Shape-Driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning

Hu, Ruizhen; Chen, Bin; Xu, Juzhan; Kaick, Oliver van; Deußen, Oliver; Huang, Hui

doi:10.1109/tvcg.2021.3052167

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Comparing to classical reinforcement learning, DRL holds the potential to address more intricate visualization problems. To handle high-dimensional input data, such as images and tables, some studies [32], [33], [46]- [48] employ deep neural networks that enable the agent to learn state abstractions and policy approximations directly from the input data. Hu et al [46] utilized the DRL [49] and pointer network [50] algorithm to address the coordinate ordering problem in starglyph sets.…”

Section: B Reinforcement Learning For Visualizationsmentioning

confidence: 99%

“…To handle high-dimensional input data, such as images and tables, some studies [32], [33], [46]- [48] employ deep neural networks that enable the agent to learn state abstractions and policy approximations directly from the input data. Hu et al [46] utilized the DRL [49] and pointer network [50] algorithm to address the coordinate ordering problem in starglyph sets. Additionally, several research efforts have focused on visualization generation and recommendation.…”

Section: B Reinforcement Learning For Visualizationsmentioning

confidence: 99%

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Hierarchical Fluctuation Shapes a Dynamic Flow Linked to States of Consciousness

Liu

Lei

et al. 2022

Preprint

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Consciousness emerges from the spatiotemporal dynamics of neural activity. However, how such an extraordinary phenomenon is supported by neural flexibility and regional specialization across the cerebral cortex remains elusive. Here, using several functional magnetic resonance imaging (fMRI) paradigms (e.g., anesthesia, sleep, and drowsiness), we show a consciousness-related signature characterized by shifting spontaneous fluctuations along a unimodal-transmodal cortical organizational axis. The signature is simple and sensitive to altered states of consciousness in single individuals, exhibiting abnormal elevation under psychedelics as well as in individuals with psychosis. Under task-free conditions, this hierarchical dynamic reflects ongoing brain state changes in global integration and connectome diversity. Applying quasi-periodic pattern detection during different vigilance states, we found that hierarchical heterogeneity also manifested as spatiotemporally propagation waves related to arousal. A similar pattern was observed in neural activity measured with cortex-wide electrocorticography (ECoG) in macaques. Last, we observed that the spatial distribution of the principle cortical gradient not only preferentially recapitulated the genetic transcription levels of the histaminergic system but also that of the functional connectome mapping of the tuberomammillary nucleus (TMN), which promotes wakefulness. Combining behavioral, neuroimaging, electrophysiological, and transcriptomic evidence, we suggest that global state of consciousness is supported by efficient hierarchical processing that can be constrained along a low-dimensional macroscale gradient.

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Section: B Reinforcement Learning For Visualizationsmentioning

confidence: 99%

Section: B Reinforcement Learning For Visualizationsmentioning

confidence: 99%

Hierarchical Fluctuation Shapes a Dynamic Flow Linked to States of Consciousness

Liu

Lei

et al. 2022

Preprint

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show abstract

“…The Scagnostics approach for detecting anomalies in SPLOM (scatterplot ma-trix) [34,35] and subsequent improvements [36][37][38][39][40][41] are representative. Following the idea of Scagnostics, researchers have proposed many other indicators for a variety of visualization techniques, such as time series [42,43], treemap [44,45], parallel coordinates [46][47][48][49][50], parallel sets [51], star glyphs [52], and pixel-oriented displays [53]. Seo and Shneiderman [54] used ordinary statistics to select the most suitable views for showing filtered data.…”

Section: Automatic Pattern Identificationmentioning

confidence: 99%

Active Pattern Classification for Automatic Visual Exploration of Multi-Dimensional Data

Tan

Huang

2022

Applied Sciences

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The practice of applying a classifier (called a pattern classifier and abbreviated as PC below) in a visual analysis system to identify patterns from interactively generated visualizations is gradually emerging. Demonstrated cases in existing works focus on ideal scenarios where the analyst can determine all the pattern types in advance without adjusting the classifier settings during the exploration process. However, in most real-world scenarios, analysts know nothing about data patterns before exploring the dataset and inevitably find novel patterns during the exploration. This difference makes the traditional classifier training and application mode less suitable. Analysts have to artificially determine whether each generated visualization contains new data patterns to adjust the classifier setting, thus affecting the automation of the data exploration. This paper proposes a novel PC-based data exploration approach. The core of the approach is an active-learning indicator for automatically identifying visualizations involving new pattern classes. Analysts thus can apply PCs to explore data while dynamically adjusting the PCs using these visualizations. We further propose a PC-based visualization framework that takes full advantage of the PC in terms of efficiency by allowing analysts to explore an exploring space, rather than a single visualization at a time. The results of the quantitative experiment and the performance of participants in the user study demonstrate the effectiveness and usability of the method.

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A Consensus Model to Manage Non-Cooperative Behaviors with Linguistic Scale Gradation-based Clustering and Mixed Adjustment Methods in Large-scale Group Decision-making

Chen

2023

2023 35th Chinese Control and Decision Conference (CCDC)

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Shape-Driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning

Cited by 5 publications

References 15 publications

Hierarchical Fluctuation Shapes a Dynamic Flow Linked to States of Consciousness

Hierarchical Fluctuation Shapes a Dynamic Flow Linked to States of Consciousness

Active Pattern Classification for Automatic Visual Exploration of Multi-Dimensional Data

A Consensus Model to Manage Non-Cooperative Behaviors with Linguistic Scale Gradation-based Clustering and Mixed Adjustment Methods in Large-scale Group Decision-making

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