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Article ; Online: Uncertainty Visualization: Concepts, Methods, and Applications in Biological Data Visualization.

Weiskopf, Daniel

Frontiers in bioinformatics

2022 Volume 2, Page(s) 793819

Abstract: This paper provides an overview of uncertainty visualization in general, along with specific examples of applications in bioinformatics. Starting from a processing and interaction pipeline of visualization, components are discussed that are relevant for ... ...

Abstract	This paper provides an overview of uncertainty visualization in general, along with specific examples of applications in bioinformatics. Starting from a processing and interaction pipeline of visualization, components are discussed that are relevant for handling and visualizing uncertainty introduced with the original data and at later stages in the pipeline, which shows the importance of making the stages of the pipeline aware of uncertainty and allowing them to propagate uncertainty. We detail concepts and methods for visual mappings of uncertainty, distinguishing between explicit and implict representations of distributions, different ways to show summary statistics, and combined or hybrid visualizations. The basic concepts are illustrated for several examples of graph visualization under uncertainty. Finally, this review paper discusses implications for the visualization of biological data and future research directions.
Language	English
Publishing date	2022-02-17
Publishing country	Switzerland
Document type	Journal Article ; Review
ISSN	2673-7647
ISSN (online)	2673-7647
DOI	10.3389/fbinf.2022.793819
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess.

Krake, Tim / Klotzl, Daniel / Hagele, David / Weiskopf, Daniel

IEEE transactions on visualization and computer graphics

2024 Volume PP

Abstract: Seasonal-trend decomposition based on loess (STL) is a powerful tool to explore time series data visually. In this paper, we present an extension of STL to uncertain data, named uncertainty-aware STL (UASTL). Our method propagates multivariate Gaussian ... ...

Abstract	Seasonal-trend decomposition based on loess (STL) is a powerful tool to explore time series data visually. In this paper, we present an extension of STL to uncertain data, named uncertainty-aware STL (UASTL). Our method propagates multivariate Gaussian distributions mathematically exactly through the entire analysis and visualization pipeline. Thereby, stochastic quantities shared between the components of the decomposition are preserved. Moreover, we present application scenarios with uncertainty modeling based on Gaussian processes, e.g., data with uncertain areas or missing values. Besides these mathematical results and modeling aspects, we introduce visualization techniques that address the challenges of uncertainty visualization and the problem of visualizing highly correlated components of a decomposition. The global uncertainty propagation enables the time series visualization with STL-consistent samples, the exploration of correlation between and within decomposition's components, and the analysis of the impact of varying uncertainty. Finally, we show the usefulness of UASTL and the importance of uncertainty visualization with several examples. Thereby, a comparison with conventional STL is performed.
Language	English
Publishing date	2024-02-13
Publishing country	United States
Document type	Journal Article
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2024.3364388
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Active Gaze Labeling: Visualization for Trust Building.

Koch, Maurice / Cao, Nan / Weiskopf, Daniel / Kurzhals, Kuno

IEEE transactions on visualization and computer graphics

2024 Volume PP

Abstract: Areas of interest (AOIs) are well-established means of providing semantic information for visualizing, analyzing, and classifying gaze data. However, the usual manual annotation of AOIs is time-consuming and further impaired by ambiguities in label ... ...

Abstract	Areas of interest (AOIs) are well-established means of providing semantic information for visualizing, analyzing, and classifying gaze data. However, the usual manual annotation of AOIs is time-consuming and further impaired by ambiguities in label assignments. To address these issues, we present an interactive labeling approach that combines visualization, machine learning, and user-centered explainable annotation. Our system provides uncertainty-aware visualization to build trust in classification with an increasing number of annotated examples. It combines specifically designed EyeFlower glyphs, dimensionality reduction, and selection and exploration techniques in an integrated workflow. The approach is versatile and hardware-agnostic, supporting video stimuli from stationary and unconstrained mobile eye tracking alike. We conducted an expert review to assess labeling strategies and trust building.
Language	English
Publishing date	2024-04-22
Publishing country	United States
Document type	Journal Article
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2024.3392476
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Article ; Online: Verhaltens- und Verhältnisprävention Hautkrebs : Umsetzung und Effektivität.

Baldermann, C / Weiskopf, D

Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete

2020 Volume 71, Issue 8, Page(s) 572–579

Abstract: Background: Ultraviolet radiation (UV)-induced malignancies, especially skin cancer, have continued to increase for decades. The main cause is natural and artificial UV radiation. The affected persons and the health care system are heavily burdened. The ...

Title translation	Behavioral and structural prevention of skin cancer : Implementation and effectiveness.
Abstract	Background: Ultraviolet radiation (UV)-induced malignancies, especially skin cancer, have continued to increase for decades. The main cause is natural and artificial UV radiation. The affected persons and the health care system are heavily burdened. The situation threatens to worsen, as climate change could lead to an increase in UV radiation exposure of the population and, thus, the risk of UV-related cancer in Germany as well. The prevention of UV-related diseases is, therefore, a health and radiation protection objective that needs to be considered. Objective: Necessary and appropriate prevention measures for the precaution of UV-related cancer are presented. Materials and methods: The currently recommended and applied primary behavioral and structural preventive measures and potential, prevention-related relief for the health care system are examined and summarized. Results: Numerous behavioral and structural preventive measures are already being applied. Sustainably designed, multicomponent and personalized behavioral preventive measures in combination with structural prevention modules are effective and have a high economic and health-related benefit. The use of modern media and multimedia measures is recommended. Conclusion: Structural prevention measures in addition to behavioral measures enable a reduction of the cancer risk caused by UV radiation. The aim must be to establish these measures nationwide for the entire population.
MeSH term(s)	Environmental Exposure/prevention & control ; Germany ; Health Promotion ; Humans ; Melanoma/prevention & control ; Radiation Protection ; Skin Neoplasms/prevention & control ; Sunlight/adverse effects ; Sunscreening Agents/therapeutic use ; Ultraviolet Rays/adverse effects
Chemical Substances	Sunscreening Agents
Language	German
Publishing date	2020-06-03
Publishing country	Germany
Document type	Journal Article ; Review
ZDB-ID	2403-x
ISSN	1432-1173 ; 0017-8470
ISSN (online)	1432-1173
ISSN	0017-8470
DOI	10.1007/s00105-020-04613-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Comparative Evaluation of Animated Scatter Plot Transitions.

Rodrigues, Nils / Dennig, Frederik L / Brandt, Vincent / Keim, Daniel A / Weiskopf, Daniel

IEEE transactions on visualization and computer graphics

2024 Volume PP

Abstract: Scatter plots are popular for displaying 2D data, but in practice, many data sets have more than two dimensions. For the analysis of such multivariate data, it is often necessary to switch between scatter plots of different dimension pairs, e.g., in a ... ...

Abstract	Scatter plots are popular for displaying 2D data, but in practice, many data sets have more than two dimensions. For the analysis of such multivariate data, it is often necessary to switch between scatter plots of different dimension pairs, e.g., in a scatter plot matrix (SPLOM). Alternative approaches include a "grand tour" for an overview of the entire data set or creating artificial axes from dimensionality reduction (DR). A cross-cutting concern in all techniques is the ability of viewers to find correspondence between data points in different views. Previous work proposed animations to preserve the mental map between view changes and to trace points as well as clusters between scatter plots of the same underlying data set. In this paper, we evaluate a variety of spline- and rotation-based view transitions in a crowdsourced user study focusing on ecological validity. Using the study results, we assess each animation's suitability for tracing points and clusters across view changes. We evaluate whether the order of horizontal and vertical rotation is relevant for task accuracy. The results show that rotations with an orthographic camera or staged expansion of a depth axis significantly outperform all other animation techniques for the traceability of individual points. Further, we provide a ranking of the animated transition techniques for traceability of individual points. However, we could not find any significant differences for the traceability of clusters. Furthermore, we identified differences by animation direction that could guide further studies to determine potential confounds for these differences. We publish the study data for reuse and provide the animation framework as a D3.js plug-in.
Language	English
Publishing date	2024-04-16
Publishing country	United States
Document type	Journal Article
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2024.3388558
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Article ; Online: Constrained Dynamic Mode Decomposition.

Krake, Tim / Klotzl, Daniel / Eberhardt, Bernhard / Weiskopf, Daniel

IEEE transactions on visualization and computer graphics

2022 Volume 29, Issue 1, Page(s) 182–192

Abstract: Frequency-based decomposition of time series data is used in many visualization applications. Most of these decomposition methods (such as Fourier transform or singular spectrum analysis) only provide interaction via pre- and post-processing, but no ... ...

Abstract	Frequency-based decomposition of time series data is used in many visualization applications. Most of these decomposition methods (such as Fourier transform or singular spectrum analysis) only provide interaction via pre- and post-processing, but no means to influence the core algorithm. A method that also belongs to this class is Dynamic Mode Decomposition (DMD), a spectral decomposition method that extracts spatio-temporal patterns from data. In this paper, we incorporate frequency-based constraints into DMD for an adaptive decomposition that leads to user-controllable visualizations, allowing analysts to include their knowledge into the process. To accomplish this, we derive an equivalent reformulation of DMD that implicitly provides access to the eigenvalues (and therefore to the frequencies) identified by DMD. By utilizing a constrained minimization problem customized to DMD, we can guarantee the existence of desired frequencies by minimal changes to DMD. We complement this core approach by additional techniques for constrained DMD to facilitate explorative visualization and investigation of time series data. With several examples, we demonstrate the usefulness of constrained DMD and compare it to conventional frequency-based decomposition methods.
Language	English
Publishing date	2022-12-16
Publishing country	United States
Document type	Journal Article
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2022.3209437
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Uncertainty-Aware Multidimensional Scaling.

Hagele, David / Krake, Tim / Weiskopf, Daniel

IEEE transactions on visualization and computer graphics

2022 Volume 29, Issue 1, Page(s) 23–32

Abstract: We present an extension of multidimensional scaling (MDS) to uncertain data, facilitating uncertainty visualization of multidimensional data. Our approach uses local projection operators that map high-dimensional random vectors to low-dimensional space ... ...

Abstract	We present an extension of multidimensional scaling (MDS) to uncertain data, facilitating uncertainty visualization of multidimensional data. Our approach uses local projection operators that map high-dimensional random vectors to low-dimensional space to formulate a generalized stress. In this way, our generic model supports arbitrary distributions and various stress types. We use our uncertainty-aware multidimensional scaling (UAMDS) concept to derive a formulation for the case of normally distributed random vectors and a squared stress. The resulting minimization problem is numerically solved via gradient descent. We complement UAMDS by additional visualization techniques that address the sensitivity and trustworthiness of dimensionality reduction under uncertainty. With several examples, we demonstrate the usefulness of our approach and the importance of uncertainty-aware techniques.
Language	English
Publishing date	2022-12-16
Publishing country	United States
Document type	Journal Article
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2022.3209420
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Laserpointer & Co-Blendattacken und Augenschäden. Laser pointer & Co-Dazzle attacks and eye injuries

Asmuss, Monika / Weiskopf, Daniela

Umwelt + Mensch Informationsdienst

2022 Volume -, Issue 1, Page(s) 29

Language	German
Document type	Article
ZDB-ID	2548774-7
ISSN	2190-1120
Database	Current Contents Medicine

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Article ; Online: Current Understanding of the Role of T Cells in Chikungunya, Dengue and Zika Infections.

Mapalagamage, Maheshi / Weiskopf, Daniela / Sette, Alessandro / De Silva, Aruna Dharshan

Viruses

2022 Volume 14, Issue 2

Abstract: Arboviral infections such as Chikungunya (CHIKV), Dengue (DENV) and Zika (ZIKV) are a major disease burden in tropical and sub-tropical countries, and there are no effective vaccinations or therapeutic drugs available at this time. Understanding the role ...

Abstract	Arboviral infections such as Chikungunya (CHIKV), Dengue (DENV) and Zika (ZIKV) are a major disease burden in tropical and sub-tropical countries, and there are no effective vaccinations or therapeutic drugs available at this time. Understanding the role of the T cell response is very important when designing effective vaccines. Currently, comprehensive identification of T cell epitopes during a DENV infection shows that CD8 and CD4 T cells and their specific phenotypes play protective and pathogenic roles. The protective role of CD8 T cells in DENV is carried out through the killing of infected cells and the production of proinflammatory cytokines, as CD4 T cells enhance B cell and CD8 T cell activities. A limited number of studies attempted to identify the involvement of T cells in CHIKV and ZIKV infection. The identification of human immunodominant ZIKV viral epitopes responsive to specific T cells is scarce, and none have been identified for CHIKV. In CHIKV infection, CD8 T cells are activated during the acute phase in the lymph nodes/blood, and CD4 T cells are activated during the chronic phase in the joints/muscles. Studies on the role of T cells in ZIKV-neuropathogenesis are limited and need to be explored. Many studies have shown the modulating actions of T cells due to cross-reactivity between DENV-ZIKV co-infections and have repeated heterologous/homologous DENV infection, which is an important factor to consider when developing an effective vaccine.
MeSH term(s)	Animals ; CD4-Positive T-Lymphocytes/immunology ; CD8-Positive T-Lymphocytes/immunology ; Chikungunya Fever/immunology ; Chikungunya Fever/therapy ; Chikungunya virus/immunology ; Cross Reactions ; Dengue/immunology ; Dengue/therapy ; Dengue Virus/immunology ; Epitopes, T-Lymphocyte/immunology ; Humans ; T-Lymphocytes/immunology ; Vaccine Development ; Viral Vaccines ; Zika Virus/immunology ; Zika Virus Infection/immunology ; Zika Virus Infection/therapy
Chemical Substances	Epitopes, T-Lymphocyte ; Viral Vaccines
Language	English
Publishing date	2022-01-25
Publishing country	Switzerland
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ZDB-ID	2516098-9
ISSN	1999-4915 ; 1999-4915
ISSN (online)	1999-4915
ISSN	1999-4915
DOI	10.3390/v14020242
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Visual analytics tool for the interpretation of hidden states in recurrent neural networks.

Garcia, Rafael / Munz, Tanja / Weiskopf, Daniel

Visual computing for industry, biomedicine, and art

2021 Volume 4, Issue 1, Page(s) 24

Abstract: In this paper, we introduce a visual analytics approach aimed at helping machine learning experts analyze the hidden states of layers in recurrent neural networks. Our technique allows the user to interactively inspect how hidden states store and process ...

Abstract	In this paper, we introduce a visual analytics approach aimed at helping machine learning experts analyze the hidden states of layers in recurrent neural networks. Our technique allows the user to interactively inspect how hidden states store and process information throughout the feeding of an input sequence into the network. The technique can help answer questions, such as which parts of the input data have a higher impact on the prediction and how the model correlates each hidden state configuration with a certain output. Our visual analytics approach comprises several components: First, our input visualization shows the input sequence and how it relates to the output (using color coding). In addition, hidden states are visualized through a nonlinear projection into a 2-D visualization space using t-distributed stochastic neighbor embedding to understand the shape of the space of the hidden states. Trajectories are also employed to show the details of the evolution of the hidden state configurations. Finally, a time-multi-class heatmap matrix visualizes the evolution of the expected predictions for multi-class classifiers, and a histogram indicates the distances between the hidden states within the original space. The different visualizations are shown simultaneously in multiple views and support brushing-and-linking to facilitate the analysis of the classifications and debugging for misclassified input sequences. To demonstrate the capability of our approach, we discuss two typical use cases for long short-term memory models applied to two widely used natural language processing datasets.
Language	English
Publishing date	2021-09-29
Publishing country	Germany
Document type	Journal Article
ISSN	2524-4442
ISSN (online)	2524-4442
DOI	10.1186/s42492-021-00090-0
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