High Efficiency Video Coding (HEVC) is the latest video coding standard that is supported by JCT-VC. In this letter, an encoding algorithm for early termination of Coding Unit (CU) and Prediction Unit (PU) based on the texture direction is proposed for the HEVC intra prediction. Experimental results show that the proposed algorithm provides an average 40% total encoding time reduction with the negligible loss of rate-distortion.

The purpose of document modeling is to learn low-dimensional semantic representations of text accurately for Natural Language Processing tasks. In this paper, proposed is a novel attention-based hybrid neural network model, which would extract semantic features of text hierarchically. Concretely, our model adopts a bidirectional LSTM module with word-level attention to extract semantic information for each sentence in text and subsequently learns high level features via a dynamic convolution neural network module. Experimental results demonstrate that our proposed approach is effective and achieve better performance than conventional methods.

In recent years, deep learning has been widely applied in relation extraction task. The method uses only word embeddings as network input, and can model relations between target named entity pairs. It equally deals with each relation mention, so it cannot effectively extract relations from the corpus with an enormous number of non-relations, which is the main reason why the performance of relation extraction is significantly lower than that of relation classification. This paper designs a deep reinforcement learning framework for relation extraction, which considers relation extraction task as a two-step decision-making game. The method models relation mentions with CNN and Tree-LSTM, which can calculate initial state and transition state for the game respectively. In addition, we can tackle the problem of unbalanced corpus by designing penalty function which can increase the penalties for first-step decision-making errors. Finally, we use Q-Learning algorithm with value function approximation to learn control policy π for the game. This paper sets up a series of experiments in ACE2005 corpus, which show that the deep reinforcement learning framework can achieve state-of-the-art performance in relation extraction task.

The main idea of filter methods in feature selection is constructing a feature-assessing criterion and searching for feature subset that optimizes the criterion. The primary principle of designing such criterion is to capture the relevance between feature subset and the class as precisely as possible. It would be difficult to compute the relevance directly due to the computation complexity when the size of feature subset grows. As a result, researchers adopt approximate strategies to measure relevance. Though these strategies worked well in some applications, they suffer from three problems: parameter determination problem, the neglect of feature interaction information and overestimation of some features. We propose a new feature selection algorithm that could compute mutual information between feature subset and the class directly without deteriorating computation complexity based on the computation of partitions. In light of the specific properties of mutual information and partitions, we propose a pruning rule and a stopping criterion to accelerate the searching speed. To evaluate the effectiveness of the proposed algorithm, we compare our algorithm to the other five algorithms in terms of the number of selected features and the classification accuracies on three classifiers. The results on the six synthetic datasets show that our algorithm performs well in capturing interaction information. The results on the thirteen real world datasets show that our algorithm selects less yet better feature subset.