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Institution

Istanbul Technical University

EducationIstanbul, Turkey
About: Istanbul Technical University is a education organization based out in Istanbul, Turkey. It is known for research contribution in the topics: Fuzzy logic & Large Hadron Collider. The organization has 12889 authors who have published 25081 publications receiving 518242 citations. The organization is also known as: İstanbul Teknik Üniversitesi & Technical University of Istanbul.


Papers
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Journal ArticleDOI
TL;DR: In this article, the in-vacuo dynamic properties of cantilever plates were investigated, such as natural frequencies and mode shapes, of the plates, partially in contact with a fluid.

124 citations

Journal ArticleDOI
TL;DR: Experimental results show that the proposed CNN architecture with a specifically ordered feature set to predict the intraday direction of Borsa Istanbul 100 stocks outperforms both Logistic Regression and CNN that utilizes randomly ordered features.
Abstract: We have extracted different types of indicator, price and temporal features.Previous instances and correlation between features are used to design CNN.We predict the hourly direction of 100 Stocks Borsa Istanbul Stock Market.Proposed method outperforms the CNN that uses randomly ordered features.On average we perform 56.3% Macro Average F-Measure rate on 100 stocks. Stock market price data have non-linear, noisy and non-stationary structure, and therefore prediction of the price or its direction are both challenging tasks. In this paper, we propose a Convolutional Neural Network (CNN) architecture with a specifically ordered feature set to predict the intraday direction of Borsa Istanbul 100 stocks. Feature set is extracted using different indicators, price and temporal information. Correlations between instances and features are utilized to order the features before they are presented as inputs to the CNN. The proposed classifier is compared with a CNN trained with randomly ordered features and Logistic Regression. Experimental results show that the proposed classifier outperforms both Logistic Regression and CNN that utilizes randomly ordered features. Feature selection methods are also utilized to reduce training time and model complexity.

124 citations

Journal ArticleDOI
TL;DR: The existence of top-quark partners with masses below 800 GeV is excluded at a 95% confidence level, and the first limit on these particles from the LHC is significantly more restrictive than previous limits.
Abstract: A search for the production of heavy partners of the top quark with charge 5/3 is performed in events with a pair of same-sign leptons. The data sample corresponds to an integrated luminosity of 19.5 fb^(−1) and was collected at s√=8 TeV by the CMS experiment. No significant excess is observed in the data above the expected background, and the existence of top-quark partners with masses below 800 GeV is excluded at a 95% confidence level, assuming they decay exclusively to tW. This is the first limit on these particles from the LHC, and it is significantly more restrictive than previous limits.

124 citations

Journal ArticleDOI
TL;DR: In this paper, evidence for the associated production of a single top quark and W boson in pp collisions at √s=7 TeV with the CMS experiment at the LHC was presented.
Abstract: Evidence is presented for the associated production of a single top quark and W boson in pp collisions at √s=7 TeV with the CMS experiment at the LHC. The analyzed data correspond to an integrated luminosity of 4.9 fb^(-1). The measurement is performed using events with two leptons and a jet originated from a b quark. A multivariate analysis based on kinematic properties is utilized to separate the tt background from the signal. The observed signal has a significance of 4.0σ and corresponds to a cross section of 16_(-4)^(+5) pb, in agreement with the standard model expectation of 15.6±0.4_(-1.2)^(+1.0) pb.

123 citations


Authors

Showing all 13155 results

NameH-indexPapersCitations
David Miller2032573204840
H. S. Chen1792401178529
Hyun-Chul Kim1764076183227
J. N. Butler1722525175561
Andrea Bocci1722402176461
Bradley Cox1692150156200
Yang Gao1682047146301
J. E. Brau1621949157675
G. A. Cowan1592353172594
David Cameron1541586126067
Andrew D. Hamilton1511334105439
Jongmin Lee1502257134772
A. Artamonov1501858119791
Teresa Lenz1501718114725
Carlos Escobar148118495346
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023137
2022338
20211,860
20201,772
20191,834
20181,643