We construct a faithful representation of the semiring of all order-preserving decreasing transformations of a chain with $n+1$ elements by Boolean upper triangular $n\times n$-matrices.

We present ExoMiner++, an enhanced deep learning model that builds on the success of ExoMiner to improve transit signal classification in 2-minute TESS data. ExoMiner++ incorporates additional diagnostic inputs, including periodogram, flux trend, difference image, unfolded flux, and spacecraft attitude control data, all of which are crucial for effectively distinguishing transit signals from more challenging sources of false positives. To further enhance performance, we leverage multi-source training by combining high-quality labeled data from the Kepler space telescope with TESS data. This approach mitigates the impact of TESS's noisier and more ambiguous labels. ExoMiner++ achieves high accuracy across various classification and ranking metrics, significantly narrowing the search space for follow-up investigations to confirm new planets. To serve the exoplanet community, we introduce new TESS catalog containing ExoMiner++ classifications and confidence scores for each transit signal. Among the 147,568 unlabeled TCEs, ExoMiner++ identifies 7,330 as planet candidates, with the remainder classified as false positives. These 7,330 planet candidates correspond to 1,868 existing TESS Objects of Interest (TOIs), 69 Community TESS Objects of Interest (CTOIs), and 50 newly introduced CTOIs. 1,797 out of the 2,506 TOIs previously labeled as planet candidates in ExoFOP are classified as planet candidates by ExoMiner++. This reduction in plausible candidates combined with the excellent ranking quality of ExoMiner++ allows the follow-up efforts to be focused on the most likely candidates, increasing the overall planet yield.

We present results of the first investigations on the correlated nature of electronic states that cross the Fermi level in Pb$_9$Cu(PO$_4$)$_6$O aka LK-99 obtained within the DFT + DMFT approach. Coulomb correlations between Cu-$d$ electrons led to the opening of the band gap between the extra-O $p$ and Cu $d_{xz}/d_{yz}$ states. We state that oxygen $p$ states play a significant role in the electronic properties of LK-99. We also assume that doping with electrons is necessary to turn the stoichiometric Pb$_9$Cu(PO$_4$)$_6$O into conducting state.

With the surge in blockchain-based cryptocurrencies, illegal mining for cryptocurrency has become a popular cyberthreat. Host-based cryptojacking, where malicious actors exploit victims systems to mine cryptocurrency without their knowledge, is on the rise. Regular cryptojacking is relatively well-known and well-studied threat, however, recently attackers started switching to GPU cryptojacking, which promises greater profits due to high GPU hash rates and lower detection chance. Additionally, GPU cryptojackers can easily propagate using, for example, modified graphic card drivers. This article considers question of GPU cryptojacking detection. First, we discuss brief history and definition of GPU cryptojacking as well as previous attempts to design a detection technique for such threats. We also propose complex exposure mechanism based on GPU load by an application and graphic card RAM consumption, which can be used to detect both browser-based and host-based cryptojacking samples. Then we design a prototype decision tree detection program based on our technique. It was tested in a controlled virtual machine environment with 80% successful detection rate against selected set of GPU cryptojacking samples and 20% false positive rate against selected number of legitimate GPU-heavy applications.

The work considers a model of charged "semi-hard-core" bosons on a square lattice with a possible filling number at each node, ranging from 0 to 2. Temperature phase diagrams of the model are obtained using numerical Monte Carlo quantum simulation methods, and the influence of local charge correlations is examined. Comparison with results from mean-field methods shows that local charge correlations contribute to an increased role of quantum fluctuations in the formation of phase states.

Machine-learned interatomic potentials (MLIPs) have become the gold standard for atomistic simulations, yet their extension to magnetic materials remains challenging because spin fluctuations must be captured either explicitly or implicitly. We address this problem for the technologically vital Fe-Cr-C system by constructing two deep machine learning potentials in DeePMD realization: one trained on non-magnetic DFT data (DP-NM) and one on spin-polarised DFT data (DP-M). Extensive validation against experiments reveals a striking dichotomy. The dynamic, collective properties, viscosity and melting temperatures are reproduced accurately by DP-NM but are incorrectly estimated by DP-M. Static, local properties, density, and lattice parameters are captured excellently by DP-M, especially in Fe-rich alloys, whereas DP-NM fails. This behaviour is explained by general properties of paramagnetic state: at high temperature, local magnetic moments self-average in space and time, so their explicit treatment is unnecessary for transport properties but essential for equilibrium volumes. Exploiting this insight, we show that a transfer-learning protocol, pre-training on non-magnetic DFT and fine-tuning on a small set of spin-polarised data, reduces the computational cost to develop magnetic MLIPs by more than an order of magnitude. Developing general-purpose potentials that capture static and dynamic behaviors throughout the whole composition space requires proper accounting for temperature-induced spin fluctuations in DFT calculations and correctly incorporating spin degrees of freedom into classical force fields.

We examine geodesics for scalar-tensor black holes in the Horndeski-Galileon non-minimal kinetic coupling framework. Our analysis shows that bound orbits may not be present within some model parameters range. Using the observational data we pose bounds on possible solution parameter values, as well as initial model parameters.

The results of numerical simulation using a modified Monte Carlo method with a heat bath algorithm for the pseudospin model of cuprates are presented. The temperature phase diagrams are constructed for various degrees of doping and for various parameters of the model, and the effect of local correlations on the critical temperatures of the model cuprate is investigated. It is shown that, in qualitative agreement with the results of the mean field, the heat bath algorithm leads to a significant decrease in the estimate of critical temperatures due to more complete accounting of fluctuations, and also makes it possible to detect phase inhomogeneous states. The possibility of using machine learning to accelerate the heat bath algorithm is discussed.

The most general way to describe localized atomic-like electronic states in strongly correlated compounds is to utilize Wannier functions. In the present paper we continue the development of widely-spread DFT+U method onto Wannier function basis set and propose the technique to calculate the Hubbard contribution to the forces. The technique was implemented as a part of plane-waves pseudopotential code Quantum-ESPRESSO and successfully tested on a charge transfer insulator NiO.

This study examines the role of human capital investment in driving sustainable socio-economic growth within the energy industry. The fuel and energy sector undeniably forms the backbone of contemporary economies, supplying vital resources that underpin industrial activities, transportation, and broader societal operations. In the context of the global shift toward sustainability, it is crucial to focus not just on technological innovation but also on cultivating human capital within this sector. This is particularly relevant considering the recent shift towards green and renewable energy solutions. In this study, we utilize bibliometric analysis, drawing from a dataset of 1933 documents (represented by research papers, conference proceedings, and book chapters) indexed in the Web of Science (WoS) database. We conduct a network cluster analysis of the textual and bibliometric data using VOSViewer software. The findings stemming from our analysis indicate that investments in human capital are perceived as important in achieving long-term sustainable economic growth in the energy companies both in Russia and worldwide. In addition, it appears that the role of human capital in the energy sector is gaining more popularity both among Russian and international researchers and academics.

In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it contains 56K annotated named entities and 39K annotated relations. Its important difference from previous datasets is annotation of nested named entities, as well as relations within nested entities and at the discourse level. NEREL can facilitate development of novel models that can extract relations between nested named entities, as well as relations on both sentence and document levels. NEREL also contains the annotation of events involving named entities and their roles in the events. The NEREL collection is available via this https URL

Increasing evidence shows that most stars in the Milky Way, including the Sun, are born in star-forming regions containing also high-mass stars, but due to both observational and theoretical challenges, our comprehension of their chemical evolution is far less clear than that of their low-mass counterparts. We present the project "CHemical Evolution of MassIve star-froming COres" (CHEMICO). The project aims at investigating any aspect of the chemical evolution of high-mass star-forming cores by observing representatives of the three main evolutionary categories: high-mass starless cores, high-mass protostellar objects, and ultra-compact HII regions. We carried out an unbiased spectral line survey of the entire bandwidth at 3, 2, and 1.2 mm with the 30m telescope of the Insitut de Radioastronomie millimetrique towards three targets that represent the three evolutionary stages. The number of lines and species detected increases with evolution. In this first work, we derive the temperature structure of the targets through the analysis of the carbon-bearing species C2H, c-C3H, c-C3H2, C4H, CH3CCH, HC3N, CH3CN, and HC5N. The excitation temperature, Tex, increases with evolution in each species, although not in the same way. Hydrocarbons tend to be associated with the smallest Tex values and enhancements with evolution, while cyanides are associated with the highest Tex values and enhancements. In each target, the higher the number of atoms in the molecule, the higher Tex tends to be. The temperature structure evolves from a cold, uniform envelope traced by simple hydrocarbons in the high-mass starless core stage, to a more stratified envelope in the protostellar stage (made by a hot core, a shell with intermediate Tex, and a larger cold envelope), to finally a hot core surrounded only by a cold envelope in the Ultracompact HII stage.

Manipulating entanglement, which reflects non-local correlations in a quantum system and defines the complexity of describing its wave function, represents the extremely tough challenge in the fields of quantum computing, quantum information, and condensed matter physics. In this work, by the example of the well-structured Dicke states we demonstrate that the complexity of these real-valued wave functions can be accurately tuned by introducing a random-sign structure, which allows us to explore the regime of the volume-law entanglement. Importantly, setting nontrivial sign structure one can increase the entanglement entropy of the Dicke state to the values that are close to Page's estimates for Haar-random states. The practical realization of these random-sign Dicke states is possible on different physical platforms with shallow quantum circuits. On the level of the measurements the change in the quantum state complexity due to sign structure can be traced out with the dissimilarity measure that estimates multi-scale variety of patterns in bit-string arrays.

Super-Earths exist around subsolar-metallicity host stars with a frequency comparable to that around solar-metallicity stars, suggesting efficient assembly of dust grains even in metal-deficient environments. In this study, we propose a pathway for the formation of multiple dust rings that will promote planetesimal formation in a subsolar-metallicity disk. We investigate the long-term evolution of a circumstellar disk with 0.1 $Z_{\odot}$ over 750 kyr from its formation stage using two-dimensional thin-disk hydrodynamic simulations. The motion of dust grains is solved separately from the gas, incorporating dust growth and self-consistent radial drift. The disk is initially gravitationally unstable and undergoes intense fragmentation. By 300 kyr, it tends toward a stable state, leaving a single gravitationally bound clump. This clump generates tightly wound spiral arms through its orbital motion. After the clump dissipates at $\sim$410 kyr, the spiral arms transition into axisymmetric substructures under the influence of viscosity. These axisymmetric substructures create local gas pressure bumps that halt the inward radial drift of dust grains, resulting in the formation of multiple-ring-shaped dust distributions. We observe several rings within $\simeq$200 au of the central star, with separations between them on the order of $\sim$10 au, and dust surface density contrasts with inter-ring gaps by factors of $\sim$10-100. We also demonstrate that turbulent viscosities at observationally suggested levels are essential for converting spiral arms into axisymmetric substructures. We speculate that the physical conditions in the dust rings may be conducive to the development of streaming instability and planetesimal formation.

HCN molecules serve as an important tracer for chemical evolution of elemental nitrogen in the regions of star and planet formation. This is largely explained by the fact that N atoms and N$_2$ molecules are poorly accessible for the observation in the radio and infrared ranges. In turn, gas-phase HCN can be observed at various stages of star formation including disks arounds young stars, cometary comas and atmospheres of the planetary satellites. Despite the large geography of gas-phase observations, an identification of interstellar HCN ice is still lacking. In this work we present a series of infrared spectroscopic measurements performed at the new ultra-high vacuum cryogenic apparatus aiming to facilitate the search for interstellar HCN ice. A series of high resolution laboratory infrared spectra of HCN molecules embedded in the H$_2$O, H$_2$O:NH$_3$, CO, CO$_2$ and CH$_3$OH ices at 10~K temperature is obtained. These interstellar ice analogues aim to simulate the surroundings of HCN molecules by the main constituents of the icy mantles on the surface of the interstellar grains. In addition, the spectra of HCN molecules embedded in the solid C$_6$H$_6$, C$_5$H$_5$N and C$_6$H$_5$NH$_2$ are obtained to somehow simulate the interaction of HCN molecules with carbonaceous material of the grains rich in polycyclic aromatic hydrocarbons. The acquired laboratory spectroscopic data are compared with the publicly available results of NIRSpec James Webb Space Telescope observations towards quiescent molecular clouds performed by the ICEAge team.

The weighted ancestor problem is a well-known generalization of the predecessor problem to trees. It is known to require $\Omega(\log\log n)$ time for queries provided $O(n\mathop{\mathrm{polylog}} n)$ space is available and weights are from $[0..n]$, where $n$ is the number of tree nodes. However, when applied to suffix trees, the problem, surprisingly, admits an $O(n)$-space solution with constant query time, as was shown by Gawrychowski, Lewenstein, and Nicholson (Proc. ESA 2014). This variant of the problem can be reformulated as follows: given the suffix tree of a string $s$, we need a data structure that can locate in the tree any substring $s[p..q]$ of $s$ in $O(1)$ time (as if one descended from the root reading $s[p..q]$ along the way). Unfortunately, the data structure of Gawrychowski et al. has no efficient construction algorithm, limiting its wider usage as an algorithmic tool. In this paper we resolve this issue, describing a data structure for weighted ancestors in suffix trees with constant query time and a linear construction algorithm. Our solution is based on a novel approach using so-called irreducible LCP values.

We analyzed publications data in WoS and Scopus to compare publications in native languages vs publications in English and find any distinctive patterns. We analyzed their distribution by research areas, languages, type of access and citation patterns. The following trends were found: share of English publications increases over time; native-language publications are read and cited less than English-language outside the origin country; open access impact on views and citation is higher for native languages; journal ranking correlates with the share of English publications for multi-language journals. We conclude also that the role of non-English publications in research evaluation in non-English speaking countries is underestimated when research in social science and humanities is assessed only by publications in Web of Science and Scopus.

The transformer architecture has become an integral part of the field of modern neural networks, playing a crucial role in a variety of tasks, such as text generation, machine translation, image and audio processing, among others. There is also an alternative approach to building intelligent systems, proposed by Jeff Hawkins and inspired by the processes occurring in the neocortex. In our article we want to combine some of these ideas and to propose the use of homeostasis mechanisms, such as RFB-kWTA and "Smart" Inhibition, in the attention mechanism of the transformer and at the output of the transformer block, as well as conducting an experiment involving the introduction of sparse distributed representations of the transformer at various points. RFB-kWTA utilizes statistics of layer activations across time to adjust the entire layer, enhancing the values of rare activations while reducing those of frequent ones. "Smart" Inhibition also uses activation statistics to sample sparsity masks, with rarer activation times are more likely to be activated. Our proposed mechanisms significantly outperform the classical transformer 0.2768 BLEU and a model that only makes use of dropout in the attention mechanism and output of the transformer block 0.3007 BLEU, achieving a score of 0.3062 on the Multi30K dataset.

We conducted observations of multiple HC3N (J = 10-9, 12-11, and 16-15) lines and the N2H+ (J = 1-0) line toward a large sample of 61 ultracompact (UC) H II regions, through the Institutde Radioastronomie Millmetrique 30 m and the Arizona Radio Observatory 12 m telescopes. The N2H+ J = 1-0 line is detected in 60 sources and HC3N is detected in 59 sources, including 40 sources with three lines, 9 sources with two lines, and 10 sources with one line. Using the rotational diagram, the rotational temperature and column density of HC3N were estimated toward sources with at least two HC3N lines. For 10 sources with only one HC3N line, their parameters were estimated, taking one average value of Trot. For N2H+, we estimated the optical depth of the N2H+ J = 1-0 line, based on the line intensity ratio of its hyperfine structure lines. Then the excitation temperature and column density were calculated. When combining our results in UC H II regions and previous observation results on high-mass starless cores and high-mass protostellar cores, the N(HC3N)/N(N2H+) ratio clearly increases from the region stage. This means that the abundance ratio changes with the evolution of high-mass star-forming regions (HMSFRs). Moreover, positive correlations between the ratio and other evolutionary indicators (dust temperature, bolometric luminosity, and luminosity-to-mass ratio) are found. Thus we propose the ratio of N(HC3N)/N(N2H+) as a reliable chemical clock of HMSFRs.

The origin of the high-energy emission in astrophysical jets from black holes is a highly debated issue. This is particularly true for jets from supermassive black holes that are among the most powerful particle accelerators in the Universe. So far, the addition of new observations and new messengers have only managed to create more questions than answers. However, the newly available X-ray polarization observations promise to finally distinguish between emission models. We use extensive multiwavelength and polarization campaigns as well as state-of-the-art polarized spectral energy distribution models to attack this problem by focusing on two X-ray polarization observations of blazar BL Lacertae in flaring and quiescent $\gamma$-ray states. We find that regardless of the jet composition and underlying emission model, inverse-Compton scattering from relativistic electrons dominates at X-ray energies.