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

An integrated optical method to readout µ-Coriolis mass flow sensors

This paper presents a novel readout for a µ-Coriolis mass flow sensor based on a differential optical reflective method, using a vertical-cavity surface-emitting laser (VCSEL) and two photodiodes (PD). The new readout detects change in applied mass flow rate by measuring the phase shift between the two photodiode signals. Such a setup offers a non-contact and robust sensing method. Measurements are presented for mass flow of DI-water up to 10 gram/hour resulting in a phase shift of 8.7 degrees.</p

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