To enable more accurate estimation of connectivity, we propose a data-driven and theoretically grounded framework for optimally designing perturbation inputs, based on formulating the neural model as ...

Light is usually described using quantum mechanics when phenomena like entanglement enter the picture. But a new paper shows that light’s polarization and entanglement obey a simple identity – written ...

NeTaGFT is written with R and uses RSpectra library for eigenvalues and vectors calculation. The ape, phytools, phangorn, phylosignal, phylobase, phylolm, and RPAND library are used for simulation ...

Recently, in order to find the principal moments of inertia of a large number of rigid bodies, it was necessary to compute the eigenvalues of many real, symmetric 3 × 3 matrices. The available ...

Sikorsky to add add cutting-edge MATRIX technology to the UH-60M Black Hawk, pushing the limits of Autonomous Military Aviation. Sikorsky, has secured a $6 million contract from DARPA (Defense ...

This article presents a from-scratch C# implementation of the second technique: using SVD to compute eigenvalues and eigenvectors from the standardized source data. If you're not familiar with PCA, ...

Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...

We compute all massive partition functions or characteristic polynomials and their complex eigenvalue correlation functions for non-Hermitean extensions of the symplectic and chiral symplectic ...

Abstract: An analytic parahermitian matrix admits in almost all cases an eigenvalue decomposition (EVD) with analytic eigenvalues and eigenvectors. We have previously defined a discrete Fourier ...