27 citations
- Heidelberg UniversityDE6 papers
- University Hospital HeidelbergDE5 papers
- Universidade de São PauloBR2 papers
- University of ViennaAT2 papers
- Vienna Institute for International Economic StudiesAT2 papers
- Assistance Publique – Hôpitaux de ParisFR1 paper
- CEA Paris-SaclayFR1 paper
- Centre BorelliFR1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- Charité - Universitätsmedizin BerlinDE1 paper
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR1 paper
- École Normale Supérieure Paris-SaclayFR1 paper
6 papers
Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts
Lisa Hemforth, Baptiste Couvy-Duchesne, Kevin De Matos +34
Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population.…
Reconciling the Contour-Improved and Fixed-Order Approaches for Hadronic Spectral Moments II: Renormalon Norm and Application in Determinations
Miguel A. Benitez-Rathgeb, Diogo Boito, André H. Hoang +1
In a previous article, we have shown that the discrepancy between the fixed-order (FOPT) and contour-improved (CIPT) perturbative expansions for hadronic spectral function mome…
Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods
Göran Köber, Raffael Kalisch, Lara Puhlmann +3
When modeling longitudinal biomedical data, often dimensionality reduction as well as dynamic modeling in the resulting latent representation is needed. This can be achieved by art…
Reconciling the Contour-Improved and Fixed-Order Approaches for Hadronic Spectral Moments I: Renormalon-Free Gluon Condensate Scheme
Miguel A. Benitez-Rathgeb, Diogo Boito, Andre H. Hoang +1
We propose a simple and easy-to-implement scheme for a renormalon-free gluon condensate (GC) matrix element, which is analogous to implementations of short-distance heavy-quark mas…
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time
Zahra Monfared, Daniel Durstewitz
Recurrent neural networks (RNN) as used in machine learning are commonly formulated in discrete time, i.e. as recursive maps. This brings a lot of advantages for training models on…
Quantifying the behavioural relevance of hippocampal neurogenesis
Stanley E. Lazic, Johannes Fuss, Peter Gass
Few studies that examine the neurogenesis--behaviour relationship formally establish covariation between neurogenesis and behaviour or rule out competing explanations. The behaviou…