DevLan/vispubs huggingface dataset:
Direct Volume Rendering with Nonparametric Models of Uncertainty
Authors: Tushar M. Athawale, Bo Ma 0002, Elham Sakhaee, Christopher R. Johnson 0001, Alireza Entezari
Published at SciVis, 2020

We present a nonparametric statistical framework for the quantification, analysis, and propagation of data uncertainty in direct volume rendering (DVR). The state-of-the-art statistical DVR framework allows for preserving the transfer function (TF) of the ground truth function when visualizing uncertain data; however, the existing framework is restricted to parametric models of uncertainty. In this paper, we address the limitations of the existing DVR framework by extending the DVR framework for nonparametric distributions. We exploit the quantile interpolation technique to derive probability distributions representing uncertainty in viewing-ray sample intensities in closed form, which allows for accurate and efficient computation. We evaluate our proposed nonparametric statistical models through qualitative and quantitative comparisons with the mean-field and parametric statistical models, such as uniform and Gaussian, as well as Gaussian mixtures. In addition, we present an extension of the state-of-the-art rendering parametric framework to 2D TFs for improved DVR classifications. We show the applicability of our uncertainty quantification framework to ensemble, downsampled, and bivariate versions of scalar field datasets.
Efficient and Flexible Hierarchical Data Layouts for a Unified Encoding of Scalar Field Precision and Resolution
Authors: Duong Hoang, Brian Summa, Harsh Bhatia, Peter Lindstrom 0001, Pavol Klacansky, Will Usher 0001, Peer-Timo Bremer, Valerio Pascucci
Published at SciVis, 2020

To address the problem of ever-growing scientific data sizes making data movement a major hindrance to analysis, we introduce a novel encoding for scalar fields: a unified tree of resolution and precision, specifically constructed so that valid cuts correspond to sensible approximations of the original field in the precision-resolution space. Furthermore, we introduce a highly flexible encoding of such trees that forms a parameterized family of data hierarchies. We discuss how different parameter choices lead to different trade-offs in practice, and show how specific choices result in known data representation schemes such as zfp [52], idx [58], and jpeg2000 [76]. Finally, we provide system-level details and empirical evidence on how such hierarchies facilitate common approximate queries with minimal data movement and time, using real-world data sets ranging from a few gigabytes to nearly a terabyte in size. Experiments suggest that our new strategy of combining reductions in resolution and precision is competitive with state-of-the-art compression techniques with respect to data quality, while being significantly more flexible and orders of magnitude faster, and requiring significantly reduced resources.
Extraction and Visualization of Poincare Map Topology for Spacecraft Trajectory Design
Authors: Xavier Tricoche, Wayne R. Schlei, Kathleen C. Howell
Published at SciVis, 2020

Mission designers must study many dynamical models to plan a low-cost spacecraft trajectory that satisfies mission constraints. They routinely use Poincare maps to search for a suitable path through the interconnected web of periodic orbits and invariant manifolds found in multi-body gravitational systems. This paper is concerned with the extraction and interactive visual exploration of this structural landscape to assist spacecraft trajectory planning. We propose algorithmic solutions that address the specific challenges posed by the characterization of the topology in astrodynamics problems and allow for an effective visual analysis of the resulting information. This visualization framework is applied to the circular restricted three-body problem (CR3BP), where it reveals novel periodic orbits with their relevant invariant manifolds in a suitable format for interactive transfer selection. Representative design problems illustrate how spacecraft path planners can leverage our topology visualization to fully exploit the natural dynamics pathways for energy-efficient trajectory designs.
Homomorphic-Encrypted Volume Rendering
Authors: Sebastian Mazza, Daniel Patel, Ivan Viola
Published at SciVis, 2020

Computationally demanding tasks are typically calculated in dedicated data centers, and real-time visualizations also follow this trend. Some rendering tasks, however, require the highest level of confidentiality so that no other party, besides the owner, can read or see the sensitive data. Here we present a direct volume rendering approach that performs volume rendering directly on encrypted volume data by using the homomorphic Paillier encryption algorithm. This approach ensures that the volume data and rendered image are uninterpretable to the rendering server. Our volume rendering pipeline introduces novel approaches for encrypted-data compositing, interpolation, and opacity modulation, as well as simple transfer function design, where each of these routines maintains the highest level of privacy. We present performance and memory overhead analysis that is associated with our privacy-preserving scheme. Our approach is open and secure by design, as opposed to secure through obscurity. Owners of the data only have to keep their secure key confidential to guarantee the privacy of their volume data and the rendered images. Our work is, to our knowledge, the first privacy-preserving remote volume-rendering approach that does not require that any server involved be trustworthy; even in cases when the server is compromised, no sensitive data will be leaked to a foreign party.
Improving the Usability of Virtual Reality Neuron Tracing with Topological Elements
Authors: Torin McDonald, Will Usher 0001, Nathan Morrical, Attila Gyulassy, Steve Petruzza, Frederick Federer, Alessandra Angelucci, Valerio Pascucci
Published at SciVis, 2020

Researchers in the field of connectomics are working to reconstruct a map of neural connections in the brain in order to understand at a fundamental level how the brain processes information. Constructing this wiring diagram is done by tracing neurons through high-resolution image stacks acquired with fluorescence microscopy imaging techniques. While a large number of automatic tracing algorithms have been proposed, these frequently rely on local features in the data and fail on noisy data or ambiguous cases, requiring time-consuming manual correction. As a result, manual and semi-automatic tracing methods remain the state-of-the-art for creating accurate neuron reconstructions. We propose a new semi-automatic method that uses topological features to guide users in tracing neurons and integrate this method within a virtual reality (VR) framework previously used for manual tracing. Our approach augments both visualization and interaction with topological elements, allowing rapid understanding and tracing of complex morphologies. In our pilot study, neuroscientists demonstrated a strong preference for using our tool over prior approaches, reported less fatigue during tracing, and commended the ability to better understand possible paths and alternatives. Quantitative evaluation of the traces reveals that users' tracing speed increased, while retaining similar accuracy compared to a fully manual approach.
Interactive Black-Hole Visualization
Authors: Annemieke Verbraeck, Elmar Eisemann
Published at SciVis, 2020

We present an efficient algorithm for visualizing the effect of black holes on its distant surroundings as seen from an observer nearby in orbit. Our solution is GPU-based and builds upon a two-step approach, where we first derive an adaptive grid to map the 360-view around the observer to the distorted celestial sky, which can be directly reused for different camera orientations. Using a grid, we can rapidly trace rays back to the observer through the distorted spacetime, avoiding the heavy workload of standard tracing solutions at real-time rates. By using a novel interpolation technique we can also simulate an observer path by smoothly transitioning between multiple grids. Our approach accepts real star catalogues and environment maps of the celestial sky and generates the resulting black-hole deformations in real time.
Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images
Authors: Xinyi Huang, Suphanut Jamonnak, Ye Zhao 0003, Boyu Wang 0001, Minh Hoai, Kevin G. Yager, Wei Xu 0020
Published at SciVis, 2020

Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However, visual analytics tools are lacking for the specific application of x-ray image classification with multiple structural attributes. In this paper, we present an interactive system for domain scientists to visually study the multiple attributes learning models applied to x-ray scattering images. It allows domain scientists to interactively explore this important type of scientific images in embedded spaces that are defined on the model prediction output, the actual labels, and the discovered feature space of neural networks. Users are allowed to flexibly select instance images, their clusters, and compare them regarding the specified visual representation of attributes. The exploration is guided by the manifestation of model performance related to mutual relationships among attributes, which often affect the learning accuracy and effectiveness. The system thus supports domain scientists to improve the training dataset and model, find questionable attributes labels, and identify outlier images or spurious data clusters. Case studies and scientists feedback demonstrate its functionalities and usefulness.
Interactive Visualization of Atmospheric Effects for Celestial Bodies
Authors: Jonathas Costa, Alexander Bock 0002, Carter Emmart, Charles D. Hansen, Anders Ynnerman, Cláudio T. Silva
Published at SciVis, 2020

We present an atmospheric model tailored for the interactive visualization of planetary surfaces. As the exploration of the solar system is progressing with increasingly accurate missions and instruments, the faithful visualization of planetary environments is gaining increasing interest in space research, mission planning, and science communication and education. Atmospheric effects are crucial in data analysis and to provide contextual information for planetary data. Our model correctly accounts for the non-linear path of the light inside the atmosphere (in Earth's case), the light absorption effects by molecules and dust particles, such as the ozone layer and the Martian dust, and a wavelength-dependent phase function for Mie scattering. The mode focuses on interactivity, versatility, and customization, and a comprehensive set of interactive controls make it possible to adapt its appearance dynamically. We demonstrate our results using Earth and Mars as examples. However, it can be readily adapted for the exploration of other atmospheres found on, for example, of exoplanets. For Earth's atmosphere, we visually compare our results with pictures taken from the International Space Station and against the CIE clear sky model. The Martian atmosphere is reproduced based on available scientific data, feedback from domain experts, and is compared to images taken by the Curiosity rover. The work presented here has been implemented in the OpenSpace system, which enables interactive parameter setting and real-time feedback visualization targeting presentations in a wide range of environments, from immersive dome theaters to virtual reality headsets.
IsoTrotter: Visually Guided Empirical Modelling of Atmospheric Convection
Authors: Juraj Pálenik, Thomas Spengler, Helwig Hauser
Published at SciVis, 2020

Empirical models, fitted to data from observations, are often used in natural sciences to describe physical behaviour and support discoveries. However, with more complex models, the regression of parameters quickly becomes insufficient, requiring a visual parameter space analysis to understand and optimize the models. In this work, we present a design study for building a model describing atmospheric convection. We present a mixed-initiative approach to visually guided modelling, integrating an interactive visual parameter space analysis with partial automatic parameter optimization. Our approach includes a new, semi-automatic technique called IsoTrotting, where we optimize the procedure by navigating along isocontours of the model. We evaluate the model with unique observational data of atmospheric convection based on flight trajectories of paragliders.
Localized Topological Simplification of Scalar Data
Authors: Jonas Lukasczyk, Christoph Garth, Ross Maciejewski, Julien Tierny
Published at SciVis, 2020

This paper describes a localized algorithm for the topological simplification of scalar data, an essential pre-processing step of topological data analysis (TDA). Given a scalar field $f$ and a selection of extrema to preserve, the proposed localized topological simplification (LTS) derives a function g that is close to $f$ and only exhibits the selected set of extrema. Specifically, sub- and superlevel set components associated with undesired extrema are first locally flattened and then correctly embedded into the global scalar field, such that these regions are guaranteed-from a combinatorial perspective-to no longer contain any undesired extrema. In contrast to previous global approaches, LTS only and independently processes regions of the domain that actually need to be simplified, which already results in a noticeable speedup. Moreover, due to the localized nature of the algorithm, LTS can utilize shared-memory parallelism to simplify regions simultaneously with a high parallel efficiency (70%). Hence, LTS significantly improves interactivity for the exploration of simplification parameters and their effect on subsequent topological analysis. For such exploration tasks, LTS brings the overall execution time of a plethora of TDA pipelines from minutes down to seconds, with an average observed speedup over state-of-the-art techniques of up to $\times 36$. Furthermore, in the special case where preserved extrema are selected based on topological persistence, an adapted version of LTS partially computes the persistence diagram and simultaneously simplifies features below a predefined persistence threshold. The effectiveness of LTS, its parallel efficiency, and its resulting benefits for TDA are demonstrated on several simulated and acquired datasets from different application domains, including physics, chemistry, and biomedical imaging.
Mode Surfaces of Symmetric Tensor Fields: Topological Analysis and Seamless Extraction
Authors: Botong Qu, Lawrence Roy, Yue Zhang 0009, Eugene Zhang
Published at SciVis, 2020

Mode surfaces are the generalization of degenerate curves and neutral surfaces, which constitute 3D symmetric tensor field topology. Efficient analysis and visualization of mode surfaces can provide additional insight into not only degenerate curves and neutral surfaces, but also how these features transition into each other. Moreover, the geometry and topology of mode surfaces can help domain scientists better understand the tensor fields in their applications. Existing mode surface extraction methods can miss features in the surfaces. Moreover, the mode surfaces extracted from neighboring cells have gaps, which make their subsequent analysis difficult. In this paper, we provide novel analysis on the topological structures of mode surfaces, including a common parameterization of all mode surfaces of a tensor field using 2D asymmetric tensors. This allows us to not only better understand the structures in mode surfaces and their interactions with degenerate curves and neutral surfaces, but also develop an efficient algorithm to seamlessly extract mode surfaces, including neutral surfaces. The seamless mode surfaces enable efficient analysis of their geometric structures, such as the principal curvature directions. We apply our analysis and visualization to a number of solid mechanics data sets.
Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models
Authors: Ngan V. T. Nguyen, Ondrej Strnad, Tobias Klein, Deng Luo, Ruwayda Alharbi, Peter Wonka, Martina Maritan, Peter Mindek, Ludovic Autin, David S. Goodsell, Ivan Viola
Published at SciVis, 2020

We present a new technique for the rapid modeling and construction of scientifically accurate mesoscale biological models. The resulting 3D models are based on a few 2D microscopy scans and the latest knowledge available about the biological entity, represented as a set of geometric relationships. Our new visual-programming technique is based on statistical and rule-based modeling approaches that are rapid to author, fast to construct, and easy to revise. From a few 2D microscopy scans, we determine the statistical properties of various structural aspects, such as the outer membrane shape, the spatial properties, and the distribution characteristics of the macromolecular elements on the membrane. This information is utilized in the construction of the 3D model. Once all the imaging evidence is incorporated into the model, additional information can be incorporated by interactively defining the rules that spatially characterize the rest of the biological entity, such as mutual interactions among macromolecules, and their distances and orientations relative to other structures. These rules are defined through an intuitive 3D interactive visualization as a visual-programming feedback loop. We demonstrate the applicability of our approach on a use case of the modeling procedure of the SARS-CoV-2 virion ultrastructure. This atomistic model, which we present here, can steer biological research to new promising directions in our efforts to fight the spread of the virus.
Objective Observer-Relative Flow Visualization in Curved Spaces for Unsteady 2D Geophysical Flows
Authors: Peter Rautek, Matej Mlejnek, Johanna Beyer, Jakob Troidl, Hanspeter Pfister, Thomas Theußl, Markus Hadwiger
Published at SciVis, 2020

Computing and visualizing features in fluid flow often depends on the observer, or reference frame, relative to which the input velocity field is given. A desired property of feature detectors is therefore that they are objective, meaning independent of the input reference frame. However, the standard definition of objectivity is only given for Euclidean domains and cannot be applied in curved spaces. We build on methods from mathematical physics and Riemannian geometry to generalize objectivity to curved spaces, using the powerful notion of symmetry groups as the basis for definition. From this, we develop a general mathematical framework for the objective computation of observer fields for curved spaces, relative to which other computed measures become objective. An important property of our framework is that it works intrinsically in 2D, instead of in the 3D ambient space. This enables a direct generalization of the 2D computation via optimization of observer fields in flat space to curved domains, without having to perform optimization in 3D. We specifically develop the case of unsteady 2D geophysical flows given on spheres, such as the Earth. Our observer fields in curved spaces then enable objective feature computation as well as the visualization of the time evolution of scalar and vector fields, such that the automatically computed reference frames follow moving structures like vortices in a way that makes them appear to be steady.
Polyphorm: Structural Analysis of Cosmological Datasets via Interactive Physarum Polycephalum Visualization
Authors: Oskar Elek, Joseph N. Burchett, J. Xavier Prochaska, Angus G. Forbes
Published at SciVis, 2020

This paper introduces Polyphorm, an interactive visualization and model fitting tool that provides a novel approach for investigating cosmological datasets. Through a fast computational simulation method inspired by the behavior of Physarum polycephalum, an unicellular slime mold organism that efficiently forages for nutrients, astrophysicists are able to extrapolate from sparse datasets, such as galaxy maps archived in the Sloan Digital Sky Survey, and then use these extrapolations to inform analyses of a wide range of other data, such as spectroscopic observations captured by the Hubble Space Telescope. Researchers can interactively update the simulation by adjusting model parameters, and then investigate the resulting visual output to form hypotheses about the data. We describe details of Polyphorm's simulation model and its interaction and visualization modalities, and we evaluate Polyphorm through three scientific use cases that demonstrate the effectiveness of our approach.
Ray Tracing Structured AMR Data Using ExaBricks
Authors: Ingo Wald, Stefan Zellmann, Will Usher 0001, Nathan Morrical, Ulrich Lang 0002, Valerio Pascucci
Published at SciVis, 2020

Structured Adaptive Mesh Refinement (Structured AMR) enables simulations to adapt the domain resolution to save computation and storage, and has become one of the dominant data representations used by scientific simulations; however, efficiently rendering such data remains a challenge. We present an efficient approach for volume- and iso-surface ray tracing of Structured AMR data on GPU-equipped workstations, using a combination of two different data structures. Together, these data structures allow a ray tracing based renderer to quickly determine which segments along the ray need to be integrated and at what frequency, while also providing quick access to all data values required for a smooth sample reconstruction kernel. Our method makes use of the RTX ray tracing hardware for surface rendering, ray marching, space skipping, and adaptive sampling; and allows for interactive changes to the transfer function and implicit iso-surfacing thresholds. We demonstrate that our method achieves high performance with little memory overhead, enabling interactive high quality rendering of complex AMR data sets on individual GPU workstations.
Sea of Genes: A Reflection on Visualising Metagenomic Data for Museums
Authors: Keshav Dasu, Kwan-Liu Ma, Joyce Ma, Jennifer Frazier
Published at SciVis, 2020

We examine the process of designing an exhibit to communicate scientific findings from a complex dataset and unfamiliar domain to the public in a science museum. Our exhibit sought to communicate new lessons based on scientific findings from the domain of metagenomics. This multi-user exhibit had three goals: (1) to inform the public about microbial communities and their daily cycles; (2) to link microbes' activity to the concept of gene expression; (3) and to highlight scientists' use of gene expression data to understand the role of microbes. To address these three goals, we derived visualization designs with three corresponding stories, each corresponding to a goal. We present three successive rounds of design and evaluation of our attempts to convey these goals. We could successfully present one story but had limited success with our second and third goals. This work presents a detailed account of an attempt to explain tightly coupled relationships through storytelling and animation in a multi-user, informal learning environment to a public with varying prior knowledge on the domain and identify lessons for future design.