Abstract: Kernel density estimation (KDE), a flexible nonparametric technique unconstrained by specific data distribution assumptions, is extensively employed in fault modeling. However, its ...

Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets ...

To analyse stroke rate (SR) and stroke length (SL) combinations among elite swimmers to better understand stroke strategies across all race distances of freestyle events. We analysed SR and SL data ...

This study introduces two-dimensional (2D) Kernel Density Estimation (KDE) plots as a novel tool for visualising Training Intensity Distribution (TID) in biathlon. The goal was to assess how KDE plots ...

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jctc.5c00129. Notes on the functional form of reorganization energy and ...

Set create_data and do_plotting to True and press play. Initiation is at the bottom of the script. Requires numpy, matplotlib, and scipy. adk_estimator: Contains the functions that does the adaptive ...

Reconstructing the diverse conformations of biomolecules from cryoelectron microscopy datasets remains a longstanding challenge. Here, we present a method that surpasses current approaches across ...

Kernel density estimation (KDE) is a cornerstone of non-parametric statistics, offering a flexible means to infer an underlying probability density from finite samples without assuming a predetermined ...

The increasing diversity of scientific and engineering data has driven the development of flexible techniques for inferring probability distributions without assuming a specific parametric family.

We retrospectively analyzed 1,080 nonactionable three-dimensional (3D) reconstructed DBT screening examinations acquired between 2011 and 2016. Reference tissue segmentations were generated using ...