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Institution

Vienna University of Technology

EducationVienna, Austria
About: Vienna University of Technology is a education organization based out in Vienna, Austria. It is known for research contribution in the topics: Laser & Cloud computing. The organization has 16723 authors who have published 49341 publications receiving 1302168 citations.


Papers
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Journal ArticleDOI
Khachatryan, Albert M. Sirunyan, Armen Tumasyan, Wolfgang Adam  +2118 moreInstitutions (3)
TL;DR: In this article, the performance and strategies used in electron reconstruction and selection at CERN LHC are presented based on data corresponding to an integrated luminosity of 19.7 inverse femtobarns, collected in proton-proton collisions at sqrt(s) = 8 TeV.
Abstract: The performance and strategies used in electron reconstruction and selection at CMS are presented based on data corresponding to an integrated luminosity of 19.7 inverse femtobarns, collected in proton-proton collisions at sqrt(s) = 8 TeV at the CERN LHC. The paper focuses on prompt isolated electrons with transverse momenta ranging from about 5 to a few 100 GeV. A detailed description is given of the algorithms used to cluster energy in the electromagnetic calorimeter and to reconstruct electron trajectories in the tracker. The electron momentum is estimated by combining the energy measurement in the calorimeter with the momentum measurement in the tracker. Benchmark selection criteria are presented, and their performances assessed using Z, Upsilon, and J/psi decays into electron-positron pairs. The spectra of the observables relevant to electron reconstruction and selection as well as their global efficiencies are well reproduced by Monte Carlo simulations. The momentum scale is calibrated with an uncertainty smaller than 0.3%. The momentum resolution for electrons produced in Z boson decays ranges from 1.7 to 4.5%, depending on electron pseudorapidity and energy loss through bremsstrahlung in the detector material.

633 citations

Journal ArticleDOI
TL;DR: A variety of algorithms have been developed by CMS to select b-quark jets based on variables such as the impact parameters of charged-particle tracks, the properties of reconstructed decay vertices, and the presence or absence of a lepton as mentioned in this paper.
Abstract: At the Large Hadron Collider, the identification of jets originating from b quarks is important for searches for new physics and for measurements of standard model processes. A variety of algorithms has been developed by CMS to select b-quark jets based on variables such as the impact parameters of charged-particle tracks, the properties of reconstructed decay vertices, and the presence or absence of a lepton, or combinations thereof. The performance of these algorithms has been measured using data from proton-proton collisions at the LHC and compared with expectations based on simulation. The data used in this study were recorded in 2011 at √s = 7 TeV for a total integrated luminosity of 5.0 fb^(-1). The efficiency for tagging b-quark jets has been measured in events from multijet and t-quark pair production. CMS has achieved a b-jet tagging efficiency of 85% for a light-parton misidentification probability of 10% in multijet events. For analyses requiring higher purity, a misidentification probability of only 1.5% has been achieved, for a 70% b-jet tagging efficiency.

631 citations

Journal ArticleDOI
TL;DR: In this paper, the state-of-the-art research, current obstacles and future needs and directions for the following four-step iterative process: (1) occupant monitoring and data collection, (2) model development, (3) model evaluation, and (4) model implementation into building simulation tools.

629 citations

Journal ArticleDOI
TL;DR: BoltzTraP2 is a software package for calculating a smoothed Fourier expression of periodic functions and the Onsager transport coefficients for extended systems using the linearized Boltzmann transport equation within the relaxation time approximation.

624 citations

Journal ArticleDOI
TL;DR: This work proposes a generic and simple framework comprising three steps: constructing a cost volume, fast cost volume filtering, and 3) Winner-Takes-All label selection that achieves 1) disparity maps in real time whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and 2) optical flow fields which contain very fine structures as well as large displacements.
Abstract: Many computer vision tasks can be formulated as labeling problems. The desired solution is often a spatially smooth labeling where label transitions are aligned with color edges of the input image. We show that such solutions can be efficiently achieved by smoothing the label costs with a very fast edge-preserving filter. In this paper, we propose a generic and simple framework comprising three steps: 1) constructing a cost volume, 2) fast cost volume filtering, and 3) Winner-Takes-All label selection. Our main contribution is to show that with such a simple framework state-of-the-art results can be achieved for several computer vision applications. In particular, we achieve 1) disparity maps in real time whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and 2) optical flow fields which contain very fine structures as well as large displacements. To demonstrate robustness, the few parameters of our framework are set to nearly identical values for both applications. Also, competitive results for interactive image segmentation are presented. With this work, we hope to inspire other researchers to leverage this framework to other application areas.

618 citations


Authors

Showing all 16934 results

NameH-indexPapersCitations
Krzysztof Matyjaszewski1691431128585
Wolfgang Wagner1562342123391
Marco Zanetti1451439104610
Sridhara Dasu1401675103185
Duncan Carlsmith1381660103642
Ulrich Heintz136168899829
Matthew Herndon133173297466
Frank Würthwein133158494613
Alain Hervé132127987763
Manfred Jeitler132127889645
David Taylor131246993220
Roberto Covarelli131151689981
Patricia McBride129123081787
David Smith1292184100917
Lindsey Gray129117081317
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023171
2022379
20212,527
20202,811
20192,846
20182,650