Results 101 to 110 of about 2,581 (224)

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, Ziyan Deng, Shiqing Cong, Chen Ran, Tao Gong, Shoulin Huang, Ting Ma   +6 more
wiley   +1 more source

A new mean-Berezin norm for operators in reproducing kernel Hilbert spaces

Journal of Inequalities and Applications
A functional Hilbert space is defined as the Hilbert space K $\mathcal{K}$ of complex-valued functions defined on a set Θ. In this space, the evaluation functionals ψ ε ( h ) = h ( ε ) $\psi _{\varepsilon}(h) = h(\varepsilon )$ , for ε ∈ Θ $\varepsilon ...
Mojtaba Bakherad
doaj   +1 more source

Multikernel Adaptive Filters Under the Minimum Cauchy Kernel Loss Criterion

IEEE Access, 2019
The Cauchy loss has been successfully applied in robust learning algorithms in the presence of large outliers, but it may suffer from performance degradation in complex nonlinear tasks.
Wei Shi, Kui Xiong, Shiyuan Wang
doaj   +1 more source

SNR-enhanced diffusion MRI with structure-preserving low-rank denoising in reproducing kernel Hilbert spaces. [PDF]

Magn Reson Med, 2021
Ramos-Llordén G, Vegas-Sánchez-Ferrero G, Liao C, Westin CF, Setsompop K, Rathi Y.   +5 more
europepmc   +1 more source

Recursive Feasibility of Nonlinear Stochastic Model Predictive Control With Gaussian Process Dynamics

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, Andreas Völz, Knut Graichen   +2 more
wiley   +1 more source

A Survey on Hilbert Spaces and Reproducing Kernels

, 2020
The main purpose of this chapter is to provide a brief review of Hilbert space with its fundamental features and introduce reproducing kernels of the corresponding spaces. We separate our analysis into two parts.
Okutmuştur, Baver, Baver Okutmuştur
core   +1 more source

Learning Reconstructive Embeddings in Reproducing Kernel Hilbert Spaces via the Representer Theorem

IEEE Open Journal of the Computer Society
Motivated by the growing interest in representation learning approaches that uncover the latent structure of high-dimensional data, this work proposes new algorithms for reconstruction-based manifold learning within Reproducing-Kernel Hilbert Spaces ...
Enrique Feito-Casares, Francisco M. Melgarejo-Meseguer, Jose Luis Rojo-Alvarez   +2 more
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

Wheat's war against stripe rust: Integrating host immunity, genomics and breeding for durable resistance

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, Mukesh Rathore, Dinesh Kumar Saini, Ayushi Singh, Naeela Qureshi, Nikita Aggarwal, Mahpara Bashir, Mehnaz Shakeel, Vikas Gupta, Sundeep Kumar, Rajeev Kumar Varshney, Reyazul Rouf Mir   +11 more
wiley   +1 more source

On the Probabilistic Approximation in Reproducing Kernel Hilbert Spaces

Complex Analysis and Operator Theory
This paper studies the probabilistic function approximation problem over reproducing kernel Hilbert spaces. We show the existence and uniqueness of the optimizer under mild assumptions. Furthermore, we generalize the celebrated representer theorem to our setting, and especially when the probability measure is finitely supported, or the Hilbert space is
Chen, Dongwei, Wang, Kai-Hsiang
openaire   +2 more sources