DevLan/vispubs huggingface dataset:
Visual Analysis of Argumentation in Essays
Authors: Dora Kiesel, Patrick Riehmann, Henning Wachsmuth, Benno Stein 0001, Bernd Fröhlich 0001
Published at VAST, 2020

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 position of an argument unit. The overview can be ordered in various ways to reveal patterns and outliers. Subsets of essays can be selected and analyzed in detail using the Argument Unit Occurrence Tree which aggregates the argument structures using hierarchical histograms. This hierarchical view facilitates the estimation of statistics and trends concerning the progression of the argumentation in the essays. It also provides insights into the commonalities and differences between selected subsets. The text view is the necessary textual basis to verify conclusions from the other views and the annotation process. Linking the views and interaction techniques for visual filtering, studying the evolution of stance within a subset of essays and scrutinizing the order of argumentative units enable a deep analysis of essay corpora. Our expert reviews confirmed the utility of the system and revealed detailed and previously unknown information about the argumentation in our sample corpus.
Visual Analytics for Temporal Hypergraph Model Exploration
Authors: Maximilian T. Fischer, Devanshu Arya, Dirk Streeb, Daniel Seebacher, Daniel A. Keim, Marcel Worring
Published at VAST, 2020

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 accurately and predict their behavior over time. However, the interactive exploration and seamless refinement of such hypergraph-based prediction models still pose a major challenge. We contribute Hyper-Matrix, a novel visual analytics technique that addresses this challenge through a tight coupling between machine-learning and interactive visualizations. In particular, the technique incorporates a geometric deep learning model as a blueprint for problem-specific models while integrating visualizations for graph-based and category-based data with a novel combination of interactions for an effective user-driven exploration of hypergraph models. To eliminate demanding context switches and ensure scalability, our matrix-based visualization provides drill-down capabilities across multiple levels of semantic zoom, from an overview of model predictions down to the content. We facilitate a focused analysis of relevant connections and groups based on interactive user-steering for filtering and search tasks, a dynamically modifiable partition hierarchy, various matrix reordering techniques, and interactive model feedback. We evaluate our technique in a case study and through formative evaluation with law enforcement experts using real-world internet forum communication data. The results show that our approach surpasses existing solutions in terms of scalability and applicability, enables the incorporation of domain knowledge, and allows for fast search-space traversal. With the proposed technique, we pave the way for the visual analytics of temporal hypergraphs in a wide variety of domains.
Visual Analytics of Multivariate Event Sequence Data in Racquet Sports
Authors: Jiang Wu, Ziyang Guo, Zuobin Wang, Qingyang Xu, Yingcai Wu
Published at VAST, 2020

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 set of attributes, such as the positions of the ball and the techniques used to hit the ball (such as drive and volley in tennis). Thus, the mentioned sequence of hits can be viewed as a multivariate event sequence. By detecting and analyzing the multivariate subsequences that frequently occur in the rallies (namely, tactical patterns), athletes can gain insights into the playing styles adopted by their opponents, and therefore help them identify systematic weaknesses of the opponents and develop counter strategies in matches. To support such analysis effectively, we propose a steerable multivariate sequential pattern mining algorithm with adjustable weights over event attributes, such that the domain expert can obtain frequent tactical patterns according to the attributes specified by himself. We also propose a re-configurable glyph design to help users simultaneously analyze multiple attributes of the hits. The framework further supports comparative analysis of the tactical patterns, e.g., for different athletes or the same athlete playing under different conditions. By applying the framework on two datasets collected in tennis and badminton matches, we demonstrate that the system is generic and effective for tactic analysis in sports and can help identify signature techniques used by individual athletes. Finally, we discuss the strengths and limitations of the proposed approach based on the feedback from the domain experts.
Visual Causality Analysis of Event Sequence Data
Authors: Zhuochen Jin, Shunan Guo, Nan Chen, Daniel Weiskopf, David Gotz, Nan Cao
Published at VAST, 2020

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 information reflecting the causal relations among event types. Unfortunately, recovering causalities from observational event sequences is challenging, as the heterogeneous and high-dimensional event variables are often connected to rather complex underlying event excitation mechanisms that are hard to infer from limited observations. Many existing automated causal analysis techniques suffer from poor explainability and fail to include an adequate amount of human knowledge. In this paper, we introduce a visual analytics method for recovering causalities in event sequence data. We extend the Granger causality analysis algorithm on Hawkes processes to incorporate user feedback into causal model refinement. The visualization system includes an interactive causal analysis framework that supports bottom-up causal exploration, iterative causal verification and refinement, and causal comparison through a set of novel visualizations and interactions. We report two forms of evaluation: a quantitative evaluation of the model improvements resulting from the user-feedback mechanism, and a qualitative evaluation through case studies in different application domains to demonstrate the usefulness of the system.
Visual cohort comparison for spatial single-cell omics-data
Authors: Antonios Somarakis, Marieke E. Ijsselsteijn, Sietse J. Luk, Boyd Kenkhuis, Noel F. C. C. de Miranda, Boudewijn P. F. Lelieveldt, Thomas Höllt
Published at VAST, 2020

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-scale cohort studies, requiring the comparison of such data at cellular level. In such studies, with little a-priori knowledge of what to expect in the data, explorative data analysis is a necessity. Here, we present an interactive visual analysis workflow for the comparison of cohorts of spatially-resolved omics-data. Our workflow allows the comparative analysis of two cohorts based on multiple levels-of-detail, from simple abundance of contained cell types over complex co-localization patterns to individual comparison of complete tissue images. As a result, the workflow enables the identification of cohort-differentiating features, as well as outlier samples at any stage of the workflow. During the development of the workflow, we continuously consulted with domain experts. To show the effectiveness of the workflow, we conducted multiple case studies with domain experts from different application areas and with different data modalities.
Visual Neural Decomposition to Explain Multivariate Data Sets
Authors: Johannes Knittel, Andrés Lalama, Steffen Koch 0001, Thomas Ertl
Published at VAST, 2020

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 independent variables, this process may become cumbersome and time-consuming due to the many possible combinations that have to be explored. In this paper, we propose a novel approach to visualize correlations between input variables and a target output variable that scales to hundreds of variables. We developed a visual model based on neural networks that can be explored in a guided way to help analysts find and understand such correlations. First, we train a neural network to predict the target from the input variables. Then, we visualize the inner workings of the resulting model to help understand relations within the data set. We further introduce a new regularization term for the backpropagation algorithm that encourages the neural network to learn representations that are easier to interpret visually. We apply our method to artificial and real-world data sets to show its utility.
VizCommender: Computing Text-Based Similarity in Visualization Repositories for Content-Based Recommendations
Authors: Michael Oppermann, Robert Kincaid, Tamara Munzner
Published at VAST, 2020

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 analytic knowledge base. However, as the size of a collection increases, so does the difficulty in finding relevant information. Content-based recommendation (CBR) systems could help analysts in finding and managing workbooks relevant to their interests. Toward this goal, we focus on text-based content that is representative of the subject matter of visualizations rather than the visual encodings and style. We discuss the challenges associated with creating a CBR based on visualization specifications and explore more concretely how to implement the relevance measures required using Tableau workbook specifications as the source of content data. We also demonstrate what information can be extracted from these visualization specifications and how various natural language processing techniques can be used to compute similarity between workbooks as one way to measure relevance. We report on a crowd-sourced user study to determine if our similarity measure mimics human judgement. Finally, we choose latent Dirichl et al.ocation (LDA) as a specific model and instantiate it in a proof-of-concept recommender tool to demonstrate the basic function of our similarity measure.
A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation
Authors: Jakob Jakob, Markus H. Gross, Tobias Günther
Published at SciVis, 2020

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 enable more scientists to engage in data-driven research. In this paper, we construct a large fluid flow data set and apply it to a deep learning problem in scientific visualization. Parameterized by the Reynolds number, the data set contains a wide spectrum of laminar and turbulent fluid flow regimes. The full data set was simulated on a high-performance compute cluster and contains 8000 time-dependent 2D vector fields, accumulating to more than 16 TB in size. Using our public fluid data set, we trained deep convolutional neural networks in order to set a benchmark for an improved post-hoc Lagrangian fluid flow analysis. In in-situ settings, flow maps are exported and interpolated in order to assess the transport characteristics of time-dependent fluids. Using deep learning, we improve the accuracy of flow map interpolations, allowing a more precise flow analysis at a reduced memory IO footprint.
A Suggestive Interface for Untangling Mathematical Knots
Authors: Huan Liu, Hui Zhang 0006
Published at SciVis, 2020

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 legal moves. Our knot interface allows one to sketch and untangle knots by proposing the Reidemeister moves, and can guide the user to untangle mathematical knots to the fewest possible number of crossings by suggesting the moves needed. The system highlights parts of the knot where the Reidemeister moves are applicable, suggests the possible moves, and constrains the user's drawing to legal moves only. This ongoing suggestion is based on a Reidemeister move analyzer, that reads the evolving knot in its Gauss code and predicts the needed Reidemeister moves towards the fewest possible number of crossings. For our principal test case of mathematical knot diagrams, this for the first time permits us to visualize, analyze, and deform them in a mathematical visual interface. In addition, understanding of a fairly long mathematical deformation sequence in our interface can be aided by visual analysis and comparison over the identified “key moments” where only critical changes occur in the sequence. Our knot interface allows users to track and trace mathematical knot deformation with a significantly reduced number of visual frames containing only the Reidemeister moves being applied. All these combine to allow a much cleaner exploratory interface for us to analyze and study mathematical knots and their dynamics in topological space.
A Testing Environment for Continuous Colormaps
Authors: Pascal Nardini, Min Chen 0001, Roxana Bujack, Michael Böttinger, Gerik Scheuermann
Published at SciVis, 2020

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 empirical studies have been the dominant means of evaluation in most other areas, such as designing colormaps. In this paper, we propose to establish a test suite for evaluating the design of colormaps. With such a suite, the users can observe the effects when different continuous colormaps are applied to planar scalar fields that may exhibit various characteristic features, such as jumps, local extrema, ridge or valley lines, different distributions of scalar values, different gradients, different signal frequencies, different levels of noise, and so on. The suite also includes an expansible collection of real-world data sets including the most popular data for colormap testing in the visualization literature. The test suite has been integrated into a web-based application for creating continuous colormaps (https://ccctool.com/), facilitating close inter-operation between design and evaluation processes. This new facility complements traditional evaluation methods such as user testimonies and empirical studies.
A Visualization Framework for Multi-scale Coherent Structures in Taylor-Couette Turbulence
Authors: Duong B. Nguyen, Rodolfo Ostilla Monico, Guoning Chen
Published at SciVis, 2020

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, it is challenging to isolate and visualize them, especially when the cylinder rotation ratio is changing. Previous approaches rely on 2D cross sections to study TCF due to its simplicity, which cannot provide the complete information of TCF. In the meantime, standard visualization techniques, such as volume rendering / iso-surfacing of certain attributes and the placement of integral curves/surfaces, usually produce cluttered visualization. To address this challenge and to support domain experts in the analysis of TCF, we developed a visualization framework to separate large-scale structures from the dense, small-scale structures and provide an effective visual representation of these structures. Instead of using a single physical attribute as the standard approach which cannot efficiently separate structures in different scales for TCF, we adapt the feature level-set method to combine multiple attributes and use them as a filter to separate large- and small-scale structures. To visualize these structures, we apply the iso-surface extraction on the kernel density estimate of the distance field generated from the feature level-set. The proposed methods successfully reveal 3D large-scale coherent structures of TCF with different control parameter settings, which are difficult to achieve with the conventional methods.
Advanced Rendering of Line Data with Ambient Occlusion and Transparency
Authors: David Groß, Stefan Gumhold
Published at SciVis, 2020

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 understanding of complex line data. We present an efficient approach for high quality GPU-based rendering of line data with ambient occlusion and transparency effects. Our approach builds on GPU-based raycasting of rounded cones, which are geometric primitives similar to truncated cones, but with spherical endcaps. Object space ambient occlusion is provided by an efficient voxel cone tracing approach. Our core contribution is a new fragment visibility sorting strategy that allows for interactive visualization of line data sets with millions of line segments. We improve performance further by exploiting hierarchical opacity maps.
ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in Virtual Screening
Authors: María Virginia Sabando, Pavol Ulbrich, Matias Nicolás Selzer, Jan Byska, Jan Mican, Ignacio Ponzoni, Axel J. Soto, Maria Luján Ganuza, Barbora Kozlíková
Published at SciVis, 2020

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 calculations often hinder interpretability of results and prevent experts from assessing the impact of individual molecular features on the resulting representations. To provide a solution for scrutinizing such complex data, we introduce ChemVA, an interactive application for the visual exploration of large molecular ensembles and their features. Our tool consists of multiple coordinated views: Hexagonal view, Detail view, 3D view, Table view, and a newly proposed Difference view designed for the comparison of DR projections. These views display DR projections combined with biological activity, selected molecular features, and confidence scores for each of these projections. This conjunction of views allows the user to drill down through the dataset and to efficiently select candidate compounds. Our approach was evaluated on two case studies of finding structurally similar ligands with similar binding affinity to a target protein, as well as on an external qualitative evaluation. The results suggest that our system allows effective visual inspection and comparison of different high-dimensional molecular representations. Furthermore, ChemVA assists in the identification of candidate compounds while providing information on the certainty behind different molecular representations.
Data-Driven Space-Filling Curves
Authors: Liang Zhou 0001, Christopher R. Johnson 0001, Daniel Weiskopf
Published at SciVis, 2020

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 Hamiltonian path that approximately minimizes an objective function that describes the similarity of data values and location coherency in a neighborhood. Our extended variant even supports multiscale data via quadtrees and octrees. Our method is useful in many areas of visualization including multivariate or comparative visualization ensemble visualization of 2D and 3D data on regular grids or multiscale visual analysis of particle simulations. The effectiveness of our method is evaluated with numerical comparisons to existing techniques and through examples of ensemble and multivariate datasets.
Deep Volumetric Ambient Occlusion
Authors: Dominik Engel, Timo Ropinski
Published at SciVis, 2020

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 function. The proposed neural network only needs to be executed upon change of this global information, and thus supports real-time volume interaction. Accordingly, we demonstrate DVAO's ability to predict volumetric ambient occlusion, such that it can be applied interactively within direct volume rendering. To achieve the best possible results, we propose and analyze a variety of transfer function representations and injection strategies for deep neural networks. Based on the obtained results we also give recommendations applicable in similar volume learning scenarios. Lastly, we show that DVAO generalizes to a variety of modalities, despite being trained on computed tomography data only.