Results 171 to 180 of about 489,537 (279)
Asymmetric canonical correlation analysis of Riemannian and high-dimensional data. [PDF]
Buenfil J, Lila E.
europepmc +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
Multidimensional frailty assessment integrating physical, mental and oral health: a multicentric study. [PDF]
Ferreira LMA +8 more
europepmc +1 more source
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto +3 more
wiley +1 more source
Covariate Adjusted Functional Mixed Membership Models. [PDF]
Marco N +5 more
europepmc +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
A note on the dynamics of extended-context disordered kinetic spin models. [PDF]
Zavatone-Veth JA, Pehlevan C.
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
Decoupling of neurophysiological activity from structure mirrors global microarchitectural and neuromodulatory trends. [PDF]
Facca M, Del Felice A, Bertoldo A.
europepmc +1 more source
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang +4 more
wiley +1 more source

