Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data

There are threefold challenges in emotion recognition. First, it is difficult to recognize human's emotional states only considering a single modality. Second, it is expensive to manually annotate the emotional data. Third, emotional data often suffers from missing modalities due to unforeseeable sensor malfunction or configuration issues. In this paper, we address all these problems under a novel multi-view deep generative framework. Specifically, we propose to model the statistical relationships of multi-modality emotional data using multiple modality-specific generative networks with a shared latent space. By imposing a Gaussian mixture assumption on the posterior approximation of the shared latent variables, our framework can learn the joint deep representation from multiple modalities and evaluate the importance of each modality simultaneously. To solve the labeled-data-scarcity problem, we extend our multi-view model to semi-supervised learning scenario by casting the semi-supervised classification problem as a specialized missing data imputation task. To address the missing-modality problem, we further extend our semi-supervised multi-view model to deal with incomplete data, where a missing view is treated as a latent variable and integrated out during inference. This way, the proposed overall framework can utilize all available (both labeled and unlabeled, as well as both complete and incomplete) data to improve its generalization ability. The experiments conducted on two real multi-modal emotion datasets demonstrated the superiority of our framework.Comment: arXiv admin note: text overlap with arXiv:1704.07548, 2018 ACM Multimedia Conference (MM'18

Comparison of dynamic changes of endogenous hormones between calli derived from mature and immature embryos of maize

Mature and immature embryos of maize inbred lines 87-1 and 137 were used as explants to induce callus on improved N6 medium. The contents of endogenous hormones abscisic acid (ABA), indoleacetic acid (IAA), gibberellic acid (GA3) and cytokinins (ZR) of immature, mature embryos and their corresponding calli were detected by method of enzyme-linked immunosorbant assay (ELISA). At the beginning of culture, IAA and GA3 levels decreased rapidly and reached their lowest levels at day 7, indicating that large amounts of IAA and GA3 are needed for germination. Levels of IAA and GA3 were highest at the beginning of embryonic callus formation from immature embryos, suggesting high levels of IAA and GA3 were beneficial to induction of embryonic callus from immature embryos (CIME). The IAA, GA3 and ABA contents and ration of IAA to ABA (IAA/ABA), GA3 to ABA (GA3/ABA) in callus of mature embryos (CME) were higher than those of CIME after the 14th day from culture initiation and the changes of ratios IAA/ABA and GA3/ABA increased rapidly in CME while they remained low in CIME during the whole experimental period. This inferred that high levels of IAA, GA3 or ABA and large increases in IAA/ABA and GA3/ABA might hinder the induction and maintenance of embryonic calli from mature embryos

SmartBFA: A passive crowdsourcing system for point-to-point barrier-free access

National Research Foundation (NRF) Singapore under its Industry Alignment Fund (Pre-positioning) Funding Initiative; Tote Board’s Enabling Lives Initiative (TB-ELI) Gran