Trajectory data uploaded by mobile devices is growing quickly. It represents the movement of an individual or a device based on the longitude and latitude coordinates collected by GPS. The location based service has a broad application prospect in the real world. As the traditional location prediction models which are based on the discrete state sequence cannot predict the locations in real time, we propose a Continuous Time Series Markov Model (CTS-MM) to solve this problem. The method takes the Gaussian Mixed Model (GMM) to simulate the posterior probability of a location in the continuous time series. The probability calculation method and state transition model of the Hidden Markov Model (HMM) are improved to get the precise location prediction. The experimental results on GeoLife data show that CTS-MM performs better for location prediction in exact minute than traditional location prediction models.

Biomedical semantic indexing is important for information retrieval and many other research fields in bioinformatics. It annotates biomedical citations with Medical Subject Headings. In face of unbalanced category distribution in the training data, sampling methods are difficult to apply for semantic indexing task. In this paper, we present a novel deep serial multi-task learning model. The primary task treats the biomedical semantic indexing as a multi-label text classification issue that considers the relations of the labels. The auxiliary task is a regression task that predicts the MeSH number of the citation and provides hints for the network to make it converge faster. The experimental results on the BioASQ-Task5A open dataset show that our model outperforms the state-of-the-art solution “MTI”, proposed by the US National Library of Medicine. Further, it not only achieves the highest precision among all the solutions in BioASQ-Task5A but also has faster convergence speed compared with some naive deep learning methods. Rather than parallel in an ordinary multi-task structure, the tasks in our model are serial and tightly coupled. It can achieve satisfied performance without any handcrafted feature.