Model-assisted estimation with a training subsample: a two-phase sampling approach with design-based variance estimation
该研究通过将训练子样本视为第二阶段抽样,解决了模型辅助估计中的不确定性量化问题,并提出了两种方差估计方法。
该研究通过将训练子样本视为第二阶段抽样,解决了模型辅助估计中的不确定性量化问题,并提出了两种方差估计方法。
本文解决了量子lambda演算中无法消除张量积的问题,通过引入新的let构造来分解和绑定单量子比特密度矩阵,从而恢复了丢弃量子比特的能力。
研究提出一种面向对象的框架,通过将协作事件日志转换为面向对象表示来改进协作过程的预测性监控,并评估了该方法在多个数据集上的有效性。
This study addresses the challenge of distributedly estimating the Fiedler vector for algebraic connectivity control in mobile agent networks by proposing the A-Fiedler method. Replacing the Fiedler vector with the principal eigenvector of the adjacency matrix as a spectral proxy, this approach decomposes the gradient controller into local interactions and graph embeddings, enabling robust distributed online repositioning under communication constraints. Experimental results demonstrate that while A-Fiedler matches conventional methods in unconstrained settings, it significantly outperforms existing approaches in communication-limited scenarios. The method effectively prevents network disconnection and maintains system robustness, thereby establishing a novel paradigm for distributed connectivity maintenance in multi-agent systems.
This study addresses the scarcity of systematic empirical research on government software engineering practices and the limited visibility of existing findings in mainstream academic venues. Through three rapid literature reviews, the authors systematically screened 984 papers published in 2024 across top-tier international software engineering conferences, regional South American conferences, and public-sector digital transformation communications. Only four relevant studies were identified, most of which consisted of regional case studies or experiential reports, confirming that empirical evidence in this domain remains sparse and highly fragmented. The findings underscore a significant gap in scholarly attention to government software engineering and inform a proposed research agenda alongside recommendations for interdisciplinary collaboration to advance the field.
该研究通过将训练子样本视为第二阶段抽样,解决了模型辅助估计中的不确定性量化问题,并提出了两种方差估计方法。
本文解决了量子lambda演算中无法消除张量积的问题,通过引入新的let构造来分解和绑定单量子比特密度矩阵,从而恢复了丢弃量子比特的能力。
研究提出一种面向对象的框架,通过将协作事件日志转换为面向对象表示来改进协作过程的预测性监控,并评估了该方法在多个数据集上的有效性。
This study addresses the challenge of distributedly estimating the Fiedler vector for algebraic connectivity control in mobile agent networks by proposing the A-Fiedler method. Replacing the Fiedler vector with the principal eigenvector of the adjacency matrix as a spectral proxy, this approach decomposes the gradient controller into local interactions and graph embeddings, enabling robust distributed online repositioning under communication constraints. Experimental results demonstrate that while A-Fiedler matches conventional methods in unconstrained settings, it significantly outperforms existing approaches in communication-limited scenarios. The method effectively prevents network disconnection and maintains system robustness, thereby establishing a novel paradigm for distributed connectivity maintenance in multi-agent systems.
This study addresses the scarcity of systematic empirical research on government software engineering practices and the limited visibility of existing findings in mainstream academic venues. Through three rapid literature reviews, the authors systematically screened 984 papers published in 2024 across top-tier international software engineering conferences, regional South American conferences, and public-sector digital transformation communications. Only four relevant studies were identified, most of which consisted of regional case studies or experiential reports, confirming that empirical evidence in this domain remains sparse and highly fragmented. The findings underscore a significant gap in scholarly attention to government software engineering and inform a proposed research agenda alongside recommendations for interdisciplinary collaboration to advance the field.