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Institution

University of Texas at Arlington

EducationArlington, Texas, United States
About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.


Papers
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Journal ArticleDOI
TL;DR: Hard magnetic nanoparticles based on the Sm(2)Co(17) and SmCo(5) systems have been successfully produced using a surfactant-assisted ball milling technique and it has been found that surfactants play multifold roles in the processing.
Abstract: Hard magnetic nanoparticles based on the Sm2Co17 and SmCo5 systems have been successfully produced using a surfactant-assisted ball milling technique. A size-selection process has been developed to obtain nanoparticles of different sizes with narrow size distribution. Significant room-temperature coercivity up to 3.1 kOe has been achieved with the Sm2Co17-based nanoparticles of an average size of 23 nm. It has been found that surfactants play multifold roles in the processing.

182 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, Jalal Abdallah3, Ovsat Abdinov4  +2813 moreInstitutions (189)
TL;DR: In this paper, a neural network is used to discriminate between signal and background events, the latter being dominated by +jets production, and an observed (expected) limit of 3.4 (2.2) times the Standard Model cross section is obtained at 95 % confidence level.
Abstract: A search for the Standard Model Higgs boson produced in association with a top-quark pair, , is presented. The analysis uses 20.3 fb(-1) of pp collision data at , collected with the ATLAS detector at the Large Hadron Collider during 2012. The search is designed for the decay mode and uses events containing one or two electrons or muons. In order to improve the sensitivity of the search, events are categorised according to their jet and b-tagged jet multiplicities. A neural network is used to discriminate between signal and background events, the latter being dominated by +jets production. In the single-lepton channel, variables calculated using a matrix element method are included as inputs to the neural network to improve discrimination of the irreducible background. No significant excess of events above the background expectation is found and an observed (expected) limit of 3.4 (2.2) times the Standard Model cross section is obtained at 95 % confidence level. The ratio of the measured signal cross section to the Standard Model expectation is found to be assuming a Higgs boson mass of 125 Gev.

182 citations

Journal ArticleDOI
Georges Aad1, T. Abajyan2, Brad Abbott3, J. Abdallah4  +2936 moreInstitutions (201)
TL;DR: In this article, the authors studied the long-range correlations observed in p + Pb collisions at root s(NN) = 5.02 TeV, the second-order anisotropy parameter of charged particles.

182 citations

Journal ArticleDOI
01 Feb 2012
TL;DR: The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests.
Abstract: Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors In these systems, the user behaviors are influenced by the hidden interests of the users Learning to leverage the information about user interests is often critical for making better recommendations However, existing collaborative-filtering-based recommender systems are usually focused on exploiting the information about the user's interaction with the systems; the information about latent user interests is largely underexplored To that end, inspired by the topic models, in this paper, we propose a novel collaborative-filtering-based recommender system by user interest expansion via personalized ranking, named iExpand The goal is to build an item-oriented model-based collaborative-filtering framework The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests Moreover, iExpand strategically deals with many issues that exist in traditional collaborative-filtering approaches, such as the overspecialization problem and the cold-start problem Finally, we evaluate iExpand on three benchmark data sets, and experimental results show that iExpand can lead to better ranking performance than state-of-the-art methods with a significant margin

181 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, Jalal Abdallah3, A. A. Abdelalim4  +2627 moreInstitutions (185)
TL;DR: The ATLAS Inner Detector as mentioned in this paper is a composite tracking system consisting of silicon pixels, silicon strips and straw tubes in a 2 T magnetic field, which was completed in 2008 and the detector took part in data-taking with single LHC beams and cosmic rays.
Abstract: The ATLAS Inner Detector is a composite tracking system consisting of silicon pixels, silicon strips and straw tubes in a 2 T magnetic field. Its installation was completed in August 2008 and the detector took part in data-taking with single LHC beams and cosmic rays. The initial detector operation, hardware commissioning and in-situ calibrations are described. Tracking performance has been measured with 7.6 million cosmic-ray events, collected using a tracking trigger and reconstructed with modular pattern-recognition and fitting software. The intrinsic hit efficiency and tracking trigger efficiencies are close to 100%. Lorentz angle measurements for both electrons and holes, specific energy-loss calibration and transition radiation turn-on measurements have been performed. Different alignment techniques have been used to reconstruct the detector geometry. After the initial alignment, a transverse impact parameter resolution of 22.1 +/- 0.9 mu m and a relative momentum resolution sigma (p) /p=(4.83 +/- 0.16)x10(-4) GeV(-1)xp (T) have been measured for high momentum tracks.

181 citations


Authors

Showing all 11918 results

NameH-indexPapersCitations
Zhong Lin Wang2452529259003
Hyun-Chul Kim1764076183227
David H. Adams1551613117783
Andrew White1491494113874
Kaushik De1391625102058
Steven F. Maier13458860382
Andrew Brandt132124694676
Amir Farbin131112583388
Evangelos Gazis131114784159
Lee Sawyer130134088419
Fernando Barreiro130108283413
Stavros Maltezos12994379654
Elizabeth Gallas129115785027
Francois Vazeille12995279800
Sotirios Vlachos12878977317
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202353
2022243
20211,721
20201,664
20191,493
20181,462