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Distributed Deep Multi-Agent Reinforcement Learning for Cooperative ...

https://cis.temple.edu/~jiewu/research/publications/Publication_files/Distributed_Deep_Multi-Agent_Reinforcement_Learning_for_Cooperative_Edge_Caching_in_Internet-of-Vehicles.pdf

2 Power of background noise -95 Bm Size of requested contents [0.5, 1.5] MB 2.4GHz Intel Xeon E5-2650 processor and 256GB RAM. The main parameters are listed in Table II. For performance comparison, the following four bench-mark caching methods are introduced:

教育博士学位论文 - sites.temple.edu

https://sites.temple.edu/nsfcareerakt/files/2019/06/140601033-%E5%8D%9A%E5%A3%AB%E6%AF%95%E4%B8%9A%E8%AE%BA%E6%96%87.pdf

接着,研 究将基于KMT的专家教师教学策略应用于新手教师专业成长的培养中,同 时也将专家教师与新手教师的教学进行对比分析,得出新手教师专业发展的三个阶段:个 人经验主导阶段,教 材解读整合阶段和分析学生学习阶段。同时,研 究进一步构建了新手教师专业成长路径的模型,模型从三个方面描述了 ...

Conditional Probability - Temple University

https://cis.temple.edu/~latecki/Courses/CIS2033-Spring12/ElementaryProbabilityforApplications/ch3.pdf

Another way of saying this is that if we ignore the outcomes that result in a sum other than 5 or 7, we reduce the sample space from Ω to E = E5 ∪ E7 and the distribution of the first outcome that lands in E follows the conditional probability P(·|E).

Topology-Aware Scheduling Framework for Microservice Applications in Cloud

https://cis.temple.edu/~wu/research/publications/Publication_files/Topology-Aware_Scheduling_Framework_for_Microservice_Applications_in_Cloud.pdf

Abstract—Loosely coupled and highly cohesived microservices running in containers are becoming the new paradigm for application development. Compared with monolithic applications, applications built on microservices architecture can be deployed and scaled independently, which promises to simplify software development and operation. However, the dramatic increase in the scale of microservices ...