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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:
https://cis.temple.edu/~latecki/Courses/CIS166-Spring10/Lectures/Ch9.ppt
The incidence matrix w.r.t. this ordering of V and E is the n m matrix M = [mij], where Incidence Matrix Example Represent the graph shown with an incidence matrix. 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 1 0 1 1 0 v1 v2 v3 v4 v5 vertices e1 e2 e3 e4 e5 e6 edges v1 v2 v3 v4 v5 e1 e2 e3 e4 e5 e6 Isomorphism Two simple graphs are ...
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 ...
https://cis.temple.edu/~latecki/Courses/CIS166-05/Lectures/graph.ppt
Start at c and take a walk: {c,d}, {d,e}, {e,c} a b c d e f “Splice” the circuits in the 2 graphs: {a,b}, {b,c}, {c,f}, {f,a} “+” {c,d}, {d,e}, {e,c} “=“ {a,b}, {b,c}, {c,d}, {d,e}, {e,c}, {c,f} {f,a} 0 0 0 e 1 0 0 d 1 1 0 c 0 0 0 f 0 1 1 b 0 0 1 a e3 e2 e1 a b c d e f e1 e2 e3 e4 e5 e6 e7 0 0 1 1 0 0 0 1 0 1 1 0 1 1 0 0 0 0 0 0 1 0 ...
https://cis.temple.edu/~wu/research/publications/Publication_files/10_bare_jrnl_compsoc.pdf
6.1 Basic Setting at runs a Linux operating system with E5-2620 CPU, NVIDIA RTX5000 GPU, 128Gb memory, and a 2Tb hard disk. We choose the Social LSTM model to predict the future trajectories of users which can achieve an average accuracy of over 70%. We used
https://cis.temple.edu/~latecki/Courses/CIS2033-Spring12/ElementaryProbabilityforApplications/ch3.pdf
The first step in analyzing craps is to compute the probability that the player makes his point. Suppose his point is 5 and let Ek be the event that the sum is k. There are 4 outcomes in E5 ((1, 4), (2, 3), (3, 2), (4, 1)), 6 in E7, and hence 26 not in E5 ∪ E7. Letting × stand for “the sum is not 5 or 7,” we see that 4 26 4 = P(5) P(× 5