Results 191 to 200 of about 3,893 (298)

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Multi-omics data integration for enhanced cancer subtyping via interactive multi-kernel learning. [PDF]

Brief Bioinform
Cao H, Wang T, Xu Z, Zhao X, Liu G, Yang X, Fang R, Luo Y, Zeng P, Yu H, Zhang Y, Cui Y.   +11 more
europepmc   +1 more source

Computing Functions of Random Variables via Reproducing Kernel Hilbert\n Space Representations [PDF]

, 2015
Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Jonas Peters   +3 more
openalex   +1 more source

Structure and Spectroscopic Characterisation of Phenanthroline‐Based Iodobismuthate(III) Complexes Utilised for Raw Acoustic Signal Classification

Advanced Intelligent Discovery, EarlyView.
Memristors based on trimethylsulfonium (phenanthroline)tetraiodobismuthate have been utilised as a nonlinear node in a delayed feedback reservoir. This system allowed an efficient classification of acoustic signals, namely differentiation of vocalisation of the brushtail possum (Trichosurus vulpecula).
Ewelina Cechosz, Gisya Abdi, Andrzej Sławek, Marlena Gryl, Konrad Szaciłowski   +4 more
wiley   +1 more source

Hirota, Fay and geometry. [PDF]

Lett Math Phys
Eynard B, Oukassi S.
europepmc   +1 more source

Radial Basis Function Network in Reproducing Kernel Hilbert Space

, 2005
Chooleewan Dachapak, Shunshoku Kanae, Zi-Jiang Yang   +2 more
openalex   +2 more sources

Useful reproducing kernel functions for solving difference equations

, 2015
...
openaire   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

Research on Driver Fatigue Detection in Real Driving Environments Based on Semi-Dry Electrodes with Automatic Conductive Fluid Replenishment. [PDF]

Sensors (Basel)
Wang F, Zhang Y, Song W, Zhang X.
europepmc   +1 more source

Radial Basis Function Network in Reproducing Kernel Hilbert Space with Application to System Identification

, 2006
Chooleewan Dachapak, Shunshoku Kanae, Zi-Jiang Yang, Kiyoshi Wada   +3 more
openalex   +2 more sources