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: VAST,TVCG,An Information-Theoretic Approach to the Cost-benefit Analysis of Visualization in Virtual Environments,J),"Min Chen, Kelly Gaither, Nigel John, Brian McCann",,,,,,,vimeo 289787910,10.1109/TVCG.2018.2865025,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:40 PM,4:00 PM
  InfoVis,TVCG,Shape-preserving Star Coordinates,J),"Vladimir Molchanov, Lars Linsen",,,,,,,vimeo 289785145,10.1109/tvcg.2018.2865118,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,2:20 PM,2:40 PM
  TVCG,TVCG,Using Dashboard Networks to Visualize Multiple Patient Histories: A Design Study on Post-operative Prostate Cancer,T),"Jürgen Bernard, David Sessler, Jörn Kohlhammer, Roy A. Ruddle",http://eprints.whiterose.ac.uk/128739/1/bernard-ieee-tvcg-dashboard-networks.pdf,"In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized.",,,,,vimeo 289789053,10.1109/tvcg.2018.2803829,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,2:40 PM,3:00 PM
  InfoVis,TVCG,SRVis: Towards Better Spatial Integration in Ranking Visualization,J),"Di Weng, Ran Chen, Zikun Deng, Feiran Wu, Jingmin Chen, Yingcai Wu",http://zjuvis.org/files/srvis.pdf,"Interactive ranking techniques have substantially promoted analysts' ability in making judicious and informed decisions effectively based on multiple criteria. However, the existing techniques cannot satisfactorily support the analysis tasks involved in ranking large-scale spatial alternatives, such as selecting optimal locations for chain stores, where the complex spatial contexts involved are essential to the decision-making process. Limitations observed in the prior attempts of integrating rankings with spatial contexts motivate us to develop a context-integrated visual ranking technique. Based on a set of generic design requirements we summarized by collaborating with domain experts, we propose SRVis, a novel spatial ranking visualization technique that supports efficient spatial multi-criteria decision-making processes by addressing three major challenges in the aforementioned context integration, namely, a) the presentation of spatial rankings and contexts, b) the scalability of rankings' visual representations, and c) the analysis of context-integrated spatial rankings. Specifically, we encode massive rankings and their cause with scalable matrix-based visualizations and stacked bar charts based on a novel two-phase optimization framework that minimizes the information loss, and the flexible spatial filtering and intuitive comparative analysis are adopted to enable the in-depth evaluation of the rankings and assist users in selecting the best spatial alternative. The effectiveness of the proposed technique has been evaluated and demonstrated with an empirical study of optimization methods, two case studies, and expert interviews.",https://srvis.zjuvis.org/,,,,vimeo 289784783,10.1109/TVCG.2018.2865126,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,3:00 PM,3:20 PM
  InfoVis,TVCG,A Declarative Rendering Model for Multiclass Density Maps,J),"Jaemin Jo, Frédéric Vernier, Pierre Dragicevic, Jean-Daniel Fekete",https://hal.inria.fr/hal-01848427/document,"Multiclass maps are scatterplots, multidimensional projections, or thematic geographic maps where data points have a categorical attribute in addition to two quantitative attributes. This categorical attribute is often rendered using shape or color, which does not scale when overplotting occurs. When the number of data points increases, multiclass maps must resort to data aggregation to remain readable. We present multiclass density maps: multiple 2D histograms computed for each of the category values. Multiclass density maps are meant as a building block to improve the expressiveness and scalability of multiclass map visualization. In this article, we first present a short survey of aggregated multiclass maps, mainly from cartography. We then introduce a declarative model�a simple yet expressive JSON grammar associated with visual semantics�that specifies a wide design space of visualizations for multiclass density maps. Our declarative model is expressive and can be efficiently implemented in visualization front-ends such as modern web browsers. Furthermore, it can be reconfigured dynamically to support data exploration tasks without recomputing the raw data. Finally, we demonstrate how our model can be used to reproduce examples from the past and support exploring data at scale.",https://jaeminjo.github.io/Multiclass-Density-Maps/,https://github.com/e-/Multiclass-Density-Maps,,,vimeo 289785026,10.1109/TVCG.2018.2865141,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,3:20 PM,3:40 PM
  InfoVis,TVCG,DimReader: Axis lines that explain non-linear projections,J),"Rebecca Faust, David Glickenstein, Carlos Scheidegger",https://arxiv.org/pdf/1710.00992.pdf,"Non-linear dimensionality reduction (NDR) methods such as LLE and t-SNE are popular with visualization researchers and experienced data analysts, but present serious problems of interpretation. In this paper, we present DimReader, a technique that recovers readable axes from such techniques. DimReader is based on analyzing infinitesimal perturbations of the dataset with respect to variables of interest. The perturbations define exactly how we want to change each point in the original dataset and we measure the effect that these changes have on the projection. The recovered axes are in direct analogy with the axis lines (grid lines) of traditional scatterplots. We also present methods for discovering perturbations on the input data that change the projection the most. The calculation of the perturbations is efficient and easily integrated into programs written in modern programming languages. We present results of DimReader on a variety of NDR methods and datasets both synthetic and real-life, and show how it can be used to compare different NDR methods. Finally, we discuss limitations of our proposal and situations where further research is needed.",,,,,vimeo 289784574,10.1109/tvcg.2018.2865194,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,3:40 PM,4:00 PM
  SciVis,TVCG,Recirculation Surfaces for Flow Visualization,J),"Thomas Wilde, Christian Rössl, Holger Theisel",http://vc.cs.ovgu.de/files/publications/2018/Wilde_2018_VISa.pdf,"We present a formal approach to the visual analysis of recirculation in flows by introducing recirculation surfaces for 3D unsteady flow fields. Recirculation surfaces are the loci where massless particle integration returns to its starting point after some variable, finite integration. We give a rigorous definition of recirculation surfaces as 2-manifolds embedded in 5D space and study their properties. Based on this we construct an algorithm for their extraction, which searches for intersections of a recirculation surface with lines defined in 3D. This reduces the problem to a repeated search for critical points in 3D vector fields. We provide a uniform sampling of the search space paired with a surface reconstruction and visualize results. This way, we present the first algorithm for a comprehensive feature extraction in the 5D flow map of a 3D flow. The problem of finding isolated closed orbits in steady vector fields occurs as a special case of recirculation surfaces. This includes isolated closed orbits with saddle behavior. We show recirculation surfaces for a number of artificial and real flow data sets.",,,,,video http://isgwww.cs.uni-magdeburg.de/visual/files/publications/2018/Wilde_2018_VISa.mp4,10.1109/tvcg.2018.2864813,2018,2018,SciVis,Estrel Hall A+B,Tuesday,Flow Features,23-Oct,2:20 PM,2:40 PM
  SciVis,TVCG,Objective Vortex Corelines of Finite-sized Objects in Fluid Flows,J),"Tobias Günther, Holger Theisel",https://isgwww.cs.uni-magdeburg.de/visual/files/publications/2018/Guenther_2018_VIS.pdf,"Vortices are one of the most-frequently studied phenomena in fluid flows. The center of the rotating motion is called the vortex coreline and its successful detection strongly depends on the choice of the reference frame. The optimal frame moves with the center of the vortex, which incidentally makes the observed fluid flow steady and thus standard vortex coreline extractors such as Sujudi-Haimes become applicable. Recently, an objective optimization framework was proposed that determines a near-steady reference frame for tracer particles. In this paper, we extend this technique to the detection of vortex corelines of inertial particles. An inertial particle is a finite-sized object that is carried by a fluid flow. In contrast to the usual tracer particles, they do not move tangentially with the flow, since they are subject to gravity and exhibit mass-dependent inertia. Their particle state is determined by their position and own velocity, which makes the search for the optimal frame a high-dimensional problem. We demonstrate in this paper that the objective detection of an inertial vortex coreline can be reduced in 2D to a critical point search in 2D. For 3D flows, however, the vortex coreline criterion remains a parallel vectors condition in 6D. To detect the vortex corelines we propose a recursive subdivision approach that is tailored to the underlying structure of the 6D vectors. The resulting algorithm is objective, and we demonstrate the vortex coreline extraction in a number of 2D and 3D vector fields.",,,,,video http://isgwww.cs.uni-magdeburg.de/visual/files/publications/2018/Guenther_2018_VIS.mp4,10.1109/TVCG.2018.2864828,2018,2018,SciVis,Estrel Hall A+B,Tuesday,Flow Features,23-Oct,2:40 PM,3:00 PM
  TVCG,TVCG,Towards High-quality Visualization of Superfluid Vortices,T),"Yulong Guo, Xiaopei Liu, Chi Xiong, Xuemiao Xu, Chi-Wing Fu",https://arxiv.org/pdf/1710.02630.pdf,"Superfluidity is a special state of matter exhibiting macroscopic quantum phenomena and acting like a fluid with zero viscosity. In such a state, superfluid vortices exist as phase singularities of the model equation with unique distributions. This paper presents novel techniques to aid the visual understanding of superfluid vortices based on the state-of-the-art non-linear Klein-Gordon equation, which evolves a complex scalar field, giving rise to special vortex lattice/ring structures with dynamic vortex formation, reconnection, and Kelvin waves, etc. By formulating a numerical model with theoretical physicists in superfluid research, we obtain high-quality superfluid flow data sets without noise-like waves, suitable for vortex visualization. By further exploring superfluid vortex properties, we develop a new vortex identification and visualization method: a novel mechanism with velocity circulation to overcome phase singularity and an orthogonal-plane strategy to avoid ambiguity. Hence, our visualizations can help reveal various superfluid vortex structures and enable domain experts for related visual analysis, such as the steady vortex lattice/ring structures, dynamic vortex string interactions with reconnections and energy radiations, where the famous Kelvin waves and decaying vortex tangle were clearly observed. These visualizations have assisted physicists to verify the superfluid model, and further explore its dynamic behavior more intuitively.",,,,,youtube TlEQbPSbYTQ ,10.1109/TVCG.2017.2719684,2017,2018,SciVis,Estrel Hall A+B,Tuesday,Flow Features,23-Oct,3:00 PM,3:20 PM
  TVCG,TVCG,Semantic Flow Graph: A Framework for Discovering Object Relationships in Flow Fields,T),"Jun Tao, Chaoli Wang, Nitesh Chawla, Lei Shi, Seung Hyun Kim",https://www3.nd.edu/~cwang11/research/tvcg18-sfg.pdf,"Visual exploration of flow fields is important for studying dynamic systems. We introduce semantic flow graph (SFG), a novel graph representation and interaction framework that enables users to explore the relationships among key objects (i.e., field lines, features, and spatiotemporal regions) of both steady and unsteady flow fields. The objects and their relationships are organized as a heterogeneous graph. We assign each object a set of attributes, based on which a semantic abstraction of the heterogeneous graph is generated. This semantic abstraction is SFG. We design a suite of operations to explore the underlying flow fields based on this graph representation and abstraction mechanism. Users can flexibly reconfigure SFG to examine the relationships among groups of objects at different abstraction levels. Three linked views are developed to display SFG, its node split criteria and history, and the objects in the spatial volume. For simplicity, we introduce SFG construction and exploration for steady flow fields with critical points being the only features. Then we demonstrate that SFG can be naturally extended to deal with unsteady flow fields and multiple types of features. We experiment with multiple data sets and conduct an expert evaluation to demonstrate the effectiveness of our approach. ",,,,,video http://www.nd.edu/~cwang11/research/tvcg18-sfg.wmv,10.1109/tvcg.2017.2773071,2018,2018,SciVis,Estrel Hall A+B,Tuesday,Flow Features,23-Oct,3:20 PM,3:40 PM
  VAST,TVCG,Visual Abstraction of the Large Scale Geospatial Origin-Destination Movement Data,J),"Zhiguang Zhou, Linhao Meng, Cheng Tang, Ying Zhao, Zhiyong Guo, Miaoxin Hu, Wei Chen",,,,,,,vimeo 289787224,10.1109/TVCG.2018.2864503,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,4:20 PM,4:40 PM
  VAST,TVCG,Analysis of Flight Variability: a Systematic Approach,J),"Natalia Andrienko, Gennady Andrienko, Jose Manuel Cordero Garcia, David Scarlatti",http://openaccess.city.ac.uk/20334/1/analysis-flight-variability.pdf,"In movement data analysis, there exists a problem of comparing multiple trajectories of moving objects to common or distinct reference trajectories. We introduce a general conceptual framework for comparative analysis of trajectories and an analytical procedure, which consists of (1) finding corresponding points in pairs of trajectories, (2) computation of pairwise difference measures, and (3) interactive visual analysis of the distributions of the differences with respect to space, time, set of moving objects, trajectory structures, and spatio-temporal context. We propose a combination of visualisation, interaction, and data transformation techniques supporting the analysis and demonstrate the use of our approach for solving a challenging problem from the aviation domain.",,,,,vimeo 289787283,10.1109/TVCG.2018.2864811,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,4:40 PM,5:00 PM
  