anchor.diff:  InfoVis,TVCG,Visual Exploration of Semantic Relationships in Neural Word Embeddings,"Shusen Liu, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Vivek Srikumar, Bei Wang, Yarden Livnat, Valerio Pascucci",http://www.sci.utah.edu/~shusenl/publications/paper157.pdf,"Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. In particular, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. Here, we introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.",,,,vimeo 230840739,,2018,2017,InfoVis,301-D,Wednesday,Multidimensional Data,3:00 PM,3:20 PM
   InfoVis,TVCG,Visualizing Nonlinear Narratives with Story Curves,"Nam Wook Kim, Benjamin Bach, Hyejin Im, Sasha Schriber, Markus Gross, Hanspeter Pfister",https://vcg.seas.harvard.edu/content/3-publications/20180101-visualizing-nonlinear-narratives-with-story-curves/paper.pdf,"In this paper, we present story curves, a visualization technique for exploring and communicating nonlinear narratives in movies. A nonlinear narrative is a storytelling device that portrays events of a story out of chronological order, e.g., in reverse order or going back and forth between past and future events. Many acclaimed movies employ unique narrative patterns which in turn have inspired other movies and contributed to the broader analysis of narrative patterns in movies. However, understanding and communicating nonlinear narratives is a difficult task due to complex temporal disruptions in the order of events as well as no explicit records specifying the actual temporal order of the underlying story. Story curves visualize the nonlinear narrative of a movie by showing the order in which events are told in the movie and comparing them to their actual chronological order, resulting in possibly meandering visual patterns in the curve. We also present Story Explorer, an interactive tool that visualizes a story curve together with complementary information such as characters and settings. Story Explorer further provides a script curation interface that allows users to specify the chronological order of events in movies. We used Story Explorer to analyze 10 popular nonlinear movies and describe the spectrum of narrative patterns that we discovered, including some novel patterns not previously described in the literature. Feedback from experts highlights potential use cases in screenplay writing and analysis, education and film production. A controlled user study shows that users with no expertise are able to understand visual patterns of nonlinear narratives using story curves.",,,,youtube XQ6xPkAZsPU,,2018,2017,InfoVis,301-D,Tuesday,Techniques,2:00 PM,2:20 PM
   InfoVis,TVCG,What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization,"Oh-Hyun Kwon, Tarik Crnovrsanin, Kwan-Liu Ma",http://vis.cs.ucdavis.edu/papers/kwon_infovis17.pdf,"Using different methods for laying out a graph can lead to very different visual appearances, with which the viewer perceives different information. Selecting a “good” layout method is thus important for visualizing a graph. The selection can be highly subjective and dependent on the given task. A common approach to selecting a good layout is to use aesthetic criteria and visual inspection. However, fully calculating various layouts and their associated aesthetic metrics is computationally expensive. In this paper, we present a machine learning approach to large graph visualization based on computing the topological similarity of graphs using graph kernels. For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics. An important contribution of our work is the development of a new framework to design graph kernels. Our experimental study shows that our estimation calculation is considerably faster than computing the actual layouts and their aesthetic metrics. Also, our graph kernels outperform the state-of-the-art ones in both time and accuracy. In addition, we conducted a user study to demonstrate that the topological similarity computed with our graph kernel matches perceptual similarity assessed by human users.",http://graphvis.net/wgl,,,vimeo 230840405,,2018,2017,InfoVis,301-D,Thursday,Graphs and Paths,4:15 PM,4:35 PM
  -VAST,TVCG,A Utility-aware Visual Approach for Anonymizing Multi-attribute Tabular Data,"Xumeng Wang, Jia-Kai Chou, Wei Chen, Huihua Guan, Wenlong Chen, Tianyi Lao, Kwan-Liu Ma",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/2017privacy.pdf,"Sharing data for public usage requires sanitization to prevent sensitive information from leaking. Previous studies have presented methods for creating privacy preserving visualizations. However, few of them provide sufficient feedback to users on how much utility is reduced (or preserved) during such a process. To address this, we design a visual interface along with a data manipulation pipeline that allows users to gauge utility loss while interactively and iteratively handling privacy issues in their data. Widely known and discussed types of privacy models, i.e., syntactic anonymity and differential privacy, are integrated and compared under different use case scenarios. Case study results on a variety of examples demonstrate the effectiveness of our approach.",,,,vimeo 230830747,,2018,2017,VAST,301-C,Thursday,Visual Representation and Design Study,9:30 AM,9:50 AM
  +VAST,TVCG,A Utility-aware Visual Approach for Anonymizing Multi-attribute Tabular Data,"Xumeng Wang, Jia-Kai Chou, Wei Chen, Huihua Guan, Wenlong Chen, Tianyi Lao, Kwan-Liu Ma",,"Sharing data for public usage requires sanitization to prevent sensitive information from leaking. Previous studies have presented methods for creating privacy preserving visualizations. However, few of them provide sufficient feedback to users on how much utility is reduced (or preserved) during such a process. To address this, we design a visual interface along with a data manipulation pipeline that allows users to gauge utility loss while interactively and iteratively handling privacy issues in their data. Widely known and discussed types of privacy models, i.e., syntactic anonymity and differential privacy, are integrated and compared under different use case scenarios. Case study results on a variety of examples demonstrate the effectiveness of our approach.",,,,vimeo 230830747,,2018,2017,VAST,301-C,Thursday,Visual Representation and Design Study,9:30 AM,9:50 AM
   VAST,TVCG,ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models,"Minsuk Kahng, Pierre Andrews, Aditya Kalro, Duen Horng Chau",https://arxiv.org/pdf/1704.01942.pdf,"While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ACTIVIS, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance- and subset-level. ACTIVIS has been deployed on Facebook’s machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ACTIVIS may work with different models.",,,,vimeo 230829900,,2018,2017,VAST,301-C,Wednesday,ML1: Deep Learning,11:10 AM,11:30 AM
   VAST,TVCG,Analyzing the Training Processes of Deep Generative Models,"Mengchen Liu, Jiaxin Shi, Kelei Cao, Jun Zhu, Shixia Liu",http://shixialiu.com/publications/dgmtracker/paper.pdf,"Among the many types of deep models, deep generative models (DGMs) provide a solution to the important problem of unsupervised and semi-supervised learning. However, training DGMs requires more skill, experience, and know-how because their training is more complex than other types of deep models such as convolutional neural networks (CNNs). We develop a visual analytics approach for better understanding and diagnosing the training process of a DGM. To help experts understand the overall training process, we first extract a large amount of time series data that represents training dynamics (e.g., activation changes over time). A blue-noise polyline sampling scheme is then introduced to select time series samples, which can both preserve outliers and reduce visual clutter. To further investigate the root cause of a failed training process, we propose a credit assignment algorithm that indicates how other neurons contribute to the output of the neuron causing the training failure. Two case studies are conducted with machine learning experts to demonstrate how our approach helps understand and diagnose the training processes of DGMs. We also show how our approach can be directly used to analyze other types of deep models, such as CNNs.",,,,video http://shixialiu.com/publications/dgmtracker/video.mp4,,2018,2017,VAST,301-C,Wednesday,ML1: Deep Learning,10:30 AM,10:50 AM
   VAST,TVCG,Applying Pragmatics Principles for Interaction with Visual Analytics,"Enamul Hoque, Vidya Setlur, Melanie Tory, Isaac Dykeman",https://research.tableau.com/sites/default/files/VAST2017_105.pdf,"Interactive visual data analysis is most productive when users can focus on answering the questions they have about their data, rather than focusing on how to operate the interface to the analysis tool. One viable approach to engaging users in interactive conversations with their data is a natural language interface to visualizations. These interfaces have the potential to be both more expressive and more accessible than other interaction paradigms. We explore how principles from language pragmatics can be applied to the flow of visual analytical conversations, using natural language as an input modality. We evaluate the effectiveness of pragmatics support in our system Evizeon, and present design considerations for conversation interfaces to visual analytics tools.",,,,vimeo 230831885,,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,2:40 PM,3:00 PM
  
positive.code: 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",,,,vimeo 230835553,,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",https://vcg.seas.harvard.edu/content/3-publications/20171001-abstractocyte/vis2017_abstractocyte.pdf,"This paper presents <em>Abstractocyte</em>, a system for the visual analysis of astrocytes and their relation to neurons, in nanoscale volumes of brain tissue. Astrocytes are glial cells, i.e., non-neuronal cells that support neurons and the nervous system. The study of astrocytes has immense potential for understanding brain function. However, their complex and widely-branching structure requires high-resolution electron microscopy imaging and makes visualization and analysis challenging. Furthermore, the structure and function of astrocytes is very different from neurons, and therefore requires the development of new visualization and analysis tools. With Abstractocyte, biologists can explore the morphology of astrocytes using various visual abstraction levels, while simultaneously analyzing neighboring neurons and their connectivity. We define a novel, conceptual 2D abstraction space for jointly visualizing astrocytes and neurons. Neuroscientists can choose a specific joint visualization as a point in this space. Interactively moving this point allows them to smoothly transition between different abstraction levels in an intuitive manner. In contrast to simply switching between different visualizations, this preserves the visual context and correlations throughout the transition. Users can smoothly navigate from concrete, highly-detailed 3D views to simplified and abstracted 2D views. In addition to investigating astrocytes, neurons, and their relationships, we enable the interactive analysis of the distribution of glycogen, which is of high importance to neuroscientists. We describe the design of Abstractocyte, and present three case studies in which neuroscientists have successfully used our system to assess astrocytic coverage of synapses, glycogen distribution in relation to synapses, and astrocytic-mitochondria coverage.",,,,vimeo 230835732,,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,,vimeo 230835941,,2018,2017,SciVis,106-ABC,Friday,Applications and Visual analysis,9:50 AM,10:10 AM
  
negative.code: 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.",http://hipiler.higlass.io/,https://github.com/flekschas/hipiler,,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",,"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,9:25 AM,9:45 AM
  