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

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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

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Class Incremental Learning with Task-Specific Batch Normalization and Out-of-Distribution Detection

arXiv

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arXiv 2024年

作者： Xie, Xuchen Qiu, Yiqiao Lin, Run Zheng, Weishi Wang, Ruixuan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Department of Computer Science and Engineering University of California San Diego United States

This study focuses on incremental learning for image classification, exploring how to reduce catastrophic forgetting [1] of all learned knowledge when access to old data is restricted due to memory or privacy constraints. The challenge of incremental learning lies in achieving an optimal balance between plasticity, the ability to learn new knowledge, and stability, the ability to retain old knowledge. Based on whether the task identifier (task-ID) of an image can be obtained during the test stage, incremental learning for image classifcation is divided into two main paradigms, which are task incremental learning (TIL) and class incremental learning (CIL). The TIL paradigm has access to the task-ID, allowing it to use multiple task-specific classification heads selected based on the task-ID. Consequently, in CIL, where the task-ID is unavailable, TIL methods must predict the task-ID to extend their application to the CIL paradigm. Our previous method [2] for TIL adds task-specific batch normalization and classification heads incrementally. This work extends the method by predicting task-ID through an "unknown" class added to each classification head. The head with the lowest "unknown" probability is selected, enabling task-ID prediction and making the method applicable to CIL. The task-specific batch normalization (BN) modules effectively adjust the distribution of output feature maps across different tasks, enhancing the model’s plasticity. Moreover, since BN has much fewer parameters compared to convolutional kernels, most of the feature extractor’s parameters remain unchanged. By only modifying the BN layers as new tasks arrive, the model can effectively manage parameter growth while ensuring stability across tasks. In summary, the proposed method can efficiently control parameter growth while ensuring both the model’s plasticity and stability. The innovation of this study lies in the first-time introduction of task-specific BN into CIL and verifying the feasibili

关键词： Image classification

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Transverse-momentum-dependent wave functions of the pion from lattice QCD

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Physical Review D 2024年第9期109卷 L091503-L091503页

作者： Min-Huan Chu Jin-Chen He Jun Hua Jian Liang Xiangdong Ji Andreas Schäfer Hai-Tao Shu Yushan Su Ji-Hao Wang Wei Wang Yi-Bo Yang Jun Zeng Jian-Hui Zhang Qi-An Zhang INPAC Shanghai Key Laboratory for Particle Physics and Cosmology Key Laboratory for Particle Astrophysics and Cosmology (MOE) School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Yang Yuanqing Scientific Computering Center Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park Maryland 20742 USA Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou 510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Institut für Theoretische Physik Universität Regensburg D-93040 Regensburg Germany CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 Guangdong Province China International Centre for Theoretical Physics Asia-Pacific Beijing/Hangzhou China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen 518172 China Center of Advanced Quantum Studies Department of Physics Beijing Normal University Beijing 100875 China School of Physics Beihang University Beijing 102206 China

We present a lattice QCD calculation of the transverse-momentum-dependent wave function (TMDWF) 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 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-TMDWF simulated on the lattice, we extract the TMDWF. The results for both lattice ensembles are compatible and a comparison with a 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.

关键词： Lattice QCD Transverse momentum dependent distribution Pions

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Exploring Patent Transformation Event: Forecasting Patent Transfer Time 26

Exploring Patent Transformation Event: Forecasting Patent Tr...

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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

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Research on patent recommendation method based on graph neural network 26

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

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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

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Controllable Secondary Through-Space Interaction and Clusteroluminescence

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CCS Chemistry 2023年第12期5卷 2832-2844页

作者： Zuping Xiong Jianyu Zhang Lei Wang Yuan Xie Yipu Wang Zujin Zhao Haoke Zhang Jing Zhi Sun Feihe Huang Ben Zhong Tang MOE Key Laboratory of Macromolecular Synthesis and Functionalization Department of Polymer Science and EngineeringZhejiang UniversityHangzhou 310027 Zhejiang-Israel Joint Laboratory of Self-Assembling Functional Materials ZJU-Hangzhou Global Scientific and Technological Innovation CenterZhejiang UniversityHangzhou 311215 Department of Chemistry Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and ReconstructionThe Hong Kong University of Science and TechnologyHong Kong Center for AIE Research College of Materials Science and EngineeringShenzhen UniversityShenzhen 518061 State Key Laboratory of Luminescent Materials and Devices Guangdong Provincial Key Laboratory of Luminescence from Molecular AggregatesSouth China University of TechnologyGuangzhou 51064 Centre of Healthcare Materials Shaoxing InstituteZhejiang UniversityShaoxing 312000 Stoddart Institute of Molecular Science Department of ChemistryZhejiang UniversityHangzhou 310058 School of Science and Engineering Shenzhen Institute of Aggregate Science and TechnologyThe Chinese University of Hong KongShenzhen 518172

Nonconjugated clusteroluminogens(CLgens),such as proteins and polystyrene,have become increasingly important in *** show many advantages over traditional conjugated dyes with fused aromatic rings in biological ***,CLgens have historically been unheeded because of their weak visible emissions in the aggregate state,namely clusteroluminescence(CL).Changing the electronic structures of CLgens by precisely regulating the intramolecular throughspace interaction(TSI)to improve their photophysical properties remains an enormous ***,we propose a general strategy to construct a higher-level intramolecular TSI,namely secondary TSI constructed by the primary TSI and a TSI linker,in multi-aryl-substituted alkanes(MAAs).By introducing methyl and phenyl into 1,1,3,3-tetraphenylpropane,the modified MAAs show efficient CL with high luminescence quantum yield(-40%)and long emission wavelength(-530 nm).Then,comprehensive experiments and theoretical studies demonstrate that molecular rigidity and overlap of subunits play pivotal roles in improving these hierarchical *** work not only provides a feasible strategy to achieve controllable manipulation of hierarchical TSIs and CL but also establishes comprehensive TSI-based aggregate photophysics.

关键词： through-space interaction clusteroluminescence aggregation-induced emission alkanes electronic structures aggregate photophysics

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

arXiv

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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

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Interpretable Drug Response Prediction through Molecule Structure-aware and Knowledge-Guided Visible Neural Network

Interpretable Drug Response Prediction through Molecule Stru...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiancong Xie Zhe Zhang Youyou Li Jiahua Rao Yuedong Yang School of Computer Science and Engineering Sun Yat-sen University China Department of Chemistry University of Chicago Illinois USA Key Laboratory of Machine Intelligence and Advanced Computing of MOE Sun Yat-sen University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Precise prediction of anti-cancer drug responses has become a crucial obstruction in anti-cancer drug design and clinical applications. In recent years, various deep learning methods have been applied to drug response prediction and become more accurate. However, they are still criticized as being non-transparent. To offer reliable drug response prediction in real-world applications, there is still a pressing demand to develop a model with high predictive performance as well as interpretability. In this study, we propose DrugVNN, an end-to-end interpretable drug response prediction framework, which extracts gene features of cell lines through a knowledge-guided visible neural network (VNN) and learns drug representation through a node-edge communicative message passing network (CMPNN). Additionally, between these two networks, a novel drug-aware gene attention gate is designed to direct the drug representation to VNN to simulate the effects of drugs. By evaluating on the GDSC dataset, DrugVNN achieved state-of-the-art performance. Moreover, DrugVNN can identify active genes and relevant signaling pathways for specific drug-cell line pairs with supporting evidence in the literature, implying the interpretability of our model.

关键词： Drugs Deep learning Message passing Biological system modeling Neural networks Pressing Predictive models Logic gates Feature extraction Reliability

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MVESF: Multi-View Enhanced Semantic Fusion for Controllable Text-to-Image Generation

MVESF: Multi-View Enhanced Semantic Fusion for Controllable ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Hai Zhang Guitao Cao Xinke Wang Jiahao Quan Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China MoE Engineering Research Center of SW/HW Co-design Technology and Application East China Normal University Shanghai China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Text-to-image(T2I) synthesis aims to generate semantically consistent images with texts. Currently, existing methods merely use the shallow semantics of the text to crudely guide image generation. They cannot fully integrate rich textual semantics with image features, leading to an inability to control image generation through text finely. To address this issue, we propose a GAN-based method named MVESF (Multi-View Enhanced Semantic Fusion), which enhances the semantic fusion of text and images from multiple perspectives for fine-grained controllable text-to-image synthesis. In multi-view, we introduce Multi-domain Semantic Guidance, Local Semantic Attention, and Visual-textual Consistency Loss to enhance the semantic fusion of text and images in image generation, image discrimination, and image supervision, respectively. Our method promotes the consistent alignment between text and images, allowing for fine-grained variations in the generated images when subtle changes in the input text without affecting unrelated regions. Extensive experimental results have demonstrated the effectiveness of our approach.

关键词： Adaptation models Image synthesis Semantics Text to image Cybernetics

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

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

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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

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