Results 91 to 100 of about 120,823 (294)
Photon Avalanching Nanoparticles: The Next Generation of Upconverting Nanomaterials?
Advanced Functional Materials, EarlyView.This Perspective outlines the mechanistic foundations that enable photon‐avalanche (PA) behavior in lanthanide nanomaterials and contrasts them with emerging application spaces and forward‐looking design strategies. By bridging threshold engineering, energy‐transfer dynamics, and materials engineering, we provide a coherent roadmap for advancing the ...
Kimoon Lee +7 more
wiley +1 more source
Kernel Exponential Family Estimation via Doubly Dual Embedding [PDF]
, 2019 We investigate penalized maximum log-likelihood estimation for exponential family distributions whose natural parameter resides in a reproducing kernel Hilbert space.
Dai, Bo +5 more
core +1 more source
Advanced Functional Materials, EarlyView.We present a fully automated Bayesian optimization (BO) protocol for the parameterization of nonbonded interactions in coarse‐grain CG force fields (BACH). Using experimental thermophysical data, we apply the protocol to a broad range of liquids, spanning linear, branched, and unsaturated hydrocarbons, esters, triglycerides, and water.
Janak Prabhu +3 more
wiley +1 more source
Light‐Actuated Fiber‐Climbing Inchworm Robot Toward Endoluminal Navigation
Advanced Functional Materials, EarlyView.A kirigami‐inspired soft inchworm robot harnesses optical energy from a customized side‐emitting optical fiber, guaranteeing its propulsion along the fiber body. The wavelength‐selective responsiveness of dye‐functionalized liquid crystal elastomers and the application of temporal illumination patterns enable sequential control of robot components. The
Antonio Lobosco +6 more
wiley +1 more source
When, where and how to perform efficiency estimation [PDF]
In this paper we compare two flexible estimators of technical efficiency in a cross-sectional setting: the nonparametric kernel SFA estimator of Fan, Li and Weersink (1996) to the nonparametric bias corrected DEA estimator of Kneip, Simar andWilson (2008)
Badunenko, Oleg +2 more
core +7 more sources
JOIV: International Journal on Informatics VisualizationResearchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors ...
Amjed Mohammed Sadek, Lekaa Ali Mohammed
doaj +1 more source
Kernel estimation of Greek weights by parameter randomization
, 2006 A Greek weight associated to a parameterized random variable $Z(\lambda)$ is a random variable $\pi$ such that $\nabla_{\lambda}E[\phi(Z(\lambda))]=E[\phi(Z(\lambda))\pi]$ for any function $\phi$.
Elie, Romuald +2 more
core +2 more sources
Advanced Functional Materials, EarlyView.ABSTRACT Traditional wearable exoskeletons rely on rigid structures, which limit comfort, flexibility, and everyday usability. This work introduces the fundamental technologies to create the first soft, lightweight, intelligent textile‐based exoskeletons (Texoskeletons) built using 1D sensors and actuators.
Amy Lukomiak +19 more
wiley +1 more source
Global Polynomial Kernel Hazard Estimation
Revista Colombiana de EstadísticaThis paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator.
MUNIR HIABU +5 more
doaj +1 more source
On Bootstrapping Kernel Spectral Estimates
The Annals of Statistics, 1992 This paper considers the problem of determining the statistical characteristics (such as probability distribution and confidence limits) of a kernel spectral density estimator, by using the bootstrap approach. A simple and natural bootstrapping scheme based on resampling the data periodogram ordinates (appropriately normalized) is introduced.
Franke, J., Hardle, W.
openaire +2 more sources