GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding

In this paper, we propose GPSP, a novel Graph Partition and Space Projection based approach, to learn the representation of a heterogeneous network that consists of multiple types of nodes and links. Concretely, we first partition the heterogeneous network into homogeneous and bipartite subnetworks. Then, the projective relations hidden in bipartite subnetworks are extracted by learning the projective embedding vectors. Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network. Extensive experiments are conducted on a real-life dataset. The results demonstrate that GPSP outperforms the state-of-the-art baselines in two key network mining tasks: node classification and clustering.Comment: WWW 2018 Poste

KINEMATICS ANALYSIS ON THE FOREHAND STROKE OF ATP TENNIS PLAYER KAREN KHACHANOV

By using the three-dimensional video analysis method to kinematical analyze the forehand stroke technique of the ATP professional tennis player, Karen Khachanov. Exploring the action mode and characteristics from him to provide a reference for all tennis learners to improve their forehand stroke. The results show that: (1) At the end of the preparation, Karen Khachanov’s center of gravity was lower, and his body control was steady; (2) After the follow through, each joint of Karen Khachanov’s racquet arm were stretched, so the swing radius is larger. His right leg was like a support point for the next stage---drive leg and rotate hip; (3) At the swing stage, the move of his driving leg and rotating hip was full, so the whole hitting action match the principle whipping technique movement. (4) The “wiper ” style follow through was natural, coordinated, and smooth. After hitting the ball, his body of control was steady, the shoulder movement was obvious, and the hitting was fast and powerful. Conclusion: The forehand stroke of Karen Khachanov was a high quality hitting action, and it’s worthy of tennis players to learn from

Conditional Random Fields as Recurrent Neural Networks

Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning techniques for image recognition to tackle pixel-level labelling tasks. One central issue in this methodology is the limited capacity of deep learning techniques to delineate visual objects. To solve this problem, we introduce a new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling. To this end, we formulate mean-field approximate inference for the Conditional Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks. This network, called CRF-RNN, is then plugged in as a part of a CNN to obtain a deep network that has desirable properties of both CNNs and CRFs. Importantly, our system fully integrates CRF modelling with CNNs, making it possible to train the whole deep network end-to-end with the usual back-propagation algorithm, avoiding offline post-processing methods for object delineation. We apply the proposed method to the problem of semantic image segmentation, obtaining top results on the challenging Pascal VOC 2012 segmentation benchmark.Comment: This paper is published in IEEE ICCV 201