anchor.diff:  CG&A,CGA,Impact of Spatial Scales on the Intercomparison of Climate Scenarios,,"Wei Luo, Michael Steptoe, Zheng Chang, Robert Link, Leon Clarke, Ross Maciejewski",,,,,,,vimeo 289785725,10.1109/mcg.2017.3621222,2017,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,11:40 AM,12:00 PM
   CG&A,CGA,Urban Space Explorer: A Visual Analytics System for Urban Planning,,"Alireza Karduni, Isaac Cho, Ginette Wessel, William Ribarsky, Eric Sauda, Wenwen Dou",,,,,,,vimeo 289785751,10.1109/mcg.2017.3621223,2017,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,12:00 PM,12:20 PM
   CG&A,CGA,Name Profiler Toolkit,,"Feng Wang, Brett Hansen, Ryan Simmons, Ross Maciejewski",http://rmaciejewski.faculty.asu.edu/papers/2017/Names-CGA.pdf,"The Name Profiler Toolkit is a visual analytics system designed to enable the interactive exploration and analysis of forename and surname geographical distributions across the United States. Using demographic data from the US Census Bureau and Zillow, the toolkit lets users interactively compare distributions of names and name attributes.",,,,,vimeo 289785766,10.1109/mcg.2017.3621224,2017,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,12:20 PM,12:40 PM
  -VAST,TVCG,"SIRIUS: Dual, Symmetric, Interactive Dimension Reductions ",J),"Michelle Dowling, John Wenskovitch, J.T. Fry, Leanna House, Scotland Leman, Chris North",,,,,,,vimeo 289787667,10.1109/tvcg.2018.2865047,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,2:20 PM,2:40 PM
  -TVCG,TVCG,A Perception-Driven Approach to Supervised Dimensionality Reduction for Visualization,T),"Yunhai Wang, Kang Feng, Xiaowei Chu, Jian Zhang, Chi-Wing Fu, Michael Sedlmair, Xiaohui Yu, Baoquan Chen",,,,,,,vimeo 289788938,10.1109/tvcg.2017.2701829,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,2:40 PM,3:00 PM
  -VAST,VAST,SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach,C),"Michael Blumenschein, Michael Behrisch, Stefanie Schmid, Simon Butscher, Deborah R. Wahl, Karoline Villinger, Britta Renner, Harald Reiterer, Daniel Keim",https://bib.dbvis.de/uploadedFiles/BlumenscheinetalVAST2018SMARTexplore.pdf,"We present SMARTEXPLORE, a novel visual analytics technique that simplifies the identification and understanding of clusters, correlations, and complex patterns in high-dimensional data. The analysis is integrated into an interactive table-based visualization that maintains a consistent and familiar representation throughout the analysis. The visualization is tightly coupled with pattern matching, subspace analysis, reordering, and layout algorithms. To increase the analyst’s trust in the revealed patterns, SMARTEXPLORE automatically selects and computes statistical measures based on dimension and data properties. While existing approaches to analyzing highdimensional data (e.g., planar projections and Parallel coordinates) have proven effective, they typically have steep learning curves for non-visualization experts. Our evaluation, based on three expert case studies, confirms that non-visualization experts successfully reveal patterns in high-dimensional data when using SMARTEXPLORE.",,,,,vimeo 289787735,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:00 PM,3:20 PM
  -TVCG,TVCG,ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color,T),"Shenghui Cheng, Wei Xu, Klaus Mueller",https://www3.cs.stonybrook.edu/~shecheng/Paper/ColormapND.pdf,"A wide variety of color schemes have been devised for mapping scalar data to color. We address the challenge of color-mapping multivariate data. While a number of methods can map low-dimensional data to color, for example, using bilinear or barycentric interpolation for two or three variables, these methods do not scale to higher data dimensions. Likewise, schemes that take a more artistic approach through color mixing and the like also face limits when it comes to the number of variables they can encode. Our approach does not have these limitations. It is data driven in that it determines a proper and consistent color map from first embedding the data samples into a circular interactive multivariate color mapping display (ICD) and then fusing this display with a convex (CIE HCL) color space. The variables (data attributes) are arranged in terms of their similarity and mapped to the ICD’s boundary to control the embedding. Using this layout, the color of a multivariate data sample is then obtained via modified generalized barycentric coordinate interpolation of the map. The system we devised has facilities for contrast and feature enhancement, supports both regular and irregular grids, can deal with multi-field as well as multispectral data, and can produce heat maps, choropleth maps, and diagrams such as scatterplots.",,,,,vimeo 289788955,10.1109/tvcg.2018.2808489,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:20 PM,3:40 PM
  -VAST,VAST,EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection,C),"Quan Li, Kristanto Sean Njotoprawiro, Hammad Haleem, Qiaoan Chen, Chris YI, Xiaojuan Ma",https://arxiv.org/pdf/1808.09074.pdf,"Constructing latent vector representation for nodes in a network through embedding models has shown its practicality in many graph analysis applications, such as node classification, clustering, and link prediction. However, despite the high efficiency and accuracy of learning an embedding model, people have little clue of what information about the original network is preserved in the embedding vectors. The abstractness of low-dimensional vector representation, stochastic nature of the construction process, and non-transparent hyper-parameters all obscure understanding of network embedding results. Visualization techniques have been introduced to facilitate embedding vector inspection, usually by projecting the embedding space to a two-dimensional display. Although the existing visualization methods allow simple examination of the structure of embedding space, they cannot support in-depth exploration of the embedding vectors. In this paper, we design an exploratory visual analytics system that supports the comparative visual interpretation of embedding vectors at the cluster, instance, and structural levels. To be more specific, it facilitates comparison of what and how node metrics are preserved across different embedding models and investigation of relationships between node metrics and selected embedding vectors. Several case studies confirm the efficacy of our system. Experts’ feedback suggests that our approach indeed helps them better embrace the understanding of network embedding models.",,,,,vimeo 289788000,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:40 PM,4:00 PM
  -InfoVis,TVCG,Evaluating 'Graphical Perception' with CNNs,,"Daniel Haehn, James Tompkin, Hanspeter Pfister",https://osf.io/8b9xs/,"Convolutional neural networks can successfully perform many computer vision tasks on images. For visualization, how do CNNs perform when applied to graphical perception tasks? We investigate this question by reproducing Cleveland and McGill's seminal 1984 experiments, which measured human perception efficiency of different visual encodings and defined elementary perceptual tasks for visualization. We measure the graphical perceptual capabilities of four network architectures on five different visualization tasks and compare to existing and new human performance baselines. While under limited circumstances CNNs are able to meet or outperform human task performance, we find that CNNs are not currently a good model for human graphical perception. We present the results of these experiments to foster the understanding of how CNNs succeed and fail when applied to data visualizations.",,https://github.com/rhoana/perception,https://osf.io/jsxdq/,,vimeo 280506639,10.1109/TVCG.2018.2865138,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,2:20 PM,2:40 PM
  -InfoVis,TVCG,NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models,J),"Shusen Liu, Zhimin Li, Tao Li, Vivek Srikumar, Valerio Pascucci, Peer-Timo Bremer",http://www.sci.utah.edu/~shusenl/publications/paper_entailVis.pdf,"With the recent advances in deep learning, neural network models have obtained state-of-the-art performances for many linguistic tasks in natural language processing. However, this rapid progress also brings enormous challenges. The opaque nature of a neural network model leads to hard-to-debug-systems and difficult-to-interpret mechanisms. Here, we introduce a visualization system that, through a tight yet flexible integration between visualization elements and the underlying model, allows a user to interrogate the model by perturbing the input, internal state, and prediction while observing changes in other parts of the pipeline. We use the natural language inference problem as an example to illustrate how a perturbation-driven paradigm can help domain experts assess the potential limitation of a model, probe its inner states, and interpret and form hypotheses about fundamental model mechanisms such as attention.",,,,,vimeo 289784737,10.1109/tvcg.2018.2865230,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,2:40 PM,3:00 PM
  -InfoVis,TVCG,Elastic Documents: Coupling Text and Tables through Contextual Visualizations for Enhanced Document Reading,J),"Sriram Karthik Badam, Zhicheng Liu, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/elastic-documents/elastic-documents.pdf,"Today's data-rich documents are often complex datasets in themselves, consisting of information in different formats such as text, gures, and data tables. These additional media augment the textual narrative in the document. However, the static layout of a traditional for-print document often impedes deep understanding of its content because of the need to navigate to access content scattered throughout the text. In this paper, we seek to facilitate enhanced comprehension of such documents through a contextual visualization technique that couples text content with data tables contained in the document. We parse the text content and data tables, cross-link the components using a keyword-based matching algorithm, and generate on-demand visualizations based on the reader's current focus within a document. We evaluate this technique in a user study comparing our approach to a traditional reading experience. Results from our study show that (1) participants comprehend the content better with tighter coupling of text and data, (2) the contextual visualizations enable participants to develop better summaries that capture the main data-rich insights within the document, and (3) overall, our method enables participants to develop a more detailed understanding of the document content.",,,,,vimeo 289784966,10.1109/TVCG.2018.2865119,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:00 PM,3:20 PM
  -InfoVis,TVCG,Augmenting Visualizations with Interactive Data Facts to Facilitate Interpretation and Communication,J),"Arjun Srinivasan, Steven Drucker, Alex Endert, John Stasko",https://arjun010.github.io/static/papers/voder-infovis18.pdf,"Recently, an increasing number of visualization systems have begun to incorporate natural language generation (NLG) capabilities into their interfaces. NLG-based visualization systems typically leverage a suite of statistical functions to automatically extract key facts about the underlying data and surface them as natural language sentences alongside visualizations. With current systems, users are typically required to read the system-generated sentences and mentally map them back to the accompanying visualization. However, depending on the features of the visualization (e.g., visualization type, data density) and the complexity of the data fact, mentally mapping facts to visualizations can be a challenging task. Furthermore, more than one visualization could be used to illustrate a single data fact. Unfortunately, current tools provide little or no support for users to explore such alternatives. In this paper, we explore how system-generated data facts can be treated as interactive widgets to help users interpret visualizations and communicate their findings. We present Voder, a system that lets users interact with automatically-generated data facts to explore both alternative visualizations to convey a data fact as well as a set of embellishments to highlight a fact within a visualization. Leveraging data facts as interactive widgets, Voder also facilitates data fact-based visualization search. To assess Voder's design and features, we conducted a preliminary user study with 12 participants having varying levels of experience with visualization tools. Participant feedback suggested that interactive data facts aided them in interpreting visualizations. Participants also stated that the suggestions surfaced through the facts helped them explore alternative visualizations and embellishments to communicate individual data facts.",,,,,vimeo 289784943,10.1109/tvcg.2018.2865145,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:20 PM,3:40 PM
  -InfoVis,TVCG,What Do We Talk About When We Talk About Dashboards?,J),"Alper Sarikaya, Michael Correll, Lyn Bartram, Melanie Tory, Danyel A Fisher",https://alper.datav.is/assets/publications/dashboards/dashboards-preprint.pdf,"Dashboards are one of the most common use cases for data visualization, and their design and contexts of use are considerably different from exploratory visualization tools. In this paper, we look at the broad scope of how dashboards are used in practice through an analysis of dashboard examples and documentation about their use. We systematically review the literature surrounding dashboard use, construct a design space for dashboards, and identify major dashboard types. We characterize dashboards by their design goals, levels of interaction, and the practices around them. Our framework and literature review suggest a number of fruitful research directions to better support dashboard design, implementation, and use.",,,,,vimeo 289785315,10.1109/tvcg.2018.2864903,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:40 PM,4:00 PM
  +VAST,TVCG,"SIRIUS: Dual, Symmetric, Interactive Dimension Reductions ",J),"Michelle Dowling, John Wenskovitch, J.T. Fry, Leanna House, Scotland Leman, Chris North",,,,,,,vimeo 289787667,10.1109/tvcg.2018.2865047,2018,2018,VAST,"Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,2:20 PM,2:40 PM
  +TVCG,TVCG,A Perception-Driven Approach to Supervised Dimensionality Reduction for Visualization,T),"Yunhai Wang, Kang Feng, Xiaowei Chu, Jian Zhang, Chi-Wing Fu, Michael Sedlmair, Xiaohui Yu, Baoquan Chen",,,,,,,vimeo 289788938,10.1109/tvcg.2017.2701829,2018,2018,VAST,"Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,2:40 PM,3:00 PM
  +VAST,VAST,SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach,C),"Michael Blumenschein, Michael Behrisch, Stefanie Schmid, Simon Butscher, Deborah R. Wahl, Karoline Villinger, Britta Renner, Harald Reiterer, Daniel Keim",https://bib.dbvis.de/uploadedFiles/BlumenscheinetalVAST2018SMARTexplore.pdf,"We present SMARTEXPLORE, a novel visual analytics technique that simplifies the identification and understanding of clusters, correlations, and complex patterns in high-dimensional data. The analysis is integrated into an interactive table-based visualization that maintains a consistent and familiar representation throughout the analysis. The visualization is tightly coupled with pattern matching, subspace analysis, reordering, and layout algorithms. To increase the analyst’s trust in the revealed patterns, SMARTEXPLORE automatically selects and computes statistical measures based on dimension and data properties. While existing approaches to analyzing highdimensional data (e.g., planar projections and Parallel coordinates) have proven effective, they typically have steep learning curves for non-visualization experts. Our evaluation, based on three expert case studies, confirms that non-visualization experts successfully reveal patterns in high-dimensional data when using SMARTEXPLORE.",,,,,vimeo 289787735,,2018,2018,VAST,"Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:00 PM,3:20 PM
  +TVCG,TVCG,ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color,T),"Shenghui Cheng, Wei Xu, Klaus Mueller",https://www3.cs.stonybrook.edu/~shecheng/Paper/ColormapND.pdf,"A wide variety of color schemes have been devised for mapping scalar data to color. We address the challenge of color-mapping multivariate data. While a number of methods can map low-dimensional data to color, for example, using bilinear or barycentric interpolation for two or three variables, these methods do not scale to higher data dimensions. Likewise, schemes that take a more artistic approach through color mixing and the like also face limits when it comes to the number of variables they can encode. Our approach does not have these limitations. It is data driven in that it determines a proper and consistent color map from first embedding the data samples into a circular interactive multivariate color mapping display (ICD) and then fusing this display with a convex (CIE HCL) color space. The variables (data attributes) are arranged in terms of their similarity and mapped to the ICD’s boundary to control the embedding. Using this layout, the color of a multivariate data sample is then obtained via modified generalized barycentric coordinate interpolation of the map. The system we devised has facilities for contrast and feature enhancement, supports both regular and irregular grids, can deal with multi-field as well as multispectral data, and can produce heat maps, choropleth maps, and diagrams such as scatterplots.",,,,,vimeo 289788955,10.1109/tvcg.2018.2808489,2018,2018,VAST,"Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:20 PM,3:40 PM
  +VAST,VAST,EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection,C),"Quan Li, Kristanto Sean Njotoprawiro, Hammad Haleem, Qiaoan Chen, Chris YI, Xiaojuan Ma",https://arxiv.org/pdf/1808.09074.pdf,"Constructing latent vector representation for nodes in a network through embedding models has shown its practicality in many graph analysis applications, such as node classification, clustering, and link prediction. However, despite the high efficiency and accuracy of learning an embedding model, people have little clue of what information about the original network is preserved in the embedding vectors. The abstractness of low-dimensional vector representation, stochastic nature of the construction process, and non-transparent hyper-parameters all obscure understanding of network embedding results. Visualization techniques have been introduced to facilitate embedding vector inspection, usually by projecting the embedding space to a two-dimensional display. Although the existing visualization methods allow simple examination of the structure of embedding space, they cannot support in-depth exploration of the embedding vectors. In this paper, we design an exploratory visual analytics system that supports the comparative visual interpretation of embedding vectors at the cluster, instance, and structural levels. To be more specific, it facilitates comparison of what and how node metrics are preserved across different embedding models and investigation of relationships between node metrics and selected embedding vectors. Several case studies confirm the efficacy of our system. Experts’ feedback suggests that our approach indeed helps them better embrace the understanding of network embedding models.",,,,,vimeo 289788000,,2018,2018,VAST,"Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:40 PM,4:00 PM
  +InfoVis,TVCG,Evaluating 'Graphical Perception' with CNNs,,"Daniel Haehn, James Tompkin, Hanspeter Pfister",https://osf.io/8b9xs/,"Convolutional neural networks can successfully perform many computer vision tasks on images. For visualization, how do CNNs perform when applied to graphical perception tasks? We investigate this question by reproducing Cleveland and McGill's seminal 1984 experiments, which measured human perception efficiency of different visual encodings and defined elementary perceptual tasks for visualization. We measure the graphical perceptual capabilities of four network architectures on five different visualization tasks and compare to existing and new human performance baselines. While under limited circumstances CNNs are able to meet or outperform human task performance, we find that CNNs are not currently a good model for human graphical perception. We present the results of these experiments to foster the understanding of how CNNs succeed and fail when applied to data visualizations.",,https://github.com/rhoana/perception,https://osf.io/jsxdq/,,vimeo 280506639,10.1109/TVCG.2018.2865138,2018,2018,InfoVis,"Hall 1, Section D",Wednesday,Text & Communication,24-Oct,2:20 PM,2:40 PM
  +InfoVis,TVCG,NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models,J),"Shusen Liu, Zhimin Li, Tao Li, Vivek Srikumar, Valerio Pascucci, Peer-Timo Bremer",http://www.sci.utah.edu/~shusenl/publications/paper_entailVis.pdf,"With the recent advances in deep learning, neural network models have obtained state-of-the-art performances for many linguistic tasks in natural language processing. However, this rapid progress also brings enormous challenges. The opaque nature of a neural network model leads to hard-to-debug-systems and difficult-to-interpret mechanisms. Here, we introduce a visualization system that, through a tight yet flexible integration between visualization elements and the underlying model, allows a user to interrogate the model by perturbing the input, internal state, and prediction while observing changes in other parts of the pipeline. We use the natural language inference problem as an example to illustrate how a perturbation-driven paradigm can help domain experts assess the potential limitation of a model, probe its inner states, and interpret and form hypotheses about fundamental model mechanisms such as attention.",,,,,vimeo 289784737,10.1109/tvcg.2018.2865230,2018,2018,InfoVis,"Hall 1, Section D",Wednesday,Text & Communication,24-Oct,2:40 PM,3:00 PM
  +InfoVis,TVCG,Elastic Documents: Coupling Text and Tables through Contextual Visualizations for Enhanced Document Reading,J),"Sriram Karthik Badam, Zhicheng Liu, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/elastic-documents/elastic-documents.pdf,"Today's data-rich documents are often complex datasets in themselves, consisting of information in different formats such as text, gures, and data tables. These additional media augment the textual narrative in the document. However, the static layout of a traditional for-print document often impedes deep understanding of its content because of the need to navigate to access content scattered throughout the text. In this paper, we seek to facilitate enhanced comprehension of such documents through a contextual visualization technique that couples text content with data tables contained in the document. We parse the text content and data tables, cross-link the components using a keyword-based matching algorithm, and generate on-demand visualizations based on the reader's current focus within a document. We evaluate this technique in a user study comparing our approach to a traditional reading experience. Results from our study show that (1) participants comprehend the content better with tighter coupling of text and data, (2) the contextual visualizations enable participants to develop better summaries that capture the main data-rich insights within the document, and (3) overall, our method enables participants to develop a more detailed understanding of the document content.",,,,,vimeo 289784966,10.1109/TVCG.2018.2865119,2018,2018,InfoVis,"Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:00 PM,3:20 PM
  +InfoVis,TVCG,Augmenting Visualizations with Interactive Data Facts to Facilitate Interpretation and Communication,J),"Arjun Srinivasan, Steven Drucker, Alex Endert, John Stasko",https://arjun010.github.io/static/papers/voder-infovis18.pdf,"Recently, an increasing number of visualization systems have begun to incorporate natural language generation (NLG) capabilities into their interfaces. NLG-based visualization systems typically leverage a suite of statistical functions to automatically extract key facts about the underlying data and surface them as natural language sentences alongside visualizations. With current systems, users are typically required to read the system-generated sentences and mentally map them back to the accompanying visualization. However, depending on the features of the visualization (e.g., visualization type, data density) and the complexity of the data fact, mentally mapping facts to visualizations can be a challenging task. Furthermore, more than one visualization could be used to illustrate a single data fact. Unfortunately, current tools provide little or no support for users to explore such alternatives. In this paper, we explore how system-generated data facts can be treated as interactive widgets to help users interpret visualizations and communicate their findings. We present Voder, a system that lets users interact with automatically-generated data facts to explore both alternative visualizations to convey a data fact as well as a set of embellishments to highlight a fact within a visualization. Leveraging data facts as interactive widgets, Voder also facilitates data fact-based visualization search. To assess Voder's design and features, we conducted a preliminary user study with 12 participants having varying levels of experience with visualization tools. Participant feedback suggested that interactive data facts aided them in interpreting visualizations. Participants also stated that the suggestions surfaced through the facts helped them explore alternative visualizations and embellishments to communicate individual data facts.",,,,,vimeo 289784943,10.1109/tvcg.2018.2865145,2018,2018,InfoVis,"Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:20 PM,3:40 PM
  +InfoVis,TVCG,What Do We Talk About When We Talk About Dashboards?,J),"Alper Sarikaya, Michael Correll, Lyn Bartram, Melanie Tory, Danyel A Fisher",https://alper.datav.is/assets/publications/dashboards/dashboards-preprint.pdf,"Dashboards are one of the most common use cases for data visualization, and their design and contexts of use are considerably different from exploratory visualization tools. In this paper, we look at the broad scope of how dashboards are used in practice through an analysis of dashboard examples and documentation about their use. We systematically review the literature surrounding dashboard use, construct a design space for dashboards, and identify major dashboard types. We characterize dashboards by their design goals, levels of interaction, and the practices around them. Our framework and literature review suggest a number of fruitful research directions to better support dashboard design, implementation, and use.",,,,,vimeo 289785315,10.1109/tvcg.2018.2864903,2018,2018,InfoVis,"Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:40 PM,4:00 PM
   SciVis,TVCG,Visualization of Bubble Formation in Porous Media,J),"Hui Zhang, Steffen Frey, Holger Steeb, David Uribe, Thomas Ertl, Wenping Wang",,,,,,,vimeo 290325359,10.1109/tvcg.2018.2864506,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,2:20 PM,2:40 PM
   SciVis,TVCG,Gaia Sky: Navigating the Gaia Catalog,J),"Toni Sagristà Sellés, Stefan Jordan, Thomas Mueller, Filip Sadlo",https://vcg.iwr.uni-heidelberg.de/static/publications/Sagrista2019gaiaSky.pdf,"In this paper, we present Gaia Sky, a free and open-source multiplatform 3D Universe system, developed since 2014 in the Data Processing and Analysis Consortium framework of ESA’s Gaia mission. Gaia’s data release 2 represents the largest catalog of the stars of our Galaxy, comprising 1.3 billion star positions, with parallaxes, proper motions, magnitudes, and colors. In this mission, Gaia Sky is the central tool for off-the-shelf visualization of these data, and for aiding production of outreach material. With its capabilities to effectively handle these data, to enable seamless navigation along the high dynamic range of distances, and at the same time to provide advanced visualization techniques including relativistic aberration and gravitational wave effects, currently no actively maintained cross-platform, modern, and open alternative exists.",,,,,vimeo 290325474,10.1109/tvcg.2018.2864508,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,2:40 PM,3:00 PM
   SciVis,TVCG,Interactive 3D Visual Analysis of Atmospheric Fronts,J),"Michael Alexander Kern, Timothy David Hewson, Andreas Schäfler, Rüdiger Westermann, Marc Rautenhaus",http://www.in.tum.de/fileadmin/w00bws/cg/Research/Publications/2018/Atmospheric_Fronts/kern_fronts_vis2018.pdf,"Atmospheric fronts play a central role in meteorology, as the boundaries between different air masses and as fundamental features of extra-tropical cyclones. They appear in numerous conceptual model depictions of extra-tropical weather systems. Conceptually, fronts are three-dimensional surfaces in space possessing an innate structural complexity, yet in meteorology, both manual and objective identification and depiction have historically focused on the structure in two dimensions. In this work, we –a team of visualization scientists and meteorologists– propose a novel visualization approach to analyze the three-dimensional structure of atmospheric fronts and related physical and dynamical processes. We build upon existing approaches to objectively identify fronts as lines in two dimensions and extend these to obtain frontal surfaces in three dimensions, using the magnitude of temperature change along the gradient of a moist potential temperature field as the primary identifying factor. We introduce the use of normal curves in the temperature gradient field to visualize a frontal zone (i.e., the transitional zone between the air masses) and the distribution of atmospheric variables in such zones. To enable for the first time a statistical analysis of frontal zones, we present a new approach to obtain the volume enclosed by a zone, by classifying grid boxes that intersect with normal curves emanating from a selected front. We introduce our method by means of an idealized numerical simulation and demonstrate its use with two real-world cases using numerical weather prediction data.",,,,,vimeo 290325737,10.1109/tvcg.2018.2864806,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,3:00 PM,3:20 PM
   SciVis,TVCG,An Interactive Framework for Visualization of Weather Forecast Ensembles,J),"Bo Ma, Alireza Entezari",,,,,,,youtube 2tsXxxLsU7U,10.1109/tvcg.2018.2864815,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,3:20 PM,3:40 PM
   TVCG,TVCG,Animation Plans for Before-And-After Satellite images,T),"María-Jesús Lobo, Caroline Appert, Emmanuel Pietriga",https://hal.inria.fr/hal-01773882/document,"Before-and-after image pairs show how entities in a given region have evolved over a specific period of time. Satellite images are a major source of such data, that capture how natural phenomena or human activity impact a geographical area. These images are used both for data analysis and to illustrate the resulting findings to diverse audiences. The simple techniques used to display them, including juxtaposing, swapping and monolithic blending, often fail to convey the underlying phenomenon in a meaningful manner. We introduce Baia, a framework to create advanced animated transitions, called animation plans, between before-and-after images. Baia relies on a pixel-based transition model that gives authors much expressive power, while keeping animations for common types of changes easy to create thanks to predefined animation primitives. We describe our model, the associated animation editor, and report on two user studies. In the first study, advanced transitions enabled by Baia were compared to monolithic blending, and perceived as more realistic and better at focusing viewer’s attention on a region of interest than the latter. The second study aimed at gathering feedback about the usability of Baia’s animation editor. ",,,,,vimeo 289789265,10.1109/TVCG.2018.2796557,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,3:40 PM,4:00 PM
  -VAST,TVCG,Vulnus: Visual Vulnerability Analysis for Network Security,J),"Marco Angelini, Graziano Blasilli, Tiziana Catarci, Simone Lenti, Giuseppe Santucci",,,,,,,vimeo 289787623,10.1109/tvcg.2018.2865028,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,9:00 AM,9:20 AM
  -VAST,TVCG,GraphProtector: a Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms,J),"Xumeng Wang, Wei Chen, Huihua Guan, Wenlong Chen, Rusheng Pan, Jia-Kai Chou, Chris Bryan, Kwan-Liu Ma",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/GraphProtector.pdf,"Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques—along with evaluating how applying the strategies will affect the utility of the anonymized results—remains a signicant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphProtector, we report several case studies and feedback collected from interviews with expert users in various scenarios.",,,,,vimeo 289787471,10.1109/TVCG.2018.2865021,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,9:20 AM,9:40 AM
  -VAST,TVCG,Situ: Identifying and explaining suspicious behavior in networks,J),"John Goodall, Eric Ragan, Chad Steed, Joel Reed, Gregory Richardson, Kelly Huffer, Robert Bridges, Jason Laska",https://www.cise.ufl.edu/~eragan/papers/Goodall_Situ_2018.pdf,"Despite the best efforts of cyber security analysts, networked computing assets are routinely compromised, resulting in the loss of intellectual property, the disclosure of state secrets, and major nancial damages. Anomaly detection methods are benecial for detecting new types of attacks and abnormal network activity, but such algorithms can be difcult to understand and trust. Network operators and cyber analysts need fast and scalable tools to help identify suspicious behavior that bypasses automated security systems, but operators do not want another automated tool with algorithms they do not trust. Experts need tools to augment their own domain expertise and to provide a contextual understanding of suspicious behavior to help them make decisions. In this paper we present Situ, a visual analytics system for discovering suspicious behavior in streaming network data. Situ provides a scalable solution that combines anomaly detection with information visualization. The system's visualizations enable operators to identify and investigate the most anomalous events and IP addresses, and the tool provides context to help operators understand why they are anomalous. Finally, operators need tools that can be integrated into their workow and with their existing tools. This paper describes the Situ platform and its deployment in an operational network setting. We discuss how operators are currently using the tool in a large organization's security operations center and present the results of expert reviews with professionals.",,,,,vimeo 289787635,10.1109/TVCG.2018.2865029,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,9:40 AM,10:00 AM
  -VAST,TVCG,A Visual Analytics Framework for the Detection of Anomalous Call Stack Trees in High Performance Computing Applications,J),"Cong Xie, Wei Xu, Klaus Mueller",,,,,,,vimeo 289787924,10.1109/tvcg.2018.2865026,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,10:00 AM,10:20 AM
  -VAST,TVCG,Lessons Learned Developing a Visual Analytics Solution for Investigative Analysis of Scamming Activities,J),"Jay Koven, Cristian Felix, Hossein Siadati, Enrico Bertini, Markus Jakobsson",,,,,,,vimeo 289787515,10.1109/tvcg.2018.2865023,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,10:20 AM,10:40 AM
  -InfoVis,TVCG,Origin-Destination Flow Maps in Immersive Environments,J),"Yalong Yang, Tim Dwyer, Bernhard Jenny, Kim Marriott, Maxime Cordeil, Haohui Chen",https://ialab.it.monash.edu/~dwyer/papers/flow-maps-ia.pdf,"Immersive virtual- and augmented-reality headsets can overlay a flat image against any surface or hang virtual objects in the space around the user. The technology is rapidly improving and may, in the long term, replace traditional flat panel displays in many situations. When displays are no longer intrinsically flat, how should we use the space around the user for abstract data visualisation? In this paper, we ask this question with respect to origin-destination flow data in a global geographic context. We report on the findings of three studies exploring different spatial encodings for flow maps. The first experiment focuses on different 2D and 3D encodings for flows on flat maps. We find that participants are significantly more accurate with raised flow paths whose height is proportional to flow distance but fastest with traditional straight line 2D flows. In our second and third experiment we compared flat maps, 3D globes and a novel interactive design we call <i>MapsLink</i>, involving a pair of linked flat maps. We find that participants took significantly more time with MapsLink than other flow maps while the 3D globe with raised flows was the fastest, most accurate, and most preferred method. Our work suggests that <i>careful</i> use of the third spatial dimension can resolve visual clutter in complex flow maps.",,,,,vimeo 279605506,10.1109/tvcg.2018.2865192,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,9:00 AM,9:20 AM
  -InfoVis,TVCG,FiberClay: Sculpting Three Dimensional Trajectories to Reveal Structural Insights,J),"Christophe Hurter, Nathalie Henry Riche, Steven Drucker, Maxime Cordeil, Richard Alligier, Romain Vuillemot",https://recherche.enac.fr/~hurter/FiberClay/FiberClay2018.pdf,"Visualizing 3D trajectories to extract insights about their similarities and spatial configuration is a critical task in several domains. Air traffic controllers for example deal with large quantities of aircrafts routes to optimize safety in airspace and neuroscientists attempt to understand neuronal pathways in the human brain by visualizing bundles of fibers from DTI images. Extracting insights from masses of 3D trajectories is challenging as the multiple three dimensional lines have complex geometries, may overlap, cross or even merge with each other, making it impossible to follow individual ones in dense areas. As trajectories are inherently spatial and three dimensional, we propose FiberClay: a system to display and interact with 3D trajectories in immersive environments. FiberClay renders a large quantity of trajectories in real time using GP-GPU techniques. FiberClay also introduces a new set of interactive techniques for composing complex queries in 3D space leveraging immersive environment controllers and user position. These techniques enable an analyst to select and compare sets of trajectories with specific geometries and data properties. We conclude by discussing insights found using FiberClay with domain experts in air traffic control and neurology.",,,,,vimeo 289784766,10.1109/tvcg.2018.2865191,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,9:20 AM,9:40 AM
  -InfoVis,TVCG,DXR: A Toolkit for Building Immersive Data Visualizations,J),"Ronell Sicat, Jiabao Li, JunYoung Choi, Maxime Cordeil, Won-Ki Jeong, Benjamin Bach, Hanspeter Pfister",https://vcg.seas.harvard.edu/publications/dxr-a-toolkit-for-building-immersive-data-visualizations/paper,"This paper presents DXR, a toolkit for building immersive data visualizations based on the Unity development platform. Over the past years, immersive data visualizations in augmented and virtual reality (AR, VR) have been emerging as a promising medium for data sense-making beyond the desktop. However, creating immersive visualizations remains challenging, and often require complex low-level programming and tedious manual encoding of data attributes to geometric and visual properties. These can hinder the iterative idea-to-prototype process, especially for developers without experience in 3D graphics, AR, and VR programming. With DXR, developers can efficiently specify visualization designs using a concise declarative visualization grammar inspired by Vega-Lite. DXR further provides a GUI for easy and quick edits and previews of visualization designs in-situ, i.e., while immersed in the virtual world. DXR also provides reusable templates and customizable graphical marks, enabling unique and engaging visualizations. We demonstrate the flexibility of DXR through several examples spanning a wide range of applications.",,https://sites.google.com/view/dxr-vis/,,,youtube p4fB_OfoaZA,10.1109/tvcg.2018.2865152,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,9:40 AM,10:00 AM
  -InfoVis,TVCG,Information Olfactation: Harnessing Scent to Convey Data,J),"Biswaksen Patnaik, Andrea Batch, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/info-olfac/info-olfac.pdf,"Olfactory feedback for analytical tasks is a virtually unexplored area in spite of the advantages it offers for information recall, feature identification, and location detection. Here we introduce the concept of information olfactation as the fragrant sibling of information visualization, and discuss how scent can be used to convey data. Building on a review of the human olfactory system and mirroring common visualization practice, we propose olfactory marks, the substrate in which they exist, and their olfactory channels that are available to designers. To exemplify this idea, we present VISCENT: A six-scent stereo olfactory display capable of conveying olfactory glyphs of varying temperature and direction, as well as a corresponding software system that integrates the display with a traditional visualization display. Finally, we present three applications that make use of the viScent system: A 2D graph visualization, a 2D line and point chart, and an immersive analytics graph visualization in 3D virtual reality. We close the paper with a review of possible extensions of viScent and applications of information olfactation for general visualization beyond the examples in this paper.",,,,,vimeo 289784509,10.1109/tvcg.2018.2865237,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,10:00 AM,10:20 AM
  -InfoVis,TVCG,Dynamic Composite Data Physicalization Using Wheeled Micro-Robots,J),"Mathieu Le Goc, Charles Perin, Sean Follmer, Jean-Daniel Fekete, Pierre Dragicevic",https://hal.inria.fr/hal-01848436/document,"This paper introduces dynamic composite physicalizations, a new class of physical visualizations that use collections of self-propelled objects to represent data. Dynamic composite physicalizations can be used both to give physical form to well-known interactive visualization techniques, and to explore new visualizations and interaction paradigms. We first propose a design space characterizing composite physicalizations based on previous work in the fields of Information Visualization and Human Computer Interaction. We illustrate dynamic composite physicalizations in two scenarios demonstrating potential benefits for collaboration and decision making, as well as new opportunities for physical interaction. We then describe our implementation using wheeled micro-robots capable of locating themselves and sensing user input, before discussing limitations and opportunities for future work.",,,,,vimeo 289784806,10.1109/tvcg.2018.2865159,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,10:20 AM,10:40 AM
  +VAST,TVCG,Vulnus: Visual Vulnerability Analysis for Network Security,J),"Marco Angelini, Graziano Blasilli, Tiziana Catarci, Simone Lenti, Giuseppe Santucci",,,,,,,vimeo 289787623,10.1109/tvcg.2018.2865028,2018,2018,VAST,"Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,9:00 AM,9:20 AM
  +VAST,TVCG,GraphProtector: a Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms,J),"Xumeng Wang, Wei Chen, Huihua Guan, Wenlong Chen, Rusheng Pan, Jia-Kai Chou, Chris Bryan, Kwan-Liu Ma",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/GraphProtector.pdf,"Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques—along with evaluating how applying the strategies will affect the utility of the anonymized results—remains a signicant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphProtector, we report several case studies and feedback collected from interviews with expert users in various scenarios.",,,,,vimeo 289787471,10.1109/TVCG.2018.2865021,2018,2018,VAST,"Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,9:20 AM,9:40 AM
  +VAST,TVCG,Situ: Identifying and explaining suspicious behavior in networks,J),"John Goodall, Eric Ragan, Chad Steed, Joel Reed, Gregory Richardson, Kelly Huffer, Robert Bridges, Jason Laska",https://www.cise.ufl.edu/~eragan/papers/Goodall_Situ_2018.pdf,"Despite the best efforts of cyber security analysts, networked computing assets are routinely compromised, resulting in the loss of intellectual property, the disclosure of state secrets, and major nancial damages. Anomaly detection methods are benecial for detecting new types of attacks and abnormal network activity, but such algorithms can be difcult to understand and trust. Network operators and cyber analysts need fast and scalable tools to help identify suspicious behavior that bypasses automated security systems, but operators do not want another automated tool with algorithms they do not trust. Experts need tools to augment their own domain expertise and to provide a contextual understanding of suspicious behavior to help them make decisions. In this paper we present Situ, a visual analytics system for discovering suspicious behavior in streaming network data. Situ provides a scalable solution that combines anomaly detection with information visualization. The system's visualizations enable operators to identify and investigate the most anomalous events and IP addresses, and the tool provides context to help operators understand why they are anomalous. Finally, operators need tools that can be integrated into their workow and with their existing tools. This paper describes the Situ platform and its deployment in an operational network setting. We discuss how operators are currently using the tool in a large organization's security operations center and present the results of expert reviews with professionals.",,,,,vimeo 289787635,10.1109/TVCG.2018.2865029,2018,2018,VAST,"Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,9:40 AM,10:00 AM
  +VAST,TVCG,A Visual Analytics Framework for the Detection of Anomalous Call Stack Trees in High Performance Computing Applications,J),"Cong Xie, Wei Xu, Klaus Mueller",,,,,,,vimeo 289787924,10.1109/tvcg.2018.2865026,2018,2018,VAST,"Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,10:00 AM,10:20 AM
  +VAST,TVCG,Lessons Learned Developing a Visual Analytics Solution for Investigative Analysis of Scamming Activities,J),"Jay Koven, Cristian Felix, Hossein Siadati, Enrico Bertini, Markus Jakobsson",,,,,,,vimeo 289787515,10.1109/tvcg.2018.2865023,2018,2018,VAST,"Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,10:20 AM,10:40 AM
  +InfoVis,TVCG,Origin-Destination Flow Maps in Immersive Environments,J),"Yalong Yang, Tim Dwyer, Bernhard Jenny, Kim Marriott, Maxime Cordeil, Haohui Chen",https://ialab.it.monash.edu/~dwyer/papers/flow-maps-ia.pdf,"Immersive virtual- and augmented-reality headsets can overlay a flat image against any surface or hang virtual objects in the space around the user. The technology is rapidly improving and may, in the long term, replace traditional flat panel displays in many situations. When displays are no longer intrinsically flat, how should we use the space around the user for abstract data visualisation? In this paper, we ask this question with respect to origin-destination flow data in a global geographic context. We report on the findings of three studies exploring different spatial encodings for flow maps. The first experiment focuses on different 2D and 3D encodings for flows on flat maps. We find that participants are significantly more accurate with raised flow paths whose height is proportional to flow distance but fastest with traditional straight line 2D flows. In our second and third experiment we compared flat maps, 3D globes and a novel interactive design we call <i>MapsLink</i>, involving a pair of linked flat maps. We find that participants took significantly more time with MapsLink than other flow maps while the 3D globe with raised flows was the fastest, most accurate, and most preferred method. Our work suggests that <i>careful</i> use of the third spatial dimension can resolve visual clutter in complex flow maps.",,,,,vimeo 279605506,10.1109/tvcg.2018.2865192,2018,2018,InfoVis,"Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,9:00 AM,9:20 AM
  +InfoVis,TVCG,FiberClay: Sculpting Three Dimensional Trajectories to Reveal Structural Insights,J),"Christophe Hurter, Nathalie Henry Riche, Steven Drucker, Maxime Cordeil, Richard Alligier, Romain Vuillemot",https://recherche.enac.fr/~hurter/FiberClay/FiberClay2018.pdf,"Visualizing 3D trajectories to extract insights about their similarities and spatial configuration is a critical task in several domains. Air traffic controllers for example deal with large quantities of aircrafts routes to optimize safety in airspace and neuroscientists attempt to understand neuronal pathways in the human brain by visualizing bundles of fibers from DTI images. Extracting insights from masses of 3D trajectories is challenging as the multiple three dimensional lines have complex geometries, may overlap, cross or even merge with each other, making it impossible to follow individual ones in dense areas. As trajectories are inherently spatial and three dimensional, we propose FiberClay: a system to display and interact with 3D trajectories in immersive environments. FiberClay renders a large quantity of trajectories in real time using GP-GPU techniques. FiberClay also introduces a new set of interactive techniques for composing complex queries in 3D space leveraging immersive environment controllers and user position. These techniques enable an analyst to select and compare sets of trajectories with specific geometries and data properties. We conclude by discussing insights found using FiberClay with domain experts in air traffic control and neurology.",,,,,vimeo 289784766,10.1109/tvcg.2018.2865191,2018,2018,InfoVis,"Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,9:20 AM,9:40 AM
  +InfoVis,TVCG,DXR: A Toolkit for Building Immersive Data Visualizations,J),"Ronell Sicat, Jiabao Li, JunYoung Choi, Maxime Cordeil, Won-Ki Jeong, Benjamin Bach, Hanspeter Pfister",https://vcg.seas.harvard.edu/publications/dxr-a-toolkit-for-building-immersive-data-visualizations/paper,"This paper presents DXR, a toolkit for building immersive data visualizations based on the Unity development platform. Over the past years, immersive data visualizations in augmented and virtual reality (AR, VR) have been emerging as a promising medium for data sense-making beyond the desktop. However, creating immersive visualizations remains challenging, and often require complex low-level programming and tedious manual encoding of data attributes to geometric and visual properties. These can hinder the iterative idea-to-prototype process, especially for developers without experience in 3D graphics, AR, and VR programming. With DXR, developers can efficiently specify visualization designs using a concise declarative visualization grammar inspired by Vega-Lite. DXR further provides a GUI for easy and quick edits and previews of visualization designs in-situ, i.e., while immersed in the virtual world. DXR also provides reusable templates and customizable graphical marks, enabling unique and engaging visualizations. We demonstrate the flexibility of DXR through several examples spanning a wide range of applications.",,https://sites.google.com/view/dxr-vis/,,,youtube p4fB_OfoaZA,10.1109/tvcg.2018.2865152,2018,2018,InfoVis,"Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,9:40 AM,10:00 AM
  +InfoVis,TVCG,Information Olfactation: Harnessing Scent to Convey Data,J),"Biswaksen Patnaik, Andrea Batch, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/info-olfac/info-olfac.pdf,"Olfactory feedback for analytical tasks is a virtually unexplored area in spite of the advantages it offers for information recall, feature identification, and location detection. Here we introduce the concept of information olfactation as the fragrant sibling of information visualization, and discuss how scent can be used to convey data. Building on a review of the human olfactory system and mirroring common visualization practice, we propose olfactory marks, the substrate in which they exist, and their olfactory channels that are available to designers. To exemplify this idea, we present VISCENT: A six-scent stereo olfactory display capable of conveying olfactory glyphs of varying temperature and direction, as well as a corresponding software system that integrates the display with a traditional visualization display. Finally, we present three applications that make use of the viScent system: A 2D graph visualization, a 2D line and point chart, and an immersive analytics graph visualization in 3D virtual reality. We close the paper with a review of possible extensions of viScent and applications of information olfactation for general visualization beyond the examples in this paper.",,,,,vimeo 289784509,10.1109/tvcg.2018.2865237,2018,2018,InfoVis,"Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,10:00 AM,10:20 AM
  +InfoVis,TVCG,Dynamic Composite Data Physicalization Using Wheeled Micro-Robots,J),"Mathieu Le Goc, Charles Perin, Sean Follmer, Jean-Daniel Fekete, Pierre Dragicevic",https://hal.inria.fr/hal-01848436/document,"This paper introduces dynamic composite physicalizations, a new class of physical visualizations that use collections of self-propelled objects to represent data. Dynamic composite physicalizations can be used both to give physical form to well-known interactive visualization techniques, and to explore new visualizations and interaction paradigms. We first propose a design space characterizing composite physicalizations based on previous work in the fields of Information Visualization and Human Computer Interaction. We illustrate dynamic composite physicalizations in two scenarios demonstrating potential benefits for collaboration and decision making, as well as new opportunities for physical interaction. We then describe our implementation using wheeled micro-robots capable of locating themselves and sensing user input, before discussing limitations and opportunities for future work.",,,,,vimeo 289784806,10.1109/tvcg.2018.2865159,2018,2018,InfoVis,"Hall 1, Section D",Thursday,Immersive Analytics,25-Oct,10:20 AM,10:40 AM
   SciVis,TVCG,Robust and Fast Extraction of 3D Symmetric Tensor Field Topology,J),"Lawrence Roy, Prashant Kumar, Yue Zhang, Eugene Zhang",http://web.engr.oregonstate.edu/~zhange/images/3DTensorTopology_Detection.pdf,"3D symmetric tensor fields appear in many science and engineering domains, and topology-driven analysis is important in many of these application domains, such as solid mechanics and fluid dynamics. Degenerate curves and neutral surfaces are important topological features in 3D symmetric tensor fields. Existing methods to extract degenerate curves and neutral surfaces often miss parts of the curves and surfaces, respectively. Moreover, they are relatively expensive to extract. These issues are due to the lack of knowledge of their overall structures. In this paper, we provide theoretical analysis on the geometric and topological structures of degenerate curves and neutral surfaces of 3D linear tensor fields. These structures lead to parameterizations for degenerate curves and neutral surfaces that can not only provide more robust extraction of these features but also incur less computational cost. We demonstrate the benefits of our approach by applying our degenerate curve and neutral surface detection to solid mechanics simulation data sets.",,,,,vimeo 290325698,10.1109/tvcg.2018.2864768,2018,2018,SciVis,Estrel Hall A+B,Thursday,Tensors,25-Oct,9:00 AM,9:20 AM
   SciVis,TVCG,DT-MRI Streamsurfaces Revisited,J),"Michael Ankele, Thomas Schultz",http://cg.cs.uni-bonn.de/aigaion2root/attachments/ankele-vis18.pdf,"DT-MRI streamsurfaces, defined as surfaces that are everywhere tangential to the major and medium eigenvector fields, have been proposed as a tool for visualizing regions of predominantly planar behavior in diffusion tensor MRI. Even though it has long been known that their construction assumes that the involved eigenvector fields satisfy an integrability condition, it has never been tested systematically whether this condition is met in real-world data. We introduce a suitable and efficiently computable test to the visualization literature, demonstrate that it can be used to distinguish integrable from nonintegrable configurations in simulations, and apply it to whole-brain datasets of 15 healthy subjects. We conclude that streamsurface integrability is approximately satisfied in a substantial part of the brain, but not everywhere, including some regions of planarity. As a consequence, algorithms for streamsurface extraction should explicitly test local integrability. Finally, we propose a novel patch-based approch to streamsurface visualization that reduces visual artifacts, and is shown to more fully sample the extent of streamsurfaces.",,,,,vimeo 290327825,10.1109/tvcg.2018.2864845,2018,2018,SciVis,Estrel Hall A+B,Thursday,Tensors,25-Oct,9:20 AM,9:40 AM
   SciVis,TVCG,Tensor Field Visualization using Fiber Surfaces of Invariant Space,J),"Felix Raith, Christian Blecha, Thomas Nagel, Francesco Parisio, Olaf Kolditz, Fabian Günther, Markus Stommel, Gerik Scheuermann",,,,,,,vimeo 290327914,10.1109/tvcg.2018.2864846,2018,2018,SciVis,Estrel Hall A+B,Thursday,Tensors,25-Oct,9:40 AM,10:00 AM
  
positive.code: 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
  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",https://commercialtools.dbvis.de/assets/data/submission/CommercialTools_TVCG_Journal_FINAL.pdf,"Five years after the first state-of-the-art report on Commercial Visual Analytics Systems we present a reevaluation of the Big Data Analytics field. We build on the success of the 2012 survey, which was influential even beyond the boundaries of the InfoVis and Visual Analytics (VA) community. While the field has matured significantly since the original survey, we find that innovation and research-driven development are increasingly sacrificed to satisfy a wide range of user groups. We evaluate new product versions on established evaluation criteria, such as available features, performance, and usability, to extend on and assure comparability with the previous survey. We also investigate previously unavailable products to paint a more complete picture of the commercial VA landscape. Furthermore, we introduce novel measures, like suitability for specific user groups and the ability to handle complex data types, and undertake a new case study to highlight innovative features. We explore the achievements in the commercial sector in addressing VA challenges and propose novel developments that should be on systems’ roadmaps in the coming years.",,https://commercialtools.dbvis.de/questions,,,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",https://arxiv.org/pdf/1707.06105.pdf,"In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of human locomotion enables clinicians to describe and analyze a patient’s gait performance in detail and allows them to base clinical decisions on objective data. These assessments generate a vast amount of complex data which need to be interpreted in a short time period. We conducted a design study in cooperation with gait analysis experts to develop a novel Knowledge-Assisted Visual Analytics solution for clinical Gait analysis (KAVAGait). KAVAGait allows the clinician to store and inspect complex data derived during clinical gait analysis. The system incorporates innovative and interactive visual interface concepts, which were developed based on the needs of clinicians. Additionally, an explicit knowledge store (EKS) allows externalization and storage of implicit knowledge from clinicians. It makes this information available for others, supporting the process of data inspection and clinical decision making. We validated our system by conducting expert reviews, a user study, and a case study. Results suggest that KAVAGait is able to support a clinician during clinical practice by visualizing complex gait data and providing knowledge of other clinicians.",,,,,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
  TVCG,TVCG,Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots,T),"G.Elisabeta Marai, Chihua Ma, Andrew T. Burks, Filippo Pellolio, Guadalupe Canahuate, David M. Vock, Abdallah S.R. Mohamed, C. David Fuller",https://www.evl.uic.edu/documents/ieee_precisionriskanalysis.pdf,"We present the design and evaluation of an integrated problem solving environment for cancer therapy analysis. The environment intertwines a statistical martingale model and a K Nearest Neighbor approach with visual encodings, including novel interactive nomograms, in order to compute and explain a patient’s probability of survival as a function of similar patient results. A coordinated views paradigm enables exploration of the multivariate, heterogeneous and few-valued data from a large head and neck cancer repository. A visual scaffolding approach further enables users to build from familiar representations to unfamiliar ones. Evaluation with domain experts show how this visualization approach and set of streamlined workflows enable the systematic and precise analysis of a patient prognosis in the context of cohorts of similar patients. We describe the design lessons learned from this successful, multi-site remote collaboration.",,,,,vimeo 289789079,10.1109/tvcg.2018.2817557,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,12:00 PM,12:20 PM
  VAST,VAST,VUSphere: Visual Analysis of Video Utilization in Online Distance Education,C),"Huan He, Qinghua Zheng, Bo Dong",,,,,,,vimeo 289788052,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,12:20 PM,12:40 PM
  InfoVis,TVCG,Juniper: A Tree+Table Approach to Multivariate Graph Visualization,J),"Carolina Nobre, Marc Streit, Alexander Lex",https://arxiv.org/pdf/1804.03261.pdf,"Analyzing large, multivariate graphs is an important problem in many domains, yet such graphs are challenging to visualize. In this paper, we introduce a novel, scalable, tree+table multivariate graph visualization technique, which makes many tasks related to multivariate graph analysis easier to achieve. The core principle we follow is to selectively query for nodes or subgraphs of interest and visualize these subgraphs as a spanning tree of the graph. The tree is laid out linearly, which enables us to juxtapose the nodes with a table visualization where diverse attributes can be shown. We also use this table as an adjacency matrix, so that the resulting technique is a hybrid node-link/adjacency matrix technique. We implement this concept in Juniper and complement it with a set of interaction techniques that enable analysts to dynamically grow, restructure, and aggregate the tree, as well as change the layout or show paths between nodes. We demonstrate the utility of our tool in usage scenarios for different multivariate networks: a bipartite network of scholars, papers, and citation metrics and a multitype network of story characters, places, books, etc.",,https://github.com/caleydo/lineage/tree/juniper,,,vimeo 289784894,10.1109/TVCG.2018.2865149,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,11:00 AM,11:20 AM
  TVCG,TVCG,Graph Thumbnails: Identifying and Comparing Multiple Graphs at a Glance,T),"Vahan Yoghourdjian, Tim Dwyer, Karsten Klein, Kim Marriott, Michael Wybrow",http://vahany.com/docs/Graph_Thumbnails_preprint.pdf,"We propose <i>Graph Thumbnails</i>, small icon-like visualisations of the high-level structure of network data. Graph Thumbnails are designed to be legible in small multiples to support rapid browsing within large graph corpora. Compared to existing graph-visualisation techniques our representation has several advantages: (1) the visualisation can be computed in linear time; (2) it is canonical in the sense that isomorphic graphs will always have identical thumbnails; and (3) it provides precise information about the graph structure. We report the results of two user studies. The first study compares Graph Thumbnails to node-link and matrix views for identifying similar graphs. The second study investigates the comprehensibility of the different representations. We demonstrate the usefulness of this representation for summarising the evolution of protein-protein interaction networks across a range of species.",,,,,vimeo 289789353,10.1109/tvcg.2018.2790961,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,11:20 AM,11:40 AM
  InfoVis,TVCG,Structure-Based Suggestive Exploration: A New Approach for Effective Exploration of Large Networks,J),"Fangzhou Guo, Wei Chen, Dongming Han, Jiacheng Pan, Xiaotao Nie, Jiazhi Xia, Xiaolong (Luke) Zhang",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/Structure-Based%20Suggestive%20Exploration.pdf,"When analyzing a visualized network, users need to explore different sections of the network to gain insight. However, effective exploration of large networks is often a challenge. While various tools are available for users to explore the global and local features of a network, these tools usually require significant interaction activities, such as repetitive navigation actions to follow network nodes and edges. In this paper, we propose a structure-based suggestive exploration approach to support effective exploration of large networks by suggesting appropriate structures upon user request. Encoding nodes with vectorized representations by transforming information of surrounding structures of nodes into a high dimensional space, our approach can identify similar structures within a large network, enable user interaction with multiple similar structures simultaneously, and guide the exploration of unexplored structures. We develop a web-based visual exploration system to incorporate this suggestive exploration approach and compare performances of our approach under different vectorizing methods and networks. We also present the usability and effectiveness of our approach through a controlled user study with two datasets.",,,,,vimeo 289785045,10.1109/tvcg.2018.2865139,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,11:40 AM,12:00 PM
  InfoVis,TVCG,Structure-aware Fisheye Views for Efficient Large Graph Exploration,J),"Yunhai Wang, Yanyan Wang, Yinqi Sun, Haifeng Zhang, Chi-Wing Fu, Michael Sedlmair, Baoquan Chen, Oliver Deussen",https://homepage.univie.ac.at/michael.sedlmair/papers/wang2019fisheye.pdf,"Traditional fisheye views for exploring large graphs introduce substantial distortions that often lead to a decreased readability of paths and other interesting structures. To overcome these problems, we propose a framework for <i>structure-aware</i> fisheye views. Using edge orientations as constraints for graph layout optimization allows us not only to reduce spatial and temporal distortions during fisheye zooms, but also to improve the readability of the graph structure. Furthermore, the framework enables us to optimize fisheye lenses towards specific tasks and design a family of new lenses: polyfocal, cluster, and path lenses. A GPU implementation lets us process large graphs with up to 15,000 nodes at interactive rates. A comprehensive evaluation, a user study, and two case studies demonstrate that our structure-aware fisheye views improve layout readability and user performance.",,,,,vimeo 289785238,10.1109/tvcg.2018.2864911,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,12:00 PM,12:20 PM
  InfoVis,TVCG,"Graphicle: Exploring Units, Networks, and Context in a Blended Visualization Approach ",J),"Timothy Major, Rahul C. Basole",http://entsci.gatech.edu/resources/basole-2019-tvcg-graphicle.pdf,"Many real-world datasets are large, multivariate, and relational in nature and relevant associated decisions frequently require a simultaneous consideration of both attributes and connections. Existing visualization systems and approaches, however, often make an explicit trade-off between either affording rich exploration of individual data units and their attributes or exploration of the underlying network structure. In doing so, important analysis opportunities and insights are potentially missed. In this study, we aim to address this gap by (1) considering visualizations and interaction techniques that blend the spectrum between unit and network visualizations, (2) discussing the nature of different forms of contexts and the challenges in implementing them, and (3) demonstrating the value of our approach for visual exploration of multivariate, relational data for a real-world use case. Specifically, we demonstrate through a system called Graphicle how network structure can be layered on top of unit visualization techniques to create new opportunities for visual exploration of physician characteristics and referral data. We report on the design, implementation, and evaluation of the system and effectiveness of our blended approach.",,,,,vimeo 262664447,10.1109/tvcg.2018.2865151,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,12:20 PM,12:40 PM
  SciVis,TVCG,Interactive Obstruction-free Lensing for Volumetric Data Visualization,J),"Michael Traoré, Christophe Hurter, Alexandru Telea",http://recherche.enac.fr/~hurter/ObstructionFreeLens/Vis2018Traore.pdf,"Occlusion is an issue in volumetric visualization as it prevents direct visualization of the region of interest. While many techniques such as transfer functions, volume segmentation or view distortion have been developed to address this, there is still room for improvement to better support the understanding of objects’ vicinity. However, most existing Focus+Context fail to solve partial occlusion in datasets where the target and the occluder are very similar density-wise. For these reasons, we investigate a new technique which maintains the general structure of the investigated volumetric dataset while addressing occlusion issues. With our technique, the user interactively defines an area of interest where an occluded region or object is partially visible. Then our lens starts pushing at its border occluding objects, thus revealing hidden volumetric data. Next, the lens is modified with an extended field of view (fish-eye deformation) to better see the vicinity of the selected region. Finally, the user can freely explore the surroundings of the area under investigation within the lens. To provide real-time exploration, we implemented our lens using a GPU accelerated ray-casting framework to handle ray deformations, local lighting, and local viewpoint manipulation. We illustrate our technique with five application scenarios in baggage inspection, 3D fluid flow visualization, chest radiology, air traffic planning, and DTI fiber exploration.",,,,,youtube FOiCmznSOz4,10.1109/TVCG.2018.2864690,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,11:00 AM,11:20 AM
  SciVis,TVCG,Dynamic Volume Lines: Visual Comparison of 3D Volumes through Space-filling Curves,J),"Johannes Weissenböck, Bernhard Fröhler, Eduard Gröller, Johann Kastner, Christoph Heinzl",,,,,,,vimeo 290325634,10.1109/tvcg.2018.2864510,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,11:20 AM,11:40 AM
  SciVis,TVCG,A Declarative Grammar of Flexible Volume Visualization Pipelines,J),"Min Shih, Charles Rozhon, Kwan-Liu Ma",,,,,,,vimeo 290327611,10.1109/tvcg.2018.2864841,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,11:40 AM,12:00 PM
  
negative.code: CG&A,CGA,Application-Driven Design: Help Students Understand Employment and See the “Big Picture”,,"Li Liu, Deborah Silver, Karen Bemis",,,,,,,vimeo 289785826,10.1109/mcg.2018.032421656,2018,2018,Other,Room III,Thursday,CG&A Session 2,25-Oct,11:40 AM,12:00 PM
  CG&A,CGA,Management of Cerebral Aneurysm Descriptors based on an Automatic Ostium Extraction,,"Monique Meuschke, Tobias Günther, Ralph Wickenhöfer, Markus Gross, Bernhard Preim, Kai Lawonn",https://cgl.ethz.ch/Downloads/Publications/Papers/2018/Meu18a/Meu18a.pdf,"We present a framework to manage cerebral aneurysms. Rupture risk evaluation is based on manually extracted descriptors, which is timeconsuming. Thus, we provide an automatic solution by considering several questions: How can expert knowledge be integrated? How should metadata be defined? Which interaction techniques are needed for data exploration?",,,,,vimeo 289785840,10.1109/mcg.2018.032421654,2018,2018,Other,Room III,Thursday,CG&A Session 2,25-Oct,12:00 PM,12:20 PM
  CG&A,CGA,Toward a Multimodal Diagnostic Exploratory Visualization of Focal Cortical Dysplasia,,"Shin-Ting Wu, Raphael Voltoline, Wallace S. Loos, J.A. Iván Rubianes, Lionis S. Watanabe, Bárbara J. Amorim, A. Carolina Coan, Fernando Cendes, Clarissa L. Yasuda",http://www.dca.fee.unicamp.br/projects/prosim/publications/journals/wu-loos-voltoline-rubianes-2017-3dmediv-draft.pdf,"Focal cortical dysplasia (FCD) is a malformation of cortical development and a common cause of pharmacoresistant epilepsy. Resective surgery of clear-cut lesions may be curative. However, the localization of the seizure focus and the evaluation of its spatial extent can be challenging in many situations. For concordance assessment, medical studies show the relevance of accurate correlation of multi-source imaging sequences. to improve the sensitivity and specificity of the evaluation. In this paper, we share the process we went through to reach our simple, but effective, solution for integrating multi-volume rendering into an exploratory visualization environment for the diagnosis of FCD. We focus on fetching of multiple data assigned to a sample when they are rendered. Knowing that the major diagnostic role of multiple volumes is to complement information, we demonstrate that appropriate geometric transformations in the texture space are sufficient for accomplishing this task. This allows us to fully implement our proposal in the OpenGL rendering pipeline and to easily integrate it into the existing visual diagnostic application. Both time performance and the visual quality of our proposal were evaluated with a set of clinical data volumes for assessing the potential practical impact of our solution in routine diagnostic use. ",,,,,vimeo 289785856,10.1109/mcg.2018.032421655,2018,2018,Other,Room III,Thursday,CG&A Session 2,25-Oct,12:20 PM,12:40 PM
  VAST,TVCG,ViBr: Visualizing Bipartite Relations at Scale with the Minimum Description Length Principle,J),"Gromit Yeuk-Yin Chan, Panpan Xu, Zeng Dai, Liu Ren",https://vgc.poly.edu/~yychan/papers/chan-vast2018.pdf,"Bipartite graphs model the key relations in many large scale real-world data: customers purchasing items, legislators voting for bills, people’s affiliation with different social groups, faults occurring in vehicles, etc. However, it is challenging to visualize large scale bipartite graphs with tens of thousands or even more nodes or edges. In this paper, we propose a novel visual summarization technique for bipartite graphs based on the minimum description length (MDL) principle. The method simultaneously groups the two different set of nodes and constructs aggregated bipartite relations with balanced granularity and precision. It addresses the key trade-off that often occurs for visualizing large scale and noisy data: acquiring a clear and uncluttered overview while maximizing the information content in it. We formulate the visual summarization task as a co-clustering problem and propose an efficient algorithm based on locality sensitive hashing (LSH) that can easily scale to large graphs under reasonable interactive time constraints that previous related methods cannot satisfy. The method leads to the opportunity of introducing a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. In the framework, we also introduce a compact visual design inspired by adjacency list representation of graphs as the building block for a small multiples display to compare the bipartite relations for different subsets of data. We showcase the applicability and effectiveness of our approach by applying it on synthetic data with ground truth and performing case studies on real-world datasets from two application domains including roll-call vote record analysis and vehicle fault pattern analysis. Interviews with experts in the political science community and the automotive industry further highlight the benefits of our approach.",,,,,vimeo 289787354,10.1109/TVCG.2018.2864826,2018,2018,VAST,"Hall 1, Section C",Thursday,Graph and Image,25-Oct,2:20 PM,2:40 PM
  VAST,VAST,Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts,C),"Po-Ming Law, Yanhong Wu, Rahul Basole",https://osf.io/ah5y6/,"Getting the overall picture of how a large number of ego-networks evolve is a common yet challenging task. Existing techniques often require analysts to inspect the evolution patterns of ego-networks one after another. In this study, we explore an approach that allows analysts to interactively create spatial layouts in which each dot is a dynamic ego-network. These spatial layouts provide overviews of the evolution patterns of ego-networks, thereby revealing different global patterns such as trends, clusters and outliers in evolution patterns. To let analysts interactively construct interpretable spatial layouts, we propose a data transformation pipeline, with which analysts can adjust the spatial layouts and convert dynamic egonetworks into event sequences to aid interpretations of the spatial positions. Based on this transformation pipeline, we develop Segue, a visual analysis system that supports thorough exploration of the evolution patterns of ego-networks. Through two usage scenarios, we demonstrate how analysts can gain insights into the overall evolution patterns of a large collection of ego-networks by interactively creating different spatial layouts.",,https://osf.io/u4jxh/,,,vimeo 289788023,,2018,2018,VAST,"Hall 1, Section C",Thursday,Graph and Image,25-Oct,2:40 PM,3:00 PM
  VAST,TVCG,A Visual Analytics Framework for Spatiotemporal Trade Network Analysis,J),"Hong Wang, Yafeng Lu, Shade Shutters, Michael Steptoe, Feng Wang, Steven Landis, Ross Maciejewski",http://www.public.asu.edu/~hxwang/trade.pdf,"Economic globalization is increasing connectedness among regions of the world, creating complex interdependencies within various supply chains. Recent studies have indicated that changes and disruptions within such networks can serve as indicators for increased risks of violence and armed conflicts. This is especially true of countries that may not be able to compete for scarce commodities during supply shocks. Thus, network-induced vulnerability to supply disruption is typically exported from wealthier populations to disadvantaged populations. As such, researchers and stakeholders concerned with supply chains, political science, environmental studies, etc. need tools to explore the complex dynamics within global trade networks and how the structure of these networks relates to regional instability. However, the multivariate, spatiotemporal nature of the network structure creates a bottleneck in the extraction and analysis of correlations and anomalies for exploratory data analysis and hypothesis generation. Working closely with experts in political science and sustainability, we have developed a highly coordinated, multi-view framework that utilizes anomaly detection, network analytics, and spatiotemporal visualization methods for exploring the relationship between global trade networks and regional instability. Requirements for analysis and initial research questions to be investigated are elicited from domain experts, and a variety of visual encoding techniques for rapid assessment of analysis and correlations between trade goods, network patterns, and time series signatures are explored. We demonstrate the application of our framework through case studies focusing on armed conflicts in Africa, regional instability measures, and their relationship to international global trade.",,,,,vimeo 289787400,10.1109/TVCG.2018.2864844,2018,2018,VAST,"Hall 1, Section C",Thursday,Graph and Image,25-Oct,3:00 PM,3:20 PM
  TVCG,TVCG,A Semantic-based Method for Visualizing Large Image Collections,T),"Xiao Xie, Xiwen Cai, Junpei Zhou, Nan Cao, Yingcai Wu",http://zjuvis.org/files/imgvis.pdf,"Interactive visualization of large image collections is important and useful in many applications, such as personal album management and user profiling on images. However, most prior studies focus on using low-level visual features of images, such as texture and color histogram, to create visualizations without considering the more important semantic information embedded in images. This paper proposes a novel visual analytic system to analyze images in a semantic-aware manner. The system mainly comprises two components: a semantic information extractor and a visual layout generator. The semantic information extractor employs an image captioning technique based on convolutional neural network (CNN) to produce descriptive captions for images, which can be transformed into semantic keywords. The layout generator employs a novel co-embedding model to project images and the associated semantic keywords to the same 2D space. Inspired by the galaxy metaphor, we further turn the projected 2D space to a galaxy visualization of images, in which semantic keywords and images are visually encoded as stars and planets. Our system naturally supports multi-scale visualization and navigation, in which users can immediately see a semantic overview of an image collection and drill down for detailed inspection of a certain group of images. Users can iteratively refine the visual layout by integrating their domain knowledge into the co-embedding process. Two task-based evaluations are conducted to demonstrate the effectiveness of our system.",,,,,vimeo 289789441,10.1109/tvcg.2018.2835485,2018,2018,VAST,"Hall 1, Section C",Thursday,Graph and Image,25-Oct,3:20 PM,3:40 PM
  TVCG,TVCG,PhotoRecomposer: Interactive Photo Recomposition by Cropping,T),"Yuan Liang, Xiting Wang, Song-Hai Zhang, Shi-Min Hu, Shixia Liu",http://shixialiu.com/publications/photorecomposer/paper.pdf,"We present a visual analysis method for interactively recomposing a large number of photos based on example photos with high-quality composition. The recomposition method is formulated as a matching problem between photos. The key to this formulation is a new metric for accurately measuring the composition distance between photos. We have also developed an earth-mover-distancebased online metric learning algorithm to support the interactive adjustment of the composition distance based on user preferences. To better convey the compositions of a large number of example photos, we have developed a multi-level, example photo layout method to balance multiple factors such as compactness, aspect ratio, composition distance, stability, and overlaps. By introducing an EulerSmooth-based straightening method, the composition of each photos is clearly displayed. The effectiveness and usefulness of the method has been demonstrated by the experimental results, user study, and case studies.",,,,,vimeo 289788924,10.1109/tvcg.2017.2764895,2017,2018,VAST,"Hall 1, Section C",Thursday,Graph and Image,25-Oct,3:40 PM,4:00 PM
  InfoVis,TVCG,Mapping Color to Meaning in Colormap Data Visualizations,J),"Karen B. Schloss, Connor C. Gramazio, Allison T. Silverman, Madeline L. Parker, Audrey S. Wang",https://schlosslab.discovery.wisc.edu/wp-content/uploads/2018/09/SchlossGramazioSilvermanParkerWanginPress.pdf,"To interpret data visualizations, people must determine how visual features map onto concepts. For example, to interpret colormaps, people must determine how dimensions of color (e.g., lightness, hue) map onto quantities of a given measure (e.g., brain activity, correlation magnitude). This process is easier when the encoded mappings in the visualization match people's predictions of how visual features will map onto concepts, their inferred mappings. To harness this principle in visualization design, it is necessary to understand what factors determine people's <i>inferred mappings</i>. In this study, we investigated how inferred color-quantity mappings for colormap data visualizations were influenced by the background color. Prior literature presents seemingly conflicting accounts of how the background color affects inferred color-quantity mappings. The present results help resolve those conflicts, demonstrating that sometimes the background has an effect and sometimes it does not, depending on whether the colormap appears to vary in opacity. When there is no apparent variation in opacity, participants infer that darker colors map to larger quantities (<i>dark-is-more bias</i>). As apparent variation in opacity increases, participants become biased toward inferring that more opaque colors map to larger quantities (<i>opaque-is-more bias</i>). These biases work together on light backgrounds and conflict on dark backgrounds. Under such conflicts, the opaque-is-more bias can negate, or even supersede the dark-is-more bias. The results suggest that if a design goal is to produce colormaps that match people's inferred mappings and are robust to changes in background color, it is beneficial to use colormaps that will not appear to vary in opacity on any background color, and to encode larger quantities in darker colors.",,,https://schlosslab.discovery.wisc.edu/resources/,,vimeo 289784916,10.1109/tvcg.2018.2865147,2018,2018,InfoVis,"Hall 1, Section D",Thursday,Perception & Cognition 1,25-Oct,2:20 PM,2:40 PM
  InfoVis,TVCG,Optimizing Color Assignment for Perception of Class Separability in Multiclass Scatterplots,J),"Yunhai Wang, Xin Chen, Tong Ge, Chen Bao, Michael Sedlmair, Chi-Wing Fu, Oliver Deussen, Baoquan Chen",http://www.yunhaiwang.org/infovis18/color/vis-color.pdf,"Appropriate choice of colors significantly aids viewers in understanding the structures in multiclass scatterplots and becomes more important with a growing number of data points and groups. An appropriate color mapping is also an important parameter for the creation of an aesthetically pleasing scatterplot. Currently, users of visualization software routinely rely on color mappings that have been pre-defined by the software. A default color mapping, however, cannot ensure an optimal perceptual separability between groups, and sometimes may even lead to a misinterpretation of the data. In this paper, we present an effective approach for color assignment based on a set of given colors that is designed to optimize the perception of scatterplots. Our approach takes into account the spatial relationships, density, degree of overlap between point clusters, and also the background color. For this purpose, we use a genetic algorithm that is able to efficiently find good color assignments. We implemented an interactive color assignment system with three extensions of the basic method that incorporates top K suggestions, user-defined color subsets, and classes of interest for the optimization. To demonstrate the effectiveness of our assignment technique, we conducted a numerical study and a controlled user study to compare our approach with default color assignments; our findings were verified by two expert studies. The results show that our approach is able to support users in distinguishing cluster numbers faster and more precisely than default assignment methods.",http://www.color-assignment.net/,,,,vimeo 289785222,10.1109/tvcg.2018.2864912,2018,2018,InfoVis,"Hall 1, Section D",Thursday,Perception & Cognition 1,25-Oct,2:40 PM,3:00 PM
  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,"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,"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,"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",https://arxiv.org/pdf/1807.11212.pdf,"This paper presents a new approach for the visualization and analysis of the spatial variability of features of interest represented by critical points in ensemble data. Our framework, called Persistence Atlas, enables the visualization of the dominant spatial patterns of critical points, along with statistics regarding their occurrence in the ensemble. The persistence atlas represents in the geometrical domain each dominant pattern in the form of a confidence map for the appearance of critical points. As a by-product, our method also provides 2-dimensional layouts of the entire ensemble, highlighting the main trends at a global level. Our approach is based on the new notion of Persistence Map, a measure of the geometrical density in critical points which leverages the robustness to noise of topological persistence to better emphasize salient features. We show how to leverage spectral embedding to represent the ensemble members as points in a low-dimensional Euclidean space, where distances between points measure the dissimilarities between critical point layouts and where statistical tasks, such as clustering, can be easily carried out. Further, we show how the notion of mandatory critical point can be leveraged to evaluate for each cluster confidence regions for the appearance of critical points. Most of the steps of this framework can be trivially parallelized and we show how to efficiently implement them. Extensive experiments demonstrate the relevance of our approach. The accuracy of the confidence regions provided by the persistence atlas is quantitatively evaluated and compared to a baseline strategy using an off-the-shelf clustering approach. We illustrate the importance of the persistence atlas in a variety of real-life datasets, where clear trends in feature layouts are identified and analyzed. We provide a lightweight VTK-based C++ implementation of our approach that can be used for reproduction purposes.",,,,,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",http://tusharathawale.info/wp-content/uploads/2018/09/vis18ProbabilisticAsympDecider.pdf,"We present a framework for the analysis of uncertainty in isocontour extraction. The marching squares (MS) algorithm for isocontour reconstruction generates a linear topology that is consistent with hyperbolic curves of a piecewise bilinear interpolation. The saddle points of the bilinear interpolant cause topological ambiguity in isocontour extraction. The midpoint decider and the asymptotic decider are well-known mathematical techniques for resolving topological ambiguities. The latter technique investigates the data values at the cell saddle points for ambiguity resolution. The uncertainty in data, however, leads to uncertainty in underlying bilinear interpolation functions for the MS algorithm, and hence, their saddle points. In our work, we study the behavior of the asymptotic decider when data at grid vertices is uncertain. First, we derive closed-form distributions characterizing variations in the saddle point values for uncertain bilinear interpolants. The derivation assumes uniform and nonparametric noise models, and it exploits the concept of ratio distribution for analytic formulations. Next, the probabilistic asymptotic decider is devised for ambiguity resolution in uncertain data using distributions of the saddle point values derived in the first step. Finally, the confidence in probabilistic topological decisions is visualized using a colormapping technique. We demonstrate the higher accuracy and stability of the probabilistic asymptotic decider in uncertain data with regard to existing decision frameworks, such as deciders in the mean field and the probabilistic midpoint decider, through the isocontour visualization of synthetic and real datasets.",,,,,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",http://www2.cs.uh.edu/~chengu/Publications/HexMesh/Vis2018_BaseComplexVis.pdf,"Understanding hexahedral (hex-) mesh structures is important for a number of hex-mesh generation and optimization tasks. However, due to various configurations of the singularities in a valid pure hex-mesh, the structure (or base complex) of the mesh can be arbitrarily complex. In this work, we present a first and effective method to help meshing practitioners understand the possible configurations in a valid 3D base complex for the characterization of their complexity. In particular, we propose a strategy to decompose the complex hex-mesh structure into multi-level sub-structures so that they can be studied separately, from which we identify a small set of the sub-structures that can most efficiently represent the whole mesh structure. Furthermore, from this set of sub-structures, we attempt to define the first metric for the quantification of the complexity of hex-mesh structure. To aid the exploration of the extracted multi-level structure information, we devise a visual exploration system coupled with a matrix view to help alleviate the common challenge of 3D data exploration (e.g., clutter and occlusion). We have applied our tool and metric to a large number of hex-meshes generated with different approaches to reveal different characteristics of these methods in terms of the mesh structures they can produce. We also use our metric to assess the existing structure simplification techniques in terms of their effectiveness.",,,,,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
  