Kernel density estimation
View article: Unveiling BYOVD Threats: Malware’s Use and Abuse of Kernel Drivers
Unveiling BYOVD Threats: Malware’s Use and Abuse of Kernel Drivers Open
View article: Statistical compressive sensing method for Hadamard-based single-pixel microscopy supported by kernel density estimators
Statistical compressive sensing method for Hadamard-based single-pixel microscopy supported by kernel density estimators Open
View article: Analysis of spatial differentiation and influencing factors of rural industrial integration efficiency in China
Analysis of spatial differentiation and influencing factors of rural industrial integration efficiency in China Open
The integrated development of rural industries is a primary method to accelerate rural revitalization, a practical path to promote agricultural and rural modernization, and an important means to build a strong agricultural nation. Based on…
View article: Distributional Random Forests for Complex Survey Designs on Reproducing Kernel Hilbert Spaces
Distributional Random Forests for Complex Survey Designs on Reproducing Kernel Hilbert Spaces Open
We study estimation of the conditional law $P(Y|X=\mathbf{x})$ and continuous functionals $Ψ(P(Y|X=\mathbf{x}))$ when $Y$ takes values in a locally compact Polish space, $X \in \mathbb{R}^p$, and the observations arise from a complex surve…
View article: Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting
Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting Open
Skin color has historically been a focal point of discrimination, yet fairness research in machine learning for medical imaging often relies on coarse subgroup categories, overlooking individual-level variations. Such group-based approache…
View article: Unsupervised Learning of Density Estimates with Topological Optimization
Unsupervised Learning of Density Estimates with Topological Optimization Open
Kernel density estimation is a key component of a wide variety of algorithms in machine learning, Bayesian inference, stochastic dynamics and signal processing. However, the unsupervised density estimation technique requires tuning a cruci…
View article: Distributional Random Forests for Complex Survey Designs on Reproducing Kernel Hilbert Spaces
Distributional Random Forests for Complex Survey Designs on Reproducing Kernel Hilbert Spaces Open
We study estimation of the conditional law $P(Y|X=\mathbf{x})$ and continuous functionals $Ψ(P(Y|X=\mathbf{x}))$ when $Y$ takes values in a locally compact Polish space, $X \in \mathbb{R}^p$, and the observations arise from a complex surve…
View article: Enhancing Kernel Search with Pattern Recognition: the Single-Source Capacitated Facility Location Problem
Enhancing Kernel Search with Pattern Recognition: the Single-Source Capacitated Facility Location Problem Open
We introduce Pattern-based Kernel Search (PaKS), a two-phase matheuristic for the solution of the Single-Source Capacitated Facility Location Problem (SSCFLP). In the first phase, PaKS employs a pattern recognition technique to identify an…
View article: Enhancing Kernel Search with Pattern Recognition: the Single-Source Capacitated Facility Location Problem
Enhancing Kernel Search with Pattern Recognition: the Single-Source Capacitated Facility Location Problem Open
We introduce Pattern-based Kernel Search (PaKS), a two-phase matheuristic for the solution of the Single-Source Capacitated Facility Location Problem (SSCFLP). In the first phase, PaKS employs a pattern recognition technique to identify an…
View article: Wishart kernel density estimation for strongly mixing time series on the cone of positive definite matrices
Wishart kernel density estimation for strongly mixing time series on the cone of positive definite matrices Open
A Wishart kernel density estimator (KDE) is introduced for density estimation in the cone of positive definite matrices. The estimator is boundary-aware and mitigates the boundary bias suffered by conventional KDEs, while remaining simple …
View article: Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting
Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting Open
Skin color has historically been a focal point of discrimination, yet fairness research in machine learning for medical imaging often relies on coarse subgroup categories, overlooking individual-level variations. Such group-based approache…
View article: Unsupervised Learning of Density Estimates with Topological Optimization
Unsupervised Learning of Density Estimates with Topological Optimization Open
Kernel density estimation is a key component of a wide variety of algorithms in machine learning, Bayesian inference, stochastic dynamics and signal processing. However, the unsupervised density estimation technique requires tuning a cruci…
View article: Wishart kernel density estimation for strongly mixing time series on the cone of positive definite matrices
Wishart kernel density estimation for strongly mixing time series on the cone of positive definite matrices Open
A Wishart kernel density estimator (KDE) is introduced for density estimation in the cone of positive definite matrices. The estimator is boundary-aware and mitigates the boundary bias suffered by conventional KDEs, while remaining simple …
View article: Nonparametric optimal density estimation for censored circular data
Nonparametric optimal density estimation for censored circular data Open
We consider the problem of estimating the probability density function of a circular random variable observed under censoring. To this end, we introduce a projection estimator constructed via a regression approach on linear sieves. We firs…
View article: Nonparametric optimal density estimation for censored circular data
Nonparametric optimal density estimation for censored circular data Open
We consider the problem of estimating the probability density function of a circular random variable observed under censoring. To this end, we introduce a projection estimator constructed via a regression approach on linear sieves. We firs…
View article: Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks
Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks Open
We propose an interpretable deep competing risks model called the Deep Kernel Aalen-Johansen (DKAJ) estimator, which generalizes the classical Aalen-Johansen nonparametric estimate of cumulative incidence functions (CIFs). Each data point …
View article: Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks
Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks Open
We propose an interpretable deep competing risks model called the Deep Kernel Aalen-Johansen (DKAJ) estimator, which generalizes the classical Aalen-Johansen nonparametric estimate of cumulative incidence functions (CIFs). Each data point …
View article: Enhanced Multimodal Video Retrieval System: Integrating Query Expansion and Cross-modal Temporal Event Retrieval
Enhanced Multimodal Video Retrieval System: Integrating Query Expansion and Cross-modal Temporal Event Retrieval Open
Multimedia information retrieval from videos remains a challenging problem. While recent systems have advanced multimodal search through semantic, object, and OCR queries - and can retrieve temporally consecutive scenes - they often rely o…
View article: Enhanced Multimodal Video Retrieval System: Integrating Query Expansion and Cross-modal Temporal Event Retrieval
Enhanced Multimodal Video Retrieval System: Integrating Query Expansion and Cross-modal Temporal Event Retrieval Open
Multimedia information retrieval from videos remains a challenging problem. While recent systems have advanced multimodal search through semantic, object, and OCR queries - and can retrieve temporally consecutive scenes - they often rely o…
View article: Kernel density estimation of excitatory and inhibitory synaptic weight <i>w</i> distributions for red tuned trial (both models yield same results).
Kernel density estimation of excitatory and inhibitory synaptic weight <i>w</i> distributions for red tuned trial (both models yield same results). Open
Colored lines from estimations at seconds of a single trial. Top row: Distributions for excitatory synapses pooled for red, green, and blue patterns. Across time, red pattern pooled excitatory synapses become bimodal with a large amount of…
View article: Bayesian nonparametric model for weighted data using mixture of Burr XII distributions
Bayesian nonparametric model for weighted data using mixture of Burr XII distributions Open
In this paper, we develop a Bayesian nonparametric approach for analyzing weighted survival data. Specifically, we employ the Dirichlet Process Burr XII Mixture Model (DPBMM) to estimate the underlying density and survival functions when t…
View article: Conservative Motion Theory – UO3: GEX Density and Spectral Flow
Conservative Motion Theory – UO3: GEX Density and Spectral Flow Open
The previous paper (UO2) established the existence, uniqueness, and minimality of theGiant Exchange Kernel (GEX) in the external UO-domain. GEX was shown to projectinto the internal universe as an effective gravitational field reproducing …
View article: Conservative Motion Theory – UO3: GEX Density and Spectral Flow
Conservative Motion Theory – UO3: GEX Density and Spectral Flow Open
The previous paper (UO2) established the existence, uniqueness, and minimality of theGiant Exchange Kernel (GEX) in the external UO-domain. GEX was shown to projectinto the internal universe as an effective gravitational field reproducing …
View article: Implementation of Vital Signs Detection Algorithm for Supervising the Evacuation of Individuals with Special Needs
Implementation of Vital Signs Detection Algorithm for Supervising the Evacuation of Individuals with Special Needs Open
The article describes a system for monitoring the vital parameters of evacuated individuals, integrating three key functionalities: pulse detection, verification of wristband contact with the skin, and motion recognition. For pulse detecti…
View article: Development of a Diagnostic Method for Open/Short Circuit Faults in a Vienna Rectifier Based on the THD Method Using SOGI FLL
Development of a Diagnostic Method for Open/Short Circuit Faults in a Vienna Rectifier Based on the THD Method Using SOGI FLL Open
The increasing demand for reliable DC fast-charging stations in electric vehicle (EV) infrastructure necessitates efficient fault detection mechanisms to ensure operational stability and user safety. This paper will present the development…
View article: Kernel density plots of DDS for children and their mothers by FAW Intensity (24 hours and 7 days recall periods).
Kernel density plots of DDS for children and their mothers by FAW Intensity (24 hours and 7 days recall periods). Open
Kernel density plots of DDS for children and their mothers by FAW Intensity (24 hours and 7 days recall periods).
View article: Group and conditional posterior inclusion probabilities for each factor in Western China, 2013––2020, using Bayesian Kernel Machine Regression hierarchical variable selection.
Group and conditional posterior inclusion probabilities for each factor in Western China, 2013––2020, using Bayesian Kernel Machine Regression hierarchical variable selection. Open
Note: GroupPIP, group posterior inclusion probabilities; CondPIP, conditional posterior inclusion probabilities; MCH, maternal and child health; Ob/Gyn, obstetrics and gynecology; PCDI, per capita disposable income. (DOCX)
View article: Factors associated with decreased total and cause-specific maternal mortality in Eastern and Western China during 2004–2012, using Bayesian Kernel Machine Regression hierarchical variable selection with missing data imputed by MICE.
Factors associated with decreased total and cause-specific maternal mortality in Eastern and Western China during 2004–2012, using Bayesian Kernel Machine Regression hierarchical variable selection with missing data imputed by MICE. Open
Note: The asterisk * and dagger † symbols in the cell represent that the factor contributes the most to the exposure–response relationship when all other factors are fixed at their 25th and 75th percentiles, respectively. White cell means …
View article: Group and conditional posterior inclusion probabilities for each factor in Western China, 2004–2012, using Bayesian Kernel Machine Regression hierarchical variable selection.
Group and conditional posterior inclusion probabilities for each factor in Western China, 2004–2012, using Bayesian Kernel Machine Regression hierarchical variable selection. Open
Note: GroupPIP, group posterior inclusion probabilities; CondPIP, conditional posterior inclusion probabilities; MCH, maternal and child health; Ob/Gyn, obstetrics and gynecology; PCDI, per capita disposable income. (DOCX)
View article: Group and conditional posterior inclusion probabilities for each factor in Western China, 2004–2012, using Bayesian Kernel Machine Regression hierarchical variable selection with missing data imputed by MICE.
Group and conditional posterior inclusion probabilities for each factor in Western China, 2004–2012, using Bayesian Kernel Machine Regression hierarchical variable selection with missing data imputed by MICE. Open
Note: GroupPIP, group posterior inclusion probabilities; CondPIP, conditional posterior inclusion probabilities; MCH, maternal and child health; Ob/Gyn, obstetrics and gynecology; PCDI, per capita disposable income. (DOCX)