One of the methods for analysis of complex spectral contours (especially for spectra of liquid objects) is their decomposition into a limited number of spectral bands with physically reasonable shapes (Gaussian, Lorentzian, Voigt etc.). Consequent analysis of the dependencies of the parameters of these bands on some external conditions in which the spectra are obtained may reveal some...
In this paper we estimate accuracy of solving the task of relation extraction from texts containing pharmacologically significant information on the set of corpora in two languages:
1) the expanded version of RDRS corpus, that contains texts of internet reviews on medications in Russian;
2) the DDI2013 dataset containing MEDLINE abstracts and documents from DrugBank database in English;
3)...
V.V. Korenkov, A.G. Reshetnikov, S.V. Ulyanov, P.V. Zrelov
MLIT, JINR
The physical interpretation of self-organization control process on quantum level is discussed based on the quantum information-thermodynamic models of the exchange and extraction of quantum (hidden) value information from/between classical particle’s trajectories in particle swarm [1,2]. Main physics and information...
Neuromorphic classification of RF-Modulation type is an on-going topic
in SIGINT applications. Neural network training approaches are varied,
each being suited to a certain application. For exemplification I show
the results for BFGS (Broyden-Fletcher-Goldfarb-Shanno) optimisation
in discriminating AM vs FM modulation and of stochastic optimisation
for the challenging case of AM-LSB vs....
Traditional linear approximation of quantum mechanical wave functions are not practically applicable for systems with more than 3 degrees of freedom due to the “the curse of dimensionality”. Indeed, the number of parameters required to describe a wave function in high-dimensional space grows exponentially with the number of degrees of freedom. Inevitably, strong model assumptions should be...
The paper presents the application of the methodology of machine learning (artificial neural networks) and the method of principal component analysis to the problem of classifying data on the base of credit institutions.
The feed-forward neural network (multilayer perceptron with hidden layers) was applied to specially prepared input data. As a result, the set of credit institutions was...
Spiking neural networks which model action potentials in biological neurons are increasingly popular for machine learning applications thanks to ongoing progress in the hardware implementation of spiking networks in low-energy-consuming neuromorphic hardware. However, obtaining a spiking neural network model that solver a classification task as accurately as a formal neural network remains a...
