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PhysioDrum: Bridging physical and digital realms in immersive musical interaction
Get PDFThe Internet of Multisensory, Multimedia, and Musical Things (Io3MT) bridges computer science, humanities, and arts, fostering transmedia services and creative applications. This demo research applies these principles alongside extended reality (XR) to enhance PhysioDrum, an immersive, multimodal system that blends physical and digital aspects to expand musical expression in virtual environments. Using a smart musical instrument (SMI) and electronic pedals as interfaces, users interact with a virtual drum kit through gestures while receiving haptic feedback. By integrating sound and multimedia elements, PhysioDrum aims to reduce cognitive load and the learning curve, merging traditional drumming practices with immersive XR. The demo emphasizes design strategies that enhance playability, accessibility, and creative potential for users of all skill levels
Bayesian uncertainty quantification and regularized reconstruction for CT-based dimensional metrology
Get PDFStatistical methods within the Bayesian framework have been widely used to address inverse imaging problems, such as computed tomography (CT) image reconstruction. These methods offer a probabilistic approach that is able to enhance the reconstruction quality by employing regularization methods while enabling uncertainty quantification of the result, providing valuable insights into the reliability of the reconstructed images. However, despite the flexibility and range of techniques within this framework, the computational intensity of this class of approaches is still impractical for large-scale datasets like those in CT. In this manuscript, we introduce a concept for determining the uncertainty caused by the noise in the observed data in CT-based dimensional measurement using a rapid, regularized, Markov Chain Monte Carlo reconstruction technique. This method provides a volumetric model where each voxel is represented by a distribution, which is then transformed into a triplet of gray value models: one for the central value and one each for the upper and lower bounds of the confidence interval. Bi-directional and uni-directional length measurements on results derived from each single-gray-value model, for real CT data, provide a task-specific measurement uncertainty. This method requires significantly less computation and storage capacity compared to classic Monte Carlo simulations by reducing the number of needed simulations for approximating a distribution while incorporating regularization techniques. The results are compared to conventional non-regularized and regularized reconstruction methods, such as Feldkamp–David–Kress (FDK), and state-of-the-art statistical methods, followed by validation of the determined uncertainty in real CT data
Ophef. Polarisatie. Is het allemaal de schuld van algoritmen? Dat blijkt te snel geoordeeld - de Volkskrant - 07-03-2025
No full textDit nieuwe systeem moet wachtrijen in verpleeghuizen minstens halveren - Reuma Arnhem - 02-06-2025
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Haarlemse Francien Bossema krijgt belangwekkende wetenschappelijke prijs. 'Fantastisch. Dit is de kroon op mijn werk' - Noordhollands Dagblad - 10-07-2025
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An exploration of sequential Bayesian variable selection -- A comment on García-Donato et al. (2025). "Model uncertainty and missing data: An objective Bayesian perspective"
Get PDFOur comment on García-Donato et al. (2025). "Model uncertainty and missing data: An objective Bayesian perspective" explores a further extension of the proposed methodology. Specifically, we consider the sequential setting where (potentially missing) data accumulate over time, with the goal of continuously monitoring statistical evidence, as opposed to assessing it only once data collection terminates. We explore a new variable selection method based on sequential model confidence sets, as proposed by Arnold et al. (2024), and show that it can help stabilise the inference of García-Donato et al. (2025). To be published as "Invited discussion" in Bayesian Analysis
A tutorial on safe anytime-valid inference: Practical maximally flexible sampling designs for experiments based on e-values
Get PDFWe demonstrate how e-values simplify both experimental design and the inference process. With e-values researchers can perform anytime-valid tests and construct confidence intervals that maintain type I error control regardless of the sample size. This enables real-time monitoring of evidence as data are collected, permitting early termination of experiments without intolerably inflating the risk of false discoveries. Early stopping not only conserves resources, but also mitigates risk for participants in clinical settings. Anytime-valid tests allow for optional continuation, that is, the extension of an experiment, for instance if more funds become available, or even if the evidence looks promising and the funding agency, a reviewer, or an editor urges the experimenter to collect more data. Analogously, a researcher can be assured that a 95% anytime-valid confidence interval will, with at least 95% probability, cover the true effect size regardless of how, or even if, data collection is stopped. We use the free and open-source software package safestats implemented in R to illustrate the practical benefits of this novel inference framework