DevLan/vispubs huggingface dataset:
In Situ Eddy Analysis in a High-Resolution Ocean Climate Model
Authors: Jonathan Woodring, Mark R. Petersen, Andre Schmeißer, John Patchett, James P. Ahrens, Hans Hagen
Published at SciVis, 2015

An eddy is a feature associated with a rotating body of fluid, surrounded by a ring of shearing fluid. In the ocean, eddies are 10 to 150 km in diameter, are spawned by boundary currents and baroclinic instabilities, may live for hundreds of days, and travel for hundreds of kilometers. Eddies are important in climate studies because they transport heat, salt, and nutrients through the world's oceans and are vessels of biological productivity. The study of eddies in global ocean-climate models requires large-scale, high-resolution simulations. This poses a problem for feasible (timely) eddy analysis, as ocean simulations generate massive amounts of data, causing a bottleneck for traditional analysis workflows. To enable eddy studies, we have developed an in situ workflow for the quantitative and qualitative analysis of MPAS-Ocean, a high-resolution ocean climate model, in collaboration with the ocean model research and development process. Planned eddy analysis at high spatial and temporal resolutions will not be possible with a postprocessing workflow due to various constraints, such as storage size and I/O time, but the in situ workflow enables it and scales well to ten-thousand processing elements.
Interactive Visualization for Singular Fibers of Functions f : R3 → R2
Authors: Daisuke Sakurai, Osamu Saeki, Hamish A. Carr, Hsiang-Yun Wu, Takahiro Yamamoto, David J. Duke, Shigeo Takahashi
Published at SciVis, 2015

Scalar topology in the form of Morse theory has provided computational tools that analyze and visualize data from scientific and engineering tasks. Contracting isocontours to single points encapsulates variations in isocontour connectivity in the Reeb graph. For multivariate data, isocontours generalize to fibers-inverse images of points in the range, and this area is therefore known as fiber topology. However, fiber topology is less fully developed than Morse theory, and current efforts rely on manual visualizations. This paper presents how to accelerate and semi-automate this task through an interface for visualizing fiber singularities of multivariate functions R3→R2. This interface exploits existing conventions of fiber topology, but also introduces a 3D view based on the extension of Reeb graphs to Reeb spaces. Using the Joint Contour Net, a quantized approximation of the Reeb space, this accelerates topological visualization and permits online perturbation to reduce or remove degeneracies in functions under study. Validation of the interface is performed by assessing whether the interface supports the mathematical workflow both of experts and of less experienced mathematicians.
Interstitial and Interlayer Ion Diffusion Geometry Extraction in Graphitic Nanosphere Battery Materials
Authors: Attila Gyulassy, Aaron Knoll, Kah Chun Lau, Bei Wang 0001, Peer-Timo Bremer, Michael E. Papka, Larry A. Curtiss, Valerio Pascucci
Published at SciVis, 2015

Large-scale molecular dynamics (MD) simulations are commonly used for simulating the synthesis and ion diffusion of battery materials. A good battery anode material is determined by its capacity to store ion or other diffusers. However, modeling of ion diffusion dynamics and transport properties at large length and long time scales would be impossible with current MD codes. To analyze the fundamental properties of these materials, therefore, we turn to geometric and topological analysis of their structure. In this paper, we apply a novel technique inspired by discrete Morse theory to the Delaunay triangulation of the simulated geometry of a thermally annealed carbon nanosphere. We utilize our computed structures to drive further geometric analysis to extract the interstitial diffusion structure as a single mesh. Our results provide a new approach to analyze the geometry of the simulated carbon nanosphere, and new insights into the role of carbon defect size and distribution in determining the charge capacity and charge dynamics of these carbon based battery materials.
Intuitive Exploration of Volumetric Data Using Dynamic Galleries
Authors: Daniel Jönsson, Martin Falk, Anders Ynnerman
Published at SciVis, 2015

In this work we present a volume exploration method designed to be used by novice users and visitors to science centers and museums. The volumetric digitalization of artifacts in museums is of rapidly increasing interest as enhanced user experience through interactive data visualization can be achieved. This is, however, a challenging task since the vast majority of visitors are not familiar with the concepts commonly used in data exploration, such as mapping of visual properties from values in the data domain using transfer functions. Interacting in the data domain is an effective way to filter away undesired information but it is difficult to predict where the values lie in the spatial domain. In this work we make extensive use of dynamic previews instantly generated as the user explores the data domain. The previews allow the user to predict what effect changes in the data domain will have on the rendered image without being aware that visual parameters are set in the data domain. Each preview represents a subrange of the data domain where overview and details are given on demand through zooming and panning. The method has been designed with touch interfaces as the target platform for interaction. We provide a qualitative evaluation performed with visitors to a science center to show the utility of the approach.
Inviwo ??? An extensible, multi-purpose visualization framework
Authors: Erik Sundén, Peter Steneteg, Sathish Kottravel, Daniel Jönsson, Rickard Englund, Martin Falk, Timo Ropinski
Published at SciVis, 2015

To enable visualization research impacting other scientific domains, the availability of easy-to-use visualization frameworks is essential. Nevertheless, an easy-to-use system also has to be adapted to the capabilities of modern hardware architectures, as only this allows for realizing interactive visualizations. With this trade-off in mind, we have designed and realized the cross-platform Inviwo (Interactive Visualization Workshop) visualization framework, that supports both interactive visualization research as well as efficient visualization application development and deployment. In this poster we give an overview of the architecture behind Inviwo, and show how its design enables us and other researchers to realize their visualization ideas efficiently. Inviwo consists of a modern and lightweight, graphics independent core, which is extended by optional modules that encapsulate visualization algorithms, well-known utility libraries and commonly used parallel-processing APIs (such as OpenGL and OpenCL). The core enables a simplistic structure for creating bridges between the different modules regarding data transfer across architecture and devices with an easy-to-use screen graph and minimalistic programming. Making the base structures in a modern way while providing intuitive methods of extending the functionality and creating modules based on other modules, we hope that Inviwo can help the visualization community to perform research through a rapid-prototyping design and GUI, while at the same time allowing users to take advantage of the results implemented in the system in any way they desire later on. Inviwo is publicly available at www.inviwo.org, and can be used freely by anyone under a permissive free software license (Simplified BSD).
Isosurface Visualization of Data with Nonparametric Models for Uncertainty
Authors: Tushar M. Athawale, Elham Sakhaee, Alireza Entezari
Published at SciVis, 2015

The problem of isosurface extraction in uncertain data is an important research problem and may be approached in two ways. One can extract statistics (e.g., mean) from uncertain data points and visualize the extracted field. Alternatively, data uncertainty, characterized by probability distributions, can be propagated through the isosurface extraction process. We analyze the impact of data uncertainty on topology and geometry extraction algorithms. A novel, edge-crossing probability based approach is proposed to predict underlying isosurface topology for uncertain data. We derive a probabilistic version of the midpoint decider that resolves ambiguities that arise in identifying topological configurations. Moreover, the probability density function characterizing positional uncertainty in isosurfaces is derived analytically for a broad class of nonparametric distributions. This analytic characterization can be used for efficient closed-form computation of the expected value and variation in geometry. Our experiments show the computational advantages of our analytic approach over Monte-Carlo sampling for characterizing positional uncertainty. We also show the advantage of modeling underlying error densities in a nonparametric statistical framework as opposed to a parametric statistical framework through our experiments on ensemble datasets and uncertain scalar fields.
JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure
Authors: Matthias Labschutz, Stefan Bruckner, M. Eduard Gröller, Markus Hadwiger, Peter Rautek
Published at SciVis, 2015

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.
Mining Graphs for Understanding Time-Varying Volumetric Data
Authors: Yi Gu, Chaoli Wang 0001, Tom Peterka, Robert L. Jacob, Seung Hyun Kim
Published at SciVis, 2015

A notable recent trend in time-varying volumetric data analysis and visualization is to extract data relationships and represent them in a low-dimensional abstract graph view for visual understanding and making connections to the underlying data. Nevertheless, the ever-growing size and complexity of data demands novel techniques that go beyond standard brushing and linking to allow significant reduction of cognition overhead and interaction cost. In this paper, we present a mining approach that automatically extracts meaningful features from a graph-based representation for exploring time-varying volumetric data. This is achieved through the utilization of a series of graph analysis techniques including graph simplification, community detection, and visual recommendation. We investigate the most important transition relationships for time-varying data and evaluate our solution with several time-varying data sets of different sizes and characteristics. For gaining insights from the data, we show that our solution is more efficient and effective than simply asking users to extract relationships via standard interaction techniques, especially when the data set is large and the relationships are complex. We also collect expert feedback to confirm the usefulness of our approach.
Multi-field Pattern Matching based on Sparse Feature Sampling
Authors: Zhongjie Wang 0001, Hans-Peter Seidel, Tino Weinkauf
Published at SciVis, 2015

We present an approach to pattern matching in 3D multi-field scalar data. Existing pattern matching algorithms work on single scalar or vector fields only, yet many numerical simulations output multi-field data where only a joint analysis of multiple fields describes the underlying phenomenon fully. Our method takes this into account by bundling information from multiple fields into the description of a pattern. First, we extract a sparse set of features for each 3D scalar field using the 3D SIFT algorithm (Scale-Invariant Feature Transform). This allows for a memory-saving description of prominent features in the data with invariance to translation, rotation, and scaling. Second, the user defines a pattern as a set of SIFT features in multiple fields by e.g. brushing a region of interest. Third, we locate and rank matching patterns in the entire data set. Experiments show that our algorithm is efficient in terms of required memory and computational efforts.
Multiresolution visualization of digital earth data via hexagonal box-spline wavelets
Authors: Mohammad Imrul Jubair, Usman R. Alim, Niklas Röber, John P. Clyne, Ali Mahdavi-Amiri, Faramarz F. Samavati
Published at SciVis, 2015

Multiresolution analysis is an important tool for exploring large-scale data sets. Such analysis provides facilities to visualize data at different levels of detail while providing the advantages of efficient data compression and transmission. In this work, an approach is presented to apply multiresolution analysis to digital Earth data where each resolution describes data at a specific level of detail. Geospatial data at a fine level is taken as the input and a hierarchy of approximation and detail coefficients is built by applying a hexagonal discrete wavelet transform. Multiresolution filters are designed for hexagonal cells based on the three directional linear box spline which is natively supported by modern GPUs.
NeuroBlocks - Visual Tracking of Segmentation and Proofreading for Large Connectomics Projects
Authors: Ali K. Al-Awami, Johanna Beyer, Daniel Haehn, Narayanan Kasthuri, Jeff Lichtman, Hanspeter Pfister, Markus Hadwiger
Published at SciVis, 2015

In the field of connectomics, neuroscientists acquire electron microscopy volumes at nanometer resolution in order to reconstruct a detailed wiring diagram of the neurons in the brain. The resulting image volumes, which often are hundreds of terabytes in size, need to be segmented to identify cell boundaries, synapses, and important cell organelles. However, the segmentation process of a single volume is very complex, time-intensive, and usually performed using a diverse set of tools and many users. To tackle the associated challenges, this paper presents NeuroBlocks, which is a novel visualization system for tracking the state, progress, and evolution of very large volumetric segmentation data in neuroscience. NeuroBlocks is a multi-user web-based application that seamlessly integrates the diverse set of tools that neuroscientists currently use for manual and semi-automatic segmentation, proofreading, visualization, and analysis. NeuroBlocks is the first system that integrates this heterogeneous tool set, providing crucial support for the management, provenance, accountability, and auditing of large-scale segmentations. We describe the design of NeuroBlocks, starting with an analysis of the domain-specific tasks, their inherent challenges, and our subsequent task abstraction and visual representation. We demonstrate the utility of our design based on two case studies that focus on different user roles and their respective requirements for performing and tracking the progress of segmentation and proofreading in a large real-world connectomics project.
Occlusion-free Blood Flow Animation with Wall Thickness Visualization
Authors: Kai Lawonn, Sylvia Saalfeld, Anna Vilanova, Bernhard Preim, Tobias Isenberg 0001
Published at SciVis, 2015

We present the first visualization tool that combines pathlines from blood flow and wall thickness information. Our method uses illustrative techniques to provide occlusion-free visualization of the flow. We thus offer medical researchers an effective visual analysis tool for aneurysm treatment risk assessment. Such aneurysms bear a high risk of rupture and significant treatment-related risks. Therefore, to get a fully informed decision it is essential to both investigate the vessel morphology and the hemodynamic data. Ongoing research emphasizes the importance of analyzing the wall thickness in risk assessment. Our combination of blood flow visualization and wall thickness representation is a significant improvement for the exploration and analysis of aneurysms. As all presented information is spatially intertwined, occlusion problems occur. We solve these occlusion problems by dynamic cutaway surfaces. We combine this approach with a glyph-based blood flow representation and a visual mapping of wall thickness onto the vessel surface. We developed a GPU-based implementation of our visualizations which facilitates wall thickness analysis through real-time rendering and flexible interactive data exploration mechanisms. We designed our techniques in collaboration with domain experts, and we provide details about the evaluation of the technique and tool.
OpenSpace: Public dissemination of space mission profiles
Authors: Alexander Bock 0002, Michal Marcinkowski, Joakim Kilby, Carter Emmart, Anders Ynnerman
Published at SciVis, 2015

This work presents a visualization system and its application to space missions. The system allows the public to disseminate the scientific findings of space craft and gain a greater understanding thereof. Instruments' field-of-views and their measurements are embedded in an accurate 3 dimensional rendering of the solar system to provide context to past measurements or the planning of future events. We tested our system with NASA's New Horizons at the Pluto Pallooza event in New York and will expose it to the greater public on the upcoming July 14th Pluto flyby.
PathlinesExplorer ??? Image-based exploration of large-scale pathline fields
Authors: Omniah H. Nagoor, Markus Hadwiger, Madhusudhanan Srinivasan
Published at SciVis, 2015

PathlinesExplorer is a novel image-based tool, which has been designed to visualize large scale pathline fields on a single computer [7]. PathlinesExplorer integrates explorable images (EI) technique [4] with order-independent transparency (OIT) method [2]. What makes this method different is that it allows users to handle large data on a single workstation. Although it is a view-dependent method, PathlinesExplorer combines both exploration and modification of visual aspects without re-accessing the original huge data. Our approach is based on constructing a per-pixel linked list data structure in which each pixel contains a list of pathline segments. With this view-dependent method, it is possible to filter, color-code, and explore large-scale flow data in real-time. In addition, optimization techniques such as early-ray termination and deferred shading are applied, which further improves the performance and scalability of our approach.
Planar Visualization of Treelike Structures
Authors: Joseph Marino, Arie E. Kaufman
Published at SciVis, 2015

We present a novel method to create planar visualizations of treelike structures (e.g., blood vessels and airway trees) where the shape of the object is well preserved, allowing for easy recognition by users familiar with the structures. Based on the extracted skeleton within the treelike object, a radial planar embedding is first obtained such that there are no self-intersections of the skeleton which would have resulted in occlusions in the final view. An optimization procedure which adjusts the angular positions of the skeleton nodes is then used to reconstruct the shape as closely as possible to the original, according to a specified view plane, which thus preserves the global geometric context of the object. Using this shape recovered embedded skeleton, the object surface is then flattened to the plane without occlusions using harmonic mapping. The boundary of the mesh is adjusted during the flattening step to account for regions where the mesh is stretched over concavities. This parameterized surface can then be used either as a map for guidance during endoluminal navigation or directly for interrogation and decision making. Depth cues are provided with a grayscale border to aid in shape understanding. Examples are presented using bronchial trees, cranial and lower limb blood vessels, and upper aorta datasets, and the results are evaluated quantitatively and with a user study.
Real-time interactive time correction on the GPU
Authors: Mai El-Shehaly, Denis Gracanin, Mohamed A. Gad, Junpeng Wang, Hicham G. Elmongui
Published at SciVis, 2015

The study of physical phenomena and their dynamic evolution is supported by the analysis and visualization of time-enabled data. In many applications, available data are sparsely distributed in the space-time domain, which leads to incomprehensible visualizations. We present an interactive approach for the dynamic tracking and visualization of measured data particles through advection in a simulated flow. We introduce a fully GPU-based technique for efficient spatio-temporal interpolation, using a kd-tree forest for acceleration. As the user interacts with the system using a time slider, particle positions are reconstructed for the time selected by the user. Our results show that the proposed technique achieves highly accurate parallel tracking for thousands of particles. The rendering performance is mainly affected by the size of the query set.
Real-Time Molecular Visualization Supporting Diffuse Interreflections and Ambient Occlusion
Authors: Robin Skånberg, Pere-Pau Vázquez, Victor Guallar, Timo Ropinski
Published at SciVis, 2015

Today molecular simulations produce complex data sets capturing the interactions of molecules in detail. Due to the complexity of this time-varying data, advanced visualization techniques are required to support its visual analysis. Current molecular visualization techniques utilize ambient occlusion as a global illumination approximation to improve spatial comprehension. Besides these shadow-like effects, interreflections are also known to improve the spatial comprehension of complex geometric structures. Unfortunately, the inherent computational complexity of interreflections would forbid interactive exploration, which is mandatory in many scenarios dealing with static and time-varying data. In this paper, we introduce a novel analytic approach for capturing interreflections of molecular structures in real-time. By exploiting the knowledge of the underlying space filling representations, we are able to reduce the required parameters and can thus apply symbolic regression to obtain an analytic expression for interreflections. We show how to obtain the data required for the symbolic regression analysis, and how to exploit our analytic solution to enhance interactive molecular visualizations.