novel compared to existing methods. We demonstrate our system on two types of block-structured AMR data and compressed scalar field data, and show how it can be easily used in existing production-ready applications through a prototypical integration in the widely used visualization program ParaView.
|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.
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).
Worksheets for Guiding Novices through the Visualization Design Process|
S. McKenna, A. Lex, M. Meyer. In CoRR, 2017.
For visualization pedagogy, an important but challenging notion to teach is design, from making to evaluating visualization encodings, user interactions, or data visualization systems. In our previous work, we introduced the design activity framework to codify the high-level activities of the visualization design process. This framework has helped structure experts' design processes to create visualization systems, but the framework's four activities lack a breakdown into steps with a concrete example to help novices utilizing this framework in their own real-world design process. To provide students with such concrete guidelines, we created worksheets for each design activity: understand, ideate, make, and deploy. Each worksheet presents a high-level summary of the activity with actionable, guided steps for a novice designer to follow. We validated the use of this framework and the worksheets in a graduate-level visualization course taught at our university. For this evaluation, we surveyed the class and conducted 13 student interviews to garner qualitative, open-ended feedback and suggestions on the worksheets. We conclude this work with a discussion and highlight various areas for future work on improving visualization design pedagogy.
Exploration of Heterogeneous Data Using Robust Similarity|
M. Mirzargar, R.T. Whitaker, R.M. Kirby. In CoRR, 2017.
Heterogeneous data pose serious challenges to data analysis tasks, including exploration and visualization. Current techniques often utilize dimensionality reductions, aggregation, or conversion to numerical values to analyze heterogeneous data. However, the effectiveness of such techniques to find subtle structures such as the presence of multiple modes or detection of outliers is hindered by the challenge to find the proper subspaces or prior knowledge to reveal the structures. In this paper, we propose a generic similarity-based exploration technique that is applicable to a wide variety of datatypes and their combinations, including heterogeneous ensembles. The proposed concept of similarity has a close connection to statistical analysis and can be deployed for summarization, revealing fine structures such as the presence of multiple modes, and detection of anomalies or outliers. We then propose a visual encoding framework that enables the exploration of a heterogeneous dataset in different levels of detail and provides insightful information about both global and local structures. We demonstrate the utility of the proposed technique using various real datasets, including ensemble data.
Visualizing Sensor Network Coverage with Location Uncertainty|
T. Sodergren, J. Hair, J.M. Phillips, B. Wang. In CoRR, Vol. abs/1710.06925, 2017.
We present an interactive visualization system for exploring the coverage in sensor networks with uncertain sensor locations. We consider a simple case of uncertainty where the location of each sensor is confined to a discrete number of points sampled uniformly at random from a region with a fixed radius. Employing techniques from topological data analysis, we model and visualize network coverage by quantifying the uncertainty defined on its simplicial complex representations. We demonstrate the capabilities and effectiveness of our tool via the exploration of randomly distributed sensor networks.
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.
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.
Taggle: Scalable Visualization of Tabular Data through Aggregation|
K. Furmanova, S. Gratzl, H. Stitz, T. Zichner, M. Jaresova, M. Ennemoser, A. Lex, M. Streit. In CoRR, 2017.
Visualization of tabular data---for both presentation and exploration purposes---is a well-researched area. Although effective visual presentations of complex tables are supported by various plotting libraries, creating such tables is a tedious process and requires scripting skills. In contrast, interactive table visualizations that are designed for exploration purposes either operate at the level of individual rows, where large parts of the table are accessible only via scrolling, or provide a high-level overview that often lacks context-preserving drill-down capabilities. In this work we present Taggle, a novel visualization technique for exploring and presenting large and complex tables that are composed of individual columns of categorical or numerical data and homogeneous matrices. The key contribution of Taggle is the hierarchical aggregation of data subsets, for which the user can also choose suitable visual representations.The aggregation strategy is complemented by the ability to sort hierarchically such that groups of items can be flexibly defined by combining categorical stratifications and by rich data selection and filtering capabilities. We demonstrate the usefulness of Taggle for interactive analysis and presentation of complex genomics data for the purpose of drug discovery.
|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.
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.
Vietoris-Rips and Cech Complexes of Metric Gluings|
M. Adamaszek, H. Adams, E. Gasparovic, M. Gommel, E. Purvine, R. Sazdanovic, B. Wang, Y. Wang, L. Ziegelmeier. In CoRR, 2017.
We study Vietoris-Rips and Cech complexes of metric wedge sums and metric gluings. We show that the Vietoris-Rips (resp. Cech) complex of a wedge sum, equipped with a natural metric, is homotopy equivalent to the wedge sum of the Vietoris-Rips (resp. Cech) complexes. We also provide generalizations for certain metric gluings, i.e. when two metric spaces are glued together along a common isometric subset. As our main example, we deduce the homotopy type of the Vietoris-Rips complex of two metric graphs glued together along a sufficiently short path. As a result, we can describe the persistent homology, in all homological dimensions, of the Vietoris-Rips complexes of a wide class of metric graphs.
Sheaf-Theoretic Stratification Learning|
A. Brown, B. Wang. In CoRR, 2017.
In this paper, we investigate a sheaf-theoretic interpretation of stratification learning. Motivated by the work of Alexandroff (1937) and McCord (1978), we aim to redirect efforts in the computational topology of triangulated compact polyhedra to the much more computable realm of sheaves on partially ordered sets. Our main result is the construction of stratification learning algorithms framed in terms of a sheaf on a partially ordered set with the Alexandroff topology. We prove that the resulting decomposition is the unique minimal stratification for which the strata are homogeneous and the given sheaf is constructible. In particular, when we choose to work with the local homology sheaf, our algorithm gives an alternative to the local homology transfer algorithm given in Bendich et al. (2012), and the cohomology stratification algorithm given in Nanda (2017). We envision that our sheaf-theoretic algorithm could give rise to a larger class of stratification beyond homology-based stratification. This approach also points toward future applications of sheaf theory in the study of topological data analysis by illustrating the utility of the language of sheaf theory in generalizing existing algorithms.
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.
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.
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.
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.
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.
Pathways for Theoretical Advances in Visualization|
M. Chen, G. Grinstein, C. R. Johnson, J. Kennedy, M. Tory. In IEEE Computer Graphics and Applications, IEEE, pp. 103--112. July, 2017.
More than a decade ago, Chris Johnson proposed the "Theory of Visualization" as one of the top research problems in visualization. Since then, there have been several theory-focused events, including three workshops and three panels at IEEE Visualization (VIS) Conferences. Together, these events have produced a set of convincing arguments.
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.
Uncertainty Footprint: Visualization of Nonuniform Behavior of Iterative Algorithms Applied to 4D Cell Tracking|
Y. Wan, C. Hansen. In Computer Graphics Forum, Wiley, 2017.
Research on microscopy data from developing biological samples usually requires tracking individual cells over time. When cells are three-dimensionally and densely packed in a time-dependent scan of volumes, tracking results can become unreliable and uncertain. Not only are cell segmentation results often inaccurate to start with, but it also lacks a simple method to evaluate the tracking outcome. Previous cell tracking methods have been validated against benchmark data from real scans or artificial data, whose ground truth results are established by manual work or simulation. However, the wide variety of real-world data makes an exhaustive validation impossible. Established cell tracking tools often fail on new data, whose issues are also difficult to diagnose with only manual examinations. Therefore, data-independent tracking evaluation methods are desired for an explosion of microscopy data with increasing scale and resolution. In this paper, we propose the uncertainty footprint, an uncertainty quantification and visualization technique that examines nonuniformity at local convergence for an iterative evaluation process on a spatial domain supported by partially overlapping bases. We demonstrate that the patterns revealed by the uncertainty footprint indicate data processing quality in two algorithms from a typical cell tracking workflow – cell identification and association. A detailed analysis of the patterns further allows us to diagnose issues and design methods for improvements. A 4D cell tracking workflow equipped with the uncertainty footprint is capable of self diagnosis and correction for a higher accuracy than previous methods whose evaluation is limited by manual examinations.
Driving Interactive Graph Exploration Using 0-Dimensional Persistent Homology Features|
A. Suh, M. Hajij, B. Wang, C. Scheidegger, P. Rosen. In CoRR, 2017.
Graphs are commonly used to encode relationships among entities, yet, their abstractness makes them incredibly difficult to analyze. Node-link diagrams are a popular method for drawing graphs. Classical techniques for the node-link diagrams include various layout methods that rely on derived information to position points, which often lack interactive exploration functionalities; and force-directed layouts, which ignore global structures of the graph. This paper addresses the graph drawing challenge by leveraging topological features of a graph as derived information for interactive graph drawing. We first discuss extracting topological features from a graph using persistent homology. We then introduce an interactive persistence barcodes to study the substructures of a force-directed graph layout; in particular, we add contracting and repulsing forces guided by the 0-dimensional persistent homology features. Finally, we demonstrate the utility of our approach across three datasets.
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.
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.