Multimodal sentiment analysis aims to accurately identify emotional tendencies and expressions from three modalities: text, audio and video, which is useful for opinion guidance and mental health analysis. Current studies have focused on fusing different sources of information from certain practical problems, focusing on the textual modality and ignoring the interaction and complementary information between modalities. In light of the aforementioned limitations, we propose a general model of hypergraph neural network. This model employs a tensor structure to fully obtain the interaction and complementary information during fusion, a low-rank decomposition of the tensor to achieve downscaling and a more accurate feature representation, and a hypergraph convolution and an attention mechanism to facilitate the capture of complex relationships in emotional expression. The experimental results on two benchmark datasets demonstrate the effectiveness of our model. Our approach outperforms existing methods by improving performance while reducing computation time, exhibiting strong generalization ability and high competitiveness.

Spectral clustering is widely used in modern data analysis. Spectral clustering methods speed up the computation and keep useful information by reducing dimensionality. Recently, graph signal filtering (GSF) has been introduced to further speed up the dimensionality reduction process by avoiding solving eigenvectors.

Spectral clustering (SC) is an important data mining method in bioinformatics due to the collection of big datasets. Calculating the number of clusters and feature vectors are two main steps of SC, both of which are related to eigenproblem. We propose a novel SC method using an adaptive filter which accelerates the SC and completely sidestepping the eigensolver. Results: We are inspired by two theories of different disciplines. The first theory is Density of State (DOS), a concept of solid-state physics. DOS roughly represents the distribution of eigenvalues. We propose a more accurate DOS and integrate it to obtain the number of clusters. The second theory is Graph Signal Processing (GSP) in irregular graph. Based on existing dimension reduction method, we use GSP to compute the feature vectors. We use Chebyshev polynomials to compute DOS and GSP and let them share the polynomials. Both the computational complexity and storage complexity are low.

Given a sparse matrix A, the selected inversion algorithm is an efficient method for computing certain selected elements of A-1. These selected elements correspond to all or some nonzero elements of the LU factors of A. In many ways, the types of matrix updates performed in the selected inversion algorithm are similar to those performed in the LU factorization, although the sequence of operations is different.

Finite difference time domain (FDTD) method is a robust and accurate algorithm which is widely used in computational electromagnetic field and the simulation of optical phenomenon. In this paper, parallel FDTD based on overlapped domain decomposition is used to simulate the band gap of photonic crystals and the quantum efficiency of thin film solar cells. The light-trapping effect is also analyzed by parallel FDTD, it's very important to improve light absorption. Numerical result demonstrates that the accuracy and the speedup of parallel FDTD are very high for large scale problem.