anchor.diff:  SciVis,TVCG,A Declarative Grammar of Flexible Volume Visualization Pipelines ,J),"Min Shih, Charles Rozhon, Kwan-Liu Ma",,,,,,,vimeo 290327611,,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,11:40 AM,12:00 PM,
   TVCG,TVCG,Multi-Material Volume Rendering with a Physically-Based Surface Reflection Model ,T),"Oleg Igouchkine, Yubo Zhang, Kwan-Liu Ma",,,,,,,vimeo 289789120,,,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,12:00 PM,12:20 PM,
   TVCG,TVCG,A Generative Model for Volume Rendering ,T),"Matthew Berger, Jixian Li, Joshua A. Levine",,,,,,,vimeo 289789468,,,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,12:20 PM,12:40 PM,
  -InfoVis,TVCG,Vistrates: A Component Model for Ubiquitous Analytics ,J),"Sriram Karthik Badam, Andreas Mathisen, Roman Rädle, Clemens Nylandsted Klokmose, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/vistrates/vistrates.pdf,"Visualization tools are often specialized for specific tasks, which turns the users analytical workflow into a fragmented process performed across many tools. In this paper, we present a component model design for data visualization to promote modular designs of visualization tools that enhance their analytical scope. Rather than fragmenting tasks across tools, the component model supports unification, where componentsthe building blocks of this modelcan be assembled to support a wide range of tasks. Furthermore, the model also provides additional key properties, such as support for collaboration, sharing across multiple devices, and adaptive usage depending on expertise, from creating visualizations using dropdown menus, through instantiating components, to actually modifying components or creating entirely new ones from scratch using JavaScript or Python source code. To realize our model, we introduce VISTRATES, a literate computing platform for developing, assembling, and sharing visualization components. From a visualization perspective, Vistrates features cross-cutting components for visual representations, interaction, collaboration, and device responsiveness maintained in a component repository. From a development perspective, Vistrates offers a collaborative programming environment where novices and experts alike can compose component pipelines for specific analytical activities. Finally, we present several Vistrates use cases that span the full range of the classic anytime and anywhere motto for ubiquitous analysis: from mobile and on-the-go usage, through office settings, to collaborative smart environments covering a variety of tasks and devices.",,https://github.com/karthikbadam/Vistrates,,,vimeo 289784994,,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:00 AM,11:20 AM,
  +InfoVis,TVCG,Vistrates: A Component Model for Ubiquitous Analytics ,J),"Sriram Karthik Badam, Andreas Mathisen, Roman Rädle, Clemens Nylandsted Klokmose, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/vistrates/vistrates.pdf,"Visualization tools are often specialized for specific tasks, which turns the user's analytical workflow into a fragmented process performed across many tools. In this paper, we present a component model design for data visualization to promote modular designs of visualization tools that enhance their analytical scope. Rather than fragmenting tasks across tools, the component model supports unification, where components�the building blocks of this model�can be assembled to support a wide range of tasks. Furthermore, the model also provides additional key properties, such as support for collaboration, sharing across multiple devices, and adaptive usage depending on expertise, from creating visualizations using dropdown menus, through instantiating components, to actually modifying components or creating entirely new ones from scratch using JavaScript or Python source code. To realize our model, we introduce VISTRATES, a literate computing platform for developing, assembling, and sharing visualization components. From a visualization perspective, Vistrates features cross-cutting components for visual representations, interaction, collaboration, and device responsiveness maintained in a component repository. From a development perspective, Vistrates offers a collaborative programming environment where novices and experts alike can compose component pipelines for specific analytical activities. Finally, we present several Vistrates use cases that span the full range of the classic �anytime� and �anywhere� motto for ubiquitous analysis: from mobile and on-the-go usage, through office settings, to collaborative smart environments covering a variety of tasks and devices.",,https://github.com/karthikbadam/Vistrates,,,vimeo 289784994,,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:00 AM,11:20 AM,
   InfoVis,TVCG,SmartCues: A Multitouch Query Approach for Details-on-Demand through Dynamically Computed Overlays ,J),"Hariharan Subramonyam, Eytan Adar",,,,,,,vimeo 289784720,,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:20 AM,11:40 AM,
   InfoVis,TVCG,"Multiple Coordinated Views at Large Displays for Multiple Users: Empirical Findings on User Behavior, Movements, and Distances ",J),"Ricardo Langner, Ulrike Kister, Raimund Dachselt",,,,,,,vimeo 289784543,,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:40 AM,12:00 PM,
   InfoVis,TVCG,Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation ,J),"Matthew Brehmer, Bongshin Lee, Petra Isenberg, Eun Kyoung Choe",https://hal.inria.fr/hal-01857469/document,"In the first crowdsourced visualization experiment conducted exclusively on mobile phones, we compare approaches to visualizing ranges over time on small displays. People routinely consume such data via a mobile phone, from temperatures in weather forecasting apps to sleep and blood pressure readings in personal health apps. However, we lack guidance on how to effectively visualize ranges on small displays in the context of different value retrieval and comparison tasks, or with respect to different data characteristics such as periodicity, seasonality, or the cardinality of ranges. Central to our experiment is a comparison between two ways to lay out ranges: a more conventional linear layout strikes a balance between quantitative and chronological scale resolution, while a less conventional radial layout emphasizes the cyclicality of time and may prioritize discrimination between values at its periphery. With results from 87 crowd workers, we found that while participants completed tasks more quickly with linear layouts than with radial ones, there were few differences in terms of error rate between layout conditions. We also found that participants performed similarly with both layouts in tasks that involved comparing superimposed observed and average ranges.",,https://github.com/Microsoft/RangesOnMobile/blob/master/StudyApp,https://github.com/Microsoft/RangesOnMobile/blob/master/StudyDataAnalysis,,vimeo 289784625,,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,12:00 PM,12:20 PM,
  
positive.code: VAST,TVCG,Cost-benefit Analysis of Visualization in Virtual Environments ,J),"Min Chen, Kelly Gaither, Nigel John, Brian McCann",,,,,,,vimeo 289787910,,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Opening & Evaluation and Theory,23-Oct,3:40 PM,4:00 PM,
  InfoVis,TVCG,Shape-preserving Star Coordinates ,J),"Vladimir Molchanov, Lars Linsen",,,,,,,vimeo 289785145,,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Opening & 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,,,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Opening & Multiple Dimensions,23-Oct,2:40 PM,3:00 PM,
  InfoVis,TVCG,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,,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Opening & 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 modela simple yet expressive JSON grammar associated with visual semanticsthat 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,,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Opening & 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,,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Opening & Multiple Dimensions,23-Oct,3:40 PM,4:00 PM,
  SciVis,TVCG,Recirculation Surfaces for Flow Visualization ,J),"Thomas Wilde, Christian Rössl, Holger Theisel",,,,,,,vimeo 290325803,,2018,2018,SciVis,Estrel Hall A+B,Tuesday,Opening & 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",,,,,,,vimeo 290327685,,2018,2018,SciVis,Estrel Hall A+B,Tuesday,Opening & Flow Features,23-Oct,2:40 PM,3:00 PM,
  
negative.code: VAST,TVCG,Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models ,J),"Jiawei Zhang, Yang Wang, Piero Molino, Lezhi Li, David Ebert",,,,,,,vimeo 289787203,,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:00 PM,5:20 PM,
  VAST,TVCG,Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution ,J),"Mennatallah El-Assady, Fabian Sperrle, Oliver Deussen, Daniel Keim, Christopher Collins",,,,,,,vimeo 289787264,,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:20 PM,5:40 PM,
  VAST,TVCG,VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning ,J),"Dominik Sacha, Matthias Kraus, Daniel Keim, Min Chen",,,,,,,vimeo 289787814,,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:40 PM,6:00 PM,
  InfoVis,TVCG,Mitigating the Attraction Effect with Visualizations ,J),"Evanthia Dimara, Gilles Bailly, Anastasia Bezerianos, Steven Franconeri",https://hal.inria.fr/hal-01845004v2/document,"Human decisions are prone to biases, and this is no less true for decisions made within data visualizations. Bias mitigation strategies often focus on the person, by educating people about their biases, typically with little success. We focus instead on the system, presenting the first evidence that altering the design of an interactive visualization tool can mitigate a strong bias � the attraction effect. Participants viewed 2D scatterplots where choices between superior alternatives were affected by the placement of other suboptimal points. We found that highlighting the superior alternatives weakened the bias, but did not eliminate it. We then tested an interactive approach where participants completely removed locally dominated points from the view, inspired by the elimination by aspects strategy in the decision-making literature. This approach strongly decreased the bias, leading to a counterintuitive suggestion: tools that allow removing inappropriately salient or distracting data from a view may help lead users to make more rational decisions.",,https://aviz.fr/deletion,https://aviz.fr/deletion,,vimeo 289784657,,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,4:20 PM,4:40 PM,
  InfoVis,TVCG,Face to Face: Evaluating Visual Comparison ,J),"Brian David Ondov, Nicole Jardine, Niklas Elmqvist, Steven Franconeri",http://users.umiacs.umd.edu/~elm/projects/face2face/face2face.pdf,"Data are often viewed as a single set of values, but those values frequently must be compared with another set. The existing evaluations of designs that facilitate these comparisons tend to be based on intuitive reasoning, rather than quantifiable measures. We build on this work with a series of crowdsourced experiments that use low-level perceptual comparison tasks that arise frequently in comparisons within data visualizations (e.g., which value changes the most between the two sets of data?). Participants completed these tasks across a variety of layouts: overlaid, two arrangements of juxtaposed small multiples, mirror-symmetric small multiples, and animated transitions. A staircase procedure sought the difficulty level (e.g., value change delta) that led to equivalent accuracy for each layout. Confirming prior intuition, we observe high levels of performance for overlaid versus standard small multiples. However, we also find performance improvements for both mirror symmetric small multiples and animated transitions. While some results are incongruent with common wisdom in data visualization, they align with previous work in perceptual psychology, and thus have potentially strong implications for visual comparison designs.",,,,,vimeo 289785372,,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,4:40 PM,5:00 PM,
  TVCG,TVCG,Task-Based Effectiveness of Basic Visualizations ,T),"Bahador Saket, Alex Endert, Çagatay Demiralp",https://arxiv.org/pdf/1709.08546.pdf,"Visualizations of tabular data are widely used; understanding their effectiveness in different task and data contexts is fundamental to scaling their impact. However, little is known about how basic tabular data visualizations perform across varying data analysis tasks. In this paper, we report results from a crowdsourced experiment to evaluate the effectiveness of five small scale (5-34 data points) two-dimensional visualization types�Table, Line Chart, Bar Chart, Scatterplot, and Pie Chart�across ten common data analysis tasks using two datasets. We find the effectiveness of these visualization types significantly varies across task, suggesting that visualization design would benefit from considering context-dependent effectiveness. Based on our findings, we derive recommendations on which visualizations to choose based on different tasks. We finally train a decision tree on the data we collected to drive a recommender, showcasing how to effectively engineer experimental user data into practical visualization systems.",,,https://github.com/gtvalab/ChartsEffectiveness/tree/master/Raw-Data,,vimeo 289789506,,,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,5:00 PM,5:20 PM,
  InfoVis,TVCG,At a Glance: Approximate Entropy as a Measure of Line Chart Visualization Complexity ,J),"Eugene Wu, Remco Chang, Abigail Mosca, Gabriel Ryan",,,,,,,vimeo 289784475,,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,5:20 PM,5:40 PM,
  TVCG,TVCG,Correlation Judgment and Visualization Features: A Comparative Study ,T),"Fumeng Yang, Lane Harrison, Ronald A. Rensink, Steven Franconeri, Remco Chang",https://github.com/Fumeng-Yang/VisualFeature_TVCG/blob/master/paper/VF_preprint.pdf,"Recent visualization research efforts have incorporated experimental techniques and perceptual models from the vision science community. Perceptual laws such as Weber's law, for example, have been used to model the perception of correlation in scatterplots. While this thread of research has progressively refined the modeling of the perception of correlation in scatterplots, it remains unclear as to why such perception can be modeled using relatively simple functions, e.g., linear and log-linear. In this paper, we investigate a longstanding hypothesis that people use visual features in a chart as a proxy for statistical measures like correlation. For a given scatterplot, we extract 49 candidate visual features and evaluate which best align with existing models and participant judgments. The results support the hypothesis that people attend to a small number of visual features when discriminating correlation in scatterplots. We discuss how this result may account for prior conflicting findings, and how visual features provide a baseline for future model-based approaches in visualization evaluation and design.",,,,,vimeo 289789613,,,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,5:40 PM,6:00 PM,
  SciVis,TVCG,Firefly: Illumination Drones for Interactive Visualization ,J),"Sergej Stoppel, Magnus Paulson Erga, Stefan Bruckner",,,,,,,vimeo 290325505,,2018,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,4:20 PM,4:40 PM,
  SciVis,TVCG,CoDDA: A Flexible Copula-based Distribution Driven Analysis Framework for Large-Scale Multivariate Data ,J),"Subhashis Hazarika, Soumya Dutta, Han-Wei Shen, Jen-Ping Chen",,,,,,,vimeo 290325658,,2018,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,4:40 PM,5:00 PM,
  SciVis,TVCG,"Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations ",J),"Timothy Basil Luciani, Andrew T Burks, Cassiano Sugiyama, Jonathan Komperda, Liz G.Elisabeta Marai",,,,,,,vimeo 290328058,,2018,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,5:00 PM,5:20 PM,
  