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Drastic increase of key rate in continuous-variable quantum key distribution using multimode states and non-Gaussian operations

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Quantum Engineering

Quantum Engineering 2020年第3期2卷

作者： Huang, Anqi Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China

Social Emergency Event Judgement based on BiLSTM-CRF 19

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Social Emergency Event Judgement based on BiLSTM-CRF

19th Chinese National Conference on Computational Linguistic, CCL 2020

作者： Hu, Huijun Wang, Cong Dai, Jianhua Liu, Maofu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

In recent years, classification and rating of social emergency event have attracted more and more attentions in emergency management. However, most of the current studies adopt the rule-based methods to identify the evidences for event judgement, and have troubles in event judgement due to the complexity of social emergency event composition and the flexibility of language description. Inspired by the idea of event extraction, this paper has proposed the event judgement method via BiLSTM (Bidirectional Long-Short Term Memory) and CRF (Conditional Radom Fields) based on event classification and evidence extraction. The social emergency event classification is carried out firstly, and then the event evidences are extracted based on event type. In the end, the rating of social emergency event is judged with the attention mechanism and the combination of event type and evidence. Experimental results show that the proposed method is more robust than rule-based ones, and effective in the social emergency event judgement. © 2020 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Extraction

Global dynamic scaling relations of Hi-rich ultra-diffuse galaxies

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arXiv 2023年

作者： Hu, Hui-Jie Guo, Qi Zheng, Zheng Yang, Hang Tsai, Chao-Wei Zhang, Hong-Xin Zhang, Zhi-Yu National Astronomical Observatories Chinese Academy of Science 20A Datun Road Beijing100101 China University of Chinese Academy of Sciences No. 19 A Yuquan Road Beijing100049 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Hefei230026 China School of Astronomy and Space Science University of Science and Technology of China Hefei230026 China School of Astronomy and Space Science Nanjing University Nanjing210023 China Ministry of Education Nanjing210023 China

The baryonic Tully-Fisher relation (BTFR), which connects the baryonic mass of galaxies with their circular velocities, has been validated across a wide range of galaxies, from dwarf galaxies to massive galaxies. Recent studies have found that several ultra-diffuse galaxies (UDGs) deviate significantly from the BTFR, indicating a galaxy population with abnormal dynamical properties. However, such studies were still confined within a small sample size. In this study, we used the 100% complete Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA) to investigate the BTFR of 88 Hi-rich UDGs (HUDGs), which is the largest UDG sample with dynamical information. We found that the HUDGs form a continuous distribution in the BTFR diagram, with high-velocity galaxies consistent with normal dwarf galaxies at 1 σ level, and low-velocity galaxies deviating from the BTFR, in line with that reported in the literature. We point out that the observed deviation may be subject to various selection effects or systemic biases. Nevertheless, we found that the significance of the deviation of HUDGs from the BTFR and TFR are different, i.e., they either deviate from the BTFR or from the TFR. Our result indicates that a high-gas fraction may play an important role in explaining the deviation of HUDGs from BTFR. Copyright © 2023, The Authors. All rights reserved.

关键词： Galaxies

Exploring spatial-temporal multi-frequency analysis for high-fidelity and temporal-consistency video prediction

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arXiv 2020年

作者： Jin, Beibei Hu, Yu Tang, Qiankun Niu, Jingyu Shi, Zhiping Han, Yinhe Li, Xiaowei Research Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Capital Normal University

Video prediction is a pixel-wise dense prediction task to infer future frames based on past frames. Missing appearance details and motion blur are still two major problems for current predictive models, which lead to image distortion and temporal inconsistency. In this paper, we point out the necessity of exploring multi-frequency analysis to deal with the two problems. Inspired by the frequency band decomposition characteristic of Human Vision System (HVS), we propose a video prediction network based on multi-level wavelet analysis to deal with spatial and temporal information in a unified manner. Specifically, multi-level spatial discrete wavelet transform decomposes each video frame into anisotropic sub-bands with multiple frequencies, helping to enrich structural information and reserve fine details. On the other hand, multi-level temporal discrete wavelet transform which operates on time axis decomposes the frame sequence into sub-band groups of different frequencies to accurately capture multi-frequency motions under fixed frame rate. Extensive experiments on diverse datasets demonstrate that our model shows significant improvements on fidelity and temporal consistency over the state-of-the-art works. Copyright © 2020, The Authors. All rights reserved.

关键词： Discrete wavelet transforms

Neither Too Much nor Too Little: Leveraging Moderate Data in Pedestrian Trajectory Prediction

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Neither Too Much nor Too Little: Leveraging Moderate Data in...

Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Meiming Wang Jing Ren Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9781728191461

ISBN: (纸本)9781728191478

Pedestrian trajectory prediction is a crucial research issue in autonomous driving and mobile robots. However, accurate prediction of pedestrian trajectory is still challenging due to the uncertainty and complexity in real world. Neither data driven methods using too much historical data nor existing non-data driven methods using little historical information can perfectly tackle this problem. In this paper, we propose a geometry-based Arc-Fitting and Tangent-predicting model that leverages moderate historical trajectory information. We propose to use a simple arc model to fit the last few positions of the observed trajectory and to predict the future trajectory along the tangent direction passing through the last point. The proposed method is compared with various models on multiple pedestrian trajectory datasets. Our model achieves state-of-the-art performance in terms of the displacement error between predicted trajectory and the ground truth.

关键词： Uncertainty Computational modeling Predictive models Data models Trajectory Complexity theory Mobile robots

Entropic uncertainty relations and quantum Fisher information of top quarks in a large hadron collider

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arXiv 2024年

作者： Ye, Biao-Liang Xue, Li-Yuan Zhu, Zhi-Qiang Shi, Dan-Dan Fei, Shao-Ming Quantum Information Research Center Shangrao Normal University Shangrao334001 China Jiangxi Province Key Laboratory of Applied Optical Technology Shangrao Normal University Shangrao334001 China School of Physical Science and Intelligent Education Shangrao Normal University Shangrao334001 China School of Chemistry and Environmental Science Shangrao Normal University Shangrao334001 China School of Mathematics and Computational Science Shangrao Normal University Shangrao334001 China School of Mathematical Sciences Capital Normal University Beijing100048 China Max-Planck-Institute for Mathematics in the Sciences Leipzig04103 Germany

We employ the entropic uncertainty relations and the quantum Fisher information to explore the formation of quark tt¯ pairs at a large hadron collider through the combination of qq¯ pair and gg pair initiated processes. A comprehensive analysis has been undertaken on the procedure of quark and gluon channel mixing in the production of top quark pairs tt¯, encompassing the tightness of the entropic uncertainty inequalities and the maximum quantum Fisher information of the system. © 2024, CC BY.

关键词： Hadrons

Rolling Bearing Fault Diagnosis Based on Single Gated Unite Recurrent Neural Networks

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Rolling Bearing Fault Diagnosis Based on Single Gated Unite ...

作者： Wenwen Tan Yuansheng Sun Dawei Qiu Yapeng An Ping Ren College of Information Science & Technology Beijing University of Chemical Technology Institute of High Energy Physics Chinese Academy of Science Beijing Key Laboratory of Mobile Computing and Pervasive Device (Institute of Computing Technology Chinese Academy of Sciences)

In order to meet the real-time requirement of bearing fault diagnosis, a simplified strategy of the traditional long short term memory(LSTM) neural network structure is *** designed a new single gated unite(SGU) recurrent neural network. In view of the nonstationary and non-linear characteristics of bearing fault vibration data, we use wavelet packet decomposition to extract the features as input signals of bi-directional single gated unite(BiSGU) to complete the diagnosis of 10 types of bearing data. Simulation results show that the proposed method can ensure the accuracy of bearing fault diagnosis, reduce the number of network parameters by 36%, and improve time efficiency by 41%.

关键词： Rolling Bearing Fault Diagnosis Based on Single Gated Unite Recurrent Neural Networks

Under-determined Blind Speech Separation via the Convolutive Transfer Function and lp Regularization

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Under-determined Blind Speech Separation via the Convolutive...

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Liu Yang Junjie Yang Yi Guo School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT (GDUT) Guangzhou China Centre for Research in Mathematics School of Computing Engineering and Mathematics Western Sydney University Parramatta Australia

ISBN: (纸本)9781665406697

Convolutive blind speech separation (CBSS) attempts to recover speech sources from recorded mixtures transmitted by room impulse response filters (RIRs). The existing BSS methods are mainly based on a multiplicative narrowband approximation (MNA) model in the short-time Fourier transform (STFT) domain. However, the MNA model may result in serious system transformation error under the situation of a strongly reverberant environment due to its sensitivity to RIRs with large lengths. Moreover, the solution of source reconstruction is not unique when the system is underdetermined even if the RIRs are given in advance. To tackle these problems, a convolutive narrowband approximation (CNA) model is explored in the proposed approach to constrain the system transformation error. Then an $l p(0\lt p\leq 1)$ regularization is provided to reconstruct the sparse sources in which p can be dynamically selected to achieve the best performance. The experimental results demonstrate the robustness of the proposed method to room reverberation under various speech separation cases in comparison to conventional methods.

关键词： Sensitivity Fourier transforms Transfer functions Reconstruction algorithms Robustness Sensors Reverberation

Maximizing the Smallest Eigenvalue of Grounded Laplacian Matrix

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arXiv 2021年

作者： Zhou, Xiaotian Wang, Run Li, Wei Zhang, Zhongzhi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Academy for Engineering and Technology Fudan University Shanghai200433 China Shanghai Engineering Research Institute of Blockchains Fudan University Shanghai200433 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China

For a connected graph G = (V, E) with n nodes, m edges, and Laplacian matrix L, a grounded Laplacian matrix L(S) of G is a (n − k) × (n − k) principal submatrix of L, obtained from L by deleting k rows and columns corresponding to k selected nodes forming a set S ⊆ V . The smallest eigenvalue λ(S) of L(S) plays a pivotal role in various dynamics defined on G. For example, λ(S) characterizes the convergence rate of leader-follower consensus, as well as the effectiveness of a pinning scheme for the pinning control problem, with larger λ(S) corresponding to smaller convergence time or better effectiveness of a pinning scheme. In this paper, we focus on the problem of optimally selecting a subset S of fixed k n nodes, in order to maximize the smallest eigenvalue λ(S) of the grounded Laplacian matrix L(S). We show that this optimization problem is NP-hard and that the objective function is non-submodular but monotone. Due to the difficulty to obtain the optimal solution, we first propose a näıve heuristic algorithm selecting one optimal node at each time for k iterations. Then we propose a fast heuristic scalable algorithm to approximately solve this problem, using derivative matrix, matrix perturbations, and Laplacian solvers as tools. Our näıve heuristic algorithm takes ˜O(knm) time, while the fast greedy heuristic has a nearly linear time complexity of ˜O(km), where ˜O(・) notation suppresses the poly(log n) factors. We also conduct numerous experiments on different networks sized up to one million nodes, demonstrating the superiority of our algorithm in terms of efficiency and effectiveness compared to baseline methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Heuristic algorithms