Results 91 to 100 of about 2,360 (218)
Engineering Reports, Volume 8, Issue 6, June 2026.Graphical abstract of the (q,τ)$$ \left(q,\tau \right) $$‐deformed kernel framework for quantum‐inspired learning and biomedical signal analysis ABSTRACT This paper introduces a weighted (q,τ)$$ \left(q,\tau \right) $$‐deformed Gram matrix framework for quantum‐inspired learning systems, with particular emphasis on applications in biomedical signal ...
Rabha W. Ibrahim +2 more
wiley +1 more source
Energy‐Based Phase‐Locking State Analysis in Brain State Identification
Human Brain Mapping, Volume 47, Issue 8, June 1, 2026.EPLSA constructs an energy landscape from BOLD phase synchrony, achieving superior brain state classification and providing novel stability metrics. It demonstrates strong clinical translatability in characterizing sleep–wake transitions and Alzheimer's disease.
Chenfei Ye +6 more
wiley +1 more source
Journal of Function Spaces, 2015 We apply the reproducing kernel Hilbert space (RKHS) method for getting analytical and approximate solutions for second-order hyperbolic integrodifferential equations with a weighted integral condition.
A. Guezane-Lakoud +2 more
doaj +1 more source
Unsupervised Domain Adaptation by Mapped Correlation Alignment
IEEE Access, 2018 The goal of unsupervised domain adaptation aims to utilize labeled data from source domain to annotate the target-domain data, which has none of the labels.
Yun Zhang +3 more
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Fortschritte der Physik, Volume 74, Issue 6, June 2026.ABSTRACT We present a clear, step‐by‐step method for counting degrees of freedom and identifying constraints in general field theories. This approach, grounded in the works of Einstein, Hilbert, Cartan, Kuranishi, and, more recently, Seiler, is neither Lagrangian nor Hamiltonian in nature. Instead, it applies directly to the field equations. We offer a
Lavinia Heisenberg
wiley +1 more source
International Journal of Robust and Nonlinear Control, Volume 36, Issue 9, Page 4957-4970, June 2026.ABSTRACT Data‐based learning of system dynamics allows model‐based control approaches to be applied to systems with partially unknown dynamics. Gaussian process regression is a preferred approach that outputs not only the learned system model but also the variance of the model, which can be seen as a measure of uncertainty.
Daniel Landgraf +2 more
wiley +1 more source
Journal of Taibah University for Science, 2019 In this work, the boundary layer flow of a Powell–Eyring non-Newtonian fluid over a stretching sheet has been investigated by a reproducing kernel method. Reproducing kernel functions are used to obtain the solutions.
Ali Akgül
doaj +1 more source
Wasserstein Regression, Forecasting, and Change‐Point Detection for Daily Traffic Flow Distributions
Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 3, June 2026.ABSTRACT We develop a distribution‐valued framework for modeling, forecasting, and monitoring traffic flow counts by treating each day as a probability distribution summarized by jittered empirical quantile signatures. Inference is conducted under the 2‐Wasserstein geometry, which in one dimension is isometric to the L2(0,1)$$ {L}^2\left(0,1\right ...
Abdolnasser Sadeghkhani
wiley +1 more source
, 2016 To the best of our knowledge, there are no general well-founded robust methods for statistical unsupervised learning. Most of the unsupervised methods explicitly or implicitly depend on the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO).
Alam, Md. Ashad +2 more
openaire +2 more sources
The Plant Genome, Volume 19, Issue 2, June 2026.Abstract Wheat (Triticum aestivum L.), a foundation of global food security, faces persistent threats from stripe rust caused by Puccinia striiformis f. sp. tritici (Pst). The pathogen thrives in cool and humid environments and regularly causes epidemics that lead to severe yield losses.
Farkhandah Jan +11 more
wiley +1 more source