Learning text representation using recurrent convolutional neural network with highway layers

Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the input. The experiment shows that our model outperforms common neural network models (CNN, RNN, Bi-RNN) on a sentiment analysis task. Besides, the analysis of how sequence length influences the RCNN with highway layers shows that our model could learn good representation for the long text.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieva

Holographic Mutual Information of Two Disjoint Spheres

We study quantum corrections to holographic mutual information for two disjoint spheres at a large separation by using the operator product expansion of the twist field. In the large separation limit, the holographic mutual information is vanishing at the semiclassical order, but receive quantum corrections from the fluctuations. We show that the leading contributions from the quantum fluctuations take universal forms as suggested from the boundary CFT. We find the universal behavior for the scalar, the vector, the tensor and the fermionic fields by treating these fields as free fields propagating in the fixed background and by using the 1/n prescription. In particular, for the fields with gauge symmetries, including the massless vector boson and massless graviton, we find that the gauge parts in the propagators play indispensable role in reading the leading order corrections to the bulk mutual information.Comment: 37 pages, 1 figure; significant revisions, corrected the discussions on the computations of the mutual information in CFT, conclusions unchange

Joint Device Activity Detection, Channel Estimation and Signal Detection for Massive Grant-free Access via BiGAMP

Massive access has been challenging for the fifth generation (5G) and beyond since the abundance of devices causes communication overload to skyrocket. In an uplink massive access scenario, device traffic is sporadic in any given coherence time. Thus, channels across the antennas of each device exhibit correlation, which can be characterized by the row sparse channel matrix structure. In this work, we develop a bilinear generalized approximate message passing (BiGAMP) algorithm based on the row sparse channel matrix structure. This algorithm can jointly detect device activities, estimate channels, and detect signals in massive multiple-input multiple-output (MIMO) systems by alternating updates between channel matrices and signal matrices. The signal observation provides additional information for performance improvement compared to the existing algorithms. We further analyze state evolution (SE) to measure the performance of the proposed algorithm and characterize the convergence condition for SE. Moreover, we perform theoretical analysis on the error probability of device activity detection, the mean square error of channel estimation, and the symbol error rate of signal detection. The numerical results demonstrate the superiority of the proposed algorithm over the state-of-the-art methods in DADCE-SD, and the numerical results are relatively close to the theoretical analysis results.Comment: 15 pages, 8 figures, IEEE TS

An Advanced Control and Extensible Configuration for Static Var Generator

An extensible configuration is proposed for static var generator (SVG) with advanced controller included for reactive power compensation of grid. Compared with the traditional configurations, the major advantage of such system configuration is that the power modules are very flexible and easy to extend or reduce without changing the main equipment of SVG under the different voltage levels. Furthermore, in order to solve the problems of modeling uncertainty, nonlinearities, and outside disturbance by using proportion integration (PI) controller, an advanced controller is proposed based on auto disturbance rejection control (ADRC). By controlling the amount and direction of reactive current, the reactive power is generated or absorbed from SVG into power grid with fast response, which can realize the excellent dynamic compensation for both the internal and external interferences. Simulations results show that the proposed controller has better performance of the transient and steady state than PI controller. Moreover, the verification tests are executed in 380 V, 6.5 kVA experiment systems, suggesting that the excellent dynamic performance and strong robustness are achieved