Caché数据库中数据的存储及其查询优化

Caché数据库的多维数据模型可以存储丰富的数据,在处理复杂的医疗数据时减少了表连接等处理过程,从而使多维数组能更快地存取数据。与主流的Oracle和SQL server等关系型数库相比,Caché主要在其存储结构上有很大的不同,Caché主要是以Global的形式存储数据,依据M语言开发应用程序。首先,介绍了Caché数据库中数据的存储形式;然后,展示了在医院HIS系统应用过程中Caché数据库中数据查询的几种方式及应用场合;最后,总结Caché数据库中SQL优化的几种办法。结果表明,Caché数据库具有更高的灵活性,适用于多种应用场合,而且在采用优化的查询方案后查询效率提高了很多倍。

内存高效的持久性分布式文件系统客户端缓存DFS-Cache

为了在数据密集型工作流下有效降低缓存碎片整理开销并提高缓存命中率,提出一种持久性分布式文件系统客户端缓存DFS-Cache(Distributed File System Cache)。DFS-Cache基于非易失性内存(NVM)设计实现,能够保证数据的持久性和崩溃一致性,并大幅减少冷启动时间。DFS-Cache包括基于虚拟内存重映射的缓存碎片整理机制和基于生存时间(TTL)的缓存空间管理策略。前者基于NVM可被内存控制器直接寻址的特性,动态修改虚拟地址和物理地址之间的映射关系,实现零拷贝的内存碎片整理;后者是一种冷热分离的分组管理策略,借助重映射的缓存碎片整理机制,提升缓存空间的管理效率。实验采用真实的Intel傲腾持久性内存设备,对比商用的分布式文件系统MooseFS和GlusterFS,采用Fio和Filebench等标准测试程序,DFS-Cache最高能提升5.73倍和1.89倍的系统吞吐量。

Multi-Level Web Cache Model Used in Data Grid Application

This paper proposed a novel multilevel data cache model by Web cache （MDWC） based on network cost in data grid. By constructing a communicating tree of grid sites based on network cost and using a single leader for each data segment within each region, the MDWC makes the most use of the Web cache of other sites whose bandwidth is as broad as covering the job executing site. The experiment result indicates that the MDWC reduces data response time and data update cost by avoiding network congestions while designing on the parameters concluded by the environment of application.

多核处理器共享Cache的划分算法

针对多核处理器性能优化问题,文中深入研究多核处理器上共享Cache的管理策略,提出了基于缓存时间公平性与吞吐率的共享Cache划分算法MT-FTP(Memory Time based Fair and Throughput Partitioning)。以公平性和吞吐率两个评价性指标建立数学模型,并分析了算法的划分流程。仿真实验结果表明,MT-FTP算法在系统吞吐率方面表现较好,其平均IPC(Instructions Per Cycles)值比UCP(Use Case Point)算法高1.3%,比LRU(Least Recently Used)算法高11.6%。MT-FTP算法对应的系统平均公平性比LRU算法的系统平均公平性高17%,比UCP算法的平均公平性高16.5%。该算法实现了共享Cache划分公平性并兼顾了系统的吞吐率。

Quantitatively characterizing sandy soil structure altered by MICP using multi-level thresholding segmentation algorithm

The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

R-DSP中二级Cache控制器的优化设计

针对二级Cache控制器(L2)对于提升R数字信号处理器(R-DSP)访存效率和整体性能的重要作用,结合L2中涉及的内存安全维护和多请求访存仲裁问题,在现有R-DSP中L2基础上实现优化。首先,采用多重分块的存储组织结构,提高访存效率;其次,并行处理一级Cache控制器请求与外存请求,减小请求处理周期;最后,增加带宽管理与存储保护功能,合理仲裁访存请求并维护存储安全。实验结果表明,相较于传统设计,新设计在保护二级存储安全的同时实现带宽管理式访存仲裁。与现有R-DSP中的L2相比,新设计的存储体单拍最大可响应访存请求数量提升了1倍,一级请求和外存请求的平均处理时钟周期数分别降低了25%和19.6%。

辣椒查尔酮合成酶基因CaCHS02的克隆及功能分析

查尔酮合成酶(Chalcone synthase,CHS)基因在植物黄酮类物质代谢过程中发挥重要作用。为研究查尔酮合成酶基因CaCHS02在辣椒(Capsicum annuum L.)中辣椒类黄酮代谢过程中的功能,本研究分析了CaCHS02基因的基因特征、蛋白特点和基因表达模式,并构建了CaCHS02过表达载体,通过农杆菌介导法获得瞬时过表达CaCHS02(35S:CaCHS02)的辣椒植株。结果发现,瞬时过表达CaCHS02的辣椒植株叶片中CaCHS02发生显著超量表达,其中编码类黄酮代谢途径中其他关键酶基因(CaCHS02、CaPAL、CaC4H、Ca4CL、CaCHI、CaFLS和CaF3H)的表达水平同步显著上调;超表达CaCHS02促进辣椒叶片中查尔酮合成酶酶活性、总黄酮含量和辣椒叶片的α-葡糖糖苷酶抑制活性的提升。研究表明,CaCHS02在辣椒类黄酮代谢过程中发挥正向调控功能,过表达CaCHS02提高了辣椒叶片的α-葡糖糖苷酶抑制活性。本研究为解析辣椒α-葡萄糖苷酶抑制剂生物合成机制奠定了基础,为选育高α-葡糖糖苷酶抑制活性的功能辣椒品种提供了理论支持。

一种带Cache加速的HyperRAM控制器设计与验证

针对目前可穿戴设备上对存储设备性能要求高、体积小、功耗低等问题,在FPGA上实现了一款可拓展的高性能HyperRAM控制器,并引入Cache缓存加速设计,以提高对频繁访问数据的命中率和优化存储器访问模式,实现更高速的数据传输和优化的系统性能。运用UVM验证方法学和FPGA进行验证,结果表明,带有Cache缓存的HyperRAM控制器相较于普通HyperRAM,在读写连续地址时性能提高61%,并具有较好的可靠性与有效性,可为嵌入式系统提供高效、灵活的存储器解决方案。

Cache侧信道攻击防御量化研究

芯片安全防护技术关系到国家、企业和个人的信息安全,相关的研究一直是计算机安全领域的热点。片上高速缓存对芯片性能起着重要作用,可以有效提升芯片内核访问效率。传统的缓存设计并没有充分考虑安全性,侧信道攻击会对Cache造成巨大威胁,可以窃取加密密钥等内存存储敏感信息。攻击者利用侧信道的技术窃取用户的隐私数据或加密算法密钥时不会改变片上系统芯片的运行状态,从而使计算机系统很难检测是否受到了攻击。与基于电磁信号和基于能量检测的侧信道攻击相比,基于存储共享的侧信道攻击只需要利用软件测量就可以实现,对芯片安全的威胁更大。目前存在多种侧信道攻击和防御手段,但缺乏一套完善的关于系统架构的安全度量方法,对Cache的安全性进行有效评估。本文对Cache侧信道攻击和防御手段进行模型化分析,提出一套Cache安全性量化研究方法。首先,我们采用CVSS漏洞评分模型对Cache侧信道攻击进行量化评分。然后,利用贝叶斯公式,构建侧信道攻击和防御的关系模型。最后,通过图模型对Cache侧信道攻击机理进行建模,计算在防御架构基础上不同威胁的攻击成功率,并结合CVSS防御得分求得不同防御方法的得分。本文针对Cache侧信道攻击进行机理建模,对攻击和防御进行评估和探索,为硬件安全人员提供理论支持。

Multilayer Satellite Network Collaborative Mobile Edge Caching:A GCN-Based Multi-Agent Approach

With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has ensued.The emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also significantly improve user experience.Integrating with the MEC and satellite networks,the network is empowered popular content ubiquitously and seamlessly.Addressing the research gap between multilayer satellite networks and MEC,we study the caching placement problem in this paper.Initially,we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale networks.Considering the constraint on satellite capacity and content propagation delay,the cache placement problem is then formulated and transformed into a markov decision process(MDP),where the content coded caching mechanism is utilized to promote the efficiency of content delivery.Furthermore,a new generic metric,content delivery cost,is proposed to elaborate the performance of caching decision in large-scale networks.Then,we introduce a graph convolutional network(GCN)-based multi-agent advantage actor-critic(A2C)algorithm to optimize the caching decision.Finally,extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.

基于Cache优化的服务调用方法

集中式服务网关通常使用共享内存进行服务实例与治理参数的本地化生产与消费,实现业务处理与服务发现逻辑的解耦,增强系统的稳定性,但频繁的共享内存操作往往带来系统资源利用率和请求处理耗时上的低效。通过引入缓存机制,在服务网关的路由组件内部实现并利用针对服务调用优化的Cache,热点数据请求直接从Cache中读取结构化信息,避免了共享内存操作与存储块的编解码,有效地利用缓存空间,提高了数据访问速度,同时减少了共享内存操作中的资源竞争,提高了系统并发。

EGSNet:An Efficient Glass Segmentation Network Based on Multi-Level Heterogeneous Architecture and Boundary Awareness

Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-seeking real-time tasks such as autonomous driving.The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers.These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers.We propose an efficient glass segmentation network(EGSNet)based on multi-level heterogeneous architecture and boundary awareness to balance the model performance and efficiency.EGSNet divides the feature layers from different stages into low-level understanding,semantic-level understanding,and global understanding with boundary guidance.Based on the information differences among the different layers,we further propose the multi-angle collaborative enhancement(MCE)module,which extracts the detailed information from shallow features,and the large-scale contextual feature extraction(LCFE)module to understand semantic logic through deep features.The models are trained and evaluated on the glass segmentation datasets HSO(Home-Scene-Oriented)and Trans10k-stuff,respectively,and EGSNet achieves the best efficiency and performance compared to advanced methods.In the HSO test set results,the IoU,Fβ,MAE(Mean Absolute Error),and BER(Balance Error Rate)of EGSNet are 0.804,0.847,0.084,and 0.085,and the GFLOPs(Giga Floating Point Operations Per Second)are only 27.15.Experimental results show that EGSNet significantly improves the efficiency of the glass segmentation task with better performance.

The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

User Preference Aware Hierarchical Edge-User Cooperative Caching Strategy

The emergence of various new services has posed a huge challenge to the existing network architecture.To improve the network delay and backhaul pressure,caching popular contents at the edge of network has been considered as a feasible scheme.However,how to efficiently utilize the limited caching resources to cache diverse contents has been confirmed as a tough problem in the past decade.In this paper,considering the time-varying user requests and the heterogeneous content sizes,a user preference aware hierarchical cooperative caching strategy in edge-user caching architecture is proposed.We divide the caching strategy into three phases,that is,the content placement,the content delivery and the content update.In the content placement phase,a cooperative content placement algorithm for local content popularity is designed to cache contents proactively.In the content delivery phase,a cooperative delivery algorithm is proposed to deliver the cached contents.In the content update phase,a content update algorithm is proposed according to the popularity of the contents.Finally,the proposed caching strategy is validated using the MovieLens dataset,and the results reveal that the proposed strategy improves the delay performance by at least 35.3%compared with the other three benchmark strategies.

Weather Classification for Autonomous Vehicles under Adverse Conditions Using Multi-Level Knowledge Distillation

Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remains a significant challenge.While advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather conditions.These challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in real-time.To address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational overhead.Specifically,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student model.This framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous systems.Our Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational demands.The proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per class.The results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning models.This work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,offering a scalable solution for weather classification in autonomous driving systems.

An SDN-Based Algorithm for Caching,Routing,and Load Balancing in ICN

One of the challenges of Informationcentric Networking(ICN)is finding the optimal location for caching content and processing users’requests.In this paper,we address this challenge by leveraging Software-defined Networking(SDN)for efficient ICN management.To achieve this,we formulate the problem as a mixed-integer nonlinear programming(MINLP)model,incorporating caching,routing,and load balancing decisions.We explore two distinct scenarios to tackle the problem.Firstly,we solve the problem in an offline mode using the GAMS environment,assuming a stable network state to demonstrate the superior performance of the cacheenabled network compared to non-cache networks.Subsequently,we investigate the problem in an online mode where the network state dynamically changes over time.Given the computational complexity associated with MINLP,we propose the software-defined caching,routing,and load balancing(SDCRL)algorithm as an efficient and scalable solution.Our evaluation demonstrates that the SDCRL algorithm significantly reduces computational time while maintaining results that closely resemble those achieved by GAMS.

EBCache:A Novel Cache-Based Mechanism for Mitigating the Spectre Attacks for RISC-V Processor

The cache-based covert channel is one of the common vulnerabilities exploited in the Spectre attacks.Current mitigation strategies focus on blocking the eviction-based channel by using a random/encrypted mapping function to translate memory address to the cache address,while the updated-based channel is still vulnerable.In addition,some mitigation strategies are also costly as it needs software and hardware modifications.In this paper,our objective is to devise low-cost,comprehensive-protection techniques for mitigating the Spectre attacks.We proposed a novel cache structure,named EBCache,which focuses on the RISC-V processor and applies the address encryption and blacklist to resist the Spectre attacks.The addresses encryption mechanism increases the difficulty of pruning a minimal eviction set.The blacklist mechanism makes the updated cache lines loaded by the malicious updates invisible.Our experiments demonstrated that the EBCache can prevent malicious modifications.The EBCache,however,reduces the processor’s performance by about 23%but involves only a low-cost modification in the hardware.

Deep Reinforcement Learning-Based Task Offloading and Service Migrating Policies in Service Caching-Assisted Mobile Edge Computing

Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.