SUMMARY: Visualization of multidimensional, categorical data is a common challenge across scientific domains and, in particular, the life sciences. The goal is to create a comprehensive overview of the underlying data which enables one to assess multiple variables. One application where such visualizations are particularly useful is gene or pathway analysis, which involves checking for dysregulation in known biological mechanisms and functions across multiple conditions. Here, we propose a new visualization approach that encodes such data in an intuitive representation: DicePlots visualize up to four distinct categorical classes in a single view using elements resembling dice faces, whereas DominoPlots add an additional layer of information for binary comparison. AVAILABILITY AND IMPLEMENTATION: The code is available as the diceplot R package and the pydiceplot on PyPI. All source code is available at https://github.com/maflot. CONTACT: The repo is managed actively and we encourage community contributions and requests.

Facial analysis is a key component in a wide range of applications such as healthcare, autonomous driving, and entertainment. Despite the availability of various facial RGB datasets, the thermal modality, which plays a crucial role in life sciences, medicine, and biometrics, has been largely overlooked. To address this gap, we introduce the T-FAKE dataset, a new large-scale synthetic thermal dataset with sparse and dense landmarks. To facilitate the creation of the dataset, we propose a novel RGB2Thermal loss function, which enables the domain-adaptive transfer of RGB faces to thermal style. By utilizing the Wasserstein distance between thermal and RGB patches and the statistical analysis of clinical temperature distributions on faces, we ensure that the generated thermal images closely resemble real samples. Using RGB2Thermal style transfer based on our RGB2Thermal loss function, we create the large-scale synthetic thermal T-FAKE dataset†with landmark and segmentation annotations. Leveraging our novel T-FAKE dataset, probabilistic landmark prediction, and label adaptation networks, we demonstrate significant improvements in landmark detection methods on thermal images across different landmark conventions. Our models show excellent performance with both sparse 70-point landmarks and dense 478-point landmark annotations. Moreover, our RGB2Thermal loss leads to notable results in terms of perceptual evaluation and temperature prediction.

Motion sickness is a phenomenon attracting increasing attention with the ever-growing popularity of highly automated driving. Understanding motion sickness is of significant interest in the context of self-driven vehicles because, in this case, all occupants of the vehicle are passengers and, therefore, more susceptible to motion sickness. In this article, we report the findings of a study wherein motion sickness was induced in 40% of the participants while driving in real-world conditions. By recording various psychophysiological parameters continuously (electrodermal activity, skin temperature, heart rate, and heart rate variability), we investigate the feasibility of using these to objectively assess motion sickness. Furthermore, the instantaneous physiological reactions of participants to unpleasant driving maneuvers are examined. The changes in the electrodermal activity show a strong correlation with the subjective ratings of motion sickness levels as reported by the participants. The phasic component of the electrodermal activity suggests differences between participants that are susceptible to motion sickness and those who are not. Several driving maneuvers (accelerations, cornering, and driving over speed bumps) were identified as events triggering significant electrodermal responses. These responses could be the result of a mismatch between visual and vestibular perception acting as an aversive, arousing stimulus. While, in this work, the driving maneuvers were partially overlapping and nonuniform, our results pave the way for future investigation of physiological responses to single driving events and their relation to motion sickness with the potential to identify real-time markers of possibly unpleasant driving maneuvers.