0000000001047706
AUTHOR
Matthias Weber
Regularized Regression Incorporating Network Information: Simultaneous Estimation of Covariate Coefficients and Connection Signs
We develop an algorithm that incorporates network information into regression settings. It simultaneously estimates the covariate coefficients and the signs of the network connections (i.e. whether the connections are of an activating or of a repressing type). For the coefficient estimation steps an additional penalty is set on top of the lasso penalty, similarly to Li and Li (2008). We develop a fast implementation for the new method based on coordinate descent. Furthermore, we show how the new methods can be applied to time-to-event data. The new method yields good results in simulation studies concerning sensitivity and specificity of non-zero covariate coefficients, estimation of networ…
Luminosity determination in pp collisions at s=7 TeV using the ATLAS detector at the LHC
Measurements of luminosity obtained using the ATLAS detector during early running of the Large Hadron Collider (LHC) at s√=7 TeV are presented. The luminosity is independently determined using several detectors and multiple algorithms, each having different acceptances, systematic uncertainties and sensitivity to background. The ratios of the luminosities obtained from these methods are monitored as a function of time and of μ, the average number of inelastic interactions per bunch crossing. Residual time- and μ-dependence between the methods is less than 2% for 0<μ<2.5. Absolute luminosity calibrations, performed using beam separation scans, have a common systematic uncertainty of ±11%, do…
Network-Constrained Covariate Coefficient and Connection Sign Estimation
Often, variables are linked to each other via a network. When such a network structure is known, this knowledge can be incorporated into regularized regression settings via a network penalty term. However, when the type of interaction via the network is unknown (that is, whether connections are of an activating or a repressing type), the connection signs have to be estimated simultaneously with the covariate coefficients. This can be done with an algorithm iterating a connection sign estimation step and a covariate coefficient estimation step. We develop such an algorithm and show detailed simulation results and an application forecasting event times. The algorithm performs well in a variet…
A detector for CLIC: main parameters and performance
Together with the recent CLIC detector model CLICdet a new software suite was introduced for the simulation and reconstruction of events in this detector. This note gives a brief introduction to CLICdet and describes the CLIC experimental conditions at 380 GeV and 3 TeV, including beam-induced backgrounds. The simulation and reconstruction tools are introduced, and the physics performance obtained is described in terms of single particles, particles in jets, jet energy resolution and flavour tagging. The performance of the very forward electromagnetic calorimeters is also discussed.
Observation of the rare B(s)(0) + decay from the combined analysis of CMS and LHCb data.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported licence.-- et al.
A detector for CLIC: main parameters and performance
Together with the recent CLIC detector model CLICdet a new software suite was introduced for the simulation and reconstruction of events in this detector. This note gives a brief introduction to CLICdet and describes the CLIC experimental conditions at 380 GeV and 3 TeV, including beam-induced backgrounds. The simulation and reconstruction tools are introduced, and the physics performance obtained is described in terms of single particles, particles in jets, jet energy resolution and flavour tagging. The performance of the very forward electromagnetic calorimeters is also discussed.