Jonathan Kajjimu,1 Mark Mohan Kaggwa,2 Felix Bongomin3 1Faculty of Medicine, Mbarara University Throw of Science and Technology, Mbarara, Uganda; 2Department of Psychiatry, Faculty of Medicine, Mbarara University of Science and Technology, Mbarara, Uganda; 3Department of Medical Microbiology and Immunology, Faculty of Medicine, Gulu University, Gul

Abstract This paper proposes a learning policy to improve the energy efficiency (EE) of heterogeneous cellular networks.The combination of active and inactive base stations (BS) that allows for maximizing EE is identified as a combinatorial learning problem and requires high computational complexity as well as a large signaling overhead.This paper

Abstract K-mer based methods have become prevalent in many areas of bioinformatics.In applications such as database search, they often work with large multi-terabyte-sized datasets.Storing such large datasets is a detriment to tool developers, tool users, and reproducibility efforts.General purpose compressors like gzip, or those designed for read