Tomsk Polytechnic University

About: Tomsk Polytechnic University is a education organization based out in Tomsk, Russia. It is known for research contribution in the topics: Combustion & Ignition system. The organization has 9190 authors who have published 13224 publications receiving 103735 citations.

Measurements of Higgs boson properties in the diphoton decay channel in proton-proton collisions at √s=13 TeV

Abstract: Measurements of Higgs boson properties in the H → γγ decay channel are reported. The analysis is based on data collected by the CMS experiment in proton-proton collisions at $ \sqrt{s}=13 $ TeV during the 2016 LHC running period, corresponding to an integrated luminosity of 35.9 fb$^{−1}$. Allowing the Higgs mass to float, the measurement yields a signal strength relative to the standard model prediction of 1.18$_{− 0.14}^{+ 0.17}$ = 1.18$_{− 0.11}^{+ 0.12}$ (stat)$_{− 0.07}^{+ 0.09}$ (syst)$_{− 0.06}^{+ 0.07}$ (theo), which is largely insensitive to the exact Higgs mass around 125 GeV. Signal strengths associated with the different Higgs boson production mechanisms, couplings to bosons and fermions, and effective couplings to photons and gluons are also measured.

Deep Learning for ECG Classification

Abstract: The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. Currently, there are many machine learning (ML) solutions which can be used for analyzing and classifying ECG data. However, the main disadvantages of these ML results is use of heuristic hand-crafted or engineered features with shallow feature learning architectures. The problem relies in the possibility not to find most appropriate features which will give high classification accuracy in this ECG problem. One of the proposing solution is to use deep learning architectures where first layers of convolutional neurons behave as feature extractors and in the end some fully-connected (FCN) layers are used for making final decision about ECG classes. In this work the deep learning architecture with 1D convolutional layers and FCN layers for ECG classification is presented and some classification results are showed.

The correlation between electrical conductivity and second-order Raman modes of laser-reduced graphene oxide

Abstract: Raman spectroscopy is the tool of choice in the physicochemical investigation of carbon nanomaterials. However, Raman analysis of graphene oxide (GO) is lagging in comparison to the rich information gained in the case of carbon nanotubes and graphene. Here, we carried out a joint current sensing atomic force microscopy (CSAFM) and Raman spectroscopy investigation of laser-reduced GO. Reduced graphene oxide (rGO) was obtained under different laser powers in the range from 0.1 to 10 mW (532 nm). We compare the Raman spectra and the electrical conductivity at the nanoscale obtained by current sensing atomic force microscopy. Our analysis shows that three bands in the second-order region (2D, D + G, 2G), in the range from 2500 to 3200 cm−1, are uniquely sensitive to the degree of reduction. Moreover, we found that the changes in peak area ratios AD+G/AD and A2G/AD show a direct correlation with the electrical resistance of rGO. We establish an optical micro-spectroscopy way to assess the degree of reduction in laser-reduced GO. These new insights provide a convenient and useful way to investigate the reduction of rGO from the fitting analysis of Raman spectra, becoming a useful tool in fundamental research and the development of rGO-based microdevices.