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Admissions - Temple University

https://www.temple.edu/admissions

After receiving recommendations from multiple professors to apply for a Goldwater Scholarship, one of the most prestigious undergraduate STEM awards, Leo Battalora won the award for his coveted research in Temple’s Electrical and Computer Engineering Department.

Montreal Cognitive Assessment - Sites

https://sites.temple.edu/rtassessment/files/2018/10/MoCA-Instructions-English_7.2.pdf

Add one point for an individual who has 12 years or fewer of formal education, for a possible maximum of 30 points. A final total score of 26 and above is considered normal.

‘Flappy Bird’ to return after a 10-year hiatus: the true story behind ...

https://news.temple.edu/news/2024-09-20/flappy-bird-return-after-10-year-hiatus-true-story-behind-world-s-most-viral-mobile

It’s an endless runner game with a one-button gaming design to control a bird to fly through gaps between green pipes without hitting them for as long as you can. The gameplay was inspired by the challenge of bouncing a ping pong ball on a paddle as many times as you can.

Brandon Enarusai - Football - Temple

https://owlsports.com/sports/football/roster/brandon-enarusai/17924

High School: Played linebacker and running back at George School in Newtown, Pa., for head coach Jordan Page … was a teammate of fellow Owl Kendall Gordon … had 100 tackles. 7.0 sacks and one interception as a senior while rushing for 1,200 on offense … as a junior, collected 89 tackles, 2.0 sacks, and a pick-six to go with 700 rushing ...

Wireshark_TCP.pdf - Temple University

https://cis.temple.edu/~tug29203/18spring-3329/reading/Lab_7_Solutions.pdf

The answers below are based on the trace file tcp-ethereal-trace-1 in in

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one-step past and operate based on simple rules, while RL-based edge caching methods can be derived from the observed historical content demands and concentrate more on the reward that agents can earn rather than users’ requests.