On the best constants of Schur multipliers of second order divided difference functions. [PDF]
Caspers M, Reimann J.
europepmc +1 more source
Detecting Critical Change in Dynamics Through Outlier Detection with Time‐Varying Parameters
Abstract Intensive longitudinal data are often found to be non‐stationary, namely, showing changes in statistical properties, such as means and variance‐covariance structures, over time. One way to accommodate non‐stationarity is to specify key parameters that show over‐time changes as time‐varying parameters (TVPs). However, the nature and dynamics of
Meng Chen +2 more
wiley +1 more source
Multipliers on bi-parameter Haar system Hardy spaces. [PDF]
Lechner R +3 more
europepmc +1 more source
Multiple Changepoint Detection for Non‐Gaussian Time Series
ABSTRACT This article combines methods from existing techniques to identify multiple changepoints in non‐Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non‐Gaussian series, enabling penalized likelihood methods to handle non‐Gaussian scenarios.
Robert Lund +3 more
wiley +1 more source
Optimised MobileNet for very lightweight and accurate plant leaf disease detection. [PDF]
Nnamdi UV, Abolghasemi V.
europepmc +1 more source
Pointwise Multipliers on the Lorentz Spaces
L^p 〓spaces ...
openaire
Turbulent Mixing and Dissipation Around Rough Seamounts
Abstract Seamounts are critical components of the global ocean energy budget, contributing significantly to turbulent dissipation through their interaction with large‐scale flows. However, most previous numerical investigations used smoothed bathymetry that omits small‐scale topographic variability. We use turbulence‐resolving large‐eddy simulations to
Tomas Chor +2 more
wiley +1 more source
Interference-aware frequency-agile onboard processor using fine-grained multilevel analysis-synthesis filter-bank channelization. [PDF]
Sarkar S, Das A, Mishra D, Gupta A.
europepmc +1 more source
Explainable AI Insights Into EEG Classification and Its Alignment to Neural Correlates
We systematically generate, filter, and cluster explanations of deep learning models of EEG data to identify universal patterns of relevance, which we validate via connections to observations from neuroscience. ABSTRACT While deep learning has drastically improved the performance of electroencephalography (EEG) analysis, it remains unclear what these ...
Hendrik Eilts +5 more
wiley +1 more source
A variational framework for residual-based adaptivity in neural PDE solvers and operator learning. [PDF]
Toscano JD +4 more
europepmc +1 more source

