Institution
National University of Defense Technology
Education•Changsha, China•
About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.
Topics: Computer science, Radar, Laser, Synthetic aperture radar, Fiber laser
Papers published on a yearly basis
Papers
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TL;DR: The proposed approach differentiates the channel busy caused by transmitting or receiving from that caused by carrier sensing, and thus improves the accuracy of estimating the overlap probability of two adjacent nodes' idle time, and achieves more accurate estimation when compared to existing research.
79 citations
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TL;DR: A new representation learning model for temporal knowledge graphs, namely CyGNet, based on a novel timeaware copy-generation mechanism, which is not only able to predict future facts from the whole entity vocabulary, but also capable of identifying facts with repetition and accordingly predicting such future facts with reference to the known facts in the past.
Abstract: Large knowledge graphs often grow to store temporal facts that model the dynamic relations or interactions of entities along the timeline. Since such temporal knowledge graphs often suffer from incompleteness, it is important to develop time-aware representation learning models that help to infer the missing temporal facts. While the temporal facts are typically evolving, it is observed that many facts often show a repeated pattern along the timeline, such as economic crises and diplomatic activities. This observation indicates that a model could potentially learn much from the known facts appeared in history. To this end, we propose a new representation learning model for temporal knowledge graphs, namely CyGNet, based on a novel timeaware copy-generation mechanism. CyGNet is not only able to predict future facts from the whole entity vocabulary, but also capable of identifying facts with repetition and accordingly predicting such future facts with reference to the known facts in the past. We evaluate the proposed method on the knowledge graph completion task using five benchmark datasets. Extensive experiments demonstrate the effectiveness of CyGNet for predicting future facts with repetition as well as de novo fact prediction.
79 citations
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TL;DR: An experiment is performed that for the first time shows secure QKKD with imperfect state preparations over long distances and achieves rigorous finite-key security bounds for decoy-state QKD against coherent attacks in the universally composable framework.
Abstract: Decoy-state quantum key distribution (QKD) is a standard technique in current quantum cryptographic implementations. Unfortunately, existing experiments have two important drawbacks: the state preparation is assumed to be perfect without errors and the employed security proofs do not fully consider the finite-key effects for general attacks. These two drawbacks mean that existing experiments are not guaranteed to be proven to be secure in practice. Here, we perform an experiment that shows secure QKD with imperfect state preparations over long distances and achieves rigorous finite-key security bounds for decoy-state QKD against coherent attacks in the universally composable framework. We quantify the source flaws experimentally and demonstrate a QKD implementation that is tolerant to channel loss despite the source flaws. Our implementation considers more real-world problems than most previous experiments, and our theory can be applied to general discrete-variable QKD systems. These features constitute a step towards secure QKD with imperfect devices.
79 citations
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TL;DR: An iterative optimization procedure to optimize under both a similarity and an energy constraint on the transmit signal, underlining the performance improvement given by a full-polarimetric design.
Abstract: We focus on the robust joint design of the transmit waveform and filtering structure for polarimetric radar. Considering the worst case signal-to-interference plus noise ratio (SINR) at the output as the figure of merit to optimize under both a similarity and an energy constraint on the transmit signal, we develop an iterative optimization procedure. The effectiveness of the proposed method is validated through experimental results, underlining the performance improvement given by a full-polarimetric design.
79 citations
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TL;DR: The present work tries to explore an effective method for extracting features from protein primary sequence and find a novel measurement of similarity among proteins for classifying a protein to its proper subcellular location, and indicates that this method outperforms some existing approaches based on amino acid composition or amino acid compositions and dipeptide composition.
79 citations
Authors
Showing all 39659 results
Name | H-index | Papers | Citations |
---|---|---|---|
Rui Zhang | 151 | 2625 | 107917 |
Jian Li | 133 | 2863 | 87131 |
Chi Lin | 125 | 1313 | 102710 |
Wei Xu | 103 | 1492 | 49624 |
Lei Liu | 98 | 2041 | 51163 |
Xiang Li | 97 | 1472 | 42301 |
Chang Liu | 97 | 1099 | 39573 |
Jian Huang | 97 | 1189 | 40362 |
Tao Wang | 97 | 2720 | 55280 |
Wei Liu | 96 | 1538 | 42459 |
Jian Chen | 96 | 1718 | 52917 |
Wei Wang | 95 | 3544 | 59660 |
Peng Li | 95 | 1548 | 45198 |
Jianhong Wu | 93 | 726 | 36427 |
Jianhua Zhang | 92 | 415 | 28085 |