Zobrazeno 1 - 10
of 3 293
pro vyhledávání: '"Ofner, A."'
Autor:
Ofner, Daniel
We study the stability of the mesoscopic fluctuations of certain orthogonal polynomial ensembles on the real line utilizing the recurrence relation of the associated orthogonal polynomials. We prove that under a sparse enough decaying perturbation of
Externí odkaz:
http://arxiv.org/abs/2410.07699
Autor:
Breuer, Jonathan, Ofner, Daniel
We study mesoscopic fluctuations of orthogonal polynomial ensembles on the unit circle. We show that asymptotics of such fluctuations are stable under decaying perturbations of the recurrence coefficients, where the appropriate decay rate depends on
Externí odkaz:
http://arxiv.org/abs/2409.09803
We demonstrate a novel phase transition from stable to unstable fluid behaviour for fluid-filled cosmological spacetimes undergoing decelerated expansion. This transition occurs when the fluid speed of sound $c_S$ exceeds a critical value relative to
Externí odkaz:
http://arxiv.org/abs/2405.03431
Autor:
Ofner, Maximilian, Hörmann, Siegfried
This paper studies linear reconstruction of partially observed functional data which are recorded on a discrete grid. We propose a novel estimation approach based on approximate factor models with increasing rank taking into account potential covaria
Externí odkaz:
http://arxiv.org/abs/2305.13152
Publikováno v:
Int. Math. Res. Not. 2024 (2024), 4328-4383
In this paper we study cosmological solutions to the Einstein--Euler equations. We first establish the future stability of nonlinear perturbations of a class of homogeneous solutions to the relativistic Euler equations on fixed linearly expanding cos
Externí odkaz:
http://arxiv.org/abs/2301.11191
Bibian et al. show in their recent paper (Bibi\'an et al. 2021) that eye and head movements can affect the EEG-based classification in a reaching motor task. These movements can generate artefacts that can cause an overoptimistic estimation of the cl
Externí odkaz:
http://arxiv.org/abs/2207.11168
Publikováno v:
IEEE/ASME Trans. on Mechatronics, 27(5):4101-4111, Oct. 2022
This paper introduces a method for the detection of knock occurrences in an internal combustion engine (ICE) using a 1D convolutional neural network trained on in-cylinder pressure data. The model architecture was based on considerations regarding th
Externí odkaz:
http://arxiv.org/abs/2201.06990
Autor:
Ofner, André, Stober, Sebastian
This paper deals with differentiable dynamical models congruent with neural process theories that cast brain function as the hierarchical refinement of an internal generative model explaining observations. Our work extends existing implementations of
Externí odkaz:
http://arxiv.org/abs/2112.03378
Autor:
Ofner, André, Stober, Sebastian
We present PredProp, a method for optimization of weights and states in predictive coding networks (PCNs) based on the precision of propagated errors and neural activity. PredProp jointly addresses inference and learning via stochastic gradient desce
Externí odkaz:
http://arxiv.org/abs/2111.08792
There is an increasing convergence between biologically plausible computational models of inference and learning with local update rules and the global gradient-based optimization of neural network models employed in machine learning. One particularl
Externí odkaz:
http://arxiv.org/abs/2111.06942