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Creating pentamer molecules by generalized stimulated Raman adiabatic passage

Physical Review A 2012年第2期85卷 023629-023629页

作者： Fu-Quan Dou Sheng-Chang Li Hui Cao Li-Bin Fu School of Physics Beijing Institute of Technology Beijing 100081 China Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province College of Physics and Electronic Engineering Northwest Normal University Lanzhou 730070 China National Laboratory of Science and Technology on Computation Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 China Key Laboratory of High Energy Density Physics Simulation Center for Applied Physics and Technology Peking University Beijing 100084 China

We study the formation of stable homonuclear and heteronuclear pentamers from ultracold atoms via a generalized stimulated Raman adiabatic passage scheme. The atom-molecule dark-state solutions for the system are obtained, and the linear instability and the adiabatic fidelity of the dark state are investigated. We also discuss the effects of external field parameters on the conversion efficiency.

关键词： ULTRACOLD molecules RAMAN effect ATOMIC theory ADIABATIC processes PARAMETER estimation ENERGY conversion

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Hybridization of evolutionary algorithm and deep reinforcement learning for multi-objective orienteering optimization

arXiv

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arXiv 2022年

作者： Liu, Wei Wang, Rui Zhang, Tao Li, Kaiwen Li, Wenhua Ishibuchi, Hisao The College of System Engineering National University of Defense Technology Changsha410073 China The Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology HKLMSI2T Changsha410073 China Guangdong Provincial Key Laboratory of Braininspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Multi-objective orienteering problems (MO-OPs) are classical multi-objective routing problems and have received a lot of attention in the past decades. This study seeks to solve MO-OPs through a problem-decomposition framework, that is, a MO-OP is decomposed into a multi-objective knapsack problem (MOKP) and a travelling salesman problem (TSP). The MOKP and TSP are then solved by a multi-objective evolutionary algorithm (MOEA) and a deep reinforcement learning (DRL) method, respectively. While the MOEA module is for selecting cities, the DRL module is for planning a Hamiltonian path for these cities. An iterative use of these two modules drives the population towards the Pareto front of MO-OPs. The effectiveness of the proposed method is compared against NSGA-II and NSGA-III on various types of MO-OP instances. Experimental results show that our method exhibits the best performance on almost all the test instances, and has shown strong generalization ability. Copyright © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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An Improved Third-order CWENO-Z Scheme at critical points

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IOP Conference Series: Materials science and engineering 2019年第3期569卷

作者： Shujiang Tang Mingjun Li Shuang Han School of mathematics and computational science Xiangtan University Xiangtan Hunan 411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan Hunan 411105 China

In this paper, by modifying the smoothness factor of the third-order CWENO scheme, we present the two-parameter CWENO-NZ3 scheme to improve accuracy at critical points. We selected some classical examples for numerical simulation, such as one-dimensional Sod shock tube, Shock-entropy wave interaction, Riemann problem with strong discontinuity and Rayleigh-Taylor instability problem. Numerically comparing with the classical third-order CWENO schemes, it is found that the improved schemes not only improve accuracy and resolution at the extreme points, but also reduce dissipation.

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An SCADTV Nonconvex Regularization Approach for Magnetic Resonance Imaging

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer science 2021年第4期48卷 1013页

作者： Luo, Zhijun Zhu, Zhibin Zhang, Benxin School of Mathematics and Finance Hunan University of Humanities Science and Technology Loudi417000 China School of Mathematics and Computing Science and Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541004 China School of Electronic Engineering and Automation and Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin541004 China

In this paper, we propose a non-convex regularization magnetic resonance imaging (MRI) reconstruction model via the smoothly clipped absolute deviation (SCAD) penalty function, which can effectively improve the fitting performance and prevent systematic underestimation compared with the classical total variation (TV) regularization. Then, we choose the alternating direction method of multipliers (ADMM) algorithm to solve the non-convex regularization model. The experiment results show that the efficiency of the proposed model and algorithm in comparison with some other typical methods. © 2021. IAENG International Journal of Computer science. All Rights Reserved.

关键词： Magnetic resonance imaging

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Multifractal analyses of daily rainfall time series in Pearl River basin of China

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Physica A: Statistical Mechanics and its Applications 2014年 405卷 193-202页

作者： Zu-Guo Yu Yee Leung Yongqin David Chen Qiang Zhang Vo Anh Yu Zhou Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan 411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia Department of Geography and Resource Management The Chinese University of Hong Kong Hong Kong China Institute of Environment Energy and Sustainability The Chinese University of Hong Kong Hong Kong China Department of Water Resources and Environment Sun Yat-sen University Guangzhou 510275 China Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute Sun Yat-sen University Guangzhou 510275 China

The multifractal properties of daily rainfall time series at the stations in Pearl River basin of China over periods of up to 45 years are examined using the universal multifractal approach based on the multiplicative cascade model and the multifractal detrended fluctuation analysis (MF-DFA). The results from these two kinds of multifractal analyses show that the daily rainfall time series in this basin have multifractal behavior in two different time scale ranges. It is found that the empirical multifractal moment function K ( q ) of the daily rainfall time series can be fitted very well by the universal multifractal model (UMM). The estimated values of the conservation parameter H from UMM for these daily rainfall data are close to zero indicating that they correspond to conserved fields. After removing the seasonal trend in the rainfall data, the estimated values of the exponent h ( 2 ) from MF-DFA indicate that the daily rainfall time series in Pearl River basin exhibit no long-term correlations. It is also found that K ( 2 ) and elevation series are negatively correlated. It shows a relationship between topography and rainfall variability.

关键词： Daily rainfall time series Multifractal property Universal multifractal model Multifractal detrended fluctuation analysis

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An Interpretable MRI Reconstruction Network with Two-grid-cycle Correction and Geometric Prior Distillation

arXiv

引用

arXiv 2022年

作者： Fan, Xiaohong Yang, Yin Chen, Ke Zhang, Jianping Dong, Ke The School of Mathematics and Computational Science Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan411105 China The School of Mathematics and Computational Science Xiangtan University Hunan National Applied Mathematics Center Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan411105 China Centre for Mathematical Imaging Techniques Department of Mathematical Sciences The University of Liverpool Merseyside LiverpoolL6972L United Kingdom The School of Mathematics and Computational Science Xiangtan University Hunan National Applied Mathematics Center Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan411105 China Department of Radiation Oncology Xiangtan Central Hospital Xiangtan411101 China

Although existing deep learning compressed-sensing-based Magnetic Resonance Imaging (CS-MRI) methods have achieved considerably impressive performance, explainability and generalizability continue to be challenging for such methods since the transition from mathematical analysis to network design not always natural enough, often most of them are not flexible enough to handle multi-sampling-ratio reconstruction assignments. In this work, to tackle explainability and generalizability, we propose a unifying deep unfolding multi-sampling-ratio interpretable CS-MRI framework. The combined approach offers more generalizability than previous works whereas deep learning gains explainability through a geometric prior module. Inspired by the multigrid algorithm, we first embed the CSMRI-based optimization algorithm into correction-distillation scheme that consists of three ingredients: pre-relaxation module, correction module and geometric prior distillation module. Furthermore, we employ a condition module to learn adaptively step-length and noise level, which enables the proposed framework to jointly train multi-ratio tasks through a single model. The proposed model not only compensates for the lost contextual information of reconstructed image which is refined from low frequency error in geometric characteristic k-space, but also integrates the theoretical guarantee of model-based methods and the superior reconstruction performances of deep learning-based methods. Therefore, it can give us a novel perspective to design biomedical imaging networks. Numerical experiments show that our framework outperforms state-of-the-art methods in terms of qualitative and quantitative evaluations. Our method achieves 3.18 dB improvement at low CS ratio 10% and average 1.42 dB improvement over other comparison methods on brain dataset using Cartesian sampling mask. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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Formation of N-body polymer molecules through generalized stimulated Raman adiabatic passage

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Physical Review A 2013年第4期87卷 043631-043631页

作者： Fu-quan Dou Li-bin Fu Jie Liu College of Physics and Electronic Engineering Northwest Normal University Lanzhou 730070 China School of Physics Beijing Institute of Technology Beijing 100081 China National Laboratory of Science and Technology on Computation Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 China Key Laboratory of High Energy Density Physics Simulation Center for Applied Physics and Technology Peking University Beijing 100084 China

We investigate the creation of stable homonuclear and heteronuclear N-body polymer molecules from ultracold atoms via a generalized stimulated Raman adiabatic passage scheme. The atom-molecule dark-state solutions for the system are obtained and are found to satisfy universal algebraic equations. We discuss the linear instability and the adiabatic fidelity of the dark state. We also explore the effects of the interparticle interactions, the atomic number of the polymer, and external field parameters on the conversion process.

关键词： DARK states (Quantum optics) ADIABATIC processes MANY-body problem ATOMS ALGEBRAIC equations ATOMIC number

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A Symmetric ADMM for Non-convex Regularization Magnetic Resonance Imaging

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer science 2021年第4期48卷 1005页

作者： Luo, Zhijun Zhu, Zhibin Zhang, Benxin School of Mathematics and Finance Hunan University of Humanities Science and Technology Loudi417000 China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541004 China School of Electronic Engineering Automation and Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin541004 China

The total variation (TV) regularization technique is a popular method for magnetic resonance imaging (MRI) reconstruction. In this paper, the generalized minimax concave (GMC) penalty function is used to construct a nonconvex regularized MRI model, which can effectively prevent the systematic underestimation characteristic of the standard TV regularization. In addition, the cost function can maintain convexity under certain conditions. To solve the new non-convex model, we describe a symmetric alternating direction method of multipliers (S-ADMM) algorithm, which is faster than the original ADMM. The experiment results show the effectiveness of the proposed model and algorithm. © 2021. IAENG International Journal of Computer science. All Rights Reserved.

关键词： Magnetic resonance imaging

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Nucleation and phase transition of decagonal quasicrystals

arXiv

引用

arXiv 2024年

作者： Zhou, Tiejun Zhang, Lei Zhang, Pingwen Shi, An-Chang Jiang, Kai Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China Beijing International Center for Mathematical Research Peking University Beijing100871 China School of Mathematics and Statistics Wuhan University Wuhan430072 China School of Mathematical Sciences Peking University Beijing100871 China Department of Physics and Astronomy McMaster University HamiltonL8S 4M1 Canada

In this work, we study the nucleation of quasicrystals from liquid or periodic crystals by developing an efficient order-order phase transition algorithm, namely the nullspace-preserving saddle search method. Specifically, we focus on nucleation and phase transitions of the decagonal quasicrystal (DQC) based on the Lifshitz-Petrich model. We present the nucleation path of DQC from the liquid and demonstrate one- and two-stage transition paths between DQC and periodic crystals. We provide a perspective of the group-subgroup phase transition and nucleation rates to understand the nucleation and phase transition mechanisms involving DQC. These results reveal the one-step and stepwise modes of symmetry breaking or recovery in the phase transition from DQC, where the stepwise modes are more probable. Copyright © 2024, The Authors. All rights reserved.

关键词： Quasicrystals

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Broadband unidirectional reflectionless aperiodic multilayer structure

Broadband unidirectional reflectionless aperiodic multilayer...

引用

Frontiers in Optics, FiO 2017

作者： You, Chenglong Matyas, Corey T. Huang, Yin Dowling, Jonathan P. Veronis, Georgios Hearne Institute for Theoretical Physics Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States Department of Optoelectrics Information Science and Engineering School of Physics and Electronics Central South University Changsha Hunan410012 China Key Laboratory of Optoelectronic Devices and Systems Ministry of Education Shenzhen University Shenzhen Guangdong Province518060 China Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States

We design non-parity-time-symmetric multilayer structures, one of which consists of alternating silicon and silica layers, while the other one includes a gain material. Our structures can achieve broadband unidirectio... 详细信息

ISBN: (纸本)9781943580330

关键词： Silica

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