Papers by Valdas Rapsevicius
BTau primary dataset in AOD format from RunB of 2010
ZeroBias primary dataset in AOD format from RunB of 2010
CERN-LHC. Measurement of inclusive charged hadron distributions in P P collisions at centre-of-ma... more CERN-LHC. Measurement of inclusive charged hadron distributions in P P collisions at centre-of-mass energies 900 and 2360 GeV. Data on multiplicity densities as functions of transverse momentum and pseusdorapidity are presented for the non-single-diffractive (NSD) class of events. In the tables all the errors shown are a linear combination of statistical and systematic uncertainties with latter being dominant in almost every case. Numerical values were supplied by CMS.
CERN-LHC. Measurement of inclusive charged hadron distributions in P P collisions at centre-of-ma... more CERN-LHC. Measurement of inclusive charged hadron distributions in P P collisions at centre-of-mass energies 900 and 2360 GeV. Data on multiplicity densities as functions of transverse momentum and pseusdorapidity are presented for the non-single-diffractive (NSD) class of events. In the tables all the errors shown are a linear combination of statistical and systematic uncertainties with latter being dominant in almost every case. Numerical values were supplied by CMS.
CERN-LHC. Measurement of inclusive charged hadron distributions in P P collisions at centre-of-ma... more CERN-LHC. Measurement of inclusive charged hadron distributions in P P collisions at centre-of-mass energies 900 and 2360 GeV. Data on multiplicity densities as functions of transverse momentum and pseusdorapidity are presented for the non-single-diffractive (NSD) class of events. In the tables all the errors shown are a linear combination of statistical and systematic uncertainties with latter being dominant in almost every case. Numerical values were supplied by CMS.
CERN-LHC. Measurement of inclusive charged hadron distributions in P P collisions at centre-of-ma... more CERN-LHC. Measurement of inclusive charged hadron distributions in P P collisions at centre-of-mass energies 900 and 2360 GeV. Data on multiplicity densities as functions of transverse momentum and pseusdorapidity are presented for the non-single-diffractive (NSD) class of events. In the tables all the errors shown are a linear combination of statistical and systematic uncertainties with latter being dominant in almost every case. Numerical values were supplied by CMS.
CERN-LHC. Measurement of inclusive charged hadron distributions in P P collisions at centre-of-ma... more CERN-LHC. Measurement of inclusive charged hadron distributions in P P collisions at centre-of-mass energies 900 and 2360 GeV. Data on multiplicity densities as functions of transverse momentum and pseusdorapidity are presented for the non-single-diffractive (NSD) class of events. In the tables all the errors shown are a linear combination of statistical and systematic uncertainties with latter being dominant in almost every case. Numerical values were supplied by CMS.
JetMETTauMonitor primary dataset in AOD format from RunB of 2010
EGMonitor primary dataset in AOD format from RunB of 2010
Electron primary dataset in AOD format from RunB of 2010
Commissioning primary dataset in AOD format from RunB of 2010
METFwd primary dataset in AOD format from RunB of 2010
Photon primary dataset in AOD format from RunB of 2010
MultiJet primary dataset in AOD format from RunB of 2010
MuOnia primary dataset in AOD format from RunB of 2010
Jet primary dataset in AOD format from RunB of 2010
MuMonitor primary dataset in AOD format from RunB of 2010
Mu primary dataset in AOD format from RunB of 2010
MinimumBias primary dataset in AOD format from RunB of 2010
Nonlinear Analysis: Modelling and Control, 2020
This work presents convolutional neural network (CNN) based methodology for electroencephalogram ... more This work presents convolutional neural network (CNN) based methodology for electroencephalogram (EEG) classification by diagnosis: benign childhood epilepsy with centrotemporal spikes (rolandic epilepsy) (Group I) and structural focal epilepsy (Group II). Manual classification of these groups is sometimes difficult, especially, when no clinical record is available, thus presenting a need for an algorithm for automatic classification. The presented algorithm has the following steps: (i) EEG spike detection by morphological filter based algorithm; (ii) classification of EEG spikes using preprocessed EEG signal data from all channels in the vicinity of the spike detected; (iii) majority rule classifier application to all EEG spikes from a single patient. Classification based on majority rule allows us to achieve 80% average accuracy (despite the fact that from a single spike one would obtain only 58% accuracy).
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Papers by Valdas Rapsevicius