anchor.diff:  InfoVis,TVCG,Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data,J),"Alex Kale, Francis Nguyen, Matthew Kay, Jessica Hullman",http://users.eecs.northwestern.edu/~jhullman/hops_jobs_pfs.pdf,"Animated representations of outcomes drawn from distributions (hypothetical outcome plots, or HOPs) are used in the media and other public venues to communicate uncertainty. HOPs greatly improve multivariate probability estimation over conventional static uncertainty visualizations and leverage the ability of the visual system to quickly, accurately, and automatically process the summary statistical properties of ensembles. However, it is unclear how well HOPs support applied tasks resembling real world judgments posed in uncertainty communication. We identify and motivate an appropriate task to investigate realistic judgments of uncertainty in the public domain through a qualitative analysis of uncertainty visualizations in the news. We contribute two crowdsourced experiments comparing the effectiveness of HOPs, error bars, and line ensembles for supporting perceptual decision-making from visualized uncertainty. Participants infer which of two possible underlying trends is more likely to have produced a sample of time series data by referencing uncertainty visualizations which depict the two trends with variability due to sampling error. By modeling each participant's accuracy as a function of the level of evidence presented over many repeated judgments, we find that observers are able to correctly infer the underlying trend in samples conveying a lower level of evidence when using HOPs rather than static aggregate uncertainty visualizations as a decision aid. Modeling approaches like ours contribute theoretically grounded and richly descriptive accounts of user perceptions to visualization evaluation.",https://medium.com/@uwdata/hypothetical-outcome-plots-hops-help-users-separate-signal-from-noise-870d4e2b75d7,,https://github.com/kalealex/jobs-report-hops,https://osf.io/8cte3/registrations/,vimeo 289785258,10.1109/tvcg.2018.2864909,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:20 AM,9:40 AM
   InfoVis,TVCG,In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation,J),"Jessica Hullman, Xiaoli Qiao, Michael Correll, Alex Kale, Matthew Kay",https://github.com/jhullman/uncertaintyVisEval/blob/master/uncertainty_vis_eval.pdf,"Understanding and accounting for uncertainty is critical to effectively reasoning about visualized data. However, evaluating the impact of an uncertainty visualization is complex due to the difficulties that people have interpreting uncertainty and the challenge of defining correct behavior with uncertainty information. Currently, evaluators of uncertainty visualization must rely on general purpose visualization evaluation frameworks which can be ill-equipped to provide guidance with the unique difficulties of assessing judgments under uncertainty. To help evaluators navigate these complexities, we present a taxonomy for characterizing decisions made in designing an evaluation of an uncertainty visualization. Our taxonomy differentiates six levels of decisions that comprise an uncertainty visualization evaluation: the behavioral targets of the study, expected effects from an uncertainty visualization, evaluation goals, measures, elicitation techniques, and analysis approaches. Applying our taxonomy to 86 user studies of uncertainty visualizations, we find that existing evaluation practice, particularly in visualization research, focuses on Performance and Satisfaction-based measures that assume more predictable and statistically-driven judgment behavior than is suggested by research on human judgment and decision making. We reflect on common themes in evaluation practice concerning the interpretation and semantics of uncertainty, the use of confidence reporting, and a bias toward evaluating performance as accuracy rather than decision quality. We conclude with a concrete set of recommendations for evaluators designed to reduce the mismatch between the conceptualization of uncertainty in visualization versus other fields.",,https://github.com/jhullman/uncertaintyVisEval,,,vimeo 289785271,10.1109/tvcg.2018.2864889,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:40 AM,10:00 AM
   InfoVis,TVCG,Where's my data? Evaluating Visualizations with Missing Data,J),"Hayeong Song, Danielle Albers Szafir",http://cmci.colorado.edu/visualab/papers/song_VIS_2018.pdf,Many real-world datasets are incomplete due to factors such as data collection failures or misalignments between fused datasets. Visualizations of incomplete datasets should allow analysts to draw conclusions from their data while effectively reasoning about the quality of the data and resulting conclusions. We conducted a pair of crowdsourced studies to measure how the methods used to impute and visualize missing data may influence analysts' perceptions of data quality and their confidence in their conclusions. Our experiments used different design choices for line graphs and bar charts to estimate averages and trends in incomplete time series datasets. Our results provide preliminary guidance for visualization designers to consider when working with incomplete data in different domains and scenarios.,,,http://cmci.colorado.edu/visualab/MissingData/,,vimeo 289785184,10.1109/TVCG.2018.2864914,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,10:00 AM,10:20 AM
  -InfoVis,TVCG,A Framework for Externalizing Implicit Error Using Visualization,,"Nina McCurdy, Julie Gerdes, Miriah Meyer",http://sci.utah.edu/~vdl/papers/2018_infovis_IE-Framework.pdf,"This paper presents a framework for externalizing and analyzing expert knowledge about discrepancies in data through the use of visualization. Grounded in an 18-month design study with global health experts, the framework formalizes the notion of data discrepancies as implicit error, both in global health data and more broadly. We use the term implicit error to describe measurement error that is inherent to and pervasive throughout a dataset, but that isn't explicitly accounted for or defined. Instead, implicit error exists in the minds of experts, is mainly qualitative, and is accounted for subjectively during expert interpretation of the data. Externalizing knowledge surrounding implicit error can assist in synchronizing, validating, and enhancing interpretation, and can inform error analysis and mitigation. The framework consists of a description of implicit error components that are important for downstream analysis, along with a process model for externalizing and analyzing implicit error using visualization. As a second contribution, we provide a rich, reflective, and verifiable description of our research process as an exemplar summary toward the ongoing inquiry into ways of increasing the validity and transferability of design study research.",,,,,vimeo 289785202,10.1109/tvcg.2018.2864913,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,10:20 AM,10:40 AM
  -SciVis,TVCG,Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps,J),"Jun Tao, Martin Imre, Chaoli Wang, Nitesh V Chawla, Hanqi Guo, Gökhan Sever, Seung Hyun Kim",,,,,,,vimeo 290325769,10.1109/tvcg.2018.2864808,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:00 AM,9:20 AM
  +InfoVis,TVCG,A Framework for Externalizing Implicit Error Using Visualization,J),"Nina McCurdy, Julie Gerdes, Miriah Meyer",http://sci.utah.edu/~vdl/papers/2018_infovis_IE-Framework.pdf,"This paper presents a framework for externalizing and analyzing expert knowledge about discrepancies in data through the use of visualization. Grounded in an 18-month design study with global health experts, the framework formalizes the notion of data discrepancies as implicit error, both in global health data and more broadly. We use the term implicit error to describe measurement error that is inherent to and pervasive throughout a dataset, but that isn't explicitly accounted for or defined. Instead, implicit error exists in the minds of experts, is mainly qualitative, and is accounted for subjectively during expert interpretation of the data. Externalizing knowledge surrounding implicit error can assist in synchronizing, validating, and enhancing interpretation, and can inform error analysis and mitigation. The framework consists of a description of implicit error components that are important for downstream analysis, along with a process model for externalizing and analyzing implicit error using visualization. As a second contribution, we provide a rich, reflective, and verifiable description of our research process as an exemplar summary toward the ongoing inquiry into ways of increasing the validity and transferability of design study research.",,https://github.com/visdesignlab/IEFramework,,,vimeo 289785202,10.1109/tvcg.2018.2864913,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,10:20 AM,10:40 AM
  +SciVis,TVCG,Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps,J),"Jun Tao, Martin Imre, Chaoli Wang, Nitesh V Chawla, Hanqi Guo, Gökhan Sever, Seung Hyun Kim",https://www3.nd.edu/~cwang11/research/vis18-mism.pdf,"We present a novel visual representation and interface named the matrix of isosurface similarity maps (MISM) for effective exploration of large time-varying multivariate volumetric data sets. MISM synthesizes three types of similarity maps (i.e., self, temporal, and variable similarity maps) to capture the essential relationships among isosurfaces of different variables and time steps. Additionally, it serves as the main visual mapping and navigation tool for examining the vast number of isosurfaces and exploring the underlying time-varying multivariate data set. We present temporal clustering, variable grouping, and interactive filtering to reduce the huge exploration space of MISM. In conjunction with the isovalue and isosurface views, MISM allows users to identify important isosurfaces or isosurface pairs and compare them over space, time, and value range. More importantly, we introduce path recommendation that suggests, animates, and compares traversal paths for effectively exploring MISM under varied criteria and at different levels-of-detail. A silhouette-based method is applied to render multiple surfaces of interest in a visually succinct manner. We demonstrate the effectiveness of our approach with case studies of several time-varying multivariate data sets and an ensemble data set, and evaluate our work with two domain experts.",,,,,vimeo 290325769,10.1109/tvcg.2018.2864808,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:00 AM,9:20 AM
   SciVis,TVCG,Visual Analysis of Spatio-temporal Relations of Pairwise Attributes in Unsteady Flow,J),"Marzieh Berenjkoub, Rodolfo Ostilla Monico, Robert S. Laramee, Guoning Chen",,,,,,,vimeo 290325964,10.1109/tvcg.2018.2864817,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:20 AM,9:40 AM
  -SciVis,TVCG,Time-Dependent Flow seen through Approximate Observer Killing Fields,J),"Markus Hadwiger, Matej Mlejnek, Thomas Theussl, Peter Rautek",,,,,,,vimeo 290327432,10.1109/tvcg.2018.2864839,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:40 AM,10:00 AM
  -TVCG,TVCG,An Exploratory Framework for Cyclone Identification and Tracking,T),"Akash Anil Valsangkar, Joy Merwin Monteiro, Vidya Narayanan, Ingrid Hotz, Vijay Natarajan",,,,,,,vimeo 289789170,10.1109/tvcg.2018.2810068,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,10:00 AM,10:20 AM
  +SciVis,TVCG,Time-Dependent Flow seen through Approximate Observer Killing Fields,J),"Markus Hadwiger, Matej Mlejnek, Thomas Theussl, Peter Rautek",http://vccvisualization.org/publications/2018_hadwiger_killingobservers.pdf,"Flow fields are usually visualized relative to a global observer, i.e., a single frame of reference. However, often no global frame can depict all flow features equally well. Likewise, objective criteria for detecting features such as vortices often use either a global reference frame, or compute a separate frame for each point in space and time. We propose the first general framework that enables choosing a smooth trade-off between these two extremes. Using global optimization to minimize specific differential geometric properties, we compute a time-dependent observer velocity field that describes the motion of a continuous field of observers adapted to the input flow. This requires developing the novel notion of an observed time derivative. While individual observers are restricted to rigid motions, overall we compute an approximate Killing field, corresponding to almost-rigid motion. This enables continuous transitions between different observers. Instead of focusing only on flow features, we furthermore develop a novel general notion of visualizing how all observers jointly perceive the input field. This in fact requires introducing the concept of an observation time, with respect to which a visualization is computed. We develop the corresponding notions of observed stream, path, streak, and time lines. For efficiency, these characteristic curves can be computed using standard approaches, by first transforming the input field accordingly. Finally, we prove that the input flow perceived by the observer field is objective. This makes derived flow features, such as vortices, objective as well.",,https://github.com/vccvisualization/killingobservers,,,vimeo 290327432,10.1109/tvcg.2018.2864839,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:40 AM,10:00 AM
  +TVCG,TVCG,An Exploratory Framework for Cyclone Identification and Tracking,T),"Akash Anil Valsangkar, Joy Merwin Monteiro, Vidya Narayanan, Ingrid Hotz, Vijay Natarajan",https://vgl.csa.iisc.ac.in/pdf/pub/CycloneTrackingTVCG_Akash.pdf,"Analyzing depressions plays an important role in meteorology, especially in the study of cyclones. In particular, the study of the temporal evolution of cyclones requires a robust depression tracking framework. To cope with this demand we propose a pipeline for the exploration of cyclones and their temporal evolution. This entails a generic framework for their identification and tracking. The fact that depressions and cyclones are not well-defined objects and their shape and size characteristics change over time makes this task especially challenging. Our method combines the robustness of topological approaches and the detailed tracking information from optical flow analysis. At first cyclones are identified within each time step based on well-established topological concepts. Then candidate tracks are computed from an optical flow field. These tracks are clustered within a moving time window to distill dominant coherent cyclone movements, which are then forwarded to a final tracking step. In contrast to previous methods our method requires only a few intuitive parameters. An integration into an exploratory framework helps in the study of cyclone movement by identifying smooth, representative tracks. Multiple case studies demonstrate the effectiveness of the method in tracking cyclones, both in the northern and southern hemisphere",,,,,youtube DLv-EKwZjSI,10.1109/tvcg.2018.2810068,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,10:00 AM,10:20 AM
   TVCG,TVCG,Popup-Plots: Warping Temporal Data Visualization,T),"Johanna Schmidt, Dominik Fleischmann, Bernhard Preim, Norbert Brandle, Gabriel Mistelbauer",,,,,,,vimeo 289789393,10.1109/tvcg.2018.2841385,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,10:20 AM,10:40 AM
positive.code: InfoVis,TVCG,Looks Good To Me: Visualizations As Sanity Checks,J),"Michael Correll, Mingwei Li, Gordon L Kindlmann, Carlos Scheidegger",https://github.com/AlgebraicVis/SanityCheck/blob/master/InfoVis/preprint.pdf,"Famous examples such as Anscombe's Quartet highlight that one of the core benefits of visualizations is allowing people to discover visual patterns that might otherwise be hidden by summary statistics. This visual inspection is particularly important in exploratory data analysis, where analysts can use visualizations such as histograms and dot plots to identify data quality issues. Yet, these visualizations are driven by parameters such as histogram bin size or mark opacity that have a great deal of impact on the final visual appearance of the chart, but are rarely optimized to make important features visible. In this paper, we show that data flaws have varying impact on the visual features of visualizations, and that the adversarial or merely uncritical setting of design parameters of visualizations can obscure the visual signatures of these flaws. Drawing on the framework of Algebraic Visualization Design, we present the results of a crowdsourced study showing that common visualization types can appear to reasonably summarize distributional data while hiding large and important flaws such as missing data and extraneous modes. We make use of these results to propose additional best practices for visualizations of distributions for data quality tasks.",https://medium.com/@mcorrell/looks-good-to-me-visualizations-as-sanity-checks-6fd1ffa37ab9,https://github.com/AlgebraicVis/SanityCheck/tree/master/study,https://github.com/AlgebraicVis/SanityCheck/tree/master/study/data,,vimeo 289785290,10.1109/tvcg.2018.2864907,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 1,25-Oct,3:00 PM,3:20 PM
  TVCG,TVCG,Is There a Robust Technique for Selecting Aspect Ratios in Line Charts?,T),"Yunhai Wang, Zeyu Wang, Lifeng Zhu, Jian Zhang, Chi-Wing Fu, Changhe Tu, Baoquan Chen, Zhanglin Cheng",http://www.yunhaiwang.org/bankto45/as-tvcg.pdf,"The aspect ratio of a line chart heavily influences the perception of the underlying data. Different methods explore different criteria in choosing aspect ratios, but so far, it was still unclear how to select aspect ratios appropriately for any given data. This paper provides a guideline for the user to choose aspect ratios for any input 1D curves by conducting an in-depth analysis of aspect ratio selection methods both theoretically and experimentally. By formulating several existing methods as line integrals, we explain their parameterization invariance. Moreover, we derive a new and improved aspect ratio selection method, namely the�L1-LOR (local orientation resolution), with a certain degree of parameterization invariance. Furthermore, we connect different methods, including AL (arc length based method), the banking to 45� principle, RV (resultant vector) and AS (average absolute slope), as well as�L1-LOR and AO (average absolute orientation). We verify these connections by a comparative evaluation involving various data sets, and show that the selections by RV and�L1-LOR are complementary to each other for most data. Accordingly, we propose the dual-scale banking technique that combines the strengths of RV and�L1-LOR, and demonstrate its practicability using multiple real-world data sets.",,,,,vimeo 289789006,10.1109/tvcg.2017.2787113,2017,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 1,25-Oct,3:20 PM,3:40 PM
  InfoVis,TVCG,Image-based Aspect Ratio Selection,J),"Yunhai Wang, Zeyu Wang, Chi-Wing Fu, Hansjörg Schmauder, Oliver Deussen, Daniel Weiskopf",http://www.yunhaiwang.org/infovis18/dbar/paper.pdf,"Selecting a good aspect ratio is crucial for effective 2D diagrams. There are several aspect ratio selection methods for function plots and line charts, but only few can handle general, discrete diagrams such as 2D scatter plots. However, these methods either lack a perceptual foundation or heavily rely on intermediate isoline representations, which depend on choosing the right isovalues and are time-consuming to compute. This paper introduces a general image-based approach for selecting aspect ratios for a wide variety of 2D diagrams, ranging from scatter plots and density function plots to line charts. Our approach is derived from Federer's co-area formula and a line integral representation that enable us to directly construct image-based versions of existing selection methods using density fields. In contrast to previous methods, our approach bypasses isoline computation, so it is faster to compute, while following the perceptual foundation to select aspect ratios. Furthermore, this approach is complemented by an anisotropic kernel density estimation to construct density fields, allowing us to more faithfully characterize data patterns, such as the subgroups in scatterplots or dense regions in time series. We demonstrate the effectiveness of our approach by quantitatively comparing to previous methods and revisiting a prior user study. Finally, we present extensions for ROI banking, multi-scale banking, and the application to image data.",,,,,vimeo 289784435,10.1109/TVCG.2018.2865266,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 1,25-Oct,3:40 PM,4:00 PM
  SciVis,TVCG,Persistence Atlas for Critical Point Variability in Ensembles,J),"Guillaume Favelier, Noura Faraj, Brian Summa, Julien Tierny",,,,,,,vimeo 290325207,10.1109/tvcg.2018.2864432,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,2:20 PM,2:40 PM
  SciVis,TVCG,Probabilistic Asymptotic Decider for Topological Ambiguity Resolution in Level-Set Extraction for Uncertain 2D Data,J),"Tushar Athawale, Chris R. Johnson",,,,,,,vimeo 290325318,10.1109/TVCG.2018.2864505,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,2:40 PM,3:00 PM
  SciVis,TVCG,Hexahedral Mesh Structure Visualization and Evaluation,J),"Kaoji Cotrik Xu, Guoning Chen",,,,,,,vimeo 290327525,10.1109/tvcg.2018.2864827,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,3:00 PM,3:20 PM
  SciVis,TVCG,Shared-Memory Parallel Computation of Morse-Smale Complexes with Improved Accuracy,J),"Attila Gyulassy, Peer-Timo Bremer, Valerio Pascucci",,,,,,,vimeo 290328033,10.1109/TVCG.2018.2864848,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,3:20 PM,3:40 PM
  SciVis,TVCG,A Study of the Trade-off between Reduced Precision and Resolution for Scientific Data Analysis and Visualization,J),"Duong Hoang, Pavol Klacansky, Harsh Bhatia, Peer-Timo Bremer, Peter Lindstrom, Valerio Pascucci",,,,,,,vimeo 290328254,10.1109/TVCG.2018.2864853,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,3:40 PM,4:00 PM
  SciVis,SciVis,An Organic Visual Metaphor for Public Understanding of Conditional Co-occurrences,,"Keshav Dasu, Takanori Fujiwara, Kwan-Liu Ma",,,,,,,vimeo 290328908,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,2:20 PM,2:32 PM
  SciVis,SciVis,VAPLI: Novel Visual Abstraction for Protein-Lipid Interactions,,"Naif Alharbi, Matthieu Chavent, Michael Krone, Robert S. Laramee",,,,,,,vimeo 290328550,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,2:32 PM,2:44 PM
  SciVis,SciVis,Color Interpolation for non-Euclidean Color Spaces,,"Max Zeyen, Tobias Post, Hans Hagen, James Ahrens, David Rogers, Roxana Bujack",,,,,,,vimeo 290328621,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,2:45 PM,2:57 PM
  SciVis,SciVis,VRGE: An Immersive Visualization Application for the Geosciences,,"David A. B. Hyde, Tyler R. Hall, Jef Caers",,,,,,,vimeo 290328472,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,2:57 PM,3:09 PM
  SciVis,SciVis,3De Interactive Lenses for Visualization in Virtual Environments,,"Roberta C. R. Mota, Allan Rocha, Julio D. Silva, Usman R. Alim, Ehud Sharlin",,,,,,,vimeo 290328419,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,3:10 PM,3:22 PM
  SciVis,SciVis,Toward A Deep Understanding of What Makes a Scientific Visualization Memorable,,"Rui Li, Jian Chen",,,,,,,vimeo 290328799,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,3:22 PM,3:34 PM
  SciVis,SciVis,Ordering Perceptions about Perceptual Order,,"Roxana Bujack, Terece L Turton, David Rogers, James Ahrens",,,,,,,vimeo 290328692,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,3:35 PM,3:47 PM
  SciVis,SciVis,QuFlow: Visualizing Parameter Flow in Quantum Circuits for Understanding Quantum Computation,,"Siyuan Lin, Hao Jiang, Lingyun Sun",,,,,,,vimeo 290328716,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,3:47 PM,3:59 PM
  VAST,TVCG,RuleMatrix: Visualizing and Understanding Classifiers with Rules,J),"Yao Ming, Huamin Qu, Enrico Bertini",https://arxiv.org/pdf/1807.06228.pdf,"With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little expertise in machine learning to understand, explore and validate predictive models. By viewing the model as a black box, we extract a standardized rule-based knowledge representation from its input-output behavior. We design RuleMatrix, a matrix-based visualization of rules to help users navigate and verify the rules and the black-box model. We evaluate the effectiveness of RuleMatrix via two use cases and a usability study.",http://myaooo.com/projects/rule-matrix/,,,,vimeo 289787299,10.1109/TVCG.2018.2864812,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,4:20 PM,4:40 PM
  
negative.code: TVCG,TVCG,Line Graph or Scatter Plot? Automatic Selection of Methods for Visualizing Trends in Time Series,T),"Yunhai Wang, Fubo Han, Lifeng Zhu, Oliver Deussen, Baoquan Chen",http://www.yunhaiwang.org/vis-selection/timeseries.pdf,"Line graphs are usually considered to be the best choice for visualizing time series data, whereas sometimes also scatter plots are used for showing main trends. So far there are no guidelines that indicate which of these visualization methods better display trends in time series for a given canvas. Assuming that the main information in a time series is its overall trend, we propose an algorithm that automatically picks the visualization method that reveals this trend best. This is achieved by measuring the visual consistency between the trend curve represented by a LOESS fit and the trend described by a scatter plot or a line graph. To measure the consistency between our algorithm and user choices, we performed an empirical study with a series of controlled experiments that show a large correspondence. In a factor analysis we furthermore demonstrate that various visual and data factors have effects on the preference for a certain type of visualization.",,,,,vimeo 289788836,10.1109/tvcg.2017.2653106,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,9:40 AM,10:00 AM
  TVCG,TVCG,A Vector Field Design Approach to Animated Transitions,T),"Yong Wang, Daniel Archambault, Carlos E. Scheidegger, Huamin Qu",http://www.cs.swansea.ac.uk/~csdarchambault/publications/animatedTransitionFinalSubVersion.pdf,"Animated transitions can be effective in explaining and exploring a small number of visualizations where there are drastic changes in the scene over a short interval of time. This is especially true if data elements cannot be visually distinguished by other means. Current research in animated transitions has mainly focused on linear transitions (all elements follow straight line paths) or enhancing coordinated motion through bundling of linear trajectories. In this paper, we introduce animated transition design, a technique to build smooth, non-linear transitions for clustered data with either minimal or no user involvement. The technique is flexible and simple to implement, and has the additional advantage that it explicitly enhances coordinated motion and can avoid crowding, which are both important factors to support object tracking in a scene. We investigate its usability, provide preliminary evidence for the effectiveness of this technique through metric evaluations and user study and discuss limitations and future directions.",,,,,vimeo 289789555,10.1109/tvcg.2017.2750689,2017,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,10:00 AM,10:20 AM
  InfoVis,TVCG,Temporal Treemaps: Static Visualization of Evolving Trees,J),"Wiebke Köpp, Tino Weinkauf",http://www.csc.kth.se/~weinkauf/publications/documents/koepp19a.pdf,"We consider temporally evolving trees with changing topology and data: tree nodes may persist for a time range, merge or split, and the associated data may change. Essentially, one can think of this as a time series of trees with a node correspondence per hierarchy level between consecutive time steps. Existing visualization approaches for such data include animated 2D treemaps, where the dynamically changing layout makes it difficult to observe the data in its entirety. We present a method to visualize this dynamic data in a static, nested, and space-filling visualization. This is based on two major contributions: First, the layout constitutes a graph drawing problem. We approach it for the entire time span at once using a combination of a heuristic and simulated annealing. Second, we propose a rendering that emphasizes the hierarchy through an adaption of the classic cushion treemaps. We showcase the wide range of applicability using data from feature tracking in time-dependent scalar fields, evolution of file system hierarchies, and world population.",http://www.csc.kth.se/~weinkauf/publications/documents/koepp19a_algorithm.mp4,,,,vimeo 289784452,10.1109/TVCG.2018.2865265,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,10:20 AM,10:40 AM
  SciVis,TVCG,Visual Analysis of Aneurysm Data using Statistical Graphics,J),"Monique Meuschke, Tobias Günther, Philipp Berg, Ralph Wickenhoefer, Markus Gross, Bernhard Preim, Kai Lawonn",https://cgl.ethz.ch/Downloads/Publications/Papers/2018/Meu18b/Meu18b.pdf,"This paper presents a framework to explore multi-field data of aneurysms occurring at intracranial and cardiac arteries by using statistical graphics. The rupture of an aneurysm is often a fatal scenario, whereas during treatment serious complications for the patient can occur. Whether an aneurysm ruptures or whether a treatment is successful depends on the interaction of different morphological such as wall deformation and thickness, and hemodynamic attributes like wall shear stress and pressure. Therefore, medical researchers are very interested in better understanding these relationships. However, the required analysis is a time-consuming process, where suspicious wall regions are difficult to detect due to the time-dependent behavior of the data. Our proposed visualization framework enables medical researchers to efficiently assess aneurysm risk and treatment options. This comprises a powerful set of views including 2D and 3D depictions of the aneurysm morphology as well as statistical plots of different scalar fields. Brushing and linking aids the user to identify interesting wall regions and to understand the influence of different attributes on the aneurysm’s state. Moreover, a visual comparison of pre- and post-treatment as well as different treatment options is provided. Our analysis techniques are designed in collaboration with domain experts, e.g., physicians, and we provide details about the evaluation.",,,,,vimeo 290325604,10.1109/tvcg.2018.2864509,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,9:00 AM,9:20 AM
  SciVis,TVCG,Interactive Visualization of 3D Histopathology in Native Resolution,J),"Martin Falk, Anders Ynnerman, Darren Treanor, Claes Lundström",http://scivis.itn.liu.se/publications/2019/FYTL19//falk-3dhistology-preprint.pdf," We present a visualization application that enables effective interactive visual analysis of large-scale 3D histopathology, that is, high-resolution 3D microscopy data of human tissue. Clinical work flows and research based on pathology have, until now, largely been dominated by 2D imaging. As we will show in the paper, studying volumetric histology data will open up novel and useful opportunities for both research and clinical practice. Our starting point is the current lack of appropriate visualization tools in histopathology, which has been a limiting factor in the uptake of digital pathology. Visualization of 3D histology data does pose difficult challenges in several aspects. The full-color datasets are dense and large in scale, on the order of 100,000 × 100,000× 100 voxels. This entails serious demands on both rendering performance and user experience design. Despite this, our developed application supports interactive study of 3D histology datasets at native resolution. Our application is based on tailoring and tuning of existing methods, system integration work, as well as a careful study of domain specific demands emanating from a close participatory design process with domain experts as team members. Results from a user evaluation employing the tool demonstrate a strong agreement among the 14 participating pathologists that 3D histopathology will be a valuable and enabling tool for their work",,,,,vimeo 290325921,10.1109/TVCG.2018.2864816,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,9:20 AM,9:40 AM
  SciVis,TVCG,Visualization of Neuronal Structures in Wide-field Microscopy Brain Images,J),"Saeed Boorboor, Shreeraj Jadhav, Mala Ananth, David Talmage, Lorna W Role, Arie Kaufman",,,,,,,vimeo 290328215,10.1109/TVCG.2018.2864852,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,9:40 AM,10:00 AM
  TVCG,TVCG,Classification of Blood Flow Patterns in Cerebral Aneurysms,T),"Monique Meuschke, Steffen Oeltze-Jafra, Oliver Beuing, Bernhard Preim, Kai Lawonn",,,,,,,youtube jI8GxKXaoU8,10.1109/tvcg.2018.2834923,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,10:00 AM,10:20 AM
  TVCG,TVCG,Bridging Text Visualization and Mining: A Task-Driven Survey,T),"Shixia Liu, Xiting Wang, Christopher Collins, Wenwen Dou, Fangxin Ouyang, Mennatallah El-Assady, Liu Jiang, Daniel Keim",,,,,,,vimeo 289789579,10.1109/tvcg.2018.2834341,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,9:00 AM,9:20 AM
  VAST,TVCG,Doccurate: A Curation-Based Approach for Clinical Text Visualization,J),Nicole Sultanum,,,,,,,vimeo 289787431,10.1109/TVCG.2018.2864905,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,9:20 AM,9:40 AM
  VAST,TVCG,VIS Author Profiles: Interactive Descriptions of Publication Records Combining Text and Visualization,J),"Shahid Latif, Fabian Beck, Devin Singh, Michael Brudno, Fanny Chevalier",https://www.vis.wiwi.uni-due.de/uploads/tx_itochairt3/publications/vap-preprint.pdf,"Publication records and collaboration networks are important for assessing the expertise and experience of researchers. Existing digital libraries show the raw publication lists in author profiles, whereas visualization techniques focus on specific subproblems. Instead, we look at publication records from various perspectives mixing low-level publication data with high-level abstractions and background information. This work presents VIS Author Profiles, a novel approach to generate integrated textual and visual descriptions to highlight patterns in publication records. We leverage template-based natural language generation to summarize notable publication statistics, evolution of research topics, and collaboration relationships. Seamlessly integrated visualizations augment the textual description and are interactively connected with each other and the text. The underlying publication data and detailed explanations of the analysis are available on demand. We compare our approach to existing systems by taking into account information needs of users and demonstrate its usefulness in two realistic application examples.",https://vis-tools.paluno.uni-due.de/vap/,,,,vimeo 289787504,10.1109/TVCG.2018.2865022,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,9:40 AM,10:00 AM
  TVCG,TVCG,A Visual Analytics Framework for Identifying Topic Drivers in Media,T),"Yafeng Lu, Hong Wang, Steven Landis, Ross Maciejewski",,,,,,,vimeo 289788885,10.1109/TVCG.2017.2752166,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,10:00 AM,10:20 AM
  TVCG,TVCG,Visualizing a Thinker’s Life,T),"Patrick Riehmann, Dora Kiesel, Martin Kohlhaas, Bernd Fröhlich",,,,,,,vimeo 289789544,10.1109/TVCG.2018.2824822,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,10:20 AM,10:40 AM
  TVCG,TVCG,Commercial Visual Analytics Systems – Advances in the Big Data Analytics Field,T),"Michael Behrisch, Dirk Streeb, Florian Stoffel, Daniel Seebacher, Stefan Hagen Weber, Sebastian Mittelstaedt, Hanspeter Pfister, Daniel Keim",,,,,,,vimeo 289789498,10.1109/TVCG.2018.2859973,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:00 AM,11:20 AM
  VAST,TVCG,BitExTract: Interactive Visualization for Extracting Bitcoin Exchange Intelligence,J),"Xuanwu Yue, Xinhuan Shu, Xinyu ZHU, Xinnan Du, Zheqing Yu, Dimitrios Papadopoulos, Siyuan Liu",,,,,,,vimeo 289787312,10.1109/TVCG.2018.2864814,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:20 AM,11:40 AM
  TVCG,TVCG,KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis,T),"Markus Wagner, Djordje Slijepcevic, Brian Horsak, Alexander Rind, Matthias Zeppelzauer, Wolfgang Aigner",,,,,,,vimeo 289788975,10.1109/tvcg.2017.2785271,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:40 AM,12:00 PM
  