Using Linear Graph Theory and the Principle of Orthogonality to Model Multibody, ... Using Linear Graph Theory and the Principle of Orthogonality to Model Multibody, Multi-Domain Systems: Author: C ...

However, such tensor models are often unable to incorporate the underlying domain knowledge when compressing high-dimensional models. To this end, we introduce a novel graph-regularized tensor ...

A domain generalized approach on surgical scene graphs to predict instrument-tissue interaction during robot-assisted surgery. We incorporate incremental learning to the feature extraction network and ...

Although the association of this viscoelastic model in a FE discretization has been largely used in previous studies devoted to linear vibrating systems, the main contribution intended for the present ...

State space models (SSMs) like Mamba are effective and efficient in modeling long-range dependencies in sequential data, but adapting them to non-sequential graph data is challenging. Many sequence ...

In this paper, we study graph contrastive learning in the context of biomedical domain, where molecular graphs are present. We propose a novel framework called MoCL, which utilizes domain knowledge at ...

The domain decomposed behavior of the adjoint Neumann-Ulam Monte Carlo method for solving linear systems is analyzed using the spectral properties of the linear oper- ator. Relationships for the ...