National University of Defense Technology

About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Radar & Synthetic aperture radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.

Experimental quantum state tomography via compressed sampling.

Abstract: A fundamental difficulty in demonstrating quantum state tomography is that the required resources grow exponentially with the system size. For pure states and nearly pure states, the task of tomography can be more efficient. We proposed two methods for state reconstruction, by (1) minimizing entropy and (2) maximizing likelihood. The algorithm of compressed sampling is employed to solve the optimization problem. Experiments are demonstrated considering 4-qubit photonic states. The results show that (1) much fewer measurements than the standard tomography are sufficient to obtain high fidelity, and (2) the method of maximizing likelihood is more accurate and noise robust than the original reconstruction method of compressed sampling. Furthermore, the physical meaning of the methods of minimizing entropy and maximizing likelihood is clear.

Predicting Asset Value through Twitter Buzz

Abstract: This paper describes early work trying to predict financial market movement such as gold price, crude oil price, currency exchange rates and stock market indicators by analyzing Twitter posts. We collected Twitter feeds for 5 months obtaining a large set of emotional retweets originating from within the US, from which six public opinion time series containing the keywords “dollar% t ”, “$% t ”, “gold% t ”, “oil% t , “job% t ” and “economy% t ” were extracted. Our results show that these variables are correlated to and even predictive of the financial market movement. Except “$% t ”, all other five public opinion time series are identified by a Granger-causal relationship with certain market movements. It is demonstrated that daily changes in the volume of economic topic retweeting seem to match the value shift occurring in the corresponding market next day.

Joint Community and Structural Hole Spanner Detection via Harmonic Modularity

Abstract: Detecting communities (or modular structures) and structural hole spanners, the nodes bridging different communities in a network, are two essential tasks in the realm of network analytics. Due to the topological nature of communities and structural hole spanners, these two tasks are naturally tangled with each other, while there has been little synergy between them. In this paper, we propose a novel harmonic modularity method to tackle both tasks simultaneously. Specifically, we apply a harmonic function to measure the smoothness of community structure and to obtain the community indicator. We then investigate the sparsity level of the interactions between communities, with particular emphasis on the nodes connecting to multiple communities, to discriminate the indicator of SH spanners and assist the community guidance. Extensive experiments on real-world networks demonstrate that our proposed method outperforms several state-of-the-art methods in the community detection task and also in the SH spanner identification task (even the methods that require the supervised community information). Furthermore, by removing the SH spanners spotted by our method, we show that the quality of other community detection methods can be further improved.

Classification, Denoising, and Deinterleaving of Pulse Streams With Recurrent Neural Networks

Abstract: Pulse streams of many emitters have flexible features and complicated patterns. They can hardly be identified or further processed from a statistical perspective. In this paper, we introduce recurrent neural networks (RNNs) to mine and exploit long-term temporal patterns in streams and solve problems of sequential pattern classification, denoising, and deinterleaving of pulse streams. RNNs mine temporal patterns from previously collected streams of certain classes via supervised learning. The learned patterns are stored in the trained RNNs, which can then be used to recognize patterns-of-interest in testing streams and categorize them to different classes, and also predict features of upcoming pulses based on features of preceding ones. As predicted features contain sufficient information for distinguishing between pulses-of-interest and noises or interfering pulses, they are then used to solve problems of denoising and deinterleaving of noise-contaminated and aliasing streams. Detailed introductions of the methods, together with explanative simulation results, are presented to describe the procedures and behaviors of the RNNs in solving the aimed problems. Statistical results are provided to show satisfying performances of the proposed methods.