DevLan/vispubs huggingface dataset:
A Prediction-Traversal Approach for Compressing Scientific Data on Unstructured Meshes with Bounded Error
Authors: Congrong Ren, Xin Liang 0001, Hanqi Guo 0001
Published at EuroVis, 2024

We explore an error-bounded lossy compression approach for reducing scientific data associated with 2D/3D unstructured meshes. While existing lossy compressors offer a high compression ratio with bounded error for regular grid data, methodologies tailored for unstructured mesh data are lacking; for example, one can compress nodal data as 1D arrays, neglecting the spatial coherency of the mesh nodes. Inspired by the SZ compressor, which predicts and quantizes values in a multidimensional array, we dynamically reorganize nodal data into sequences. Each sequence starts with a seed cell; based on a predefined traversal order, the next cell is added to the sequence if the current cell can predict and quantize the nodal data in the next cell with the given error bound. As a result, one can efficiently compress the quantized nodal data in each sequence until all mesh nodes are traversed. This paper also introduces a suite of novel error metrics, namely continuous mean squared error (CMSE) and continuous peak signal-to-noise ratio (CPSNR), to assess compression results for unstructured mesh data. The continuous error metrics are defined by integrating the error function on all cells, providing objective statistics across nonuniformly distributed nodes/cells in the mesh. We evaluate our methods with several scientific simulations ranging from ocean-climate models and computational fluid dynamics simulations with both traditional and continuous error metrics. We demonstrated superior compression ratios and quality than existing lossy compressors.
A Systematic Literature Review of User Evaluation in Immersive Analytics
Authors: Judith Friedl-Knirsch, Fabian Pointecker, S. Pfistermüller, Christian Stach, Christoph Anthes, Daniel Roth 0001
Published at EuroVis, 2024

User evaluation is a common and useful tool for systematically generating knowledge and validating novel approaches in the domain of Immersive Analytics. Since this research domain centres around users, user evaluation is of extraordinary relevance. Additionally, Immersive Analytics is an interdisciplinary field of research where different communities bring in their own methodologies. It is vital to investigate and synchronise these different approaches with the long-term goal to reach a shared evaluation framework. While there have been several studies focusing on Immersive Analytics as a whole or on certain aspects of the domain, this is the first systematic review of the state of evaluation methodology in Immersive Analytics. The main objective of this systematic literature review is to illustrate methodologies and research areas that are still underrepresented in user studies by identifying current practice in user evaluation in the domain of Immersive Analytics in coherence with the PRISMA protocol. (see https://www.acm.org/publications/class-2012)
An Experimental Evaluation of Viewpoint-Based 3D Graph Drawing
Authors: Simon van Wageningen, Tamara Mchedlidze, Alexandru C. Telea
Published at EuroVis, 2024

Node-link diagrams are a widely used metaphor for creating visualizations of relational data. Most frequently, such techniques address creating 2D graph drawings, which are easy to use on computer screens and in print. In contrast, 3D node-link graph visualizations are far less used, as they have many known limitations and comparatively few well-understood advantages. A key issue here is that such 3D visualizations require users to select suitable viewpoints. We address this limitation by studying the ability of layout techniques to produce high-quality views of 3D graph drawings. For this, we perform a thorough experimental evaluation, comparing 3D graph drawings, rendered from a covering sampling of all viewpoints, with their 2D counterparts across various state-of-the-art node-link drawing algorithms, graph families, and quality metrics. Our results show that, depending on the graph family, 3D node-link diagrams can contain a many viewpoints that yield 2D visualizations that are of higher quality than those created by directly using 2D node-link diagrams. This not only sheds light on the potential of 3D node-link diagrams but also gives a simple approach to produce high-quality 2D node-link diagrams.
Antarstick: Extracting Snow Height From Time-Lapse Photography
Authors: Matej Lang, Radoslav Mráz, Marek Trtík, Sergej Stoppel, Jan Byska, Barbora Kozlíková
Published at EuroVis, 2024

The evolution and accumulation of snow cover are among the most important characteristics influencing Antarctica's climate and biotopes. The changes in Antarctica are also substantially impacting global climate change. Therefore, detailed monitoring of snow evolution is key to understanding such changes. One way to conduct this monitoring is by installing trail cameras in a particular region and then processing the captured information. This option is affordable, but has some drawbacks, such as the fully automatic solution for the extraction of snow height from these images is not feasible. Therefore, it still requires human intervention, manually correcting the inaccurately extracted information. In this paper, we present Antarstick, a tool for visual guidance of the user to potentially wrong values extracted from poor-quality images and support for their interactive correction. This tool allows for much quicker and semi-automated processing of snow height from time-lapse photography.
AutoVizuA11y: A Tool to Automate Screen Reader Accessibility in Charts
Authors: Diogo Duarte, Rita Costa, Pedro Bizarro, Carlos Duarte
Published at EuroVis, 2024

Charts remain widely inaccessible on the web for users of assistive technologies like screen readers. This is, in part, due to data visualization experts still lacking the experience, knowledge, and time to consistently implement accessible charts. As a result, screen reader users are prevented from accessing information and are forced to resort to tabular alternatives (if available), limiting the insights that they can gather. We worked with both groups to develop AutoVizuA11y, a tool that automates the addition of accessible features to web-based charts. It generates human-like descriptions of the data using a large language model, calculates statistical insights from the data, and provides keyboard navigation between multiple charts and underlying elements. Fifteen screen reader users interacted with charts made accessible with AutoVizuA11y in a usability test, thirteen of which praised the tool for its intuitive design, short learning curve, and rich information. On average, they took 66 seconds to complete each of the eight analytical tasks presented and achieved a success rate of 89%. Through a SUS questionnaire, the participants gave AutoVizuA11y an "Excellent" score — 83.5/100 points. We also gathered feedback from two data visualization experts who used the tool. They praised the tool availability, ease of use and functionalities, and provided feedback to add AutoVizuA11y support for other technologies in the future.
AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making
Authors: Shusen Liu 0001, Haichao Miao, Zhimin Li, Matthew L. Olson, Valerio Pascucci, Peer-Timo Bremer
Published at EuroVis, 2024

With recent advances in multi-modal foundation models, the previously text-only large language models (LLM) have evolved to incorporate visual input, opening up unprecedented opportunities for various applications in visualization. Compared to existing work on LLM-based visualization works that generate and control visualization with textual input and output only, the proposed approach explores the utilization of the visual processing ability of multi-modal LLMs to develop Autonomous Visualization Agents (AVAs) that can evaluate the generated visualization and iterate on the result to accomplish user-defined objectives defined through natural language. We propose the first framework for the design of AVAs and present several usage scenarios intended to demonstrate the general applicability of the proposed paradigm. Our preliminary exploration and proof-of-concept agents suggest that this approach can be widely applicable whenever the choices of appropriate visualization parameters require the interpretation of previous visual output. Our study indicates that AVAs represent a general paradigm for designing intelligent visualization systems that can achieve high-level visualization goals, which pave the way for developing expert-level visualization agents in the future.
Beyond ExaBricks: GPU Volume Path Tracing of AMR Data
Authors: Stefan Zellmann, Qi Wu 0015, Alper Sahistan, Kwan-Liu Ma, Ingo Wald
Published at EuroVis, 2024

Adaptive Mesh Refinement (AMR) is becoming a prevalent data representation for HPC, and thus also for scientific visualization. AMR data is usually cell centric (which imposes numerous challenges), complex, and generally hard to render. Recent work on GPU-accelerated AMR rendering has made much progress towards real-time volume and isosurface rendering of such data, but so far this work has focused exclusively on ray marching, with simple lighting models and without scattering events or global illumination. True high-quality rendering requires a modified approach that is able to trace arbitrary incoherent paths; but this may not be a perfect fit for the types of data structures recently developed for ray marching. In this paper, we describe a novel approach to high-quality path tracing of complex AMR data, with a specific focus on analyzing and comparing different data structures and algorithms to achieve this goal.
CAN: Concept-Aligned Neurons for Visual Comparison of Deep Neural Network Models
Authors: Mingwei Li, Sangwon Jeong, Shusen Liu 0001, Matthew Berger
Published at EuroVis, 2024

We present concept-aligned neurons, or CAN, a visualization design for comparing deep neural networks. The goal of CAN is to support users in understanding the similarities and differences between neural networks, with an emphasis on comparing neuron functionality across different models. To make this comparison intuitive, CAN uses concept-based representations of neurons to visually align models in an interpretable manner. A key feature of CAN is the hierarchical organization of concepts, which permits users to relate sets of neurons at different levels of detail. CAN's visualization is designed to help compare the semantic coverage of neurons, as well as assess the distinctiveness, redundancy, and multi-semantic alignment of neurons or groups of neurons, all at different concept granularity. We demonstrate the generality and effectiveness of CAN by comparing models trained on different datasets, neural networks with different architectures, and models trained for different objectives, e.g. adversarial robustness, and robustness to out-of-distribution data.
ChoreoVis: Planning and Assessing Formations in Dance Choreographies
Authors: Samuel Beck, Nina Doerr, Kuno Kurzhals, Alexander Riedlinger, Fabian Schmierer, Michael Sedlmair, Steffen Koch 0001
Published at EuroVis, 2024

Sports visualization has developed into an active research field over the last decades. Many approaches focus on analyzing movement data recorded from unstructured situations, such as soccer. For the analysis of choreographed activities like formation dancing, however, the goal differs, as dancers follow specific formations in coordinated movement trajectories. To date, little work exists on how visual analytics methods can support such choreographed performances. To fill this gap, we introduce a new visual approach for planning and assessing dance choreographies. In terms of planning choreographies, we contribute a web application with interactive authoring tools and views for the dancers' positions and orientations, movement trajectories, poses, dance floor utilization, and movement distances. For assessing dancers' real-world movement trajectories, extracted by manual bounding box annotations, we developed a timeline showing aggregated trajectory deviations and a dance floor view for detailed trajectory comparison. Our approach was developed and evaluated in collaboration with dance instructors, showing that introducing visual analytics into this domain promises improvements in training efficiency for the future.
CUPID: Contextual Understanding of Prompt-conditioned Image Distributions
Authors: Yayan Zhao, Mingwei Li, Matthew Berger
Published at EuroVis, 2024

We present CUPID: a visualization method for the contextual understanding of prompt-conditioned image distributions. CUPID targets the visual analysis of distributions produced by modern text-to-image generative models, wherein a user can specify a scene via natural language, and the model generates a set of images, each intended to satisfy the user's description. CUPID is designed to help understand the resulting distribution, using contextual cues to facilitate analysis: objects mentioned in the prompt, novel, synthesized objects not explicitly mentioned, and their potential relationships. Central to CUPID is a novel method for visualizing high-dimensional distributions, wherein contextualized embeddings of objects, those found within images, are mapped to a low-dimensional space via density-based embeddings. We show how such embeddings allows one to discover salient styles of objects within a distribution, as well as identify anomalous, or rare, object styles. Moreover, we introduce conditional density embeddings, whereby conditioning on a given object allows one to compare object dependencies within the distribution. We employ CUPID for analyzing image distributions produced by large-scale diffusion models, where our experimental results offer insights on language misunderstanding from such models and biases in object composition, while also providing an interface for discovery of typical, or rare, synthesized scenes.
Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptation
Authors: Steffen Holter, Mennatallah El-Assady
Published at EuroVis, 2024

As full AI-based automation remains out of reach in most real-world applications, the focus has instead shifted to leveraging the strengths of both human and AI agents, creating effective collaborative systems. The rapid advances in this area have yielded increasingly more complex systems and frameworks, while the nuance of their characterization has gotten more vague. Similarly, the existing conceptual models no longer capture the elaborate processes of these systems nor describe the entire scope of their collaboration paradigms. In this paper, we propose a new unified set of dimensions through which to analyze and describe human-AI systems. Our conceptual model is centered around three high-level aspects - agency, interaction, and adaptation - and is developed through a multi-step process. Firstly, an initial design space is proposed by surveying the literature and consolidating existing definitions and conceptual frameworks. Secondly, this model is iteratively refined and validated by conducting semi-structured interviews with nine researchers in this field. Lastly, to illustrate the applicability of our design space, we utilize it to provide a structured description of selected human-AI systems.
Depth for Multi-Modal Contour Ensembles
Authors: Nicolas F. Chaves-de-Plaza, Mathijs Molenaar, Prerak Mody, Marius Staring, René van Egmond, Elmar Eisemann, Anna Vilanova, Klaus Hildebrandt
Published at EuroVis, 2024

The contour depth methodology enables non-parametric summarization of contour ensembles by extracting their representatives, confidence bands, and outliers for visualization (via contour boxplots) and robust downstream procedures. We address two shortcomings of these methods. Firstly, we significantly expedite the computation and recomputation of Inclusion Depth (ID), introducing a linear-time algorithm for epsilon ID, a variant used for handling ensembles with contours with multiple intersections. We also present the inclusion matrix, which contains the pairwise inclusion relationships between contours, and leverage it to accelerate the recomputation of ID. Secondly, extending beyond the single distribution assumption, we present the Relative Depth (ReD), a generalization of contour depth for ensembles with multiple modes. Building upon the linear-time eID, we introduce CDclust, a clustering algorithm that untangles ensemble modes of variation by optimizing ReD. Synthetic and real datasets from medical image segmentation and meteorological forecasting showcase the speed advantages, illustrate the use case of progressive depth computation and enable non-parametric multimodal analysis. To promote research and adoption, we offer the contour-depth Python package.
DynTrix: A Hybrid Representation for Dynamic Graphs
Authors: B. Vago, Daniel Archambault, Alessio Arleo
Published at EuroVis, 2024

Hybrid graph representations combine two or more network visualization techniques in a unique drawing, simultaneously leveraging their strong traits. Since their introduction in the early 2000s, hybrid representations have gained significant research interest, with the introduction of new techniques and comparative user studies. However, all this research has not considered dynamic graphs. In this paper, we investigate hybrid graph representations in a dynamic network context and present DynTrix. Our system uses the NodeTrix representation as a basis, but the research extends this representation to the dynamic network domain. DynTrix supports automatic or manually created clusters/matrices across time. Drawing stability is implemented through aggregation and users can rearrange the nodes/matrix positions and pin them. DynTrix visualizes the temporal dynamics of the network through a combination of movement and element highlighting. We also introduce the concept of volatility, that allows the identification of actors in the network that are the most volatile. Matrices can be ordered such that stable cores gravitate towards the centre of the matrix. We integrate this technique in a visual analytics application for the exploration of offline dynamic networks and evaluate our system through case studies and qualitative expert interviews. Experts agree on the capabilities of the system, noting its potential for the analysis of dynamic networks through hybrid representations.
Exploring Classifiers with Differentiable Decision Boundary Maps
Authors: Alister Machado, Michael Behrisch 0001, Alexandru C. Telea
Published at EuroVis, 2024

Explaining Machine Learning (ML) — and especially Deep Learning (DL) — classifiers' decisions is a subject of interest across fields due to the increasing ubiquity of such models in computing systems. As models get increasingly complex, relying on sophisticated machinery to recognize data patterns, explaining their behavior becomes more difficult. Directly visualizing classifier behavior is in general infeasible, as they create partitions of the data space, which is typically high dimensional. In recent years, Decision Boundary Maps (DBMs) have been developed, taking advantage of projection and inverse projection techniques. By being able to map 2D points back to the data space and subsequently run a classifier, DBMs represent a slice of classifier outputs. However, we recognize that DBMs without additional explanatory views are limited in their applicability. In this work, we propose augmenting the naive DBM generating process with views that provide more in-depth information about classifier behavior, such as whether the training procedure is locally stable. We describe our proposed views — which we term Differentiable Decision Boundary Maps — over a running example, explaining how our work enables drawing new and useful conclusions from these dense maps. We further demonstrate the value of these conclusions by showing how useful they would be in carrying out or preventing a dataset poisoning attack. We thus provide evidence of the ability of our proposed views to make DBMs significantly more trustworthy and interpretable, increasing their utility as a model understanding tool.
Exploring the Design Space of BioFabric Visualization for Multivariate Network Analysis
Authors: Johannes Fuchs 0001, Frederik L. Dennig, Maria-Viktoria Heinle, Daniel A. Keim, Sara Di Bartolomeo
Published at EuroVis, 2024

The visual analysis of multivariate network data is a common yet difficult task in many domains. The major challenge is to visualize the network's topology and additional attributes for entities and their connections. Although node-link diagrams and adjacency matrices are widespread, they have inherent limitations. Node-link diagrams struggle to scale effectively, while adjacency matrices can fail to represent network topologies clearly. In this paper, we delve into the design space of BioFabric, which aligns entities along rows and relationships along columns, providing a way to encapsulate multiple attributes for both. We explore how we can leverage the unique opportunities offered by BioFabric's design space to visualize multivariate network data — focusing on three main categories: juxtaposed visualizations, embedded on-node and on-edge encoding, and transformed node and edge encoding. We complement our exploration with a quantitative assessment comparing BioFabric to adjacency matrices. We postulate that the expansive design possibilities introduced in BioFabric network visualization have the potential for the visualization of multivariate data, and we advocate for further evaluation of the associated design space. Our supplemental material is available on osf.io.
From Delays to Densities: Exploring Data Uncertainty through Speech, Text, and Visualization
Authors: Chase Stokes, Chelsea Sanker, Bridget Cogley, Vidya Setlur
Published at EuroVis, 2024

Understanding and communicating data uncertainty is crucial for making informed decisions in sectors like finance and healthcare. Previous work has explored how to express uncertainty in various modes. For example, uncertainty can be expressed visually with quantile dot plots or linguistically with hedge words and prosody. Our research aims to systematically explore how variations within each mode contribute to communicating uncertainty to the user; this allows us to better understand each mode's affordances and limitations. We completed an exploration of the uncertainty design space based on pilot studies and ran two crowdsourced experiments examining how speech, text, and visualization modes and variants within them impact decision-making with uncertain data. Visualization and text were most effective for rational decision-making, though text resulted in lower confidence. Speech garnered the highest trust despite sometimes leading to risky decisions. Results from these studies indicate meaningful trade-offs among modes of information and encourage exploration of multimodal data representations.