Institution
National Academy of Sciences of Belarus
Government•Minsk, Belarus•
About: National Academy of Sciences of Belarus is a government organization based out in Minsk, Belarus. It is known for research contribution in the topics: Laser & Large Hadron Collider. The organization has 13131 authors who have published 16413 publications receiving 202912 citations. The organization is also known as: NASB.
Topics: Laser, Large Hadron Collider, Heat transfer, Luminescence, Excited state
Papers published on a yearly basis
Papers
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TL;DR: In this article, a search for the Standard Model Higgs boson in proton-proton collisions with the ATLAS detector at the LHC is presented, which has a significance of 5.9 standard deviations, corresponding to a background fluctuation probability of 1.7×10−9.
9,282 citations
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23 Feb 2020
TL;DR: The ATLAS detector as installed in its experimental cavern at point 1 at CERN is described in this paper, where a brief overview of the expected performance of the detector when the Large Hadron Collider begins operation is also presented.
Abstract: The ATLAS detector as installed in its experimental cavern at point 1 at CERN is described in this paper. A brief overview of the expected performance of the detector when the Large Hadron Collider begins operation is also presented.
3,111 citations
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Radboud University Nijmegen1, Eindhoven University of Technology2, Utrecht University3, Beth Israel Deaconess Medical Center4, Massachusetts Institute of Technology5, Harvard University6, The Chinese University of Hong Kong7, Munich Business School8, Middle East Technical University9, University of Toronto10, University of Warwick11, Coventry Health Care12, Qatar University13, HTW Berlin - University of Applied Sciences14, Tampere University of Technology15, University of Tampere16, Technische Universität München17, Osaka University18, University of South Florida19, National Academy of Sciences of Belarus20, University of Castilla–La Mancha21, Pierre-and-Marie-Curie University22, Pontifical Catholic University of Peru23
TL;DR: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.
Abstract: Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884];P Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.
2,116 citations
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TL;DR: A measurement of the Higgs boson mass is presented based on the combined data samples of the ATLAS and CMS experiments at the CERN LHC in the H→γγ and H→ZZ→4ℓ decay channels.
Abstract: A measurement of the Higgs boson mass is presented based on the combined data samples of the ATLAS and CMS experiments at the CERN LHC in the H→γγ and H→ZZ→4l decay channels. The results are obtained from a simultaneous fit to the reconstructed invariant mass peaks in the two channels and for the two experiments. The measured masses from the individual channels and the two experiments are found to be consistent among themselves. The combined measured mass of the Higgs boson is mH=125.09±0.21 (stat)±0.11 (syst) GeV.
1,567 citations
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TL;DR: An extensive review of the scheduling literature on models with setup times (costs) from then to date covering more than 300 papers is provided, which classifies scheduling problems into those with batching and non-batching considerations, and with sequence-independent and sequence-dependent setup times.
1,264 citations
Authors
Showing all 13177 results
Name | H-index | Papers | Citations |
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David Cameron | 154 | 1586 | 126067 |
Steinar Stapnes | 129 | 932 | 76610 |
Sergey Kuleshov | 129 | 1107 | 82935 |
Heidi Sandaker | 128 | 999 | 76517 |
Vincent Garonne | 128 | 921 | 76980 |
Farid Ould-Saada | 128 | 931 | 76394 |
Ole Røhne | 128 | 1038 | 75752 |
James Catmore | 127 | 892 | 75086 |
Børge Kile Gjelsten | 126 | 670 | 68535 |
Pavel Tsiareshka | 126 | 874 | 73362 |
Lidia Dell'Asta | 124 | 825 | 69464 |
Katarina Pajchel | 120 | 571 | 62023 |
Are Strandlie | 120 | 576 | 67830 |
Andrey L. Rogach | 117 | 576 | 46820 |
Francesco Ragusa | 109 | 923 | 50931 |