研究探讨了澳大利亚高等教育中网络安全教育缺乏性别包容性和交叉性视角的问题，通过半结构化访谈识别了整合这些视角的障碍及未来所需支持。
This work addresses the challenges of multi-domain named entity recognition (NER) under conditions of scarce labeled data, where conventional approaches suffer from domain discrepancy, data sparsity, and overfitting. To overcome these limitations, the paper proposes a unified framework that integrates unsupervised pre-training, transfer learning, data augmentation, few-shot learning, and domain-adversarial training. This approach enables effective adaptation to target domains without requiring any annotated data therein, significantly enhancing model generalization and robustness in low-resource, multi-domain settings. Experimental results demonstrate that the proposed method substantially outperforms existing baselines, offering a novel and practical pathway toward efficient and transferable NER in resource-constrained environments.
This study addresses the unreliability of Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO) in hierarchical models when group-level random effects are data-driven, which can lead to misleading model assessments. To overcome this limitation, the authors propose a weight-free diagnostic for detecting fold failures, integrated with an observation-level Rao–Blackwellized leave-one-out (RB-LOO) estimator. By marginalizing over latent parameters via importance sampling and leveraging structural leverage combined with a closed-form triangular triage strategy, the method substantially enhances computational efficiency and numerical stability. Evaluated on logistic and count generalized linear mixed models, RB-LOO achieves threefold higher accuracy than moment matching and reproduces exact refitting results within 82 minutes (elpd RMSE = 0.04), thereby effectively preventing erroneous model selection.
This study addresses the complex interplay of dynamically evolving customer classes, abandonment behavior, and dynamic prioritization in finite-capacity, multi-server queueing systems. To tackle this challenge, the authors propose a scalable continuous-time Markov chain (CTMC) modeling framework that integrates quasi-birth–death processes, matrix-analytic methods, and Krylov subspace approximations to efficiently compute both conditional and steady-state waiting time distributions for two customer classes. Notably, this work is the first to incorporate dynamic customer-type evolution and reneging into waiting time analysis for such systems. The model’s validity is demonstrated using real-world data from a tertiary referral hospital in Australia, where it successfully quantifies the disparity in waiting times between complex and routine patients, thereby offering actionable, quantitative insights for healthcare operational decision-making.
Traditional piecewise regression models often assume abrupt change points and ignore population heterogeneity, limiting their ability to capture smooth transitions and systematic differences observed in real-world data. This work proposes the smoothbp R package, which implements a Bayesian hierarchical framework incorporating a logistic smooth transition mechanism and Kuo–Mallick spike-and-slab priors to enable automatic selection of the number of breakpoints. The model accommodates random intercepts, random breakpoints, and covariate effects. Computational efficiency is substantially enhanced through a custom Metropolis-within-Gibbs sampler implemented in Rust, which integrates conjugate updates with Hamiltonian Monte Carlo (HMC). Simulation studies demonstrate that the method yields accurate parameter estimates and well-calibrated posterior inference, outperforming existing tools such as brms and mcp in both modeling flexibility and computational performance.
研究探讨了澳大利亚高等教育中网络安全教育缺乏性别包容性和交叉性视角的问题，通过半结构化访谈识别了整合这些视角的障碍及未来所需支持。
This work addresses the challenges of multi-domain named entity recognition (NER) under conditions of scarce labeled data, where conventional approaches suffer from domain discrepancy, data sparsity, and overfitting. To overcome these limitations, the paper proposes a unified framework that integrates unsupervised pre-training, transfer learning, data augmentation, few-shot learning, and domain-adversarial training. This approach enables effective adaptation to target domains without requiring any annotated data therein, significantly enhancing model generalization and robustness in low-resource, multi-domain settings. Experimental results demonstrate that the proposed method substantially outperforms existing baselines, offering a novel and practical pathway toward efficient and transferable NER in resource-constrained environments.
This study addresses the unreliability of Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO) in hierarchical models when group-level random effects are data-driven, which can lead to misleading model assessments. To overcome this limitation, the authors propose a weight-free diagnostic for detecting fold failures, integrated with an observation-level Rao–Blackwellized leave-one-out (RB-LOO) estimator. By marginalizing over latent parameters via importance sampling and leveraging structural leverage combined with a closed-form triangular triage strategy, the method substantially enhances computational efficiency and numerical stability. Evaluated on logistic and count generalized linear mixed models, RB-LOO achieves threefold higher accuracy than moment matching and reproduces exact refitting results within 82 minutes (elpd RMSE = 0.04), thereby effectively preventing erroneous model selection.
This study addresses the complex interplay of dynamically evolving customer classes, abandonment behavior, and dynamic prioritization in finite-capacity, multi-server queueing systems. To tackle this challenge, the authors propose a scalable continuous-time Markov chain (CTMC) modeling framework that integrates quasi-birth–death processes, matrix-analytic methods, and Krylov subspace approximations to efficiently compute both conditional and steady-state waiting time distributions for two customer classes. Notably, this work is the first to incorporate dynamic customer-type evolution and reneging into waiting time analysis for such systems. The model’s validity is demonstrated using real-world data from a tertiary referral hospital in Australia, where it successfully quantifies the disparity in waiting times between complex and routine patients, thereby offering actionable, quantitative insights for healthcare operational decision-making.
Traditional piecewise regression models often assume abrupt change points and ignore population heterogeneity, limiting their ability to capture smooth transitions and systematic differences observed in real-world data. This work proposes the smoothbp R package, which implements a Bayesian hierarchical framework incorporating a logistic smooth transition mechanism and Kuo–Mallick spike-and-slab priors to enable automatic selection of the number of breakpoints. The model accommodates random intercepts, random breakpoints, and covariate effects. Computational efficiency is substantially enhanced through a custom Metropolis-within-Gibbs sampler implemented in Rust, which integrates conjugate updates with Hamiltonian Monte Carlo (HMC). Simulation studies demonstrate that the method yields accurate parameter estimates and well-calibrated posterior inference, outperforming existing tools such as brms and mcp in both modeling flexibility and computational performance.