Results 121 to 130 of about 260,346 (290)
Plant Genetic Engineering: Technological Pathways, Application Scenarios, and Future Directions
Advanced Science, EarlyView.This review maps the fast‐evolving landscape of plant genetic engineering, linking enabling platforms with trait‐focused applications in architecture optimization, stress resilience, yield improvement, and quality enhancement. It highlights how genome editing, transgenic strategies, and emerging multi‐gene approaches reshape breeding pipelines, while ...
Peilin Wang +4 more
wiley +1 more source
ROBUST KERNEL ESTIMATOR FOR DENSITIES OF UNKNOWN [PDF]
Results on nonparametric kernel estimators of density differ according to the assumed degree of density smoothness; it is often assumed that the density function is at least twice differentiable.
Victoria Zinde-Walsh, Yulia Kotlyarova
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Kernel Density Estimators in Large Dimensions
SIAM Journal on Mathematics of Data ScienceThis paper studies Kernel Density Estimation for a high-dimensional distribution $ρ(x)$. Traditional approaches have focused on the limit of large number of data points $n$ and fixed dimension $d$. We analyze instead the regime where both the number $n$ of data points $y_i$ and their dimensionality $d$ grow with a fixed ratio $α=(\log n)/d$.
Biroli, Giulio, Mezard, Marc
openaire +3 more sources
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
Bayesian Adaptive Bandwidth Kernel Density Estimation of Irregular Multivariate Distributions [PDF]
Kernel density estimation is an important technique for understanding the distributional properties of data. Some investigations have found that the estimation of a global bandwidth can be heavily affected by observations in the tail.
D.S. Poskitt, Shuowen Hu, Xibin Zhang
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Kernel estimation of density level sets
Journal of Multivariate Analysis, 2006 Let $f$ be a multivariate density and $f\_n$ be a kernel estimate of $f$ drawn from the $n$-sample $X\_1,...,X\_n$ of i.i.d. random variables with density $f$. We compute the asymptotic rate of convergence towards 0 of the volume of the symmetric difference between the $t$-level set $\{f\geq t\}$ and its plug-in estimator $\{f\_n\geq t\}$.
openaire +4 more sources
Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Advanced Science, EarlyView.Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source
Weighted 2D-kernel density estimations provide a new probabilistic measure for epigenetic age
Genome BiologyEpigenetic aging signatures provide insights into human aging, but traditional clocks rely on linear regression of DNA methylation levels, assuming linear trajectories.
Juan-Felipe Perez-Correa +6 more
doaj +1 more source
AxiomsThe article investigates the accuracy of nonparametric univariate density estimation methods applied to various Gaussian mixture models. A comprehensive comparative analysis is performed for four popular estimation approaches: adaptive kernel density ...
Tomas Ruzgas +3 more
doaj +1 more source
Kernel Density Estimation of Actuarial Loss Functions. [PDF]
No abstractLoss models; Transformation; Skewness; Weighted integrated squared ...
Bolance, Catalina +2 more
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