—Reinforcement learning is a framework in which an agent can learn behavior without knowledge on a task or an environment by exploration and exploitation. Striking a balance betw...
Zhengqiao Ji, Q. M. Jonathan Wu, Maher A. Sid-Ahme...
Applications that query data streams in order to identify trends, patterns, or anomalies can often benefit from comparing the live stream data with archived historical stream dat...
Frederick Reiss, Kurt Stockinger, Kesheng Wu, Arie...
Designing a collaborative architecture for real-time applications is an intricate challenge that usually involves dealing with the real-time constraints, resource limitations and ...
Dewan Tanvir Ahmed, Shervin Shirmohammadi, Abdulmo...
The complexity of dynamical laws governing 3D atmospheric flows associated to incomplete and noisy observations makes very difficult the recovery of atmospheric dynamics from sate...
We investigate the problem of constructing the maximal number of node disjoint paths between two distinct nodes in Swapped/OTIS networks. A general construction of node disjoint pa...