Trajectory Vorticity - Computation and Visualization of Rotational Trajectory Behavior in an Objective Way.

Trajectory data consisting of a low number of smooth parametric curves are standard data sets in visualization. For a visual analysis, not only the behavior of the individual trajectories is of interest but also the relation of the trajectories to each other. Moving objects represented by the trajectories may rotate around each other or around a moving center. We present an approach to compute and visually analyze such rotational behavior in an objective way. We introduce trajectory vorticity (TRV), a measure of rotational behavior of a low number of trajectories. We show that it is objective and that it can be introduced in two independent ways: by approaches for unsteadiness minimization and by considering the relative spin tensor. We compare TRV against single-trajectory methods and apply it to a number of constructed and real trajectory data sets, including drifting buoys in the Atlantic, midge swarm tracking data, pedestrian tracking data, pigeon flocks, and a simulated vortex street.

GRay: Ray Casting for Visualization and Interactive Data Exploration of Gaussian Mixture Models.

The Gaussian mixture model (GMM) describes the distribution of random variables from several different populations. GMMs have widespread applications in probability theory, statistics, machine learning for unsupervised cluster analysis and topic modeling, as well as in deep learning pipelines. So far, few efforts have been made to explore the underlying point distribution in combination with the GMMs, in particular when the data becomes high-dimensional and when the GMMs are composed of many Gaussians. We present an analysis tool comprising various GPU-based visualization techniques to explore such complex GMMs. To facilitate the exploration of high-dimensional data, we provide a novel navigation system to analyze the underlying data. Instead of projecting the data to 2D, we utilize interactive 3D views to better support users in understanding the spatial arrangements of the Gaussian distributions. The interactive system is composed of two parts: (1) raycasting-based views that visualize cluster memberships, spatial arrangements, and support the discovery of new modes. (2) overview visualizations that enable the comparison of Gaussians with each other, as well as small multiples of different choices of basis vectors. Users are supported in their exploration with customization tools and smooth camera navigations. Our tool was developed and assessed by five domain experts, and its usefulness was evaluated with 23 participants. To demonstrate the effectiveness, we identify interesting features in several data sets.

A Survey of Smooth Vector Graphics: Recent Advances in Representation, Creation, Rasterization and Image Vectorization.

The field of smooth vector graphics explores the representation, creation, rasterization, and automatic generation of light-weight image representations, frequently used for scalable image content. Over the past decades, several conceptual approaches on the representation of images with smooth gradients have emerged that each led to separate research threads, including the popular gradient meshes and diffusion curves. As the computational models matured, the mathematical descriptions diverged and papers started to focus more narrowly on subproblems, such as on the representation and creation of vector graphics, or the automatic vectorization from raster images. Most of the work concentrated on a specific mathematical model only. With this survey, we describe the established computational models in a consistent notation to spur further knowledge transfer, leveraging the recent advances in each field. We therefore categorize vector graphics papers from the last decades based on their underlying mathematical representations as well as on their contribution to the vector graphics content creation pipeline, comprising representation, creation, rasterization, and automatic image vectorization. This survey is meant as an entry point for both artists and researchers. We conclude this survey with an outlook on promising research directions and challenges to overcome in the future.

News Globe: Visualization of Geolocalized News Articles.

The number of online news articles available nowadays is rapidly increasing. When exploring articles on online news portals, navigation is mostly limited to the most recent ones. The spatial context and the history of topics are not immediately accessible. To support readers in the exploration or research of articles in large datasets, we developed an interactive 3D globe visualization. We worked with datasets from multiple online news portals containing up to 45,000 articles. Using agglomerative hierarchical clustering, we represent the referenced locations of news articles on a globe with different levels of detail. We employ two interaction schemes for navigating the viewpoint on the visualization, including support for hand-held devices and desktop PCs, and provide search functionality and interactive filtering. Based on this framework, we explore additional modules for jointly exploring the spatial and temporal domain of the dataset and incorporating live news into the visualization.

MineTime Insight: Visualizing Meeting Habits to Promote Informed Scheduling Decisions.

Corporate meetings are a crucial part of business activities. While numerous academic papers investigated how to make the scheduling process of meetings faster or even automatic, little work has been done yet to facilitate the retrospective reasoning about how time is spent on meetings. Traditional calendar applications do not allow users to extract actionable statistics although it has been shown that reflection-oriented design can increase the users' understanding of their habits and can thereby encourage a shift towards better practices. In this paper, we present MineTime Insight, a tool made of multiple coordinated views for the exploration of personal calendar data, with the overarching goal of improving short and long-term scheduling decisions. Despite being focused on the working environment, our work builds upon recent results in the field of Personal Visual Analytics, as it targets users not necessarily expert in visualization and data analysis. We demonstrate the potential of MineTime Insight, when applied to the agenda of an executive manager. Finally, we discuss the results of an informal user study and a field study. Our results suggest that our visual representations are perceived as easy to understand and helpful towards a change in the scheduling habits.

A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation.

In recent years, deep learning has opened countless research opportunities across many different disciplines. At present, visualization is mainly applied to explore and explain neural networks. Its counterpart-the application of deep learning to visualization problems-requires us to share data more openly in order to enable more scientists to engage in data-driven research. In this paper, we construct a large fluid flow data set and apply it to a deep learning problem in scientific visualization. Parameterized by the Reynolds number, the data set contains a wide spectrum of laminar and turbulent fluid flow regimes. The full data set was simulated on a high-performance compute cluster and contains 8000 time-dependent 2D vector fields, accumulating to more than 16 TB in size. Using our public fluid data set, we trained deep convolutional neural networks in order to set a benchmark for an improved post-hoc Lagrangian fluid flow analysis. In in-situ settings, flow maps are exported and interpolated in order to assess the transport characteristics of time-dependent fluids. Using deep learning, we improve the accuracy of flow map interpolations, allowing a more precise flow analysis at a reduced memory IO footprint.

Visibility, Topology, and Inertia: New Methods in Flow Visualization.

In this article, we address three different topics in scientific visualization. The first part introduces optimization strategies that determine the visibility of line and surface geometry, such that a balance between occlusion avoidance and preservation of context is found. The second part proposes new methods for the visualization of time-dependent fluid flows, including the accurate depiction of Lagrangian scalar fields, as well as a new category of vortex identification methods. The third part introduces finite-sized particles as new application area for flow visualization, covering geometry-based methods, particle separation, topology, vortex corelines, and the determination of the origin of finite-sized particles.

Fourier Opacity Optimization for Scalable Exploration.

Over the past decades, scientific visualization became a fundamental aspect of modern scientific data analysis. Across all data-intensive research fields, ranging from structural biology to cosmology, data sizes increase rapidly. Dealing with the growing large-scale data is one of the top research challenges of this century. For the visual exploratory data analysis, interactivity, a view-dependent visibility optimization and frame coherence are indispensable. In this work, we extend the recent decoupled opacity optimization framework to enable a navigation without occlusion of important features through large geometric data. By expressing the accumulation of importance and optical depth in Fourier basis, the computation, evaluation and rendering of optimized transparent geometry become not only order-independent, but also operate within a fixed memory bound. We study the quality of our Fourier approximation in terms of accuracy, memory requirements and efficiency for both the opacity computation, as well as the order-independent compositing. We apply the method to different point, line and surface data sets originating from various research fields, including meteorology, health science, astrophysics and organic chemistry.

Extraction and Visual Analysis of Potential Vorticity Banners around the Alps.

Potential vorticity is among the most important scalar quantities in atmospheric dynamics. For instance, potential vorticity plays a key role in particularly strong wind peaks in extratropical cyclones and it is able to explain the occurrence of frontal rain bands. Potential vorticity combines the key quantities of atmospheric dynamics, namely rotation and stratification. Under suitable wind conditions elongated banners of potential vorticity appear in the lee of mountains. Their role in atmospheric dynamics has recently raised considerable interest in the meteorological community for instance due to their influence in aviation wind hazards and maritime transport. In order to support meteorologists and climatologists in the analysis of these structures, we developed an extraction algorithm and a visual exploration framework consisting of multiple linked views. For the extraction we apply a predictor-corrector algorithm that follows streamlines and realigns them with extremal lines of potential vorticity. Using the agglomerative hierarchical clustering algorithm, we group banners from different sources based on their proximity. To visually analyze the time-dependent banner geometry, we provide interactive overviews and enable the query for detail on demand, including the analysis of different time steps, potentially correlated scalar quantities, and the wind vector field. In particular, we study the relationship between relative humidity and the banners for their potential in indicating the development of precipitation. Working with our method, the collaborating meteorologists gained a deeper understanding of the three-dimensional processes, which may spur follow-up research in the future.

Accelerated Monte Carlo Rendering of Finite-Time Lyapunov Exponents.

Time-dependent fluid flows often contain numerous hyperbolic Lagrangian coherent structures, which act as transport barriers that guide the advection. The finite-time Lyapunov exponent is a commonly-used approximation to locate these repelling or attracting structures. Especially on large numerical simulations, the FTLE ridges can become arbitrarily sharp and very complex. Thus, the discrete sampling onto a grid for a subsequent direct volume rendering is likely to miss sharp ridges in the visualization. For this reason, an unbiased Monte Carlo-based rendering approach was recently proposed that treats the FTLE field as participating medium with single scattering. This method constructs a ground truth rendering without discretization, but it is prohibitively slow with render times in the order of days or weeks for a single image. In this paper, we accelerate the rendering process significantly, which allows us to compute video sequence of high-resolution FTLE animations in a much more reasonable time frame. For this, we follow two orthogonal approaches to improve on the rendering process: the volumetric light path integration in gradient domain and an acceleration of the transmittance estimation. We analyze the convergence and performance of the proposed method and demonstrate the approach by rendering complex FTLE fields in several 3D vector fields.

Vector Field Topology of Time-Dependent Flows in a Steady Reference Frame.

The topological analysis of unsteady vector fields remains to this day one of the largest challenges in flow visualization. We build up on recent work on vortex extraction to define a time-dependent vector field topology for 2D and 3D flows. In our work, we split the vector field into two components: a vector field in which the flow becomes steady, and the remaining ambient flow that describes the motion of topological elements (such as sinks, sources and saddles) and feature curves (vortex corelines and bifurcation lines). To this end, we expand on recent local optimization approaches by modeling spatially-varying deformations through displacement transformations from continuum mechanics. We compare and discuss the relationships with existing local and integration-based topology extraction methods, showing for instance that separatrices seeded from saddles in the optimal frame align with the integration-based streakline vector field topology. In contrast to the streakline-based approach, our method gives a complete picture of the topology for every time slice, including the steps near the temporal domain boundaries. With our work it now becomes possible to extract topological information even when only few time slices are available. We demonstrate the method in several analytical and numerically-simulated flows and discuss practical aspects, limitations and opportunities for future work.

Hyper-Objective Vortices.

Almost all properties of vector fields, including magnitude, direction, λ2 and vorticity change under arbitrary movements of the observer. This is undesirable since measurements of physical properties should ideally not depend on the way the (virtual) measurement device moves. There are some properties that are invariant under certain types of reference frame transformations: Galilean invariance (invariance under equal-speed translation) and objectivity (invariance under any smooth rotation and translation of the reference frame). In this paper, we introduce even harder conditions than objectivity: we demand invariance under any smooth similarity transformation (rotation, translation and uniform scale) as well as invariance under any smooth affine transformation of the reference frame. We show that these new hyper-objective measures allow the extraction of vortices that change their volume or deform. Further, we present a generic approach that transforms almost any vortex measure into a hyper-objective one. We apply our methods to vortex extraction in 2D and 3D vector fields, and analyze the numerical robustness, extraction time and the minimization residuals for the Galilean invariant, objective, and the two new hyper-objective approaches.

Visual Analysis of Aneurysm Data using Statistical Graphics.

This paper presents a framework to explore multi-field data of aneurysms occurring at intracranial and cardiac arteries by using statistical graphics. The rupture of an aneurysm is often a fatal scenario, whereas during treatment serious complications for the patient can occur. Whether an aneurysm ruptures or whether a treatment is successful depends on the interaction of different morphological such as wall deformation and thickness, and hemodynamic attributes like wall shear stress and pressure. Therefore, medical researchers are very interested in better understanding these relationships. However, the required analysis is a time-consuming process, where suspicious wall regions are difficult to detect due to the time-dependent behavior of the data. Our proposed visualization framework enables medical researchers to efficiently assess aneurysm risk and treatment options. This comprises a powerful set of views including 2D and 3D depictions of the aneurysm morphology as well as statistical plots of different scalar fields. Brushing and linking aids the user to identify interesting wall regions and to understand the influence of different attributes on the aneurysm's state. Moreover, a visual comparison of pre- and post-treatment as well as different treatment options is provided. Our analysis techniques are designed in collaboration with domain experts, e.g., physicians, and we provide details about the evaluation.

Objective Vortex Corelines of Finite-sized Objects in Fluid Flows.

Vortices are one of the most-frequently studied phenomena in fluid flows. The center of the rotating motion is called the vortex coreline and its successful detection strongly depends on the choice of the reference frame. The optimal frame moves with the center of the vortex, which incidentally makes the observed fluid flow steady and thus standard vortex coreline extractors such as Sujudi-Haimes become applicable. Recently, an objective optimization framework was proposed that determines a near-steady reference frame for tracer particles. In this paper, we extend this technique to the detection of vortex corelines of inertial particles. An inertial particle is a finite-sized object that is carried by a fluid flow. In contrast to the usual tracer particles, they do not move tangentially with the flow, since they are subject to gravity and exhibit mass-dependent inertia. Their particle state is determined by their position and own velocity, which makes the search for the optimal frame a high-dimensional problem. We demonstrate in this paper that the objective detection of an inertial vortex coreline can be reduced in 2D to a critical point search in 2D. For 3D flows, however, the vortex coreline criterion remains a parallel vectors condition in 6D. To detect the vortex corelines we propose a recursive subdivision approach that is tailored to the underlying structure of the 6D vectors. The resulting algorithm is objective, and we demonstrate the vortex coreline extraction in a number of 2D and 3D vector fields.

Management of Cerebral Aneurysm Descriptors based on an Automatic Ostium Extraction.

We present a framework to manage cerebral aneurysms. Rupture risk evaluation is based on manually extracted descriptors, which is time-consuming. Thus, we provide an automatic solution by considering several questions: How can expert knowledge be integrated? How should meta data be defined? Which interaction techniques are needed for data exploration.

Backward Finite-Time Lyapunov Exponents in Inertial Flows.

Inertial particles are finite-sized objects that are carried by fluid flows and in contrast to massless tracer particles they are subject to inertia effects. In unsteady flows, the dynamics of tracer particles have been extensively studied by the extraction of Lagrangian coherent structures (LCS), such as hyperbolic LCS as ridges of the Finite-Time Lyapunov Exponent (FTLE). The extension of the rich LCS framework to inertial particles is currently a hot topic in the CFD literature and is actively under research. Recently, backward FTLE on tracer particles has been shown to correlate with the preferential particle settling of small inertial particles. For larger particles, inertial trajectories may deviate strongly from (massless) tracer trajectories, and thus for a better agreement, backward FTLE should be computed on inertial trajectories directly. Inertial backward integration, however, has not been possible until the recent introduction of the influence curve concept, which - given an observation and an initial velocity - allows to recover all sources of inertial particles as tangent curves of a derived vector field. In this paper, we show that FTLE on the influence curve vector field is in agreement with preferential particle settling and more importantly it is not only valid for small (near-tracer) particles. We further generalize the influence curve concept to general equations of motion in unsteady spatio-velocity phase spaces, which enables backward integration with more general equations of motion. Applying the influence curve concept to tracer particles in the spatio-velocity domain emits streaklines in massless flows as tangent curves of the influence curve vector field. We demonstrate the correlation between inertial backward FTLE and the preferential particle settling in a number of unsteady vector fields.

S-Layer-Based Nanocomposites for Industrial Applications.

This chapter covers the fundamental aspects of bacterial S-layers: what are S-layers, what is known about them, and what are their main features that makes them so interesting for the production of nanostructures. After a detailed introduction of the paracrystalline protein lattices formed by S-layer systems in nature the chapter explores the engineering of S-layer-based materials. How can S-layers be used to produce "industry-ready" nanoscale bio-composite materials, and which kinds of nanomaterials are possible (e.g., nanoparticle synthesis, nanoparticle immobilization, and multifunctional coatings)? What are the advantages and disadvantages of S-layer-based composite materials? Finally, the chapter highlights the potential of these innovative bacterial biomolecules for future technologies in the fields of metal filtration, catalysis, and bio-functionalization.

Characterization of Three Different Unusual S-Layer Proteins from Viridibacillus arvi JG-B58 That Exhibits Two Super-Imposed S-Layer Proteins.

Genomic analyses of Viridibacillus arvi JG-B58 that was previously isolated from heavy metal contaminated environment identified three different putative surface layer (S-layer) protein genes namely slp1, slp2, and slp3. All three genes are expressed during cultivation. At least two of the V. arvi JG-B58 S-layer proteins were visualized on the surface of living cells via atomic force microscopy (AFM). These S-layer proteins form a double layer with p4 symmetry. The S-layer proteins were isolated from the cells using two different methods. Purified S-layer proteins were recrystallized on SiO2 substrates in order to study the structure of the arrays and self-assembling properties. The primary structure of all examined S-layer proteins lack some features that are typical for Bacillus or Lysinibacillus S-layers. For example, they possess no SLH domains that are usually responsible for the anchoring of the proteins to the cell wall. Further, the pI values are relatively high ranging from 7.84 to 9.25 for the matured proteins. Such features are typical for S-layer proteins of Lactobacillus species although sequence comparisons indicate a close relationship to S-layer proteins of Lysinibacillus and Bacillus strains. In comparison to the numerous descriptions of S-layers, there are only a few studies reporting the concomitant existence of two different S-layer proteins on cell surfaces. Together with the genomic data, this is the first description of a novel type of S-layer proteins showing features of Lactobacillus as well as of Bacillus-type S-layer proteins and the first study of the cell envelope of Viridibacillus arvi.

Visualizing Volcanic Clouds in the Atmosphere and Their Impact on Air Traffic.

Volcanic eruptions are not only hazardous in the direct vicinity of a volcano, but they also affect the climate and air travel for great distances. This article sheds light on the Grímsvötn, Puyehue-Cordón Caulle, and Nabro eruptions in 2011. The authors study the agreement of the complementary satellite data, reconstruct sulfate aerosol and volcanic ash clouds, visualize endangered flight routes, minimize occlusion in particle trajectory visualizations, and focus on the main pathways of Nabro's sulfate aerosol into the stratosphere. The results here were developed for the 2014 IEEE Scientific Visualization Contest, which centers around the fusion of multiple satellite data modalities to reconstruct and assess the movement of volcanic ash and sulfate aerosol emissions. Using data from three volcanic eruptions that occurred in the span of approximately three weeks, the authors study the agreement of the complementary satellite data, reconstruct sulfate aerosol and volcanic ash clouds, visualize endangered flight routes, minimize occlusion in particle trajectory visualizations, and focus on the main pathways of sulfate aerosol into the stratosphere. This video provides animations of the reconstructed ash clouds. https://youtu.be/D9DvJ5AvZAs.

Rotation Invariant Vortices for Flow Visualization.

We propose a new class of vortex definitions for flows that are induced by rotating mechanical parts, such as stirring devices, helicopters, hydrocyclones, centrifugal pumps, or ventilators. Instead of a Galilean invariance, we enforce a rotation invariance, i.e., the invariance of a vortex under a uniform-speed rotation of the underlying coordinate system around a fixed axis. We provide a general approach to transform a Galilean invariant vortex concept to a rotation invariant one by simply adding a closed form matrix to the Jacobian. In particular, we present rotation invariant versions of the well-known Sujudi-Haimes, Lambda-2, and Q vortex criteria. We apply them to a number of artificial and real rotating flows, showing that for these cases rotation invariant vortices give better results than their Galilean invariant counterparts.