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 ...

Derivative-free method to find zeros of analytic (holomorphic) functions / solve nonlinear (polynomial / generalized) eigenvalue problems using contour integration. (Block SS-Hankel method, Block ...

Abstract: In this paper, we study the eigenvalue inverse problem for a class of bifurcation matrices. The existence of a unique identity of the matrix is obtained for two given pairs of eigenvalues.

Practical word problems are often considered one of the most complex parts of elementary school mathematics. They require students to understand the context of a math problem, identify what the ...

The Principal Component Analysis (PCA) is a procedure extensively employed in data science with diverse purposes. It has found widespread use in making sense of data collected from Molecular Dynamics ...

The Nature Index 2025 Research Leaders — previously known as Annual Tables — reveal the leading institutions and countries/territories in the natural and health sciences, according to their output in ...

Transforming a dataset into one with fewer columns is more complicated than it might seem, explains Dr. James McCaffrey of Microsoft Research in this full-code, step-by-step machine learning tutorial.

A printer is a wonderful thing when it works, making your life easier by providing lightweight, foldable documents you can tuck in a folder or jam into a pocket. Unfortunately, even the best printers ...