Matrix structures don’t work on their own. The work is less about control and more about integration, often without formal ...

a notebook-first introduction to QSVT and QSP a reusable Python package for polynomial design, spectral transforms, and small PennyLane QSVT checks where the backend can synthesize the transform ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Many electronic structure methods rely on the minimization of the energy of the system ...

We extend the Gibbons–Ross–Shanken test to high-dimensional cases, when the num-ber of test assets far exceeds the sample size and the return covariance matrix is ill-conditioned or singular, as ...

Abstract: Multi-layered graphs are popular in mobility studies because transportation data include multiple modalities, such as railways, buses, and taxis. Another example of a multi-layered graph is ...

Abstract: We propose a new dimension reduction method for matrix-valued data called Matrix Non-linear PCA (MNPCA), which is a non-linear generalization of (2D) ${}^{2}$ PCA. MNPCA is based on ...

Understanding Singular Value Decomposition If you have a matrix A with dim = n, it is possible to compute n eigenvalues (ordinary numbers like 1.234) and n associated eigenvectors, each with n values.

Relative pose directly from matches, without additional steps for disambiguation and pure rotation checks. The relative rotation and translation, $\mathbf{R} _{01}$ and $\mathbf{t} _{01}$, map a 3D ...