Aimin Yang

Testing conditional independence is a critical task, particularly in causal discovery and learning in Bayesian networks. However, in many real-world scenarios, variables are often measured with errors, such as those introduced by insufficient measurement accuracy, complicating the testing process. This paper focuses on testing conditional independence in the linear non-Gaussian measurement error model, under the condition that measurement error noise follows a Gaussian distribution. By leveraging high-order cumulants, we derive rank constraints on the cumulant matrix and establish their role in effectively assessing conditional independence, even in the presence of measurement errors. Based on these theoretical results, we leverage the rank constraints of the cumulant matrix as a tool for conditional independence testing and incorporate it into the PC algorithm, resulting in the PC-ME algorithm — a method designed to learn causal structures from observed data while accounting for measurement errors. Experimental results demonstrate that the proposed method outperforms existing approaches, particularly in cases other methods encounter difficulties.

The prevalence of fake news online has become a significant societal concern. To combat this, multimodal detection techniques based on images and text have shown promise. Yet, these methods struggle to analyze complex relationships within and between modalities due to the diverse discriminative elements in the news content. In addition, research on multimodal and multi-class fake news detection remains insufficient. To address the above challenges, in this article, we propose a novel detection model, GS ² F, leveraging g raph s tructure and g uided s emantic f usion. Specifically, we construct a multimodal graph structure to align two modalities and employ graph contrastive learning for refined fusion representations. Furthermore, a guided semantic fusion module is introduced to maximize the utilization of single-modal information and a dynamic contribution assignment layer is designed to weigh the importance of image, text, and multimodal features. Experimental results on Fakeddit demonstrate that our model outperforms existing methods, marking a step forward in the multimodal and multi-class fake news detection.

Each object has unique inherent frequencies and vibration modes, which are known as modal parameters. The modal analysis aims to study the free vibration characteristics of an object under an external force action. In modal analysis, finite element method (FEM) is widely used to build dynamic model structures and solve for modal parameters. Nevertheless, despite its widespread application, FEM does come with certain drawbacks related to computational efficiency. FEM necessitates the construction of stiffness and mass matrices for the structure, alongside an eigenvalue analysis during modal analysis, which can result in extensive computational time. Additionally, meshing the object is a fundamental requirement for FEM, and achieving proper meshing can be a laborious and time-consuming task. In the case of nonlinear problems, FEM demands iterative solutions, with each iteration addressing a linear system. To that end, in this article, we propose a MODAL-DRN-BL framework to improve the computational efficiency against FEM. MODAL-DRN-BL utilizes convolution operation to effectively expand the receptive field and capture vibration information at longer distances. It also handles sparse interaction between features through a broad learning module. Experimental results demonstrate that our proposed MODAL-DRN-BL framework achieves a mean absolute error of 1.49 in modal analysis benchmark ansys apdl (Ansys Parametric Design Language). Moreover, in terms of computational time, the MODAL-DRN-BL framework exhibits significant optimization compared to ansys apdl, resulting in a five-order-of-magnitude improvement in computational efficiency.