The continuation task (Wang & Wang, 2014) proves to have language learning potentials by many empirical studies (Peng, 2018). This study intends to explore its underlining alignment mechanisms through two continua...The continuation task (Wang & Wang, 2014) proves to have language learning potentials by many empirical studies (Peng, 2018). This study intends to explore its underlining alignment mechanisms through two continuation tasks on English materi-als called Chon and Charles by two Chinese linguistics-major graduates (EFL learners). The results show that alignment is ubiqui-tous both linguistically and thematically, and the reading and writing are tightly coupled. This indicates that the continuation task will significantly benefit second language writing pedagogy if applied appropriately.展开更多
On April 27,2016,a striking true-color satellite image acquired by the Moderate Resolution Imaging Spectroradiometer（MODIS）onboard National Aeronautics and Space Administration’s（NASA’s）Aqua satellite showed sev...On April 27,2016,a striking true-color satellite image acquired by the Moderate Resolution Imaging Spectroradiometer（MODIS）onboard National Aeronautics and Space Administration’s（NASA’s）Aqua satellite showed several groups of very well structured arc cloud patterns（Fig.1）,which are associaed with atmospheric gravity waves,aligned in the middle of the Atlantic Ocean between展开更多
Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem.H...Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem.However, it is worth noting that interactions in biological networks, like many other processes in the biological realm,are probabilistic events. Therefore, more accurate and better results can be obtained if biological networks are characterized by probabilistic graphs. This probabilistic information, however, increases difficulties in analyzing networks and only few methods can handle the probabilistic information. Therefore, in this paper, an improved Probabilistic Biological Network Alignment（PBNA） is proposed. Based on Iso Rank, PBNA is able to use the probabilistic information. Furthermore, PBNA takes advantages of Contributor and Probability Generating Function（PGF） to improve the accuracy of node similarity value and reduce the computational complexity of random variables in similarity matrix. Experimental results on dataset of the Protein-Protein Interaction（PPI） networks provided by Todor demonstrate that PBNA can produce some alignment results that ignored by the deterministic methods, and produce more biologically meaningful alignment results than Iso Rank does in most of the cases based on the Gene Ontology Consistency（GOC） measure. Compared with Prob method, which is designed exactly to solve the probabilistic alignment problem, PBNA can obtain more biologically meaningful mappings in less time.展开更多
文摘The continuation task (Wang & Wang, 2014) proves to have language learning potentials by many empirical studies (Peng, 2018). This study intends to explore its underlining alignment mechanisms through two continuation tasks on English materi-als called Chon and Charles by two Chinese linguistics-major graduates (EFL learners). The results show that alignment is ubiqui-tous both linguistically and thematically, and the reading and writing are tightly coupled. This indicates that the continuation task will significantly benefit second language writing pedagogy if applied appropriately.
文摘On April 27,2016,a striking true-color satellite image acquired by the Moderate Resolution Imaging Spectroradiometer（MODIS）onboard National Aeronautics and Space Administration’s（NASA’s）Aqua satellite showed several groups of very well structured arc cloud patterns（Fig.1）,which are associaed with atmospheric gravity waves,aligned in the middle of the Atlantic Ocean between
基金supported by the Natural Science Foundation of Jiangsu Province under Grant No. BK2012742
文摘Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem.However, it is worth noting that interactions in biological networks, like many other processes in the biological realm,are probabilistic events. Therefore, more accurate and better results can be obtained if biological networks are characterized by probabilistic graphs. This probabilistic information, however, increases difficulties in analyzing networks and only few methods can handle the probabilistic information. Therefore, in this paper, an improved Probabilistic Biological Network Alignment（PBNA） is proposed. Based on Iso Rank, PBNA is able to use the probabilistic information. Furthermore, PBNA takes advantages of Contributor and Probability Generating Function（PGF） to improve the accuracy of node similarity value and reduce the computational complexity of random variables in similarity matrix. Experimental results on dataset of the Protein-Protein Interaction（PPI） networks provided by Todor demonstrate that PBNA can produce some alignment results that ignored by the deterministic methods, and produce more biologically meaningful alignment results than Iso Rank does in most of the cases based on the Gene Ontology Consistency（GOC） measure. Compared with Prob method, which is designed exactly to solve the probabilistic alignment problem, PBNA can obtain more biologically meaningful mappings in less time.
