Results 181 to 190 of about 52,154 (284)

Harnessing genomic prediction in Brassica napus through a nested association mapping population. [PDF]

Plant Genome
Perumal S, Higgins EE, Sra S, Khedikar Y, Moore J, Chaudary R, Haile T, Koh CS, Vail S, Robinson SJ, Horner K, Hope B, Klein-Gebbinck H, Herrmann DT, Navabi K, Sharpe AG, Parkin IAP.   +16 more
europepmc   +1 more source

Holographic Mapping of Orbital Angular Momentum using a Terahertz Diffractive Optical Neural Network

Advanced Intelligent Discovery, EarlyView.
A compact six‐layer diffractive optical neural network enables direct recognition and spatial mapping of terahertz (THz) orbital angular momentum (OAM) beams. Fabricated by 3D printing, the system distinguishes nine OAM modes and their superpositions with high fidelity, good efficiency, and low crosstalk, offering a scalable solution for THz ...
Wei Jia, Miguel Gomez, Steve Blair, Berardi Sensale‐Rodriguez   +3 more
wiley   +1 more source

Takens-Based Kernel Transfer Entropy Connectivity Network for Motor Imagery Classification. [PDF]

Sensors (Basel)
Gomez-Rivera A, Álvarez-Meza AM, Cárdenas-Peña D, Orozco-Gutierrez A.   +3 more
europepmc   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Interactive visualization of metric distortion in nonlinear data embeddings using the distortions package. [PDF]

Brief Bioinform
Sankaran K, Zhang S, Chenab, Meilă M.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Using a meshless method to investigate the effects of confining pressure on the hydraulic fracturing processes of hydraulic tunnels. [PDF]

Sci Rep
Zhang H, Shi Y, Mu J, Hao R, Qi D, Li W, Zhang B, Yu S.   +7 more
europepmc   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

Application of Bivariate Reproducing Kernel-Based Best Interpolation Method in Electrical Tomography. [PDF]

Sensors (Basel)
Tan Y, Wang J, Yu J, Wu B, Shen J, Guo X.   +5 more
europepmc   +1 more source

Some Open Problems in the Reproducing Kernel Method

, 2017
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openaire   +1 more source