Abstract: This article investigates a novel robust Kalman filter (RKF) by incorporating kernel density estimation (KDE) in the Kalman filtering framework to address the disturbance of measurement ...

Kernel density estimation (KDE) is a cornerstone of non-parametric statistics, offering a flexible means to infer an underlying probability density from finite samples without assuming a predetermined ...

The stable release of Linux Kernel 6.18 was officially tagged on November 30, 2025. It’s expected to become this year’s major long-term support (LTS) kernel, something many users and distributions ...

As the global population continues to surge, cities become increasingly complex ecosystems, dense and bustling environments home to millions of people. Today, more than half of the world's population ...

AKDE provides an accurate, adaptive kernel density estimator based on the Gaussian Mixture Model for multidimensional data. This Python implementation includes automatic grid construction for ...

ABSTRACT: Singh, Gewali, and Khatiwada proposed a skewness measure for probability distributions called Area Skewness (AS), which has desirable properties but has not been widely applied in practice.

CISA has warned U.S. federal agencies to secure their systems against ongoing attacks targeting a high-severity Windows kernel vulnerability. Tracked as CVE-2024-35250, this security flaw is due to an ...

We retrospectively analyzed 1,080 nonactionable three-dimensional (3D) reconstructed DBT screening examinations acquired between 2011 and 2016. Reference tissue segmentations were generated using ...

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Density estimation methods often involve kernels, but there are advantages to using splines. Especially if the shape of the density is known to be decreasing, or unimodal, or bimodal, or if the ...