Creative inventions and ideas that show next-level thinking. Terror charge filed in Jan. 6 case I asked 3 restaurant pros to name the most annoying thing diners do ...

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 ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.

It’s got a great cast. It looks cinematic. It’s, um … fine. And it’s everywhere. Credit...Photo Illustration by Alex Merto Supported by By James Poniewozik A few years ago, “Atlanta” and “PEN15” were ...

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 ...

I'm trying to solve a generalized eigenvalue problem but I have no idea what functions in this toolbox I should use. Are there any eigenvalue solvers in this toolbox? I really appreciate it if you ...

ABSTRACT: The importance of perturbation theory in many fields is very clear through almost a century or even more. Its importance was exemplified in solving many problems in physics and other applied ...

Abstract: This paper studies the generalized inverse eigenvalue problem for generalized snow-like matrices. It is to solve the class of generalized snow-like matrices by two given characteristic pairs ...

In this work, we present a computational method for solving eigenvalue problems of fourth-order ordinary differential equations which based on the use of Chebychev method. The efficiency of the method ...