Results 111 to 120 of about 379,537 (253)
Sparse regression using mixed norms
Applied and Computational Harmonic Analysis, 2009 Mixed norms are used to exploit in an easy way, both structure and sparsity in the framework of regression problems, and introduce implicitly couplings between regression coefficients. Regression is done through optimization problems, and corresponding algorithms are described and analyzed.
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Advanced Healthcare Materials, EarlyView.This study develops a dual‐bioinspired hydrogel, MSA@PGel (macrophage membrane‐coated and salvianolic acid B/5‐aminolevulinic acid co‐loaded liposomes embedded in a polydopamine‐based gel), that integrates macrophage membrane‐mediated active targeting and mussel‐inspired wet adhesion for programmed intervention in oral precancerous lesions.
Xiaoxian Zhao +8 more
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Automating the discovery of partial differential equations in dynamical systems
Machine Learning: Science and TechnologyIdentifying partial differential equations (PDEs) from data is crucial for understanding the governing mechanisms of natural phenomena, yet it remains a challenging task.
Weizhen Li, Rui Carvalho
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IEEE Transactions on Neural Systems and Rehabilitation EngineeringThe early diagnosis of Alzheimer’s disease (AD) is crucial because individuals may first experience mild cognitive impairment (MCI), which can then develop into AD, enabling timely intervention, slowing disease progression, and advancing the ...
Liqiang Xu +6 more
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Advanced Healthcare Materials, EarlyView.A paracrine factor local gradient (PFLG)‐generating system enables microvessel penetration across 3D hepatocyte tissues. The resulting vascularized constructs recapitulate hepatic sinusoidal hepatocyte—endothelial contact architecture and enhance hepatic functions in vitro.
Yen‐Hsiang Huang +2 more
wiley +1 more source
Sparse Additive Gaussian Process Regression
, 2019 In this paper we introduce a novel model for Gaussian process (GP) regression in the fully Bayesian setting. Motivated by the ideas of sparsification, localization and Bayesian additive modeling, our model is built around a recursive partitioning (RP) scheme. Within each RP partition, a sparse GP (SGP) regression model is fitted.
Luo, Hengrui +2 more
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Advanced Materials, EarlyView.AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll +19 more
wiley +1 more source
Advanced Materials, EarlyView.Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu +3 more
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Remodeling and Estimation for Sparse Partially Linear Regression Models
Abstract and Applied Analysis, 2013 When the dimension of covariates in the regression model is high, one usually uses a submodel as a working model that contains significant variables. But it may be highly biased and the resulting estimator of the parameter of interest may be very poor ...
Yunhui Zeng, Xiuli Wang, Lu Lin
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Advanced Materials, EarlyView.An ion‐mediated structural engineering strategy enables versatile modulation of PVA–Alginate hydrogel microstructure. We systematically examine how different ion combinations affect hydrogel mechanical properties and analyte diffusivity. Applying this novel strategy to hydrogel as the outer membrane in an electrochemical glucose biosensor allows ...
Dongwook Lee +14 more
wiley +1 more source