Metal-Insulator Transition and Lattice Instability of Paramagnetic V2O3

We determine the electronic structure and phase stability of paramagnetic V$_2$O$_3$ at the Mott-Hubbard metal-insulator phase transition, by employing a combination of an ab initio method for calculating band structures with dynamical mean-field theory. The structural transformation associated with the metal-insulator transition is found to occur upon a slight expansion of the lattice volume by $\sim 1.5$ %, in agreement with experiment. Our results show that the structural change precedes the metal-insulator transition, implying a complex interplay between electronic and lattice degrees of freedom at the transition. Electronic correlations and full charge self-consistency are found to be crucial for a correct description of the properties of V$_2$O$_3$.Comment: 5 pages, 4 figure

The neural network art which uses the Hamming distance to measure an image similarity score

This study reports a new discrete neural network of Adaptive Resonance Theory (ART-1H) in which the Hamming distance is used for the first time to estimate the measure of binary images (vectors) proximity. For the development of a new neural network of adaptive resonance theory, architectures and operational algorithms of discrete neural networks ART-1 and discrete Hamming neural networks are used. Unlike the discrete neural network adaptive resonance theory ART-1 in which the similarity parameter which takes into account single images components only is used as a measure of images (vectors) proximity in the new network in the Hamming distance all the components of black and white images are taken into account. In contrast to the Hamming network, the new network allows the formation of typical vector classes representatives in the learning process not using information from the teacher which is not always reliable. New neural network can combine the advantages of the Hamming neural network and ART-1 by setting a part of source information in the form of reference images (distinctive feature and advantage of the Hamming neural network) and obtaining some of typical image classes representatives using learning algorithms of the neural network ART-1 (the dignity of the neural network ART-1). The architecture and functional algorithms of the new neural network ART which has the properties of both neural network ART-1 and the Hamming network were proposed and investigated. The network can use three methods to get information about typical image classes representatives: teacher information, neural network learning process, third method uses a combination of first two methods. Property of neural network ART-1 and ART-1H, related to the dependence of network learning outcomes or classification of input information to the order of the vectors (images) can be considered not as a disadvantage of the networks but as a virtue. This property allows to receive various types of input information classification which cannot be obtained using other neural networks

Correlation strength, Lifshitz transition and the emergence of a two- to three-dimensional crossover in FeSe under pressure

We report a detailed theoretical study of the electronic structure, spectral properties, and lattice parameters of bulk FeSe under pressure using a fully charge self-consistent implementation of the density functional theory plus dynamical mean-field theory method (DFT+DMFT). In particular, we perform a structural optimization and compute the evolution of the lattice parameters (volume, $c/a$ ratio, and the internal $z$ position of Se) and the electronic structure of the tetragonal (space group $P4/nmm$) paramagnetic FeSe. Our results for the lattice parameters are in good quantitative agreement with experiment. The $c/a$ ratio is slightly overestimated by about $3$~\%, presumably due to the absence of the van der Waals interactions between the FeSe layers in our calculations. The lattice parameters determined within DFT are off the experimental values by a remarkable $\sim$$6$-$15$~\%, implying a crucial importance of electron correlations. Upon compression to $10$~GPa, the $c/a$ ratio and the lattice volume show a decrease by $2$ and $10$~\%, respectively, while the Se $z$ coordinate weakly increases by $\sim$$2$~\%. Most importantly, our results reveal a topological change of the Fermi surface (Lifshitz transition) which is accompanied by a two- to three-dimensional crossover. Our results indicate a small reduction of the quasiparticle mass renormalization $m^*/m$ by about $5$~\% for the $e$ and less than $1$~\% for the $t_2$ states, as compared to ambient pressure. The behavior of the momentum-resolved magnetic susceptibility $\chi({\bf q})$ shows no topological changes of magnetic correlations under pressure, but demonstrates a reduction of the degree of the in-plane $(\pi,\pi)$ stripe-type nesting. Our results for the electronic structure and lattice parameters of FeSe are in good qualitative agreement with recent experiments on its isoelectronic counterpart FeSe$_{1-x}$S$_x$.Comment: 10 pages, 6 figure

The Study of Nitrogen Transformation in Fresh Water: Experiments and Mathematical Modeling

Two main transformations occur in water environments -- one due to the effect of microorganisms and the other due to chemical reactions. Both transformations are very closely interwoven. Of the transformation processes in the water environment, nitrogen transformation is perhaps the most interesting because nitrogen and its compounds (both organic and mineral), affect the development of practically all aquatic microorganisms and therefore determine the trophic state and the quality of a water environment. Nitrogen compounds are present in sewage and other waste water discharged into water bodies. Therefore, it is quite understandable why during the last few years nitrogen transformation is the subject of study at descriptive and experimental levels, as well as by mathematical modeling techniques. This paper reports on the results of a collaborative study between IIASA and the Institute of Experimental Biology and Ecology of the Slovak Academy of Sciences in Bratislava, on nitrogen transformations. The data of twelve experiments covering a broad set of initial conditions in nitrogen concentrations and at two temperatures (180 degrees Celsius and 120 degrees Celsius) are presented in this report. These experimental data were analyzed with the help of the mathematical model developed at IIASA (WP-80-86) and intended for understanding processes of nitrogen transformation in water environments. The results of model description of nitrogen compound dynamics are evaluated by statistics to find a quantitative criteria in model assessment. In the discussion of simulation results, attention was focused on the analysis of bacterial activities in the conversion of organic as well as mineral nitrogen forms. The results reported here are considered to be the basis for the simulation of nitrogen dynamics in water bodies and for studying various aspects of ecology and aquatic ecosystem behavior

Neural networks art: solving problems with multiple solutions and new teaching algorithm

A new discrete neural networks adaptive resonance theory (ART), which allows solving problems with multiple solutions, is developed. New algorithms neural networks teaching ART to prevent degradation and reproduction classes at training noisy input data is developed. Proposed learning algorithms discrete ART networks, allowing obtaining different classification methods of input

Hidden attractors in fundamental problems and engineering models

Recently a concept of self-excited and hidden attractors was suggested: an attractor is called a self-excited attractor if its basin of attraction overlaps with neighborhood of an equilibrium, otherwise it is called a hidden attractor. For example, hidden attractors are attractors in systems with no equilibria or with only one stable equilibrium (a special case of multistability and coexistence of attractors). While coexisting self-excited attractors can be found using the standard computational procedure, there is no standard way of predicting the existence or coexistence of hidden attractors in a system. In this plenary survey lecture the concept of self-excited and hidden attractors is discussed, and various corresponding examples of self-excited and hidden attractors are considered