Basal stem cluster bud induction and efficient regeneration for the Tibetan endemic medicinal plant Swertia conaensis

The artificial rapid propagation system for Swertia conaensis T. N. Ho et S. W. Liu was explored to screen the appropriate plant regeneration method and to provide an efficient propagation mode, useful for artificial breeding technology or for further research and development of the Tibetan endemic medicinal plant. In this study, the most suitable explant and hormone were chosen according to single factor test. Next, the effects of different hormone combinations on basal stem cluster bud induction, callus induction, adventitious bud occurrence and plant regeneration were investigated by using complete combination and orthogonal experiment. The obtained results showed that the explants suitable for in vitro of S. conaensis were stem tips with leaves, which were regenerated through the method of basal stem cluster bud occurrence in the MS medium with 2.0 mg∙L-1 6-BA, 0.5 mg∙L-1 NAA, but the proliferation coefficient was low, only 3.16 after 40 days of culture. Subsequently, the proliferation coefficient failed to improve, irrespective of change of the concentration ratio of 6-BA and NAA. Therefore, in the orthogonal experiment of adding ZT, the MS medium with 1.0 mg∙L-1 ZT, 0.5 mg∙L-1 NAA and 2.5 mg∙L-1 6-BA induced a large number of callus green and compact, with 86.30% callus occurrence rate. After 40 days of culture, the rate of adventitious bud occurrence was 96.55% and the proliferation coefficient was high (10.37). The rooting rate was 100% in the 1/2MS medium with 0.5 mg∙L-1 NAA. The survival rate of regenerated plants was more than 95%. Indirect organogenesis was more efficient than direct organogenesis in in vitro culture of S. conaensis. In this study, the efficient and stable regeneration system of S. conaensis was achieved through the method of explant to callus to adventitious buds, which provided an effective way to an endangered species

Acquiring Knowledge from Pre-trained Model to Neural Machine Translation

Pre-training and fine-tuning have achieved great success in the natural language process field. The standard paradigm of exploiting them includes two steps: first, pre-training a model, e.g. BERT, with a large scale unlabeled monolingual data. Then, fine-tuning the pre-trained model with labeled data from downstream tasks. However, in neural machine translation (NMT), we address the problem that the training objective of the bilingual task is far different from the monolingual pre-trained model. This gap leads that only using fine-tuning in NMT can not fully utilize prior language knowledge. In this paper, we propose an APT framework for acquiring knowledge from the pre-trained model to NMT. The proposed approach includes two modules: 1). a dynamic fusion mechanism to fuse task-specific features adapted from general knowledge into NMT network, 2). a knowledge distillation paradigm to learn language knowledge continuously during the NMT training process. The proposed approach could integrate suitable knowledge from pre-trained models to improve the NMT. Experimental results on WMT English to German, German to English and Chinese to English machine translation tasks show that our model outperforms strong baselines and the fine-tuning counterparts

Forecasting the All-Weather Short-Term Metro Passenger Flow Based on Seasonal and Nonlinear LSSVM

Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term passenger flow prediction needs to consider many factors, and the results of the existing models for short-term subway passenger flow forecasting are often unsatisfactory. Along this line, we propose a parallel architecture, called the seasonal and nonlinear least squares support vector machine (SN-LSSVM), to extract the periodicity and nonlinearity characteristics of passenger flow. Various forecasting models, including auto-regressive integrated moving average, long short-term memory network, and support vector machine, are employed for evaluating the performance of the proposed architecture. Moreover, we first applied the method to the Tiyu Xilu station which is the most crowded station in the Guangzhou metro. The results indicate that the proposed model can effectively make all-weather and year-round passenger flow predictions, thus contributing to the management of the station

A SEEMINGLY UNRELETED REGRESSION ANALYSIS ON THE TRADING BEHAVIOR OF MUTUAL FUND INVESTORS

This paper provides a comprehensive investigation on the causality relationship between fund performance and trading flows. We analyze if investors behave asymmetrically in fund purchasing and selling by seemingly unrelated regression which comprises several individual relationships that are linked by the fact that their disturbances or the error terms are correlated. The empirical result shows a significantly negative relationship between fund performance and purchase flows for domestic funds. The magnitude of domestic funds redemption negatively affects current return, but not for international funds. As previousfund return positively affects current net flows,the further lagged performances have no significant impact on the trading flows, revealing that fund investors are sensitive only to short-term past performance. Most importantly, while negative fund performance leads to the increases in redemption, positive performance contrarily leads to the decreases in purchase. The evidences strongly indicate an asymmetry behavior of fund investors in the return-purchase causality relations