Optical design unlocks direct Raman detection of ångström-scale ultrathin molecular layers at interfaces

Conventional spontaneous Raman spectroscopy of interfacial molecules typically requires plasmonic or electronic enhancement, ...
IEEE

Cauchy Kernel Center Differential Filtering Based on Pseudo-Inverse Mechanism for Nonlinear Non-Gaussian Systems

Abstract: This paper addresses the non-Gaussian filtering problem for nonlinear systems. An iterative updating center differential filter is designed under Cauchy kernel maximum correntropy, on the ...
Frontiers

KSRV: a Kernel PCA-Based framework for inferring spatial RNA velocity at single-cell resolution

Understanding the temporal dynamics of gene expression within spatial contexts is essential for deciphering cellular differentiation. RNA velocity, which estimates the future state of gene expression ...
Frontiers

Nonlinear kernel-based fMRI activation detection

Kernel Canonical Correlation Analysis (KCCA) is an effective method for globally detecting brain activation with reduced computational complexity. However, the current KCCA is limited to linear ...
Nature

Kernel Methods and Support Vector Machines

Kernel methods and support vector machines (SVMs) serve as cornerstones in modern machine learning, offering robust techniques for both classification and regression tasks. At their core, kernel ...
Scientific Research Publishing

Wu, Y. and Yin, G. (2015) Conditional Quantile Screening in Ultrahigh-Dimensional Heterogeneous Data. Biometrika, 102, 65-76.

ABSTRACT: We introduce the Kernel-based Partial Conditional Mean Dependence, a scalar-valued measure of conditional mean dependence of Y given X , while adjusting for the nonlinear dependence on Z .
IEEE

Towards Robust Nonlinear Subspace Clustering: A Kernel Learning Approach

Abstract: Kernel-based subspace clustering, which addresses the nonlinear structures in data, is an evolving area of research. Despite noteworthy progressions, prevailing methodologies predominantly ...
eLife

Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel

This important study combines the use of Fisher Kernels with Hidden Markov models aiming to improve brain-behaviour prediction. The evidence supporting the authors' conclusions is compelling, ...