anchor.diff:  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,"Before seeing a patient, physicians seek to obtain an overview of the patient’s medical history. Text plays a major role in this activity since it represents the bulk of the clinical documentation, but reviewing it quickly becomes onerous when patient charts grow too large. Text visualization methods have been widely explored to manage this large scale through visual summaries that rely on information retrieval algorithms to structure text and make it amenable to visualization. However, the integration with such automated approaches comes with a number of limitations, including significant error rates and the need for healthcare providers to fine-tune algorithms without expert knowledge of their inner mechanics. In addition, several of these approaches obscure or substitute the original clinical text and therefore fail to leverage qualitative and rhetorical flavours of the clinical notes. These drawbacks have limited the adoption of text visualization and other summarization technologies in clinical practice. In this work we present <i>Doccurate</i>, a novel system embodying a curation-based approach for the visualization of large clinical text datasets. Our approach offers automation auditing and customizability to physicians while also preserving and extensively linking to the original text. We discuss findings of a formal qualitative evaluation conducted with 6 domain experts, shedding light onto physicians’ information needs, perceived strengths and limitations of automated tools, and the importance of customization while balancing efficiency. We also present use case scenarios to showcase Doccurate’s envisioned usage in practice.",J),"Nicole Sultanum, Devin Singh, Michael Brudno, Fanny Chevalier",https://nicksultanum.github.io/web/files/pubs/2018-doccurate.pdf,"Before seeing a patient, physicians seek to obtain an overview of the patient’s medical history. Text plays a major role in this activity since it represents the bulk of the clinical documentation, but reviewing it quickly becomes onerous when patient charts grow too large. Text visualization methods have been widely explored to manage this large scale through visual summaries that rely on information retrieval algorithms to structure text and make it amenable to visualization. However, the integration with such automated approaches comes with a number of limitations, including significant error rates and the need for healthcare providers to fine-tune algorithms without expert knowledge of their inner mechanics. In addition, several of these approaches obscure or substitute the original clinical text and therefore fail to leverage qualitative and rhetorical flavours of the clinical notes. These drawbacks have limited the adoption of text visualization and other summarization technologies in clinical practice. In this work we present Doccurate, a novel system embodying a curation-based approach for the visualization of large clinical text datasets. Our approach offers automation auditing and customizability to physicians while also preserving and extensively linking to the original text. We discuss findings of a formal qualitative evaluation conducted with 6 domain experts, shedding light onto physicians’ information needs, perceived strengths and limitations of automated tools, and the importance of customization while balancing efficiency. We also present use case scenarios to showcase Doccurate’s envisioned usage in practice.",,,,,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",http://rmaciejewski.faculty.asu.edu/papers/2018/Identifying%20Topic%20Drivers.pdf,"Media data has been the subject of large scale analysis with applications of text mining being used to provide overviews of media themes and information flows. Such information extracted from media articles has also shown its contextual value of being integrated with other data, such as criminal records and stock market pricing. In this work, we explore linking textual media data with curated secondary textual data sources through user-guided semantic lexical matching for identifying relationships and data links. In this manner, critical information can be identified and used to annotate media timelines in order to provide a more detailed overview of events that may be driving media topics and frames. These linked events are further analyzed through an application of causality modeling to model temporal drivers between the data series. Such causal links are then annotated through automatic entity extraction which enables the analyst to explore persons, locations, and organizations that may be pertinent to the media topic of interest. To demonstrate the proposed framework, two media datasets and an armed conflict event dataset are explored.",,,,,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
  
positive.code: TVCG,TVCG,A Generative Model for Volume Rendering,T),"Matthew Berger, Jixian Li, Joshua A. Levine",https://arxiv.org/pdf/1710.09545.pdf,"We present a technique to synthesize and analyze volume-rendered images using generative models. We use the Generative Adversarial Network (GAN) framework to compute a model from a large collection of volume renderings, conditioned on (1) viewpoint and (2) transfer functions for opacity and color. Our approach facilitates tasks for volume analysis that are challenging to achieve using existing rendering techniques such as ray casting or texture-based methods. We show how to guide the user in transfer function editing by quantifying expected change in the output image. Additionally, the generative model transforms transfer functions into a view-invariant latent space specifically designed to synthesize volume-rendered images. We use this space directly for rendering, enabling the user to explore the space of volume-rendered images. As our model is independent of the choice of volume rendering process, we show how to analyze volume-rendered images produced by direct and global illumination lighting, for a variety of volume datasets.",,https://github.com/matthewberger/tfgan,,,vimeo 289789468,10.1109/tvcg.2018.2816059,2018,2018,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 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 289784994,10.1109/tvcg.2018.2865144,2018,2018,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 289784720,10.1109/tvcg.2018.2865231,2018,2018,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 289784543,10.1109/tvcg.2018.2865235,2018,2018,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 289784625,10.1109/tvcg.2018.2865234,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,12:00 PM,12:20 PM
  InfoVis,TVCG,Glanceable Visualization: Studies of Data Comparison Performance on Smartwatches,J),"Tanja Blascheck, Lonni Besançon, Anastasia Bezerianos, Bongshin Lee, Petra Isenberg",https://hal.inria.fr/hal-01851306/file/Blascheck_2018_Glanceable_Visualization.pdf,"We present the results of two perception studies to assess how quickly people can perform a simple data comparison task for small-scale visualizations on a smartwatch. The main goal of these studies is to extend our understanding of design constraints for smartwatch visualizations. Previous work has shown that a vast majority of smartwatch interactions last under 5 s. It is still unknown what people can actually perceive from visualizations during such short glances, in particular with such a limited display space of smartwatches. To shed light on this question, we conducted two perception studies that assessed the lower bounds of task time for a simple data comparison task. We tested three chart types common on smartwatches: bar charts, donut charts, and radial bar charts with three different data sizes: 7, 12, and 24 data values. In our first study, we controlled the differences of the two target bars to be compared, while the second study varied the difference randomly. For both studies, we found that participants performed the task on average in <300 ms for the bar chart, <220 ms for the donut chart, and in <1780 ms for the radial bar chart. Thresholds in the second study per chart type were on average 1.14–1.35× higher than in the first study. Our results show that bar and donut charts should be preferred on smartwatch displays when quick data comparisons are necessary.",,,,,vimeo 289785013,10.1109/tvcg.2018.2865142,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,12:20 PM,12:40 PM
  CG&A,CGA,Physical Visualization of Geospatial Datasets,,"Hessam Djavaherpour, Ali Mahdavi-Amiri, Faramarz F. Samavati",,,,,,,vimeo 289785692,10.1109/mcg.2017.38,2017,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,11:00 AM,11:20 AM
  
negative.code: 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
  VAST,TVCG,ForVizor: Visualizing Spatio-Temporal Team Formations in Soccer,J),"Yingcai Wu, Xiao Xie, Jiachen Wang, Dazhen Deng, Hongye Liang, Hui Zhang, Shoubin Cheng, Wei Chen",http://zjuvis.org/files/forvizor.pdf,"Regarded as a high-level tactic in soccer, a team formation assigns players different tasks and indicates their active regions on the pitch, thereby influencing the team performance significantly. Analysis of formations in soccer has become particularly indispensable for soccer analysts. However, formations of a team are intrinsically time-varying and contain inherent spatial information. The spatio-temporal nature of formations and other characteristics of soccer data, such as multivariate features, make analysis of formations in soccer a challenging problem. In this study, we closely worked with domain experts to characterize domain problems of formation analysis in soccer and formulated several design goals. We design a novel spatio-temporal visual representation of changes in team formation, allowing analysts to visually analyze the evolution of formations and track the spatial flow of players within formations over time. Based on the new design, we further design and develop ForVizor, a visual analytics system, which empowers users to track the spatio-temporal changes in formation and understand how and why such changes occur. With ForVizor, domain experts conduct formation analysis of two games. Analysis results with insights and useful feedback are summarized in two case studies.",,,,,vimeo 289787566,10.1109/tvcg.2018.2865041,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,5:00 PM,5:20 PM
  VAST,TVCG,MotionRugs: Visualizing Collective Trends in Space and Time,J),"Juri Buchmuller, Dominik Jackle, Eren Cakmak, Ulrik Brandes, Daniel Keim",https://bib.dbvis.de/uploadedFiles/MotionRugsPreprint.pdf,"Understanding the movement patterns of collectives, such as flocks of birds or fish swarms, is an interesting open research question. The collectives are driven by mutual objectives or react to individual direction changes and external influence factors and stimuli. The challenge in visualizing collective movement data is to show space and time of hundreds of movements at the same time to enable the detection of spatiotemporal patterns. In this paper, we propose MotionRugs, a novel space efficient technique for visualizing moving groups of entities. Building upon established space-partitioning strategies, our approach reduces the spatial dimensions in each time step to a one-dimensional ordered representation of the individual entities. By design, MotionRugs provides an overlap-free, compact overview of the development of group movements over time and thus, enables analysts to visually identify and explore group-specific temporal patterns. We demonstrate the usefulness of our approach in the field of fish swarm analysis and report on initial feedback of domain experts from the field of collective behavior.",,https://github.com/jbuchmueller/motionrugs,,,youtube xSewCmdgN40,10.1109/tvcg.2018.2865049,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,5:20 PM,5:40 PM
  VAST,TVCG,Identification of Temporally Varying Areas of Interest in Long Duration Eye Tracking Data Sets,J),"Prithiviraj Kaliappa Gounder Muthumanickam, Katerina Vrotsou, Aida Nordman, Jimmy Johansson, Matthew Cooper",,,,,,,vimeo 289787582,10.1109/TVCG.2018.2865042,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,5:40 PM,6:00 PM
  InfoVis,TVCG,A Heuristic Approach to Value-Driven Evaluation of Visualizations,J),"Emily Wall, Meeshu Agnihotri, Laura Matzen, Kristin Divis, Michael Haass, Alex Endert, John Stasko",https://www.cc.gatech.edu/~ewall9/media/papers/ValueVIS2018.pdf,"Recently, an approach for determining the value of a visualization was proposed, one moving beyond simple measurements of task accuracy and speed. The value equation contains components for the time savings a visualization provides, the insights and insightful questions it spurs, the overall essence of the data it conveys, and the confidence about the data and its domain it inspires. This articulation of value is purely descriptive, however, providing no actionable method of assessing a visualization's value. In this work, we create a heuristic-based evaluation methodology to accompany the value equation for assessing interactive visualizations. We refer to the methodology colloquially as ICE-T, based on an anagram of the four value components. Our approach breaks the four components down into guidelines, each of which is made up of a small set of low-level heuristics. Evaluators who have knowledge of visualization design principles then assess the visualization with respect to the heuristics. We conducted an initial trial of the methodology on three interactive visualizations of the same data set, each evaluated by 15 visualization experts. We found that the methodology showed promise, obtaining consistent ratings across the three visualizations and mirroring judgments of the utility of the visualizations by instructors of the course in which they were developed.",,,,,vimeo 289784928,10.1109/tvcg.2018.2865146,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Evaluation & Applications,23-Oct,4:20 PM,4:40 PM
  InfoVis,TVCG,Patterns and Pace: Quantifying Diverse Exploration Behavior with Visualizations on the Web,J),"Mi Feng, Evan Peck, Lane Harrison",https://osf.io/9wqgk/,"The diverse and vibrant ecosystem of interactive visualizations on the web presents an opportunity for researchers and practitioners to observe and analyze how everyday people interact with data visualizations. However, existing metrics of visualization interaction behavior used in research do not fully reveal the breadth of peoples' open-ended explorations with visualizations. One possible way to address this challenge is to determine high-level goals for visualization interaction metrics, and infer corresponding features from user interaction data that characterize different aspects of peoples' explorations of visualizations. In this paper, we address this challenge by identifying needs for visualization behavior analysis, and by developing corresponding candidate features that can be inferred from users' interaction data with visualization. We then propose metrics that capture novel aspects of peoples' open-ended explorations, including exploration uniqueness and exploration pacing. We evaluate these metrics along with four other metrics recently proposed in visualization literature by applying them to interaction data from prior visualization studies. The results of these evaluations suggest that these new metrics 1) reveal new characteristics of peoples' use of visualizations, 2) can be used to evaluate statistical differences between visualization designs, and 3) are statistically independent of prior metrics used in visualization research. We discuss implications of these results for future studies, including the potential for applying these metrics in visualization interaction analysis, as well as emerging challenges in developing a design space of metrics for visualization engagement.",,,https://osf.io/dx43q/wiki/home/,,vimeo 289785167,10.1109/tvcg.2018.2865117,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Evaluation & Applications,23-Oct,4:40 PM,5:00 PM
  