Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos

Deep learning has been demonstrated to achieve excellent results for image classification and object detection. However, the impact of deep learning on video analysis (e.g. action detection and recognition) has been limited due to complexity of video data and lack of annotations. Previous convolutional neural networks (CNN) based video action detection approaches usually consist of two major steps: frame-level action proposal detection and association of proposals across frames. Also, these methods employ two-stream CNN framework to handle spatial and temporal feature separately. In this paper, we propose an end-to-end deep network called Tube Convolutional Neural Network (T-CNN) for action detection in videos. The proposed architecture is a unified network that is able to recognize and localize action based on 3D convolution features. A video is first divided into equal length clips and for each clip a set of tube proposals are generated next based on 3D Convolutional Network (ConvNet) features. Finally, the tube proposals of different clips are linked together employing network flow and spatio-temporal action detection is performed using these linked video proposals. Extensive experiments on several video datasets demonstrate the superior performance of T-CNN for classifying and localizing actions in both trimmed and untrimmed videos compared to state-of-the-arts

Quantum pumping with adiabatically modulated barriers in graphene

We study the adiabatic quantum pumping characteristics in the graphene modulated by two oscillating gate potentials out of phase. The angular and energy dependence of the pumped current is presented. The direction of the pumped current can be reversed when a high barrier demonstrates stronger transparency than a low one, which results from the Klein paradox. The underlying physics of the pumping process is illuminated.Comment: 14 pages, 4 figure

Performance Analysis for Physical Layer Security in Multi-Antenna Downlink Networks with Limited CSI Feedback

Channel state information (CSI) at the transmitter is of importance to the performance of physical layer security based on multi-antenna networks. Specifically, CSI is not only beneficial to improve the capacity of the legitimate channel, but also can be used to degrade the performance of the eavesdropper channel. Thus, the secrecy rate increases accordingly. This letter focuses on the quantitative analysis of the ergodic secrecy sum-rate in terms of feedback amount of the CSI from the legitimate users in multiuser multi-antenna downlink networks. Furthermore, the asymptotic characteristics of the ergodic secrecy sum-rate in two extreme cases is investigated in some detail. Finally, our theoretical claims are confirmed by the numerical results.Comment: 4 pages, 2 figures. In IEEE Wireless Communications Letters, 201

Empirical information on nuclear matter fourth-order symmetry energy from an extended nuclear mass formula

We establish a relation between the equation of state (EOS) of nuclear matter and the fourth-order symmetry energy $a_{\rm{sym,4}}(A)$ of finite nuclei in a semi-empirical nuclear mass formula by self-consistently considering the bulk, surface and Coulomb contributions to the nuclear mass. Such a relation allows us to extract information on nuclear matter fourth-order symmetry energy $E_{\rm{sym,4}}(\rho_0)$ at normal nuclear density $\rho_0$ from analyzing nuclear mass data. Based on the recent precise extraction of $a_{\rm{sym,4}}(A)$ via the double difference of the "experimental" symmetry energy extracted from nuclear masses, for the first time, we estimate a value of $E_{\rm{sym,4}}(\rho_0) = 20.0\pm4.6$ MeV. Such a value of $E_{\rm{sym,4}}(\rho_0)$ is significantly larger than the predictions from mean-field models and thus suggests the importance of considering the effects of beyond the mean-field approximation in nuclear matter calculations.Comment: 7 pages, 1 figure. Presentation improved and discussions added. Accepted version to appear in PL