anchor.diff:  TVCG,TVCG,Decal-Lenses: Interactive Lenses on Surfaces for Multivariate Visualization,T),"Allan Rocha, Julio Daniel Silva, Usman R. Alim, Sheelagh Carpendale, Mario Costa Sousa",http://pages.cpsc.ucalgary.ca/~acarocha/projects/decal_lenses/files/camera_ready.pdf,"We present decal-lenses, a new interaction technique that extends the concept of magic lenses to augment and manage multivariate visualizations on arbitrary surfaces. Our object-space lenses follow the surface geometry and allow the user to change the point of view during data exploration while maintaining a spatial reference to positions where one or more lenses were placed. Each lens delimits specific regions of the surface where one or more attributes can be selected or combined. Similar to 2D lenses, the user interacts with our lenses in real-time, switching between different attributes within the lens context. The user can also visualize the surface data representations from the point of view of each lens by using local cameras. To place lenses on surfaces of intricate geometry, such as the human brain, we introduce the concept of support surfaces for designing interaction techniques. Support surfaces provide a way to place and interact with the lenses while avoiding holes and occluded regions during data exploration. We further extend decal-lenses to arbitrary regions using brushing and lassoing operations. We discuss the applicability of our technique and present several examples where our lenses can be useful to create a customized exploration of multivariate data on surfaces. ",,,,,vimeo 295735599,10.1109/tvcg.2018.2850781,2018,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,5:40 PM,6:00 PM
   SciVis,SciVis,Cluster-Based Visualization for Merger Tree Data: The Challenge of Missing Expectations,,"Annie Preston, Kwan-Liu Ma",,,,,,,vimeo 290328886,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,4:20 PM,4:32 PM
   SciVis,SciVis,Visualization of Uncertainty for Computationally Intensive Simulations Using High Fidelity Emulators,,"Ayan Biswas, Earl Lawrence, James Ahrens",http://ayanbiswas.net/vis18m-sub1022-cam-i7.pdf,"Visualization of high-fidelity scientific simulations with high-dimensional inputs and outputs is an important task. Existing high-dimensional data visualization approaches generally assume a substantial amount of data are available or can be generated as needed. However, many of these simulations can be very computationally intensive, taking minutes or hours to run. Analysis and visualization of such expensive simulations poses a challenge. Statistical emulators are frequently used to approximate simulations for statistical analyses. In this work, we propose a visualization tool for an emulator of the simulator and describe how emulators can be used to create effective visualization systems. We choose Gaussian process emulators for this purpose as they enable fast and accurate prediction with uncertainty information. Using these predictions, we design a system that enables visualization of high-dimensional input and output spaces of complex physics simulations. Users of our system can get a detailed understanding of the uncertainties associated with the emulator predictions in both input and output space for a high-dimensional simulation.",,,,,vimeo 290328642,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,4:32 PM,4:44 PM
  -SciVis,SciVis,A Lagrangian Method for Extracting Eddy Boundaries in the Red Sea and the Gulf of Aden,,"Anke Friederici, Habib Toye, Ibrahim Hoteit, Tino Weinkauf, Holger Theisel, Markus Hadwiger",http://www.medvis-award.de/visual/files/publications/2018/Friederici_2018_VIS_paper.pdf,"Mesoscale ocean eddies play a major role for both the intermixing of water and the transport of biological mass. This makes the identification and tracking of their shape, location and deformation over time highly important for a number of applications. While eddies maintain a roughly circular shape in the free ocean, the narrow basins of the Red Sea and Gulf of Aden lead to the formation of irregular eddy shapes that existing methods struggle to identify. We propose the following model: Inside an eddy, particles rotate around a common core and thereby remain at a constant distance under a certain parametrization. The transition to the more unpredictable flow on the outside can thus be identified as the eddy boundary. We apply this algorithm on a realistic simulation of the Red Sea circulation, where we are able to identify the shape of irregular eddies robustly and more coherently than previous methods. We visualize the eddies as tubes in space-time to enable the analysis of their movement and deformation over several weeks.",,,,,http://vccvisualization.org/publications/2018_friederici_redseaeddies.mp4,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,4:45 PM,4:57 PM
  +SciVis,SciVis,A Lagrangian Method for Extracting Eddy Boundaries in the Red Sea and the Gulf of Aden,,"Anke Friederici, Habib Toye, Ibrahim Hoteit, Tino Weinkauf, Holger Theisel, Markus Hadwiger",http://www.medvis-award.de/visual/files/publications/2018/Friederici_2018_VIS_paper.pdf,"Mesoscale ocean eddies play a major role for both the intermixing of water and the transport of biological mass. This makes the identification and tracking of their shape, location and deformation over time highly important for a number of applications. While eddies maintain a roughly circular shape in the free ocean, the narrow basins of the Red Sea and Gulf of Aden lead to the formation of irregular eddy shapes that existing methods struggle to identify. We propose the following model: Inside an eddy, particles rotate around a common core and thereby remain at a constant distance under a certain parametrization. The transition to the more unpredictable flow on the outside can thus be identified as the eddy boundary. We apply this algorithm on a realistic simulation of the Red Sea circulation, where we are able to identify the shape of irregular eddies robustly and more coherently than previous methods. We visualize the eddies as tubes in space-time to enable the analysis of their movement and deformation over several weeks.",,,,,video http://vccvisualization.org/publications/2018_friederici_redseaeddies.mp4,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,4:45 PM,4:57 PM
   SciVis,SciVis,FTLE Ridge Lines for Long Integration Times,,"Thomas Wilde, Christian Rössl, Holger Theisel",http://www.medvis-award.de/visual/files/publications/2018/Wilde_2018_VISb.pdf,"We present an approach to the extraction of FTLE ridges for 2D unsteady vector fields under long integration times. This is a hard problem because such FTLE ridges tend to be sharp and close to each other. The main feature of our approach is that it does not only use an FTLE sampling at the desired final integration time but incorporates samples from prior integration times as well. With this additional information, the new method produces more and finer ridge lines than previous approaches. Based on this output, we can consider FTLE ridge statistics. We test the approach on synthetic benchmarks and real-world data sets.",,,,,vimeo 290328753,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,4:57 PM,5:09 PM
   SciVis,SciVis,Ocean Current Segmentation at Different Depths and Correlation with Temperature in a MPAS-Ocean Simulation,,"Petra Gospodnetic, Divya Banesh, Philip Wolfram, Mark Petersen, Hans Hagen, James Ahrens, Markus Rauhut",,,,,,,vimeo 290328665,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,5:10 PM,5:22 PM
   SciVis,SciVis,"TimeTubes: Automatic Extraction of Observable Blazar Features from Long-Term, Multi-Dimensional Datasets",,"Naoko Sawada, Masanori Nakayama, Makoto Uemura, Issei Fujishiro",,,,,,,vimeo 290328934,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,5:22 PM,5:34 PM
  
positive.code: TVCG,TVCG,Is There a Robust Technique for Selecting Aspect Ratios in Line Charts?,T),"Yunhai Wang, Zeyu Wang, Lifeng Zhu, Jian Zhang, Chi-Wing Fu, Changhe Tu, Baoquan Chen, Zhanglin Cheng",http://www.yunhaiwang.org/bankto45/as-tvcg.pdf,"The aspect ratio of a line chart heavily influences the perception of the underlying data. Different methods explore different criteria in choosing aspect ratios, but so far, it was still unclear how to select aspect ratios appropriately for any given data. This paper provides a guideline for the user to choose aspect ratios for any input 1D curves by conducting an in-depth analysis of aspect ratio selection methods both theoretically and experimentally. By formulating several existing methods as line integrals, we explain their parameterization invariance. Moreover, we derive a new and improved aspect ratio selection method, namely the�L1-LOR (local orientation resolution), with a certain degree of parameterization invariance. Furthermore, we connect different methods, including AL (arc length based method), the banking to 45� principle, RV (resultant vector) and AS (average absolute slope), as well as�L1-LOR and AO (average absolute orientation). We verify these connections by a comparative evaluation involving various data sets, and show that the selections by RV and�L1-LOR are complementary to each other for most data. Accordingly, we propose the dual-scale banking technique that combines the strengths of RV and�L1-LOR, and demonstrate its practicability using multiple real-world data sets.",,,,,vimeo 289789006,10.1109/tvcg.2017.2787113,2017,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 1,25-Oct,3:20 PM,3:40 PM
  InfoVis,TVCG,Image-based Aspect Ratio Selection,J),"Yunhai Wang, Zeyu Wang, Chi-Wing Fu, Hansjörg Schmauder, Oliver Deussen, Daniel Weiskopf",http://www.yunhaiwang.org/infovis18/dbar/paper.pdf,"Selecting a good aspect ratio is crucial for effective 2D diagrams. There are several aspect ratio selection methods for function plots and line charts, but only few can handle general, discrete diagrams such as 2D scatter plots. However, these methods either lack a perceptual foundation or heavily rely on intermediate isoline representations, which depend on choosing the right isovalues and are time-consuming to compute. This paper introduces a general image-based approach for selecting aspect ratios for a wide variety of 2D diagrams, ranging from scatter plots and density function plots to line charts. Our approach is derived from Federer's co-area formula and a line integral representation that enable us to directly construct image-based versions of existing selection methods using density fields. In contrast to previous methods, our approach bypasses isoline computation, so it is faster to compute, while following the perceptual foundation to select aspect ratios. Furthermore, this approach is complemented by an anisotropic kernel density estimation to construct density fields, allowing us to more faithfully characterize data patterns, such as the subgroups in scatterplots or dense regions in time series. We demonstrate the effectiveness of our approach by quantitatively comparing to previous methods and revisiting a prior user study. Finally, we present extensions for ROI banking, multi-scale banking, and the application to image data.",,,,,vimeo 289784435,10.1109/TVCG.2018.2865266,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 1,25-Oct,3:40 PM,4:00 PM
  SciVis,TVCG,Persistence Atlas for Critical Point Variability in Ensembles,J),"Guillaume Favelier, Noura Faraj, Brian Summa, Julien Tierny",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",,,,,,,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
  SciVis,TVCG,Shared-Memory Parallel Computation of Morse-Smale Complexes with Improved Accuracy,J),"Attila Gyulassy, Peer-Timo Bremer, Valerio Pascucci",,,,,,,vimeo 290328033,10.1109/TVCG.2018.2864848,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,3:20 PM,3:40 PM
  SciVis,TVCG,A Study of the Trade-off between Reduced Precision and Resolution for Scientific Data Analysis and Visualization,J),"Duong Hoang, Pavol Klacansky, Harsh Bhatia, Peer-Timo Bremer, Peter Lindstrom, Valerio Pascucci",,,,,,,vimeo 290328254,10.1109/TVCG.2018.2864853,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,3:40 PM,4:00 PM
  
negative.code: 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
  TVCG,TVCG,Multi-Material Volume Rendering with a Physically-Based Surface Reflection Model,T),"Oleg Igouchkine, Yubo Zhang, Kwan-Liu Ma",,,,,,,vimeo 289789120,10.1109/tvcg.2017.2784830,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,12:00 PM,12:20 PM
  TVCG,TVCG,A Generative Model for Volume Rendering,T),"Matthew Berger, Jixian Li, Joshua A. Levine",https://arxiv.org/pdf/1710.09545.pdf,"We present a technique to synthesize and analyze volume-rendered images using generative models. We use the Generative Adversarial Network (GAN) framework to compute a model from a large collection of volume renderings, conditioned on (1) viewpoint and (2) transfer functions for opacity and color. Our approach facilitates tasks for volume analysis that are challenging to achieve using existing rendering techniques such as ray casting or texture-based methods. We show how to guide the user in transfer function editing by quantifying expected change in the output image. Additionally, the generative model transforms transfer functions into a view-invariant latent space specifically designed to synthesize volume-rendered images. We use this space directly for rendering, enabling the user to explore the space of volume-rendered images. As our model is independent of the choice of volume rendering process, we show how to analyze volume-rendered images produced by direct and global illumination lighting, for a variety of volume datasets.",,,,,vimeo 289789468,10.1109/tvcg.2018.2816059,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,12:20 PM,12:40 PM
  InfoVis,TVCG,Vistrates: A Component Model for Ubiquitous Analytics,J),"Sriram Karthik Badam, Andreas Mathisen, Roman Rädle, Clemens Nylandsted Klokmose, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/vistrates/vistrates.pdf,"Visualization tools are often specialized for specific tasks, which turns the user's analytical workflow into a fragmented process performed across many tools. In this paper, we present a component model design for data visualization to promote modular designs of visualization tools that enhance their analytical scope. Rather than fragmenting tasks across tools, the component model supports unification, where components�the building blocks of this model�can be assembled to support a wide range of tasks. Furthermore, the model also provides additional key properties, such as support for collaboration, sharing across multiple devices, and adaptive usage depending on expertise, from creating visualizations using dropdown menus, through instantiating components, to actually modifying components or creating entirely new ones from scratch using JavaScript or Python source code. To realize our model, we introduce VISTRATES, a literate computing platform for developing, assembling, and sharing visualization components. From a visualization perspective, Vistrates features cross-cutting components for visual representations, interaction, collaboration, and device responsiveness maintained in a component repository. From a development perspective, Vistrates offers a collaborative programming environment where novices and experts alike can compose component pipelines for specific analytical activities. Finally, we present several Vistrates use cases that span the full range of the classic �anytime� and �anywhere� motto for ubiquitous analysis: from mobile and on-the-go usage, through office settings, to collaborative smart environments covering a variety of tasks and devices.",,https://github.com/karthikbadam/Vistrates,,,vimeo 289784994,10.1109/tvcg.2018.2865144,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:00 AM,11:20 AM
  InfoVis,TVCG,SmartCues: A Multitouch Query Approach for Details-on-Demand through Dynamically Computed Overlays,J),"Hariharan Subramonyam, Eytan Adar",,,,,,,vimeo 289784720,10.1109/tvcg.2018.2865231,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:20 AM,11:40 AM
  InfoVis,TVCG,"Multiple Coordinated Views at Large Displays for Multiple Users: Empirical Findings on User Behavior, Movements, and Distances ",J),"Ricardo Langner, Ulrike Kister, Raimund Dachselt",,,,,,,vimeo 289784543,10.1109/tvcg.2018.2865235,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:40 AM,12:00 PM
  