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In quantum mechanics, randomness is postulated as a separate axiom. De Broglie's theory allows one to reproduce quantum phenomena from completely deterministic formalism. But the question of the quantum randomness emergency in the de Broglie-Bohm theory needs special attention. In the work [G. Tastevin, F. Lalo\"e, Comptes Rendus. Physique, 2021, 22, 1, pp. 99-116], it was shown that it arises as a result of the device microscopic state influence on the measurement result. In our work, we investigate the genesis of the quantum randomness in the de Broglie's theory in more details. Namely, we investigate the target system and the device behaviour in the decoherence process and model the measurement of canonical-conjugate observables. We propose a thought experiment which tests the opportunity of the information transition using beable-parameters violating the uncertainty relation. We show that in the measurement process, the strong stochastic fluctuations of beable parameters arise randomising the system in accordance with the uncertainty relation. Nevertheless, we find anomalous models of measurement in which these fluctuations can be neglected. These special models require further investigation., Comment: 8 pages (2 columns), 5 figures

We present a theoretical study of an exciton-polariton annular microcavity with an additional quasiperiodic structure along the ring which is implemented in the form of a bicosine dependence. We demonstrate that for a sufficiently strong quasiperiodic modulation, the microcavity features a sharp mobility edge separating a cluster of localized states from the rest of the spectrum consisting of states extended over the whole ring. Localized modes can be excited using a resonant pump whose topological charge determines the phase distribution of excited patterns. Repulsive polariton interactions make the resonance peaks distinctively asymmetric and enable the formation of multistable states which feature the attractor-like dynamical behavior \rev{and hysteresis}. We also demonstrate that the localized states can be realized in a biannular cavity that consists of two rings, each having periodic modulation, such that the periods of two modulations are different., Comment: 8 pages, 6 figures; to appear in Phys. Rev. B

We present first theoretical predictions for various observables related to the $\bar B_d \to \mu^+ \mu^- e^+ e^-$ decay within the framework of the Standard Model. Our study encompasses the branching ratios, differential distributions, and forward-backward leptonic asymmetries. To obtain these predictions, we account for several contributions: resonance contribution from $\rho(770)$ and significant contribution $\omega(782)$; contributions from four charmonium resonances: $\psi(3770)$, $\psi(4040)$, $\psi(4160)$, and $\psi(4415)$; additional contributions from the "tails" of $J/\psi$ and $\psi(2S)$ resonances; non-resonant contributions arising from virtual photon emission by a $b$-quark of $B_d$ meson, bremsstrahlung, and weak annihilation. In our calculations we employ the model of vector meson dominance (VMD) to assess the resonance contributions accurately. We provide the predictions for the branching ratio of the $\bar B_d \to \mu^+ \mu^- e^+ e^-$ decay both with and without the resonant contribution from $\omega(782)$., Comment: arXiv admin note: text overlap with arXiv:2112.06093

Fast multi-phase processes in methane hydrate-bearing samples are challenging for micro-CT quantitative study because of complex tomographic data analysis involving time-consuming segmentation procedures. This is due to the sample multi-scale structure changing in time, low X-ray attenuation and phase contrast between solid and fluid materials, as well as large amount of data acquired during dynamic processes. We propose a hybrid approach for automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media. First, we use an optimized 3D U-net neural network to perform segmentation of mineral grains that are characterized by low contrast to the surrounding pore brine-saturated phases. Then, we perform statistical clustering based on the Gaussian mixture model for separating the pore-space phases that are characterized by gray-level instabilities caused by dynamic processes during hydrate formation. The proposed approach was used for segmenting several hundred gigabytes of data acquired during an in-situ tomographic experiment at a synchrotron. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as the incremental pore-brine flow and redistribution.

In the framework of the Standard Model we present new theoretical predictions for the branching ratios, double and single differential distributions and forward -- backward leptonic asymmetries for the $\bar B_s \to \mu^+ \mu^- e^+ e^-$ decay. In our consideration we take into account the $\phi(1020)$ -- resonance contribution; the main contributions of four charmonium resonances : $\psi(3770)$, $\psi(4040)$, $\psi(4160)$ and $\psi(4415)$; $u\bar{u}$ -- resonant contribution from $\rho(770)$ and $\omega(782)$; tails contributions from $J/\psi$ and $\psi(2S)$ resonances; non -- resonant contribution of the $b\,\bar{b}$ - pairs, bremsstrahlung and the contribution of the weak annihilation. We provide the prediction for the branching ratio of $\bar B_s \to \mu^+ \mu^- e^+ e^-$ decay with and without the $\phi(1020)$ -- resonance contribution. We use the model of vector meson dominance (VMD) for calculation of resonances contributions and take into account all substantive terms in $\bar B_s \to \mu^+ \mu^- e^+ e^-$ amplitude that was not considered in the previously papers.

This paper presents a novel way to predict options price for one day in advance, utilizing the method of Quasi-Reversibility for solving the Black-Scholes equation. The Black-Scholes equation solved forwards in time with Tikhonov regularization as an ill-posed problem allows for extrapolation of option prices. This provides high-accuracy results, which can be further improved by applying Neural Network Machine Learning to the solution of the Black-Scholes equation as well as initial and boundary conditions and implied volatility. Using historical option and stock price data the results obtained from the method of Quasi-Reversibility and Machine Learning method are compared in terms of accuracy, precision and recall. It is shown that these methods can be applied to the real-world trading within a variety of trading strategies.