A5059941239- Model Reduction and Neural Networks
- Neural Networks and Applications
- Neurotransmitter Receptor Influence on Behavior
- Psychedelics and Drug Studies
- Cardiovascular and exercise physiology
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Landslides and related hazards
- Rangeland Management and Livestock Ecology
- Olfactory and Sensory Function Studies
- Machine Learning in Materials Science
- Nuclear Engineering Thermal-Hydraulics
- Lattice Boltzmann Simulation Studies
- Computational Drug Discovery Methods
- Chemical synthesis and alkaloids
- Soil erosion and sediment transport
- Cardiac Health and Mental Health
- Seismic Imaging and Inversion Techniques
University of Basel
2022-2024
Abstract Psychedelics have emerged as promising candidate treatments for various psychiatric conditions, and given their clinical potential, there is a need to identify biomarkers that underlie effects. Here, we investigate the neural mechanisms of lysergic acid diethylamide (LSD) using regression dynamic causal modelling (rDCM), novel technique assesses whole-brain effective connectivity (EC) during resting-state functional magnetic resonance imaging (fMRI). We modelled data from two...
Partial differential equations (PDEs) are widely used to describe relevant phenomena in dynamical systems. In real-world applications, we commonly need combine formal PDE models with (potentially noisy) observations. This is especially settings where lack information about boundary or initial conditions, identify unknown model parameters. recent years, Physics-Informed Neural Networks (PINNs) have become a popular tool for this kind of problems. high-dimensional settings, however, PINNs...
ABSTRACT Introduction Well-trained staff is needed to interpret cardiopulmonary exercise tests (CPET). We aimed examine the accuracy of machine learning–based algorithms classify limitations and their severity in clinical practice compared with expert consensus using patients presenting at a pulmonary clinic. Methods This study included 200 historical CPET data sets (48.5% female) older than 40 yr referred for because unexplained dyspnea, preoperative examination, evaluation therapy...
Physics-informed Neural Networks (PINNs) have recently emerged as a principled way to include prior physical knowledge in form of partial differential equations (PDEs) into neural networks. Although PINNs are generally viewed mesh-free, current approaches still rely on collocation points within bounded region, even settings with spatially sparse signals. Furthermore, if the boundaries not known, selection such region is difficult and often results large proportion being selected areas low...
Partial differential equations (PDEs) can describe many relevant phenomena in dynamical systems. In real-world applications, we commonly need to combine formal PDE models with (potentially noisy) observations. This is especially settings where lack information about boundary or initial conditions, identify unknown model parameters. recent years, Physics-informed neural networks (PINNs) have become a popular tool for problems of this kind. high-dimensional settings, however, PINNs often...
<p>Understanding the occurrence of soil erosion phenomena is vital importance for ecology and agriculture, especially under changing climate conditions. In Alpine grasslands, susceptibility to predominately due prevailing geological, morphological conditions but also affected by anthropogenic aspects such as agricultural land use. Climate change expected have a relevant impact on driving factors like strong precipitation events altered snow dynamics. order assess spatial...
Considering smooth mappings from input vectors to continuous targets, our goal is characterise subspaces of the domain, which are invariant under such mappings. Thus, we want manifolds implicitly defined by level sets. Specifically, this characterisation should be a global parametric form, especially useful for different informed data exploration tasks, as building grid-based approximations, sampling points along curves, or finding trajectories on manifold. However, parameterisations can...
Abstract Psychedelics have emerged as promising candidate treatments for various psychiatric conditions, and given their clinical potential, there is a need to identify biomarkers that underlie effects. Here, we investigate the neural mechanisms of lysergic acid diethylamide (LSD) using regression dynamic causal modelling (rDCM), novel technique assesses whole-brain effective connectivity (EC) during resting-state functional magnetic resonance imaging (fMRI). We modelled data from two...