43-Issue 3

Permanent URI for this collection

EuroVis 2024 - 26th EG Conference on Visualization
Odense, Denmark | May 27 - 31, 2024
Graph Visualization
DynTrix: A Hybrid Representation for Dynamic Graphs
Benjamin Vago, Daniel Archambault, and Alessio Arleo
An Experimental Evaluation of Viewpoint-Based 3D Graph Drawing
Simon van Wageningen, Tamara Mchedlidze, and Alexandru Telea
ProtEGOnist: Visual Analysis of Interactions in Small World Networks Using Ego-graphs
Nicolas Brich, Theresa A. Harbig, Mathias Witte Paz, Kay Nieselt, and Michael Krone
Exploring the Design Space of BioFabric Visualization for Multivariate Network Analysis
Johannes Fuchs, Frederik L. Dennig, Maria-Viktoria Heinle, Daniel A. Keim, and Sara Di Bartolomeo
Medical Visualization
InverseVis: Revealing the Hidden with Curved Sphere Tracing
Kai Lawonn, Monique Meuschke, and Tobias Günther
Instantaneous Visual Analysis of Blood Flow in Stenoses Using Morphological Similarity
Pepe Eulzer, Kevin Richter, Anna Hundertmark, Ralph Wickenhoefer, Carsten Klingner, and Kai Lawonn
Sparse q-ball imaging towards efficient visual exploration of HARDI data
Danhua Lei, Ehsan Miandji, Jonas Unger, and Ingrid Hotz
Scalars, Vectors, and Topology
Depth for Multi-Modal Contour Ensembles
Nicolas F. Chaves-de-Plaza, Mathijs Molenaar, Prerak Mody, Marius Staring, René van Egmond, Elmar Eisemann, Anna Vilanova, and Klaus Hildebrandt
Topological Characterization and Uncertainty Visualization of Atmospheric Rivers
Fangfei Lan, Brandi Gamelin, Lin Yan, Jiali Wang, Bei Wang, and Hanqi Guo
AI4Vis and Vis4AI
CAN: Concept-aligned Neurons for Visual Comparison of Neural Networks
Mingwei Li, Sangwon Jeong, Shusen Liu, and Matthew Berger
CUPID: Contextual Understanding of Prompt-conditioned Image Distributions
Yayan Zhao, Mingwei Li, and Matthew Berger
It's All About Time
Improving Temporal Treemaps by Minimizing Crossings
Alexander Dobler and Martin Nöllenburg
Antarstick: Extracting Snow Height From Time-Lapse Photography
Matěj Lang, Radoslav Mráz, Marek Trtík, Sergej Stoppel, Jan Byška, and Barbora Kozlikova
Geospatial Data and Optimization
Generating Euler Diagrams Through Combinatorial Optimization
Peter Rottmann, Peter Rodgers, Xinyuan Yan, Daniel Archambault, Bei Wang, and Jan-Henrik Haunert
Interactive Optimization for Cartographic Aggregation of Building Features
Shigeo Takahashi, Ryo Kokubun, Satoshi Nishimura, Kazuo Misue, and Masatoshi Arikawa
RouteVis: Quantitative Visual Analytics of Various Factors to Understand Route Choice Preferences
Cheng Lv, Huijie Zhang, Yiming Lin, Jialu Dong, and Liang Tian
Workflows and Decision Making
Persist: Persistent and Reusable Interactions in Computational Notebooks
Kiran Gadhave, Zach Cutler, and Alexander Lex
AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making
Shusen Liu, Haichao Miao, Zhimin Li, Matthew Olson, Valerio Pascucci, and Peer-Timo Bremer
Transparent Risks: The Impact of the Specificity and Visual Encoding of Uncertainty on Decision Making
Laura E. Matzen, Breannan C. Howell, Marie Tuft, and Kristin M. Divis
Volume Rendering and Large Data
Beyond ExaBricks: GPU Volume Path Tracing of AMR Data
Stefan Zellmann, Qi Wu, Alper Sahistan, Kwan-Liu Ma, and Ingo Wald
Transmittance-based Extinction and Viewpoint Optimization
Paul Himmler and Tobias Günther
A Prediction-Traversal Approach for Compressing Scientific Data on Unstructured Meshes with Bounded Error
Congrong Ren, Xin Liang, and Hanqi Guo
Text and Speech
Visual Analytics for Fine-grained Text Classification Models and Datasets
Munkhtulga Battogtokh, Yiwen Xing, Cosmin Davidescu, Alfie Abdul-Rahman, Michael Luck, and Rita Borgo
AutoVizuA11y: A Tool to Automate Screen Reader Accessibility in Charts
Diogo Duarte, Rita Costa, Pedro Bizarro, and Carlos Duarte
From Delays to Densities: Exploring Data Uncertainty through Speech, Text, and Visualization
Chase Stokes, Chelsea Sanker, Bridget Cogley, and Vidya Setlur
Perception and Cognition
GerontoVis: Data Visualization at the Confluence of Aging
Zack While, R. Jordan Crouser, and Ali Sarvghad
Should I make it round? Suitability of circular and linear layouts for comparative tasks with matrix and connective data
Emilia Ståhlbom, Jesper Molin, Anders Ynnerman, and Claes Lundström
psudo: Exploring Multi-Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring
Simon Warchol, Jakob Troidl, Jeremy Muhlich, Robert Krueger, John Hoffer, Tica Lin, Johanna Beyer, Elena Glassman, Peter Sorger, and Hanspeter Pfister
Interactions and Human Movement
ChoreoVis: Planning and Assessing Formations in Dance Choreographies
Samuel Beck, Nina Doerr, Kuno Kurzhals, Alexander Riedlinger, Fabian Schmierer, Michael Sedlmair, and Steffen Koch
Visual Highlighting for Situated Brushing and Linking
Nina Doerr, Benjamin Lee, Katarina Baricova, Dieter Schmalstieg, and Michael Sedlmair
Investigating the Effect of Operation Mode and Manifestation on Physicalizations of Dynamic Processes
Daniel Pahr, Henry Ehlers, Hsiang-Yun Wu, Manuela Waldner, and Renata Georgia Raidou
Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptation
Steffen Holter and Mennatallah El-Assady
Best Paper
HORA 3D: Personalized Flood Risk Visualization as an Interactive Web Service
Silvana Rauer-Zechmeister, Daniel Cornel, Bernhard Sadransky, Zsolt Horváth, Artem Konev, Andreas Buttinger-Kreuzhuber, Raimund Heidrich, Günter Blöschl, Eduard Gröller, and Jürgen Waser
Honorable Mention
Guided By AI: Navigating Trust, Bias, and Data Exploration in AI-Guided Visual Analytics
Sunwoo Ha, Shayan Monadjemi, and Alvitta Ottley
Exploring Classifiers with Differentiable Decision Boundary Maps
Alister Machado, Michael Behrisch, and Alexandru Telea

BibTeX (43-Issue 3)
                
@article{
10.1111:cgf.15068,
journal = {Computer Graphics Forum}, title = {{
EuroVis 2024 CGF 43-3: Frontmatter}},
author = {
Aigner, Wolfgang
and
Archambault, Daniel
and
Bujack, Roxana
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15068}
}
                
@article{
10.1111:cgf.15076,
journal = {Computer Graphics Forum}, title = {{
DynTrix: A Hybrid Representation for Dynamic Graphs}},
author = {
Vago, Benjamin
and
Archambault, Daniel
and
Arleo, Alessio
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15076}
}
                
@article{
10.1111:cgf.15077,
journal = {Computer Graphics Forum}, title = {{
An Experimental Evaluation of Viewpoint-Based 3D Graph Drawing}},
author = {
Wageningen, Simon van
and
Mchedlidze, Tamara
and
Telea, Alexandru
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15077}
}
                
@article{
10.1111:cgf.15078,
journal = {Computer Graphics Forum}, title = {{
ProtEGOnist: Visual Analysis of Interactions in Small World Networks Using Ego-graphs}},
author = {
Brich, Nicolas
and
Harbig, Theresa A.
and
Witte Paz, Mathias
and
Nieselt, Kay
and
Krone, Michael
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15078}
}
                
@article{
10.1111:cgf.15079,
journal = {Computer Graphics Forum}, title = {{
Exploring the Design Space of BioFabric Visualization for Multivariate Network Analysis}},
author = {
Fuchs, Johannes
and
Dennig, Frederik L.
and
Heinle, Maria-Viktoria
and
Keim, Daniel A.
and
Bartolomeo, Sara Di
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15079}
}
                
@article{
10.1111:cgf.15080,
journal = {Computer Graphics Forum}, title = {{
InverseVis: Revealing the Hidden with Curved Sphere Tracing}},
author = {
Lawonn, Kai
and
Meuschke, Monique
and
Günther, Tobias
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15080}
}
                
@article{
10.1111:cgf.15081,
journal = {Computer Graphics Forum}, title = {{
Instantaneous Visual Analysis of Blood Flow in Stenoses Using Morphological Similarity}},
author = {
Eulzer, Pepe
and
Richter, Kevin
and
Hundertmark, Anna
and
Wickenhoefer, Ralph
and
Klingner, Carsten
and
Lawonn, Kai
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15081}
}
                
@article{
10.1111:cgf.15082,
journal = {Computer Graphics Forum}, title = {{
Sparse q-ball imaging towards efficient visual exploration of HARDI data}},
author = {
Lei, Danhua
and
Miandji, Ehsan
and
Unger, Jonas
and
Hotz, Ingrid
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15082}
}
                
@article{
10.1111:cgf.15083,
journal = {Computer Graphics Forum}, title = {{
Depth for Multi-Modal Contour Ensembles}},
author = {
Chaves-de-Plaza, Nicolas F.
and
Molenaar, Mathijs
and
Mody, Prerak
and
Staring, Marius
and
Egmond, René van
and
Eisemann, Elmar
and
Vilanova, Anna
and
Hildebrandt, Klaus
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15083}
}
                
@article{
10.1111:cgf.15084,
journal = {Computer Graphics Forum}, title = {{
Topological Characterization and Uncertainty Visualization of Atmospheric Rivers}},
author = {
Lan, Fangfei
and
Gamelin, Brandi
and
Yan, Lin
and
Wang, Jiali
and
Wang, Bei
and
Guo, Hanqi
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15084}
}
                
@article{
10.1111:cgf.15085,
journal = {Computer Graphics Forum}, title = {{
CAN: Concept-aligned Neurons for Visual Comparison of Neural Networks}},
author = {
Li, Mingwei
and
Jeong, Sangwon
and
Liu, Shusen
and
Berger, Matthew
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15085}
}
                
@article{
10.1111:cgf.15086,
journal = {Computer Graphics Forum}, title = {{
CUPID: Contextual Understanding of Prompt-conditioned Image Distributions}},
author = {
Zhao, Yayan
and
Li, Mingwei
and
Berger, Matthew
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15086}
}
                
@article{
10.1111:cgf.15087,
journal = {Computer Graphics Forum}, title = {{
Improving Temporal Treemaps by Minimizing Crossings}},
author = {
Dobler, Alexander
and
Nöllenburg, Martin
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15087}
}
                
@article{
10.1111:cgf.15088,
journal = {Computer Graphics Forum}, title = {{
Antarstick: Extracting Snow Height From Time-Lapse Photography}},
author = {
Lang, Matěj
and
Mráz, Radoslav
and
Trtík, Marek
and
Stoppel, Sergej
and
Byška, Jan
and
Kozlikova, Barbora
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15088}
}
                
@article{
10.1111:cgf.15089,
journal = {Computer Graphics Forum}, title = {{
Generating Euler Diagrams Through Combinatorial Optimization}},
author = {
Rottmann, Peter
and
Rodgers, Peter
and
Yan, Xinyuan
and
Archambault, Daniel
and
Wang, Bei
and
Haunert, Jan-Henrik
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15089}
}
                
@article{
10.1111:cgf.15090,
journal = {Computer Graphics Forum}, title = {{
Interactive Optimization for Cartographic Aggregation of Building Features}},
author = {
Takahashi, Shigeo
and
Kokubun, Ryo
and
Nishimura, Satoshi
and
Misue, Kazuo
and
Arikawa, Masatoshi
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15090}
}
                
@article{
10.1111:cgf.15091,
journal = {Computer Graphics Forum}, title = {{
RouteVis: Quantitative Visual Analytics of Various Factors to Understand Route Choice Preferences}},
author = {
Lv, Cheng
and
Zhang, Huijie
and
Lin, Yiming
and
Dong, Jialu
and
Tian, Liang
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15091}
}
                
@article{
10.1111:cgf.15092,
journal = {Computer Graphics Forum}, title = {{
Persist: Persistent and Reusable Interactions in Computational Notebooks}},
author = {
Gadhave, Kiran
and
Cutler, Zach
and
Lex, Alexander
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15092}
}
                
@article{
10.1111:cgf.15093,
journal = {Computer Graphics Forum}, title = {{
AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making}},
author = {
Liu, Shusen
and
Miao, Haichao
and
Li, Zhimin
and
Olson, Matthew
and
Pascucci, Valerio
and
Bremer, Peer-Timo
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15093}
}
                
@article{
10.1111:cgf.15094,
journal = {Computer Graphics Forum}, title = {{
Transparent Risks: The Impact of the Specificity and Visual Encoding of Uncertainty on Decision Making}},
author = {
Matzen, Laura E.
and
Howell, Breannan C.
and
Tuft, Marie
and
Divis, Kristin M.
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15094}
}
                
@article{
10.1111:cgf.15095,
journal = {Computer Graphics Forum}, title = {{
Beyond ExaBricks: GPU Volume Path Tracing of AMR Data}},
author = {
Zellmann, Stefan
and
Wu, Qi
and
Sahistan, Alper
and
Ma, Kwan-Liu
and
Wald, Ingo
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15095}
}
                
@article{
10.1111:cgf.15096,
journal = {Computer Graphics Forum}, title = {{
Transmittance-based Extinction and Viewpoint Optimization}},
author = {
Himmler, Paul
and
Günther, Tobias
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15096}
}
                
@article{
10.1111:cgf.15097,
journal = {Computer Graphics Forum}, title = {{
A Prediction-Traversal Approach for Compressing Scientific Data on Unstructured Meshes with Bounded Error}},
author = {
Ren, Congrong
and
Liang, Xin
and
Guo, Hanqi
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15097}
}
                
@article{
10.1111:cgf.15098,
journal = {Computer Graphics Forum}, title = {{
Visual Analytics for Fine-grained Text Classification Models and Datasets}},
author = {
Battogtokh, Munkhtulga
and
Xing, Yiwen
and
Davidescu, Cosmin
and
Abdul-Rahman, Alfie
and
Luck, Michael
and
Borgo, Rita
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15098}
}
                
@article{
10.1111:cgf.15099,
journal = {Computer Graphics Forum}, title = {{
AutoVizuA11y: A Tool to Automate Screen Reader Accessibility in Charts}},
author = {
Duarte, Diogo
and
Costa, Rita
and
Bizarro, Pedro
and
Duarte, Carlos
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15099}
}
                
@article{
10.1111:cgf.15100,
journal = {Computer Graphics Forum}, title = {{
From Delays to Densities: Exploring Data Uncertainty through Speech, Text, and Visualization}},
author = {
Stokes, Chase
and
Sanker, Chelsea
and
Cogley, Bridget
and
Setlur, Vidya
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15100}
}
                
@article{
10.1111:cgf.15101,
journal = {Computer Graphics Forum}, title = {{
GerontoVis: Data Visualization at the Confluence of Aging}},
author = {
While, Zack
and
Crouser, R. Jordan
and
Sarvghad, Ali
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15101}
}
                
@article{
10.1111:cgf.15102,
journal = {Computer Graphics Forum}, title = {{
Should I make it round? Suitability of circular and linear layouts for comparative tasks with matrix and connective data}},
author = {
Ståhlbom, Emilia
and
Molin, Jesper
and
Ynnerman, Anders
and
Lundström, Claes
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15102}
}
                
@article{
10.1111:cgf.15103,
journal = {Computer Graphics Forum}, title = {{
psudo: Exploring Multi-Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring}},
author = {
Warchol, Simon
and
Troidl, Jakob
and
Muhlich, Jeremy
and
Krueger, Robert
and
Hoffer, John
and
Lin, Tica
and
Beyer, Johanna
and
Glassman, Elena
and
Sorger, Peter
and
Pfister, Hanspeter
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15103}
}
                
@article{
10.1111:cgf.15104,
journal = {Computer Graphics Forum}, title = {{
ChoreoVis: Planning and Assessing Formations in Dance Choreographies}},
author = {
Beck, Samuel
and
Doerr, Nina
and
Kurzhals, Kuno
and
Riedlinger, Alexander
and
Schmierer, Fabian
and
Sedlmair, Michael
and
Koch, Steffen
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15104}
}
                
@article{
10.1111:cgf.15105,
journal = {Computer Graphics Forum}, title = {{
Visual Highlighting for Situated Brushing and Linking}},
author = {
Doerr, Nina
and
Lee, Benjamin
and
Baricova, Katarina
and
Schmalstieg, Dieter
and
Sedlmair, Michael
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15105}
}
                
@article{
10.1111:cgf.15106,
journal = {Computer Graphics Forum}, title = {{
Investigating the Effect of Operation Mode and Manifestation on Physicalizations of Dynamic Processes}},
author = {
Pahr, Daniel
and
Ehlers, Henry
and
Wu, Hsiang-Yun
and
Waldner, Manuela
and
Raidou, Renata Georgia
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15106}
}
                
@article{
10.1111:cgf.15107,
journal = {Computer Graphics Forum}, title = {{
Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptation}},
author = {
Holter, Steffen
and
El-Assady, Mennatallah
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15107}
}
                
@article{
10.1111:cgf.15110,
journal = {Computer Graphics Forum}, title = {{
HORA 3D: Personalized Flood Risk Visualization as an Interactive Web Service}},
author = {
Rauer-Zechmeister, Silvana
and
Cornel, Daniel
and
Sadransky, Bernhard
and
Horváth, Zsolt
and
Konev, Artem
and
Buttinger-Kreuzhuber, Andreas
and
Heidrich, Raimund
and
Blöschl, Günter
and
Gröller, Eduard
and
Waser, Jürgen
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15110}
}
                
@article{
10.1111:cgf.15108,
journal = {Computer Graphics Forum}, title = {{
Guided By AI: Navigating Trust, Bias, and Data Exploration in AI-Guided Visual Analytics}},
author = {
Ha, Sunwoo
and
Monadjemi, Shayan
and
Ottley, Alvitta
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15108}
}
                
@article{
10.1111:cgf.15109,
journal = {Computer Graphics Forum}, title = {{
Exploring Classifiers with Differentiable Decision Boundary Maps}},
author = {
Machado, Alister
and
Behrisch, Michael
and
Telea, Alexandru
}, year = {
2024},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.15109}
}

Browse

Recent Submissions

Now showing 1 - 36 of 36
  • Item
    EuroVis 2024 CGF 43-3: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
  • Item
    DynTrix: A Hybrid Representation for Dynamic Graphs
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Vago, Benjamin; Archambault, Daniel; Arleo, Alessio; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.
  • Item
    An Experimental Evaluation of Viewpoint-Based 3D Graph Drawing
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Wageningen, Simon van; Mchedlidze, Tamara; Telea, Alexandru; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.
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    ProtEGOnist: Visual Analysis of Interactions in Small World Networks Using Ego-graphs
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Brich, Nicolas; Harbig, Theresa A.; Witte Paz, Mathias; Nieselt, Kay; Krone, Michael; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    Visualizing small-world networks such as protein-protein interaction networks or social networks often leads to visual clutter and limited interpretability. To overcome these problems, we present ProtEGOnist, a visualization approach designed to explore small-world networks. ProtEGOnist visualizes networks using ego-graphs that represent local neighborhoods. Egographs are visualized in an aggregated state as a glyph where the size encodes the size of the neighborhood and in a detailed version where the original network nodes can be explored. The ego-graphs are arranged in an ego-graph network, where edges encode similarity using the Jaccard index. Our design aims to reduce visual complexity and clutter while enabling detailed exploration and facilitating the discovery of meaningful patterns. To achieve this, our approach offers a network overview using ego-graphs, a radar chart for a one-to-many ego-graph comparison and meta-data integration, and detailed ego-graph subnetworks for interactive exploration. We demonstrate the applicability of our approach on a co-author network and two different protein-protein interaction networks. A web-based prototype of ProtEGOnist can be accessed online at https://protegonist-tuevis.cs.uni-tuebingen.de/.
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    Exploring the Design Space of BioFabric Visualization for Multivariate Network Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Fuchs, Johannes; Dennig, Frederik L.; Heinle, Maria-Viktoria; Keim, Daniel A.; Bartolomeo, Sara Di; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.
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    InverseVis: Revealing the Hidden with Curved Sphere Tracing
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Lawonn, Kai; Meuschke, Monique; Günther, Tobias; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    Exploratory analysis of scalar fields on surface meshes presents significant challenges in identifying and visualizing important regions, particularly on the surface's backside. Previous visualization methods achieved only a limited visibility of significant features, i.e., regions with high or low scalar values, during interactive exploration. In response to this, we propose a novel technique, InverseVis, which leverages curved sphere tracing and uses the otherwise unused space to enhance visibility. Our approach combines direct and indirect rendering, allowing camera rays to wrap around the surface and reveal information from the backside. To achieve this, we formulate an energy term that guides the image synthesis in previously unused space, highlighting the most important regions of the backside. By quantifying the amount of visible important features, we optimize the camera position to maximize the visibility of the scalar field on both the front and backsides. InverseVis is benchmarked against state-of-the-art methods and a derived technique, showcasing its effectiveness in revealing essential features and outperforming existing approaches.
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    Instantaneous Visual Analysis of Blood Flow in Stenoses Using Morphological Similarity
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Eulzer, Pepe; Richter, Kevin; Hundertmark, Anna; Wickenhoefer, Ralph; Klingner, Carsten; Lawonn, Kai; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    The emergence of computational fluid dynamics (CFD) enabled the simulation of intricate transport processes, including flow in physiological structures, such as blood vessels. While these so-called hemodynamic simulations offer groundbreaking opportunities to solve problems at the clinical forefront, a successful translation of CFD to clinical decision-making is challenging. Hemodynamic simulations are intrinsically complex, time-consuming, and resource-intensive, which conflicts with the timesensitive nature of clinical workflows and the fact that hospitals usually do not have the necessary resources or infrastructure to support CFD simulations. To address these transfer challenges, we propose a novel visualization system which enables instant flow exploration without performing on-site simulation. To gain insights into the viability of the approach, we focus on hemodynamic simulations of the carotid bifurcation, which is a highly relevant arterial subtree in stroke diagnostics and prevention. We created an initial database of 120 high-resolution carotid bifurcation flow models and developed a set of similarity metrics used to place a new carotid surface model into a neighborhood of simulated cases with the highest geometric similarity. The neighborhood can be immediately explored and the flow fields analyzed.We found that if the artery models are similar enough in the regions of interest, a new simulation leads to coinciding results, allowing the user to circumvent individual flow simulations. We conclude that similarity-based visual analysis is a promising approach toward the usability of CFD in medical practice.
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    Sparse q-ball imaging towards efficient visual exploration of HARDI data
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Lei, Danhua; Miandji, Ehsan; Unger, Jonas; Hotz, Ingrid; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    Diffusion-weighted magnetic resonance imaging (D-MRI) is a technique to measure the diffusion of water, in biological tissues. It is used to detect microscopic patterns, such as neural fibers in the living human brain, with many medical and neuroscience applications e.g. for fiber tracking. In this paper, we consider High-Angular Resolution Diffusion Imaging (HARDI) which provides one of the richest representations of water diffusion. It records the movement of water molecules by measuring diffusion under 64 or more directions. A key challenge is that it generates high-dimensional, large, and complex datasets. In our work, we develop a novel representation that exploits the inherent sparsity of the HARDI signal by approximating it as a linear sum of basic atoms in an overcomplete data-driven dictionary using only a sparse set of coefficients. We show that this approach can be efficiently integrated into the standard q-ball imaging pipeline to compute the diffusion orientation distribution function (ODF). Sparse representations have the potential to reduce the size of the data while also giving some insight into the data. To explore the results, we provide a visualization of the atoms of the dictionary and their frequency in the data to highlight the basic characteristics of the data. We present our proposed pipeline and demonstrate its performance on 5 HARDI datasets.
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    Depth for Multi-Modal Contour Ensembles
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Chaves-de-Plaza, Nicolas F.; Molenaar, Mathijs; Mody, Prerak; Staring, Marius; Egmond, René van; Eisemann, Elmar; Vilanova, Anna; Hildebrandt, Klaus; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.
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    Topological Characterization and Uncertainty Visualization of Atmospheric Rivers
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Lan, Fangfei; Gamelin, Brandi; Yan, Lin; Wang, Jiali; Wang, Bei; Guo, Hanqi; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    Atmospheric rivers (ARs) are long, narrow regions of water vapor in the Earth's atmosphere that transport heat and moisture from the tropics to the mid-latitudes. ARs are often associated with extreme weather events in North America and contribute significantly to water supply and flood risk. However, characterizing ARs has been a major challenge due to the lack of a universal definition and their structural variations. Existing AR detection tools (ARDTs) produce distinct AR boundaries for the same event, making the risk assessment of ARs a difficult task. Understanding these uncertainties is crucial to improving the predictability of AR impacts, including their landfall areas and associated precipitation, which could cause catastrophic flooding and landslides over the coastal regions. In this work, we develop an uncertainty visualization framework that captures boundary and interior uncertainties, i.e., structural variations, of an ensemble of ARs that arise from a set of ARDTs. We first provide a statistical overview of the AR boundaries using the contour boxplots of Whitaker et al. that highlight the structural variations of AR boundaries based on their nesting relationships. We then introduce the topological skeletons of ARs based on Morse complexes that characterize the interior variation of an ensemble of ARs. We propose an uncertainty visualization of these topological skeletons, inspired by MetroSets of Jacobson et al. that emphasizes the agreements and disagreements across the ensemble members. Through case studies and expert feedback, we demonstrate that the two approaches complement each other, and together they could facilitate an effective comparative analysis process and provide a more confident outlook on an AR's shape, area, and onshore impact.
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    CAN: Concept-aligned Neurons for Visual Comparison of Neural Networks
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Li, Mingwei; Jeong, Sangwon; Liu, Shusen; Berger, Matthew; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.
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    CUPID: Contextual Understanding of Prompt-conditioned Image Distributions
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Zhao, Yayan; Li, Mingwei; Berger, Matthew; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.
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    Improving Temporal Treemaps by Minimizing Crossings
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Dobler, Alexander; Nöllenburg, Martin; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    Temporal trees are trees that evolve over a discrete set of time steps. Each time step is associated with a node-weighted rooted tree and consecutive trees change by adding new nodes, removing nodes, splitting nodes, merging nodes, and changing node weights. Recently, two-dimensional visualizations of temporal trees called temporal treemaps have been proposed, representing the temporal dimension on the x-axis, and visualizing the tree modifications over time as temporal edges of varying thickness. The tree hierarchy at each time step is depicted as a vertical, one-dimensional nesting relationships, similarly to standard, nontemporal treemaps. Naturally, temporal edges can cross in the visualization, decreasing readability. Heuristics were proposed to minimize such crossings in the literature, but a formal characterization and minimization of crossings in temporal treemaps was left open. In this paper, we propose two variants of defining crossings in temporal treemaps that can be combinatorially characterized. For each variant, we propose an exact optimization algorithm based on integer linear programming and heuristics based on graph drawing techniques. In an extensive experimental evaluation, we show that on the one hand the exact algorithms reduce the number of crossings by a factor of 20 on average compared to the previous algorithms. On the other hand, our new heuristics are faster by a factor of more than 100 and still reduce the number of crossings by a factor of almost three.
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    Antarstick: Extracting Snow Height From Time-Lapse Photography
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Lang, Matěj; Mráz, Radoslav; Trtík, Marek; Stoppel, Sergej; Byška, Jan; Kozlikova, Barbora; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.
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    Generating Euler Diagrams Through Combinatorial Optimization
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Rottmann, Peter; Rodgers, Peter; Yan, Xinyuan; Archambault, Daniel; Wang, Bei; Haunert, Jan-Henrik; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    Can a given set system be drawn as an Euler diagram? We present the first method that correctly decides this question for arbitrary set systems if the Euler diagram is required to represent each set with a single connected region. If the answer is yes, our method constructs an Euler diagram. If the answer is no, our method yields an Euler diagram for a simplified version of the set system, where a minimum number of set elements have been removed. Further, we integrate known wellformedness criteria for Euler diagrams as additional optimization objectives into our method. Our focus lies on the computation of a planar graph that is embedded in the plane to serve as the dual graph of the Euler diagram. Since even a basic version of this problem is known to be NP-hard, we choose an approach based on integer linear programming (ILP), which allows us to compute optimal solutions with existing mathematical solvers. For this, we draw upon previous research on computing planar supports of hypergraphs and adapt existing ILP building blocks for contiguity-constrained spatial unit allocation and the maximum planar subgraph problem. To generate Euler diagrams for large set systems, for which the proposed simplification through element removal becomes indispensable, we also present an efficient heuristic. We report on experiments with data from MovieDB and Twitter. Over all examples, including 850 non-trivial instances, our exact optimization method failed only for one set system to find a solution without removing a set element. However, with the removal of only a few set elements, the Euler diagrams can be substantially improved with respect to our wellformedness criteria.
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    Interactive Optimization for Cartographic Aggregation of Building Features
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Takahashi, Shigeo; Kokubun, Ryo; Nishimura, Satoshi; Misue, Kazuo; Arikawa, Masatoshi; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    Aggregation, as an operation of cartographic generalization, provides an effective means of abstracting the configuration of building features by combining them according to the scale reduction of the 2D map. Automating this design process effectively helps professional cartographers design both paper and digital maps, but finding the best aggregation result from the numerous combinations of building features has been a challenge. This paper presents a novel approach to assist cartographers in interactively designing the aggregation of building features in scale-aware map visualization. Our contribution is to provide an appropriate set of candidates for the cartographer to choose from among a limited number of possible combinations of building features. This is achieved by collecting locally optimal solutions that emerge in the course of aggregation operations, formulated as a label cost optimization problem. Users can also explore better aggregation results by interactively adjusting the design parameters to update the set of possible combinations, along with an operator to force the combination of manually selected building features. Each cluster of aggregated building features is tightly enclosed by a concave hull, which is later adaptively simplified to abstract its boundary shapes. Experimental design examples and evaluations by expert cartographers demonstrate the feasibility of the proposed approach to interactive aggregation.
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    RouteVis: Quantitative Visual Analytics of Various Factors to Understand Route Choice Preferences
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Lv, Cheng; Zhang, Huijie; Lin, Yiming; Dong, Jialu; Tian, Liang; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    Analyzing the preference of route choice not only facilitates the understanding of individuals' decision-making behavior, but also provides valuable information for improving traffic management strategies. As the layout of the road network, the variability of individual preferences and the spatial distribution of origins and destinations all play a role in route choice, it is a great challenge to reveal the interplay of such numerous complex factors. In this paper, we propose RouteVis, an interactive visual analytics system that enables traffic analysts to gain insight into what factors drive individuals to choose a specific route. To uncover the relationship between route choice and influencing factors, we design a quantitative analytical framework that supports analysts in conducting closed-loop analysis of various factors, i.e., data preprocessing, route identification, and the quantification of influence and contribution. Furthermore, given the multidimensional and spatio-temporal characteristics of the analysis results, we customize a set of coordinated views and visual designs to provide an intuitive presentation of the factors affecting people's travels, thus freeing analysts from tedious repetitive tasks and significantly enhancing work efficiency. Two typical usage scenarios and expert feedback on the system's functionality demonstrate that RouteVis can greatly enhance the capabilities of understanding the travel status.
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    Persist: Persistent and Reusable Interactions in Computational Notebooks
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Gadhave, Kiran; Cutler, Zach; Lex, Alexander; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    Computational notebooks, such as Jupyter, support rich data visualization. However, even when visualizations in notebooks are interactive, they are a dead end: Interactive data manipulations, such as selections, applying labels, filters, categorizations, or fixes to column or cell values, could be efficiently applied in interactive visual components, but interactive components typically cannot manipulate Python data structures. Furthermore, actions performed in interactive plots are lost as soon as the cell is re-run, prohibiting reusability and reproducibility. To remedy this problem, we introduce Persist, a family of techniques to (a) capture interaction provenance, enabling the persistence of interactions, and (b) map interactions to data manipulations that can be applied to dataframes.We implement our approach as a JupyterLab extension that supports tracking interactions in Vega- Altair plots and in a data table view. Persist can re-execute interaction provenance when a notebook or a cell is re-executed, enabling reproducibility and re-use.We evaluate Persist in a user study targeting data manipulations with 11 participants skilled in Python and Pandas, comparing it to traditional code-based approaches. Participants were consistently faster and were able to correctly complete more tasks with Persist.
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    AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Liu, Shusen; Miao, Haichao; Li, Zhimin; Olson, Matthew; Pascucci, Valerio; Bremer, Peer-Timo; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.
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    Transparent Risks: The Impact of the Specificity and Visual Encoding of Uncertainty on Decision Making
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Matzen, Laura E.; Howell, Breannan C.; Tuft, Marie; Divis, Kristin M.; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    People frequently make decisions based on uncertain information. Prior research has shown that visualizations of uncertainty can help to support better decision making. However, research has also shown that different representations of the same information can lead to different patterns of decision making. It is crucial for researchers to develop a better scientific understanding of when, why and how different representations of uncertainty lead viewers to make different decisions. This paper seeks to address this need by comparing geospatial visualizations of wildfire risk to verbal descriptions of the same risk. In three experiments, we manipulated the specificity of the uncertain information as well as the visual cues used to encode risk in the visualizations. All three experiments found that participants were more likely to evacuate in response to a hypothetical wildfire if the risk information was presented verbally. When the risk was presented visually, participants were less likely to evacuate, particularly when transparency was used to encode the risk information. Experiment 1 showed that evacuation rates were lower for transparency maps than for other types of visualizations. Experiments 2 and 3 sought to replicate this effect and to test how it related to other factors. Experiment 2 varied the hue used for the transparency maps and Experiment 3 manipulated the salience of the borders between the different risk levels. These experiments showed lower evacuation rates in response to transparency maps regardless of hue. The effect was partially, but not entirely, mitigated by adding salient borders to the transparency maps. Taken together, these experiments show that using transparency to encode information about risk can lead to very different patterns of decision making than other encodings of the same information.
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    Beyond ExaBricks: GPU Volume Path Tracing of AMR Data
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Zellmann, Stefan; Wu, Qi; Sahistan, Alper; Ma, Kwan-Liu; Wald, Ingo; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.
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    Transmittance-based Extinction and Viewpoint Optimization
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Himmler, Paul; Günther, Tobias; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    A long-standing challenge in volume visualization is the effective communication of relevant spatial structures that might be hidden due to occlusions. Given a scalar field that indicates the importance of every point in the domain, previous work synthesized volume visualizations by weighted averaging of samples along view rays or by optimizing a spatially-varying extinction field through an energy minimization. This energy minimization, however, did not directly measure the contribution of an individual sample to the final pixel color. In this paper, we measure the visibility of relevant structures directly by incorporating the transmittance into a non-linear energy minimization. For the first time, we not only perform a transmittance-based extinction optimization, we concurrently optimize the camera position to find ideal viewpoints. We derive the partial derivatives for the gradient-based optimization symbolically, which makes the application of automatic differentiation methods unnecessary. The transmittance-based formulation gives a direct visibility measure that is communicated to the user in order to make aware of potentially overlooked relevant structures. Our approach is compatible with any measure of importance and its versatility is demonstrated in multiple data sets.
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    A Prediction-Traversal Approach for Compressing Scientific Data on Unstructured Meshes with Bounded Error
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Ren, Congrong; Liang, Xin; Guo, Hanqi; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.
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    Visual Analytics for Fine-grained Text Classification Models and Datasets
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Battogtokh, Munkhtulga; Xing, Yiwen; Davidescu, Cosmin; Abdul-Rahman, Alfie; Luck, Michael; Borgo, Rita; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    In natural language processing (NLP), text classification tasks are increasingly fine-grained, as datasets are fragmented into a larger number of classes that are more difficult to differentiate from one another. As a consequence, the semantic structures of datasets have become more complex, and model decisions more difficult to explain. Existing tools, suited for coarse-grained classification, falter under these additional challenges. In response to this gap, we worked closely with NLP domain experts in an iterative design-and-evaluation process to characterize and tackle the growing requirements in their workflow of developing fine-grained text classification models. The result of this collaboration is the development of SemLa, a novel Visual Analytics system tailored for 1) dissecting complex semantic structures in a dataset when it is spatialized in model embedding space, and 2) visualizing fine-grained nuances in the meaning of text samples to faithfully explain model reasoning. This paper details the iterative design study and the resulting innovations featured in SemLa. The final design allows contrastive analysis at different levels by unearthing lexical and conceptual patterns including biases and artifacts in data. Expert feedback on our final design and case studies confirm that SemLa is a useful tool for supporting model validation and debugging as well as data annotation.
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    AutoVizuA11y: A Tool to Automate Screen Reader Accessibility in Charts
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Duarte, Diogo; Costa, Rita; Bizarro, Pedro; Duarte, Carlos; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.
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    From Delays to Densities: Exploring Data Uncertainty through Speech, Text, and Visualization
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Stokes, Chase; Sanker, Chelsea; Cogley, Bridget; Setlur, Vidya; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.
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    GerontoVis: Data Visualization at the Confluence of Aging
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) While, Zack; Crouser, R. Jordan; Sarvghad, Ali; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    Despite the explosive growth of the aging population worldwide, older adults have been largely overlooked by visualization research. This paper is a critical reflection on the underrepresentation of older adults in visualization research. We discuss why investigating visualization at the intersection of aging matters, why older adults may have been omitted from sample populations in visualization research, how aging may affect visualization use, and how this differs from traditional accessibility research. To encourage further discussion and novel scholarship in this area, we introduce GerontoVis, a term which encapsulates research and practice of data visualization design that primarily focuses on older adults. By introducing this new subfield of visualization research, we hope to shine a spotlight on this growing user population and stimulate innovation toward the development of aging-aware visualization tools. We offer a birds-eye view of the GerontoVis landscape, explore some of its unique challenges, and identify promising areas for future research.
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    Should I make it round? Suitability of circular and linear layouts for comparative tasks with matrix and connective data
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Ståhlbom, Emilia; Molin, Jesper; Ynnerman, Anders; Lundström, Claes; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    Visual representations based on circular shapes are frequently used in visualization applications. One example are circos plots within bioinformatics, which bend graphs into a wheel of information with connective lines running through the center like spokes. The results are aesthetically appealing and impressive visualizations that fit long data sequences into a small quadratic space. However, the authors' experiences are that when asked, a visualization researcher would generally advise against making visualizations with radial layouts. Upon reviewing the literature we found that there is evidence that circular layouts are preferable in some cases, but we found no clear evidence for what layout is preferable for matrices and connective data in particular, which both are common data types in circos plots. In this work, we thus performed a user study to compare circular and linear layouts. The tasks are inspired by genomics data, but our results generalize to many other application areas, involving comparison and connective data. To build the prototype we utilized Gosling, a grammar for visualizing genomics data. We contribute empirical evidence on the suitedness of linear versus circular layouts, adding to the specific and general knowledge concerning perception of circular graphs. In addition, we contribute a case study evaluation of the grammar Gosling as a rapid prototyping language, confirming its utility and providing guidance on suitable areas for future development.
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    psudo: Exploring Multi-Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Warchol, Simon; Troidl, Jakob; Muhlich, Jeremy; Krueger, Robert; Hoffer, John; Lin, Tica; Beyer, Johanna; Glassman, Elena; Sorger, Peter; Pfister, Hanspeter; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    Over the past century, multichannel fluorescence imaging has been pivotal in myriad scientific breakthroughs by enabling the spatial visualization of proteins within a biological sample. With the shift to digital methods and visualization software, experts can now flexibly pseudocolor and combine image channels, each corresponding to a different protein, to explore their spatial relationships. We thus propose psudo, an interactive system that allows users to create optimal color palettes for multichannel spatial data. In psudo, a novel optimization method generates palettes that maximize the perceptual differences between channels while mitigating confusing color blending in overlapping channels. We integrate this method into a system that allows users to explore multi-channel image data and compare and evaluate color palettes for their data. An interactive lensing approach provides on-demand feedback on channel overlap and a color confusion metric while giving context to the underlying channel values. Color palettes can be applied globally or, using the lens, to local regions of interest. We evaluate our palette optimization approach using three graphical perception tasks in a crowdsourced user study with 150 participants, showing that users are more accurate at discerning and comparing the underlying data using our approach. Additionally, we showcase psudo in a case study exploring the complex immune responses in cancer tissue data with a biologist.
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    ChoreoVis: Planning and Assessing Formations in Dance Choreographies
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Beck, Samuel; Doerr, Nina; Kurzhals, Kuno; Riedlinger, Alexander; Schmierer, Fabian; Sedlmair, Michael; Koch, Steffen; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.
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    Visual Highlighting for Situated Brushing and Linking
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Doerr, Nina; Lee, Benjamin; Baricova, Katarina; Schmalstieg, Dieter; Sedlmair, Michael; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    Brushing and linking is widely used for visual analytics in desktop environments. However, using this approach to link many data items between situated (e.g., a virtual screen with data) and embedded views (e.g., highlighted objects in the physical environment) is largely unexplored. To this end, we study the effectiveness of visual highlighting techniques in helping users identify and link physical referents to brushed data marks in a situated scatterplot. In an exploratory virtual reality user study (N=20), we evaluated four highlighting techniques under different physical layouts and tasks. We discuss the effectiveness of these techniques, as well as implications for the design of brushing and linking operations in situated analytics.
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    Investigating the Effect of Operation Mode and Manifestation on Physicalizations of Dynamic Processes
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Pahr, Daniel; Ehlers, Henry; Wu, Hsiang-Yun; Waldner, Manuela; Raidou, Renata Georgia; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    We conducted a study to systematically investigate the communication of complex dynamic processes along a two-dimensional design space, where the axes represent a representation's manifestation (physical or virtual) and operation (manual or automatic).We exemplify the design space on a model embodying cardiovascular pathologies, represented by a mechanism where a liquid is pumped into a draining vessel, with complications illustrated through modifications to the model. The results of a mixed-methods lab study with 28 participants show that both physical manifestation and manual operation have a strong positive impact on the audience's engagement. The study does not show a measurable knowledge increase with respect to cardiovascular pathologies using manually operated physical representations. However, subjectively, participants report a better understanding of the process-mainly through non-visual cues like haptics, but also auditory cues. The study also indicates an increased task load when interacting with the process, which, however, seems to play a minor role for the participants. Overall, the study shows a clear potential of physicalization for the communication of complex dynamic processes, which only fully unfold if observers have to chance to interact with the process.
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    Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptation
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Holter, Steffen; El-Assady, Mennatallah; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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 semistructured 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.
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    HORA 3D: Personalized Flood Risk Visualization as an Interactive Web Service
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Rauer-Zechmeister, Silvana; Cornel, Daniel; Sadransky, Bernhard; Horváth, Zsolt; Konev, Artem; Buttinger-Kreuzhuber, Andreas; Heidrich, Raimund; Blöschl, Günter; Gröller, Eduard; Waser, Jürgen; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    We propose an interactive web-based application to inform the general public about personal flood risks. Flooding is the natural hazard affecting most people worldwide. Protection against flooding is not limited to mitigation measures, but also includes communicating its risks to affected individuals to raise awareness and preparedness for its adverse effects. Until now, this is mostly done with static and indiscriminate 2D maps of the water depth. These flood hazard maps can be difficult to interpret and the user has to derive a personal flood risk based on prior knowledge. In addition to the hazard, the flood risk has to consider the exposure of the own house and premises to high water depths and flow velocities as well as the vulnerability of particular parts. Our application is centered around an interactive personalized visualization to raise awareness of these risk factors for an object of interest. We carefully extract and show only the relevant information from large precomputed flood simulation and geospatial data to keep the visualization simple and comprehensible. To achieve this goal, we extend various existing approaches and combine them with new real-time visualization and interaction techniques in 3D. A new view-dependent focus+context design guides user attention and supports an intuitive interpretation of the visualization to perform predefined exploration tasks. HORA 3D enables users to individually inform themselves about their flood risks. We evaluated the user experience through a broad online survey with 87 participants of different levels of expertise, who rated the helpfulness of the application with 4.7 out of 5 on average.
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    Guided By AI: Navigating Trust, Bias, and Data Exploration in AI-Guided Visual Analytics
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Ha, Sunwoo; Monadjemi, Shayan; Ottley, Alvitta; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    The increasing integration of artificial intelligence (AI) in visual analytics (VA) tools raises vital questions about the behavior of users, their trust, and the potential of induced biases when provided with guidance during data exploration. We present an experiment where participants engaged in a visual data exploration task while receiving intelligent suggestions supplemented with four different transparency levels. We also modulated the difficulty of the task (easy or hard) to simulate a more tedious scenario for the analyst. Our results indicate that participants were more inclined to accept suggestions when completing a more difficult task despite the AI's lower suggestion accuracy. Moreover, the levels of transparency tested in this study did not significantly affect suggestion usage or subjective trust ratings of the participants. Additionally, we observed that participants who utilized suggestions throughout the task explored a greater quantity and diversity of data points. We discuss these findings and the implications of this research for improving the design and effectiveness of AI-guided VA tools.
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    Exploring Classifiers with Differentiable Decision Boundary Maps
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Machado, Alister; Behrisch, Michael; Telea, Alexandru; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    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.