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arXiv 2019年

作者： Zhao, Wen-Lei Wang, Jiaozi Wang, Xiaohui Tong, Peiqing School of Science Jiangxi University of Science and Technology Ganzhou341000 China Department of Modern Physics University of Science and Technology of China Hefei230026 China School of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China Department of Physics Institute of Theoretical Physics Nanjing Normal University Nanjing210023 China Jiangsu Provincial Key Laboratory for Numerical Simulation of Large Scale Complex Systems Nanjing Normal University Nanjing Jiangsu210023 China

We investigate the directed momentum current in the quantum kicked rotor model with PT symmetric deriving potential. For the quantum non-resonance case, the values of quasi-energy become to be complex when the strength of imaginary part of the kicking potential exceeds a threshold value, which demonstrates the appearance of the spontaneous PT symmetry breaking. In the vicinity of the transition point, the momentum current exhibits a staircase growth with time. Each platform of the momentum current corresponds to the mean momentum of some eigenstates of the Floquet operator whose imaginary parts of the quasi-energy are significantly large. Above the transition point, the momentum current increases linearly with time. Interestingly, its acceleration rate exhibits a kind of "quantized" increment with the kicking strength. We propose a modified classical acceleration mode of the kicked rotor model to explain such an intriguing phenomenon. Our theoretical prediction is in good agreement with numerical results. Copyright © 2019, The Authors. All rights reserved.

关键词： Momentum

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S-wave resonance contributions to B(s)^0→ηc(2S)π^+π^-in the perturbative QCD factorization approach

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Chinese Physics C 2017年第8期41卷 47-52页

作者： Ai-Jun Ma Ya Li Wen-Fei Wang Zhen-Jun Xiao Department of Physics and Institute of Theoretical Physics Nanjing Normal UniversityNanjing 210023China Institute of Theoretical Physics Shanxi UniversityTaiyuanShanxi 030006China Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems Nanjing Normal UniversityNanjing 210023China

By employing the perturbative QCD（PQCD） factorization approach, we study the quasi-two-body B（s）^0→ηc（2S）π^＋π^- decays, where the pion pair comes from the S-wave resonance f0（X）. The Breit-Wigner formula for the f0（500） and f0（1500） resonances and the Flatt′e model for the f0（980） resonance are adopted to parameterize the time-like scalar form factors in the two-pion distribution amplitudes. As a comparison, Bugg＇s model is also used for the wide f0（500） in this work. For decay rates, we found the following PQCD predictions：（a）-B（Bs^0 →ηc（2S）f0（X）[π^＋π^-]s）= 2.67-1.08^＋1.78 ×10^-5 when the contributions from f0（980） and f0（1500） are all taken into account;（b）B（B0→η-c（2S）f0（500）[π^＋π^-]s） = 1.40（-0.56）（＋0.92） ×10^-6 in the Breit-Wigner model and 1.53（-0.61）^（＋0.97） ×10^-6 in Bugg＇s model.

关键词： PQCD factorization approach two-pion distribution amplitudes quasi-two-body decay

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Fine-grained image recognition via weakly supervised click data guided bilinear CNN model

Fine-grained image recognition via weakly supervised click d...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Guangjian Zheng Min Tan Jun Yu Qing Wu Jianping Fan Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Department of Computer Science University of North Carolina at Charlotte

ISBN: (纸本)9781509060689

Bilinear convolutional neural networks (BCNN) model, the state-of-the-art in fine-grained image recognition, fails in distinguishing the categories with subtle visual differences. We design a novel BCNN model guided by user click data (C-BCNN) to improve the performance via capturing both the visual and semantical content in images. Specially, to deal with the heavy noise in large-scale click data, we propose a weakly supervised learning approach to learn the C-BCNN, namely W-C-BCNN. It can automatically weight the training images based on their reliability. Extensive experiments are conducted on the public Clickture-Dog dataset. It shows that: (1) integrating CNN with click feature largely improves the performance; (2) both the click data and visual consistency can help to model image reliability. Moreover, the method can be easily customized to medical image recognition. Our model performs much better than conventional BCNN models on both the Clickture-Dog and medical image dataset.

关键词： Reliability Visualization Image recognition Data models Training Feature extraction Computational modeling

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Possible existence of a dibaryon candidate N▵ (D21)

arXiv

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arXiv 2018年

作者： Huang, Hongxia Zhu, Xinmei Ping, Jialun Wang, Fan Dept. of Physics and Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems Nanjing Normal University Nanjing210023 China Department of Physics Yangzhou University Yangzhou225009 China Department of Physics Nanjing University Nanjing210093 China

Inspired by the experimental report by WASA-at-COSY Collaboration, we investigate the possibile existence of the dibaryon candidate N▵ with quantum numbers IJP= 21+(D21). The dynamical calculation shows that we cannot obtain the bound D21 state in the models which can obtain the experimental d. The low-energy scattering phase shifts of the N-▵ scattering give the same conclusion. Besides, the mass calculation by using the Gursey-Radicati mass formula and the analysis of the matrix elements of the color magnetic interaction show that the mass of D21is larger than that of D12(N▵ with IJP= 12+), which indicate that it is less possible for the D21than the D12to form bound state. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum theory

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Deep Mixture of Experts with Diverse Task Spaces

Deep Mixture of Experts with Diverse Task Spaces

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International Conference on Machine Learning and Applications (ICMLA)

作者： Jianping Fan Tianyi Zhao Zhenzhong Kuang Zhou Yu Jun Yu Department of Computer Science UNC-Charlotte Charlotte NC USA Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou China

In this paper, a deep mixture algorithm is developed to support large-scale visual recognition (e.g., recognizing tens of thousands of object classes) by seamlessly combining a set of base deep CNNs (AlexNet) with diverse task spaces, e.g., such base deep CNNs (i.e., diverse experts) are trained to recognize different subsets of tens of thousands of object classes rather than the same set of object classes. Our experimental results have demonstrated that our deep mixture algorithm can achieve very competitive results on large-scale visual recognition.

关键词： Ontologies Proposals Prediction algorithms Visualization Semantics Atomic layer deposition

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BPaaS: A platform for artifact-centric business process customization in cloud computing 29

BPaaS: A platform for artifact-centric business process cust...

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29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017

作者： Gao, Min Yin, Yu Wang, Xingfei Li, Ying Chen, Zaidie Guo, Lipeng Key Laboratory of Complex Systems Modeling and Simulation of Ministry of Education College of Electrical Engineering Hangzhou Dianzi University Zhejiang China College of Computer Science Zhejiang University Zhejiang Hangzhou China College of Computer Science Fudan University Shanghai China Hangzhou Housing Management Bureaus Hangzhou Zhejiang China

ISBN: (纸本)1891706411

As a new service paradigm of Software-As-A-Service (SaaS),Business Process-As-A-Service (BPaaS). BPaaS is used to build a cost-effective Business Process Management (BPM) system. Based on universal Artifacts, we develop a framework named SeGA (Self-Guided Artifact). In SeGA, a BPM system is capable of executing business processes from multiple clients, and responding query at runtime. what's more, by the template-based cascading data mapping method, entity to be synchronized with database automatically, so each BP instance can modify its process entity without worrying about the database access. In this paper, we conduct a deep research and implement a prototype system for Artifact process design.

关键词： Process design

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An LQP-Based Two-Step Method for Structured Variational Inequalities

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Journal of the Operations Research Society of China 2017年第3期5卷 301-317页

作者： Hong-Jin He Kai Wang Xing-Ju Cai De-Ren Han Department of Mathematics School of ScienceHangzhou Dianzi UniversityHangzhou 310018China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore School of Mathematical Sciences Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex SystemsNanjing Normal UniversityNanjing 210023China

t The logarithmic quadratic proximal(LQP)regularization is a popular and powerful proximal regularization technique for solving monotone variational inequalities with nonnegative *** this paper,we propose an implementable two-step method for solving structured variational inequality problems by combining LQP regularization and projection *** proposed algorithm consists of two *** first step generates a pair of predictors via inexactly solving a system of nonlinear ***,the second step updates the iterate via a simple correction *** establish the global convergence of the new method under mild *** improve the numerical performance of our new method,we further present a self-adaptive version and implement it to solve a traffic equilibrium *** numerical results further demonstrate the efficiency of the proposed method.

关键词： Logarithmic quadratic proximal Projection method Variational inequality problem Traffic equilibrium problem

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Convolutional neural networks for intestinal hemorrhage detection in wireless capsule endoscopy images

Convolutional neural networks for intestinal hemorrhage dete...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Panpeng Li Ziyun Li Fei Gao Li Wan Jun Yu Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University School of Communication Engineering Hangzhou Dianzi University College of Computer Science Chongqing University

ISBN: (纸本)9781509060689

Wireless capsule endoscopy (WCE) can painlessly capture a large number of images inside the intestine. However, only a small portion of these WCE images contain hemorrhage. It is thus critical to develop automated hemorrhage detection method to facilitate the diagnosis of intestinal diseases. However, automated hemorrhage detection is complicated by 1) the extreme imbalance between the amount of hemorrhage images and that of normal images; and 2) the variety of the appearance, texture, and luminance inside the intestine. In this paper, we proposed to learn a robust intestinal hemorrhage detection model via Convolutional Neural Networks (CNNs), because of CNNs' extraordinary performance in solving various image understanding tasks. Specially, we explored different CNN architectures and data augmentation methods. Besides, we investigated the correlation between hemorrhage detection accuracy and image quality. Across about 1.3k hemorrhage images and 40k normal images, the learned CNN model achieves an F-measure of 98.87%.

关键词： Hemorrhaging Optical wavelength conversion Endoscopes Machine learning Biomedical imaging Training Wireless communication

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Author Correction: Malaria elimination on Hainan Island despite climate change

Communications Medicine

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Communications Medicine 2022年第1期2卷 1页

作者： Huaiyu Tian Naizhe Li Yonghong Liu Yidan Li Ben Wang Peiyi Wu Yapin Li Dongqi Gao Wenjing Song Moritz U. G. Kraemer José Lourenço Oliver G. Pybus Christopher Dye Hua Tan Bernard Cazelles Dingwei Sun Yuchun Li Guangze Wang State Key Laboratory of Remote Sensing Science Center for Global Change and Public Health College of Global Change and Earth System Science Beijing Normal University Beijing China Central Theater Center for Disease Control and Prevention of PLA Beijing China Department of Zoology University of Oxford Oxford UK Department of Pathobiology and Population Science The Royal Veterinary College London UK Oxford Martin School University of Oxford Oxford UK School of Biomedical Informatics the University of Texas Health Science Center at Houston Houston TX USA Institut de Biologie de l’École Normale Supérieure Unité Mixte de Recherche Centre National de la Recherche Scientifique et École Normale Supérieure Paris France Unité Mixte Internationnale Mathematical and Computational Modeling of Complex Systems Institut de Recherche pour le Développement et Sorbonne Université Bondy France Hainan Center for Disease Control and Prevention Haikou China

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The next-to-leading order corrections to B →-transition in the kTfactorization

arXiv

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arXiv 2018年

作者： Hua, Jun Zhang, Ya-Lan Xiao, Zhen-Jun Department of Physics Institute of Theoretical Physics Nanjing Normal University Nanjing Jiangsu210023 China Department of Faculty of Mathematics and Physics Huaiyin Institute of Technology Huaian Jiangsu223001 China Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems Nanjing Normal University Nanjing210023 China

In this paper, we investigate the factorization hypothesis step by step for the exclusive process B → ρ at next-to-leading order (NLO) with the collinear factorization approach, and then we extend our results to the kTfactorization frame. We show that the soft divergence from the specific NLO diagrams will cancel each other at the quark level, while the remaining collinear divergence can be absorbed into the NLO wave functions. The full NLO amplitudes can be factorized into two parts: The NLO B and ρ meson wave functions containing the collinear divergence and the leading order (LO) finite hard kernels. We give the general form of the nonlocal hadron matrix for the NLO B and ρ meson wave functions and all results of factorization for different twists' combinations. Copyright © 2018, The Authors. All rights reserved.

关键词： Factorization

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