10,707 research outputs found
On semi-supervised estimation using exponential tilt mixture models
Consider a semi-supervised setting with a labeled dataset of binary responses
and predictors and an unlabeled dataset with only the predictors. Logistic
regression is equivalent to an exponential tilt model in the labeled
population. For semi-supervised estimation, we develop further analysis and
understanding of a statistical approach using exponential tilt mixture (ETM)
models and maximum nonparametric likelihood estimation, while allowing that the
class proportions may differ between the unlabeled and labeled data. We derive
asymptotic properties of ETM-based estimation and demonstrate improved
efficiency over supervised logistic regression in a random sampling setup and
an outcome-stratified sampling setup previously used. Moreover, we reconcile
such efficiency improvement with the existing semiparametric efficiency theory
when the class proportions in the unlabeled and labeled data are restricted to
be the same. We also provide a simulation study to numerically illustrate our
theoretical findings
Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation
Source-free test-time adaptation for medical image segmentation aims to
enhance the adaptability of segmentation models to diverse and previously
unseen test sets of the target domain, which contributes to the
generalizability and robustness of medical image segmentation models without
access to the source domain. Ensuring consistency between target edges and
paired inputs is crucial for test-time adaptation. To improve the performance
of test-time domain adaptation, we propose a multi task consistency guided
source-free test-time domain adaptation medical image segmentation method which
ensures the consistency of the local boundary predictions and the global
prototype representation. Specifically, we introduce a local boundary
consistency constraint method that explores the relationship between tissue
region segmentation and tissue boundary localization tasks. Additionally, we
propose a global feature consistency constraint toto enhance the intra-class
compactness. We conduct extensive experiments on the segmentation of benchmark
fundus images. Compared to prediction directly by the source domain model, the
segmentation Dice score is improved by 6.27\% and 0.96\% in RIM-ONE-r3 and
Drishti GS datasets, respectively. Additionally, the results of experiments
demonstrate that our proposed method outperforms existing competitive domain
adaptation segmentation algorithms.Comment: 31 pages,7 figure
Solving optimal power flow problems via a constrained many-objective co-evolutionary algorithm
The optimal power flow problem in power systems is characterized by a number of complex objectives and constraints, which aim to optimize the total fuel cost, emissions, active power loss, voltage magnitude deviation, and other metrics simultaneously. These conflicting objectives and strict constraints challenge existing optimizers in balancing between active power and reactive power, along with good trade-offs among many metrics. To address these difficulties, this paper develops a co-evolutionary algorithm to solve the constrained many-objective optimization problem of optimal power flow, which evolves three populations with different selection strategies. These populations are evolved towards different parts of the huge objective space divided by large infeasible regions, and the cooperation between them renders assistance to the search for feasible and Pareto-optimal solutions. According to the experimental results on benchmark problems and the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems, the proposed algorithm is superior over peer algorithms in solving constrained many-objective optimization problems, especially the optimal power flow problems
Trends in precipitation extremes and long-term memory of runoff records in Zhejiang, East China
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