Revisiting Text Guide, a Truncation Method for Long Text Classification

The quality of text classification has greatly improved with the introduction of deep learning, and more recently, models using attention mechanism. However, to address the problem of classifying text instances that are longer than the length limit adopted by most of the best performing transformer models, the most common method is to naively truncate the text so that it meets the model limit. Researchers have proposed other approaches, but they do not appear to be popular, because of their high computational cost and implementation complexity. Recently, another method called Text Guide has been proposed, which allows for text truncation that outperforms the naive approach and simultaneously is less complex and costly than earlier proposed solutions. Our study revisits Text Guide by testing the influence of certain modifications on the method’s performance. We found that some aspects of the method can be altered to further improve performance and confirmed several assumptions regarding the dependence of the method’s quality on certain factors.

A nonlinear fractional Rayleigh–Stokes equation under nonlocal integral conditions

In this paper, we study the fractional nonlinear Rayleigh–Stokes equation under nonlocal integral conditions, and the existence and uniqueness of the mild solution to our problem are considered. The ill-posedness of the mild solution to the problem recovering the initial value is also investigated. To tackle the ill-posedness, a regularized solution is constructed by the Fourier truncation method, and the convergence rate to the exact solution of this method is demonstrated.

Sequential Truncation of R-Vine Copula Mixture Model for High-Dimensional Datasets

Uncovering hidden mixture dependencies among variables has been investigated in the literature using mixture R-vine copula models. They provide considerable flexibility for modeling multivariate data. As the dimensions increase, the number of the model parameters that need to be estimated is increased dramatically, which comes along with massive computational times and efforts. This situation becomes even much more complex and complicated in the regular vine copula mixture models. Incorporating the truncation method with a mixture of regular vine models will reduce the computation difficulty for the mixture-based models. In this paper, the tree-by-tree estimation mixture model is joined with the truncation method to reduce computational time and the number of parameters that need to be estimated in the mixture vine copula models. A simulation study and real data applications illustrated the performance of the method. In addition, the real data applications show the effect of the mixture components on the truncation level.

Improved uncertain method for safety analysis of aircraft landing gear

There are various uncertain factors in most practical engineering applications, such as input loads, structural sizes, manufacturing tolerance, and initial and boundary conditions. The interval method and grey number theory are common methods to deal with uncertainty. In this article, the interval truncation method and grey number theory are improved. And a mixed method is proposed to represent the confidence interval of output result based on the improved interval truncation method and improved grey number theory. The proposed methods’ feasibility is verified by a stepped bar; the methods are applied to the analysis of aircraft landing gear safety uncertainty.

Truncation Mixture R-Vine Copulas

Uncovering hidden mixture correlation among variables have been investigating in the literature using mixture R-vine copula models. These models are hierarchical in nature. They provides a huge flexibility for modelling multivariate data. As the dimensions increases, the number of the model parameters that need to be estimated is increased dramatically, which becomes along with huge computational times and efforts. This situation becomes even much more harder and complicated in the mixture Regular vine models. Incorporating truncation method with mixture Regular vine models will reduce the computation difficulty for the mixture based models. In this paper, tree-by-tree estimation mixture model is joined with the truncation method, in order to reduce the computational time and the number of the parameters that need to be estimated in the mixture vine copula models. A simulation study and a real data applications illustrated the performance of the method. In addition, the real data applications show the affect of the mixture components on the truncation level.