检索结果-内蒙古大学图书馆

Complex generalized Gauss-Radau quadrature rules for Hankel transforms of integer order

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Haiyong Wu, Menghan School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China

Complex Gaussian quadrature rules for oscillatory integral transforms have the advantage that they can achieve optimal asymptotic order. However, their existence for Hankel transform can only be guaranteed when the order of the transform belongs to [0, 1/2]. In this paper we consider the construction of generalized Gauss-Radau quadrature rules for Hankel transform. We show that, if adding certain value and derivative information at the left endpoint, then complex generalized Gauss-Radau quadrature rules for Hankel transform of integer order can be constructed with theoretical guarantees. Orthogonal polynomials that are closely related to such quadrature rules are investigated and their existence for even degrees is proved. Numerical experiments are presented to confirm our *** Codes 65R10, 65D32 © 2024, CC BY-NC-ND.

关键词： Integral equations

Dendritic integration inspired artificial neural networks capture data correlation 24

学校读者我要写书评

暂无评论

Dendritic integration inspired artificial neural networks ca...

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Chongming Liu Jingyang Ma Songting Li Douglas Zhou School of Mathematical Sciences and Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematical Sciences and Institute of Natural Sciences and Ministry of Education Key Laboratory of Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai China School of Mathematical Sciences and Institute of Natural Sciences and Ministry of Education Key Laboratory of Scientific and Engineering Computing and Shanghai Frontier Science Center of Modern Analysis Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331314385

Incorporating biological neuronal properties into Artificial Neural Networks (ANNs) to enhance computational capabilities is under active investigation in the field of deep learning. Inspired by recent findings indicating that dendrites adhere to quadratic integration rule for synaptic inputs, this study explores the computational benefits of quadratic neurons. We theoretically demonstrate that quadratic neurons inherently capture correlation within structured data, a feature that grants them superior generalization abilities over traditional neurons. This is substantiated by few-shot learning experiments. Furthermore, we integrate the quadratic rule into Convolutional Neural Networks (CNNs) using a biologically plausible approach, resulting in innovative architectures—Dendritic integration inspired CNNs (Dit-CNNs). Our Dit-CNNs compete favorably with state-of-the-art models across multiple classification benchmarks, e.g., ImageNet-1K, while retaining the simplicity and efficiency of traditional CNNs. All source code are available at https://***/liuchongming1999/Dendritic-integration-inspired-CNN-NeurIPS-2024.

关键词：

XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of Software Nanjing University of Information Science and Technology School of Computing and Communications Lancaster University School of Computing Technologies Royal Melbourne Institute of Technology State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental *** results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),a

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

Exploring Patent Transformation Event: Forecasting Patent Transfer Time 26

学校读者我要写书评

暂无评论

Exploring Patent Transformation Event: Forecasting Patent Tr...

26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Huo, Jinchen Liu, Weidong Li, Yang Cao, Yan Institute of Scientific and Technical Information of China China College of Computer Science Inner Mongolia University China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350331684

As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only to enhancing economic efficiency, but also to improving productivity and the rational utilization of resources. There is an imbalance between high patent ownership and low transformation rates. We try to predict the occurrence of transformation events from the patent assignment. However, there are some challenges in predicting patent transformation: (1) how to capture transformation features of patents, especially combined with the transfer time factor. (2) how to predict patent transfer time effectively. To address these challenges, a Patent Transfer Time Forecasting Model (PTTFM) is proposed. The model includes: (1) extraction of time-varying features of patents. (2) the patent transfer time is forecast using a Neural Temporal Point Process. By testing the model on patents under different classifications, the experimental results are obtained to show that the proposed model is applicable to predict the timing of patent assignment within a certain time frame, especially one month. Our work may facilitate patent transformation while interpretability is ensured for transformation events. © 2023 IEEE.

关键词： Forecasting

Research on patent recommendation method based on graph neural network 26

学校读者我要写书评

暂无评论

Research on patent recommendation method based on graph neur...

26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Enchao Liu, Weidong Liu, Shi Cao, Yan Institute of Scientific and Technical Information of China China College of Computer Science Inner Mongolia University China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350331684

Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation. © 2023 IEEE.

关键词： Patents and inventions

Density functions for the overdamped generalized Langevin equation and its Euler-Maruyama method: smoothness and convergence

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Dai, Xinjie Jin, Diancong School of Mathematics and Statistics Yunnan University Kunming650504 China School of Mathematics and Statistics Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China

This paper focuses on studying the convergence rate of the density function of the Euler- Maruyama (EM) method, when applied to the overdamped generalized Langevin equation with fractional noise which serves as an important model in many fields. Firstly, we give an improved upper bound estimate for the total variation distance between random variables by their Malliavin-Sobolev norms. Secondly, we establish the existence and smoothness of the density function for both the exact solution and the numerical one. Based on the above results, the convergence rate of the density function of the numerical solution is obtained, which relies on the regularity of the noise and kernel. This convergence result provides a powerful support for numerically capturing the statistical information of the exact solution through the EM method. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential equations

Multiple-relaxation-time finite-difference lattice Boltzmann model for the nonlinear convection-diffusion equation

学校读者我要写书评

暂无评论

Multiple-relaxation-time finite-difference lattice Boltzmann...

第十二届全国流体力学学术会议

作者： Xinmeng Chen Zhenhua Chai Jinlong Shang Baochang Shi School of Mathematics and Statistics Huazhong University of Science and Technology Hubei Key Laboratory of Engineering Modeling and Scientific Computing

In this paper, a multiple-relaxation-time finite-difference lattice Boltzmann method(MRT-FDLBM) is developed for the nonlinear convection-diffusion equation(NCDE). Through designing the equilibrium distribution function and the source term properly, the NCDE can be recovered exactly from MRT-FDLBM. We also conduct the von Neumann stability analysis on the present MRT-FDLBM and its special case, i.e., single-relaxationtime finite-difference lattice Boltzmann method(SRT-FDLBM). Then, a simplified version of MRT-FDLBM(SMRT-FDLBM) is also proposed, which can save about 15% computational cost. In addition, a series of real and complex-value NCDEs, including the isotropic convection-diffusion equation, Burgers-Fisher equation, sine-Gordon equation, heat-conduction equation, and Schr?dinger equation, are used to test the performance of MRT-FDLBM. The results show that both MRT-FDLBM and SMRT-FDLBM have second-order convergence rates in space and time. Finally, the stability and accuracy of five different models are compared, including the MRT-FDLBM, SMRT-FDLBM,SRT-FDLBM, the previous finite-difference lattice Boltzmann method [H. Wang, B. Shi et al., ***. Comput. 309, 334(2017)], and the lattice Boltzmann method(LBM). The stability tests show that the sequence of stability from high to low is as follows: MRT-FDLBM, SMRT-FDLBM,SRT-FDLBM, the previous finite-difference lattice Boltzmann method, and LBM. In most of the precision test results, it is found that the order from high to low of precision is MRT-FDLBM,SMRT-FDLBM, SRT-FDLBM, and the previous finite-difference lattice Boltzmann method.

关键词： lattice Boltzmann method finite-difference multiple-relaxation-time

Optimal-rate error estimates and a twice decoupled solver for a backward Euler finite element scheme of the Doyle-Fuller-Newman model of lithium-ion batteries

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Xu, Shu Cao, Liqun School of Mathematical Sciences Peking University Beijing100871 China Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Scientific and Engineering Computing Beijing100190 China National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences Beijing100190 China

We investigate the convergence of a backward Euler finite element discretization applied to a multi-domain and multi-scale elliptic-parabolic system, derived from the Doyle-Fuller-Newman model for lithium-ion batteries. We establish optimal-order error estimates for the solution in the norms l2(H1) and l2(L2(Hrq)), q = 0,1. To improve computational efficiency, we propose a novel solver that accelerates the solution process and controls memory usage. Numerical experiments with realistic battery parameters validate the theoretical error rates and demonstrate the significantly superior performance of the proposed solver over existing solvers. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

An Efficient Benders Decomposition Approach for Optimal Large-Scale Network Slicing

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Chen, Wei-Kun Wu, Zheyu Zhang, Rui-Jin Liu, Ya-Feng Dai, Yu-Hong Luo, Zhi-Quan The School of Mathematics and Statistics Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing100081 China 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 Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China

This paper considers the network slicing (NS) problem which attempts to map multiple customized virtual network requests to a common shared network infrastructure and allocate network resources to meet diverse service requirements. This paper proposes an efficient customized Benders decomposition algorithm for globally solving the large-scale NP-hard NS problem. The proposed algorithm decomposes the hard NS problem into two relatively easy function placement (FP) and traffic routing (TR) subproblems and iteratively solves them enabling the information feedback between each other, which makes it particularly suitable to solve large-scale problems. Specifically, the FP subproblem is to place service functions into cloud nodes in the network, and solving it can return a function placement strategy based on which the TR subproblem is defined;and the TR subproblem is to find paths connecting two nodes hosting two adjacent functions in the network, and solving it can either verify that the solution of the FP subproblem is an optimal solution of the original problem, or return a valid inequality to the FP subproblem that cuts off the current infeasible solution. The proposed algorithm is guaranteed to find the globally optimal solution of the NS problem. By taking the special structure of the NS problem into consideration, we successfully develop two families of valid inequalities that render the proposed algorithm converge much more quickly and thus much more efficient. Numerical results demonstrate that the proposed valid inequalities effectively accelerate the convergence of the decomposition algorithm, and the proposed algorithm significantly outperforms the existing algorithms in terms of both solution efficiency and quality. Copyright © 2023, The Authors. All rights reserved.

关键词： Resource allocation