Results 201 to 210 of about 993,426 (339)
Reconstructing Spatial Localization Error Maps via Physics-Informed Tensor Completion for Passive Sensor Systems. [PDF]
Zhang Z, Huang Z, Wang C, Jiang Q.
europepmc +1 more source
This study integrates random matrix theory (RMT) and principal component analysis (PCA) to improve the identification of correlated regions in HIV protein sequences for vaccine design. PCA validation enhances the reliability of RMT‐derived correlations, particularly in small‐sample, high‐dimensional datasets, enabling more accurate detection of ...
Mariyam Siddiqah +3 more
wiley +1 more source
Econometric Analysis of Spot Variances, Covariances and Correlations [PDF]
Saavedra Acosta
openalex
Why Does the Fed React to the Stock Market Changes?: A Covariance Decomposition Analysis
Bedri Kamil Onur Taş
openalex +1 more source
Deep learning‐based denoising models are applied to DNA data storage systems to enhance error reduction and data fidelity. By integrating DnCNN with DNA sequence encoding methods, the study demonstrates significant improvements in image quality and correction of substitution errors, revealing a promising path toward robust and efficient DNA‐based ...
Seongjun Seo +5 more
wiley +1 more source
MUSICiAn: genome-wide identification of genes involved in DNA repair via control-free mutational spectra analysis. [PDF]
Seale C +4 more
europepmc +1 more source
Range-Clearing: A Market-Structure Analysis Framework Based on Intervals and Covariance
Xuebin Zhao
openalex +1 more source
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley +1 more source
Phase II monitoring of process variability in multichannel profiles. [PDF]
Jalilibal Z, Amiri A, Ahmadi O.
europepmc +1 more source
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley +1 more source

