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1.
Artigo em Inglês | MEDLINE | ID: mdl-36191101

RESUMO

This work investigates and compares the performance of node-link diagrams, adjacency matrices, and bipartite layouts for visualizing networks. In a crowd-sourced user study (n = 150), we measure the task accuracy and completion time of the three representations for different network classes and properties. In contrast to the literature, which covers mostly topology-based tasks (e.g., path finding) in small datasets, we mainly focus on overview tasks for large and directed networks. We consider three overview tasks on networks with 500 nodes: (T1) network class identification, (T2) cluster detection, and (T3) network density estimation, and two detailed tasks: (T4) node in-degree vs. out-degree and (T5) representation mapping, on networks with 50 and 20 nodes, respectively. Our results show that bipartite layouts are beneficial for revealing the overall network structure, while adjacency matrices are most reliable across the different tasks.

2.
Front Bioinform ; 2: 793819, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36304261

RESUMO

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.

3.
Artigo em Inglês | MEDLINE | ID: mdl-36191104

RESUMO

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.

4.
Artigo em Inglês | MEDLINE | ID: mdl-36166524

RESUMO

We introduce relaxed dot plots as an improvement of nonlinear dot plots for unit visualization. Our plots produce more faithful data representations and reduce moire´ effects. Their contour is based on a customized kernel frequency estimation to match the shape of the distribution of underlying data values. Previous nonlinear layouts introduce column-centric nonlinear scaling of dot diameters for visualization of high-dynamic-range data with high peaks. We provide a mathematical approach to convert that column-centric scaling to our smooth envelope shape. This formalism allows us to use linear, root, and logarithmic scaling to find ideal dot sizes. Our method iteratively relaxes the dot layout for more correct and aesthetically pleasing results. To achieve this, we modified Lloyd's algorithm with additional constraints and heuristics. We evaluate the layouts of relaxed dot plots against a previously existing nonlinear variant and show that our algorithm produces less error regarding the underlying data while establishing the blue noise property that works against moire´ effects. Further, we analyze the readability of our relaxed plots in three crowd-sourced experiments. The results indicate that our proposed technique surpasses traditional dot plots.

5.
Artigo em Inglês | MEDLINE | ID: mdl-36170398

RESUMO

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.

6.
IEEE Comput Graph Appl ; 42(2): 33-44, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35263250

RESUMO

Modern machines continuously log status reports over long periods of time, which are valuable data to optimize working routines. Data visualization is a commonly used tool to gain insights into these data, mostly in retrospective (e.g., to determine causal dependencies between the faults of different machines). We present an approach to bring such visual analyses to the shop floor to support reacting to faults in real time. This approach combines spatio-temporal analyses of time series using a handheld touch device with augmented reality for live monitoring. Important information augments machines directly in their real-world context, and detailed logs of current and historical events are displayed on the handheld device. In collaboration with an industry partner, we designed and tested our approach on a live production line to obtain feedback from operators. We compare our approach for monitoring and analysis with existing solutions that are currently deployed.


Assuntos
Realidade Aumentada , Comércio , Retroalimentação , Indústrias , Estudos Retrospectivos
7.
IEEE Comput Graph Appl ; 42(2): 10-20, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35139011

RESUMO

Our built world is one of the most important factors for a livable future, accounting for massive impact on resource and energy use, as well as climate change, but also the social and economic aspects that come with population growth. The architecture, engineering, and construction industry is facing the challenge that it needs to substantially increase its productivity, let alone the quality of buildings of the future. In this article, we discuss these challenges in more detail, focusing on how digitization can facilitate this transformation of the industry, and link them to opportunities for visualization and augmented reality research. We illustrate solution strategies for advanced building systems based on wood and fiber.


Assuntos
Indústria da Construção , Engenharia , Previsões
8.
Vis Comput Ind Biomed Art ; 4(1): 24, 2021 Sep 29.
Artigo em Inglês | MEDLINE | ID: mdl-34585277

RESUMO

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.

9.
In Vivo ; 35(4): 2187-2196, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34182496

RESUMO

BACKGROUND/AIM: To date, multiple different surgical techniques have been established for hallux valgus surgery, with each technique having its unique advantages and limitations. The open distal chevron osteotomy is widely accepted, but increasing patient demands have led several minimally invasive (MIS) techniques to be described in recent years. The aim of this study was to compare outcomes after minimally invasive (MIS) distal chevron osteotomy and the minimally invasive Reverdin-Isham method. PATIENTS AND METHODS: We assessed clinical and radiographic outcomes after MIS chevron osteotomy in 57 feet of 49 consecutive patients with a mean follow-up of 58.9 (range=39.0-85.4) months. Outcomes after MIS Reverdin-Isham osteotomy were analyzed by means of a systematic literature review with a minimum follow-up of 6 months. RESULTS: Radiographic outcomes were significantly better in the MIS chevron cohort for intermetatarsal angle (p<0.001), hallux valgus angle and distal metacarpal articular angle (p<0.05). Concerning clinical outcomes, both methods provided comparable improvement. CONCLUSION: MIS distal chevron osteotomy in mild to moderate hallux valgus deformity correction results in superior radiographic outcomes compared to the MIS Reverdin-Isham osteotomy. Sufficient correction of IMA cannot be achieved with the MIS Reverdin-Isham osteotomy.


Assuntos
Hallux Valgus , Estudos de Coortes , Hallux Valgus/diagnóstico por imagem , Hallux Valgus/cirurgia , Humanos , Osteotomia , Resultado do Tratamento
10.
IEEE Trans Med Imaging ; 40(7): 1778-1791, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33729932

RESUMO

The concept of biological age (BA) - although important in clinical practice - is hard to grasp mainly due to the lack of a clearly defined reference standard. For specific applications, especially in pediatrics, medical image data are used for BA estimation in a routine clinical context. Beyond this young age group, BA estimation is mostly restricted to whole-body assessment using non-imaging indicators such as blood biomarkers, genetic and cellular data. However, various organ systems may exhibit different aging characteristics due to lifestyle and genetic factors. Thus, a whole-body assessment of the BA does not reflect the deviations of aging behavior between organs. To this end, we propose a new imaging-based framework for organ-specific BA estimation. In this initial study we focus mainly on brain MRI. As a first step, we introduce a chronological age (CA) estimation framework using deep convolutional neural networks (Age-Net). We quantitatively assess the performance of this framework in comparison to existing state-of-the-art CA estimation approaches. Furthermore, we expand upon Age-Net with a novel iterative data-cleaning algorithm to segregate atypical-aging patients (BA [Formula: see text] CA) from the given population. We hypothesize that the remaining population should approximate the true BA behavior. We apply the proposed methodology on a brain magnetic resonance image (MRI) dataset containing healthy individuals as well as Alzheimer's patients with different dementia ratings. We demonstrate the correlation between the predicted BAs and the expected cognitive deterioration in Alzheimer's patients. A statistical and visualization-based analysis has provided evidence regarding the potential and current challenges of the proposed methodology.


Assuntos
Envelhecimento , Imageamento por Ressonância Magnética , Algoritmos , Encéfalo/diagnóstico por imagem , Criança , Humanos , Redes Neurais de Computação
11.
IEEE Trans Vis Comput Graph ; 27(2): 1558-1568, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33048698

RESUMO

We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation. Here, we understand the local subspace as the multidimensional local neighborhood of data points. Existing methods focus on the projection of multidimensional data points, and the neighborhood information is ignored. Our method is able to analyze the shape and directional information of the local subspace to gain more insights into the global structure of the data through the perception of local structures. Local subspaces are fitted by multidimensional ellipses that are spanned by basis vectors. An accurate and efficient vector transformation method is proposed based on analytical differentiation of multidimensional projections formulated as implicit functions. The results are visualized as glyphs and analyzed using a full set of specifically-designed interactions supported in our efficient web-based visualization tool. The usefulness of our method is demonstrated using various multi- and high-dimensional benchmark datasets. Our implicit differentiation vector transformation is evaluated through numerical comparisons; the overall method is evaluated through exploration examples and use cases.

12.
IEEE Trans Vis Comput Graph ; 27(2): 1343-1352, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33048746

RESUMO

Causality is crucial to understanding the mechanisms behind complex systems and making decisions that lead to intended outcomes. Event sequence data is widely collected from many real-world processes, such as electronic health records, web clickstreams, and financial transactions, which transmit a great deal of information reflecting the causal relations among event types. Unfortunately, recovering causalities from observational event sequences is challenging, as the heterogeneous and high-dimensional event variables are often connected to rather complex underlying event excitation mechanisms that are hard to infer from limited observations. Many existing automated causal analysis techniques suffer from poor explainability and fail to include an adequate amount of human knowledge. In this paper, we introduce a visual analytics method for recovering causalities in event sequence data. We extend the Granger causality analysis algorithm on Hawkes processes to incorporate user feedback into causal model refinement. The visualization system includes an interactive causal analysis framework that supports bottom-up causal exploration, iterative causal verification and refinement, and causal comparison through a set of novel visualizations and interactions. We report two forms of evaluation: a quantitative evaluation of the model improvements resulting from the user-feedback mechanism, and a qualitative evaluation through case studies in different application domains to demonstrate the usefulness of the system.

13.
IEEE Trans Vis Comput Graph ; 27(2): 1591-1600, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33048752

RESUMO

Abstract-We propose a data-driven space-filling curve method for 2D and 3D visualization. Our flexible curve traverses the data elements in the spatial domain in a way that the resulting linearization better preserves features in space compared to existing methods. We achieve such data coherency by calculating a Hamiltonian path that approximately minimizes an objective function that describes the similarity of data values and location coherency in a neighborhood. Our extended variant even supports multiscale data via quadtrees and octrees. Our method is useful in many areas of visualization including multivariate or comparative visualization ensemble visualization of 2D and 3D data on regular grids or multiscale visual analysis of particle simulations. The effectiveness of our method is evaluated with numerical comparisons to existing techniques and through examples of ensemble and multivariate datasets.

14.
IEEE Comput Graph Appl ; 41(6): 101-110, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-32746086

RESUMO

Simulations of cosmic evolution are a means to explain the formation of the universe as we see it today. The resulting data of such simulations comprise numerous physical quantities, which turns their analysis into a complex task. Here, we analyze such high-dimensional and time-varying particle data using various visualization techniques from the fields of particle visualization, flow visualization, volume visualization, and information visualization. Our approach employs specialized filters to extract and highlight the development of so-called active galactic nuclei and filament structures formed by the particles. Additionally, we calculate X-ray emission of the evolving structures in a preprocessing step to complement visual analysis. Our approach is integrated into a single visual analytics framework to allow for analysis of star formation at interactive frame rates. Finally, we lay out the methodological aspects of our work that led to success at the 2019 IEEE SciVis Contest.

15.
Stud Hist Philos Biol Biomed Sci ; 84: 101328, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32771278

RESUMO

Indigenous peoples possess enormously rich and articulated knowledge of the natural world. A major goal of research in anthropology and ethnobiology as well as ecology, conservation biology, and development studies is to find ways of integrating this knowledge with that produced by academic and other institutionalized scientific communities. Here I present a challenge to this integration project. I argue, by reference to ethnographic and cross-cultural psychological studies, that the models of the world developed within specialized academic disciplines do not map onto anything existing within traditional beliefs and practices for coping with nature. Traditional ecological knowledge is distributed across a heterogeneous array of overlapping practices within Indigenous cultures, including spiritual and ritual practices that invoke categories, properties, and causal-explanatory models that do not in general converge with those of the academic sciences. In light of this divergence I argue that we should abandon the integration project, and conclude by sketching a notion of knowledge coordination as a possible successor framework.


Assuntos
Antropologia Cultural , Ecologia , Conhecimento
16.
IEEE Trans Vis Comput Graph ; 26(6): 2156-2167, 2020 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-32175863

RESUMO

We propose a photographic method to show scalar values of high dynamic range (HDR) by color mapping for 2D visualization. We combine (1) tone-mapping operators that transform the data to the display range of the monitor while preserving perceptually important features, based on a systematic evaluation, and (2) simulated glares that highlight high-value regions. Simulated glares are effective for highlighting small areas (of a few pixels) that may not be visible with conventional visualizations; through a controlled perception study, we confirm that glare is preattentive. The usefulness of our overall photographic HDR visualization is validated through the feedback of expert users.

17.
IEEE Trans Vis Comput Graph ; 26(1): 822-831, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31603820

RESUMO

We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions. In comparison to non-linear methods, linear dimensionality reduction techniques have the advantage that the characteristics of such probability distributions remain intact after projection. We derive a representation of the PCA sample covariance matrix that respects potential uncertainty in each of the inputs, building the mathematical foundation of our new method: uncertainty-aware PCA. In addition to the accuracy and performance gained by our approach over sampling-based strategies, our formulation allows us to perform sensitivity analysis with regard to the uncertainty in the data. For this, we propose factor traces as a novel visualization that enables to better understand the influence of uncertainty on the chosen principal components. We provide multiple examples of our technique using real-world datasets. As a special case, we show how to propagate multivariate normal distributions through PCA in closed form. Furthermore, we discuss extensions and limitations of our approach.

18.
IEEE Trans Vis Comput Graph ; 26(1): 1129-1139, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31443011

RESUMO

Good code quality is a prerequisite for efficiently developing maintainable software. In this paper, we present a novel approach to generate exploranative (explanatory and exploratory) data-driven documents that report code quality in an interactive, exploratory environment. We employ a template-based natural language generation method to create textual explanations about the code quality, dependent on data from software metrics. The interactive document is enriched by different kinds of visualization, including parallel coordinates plots and scatterplots for data exploration and graphics embedded into text. We devise an interaction model that allows users to explore code quality with consistent linking between text and visualizations; through integrated explanatory text, users are taught background knowledge about code quality aspects. Our approach to interactive documents was developed in a design study process that included software engineering and visual analytics experts. Although the solution is specific to the software engineering scenario, we discuss how the concept could generalize to multivariate data and report lessons learned in a broader scope.

19.
J Vis Lang Comput ; 552019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31827316

RESUMO

We propose an image-space contrast enhancement method for color-encoded visualization. The contrast of an image is enhanced through a perceptually guided approach that interfaces with the user with a single and intuitive parameter of the virtual viewing distance. To this end, we analyze a multiscale contrast model of the input image and test the visibility of bandpass images of all scales at a virtual viewing distance. By adapting weights of bandpass images with a threshold model of spatial vision, this image-based method enhances contrast to compensate for contrast loss caused by viewing the image at a certain distance. Relevant features in the color image can be further emphasized by the user using overcompensation. The weights can be assigned with a simple band-based approach, or with an efficient pixel-based approach that reduces ringing artifacts. The method is efficient and can be integrated into any visualization tool as it is a generic image-based post-processing technique. Using highly diverse datasets, we show the usefulness of perception compensation across a wide range of typical visualizations.

20.
IEEE Trans Vis Comput Graph ; 25(6): 2193-2204, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-30892212

RESUMO

We propose a technique to represent two-dimensional data using stipples. While stippling is often regarded as an illustrative method, we argue that it is worth investigating its suitability for the visualization domain. For this purpose, we generalize the Linde-Buzo-Gray stippling algorithm for information visualization purposes to encode continuous and discrete 2D data. Our proposed modifications provide more control over the resulting distribution of stipples for encoding additional information into the representation, such as contours. We show different approaches to depict contours in stipple drawings based on locally adjusting the stipple distribution. Combining stipple-based gradients and contours allows for simultaneous assessment of the overall structure of the data while preserving important local details. We discuss the applicability of our technique using datasets from different domains and conduct observation-validating studies to assess the perception of stippled representations.

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