A Systematic Literature Review on Distributed Machine Learning in Edge Computing.

Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical cloud devices, many hindrances have to be overcome to fully extract the potential benefits of such an approach (such as data-in-motion analytics). In this paper, we investigate the challenges of running ML/DL on edge devices in a distributed way, paying special attention to how techniques are adapted or designed to execute on these restricted devices. The techniques under discussion pervade the processes of caching, training, inference, and offloading on edge devices. We also explore the benefits and drawbacks of these strategies.

A discrete search algorithm for finding the structure of protein backbones and side chains.

Some information about protein structure can be obtained by using Nuclear Magnetic Resonance (NMR) techniques, but they provide only a sparse set of distances between atoms in a protein. The Molecular Distance Geometry Problem (MDGP) consists in determining the three-dimensional structure of a molecule using a set of known distances between some atoms. Recently, a Branch and Prune (BP) algorithm was proposed to calculate the backbone of a protein, based on a discrete formulation for the MDGP. We present an extension of the BP algorithm that can calculate not only the protein backbone, but the whole three-dimensional structure of proteins.