anchor.diff:  VAST,TVCG,Supporting Handoff in Asynchronous Collaborative Sensemaking Using Knowledge-Transfer Graphs,"Jian Zhao, Michael Glueck, Petra Isenberg, Fanny Chevalier, Azam Khan",,"During asynchronous collaborative analysis, handoff of partial findings is challenging because externalizations produced by analysts may not adequately communicate their investigative process. To address this challenge, we developed techniques to automatically capture and help encode tacit aspects of the investigative process based on an analyst’s interactions, and streamline explicit authoring of handoff annotations. We designed our techniques to mediate awareness of analysis coverage, support explicit communication of progress and uncertainty with annotation, and implicit communication through playback of investigation histories. To evaluate our techniques, we developed an interactive visual analysis system, KTGraph, that supports an asynchronous investigative document analysis task. We conducted a two-phase user study to characterize a set of handoff strategies and to compare investigative performance with and without our techniques. The results suggest that our techniques promote the use of more effective handoff strategies, help increase an awareness of prior investigative process and insights, as well as improve final investigative outcomes.",,,,youtube 5iEyczTOvxQ,10.1109/TVCG.2017.2745279,2018,2017,VAST,207,Thursday,Sensemaking,4:55 PM,5:15 PM
   VAST,TVCG,The Interactive Visualization Gap in Initial Exploratory Data Analysis,"Andrea Batch, Niklas Elmqvist",http://www.umiacs.umd.edu/~elm/projects/visgap/visgap.pdf,"Data scientists and other analytic professionals often use interactive visualization in the dissemination phase at the end of a workflow during which findings are communicated to a wider audience. Visualization scientists, however, hold that interactive representation of data can also be used during exploratory analysis itself. Since the use of interactive visualization is optional rather than mandatory, this leaves a “visualization gap” during initial exploratory analysis that is the onus of visualization researchers to fill. In this paper, we explore areas where visualization would be beneficial in applied research by conducting a design study using a novel variation on contextual inquiry conducted with professional data analysts. Based on these interviews and experiments, we propose a set of interactive initial exploratory visualization guidelines which we believe will promote adoption by this type of user.",,,,vimeo 230830233,10.1109/TVCG.2017.2743990,2018,2017,VAST,301-C,Thursday,Theory and Analysis Process,11:50 AM,12:10 PM
   VAST,TVCG,Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics,"John Wenskovitch, Ian Crandell, Naren Ramakrishnan, Leanna House, Scotland Leman, Chris North",https://infovis.cs.vt.edu/sites/default/files/systematic-combination-dimension.pdf,"Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems. However, these algorithmic combinations are often ad hoc or disconnected, working independently and in parallel rather than integrating some degree of interdependence. A number of design decisions must be addressed when employing dimension reduction and clustering algorithms concurrently in a visualization system, including the selection of each algorithm, the order in which they are processed, and how to present and interact with the resulting projection. This paper contributes an overview of combining dimension reduction and clustering into a visualization system, discussing the challenges inherent in developing a visualization system that makes use of both families of algorithms.",,,,vimeo 230830954,,2018,2017,VAST,301-C,Thursday,ML2: Cluster Analysis,4:55 PM,5:15 PM
  -VAST,TVCG,TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees,"Thomas Mühlbacher, Lorenz Linhardt, Torsten Möller, Harald Piringer",,,,,,youtube a3QC3crIsxc,,2018,2017,VAST,102-ABC,Friday,ML3: Classification,9:30 AM,9:50 AM
  +VAST,TVCG,TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees,"Thomas Mühlbacher, Lorenz Linhardt, Torsten Möller, Harald Piringer",http://eprints.cs.univie.ac.at/5263/1/treepod.pdf,"Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.",,,,youtube a3QC3crIsxc,,2018,2017,VAST,102-ABC,Friday,ML3: Classification,9:30 AM,9:50 AM
   VAST,TVCG,Understanding a sequence of sequences: Visual exploration of categorical states in lake sediment cores,"Andrea Unger, Nadine Dräger, Mike Sips, Dirk Lehmann",http://46.38.235.241/webpage/dirkfiles/publications/Lehmann_2017_TVCG2_VAST.pdf,"This design study focuses on the analysis of a time sequence of categorical sequences. Such data is relevant for the geoscientific research field of landscape and climate development. It results from microscopic analysis of lake sediment cores. The goal is to gain hypotheses about landscape evolution and climate conditions in the past. To this end, geoscientists identify which categorical sequences are similar in the sense that they indicate similar conditions. Categorical sequences are similar if they have similar meaning (semantic similarity) and appear in similar time periods (temporal similarity). For data sets with many different categorical sequences, the task to identify similar sequences becomes a challenge. Our contribution is a tailored visual analysis concept that effectively supports the analytical process. Our visual interface comprises coupled visualizations of semantics and temporal context for the exploration and assessment of the similarity of categorical sequences. Integrated automatic methods reduce the analytical effort substantially. They (1) extract unique sequences in the data and (2) rank sequences by a similarity measure during the search for similar sequences. We evaluated our concept by demonstrations of our prototype to a larger audience and hands-on analysis sessions for two different lakes. According to geoscientists, our approach fills an important methodological gap in the application domain",,,,youtube upjkAJTgVXI,,2018,2017,VAST,301-C,Wednesday,Sequences and Events,3:20 PM,3:40 PM
  -VAST,TVCG,Understanding the Relationship between Interactive Optimisation and Visual Analytics in the Context of Prostate Brachytherapy,"Jie Liu, Tim Dwyer, Kim Marriott, Jeremy Millar, Annette Haworth",,,,,,,10.1109/TVCG.2017.2744418,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,3:00 PM,3:20 PM
  +VAST,TVCG,Understanding the Relationship between Interactive Optimisation and Visual Analytics in the Context of Prostate Brachytherapy,"Jie Liu, Tim Dwyer, Kim Marriott, Jeremy Millar, Annette Haworth",,"The fields of operations research and computer science have long sought to find automatic solver techniques that can find high-quality solutions to difficult real-world optimisation problems. The traditional workflow is to exactly model the problem and then enter this model into a general-purpose &#x201C;black-box&#x201D; solver. In practice, however, many problems cannot be solved completely automatically, but require a &#x201C;human-in-the-loop&#x201D; to iteratively refine the model and give hints to the solver. In this paper, we explore the parallels between this interactive optimisation workflow and the visual analytics sense-making loop. We assert that interactive optimisation is essentially a visual analytics task and propose a problem-solving loop analogous to the sense-making loop. We explore these ideas through an in-depth analysis of a use-case in prostate brachytherapy, an application where interactive optimisation may be able to provide significant assistance to practitioners in creating prostate cancer treatment plans customised to each patient&#x0027;s tumour characteristics. However, current brachytherapy treatment planning is usually a careful, mostly manual process involving multiple professionals. We developed a prototype interactive optimisation tool for brachytherapy that goes beyond current practice in supporting focal therapy - targeting tumour cells directly rather than simply seeking coverage of the whole prostate gland. We conducted semi-structured interviews, in two stages, with seven radiation oncology professionals in order to establish whether they would prefer to use interactive optimisation for treatment planning and whether such a tool could improve their trust in the novel focal therapy approach and in machine generated solutions to the problem.",,,,,10.1109/TVCG.2017.2744418,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,3:00 PM,3:20 PM
   VAST,TVCG,VIGOR: Interactive Visual Exploration of Graph Query Results,"Robert Pienta, Fred Hohman, Alex Endert, Acar Tamersoy, Kevin Roundy, Chris Gates, Shamkant Navathe, Duen Horng Chau",https://www.cc.gatech.edu/~dchau/papers/17-vast-vigor.pdf,"Finding patterns in graphs has become a vital challenge in many domains from biological systems, network security, to finance (e.g., finding money laundering rings of bankers and business owners). While there is significant interest in graph databases and querying techniques, less research has focused on helping analysts make sense of underlying patterns within a group of subgraph results. Visualizing graph query results is challenging, requiring effective summarization of a large number of subgraphs, each having potentially shared node-values, rich node features, and flexible structure across queries. We present VIGOR, a novel interactive visual analytics system, for exploring and making sense of query results. VIGOR uses multiple coordinated views, leveraging different data representations and organizations to streamline analysts sensemaking process. VIGOR contributes: (1) an exemplar-based interaction technique, where an analyst starts with a specific result and relaxes constraints to find other similar results or starts with only the structure (i.e., without node value constraints), and adds constraints to narrow in on specific results; and (2) a novel feature-aware subgraph result summarization. Through a collaboration with Symantec, we demonstrate how VIGOR helps tackle real-world problems through the discovery of security blindspots in a cybersecurity dataset with over 11,000 incidents. We also evaluate VIGOR with a within-subjects study, demonstrating VIGOR’s ease of use over a leading graph database management system, and its ability to help analysts understand their results at higher speed and make fewer errors.",,,,youtube -jeck0yrT_Q,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,5:15 PM,5:35 PM
   VAST,TVCG,Visual Diagnosis of Tree Boosting Methods,"Jiannan Xiao, Junlin Liu, Xiting Wang, Jing Wu, Jun Zhu, Shixia Liu",http://shixialiu.com/publications/boostvis/paper.pdf,"Tree boosting, which combines weak learners (typically decision trees) to generate a strong learner, is a highly effective and widely used machine learning method. However, the development of a high performance tree boosting model is a time-consuming process that requires numerous trial-and-error experiments. To tackle this issue, we have developed a visual diagnosis tool, BOOSTVis, to help experts quickly analyze and diagnose the training process of tree boosting. In particular, we have designed a temporal confusion matrix visualization, and combined it with a t-SNE projection and a tree visualization. These visualization components work together to provide a comprehensive overview of a tree boosting model, and enable an effective diagnosis of an unsatisfactory training process. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms.",http://www.shixialiu.com:8082/static/index.html,,,video http://shixialiu.com/publications/boostvis/video.mp4,,2018,2017,VAST,102-ABC,Friday,ML3: Classification,8:50 AM,9:10 AM
   VAST,TVCG,Visualizing Big Data Outliers through Distributed Aggregation,Leland Wilkinson,,,,,,,,2018,2017,VAST,301-C,Wednesday,High-dimensional Data,9:30 AM,9:50 AM
  
positive.code: VAST,TVCG,Analyzing the Training Processes of Deep Generative Models,"Mengchen Liu, Jiaxin Shi, Kelei Cao, Jun Zhu, Shixia Liu",http://shixialiu.com/publications/dgmtracker/paper.pdf,"Among the many types of deep models, deep generative models (DGMs) provide a solution to the important problem of unsupervised and semi-supervised learning. However, training DGMs requires more skill, experience, and know-how because their training is more complex than other types of deep models such as convolutional neural networks (CNNs). We develop a visual analytics approach for better understanding and diagnosing the training process of a DGM. To help experts understand the overall training process, we first extract a large amount of time series data that represents training dynamics (e.g., activation changes over time). A blue-noise polyline sampling scheme is then introduced to select time series samples, which can both preserve outliers and reduce visual clutter. To further investigate the root cause of a failed training process, we propose a credit assignment algorithm that indicates how other neurons contribute to the output of the neuron causing the training failure. Two case studies are conducted with machine learning experts to demonstrate how our approach helps understand and diagnose the training processes of DGMs. We also show how our approach can be directly used to analyze other types of deep models, such as CNNs.",,,,video http://shixialiu.com/publications/dgmtracker/video.mp4,,2018,2017,VAST,301-C,Wednesday,ML1: Deep Learning,10:30 AM,10:50 AM
  VAST,TVCG,Applying Pragmatics Principles for Interaction with Visual Analytics,"Enamul Hoque, Vidya Setlur, Melanie Tory, Isaac Dykeman",https://research.tableau.com/sites/default/files/VAST2017_105.pdf,"Interactive visual data analysis is most productive when users can focus on answering the questions they have about their data, rather than focusing on how to operate the interface to the analysis tool. One viable approach to engaging users in interactive conversations with their data is a natural language interface to visualizations. These interfaces have the potential to be both more expressive and more accessible than other interaction paradigms. We explore how principles from language pragmatics can be applied to the flow of visual analytical conversations, using natural language as an input modality. We evaluate the effectiveness of pragmatics support in our system Evizeon, and present design considerations for conversation interfaces to visual analytics tools.",,,,vimeo 230831885,,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,2:40 PM,3:00 PM
  VAST,TVCG,Beyond Tasks: An Activity Typology for Visual Analytics,"Darren Edge, Nathalie Henry Riche, Jonathan Larson, Christopher White",,,,,,,,2018,2017,VAST,301-C,Thursday,Theory and Analysis Process,10:30 AM,10:50 AM
  VAST,TVCG,BiDots: Visual Exploration of Weighted Biclusters,"Jian Zhao, Maoyuan Sun, Francine Chen, Patrick Chiu",,,,,,youtube 79VSgVIl11M,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,4:35 PM,4:55 PM
  VAST,TVCG,Bring it to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis,"Manuel Stein, Halldór Janetzko, Andreas Lamprecht, Thorsten Breitkreutz, Philipp Zimmermann, Bastian Goldlücke, Tobias Schreck, Gennady Andrienko, Michael Grossniklaus, Daniel Keim",,,,,,vimeo 230830922,,2018,2017,VAST,301-C,Tuesday,"Space, Time, Movement",2:00 PM,2:20 PM
  VAST,TVCG,Clustering Trajectories by Relevant Parts for Air Traffic Analysis,"Gennady Andrienko, Natalia Andrienko, Georg Fuchs, Jose Manuel Cordero Garcia",http://openaccess.city.ac.uk/18119/1/main-reducedSize.pdf,"Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.",,,,vimeo 227918021,,2018,2017,VAST,301-C,Tuesday,"Space, Time, Movement",2:40 PM,3:00 PM
  VAST,TVCG,Clustervision: Visual Supervision of Unsupervised Clustering,"Bum Chul Kwon, Ben Eysenbach, Janu Verma, Kenney Ng, Christopher deFilippi, Walter Stewart, Adam Perer",http://perer.org/papers/adamPerer-Clustervision-VAST2017.pdf,"Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built <em>Clustervision</em>, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.",,,,vimeo 232177941,,2018,2017,VAST,301-C,Thursday,ML2: Cluster Analysis,5:15 PM,5:35 PM
  
negative.code: SciVis,TVCG,An Intelligent System Approach for Probabilistic Volume Rendering using Hierarchical 3D Convolutional Sparse Coding,"Tran Minh Quan, JunYoung Choi, HaeJin Jeong, Won-Ki Jeong",,,,,,,,2018,2017,SciVis,207,Tuesday,Volume Rendering,4:15 PM,4:35 PM
  SciVis,TVCG,BASTet: Shareable and reproducible analysis and visualization of mass spectrometry imaging data via OpenMSI,"Oliver Ruebel, Benjamin P. Bowen",,,https://openmsi.nersc.gov/openmsi/client/bastet.html,https://github.com/biorack/BASTet,,vimeo 230835941,,2018,2017,SciVis,106-ABC,Friday,Applications and Visual analysis,9:50 AM,10:10 AM
  SciVis,TVCG,Clique Community Persistence: A Topological Visual Analysis Approach for Complex Networks,"Bastian Rieck, Ulderico Fugacci, Jonas Lukasczyk, Heike Leitte",https://osf.io/td973/,"Complex networks require effective tools and visualizations for their analysis and comparison. Clique communities have been recognized as a powerful concept for describing cohesive structures in networks. We propose an approach that extends the computation of clique communities by considering persistent homology, a topological paradigm originally introduced to characterize and compare the global structure of shapes. Our persistence-based algorithm is able to detect clique communities and to keep track of their evolution according to different edge weight thresholds. We use this information to define comparison metrics and a new centrality measure, both reflecting the relevance of the clique communities inherent to the network. Moreover, we propose an interactive visualization tool based on nested graphs that is capable of compactly representing the evolving relationships between communities for different thresholds and clique degrees. We demonstrate the effectiveness of our approach on various network types",,https://osf.io/rdktg/,,vimeo 230835775,,2018,2017,SciVis,207,Wednesday,Topology-based methods,2:20 PM,2:40 PM
  SciVis,TVCG,Dynamic Load Balancing Based on Constrained K-D Tree Decomposition for Parallel Particle Tracing,"Jiang Zhang, Hanqi Guo, Fan Hong, Xiaoru Yuan, Tom Peterka",http://www.mcs.anl.gov/papers/P7034-0417.pdf,"Particle tracing is a fundamental technique in flow field data visualization. In this work, we present a novel dynamic load balancing method for parallel particle tracing. Specifically, we employ a constrained k-d tree decomposition approach to dynamically redistribute tasks among processes. Each process is initially assigned a regularly partitioned block along with duplicated ghost layer under the memory limit. During particle tracing, the k-d tree decomposition is dynamically performed by constraining the cutting planes in the overlap range of duplicated data. This ensures that each process is reassigned particles as even as possible, and on the other hand the new assigned particles for a process always locate in its block. Result shows good load balance and high efficiency of our method.",,,,,,2018,2017,SciVis,207,Tuesday,"Mix: Foundations, uncertainty, particles",3:20 PM,3:40 PM
  SciVis,TVCG,Globe Browsing: Contextualized Spatio-Temporal Planetary Surface Visualization,"Karl Bladin, Emil Axelsson, Erik Broberg, Carter Emmart, Patric Ljung, Alexander Bock, Anders Ynnerman",https://alexanderbock.github.io/papers/vis17-bladin-globe_browsing.pdf,"Results of planetary mapping are often shared openly for use in scientific research and mission planning. In its raw format, however, the data is not accessible to non-experts due to the difficulty in grasping the context and the intricate acquisition process. We present work on tailoring and integration of multiple data processing and visualization methods to interactively contextualize geospatial surface data of celestial bodies for use in science communication. As our approach handles dynamic data sources, streamed from online repositories, we are significantly shortening the time between discovery and dissemination of data and results. We describe the image acquisition pipeline, the pre-processing steps to derive a 2.5D terrain, and a chunked level-of-detail, out-of-core rendering approach to enable interactive exploration of global maps and high-resolution digital terrain models. The results are demonstrated for three different celestial bodies. The first case addresses high-resolution map data on the surface of Mars. A second case is showing dynamic processes, such as concurrent weather conditions on Earth that require temporal datasets. As a final example we use data from the New Horizons spacecraft which acquired images during a single flyby of Pluto. We visualize the acquisition process as well as the resulting surface data. Our work has been implemented in the OpenSpace software [8], which enables interactive presentations in a range of environments such as immersive dome theaters, interactive touch tables, and virtual reality headsets.",,,,vimeo 230835323,,2018,2017,Other,207,Tuesday,VIS Awards & Best Papers,10:10 AM,10:30 AM
  SciVis,TVCG,Instant Construction and Visualization of Crowded Biological Environments,"Tobias Klein, Ludovic K. Autin, Barbora Kozlikova, David Goodsell, Arthur Olson, Eduard Gröller, Ivan Viola",https://www.cg.tuwien.ac.at/research/publications/2017/klein_2017_IM/klein_2017_IM-paper.pdf,"We present the first approach to integrative structural modeling of the biological mesoscale within an interactive visual environment. These complex models can comprise up to millions of molecules with defined atomic structures, locations, and interactions. Their construction has previously been attempted only within a non-visual and non-interactive environment. Our solution unites the modeling and visualization aspect, enabling interactive construction of atomic resolution mesoscale models of large portions of a cell. We present a novel set of GPU algorithms that build the basis for the rapid construction of complex biological structures. These structures consist of multiple membrane-enclosed compartments including both soluble molecules and fibrous structures. The compartments are defined using volume voxelization of triangulated meshes. For membranes, we present an extension of the Wang Tile concept that populates the bilayer with individual lipids. Soluble molecules are populated within compartments using the Halton sequence for their distribution. Fibrous structures, such as RNA or actin filaments, are created by self-avoiding random walks. Resulting overlaps of molecules are resolved by a forced-based system. Our approach opens new possibilities to the world of interactive construction of cellular compartments. We demonstrate its effectiveness by showcasing scenes of different scale and complexity that comprise blood plasma, mycoplasma, and HIV.",,,,vimeo 230835707,,2018,2017,SciVis,207,Thursday,Visualization in Biology and Medicine,8:50 AM,9:10 AM
  SciVis,TVCG,Interactive Design and Visualization of Branched Covering Spaces,"Lawrence Roy, Prashant Kumar, Sanaz Golbabaei, Yue Zhang, Eugene Zhang",http://web.engr.oregonstate.edu/~zhange/images/Paper_BCSVis_main.pdf,"Branched covering spaces are a mathematical concept which originates from complex analysis and topology and has applications in tensor field topology and geometry remeshing. Given a manifold surface and an N-way rotational symmetry field, a branched covering space is a manifold surface that has an N-to-1 map to the original surface except at the ramification points, which correspond to the singularities in the rotational symmetry field. Understanding the notion and mathematical properties of branched covering spaces is important to researchers in tensor field visualization and geometry processing, and their application areas. In this paper, we provide a framework to interactively design and visualize the branched covering space (BCS) of an input mesh surface and a rotational symmetry field defined on it. In our framework, the user can visualize not only the BCSs but also their construction process. In addition, our system allows the user to design the geometric realization of the BCS using mesh deformation techniques as well as connecting tubes. This enables the user to verify important facts about BCSs such as that they are manifold surfaces around singularities, as well as the Riemann-Hurwitz formula which relates the Euler characteristic of the BCS to that of the original mesh. Our system is evaluated by student researchers in scientific visualization and geometry processing as well as faculty members in mathematics at our university who teach topology. We include their evaluations and feedback in the paper.",,,,vimeo 230835443,,2018,2017,SciVis,207,Wednesday,Topology-based methods,3:20 PM,3:40 PM
  SciVis,TVCG,Interactive Dynamic Volume Illumination with Refraction and Caustics,"Jens Magnus, Stefan Bruckner",,,,,,youtube 3tn6sSXw4NQ,,2018,2017,SciVis,207,Tuesday,Volume Rendering,5:15 PM,5:35 PM
  SciVis,TVCG,Multiscale Visualization and Scale-Adaptive Modification of DNA Nanostructures,"Haichao Miao, Elisa De Llano, Johannes Sorger, Yasaman Ahmadi, Tadija Kekic, Tobias Isenberg, Eduard Gröller, Ivan Barisic, Ivan Viola",https://hal.inria.fr/hal-01581203/file/Miao_2018_MVS.pdf,"We present an approach to represent DNA nanostructures in varying forms of semantic abstraction, describe ways to smoothly transition between them, and thus create a continuous multiscale visualization and interaction space for applications in DNA nanotechnology. This new way of observing, interacting with, and creating DNA nanostructures enables domain experts to approach their work in any of the semantic abstraction levels, supporting both low-level manipulations and high-level visualization and modifications. Our approach allows them to deal with the increasingly complex DNA objects that they are designing, to improve their features, and to add novel functions in a way that no existing single-scale approach offers today. For this purpose we collaborated with DNA nanotechnology experts to design a set of ten semantic scales. These scales take the DNA’s chemical and structural behavior into account and depict it from atoms to the targeted architecture with increasing levels of abstraction. To create coherence between the discrete scales, we seamlessly transition between them in a well-defined manner. We use special encodings to allow experts to estimate the nanoscale object’s stability. We also add scale-adaptive interactions that facilitate the intuitive modification of complex structures at multiple scales. We demonstrate the applicability of our approach on an experimental use case. Moreover, feedback from our collaborating domain experts confirmed an increased time efficiency and certainty for analysis and modification tasks on complex DNA structures. Our method thus offers exciting new opportunities with promising applications in medicine and biotechnology",,,,vimeo 230835588,,2018,2017,SciVis,106-ABC,Friday,Applications and Visual analysis,8:30 AM,8:50 AM
  SciVis,TVCG,On the Treatment of Field Quantities and Elemental Continuity in FEM Solutions,"Ashok Jallepalli, Julia Docampo, Jennifer Ryan, Robert Haimes, Mike Kirby",,,,,,,,2018,2017,SciVis,207,Wednesday,Flow Visualization,9:30 AM,9:50 AM
  SciVis,TVCG,Robust detection and visualization of jet-stream core lines in atmospheric flow,"Michael Kern, Tim Hewson, Filip Sadlo, Rüdiger Westermann, Marc Rautenhaus",http://wwwcg.in.tum.de/fileadmin/user_upload/Lehrstuehle/Lehrstuhl_XV/Research/Publications/2017/Jetcore_Detection/kern_jetcores_vis2017.pdf,"Jet-streams, their core lines and their role in atmospheric dynamics have been subject to considerable meteorological research since the first half of the twentieth century. Yet, until today no consistent automated feature detection approach has been proposed to identify jet-stream core lines from 3D wind fields. Such 3D core lines can facilitate meteorological analyses previously not possible. Although jet-stream cores can be manually analyzed by meteorologists in 2D as height ridges in the wind speed field, to the best of our knowledge no automated ridge detection approach has been applied to jet-stream core detection. In this work, we –a team of visualization scientists and meteorologists– propose a method that exploits directional information in the wind field to extract core lines in a robust and numerically less involved manner than traditional 3D ridge detection. For the first time, we apply the extracted 3D core lines to meteorological analysis, considering real-world case studies and demonstrating our method’s benefits for weather forecasting and meteorological research.",,,,vimeo 230835424,,2018,2017,SciVis,207,Wednesday,Flow Visualization,8:30 AM,8:50 AM
  SciVis,TVCG,Screen-Space Normal Distribution Function Caching for Consistent Multi-Resolution Rendering of Large Particle Data,"Mohamed Ibrahim, Patrick Wickenhäuser, Peter Rautek, Guido Reina, Markus Hadwiger",http://vccvisualization.org/publications/2017_ibrahim_SNDF.pdf,"Molecular dynamics (MD) simulations are crucial to investigating important processes in physics and thermodynamics. The simulated atoms are usually visualized as hard spheres with Phong shading, where individual particles and their local density can be perceived well in close-up views. However, for large-scale simulations with 10 million particles or more, the visualization of large fields-of-view usually suffers from strong aliasing artifacts, because the mismatch between data size and output resolution leads to severe under-sampling of the geometry. Excessive super-sampling can alleviate this problem, but is prohibitively expensive. This paper presents a novel visualization method for large-scale particle data that addresses aliasing while enabling interactive high-quality rendering. We introduce the novel concept of screen-space normal distribution functions (S-NDFs) for particle data. S-NDFs represent the distribution of surface normals that map to a given pixel in screen space, which enables high-quality re-lighting without re-rendering particles. In order to facilitate interactive zooming, we cache S-NDFs in a screen-space mipmap (S-MIP). Together, these two concepts enable interactive, scale-consistent re-lighting and shading changes, as well as zooming, without having to re-sample the particle data. We show how our method facilitates the interactive exploration of real-world large-scale MD simulation data in different scenarios.",,,,video http://vccvisualization.org/publications/2017_ibrahim_SNDF.mp4,10.1109/TVCG.2017.2743979,2018,2017,SciVis,207,Tuesday,"Mix: Foundations, uncertainty, particles",3:00 PM,3:20 PM
  SciVis,TVCG,SparseLeap: Efficient Empty Space Skipping for Large-Scale Volume Rendering,"Markus Hadwiger, Ali K. Al-Awami, Johanna Beyer, Marco Agus, Hanspeter Pfister",https://johanna-b.github.io/files/documents/sparseleap_vis17.pdf,"Recent advances in data acquisition produce volume data of very high resolution and large size, such as terabyte-sized microscopy volumes. These data often contain many fine and intricate structures, which pose huge challenges for volume rendering, and make it particularly important to efficiently skip empty space. This paper addresses two major challenges: (1) The complexity of large volumes containing fine structures often leads to highly fragmented space subdivisions that make empty regions hard to skip efficiently. (2) The classification of space into empty and non-empty regions changes frequently, because the user or the evaluation of an interactive query activate a different set of objects, which makes it unfeasible to pre-compute a well-adapted space subdivision. We describe the novel SparseLeap method for efficient empty space skipping in very large volumes, even around fine structures. The main performance characteristic of SparseLeap is that it moves the major cost of empty space skipping out of the ray-casting stage. We achieve this via a hybrid strategy that balances the computational load between determining empty ray segments in a rasterization (object-order) stage, and sampling non-empty volume data in the ray-casting (image-order) stage. Before ray-casting, we exploit the fast hardware rasterization of GPUs to create a ray segment list for each pixel, which identifies non-empty regions along the ray. The ray-casting stage then leaps over empty space without hierarchy traversal. Ray segment lists are created by rasterizing a set of fine-grained, view-independent bounding boxes. Frame coherence is exploited by re-using the same bounding boxes unless the set of active objects changes. We show that SparseLeap scales better to large, sparse data than standard octree empty space skipping.",,,,vimeo 230835661,,2018,2017,SciVis,207,Tuesday,Volume Rendering,4:55 PM,5:15 PM
  SciVis,TVCG,StreetVizor: Visual Exploration of Human-Scale Urban Forms based on Street Views,"Qiaomu Shen, Wei Zeng, Yu Ye, Stefan Mueller Arisona, Simon Schubiger, Remo Burkhard, Huamin Qu",,,,,,,,2018,2017,SciVis,106-ABC,Friday,Applications and Visual analysis,9:30 AM,9:50 AM
  SciVis,TVCG,"The Good, the Bad, and the Ugly: A Theoretical Framework for the Assessment of Continuous Colormaps","Roxana Bujack, Terece Turton, Francesca Samsel, David Rogers, James Ahrens, Colin Ware",,,,,,vimeo 230835348,,2018,2017,SciVis,207,Tuesday,"Mix: Foundations, uncertainty, particles",2:20 PM,2:40 PM
  