A weighted biobjective transformation technique for locating multiple optimal solutions of nonlinear equation systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Due to the fact that a nonlinear equation system may contain multiple optimal solutions, solving nonlinear equation systems is one of the most important challenges in numerical computation. When applying evolutionary algorithms to solve nonlinear equation systems, two issues should be considered: i) how to transform a nonlinear equation system into a kind of optimization problem, and ii) how to develop an optimization algorithm to solve the transformed optimization problem. In this paper, we tackle the first issue by transforming a nonlinear equation system into a weighted biobjective optimization problem. By the above transformation, not only do all the optimal solutions of an original nonlinear equation system become the Pareto optimal solutions of the transformed biobjective optimization problem, but also their images are different points on a linear Pareto front in the objective space. In addition, we suggest an adaptive multiobjective differential evolution, the goal of which is to effectively locate the Pareto optimal solutions of the transformed biobjective optimization problem. Once these solutions are found, the optimal solutions of the original nonlinear equation system can also be obtained correspondingly. By combining the weighted biobjective transformation technique with the adaptive multiobjective differential evolution, we propose a generic framework for the simultaneous locating of multiple optimal solutions of nonlinear equation systems. Comprehensive experiments on 38 nonlinear equation systems with various features have demonstrated that our framework provides very competitive overall performance compared with several state-of-the-art methods

Identification, Expression and Target Gene Analyses of MicroRNAs in Spodoptera litura

MicroRNAs (miRNAs) are small RNAs widely present in animals and plants and involved in post-transcriptional regulation of gene transcripts. In this study we identified and validated 58 miRNAs from an EST dataset of Spodoptera litura based on the computational and experimental analysis of sequence conservation and secondary structure of miRNA by comparing the miRNA sequences in the miRbase. RT-PCR was conducted to examine the expression of these miRNAs and stem-loop RT-PCR assay was performed to examine expression of 11 mature miRNAs (out of the 58 putative miRNA) that showed significant changes in different tissues and stages of the insect development. One hundred twenty eight possible target genes against the 11 miRNAs were predicted by using computational methods. Binding of one miRNA (sli-miR-928b) with the three possible target mRNAs was confirmed by Southern blotting, implying its possible function in regulation of the target genes

Short text similarity based on probabilistic topics

In this paper, we propose a new method for measuring the similarity between two short text snippets by comparing each of them with the probabilistic topics. Specifically, our method starts by firstly finding the distinguishing terms between the two short text snippets and comparing them with a series of probabilistic topics, extracted by Gibbs sampling algorithm. The relationship between the distinguishing terms of the short text snippets can be discovered by examining their probabilities under each topic. The similarity between two short text snippets is calculated based on their common terms and the relationship of their distinguishing terms. Extensive experiments on paraphrasing and question categorization show that the proposed method can calculate the similarity of short text snippets more accurately than other methods including the pure TF-IDF measure.Xiaojun Quan, Gang Liu, Zhi Lu, Xingliang Ni, Liu Wenyi

Categorizing and ranking search engine's results by semantic similarity

An automatic method for text categorizing and ranking search engine's results by semantic similarity is proposed in this paper. We first obtain nouns and verbs from snippets obtained from search engine using Name Entity Recognition and part-of speech. A semantic similarity algorithm based on WordNet is proposed to calculate the similarity of each snippet to each of the pre-defined categories. A balanced similarity ranking method combined with Google's rank and timeliness of the pages is proposed to rank these snippets. Preliminary experiments with 500 labeled questions from TREC03 show that 72.7% are correctly categorized.Tianyong Hao, Zhi Lu, Shitong Wang, Tiansong Zou, Shenhua Gu, Liu Wenyi