DevLan/vispubs huggingface dataset:
A General Illumination Model for Molecular Visualization
Authors: Pedro Hermosilla, Pere-Pau Vázquez, Àlvar Vinacua, Timo Ropinski
Published at EuroVis, 2018

Several visual representations have been developed over the years to visualize molecular structures, and to enable a better understanding of their underlying chemical processes. Today, the most frequently used atom‐based representations are the Space‐filling, the Solvent Excluded Surface, the Balls‐and‐Sticks, and the Licorice models. While each of these representations has its individual benefits, when applied to large‐scale models spatial arrangements can be difficult to interpret when employing current visualization techniques. In the past it has been shown that global illumination techniques improve the perception of molecular visualizations; unfortunately existing approaches are tailored towards a single visual representation. We propose a general illumination model for molecular visualization that is valid for different representations. With our illumination model, it becomes possible, for the first time, to achieve consistent illumination among all atom‐based molecular representations. The proposed model can be further evaluated in real‐time, as it employs an analytical solution to simulate diffuse light interactions between objects. To be able to derive such a solution for the rather complicated and diverse visual representations, we propose the use of regression analysis together with adapted parameter sampling strategies as well as shape parametrization guided sampling, which are applied to the geometric building blocks of the targeted visual representations. We will discuss the proposed sampling strategies, the derived illumination model, and demonstrate its capabilities when visualizing several dynamic molecules.
A Survey of Flattening-Based Medical Visualization Techniques
Authors: Julian Kreiser, Monique Meuschke, Gabriel Mistelbauer, Bernhard Preim, Timo Ropinski
Published at EuroVis, 2018

In many areas of medicine, visualization research can help with task simplification, abstraction or complexity reduction. A common visualization approach is to facilitate parameterization techniques which flatten a usually 3D object into a 2D plane. Within this state of the art report (STAR), we review such techniques used in medical visualization and investigate how they can be classified with respect to the handled data and the underlying tasks. Many of these techniques are inspired by mesh parameterization algorithms which help to project a triangulation in ℝ3 to a simpler domain in ℝ2. It is often claimed that this makes complex structures easier to understand and compare by humans and machines. Within this STAR we review such flattening techniques which have been developed for the analysis of the following medical entities: the circulation system, the colon, the brain, tumors, and bones. For each of these five application scenarios, we have analyzed the tasks and requirements, and classified the reviewed techniques with respect to a developed coding system. Furthermore, we present guidelines for the future development of flattening techniques in these areas.
An Approximate Parallel Vectors Operator for Multiple Vector Fields
Authors: Tim Gerrits, Christian Rössl, Holger Theisel
Published at EuroVis, 2018

The Parallel Vectors (PV) Operator extracts the locations of points where two vector fields are parallel. In general, these features are line structures. The PV operator has been used successfully for a variety of problems, which include finding vortex‐core lines or extremum lines. We present a new generic feature extraction method for multiple 3D vector fields: The Approximate Parallel Vectors (APV) Operator extracts lines where all fields are approximately parallel. The definition of the APV operator is based on the application of PV for two vector fields that are derived from the given set of fields. The APV operator enables the direct visualization of features of vector field ensembles without processing fields individually and without causing visual clutter. We give a theoretical analysis of the APV operator and demonstrate its utility for a number of ensemble data.
Analyzing Residue Surface Proximity to Interpret Molecular Dynamics
Authors: Nils Lichtenberg, Raphael Menges, V. Ageev, Ajay Abisheck Paul George, P. Heimer, Diana Imhof, Kai Lawonn
Published at EuroVis, 2018

The surface of a molecule holds important information about the interaction behavior with other molecules. In dynamic folding or docking processes, residues of amino acids with different properties change their position within the molecule over time. The atoms of the residues that are accessible to the solvent can directly contribute to binding interactions, while residues buried within the molecular structure contribute to the stability of the molecule. Understanding patterns and causality of structural changes is important for experts in the pharmaceutical domain, e.g., in the process of drug design. We apply an iterative computation of the Solvent Accessible Surface in order to extract virtual layers of a molecule. The extraction allows to track the movement of residues in the body of the molecule, with respect to the distance of the residue to the surface or the core during dynamics simulations. We visualize the obtained layer information for the complete time span of the molecular dynamics simulation as a 2D‐map and for individual time‐steps as a 3D‐representation of the molecule. The data acquisition has been implemented alongside with further analysis functionality in a prototypical application, which is available to the public domain. We underline the feasibility of our approach with a study from the pharmaceutical domain, where our approach has been used for novel insights into the folding behavior of μ‐conotoxins.
Assessing Effects of Task and Data Distribution on the Effectiveness of Visual Encodings
Authors: Younghoon Kim, Jeffrey Heer
Published at EuroVis, 2018

In addition to the choice of visual encodings, the effectiveness of a data visualization may vary with the analytical task being performed and the distribution of data values. To better assess these effects and create refined rankings of visual encodings, we conduct an experiment measuring subject performance across task types (e.g., comparing individual versus aggregate values) and data distributions (e.g., with varied cardinalities and entropies). We compare performance across 12 encoding specifications of trivariate data involving 1 categorical and 2 quantitative fields, including the use of x, y, color, size, and spatial subdivision (i.e., faceting). Our results extend existing models of encoding effectiveness and suggest improved approaches for automated design. For example, we find that colored scatterplots (with positionally‐coded quantities and color‐coded categories) perform well for comparing individual points, but perform poorly for summary tasks as the number of categories increases.
Baseball Timeline: Summarizing Baseball Plays Into a Static Visualization
Authors: Jorge Piazentin Ono, Carlos A. Dietrich, Cláudio T. Silva
Published at EuroVis, 2018

In sports, Play Diagrams are the standard way to represent and convey information. They are widely used by coaches, managers, journalists and fans in general. There are situations where diagrams may be hard to understand, for example, when several actions are packed in a certain region of the field or there are just too many actions to be transformed in a clear depiction of the play. The representation of how actions develop through time, in particular, may be hardly achieved on such diagrams. The time, and the relationship among the actions of the players through time, is critical on the depiction of complex plays. In this context, we present a study on how player actions may be clearly depicted on 2D diagrams. The study is focused on Baseball plays, a sport where diagrams are heavily used to summarize the actions of the players. We propose a new and simple approach to represent spatiotemporal information in the form of a timeline. We designed our visualization with a requirement driven approach, conducting interviews and fulfilling the needs of baseball experts and expert‐fans. We validate our approach by presenting a detailed analysis of baseball plays and conducting interviews with four domain experts.
Bladder Runner: Visual Analytics for the Exploration of RT-Induced Bladder Toxicity in a Cohort Study
Authors: Renata G. Raidou, Oscar Casares-Magaz, Artem Amirkhanov, Vitali Moiseenko, Ludvig P. Muren, John P. Einck, Anna Vilanova, M. Eduard Gröller
Published at EuroVis, 2018

We present the Bladder Runner, a novel tool to enable detailed visual exploration and analysis of the impact of bladder shape variation on the accuracy of dose delivery, during the course of prostate cancer radiotherapy (RT). Our tool enables the investigation of individual patients and cohorts through the entire treatment process, and it can give indications of RT‐induced complications for the patient. In prostate cancer RT treatment, despite the design of an initial plan prior to dose administration, bladder toxicity remains very common. The main reason is that the dose is delivered in multiple fractions over a period of weeks, during which, the anatomical variation of the bladder – due to differences in urinary filling – causes deviations between planned and delivered doses. Clinical researchers want to correlate bladder shape variations to dose deviations and toxicity risk through cohort studies, to understand which specific bladder shape characteristics are more prone to side effects. This is currently done with Dose‐Volume Histograms (DVHs), which provide limited, qualitative insight. The effect of bladder variation on dose delivery and the resulting toxicity cannot be currently examined with the DVHs. To address this need, we designed and implemented the Bladder Runner, which incorporates visualization strategies in a highly interactive environment with multiple linked views. Individual patients can be explored and analyzed through the entire treatment period, while inter‐patient and temporal exploration, analysis and comparison are also supported. We demonstrate the applicability of our presented tool with a usage scenario, employing a dataset of 29 patients followed through the course of the treatment, across 13 time points. We conducted an evaluation with three clinical researchers working on the investigation of RT‐induced bladder toxicity. All participants agreed that Bladder Runner provides better understanding and new opportunities for the exploration and analysis of the involved cohort data.
CFGExplorer: Designing a Visual Control Flow Analytics System around Basic Program Analysis Operations
Authors: Sabin Devkota, Katherine E. Isaacs
Published at EuroVis, 2018

To develop new compilation and optimization techniques, computer scientists frequently Consult program analysis artifacts such as Control flow graphs (CFGs) and traces of executed instructions. A CFG is a directed graph representing possible execution paths in a program. CFGs are commonly visualized as node‐link diagrams while traces are commonly viewed in raw text format. Visualizing and exploring CFGs and traces is challenging because of the complexity and specificity of the operations researchers perform. We present a design study where we collaborate with computer scientists researching dynamic binary analysis and compilation techniques. The research group primarily employs CFGs and traces to reason about and develop new algorithms for program optimization and parallelization. Through questionnaires, interviews, and a year‐long observation, we analyzed their use of visualization, noting that the tasks they perform match common subroutines they employ in their techniques. Based on this task analysis, we designed CFGExplorer, a visual analytics system that supports computer scientists with interactions that are integrated with the program structure. We developed a domain‐specific graph modification to generate graph layouts that reflect program structure. CFGExplorer incorporates structures such as functions and loops, and uses the correspondence between CFGs and traces to support navigation. We further augment the system to highlight the output of program analysis techniques, facilitating exploration at a higher level. We evaluate the tool through guided sessions and semi‐structured interviews as well as deployment. Our collaborators have integrated CFGExplorer into their workflow and use it to reason about programs, develop and debug new algorithms, and share their findings.
ChangeCatcher: Increasing Inter-author Awareness for Visualization Development
Authors: Mona Hosseinkhani Loorak, Melanie Tory, Sheelagh Carpendale
Published at EuroVis, 2018

We introduce an approach for explicitly revealing changes between versions of a visualization workbook to support version comparison tasks. Visualization authors may need to understand version changes for a variety of reasons, analogous to document editing. An author who has been away for a while may need to catch up on the changes made by their co‐author, or a person responsible for formatting compliance may need to check formatting changes that occurred since the last time they reviewed the work. We introduce ChangeCatcher, a prototype tool to help people find and understand changes in a visualization workbook, specifically, a Tableau workbook. Our design is based on interviews we conducted with experts to investigate user needs and practices around version comparison. ChangeCatcher provides an overview of changes across six categories, and employs a multi‐level details‐on‐demand approach to progressively reveal details. Our qualitative study showed that ChangeCatcher's methods for explicitly revealing and categorizing version changes were helpful in version comparison tasks.
Chart Constellations: Effective Chart Summarization for Collaborative and Multi-User Analyses
Authors: Shenyu Xu, Chris Bryan, Jianping Kelvin Li, Jian Zhao 0010, Kwan-Liu Ma
Published at EuroVis, 2018

Many data problems in the real world are complex and require multiple analysts working together to uncover embedded insights by creating chart‐driven data stories. How, as a subsequent analysis step, do we interpret and learn from these collections of charts? We present Chart Constellations, a system to interactively support a single analyst in the review and analysis of data stories created by other collaborative analysts. Instead of iterating through the individual charts for each data story, the analyst can project, cluster, filter, and connect results from all users in a meta‐visualization approach. Constellations supports deriving summary insights about prior investigations and supports the exploration of new, unexplored regions in the dataset. To evaluate our system, we conduct a user study comparing it against data science notebooks. Results suggest that Constellations promotes the discovery of both broad and high‐level insights, including theme and trend analysis, subjective evaluation, and hypothesis generation.
ConcaveCubes: Supporting Cluster-based Geographical Visualization in Large Data Scale
Authors: Mingzhao Li 0001, Farhana Murtaza Choudhury, Zhifeng Bao, Hanan Samet, Timos Sellis
Published at EuroVis, 2018

In this paper we study the problem of supporting effective and scalable visualization for the rapidly increasing volumes of urban data. From an extensive literature study, we find that the existing solutions suffer from at least one of the drawbacks below: (i) loss of interesting structures/outliers due to sampling; (ii) supporting heatmaps only, which provides limited information; and (iii) no notion of real‐world geography semantics (e.g., country, state, city) is captured in the visualization result as well as the underlying index. Therefore, we propose ConcaveCubes, a cluster‐based data cube to support interactive visualization of large‐scale multidimensional urban data. Specifically, we devise an appropriate visualization abstraction and visualization design based on clusters. We propose a novel concave hull construction method to support boundary based cluster map visualization, where real‐world geographical semantics are preserved without any information loss. Instead of calculating the clusters on demand, ConcaveCubes (re)utilizes existing calculation and visualization results to efficiently support different kinds of user interactions. We conduct extensive experiments using real‐world datasets and show the efficiency and effectiveness of ConcaveCubes by comparing with the state‐of‐the‐art cube‐based solutions.
Core Lines in 3D Second-Order Tensor Fields
Authors: Timo Oster, Christian Rössl, Holger Theisel
Published at EuroVis, 2018

Vortices are important features in vector fields that show a swirling behavior around a common core. The concept of a vortex core line describes the center of this swirling behavior. In this work, we examine the extension of this concept to 3D second‐order tensor fields. Here, a behavior similar to vortices in vector fields can be observed for trajectories of the eigenvectors. Vortex core lines in vector fields were defined by Sujudi and Haimes to be the locations where stream lines are parallel to an eigenvector of the Jacobian. We show that a similar criterion applied to the eigenvector trajectories of a tensor field yields structurally stable lines that we call tensor core lines. We provide a formal definition of these structures and examine their mathematical properties. We also present a numerical algorithm for extracting tensor core lines in piecewise linear tensor fields. We find all intersections of tensor core lines with the faces of a dataset using a simple and robust root finding algorithm. Applying this algorithm to tensor fields obtained from structural mechanics simulations shows that it is able to effectively detect and visualize regions of rotational or hyperbolic behavior of eigenvector trajectories.
Cosine-Weighted B-Spline Interpolation on the Face-Centered Cubic Lattice
Authors: Gergely Rácz, Balázs Csébfalvi
Published at EuroVis, 2018

Cosine‐Weighted B‐spline (CWB) interpolation [Csé13] has been originally proposed for volumetric data sampled on the Body‐Centered Cubic (BCC) lattice. The BCC lattice is well known to be optimal for sampling isotropically band‐limited signals above the Nyquist limit. However, the Face‐Centered Cubic (FCC) lattice has been recently proven to be optimal for low‐rate sampling. The CWB interpolation is a state‐of‐the‐art technique on the BCC lattice, which outperforms, for example, the previously proposed box‐spline interpolation in terms of both efficiency and visual quality. In this paper, we show that CWB interpolation can be adapted to the FCC lattice as well, and results in similarly isotropic signal reconstructions as on the BCC lattice.
Design Factors for Summary Visualization in Visual Analytics
Authors: Alper Sarikaya, Michael Gleicher, Danielle Albers Szafir
Published at EuroVis, 2018

Data summarization allows analysts to explore datasets that may be too complex or too large to visualize in detail. Designers face a number of design and implementation choices when using summarization in visual analytics systems. While these choices influence the utility of the resulting system, there are no clear guidelines for the use of these summarization techniques. In this paper, we codify summarization use in existing systems to identify key factors in the design of summary visualizations. We use quantitative content analysis to systematically survey examples of visual analytics systems and enumerate the use of these design factors in data summarization. Through this analysis, we expose the relationship between design considerations, strategies for data summarization in visualization systems, and how different summarization methods influence the analyses supported by systems. We use these results to synthesize common patterns in real‐world use of summary visualizations and highlight open challenges and opportunities that these patterns offer for designing effective systems. This work provides a more principled understanding of design practices for summary visualization and offers insight into underutilized approaches.
DimSUM: Dimension and Scale Unifying Map for Visual Abstraction of DNA Origami Structures
Authors: Haichao Miao, Elisa De Llano, Tobias Isenberg 0001, M. Eduard Gröller, Ivan Barisic, Ivan Viola
Published at EuroVis, 2018

We present a novel visualization concept for DNA origami structures that integrates a multitude of representations into a Dimension and Scale Unifying Map (DimSUM). This novel abstraction map provides means to analyze, smoothly transition between, and interact with many visual representations of the DNA origami structures in an effective way that was not possible before. DNA origami structures are nanoscale objects, which are challenging to model in silico. In our holistic approach we seamlessly combine three‐dimensional realistic shape models, two‐dimensional diagrammatic representations, and ordered alignments in one‐dimensional arrangements, with semantic transitions across many scales. To navigate through this large, two‐dimensional abstraction map we highlight locations that users frequently visit for certain tasks and datasets. Particularly interesting viewpoints can be explicitly saved to optimize the workflow. We have developed DimSUM together with domain scientists specialized in DNA nanotechnology. In the paper we discuss our design decisions for both the visualization and the interaction techniques. We demonstrate two practical use cases in which our approach increases the specialists’ understanding and improves their effectiveness in the analysis. Finally, we discuss the implications of our concept for the use of controlled abstraction in visualization in general.
Explorative Blood Flow Visualization using Dynamic Line Filtering based on Surface Features
Authors: Benjamin Behrendt, Philipp Berg, Oliver Beuing, Bernhard Preim, Sylvia Saalfeld
Published at EuroVis, 2018

Rupture risk assessment is a key to devise patient‐specific treatment plans of cerebral aneurysms. To understand and predict the development of aneurysms and other vascular diseases over time, both hemodynamic flow patterns and their effect on the vessel surface need to be analyzed. Flow structures close to the vessel wall often correlate directly with local changes in surface parameters, such as pressure or wall shear stress. Yet, in many existing applications, the analyses of flow and surface features are either somewhat detached from one another or only globally available. Especially for the identification of specific blood flow characteristics that cause local startling parameters on the vessel surface, like elevated pressure values, an interactive analysis tool is missing.The explorative visualization of flow data is challenging due to the complexity of the underlying data. In order to find meaningful structures in the entirety of the flow, the data has to be filtered based on the respective explorative aim. In this paper, we present a combination of visualization, filtering and interaction techniques for explorative analysis of blood flow with a focus on the relation of local surface parameters and underlying flow structures. Coherent bundles of pathlines can be interactively selected based on their relation to features of the vessel wall and further refined based on their own hemodynamic features. This allows the user to interactively select and explore flow structures locally affecting a certain region on the vessel wall and therefore to understand the cause and effect relationship between these entities. Additionally, multiple selected flow structures can be compared with respect to their quantitative parameters, such as flow speed. We confirmed the usefulness of our approach by conducting an informal interview with two expert neuroradiologists and an expert in flow simulation. In addition, we recorded several insights the neuroradiologists were able to gain with the help of our tool.