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

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

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

Boundary Dynamics Driven Entanglement

We will show how it is possible to generate entangled states out of unentangled ones on a bipartite system by means of dynamical boundary conditions. The auxiliary system is defined by a symmetric but not self-adjoint Hamiltonian and the space of self-adjoint extensions of the bipartite system is studied. It is shown that only a small set of them leads to separable dynamics and they are characterized. Various simple examples illustrating this phenomenon are discussed, in particular we will analyze the hybrid system consisting on a planar quantum rotor and a spin system under a wide class of boundary conditions.Comment: 26 pages, 5 figure

Fast on-wafer electrical, mechanical, and electromechanical characterization of piezoresistive cantilever force sensors

Validation of a technological process requires an intensive characterization of the performance of the resulting devices, circuits, or systems. The technology for the fabrication of micro and nanoelectromechanical systems (MEMS and NEMS) is evolving rapidly, with new kind of device concepts for applications like sensing or harvesting are being proposed and demonstrated. However, the characterization tools and methods for these new devices are still not fully developed. Here, we present an on-wafer, highly precise, and rapid characterization method to measure the mechanical, electrical, and electromechanical properties of piezoresistive cantilevers. The setup is based on a combination of probe-card and atomic force microscopy technology, it allows accessing many devices across a wafer and it can be applied to a broad range of MEMS and NEMS. Using this setup we have characterized the performance of multiple submicron thick piezoresistive cantilever force sensors. For the best design we have obtained a force sensitivity ℜ_F = 158μV/nN, a noise of 5.8 μV (1 Hz–1 kHz) and a minimum detectable force of 37 pN with a relative standard deviation of σ_r ≈ 8%. This small value of σr, together with a high fabrication yield >95%, validates our fabrication technology. These devices are intended to be used as bio-molecular detectors for the measurement of intermolecular forces between ligand and receptor molecule pairs

Probing the strange Higgs coupling at lepton colliders using light-jet flavor tagging

We propose a method to probe the coupling of the Higgs to strange quarks by tagging strange jets at future lepton colliders. For this purpose we describe a jet-flavor observable, $J_F$, that is correlated with the flavor of the quark associated with the hard part of the jet. Using this variable, we set up a strangeness tagger aimed at studying the decay $h\to s\bar{s}$. We determine the sensitivity of our method to the strange Yukawa coupling, and find it to be of the order of the standard-model expectation.Comment: 6 pages, v2 accepted for publication in PR