bluuebunny/crossref_metadata_2025 huggingface dataset:
CPU Ray Tracing of Tree‐Based Adaptive Mesh Refinement Data
Authors: Wang Feng , Marshak Nathan , Usher Will , Burstedde Carsten , Knoll Aaron , Heister Timo , Johnson Chris R.
Published: July 2020

Adaptive mesh refinement (AMR) techniques allow for representing a simulation's computation domain in an adaptive fashion. Although these techniques have found widespread adoption in high‐performance computing simulations, visualizing their data output interactively and without cracks or artifacts remains challenging. In this paper, we present an efficient solution for direct volume rendering and hybrid implicit isosurface ray tracing of tree‐based AMR (TB‐AMR) data. We propose a novel reconstruction strategy, Generalized Trilinear Interpolation (GTI), to interpolate across AMR level boundaries without cracks or discontinuities in the surface normal. We employ a general sparse octree structure supporting a wide range of AMR data, and use it to accelerate volume rendering, hybrid implicit isosurface rendering and value queries. We demonstrate that our approach achieves artifact‐free isosurface and volume rendering and provides higher quality output images compared to existing methods at interactive rendering rates.
DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning
Authors: Jaunet T. , Vuillemot R. , Wolf C.
Published: July 2020

We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co‐correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explain‐ability in the field of visual analytics.
Reading Traces: Scalable Exploration in Elastic Visualizations of Cultural Heritage Data
Authors: Bludau M.‐J. , Brüggemann V. , Busch A. , Dörk M.
Published: July 2020

Through a design study, we develop an approach to data exploration that utilizes elastic visualizations designed to support varying degrees of detail and abstraction. Examining the notions of scalability and elasticity in interactive visualizations, we introduce a visualization of personal reading traces such as marginalia or markings inside the reference library of German realist author Theodor Fontane. To explore such a rich and extensive collection, meaningful visual forms of abstraction and detail are as important as the transitions between those states. Following a growing research interest in the role of fluid interactivity and animations between views, we are particularly interested in the potential of carefully designed transitions and consistent representations across scales. The resulting prototype addresses humanistic research questions about the interplay of distant and close reading with visualization research on continuous navigation along several granularity levels, using scrolling as one of the main interaction mechanisms. In addition to presenting the design process and resulting prototype, we present findings from a qualitative evaluation of the tool, which suggest that bridging between distant and close views can enhance exploration, but that transitions between views need to be crafted very carefully to facilitate comprehension.
VA‐TRAC: Geospatial Trajectory Analysis for Monitoring, Identification, and Verification in Fishing Vessel Operations
Authors: Storm‐Furru S. , Bruckner S.
Published: July 2020

In order to ensure sustainability, fishing operations are governed by many rules and regulations that restrict the use of certain techniques and equipment, specify the species and size offish that can be harvested, and regulate commercial activities based on licensing schemes. As the world's second largest exporter offish and seafood products, Norway invests a significant amount of effort into maintaining natural ecosystem dynamics by ensuring compliance with its constantly evolving science‐based regulatory body. This paper introduces VA‐TRAC, a geovisual analytics application developed in collaboration with the Norwegian Directorate of Fisheries in order to address this complex task. Our approach uses automatic methods to identify possible catch operations based on fishing vessel trajectories, embedded in an interactive web‐based visual interface used to explore the results, compare them with licensing information, and incorporate the analysts’ domain knowledge into the decision making process. We present a data and task analysis based on a close collaboration with domain experts, and the design and implementation of VA‐TRAC to address the identified requirements.
Orchard: Exploring Multivariate Heterogeneous Networks on Mobile Phones
Authors: Eichmann P. , Edge D. , Evans N. , Lee B. , Brehmer M. , White C.
Published: July 2020

People are becoming increasingly sophisticated in their ability to navigate information spaces using search, hyperlinks, and visualization. But, mobile phones preclude the use of multiple coordinated views that have proven effective in the desktop environment (e.g., for business intelligence or visual analytics). In this work, we propose to model information as multivariate heterogeneous networks to enable greater analytic expression for a range of sensemaking tasks while suggesting a new, list‐based paradigm with gestural navigation of structured information spaces on mobile phones. We also present a mobile application, called Orchard, which combines ideas from both faceted search and interactive network exploration in a visual query language to allow users to collect facets of interest during exploratory navigation. Our study showed that users could collect and combine these facets with Orchard, specifying network queries and projections that would only have been possible previously using complex data tools or custom data science.
QUESTO: Interactive Construction of Objective Functions for Classification Tasks
Authors: Das Subhajit , Xu Shenyu , Gleicher Michael , Chang Remco , Endert Alex
Published: July 2020

Building effective classifiers requires providing the modeling algorithms with information about the training data and modeling goals in order to create a model that makes proper tradeoffs. Machine learning algorithms allow for flexible specification of such meta‐information through the design of the objective functions that they solve. However, such objective functions are hard for users to specify as they are a specific mathematical formulation of their intents. In this paper, we present an approach that allows users to generate objective functions for classification problems through an interactive visual interface. Our approach adopts a semantic interaction design in that user interactions over data elements in the visualization are translated into objective function terms. The generated objective functions are solved by a machine learning solver that provides candidate models, which can be inspected by the user, and used to suggest refinements to the specifications. We demonstrate a visual analytics system QUESTO for users to manipulate objective functions to define domain‐specific constraints. Through a user study we show that QUESTO helps users create various objective functions that satisfy their goals.
Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning
Authors: Lekschas Fritz , Peterson Brant , Haehn Daniel , Ma Eric , Gehlenborg Nils , Pfister Hanspeter
Published: July 2020

We present Peax, a novel feature‐based technique for interactive visual pattern search in sequential data, like time series or data mapped to a genome sequence. Visually searching for patterns by similarity is often challenging because of the large search space, the visual complexity of patterns, and the user's perception of similarity. For example, in genomics, researchers try to link patterns in multivariate sequential data to cellular or pathogenic processes, but a lack of ground truth and high variance makes automatic pattern detection unreliable. We have developed a convolutional autoencoder for unsupervised representation learning of regions in sequential data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned representation as features of the sequential data, our accompanying visual query system enables interactive feedback‐driven adjustments of the pattern search to adapt to the users’ perceived similarity. Using an active learning sampling strategy, Peax collects user‐generated binary relevance feedback. This feedback is used to train a model for binary classification, to ultimately find other regions that exhibit patterns similar to the search target. We demonstrate Peax's features through a case study in genomics and report on a user study with eight domain experts to assess the usability and usefulness of Peax. Moreover, we evaluate the effectiveness of the learned feature representation for visual similarity search in two additional user studies. We find that our models retrieve significantly more similar patterns than other commonly used techniques.
VisuaLint: Sketchy In Situ Annotations of Chart Construction Errors
Authors: Hopkins Aspen K. , Correll Michael , Satyanarayan Arvind
Published: July 2020

Chart construction errors, such as truncated axes or inexpressive visual encodings, can hinder reading a visualization, or worse, imply misleading facts about the underlying data. These errors can be caught by critical readings of visualizations, but readers must have a high level of data and design literacy and must be paying close attention. To address this issue, we introduce VisuaLint: a technique for surfacing chart construction errors in situ. Inspired by the ubiquitous red wavy underline that indicates spelling mistakes, visualization elements that contain errors (e.g., axes and legends) are sketchily rendered and accompanied by a concise annotation. VisuaLint is unobtrusive — it does not interfere with reading a visualization — and its direct display establishes a close mapping between erroneous elements and the expression of error. We demonstrate five examples of VisualLint and present the results of a crowdsourced evaluation (N = 62) of its efficacy. These results contribute an empirical baseline proficiency for recognizing chart construction errors, and indicate near‐universal difficulty in error identification. We find that people more reliably identify chart construction errors after being shown examples of VisuaLint, and prefer more verbose explanations for unfamiliar or less obvious flaws.
Many At Once: Capturing Intentions to Create And Use Many Views At Once In Large Display Environments
Authors: Aurisano Jillian , Kumar Abhinav , Alsaiari Abeer , Eugenio Barbara Di , Johnson Andrew
Published: July 2020

This paper describes results from an observational, exploratory study of visual data exploration in a large, multi‐view, flexible canvas environment. Participants were provided with a set of data exploration sub‐tasks associated with a local crime dataset and were instructed to pose questions to a remote mediator who would respond by generating and organizing visualizations on the large display. We observed that participants frequently posed requests to cast a net around one or several subsets of the data or a set of data attributes. They accomplished this directly and by utilizing existing views in unique ways, including by requesting to copy and pivot a group of views collectively and posing a set of parallel requests on target views expressed in one command. These observed actions depart from multi‐view flexible canvas environments that typically provide interfaces in support of generating one view at a time or actions that operate on one view at a time. We describe how participants used these ‘cast‐a‐net’ requests for tasks that spanned more than one view and describe design considerations for multi‐view environments that would support the observed multi‐view generation actions.
Quantitative Evaluation of Time‐Dependent Multidimensional Projection Techniques
Authors: Vernier E. F. , Garcia R. , Silva I. P. da , Comba J. L. D. , Telea A. C.
Published: July 2020

Dimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time‐dependent multivariate datasets. There are, however, few studies and proposals that leverage on the concise power of expression of projections in the context of dynamic/temporal data. In this paper, we aim at providing an approach to assess projection techniques for dynamic data and understand the relationship between visual quality and stability. Our approach relies on an experimental setup that consists of existing techniques designed for time‐dependent data and new variations of static methods. To support the evaluation of these techniques, we provide a collection of datasets that has a wide variety of traits that encode dynamic patterns, as well as a set of spatial and temporal stability metrics that assess the quality of the layouts. We present an evaluation of 9 methods, 10 datasets, and 12 quality metrics, and elect the best‐suited methods for projecting time‐dependent multivariate data, exploring the design choices and characteristics of each method. Additional results can be found in the online benchmark repository. We designed our evaluation pipeline and benchmark specifically to be a live resource, open to all researchers who can further add their favorite datasets and techniques at any point in the future.
Phase Space Projection of Dynamical Systems
Authors: Bartolovic Nemanja , Gross Markus , Günther Tobias
Published: July 2020

Dynamical systems are commonly used to describe the state of time‐dependent systems. In many engineering and control problems, the state space is high‐dimensional making it difficult to analyze and visualize the behavior of the system for varying input conditions. We present a novel dimensionality reduction technique that is tailored to high‐dimensional dynamical systems. In contrast to standard general purpose dimensionality reduction algorithms, we use energy minimization to preserve properties of the flow in the high‐dimensional space. Once the projection operator is optimized, further high‐dimensional trajectories are projected easily. Our 3D projection maintains a number of useful flow properties, such as critical points and flow maps, and is optimized to match geometric characteristics of the high‐dimensional input, as well as optional user constraints. We apply our method to trajectories traced in the phase spaces of second‐order dynamical systems, including finite‐sized objects in fluids, the circular restricted three‐body problem and a damped double pendulum. We compare the projections with standard visualization techniques, such as PCA, t‐SNE and UMAP, and visualize the dynamical systems with multiple coordinated views interactively, featuring a spatial embedding, projection to subspaces, our dimensionality reduction and a seed point exploration tool.
Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude
Authors: Liem J. , Perin C. , Wood J.
Published: July 2020

In the visualization community, it is often assumed that visual data storytelling increases memorability and engagement, making it more effective at communicating information. However, many assumptions about the efficacy of storytelling in visualization lack empirical evaluation. Contributing to an emerging body of work, we study whether selected techniques commonly used in visual data storytelling influence people's attitudes towards immigration. We compare (a) personal visual narratives designed to generate empathy; (b) structured visual narratives of aggregates of people; and (c) an exploratory visualization without narrative acting as a control condition. We conducted two crowdsourced between‐subject studies comparing the three conditions, each with 300 participants. To assess the differences in attitudes between conditions, we adopted established scales from the social sciences used in the European Social Survey (ESS). Although we found some differences between conditions, the effects on people's attitudes are smaller than we expected. Our findings suggest that we need to be more careful when it comes to our expectations about the effects visual data storytelling can have on attitudes. Additional material: https://flowstory.github.io/attitudes/.
Co‐creating Visualizations: A First Evaluation with Social Science Researchers
Authors: León G. Molina , Breiter A.
Published: July 2020

Co‐creation is a design method where designers and domain experts work together to develop a product. In this paper, we present and evaluate the use of co‐creation to design a visual information system with social science researchers in order to explore and analyze their data. Co‐creation proposes involving the future users in the design process to ensure that they play a critical role in the design, and to increase the chances of long‐term adoption. We evaluated the co‐creation process through surveys, interviews and a user study. According to the participants’ feedback, they felt listened to through co‐creation, and considered the methodology helpful to develop visualizations that support their research in the near future. However, participation was far from perfect, particularly early career researchers showed limited interest in participating because they did not see the process as beneficial for their research publication goals. We summarize benefits and limitations of co‐creation, together with our recommendations, as lessons learned.
Extraction of Distinguished Hyperbolic Trajectories for 2D Time‐Dependent Vector Field Topology
Authors: Hofmann Lutz , Sadlo Filip
Published: July 2020

This paper does two main contributions to 2D time‐dependent vector field topology. First, we present a technique for robust, accurate, and efficient extraction of distinguished hyperbolic trajectories (DHT), the generative structures of 2D time‐dependent vector field topology. It is based on refinement of initial candidate curves. In contrast to previous approaches, it is robust because the refinement converges for reasonably close initial candidates, it is accurate due to its adaptive scheme, and it is efficient due to its high convergence speed. Second, we provide a detailed evaluation and discussion of previous approaches for the extraction of DHTs and time‐dependent vector field topology in general. We demonstrate the utility of our approach using analytical flows, as well as data from computational fluid dynamics.
Fuzzy Contour Trees: Alignment and Joint Layout of Multiple Contour Trees
Authors: Lohfink Anna‐Pia , Wetzels Florian , Lukasczyk Jonas , Weber Gunther H. , Garth Christoph
Published: July 2020

We describe a novel technique for the simultaneous visualization of multiple scalar fields, e.g. representing the members of an ensemble, based on their contour trees. Using tree alignments, a graph‐theoretic concept similar to edit distance mappings, we identify commonalities across multiple contour trees and leverage these to obtain a layout that can represent all trees simultaneously in an easy‐to‐interpret, minimally‐cluttered manner. We describe a heuristic algorithm to compute tree alignments for a given similarity metric, and give an algorithm to compute a joint layout of the resulting aligned contour trees. We apply our approach to the visualization of scalar field ensembles, discuss basic visualization and interaction possibilities, and demonstrate results on several analytic and real‐world examples.
Metro Maps on Octilinear Grid Graphs
Authors: Bast Hannah , Brosi Patrick , Storandt Sabine
Published: July 2020

Schematic transit maps (often called “metro maps” in the literature) are important to produce comprehensible visualizations of complex public transit networks. In this work, we investigate the problem of automatically drawing such maps on an octilinear grid with an arbitrary (but optimal) number of edge bends. Our approach can naturally deal with obstacles that should be respected in the final drawing (points of interest, rivers, coastlines) and can prefer grid edges near the real‐world course of a line. This allows our drawings to be combined with existing maps, for example as overlays in map services. We formulate an integer linear program which can be used to solve the problem exactly. We also provide a fast approximation algorithm which greedily calculates shortest paths between node candidates on the underlying octilinear grid graph. Previous work used local search techniques to update node positions until a local optimum was found, but without guaranteeing octilinearity. We can thus calculate nearly optimal metro maps in a fraction of a second even for complex networks, enabling the interactive use of our method in map editors.
Set Streams: Visual Exploration of Dynamic Overlapping Sets
Authors: Agarwal Shivam , Beck Fabian
Published: July 2020

In many applications, membership of set elements changes over time. Since each element can be present in multiple sets, the sets also overlap. As a result, it becomes challenging to visualize the temporal change in set membership of elements across several timesteps while showing individual set intersections. We propose Set Streams, a visualization technique that represents changing set structures on a timeline as branching and merging streams. The streams encode the changing membership of elements with set intersections. A query‐based selection mechanism supports a flexible comparison of selected groups of elements across the temporal evolution. The main timeline view is complemented with additional panels to provide details about the elements. While the proposed visualization is an application‐independent visualization technique for dynamic sets, we demonstrate its effectiveness and applicability through three diverse application examples and expert feedback.
PAVED: Pareto Front Visualization for Engineering Design
Authors: Cibulski Lena , Mitterhofer Hubert , May Thorsten , Kohlhammer Jörn
Published: July 2020

Design problems in engineering typically involve a large solution space and several potentially conflicting criteria. Selecting a compromise solution is often supported by optimization algorithms that compute hundreds of Pareto‐optimal solutions, thus informing a decision by the engineer. However, the complexity of evaluating and comparing alternatives increases with the number of criteria that need to be considered at the same time. We present a design study on Pareto front visualization to support engineers in applying their expertise and subjective preferences for selection of the most‐preferred solution. We provide a characterization of data and tasks from the parametric design of electric motors. The requirements identified were the basis for our development ofPAVED, an interactive parallel coordinates visualization for exploration of multi‐criteria alternatives. We reflect on our user‐centered design process that included iterative refinement with real data in close collaboration with a domain expert as well as a summative evaluation in the field. The results suggest a high usability of our visualization as part of a real‐world engineering design workflow. Our lessons learned can serve as guidance to future visualization developers targeting multi‐criteria optimization problems in engineering design or alternative domains.
Feature Driven Combination of Animated Vector Field Visualizations
Authors: Lobo M‐J. , Telea A.C. , Hurter C.
Published: July 2020

Animated visualizations are one of the methods for finding and understanding complex structures of time‐dependent vector fields. Many visualization designs can be used to this end, such as streamlines, vector glyphs, and image‐based techniques. While all such designs can depict any vector field, their effectiveness in highlighting particular field aspects has not been fully explored. To fill this gap, we compare three animated vector field visualization techniques, OLIC, IBFV, and particles, for a critical point detection‐and‐classification task through a user study. Our results show that the effectiveness of the studied techniques depends on the nature of the critical points. We use these results to design a new flow visualization technique that combines all studied techniques in a single view by locally using the most effective technique for the patterns present in the flow data at that location. A second user study shows that our technique is more efficient and less error prone than the three other techniques used individually for the critical point detection task.
LOCALIS: Locally‐adaptive Line Simplification for GPU‐based Geographic Vector Data Visualization
Authors: Amiraghdam Alireza , Diehl Alexandra , Pajarola Renato
Published: July 2020

Visualization of large vector line data is a core task in geographic and cartographic systems. Vector maps are often displayed at different cartographic generalization levels, traditionally by using several discrete levels‐of‐detail (LODs). This limits the generalization levels to a fixed and predefined set of LODs, and generally does not support smooth LOD transitions. However, fast GPUs and novel line rendering techniques can be exploited to integrate dynamic vector map LOD management into GPU‐based algorithms for locally‐adaptive line simplification and real‐time rendering. We propose a new technique that interactively visualizes large line vector datasets at variable LODs. It is based on the Douglas‐Peucker line simplification principle, generating an exhaustive set of line segments whose specific subsets represent the lines at any variable LOD. At run time, an appropriate and view‐dependent error metric supports screen‐space adaptive LOD levels and the display of the correct subset of line segments accordingly. Our implementation shows that we can simplify and display large line datasets interactively. We can successfully apply line style patterns, dynamic LOD selection lenses, and anti‐aliasing techniques to our line rendering.
Warehouse Vis: A Visual Analytics Approach to Facilitating Warehouse Location Selection for Business Districts
Authors: Li Q. , Liu Q. Q. , Tang C. F. , Li Z. W. , Wei S. C. , Peng X. R. , Zheng M. H. , Chen T. J. , Yang Q.
Published: July 2020

Selecting a proper warehouse location serving to satisfy the demands of the goods from a certain business area is important to a successful retail business. However, the large solution space, uncertain traffic conditions, and varying business preferences impose great challenges on warehouse location selection. Conventional approaches mainly summarize relevant evaluation criteria and compile them into an analysis report to facilitate rapid data absorption but fail to support a comprehensive and joint decision‐making process in warehouse location selection. In this paper, we propose a visual analytics approach to facilitating warehouse location selection. We first visually centralize relevant information of warehouses and adapts a widely‐used methodology to efficiently rank warehouse candidates. We then design a delivering estimation model based on massive logistics trajectories to resolve the uncertainty issue of traffic conditions of warehouses. Based on these techniques, an interactive framework is proposed to generate and explore the candidate warehouses. We conduct a case study and a within‐subject study with baseline systems to assess the efficacy of our system. Experts ‘feedback also suggests that our approach indeed helps them better tackle the problem of finding an ideal warehouse in the field of retail logistics management.
Resolving Conflicting Insights in Asynchronous Collaborative Visual Analysis
Authors: Li Jianping Kelvin , Xu Shenyu , (Chris) Ye Yecong , Ma Kwan‐Liu
Published: July 2020

Analyzing large and complex datasets for critical decision making can benefit from a collective effort involving a team of analysts. However, insights and findings from different analysts are often incomplete, disconnected, or even conflicting. Most existing analysis tools lack proper support for examining and resolving the conflicts among the findings in order to consolidate the results of collaborative data analysis. In this paper, we present CoVA, a visual analytics system incorporating conflict detection and resolution for supporting asynchronous collaborative data analysis. By using a declarative visualization language and graph representation for managing insights and insight provenance, CoVA effectively leverages distributed revision control workflow from software engineering to automatically detect and properly resolve conflicts in collaborative analysis results. In addition, CoVA provides an effective visual interface for resolving conflicts as well as combining the analysis results. We conduct a user study to evaluate CoVA for collaborative data analysis. The results show that CoVA allows better understanding and use of the findings from different analysts.
SeqDynamics: Visual Analytics for Evaluating Online Problem‐solving Dynamics
Authors: Xia Meng , Xu Min , Lin Chuan‐en , Cheng Ta Ying , Qu Huamin , Ma Xiaojuan
Published: July 2020

Problem‐solving dynamics refers to the process of solving a series of problems over time, from which a student's cognitive skills and non‐cognitive traits and behaviors can be inferred. For example, we can derive a student's learning curve (an indicator of cognitive skill) from the changes in the difficulty level of problems solved, or derive a student's self‐regulation patterns (an example of non‐cognitive traits and behaviors) based on the problem‐solving frequency over time. Few studies provide an integrated overview of both aspects by unfolding the problem‐solving process. In this paper, we present a visual analytics system named SeqDynamics that evaluates students ‘problem‐solving dynamics from both cognitive and non‐cognitive perspectives. The system visualizes the chronological sequence of learners’ problem‐solving behavior through a set of novel visual designs and coordinated contextual views, enabling users to compare and evaluate problem‐solving dynamics on multiple scales. We present three scenarios to demonstrate the usefulness of SeqDynamics on a real‐world dataset which consists of thousands of problem‐solving traces. We also conduct five expert interviews to show that SeqDynamics enhances domain experts’ understanding of learning behavior sequences and assists them in completing evaluation tasks efficiently.
SEEVis: A Smart Emergency Evacuation Plan Visualization System with Data‐Driven Shot Designs
Authors: Li Q. , Liu Y. J. , Chen L. , Yang X. C. , Peng Y. , Yuan X. R. , Wijerathne M. L. L.
Published: July 2020

Despite the significance of tracking human mobility dynamics in a large‐scale earthquake evacuation for an effective first response and disaster relief, the general understanding of evacuation behaviors remains limited. Numerous individual movement trajectories, disaster damages of civil engineering, associated heterogeneous data attributes, as well as complex urban environment all obscure disaster evacuation analysis. Although visualization methods have demonstrated promising performance in emergency evacuation analysis, they cannot effectively identify and deliver the major features like speed or density, as well as the resulting evacuation events like congestion or turn‐back. In this study, we propose a shot design approach to generate customized and narrative animations to track different evacuation features with different exploration purposes of users. Particularly, an intuitive scene feature graph that identifies the most dominating evacuation events is first constructed based on user‐specific regions or their tracking purposes on a certain feature. An optimal camera route, i.e., a storyboard is then calculated based on the previous user‐specific regions or features. For different evacuation events along this route, we employ the corresponding shot design to reveal the underlying feature evolution and its correlation with the environment. Several case studies confirm the efficacy of our system. The feedback from experts and users with different backgrounds suggests that our approach indeed helps them better embrace a comprehensive understanding of the earthquake evacuation.
Evaluating Reordering Strategies for Cluster Identification in Parallel Coordinates
Authors: Blumenschein Michael , Zhang Xuan , Pomerenke David , Keim Daniel A. , Fuchs Johannes
Published: July 2020

The ability to perceive patterns in parallel coordinates plots (PCPs) is heavily influenced by the ordering of the dimensions. While the community has proposed over 30 automatic ordering strategies, we still lack empirical guidance for choosing an appropriate strategy for a given task. In this paper, we first propose a classification of tasks and patterns and analyze which PCP reordering strategies help in detecting them. Based on our classification, we then conduct an empirical user study with 31 participants to evaluate reordering strategies for cluster identification tasks. We particularly measure time, identification quality, and the users’ confidence for two different strategies using both synthetic and real‐world datasets. Our results show that, somewhat unexpectedly, participants tend to focus on dissimilar rather than similar dimension pairs when detecting clusters, and are more confident in their answers. This is especially true when increasing the amount of clutter in the data. As a result of these findings, we propose a new reordering strategy based on the dissimilarity of neighboring dimension pairs.
Sunspot Plots: Model‐based Structure Enhancement for Dense Scatter Plots
Authors: Trautner T. , Bolte F. , Stoppel S. , Bruckner S.
Published: July 2020

Scatter plots are a powerful and well‐established technique for visualizing the relationships between two variables as a collection of discrete points. However, especially when dealing with large and dense data, scatter plots often exhibit problems such as overplotting, making the data interpretation arduous. Density plots are able to overcome these limitations in highly populated regions, but fail to provide accurate information of individual data points. This is particularly problematic in sparse regions where the density estimate may not provide a good representation of the underlying data. In this paper, we present sunspot plots, a visualization technique that communicates dense data as a continuous data distribution, while preserving the discrete nature of data samples in sparsely populated areas. We furthermore demonstrate the advantages of our approach on typical failure cases of scatter plots within synthetic and real‐world data sets and validate its effectiveness in a user study.
The State of the Art in Map‐Like Visualization
Authors: Hogräfer Marius , Heitzler Magnus , Schulz Hans‐Jörg
Published: July 2020

Cartographic maps have been shown to provide cognitive benefits when interpreting data in relation to a geographic location. In visualization, the term map‐like describes techniques that incorporate characteristics of cartographic maps in their representation of abstract data. However, the field of map‐like visualization is vast and currently lacks a clear classification of the existing techniques. Moreover, choosing the right technique to support a particular visualization task is further complicated, as techniques are scattered across different domains, with each considering different characteristics as map‐like. In this paper, we give an overview of the literature on map‐like visualization and provide a hierarchical classification of existing techniques along two general perspectives: imitation and schematization of cartographic maps. Each perspective is further divided into four principal categories that group common map‐like techniques along the visual primitives they affect. We further discuss this classification from a task‐centered view and highlight open research questions.
Privacy‐Preserving Data Visualization: Reflections on the State of the Art and Research Opportunities
Authors: Bhattacharjee Kaustav , Chen Min , Dasgupta Aritra
Published: July 2020

Preservation of data privacy and protection of sensitive information from potential adversaries constitute a key socio‐technical challenge in the modern era of ubiquitous digital transformation. Addressing this challenge needs analysis of multiple factors: algorithmic choices for balancing privacy and loss of utility, potential attack scenarios that can be undertaken by adversaries, implications for data owners, data subjects, and data sharing policies, and access control mechanisms that need to be built into interactive data interfaces. Visualization has a key role to play as part of the solution space, both as a medium of privacy‐aware information communication and also as a tool for understanding the link between privacy parameters and data sharing policies. The field of privacy‐preserving data visualization has witnessed progress along many of these dimensions. In this state‐of‐the‐art report, our goal is to provide a systematic analysis of the approaches, methods, and techniques used for handling data privacy in visualization. We also reflect on the road‐map ahead by analyzing the gaps and research opportunities for solving some of the pressing socio‐technical challenges involving data privacy with the help of visualization.
Effect of sacral neuromodulation on bowel dysfunction in patients with neurogenic bladder
Authors: Chen G. , Liao L. , Wang Y. , Ying X.
Published: July 2020

Aim

The aim of this retrospective study was to evaluate the clinical effect of sacral neuromodulation (SNM) on bowel dysfunction in patients with neurogenic bladder using the neurogenic bowel dysfunction (NBD) score.

Method

Between July 2012 and July 2019, 41 patients with both neurogenic bladder and bowel dysfunction underwent permanent SNM implantation. The NBD score was used to evaluate the bowel symptoms before the testing phase and at follow‐up. The first follow‐up was at the time of discharge after permanent implantation. The second follow‐up was conducted by telephone after discharge.

Results

The mean NBD score before the testing phase was 11.0 ± 5.83, whilst it significantly decreased to 5.2 ± 5.32 (n = 41, P < 0.05) at the first follow‐up (32 ± 3.9 days). Before the testing phase, there were 9 patients with very minor NBD, 10 with minor NBD, 13 with moderate NBD and 9 with severe NBD. At the first follow‐up, there were 26 patients with very minor NBD, 7 with minor NBD, 4 with moderate NBD and 4 with severe NBD (P < 0.05). Twenty‐six patients were interviewed in the long‐term follow‐up (34 ± 30.9 months). The NBD score in these 26 patients was 5.6 ± 5.18, which was not significantly different from the NBD score (5.5 ± 5.33) at the first follow‐up (n = 26, P > 0.05).

Conclusion

SNM facilitates a significant reduction in NBD score in patients with neurogenic bladder and bowel dysfunction. The improvement in NBD symptoms can also be used as a future indicator to determine the clinical efficacy of permanent SNM implantation in the treatment of neurogenic bladder.
Dasatinib‐related effusion lymphoma in a patient treated for chronic myeloid leukaemia
Authors: Miyagi Daichi , Chen Wei‐Yu , Chen Bo‐Jung , Su Ying‐Zhen , Kuo Chun‐Chi , Karube Kennosuke , Chuang Shih‐Sung
Published: July 2020

This report presents the clinical, cytological and molecular features of pleural effusion lymphoma which developed in a patient treated for chronic myeloid leukaemia after five years of dasatinib therapy. The effusion lymphoma recurred three times but largely resolved after pleurocentesis and steroid treatment. More reports on such unusual cases may improve our understanding and management of patients with this rare adverse effect associated with dasatinib.
How should our testing behaviour change with time in children in current COVID‐19 pandemic?
Authors: Zhang Yin , Lin Jilei , Xu Hongmei , Liu Enmei , Luo Zhengxiu , Li Qubei , Xu Feng , He Ling , Zou Lin , Fu Zhou , Dai Jihong
Published: July 2020

Backgrounds

More paediatric‐confirmed cases have been reported with the global pandemic of COVID‐19. This study aims to summarize the key points and supply suggestions on screening paediatric COVID‐19 patients more appropriately.

Materials and Methods

We retrospectively included paediatric patients who have accepted SARS‐CoV‐2 RT‐PCR testing in Children's Hospital of Chongqing Medical University (30 January 2020 to 13 February 2020) and compared them with paediatric‐confirmed COVID‐19 cases. Besides, a review was carried out by analysing all current literature about laboratory‐confirmed paediatric cases with COVID‐19.

Results

There were 46 suspected cases included in the descriptive study. The results of SARS‐CoV‐2 RT‐PCR testing were all negative. Compared with paediatric‐confirmed cases, the incidence of epidemic history was lower in suspected cases (P < .001). The rate of fever (P < .001), cough (P < .001), headache or dizziness (P < .001), vomiting (P < .001) and abdominal discomfort or distention (P = .01) were more observed in the included suspected children. There were more children having decreased WBC count in the confirmed group. In the literature review, twenty‐nine studies were obtained with 488 paediatric COVID‐19 cases. 88.6% of them had epidemiological history. Cough and fever were the most common symptoms. Compared with older patients, the incidence of fever, respiratory symptoms, lethargy and headache or dizziness was lower, while gastrointestinal symptoms were reported more.

Conclusions

Children with a history of close contact with confirmed cases, manifested as cough and fever should be paid more attention to after excluding infection of other common pathogens. Atypical symptoms should not be over‐emphasized in screening paediatric COVID‐19. More studies are needed for guiding efficient recognition in paediatric COVID‐19.
Obesity of politicians and corruption in post‐Soviet countries
Authors: Blavatskyy Pavlo
Published: July 2020

We collected 299 frontal face images of 2017 cabinet ministers from 15 post‐Soviet states (Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan). For each image, the minister's body‐mass index is estimated using a computer vision algorithm. The median estimated body‐mass index of cabinet ministers is highly correlated with conventional measures of corruption (Transparency International Corruption Perceptions Index, World Bank worldwide governance indicator Control of Corruption, Index of Public Integrity). This result suggests that physical characteristics of politicians such as their body‐mass index can be used as proxy variables for political corruption when the latter are not available, for instance at a very local level.
Coexistence of two termite‐eating specialists (Araneae)
Authors: Pekár Stano , Petráková Dušátková Lenka , Michálek Ondřej , Haddad Charles R.
Published: July 2020

Competition among closely‐related specialist predators has rarely been studied, and thus the mechanism of their coexistence remains enigmatic. Interspecific competition among specialised co‐occurring predators capturing termites should be high.

Here we investigated various niche dimensions, namely temporal, spatial and trophic, of a couple of jumping spider species of the genus Stenaelurillus (Stenaelurillus guttiger and S. modestus) from South Africa, to find whether these two species co‐exist and along which niche dimension(s) they differentiate.

The two species co‐occurred in two out of five study sites. Body size was not significantly different between the species. The phenology was shifted so that one species matured earlier. Circadian activity was not different, as both species were diurnal and active at similar times. Both species preyed almost exclusively on termites. The fundamental trophic niche was very similar and rather narrow. The realised trophic niche at the prey order level of both species was similar, but at the genus level it was different. In S. modestus it was narrower, as it captured mainly Odontotermes, while S. guttiger exploited a few termite species. The size of prey captured was also similar between the two species. The frequency of intraguild predation was negligible.

We conclude that both Stenaelurillus species are specialised termitophagous predators. The two species can coexist across broad spatial scales due to spatial segregation on the landscape. At the sites where they co‐occur, the two species specialise on different termite prey, promoting local coexistence.
Ants climb plants because they cannot swim: ant presence on flowers during the flood season reduces the frequency of floral visitors
Authors: Souza Camila S. , Baronio Gudryan J. , Weirich Carlos E. , Oliveira Andressa F. , dos Santos Ferreira Bruno H. , Arruda Rafael , Aoki Camila
Published: July 2020

Seasonal changes in environments may not only affect habitat connectivity but may also affect its use by species and their interactions. Thus, during the flood season, ants are forced to develop survival strategies such as vertical plant migration.

According to this, it has been hypothesized that the presence of ants may directly affect plant‐pollinator interactions.

Thus, we asked the following questions: (i) Are floral visitors of Hyptis brevipes expelled due to ant presence on inflorescences during the flood period? (ii) Is the ant effect mediated by the abundance of ants foraging on inflorescences? And, (iii) Does flower abundance predict the abundance of floral visits and ants?

We experimentally sampled 59 H. brevipes plants with and without ants during the flooded season, and observed no differences in flower abundance between ant treatments.

The probability of detaining floral visitors on H. brevipes increased with ant abundance and exceeded 50% possible repellency, but the probability of visitor deterrence was not related to flower abundance. Furthermore, the abundance of flowers did not predict the number of ants on H. brevipes individuals or the frequency of floral visits.

Consequently, ant repelling effects are pronounced when there are more ants foraging on plants. However, the ant repelling effect can be mitigated when plants flourish all year‐round and exhibit higher concentrations of flowers in the dry months. Additionally, the different sexual functions of plants may present specific responses due to the explosive pollination mechanism associated with ant effects.