Results 111 to 120 of about 55,553 (309)
KDENS: Stata module for univariate kernel density estimation [PDF]
, 2005 kdens produces univariate kernel density estimates and graphs the result. kdens supplements official Stata's kdensity. Important additions are: adaptive (i.e.
Jann, Ben
core
Advanced Science, EarlyView.This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo +6 more
wiley +1 more source
Kernel Density Estimators for Gaussian Mixture Models
Lithuanian Journal of Statistics, 2013 The problem of nonparametric estimation of probability density function is considered. The performance of kernel estimators based on various common kernels and a new kernel K (see (14)) with both fixed and adaptive smoothing bandwidth is compared in ...
Tomas Ruzgas, Indrė Drulytė
doaj +1 more source
Advanced Science, EarlyView.High‐throughput single‐cell analysis of resuscitating bacteria reveals a starvation‐history‐dependent transiently tolerant subpopulation that survives β$\beta$‐lactam exposure by temporarily reducing growth. Distinct from classical persisters, these actively growing yet dynamically modulated cells dominate survival across clinically relevant antibiotic
Kieran Abbott +5 more
wiley +1 more source
Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data [PDF]
Journal of Sciences, Islamic Republic of Iran, 2014 Kernel density estimators are the basic tools for density estimation in non-parametric statistics. The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in which the bandwidth is varied depending on the ...
V. Fakoor
doaj
Mapping the “Supply–Demand–Flow” of Ecosystem Services for Ecosystem Management in China
Advanced Science, EarlyView.This study develops a “supply–demand–flow” framework clarifies how ecosystem services move between regions by distinguishing potential and actual supply and demand. Using integrated biophysical–socioeconomic modeling, nine services in China were mapped.
Yikun Zhang +3 more
wiley +1 more source
Traction Force Microscopy for Viscoelastic Substrates: A Semi‐Analytical Method
Advanced Science, EarlyView.A semi‐analytical viscoelastic traction force microscopy framework is introduced for quantifying time‐resolved cell tractions on flat finite‐thickness substrates. The method generalizes elastic traction force microscopy to Generalized Maxwell materials, identifies when elastic approximations remain valid and, when they do not, shows that inferred ...
Adrià Villacrosa‐Ribas +10 more
wiley +1 more source
Weakly supervised segment annotation via expectation kernel density estimation
IET Computer Vision, 2019 Since the labelling for the positive images/videos is ambiguous in weakly supervised segment annotation, negative mining‐based methods that only use the intra‐class information emerge. In these methods, negative instances are utilised to penalise unknown
Liantao Wang, Qingwu Li, Jianfeng Lu
doaj +1 more source
Normal Reference Bandwidths for the General Order, Multivariate Kernel Density Derivative Estimator [PDF]
This note derives the general form of the approximate mean integrated squared error for the q-variate, th-order kernel density r th derivative estimator. This formula allows for normal reference rule-of-thumb bandwidths to be derived.
Christopher F. Parmeter +1 more
core
Target tracking based on non-linear kernel density estimation and Kalman filter [PDF]
, 2015 This paper chooses Mean Shift algorithm to track target based on non-linear kernel density estimation and Kalman filter. Kernel density estimation is a probability density estimation method, which is used to detect moving target and update the target ...
Zhang YC(张宜弛) +2 more
core