Cross-Domain Labeled LDA for Cross-Domain Text Classification

Cross-domain text classification aims at building a classifier for a target domain which leverages data from both source and target domain. One promising idea is to minimize the feature distribution differences of the two domains. Most existing studies explicitly minimize such differences by an exact alignment mechanism (aligning features by one-to-one feature alignment, projection matrix etc.). Such exact alignment, however, will restrict models' learning ability and will further impair models' performance on classification tasks when the semantic distributions of different domains are very different. To address this problem, we propose a novel group alignment which aligns the semantics at group level. In addition, to help the model learn better semantic groups and semantics within these groups, we also propose a partial supervision for model's learning in source domain. To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and Reuters dataset, extensive quantitative (classification, perplexity etc.) and qualitative (topic detection) experiments are conducted to show the effectiveness of the proposed group alignment and partial supervision.Comment: ICDM 201

Tuning the Conductance of Monatomic Carbon Chain

Ab initio calculations show that the conductance of short monatomic carbon chain can be dramatically modified by adhering a single H, N, or O atom to the chain. For example, the conductance of the pristine chain gets about two orders of magnitude smaller if an H atom is adhered to the chain. By a statistical model, the structure of the carbon chain with the single atom adhered is found to be quite stable at room temperature, indicating that the method can be used to tune the conductance of monatomic carbon chain.Comment: 11pages, 6figure

L-Theanine Content and Related Gene Expression: Novel Insights into Theanine Biosynthesis and Hydrolysis among Different Tea Plant (Camellia sinensis L.) Tissues and Cultivars

L-Theanine content has tissues and cultivars specificity in tea plant (Camellia sinensis L.), the correlations of theanine metabolic related genes expression profiles with theanine contents were explored in this study. L-theanine contents in the bud and 1st leaf, 2nd leaf, 3rd leaf, old leaf, stem, and lateral root were determined by HPLC from three C. sinensis cultivars, namely ‘Huangjinya’, ‘Anjibaicha’, and ‘Yingshuang’, respectively. The theanine contents in leaves and root of ‘Huangjinya’ were the highest, followed by ‘Anjibaicha’, and ‘Yingshuang’. The theanine contents in the leaves reduced as the leaf mature gradually, and in stem were the least. Seventeen genes encoding enzymes involved in theanine metabolism were identified from GenBank and our tea transcriptome database, including CsTS1, CsTS2, CsGS1, CsGS2, CsGOGAT-Fe, CsGOGAT-NAD(P)H, CsGDH1, CsGDH2, CsALT, CsSAMDC, CsADC, CsCuAO, CsPAO, CsNiR, CsNR, CsGGT1, and CsGGT3. The transcript profiles of those seventeen genes in the different tissues of three tea plant cultivars were analyzed comparatively. Among the different cultivars, the transcript levels of most selected genes in ‘Huangjinya’ were significantly higher than that in the ‘Anjibaicha’ and ‘Yingshuang’. Among the different tissues, the transcript levels of CsTS2, CsGS1, and CsGDH2 almost showed positive correlation with the theanine contents, while the other genes showed negative correlation with the theanine contents in most cases. The theanine contents showed correlations with related genes expression levels among cultivars and tissues of tea plant, and were determined by the integrated effect of the metabolic related genes