Design and setting A nested case–control study within two prospective cohort studies, the Shanghai Women’s Health Study (n=74 941) and the Shanghai Men’s Health Study (n=61 480). Participants Lifetime ...

Abstract: Recent advances of kernel regression assume that target signals lie over a feature graph such that their values can be predicted with the assistance of the graph learned from training data.

pwtools is a Python package for pre- and postprocessing of atomistic calculations, mostly targeted to Quantum Espresso, CPMD, CP2K and LAMMPS. It is almost, but not quite, entirely unlike ASE, with ...

Abstract: We propose a kernel regression method to predict a target signal lying over a graph when an input observation is given. The input and the output could be two different physical quantities.

The purpose of this package is to solve the Distribution Regression problem for noncontiguous geospatial features. The use case documented here is for modeling archaeological site locations. The aim ...

The k-nearest neighbors (KNN) regression method, known for its nonparametric nature, is highly valued for its simplicity and its effectiveness in handling complex structured data, particularly in big ...

Linux kernel 6.9 has been released after several months of attentive development. Linux founder Linus Torvalds announced the final release on the Linux Kernel Mailing List in his usual relaxed, ...

KRR is especially useful when there is limited training data, says Dr. James McCaffrey of Microsoft Research in this full-code, step-by-step tutorial. The goal of a machine learning regression problem ...