anchor.diff:  VAST,TVCG,Clustervision: Visual Supervision of Unsupervised Clustering,"Bum Chul Kwon, Ben Eysenbach, Janu Verma, Kenney Ng, Christopher deFilippi, Walter Stewart, Adam Perer",,,,,,,,2018,2017,VAST,301-C,Thursday,ML2: Cluster Analysis,5:15 PM,5:35 PM
   VAST,TVCG,Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study,"Jürgen Bernard, Marco Hutter, Matthias Zeppelzauer, Dieter Fellner, Michael Sedlmair",,,,,,,,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,2:20 PM,2:40 PM
   VAST,TVCG,ConceptVector: Text Visual Analytics via Interactive Lexicon Building using Word Embedding,"Deokgun Park, Seungyeon Kim, Jurim Lee, Jaegul Choo, Nicholas Diakopoulos, Niklas Elmqvist",http://www.umiacs.umd.edu/~elm/projects/conceptvector/conceptvector.pdf,"Central to many text analysis methods is the notion of a concept: a set of semantically related keywords characterizing a specific object, phenomenon, or theme. Advances in word embedding allow building such concepts from a small set of seed terms. However, naive application of such techniques may result in false positive errors because of the polysemy of human language. To mitigate this problem, we present a visual analytics system called ConceptVector that guides the user in building such concepts and then using them to analyze documents. Document-analysis case studies with real-world datasets demonstrate the fine-grained analysis provided by ConceptVector. To support the elaborate modeling of concepts using user seed terms, we introduce a bipolar concept model and support for irrelevant words. We validate the interactive lexicon building interface via a user study and expert reviews. The quantitative evaluation shows that the bipolar lexicon generated with our methods is comparable to human-generated ones.",,,,youtube -_gANFu-ulY,,2018,2017,VAST,207,Wednesday,Text Analytics,11:10 AM,11:30 AM
  -VAST,TVCG,DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks,"Nicola Pezzotti, Thomas Höllt, Jan van Gemert, Boudewijn P. F. Lelieveldt, Elmar Eisemann, Anna Vilanova",https://graphics.tudelft.nl/Publications-new/2018/PHVLEV18/paper216.pdf,"Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform due to the large datasets used for training. In this paper, we present DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems.",,,,,,2018,2017,VAST,301-C,Wednesday,ML1: Deep Learning,11:30 AM,11:50 AM
  -VAST,TVCG,Do Convolutional Neural Networks learn Class Hierarchy?,"Bilal Alsallakh, Amin Jourabloo, Mao Ye, Xiaoming Liu, Liu Ren",http://cvlab.cse.msu.edu/pdfs/DoConvolutionalNeuralNetworksLearnClassHierarchy.pdf,"Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.",,,,,,2018,2017,VAST,102-ABC,Friday,ML3: Classification,8:30 AM,8:50 AM
  -VAST,TVCG,Dynamic Influence Networks for Rule-based Models,"Angus Forbes, Andrew Burks, Kristine Lee, Xing Li, Pierre Boutillier, Jean Krivine, Walter Fontana",https://osf.io/ayb3u/,"We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological modelsthat are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-componentbiological molecules.  Our technique visualizes the dynamics of these rules as they evolve over time.  Using the data produced byKaSim, an open source stochastic simulator of rule-based models written in theKappalanguage, DINs provide a node-link diagramthat represents the influence that each rule has on the other rules. That is, rather than representing individual biological components ortypes, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactiveDIN-Vizsoftware tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, andto identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation ofa circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.",https://creativecodinglab.github.io/DynamicInfluenceNetworks/,https://github.com/CreativeCodingLab/DynamicInfluenceNetworks,,,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,4:15 PM,4:35 PM
  +VAST,TVCG,DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks,"Nicola Pezzotti, Thomas Höllt, Jan van Gemert, Boudewijn P. F. Lelieveldt, Elmar Eisemann, Anna Vilanova",https://graphics.tudelft.nl/Publications-new/2018/PHVLEV18/paper216.pdf,"Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform due to the large datasets used for training. In this paper, we present DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems.",,,,vimeo 230830385,,2018,2017,VAST,301-C,Wednesday,ML1: Deep Learning,11:30 AM,11:50 AM
  +VAST,TVCG,Do Convolutional Neural Networks learn Class Hierarchy?,"Bilal Alsallakh, Amin Jourabloo, Mao Ye, Xiaoming Liu, Liu Ren",http://cvlab.cse.msu.edu/pdfs/DoConvolutionalNeuralNetworksLearnClassHierarchy.pdf,"Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.",,,,vimeo 230831852,,2018,2017,VAST,102-ABC,Friday,ML3: Classification,8:30 AM,8:50 AM
  +VAST,TVCG,Dynamic Influence Networks for Rule-based Models,"Angus Forbes, Andrew Burks, Kristine Lee, Xing Li, Pierre Boutillier, Jean Krivine, Walter Fontana",https://osf.io/ayb3u/,"We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological modelsthat are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-componentbiological molecules.  Our technique visualizes the dynamics of these rules as they evolve over time.  Using the data produced byKaSim, an open source stochastic simulator of rule-based models written in theKappalanguage, DINs provide a node-link diagramthat represents the influence that each rule has on the other rules. That is, rather than representing individual biological components ortypes, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactiveDIN-Vizsoftware tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, andto identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation ofa circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.",https://creativecodinglab.github.io/DynamicInfluenceNetworks/,https://github.com/CreativeCodingLab/DynamicInfluenceNetworks,,vimeo 230831007,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,4:15 PM,4:35 PM
   VAST,TVCG,EVA: Visual Analytics to Identify Fraudulent Events,"Roger Leite, Theresia Gschwandtner, Silvia Miksch, Simone Kriglstein, Margit Pohl, Erich Gstrein, Johannes Kuntner",,,,,,,,2018,2017,VAST,207,Thursday,Sensemaking,4:35 PM,4:55 PM
  -VAST,TVCG,EventThread: Visual Summarization and Stage Analysis of Event Sequence Data,"Shunan Guo, Ke Xu, Rongwen Zhao, David Gotz, Hongyuan Zha, Nan Cao",http://gotz.web.unc.edu/files/2013/10/PREPRINT-Guo-VIS-2017-EventThread-Preprint.pdf,"Event sequence data such as electronic health records, a person’s academic records, or car service records, are ordered series of events which have occurred over a period of time. Analyzing collections of event sequences can reveal common or semantically important sequential patterns. For example, event sequence analysis might reveal frequently used care plans for treating a disease, typical publishing patterns of professors, and the patterns of service that result in a well-maintained car. It is challenging, however, to visually explore large numbers of event sequences, or sequences with large numbers of event types. Existing methods focus on extracting explicitly matching patterns of events using statistical analysis to create stages of event progression over time. However, these methods fail to capture latent clusters of similar but not identical evolutions of event sequences. In this paper, we introduce a novel visualization system named EventThread which clusters event sequences into threads based on tensor analysis and visualizes the latent stage categories and evolution patterns by interactively grouping the threads by similarity into time-specific clusters. We demonstrate the effectiveness of EventThread through usage scenarios in three different application domains and via interviews with an expert user.",,,,,,2018,2017,VAST,301-C,Wednesday,Sequences and Events,2:20 PM,2:40 PM
  +VAST,TVCG,EventThread: Visual Summarization and Stage Analysis of Event Sequence Data,"Shunan Guo, Ke Xu, Rongwen Zhao, David Gotz, Hongyuan Zha, Nan Cao",http://gotz.web.unc.edu/files/2013/10/PREPRINT-Guo-VIS-2017-EventThread-Preprint.pdf,"Event sequence data such as electronic health records, a person’s academic records, or car service records, are ordered series of events which have occurred over a period of time. Analyzing collections of event sequences can reveal common or semantically important sequential patterns. For example, event sequence analysis might reveal frequently used care plans for treating a disease, typical publishing patterns of professors, and the patterns of service that result in a well-maintained car. It is challenging, however, to visually explore large numbers of event sequences, or sequences with large numbers of event types. Existing methods focus on extracting explicitly matching patterns of events using statistical analysis to create stages of event progression over time. However, these methods fail to capture latent clusters of similar but not identical evolutions of event sequences. In this paper, we introduce a novel visualization system named EventThread which clusters event sequences into threads based on tensor analysis and visualizes the latent stage categories and evolution patterns by interactively grouping the threads by similarity into time-specific clusters. We demonstrate the effectiveness of EventThread through usage scenarios in three different application domains and via interviews with an expert user.",,,,vimeo 230831051,,2018,2017,VAST,301-C,Wednesday,Sequences and Events,2:20 PM,2:40 PM
   VAST,TVCG,Graphiti: Interactive Specification of Attribute-based Edges for Network Modeling and Visualization,"Arjun Srinivasan, Hyunwoo Park, Alex Endert, Rahul Basole",,,,,,,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,5:35 PM,5:55 PM
   VAST,TVCG,How Do Ancestral Traits Shape Family Trees over Generations?,"Siwei Fu, Hao Dong, Weiwei Cui, Jian Zhao, Huamin Qu",,,,,,,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,4:55 PM,5:15 PM
  -VAST,TVCG,LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets,"Jiazhi Xia, Fenjin Ye, Wei Chen, Yusi Wang, Weifeng Chen, Yuxin Ma, Anthony K. H. Tung",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/LDSScanner.pdf,"Many approaches for analyzing a high-dimensional dataset assume that the dataset contains specific structures, e.g., clusters in linear subspaces or non-linear manifolds. This yields a trial-and-error process to verify the appropriate model and parameters. This paper contributes an exploratory interface that supports visual identification of low-dimensional structures in a high-dimensional dataset, and facilitates the optimized selection of data models and configurations. Our key idea is to abstract a set of global and local feature descriptors from the neighborhood graph-based representation of the latent low-dimensional structure, such as pairwise geodesic distance (GD) among points and pairwise local tangent space divergence (LTSD) among pointwise local tangent spaces (LTS). We propose a new LTSD-GD view, which is constructed by mapping LTSD and GD to the x axis and y axis using 1D multidimensional scaling, respectively. Unlike traditional dimensionality reduction methods that preserve various kinds of distances among points, the LTSD-GD view presents the distribution of pointwise LTS (x axis) and the variation of LTS in structures (the combination of x axis and y axis). We design and implement a suite of visual tools for navigating and reasoning about intrinsic structures of a high-dimensional dataset. Three case studies verify the effectiveness of our approach.",,,,,,2018,2017,VAST,301-C,Wednesday,High-dimensional Data,8:30 AM,8:50 AM
  +VAST,TVCG,LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets,"Jiazhi Xia, Fenjin Ye, Wei Chen, Yusi Wang, Weifeng Chen, Yuxin Ma, Anthony K. H. Tung",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/LDSScanner.pdf,"Many approaches for analyzing a high-dimensional dataset assume that the dataset contains specific structures, e.g., clusters in linear subspaces or non-linear manifolds. This yields a trial-and-error process to verify the appropriate model and parameters. This paper contributes an exploratory interface that supports visual identification of low-dimensional structures in a high-dimensional dataset, and facilitates the optimized selection of data models and configurations. Our key idea is to abstract a set of global and local feature descriptors from the neighborhood graph-based representation of the latent low-dimensional structure, such as pairwise geodesic distance (GD) among points and pairwise local tangent space divergence (LTSD) among pointwise local tangent spaces (LTS). We propose a new LTSD-GD view, which is constructed by mapping LTSD and GD to the x axis and y axis using 1D multidimensional scaling, respectively. Unlike traditional dimensionality reduction methods that preserve various kinds of distances among points, the LTSD-GD view presents the distribution of pointwise LTS (x axis) and the variation of LTS in structures (the combination of x axis and y axis). We design and implement a suite of visual tools for navigating and reasoning about intrinsic structures of a high-dimensional dataset. Three case studies verify the effectiveness of our approach.",,,,vimeo 230830366,,2018,2017,VAST,301-C,Wednesday,High-dimensional Data,8:30 AM,8:50 AM
   VAST,TVCG,PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models,"Michael Glueck, Mahdi Pakdaman Naeini, Finale Doshi-Velez, Fanny Chevalier, Azam Khan, Daniel Wigdor, Michael Brudno",,,,,,,,2018,2017,VAST,207,Wednesday,Text Analytics,11:30 AM,11:50 AM
   VAST,TVCG,Podium: Ranking Data Using Mixed-Initiative Visual Analytics,"Emily Wall, Subhajit Das, Ravish Chawla, Bharath Kalidindi, Eli T. Brown, Alex Endert",https://www.cc.gatech.edu/~ewall9/media/papers/PodiumVAST17.pdf,"People often rank and order data points as a vital part of making decisions. Multi-attribute ranking systems are a common tool used to make these data-driven decisions. Such systems often take the form of a table-based visualization in which users assign weights to the attributes representing the quantifiable importance of each attribute to a decision, which the system then uses to compute a ranking of the data. However, these systems assume that users are able to quantify their conceptual understanding of how important particular attributes are to a decision. This is not always easy or even possible for users to do. Rather, people often have a more holistic understanding of the data. They form opinions that data point A is better than data point B but do not necessarily know which attributes are important. To address these challenges, we present a visual analytic application to help people rank multi-variate data points. We developed a prototype system, Podium, that allows users to drag rows in the table to rank order data points based on their perception of the relative value of the data. Podium then infers a weighting model using Ranking SVM that satisfies the user’s data preferences as closely as possible. Whereas past systems help users understand the relationships between data points based on changes to attribute weights, our approach helps users to understand the attributes that might inform their understanding of the data. We present two usage scenarios to describe some of the potential uses of our proposed technique: (1) understanding which attributes contribute to a user’s subjective preferences for data, and (2) deconstructing attributes of importance for existing rankings.Our proposed approach makes powerful machine learning techniques more usable to those who may not have expertise in these areas",,,,youtube Z4N9wKxK2i4,,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,2:00 PM,2:20 PM
   VAST,TVCG,Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework,"Mennatallah El-Assady, Rita Sevastjanova, Fabian Sperrle, Daniel Keim, Christopher Collins",,,,,,,,2018,2017,VAST,207,Wednesday,Text Analytics,11:50 AM,12:10 PM
  -VAST,TVCG,Sequence Synopsis: Optimize Visual Summary of Temporal Event Data,"Yuanzhe Chen, Panpan Xu, Liu Ren",http://lliquid.github.io/homepage/files/ss_vast17.pdf,"Event sequences analysis plays an important role in many application domains such as customer behavior analysis, electronic health record analysis and vehicle fault diagnosis. Real-world event sequence data is often noisy and complex with high event cardinality, making it a challenging task to construct concise yet comprehensive overviews for such data. In this paper, we propose a novel visualization technique based on the minimum description length (MDL) principle to construct a coarse-level overview of event sequence data while balancing the information loss in it. The method addresses a fundamental trade-off in visualization design: reducing visual clutter vs. increasing the information content in a visualization. The method enables simultaneous sequence clustering and pattern extraction and is highly tolerant to noises such as missing or additional events in the data. Based on this approach we propose a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. We demonstrate the usability and effectiveness of our approach through case studies with two real-world datasets. One dataset showcases a new application domain for event sequence visualization, i.e., fault development path analysis in vehicles for predictive maintenance. We also discuss the strengths and limitations of the proposed method based on user feedback.",,http://lliquid.github.io/homepage/files/ss_vast17_sup.pdf,,,,2018,2017,VAST,301-C,Wednesday,Sequences and Events,2:00 PM,2:20 PM
  -VAST,TVCG,SkyLens: Visual Analysis of Skyline on Multi-dimensional Data,"Xun Zhao, Yanhong Wu, Weiwei Cui, Xinnan Du, Yuan Chen, Yong Wang, Dik-Lun Lee, Huamin Qu",http://yhwu.me/publications/skylens_vast17.pdf,"Skyline queries have wide-ranging applications in fields that involve multi-criteria decision making, including tourism, retail industry, and human resources. By automatically removing incompetent candidates, skyline queries allow users to focus on a subset of superior data items (i.e., the skyline), thus reducing the decision-making overhead. However, users are still required to interpret and compare these superior items manually before making a successful choice. This task is challenging because of two issues. First, people usually have fuzzy, unstable, and inconsistent preferences when presented with multiple candidates. Second, skyline queries do not reveal the reasons for the superiority of certain skyline points in a multi-dimensional space. To address these issues, we propose SkyLens, a visual analytic system aiming at revealing the superiority of skyline points from different perspectives and at different scales to aid users in their decision making. Two scenarios demonstrate the usefulness of SkyLens on two datasets with a dozen of attributes. A qualitative study is also conducted to show that users can efficiently accomplish skyline understanding and comparison tasks with SkyLens.",,,,,,2018,2017,VAST,301-C,Wednesday,High-dimensional Data,9:10 AM,9:30 AM
  +VAST,TVCG,Sequence Synopsis: Optimize Visual Summary of Temporal Event Data,"Yuanzhe Chen, Panpan Xu, Liu Ren",http://lliquid.github.io/homepage/files/ss_vast17.pdf,"Event sequences analysis plays an important role in many application domains such as customer behavior analysis, electronic health record analysis and vehicle fault diagnosis. Real-world event sequence data is often noisy and complex with high event cardinality, making it a challenging task to construct concise yet comprehensive overviews for such data. In this paper, we propose a novel visualization technique based on the minimum description length (MDL) principle to construct a coarse-level overview of event sequence data while balancing the information loss in it. The method addresses a fundamental trade-off in visualization design: reducing visual clutter vs. increasing the information content in a visualization. The method enables simultaneous sequence clustering and pattern extraction and is highly tolerant to noises such as missing or additional events in the data. Based on this approach we propose a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. We demonstrate the usability and effectiveness of our approach through case studies with two real-world datasets. One dataset showcases a new application domain for event sequence visualization, i.e., fault development path analysis in vehicles for predictive maintenance. We also discuss the strengths and limitations of the proposed method based on user feedback.",,http://lliquid.github.io/homepage/files/ss_vast17_sup.pdf,,vimeo 230830645,,2018,2017,VAST,301-C,Wednesday,Sequences and Events,2:00 PM,2:20 PM
  +VAST,TVCG,SkyLens: Visual Analysis of Skyline on Multi-dimensional Data,"Xun Zhao, Yanhong Wu, Weiwei Cui, Xinnan Du, Yuan Chen, Yong Wang, Dik-Lun Lee, Huamin Qu",http://yhwu.me/publications/skylens_vast17.pdf,"Skyline queries have wide-ranging applications in fields that involve multi-criteria decision making, including tourism, retail industry, and human resources. By automatically removing incompetent candidates, skyline queries allow users to focus on a subset of superior data items (i.e., the skyline), thus reducing the decision-making overhead. However, users are still required to interpret and compare these superior items manually before making a successful choice. This task is challenging because of two issues. First, people usually have fuzzy, unstable, and inconsistent preferences when presented with multiple candidates. Second, skyline queries do not reveal the reasons for the superiority of certain skyline points in a multi-dimensional space. To address these issues, we propose SkyLens, a visual analytic system aiming at revealing the superiority of skyline points from different perspectives and at different scales to aid users in their decision making. Two scenarios demonstrate the usefulness of SkyLens on two datasets with a dozen of attributes. A qualitative study is also conducted to show that users can efficiently accomplish skyline understanding and comparison tasks with SkyLens.",,,,vimeo 230829927,,2018,2017,VAST,301-C,Wednesday,High-dimensional Data,9:10 AM,9:30 AM
   VAST,TVCG,SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance,"Dominik Sacha, Matthias Kraus, Jürgen Bernard, Michael Behrisch, Tobias Schreck, Yuki Asano, Daniel Keim",,,,,,,,2018,2017,VAST,301-C,Thursday,ML2: Cluster Analysis,4:35 PM,4:55 PM
   VAST,TVCG,Supporting Handoff in Asynchronous Collaborative Sensemaking Using Knowledge-Transfer Graphs,"Jian Zhao, Michael Glueck, Petra Isenberg, Fanny Chevalier, Azam Khan",,,,,,,,2018,2017,VAST,207,Thursday,Sensemaking,4:55 PM,5:15 PM
   VAST,TVCG,The Interactive Visualization Gap in Initial Exploratory Analysis,"Andrea Batch, Niklas Elmqvist",,,,,,,,2018,2017,VAST,301-C,Thursday,Theory and Analysis Process,11:50 AM,12:10 PM
  
positive.code: 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",,,,,,,,2018,2017,VAST,102-ABC,Friday,ML3: Classification,8:50 AM,9:10 AM
  VAST,TVCG,Visualizing Big Data Outliers through Distributed Aggregation,Author: Leland Wilkinson,,,,,,,,2018,2017,VAST,301-C,Wednesday,High-dimensional Data,9:30 AM,9:50 AM
  VAST,TVCG,Visualizing Confidence in Cluster-based Ensemble Weather Forecast Analyses,"Alexander Kumpf, Bianca Tost, Marlene Baumgart, Michael Riemer, Rüdiger Westermann, Marc Rautenhaus",https://wwwcg.in.tum.de/fileadmin/user_upload/Lehrstuehle/Lehrstuhl_XV/Research/Publications/2017/Cluster_Confidence/kumpf_etal_clustering_2017.pdf,"In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we –a team of visualization scientists and meteorologists– deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of “Tropical Cyclone Karl”, guiding the user towards the cluster robustness information required for subsequent ensemble analysis",,,,,,2018,2017,VAST,301-C,Thursday,ML2: Cluster Analysis,4:15 PM,4:35 PM
  VAST,TVCG,Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow,"Kanit Wongsuphasawat, Daniel Smilkov, James Wexler, Jimbo Wilson, Dandelion Mané, Doug Fritz, Dilip Krishnan, Fernanda B. Viégas, Martin Wattenberg",,,,,,,,2018,2017,Other,301-CD,Tuesday,VIS Awards & Best Papers,9:30 AM,9:50 AM
  VAST,TVCG,Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data,"Nan Cao, Chaoguang Lin, Qiuhan Zhu, Yu-Ru Lin, Xian Teng, Xidao Wen",,,,,,,,2018,2017,VAST,301-C,Tuesday,"Space, Time, Movement",2:20 PM,2:40 PM
  VAST,VAST,A Visual Analytics System for Optimizing Communications in Massively Parallel Applications,"Takanori Fujiwara, Preeti Malakar, Khairi Reda, Venkatram Vishwanath, Michael Papka, Kwan-Liu Ma",,,,,,,,2017,2017,VAST,301-C,Thursday,Visual Representation and Design Study,8:50 AM,9:10 AM
  VAST,VAST,A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations,"Josua Krause, Aritra Dasgupta, Jordan Swartz, Yindalon Aphinyanaphongs, Enrico Bertini",https://arxiv.org/pdf/1705.01968.pdf,"Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary classifier. The approach leverages “instance-level explanations”, measures of local feature relevance that explain single instances, and uses them to build a set of visual representations that guide the users in their investigation. The workflow is based on three main visual representations and steps: one based on aggregate statistics to see how data distributes across correct / incorrect decisions; one based on explanations to understand which features are used to make these decisions; and one based on raw data, to derive insights on potential root causes for the observed patterns. The workflow is derived from a long-term collaboration with a group of machine learning and healthcare professionals who used our method to make sense of machine learning models they developed. The case study from this collaboration demonstrates that the proposed workflow helps experts derive useful knowledge about the model and the phenomena it describes, thus experts can generate useful hypotheses on how a model can be improved.",,,,,,2017,2017,VAST,102-ABC,Friday,ML3: Classification,9:10 AM,9:30 AM
  VAST,VAST,CRICTO: Supporting Sensemaking through Crowdsourced Information Schematization,"Haeyong Chung, Sai Prashanth Dasari, Santhosh Nandhakumar, Christopher Andrews",,,,,,,,2017,2017,VAST,207,Thursday,Sensemaking,5:15 PM,5:35 PM
  VAST,VAST,CrystalBall: A Visual Analytic System for Future Event Discovery and Analysis from Social Media Data,"Isaac Cho, Ryan Wesslen, Svitlana Volkova, Bill Ribarsky, Wenwen Dou",,,,,,,,2017,2017,VAST,301-C,Wednesday,Sequences and Events,2:40 PM,3:00 PM
  VAST,VAST,E-Map: A Visual Analytics Approach for Exploring Significant Event Evolutions in Social Media,"Siming Chen, Shuai Chen, Lijing Lin, Xiaoru Yuan, Jie Liang, Xiaolong (Luke) Zhang",,,,,,,,2017,2017,VAST,301-C,Wednesday,Sequences and Events,3:00 PM,3:20 PM
  VAST,VAST,Interactive Visual Alignment of Medieval Text Versions,"Stefan Jänicke, David Wrisley",,,,,,,,2017,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,3:20 PM,3:40 PM
  VAST,VAST,Pattern Trails: Visual Analysis of Pattern Transitions in Subspaces,"Dominik Jäckle, Michael Hund, Michael Behrisch, Daniel A. Keim, Tobias Schreck",https://dominikjaeckle.com/projects/2017/pattern_trails/paper/2017_jaeckle_pattern_trails.pdf,"Subspace analysis methods have gained interest for identifying patterns in subspaces of high-dimensional data. Existing techniques allow to visualize and compare patterns in subspaces. However, many subspace analysis methods produce an abundant amount of patterns, which often remain redundant and are difficult to relate. Creating effective layouts for comparison of subspace patterns remains challenging. We introduce Pattern Trails, a novel approach for visually ordering and comparing subspace patterns. Central to our approach is the notion of pattern transitions as an interpretable structure imposed to order and compare patterns between subspaces. The basic idea is to visualize projections of subspaces side-by-side, and indicate changes between adjacent patterns in the subspaces by a linked representation, hence introducing pattern transitions. Our contributions comprise a systematization for how pairs of subspace patterns can be compared, and how changes can be interpreted in terms of pattern transitions. We also contribute a technique for visual subspace analysis based on a data-driven similarity measure between subspace representations. This measure is useful to order the patterns, and interactively group subspaces to reduce redundancy. We demonstrate the usefulness of our approach by application to several use cases, indicating that data can be meaningfully ordered and interpreted in terms of pattern transitions.",,,,,,2017,2017,VAST,301-C,Wednesday,High-dimensional Data,8:50 AM,9:10 AM
  VAST,VAST,QSAnglyzer: Visual Analytics for Prismatic Analysis of Question Answering System Evaluations,"Nan-Chen Chen, Been Kim",http://nanchen.csie.org/publications/qsanglyzer_vast2017.pdf,"Developing sophisticated artificial intelligence (AI) systems requires AI researchers to experiment with different designs and analyze results from evaluations (we refer this task as evaluation analysis). In this paper, we tackle the challenges of evaluation analysis in the domain of question-answering (QA) systems. Through in-depth studies with QA researchers, we identify tasks and goals of evaluation analysis and derive a set of design rationales, based on which we propose a novel approach termed prismatic analysis. Prismatic analysis examines data through multiple ways of categorization (referred as angles). Categories in each angle are measured by aggregate metrics to enable diverse comparison scenarios.<br/>To facilitate prismatic analysis of QA evaluations, we design and implement the Question Space Anglyzer (QSAnglyzer), a visual analytics (VA) tool. In QSAnglyzer, the high-dimensional space formed by questions is divided into categories based on several angles (e.g., topic and question type). Each category is aggregated by accuracy, the number of questions, and accuracy variance across evaluations. QSAnglyzer visualizes these angles so that QA researchers can examine and compare evaluations from various aspects both individually and collectively. Furthermore, QA researchers filter questions based on any angle by clicking to construct complex queries. We validate QSAnglyzer through controlled experiments and by expert reviews. The results indicate that when using QSAnglyzer, users perform analysis tasks faster (p < 0.01) and more accurately (p < 0.05), and are quick to gain new insight. We discuss how *e-mail: nanchen@uw.edu † e-mail: beenkim@csail.mit.edu prismatic analysis and QSAnglyzer scaffold evaluation analysis, and provide directions for future research.",,,,,,2017,2017,VAST,301-C,Thursday,Visual Representation and Design Study,8:30 AM,8:50 AM
  VAST,VAST,"The y of it Matters, Even for Storyline Visualization","Dustin Arendt, Megan Pirrung",,,,,,,,2017,2017,VAST,301-C,Thursday,Visual Representation and Design Study,9:50 AM,10:10 AM
  VAST,VAST,The Anchoring Effect in Decision-Making with Visual Analytics,"Isaac Cho, Ryan Wesslen, Alireza Karduni, Sashank Santhanam, Samira Shaikh, Wenwen Dou",,,,,,,,2017,2017,VAST,301-C,Thursday,Theory and Analysis Process,11:30 AM,11:50 AM
  VAST,VAST,The Role of Explicit Knowledge: A Conceptual Model of Knowledge-Assisted Visual Analytics,"Paolo Federico, Markus Wagner, Alexander Rind, Albert Amor-Amorós, Silvia Miksch, Wolfgang Aigner",http://www.cvast.tuwien.ac.at/sites/default/files/federico-2017-vast.pdf,"Visual Analytics (VA) aims to combine the strengths of humans and computers for effective data analysis. In this endeavor, humans’ tacit knowledge from prior experience is an important asset that can be leveraged by both human and computer to improve the analytic process. While VA environments are starting to include features to formalize, store, and utilize such knowledge, the mechanisms and degree in which these environments integrate explicit knowledge varies widely. Additionally, this important class of VA environments has never been elaborated on by existing work on VA theory. This paper proposes a conceptual model of Knowledge-assisted VA conceptually grounded on the visualization model by van Wijk. We apply the model to describe various examples of knowledge-assisted VA from the literature and elaborate on three of them in finer detail. Moreover, we illustrate the utilization of the model to compare different design alternatives and to evaluate existing approaches with respect to their use of knowledge. Finally, the model can inspire designers to generate novel VA environments using explicit knowledge effectively",,,,,,2017,2017,VAST,301-C,Thursday,Theory and Analysis Process,10:50 AM,11:10 AM
  VAST,VAST,Understanding Hidden Memories of Recurrent Neural Networks,"Yao Ming, Shaozu CAO, Ruixiang Zhang, Zhen LI, Yuanzhe Chen, Yangqiu Song, Huamin Qu",,,,,,,,2017,2017,VAST,301-C,Wednesday,ML1: Deep Learning,10:50 AM,11:10 AM
  VAST,VAST,Visual Causality Analysis Made Practical,"Jun Wang, Klaus Mueller",,,,,,,,2017,2017,VAST,207,Thursday,Sensemaking,5:35 PM,5:55 PM
  VAST,VAST,Visualizing Real-Time Strategy Games: The Example of StarCraft II,"Yen-Ting Kuan, Yu-Shuen Wang, Jung-Hong Chuang",,,,,,,,2017,2017,VAST,301-C,Thursday,Visual Representation and Design Study,9:10 AM,9:30 AM
  VAST,VAST,"Warning, Bias May Occur: A Proposed Approach to Detecting Cognitive Bias in Interactive Visual Analytics","Emily Wall, Leslie M. Blaha, Lyndsey Franklin, Alex Endert",https://www.cc.gatech.edu/~ewall9/media/papers/BiasVAST17.pdf,"Visual analytic tools combine the complementary strengths of humans and machines in human-in-the-loop systems. Humans provide invaluable domain expertise and sensemaking capabilities to this discourse with analytic models; however, little consideration has yet been given to the ways inherent human biases might shape the visual analytic process. In this paper, we establish a conceptual framework for considering bias assessment through human-in-the-loop systems and lay the theoretical foundations for bias measurement. We propose six preliminary metrics to systematically detect and quantify bias from user interactions and demonstrate how the metrics might be implemented in an existing visual analytic system, InterAxis. We discuss how our proposed metrics could be used by visual analytic systems to mitigate the negative effects of cognitive biases by making users aware of biased processes throughout their analyses.",,,,youtube W9LWi3oXjM0,,2017,2017,VAST,301-C,Thursday,Theory and Analysis Process,11:10 AM,11:30 AM
  SciVis,TVCG,A Virtual Reality Visualization Tool for Neuron Tracing,"Will Usher, Pavol Klacansky, Frederick Federer, Peer-Timo Bremer, Aaron Knoll, Alessandra Angelucci, Valerio Pascucci",http://sci.utah.edu/~will/papers/vrnt/vr-neuron-tracing.pdf,"Tracing neurons in large-scale microscopy data is crucial to establishing a wiring diagram of the brain, which is needed to understand how neural circuits in the brain process information and generate behavior. Automatic techniques often fail for large and complex datasets, and connectomics researchers may spend weeks or months manually tracing neurons using 2D image stacks. We present a design study of a new virtual reality (VR) system, developed in collaboration with trained neuroanatomists, to trace neurons in microscope scans of the visual cortex of primates. We hypothesize that using consumer-grade VR technology to interact with neurons directly in 3D will help neuroscientists better resolve complex cases and enable them to trace neurons faster and with less physical and mental strain. We discuss both the design process and technical challenges in developing an interactive system to navigate and manipulate terabyte-sized image volumes in VR. Using a number of different datasets, we demonstrate that, compared to widely used commercial software, consumer-grade VR presents a promising alternative for scientists",,,,,,2018,2017,SciVis,207,Tuesday,Volume Rendering,5:35 PM,5:55 PM
  SciVis,TVCG,Abstractocyte: A Visual Tool for Exploring Nanoscale Astroglial Cells,"Haneen Mohammed, Ali Al-Awami, Johanna Beyer, Corrado Cali, Pierre Magistretti, Hanspeter Pfister, Markus Hadwiger",,,,,,,,2018,2017,SciVis,207,Thursday,Visualization in Biology and Medicine,8:30 AM,8:50 AM
  SciVis,TVCG,Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization,Liz Marai,,,,,,,,2018,2017,SciVis,207,Tuesday,"Mix: Foundations, uncertainty, particles",2:00 PM,2:20 PM
  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,,,,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",http://bastian.rieck.ru/research/Vis2017_Networks.pdf,"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",,,,,,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",,,,,,,,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",,,,,,,,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.",,,,,,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.",,,,,,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",,,,,,,,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://www.cg.tuwien.ac.at/research/publications/2018/miao_tvcg_2018/miao_tvcg_2018-paper.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",,,,,,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.",,,,,,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",,,,,,,,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.",,,,,,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",,,,,,,,2018,2017,SciVis,207,Tuesday,"Mix: Foundations, uncertainty, particles",2:20 PM,2:40 PM
  SciVis,TVCG,The Topology ToolKit,"Julien Tierny, Guillaume Favelier, Joshua Levine, Charles Gueunet, Michaël Michaux",https://hal.archives-ouvertes.fr/hal-01499905/document,"This system paper presents the Topology ToolKit (TTK), a software platform designed for the topological analysis of scalar data in scientific visualization. While topological data analysis has gained in popularity over the last two decades, it has not yet been widely adopted as a standard data analysis tool for end users or developers. TTK aims at addressing this problem by providing a unified, generic, efficient, and robust implementation of key algorithms for the topological analysis of scalar data, including: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due to a tight integration with ParaView. It is also easily accessible to developers through a variety of bindings (Python, VTK/C++) for fast prototyping or through direct, dependency-free, C++, to ease integration into pre-existing complex systems. While developing TTK, we faced several algorithmic and software engineering challenges, which we document in this paper. In particular, we present an algorithm for the construction of a discrete gradient that complies to the critical points extracted in the piecewise-linear setting. This algorithm guarantees a combinatorial consistency across the topological abstractions supported by TTK, and importantly, a unified implementation of topological data simplification for multi-scale exploration and analysis. We also present a cached triangulation data structure, that supports time efficient and generic traversals, which self-adjusts its memory usage on demand for input simplicial meshes and which implicitly emulates a triangulation for regular grids with no memory overhead. Finally, we describe an original software architecture, which guarantees memory efficient and direct accesses to TTK features, while still allowing for researchers powerful and easy bindings and extensions. TTK is open source (BSD license) and its code, online documentation and video tutorials are available on TTK’s website [108].",https://topology-tool-kit.github.io/,https://github.com/topology-tool-kit/ttk,,,,2018,2017,SciVis,207,Wednesday,Topology-based methods,2:40 PM,3:00 PM
  SciVis,TVCG,TopoAngler: Interactive Topology-based Extraction of Fishes,"Alexander Bock, Harish Doraiswamy, Adam Summers, Claudio Silva",,,,,,youtube gz22DjMRJjk,,2018,2017,SciVis,207,Wednesday,Topology-based methods,2:00 PM,2:20 PM
  SciVis,TVCG,Uncertainty Visualization Using Copula-Based Analysis in Mixed Distribution Models,"Subhashis Hazarika, Ayan Biswas, Han-Wei Shen",,,,,,,,2018,2017,SciVis,207,Tuesday,"Mix: Foundations, uncertainty, particles",2:40 PM,3:00 PM
  SciVis,TVCG,Visualization Multipipeline for Communicating Biology,"Peter Mindek, David Kouřil, Johannes Sorger, Daniel Toloudis, Blair Lyons, Graham Johnson, M. Eduard Gröller, Ivan Viola",,,,,,,,2018,2017,SciVis,207,Thursday,Visualization in Biology and Medicine,9:30 AM,9:50 AM
  
negative.code: TVCG,TVCG,A Survey on Visual Approaches for Analyzing Scientific Literature and Patents,"Paolo Federico, Florian Heimerl, Steffen Koch, Silvia Miksch",,,,,,,10.1109/TVCG.2016.2610422,2016,2017,VAST,207,Wednesday,Text Analytics,10:30 AM,10:50 AM
  TVCG,TVCG,Uncertainty Visualization by Representative Sampling from Prediction Ensembles,"Le Liu, Alexander Boone, Ian Ruginski, Lace Padilla, Mary Hegarty, Sarah H. Creem-Regehr, William B. Thompson, Cem Yuksel, Donald H. House",,,,,,,10.1109/TVCG.2016.2607204,2016,2017,InfoVis,301-D,Tuesday,Techniques,3:20 PM,3:40 PM
  TVCG,TVCG,Revealing Patterns and Trends of Mass Mobility through Spatial and Temporal Abstraction of Origin-Destination Movement Data,"Gennady Andrienko, Natalia Andrienko, Georg Fuchs, Jo Wood",http://openaccess.city.ac.uk/15488/1/andrienko_revealing_2016_AuthorCopy.pdf,"Origin-destination (OD) movement data describe moves or trips between spatial locations by specifying the origins, destinations, start, and end times, but not the routes travelled. For studying the spatio-temporal patterns and trends of mass mobility, individual OD moves of many people are aggregated into flows (collective moves) by time intervals. Time-variant flow data pose two difficult challenges for visualization and analysis. First, flows may connect arbitrary locations (not only neighbors), thus making a graph with numerous edge intersections, which is hard to visualize in a comprehensible way. Even a single spatial situation consisting of flows in one time step is hard to explore. The second challenge is the need to analyze long time series consisting of numerous spatial situations. We present an approach facilitating exploration of long-term flow data by means of spatial and temporal abstraction. It involves a special way of data aggregation, which allows representing spatial situations by diagram maps instead of flow maps, thus reducing the intersections and occlusions pertaining to flow maps. The aggregated data are used for clustering of time intervals by similarity of the spatial situations. Temporal and spatial displays of the clustering results facilitate the discovery of periodic patterns and longer-term trends in the mass mobility behavior.",,,,vimeo 230834183,10.1109/TVCG.2016.2616404,2016,2017,VAST,301-C,Tuesday,"Space, Time, Movement",3:00 PM,3:20 PM
  TVCG,TVCG,An Exploratory Study of Word-Scale Graphics in Data-Rich Text Documents,"Pascal Goffin, Jeremy Boy, Wesley Willett, Petra Isenber",https://www.archives-ouvertes.fr/hal-01389998/document,"We contribute an investigation of the design and function of word-scale graphics and visualizations embedded in text documents. Word-scale graphics include both data-driven representations such as word-scale visualizations and sparklines, and non-data-driven visual marks. Their design, function, and use has so far received little research attention. We present the results of an open ended exploratory study with 9 graphic designers. The study resulted in a rich collection of different types of graphics, data provenance, and relationships between text, graphics, and data. Based on this corpus, we present a systematic overview of word-scale graphic designs, and examine how designers used them. We also discuss the designers’ goals in creating their graphics, and characterize how they used word-scale graphics to visualize data, add emphasis, and create alternative narratives. Building on these examples, we discuss implications for the design of authoring tools for word-scale graphics and visualizations, and explore how new authoring environments could make it easier for designers to integrate them into documents",,,,vimeo 230834366,10.1109/TVCG.2016.2618797,2016,2017,InfoVis,301-D,Thursday,Text and Machine Learning,9:10 AM,9:30 AM
  TVCG,TVCG,vispubdata.org: A Metadata Collection about IEEE Visualization (VIS) Publications,"Petra Isenberg, Florian Heimerl, Steffen Koch, Tobias Isenberg, Panpan Xu, Charles Stolper, Michael Sedlmair, Jian Chen, Torsten Moller, John T. Stasko",https://hal.inria.fr/hal-01376597/file/Isenberg_2017_VMC.pdf,"We have created and made available to all a dataset with information about every paper that has appeared at the IEEE Visualization (VIS) set of conferences: InfoVis, SciVis, VAST, and Vis. The information about each paper includes its title, abstract, authors, and citations to other papers in the conference series, among many other attributes. This article describes the motivation for creating the dataset, as well as our process of coalescing and cleaning the data, and a set of three visualizations we created to facilitate exploration of the data. This data is meant to be useful to the broad data visualization community to help understand the evolution of the field and as an example document collection for text data visualization research.",http://vispubdata.org,https://docs.google.com/spreadsheets/d/1xgoOPu28dQSSGPIp_HHQs0uvvcyLNdkMF9XtRajhhxU/edit?usp=sharing,,vimeo 230834115,10.1109/TVCG.2016.2615308,2016,2017,VAST,207,Wednesday,Text Analytics,10:50 AM,11:10 AM
  TVCG,TVCG,A Systematic Review of Experimental Studies on Data Glyphs,"Johannes Fuchs, Petra Isenberg, Anastasia Bezerianos, Daniel Keim",https://hal.inria.fr/hal-01378429/document,"We systematically reviewed 64 user-study papers on data glyphs to help researchers and practitioners gain an informed understanding of tradeoffs in the glyph design space. The glyphs we consider are individual representations of multi-dimensional data points, often meant to be shown in small-multiple settings. Over the past 60 years many different glyph designs were proposed and many of these designs have been subjected to perceptual or comparative evaluations. Yet, a systematic overview of the types of glyphs and design variations tested, the tasks under which they were analyzed, or even the study goals and results does not yet exist. In this paper we provide such an overview by systematically sampling and tabulating the literature on data glyph studies, listing their designs, questions, data, and tasks. In addition we present a concise overview of the types of glyphs and their design characteristics analyzed by researchers in the past, and a synthesis of the study results. Based on our meta analysis of all results we further contribute a set of design implications and a discussion on open research directions.",,,,vimeo 230834154,10.1109/TVCG.2016.2549018,2017,2017,InfoVis,105-ABC,Friday,Evaluation,8:50 AM,9:10 AM
  TVCG,TVCG,Evaluating Cartogram Effectiveness,"Sabrina Nusrat, Muhammad Jawaherul Alam, Stephen Kobourov",https://arxiv.org/pdf/1504.02218.pdf,"Cartograms are maps in which areas of geographic regions, such as countries and states, appear in proportion to some variable of interest, such as population or income. Cartograms are popular visualizations for geo-referenced data that have been used for over a century to illustrate patterns and trends in the world around us. Despite the popularity of cartograms, and the large number of cartogram types, there are few studies evaluating the effectiveness of cartograms in conveying information. Based on a recent task taxonomy for cartograms, we evaluate four major types of cartograms: contiguous, non-contiguous, rectangular, and Dorling cartograms. We first evaluate the effectiveness of these cartogram types by quantitative performance analysis (time and error). Second, we collect qualitative data with an attitude study and by analyzing subjective preferences. Third, we compare the quantitative and qualitative results with the results of a metrics-based cartogram evaluation. Fourth, we analyze the results of our study in the context of cartography, geography, visual perception, and demography. Finally, we consider implications for design and possible improvements.",,,,vimeo 230834493,10.1109/TVCG.2016.2642109,2016,2017,InfoVis,105-ABC,Friday,Evaluation,9:30 AM,9:50 AM
  TVCG,TVCG,A Statistical Direct Volume Rendering Framework for Visualization of Uncertain Data,"Elham Sakhaee, Alireza Entezari",https://www.cise.ufl.edu/~esakhaee/papers/VIS/uncertaintyDVR.pdf,"With uncertainty present in almost all modalities of data acquisition, reduction, transformation, and representation, there is a growing demand for mathematical analysis of uncertainty propagation in data processing pipelines. In this paper, we present a statistical framework for quantification of uncertainty and its propagation in the main stages of the visualization pipeline. We propose a novel generalization of Irwin-Hall distributions from the statistical viewpoint of splines and box-splines, that enables interpolation of random variables. Moreover, we introduce a probabilistic transfer function classification model that allows for incorporating probability density functions into the volume rendering integral. Our statistical framework allows for incorporating distributions from various sources of uncertainty which makes it suitable in a wide range of visualization applications. We demonstrate effectiveness of our approach in visualization of ensemble data, visualizing large datasets at reduced scale, iso-surface extraction, and visualization of noisy data.",,,,vimeo 230834547,10.1109/TVCG.2016.2637333,2016,2017,SciVis,207,Tuesday,Volume Rendering,4:35 PM,4:55 PM
  TVCG,TVCG,The Subspace Voyager: Exploring High-Dimensional Data along a Continuum of Salient 3D Subspaces,"Bing Wang, Klaus Mueller",https://arxiv.org/ftp/arxiv/papers/1603/1603.04781.pdf,"Analyzing high-dimensional data and finding hidden patterns is a difficult problem and has attracted numerous research efforts. Automated methods can be useful to some extent but bringing the data analyst into the loop via interactive visual tools can help the discovery process tremendously. An inherent problem in this effort is that humans lack the mental capacity to truly understand spaces exceeding three spatial dimensions. To keep within this limitation, we describe a framework that decomposes a high-dimensional data space into a continuum of generalized 3D subspaces. Analysts can then explore these 3D subspaces individually via the familiar trackball interface while using additional facilities to smoothly transition to adjacent subspaces for expanded space comprehension. Since the number of such subspaces suffers from combinatorial explosion, we provide a set of data-driven subspace selection and navigation tools which can guide users to interesting subspaces and views. A subspace trail map allows users to manage the explored subspaces, keep their bearings, and return to interesting subspaces and views. Both trackball and trail map are each embedded into a word cloud of attribute labels which aid in navigation. We demonstrate our system via several use cases in a diverse set of application areas – cluster analysis and refinement, information discovery, and supervised training of classifiers. We also report on a user study that evaluates the usability of the various interactions our system provides.",,,,youtube c-yKtajfGQE,10.1109/TVCG.2017.2672987,2016,2017,VAST,301-C,Wednesday,High-dimensional Data,9:50 AM,10:10 AM
  TVCG,TVCG,TopKube: A Rank-Aware Data Cube for Real-Time Exploration of Spatiotemporal Data,"Fabio Miranda, Lauro Lins, James Klosowski, Claudio Silva",,,,,,,10.1109/TVCG.2017.2671341,2017,2017,InfoVis,301-D,Tuesday,Time and Space,5:35 PM,5:55 PM
  TVCG,TVCG,Perceptual Biases in Font Size as a Data Encoding,"Eric Carlson Alexander, Chih-Ching Chang, Mariana Shimabukuro, Steve Franconeri, Christopher Collins, Michael Gleicher",,,,,,,10.1109/TVCG.2017.2723397,2017,2017,InfoVis,301-D,Wednesday,Perception,9:10 AM,9:30 AM
  TVCG,TVCG,Data Flow Analysis and Visualization for Spatiotemporal Statistical Data without Trajectory Information,"Seokyeon Kim, Seongmin Jeong, Insoo Woo, Yun Jang, Ross Maciejewski, David Ebert",http://rmaciejewski.faculty.asu.edu/papers/2017/Yun-Flow.pdf,"Geographic visualization research has focused on a variety of techniques to represent and explore spatiotemporal data. The goal of those techniques is to enable users to explore events and interactions over space and time in order to facilitate the discovery of patterns, anomalies and relationships within the data. However, it is difficult to extract and visualize data flow patterns over time for non-directional statistical data without trajectory information. In this work, we develop a novel flow analysis technique to extract, represent, and analyze flow maps of non-directional spatiotemporal data unaccompanied by trajectory information. We estimate a continuous distribution of these events over space and time, and extract flow fields for spatial and temporal changes utilizing a gravity model. Then, we effectively visualize the spatiotemporal patterns in the data by employing flow visualization techniques. The user is presented with temporal trends of geo-referenced discrete events on a map. As such, overall spatiotemporal data flow patterns help users analyze geo-referenced temporal events, such as disease outbreaks, crime patterns, etc. To validate our model, we discard the trajectory information in an origin-destination dataset and apply our technique to the data and compare the derived trajectories and the original. Finally, we present spatiotemporal trend analysis for statistical datasets including twitter data, maritime search and rescue events, and syndromic surveillance",,,,vimeo 230834322,10.1109/TVCG.2017.2666146,2017,2017,VAST,301-C,Tuesday,"Space, Time, Movement",3:20 PM,3:40 PM
  TVCG,TVCG,Evaluating Interactive Graphical Encodings for Data Visualization,"Bahador Saket, Arjun Srinivasan, Eric D. Ragan, Alex Endert",http://bahadorsaket.com/publication/encodingsPaper.pdf,"User interfaces for data visualization often consist of two main components: control panels for user interaction and visual representation. A recent trend in visualization is directly embedding user interaction into the visual representations. For example, instead of using control panels to adjust visualization parameters, users can directly adjust basic graphical encodings (e.g., changing distances between points in a scatterplot) to perform similar parameterizations. However, enabling embedded interactions for data visualization requires a strong understanding of how user interactions influence the ability to accurately control and perceive graphical encodings. In this paper, we study the effectiveness of these graphical encodings when serving as the method for interaction. Our user study includes 12 interactive graphical encodings. We discuss the results in terms of task performance and interaction effectiveness metrics.",https://medium.com/@GT_Vis/evaluating-interactive-graphical-encodings-for-data-visualization-b9b8b4bf47f8,https://github.com/gtvalab/interactive-graphical-encodings,https://github.com/gtvalab/interactive-graphical-encodings/tree/master/Supplementary%20Materials/Raw%20Data,vimeo 230834446,10.1109/TVCG.2017.2680452,2017,2017,InfoVis,301-D,Wednesday,Perception,9:50 AM,10:10 AM
  TVCG,TVCG,PETMiner - A Visual Analysis Tool for Petrophysical Properties of Core Sample Data,"Dave G. Harrison, Nick D. Efford, Quentin J. Fisher, Roy A. Ruddle",http://eprints.whiterose.ac.uk/113580/1/harrison-ieee-tvcg-2017.pdf,"The aim of the PETMiner software is to reduce the time and monetary cost of analysing petrophysical data that is obtained from reservoir sample cores. Analysis of these data requires tacit knowledge to fill ’gaps’ so that predictions can be made for incomplete data. Through discussions with 30 industry and academic specialists, we identified three analysis use cases that exemplified the limitations of current petrophysics analysis tools. We used those use cases to develop nine core requirements for PETMiner, which is innovative because of its ability to display detailed images of the samples as data points, directly plot multiple sample properties and derived measures for comparison, and substantially reduce interaction cost. An 11-month evaluation demonstrated benefits across all three use cases by allowing a consultant to: (1) generate more accurate reservoir flow models, (2) discover a previously unknown relationship between one easy-to-measure property and another that is costly, and (3) make a 100-fold reduction in the time required to produce plots for a report.",,,,vimeo 230834380,10.1109/TVCG.2017.2682865,2017,2017,SciVis,106-ABC,Friday,Applications and Visual analysis,9:10 AM,9:30 AM
  TVCG,TVCG,Indexed-Points Parallel Coordinates Visualization of Multivariate Correlations,"Liang Zhou, Daniel Weiskopf",http://www.cs.utah.edu/~zhoul/Research/mypapers/pflatsInPCP_authors.pdf,"We address the problem of visualizing multivariate correlations in parallel coordinates. We focus on multivariate correlation in the form of linear relationships between multiple variables. Traditional parallel coordinates are well prepared to show negative correlations between two attributes by distinct visual patterns. However, it is difficult to recognize positive correlations in parallel coordinates. Furthermore, there is no support to highlight multivariate correlations in parallel coordinates. In this paper, we exploit the indexed point representation of p-flats (planes in multidimensional data) to visualize local multivariate correlations in parallel coordinates. Our method yields clear visual signatures for negative and positive correlations alike, and it supports large datasets. All information is shown in a unified parallel coordinates framework, which leads to easy and familiar user interactions for analysts who have experience with traditional parallel coordinates. The usefulness of our method is demonstrated through examples of typical multidimensional datasets.",,,,vimeo 230834568,10.1109/TVCG.2017.2698041,2017,2017,InfoVis,301-D,Wednesday,Multidimensional Data,3:20 PM,3:40 PM
  TVCG,TVCG,Visual Analysis of Inclusion Dynamics in Two-Phase Flow,"Grzegorz Karch, Fabian Beck, Moritz Ertl, Christian Meister, Kathrin Schulte, Bernhard Weigand, Thomas Ertl, Filip Sadlo",,,,,,,10.1109/TVCG.2017.2692781,2017,2017,SciVis,207,Wednesday,Flow Visualization,8:50 AM,9:10 AM
  TVCG,TVCG,A Combined Eulerian-Lagrangian Data Representation for Large-scale Applications,"Franz Sauer, Jinrong Xie, Kwan-Liu Ma",http://vis.cs.ucdavis.edu/~fsauer/TVCG_17/TVCG_17_Paper-1.pdf,"The Eulerian and Lagrangian reference frames each provide a unique perspective when studying and visualizing results from scientific systems. As a result, many large-scale simulations produce data in both formats, and analysis tasks that simultaneously utilize information from both representations are becoming increasingly popular. However, due to their fundamentally different nature, drawing correlations between these data formats is a computationally difficult task, especially in a large-scale setting. In this work, we present a new data representation which combines both reference frames into a joint Eulerian-Lagrangian format. By reorganizing Lagrangian information according to the Eulerian simulation grid into a “unit cell” based approach, we can provide an efficient out-of-core means of sampling, querying, and operating with both representations simultaneously. We also extend this design to generate multi-resolution subsets of the full data to suit the viewer’s needs and provide a fast flow-aware trajectory construction scheme. We demonstrate the effectiveness of our method using three large-scale real world scientific datasets and provide insight into the types of performance gains that can be achieved.",,,,vimeo 230834211,10.1109/TVCG.2016.2620975,2016,2017,SciVis,207,Wednesday,Flow Visualization,9:10 AM,9:30 AM
  TVCG,TVCG,Keshif: Rapid and Expressive Tabular Data Exploration for Novices,"Mehmet Adil Yalcin, Niklas Elmqvist, Benjamin B Bederson",http://www.umiacs.umd.edu/~elm/projects/keshif/keshif.pdf,"General purpose graphical interfaces for data exploration are typically based on manual visualization and interaction specifications. While designing manual specification can be very expressive, it demands high efforts to make effective decisions, therefore reducing exploratory speed. Instead, principled automated designs can increase exploratory speed, decrease learning efforts, help avoid ineffective decisions, and therefore better support data analytics novices. Towards these goals, we present Keshif, a new systematic design for tabular data exploration. To summarize a given dataset, Keshif aggregates records by value within attribute summaries, and visualizes aggregate characteristics using a consistent design based on data types. To reveal data distribution details, Keshif features three complementary linked selections: highlighting, filtering, and comparison. Keshif further increases expressiveness through aggregate metrics, absolute/part-of scale modes, calculated attributes, and saved selections, all working in synchrony. Its automated design approach also simplifies authoring of dashboards composed of summaries and individual records from raw data using fluid interaction. We show examples selected from 160+ datasets from diverse domains. Our study with novices shows that after exploring raw data for 15 minutes, our participants reached close to 30 data insights on average, comparable to other studies with skilled users using more complex tools.",,,,youtube 3Hmvms-1grU,10.1109/TVCG.2017.2723393,2017,2017,InfoVis,301-D,Wednesday,Multidimensional Data,2:40 PM,3:00 PM
  TVCG,TVCG,DSPCP: A Data Scalable Approach for Identifying Relationships in Parallel Coordinates,"Hoa Nguyen, Paul Rosen",,,,,,,10.1109/TVCG.2017.2661309,2017,2017,SciVis,106-ABC,Friday,Applications and Visual analysis,8:50 AM,9:10 AM
  TVCG,TVCG,RCLens: Interactive Rare Category Exploration and Identification,"Hanfei Lin, Siyuan Gao, David Gotz, Fan Du, Jingrui He, Nan Cao",http://frankdu.org/papers/lin2017tvcg.pdf,"Rare category identification is an important task in many application domains, ranging from network security, to financial fraud detection, to personalized medicine. These are all applications which require the discovery and characterization of sets of rare but structurally-similar data entities which are obscured within a larger but structurally different dataset. This paper introduces RCLens, a visual analytics system designed to support user-guided rare category exploration and identification. RCLens adopts a novel active learning-based algorithm to iteratively identify more accurate rare categories in response to user-provided feedback. The algorithm is tightly integrated with an interactive visualization-based interface which supports a novel and effective workflow for rare category identification. This paper (1) defines RCLens’ underlying active-learning algorithm; (2) describes the visualization and interaction designs, including a discussion of how the designs support user-guided rare category identification; and (3) presents results from an evaluation demonstrating RCLens’ ability to support the rare category identification process",,,,vimeo 230834420,10.1109/TVCG.2017.2711030,2017,2017,VAST,207,Thursday,Sensemaking,4:15 PM,4:35 PM
  CG&A,CG&A,ARIES: Enabling Visual Exploration and Organization of Art Image Collections,"Lhaylla Crissaff, Louisa Wood Ruby, Samantha Deutch, R. Luke DuBois, Jean-Daniel Fekete, Juliana Freire, Claudio Silva",,,,,,,,2017,2017,Other,101-ABC,Wednesday,Spatiotemporal Applications,10:30 AM,10:50 AM
  CG&A,CG&A,BKViz: A Basketball Visual Analysis Tool,"Antonio G. Losada, Roberto Theron, Alejandro Benito",,"The amount of data available nowadays in the sports field is hard to comprehend using classic analytic methods. This calls for the development of systems such as the prototype discussed here, which makes it possible to manipulate chunks of data to then portray them in visual ways, easing their understanding. Based on basketball, this tool helps users in reaching conclusions regarding performances during individual matches. This enables them to gather knowledge about play sequences, the events occurring at different moments, and the style of play teams employ based on player chemistry. Using multiple visualizations in an integrated system, data can be manipulated to vary how they are shown and change their analytic power as needed. With interaction and sim- ple comprehension based on visualization being the two cornerstones, the challenge was to provide a tool able to showcase patterns and study actions in an innovative and combined way not yet exploited.",http://vis.usal.es/bkviz/,,,,10.1109/MCG.2016.124,2016,2017,Other,101-ABC,Thursday,Sports Data Visual Analytics,10:30 AM,10:50 AM
  CG&A,CG&A,Director's Cut: Analysis and Annotation of Soccer Matches,"Manuel Stein, Halldór Janetzko, Thorsten Breitkreutz, Daniel Seebacher, Tobias Schreck, Michael Grossniklaus, Iain Couzin, Daniel Keim",https://kops.uni-konstanz.de/bitstream/handle/123456789/35829/Stein_0-371514.pdf,"For development and alignment of tactics and strategies, professional soccer analysts spend up to three working days manually analyzing and annotating professional soccer matches. In an effort to improve soccer player and match analysis, a visual-interactive and data-analysis support system focuses on key situations by using rule-based filtering and automatically annotating key types of soccer match elements. The authors evaluate the proposed approach by analyzing real-world soccer matches and several expert studies. Quantitative measures show the proposed methods can significantly outperform naive solutions.",,,,vimeo 230841435,10.1109/MCG.2016.102,2016,2017,Other,101-ABC,Thursday,Sports Data Visual Analytics,10:50 AM,11:10 AM
  CG&A,CG&A,GapChart: a Gap Strategy to Visualize the Temporal Evolution of both Ranks and Scores,"Charles Perin, Jeremy Boy, Frederic Vernier",,,,,,,,2016,2017,Other,101-ABC,Thursday,Sports Data Visual Analytics,11:10 AM,11:30 AM
  CG&A,CG&A,Glyph Visualization: A Fail-Safe Design Scheme Based on Quasi-Hamming Distances,"Philip A. Legg, Eamonn Maguire, Simon Walton, Min Chen",,,,,,,10.1109/MCG.2016.66,2017,2017,Other,101-ABC,Wednesday,Spatiotemporal Applications,10:50 AM,11:10 AM
  CG&A,CG&A,Sport Tournament Predictions by Direct Manipulation,"Romain Vuillemot, Charles Perin",http://romain.vuillemot.net/publis/cga16-sports-tournament-predictions-using-direct-manipulation.pdf,"We present an advanced interface for predicting sport tournaments by direct manipulation. The interface allows users to focus on their prediction tasks, by dragging and dropping teams to their final outcome in the competition, e. g., as winner or semi-finalist. This interface allows predictions to be made non-linearly, such as prediction winner first and filling up the other games. This better matches the way people actually make predictions, while the current interfaces can only be filled linearly, with text fields widgets as input. We released a first version of the interface for the 2014 FIFA soccer World Cup that validated the use of direct manipulation as alternative to widgets. We released an improved version a year later for the 2015/2016 UEFA soccer Champions League, which included a tracking system to understand users interactions. We recorded a total of 504, 307 interaction logs from 3, 029 unique visitors, among them 198 fully completed the prediction from scratch. From our analysis of logs, we provide a list of strategies that people employ to perform predictions, and which will help inform the design of further prediction interfaces that preserve the flow and concentration of users",,,,vimeo 230841474,,2016,2017,Other,101-ABC,Thursday,Sports Data Visual Analytics,11:30 AM,11:50 AM
  CG&A,CG&A,StatCast Dashboard: Exploration of Spatiotemporal Baseball Data,"Marcos Lage, Jorge Henrique Ono, Daniel Cervone, Justin Chiang, Carlos Dietrich, Claudio Silva",,,,,,,10.1109/MCG.2016.101,2016,2017,Other,101-ABC,Wednesday,Spatiotemporal Applications,11:10 AM,11:30 AM
  CG&A,CG&A,VisAdapt: A Visualization Tool to Support Climate Change Adaptation,"Jimmy Johansson, Tomasz Opach, Erik Glaas, Tina-Simone Neset, Carlo Navarra, Bjorn-Ola Linner, Jan Ketil Rod",http://webstaff.itn.liu.se/~jimjo94/papers/CG_and_A-2015-06-Johansson.pdf,"In this article we present the design and implementation of the web-based visualization tool VisAdapt, developed to support homeowners in the Nordic countries to assess anticipated climate change risks, which are expected to negatively impact their living conditions and to identify possible adaptation measures. The tool guides the user through a three-step visual exploration process to facilitate the exploration of risks and adaptive action, specifically modified to the users’ location and house type. We have developed VisAdapt over the course of three years in close collaboration with domain experts and end users to ensure the validity of the included data and the efficiency of the visual interface. Although Nordic homeowners are the targeted end-users of VisAdapt, the insights gained from the development process and the lessons learned from the project could be valuable for researchers in a wide area of application domains. These include how to make global changes tangible on the local level, how to develop easily accessible flow of information and how to incorporate end-user evaluations in the development process.",,,,vimeo 230841562,10.1109/MCG.2016.49,2017,2017,Other,101-ABC,Wednesday,Spatiotemporal Applications,11:30 AM,11:50 AM
  InfoVis,TVCG,Active Reading of Visualizations,"Jagoda Walny, Samuel Huron, Charles Perin, Tiffany Wun, Richard Pusch, Sheelagh Carpendale",,,,,,,,2018,2017,InfoVis,301-D,Thursday,Understanding Visualization,2:40 PM,3:00 PM
  InfoVis,TVCG,Assessing the Graphical Perception of Time and Speed on 2D + Time Trajectories,"Charles Perin, Tiffany Wun, Richard Pusch, Sheelagh Carpendale",,,,,,,,2018,2017,InfoVis,301-D,Tuesday,Time and Space,4:55 PM,5:15 PM
  InfoVis,TVCG,Blinded with Science or Informed by Charts? A Replication Study,"Pierre Dragicevic, Yvonne Jansen",,,,,,,,2018,2017,InfoVis,301-D,Thursday,Understanding Visualization,3:00 PM,3:20 PM
  InfoVis,TVCG,Bridging From Goals to Tasks with Design Study Analysis Reports,"Heidi Lam, Melanie Tory, Tamara Munzner",https://research.tableau.com/sites/default/files/GoalsToTasks.pdf,"Visualization researchers and practitioners engaged in generating or evaluating designs are faced with the difficult problem of transforming the questions asked and actions taken by target users from domain-specific language and context into more abstract forms. Existing abstract task classifications aim to provide support for this endeavour by providing a carefully delineated suite of actions. Our experience is that this bottom-up approach is part of the challenge: low-level actions are difficult to interpret without a higher-level context of analysis goals and the analysis process. To bridge this gap, we propose a framework based on analysis reports derived from open-coding 20 design study papers published at IEEE InfoVis 2009-2015, to build on the previous work of abstractions that collectively encompass a broad variety of domains. The framework is organized in two axes illustrated by nine analysis goals. It helps situate the analysis goals by placing each goal under axes of specificity (Explore, Describe, Explain, Confirm) and number of data populations (Single, Multiple). The single-population types are Discover Observation, Describe Observation, Identify Main Cause, and Collect Evidence. The multiple-population types are Compare Entities, Explain Differences, and Evaluate Hypothesis. Each analysis goal is scoped by an input and an output and is characterized by analysis steps reported in the design study papers. We provide examples of how we and others have used the framework in a top-down approach to abstracting domain problems: visualization designers or researchers first identify the analysis goals of each unit of analysis in an analysis stream, and then encode the individual steps using existing task classifications with the context of the goal, the level of specificity, and the number of populations involved in the analysis.",,http://tinyurl.com/gt27fau,,vimeo 230841235,,2018,2017,InfoVis,301-D,Wednesday,Design,11:30 AM,11:50 AM
  InfoVis,TVCG,Bubble Treemaps for Uncertainty Visualization,"Jochen Görtler, Christoph Schulz, Daniel Weiskopf, Oliver Deussen",http://graphics.uni-konstanz.de/publikationen/Goertler2018BubbleTreemapsUncertainty/bubble-treemaps.pdf,"We present a novel type of circular treemap, where we intentionally allocate extra space for additional visual variables. With this extended visual design space, we encode hierarchically structured data along with their uncertainties in a combined diagram. We introduce a hierarchical and force-based circle-packing algorithm to compute Bubble Treemaps, where each node is visualized using nested contour arcs. Bubble Treemaps do not require any color or shading, which offers unrestricted design choices. We explore uncertainty visualization as an application of our treemaps using standard error and Monte Carlo-based statistical models. To this end, we discuss how uncertainty propagates within hierarchies. Furthermore, we show the effectiveness of our visualization using three different examples: the package structure of Flare, the S&P 500 index, and the US consumer expenditure survey.",,,,vimeo 230840520,,2018,2017,InfoVis,301-D,Thursday,Trees and Table Tennis,10:50 AM,11:10 AM
  InfoVis,TVCG,CasCADe: A Novel 4D Visualization System for Virtual Construction Planning,"Paulo Ivson, Daniel Nascimento, Waldemar Celes, Simone Barbosa",,,,,,,,2018,2017,InfoVis,301-D,Tuesday,Time and Space,4:35 PM,4:55 PM
  InfoVis,TVCG,Conceptual and Methodological Issues in Evaluating Multidimensional Visualizations for Decision Support,"Evanthia Dimara, Anastasia Bezerianos, Pierre Dragicevic",,,,,,,,2018,2017,InfoVis,301-D,Thursday,Understanding Visualization,2:00 PM,2:20 PM
  InfoVis,TVCG,Considerations for Visual Comparison,Michael Gleicher,http://graphics.cs.wisc.edu/Papers/2018/Gle18/viscomp.pdf,"Supporting comparison is a common and diverse challenge in visualization. Such support is difficult to design because solutions must address both the specifics of their scenario as well as the general issues of comparison. This paper aids designers by providing a strategy for considering those general issues. It presents four considerations that abstract comparison. These considerations identify issues and categorize solutions in a domain independent manner. The first considers how the common elements of comparison—a target set of items that are related and an action the user wants to perform on that relationship—are present in an analysis problem. The second considers why these elements lead to challenges because of their scale, in number of items, complexity of items, or complexity of relationship. The third considers what strategies address the identified scaling challenges, grouping solutions into three broad categories. The fourth considers which visual designs map to these strategies to provide solutions for a comparison analysis problem. In sequence, these considerations provide a process for developers to consider support for comparison in the design of visualization tools. Case studies show how these considerations can help in the design and evaluation of visualization solutions for comparison problems.",,,,vimeo 230841111,,2018,2017,InfoVis,301-D,Wednesday,Design,10:50 AM,11:10 AM
  InfoVis,TVCG,CyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis,"Thomas Höllt, Nicola Pezzotti, Vincent van Unen, Frits Koning, Boudewijn P. F. Lelieveldt, Anna Vilanova",https://www.thomashollt.com/assets/papers/hollt_vis_2017/vis17_cyteguide_visual_guidance_for_hierarchical_single-cell_analysis.pdf,"Single-cell analysis through mass cytometry has become an increasingly important tool for immunologists to study the immune system in health and disease. Mass cytometry creates a high-dimensional description vector for single cells by time-of-flight measurement. Recently, t-Distributed Stochastic Neighborhood Embedding (t-SNE) has emerged as one of the state-of-the-art techniques for the visualization and exploration of single-cell data. Ever increasing amounts of data lead to the adoption of Hierarchical Stochastic Neighborhood Embedding (HSNE), enabling the hierarchical representation of the data. Here, the hierarchy is explored selectively by the analyst, who can request more and more detail in areas of interest. Such hierarchies are usually explored by visualizing disconnected plots of selections in different levels of the hierarchy. This poses problems for navigation, by imposing a high cognitive load on the analyst. In this work, we present an interactive summary-visualization to tackle this problem. CyteGuide guides the analyst through the exploration of hierarchically represented single-cell data, and provides a complete overview of the current state of the analysis. We conducted a two-phase user study with domain experts that use HSNE for data exploration. We first studied their problems with their current workflow using HSNE and the requirements to ease this workflow in a field study. These requirements have been the basis for our visual design. In the second phase, we verified our proposed solution in a user evaluation.",https://cyteguide.cytosplore.org/,https://cyteguide.cytosplore.org/assets/files/tasks.pdf,https://cyteguide.cytosplore.org/assets/files/raw_timings.csv,vimeo 223373844,,2018,2017,InfoVis,301-D,Thursday,Trees and Table Tennis,11:30 AM,11:50 AM
  InfoVis,TVCG,Data Through Others' Eyes: The Impact of Visualizing Others' Expectations on Visualization Interpretation,"Yea-Seul Kim, Katharina Reinecke, Jessica Hullman",http://faculty.washington.edu/jhullman/VIS17_Expectations_SocialVis.pdf,"In addition to visualizing input data, interactive visualizations have the potential to be social artifacts that reveal other people’s perspectives on the data. However, how such social information embedded in a visualization impacts a viewer’s interpretation of the data remains unknown. Inspired by recent interactive visualizations that display people’s expectations of data against the data, we conducted a controlled experiment to evaluate the effect of showing social information in the form of other people’s expectations on people’s ability to recall the data, the degree to which they adjust their expectations to align with the data, and their trust in the accuracy of the data. We found that social information that exhibits a high degree of consensus lead participants to recall the data more accurately relative to participants who were exposed to the data alone. Additionally, participants trusted the accuracy of the data less and were more likely to maintain their initial expectations when other people’s expectations aligned with their own initial expectations but not with the data. We conclude by characterizing the design space for visualizing others’ expectations alongside data.",,,,vimeo 230840801,,2018,2017,InfoVis,301-D,Thursday,Understanding Visualization,2:20 PM,2:40 PM
  InfoVis,TVCG,Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations,"Laura Matzen, Michael Haass, Kristin Divis, Zhiyuan Wang, Andrew Wilson",,,,,,,,2018,2017,InfoVis,301-D,Wednesday,Perception,8:30 AM,8:50 AM
  InfoVis,TVCG,EdWordle: Consistency-preserving Word Cloud Editing,"Yunhai Wang, Xiaowei Chu, Chen Bao, Lifeng Zhu, Oliver Deussen, Baoquan Chen, Michael Sedlmair",http://graphics.uni-konstanz.de/publikationen/Wang2018EdWordleConsistencypreserving/Edwordle.pdf,"We present EdWordle, a method for consistently editing word clouds. At its heart, EdWordle allows users to move and edit words while preserving the neighborhoods of other words. To do so, we combine a constrained rigid body simulation with a neighborhood-aware local Wordle algorithm to update the cloud and to create very compact layouts. The consistent and stable behavior of EdWordle enables users to create new forms of word clouds such as storytelling clouds in which the position of words is carefully edited. We compare our approach with state-of-the-art methods and show that we can improve user performance, user satisfaction, as well as the layout itself.",http://www.edwordle.net,,,vimeo 230840872,,2018,2017,InfoVis,301-D,Thursday,Text and Machine Learning,8:50 AM,9:10 AM
  InfoVis,TVCG,"Exploring Multivariate Event Sequences using Rules, Aggregations, and Selections","Bram Cappers, Jarke van Wijk",http://www.bramcappers.nl/files/papers/eventPad.pdf,"Multivariate event sequences are ubiquitous: travel history, telecommunication conversations, and server logs are some examples. Besides standard properties such as type and timestamp, events often have other associated multivariate data. Current exploration and analysis methods either focus on the temporal analysis of a single attribute or the structural analysis of the multivariate data only. We present an approach where users can explore event sequences at multivariate and sequential level simultaneously by interactively defining a set of rewrite rules using multivariate regular expressions. Users can store resulting patterns as new types of events or attributes to interactively enrich or simplify event sequences for further investigation. In Eventpad we provide a bottom-up glyph-oriented approach for multivariate event sequence analysis by searching, clustering, and aligning them according to newly defined domain specific properties. We illustrate the effectiveness of our approach with real-world data sets including telecommunication traffic and hospital treatments",,,,youtube 2DWVW-vLN8Q,,2018,2017,InfoVis,301-D,Wednesday,Multidimensional Data,2:00 PM,2:20 PM
  InfoVis,TVCG,Extracting and Retargeting Color Mappings from Bitmap Images of Visualizations,"Jorge Poco, Angela Mayhua, Jeffrey Heer",https://idl.cs.washington.edu/papers/extracting-color-mappings/,"Visualization designers regularly use color to encode quantitative or categorical data. However, visualizations ""in the wild"" often violate perceptual color design principles and may only be available as bitmap images. In this work, we contribute a method to semi-automatically extract color encodings from a bitmap visualization image. Given an image and a legend location, we classify the legend as describing either a discrete or continuous color encoding, identify the colors used, and extract legend text using OCR methods. We then combine this information to recover the specific color mapping. Users can also correct interpretation errors using an annotation interface. We evaluate our techniques using a corpus of images extracted from scientific papers and demonstrate accurate automatic inference of color mappings across a variety of chart types. In addition, we present two applications of our method: automatic recoloring to improve perceptual effectiveness, and interactive overlays to enable improved reading of static visualizations.",,,,vimeo 230841267,,2018,2017,InfoVis,301-D,Thursday,Text and Machine Learning,8:30 AM,8:50 AM
  InfoVis,TVCG,Functional Decomposition for Bundled Simpliﬁcation of Trail Sets,"Christophe Hurter, Stephane Puechmorel, Florence Nicol, Alex Telea",http://www.cs.rug.nl/~alext/PAPERS/InfoVis17/paper1.pdf,"Bundling visually aggregates curves to reduce clutter and help finding important patterns in trail-sets or graph drawings. We propose a new approach to bundling based on functional decomposition of the underling dataset. We recover the functional nature of the curves by representing them as linear combinations of piecewise-polynomial basis functions with associated expansion coefficients. Next, we express all curves in a given cluster in terms of a centroid curve and a complementary term, via a set of so-called principal component functions. Based on the above, we propose a two-fold contribution: First, we use cluster centroids to design a new bundling method for 2D and 3D curve-sets. Secondly, we deform the cluster centroids and generate new curves along them, which enables us to modify the underlying data in a statistically-controlled way via its simplified (bundled) view. We demonstrate our method by applications on real-world 2D and 3D datasets for graph bundling, trajectory analysis, and vector field and tensor field visualization",,,,vimeo 230841191,,2018,2017,InfoVis,301-D,Thursday,Graphs and Paths,4:55 PM,5:15 PM
  InfoVis,TVCG,HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples,"Fritz Lekschas, Benjamin Bach, Peter Kerpedjiev, Nils Gehlenborg, Hanspeter Pfister",http://www.biorxiv.org/content/early/2017/07/09/123588.full.pdf,"This paper presents an interactive visualization interface - HiPiler - for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of genomic regions to each other and can contain up to 3 million rows and columns with many sparse regions. Traditional matrix aggregation or pan-and-zoom interfaces largely fail in supporting search, inspection, and comparison of local regions-of-interest (ROIs). ROIs can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. ROIs are first-class objects in HiPiler, which represents them as thumbnail-like “snippets”. Snippets can be laid out automatically based on their data and meta attributes. They are linked back to the matrix and can be explored interactively. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. We describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data.",,,,youtube qoLqje5OYKg,,2018,2017,InfoVis,301-D,Thursday,Graphs and Paths,5:35 PM,5:55 PM
  InfoVis,TVCG,Imagining Replications: Graphical Prediction & Discrete Visualizations Improve Recall & Estimation of Effect Uncertainty,"Jessica Hullman, Matthew Kay, Yea-Seul Kim, Samana Shrestha",http://faculty.washington.edu/jhullman/imagining_replications_cr.pdf,"People often have erroneous intuitions about the results of uncertain processes, such as scientific experiments. Many uncertainty visualizations assume considerable statistical knowledge, but have been shown to prompt erroneous conclusions even when users possess this knowledge. Active learning approaches been shown to improve statistical reasoning, but are rarely applied in visualizing uncertainty in scientific reports. We present a controlled study to evaluate the impact of an interactive, graphical uncertainty prediction technique for communicating uncertainty in experiment results. Using our technique, users sketch their prediction of the uncertainty in experimental effects prior to viewing the true sampling distribution from an experiment. We find that having a user graphically predict the possible effects from experiment replications is an effective way to improve one’s ability to make predictions about replications of new experiments. Additionally, visualizing uncertainty as a set of discrete outcomes, as opposed to a continuous probability distribution, can improve recall of a sampling distribution from a single experiment. Our work has implications for various applications where it is important to elicit peoples’ estimates of probability distributions and to communicate uncertainty effectively",,,,,,2018,2017,InfoVis,301-D,Friday,Evaluation,8:30 AM,8:50 AM
  InfoVis,TVCG,iTTVis: Interactive Visualization of Table Tennis Data,"Yingcai Wu, Ji Lan, Xinhuan Shu, Chenyang Ji, Kejian Zhao, Jiachen Wang, Hui Zhang",,,,,,,,2018,2017,InfoVis,301-D,Thursday,Trees and Table Tennis,10:30 AM,10:50 AM
  InfoVis,TVCG,"Keeping Multiple Views Consistent: Constraints, Validations and Exceptions in Visualization Authoring","Zening Qu, Jessica Hullman",http://faculty.washington.edu/jhullman/VIS17_Consistency_CR.pdf,"Visualizations often appear in multiples, either in a single display (e.g., small multiples, dashboard) or across time or space (e.g., slideshow, set of dashboards). However, existing visualization design guidelines typically focus on single rather than multiple views. Solely following these guidelines can lead to effective yet inconsistent views (e.g., the same field has different axes domains across charts), making interpretation slow and error-prone. Moreover, little is known how consistency balances with other design considerations, making it difficult to incorporate consistency mechanisms in visualization authoring software. We present a wizard-of-oz study in which we observed how Tableau users achieve and sacrifice consistency in an exploration-to-presentation visualization design scenario. We extend (from our prior work) a set of encoding-specific constraints defining consistency across multiple views. Using the constraints as a checklist in our study, we observed cases where participants spontaneously maintained consistent encodings and warned cases where consistency was overlooked. In response to the warnings, participants either revised views for consistency or stated why they thought consistency should be overwritten. We categorize participants’ actions and responses as constraint validations and exceptions, depicting the relative importance of consistency and other design considerations under various circumstances (e.g., data cardinality, available encoding resources, chart layout). We discuss automatic consistency checking as a constraint-satisfaction problem and provide design implications for communicating inconsistencies to users.",,,,vimeo 230841093,,2018,2017,InfoVis,301-D,Friday,Evaluation,9:50 AM,10:10 AM
  InfoVis,TVCG,LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks,"Hendrik Strobelt, Sebastian Gehrmann, Hanspeter Pfister, Alexander Rush",https://arxiv.org/pdf/1606.07461.pdf,"Recurrent neural networks, and in particular long short-term memory networks (LSTMs), are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noise. In this work, we present LSTMVis a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows a user to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with domain specific structural annotations. We further show several use cases of the tool for analyzing specific hidden state properties on datasets containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis.",http://lstm.seas.harvard.edu/,https://github.com/HendrikStrobelt/LSTMVis,,vimeo 172145096,,2018,2017,InfoVis,301-D,Thursday,Text and Machine Learning,9:50 AM,10:10 AM
  InfoVis,TVCG,Modeling Color Difference for Visualization Design,Danielle Albers Szafir,http://danielleszafir.com/colordiff_vis2017.pdf,"Color is frequently used to encode values in visualizations. For color encodings to be effective, the mapping between colors and values must preserve important differences in the data. However, most guidelines for effective color choice in visualization are based on either color perceptions measured using large, uniform fields in optimal viewing environments or on qualitative intuitions. These limitations may cause data misinterpretation in visualizations, which frequently use small, elongated marks. Our goal is to develop quantitative metrics to help people use color more effectively in visualizations. We present a series of crowdsourced studies measuring color difference perceptions for three common mark types: points, bars, and lines. Our results indicate that peoples’ abilities to perceive color differences varies significantly across mark types. Probabilistic models constructed from the resulting data can provide objective guidance for designers, allowing them to anticipate viewer perceptions in order to inform effective encoding design.",,,http://cmci.colorado.edu/visualab/VisColors/,vimeo 230841251,,2018,2017,Other,301-CD,Tuesday,VIS Awards & Best Papers,9:50 AM,10:10 AM
  InfoVis,TVCG,MyBrush: Brushing and Linking with Personal Agency,"Philipp Koytek, Charles Perin, Jo Vermeulen, Elisabeth André, Sheelagh Carpendale",,,,,,,,2018,2017,InfoVis,301-D,Tuesday,Techniques,2:20 PM,2:40 PM
  