Biblio

Although sequence-to-sequence attentional neural machine translation (NMT) has achieved great progress recently, it is confronted with two challenges: learning optimal model parameters for long parallel sentences and well exploiting different scopes of contexts. In this paper, partially inspired by the idea of segmenting a long sentence into short clauses, each of which can be easily translated by NMT, we propose a hierarchy-to-sequence attentional NMT model to handle these two challenges. Our encoder takes the segmented clause sequence as input and explores a hierarchical neural network structure to model words, clauses, and sentences at different levels, particularly with two layers of recurrent neural networks modeling semantic compositionality at the word and clause level. Correspondingly, the decoder sequentially translates segmented clauses and simultaneously applies two types of attention models to capture contexts of interclause and intraclause for translation prediction. In this way, we can not only improve parameter learning, but also well explore different scopes of contexts for translation. Experimental results on Chinese-English and English-German translation demonstrate the superiorities of the proposed model over the conventional NMT model.

Genes, proteins and other metabolites present in cellular environment exhibit a virtual network that represents the regulatory relationship among its constituents. This network is called Gene Regulatory Network (GRN). Computational reconstruction of GRN reveals the normal metabolic pathway as well as disease motifs. Availability of microarray gene expression data from normal and diseased tissues makes the job easier for computational biologists. Reconstruction of GRN is based on neural modeling. Here we have used discrete and continuous versions of a meta-heuristic algorithm named Firefly algorithm for structure and parameter learning of GRNs respectively. The discrete version for this problem is proposed by us and it has been applied to explore the discrete search space of GRN structure. To evaluate performance of the algorithm, we have used a widely used synthetic GRN data set. The algorithm shows an accuracy rate above 50% in finding GRN. The accuracy level of the performance of Firefly algorithm in structure and parameter optimization of GRN is promising.