Results 51 to 60 of about 43,727 (260)
Quantum circuits from non-unitary sparse binary matrices
Quantum computing leverages unitary matrices to perform reversible computations while preserving probability norms. However, many real-world applications involve non-unitary sparse matrices, posing a challenge for quantum implementation.
Krishnageetha Karuppasamy +3 more
doaj +1 more source
Given a polynomial $p(z)$, a companion matrix can be thought of as a simple template for placing the coefficients of $p(z)$ in a matrix such that the characteristic polynomial is $p(z)$. The Frobenius companion and the more recently-discovered Fiedler companion matrices are examples.
Deaett, Louis +3 more
openaire +2 more sources
Identification of Matrices Having a Sparse Representation [PDF]
We consider the problem of recovering a matrix from its action on a known vector in the setting where the matrix can be represented efficiently in a known matrix dictionary. Connections with sparse signal recovery allows for the use of efficient reconstruction techniques such as Basis Pursuit.
Götz E. Pfander +2 more
openaire +4 more sources
Uncovering G Protein‐Coupled Receptors: Novel Targets and Biomarkers for Predicting Glioma Prognosis
ABSTRACT Background Low‐grade gliomas (LGG) exhibit significant heterogeneity and recurrence risk. G protein‐coupled receptors (GPCR) contribute to glioma malignant progression, but their prognostic value remains unclear. This work attempts to formulate a GPCR‐based outcome‐predicting model for LGG. Methods Based on TCGA LGG data, the enrichment scores
Jun Yang +4 more
wiley +1 more source
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source
bspcov: An R Package for Bayesian sparse covariance matrix estimation
The bspcov R package provides a Bayesian inference for covariance matrices. The bspcov is developed to aid in research that involves estimating constrained covariance matrices by enabling the use of state-of-the-art Bayesian inference methods.
Kyeongwon Lee +3 more
doaj +1 more source
Matrix Depot: an extensible test matrix collection for Julia [PDF]
Matrix Depot is a Julia software package that provides easy access to a large and diverse collection of test matrices. Its novelty is threefold. First, it is extensible by the user, and so can be adapted to include the user’s own test problems.
Weijian Zhang, Nicholas J. Higham
doaj +2 more sources
Sparse Recovery Using Sparse Random Matrices [PDF]
Over the recent years, a new linear method for compressing high-dimensional data (e.g., images) has been discovered. For any high-dimensional vector x, its sketch is equal to Ax, where A is an m×n matrix (possibly chosen at random). Although typically the sketch length m is much smaller than the number of dimensions n, the sketch contains enough ...
openaire +1 more source
This review comprehensively evaluates extrusion‐based additive manufacturing for advanced ceramics, detailing feedstock options and key process parameters. By critically addressing defect mechanisms like porosity and cracking, the work highlights optimization strategies through machine learning and advanced postprocessing.
Meisam Bakhtiari +4 more
wiley +1 more source
Shellac, a centuries‐old natural resin, is reimagined as a green material for flexible electronics. When combined with silver nanowires, shellac films deliver transparency, conductivity, and stability against humidity. These results position shellac as a sustainable alternative to synthetic polymers for transparent conductors in next‐generation ...
Rahaf Nafez Hussein +4 more
wiley +1 more source

