AI-enhanced approaches are becoming common in astronomical data analysis, including in the galaxy morphological classification. In this study we develop an approach that enhances galaxy classification by incorporating an image denoising pre-processing step, utilizing the U-Net Variational Autoencoder (VAE) architecture and effectively mitigating noise in galaxy images and leading to improved classification performance. Our methodology involves training U-Net VAEs on the EFIGI dataset. To simulate realistic observational conditions, we introduce artifacts such as projected stars, satellite trails, and diffraction patterns into clean galaxy images. The denoised images generated are evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), to quantify the quality improvements. We utilize the denoised images for galaxy classification tasks using models such as DenseNet-201, ResNet50, VGG16 and GCNN. Simulations do reveal that, the models trained on denoised images consistently outperform those trained on noisy images, thus demonstrating the efficiency of the used denoising procedure. The developed approach can be used for other astronomical datasets, via refining the VAE architecture and integrating additional pre-processing strategies, e.g. in revealing of gravitational lenses, cosmic web structures.

Nonnegative matrix factorization (NMF) is a known unsupervised data-reduction method. The principle of the common cause (PCC) is a basic methodological approach in probabilistic causality, which seeks an independent mixture model for the joint probability of two dependent random variables. It turns out that these two concepts are closely related. This relationship is explored reciprocally for several datasets of gray-scale images, which are conveniently mapped into probability models. On one hand, PCC provides a predictability tool that leads to a robust estimation of the effective rank of NMF. Unlike other estimates (e.g., those based on the Bayesian Information Criteria), our estimate of the rank is stable against weak noise. We show that NMF implemented around this rank produces features (basis images) that are also stable against noise and against seeds of local optimization, thereby effectively resolving the NMF nonidentifiability problem. On the other hand, NMF provides an interesting possibility of implementing PCC in an approximate way, where larger and positively correlated joint probabilities tend to be explained better via the independent mixture model. We work out a clustering method, where data points with the same common cause are grouped into the same cluster. We also show how NMF can be employed for data denoising.

High-precision, robust quantum gates are essential components in quantum computation and information processing. In this study, we present an alternative perspective, exploring the potential applicability of quantum gates that exhibit heightened sensitivity to errors. We investigate such sensitive quantum gates, which, beyond their established use in in vivo NMR spectroscopy, quantum sensing, and polarization optics, may offer significant utility in precision quantum metrology and error characterization. Utilizing the composite pulses technique, we derive three fundamental quantum gates with narrowband and passband characteristics -- the X (NOT) gate, the Hadamard gate, and gates enabling arbitrary rotations. To systematically design these composite pulse sequences, we introduce the SU(2), modified-SU(2), and regularization random search methodologies. These approaches, many of which are novel, demonstrate superior performance compared to established sequences in the literature, including NB1, SK1, and PB1.

We study the properties of the symplectic sp(2N) algebra deformed using Dunkl operators, which describe the dynamical symmetry of the generalized N-particle quantum Calogero model. It contains a symmetry subalgebra formed by the deformed unitary generators as well as the (nondeformed) sl(2,R) conformal subalgebra. An explicit relation among the deformed symplectic generators is derived. Based on the matching between the Casimir elements of the conformal spin and Dunkl angular momentum algebras, the independent wavefunctions of the both the standard and generalized Calogero models, expressed in terms of the deformed spherical harmonics, are classified according to infinite-dimensional lowest-state sl(2,R) multiplets. Meanwhile, any polynomial integral of motion of the (generalized) Calogero-Moser model generates a finite-dimensional highest-state conformal multiplet with descendants expressed via the Weyl-ordered product in quantum field theory.

Rotational equilibrated systems are widespread, but relatively little attention has been devoted to studying them from the first principles of statistical mechanics. Here we bridge this gap, as we look at a Brownian particle coupled with a rotational thermal bath modeled via Caldeira-Leggett oscillators. We show that the Langevin equation that describes the dynamics of the Brownian particle contains (due to rotation) long-range correlated noise. In contrast to the usual situation of non-rotational equilibration, the rotational Gibbs distribution is recovered only for a weak coupling with the bath. However, the presence of a uniform magnetic field disrupts equilibrium, even under weak coupling conditions. In this context, we clarify the applicability of the Bohr-van Leeuwen theorem to classical systems in rotational equilibrium, as well as the concept of work done by a changing magnetic field. Additionally, we show that the Brownian particle under a rotationally symmetric potential reaches a stationary state that behaves as an effective equilibrium, characterized by a free energy. As a result, no work can be extracted via cyclic processes that respect the rotation symmetry. However, if the external potential exhibits asymmetry, then work extraction via slow cyclic processes is possible. This is illustrated by a general scenario, involving a slow rotation of a non-rotation-symmetric potential.

Angular momentum conservation influences equilibrium statistical mechanics, leading to a generalized microcanonical density for an isolated system and a generalized Gibbs density for a weakly coupled system. We study the stochastic decay of angular momentum due to weakly imperfect rotational symmetry of the external potential that confines the isolated many-particle system. We present a mesoscopic description of the system, deriving Langevin and Fokker-Planck equations, which are consistent with equilibrium statistical mechanics when rotational symmetry is maintained. When the symmetry is weakly violated, we formulate a coarse-grained stochastic differential equation governing the decay of total angular momentum over time. To validate our analytical predictions, we conduct numerical simulations of the microcanonical ensemble, an isolated system undergoing thermalization due to weak two-body interactions. Our coarse-grained Langevin equation accurately characterizes both the decay of the angular momentum and its fluctuations in a steady state. Furthermore, we estimate the parameters of our mesoscopic model directly from simulations, providing insights into the dissipative phenomenological coefficients, such as friction. More generally, this study contributes to a deeper understanding of the behavior of the integrals of motion when the corresponding symmetry is weakly violated.

A text written using symbols from a given alphabet can be compressed using the Huffman code, which minimizes the length of the encoded text. It is necessary, however, to employ a text-specific codebook, i.e. the symbol-codeword dictionary, to decode the original text. Thus, the compression performance should be evaluated by the full code length, i.e. the length of the encoded text plus the length of the codebook. We studied several alphabets for compressing texts -- letters, n-grams of letters, syllables, words, and phrases. If only sufficiently short texts are retained, an alphabet of letters or two-grams of letters is optimal. For the majority of Project Gutenberg texts, the best alphabet (the one that minimizes the full code length) is given by syllables or words, depending on the representation of the codebook. Letter 3 and 4-grams, having on average comparable length to syllables/words, perform noticeably worse than syllables or words. Word 2-grams also are never the best alphabet, on the account of having a very large codebook. We also show that the codebook representation is important -- switching from a naive representation to a compact one significantly improves the matters for alphabets with large number of symbols, most notably the words. Thus, meaning-expressing elements of the language (syllables or words) provide the best compression alphabet.