Balancing Transferability and Discriminability for Unsupervised Domain Adaptation.

Unsupervised domain adaptation (UDA) aims to leverage a sufficiently labeled source domain to classify or represent the fully unlabeled target domain with a different distribution. Generally, the existing approaches try to learn a domain-invariant representation for feature transferability and add class discriminability constraints for feature discriminability. However, the feature transferability and discriminability are usually not synchronized, and there are even some contradictions between them, which is often ignored and, thus, reduces the accuracy of recognition. In this brief, we propose a deep multirepresentations adversarial learning (DMAL) method to explore and mitigate the inconsistency between feature transferability and discriminability in UDA task. Specifically, we consider feature representation learning at both the domain level and class level and explore four types of feature representations: domain-invariant, domain-specific, class-invariant, and class-specific. The first two types indicate the transferability of features, and the last two indicate the discriminability. We develop an adversarial learning strategy between the four representations to make the feature transferability and discriminability to be gradually synchronized. A series of experimental results verify that the proposed DMAL achieves comparable and promising results on six UDA datasets.

Identify of Fast-Growing Related Genes Especially in Height Growth by Combining QTL Analysis and Transcriptome in Salix matsudana (Koidz).

The study on the fast-growing traits of trees, mainly valued by tree height (TH) and diameter at breast height (DBH), is of great significance to promote the development of the forest industry. Quantitative trait locus (QTL) mapping based on high-density genetic maps is an efficient approach to identify genetic regions for fast-growing traits. In our study, a high-density genetic map for the F1 population was constructed. The genetic map had a total size of 5,484.07 centimorgan (cM), containing 5,956 single nucleotide polymorphisms (SNPs) based on Specific Length Amplified Fragment sequencing. Six fast-growing related stable QTL were identified on six chromosomes, and five stable QTL were identified by a principal component analysis (PCA). By combining the RNA-seq analysis for the two parents and two progenies with the qRT-PCR analysis, four candidate genes, annotated as DnaJ, 1-aminocyclopropane-1-carboxylate oxidase 1 (ACO1), Caffeic acid 3-O-methyltransferase 1 (COMT1), and Dirigent protein 6 (DIR6), that may regulate height growth were identified. Several lignin biosynthesis-related genes that may take part in height growth were detected. In addition, 21 hotspots in this population were found. The results of this study will provide an important foundation for further studies on the molecular and genetic regulation of TH and DBH.