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arXiv 2020年

作者： Liu, Liang Liu, Ya-Feng Patil, Pratik Yu, Wei the Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong the State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Edward S. Rogers Sr. Department of Electrical and Computer Engineering the University of Toronto the Department of Statistics and Data Science and the Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S3G4 Canada

—Uplink-downlink duality refers to the fact that under a sum-power constraint, the capacity regions of a Gaussian multiple-access channel and a Gaussian broadcast channel with Hermitian transposed channel matrices are identical. This paper generalizes this result to a cooperative cellular network, in which remote access-points are deployed as relays in serving the users under the coordination of a central processor (CP). In this model, the users and the relays are connected over noisy wireless links, while the relays and the CP are connected over noiseless but rate-limited fronthaul links. Based on a Lagrangian technique, this paper establishes a duality relationship between such a multiple-access relay channel and broadcast relay channel, under the assumption that the relays use compression-based strategies. Specifically, we show that under the same total transmit power constraint and individual fronthaul rate constraints, the achievable rate regions of the Gaussian multiple-access and broadcast relay channels are identical, when either independent compression or Wyner-Ziv and multivariate compression strategies are used. The key observations are that if the beamforming vectors at the relays are fixed, the sum-power minimization problems under the achievable rate and fronthaul constraints in both the uplink and the downlink can be transformed into either a linear programming or a semidefinite programming problem depending on the compression technique, and that the uplink and downlink problems are Lagrangian duals of each other. Moreover, the dual variables corresponding to the downlink rate constraints become the uplink powers;the dual variables corresponding to the downlink fronthaul constraints become the uplink quantization noises. This duality relationship enables an efficient algorithm for optimizing the downlink transmission and relaying strategies based on the uplink. Copyright © 2020, The Authors. All rights reserved.

关键词： Linear programming

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Transverse-Momentum-Dependent Wave Functions of Pion from Lattice QCD

arXiv

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arXiv 2023年

作者： Chu, Min-Huan He, Jin-Chen Hua, Jun Liang, Jian Ji, Xiangdong Schäfer, Andreas Shu, Hai-Tao Su, Yushan Wang, Ji-Hao Wang, Wei Yang, Yi-Bo Zeng, Jun Zhang, Jian-Hui Zhang, Qi-An School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Yang Yuanqing Scientific Computering Center Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai200240 China Department of Physics University of Maryland College ParkMD20742 United States Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou510006 China Institut für Theoretische Physik Universität Regensburg RegensburgD-93040 Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou310024 China Institute of Modern Physics Chinese Academy of Sciences Guangdong Province Huizhou516000 China International Centre for Theoretical Physics Asia-Pacific Hangzhou Beijing China School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China Center of Advanced Quantum Studies Department of Physics Beijing Normal University Beijing100875 China School of Physics Beihang University Beijing102206 China

We present a first lattice QCD calculation of the transverse-momentum-dependent wave functions (TMDWFs) of the pion using large-momentum effective theory. Numerical simulations are based on one ensemble with 2+1+1 flavors of highly improved staggered quarks action with lattice spacing a = 0.121 fm from the MILC Collaboration, and one with 2 +1 flavor clover fermions and tree-level Symanzik gauge action generated by the CLS Collaboration with a = 0.098 fm. As a key ingredient, the soft function is first obtained by incorporating the one-loop perturbative contributions and a proper normalization. Based on this and the equal-time quasi-TMDWFs simulated on the lattice, we extract the light-cone TMDWFs. The results are comparable between the two lattice ensembles and a comparison with phenomenological parametrization is made. Our studies provide a first attempt of ab initio calculation of TMDWFs which will eventually lead to crucial theory inputs for making predictions for exclusive processes under QCD factorization. Copyright © 2023, The Authors. All rights reserved.

关键词： Wave functions

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Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules

Drug3D-DTI: Improved Drug-target Interaction Prediction by I...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhirui Liao Xiaodi Huang Hiroshi Mamitsuka Shanfeng Zhu School of Computer Science Fudan University Shanghai China Institute of Artificial Intelligence Biomedicine Nanjing University Nanjing China School of Computing Mathematics and Engineering Charles Sturt University Albury NSW Australia Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto Japan Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education China MOE Frontiers Center for Brain Science Fudan University Shanghai China Zhangjiang Fudan International Innovation Center Shanghai China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9781665429825

A number of machine learning (ML) approaches for drug discovery have been available that rely only on sequential (1D) and planar (2D) information without effectively using the 3D information for generating features of drugs. However, 3D information of small molecules can reflect relative position of atoms more directly, which affects molecular properties. In this work, we present a new deep learning model called Drug3D-DTI for drug-target interaction prediction. Drug3D-DTI takes advantage of molecular spatial information, i.e., atom proximity in three-dimensional (3D) structures. We comprehensively evaluated the performance of Drug3D-DTI on two datasets with two tasks of regression and classification. In particular, we compared Drug3D-DTI with several existing methods including the two cutting-edge methods for compound-protein interaction prediction. From the experimental results, Drug3D-DTI clearly outperformed other methods under all settings. Further, this performance improvement was validated by ablation experiments and a case study. The implementation of Drug3D-DTI is available at (https://***/zhiruiliao/Drug3D-DTI).

关键词： Drugs Proteins Deep learning Solid modeling Three-dimensional displays Conferences Predictive models

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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ALOR: Adaptive layout optimization of raft groups for heterogeneous distributed key-value stores 1

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15th IFIP International Conference on Network and Parallel computing, NPC 2018

作者： Wang, Yangyang Chai, Yunpeng Wang, Xin Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China School of Information Renmin University of China Beijing China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9783030056773

ISBN: (纸本)9783030056766

Many distributed key-value storage systems employ the simple and effective Raft protocol to ensure data consistency. They usually assume a homogeneous node hardware configuration for the underlying cluster and thus adopt even data distribution schemes. However, today’s distributed systems tend to be heterogeneous in nodes’ I/O devices due to the regular worn I/O device replacement and the emergence of expensive new storage media (e.g., non-volatile memory). In this paper, we propose a new data layout scheme called Adaptive Layout Optimization of Raft groups (ALOR), considering the hardware heterogeneity of the cluster. ALOR aims to optimize the data layout of Raft groups to achieve a better practical load balance, which leads to higher performance. ALOR consists of two components: leader migration in Raft groups and skewed data layout based on cold data migration. We conducted experiments on a practical heterogeneous cluster, and the results indicate that, on average, ALOR improves throughput by 36.89%, reduces latency and 99th percentile tail latency by 24.54% and 21.32%, respectively. © IFIP International Federation for Information Processing 2018.

关键词： Digital storage

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Investigation of natural contamination layer growth on optical substrates

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Chinese Physics C 2018年第11期42卷 107-119页

作者： Li Jiang Qiu-Shi Huang Igor V.Kozhevnikov Yi-Yun Yao Jiang-Tao Feng Yu-Fei Feng Bin Ma Hong-Fei Jiao Hong Chen Zhong Zhang Zhan-Shan Wang Key Laboratory of Advanced Micro-Structured Materials MOE Institute of Precision Optical Engineering School of Physics Science and Engineering Tongji University Shanghai 200092 China Shubnikov Institute of Crystallography of Federal Scientific Research Centre "Crystallography and Photonics" of the Russian Academy of Sciences Leninskiy pr. 59 Moscow 119333 Russia

The surface contamination layer on mirrors can cause significant degradation of the optical performance, which is widely observed in applications, particularly in the fabrication of X-ray focusing telescopes. In this paper, we study the natural contamination layer arising from adsorption precipitation of hydrocarbons or other organic and water molecules in the absence of any external factor. Temporal evolution of the layer formed on super-smooth fused silica, borosilicate glass, and silicon substrates is studied by X-ray reflectometry, atomic force microscopy, and transmission electron microscopy for a one-year period after surface cleaning. The general characteristics of adhesion layer growth are established and discussed. The reconstructed dielectric constant profiles demonstrate that an increase in the adhesion layer thickness, deposited mass and density over time obeys power laws with extremely small exponents. Therefore, the adhesion layer growth is rapid immediately after surface cleaning, with a - 1 nm thick layer formed within the first day on all three substrates studied, while the layer density is low （- 1 g/cm^3）. The layer growth on the fused silica and silicon substrates became very slow in the succeeding days, with only a 1.4-1.5 nm thick layer and 1.2-1.3 g/cm^3 density after one year of storage in air. At the same time, the adhesion layer growth on the glass substrate showed unexpected acceleration about two months after cleaning, so that the layer thickness reached - 2.2 nm after one year of storage. The reason for this effect, which is connected with leaching of the glass, is discussed briefly.

关键词： X-ray telescope surface contamination surface dynamics X-ray reflectometry inverse problem

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DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

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Genomics, Proteomics & Bioinformatics 2020年第2期18卷 104-119页

作者： Tiansheng Zhu Yi Zhu Yue Xuan Huanhuan Gao Xue Cai Sander R.Piersma Thang V.Pham Tim Schelfhorst Richard R.G.D.Haas Irene V.Bijnsdorp Rui Sun Liang Yue Guan Ruan Qiushi Zhang Mo Hu Yue Zhou Winan J.Van Houdt Tessa Y.S.Le Large Jacqueline Cloos Anna Wojtuszkiewicz Danijela Koppers-Lalic Franziska Bottger Chantal Scheepbouwer Ruud H.Brakenhoff Geert J.L.H.van Leenders Jan N.M.Ijzermans John W.M.Martens Renske D.M.Steenbergen Nicole C.Grieken Sathiyamoorthy Selvarajan Sangeeta Mantoo Sze S.Lee Serene J.Y.Yeow Syed M.F.Alkaff Nan Xiang Yaoting Sun Xiao Yi Shaozheng Dai Wei Liu Tian Lu Zhicheng Wu Xiao Liang Man Wang Yingkuan Shao Xi Zheng Kailun Xu Qin Yang Yifan Meng Cong Lu Jiang Zhu Jin'e Zheng Bo Wang Sai Lou Yibei Dai Chao Xu Chenhuan Yu Huazhong Ying Tony K.Lim Jianmin Wu Xiaofei Gao Zhongzhi Luan Xiaodong Teng Peng Wu Shi'ang Huang Zhihua Tao Narayanan G.Iyer Shuigeng Zhou Wenguang Shao Henry Lam Ding Ma Jiafu Ji Oi L.Kon Shu Zheng Ruedi Aebersold Connie R.Jimenez Tiannan Guo Zhejiang Provincial Laboratory of Life Sciences and Biomedicine Westlake UniversityHangzhou 310024China Key Laboratory of Structural Biology of Zhejiang Province School of Life SciencesWestlake UniversityHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced StudyHangzhou 310024China School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China Thermo Fisher Scientific(BREMEN)GmbH Bremen 28195Germany OncoProteomics Laboratory Department of Medical OncologyVU University Medical CenterVU UniversityAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamUrologyCancer Center AmsterdamAmsterdam 1011The Netherlands Thermo Fisher Scientific Shanghai 201206China The Netherlands Cancer Institute Surgical OncologyAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamSurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPediatric Oncology/HematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamHematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamMedical OncologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamNeurosurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPathologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamOtolaryngology/Head and Neck SurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Erasmus MC University Medical Center PathologyRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center SurgeryRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center Medical OncologyRotterdam 1016LVThe Netherlands Department of Anatomical Pathology Singapore General HospitalSingapore 169608Singapore Division of Medical Scie

To address the increasing need for detecting and validating protein biomarkers in clinical specimens,mass spectrometry(MS)-based targeted proteomic techniques,including the selected reaction monitoring(SRM),parallel reaction monitoring(PRM),and massively parallel dataindependent acquisition(DIA),have been *** optimal performance,they require the fragment ion spectra of targeted peptides as prior *** this report,we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue *** build the spectral resource,we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on *** then applied the workflow to generate DPHL,a comprehensive DIA pan-human library,from 1096 data-dependent acquisition(DDA)MS raw files for 16 types of cancer *** extensive spectral resource was then applied to a proteomic study of 17 prostate cancer(PCa)***,PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was *** a second application,the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma(DLBCL)patients and 18 healthy control *** expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by *** data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker *** is freely accessible at https://***/page/***?id=IPX0001400000.

关键词： Data-independent acquisition Parallel reaction monitoring Spectral library Prostate cancer Diffuse large B cell lymphoma

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NIR Clusteroluminescence of Non-conjugated Phenolic Resins Enabled by Through-Space Interactions

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Angewandte Chemie 2023年第30期135卷

作者： Ziteng Zhang Dr. Jianyu Zhang Zuping Xiong Bo Chu Prof. Chengjian Zhang Prof. Jing Zhi Sun Prof. Haoke Zhang Prof. Xing-Hong Zhang Prof. Ben Zhong Tang Department of Polymer Science and Engineering Zhejiang University 310058 Hangzhou China State Key Laboratory of Motor Vehicle Biofuel Technology International Research Center for X Polymers Zhejiang University 310058 Hangzhou China Zhejiang-Israel Joint Laboratory of Self-Assembling Functional Materials ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University 311215 Hangzhou China Department of Chemistry Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction and Institute for Advanced Study The Hong Kong University of Science and Technology 999077 Hong Kong China MOE Key Laboratory of Macromolecular Synthesis and Functionalization Zhejiang University 310058 Hangzhou China Centre of Healthcare Materials Shaoxing Institute Zhejiang University 312000 Shaoxing China School of Science and Engineering Shenzhen Institute of Aggregate Science and Technology The Chinese University of Hong Kong 518172 Shenzhen Guangdong China

Clusteroluminescence (CL) and through-space interactions (TSIs) of non-conjugated molecules have drawn more attention due to their unique photophysical behaviors that are different from largely conjugated luminogens. However, achieving red and even near-infrared (NIR) emission from such systems is still challenging due to the intrinsic drawbacks of non-conjugated molecules and the lack of theories for structure–property relationships. In this work, six phenolic resins are designed and synthesized based on two molecule-engineering strategies: increasing the number of TSIs units and introducing electron-donating/-withdrawing groups. All phenolic resins are verified as luminogens with CL property (CLgens), and the first example of CLgens with NIR emission (maximum emission wavelength ≥680 nm) and high absolute quantum yield (47 %) is reported. Experiments and theoretical analysis reveal that two TSIs types, through-space locally excited state and through-space charge transfer state, play essential roles in achieving CL from these non-conjugated polymers, which could be manipulated via changing structural conformation and electron density or altering electron transition behaviors. This work not only provides an approach to manipulate TSIs and CL of non-conjugated polymers but also endows commercially available phenolic resins with high practical value as luminescence materials.

关键词： Clusteroluminescence Near-Infrared Luminescence Phenolic Resin Through-Space Interactions

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Joint switch upgrade and controller deployment in hybrid software-defined networks

arXiv

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arXiv 2019年

作者： Guo, Zehua Chen, Weikun Liu, Ya-Feng Xu, Yang Zhang, Zhi-Li Department of Computer Science and Engineering University of Minnesota Twin Cities MinneapolisMN55455 United States State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School Of Computer Science Fudan University Shanghai200433 China

To improve traffic management ability, Internet Service Providers (ISPs) are gradually upgrading legacy network devices to programmable devices that support Software-Defined Networking (SDN). The coexistence of legacy and SDN devices gives rise to a hybrid SDN. Existing hybrid SDNs do not consider the potential performance issues introduced by a centralized SDN controller: flow requests processed by a highly loaded controller may experience long tail processing delay;inappropriate multi-controller deployment could increase the propagation delay of flow requests. In this paper, we propose to jointly consider the deployment of SDN switches and their controllers for hybrid SDNs. We formulate the joint problem as an optimization problem that maximizes the number of flows that can be controlled and managed by the SDN and minimizes the propagation delay of flow requests between SDN controllers and switches under a given upgrade budget constraint. We show this problem is NP-hard. To efficiently solve the problem, we propose some techniques (e.g., strengthening the constraints and adding additional valid inequalities) to accelerate the global optimization solver for solving the problem for small networks and an efficient heuristic algorithm for solving it for large networks. The simulation results from real network topologies illustrate the effectiveness of the proposed techniques and show that our proposed heuristic algorithm uses a small number of controllers to manage a high amount of flows with good performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Software defined networking

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Efficient Numerical Computation of Time-Fractional Nonlinear Schrodinger Equations in Unbounded Domain

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Communications in Computational Physics 2019年第1期25卷 218-243页

作者： Jiwei Zhang Dongfang Li Xavier Antoine Beijing Computational Science Research Center Beijing 100193P.R.China School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China Institut Elie Cartan de Lorraine UMR CNRS 7502Universit́e de LorraineInria Nancy-Grand EstSPHINX TeamF-54506 Vandoeuvre-l`es-Nancy CedexFrance

The aim of this paper is to derive a stable and efficient scheme for solving the one-dimensional time-fractional nonlinear Schrodinger equation set in an unbounded *** first derive absorbing boundary conditions for the fractional system by using the unified approach introduced in[47,48]and a linearization ***,the initial boundary-value problem for the fractional system with ABCs is discretized,a stability analysis is developed and the error estimate O(h^(2)+τ)is *** accel-erate the L1-scheme in time,a sum-of-exponentials approximation is introduced to speed-up the evaluation of the Caputo fractional *** resulting algorithm is highly efficient for long time ***,we end the paper by reporting some numerical simulations to validate the properties(accuracy and efficiency)of the derived scheme.

关键词： Time-fractional nonlinear Schrodinger equation absorbing boundary condition sta-bility analysis convergence analysis sum-of-exponentials approximation

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