96 research outputs found
A simulation framework for reciprocal recurrent selection-based hybrid breeding under transparent and opaque simulators
Hybrid breeding is an established and effective process to improve offspring performance, while it is resource-intensive and time-consuming for the recurrent process in reality. To enable breeders and researchers to evaluate the effectiveness of competing decision-making strategies, we present a modular simulation framework for reciprocal recurrent selection-based hybrid breeding. Consisting of multiple modules such as heterotic separation, genomic prediction, and genomic selection, this simulation framework allows breeders to efficiently simulate the hybrid breeding process with multiple options of simulators and decision-making strategies. We also integrate the recently proposed concepts of transparent and opaque simulators into the framework in order to reflect the breeding process more realistically. Simulation results show the performance comparison among different breeding strategies under the two simulators
Simultaneously detecting spatiotemporal changes with penalized Poisson regression models
In the realm of large-scale spatiotemporal data, abrupt changes are commonly
occurring across both spatial and temporal domains. This study aims to address
the concurrent challenges of detecting change points and identifying spatial
clusters within spatiotemporal count data. We introduce an innovative method
based on the Poisson regression model, employing doubly fused penalization to
unveil the underlying spatiotemporal change patterns. To efficiently estimate
the model, we present an iterative shrinkage and threshold based algorithm to
minimize the doubly penalized likelihood function. We establish the statistical
consistency properties of the proposed estimator, confirming its reliability
and accuracy. Furthermore, we conduct extensive numerical experiments to
validate our theoretical findings, thereby highlighting the superior
performance of our method when compared to existing competitive approaches
Transposed Poisson structures on Galilean and solvable Lie algebras
Transposed Poisson structures on complex Galilean type Lie algebras and
superalgebras are described. It was proven that all principal Galilean Lie
algebras do not have non-trivial -derivations and as it follows
they do not admit non-trivial transposed Poisson structures. Also, we proved
that each complex finite-dimensional solvable Lie algebra admits a non-trivial
transposed Poisson structure and a non-trivial -Lie structure
- …