Improvement in Finite-Sample Properties of GMM-Based Wald Tests

  GMM-based Wald tests tend to overreject when used for small samples, mainly due to inaccurate estimation of the weighting matrix. This article proposes applying the shrinkage method to address this problem. Using a possibly-misspecied factor model, the shrinkage method can provide a good estimator for the weighting matrix, and hence improve the nite-sample performance of the GMM-based Wald tests.  This paper is accepted by Computational Statistics

FAF: A novel multimodal emotion recognition approach integrating face, body and text

Multimodal emotion analysis performed better in emotion recognition depending on more comprehensive emotional clues and multimodal emotion dataset. In this paper, we developed a large multimodal emotion dataset, named "HED" dataset, to facilitate the emotion recognition task, and accordingly propose a multimodal emotion recognition method. To promote recognition accuracy, "Feature After Feature" framework was used to explore crucial emotional information from the aligned face, body and text samples. We employ various benchmarks to evaluate the "HED" dataset and compare the performance with our method. The results show that the five classification accuracy of the proposed multimodal fusion method is about 83.75%, and the performance is improved by 1.83%, 9.38%, and 21.62% respectively compared with that of individual modalities. The complementarity between each channel is effectively used to improve the performance of emotion recognition. We had also established a multimodal online emotion prediction platform, aiming to provide free emotion prediction to more users

Profiling variable-number tandem repeat variation across populations using repeat-pangenome graphs.

Variable number tandem repeats (VNTRs) are composed of consecutive repetitive DNA with hypervariable repeat count and composition. They include protein coding sequences and associations with clinical disorders. It has been difficult to incorporate VNTR analysis in disease studies that use short-read sequencing because the traditional approach of mapping to the human reference is less effective for repetitive and divergent sequences. In this work, we solve VNTR mapping for short reads with a repeat-pangenome graph (RPGG), a data structure that encodes both the population diversity and repeat structure of VNTR loci from multiple haplotype-resolved assemblies. We develop software to build a RPGG, and use the RPGG to estimate VNTR composition with short reads. We use this to discover VNTRs with length stratified by continental population, and expression quantitative trait loci, indicating that RPGG analysis of VNTRs will be critical for future studies of diversity and disease

Dimensionless Study on Efficiency and Speed Characteristics of a Compressed Air Engine

To eliminate the pollutants exhausting, this paper presents an idea of using compressed air as the power source for engines. Instead of an internal combustion (IC) engine, this automobile is equipped with a compressed air engines (CAEs), which transforms the energy of the compressed air into mechanical kinematic energy. Through analysis of the working process of a CAE, the mathematical model is setup. Experiments are carried out to verify the engine performance and the basic model's validity. By selecting the appropriate reference values, the mathematical model is transformed to a dimensionless expression. The dimensionless speed and efficiency characteristics of the CAE are obtained. Through analysis, it can be obtained that the dimensionless average rotating speed is mainly determined by the intake duration angle, the dimensionless inertia parameter, the dimensionless exhaust pressure, and the scale factor of exhaust valve. Moreover, the efficiency of the CAE is mainly determined by the dimensionless exhaust pressure, the intake duration angle and the dimensionless cylinder clearance. This research can be referred to in the design of CAE and the study on optimization of the CAE