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"title": "Evaluating Perceptual Bias During Geometric Scaling of Scatterplots",
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"abstract": "Electroencephalogram (EEG) has been widely used for the detection of anxiety because of its ability to reflect the functional activities of the brain. However, EEG alone may not provide precision in the detection of anxiety because other emotional disorders usually trigger the same changes in brain function. To discover effective diagnostic indicators and to achieve more precise anxiety detection, we integrate eye movement information into EEG and divide the features into groups according to their respective characteristics. Then, we use group sparse canonical correlation analysis (GSCCA) to investigate group structure information among EEG and eye movement features and obtain an effective fusion representation of EEG and eye movement to achieve more precise detection of anxiety mood. The experimental results from 45 anxious subjects and 47 normal controls from the Healthy Brain Network (HBN) dataset showed that GSCCA could be effectively used to explore the correlation between EEG features within different scalp regions and eye movement features from several aspects. Visual behaviors, including saccades and fixation, are more linearly related to the power spectrum of EEG on the scalp area corresponding to the visual region of the brain. The ultimate fusion representation achieved an optimal classification accuracy of 82.70 percent with the support vector machine (SVM) classifier on the gamma band of EEG.",
"abstracts": [
{
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"content": "Electroencephalogram (EEG) has been widely used for the detection of anxiety because of its ability to reflect the functional activities of the brain. However, EEG alone may not provide precision in the detection of anxiety because other emotional disorders usually trigger the same changes in brain function. To discover effective diagnostic indicators and to achieve more precise anxiety detection, we integrate eye movement information into EEG and divide the features into groups according to their respective characteristics. Then, we use group sparse canonical correlation analysis (GSCCA) to investigate group structure information among EEG and eye movement features and obtain an effective fusion representation of EEG and eye movement to achieve more precise detection of anxiety mood. The experimental results from 45 anxious subjects and 47 normal controls from the Healthy Brain Network (HBN) dataset showed that GSCCA could be effectively used to explore the correlation between EEG features within different scalp regions and eye movement features from several aspects. Visual behaviors, including saccades and fixation, are more linearly related to the power spectrum of EEG on the scalp area corresponding to the visual region of the brain. The ultimate fusion representation achieved an optimal classification accuracy of 82.70 percent with the support vector machine (SVM) classifier on the gamma band of EEG.",
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"normalizedAbstract": "Electroencephalogram (EEG) has been widely used for the detection of anxiety because of its ability to reflect the functional activities of the brain. However, EEG alone may not provide precision in the detection of anxiety because other emotional disorders usually trigger the same changes in brain function. To discover effective diagnostic indicators and to achieve more precise anxiety detection, we integrate eye movement information into EEG and divide the features into groups according to their respective characteristics. Then, we use group sparse canonical correlation analysis (GSCCA) to investigate group structure information among EEG and eye movement features and obtain an effective fusion representation of EEG and eye movement to achieve more precise detection of anxiety mood. The experimental results from 45 anxious subjects and 47 normal controls from the Healthy Brain Network (HBN) dataset showed that GSCCA could be effectively used to explore the correlation between EEG features within different scalp regions and eye movement features from several aspects. Visual behaviors, including saccades and fixation, are more linearly related to the power spectrum of EEG on the scalp area corresponding to the visual region of the brain. The ultimate fusion representation achieved an optimal classification accuracy of 82.70 percent with the support vector machine (SVM) classifier on the gamma band of EEG.",
"title": "Fusing of Electroencephalogram and Eye Movement With Group Sparse Canonical Correlation Analysis for Anxiety Detection",
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"givenName": "Xiaowei",
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"abstract": "Natural language and visualization are being increasingly deployed together for supporting data analysis in different ways, from multimodal interaction to enriched data summaries and insights. Yet, researchers still lack systematic knowledge on how viewers verbalize their interpretations of visualizations, and how they interpret verbalizations of visualizations in such contexts. We describe two studies aimed at identifying characteristics of data and charts that are relevant in such tasks. The first study asks participants to verbalize what they see in scatterplots that depict various levels of correlations. The second study then asks participants to choose visualizations that match a given verbal description of correlation. We extract key concepts from responses, organize them in a taxonomy and analyze the categorized responses. We observe that participants use a wide range of vocabulary across all scatterplots, but particular concepts are preferred for higher levels of correlation. A comparison between the studies reveals the ambiguity of some of the concepts. We discuss how the results could inform the design of multimodal representations aligned with the data and analytical tasks, and present a research roadmap to deepen the understanding about visualizations and natural language.",
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"content": "Natural language and visualization are being increasingly deployed together for supporting data analysis in different ways, from multimodal interaction to enriched data summaries and insights. Yet, researchers still lack systematic knowledge on how viewers verbalize their interpretations of visualizations, and how they interpret verbalizations of visualizations in such contexts. We describe two studies aimed at identifying characteristics of data and charts that are relevant in such tasks. The first study asks participants to verbalize what they see in scatterplots that depict various levels of correlations. The second study then asks participants to choose visualizations that match a given verbal description of correlation. We extract key concepts from responses, organize them in a taxonomy and analyze the categorized responses. We observe that participants use a wide range of vocabulary across all scatterplots, but particular concepts are preferred for higher levels of correlation. A comparison between the studies reveals the ambiguity of some of the concepts. We discuss how the results could inform the design of multimodal representations aligned with the data and analytical tasks, and present a research roadmap to deepen the understanding about visualizations and natural language.",
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"abstracts": [
{
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"content": "In NURBS-based isogeometric analysis, the basis functions of a 3D model's geometric description also form the basis for the solution space of variational formulations of partial differential equations. In order to visualize the results of a NURBS-based isogeometric analysis, we developed a novel GPU-based multi-pass isosurface visualization technique which performs directly on an equivalent rational Bézier representation without the need for discretization or approximation. Our approach utilizes rasterization to generate a list of intervals along the ray that each potentially contain boundary or isosurface intersections. Depth-sorting this list for each ray allows us to proceed in front-to-back order and enables early ray termination. We detect multiple intersections of a ray with the higher-order surface of the model using a sampling-based root-isolation method. The model's surfaces and the isosurfaces always appear smooth, independent of the zoom level due to our pixel-precise processing scheme. Our adaptive sampling strategy minimizes costs for point evaluations and intersection computations. The implementation shows that the proposed approach interactively visualizes volume meshes containing hundreds of thousands of Bézier elements on current graphics hardware. A comparison to a GPU-based ray casting implementation using spatial data structures indicates that our approach generally performs significantly faster while being more accurate.",
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"title": "Direct Isosurface Ray Casting of NURBS-Based Isogeometric Analysis",
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"abstract": "Curve reconstruction from data points is an important issue for advanced medical imaging techniques, such as computer tomography (CT) and magnetic resonance imaging (MRI). The most powerful fitting functions for this purpose are the NURBS (non-uniform rational B-splines). Solving the general reconstruction problem with NURBS requires computing all free variables of the problem (data parameters, breakpoints, control points, and their weights). This leads to a very difficult non-convex, nonlinear, high-dimensional, multimodal, and continuous optimization problem. Previous methods simplify the problem by guessing the values for some variables and computing only the remaining ones. As a result, unavoidable approximations errors are introduced. In this paper, we describe the first method in the literature to solve the full NURBS curve reconstruction problem in all its generality. Our method is based on a combination of two techniques: an immunological approach to perform data parameterization, breakpoint placement, and weight calculation, and least squares minimization to compute the control points. This procedure is repeated iteratively (until no further improvement is achieved) for higher accuracy. The method has been applied to reconstruct some outline curves from MRI brain images with satisfactory results. Comparative work shows that our method outperforms the previous related approaches in the literature for all instances in our benchmark.",
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"content": "Curve reconstruction from data points is an important issue for advanced medical imaging techniques, such as computer tomography (CT) and magnetic resonance imaging (MRI). The most powerful fitting functions for this purpose are the NURBS (non-uniform rational B-splines). Solving the general reconstruction problem with NURBS requires computing all free variables of the problem (data parameters, breakpoints, control points, and their weights). This leads to a very difficult non-convex, nonlinear, high-dimensional, multimodal, and continuous optimization problem. Previous methods simplify the problem by guessing the values for some variables and computing only the remaining ones. As a result, unavoidable approximations errors are introduced. In this paper, we describe the first method in the literature to solve the full NURBS curve reconstruction problem in all its generality. Our method is based on a combination of two techniques: an immunological approach to perform data parameterization, breakpoint placement, and weight calculation, and least squares minimization to compute the control points. This procedure is repeated iteratively (until no further improvement is achieved) for higher accuracy. The method has been applied to reconstruct some outline curves from MRI brain images with satisfactory results. Comparative work shows that our method outperforms the previous related approaches in the literature for all instances in our benchmark.",
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