DevLan/vispubs huggingface dataset:
VIANA: Visual Interactive Annotation of Argumentation
Authors: Fabian Sperrle, Rita Sevastjanova, Rebecca Kehlbeck, Mennatallah El-Assady
Published at VAST, 2019

Argumentation Mining addresses the challenging tasks of identifying boundaries of argumentative text fragments and extracting their relationships. Fully automated solutions do not reach satisfactory accuracy due to their insufficient incorporation of semantics and domain knowledge. Therefore, experts currently rely on time-consuming manual annotations. In this paper, we present a visual analytics system that augments the manual annotation process by automatically suggesting which text fragments to annotate next. The accuracy of those suggestions is improved over time by incorporating linguistic knowledge and language modeling to learn a measure of argument similarity from user interactions. Based on a long-term collaboration with domain experts, we identify and model five high-level analysis tasks. We enable close reading and note-taking, annotation of arguments, argument reconstruction, extraction of argument relations, and exploration of argument graphs. To avoid context switches, we transition between all views through seamless morphing, visually anchoring all text- and graph-based layers. We evaluate our system with a two-stage expert user study based on a corpus of presidential debates. The results show that experts prefer our system over existing solutions due to the speedup provided by the automatic suggestions and the tight integration between text and graph views.
Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation
Authors: David Gotz, Jonathan Zhang, Wenyuan Wang, Joshua Shrestha, David Borland
Published at VAST, 2019

Temporal event data are collected across a broad range of domains, and a variety of visual analytics techniques have been developed to empower analysts working with this form of data. These techniques generally display aggregate statistics computed over sets of event sequences that share common patterns. Such techniques are often hindered, however, by the high-dimensionality of many real-world event sequence datasets which can prevent effective aggregation. A common coping strategy for this challenge is to group event types together prior to visualization, as a pre-process, so that each group can be represented within an analysis as a single event type. However, computing these event groupings as a pre-process also places significant constraints on the analysis. This paper presents a new visual analytics approach for dynamic hierarchical dimension aggregation. The approach leverages a predefined hierarchy of dimensions to computationally quantify the informativeness, with respect to a measure of interest, of alternative levels of grouping within the hierarchy at runtime. This information is then interactively visualized, enabling users to dynamically explore the hierarchy to select the most appropriate level of grouping to use at any individual step within an analysis. Key contributions include an algorithm for interactively determining the most informative set of event groupings for a specific analysis context, and a scented scatter-plus-focus visualization design with an optimization-based layout algorithm that supports interactive hierarchical exploration of alternative event type groupings. We apply these techniques to high-dimensional event sequence data from the medical domain and report findings from domain expert interviews.
Visual Analytics for Electromagnetic Situation Awareness in Radio Monitoring and Management
Authors: Ying Zhao 0001, Xiaobo Luo, Xiaoru Lin, Hairong Wang, Xiaoyan Kui, Fangfang Zhou, Jinsong Wang, Yi Chen 0007, Wei Chen 0001
Published at VAST, 2019

Traditional radio monitoring and management largely depend on radio spectrum data analysis, which requires considerable domain experience and heavy cognition effort and frequently results in incorrect signal judgment and incomprehensive situation awareness. Faced with increasingly complicated electromagnetic environments, radio supervisors urgently need additional data sources and advanced analytical technologies to enhance their situation awareness ability. This paper introduces a visual analytics approach for electromagnetic situation awareness. Guided by a detailed scenario and requirement analysis, we first propose a signal clustering method to process radio signal data and a situation assessment model to obtain qualitative and quantitative descriptions of the electromagnetic situations. We then design a two-module interface with a set of visualization views and interactions to help radio supervisors perceive and understand the electromagnetic situations by a joint analysis of radio signal data and radio spectrum data. Evaluations on real-world data sets and an interview with actual users demonstrate the effectiveness of our prototype system. Finally, we discuss the limitations of the proposed approach and provide future work directions.
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference
Authors: Sebastian Gehrmann, Hendrik Strobelt, Robert Krüger, Hanspeter Pfister, Alexander M. Rush
Published at VAST, 2019

Automation of tasks can have critical consequences when humans lose agency over decision processes. Deep learning models are particularly susceptible since current black-box approaches lack explainable reasoning. We argue that both the visual interface and model structure of deep learning systems need to take into account interaction design. We propose a framework of collaborative semantic inference (CSI) for the co-design of interactions and models to enable visual collaboration between humans and algorithms. The approach exposes the intermediate reasoning process of models which allows semantic interactions with the visual metaphors of a problem, which means that a user can both understand and control parts of the model reasoning process. We demonstrate the feasibility of CSI with a co-designed case study of a document summarization system.
You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems
Authors: Doris Jung Lin Lee, John Lee 0005, Tarique Siddiqui, Jaewoo Kim, Karrie Karahalios, Aditya G. Parameswaran
Published at VAST, 2019

Visual query systems (VQSs) empower users to interactively search for line charts with desired visual patterns, typically specified using intuitive sketch-based interfaces. Despite decades of past work on VQSs, these efforts have not translated to adoption in practice, possibly because VQSs are largely evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we collaborated with experts from three diverse domains—astronomy, genetics, and material science—via a year-long user-centered design process to develop a VQS that supports their workflow and analytical needs, and evaluate how VQSs can be used in practice. Our study results reveal that ad-hoc sketch-only querying is not as commonly used as prior work suggests, since analysts are often unable to precisely express their patterns of interest. In addition, we characterize three essential sensemaking processes supported by our enhanced VQS. We discover that participants employ all three processes, but in different proportions, depending on the analytical needs in each domain. Our findings suggest that all three sensemaking processes must be integrated in order to make future VQSs useful for a wide range of analytical inquiries.
A Structural Average of Labeled Merge Trees for Uncertainty Visualization
Authors: Lin Yan, Yusu Wang 0001, Elizabeth Munch, Ellen Gasparovic, Bei Wang 0001
Published at SciVis, 2019

Physical phenomena in science and engineering are frequently modeled using scalar fields. In scalar field topology, graph-based topological descriptors such as merge trees, contour trees, and Reeb graphs are commonly used to characterize topological changes in the (sub)level sets of scalar fields. One of the biggest challenges and opportunities to advance topology-based visualization is to understand and incorporate uncertainty into such topological descriptors to effectively reason about their underlying data. In this paper, we study a structural average of a set of labeled merge trees and use it to encode uncertainty in data. Specifically, we compute a 1-center tree that minimizes its maximum distance to any other tree in the set under a well-defined metric called the interleaving distance. We provide heuristic strategies that compute structural averages of merge trees whose labels do not fully agree. We further provide an interactive visualization system that resembles a numerical calculator that takes as input a set of merge trees and outputs a tree as their structural average. We also highlight structural similarities between the input and the average and incorporate uncertainty information for visual exploration. We develop a novel measure of uncertainty, referred to as consistency, via a metric-space view of the input trees. Finally, we demonstrate an application of our framework through merge trees that arise from ensembles of scalar fields. Our work is the first to employ interleaving distances and consistency to study a global, mathematically rigorous, structural average of merge trees in the context of uncertainty visualization.
Accelerated Monte Carlo Rendering of Finite-Time Lyapunov Exponents
Authors: Irene Baeza Rojo, Markus H. Gross, Tobias Günther
Published at SciVis, 2019

Time-dependent fluid flows often contain numerous hyperbolic Lagrangian coherent structures, which act as transport barriers that guide the advection. The finite-time Lyapunov exponent is a commonly-used approximation to locate these repelling or attracting structures. Especially on large numerical simulations, the FTLE ridges can become arbitrarily sharp and very complex. Thus, the discrete sampling onto a grid for a subsequent direct volume rendering is likely to miss sharp ridges in the visualization. For this reason, an unbiased Monte Carlo-based rendering approach was recently proposed that treats the FTLE field as participating medium with single scattering. This method constructs a ground truth rendering without discretization, but it is prohibitively slow with render times in the order of days or weeks for a single image. In this paper, we accelerate the rendering process significantly, which allows us to compute video sequence of high-resolution FTLE animations in a much more reasonable time frame. For this, we follow two orthogonal approaches to improve on the rendering process: the volumetric light path integration in gradient domain and an acceleration of the transmittance estimation. We analyze the convergence and performance of the proposed method and demonstrate the approach by rendering complex FTLE fields in several 3D vector fields.
Analysis of the Near-Wall Flow in a Turbine Cascade by Splat Visualization
Authors: Baldwin Nsonga, Gerik Scheuermann, Stefan Gumhold, Jordi Ventosa-Molina, Denis Koschichow, Jochen Fröhlich
Published at SciVis, 2019

Turbines are essential components of jet planes and power plants. Therefore, their efficiency and service life are of central engineering interest. In the case of jet planes or thermal power plants, the heating of the turbines due to the hot gas flow is critical. Besides effective cooling, it is a major goal of engineers to minimize heat transfer between gas flow and turbine by design. Since it is known that splat events have a substantial impact on the heat transfer between flow and immersed surfaces, we adapt a splat detection and visualization method to a turbine cascade simulation in this case study. Because splat events are small phenomena, we use a direct numerical simulation resolving the turbulence in the flow as the base of our analysis. The outcome shows promising insights into splat formation and its relation to vortex structures. This may lead to better turbine design in the future.
Artifact-Based Rendering: Harnessing Natural and Traditional Visual Media for More Expressive and Engaging 3D Visualizations
Authors: Seth Johnson, Francesca Samsel, Greg Abram, Daniel Olson, Andrew J. Solis, Bridger Herman, Phillip J. Wolfram, Christophe Lenglet, Daniel F. Keefe
Published at SciVis, 2019

We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-to-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-the-research-team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication.
Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration
Authors: Andrew Wentzel, Peter Hanula, Timothy Luciani, Baher Elgohari, Hesham Elhalawani, Guadalupe Canahuate, David M. Vock, Clifton D. Fuller, G. Elisabeta Marai
Published at SciVis, 2019

We describe a visual computing approach to radiation therapy (RT) planning, based on spatial similarity within a patient cohort. In radiotherapy for head and neck cancer treatment, dosage to organs at risk surrounding a tumor is a large cause of treatment toxicity. Along with the availability of patient repositories, this situation has lead to clinician interest in understanding and predicting RT outcomes based on previously treated similar patients. To enable this type of analysis, we introduce a novel topology-based spatial similarity measure, T-SSIM, and a predictive algorithm based on this similarity measure. We couple the algorithm with a visual steering interface that intertwines visual encodings for the spatial data and statistical results, including a novel parallel-marker encoding that is spatially aware. We report quantitative results on a cohort of 165 patients, as well as a qualitative evaluation with domain experts in radiation oncology, data management, biostatistics, and medical imaging, who are collaborating remotely.
Deadeye Visualization Revisited: Investigation of Preattentiveness and Applicability in Virtual Environments
Authors: Andrey Krekhov, Sebastian Cmentowski, Andre Waschk, Jens H. Krüger
Published at SciVis, 2019

Visualizations rely on highlighting to attract and guide our attention. To make an object of interest stand out independently from a number of distractors, the underlying visual cue, e.g., color, has to be preattentive. In our prior work, we introduced Deadeye as an instantly recognizable highlighting technique that works by rendering the target object for one eye only. In contrast to prior approaches, Deadeye excels by not modifying any visual properties of the target. However, in the case of 2D visualizations, the method requires an additional setup to allow dichoptic presentation, which is a considerable drawback. As a follow-up to requests from the community, this paper explores Deadeye as a highlighting technique for 3D visualizations, because such stereoscopic scenarios support dichoptic presentation out of the box. Deadeye suppresses binocular disparities for the target object, so we cannot assume the applicability of our technique as a given fact. With this motivation, the paper presents quantitative evaluations of Deadeye in VR, including configurations with multiple heterogeneous distractors as an important robustness challenge. After confirming the preserved preattentiveness (all average accuracies above 90%) under such real-world conditions, we explore VR volume rendering as an example application scenario for Deadeye. We depict a possible workflow for integrating our technique, conduct an exploratory survey to demonstrate benefits and limitations, and finally provide related design implications.
DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network
Authors: Yifan Wang, Zichun Zhong, Jing Hua 0001
Published at SciVis, 2019

This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize fully high-fidelity 3D / 4D organ geometric models from single-view medical images with complicated background in real time. Traditional 3D / 4D medical image reconstruction requires near hundreds of projections, which cost insufferable computational time and deliver undesirable high imaging / radiation dose to human subjects. Moreover, it always needs further notorious processes to segment or extract the accurate 3D organ models subsequently. The computational time and imaging dose can be reduced by decreasing the number of projections, but the reconstructed image quality is degraded accordingly. To our knowledge, there is no method directly and explicitly reconstructing multiple 3D organ meshes from a single 2D medical grayscale image on the fly. Given single-view 2D medical images, e.g., 3D / 4D-CT projections or X-ray images, our end-to-end DeepOrganNet framework can efficiently and effectively reconstruct 3D / 4D lung models with a variety of geometric shapes by learning the smooth deformation fields from multiple templates based on a trivariate tensor-product deformation technique, leveraging an informative latent descriptor extracted from input 2D images. The proposed method can guarantee to generate high-quality and high-fidelity manifold meshes for 3D / 4D lung models; while, all current deep learning based approaches on the shape reconstruction from a single image cannot. The major contributions of this work are to accurately reconstruct the 3D organ shapes from 2D single-view projection, significantly improve the procedure time to allow on-the-fly visualization, and dramatically reduce the imaging dose for human subjects. Experimental results are evaluated and compared with the traditional reconstruction method and the state-of-the-art in deep learning, by using extensive 3D and 4D examples, including both synthetic phantom and real patient datasets. The efficiency of the proposed method shows that it only needs several milliseconds to generate organ meshes with 10K vertices, which has great potential to be used in real-time image guided radiation therapy (IGRT).
Dynamic Nested Tracking Graphs
Authors: Jonas Lukasczyk, Christoph Garth, Gunther H. Weber, Tim Biedert, Ross Maciejewski, Heike Leitte
Published at SciVis, 2019

This work describes an approach for the interactive visual analysis of large-scale simulations, where numerous superlevel set components and their evolution are of primary interest. The approach first derives, at simulation runtime, a specialized Cinema database that consists of images of component groups, and topological abstractions. This database is processed by a novel graph operation-based nested tracking graph algorithm (GO-NTG) that dynamically computes NTGs for component groups based on size, overlap, persistence, and level thresholds. The resulting NTGs are in turn used in a feature-centered visual analytics framework to query specific database elements and update feature parameters, facilitating flexible post hoc analysis.
Extraction and Visual Analysis of Potential Vorticity Banners around the Alps
Authors: Robin Bader, Michael Sprenger, Nikolina Ban, Stefan Rüdisühli, Christoph Schär, Tobias Günther
Published at SciVis, 2019

Potential vorticity is among the most important scalar quantities in atmospheric dynamics. For instance, potential vorticity plays a key role in particularly strong wind peaks in extratropical cyclones and it is able to explain the occurrence of frontal rain bands. Potential vorticity combines the key quantities of atmospheric dynamics, namely rotation and stratification. Under suitable wind conditions elongated banners of potential vorticity appear in the lee of mountains. Their role in atmospheric dynamics has recently raised considerable interest in the meteorological community for instance due to their influence in aviation wind hazards and maritime transport. In order to support meteorologists and climatologists in the analysis of these structures, we developed an extraction algorithm and a visual exploration framework consisting of multiple linked views. For the extraction we apply a predictor-corrector algorithm that follows streamlines and realigns them with extremal lines of potential vorticity. Using the agglomerative hierarchical clustering algorithm, we group banners from different sources based on their proximity. To visually analyze the time-dependent banner geometry, we provide interactive overviews and enable the query for detail on demand, including the analysis of different time steps, potentially correlated scalar quantities, and the wind vector field. In particular, we study the relationship between relative humidity and the banners for their potential in indicating the development of precipitation. Working with our method, the collaborating meteorologists gained a deeper understanding of the three-dimensional processes, which may spur follow-up research in the future.
High-throughput feature extraction for measuring attributes of deforming open-cell foams
Authors: Steve Petruzza, Attila Gyulassy, Samuel Leventhal, John J. Baglino, Michael Czabaj, Ashley D. Spear, Valerio Pascucci
Published at SciVis, 2019

Metallic open-cell foams are promising structural materials with applications in multifunctional systems such as biomedical implants, energy absorbers in impact, noise mitigation, and batteries. There is a high demand for means to understand and correlate the design space of material performance metrics to the material structure in terms of attributes such as density, ligament and node properties, void sizes, and alignments. Currently, X-ray Computed Tomography (CT) scans of these materials are segmented either manually or with skeletonization approaches that may not accurately model the variety of shapes present in nodes and ligaments, especially irregularities that arise from manufacturing, image artifacts, or deterioration due to compression. In this paper, we present a new workflow for analysis of open-cell foams that combines a new density measurement to identify nodal structures, and topological approaches to identify ligament structures between them. Additionally, we provide automated measurement of foam properties. We demonstrate stable extraction of features and time-tracking in an image sequence of a foam being compressed. Our approach allows researchers to study larger and more complex foams than could previously be segmented only manually, and enables the high-throughput analysis needed to predict future foam performance.