Results 81 to 90 of about 615,565 (289)
Multivariate time series classification using kernel matrix
Multivariate time series (MTS) classification is a fundamental problem in time series mining, and the approach based on covariance matrix is an attractive way to solve the classification. In this study, it is noted that a traditional covariance matrix is
Jiancheng Sun +4 more
doaj +1 more source
The rank of the covariance matrix of an evanescent field
Evanescent random fields arise as a component of the 2-D Wold decomposition of homogenous random fields. Besides their theoretical importance, evanescent random fields have a number of practical applications, such as in modeling the observed signal in the space time adaptive processing (STAP) of airborne radar data.
Mark Kliger, Joseph M. Francos
openaire +2 more sources
Predicting Atomic Charges in MOFs by Topological Charge Equilibration
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi +2 more
wiley +1 more source
A large number of MoS2 flakes were screened to obtain high‐quality flakes based on optical intensities in R, G, and B channel images. The flakes were classified from Level 1 to 6 based on optical intensities in the R, G, and B channel images. Low‐quality flake exhibited wrinkled, folded, or overlapped features, while high‐quality displayed a neat ...
Sanghyun Lee +11 more
wiley +1 more source
Some quantitative characteristics of error covariance for Kalman filters
Some quantitative characteristics of error covariance are studied for linear Kalman filters. These quantitative characteristics include the peak value and location in the matrix, the decay rate from peak to bottom, and some algebraic constraints of the ...
Wei Kang, Liang Xu
doaj +1 more source
Group Lasso estimation of high-dimensional covariance matrices [PDF]
In this paper, we consider the Group Lasso estimator of the covariance matrix of a stochastic process corrupted by an additive noise. We propose to estimate the covariance matrix in a high-dimensional setting under the assumption that the process has a ...
Alvarez, Lilian Muniz +3 more
core +2 more sources
Transition metal oxy/carbo‐nitrides show great promise as catalysts for sustainable processes. A Mn‐Mo mixed‐metal oxynitride attains remarkable performance for the direct synthesis of acetonitrile, an important commodity chemical, via sequential C─N and C─C coupling from syngas (C1) and ammonia (N1) feedstocks.
M. Elena Martínez‐Monje +7 more
wiley +1 more source
Random matrix-improved estimation of covariance matrix distances [PDF]
Given two sets $x_1^{(1)},\ldots,x_{n_1}^{(1)}$ and $x_1^{(2)},\ldots,x_{n_2}^{(2)}\in\mathbb{R}^p$ (or $\mathbb{C}^p$) of random vectors with zero mean and positive definite covariance matrices $C_1$ and $C_2\in\mathbb{R}^{p\times p}$ (or $\mathbb{C}^{p\times p}$), respectively, this article provides novel estimators for a wide range of distances ...
Couillet, Romain +3 more
openaire +3 more sources
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu +3 more
wiley +1 more source
Magnetic doping of the topological insulator Bi2Te3 with erbium adatoms induces out‐of‐plane magnetism and breaks time‐reversal symmetry, opening a Dirac gap and driving a Fermi surface transition from hexagonal to star‐of‐David geometry. Microscopy, spectroscopy, and magnetic dichroism reveal atomically controlled magnetic interactions that tailor the
Beatriz Muñiz Cano +18 more
wiley +1 more source

