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Peter Morris | Temple University, Japan Campus - tuj.ac.jp

https://www.tuj.ac.jp/about/faculty-staff/peter-morris

IP is one of Peter’s legal focus areas and he has lectured on copyright law and other IP issues at various forums, including UTEM Law School in Malaysia. After obtaining his bar license in 2019, Peter opened his own law firm in Irvine, California.

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

This situation may occur because LFU and LRU learn only from one-step past and operate based on simple rules, while RL-based edge caching methods can be derived from the observed historical content demands and concentrate more on the reward that agents can earn rather than users’ requests.

Joint Mobile Edge Caching and Pricing: A Mean-Field Game Approach

https://cis.temple.edu/~wu/research/publications/Publication_files/ICDE2024_Xu.pdf

Here, the popularity of v1 is higher than v2, and Alice (or Bob) is capable of only caching one video due to the limited storage resources. Considering that high-popularity videos can generate more trading incomes, Alice and Bob generally tend to cache v1 to improve their utilities (i.e., net profits).

Zhanteng Xie, Pujie Xin, and Philip Dames - Sites

https://sites.temple.edu/trail/files/2021/11/XieXinDamesIROS2021.pdf

architecture of our deep neural network. Our input data consists of three separate channels, one from lidar and two containing pedestr an data, each of which is an 80 80 array. These 3-channel input data are fed to the deep neural network consisting of 1 convolutional layer, 6 bottleneck r

Prediction of Dental Caries in Pediatric Patients Using Machine ...

https://scholarshare.temple.edu/bitstreams/1c1f1a6d-0f34-4234-8b39-41d9eeb397f0/download

One of the limitations of this study was that none of the studies had a consistent way to the accuracy of the ML algorith s. For instance, some studies us sensitivity scores to analyze performance, while others did not. Additionally, each study tested different sets of ML algorithms. For example, if Montenegro et al included RF as an ML