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,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",http://arjun010.github.io/static/papers/graphiti-vast-17.pdf,"Network visualizations, often in the form of node-link diagrams, are an effective means to understand relationships between entities, discover entities with interesting characteristics, and to identify clusters. While several existing tools allow users to visualize pre-defined networks, creating these networks from raw data remains a challenging task, often requiring users to program custom scripts or write complex SQL commands. Some existing tools also allow users to both visualize and model networks. Interaction techniques adopted by these tools often assume users know the exact conditions for defining edges in the resulting networks. This assumption may not always hold true, however. In cases where users do not know much about attributes in the dataset or when there are several attributes to choose from, users may not know which attributes they could use to formulate linking conditions. We propose an alternate interaction technique to model networks that allows users to demonstrate to the system a subset of nodes and links they wish to see in the resulting network. The system, in response, recommends conditions that can be used to model networks based on the specified nodes and links. In this paper, we show how such a demonstration-based interaction technique can be used to model networks by employing it in a prototype tool, Graphiti. Through multiple usage scenarios, we show how Graphiti not only allows users to model networks from a tabular dataset but also facilitates updating a pre-defined network with additional edge types.",,,,vimeo 230830104,,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",,"Whether and how does the structure of family trees differ by ancestral traits over generations? This is a fundamental question regarding the structural heterogeneity of family trees for the multi-generational transmission research. However, previous work mostly focuses on parent-child scenarios due to the lack of proper tools to handle the complexity of extending the research to multi-generational processes. Through an iterative design study with social scientists and historians, we develop TreeEvo that assists users to generate and test empirical hypotheses for multi-generational research. TreeEvo summarizes and organizes family trees by structural features in a dynamic manner based on a traditional Sankey diagram. A pixel-based technique is further proposed to compactly encode trees with complex structures in each Sankey Node. Detailed information of trees is accessible through a space-efficient visualization with semantic zooming. Moreover, TreeEvo embeds Multinomial Logit Model (MLM) to examine statistical associations between tree structure and ancestral traits. We demonstrate the effectiveness and usefulness of TreeEvo through an in-depth case-study with domain experts using a real-world dataset (containing 54,128 family trees of 126,196 individuals).",,,,youtube 7MqeoD49wVg,,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.",,,,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",https://bib.dbvis.de/uploadedFiles/progressivelearningtopic.pdf,"Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.",,,,vimeo 230830619,,2018,2017,VAST,207,Wednesday,Text Analytics,11:50 AM,12:10 PM
  
negative.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",,"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
  