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train · 6.09k rows

Conference stringclasses 6 values	Year int64 1.99k 2.03k	Title stringlengths 8 187	DOI stringlengths 16 32	Abstract stringlengths 128 7.15k ⌀	Accessible bool 2 classes	Early bool 2 classes	AuthorNames-Deduped listlengths 1 24	Award listlengths 0 2	Resources listlengths 0 5	ResourceLinks listlengths 0 10
VAST	2,020	A Visual Analytics Framework for Contrastive Network Analysis	10.1109/VAST50239.2020.00010	A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other. For example, when comparing protein interaction networks derived from normal and cancer tissues, one essential task is to discover protein-protein interactions unique to cancer tissu...	false	false	[ "Takanori Fujiwara", "Jian Zhao 0010", "Francine Chen 0001", "Kwan-Liu Ma" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.00151v2", "icon": "paper" } ]
VAST	2,020	A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes	10.1109/TVCG.2020.3028888	Many statistical learning models hold an assumption that the training data and the future unlabeled data are drawn from the same distribution. However, this assumption is difficult to fulfill in real-world scenarios and creates barriers in reusing existing labels from similar application domains. Transfer Learning is i...	false	false	[ "Yuxin Ma", "Arlen Fan", "Jingrui He", "Arun Reddy Nelakurthi", "Ross Maciejewski" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.06876v1", "icon": "paper" } ]
VAST	2,020	A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction	10.1109/TVCG.2020.3028889	Data-driven problem solving in many real-world applications involves analysis of time-dependent multivariate data, for which dimensionality reduction (DR) methods are often used to uncover the intrinsic structure and features of the data. However, DR is usually applied to a subset of data that is either single-time-poi...	false	false	[ "Takanori Fujiwara", "Shilpika", "Naohisa Sakamoto", "Jorji Nonaka", "Keiji Yamamoto", "Kwan-Liu Ma" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.01645v3", "icon": "paper" } ]
VAST	2,020	An Examination of Grouping and Spatial Organization Tasks for High-Dimensional Data Exploration	10.1109/TVCG.2020.3028890	How do analysts think about grouping and spatial operations? This overarching research question incorporates a number of points for investigation, including understanding how analysts begin to explore a dataset, the types of grouping/spatial structures created and the operations performed on them, the relationship betw...	false	false	[ "John E. Wenskovitch", "Chris North 0001" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.09233v1", "icon": "paper" } ]
VAST	2,020	Argus: Interactive a priori Power Analysis	10.1109/TVCG.2020.3028894	A key challenge HCl researchers face when designing a controlled experiment is choosing the appropriate number of participants, or sample size. A priori power analysis examines the relationships among multiple parameters, including the complexity associated with human participants, e.g., order and fatigue effects, to c...	false	false	[ "Xiaoyi Wang", "Alexander Eiselmayer", "Wendy E. Mackay", "Kasper Hornbæk", "Chat Wacharamanotham" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.07564v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/gWoDjnGejGQ", "icon": "video" } ]
VAST	2,020	Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models	10.1109/TVCG.2020.3028976	Advances in language modeling have led to the development of deep attention-based models that are performant across a wide variety of natural language processing (NLP) problems. These language models are typified by a pre-training process on large unlabeled text corpora and subsequently fine-tuned for specific tasks. A...	false	false	[ "Joseph F. DeRose", "Jiayao Wang", "Matthew Berger" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.07053v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/rz3BpEVpS7E", "icon": "video" } ]
VAST	2,020	Auditing the Sensitivity of Graph-based Ranking with Visual Analytics	10.1109/TVCG.2020.3028958	Graph mining plays a pivotal role across a number of disciplines, and a variety of algorithms have been developed to answer who/what type questions. For example, what items shall we recommend to a given user on an e-commerce platform? The answers to such questions are typically returned in the form of a ranked list, an...	false	false	[ "Tiankai Xie", "Yuxin Ma", "Hanghang Tong", "My T. Thai", "Ross Maciejewski" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.07227v1", "icon": "paper" } ]
VAST	2,020	Boba: Authoring and Visualizing Multiverse Analyses	10.1109/TVCG.2020.3028985	Multiverse analysis is an approach to data analysis in which all “reasonable” analytic decisions are evaluated in parallel and interpreted collectively, in order to foster robustness and transparency. However, specifying a multiverse is demanding because analysts must manage myriad variants from a cross-product of anal...	false	false	[ "Yang Liu 0136", "Alex Kale", "Tim Althoff", "Jeffrey Heer" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2007.05551v2", "icon": "paper" } ]
VAST	2,020	CAVA: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge Graphs	10.1109/TVCG.2020.3030443	Most visual analytics systems assume that all foraging for data happens before the analytics process; once analysis begins, the set of data attributes considered is fixed. Such separation of data construction from analysis precludes iteration that can enable foraging informed by the needs that arise in-situ during the ...	false	false	[ "Dylan Cashman", "Shenyu Xu", "Subhajit Das 0002", "Florian Heimerl", "Cong Liu", "Shah Rukh Humayoun", "Michael Gleicher", "Alex Endert", "Remco Chang" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.02865v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/mOidkJ_0_3U", "icon": "video" } ]
VAST	2,020	CcNav: Understanding Compiler Optimizations in Binary Code	10.1109/TVCG.2020.3030357	Program developers spend significant time on optimizing and tuning programs. During this iterative process, they apply optimizations, analyze the resulting code, and modify the compilation until they are satisfied. Understanding what the compiler did with the code is crucial to this process but is very time-consuming a...	false	false	[ "Sabin Devkota", "Pascal Aschwanden", "Adam Kunen", "Matthew P. LeGendre", "Katherine E. Isaacs" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.00956v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/Y3yjiyfNf48", "icon": "video" } ]
VAST	2,020	CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization	10.1109/TVCG.2020.3030418	Deep learning's great success motivates many practitioners and students to learn about this exciting technology. However, it is often challenging for beginners to take their first step due to the complexity of understanding and applying deep learning. We present CNN Explainer, an interactive visualization tool designed...	false	false	[ "Zijie J. Wang", "Robert Turko", "Omar Shaikh", "Haekyu Park", "Nilaksh Das", "Fred Hohman", "Minsuk Kahng", "Polo Chau" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2004.15004v3", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/SlEvmkS4Rs4", "icon": "video" } ]
VAST	2,020	CNNPruner: Pruning Convolutional Neural Networks with Visual Analytics	10.1109/TVCG.2020.3030461	Convolutional neural networks (CNNs) have demonstrated extraordinarily good performance in many computer vision tasks. The increasing size of CNN models, however, prevents them from being widely deployed to devices with limited computational resources, e.g., mobile/embedded devices. The emerging topic of model pruning ...	false	false	[ "Guan Li", "Junpeng Wang", "Han-Wei Shen", "Kaixin Chen 0004", "Guihua Shan", "Zhonghua Lu" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.09940v1", "icon": "paper" } ]
VAST	2,020	Co-Bridges: Pair-wise Visual Connection and Comparison for Multi-item Data Streams	10.1109/TVCG.2020.3030411	In various domains, there are abundant streams or sequences of multi-item data of various kinds, e.g. streams of news and social media texts, sequences of genes and sports events, etc. Comparison is an important and general task in data analysis. For comparing data streams involving multiple items (e.g., words in texts...	false	false	[ "Siming Chen 0001", "Natalia V. Andrienko", "Gennady L. Andrienko", "Jie Li 0006", "Xiaoru Yuan" ]	[]	[]	[]
VAST	2,020	Competing Models: Inferring Exploration Patterns and Information Relevance via Bayesian Model Selection	10.1109/TVCG.2020.3030430	Analyzing interaction data provides an opportunity to learn about users, uncover their underlying goals, and create intelligent visualization systems. The first step for intelligent response in visualizations is to enable computers to infer user goals and strategies through observing their interactions with a system. R...	false	false	[ "Shayan Monadjemi", "Roman Garnett", "Alvitta Ottley" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.06042v2", "icon": "paper" } ]
VAST	2,020	ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data	10.1109/VAST50239.2020.00006	Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and ana...	false	false	[ "Xumeng Wang", "Wei Chen 0001", "Jiazhi Xia", "Zexian Chen", "Dongshi Xu", "Xiangyang Wu", "Mingliang Xu", "Tobias Schreck" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2007.15272v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/KqB3Gy1eHvQ", "icon": "video" } ]
VAST	2,020	DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models	10.1109/TVCG.2020.3030342	With machine learning models being increasingly applied to various decision-making scenarios, people have spent growing efforts to make machine learning models more transparent and explainable. Among various explanation techniques, counterfactual explanations have the advantages of being human-friendly and actionable-a...	false	false	[ "Furui Cheng", "Yao Ming", "Huamin Qu" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.08353v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/wVrJ5youWNU", "icon": "video" } ]
VAST	2,020	Diagnosing Concept Drift with Visual Analytics	10.1109/VAST50239.2020.00007	Concept drift is a phenomenon in which the distribution of a data stream changes over time in unforeseen ways, causing prediction models built on historical data to become inaccurate. While a variety of automated methods have been developed to identify when concept drift occurs, there is limited support for analysts wh...	false	false	[ "Weikai Yang", "Zhen Li 0044", "Mengchen Liu", "Yafeng Lu", "Kelei Cao", "Ross Maciejewski", "Shixia Liu" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2007.14372v3", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/449t1pfeKq0", "icon": "video" } ]
VAST	2,020	Evaluation of Sampling Methods for Scatterplots	10.1109/TVCG.2020.3030432	Given a scatterplot with tens of thousands of points or even more, a natural question is which sampling method should be used to create a small but “good” scatterplot for a better abstraction. We present the results of a user study that investigates the influence of different sampling strategies on multi-class scatterp...	false	false	[ "Jun Yuan 0003", "Shouxing Xiang", "Jiazhi Xia", "Lingyun Yu 0001", "Shixia Liu" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2007.14666v4", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/gPtAZsJKO5I", "icon": "video" } ]
VAST	2,020	Explainable Matrix - Visualization for Global and Local Interpretability of Random Forest Classification Ensembles	10.1109/TVCG.2020.3030354	Over the past decades, classification models have proven to be essential machine learning tools given their potential and applicability in various domains. In these years, the north of the majority of the researchers had been to improve quantitative metrics, notwithstanding the lack of information about models' decisio...	false	false	[ "Mário Popolin Neto", "Fernando Vieira Paulovich" ]	[]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/qlthySP_mwA", "icon": "video" } ]
VAST	2,020	Githru: Visual Analytics for Understanding Software Development History Through Git Metadata Analysis	10.1109/TVCG.2020.3030414	Git metadata contains rich information for developers to understand the overall context of a large software development project. Thus it can help new developers, managers, and testers understand the history of development without needing to dig into a large pile of unfamiliar source code. However, the current tools for...	false	false	[ "Youngtaek Kim", "Jaeyoung Kim", "Hyeon Jeon", "Young-Ho Kim", "Hyunjoo Song", "Bo Hyoung Kim", "Jinwook Seo" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.03115v2", "icon": "paper" } ]
VAST	2,020	HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks	10.1109/TVCG.2020.3030380	To mitigate the pain of manually tuning hyperparameters of deep neural networks, automated machine learning (AutoML) methods have been developed to search for an optimal set of hyperparameters in large combinatorial search spaces. However, the search results of AutoML methods significantly depend on initial configurati...	false	false	[ "Heungseok Park", "Yoonsoo Nam", "Jihoon Kim", "Jaegul Choo" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.02078v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/3nD6kXCL2xI", "icon": "video" } ]
VAST	2,020	HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models	10.1109/TVCG.2020.3030449	In this paper, we present a visual analytics tool for enabling hypothesis-based evaluation of machine learning (ML) models. We describe a novel ML-testing framework that combines the traditional statistical hypothesis testing (commonly used in empirical research) with logical reasoning about the conclusions of multiple...	false	false	[ "Qianwen Wang", "William Alexander", "Jack Pegg", "Huamin Qu", "Min Chen 0001" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2002.05271v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/rf-amrd2Goc", "icon": "video" } ]
VAST	2,020	iConViz: Interactive Visual Exploration of the Default Contagion Risk of Networked-Guarantee Loans	10.1109/VAST50239.2020.00013	Groups of enterprises can serve as guarantees for one another and form complex networks when obtaining loans from commercial banks. During economic slowdowns, corporate default may spread like a virus and lead to large-scale defaults or even systemic financial crises. To help financial regulatory authorities and banks ...	false	false	[ "Zhibin Niu", "Runlin Li", "Junqi Wu", "Dawei Cheng", "Jiawan Zhang" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2006.09542v3", "icon": "paper" } ]
VAST	2,020	II-20: Intelligent and pragmatic analytic categorization of image collections	10.1109/TVCG.2020.3030383	In this paper, we introduce 11–20 (Image Insight 2020), a multimedia analytics approach for analytic categorization of image collections. Advanced visualizations for image collections exist, but they need tight integration with a machine model to support the task of analytic categorization. Directly employing computer ...	false	false	[ "Jan Zahálka", "Marcel Worring", "Jarke J. van Wijk" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2005.02149v3", "icon": "paper" } ]
VAST	2,020	In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals	10.1109/TVCG.2020.3030437	Pathogen outbreaks (i.e., outbreaks of bacteria and viruses) in hospitals can cause high mortality rates and increase costs for hospitals significantly. An outbreak is generally noticed when the number of infected patients rises above an endemic level or the usual prevalence of a pathogen in a defined population. Recon...	false	false	[ "Tom Baumgartl", "Markus Petzold", "Marcel Wunderlich", "Markus Höhn", "Daniel Archambault", "M. Lieser", "A. Dalpke", "Simone Scheithauer", "Michael Marschollek", "Vanessa Eichel", "Nico T. Mutters", "Highmed Consortium", "Tatiana von Landesberger" ]	[ "HM" ]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.09552v3", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/Y3fGnxKLFIM", "icon": "video" } ]
VAST	2,020	InCorr: Interactive Data-Driven Correlation Panels for Digital Outcrop Analysis	10.1109/TVCG.2020.3030409	Geological analysis of 3D Digital Outcrop Models (DOMs) for reconstruction of ancient habitable environments is a key aspect of the upcoming ESA ExoMars 2022 Rosalind Franklin Rover and the NASA 2020 Rover Perseverance missions in seeking signs of past life on Mars. Geologists measure and interpret 3D DOMs, create sedi...	false	false	[ "Thomas Ortner", "Andreas Walch", "Rebecca Nowak", "Robert Barnes", "Thomas Höllt", "M. Eduard Gröller" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2007.11512v2", "icon": "paper" } ]
VAST	2,020	Insight Beyond Numbers: The Impact of Qualitative Factors on Visual Data Analysis	10.1109/TVCG.2020.3030376	As of today, data analysis focuses primarily on the findings to be made inside the data and concentrates less on how those findings relate to the domain of investigation. Contemporary visualization as a field of research shows a strong tendency to adopt this data-centrism. Despite their decisive influence on the analys...	false	false	[ "Benjamin Karer", "Hans Hagen", "Dirk J. Lehmann" ]	[]	[]	[]
VAST	2,020	Integrating Prior Knowledge in Mixed-Initiative Social Network Clustering	10.1109/TVCG.2020.3030347	We propose a new approach-called PK-clustering-to help social scientists create meaningful clusters in social networks. Many clustering algorithms exist but most social scientists find them difficult to understand, and tools do not provide any guidance to choose algorithms, or to evaluate results taking into account th...	false	false	[ "Alexis Pister", "Paolo Buono", "Jean-Daniel Fekete", "Catherine Plaisant", "Paola Valdivia" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2005.02972v2", "icon": "paper" } ]
VAST	2,020	LineSmooth: An Analytical Framework for Evaluating the Effectiveness of Smoothing Techniques on Line Charts	10.1109/TVCG.2020.3030421	We present a comprehensive framework for evaluating line chart smoothing methods under a variety of visual analytics tasks. Line charts are commonly used to visualize a series of data samples. When the number of samples is large, or the data are noisy, smoothing can be applied to make the signal more apparent. However,...	false	false	[ "Paul Rosen 0001", "Ghulam Jilani Quadri" ]	[]	[ "PW", "P", "V", "C" ]	[ { "name": "Project Website with Demo", "url": "https://usfdatavisualization.github.io/LineSmoothDemo/", "icon": "project_website" }, { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2007.13882v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/K...
VAST	2,020	Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs	10.1109/TVCG.2020.3030398	The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summaries of dynamic graphs at multiple temporal scales. First, we recursively generate temporal summaries to abstract overlapping sequences of grap...	false	false	[ "Eren Cakmak", "Udo Schlegel", "Dominik Jäckle", "Daniel A. Keim", "Tobias Schreck" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.08282v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/qqNPRLmFqDM", "icon": "video" } ]
VAST	2,020	MultiSegVA: Using Visual Analytics to Segment Biologging Time Series on Multiple Scales	10.1109/TVCG.2020.3030386	Segmenting biologging time series of animals on multiple temporal scales is an essential step that requires complex techniques with careful parameterization and possibly cross-domain expertise. Yet, there is a lack of visual-interactive tools that strongly support such multi-scale segmentation. To close this gap, we pr...	false	false	[ "Philipp Meschenmoser", "Juri Buchmüller", "Daniel Seebacher", "Martin Wikelski", "Daniel A. Keim" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.00548v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/Zqqlgv7ZaV0", "icon": "video" } ]
VAST	2,020	Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality	10.1109/TVCG.2020.3030358	Causality visualization can help people understand temporal chains of events, such as messages sent in a distributed system, cause and effect in a historical conflict, or the interplay between political actors over time. However, as the scale and complexity of these event sequences grows, even these visualizations can ...	false	false	[ "Arjun Choudhry", "Mandar Sharma", "Pramod Chundury", "Thomas Kapler", "Derek W. S. Gray", "Naren Ramakrishnan", "Niklas Elmqvist" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.02649v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/Ra5hihtc8c0", "icon": "video" } ]
VAST	2,020	P6: A Declarative Language for Integrating Machine Learning in Visual Analytics	10.1109/TVCG.2020.3030453	We present P6, a declarative language for building high performance visual analytics systems through its support for specifying and integrating machine learning and interactive visualization methods. As data analysis methods based on machine learning and artificial intelligence continue to advance, a visual analytics s...	false	false	[ "Jianping Kelvin Li", "Kwan-Liu Ma" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.01399v1", "icon": "paper" } ]
VAST	2,020	PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes	10.1109/TVCG.2020.3030359	In soccer, passing is the most frequent interaction between players and plays a significant role in creating scoring chances. Experts are interested in analyzing players' passing behavior to learn passing tactics, i.e., how players build up an attack with passing. Various approaches have been proposed to facilitate the...	false	false	[ "Xiao Xie", "Jiachen Wang", "Hongye Liang", "Dazhen Deng", "Shoubin Cheng", "Hui Zhang 0051", "Wei Chen 0001", "Yingcai Wu" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.02464v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/Lr6yuBBrMQw", "icon": "video" } ]
VAST	2,020	PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines	10.1109/TVCG.2020.3030361	In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to generate end-to-end ML pipelines. While these techniques facilitate the creation of models, given their black-box nature, the complexity of the underlying algorithms, and the large number of pipelines they derive, they ...	false	false	[ "Jorge Henrique Piazentin Ono", "Sonia Castelo", "Roque Lopez", "Enrico Bertini", "Juliana Freire", "Cláudio T. Silva" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2005.00160v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/0FlwKtToYLQ", "icon": "video" } ]
VAST	2,020	Preserving Minority Structures in Graph Sampling	10.1109/TVCG.2020.3030428	Sampling is a widely used graph reduction technique to accelerate graph computations and simplify graph visualizations. By comprehensively analyzing the literature on graph sampling, we assume that existing algorithms cannot effectively preserve minority structures that are rare and small in a graph but are very import...	false	false	[ "Ying Zhao 0001", "Haojin Jiang", "Qi'an Chen", "Yaqi Qin", "Huixuan Xie", "Yitao Wu", "Shixia Liu", "Zhiguang Zhou", "Jiazhi Xia", "Fangfang Zhou" ]	[ "HM" ]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.02498v2", "icon": "paper" } ]
VAST	2,020	QLens: Visual Analytics of MUlti-step Problem-solving Behaviors for Improving Question Design	10.1109/TVCG.2020.3030337	With the rapid development of online education in recent years, there has been an increasing number of learning platforms that provide students with multi-step questions to cultivate their problem-solving skills. To guarantee the high quality of such learning materials, question designers need to inspect how students' ...	false	false	[ "Meng Xia", "Reshika Palaniyappan Velumani", "Yong Wang 0021", "Huamin Qu", "Xiaojuan Ma" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.12833v1", "icon": "paper" } ]
VAST	2,020	Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics	10.1109/TVCG.2020.3030410	Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal traffic data is aggregated into sequentially organized matrices that are then fed into convolution-based residual neural networks. However, the widely known modifiable areal unit problem within such aggregation processes...	false	false	[ "Wei Zeng 0004", "Chengqiao Lin", "Juncong Lin", "Jincheng Jiang", "Jiazhi Xia", "Cagatay Turkay", "Wei Chen 0001" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2007.15486v3", "icon": "paper" } ]
VAST	2,020	Selection-Bias-Corrected Visualization via Dynamic Reweighting	10.1109/TVCG.2020.3030455	The collection and visual analysis of large-scale data from complex systems, such as electronic health records or clickstream data, has become increasingly common across a wide range of industries. This type of retrospective visual analysis, however, is prone to a variety of selection bias effects, especially for high-...	false	false	[ "David Borland", "Jonathan Zhang", "Smiti Kaul", "David Gotz" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2007.14964v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/pqoQZZ07HOo", "icon": "video" } ]
VAST	2,020	SilkViser: A Visual Explorer of Blockchain-based Cryptocurrency Transaction Data	10.1109/VAST50239.2020.00014	Many blockchain-based cryptocurrencies provide users with online blockchain explorers for viewing online transaction data. However, traditional blockchain explorers mostly present transaction information in textual and tabular forms. Such forms make understanding cryptocurrency transaction mechanisms difficult for novi...	false	false	[ "Zengsheng Zhong", "Shuirun Wei", "Yeting Xu", "Ying Zhao 0001", "Fangfang Zhou", "Feng Luo", "Ronghua Shi" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.02651v1", "icon": "paper" } ]
VAST	2,020	SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization	10.1109/VAST50239.2020.00015	Nowadays, as data becomes increasingly complex and distributed, data analyses often involve several related datasets that are stored on different servers and probably owned by different stakeholders. While there is an emerging need to provide these stakeholders with a full picture of their data under a global context, ...	false	false	[ "Jiazhi Xia", "Tianxiang Chen", "Lei Zhang", "Wei Chen 0001", "Yang Chen", "Xiaolong Zhang 0001", "Cong Xie", "Tobias Schreck" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2007.15591v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/ckm5b5slF7Y", "icon": "video" } ]
VAST	2,020	StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics	10.1109/TVCG.2020.3030352	In machine learning (ML), ensemble methods-such as bagging, boosting, and stacking-are widely-established approaches that regularly achieve top-notch predictive performance. Stacking (also called “stacked generalization”) is an ensemble method that combines heterogeneous base models, arranged in at least one layer, and...	false	false	[ "Angelos Chatzimparmpas", "Rafael Messias Martins", "Kostiantyn Kucher", "Andreas Kerren" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2005.01575v8", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/9lvdgPHGfsQ", "icon": "video" } ]
VAST	2,020	STULL: Unbiased Online Sampling for Visual Exploration of Large Spatiotemporal Data	10.1109/VAST50239.2020.00012	Online sampling-supported visual analytics is increasingly important, as it allows users to explore large datasets with acceptable approximate answers at interactive rates. However, existing online spatiotemporal sampling techniques are often biased, as most researchers have primarily focused on reducing computational ...	false	false	[ "Guizhen Wang", "Jingjing Guo", "MingJie Tang", "Jose Florencio de Queiroz Neto", "Calvin Yau", "Anas Daghistani", "Morteza Karimzadeh", "Walid G. Aref", "David S. Ebert" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.13028v1", "icon": "paper" } ]
VAST	2,020	Supporting the Problem-Solving Loop: Designing Highly Interactive Optimisation Systems	10.1109/TVCG.2020.3030364	Efficient optimisation algorithms have become important tools for finding high-quality solutions to hard, real-world problems such as production scheduling, timetabling, or vehicle routing. These algorithms are typically “black boxes” that work on mathematical models of the problem to solve. However, many problems are ...	false	false	[ "Jie Liu", "Tim Dwyer", "Guido Tack", "Samuel Gratzl", "Kim Marriott" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.03163v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/nT55ocI-73o", "icon": "video" } ]
VAST	2,020	TaxThemis: Interactive Mining and Exploration of Suspicious Tax Evasion Groups	10.1109/TVCG.2020.3030370	Tax evasion is a serious economic problem for many countries, as it can undermine the government's tax system and lead to an unfair business competition environment. Recent research has applied data analytics techniques to analyze and detect tax evasion behaviors of individual taxpayers. However, they have failed to su...	false	false	[ "Yating Lin", "Kamkwai Wong", "Yong Wang 0021", "Rong Zhang", "Bo Dong 0001", "Huamin Qu", "Qinghua Zheng" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.03179v1", "icon": "paper" } ]
VAST	2,020	Topology Density Map for Urban Data Visualization and Analysis	10.1109/TVCG.2020.3030469	Density map is an effective visualization technique for depicting the scalar field distribution in 2D space. Conventional methods for constructing density maps are mainly based on Euclidean distance, limiting their applicability in urban analysis that shall consider road network and urban traffic. In this work, we prop...	false	false	[ "Zezheng Feng", "Haotian Li 0001", "Wei Zeng 0004", "Shuang-Hua Yang", "Huamin Qu" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2007.15828v4", "icon": "paper" } ]
VAST	2,020	Towards Better Bus Networks: A Visual Analytics Approach	10.1109/TVCG.2020.3030458	Bus routes are typically updated every 3–5 years to meet constantly changing travel demands. However, identifying deficient bus routes and finding their optimal replacements remain challenging due to the difficulties in analyzing a complex bus network and the large solution space comprising alternative routes. Most of ...	false	false	[ "Di Weng", "Chengbo Zheng", "Zikun Deng", "Mingze Ma", "Jie Bao 0003", "Yu Zheng 0004", "Mingliang Xu", "Yingcai Wu" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.10915v3", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/DEAfK8F2dQE", "icon": "video" } ]
VAST	2,020	Uplift: A Tangible and Immersive Tabletop System for Casual Collaborative Visual Analytics	10.1109/TVCG.2020.3030334	Collaborative visual analytics leverages social interaction to support data exploration and sensemaking. These processes are typically imagined as formalised, extended activities, between groups of dedicated experts, requiring expertise with sophisticated data analysis tools. However, there are many professional domain...	false	false	[ "Barrett Ens", "Sarah Goodwin", "Arnaud Prouzeau", "Fraser Anderson", "Florence Y. Wang", "Samuel Gratzl", "Zac Lucarelli", "Brendan Moyle", "Jim Smiley", "Tim Dwyer" ]	[]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/JrH2dVuxa1I", "icon": "video" } ]
VAST	2,020	VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection	10.1109/TVCG.2020.3030350	Traffic light detection is crucial for environment perception and decision-making in autonomous driving. State-of-the-art detectors are built upon deep Convolutional Neural Networks (CNNs) and have exhibited promising performance. However, one looming concern with CNN based detectors is how to thoroughly evaluate the p...	false	false	[ "Liang Gou", "Lincan Zou", "Nanxiang Li", "Michael Hofmann 0010", "Arvind Kumar Shekar", "Axel Wendt", "Ren Liu" ]	[ "BP" ]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.12975v1", "icon": "paper" } ]
VAST	2,020	Visilant: Visual Support for the Exploration and Analytical Process Tracking in Criminal Investigations	10.1109/TVCG.2020.3030356	The daily routine of criminal investigators consists of a thorough analysis of highly complex and heterogeneous data of crime cases. Such data can consist of case descriptions, testimonies, criminal networks, spatial and temporal information, and virtually any other data that is relevant for the case. Criminal investig...	false	false	[ "Kristína Zákopcanová", "Marko Rehácek", "Jozef Bátrna", "Daniel Plakinger", "Sergej Stoppel", "Barbora Kozlíková" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.09082v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/xMcE5toAoxY", "icon": "video" } ]
VAST	2,020	Visual Abstraction of Geographical Point Data with Spatial Autocorrelations	10.1109/VAST50239.2020.00011	Scatterplots are always employed to visualize geographical point datasets, which often suffer from an overdraw problem due to the increase of data sizes. A variety of sampling strategies have been proposed to reduce overdraw and visual clutter with the spatial densities of points taken into account. However, informativ...	false	false	[ "Zhiguang Zhou", "Xinlong Zhang", "Zhendong Yang", "Yuanyuan Chen", "Yuhua Liu", "Jin Wen", "Binjie Chen", "Ying Zhao 0001", "Wei Chen 0001" ]	[]	[]	[]
VAST	2,020	Visual Analysis of Argumentation in Essays	10.1109/TVCG.2020.3030425	This paper presents a visual analytics system for exploring, analyzing and comparing argument structures in essay corpora. We provide an overview of the corpus by a list of ArguLines which represent the argument units of each essay by a sequence of glyphs. Each glyph encodes the stance, the depth and the relative posit...	false	false	[ "Dora Kiesel", "Patrick Riehmann", "Henning Wachsmuth", "Benno Stein 0001", "Bernd Fröhlich 0001" ]	[]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/Nongy_HBaM8", "icon": "video" } ]
VAST	2,020	Visual Analytics for Temporal Hypergraph Model Exploration	10.1109/TVCG.2020.3030408	Many processes, from gene interaction in biology to computer networks to social media, can be modeled more precisely as temporal hypergraphs than by regular graphs. This is because hypergraphs generalize graphs by extending edges to connect any number of vertices, allowing complex relationships to be described more acc...	false	false	[ "Maximilian T. Fischer", "Devanshu Arya", "Dirk Streeb", "Daniel Seebacher", "Daniel A. Keim", "Marcel Worring" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.07299v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/Z1J6RX0W2ao", "icon": "video" } ]
VAST	2,020	Visual Analytics of Multivariate Event Sequence Data in Racquet Sports	10.1109/VAST50239.2020.00009	In this work, we propose a generic visual analytics framework to support tactic analysis based on data collected from racquet sports (such as tennis and badminton). The proposed approach models each rally in a game as a sequence of hits (i.e., events) until one athlete scores a point. Each hit can be described with a s...	false	false	[ "Jiang Wu", "Ziyang Guo", "Zuobin Wang", "Qingyang Xu", "Yingcai Wu" ]	[]	[]	[]
VAST	2,020	Visual Causality Analysis of Event Sequence Data	10.1109/TVCG.2020.3030465	Causality is crucial to understanding the mechanisms behind complex systems and making decisions that lead to intended outcomes. Event sequence data is widely collected from many real-world processes, such as electronic health records, web clickstreams, and financial transactions, which transmit a great deal of informa...	false	false	[ "Zhuochen Jin", "Shunan Guo", "Nan Chen", "Daniel Weiskopf", "David Gotz", "Nan Cao" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.00219v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/JWhyQxA7SEg", "icon": "video" } ]
VAST	2,020	Visual cohort comparison for spatial single-cell omics-data	10.1109/TVCG.2020.3030336	Spatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To understand what causes or deteriorates a disease and identify related biomarkers, clinical researchers regularly perform large-sc...	false	false	[ "Antonios Somarakis", "Marieke E. Ijsselsteijn", "Sietse J. Luk", "Boyd Kenkhuis", "Noel F. C. C. de Miranda", "Boudewijn P. F. Lelieveldt", "Thomas Höllt" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2006.05175v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/xGFBiyBkm38", "icon": "video" } ]
VAST	2,020	Visual Neural Decomposition to Explain Multivariate Data Sets	10.1109/TVCG.2020.3030420	Investigating relationships between variables in multi-dimensional data sets is a common task for data analysts and engineers. More specifically, it is often valuable to understand which ranges of which input variables lead to particular values of a given target variable. Unfortunately, with an increasing number of ind...	false	false	[ "Johannes Knittel", "Andrés Lalama", "Steffen Koch 0001", "Thomas Ertl" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.05502v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/dD9wY6R_gt0", "icon": "video" } ]
VAST	2,020	VizCommender: Computing Text-Based Similarity in Visualization Repositories for Content-Based Recommendations	10.1109/TVCG.2020.3030387	Cloud-based visualization services have made visual analytics accessible to a much wider audience than ever before. Systems such as Tableau have started to amass increasingly large repositories of analytical knowledge in the form of interactive visualization workbooks. When shared, these collections can form a visual a...	false	false	[ "Michael Oppermann", "Robert Kincaid", "Tamara Munzner" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.07702v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/wp4CWYFAbZw", "icon": "video" } ]
SciVis	2,020	A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation	10.1109/TVCG.2020.3028947	In recent years, deep learning has opened countless research opportunities across many different disciplines. At present, visualization is mainly applied to explore and explain neural networks. Its counterpart-the application of deep learning to visualization problems-requires us to share data more openly in order to e...	false	false	[ "Jakob Jakob", "Markus H. Gross", "Tobias Günther" ]	[]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/6gKr7YmA0QE", "icon": "video" } ]
SciVis	2,020	A Suggestive Interface for Untangling Mathematical Knots	10.1109/TVCG.2020.3028893	In this paper we present a user-friendly sketching-based suggestive interface for untangling mathematical knots with complicated structures. Rather than treating mathematical knots as if they were 3D ropes, our interface is designed to assist the user to interact with knots with the right sequence of mathematically leg...	false	false	[ "Huan Liu", "Hui Zhang 0006" ]	[]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/D0ltvMJteoE", "icon": "video" } ]
SciVis	2,020	A Testing Environment for Continuous Colormaps	10.1109/TVCG.2020.3028955	Many computer science disciplines (e.g., combinatorial optimization, natural language processing, and information retrieval) use standard or established test suites for evaluating algorithms. In visualization, similar approaches have been adopted in some areas (e.g., volume visualization), while user testimonies and em...	false	false	[ "Pascal Nardini", "Min Chen 0001", "Roxana Bujack", "Michael Böttinger", "Gerik Scheuermann" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.13133v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/QbWd1Iv7eXc", "icon": "video" } ]
SciVis	2,020	A Visualization Framework for Multi-scale Coherent Structures in Taylor-Couette Turbulence	10.1109/TVCG.2020.3028892	Taylor-Couette flow (TCF) is the turbulent fluid motion created between two concentric and independently rotating cylinders. It has been heavily researched in fluid mechanics thanks to the various nonlinear dynamical phenomena that are exhibited in the flow. As many dense coherent structures overlap each other in TCF, ...	false	false	[ "Duong B. Nguyen", "Rodolfo Ostilla Monico", "Guoning Chen" ]	[]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/Al7uuUlkCa4", "icon": "video" } ]
SciVis	2,020	Advanced Rendering of Line Data with Ambient Occlusion and Transparency	10.1109/TVCG.2020.3028954	3D Lines are a widespread rendering primitive for the visualization of data from research fields like fluid dynamics or fiber tractography. Global illumination effects and transparent rendering improve the perception of three-dimensional features and decrease occlusion within the data set, thus enabling better understa...	false	false	[ "David Groß", "Stefan Gumhold" ]	[]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/nP6r-ItI8u4", "icon": "video" } ]
SciVis	2,020	ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in Virtual Screening	10.1109/TVCG.2020.3030438	In the modern drug discovery process, medicinal chemists deal with the complexity of analysis of large ensembles of candidate molecules. Computational tools, such as dimensionality reduction (DR) and classification, are commonly used to efficiently process the multidimensional space of features. These underlying calcul...	false	false	[ "María Virginia Sabando", "Pavol Ulbrich", "Matias Nicolás Selzer", "Jan Byska", "Jan Mican", "Ignacio Ponzoni", "Axel J. Soto", "Maria Luján Ganuza", "Barbora Kozlíková" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.13150v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/vKMRGer-pAY", "icon": "video" } ]
SciVis	2,020	Data-Driven Space-Filling Curves	10.1109/TVCG.2020.3030473	Abstract-We propose a data-driven space-filling curve method for 2D and 3D visualization. Our flexible curve traverses the data elements in the spatial domain in a way that the resulting linearization better preserves features in space compared to existing methods. We achieve such data coherency by calculating a Hamilt...	false	false	[ "Liang Zhou 0001", "Christopher R. Johnson 0001", "Daniel Weiskopf" ]	[]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/gEujag3akYw", "icon": "video" } ]
SciVis	2,020	Deep Volumetric Ambient Occlusion	10.1109/TVCG.2020.3030344	We present a novel deep learning based technique for volumetric ambient occlusion in the context of direct volume rendering. Our proposed Deep Volumetric Ambient Occlusion (DVAO) approach can predict per-voxel ambient occlusion in volumetric data sets, while considering global information provided through the transfer ...	false	false	[ "Dominik Engel", "Timo Ropinski" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.08345v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/AMLlnwqGiIU", "icon": "video" } ]
SciVis	2,020	Direct Volume Rendering with Nonparametric Models of Uncertainty	10.1109/TVCG.2020.3030394	We present a nonparametric statistical framework for the quantification, analysis, and propagation of data uncertainty in direct volume rendering (DVR). The state-of-the-art statistical DVR framework allows for preserving the transfer function (TF) of the ground truth function when visualizing uncertain data; however, ...	false	false	[ "Tushar M. Athawale", "Bo Ma 0002", "Elham Sakhaee", "Christopher R. Johnson 0001", "Alireza Entezari" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.13576v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/3R2dlFj5qoc", "icon": "video" } ]
SciVis	2,020	Efficient and Flexible Hierarchical Data Layouts for a Unified Encoding of Scalar Field Precision and Resolution	10.1109/TVCG.2020.3030381	To address the problem of ever-growing scientific data sizes making data movement a major hindrance to analysis, we introduce a novel encoding for scalar fields: a unified tree of resolution and precision, specifically constructed so that valid cuts correspond to sensible approximations of the original field in the pre...	false	false	[ "Duong Hoang", "Brian Summa", "Harsh Bhatia", "Peter Lindstrom 0001", "Pavol Klacansky", "Will Usher 0001", "Peer-Timo Bremer", "Valerio Pascucci" ]	[]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/4V6r8RUlrX4", "icon": "video" } ]
SciVis	2,020	Extraction and Visualization of Poincare Map Topology for Spacecraft Trajectory Design	10.1109/TVCG.2020.3030402	Mission designers must study many dynamical models to plan a low-cost spacecraft trajectory that satisfies mission constraints. They routinely use Poincare maps to search for a suitable path through the interconnected web of periodic orbits and invariant manifolds found in multi-body gravitational systems. This paper i...	false	false	[ "Xavier Tricoche", "Wayne R. Schlei", "Kathleen C. Howell" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.03454v1", "icon": "paper" } ]
SciVis	2,020	Homomorphic-Encrypted Volume Rendering	10.1109/TVCG.2020.3030436	Computationally demanding tasks are typically calculated in dedicated data centers, and real-time visualizations also follow this trend. Some rendering tasks, however, require the highest level of confidentiality so that no other party, besides the owner, can read or see the sensitive data. Here we present a direct vol...	false	false	[ "Sebastian Mazza", "Daniel Patel", "Ivan Viola" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.02122v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/v0eO7uXGzG4", "icon": "video" } ]
SciVis	2,020	Improving the Usability of Virtual Reality Neuron Tracing with Topological Elements	10.1109/TVCG.2020.3030363	Researchers in the field of connectomics are working to reconstruct a map of neural connections in the brain in order to understand at a fundamental level how the brain processes information. Constructing this wiring diagram is done by tracing neurons through high-resolution image stacks acquired with fluorescence micr...	false	false	[ "Torin McDonald", "Will Usher 0001", "Nathan Morrical", "Attila Gyulassy", "Steve Petruzza", "Frederick Federer", "Alessandra Angelucci", "Valerio Pascucci" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.01891v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/8l5d6hC_K_4", "icon": "video" } ]
SciVis	2,020	Interactive Black-Hole Visualization	10.1109/TVCG.2020.3030452	We present an efficient algorithm for visualizing the effect of black holes on its distant surroundings as seen from an observer nearby in orbit. Our solution is GPU-based and builds upon a two-step approach, where we first derive an adaptive grid to map the 360-view around the observer to the distorted celestial sky, ...	false	false	[ "Annemieke Verbraeck", "Elmar Eisemann" ]	[ "HM" ]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/k5YB_4cCJG4", "icon": "video" } ]
SciVis	2,020	Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images	10.1109/TVCG.2020.3030384	Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However, visual analytics tools are lacking for the specific application of x-ray image classification with multiple structural attributes. In this...	false	false	[ "Xinyi Huang", "Suphanut Jamonnak", "Ye Zhao 0003", "Boyu Wang 0001", "Minh Hoai", "Kevin G. Yager", "Wei Xu 0020" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.02256v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/GKaNHZPqM6I", "icon": "video" } ]
SciVis	2,020	Interactive Visualization of Atmospheric Effects for Celestial Bodies	10.1109/TVCG.2020.3030333	We present an atmospheric model tailored for the interactive visualization of planetary surfaces. As the exploration of the solar system is progressing with increasingly accurate missions and instruments, the faithful visualization of planetary environments is gaining increasing interest in space research, mission plan...	false	false	[ "Jonathas Costa", "Alexander Bock 0002", "Carter Emmart", "Charles D. Hansen", "Anders Ynnerman", "Cláudio T. Silva" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2010.03534v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/e-JPG3Ki2f4", "icon": "video" } ]
SciVis	2,020	IsoTrotter: Visually Guided Empirical Modelling of Atmospheric Convection	10.1109/TVCG.2020.3030389	Empirical models, fitted to data from observations, are often used in natural sciences to describe physical behaviour and support discoveries. However, with more complex models, the regression of parameters quickly becomes insufficient, requiring a visual parameter space analysis to understand and optimize the models. ...	false	false	[ "Juraj Pálenik", "Thomas Spengler", "Helwig Hauser" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.10301v1", "icon": "paper" } ]
SciVis	2,020	Localized Topological Simplification of Scalar Data	10.1109/TVCG.2020.3030353	This paper describes a localized algorithm for the topological simplification of scalar data, an essential pre-processing step of topological data analysis (TDA). Given a scalar field $f$ and a selection of extrema to preserve, the proposed localized topological simplification (LTS) derives a function g that is close t...	false	false	[ "Jonas Lukasczyk", "Christoph Garth", "Ross Maciejewski", "Julien Tierny" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.00083v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/bP6zVaBXIlA", "icon": "video" } ]
SciVis	2,020	Mode Surfaces of Symmetric Tensor Fields: Topological Analysis and Seamless Extraction	10.1109/TVCG.2020.3030431	Mode surfaces are the generalization of degenerate curves and neutral surfaces, which constitute 3D symmetric tensor field topology. Efficient analysis and visualization of mode surfaces can provide additional insight into not only degenerate curves and neutral surfaces, but also how these features transition into each...	false	false	[ "Botong Qu", "Lawrence Roy", "Yue Zhang 0009", "Eugene Zhang" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.04601v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/Q12NMVybRUs", "icon": "video" } ]
SciVis	2,020	Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models	10.1109/TVCG.2020.3030415	We present a new technique for the rapid modeling and construction of scientifically accurate mesoscale biological models. The resulting 3D models are based on a few 2D microscopy scans and the latest knowledge available about the biological entity, represented as a set of geometric relationships. Our new visual-progra...	false	false	[ "Ngan V. T. Nguyen", "Ondrej Strnad", "Tobias Klein", "Deng Luo", "Ruwayda Alharbi", "Peter Wonka", "Martina Maritan", "Peter Mindek", "Ludovic Autin", "David S. Goodsell", "Ivan Viola" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2005.01804v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/g0Ks7a-tITQ", "icon": "video" } ]
SciVis	2,020	Objective Observer-Relative Flow Visualization in Curved Spaces for Unsteady 2D Geophysical Flows	10.1109/TVCG.2020.3030454	Computing and visualizing features in fluid flow often depends on the observer, or reference frame, relative to which the input velocity field is given. A desired property of feature detectors is therefore that they are objective, meaning independent of the input reference frame. However, the standard definition of obj...	false	false	[ "Peter Rautek", "Matej Mlejnek", "Johanna Beyer", "Jakob Troidl", "Hanspeter Pfister", "Thomas Theußl", "Markus Hadwiger" ]	[ "BP" ]	[]	[]
SciVis	2,020	Polyphorm: Structural Analysis of Cosmological Datasets via Interactive Physarum Polycephalum Visualization	10.1109/TVCG.2020.3030407	This paper introduces Polyphorm, an interactive visualization and model fitting tool that provides a novel approach for investigating cosmological datasets. Through a fast computational simulation method inspired by the behavior of Physarum polycephalum, an unicellular slime mold organism that efficiently forages for n...	false	false	[ "Oskar Elek", "Joseph N. Burchett", "J. Xavier Prochaska", "Angus G. Forbes" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.02441v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/V_JivWRFJKA", "icon": "video" } ]
SciVis	2,020	Ray Tracing Structured AMR Data Using ExaBricks	10.1109/TVCG.2020.3030470	Structured Adaptive Mesh Refinement (Structured AMR) enables simulations to adapt the domain resolution to save computation and storage, and has become one of the dominant data representations used by scientific simulations; however, efficiently rendering such data remains a challenge. We present an efficient approach ...	false	false	[ "Ingo Wald", "Stefan Zellmann", "Will Usher 0001", "Nathan Morrical", "Ulrich Lang 0002", "Valerio Pascucci" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.03076v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/WdieKs0m-gY", "icon": "video" } ]
SciVis	2,020	Sea of Genes: A Reflection on Visualising Metagenomic Data for Museums	10.1109/TVCG.2020.3030412	We examine the process of designing an exhibit to communicate scientific findings from a complex dataset and unfamiliar domain to the public in a science museum. Our exhibit sought to communicate new lessons based on scientific findings from the domain of metagenomics. This multi-user exhibit had three goals: (1) to in...	false	false	[ "Keshav Dasu", "Kwan-Liu Ma", "Joyce Ma", "Jennifer Frazier" ]	[]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/r8cFMh0Ze8I", "icon": "video" } ]
SciVis	2,020	The Mixture Graph-A Data Structure for Compressing, Rendering, and Querying Segmentation Histograms	10.1109/TVCG.2020.3030451	In this paper, we present a novel data structure, called the Mixture Graph. This data structure allows us to compress, render, and query segmentation histograms. Such histograms arise when building a mipmap of a volume containing segmentation IDs. Each voxel in the histogram mipmap contains a convex combination (mixtur...	false	false	[ "Khaled A. Al-Thelaya", "Marco Agus", "Jens Schneider 0002" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.02702v1", "icon": "paper" } ]
SciVis	2,020	TopoMap: A 0-dimensional Homology Preserving Projection of High-Dimensional Data	10.1109/TVCG.2020.3030441	Multidimensional Projection is a fundamental tool for high-dimensional data analytics and visualization. With very few exceptions, projection techniques are designed to map data from a high-dimensional space to a visual space so as to preserve some dissimilarity (similarity) measure, such as the Euclidean distance for ...	false	false	[ "Harish Doraiswamy", "Julien Tierny", "Paulo J. S. Silva", "Luis Gustavo Nonato", "Cláudio T. Silva" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.01512v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/2HeB4z_bsOA", "icon": "video" } ]
SciVis	2,020	Uncertainty in Continuous Scatterplots, Continuous Parallel Coordinates, and Fibers	10.1109/TVCG.2020.3030466	In this paper, we introduce uncertainty to continuous scatterplots and continuous parallel coordinates. We derive respective models, validate them with sampling-based brute-force schemes, and present acceleration strategies for their computation. At the same time, we show that our approach lends itself as well for intr...	false	false	[ "Boyan Zheng", "Filip Sadlo" ]	[]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/fcOvkYSbExY", "icon": "video" } ]
SciVis	2,020	Uncertainty-Oriented Ensemble Data Visualization and Exploration using Variable Spatial Spreading	10.1109/TVCG.2020.3030377	As an important method of handling potential uncertainties in numerical simulations, ensemble simulation has been widely applied in many disciplines. Visualization is a promising and powerful ensemble simulation analysis method. However, conventional visualization methods mainly aim at data simplification and highlight...	false	false	[ "Mingdong Zhang", "Li Chen", "Quan Li", "Xiaoru Yuan", "Junhai Yong" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2011.01497v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/nP0jC8QnHHY", "icon": "video" } ]
SciVis	2,020	V2V: A Deep Learning Approach to Variable-to-Variable Selection and Translation for Multivariate Time-Varying Data	10.1109/TVCG.2020.3030346	We present V2V, a novel deep learning framework, as a general-purpose solution to the variable-to-variable (V2V) selection and translation problem for multivariate time-varying data (MTVD) analysis and visualization. V2V leverages a representation learning algorithm to identify transferable variables and utilizes Kullb...	false	false	[ "Jun Han 0010", "Hao Zheng 0006", "Yunhao Xing", "Danny Ziyi Chen", "Chaoli Wang 0001" ]	[]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/OsX3v4aUONE", "icon": "video" } ]
SciVis	2,020	VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data	10.1109/TVCG.2020.3030374	The fundamental motivation of the proposed work is to present a new visualization-guided computing paradigm to combine direct 3D volume processing and volume rendered clues for effective 3D exploration. For example, extracting and visualizing microstructures in-vivo have been a long-standing challenging problem. Howeve...	false	false	[ "Yifan Wang", "Guoli Yan", "Haikuan Zhu", "Sagar Buch", "Ying Wang 0060", "E. Mark Haacke", "Jing Hua 0001", "Zichun Zhong" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.06184v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/aF7PxeHloDs", "icon": "video" } ]
SciVis	2,020	Visual Analysis of Large Multivariate Scattered Data using Clustering and Probabilistic Summaries	10.1109/TVCG.2020.3030379	Rapidly growing data sizes of scientific simulations pose significant challenges for interactive visualization and analysis techniques. In this work, we propose a compact probabilistic representation to interactively visualize large scattered datasets. In contrast to previous approaches that represent blocks of volumet...	false	false	[ "Tobias Rapp", "Christoph Peters 0002", "Carsten Dachsbacher" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.09544v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/qbXFFvO9Y1M", "icon": "video" } ]
SciVis	2,020	Visualization of Human Spine Biomechanics for Spinal Surgery	10.1109/TVCG.2020.3030388	We propose a visualization application, designed for the exploration of human spine simulation data. Our goal is to support research in biomechanical spine simulation and advance efforts to implement simulation-backed analysis in surgical applications. Biomechanical simulation is a state-of-the-art technique for analyz...	false	false	[ "Pepe Eulzer", "Sabine Bauer 0001", "Francis Kilian", "Kai Lawonn" ]	[]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.11148v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/PvH4suYfU-o", "icon": "video" } ]
InfoVis	2,020	A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations	10.1109/TVCG.2020.3029412	Understanding correlation judgement is important to designing effective visualizations of bivariate data. Prior work on correlation perception has not considered how factors including prior beliefs and uncertainty representation impact such judgements. The present work focuses on the impact of uncertainty communication...	false	false	[ "Alireza Karduni", "Douglas Markant", "Ryan Wesslen", "Wenwen Dou" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2008.00058v1", "icon": "paper" } ]
InfoVis	2,020	A Design Space of Vision Science Methods for Visualization Research	10.1109/TVCG.2020.3029413	A growing number of efforts aim to understand what people see when using a visualization. These efforts provide scientific grounding to complement design intuitions, leading to more effective visualization practice. However, published visualization research currently reflects a limited set of available methods for unde...	false	false	[ "Madison A. Elliott", "Christine Nothelfer", "Cindy Xiong", "Danielle Albers Szafir" ]	[ "HM" ]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2009.06855v1", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/v6bJwsHxRLY", "icon": "video" } ]
InfoVis	2,020	A Generic Framework and Library for Exploration of Small Multiples through Interactive Piling	10.1109/TVCG.2020.3028948	Small multiples are miniature representations of visual information used generically across many domains. Handling large numbers of small multiples imposes challenges on many analytic tasks like inspection, comparison, navigation, or annotation. To address these challenges, we developed a framework and implemented a li...	false	false	[ "Fritz Lekschas", "Xinyi Zhou 0005", "Wei Chen 0001", "Nils Gehlenborg", "Benjamin Bach", "Hanspeter Pfister" ]	[ "HM" ]	[ "P", "V" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2005.00595v2", "icon": "paper" }, { "name": "Fast Forward", "url": "https://youtu.be/mojW-9Mc2qs", "icon": "video" } ]
InfoVis	2,020	A Simple Pipeline for Coherent Grid Maps	10.1109/TVCG.2020.3028953	Grid maps are spatial arrangements of simple tiles (often squares or hexagons), each of which represents a spatial element. They are an established, effective way to show complex data per spatial element, using visual encodings within each tile ranging from simple coloring to nested small-multiples visualizations. An e...	false	false	[ "Wouter Meulemans", "Max Sondag", "Bettina Speckmann" ]	[ "HM" ]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/ve7qn0VDyUg", "icon": "video" } ]
InfoVis	2,020	A Structured Review of Data Management Technology for Interactive Visualization and Analysis	10.1109/TVCG.2020.3028891	In the last two decades, interactive visualization and analysis have become a central tool in data-driven decision making. Concurrently to the contributions in data visualization, research in data management has produced technology that directly benefits interactive analysis. Here, we contribute a systematic review of ...	false	false	[ "Leilani Battle", "Carlos Scheidegger" ]	[]	[]	[]
InfoVis	2,020	A Survey of Text Alignment Visualization	10.1109/TVCG.2020.3028975	Text alignment is one of the fundamental techniques text-related domains like natural language processing, computational linguistics, and digital humanities. It compares two or more texts with each other aiming to find similar textual patterns, or to estimate in general how different or similar the texts are. Visualizi...	false	false	[ "Tariq Yousef", "Stefan Jänicke" ]	[]	[ "V" ]	[ { "name": "Fast Forward", "url": "https://youtu.be/iM3vrZRYAfE", "icon": "video" } ]
InfoVis	2,020	Bayesian-Assisted Inference from Visualized Data	10.1109/TVCG.2020.3028984	A Bayesian view of data interpretation suggests that a visualization user should update their existing beliefs about a parameter's value in accordance with the amount of information about the parameter value captured by the new observations. Extending recent work applying Bayesian models to understand and evaluate beli...	false	false	[ "Yea-Seul Kim", "Paula Kayongo", "Madeleine Grunde-McLaughlin", "Jessica Hullman" ]	[]	[]	[]
InfoVis	2,020	Calliope: Automatic Visual Data Story Generation from a Spreadsheet	10.1109/TVCG.2020.3030403	Visual data stories shown in the form of narrative visualizations such as a poster or a data video, are frequently used in data-oriented storytelling to facilitate the understanding and memorization of the story content. Although useful, technique barriers, such as data analysis, visualization, and scripting, make the ...	false	false	[ "Danqing Shi", "Xinyue Xu", "Fuling Sun", "Yang Shi 0007", "Nan Cao" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2010.09975v1", "icon": "paper" } ]
InfoVis	2,020	Cartographic Relief Shading with Neural Networks	10.1109/TVCG.2020.3030456	Shaded relief is an effective method for visualising terrain on topographic maps, especially when the direction of illumination is adapted locally to emphasise individual terrain features. However, digital shading algorithms are unable to fully match the expressiveness of hand-crafted masterpieces, which are created th...	false	false	[ "Bernhard Jenny", "Magnus Heitzler", "Dilpreet Singh", "Marianna Farmakis-Serebryakova", "Jeffery Chieh Liu", "Lorenz Hurni" ]	[]	[ "P" ]	[ { "name": "Paper Preprint", "url": "http://arxiv.org/pdf/2010.01256v1", "icon": "paper" } ]
InfoVis	2,020	Chartem: Reviving Chart Images with Data Embedding	10.1109/TVCG.2020.3030351	In practice, charts are widely stored as bitmap images. Although easily consumed by humans, they are not convenient for other uses. For example, changing the chart style or type or a data value in a chart image practically requires creating a completely new chart, which is often a time-consuming and error-prone process...	false	false	[ "Jiayun Fu", "Bin Zhu", "Weiwei Cui", "Song Ge", "Yun Wang 0012", "Haidong Zhang", "He Huang", "Yuanyuan Tang", "Dongmei Zhang 0001", "Xiaojing Ma 0002" ]	[]	[]	[]

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