HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems

Graph neural networks (GNNs) have shown promising performance in solving both Boolean satisfiability (SAT) and Maximum Satisfiability (MaxSAT) problems due to their ability to efficiently model and capture the structural dependencies between literals and clauses. However, GNN methods for solving Weighted MaxSAT problems remain underdeveloped. The challenges arise from the non-linear dependency and sensitive objective function, which are caused by the non-uniform distribution of weights across clauses. In this paper, we present HyperSAT, a novel neural approach that employs an unsupervised hypergraph neural network model to solve Weighted MaxSAT problems. We propose a hypergraph representation for Weighted MaxSAT instances and design a cross-attention mechanism along with a shared representation constraint loss function to capture the logical interactions between positive and negative literal nodes in the hypergraph. Extensive experiments on various Weighted MaxSAT datasets demonstrate that HyperSAT achieves better performance than state-of-the-art competitors.

Explosion prediction of oil gas using SVM and Logistic Regression

The prevention of dangerous chemical accidents is a primary problem of industrial manufacturing. In the accidents of dangerous chemicals, the oil gas explosion plays an important role. The essential task of the explosion prevention is to estimate the better explosion limit of a given oil gas. In this paper, Support Vector Machines (SVM) and Logistic Regression (LR) are used to predict the explosion of oil gas. LR can get the explicit probability formula of explosion, and the explosive range of the concentrations of oil gas according to the concentration of oxygen. Meanwhile, SVM gives higher accuracy of prediction. Furthermore, considering the practical requirements, the effects of penalty parameter on the distribution of two types of errors are discussed.