We describe how video data can be organized and structured so as to facilitate efficient querying. We develop a formal model for video data and show how spatial data structures, suitably modified, provide an elegant way of storing such data. We develop algorithms to process various kinds of video queries and show that, in most cases, the complexity of these algorithms is linear. A prototype system, called the Advanced Video Information System (AVIS), based on these concepts, has been designed at the University of Maryland.

Drug-drug interactions (DDIs) may bring huge health risks and dangerous effects to a patient’s body when taking two or more drugs at the same time or within a certain period of time. Therefore, the automatic extraction of unknown DDIs has great potential for the development of pharmaceutical agents and the safety of drug use. In this article, we propose a novel recurrent hybrid convolutional neural network (RHCNN) for DDI extraction from biomedical literature. In the embedding layer, the texts mentioning two entities are represented as a sequence of semantic embeddings and position embeddings. In particular, the complete semantic embedding is obtained by the information fusion between a word embedding and its contextual information which is learnt by recurrent structure. After that, the hybrid convolutional neural network is employed to learn the sentence-level features which consist of the local context features from consecutive words and the dependency features between separated words for DDI extraction. Lastly but most significantly, in order to make up for the defects of the traditional cross-entropy loss function when dealing with class imbalanced data, we apply an improved focal loss function to mitigate against this problem when using the DDIExtraction 2013 dataset. In our experiments, we achieve DDI automatic extraction with a micro F-score of 75.48% on the DDIExtraction 2013 dataset, outperforming the state-of-the-art approach by 2.49%.

Neurotrauma in the form of traumatic brain injury (TBI) afflicts more Americans annually than Alzheimer's and Parkinson's disease combined, yet few researchers have used neuroproteomics to investigate the underlying complex molecular events that exacerbate TBI. Discussed in this review is the methodology needed to explore the neurotrauma proteome-from the types of samples used to the mass spectrometry identification and quantification techniques available. This neuroproteomics survey presents a framework for large-scale protein research in neurotrauma, as applied for immediate TBI biomarker discovery and the far-reaching systems biology understanding of how the brain responds to trauma. Ultimately, knowledge attained through neuroproteomics could lead to clinical diagnostics and therapeutics to lessen the burden of neurotrauma on society.

Active learning is an e ective learning approach. In this paper, we present a n i n telligent a g e n t assisted environment for active learning. The system is to better support studentcentered, self-paced, and highly interactive learning approach. Students' learning-related pro les, such as learning styles and background knowledge, are used in selecting, organizing, and presenting learning materials. A new approach t o course content organization and delivery is being developed based on smart instructional components, which c a n b e i ntegrated into a wide range of courses. The system is being implemented using the prevalent I n ternet, Web, digital library, and multi-agent technologies.