该研究提出一种基于图的CAD辅助方法，可在参数化设计序列中预测下一个建模操作。研究人员将来自汽车领域的真实CATIA V5模型转换为有向无环图（Directed Acyclic Graph，DAG）以捕获特征依赖关系，从而实现直接从结构设计数据中学习。所采用的 该研究提出一种基于图的CAD辅助方法，可在参数化设计序列中预测下一个建模操作。研究人员将来自汽车领域的真实CATIA V5模型转换为有向无 ...

A new framework called SkillWeaver tackles AI agent tool routing by skipping full-library loading, cutting token use 99% on ...

Title: Have directed acyclic graphs (DAGs) fulfilled their promise in epidemiology and health research? Abstract: Causal directed acyclic graphs (DAGs) are among the most widely used causal diagrams.

Abstract: Directed acyclic graphs (DAGs) are central to science and engineering applications including causal inference, scheduling, and the automated design of neural architectures. In this work, we ...

Aim Causal inference relies on correct background knowledge, which epidemiologists generally understand to come from academic experts. Our community-engaged study augments scientific domain knowledge ...

The term evidence-based medicine, coined by Dr. Guyatt in 1991 (1), describes the practice of medicine rooted in the best available scientific evidence (2). Since its inception, evidence-based ...

Evidence-based Directed Acyclic Graphs (DAGs) are effective tools to comprehensively visualize complex causal and biasing pathways in pharmacoepidemiologic research in rheumatology. This paper ...

Department of Chemistry and Centro de Innovación en Química Avanzada (ORFEO−CINQA), Universitat Autónoma de Barcelona, 08193 Cerdanyola, Spain ...

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

Directed graphs are crucial in modeling complex real-world systems, from gene regulatory networks and flow networks to stochastic processes and graph metanetworks. Representing these directed graphs ...