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We conducted a study to systematically investigate the communication of complex dynamic processes along a two‐dimensional design space, where the axes represent a representation's manifestation (physical or virtual) and operation (manual or automatic). We exemplify the design space on a model embodying cardiovascular pathologies, represented by a mechanism where a liquid is pumped into a draining vessel, with complications illustrated through modifications to the model. The results of a mixed‐methods lab study with 28 participants show that both physical manifestation and manual operation have a strong positive impact on the audience's engagement. The study does not show a measurable knowledge increase with respect to cardiovascular pathologies using manually operated physical representations. However, subjectively, participants report a better understanding of the process—mainly through non‐visual cues like haptics, but also auditory cues. The study also indicates an increased task load when interacting with the process, which, however, seems to play a minor role for the participants. Overall, the study shows a clear potential of physicalization for the communication of complex dynamic processes, which only fully unfold if observers have to chance to interact with the process. [ABSTRACT FROM AUTHOR]

3D animations are an effective method to learn about complex dynamic phenomena, such as mesoscale biological processes. The animators' goals are to convey a sense of the scene's overall complexity while, at the same time, visually guiding the user through a story of subsequent events embedded in the chaotic environment. Animators use a variety of visual emphasis techniques to guide the observers' attention through the story, such as highlighting, halos – or by manipulating motion parameters of the scene. In this paper, we investigate the effect of smoothing the motion of contextual scene elements to attract attention to focus elements of the story exhibiting high‐frequency motion. We conducted a crowdsourced study with 108 participants observing short animations with two illustrative motion smoothing strategies: geometric smoothing through noise reduction of contextual motion trajectories and visual smoothing through motion blur of context items. We investigated the observers' ability to follow the story as well as the effect of the techniques on speed perception in a molecular scene. Our results show that moderate motion blur significantly improves users' ability to follow the story. Geometric motion smoothing is less effective but increases the visual appeal of the animation. However, both techniques also slow down the perceived speed of the animation. We discuss the implications of these results and derive design guidelines for animators of complex dynamic visualizations. [ABSTRACT FROM AUTHOR]

While convolutional neural networks (CNNs) have found wide adoption as state‐of‐the‐art models for image‐related tasks, their predictions are often highly sensitive to small input perturbations, which the human vision is robust against. This paper presents Perturber, a web‐based application that allows users to instantaneously explore how CNN activations and predictions evolve when a 3D input scene is interactively perturbed. Perturber offers a large variety of scene modifications, such as camera controls, lighting and shading effects, background modifications, object morphing, as well as adversarial attacks, to facilitate the discovery of potential vulnerabilities. Fine‐tuned model versions can be directly compared for qualitative evaluation of their robustness. Case studies with machine learning experts have shown that Perturber helps users to quickly generate hypotheses about model vulnerabilities and to qualitatively compare model behavior. Using quantitative analyses, we could replicate users' insights with other CNN architectures and input images, yielding new insights about the vulnerability of adversarially trained models. [ABSTRACT FROM AUTHOR]

Many professionals, like journalists, writers, or consultants, need to acquire information from various sources, make sense of this unstructured evidence, structure their observations, and finally create and deliver their product, such as a report or a presentation. In formative interviews, we found that tools allowing structuring of observations are often disconnected from the corresponding evidence. Therefore, we designed a sensemaking environment with a flexible observation graph that visually ties together evidence in unstructured documents with the user's structured knowledge. This is achieved through bi-directional deep links between highlighted document portions and nodes in the observation graph. In a controlled study, we compared users' sensemaking strategies using either the observation graph or a simple text editor on a large display. Results show that the observation graph represents a holistic, compact representation of users' observations, which can be linked to unstructured evidence on demand. In contrast, users taking textual notes required much more display space to spatially organize source documents containing unstructured evidence. This implies that spatial organization is a powerful strategy to structure observations even if the available space is limited. [ABSTRACT FROM AUTHOR]

Radial charts are generally considered less effective than linear charts. Perhaps the only exception is in visualizing periodical time-dependent data, which is believed to be naturally supported by the radial layout. It has been demonstrated that the drawbacks of radial charts outweigh the benefits of this natural mapping. Visualization of daily patterns, as a special case, has not been systematically evaluated using radial charts. In contrast to yearly or weekly recurrent trends, the analysis of daily patterns on a radial chart may benefit from our trained skill on reading radial clocks that are ubiquitous in our culture. In a crowd-sourced experiment with 92 non-expert users, we evaluated the accuracy, efficiency, and subjective ratings of radial and linear charts for visualizing daily traffic accident patterns. We systematically compared juxtaposed 12-hours variants and single 24-hours variants for both layouts in four low-level tasks and one high-level interpretation task. Our results show that over all tasks, the most elementary 24-hours linear bar chart is most accurate and efficient and is also preferred by the users. This provides strong evidence for the use of linear layouts – even for visualizing periodical daily patterns. [ABSTRACT FROM AUTHOR]

Abstract: When performing queries in web search engines, users often face difficulties choosing appropriate query terms. Search engines therefore usually suggest a list of expanded versions of the user query to disambiguate it or to resolve potential term mismatches. However, it has been shown that users find it difficult to choose an expanded query from such a list. In this paper, we describe the adoption of set‐based text visualization techniques to visualize how query expansions enrich the result space of a given user query and how the result sets relate to each other. Our system uses a linguistic approach to expand queries and topic modeling to extract the most informative terms from the results of these queries. In a user study, we compare a common text list of query expansion suggestions to three set‐based text visualization techniques adopted for visualizing expanded query results – namely, Compact Euler Diagrams, Parallel Tag Clouds, and a List View – to resolve ambiguous queries using interactive query expansion. Our results show that text visualization techniques do not increase retrieval efficiency, precision, or recall. Overall, users rate Parallel Tag Clouds visualizing key terms of the expanded query space lowest. Based on the results, we derive recommendations for visualizations of query expansion results, text visualization techniques in general, and discuss alternative use cases of set‐based text visualization techniques in the context of web search. [ABSTRACT FROM AUTHOR]

Focus-context techniques provide visual guidance in visualizations by giving strong visual prominence to elements of interest while the context is suppressed. However, finding a visual feature to enhance for the focus to pop out from its context in a large dynamic scene, while leading to minimal visual deformation and subjective disturbance, is challenging. This paper proposes Attractive Flicker, a novel technique for visual guidance in dynamic narrative visualizations. We first show that flicker is a strong visual attractor in the entire visual field, without distorting, suppressing, or adding any scene elements. The novel aspect of our Attractive Flicker technique is that it consists of two signal stages: The first “orientation stage” is a short but intensive flicker stimulus to attract the attention to elements of interest. Subsequently, the intensive flicker is reduced to a minimally disturbing luminance oscillation (“engagement stage”) as visual support to keep track of the focus elements. To find a good trade-off between attraction effectiveness and subjective annoyance caused by flicker, we conducted two perceptual studies to find suitable signal parameters. We showcase Attractive Flicker with the parameters obtained from the perceptual statistics in a study of molecular interactions. With Attractive Flicker, users were able to easily follow the narrative of the visualization on a large display, while the flickering of focus elements was not disturbing when observing the context. [ABSTRACT FROM PUBLISHER]

In this paper, we present the design and implementation of a dynamic window management technique that changes the perception of windows as fixed-sized rectangles. The primary goal of self-organizing windows is to automatically display the most relevant information for a user's current activity, which removes the burden of organizing and arranging windows from the user. We analyze the image-based representation of each window and identify coherent pieces of information. The windows are then automatically moved, scaled and composed in a content-aware manner to fit the most relevant information into the limited area of the screen. During the design process, we consider findings from previous experiments and show how users can benefit from our system. We also describe how the immense processing power of current graphics processing units can be exploited to build an interactive system that finds an optimal solution within the complex design space of all possible window transformations in real time. [ABSTRACT FROM AUTHOR]

Evaluating, comparing, and interpreting related pieces of information are tasks that are commonly performed during visual data analysis and in many kinds of information-intensive work. Synchronized visual highlighting of related elements is a well-known technique used to assist this task. An alternative approach, which is more invasive but also more expressive is visual linking in which line connections are rendered between related elements. In this work, we present context-preserving visual links as a new method for generating visual links. The method specifically aims to fulfill the following two goals: first, visual links should minimize the occlusion of important information; second, links should visually stand out from surrounding information by minimizing visual interference. We employ an image-based analysis of visual saliency to determine the important regions in the original representation. A consequence of the image-based approach is that our technique is application-independent and can be employed in a large number of visual data analysis scenarios in which the underlying content cannot or should not be altered. We conducted a controlled experiment that indicates that users can find linked elements in complex visualizations more quickly and with greater subjective satisfaction than in complex visualizations in which plain highlighting is used. Context-preserving visual links were perceived as visually more attractive than traditional visual links that do not account for the context information. [ABSTRACT FROM AUTHOR]

We introduce a workflow for the visual assessment of interobserver variability in medical image segmentation. Image segmentation is a crucial step in the diagnosis, prognosis, and treatment of many diseases. Despite the advancements in autosegmentation, clinical practice widely relies on manual delineations performed by radiologists. Our work focuses on designing a solution for understanding the radiologists' thought processes during segmentation and for unveiling reasons that lead to interobserver variability. To this end, we propose a visual analysis tool connecting multiple radiologists' delineation processes with their outcomes, and we demonstrate its potential in a case study.

Immersive virtual reality environments are gaining popularity for studying and exploring crowded three-dimensional structures. When reaching very high structural densities, the natural depiction of the scene produces impenetrable clutter and requires visibility and occlusion management strategies for exploration and orientation. Strategies developed to address the crowdedness in desktop applications, however, inhibit the feeling of immersion. They result in nonimmersive, desktop-style outside-in viewing in virtual reality. This article proposes Nanotilus-a new visibility and guidance approach for very dense environments that generates an endoscopic inside-out experience instead of outside-in viewing, preserving the immersive aspect of virtual reality. The approach consists of two novel, tightly coupled mechanisms that control scene sparsification simultaneously with camera path planning. The sparsification strategy is localized around the camera and is realized as a multi-scale, multi-shell, variety-preserving technique. When Nanotilus dives into the structures to capture internal details residing on multiple scales, it guides the camera using depth-based path planning. In addition to sparsification and path planning, we complete the tour generation with an animation controller, textual annotation, and text-to-visualization conversion. We demonstrate the generated guided tours on mesoscopic biological models - SARS-CoV-2 and HIV. We evaluate the Nanotilus experience with a baseline outside-in sparsification and navigational technique in a formal user study with 29 participants. While users can maintain a better overview using the outside-in sparsification, the study confirms our hypothesis that Nanotilus leads to stronger engagement and immersion.

We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.

Visualization of structural biology data uses color to categorize or separate dense structures into particular semantic units. In multiscale models of viruses or bacteria, there are atoms on the finest level of detail, then amino-acids, secondary structures, macromolecules, up to the compartment level and, in all these levels, elements can be visually distinguished by color. However, currently only single scale coloring schemes are utilized that show information for one particular scale only. We present a novel technology which adaptively, based on the current scale level, adjusts the color scheme to depict or distinguish the currently best visible structural information. We treat the color as a visual resource that is distributed given a particular demand. The changes of the color scheme are seamlessly interpolated between the color scheme from the previous views into a given new one. With such dynamic multi-scale color mapping we ensure that the viewer is able to distinguish structural detail that is shown on any given scale. This technique has been tested by users with an expertise in structural biology and has been overall well received.

The gauge-figure task (GFT) is a widespread method used to study surface perception for evaluating rendering and visualization techniques. The authors investigate how accurately slant angles probed on well-defined objects align with the ground truth (GT) in monoscopic and stereoscopic displays. Their results show that the GFT probes taken with well-defined objects align well with the GT in the all-monoscopic and all-stereoscopic conditions. However, they found that a GF rendered in stereo over a monoscopic stimulus results in a strong slant underestimation and that an overestimation occurred in the inverse case (monoscopic GF andstereoscopic stimulus). They discuss how their findings affect the interpretation of absolute GFT measures, compared to the GT normal.