anchor.diff:  VAST,TVCG,Visual Diagnosis of Tree Boosting Methods,"Jiannan Xiao, Junlin Liu, Xiting Wang, Jing Wu, Jun Zhu, Shixia Liu",http://shixialiu.com/publications/boostvis/paper.pdf,"Tree boosting, which combines weak learners (typically decision trees) to generate a strong learner, is a highly effective and widely used machine learning method. However, the development of a high performance tree boosting model is a time-consuming process that requires numerous trial-and-error experiments. To tackle this issue, we have developed a visual diagnosis tool, BOOSTVis, to help experts quickly analyze and diagnose the training process of tree boosting. In particular, we have designed a temporal confusion matrix visualization, and combined it with a t-SNE projection and a tree visualization. These visualization components work together to provide a comprehensive overview of a tree boosting model, and enable an effective diagnosis of an unsatisfactory training process. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms.",http://www.shixialiu.com:8082/static/index.html,,,video http://shixialiu.com/publications/boostvis/video.mp4,,2018,2017,VAST,102-ABC,Friday,ML3: Classification,8:50 AM,9:10 AM
   VAST,TVCG,Visualizing Big Data Outliers through Distributed Aggregation,Leland Wilkinson,,,,,,,,2018,2017,VAST,301-C,Wednesday,High-dimensional Data,9:30 AM,9:50 AM
   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",,,,vimeo 230830865,,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",https://idl.cs.washington.edu/files/2018-TensorFlowGraph-VAST.pdf,"We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the graph, we decouple non-critical nodes from the layout. To provide an overview, we build a clustered graph using the hierarchical structure annotated in the source code. To support exploration of nested structure on demand, we perform edge bundling to enable stable and responsive cluster expansion. Finally, we detect and highlight repeated structures to emphasize a model’s modular composition. To demonstrate the utility of the visualizer, we describe example usage scenarios and report user feedback. Overall, users find the visualizer useful for understanding, debugging, and sharing the structures of their models.",,,,vimeo 232930758,,2018,2017,Other,301-CD,Tuesday,VIS Awards & Best Papers,9:30 AM,9:50 AM
  +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",https://idl.cs.washington.edu/files/2018-TensorFlowGraph-VAST.pdf,"We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the graph, we decouple non-critical nodes from the layout. To provide an overview, we build a clustered graph using the hierarchical structure annotated in the source code. To support exploration of nested structure on demand, we perform edge bundling to enable stable and responsive cluster expansion. Finally, we detect and highlight repeated structures to emphasize a model’s modular composition. To demonstrate the utility of the visualizer, we describe example usage scenarios and report user feedback. Overall, users find the visualizer useful for understanding, debugging, and sharing the structures of their models.",,,,vimeo 232930758,,2018,2017,Other,301-CD,Tuesday,VIS Awards & Best Papers,9:05 AM,9:25 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",,,,,,youtube RzXfxXjAoYk,,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.",,https://github.com/nyuvis/explanation_explorer,,vimeo 235631465,,2017,2017,VAST,102-ABC,Friday,ML3: Classification,9:10 AM,9:30 AM
  
positive.code: InfoVis,TVCG,iTTVis: Interactive Visualization of Table Tennis Data,"Yingcai Wu, Ji Lan, Xinhuan Shu, Chenyang Ji, Kejian Zhao, Jiachen Wang, Hui Zhang",http://www.cad.zju.edu.cn/home/ycwu/Files/iTTVis.pdf,"The rapid development of information technology paved the way for the recording of fine-grained data, such as stroke techniques and stroke placements, during a table tennis match. This data recording creates opportunities to analyze and evaluate matches from new perspectives. Nevertheless, the increasingly complex data poses a significant challenge to make sense of and gain insights into. Analysts usually employ tedious and cumbersome methods which are limited to watching videos and reading statistical tables. However, existing sports visualization methods cannot be applied to visualizing table tennis competitions due to different competition rules and particular data attributes. In this work, we collaborate with data analysts to understand and characterize the sophisticated domain problem of analysis of table tennis data. We propose iTTVis, a novel interactive table tennis visualization system, which to our knowledge, is the first visual analysis system for analyzing and exploring table tennis data. iTTVis provides a holistic visualization of an entire match from three main perspectives, namely, time-oriented, statistical, and tactical analyses. The proposed system with several well-coordinated views not only supports correlation identification through statistics and pattern detection of tactics with a score timeline but also allows cross analysis to gain insights. Data analysts have obtained several new insights by using iTTVis. The effectiveness and usability of the proposed system are demonstrated with four case studies.",,,,video http://www.cad.zju.edu.cn/home/ycwu/Files/iTTVis.mp4,,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.",,,,video http://cmci.colorado.edu/visualab/papers/infovis_paper-333.m4v,,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",http://innovis.cpsc.ucalgary.ca/supplemental/MyBrush/2018_VIS_mybrush.pdf,"We extend the popular brushing and linking technique by incorporating personal agency in the interaction. We map existing research related to brushing and linking into a design space that deconstructs the interaction technique into three components: source (what is being brushed), link (the expression of relationship between source and target), and target (what is revealed as related to the source). Using this design space, we created MyBrush, a unified interface that offers personal agency over brushing and linking by giving people the flexibility to configure the source, link, and target of multiple brushes. The results of three focus groups demonstrate that people with different backgrounds leveraged personal agency in different ways, including performing complex tasks and showing links explicitly. We reflect on these results, paving the way for future research on the role of personal agency in information visualization.",http://innovis.cpsc.ucalgary.ca/supplemental/MyBrush/demo/,https://github.com/philippkoytek/mybrush,,youtube 1iAtPhWEV9I,,2018,2017,InfoVis,301-D,Tuesday,Techniques,2:20 PM,2:40 PM
  InfoVis,TVCG,Nonlinear Dot Plots,"Nils Rodrigues, Daniel Weiskopf",,,,,,,,2018,2017,InfoVis,301-D,Tuesday,Techniques,2:40 PM,3:00 PM
  InfoVis,TVCG,Open vs Closed Shapes: New Perceptual Categories?,"David Burlinson, Kalpathi Subramanian, Paula Goolkasian",,,,,,,,2018,2017,InfoVis,301-D,Wednesday,Perception,8:50 AM,9:10 AM
  
negative.code: SciVis,TVCG,BASTet: Shareable and reproducible analysis and visualization of mass spectrometry imaging data via OpenMSI,"Oliver Ruebel, Benjamin P. Bowen",,,https://openmsi.nersc.gov/openmsi/client/bastet.html,https://github.com/biorack/BASTet,,vimeo 230835941,,2018,2017,SciVis,106-ABC,Friday,Applications and Visual analysis,9:50 AM,10:10 AM
  SciVis,TVCG,Clique Community Persistence: A Topological Visual Analysis Approach for Complex Networks,"Bastian Rieck, Ulderico Fugacci, Jonas Lukasczyk, Heike Leitte",https://osf.io/td973/,"Complex networks require effective tools and visualizations for their analysis and comparison. Clique communities have been recognized as a powerful concept for describing cohesive structures in networks. We propose an approach that extends the computation of clique communities by considering persistent homology, a topological paradigm originally introduced to characterize and compare the global structure of shapes. Our persistence-based algorithm is able to detect clique communities and to keep track of their evolution according to different edge weight thresholds. We use this information to define comparison metrics and a new centrality measure, both reflecting the relevance of the clique communities inherent to the network. Moreover, we propose an interactive visualization tool based on nested graphs that is capable of compactly representing the evolving relationships between communities for different thresholds and clique degrees. We demonstrate the effectiveness of our approach on various network types",,https://osf.io/rdktg/,,vimeo 230835775,,2018,2017,SciVis,207,Wednesday,Topology-based methods,2:20 PM,2:40 PM
  SciVis,TVCG,Dynamic Load Balancing Based on Constrained K-D Tree Decomposition for Parallel Particle Tracing,"Jiang Zhang, Hanqi Guo, Fan Hong, Xiaoru Yuan, Tom Peterka",http://www.mcs.anl.gov/papers/P7034-0417.pdf,"Particle tracing is a fundamental technique in flow field data visualization. In this work, we present a novel dynamic load balancing method for parallel particle tracing. Specifically, we employ a constrained k-d tree decomposition approach to dynamically redistribute tasks among processes. Each process is initially assigned a regularly partitioned block along with duplicated ghost layer under the memory limit. During particle tracing, the k-d tree decomposition is dynamically performed by constraining the cutting planes in the overlap range of duplicated data. This ensures that each process is reassigned particles as even as possible, and on the other hand the new assigned particles for a process always locate in its block. Result shows good load balance and high efficiency of our method.",,,,,,2018,2017,SciVis,207,Tuesday,"Mix: Foundations, uncertainty, particles",3:20 PM,3:40 PM
  SciVis,TVCG,Globe Browsing: Contextualized Spatio-Temporal Planetary Surface Visualization,"Karl Bladin, Emil Axelsson, Erik Broberg, Carter Emmart, Patric Ljung, Alexander Bock, Anders Ynnerman",https://alexanderbock.github.io/papers/vis17-bladin-globe_browsing.pdf,"Results of planetary mapping are often shared openly for use in scientific research and mission planning. In its raw format, however, the data is not accessible to non-experts due to the difficulty in grasping the context and the intricate acquisition process. We present work on tailoring and integration of multiple data processing and visualization methods to interactively contextualize geospatial surface data of celestial bodies for use in science communication. As our approach handles dynamic data sources, streamed from online repositories, we are significantly shortening the time between discovery and dissemination of data and results. We describe the image acquisition pipeline, the pre-processing steps to derive a 2.5D terrain, and a chunked level-of-detail, out-of-core rendering approach to enable interactive exploration of global maps and high-resolution digital terrain models. The results are demonstrated for three different celestial bodies. The first case addresses high-resolution map data on the surface of Mars. A second case is showing dynamic processes, such as concurrent weather conditions on Earth that require temporal datasets. As a final example we use data from the New Horizons spacecraft which acquired images during a single flyby of Pluto. We visualize the acquisition process as well as the resulting surface data. Our work has been implemented in the OpenSpace software [8], which enables interactive presentations in a range of environments such as immersive dome theaters, interactive touch tables, and virtual reality headsets.",,,,vimeo 230835323,,2018,2017,Other,207,Tuesday,VIS Awards & Best Papers,9:45 AM,10:05 AM
  SciVis,TVCG,Instant Construction and Visualization of Crowded Biological Environments,"Tobias Klein, Ludovic K. Autin, Barbora Kozlikova, David Goodsell, Arthur Olson, Eduard Gröller, Ivan Viola",https://www.cg.tuwien.ac.at/research/publications/2017/klein_2017_IM/klein_2017_IM-paper.pdf,"We present the first approach to integrative structural modeling of the biological mesoscale within an interactive visual environment. These complex models can comprise up to millions of molecules with defined atomic structures, locations, and interactions. Their construction has previously been attempted only within a non-visual and non-interactive environment. Our solution unites the modeling and visualization aspect, enabling interactive construction of atomic resolution mesoscale models of large portions of a cell. We present a novel set of GPU algorithms that build the basis for the rapid construction of complex biological structures. These structures consist of multiple membrane-enclosed compartments including both soluble molecules and fibrous structures. The compartments are defined using volume voxelization of triangulated meshes. For membranes, we present an extension of the Wang Tile concept that populates the bilayer with individual lipids. Soluble molecules are populated within compartments using the Halton sequence for their distribution. Fibrous structures, such as RNA or actin filaments, are created by self-avoiding random walks. Resulting overlaps of molecules are resolved by a forced-based system. Our approach opens new possibilities to the world of interactive construction of cellular compartments. We demonstrate its effectiveness by showcasing scenes of different scale and complexity that comprise blood plasma, mycoplasma, and HIV.",,,,vimeo 230835707,,2018,2017,SciVis,207,Thursday,Visualization in Biology and Medicine,8:50 AM,9:10 AM
  SciVis,TVCG,Interactive Design and Visualization of Branched Covering Spaces,"Lawrence Roy, Prashant Kumar, Sanaz Golbabaei, Yue Zhang, Eugene Zhang",http://web.engr.oregonstate.edu/~zhange/images/Paper_BCSVis_main.pdf,"Branched covering spaces are a mathematical concept which originates from complex analysis and topology and has applications in tensor field topology and geometry remeshing. Given a manifold surface and an N-way rotational symmetry field, a branched covering space is a manifold surface that has an N-to-1 map to the original surface except at the ramification points, which correspond to the singularities in the rotational symmetry field. Understanding the notion and mathematical properties of branched covering spaces is important to researchers in tensor field visualization and geometry processing, and their application areas. In this paper, we provide a framework to interactively design and visualize the branched covering space (BCS) of an input mesh surface and a rotational symmetry field defined on it. In our framework, the user can visualize not only the BCSs but also their construction process. In addition, our system allows the user to design the geometric realization of the BCS using mesh deformation techniques as well as connecting tubes. This enables the user to verify important facts about BCSs such as that they are manifold surfaces around singularities, as well as the Riemann-Hurwitz formula which relates the Euler characteristic of the BCS to that of the original mesh. Our system is evaluated by student researchers in scientific visualization and geometry processing as well as faculty members in mathematics at our university who teach topology. We include their evaluations and feedback in the paper.",,,,vimeo 230835443,,2018,2017,SciVis,207,Wednesday,Topology-based methods,3:20 PM,3:40 PM
  SciVis,TVCG,Interactive Dynamic Volume Illumination with Refraction and Caustics,"Jens Magnus, Stefan Bruckner",,,,,,youtube 3tn6sSXw4NQ,,2018,2017,SciVis,207,Tuesday,Volume Rendering,5:15 PM,5:35 PM
  