Search for a heavy gauge boson decaying to a charged lepton and a neutrino in 1 fb^(−1) of pp collisions at √s = 7 TeV using the ATLAS detector

The ATLAS detector at the LHC is used to search for high-mass states, such as heavy charged gauge bosons (W'), decaying to a charged lepton (electron or muon) and a neutrino. Results are presented based on the analysis of pp collisions at a center-of-mass energy of 7 TeV corresponding to an integrated luminosity of 1.04 fb^(−1). No excess above Standard Model expectations is observed. A W' with Sequential Standard Model couplings is excluded at the 95% confidence level for masses up to 2.15 Te

Review of the West Indian species of Efferia Coquillett (Diptera: Asilidae): Part 1. Bahamas, Cayman Islands, Cuba, and Jamaica

The genus Efferia Coquillett from the Bahamas, Cayman Islands, Cuba, and Jamaica is reviewed. The fauna now totals 16 species with 6 new species described (Ef. bellardii n. sp., Ef. bromleyi n. sp., Ef. hinei n. sp., Ef. insula n. sp., Ef. pina n. sp., and Ef. vinalensis n. sp.). Cuba has the greatest diversity with 10 species, Jamaica 3, the Bahamas 2, and the Cayman Islands 1. Efferia stylata (Fabricius) is removed from the species list of these West Indian islands. The wings of Ef. caymanensis Scarbrough and Ef. bromleyi, spermathecae of Ef. bromleyi, Ef. cubensis (Bromley), Ef. insula, Ef. nigritarsis (Hine), and terminalia of all species are illustrated. Keys for the identification of the species are provided. Specimens of two additional species from Cuba are in too poor a condition to be described but their terminalia are illustrated and the species are included in the key to the males

Filling the Gap: a Tool to Automate Parameter Estimation for Software Performance Models

© 2015 ACM.Software performance engineering heavily relies on application and resource models that enable the prediction of Quality-of-Service metrics. Critical to these models is the accuracy of their parameters, the value of which can change with the application and the resources where it is deployed. In this paper we introduce the Filling-the-gap (FG) tool, which automates the parameter estimation of application performance models. This tool implements a set of statistical routines to estimate the parameters of performance models, which are automatically executed using monitoring information kept in a local database