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Institution

National University of Defense Technology

EducationChangsha, 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.


Papers
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Journal ArticleDOI
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

Posted Content
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

NameH-indexPapersCitations
Rui Zhang1512625107917
Jian Li133286387131
Chi Lin1251313102710
Wei Xu103149249624
Lei Liu98204151163
Xiang Li97147242301
Chang Liu97109939573
Jian Huang97118940362
Tao Wang97272055280
Wei Liu96153842459
Jian Chen96171852917
Wei Wang95354459660
Peng Li95154845198
Jianhong Wu9372636427
Jianhua Zhang9241528085
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
202397
2022469
20212,986
20203,468
20193,695