no code implementations • 11 Nov 2022 • Marina Zapater, José L. Risco-Martín, Patricia Arroba, José L. Ayala, José M. Moya, Román Hermida
By using Gramatical Evolution techniques, this paper presents a methodology for the generation of temperature models for data centers and the runtime prediction of CPU and inlet temperature under variable cooling setups.
no code implementations • 4 Mar 2021 • Giovanni Agosta, William Fornaciari, David Atienza, Ramon Canal, Alessandro Cilardo, José Flich Cardo, Carles Hernandez Luz, Michal Kulczewski, Giuseppe Massari, Rafael Tornero Gavilá, Marina Zapater
RECIPE (REliable power and time-ConstraInts-aware Predictive management of heterogeneous Exascale systems) is a recently started project funded within the H2020 FETHPC programme, which is expressly targeted at exploring new High-Performance Computing (HPC) technologies.
Weather Forecasting Distributed, Parallel, and Cluster Computing
no code implementations • 22 Dec 2020 • Valentin Gabeff, Tomas Teijeiro, Marina Zapater, Leila Cammoun, Sylvain Rheims, Philippe Ryvlin, David Atienza
Specifically, we focused the discussion on three main aspects: 1) how to aggregate the classification results on signal segments provided by the DL model into a larger time scale, at the seizure-level; 2) what are the relevant frequency patterns learned in the first convolutional layer of different models, and their relation with the delta, theta, alpha, beta and gamma frequency bands on which the visual interpretation of EEG is based; and 3) the identification of the signal waveforms with larger contribution towards the ictal class, according to the activation differences highlighted using the DeepLIFT method.