Abstract: Kernel principal component analysis (kernel-PCA) is an elegant nonlinear extension of one of the most used data analysis and dimensionality reduction techniques, the principal component ...
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 ...
Our goal is to implement this in our project so that we can use the shapes generated for overlay-analysis and visualization.
Inside living cells, mitochondria divide, lysosomes travel, and synaptic vesicles pulse—all in three dimensions (3Ds) and constant motion. Capturing these events with clarity is vital not just for ...
ABSTRACT: This article examines the effect of economic vulnerability on inclusive growth across 49 developing countries from 1991 to 2020, focusing on the mitigating role of agricultural structural ...
The authors present a critique of current usage of principal component analysis in geometric morphometrics, making a compelling case with benchmark data that standard techniques perform poorly. The ...
Attackers can downgrade Windows kernel components to bypass security features such as Driver Signature Enforcement and deploy rootkits on fully patched systems. This is possible by taking control of ...
Abstract: Kernel Principal Component Analysis (Kernel PCA) is one of the methods to perform PCA in high dimensional space. The purpose of this paper is to examine what components are obtained by ...
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