Earticle

A matching approach is proposed for solving the two-sided matching problem, where the preferences given by the two-sided agents are in the format of strict order relations and threshold orders. The two-sided matching problem under the conditions of strict order relations and threshold orders is firstly described. The related concepts of the two-sided matching are also introduced. In order to solve the considered two-sided matching problem, the strict order relations are converted into the Borda number matrix, and the threshold orders are transformed into the threshold Borda numbers. According to the Borda number matrix and the threshold Borda numbers of each side, the two Borda number cut matrix can be established, and then the two normalized Borda number cut matrixes can be set up. According to the two normalized Borda number cut matrixes, the synthetical normalized Borda number cut matrix can be established. Based on the synthetical normalized Borda number cut matrix, a matching model considering the two-sided matching constraint conditions can be developed. The matching alternative can be obtained by solving the matching model. Finally, a matching example between positions and staffs is given to illustrate the use of the proposed approach.

Convolutional neural networks are a technology that combines artificial neural networks and recent deep learning methods. They have been applied to many image recognition tasks and have attracted the attention of the researchers of many countries in recent years. This paper summarizes the latest development of convolutional neural networks and expounds the relative research of image recognition technology and elaborates on the application of convolutional neural networks in handwritten numeral recognition.

The electromagnetic anomaly observations before earthquake, have been confirmed by many cases of strong earthquakes. The analysis of earthquake magnetic anomaly is an effective approach for seismo-precursor detection. Traditional frequent mining methods for electromagnetic matrix datasets analysis often find the co-related items. However, these methods may miss the items which are differential co-related patters under different datasets. Mining these differential co-related patterns is more valuable for inferring potential knowledge. In this paper, we develop an algorithm, MSPattern, to mine maximal subspace differential co-expression patterns. MSPattern constructs a weighted undirected item-item relational graph firstly. Then all the maximal co-related patterns would be mined using item-growth method in above graph. MSPattern also utilizes several techniques for producing maximal patterns without candidate patterns maintenance. Evaluated by real electromagnetic matrix datasets and the gene expression datasets, the experimental results show our algorithm can find some potential knowledge for earthquake analysis, and MSPattern algorithm is more efficient than traditional ones. The performance of MSPattern is also evaluated by empirical p-value and gene ontology, the results show our algorithm can find statistical significant and biological differential co-expression patterns.