... effects in distinct subgroups showing different attributes. A tailored classification expectation-maximization algorithm is developed for parameter estimation, and extensive simulation studies are conducted to evaluate its performance against competing models. The novel approach is applied...

... in the accurate dose conversion scheme for photon dose-calculation algorithms [ 29 ]. Therefore, we also added a CT image as an input for the conversion from the PB dose to the MC dose. This additional input allows the model to account for the heterogeneity that causes difference between the PB and MC dose...

... spatial pyramid pooling module from YOLOv7 was added at the end of the backbone network, which improved the ability of the model to recognize multi-scale sheep behaviors by increasing the receptive field. In the neck network part, a new algorithm named CiConcat was proposed to optimize the regular Concat...

... frequencies, suggesting potential diversification benefits for insurers as climate change leads to more frequent weather-related hazards. climate change compound frequency models dependence modelling expectation-conditional maximization algorithm geospatial effects natural phenomena coverage Economic...

... to rely on local moves, often leading to poor mixing. A promising approach is to instead directly sample spanning trees on an auxiliary graph. Current spanning tree samplers, such as the celebrated Aldous–Broder algorithm, rely predominantly on simulating random walks that are required to visit all...

.... Res, 63, 1026–1043) can be highly efficient at approximating expectations of path functionals associated with stochastic differential equations. However, algorithms for calculating the optimal distributions with an infinite horizon are lacking. In this article, based on the method of Cui et al. (2021...

... Karhunen–Loéve expansions, where splines are employed to capture the mean and eigenfunctions. Treating the latent subgroup membership and the functional principal component (FPC) scores as missing data, we propose an Expectation-Maximization algorithm to effectively fit the joint model, combining...

...-locked loop. It subsequently presents a unique method for analyzing small-signal stability in islanded MGs. A virtual impedance setting strategy is created using the gray wolf optimization algorithm. It was found that the voltage and frequency of the MG stay within acceptable boundaries. The MG stability...

... socioeconomic conditions. To achieve this, it is essential to forecast water demand accurately. This investigation introduces a predictive framework that utilizes a convolutional neural network-based Xception model, which has been optimized through the developed maritime search and rescue algorithm to increase...

... [email protected]. Abstract In this paper, the optimal allocation of hydrogen storage capacity is studied by using fast nondominated sorting genetic algorithm. By analyzing the multienergy characteristics of hydrogen storage units, optimizing hydrogen storage distribution and improving energy efficiency...

.... Specifically, the watermark is cleverly embedded within large DNA fragments, allowing for the direct localization of raw reads via watermark alignment to avoid complex read assembly. A soft-decision forward–backward algorithm is proposed, which can identify indel errors and provide probability information...

... of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Objective To validate a phenotyping algorithm for gradations of diverticular...

... spectroscopy machine learning algorithm benchmark single-cell taxonomy identification microbial ecology environmental microbiome United States USDA-NIFA 2019–67013-29364 Cornell Initiative for Digital Agriculture funds National Science Foundation Center for Research on Programmable Plant...

... of CAC and the covariance on scalar regression model with the positive-definiteness constraints. Section  3 presents the proposed estimation approach and the ADMM algorithm. Section  4 shows the convergence of the proposed algorithm. Section  5 establishes the convergence rates and theoretical...

...Jee A Baik; Jun Won Choi; Jung Jin Kim Correspondence: [email protected] Graphical Abstract Figure. Fracture path prediction in the proximal femur using Dijkstra’s algorithm. 19 10...

... within a modified Expectation–Maximization algorithm, collapsing support points of the discrete distribution with overlapping estimated confidence intervals or regions, derived from the asymptotic properties of maximum likelihood estimators. This approach offers a transparent interpretation of the latent...

... Beardsley mealybug under different climate scenarios with MaxEnt, and discussed preventive and control measures against this pest. species distribution model maximum algorithm invasive pest climate niche Science and Technology Project of Hebei Education Department BJ2020052 Pseudococcus...

... algorithm with a deep neural network model. The statistical algorithm ranks differentially expressed genes by correlating their expression scores with the semantic similarity of their biological context, utilizing gene ontology information to align genes with known pathways. The deep neural network...

... contact [email protected] Abstract Pairwise sequence alignment (PSA) serves as the cornerstone in computational bioinformatics, facilitating multiple sequence alignment and phylogenetic analysis. This paper introduces the FORAlign algorithm, leveraging the Four Russians algorithm...

... data in a least squares setting. We develop a fast algorithm that does not only compute a minimizer but also determines an optimal hyperparameter—in the sense of rolling cross validation with the one standard error rule—exactly. This eliminates manual hyperparameter selection. Our method supports...