Parallel Computation of Piecewise Linear Morse-Smale Segmentations.

This article presents a well-scaling parallel algorithm for the computation of Morse-Smale (MS) segmentations, including the region separators and region boundaries. The segmentation of the domain into ascending and descending manifolds, solely defined on the vertices, improves the computational time using path compression and fully segments the border region. Region boundaries and region separators are generated using a multi-label marching tetrahedra algorithm. This enables a fast and simple solution to find optimal parameter settings in preliminary exploration steps by generating an MS complex preview. It also poses a rapid option to generate a fast visual representation of the region geometries for immediate utilization. Two experiments demonstrate the performance of our approach with speedups of over an order of magnitude in comparison to two publicly available implementations. The example section shows the similarity to the MS complex, the useability of the approach, and the benefits of this method with respect to the presented datasets. We provide our implementation with the paper.

Mapping regional funding for COVID-19 research in the Asia-Pacific region.

INTRODUCTION: The Global Research Collaboration for Infectious Disease Preparedness (GloPID-R) is a network of funders supporting research on infectious diseases of epidemic/pandemic potential. GloPID-R is establishing regional hubs to strengthen stakeholder engagement particularly among low-income and middle-income countries. The first pilot hub, led from Republic of Korea (South Korea), has been launched in the Asia-Pacific region, a region highly prone to outbreaks of emerging infectious diseases. We present findings of mapping research undertaken in support of the hub's development. METHODS: We analysed five COVID-19 research databases in September 2022 to identify research funders and intermediary funding sources supporting research in infectious diseases in the Asia-Pacific region. This was complemented with an in-depth analysis of the UK Collaborative on Development Research (UKCDR) and GloPID-R COVID-19 Research Project Tracker to assess the alignment of funded projects in the region to the WHO COVID-19 research priorities. RESULTS: We identified 453 funders and funding sources supporting COVID-19 research in the Asia-Pacific Region including public, private and philanthropic organisations and universities. However, these organisations were clustered in few countries in the region. The in-depth analysis of the UKCDR and GloPID-R COVID-19 Research project Tracker found limited research involving Asia-Pacific countries with the 117 funders supporting these projects investing at least US$604m in COVID-19 research in the region. Social Sciences was the dominant theme on which funded projects focused whereas the priority areas with the least number of projects were research on 'animal and environmental health' and 'ethics considerations for research'. CONCLUSION: Our analyses show the diversity of funding sources for research on infectious diseases in the Asia-Pacific region. Engagement between multiple actors in the health research system is likely to promote enhanced coordination for greater research impact. GloPID-R's Asia-Pacific regional hub aims to support activities for the enhancement of preparedness for outbreaks of emerging infectious diseases in the region.

Procedural Defect Modeling for Virtual Surface Inspection Environments.

Development of automated visual surface inspection systems heavily depends on the availability of defected product samples. Both inspection hardware configuration and training of defect detection models require diversified, representative and precisely annotated data. Reliable training data of sufficient size is frequently challenging to obtain. Using virtual environments, it is possible to simulate defected products which would serve both for configuration of acquisition hardware as well as for generation of required datasets. In this work, we present parameterized models for adaptable simulation of geometrical defects, based on procedural methods. Presented models are suitable for creating defected products in virtual surface inspection planning environments. As such, they enable inspection planning experts to assess defect visibility for various configurations of acquisition hardware. Finally, the presented method enables pixel-precise annotations alongside image synthesis for the creation of training-ready datasets.

Insight Beyond Numbers: The Impact of Qualitative Factors on Visual Data Analysis.

As of today, data analysis focuses primarily on the findings to be made inside the data and concentrates less on how those findings relate to the domain of investigation. Contemporary visualization as a field of research shows a strong tendency to adopt this data-centrism. Despite their decisive influence on the analysis result, qualitative aspects of the analysis process such as the structure, soundness, and complexity of the applied reasoning strategy are rarely discussed explicitly. We argue that if the purpose of visualization is the provision of domain insight rather than the depiction of data analysis results, a holistic perspective requires a qualitative component to to be added to the discussion of quantitative and human factors. To support this point, we demonstrate how considerations of qualitative factors in visual analysis can be applied to obtain explanations and possible solutions for a number of practical limitations inherent to the data-centric perspective on analysis. Based on this discussion of what we call qualitative visual analysis, we develop an inside-outside principle of nested levels of context that can serve as a conceptual basis for the development of visualization systems that optimally support the emergence of insight during analysis.

Hierarchical Image Semantics Using Probabilistic Path Propagations for Biomedical Research.

Image segmentation is an important subtask in biomedical research applications, such as estimating the position and shape of a tumor. Unfortunately, advanced image segmentation methods are not widely applied in research applications as they often miss features, such as uncertainty communication, and may lack an intuitive approach for the use of the underlying algorithm. To solve this problem, this paper fuses a fuzzy and a hierarchical segmentation approach together, thus providing a flexible multiclass segmentation method based on probabilistic path propagations. By utilizing this method, analysts and physicians can map their mental model of image components and their composition to higher level objects. The probabilistic segmentation of higher order components is propagated along the user-defined hierarchy to highlight the potential of improvement resulting in each level of hierarchy by providing an intuitive representation. The effectiveness of this approach is demonstrated by evaluating our segmentations of biomedical datasets, comparing it to the state-of-the-art segmentation approaches, and an extensive user study.

Empirical Comparison of Curvature Estimators on Volume Images and Triangle Meshes.

In the development of graphical algorithms, choosing an appropriate data representation plays a pivotal role. Hence, there is a need for studies that support corresponding decision making. Here, we investigate curvature estimation based on two discrete representations-volume images and triangle meshes-and present a comprehensive cross-comparison. For doing so, four carefully selected geometries, represented as implicit functions, have been discretized to volume images and triangle meshes in different resolutions on a comparable scale. Afterwards, implementations available in open-source libraries (CGAL, DIPimage, libigl, trimesh2, VTK) and our own implementation of a relevant paper [1] were applied to them and the resulting estimations of mean and Gaussian curvature were compared in terms of quality and runtime. Independent of the underlying discrete representation, all estimators generated similar errors, but overall, mesh-based methods allowed for more accurate estimations. We measured a maximum normalized mean absolute error difference of 6.36 percent between the most precise mesh-based method compared to corresponding image-based methods when considering only discretizations of sufficient resolution. In terms of runtime, methods working on triangle meshes were faster when geometries had a small surface density. For geometries with larger surface densities, which is fairly common when considering real data, e.g., in material or medical science, the runtimes for both representations were similar.

Apply or Die: On the Role and Assessment of Application Papers in Visualization.

Application-oriented papers provide an important way to invigorate and cross-pollinate the visualization field, but the exact criteria for judging an application paper's merit remain an open question. This article builds on a panel at the 2016 IEEE Visualization Conference entitled "Application Papers: What Are They, and How Should They Be Evaluated?" that sought to gain a better understanding of prevalent views in the visualization community. This article surveys current trends that favor application papers, reviews the benefits and contributions of this paper type, and discusses their assessment in the review process. It concludes with recommendations to ensure that the visualization community is more inclusive to application papers.

STRAD Wheel: Web-Based Library for Visualizing Temporal Data.

Recent advances in web development, including the introduction of HTML5, have opened a door for visualization researchers and developers to quickly access larger audiences worldwide. Open source libraries for the creation of interactive visualizations are becoming more specialized but also modular, which makes them easy to incorporate in domain-specific applications. In this context, the authors developed STRAD (Spatio-Temporal-Radar) Wheel, a web-based library that focuses on the visualization and interactive query of temporal data in a compact view with multiple temporal granularities. This article includes two application examples in urban planning to help illustrate the proposed visualization's use in practice.

A Decision-Support System for Sustainable Water Distribution System Planning.

An interactive decision-support system (DSS) can help experts prepare water resource management plans for decision makers and stakeholders. The design of the proposed prototype incorporates visualization techniques such as circle views, grid layout, small multiple maps, and node simplification to improve the data readability of water distribution systems. A case study with three urban water management and sanitary engineering experts revealed that the proposed DSS is satisfactory, efficient, and effective.

In Situ Eddy Analysis in a High-Resolution Ocean Climate Model.

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.

Direct Feature Visualization Using Morse-Smale Complexes.

In this paper, we characterize the range of features that can be extracted from an Morse-Smale complex and describe a unified query language to extract them. We provide a visual dictionary to guide users when defining features in terms of these queries. We demonstrate our topology-rich visualization pipeline in a tool that interactively queries the MS complex to extract features at multiple resolutions, assigns rendering attributes, and combines traditional volume visualization with the extracted features. The flexibility and power of this approach is illustrated with examples showing novel features.

On mesh-free valley surface extraction with application to low frequency sound simulation.

Crease surfaces describe extremal structures of 3D scalar fields. We present a new region-growing-based approach to the meshless extraction of adaptive nonmanifold valley and ridge surfaces that overcomes limitations of previous approaches by decoupling point seeding and triangulation of the surface. Our method is capable of extracting valley surface skeletons as connected minimum structures. As our algorithm is inherently mesh-free and curvature adaptive, it is suitable for surface construction in fields with an arbitrary neighborhood structure. As an application for insightful visualization with valley surfaces, we choose a low frequency acoustics simulation. We use our valley surface construction approach to visualize the resulting complex-valued scalar pressure field for arbitrary frequencies to identify regions of sound cancellation. This provides an expressive visualization of the topology of wave node and antinode structures in simulated acoustics.

A scale space based persistence measure for critical points in 2D scalar fields.

This paper introduces a novel importance measure for critical points in 2D scalar fields. This measure is based on a combination of the deep structure of the scale space with the well-known concept of homological persistence. We enhance the noise robust persistence measure by implicitly taking the hill-, ridge- and outlier-like spatial extent of maxima and minima into account. This allows for the distinction between different types of extrema based on their persistence at multiple scales. Our importance measure can be computed efficiently in an out-of-core setting. To demonstrate the practical relevance of our method we apply it to a synthetic and a real-world data set and evaluate its performance and scalability.

IRIS: illustrative rendering for integral surfaces.

Integral surfaces are ideal tools to illustrate vector fields and fluid flow structures. However, these surfaces can be visually complex and exhibit difficult geometric properties, owing to strong stretching, shearing and folding of the flow from which they are derived. Many techniques for non-photorealistic rendering have been presented previously. It is, however, unclear how these techniques can be applied to integral surfaces. In this paper, we examine how transparency and texturing techniques can be used with integral surfaces to convey both shape and directional information. We present a rendering pipeline that combines these techniques aimed at faithfully and accurately representing integral surfaces while improving visualization insight. The presented pipeline is implemented directly on the GPU, providing real-time interaction for all rendering modes, and does not require expensive preprocessing of integral surfaces after computation.

Integrating data clustering and visualization for the analysis of 3D gene expression data.

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex data sets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss 1) the integration of data clustering and visualization into one framework, 2) the application of data clustering to 3D gene expression data, 3) the evaluation of the number of clusters k in the context of 3D gene expression clustering, and 4) the improvement of overall analysis quality via dedicated postprocessing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.

Tiled++: an enhanced tiled hi-res display wall.

In recent years, high-resolution displays have become increasingly important to decision makers and scientists because large screens combined with a high pixel count facilitate content rich, simultaneous display of computer-generated imagery and high-definition video data from multiple sources. Tiled displays are attractive due to their extended screen real estate, scalability, and low cost. LCD panels are usually preferred over projectors because of their superior resolution. One of the drawbacks of LCD-based tiled displays is the fact that users sometimes get distracted by the screens' bezels, which cause discontinuities in rendered images, animations, or videos. Most conventional solutions either ignore the bezels and display all pixels, causing objects to become distorted, or eliminate the pixels that would normally fall under the bezels, causing pixels to be missing in the display of static images. In animations, the missing pixels will eventually reappear when the object moves, providing an experience that is similar to looking through a French window. In this paper, we present a new scalable approach that leads neither to discontinuities nor to significant loss of information. By projecting onto the bezels, we demonstrate that a combination of LCD-based tiled displays and projection significantly reduces the bezel problem. Our technique eliminates ambiguities that commonly occur on tiled displays in the fields of information visualization, visual data analysis, human-computer interaction, and scientific data display. It improves the usability of multimonitor systems by virtually eliminating the bezels. We describe a setup and provide results from an evaluation experiment conducted on a 3 x 3 and on a 10 x 5 tiled display wall.

Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data.

Knowledge discovery from large and complex scientific data is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. The combination and close integration of methods from scientific visualization, information visualization, automated data analysis, and other enabling technologies -such as efficient data management- supports knowledge discovery from multi-dimensional scientific data. This paper surveys two distinct applications in developmental biology and accelerator physics, illustrating the effectiveness of the described approach.