7 papers
Modern aerodynamics models do not capture important unsteady forces--the failure of the quasi-steady approximation
Victoria M. Malarczyk, Marcus Hultmark
Aerodynamic unsteadiness is inherent to the operation of many engineering applications, especially those that involve large-scale rotating blades, such as modern wind turbines. Ove…
Deep Shape Regression for Planar Curves with Multimodal Covariates
Manuel Pfeuffer, Roshan Prakash Rane, Hadya Yassin +2
The shape of a planar curve is the geometric information that remains once translation, rotation, scale and reparametrisation are removed and is of interest in many health applicat…
Measurement noise limits the advantage of nonlinear models over linear models in biomedical prediction
Marc-Andre Schulz, Kerstin Ritter
On biomedical tabular data, flexible models such as deep networks, gradient-boosted trees, and kernel methods are repeatedly matched or beaten by linear and logistic regression giv…
Flow Matching with In-Context Priors for Out-of-Distribution Brain Dynamics
Sam Gijsen, MichaÅ Åukomski, Marc-André Schulz +1
Flow matching and diffusion models enable conditional generation across domains ranging from images to proteins, with recent extensions to out-of-distribution contexts. Yet generat…
Beyond Binary: Speech Representations Across the Cognitive Score Hierarchy
Serli Kopar, Roshan Prakash Rane, Christian Mychajliw +6
This study examines the relationship between speech representations and the hierarchical structure of cognitive assessment in mild cognitive impairment. Utilizing 5,754 German neur…
Brain-Semantoks: Learning Semantic Tokens of Brain Dynamics with a Self-Distilled Foundation Model
Sam Gijsen, Marc-Andre Schulz, Kerstin Ritter
The development of foundation models for functional magnetic resonance imaging (fMRI) time series holds significant promise for predicting phenotypes related to disease and cogniti…