Graduate Student Source Analysis Method Based on Annual Report Data of Degree-Granting Information

Based on the annual data of degree-granting information accumulated by degree-granting institutions, this paper analyzes the storage structure of the database, excavates the critical information in the degree-granting information data structure, and puts forward a multi-dimensional graduate source analysis method, such as first-choice admission rate, the admission rate of recommended students, pre-graduation institution type, residential registration origin and types of colleges and universities transferring students. The limitation of using a single index of first choice admission rate to evaluate the quality of student source is improved

A deep learning approach to 3D segmentation of brain vasculature

The segmentation of blood-vessels is an important preprocessing step for the quantitative analysis of brain vasculature. We approach the segmentation task for two-photon brain angiograms using a fully convolutional 3D deep neural network.Published versio

User Interest Modeling and Self-Adaptive Update Using Relevance Feedback Technology

AbstractUser interest modeling is the core component of personalized service system. To improve the accuracy of the resulting model, this paper presents a method for modeling user's interest by collecting information of interactive behaviors and combining with implicit feedback techniques. This method also applies explicit feedback and Timeattenuation to self-adaptively update the interest model. It improves the accuracy of the model and will be pretty useful for personalized searching and filtering systems