Results 171 to 180 of about 7,587 (302)

Reproducing kernel Hilbert spaces

, 2016
105
openaire   +2 more sources

Uncertainty‐Aware Deep Ensembles for Robust and Reliable Chemical Sensor Arrays

Advanced Science, EarlyView.
A reliability‐aware electronic nose is developed using photothermally anchored metal‐catalyst decorated metal oxide nanofiber sensor arrays combined with deep ensemble learning. Diverse catalytic nanofiber channels generate gas‐specific response patterns, enabling selective identification and quantification of sulfur‐containing gases.
Sungwoo Eo, Ji‐Hwan Eum, Suk‐Jeong Kwon, Minji Sagong, Dongchan Kim, Dong‐Ha Kim   +5 more
wiley   +1 more source

Fast and interpretable quantification of biological shape heterogeneity via stratified Wasserstein kernel. [PDF]

PLoS Comput Biol
Zhao W, Sutherland DJ, Dao Duc K.
europepmc   +1 more source

SPADE: A Deep Learning Framework for Spatial Mapping and Quantitative Cell–Cell Interaction Inference

Advanced Science, EarlyView.
SPADE integrates spatial transcriptomics with single‐cell RNA sequencing by using cell–cell communications (CCC) as a guide for spatial mapping. It improves cell‐type localization, enhances sparse gene‐expression signals, and reveals CCC programs at single‐spot resolution.
Xinyi Li, Ning Zhang, Zijie Jin
wiley   +1 more source

Proton-driven many-body interactions and structural organization in He _<i>n</i> H⁺ clusters. [PDF]

RSC Adv
Montes de Oca-Estévez MJ, Hernández-Rojas J, Prosmiti R.   +2 more
europepmc   +1 more source

Reproducing kernel almost Pontryagin spaces

, 2014
An almost Pontryagin space A is an inner product space which admits a direct and orthogonal decomposition of the form A = A>[+_ ]A with a Hilbert space A> and a nite-dimensional negative semide nite space A . A reproducing kernel almost Pontryagin space
Woracek, Harald
core  

Dual‐Mode Nanoporous SiO2 Memristors with Coexisting Volatile and Nonvolatile Dynamics for Reservoir Computing

Advanced Science, EarlyView.
A nanoporous SiO2 memristor enabling reconfigurable volatile and non‐volatile switching within a single device is demonstrated. The dual‐mode functionality supports both physical reservoir dynamics and synaptic weight storage, allowing unified hardware implementation of reservoir computing for temporal information processing, including image and ...
Bohao Ding, Tongjun Zhang, Li Shao, Nikolay Zhelev, Andrew L. Hector, Ruomeng Huang   +5 more
wiley   +1 more source

Identifying stochastic dynamics from non-sequential data (DyNoSeD). [PDF]

Chaos
Lu Z, Kuśmierz Ł, Mihalas S.
europepmc   +1 more source

Ridge Regression Learning Algorithm in Dual Variables

, 1998
In this paper we study a dual version of the Ridge Regression procedure. It allows us to perform non-linear regression by constructing a linear regression function in a high dimensional feature space.
C. Saunders, V. Vovk, Gammerman, A., Vovk, V., A. Gammerman, Saunders, C.   +5 more
core  

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source