The integration of Central and Eastern European (CEE) countries into the European Economic Area serves as a valuable experiment for the regional economic development theory. The long-lasting convergence of these economies with more advanced Western Europe exhibits a few standard features and varying policies implemented. Even the Baltic countries, which started from very similar starting positions, demonstrate their unique trajectories of development. We employ a panel data regression model that allows coefficients to vary over time to compare the contributions of a few macroeconomic factors to the GDP growth of CEE countries. In particular, we regress the annual change of GDP per capita in PPP terms as a function of achieved GDP, price, trade, investment, and debt levels. Time-varying common slope coefficients in this approach describe the external economic environment in which countries implement their own policies. The panel consists of 11 Central and Eastern European countries (Bulgaria, Czechia, Estonia, Croatia, Latvia, Lithuania, Hungary, Poland, Romania, Slovenia, and Slovakia), which have been observed annually from 1995 to 2024. While the main selected factors of this investigation contribute to economic growth, in agreement with previous findings, the role of private debt appears vital in determining the pace of economic growth.

Context. Low- and intermediate-mass giants undergo a complex chemical evolution that has yet to be observationally probed. The influence of core helium flash on the chemical composition of stellar atmospheres has been an open question since its theoretical prediction 60 years ago. Aims. Based on high-resolution spectral observations of 44 open star clusters in the Gaia-ESO survey, our aim is to perform the first large-scale homogeneous investigation into the carbon and nitrogen photospheric content of low- and intermediate-mass giant stars in different phases of evolution. Methods. We determined carbon and nitrogen abundances using spectral synthesis of the C2 Swan (1,0) band head at 5135 Å and C2 Swan (0,1) band head at 5635.5 Å, 12C14N bands in the interval 6470 - 6490 Å, and the forbidden [O i] line at 6300.31 Å. Results. We revealed differences in C/N abundance ratios between pre- and post-core-He-flash stars. The lower C/N ratios in core He-burning red clump stars are mainly due to the enhancement of nitrogen abundances. We presented calibrations of the relationship between [C/N] and stellar age for solar metallicity low- and intermediate-mass giants taking into account different evolutionary stages. Conclusions. The C/N abundance ratios in the investigated first-ascent giant stars are slightly less affected by the first dredge-up than predicted by the theoretical models. The rotation-induced extra mixing is not as efficient as theoretically predicted. The core helium flash may trigger additional alterations in carbon and nitrogen abundances that are not yet theoretically modelled. We found that the evolutionary stage of stars must be taken into account when using [C/N] as an age indicator.

How we should design and interact with social artificial intelligence depends on the socio-relational role the AI is meant to emulate or occupy. In human society, relationships such as teacher-student, parent-child, neighbors, siblings, or employer-employee are governed by specific norms that prescribe or proscribe cooperative functions including hierarchy, care, transaction, and mating. These norms shape our judgments of what is appropriate for each partner. For example, workplace norms may allow a boss to give orders to an employee, but not vice versa, reflecting hierarchical and transactional expectations. As AI agents and chatbots powered by large language models are increasingly designed to serve roles analogous to human positions - such as assistant, mental health provider, tutor, or romantic partner - it is imperative to examine whether and how human relational norms should extend to human-AI interactions. Our analysis explores how differences between AI systems and humans, such as the absence of conscious experience and immunity to fatigue, may affect an AI's capacity to fulfill relationship-specific functions and adhere to corresponding norms. This analysis, which is a collaborative effort by philosophers, psychologists, relationship scientists, ethicists, legal experts, and AI researchers, carries important implications for AI systems design, user behavior, and regulation. While we accept that AI systems can offer significant benefits such as increased availability and consistency in certain socio-relational roles, they also risk fostering unhealthy dependencies or unrealistic expectations that could spill over into human-human relationships. We propose that understanding and thoughtfully shaping (or implementing) suitable human-AI relational norms will be crucial for ensuring that human-AI interactions are ethical, trustworthy, and favorable to human well-being.

Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. However, current tools including genomic assays lack the accuracy required for optimal clinical decision-making. We developed a novel artificial intelligence (AI)-based approach that integrates digital pathology images with clinical data, providing a more robust and effective method for predicting the risk of cancer recurrence in breast cancer patients. Specifically, we utilized a vision transformer pan-cancer foundation model trained with self-supervised learning to extract features from digitized H&E-stained slides. These features were integrated with clinical data to form a multi-modal AI test predicting cancer recurrence and death. The test was developed and evaluated using data from a total of 8,161 female breast cancer patients across 15 cohorts originating from seven countries. Of these, 3,502 patients from five cohorts were used exclusively for evaluation, while the remaining patients were used for training. Our test accurately predicted our primary endpoint, disease-free interval, in the five evaluation cohorts (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p<0.001]). In a direct comparison (n=858), the AI test was more accurate than Oncotype DX, the standard-of-care 21-gene assay, achieving a C-index of 0.67 [0.61-0.74] versus 0.61 [0.49-0.73], respectively. Additionally, the AI test added independent prognostic information to Oncotype DX in a multivariate analysis (HR: 3.11 [1.91-5.09, p<0.001)]). The test demonstrated robust accuracy across major molecular breast cancer subtypes, including TNBC (C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]), where no diagnostic tools are currently recommended by clinical guidelines. These results suggest that our AI test improves upon the accuracy of existing prognostic tests, while being applicable to a wider range of patients.

Artificial Intelligence (XAI) has found numerous applications in computer vision. While image classification-based explainability techniques have garnered significant attention, their counterparts in semantic segmentation have been relatively neglected. Given the prevalent use of image segmentation, ranging from medical to industrial deployments, these techniques warrant a systematic look. In this paper, we present the first comprehensive survey on XAI in semantic image segmentation. This work focuses on techniques that were either specifically introduced for dense prediction tasks or were extended for them by modifying existing methods in classification. We analyze and categorize the literature based on application categories and domains, as well as the evaluation metrics and datasets used. We also propose a taxonomy for interpretable semantic segmentation, and discuss potential challenges and future research directions.

Extensive irradiation of silicon crystal sensors by high energy particles in e. g. accelerators yield defect clusters of different types. Trapping of electrons and holes result in extended internal electric fields that drive remaining free charges. The question whether these internal electric fields affect the experimental observables, e.g. recombination process and lifetime of free charges is the main focus of this paper. Including the drift and diffusion of electrons and holes we calculate the recombination rate in a cubic sample with a single dipolar cluster of defects. It is shown that the large effect on charge lifetime is to be expected when charge diffusion length during the charge lifetime is comparable to the dimensions of the cluster. If the diffusion length exceeds the cluster size, the cluster barely affects the recombination rate.

The spin-orbit coupling (SOC) affecting the center of mass of ultracold atoms can be simulated using a properly chosen periodic sequence of magnetic pulses. Yet such a method is generally accompanied by micro-motion which hinders a precise control of atomic dynamics and thus complicating practical applications. Here we show how to by-pass the micro-motion emerging in the magnetically induced SOC by switching on and off properly the oscillating magnetic fields at the initial and final times. We consider the exact dynamics of the system and demonstrate that the overall dynamics can be immune to the micro-motion. The exact dynamics is shown to agree well with the evolution of the system described by slowly changing effective Floquet Hamiltonian including the SOC term. The agreement is shown to be the best when the phase of the periodic driving takes a specific value for which the effect of the spin-orbit coupling is maximum.

In this article, we focus on the pre-training of visual autonomous driving agents in the context of imitation learning. Current methods often rely on a classification-based pre-training, which we hypothesise to be holding back from extending capabilities of implicit image understanding. We propose pre-training the visual encoder of a driving agent using the self-distillation with no labels (DINO) method, which relies on a self-supervised learning paradigm.% and is trained on an unrelated task. Our experiments in CARLA environment in accordance with the Leaderboard benchmark reveal that the proposed pre-training is more efficient than classification-based pre-training, and is on par with the recently proposed pre-training based on visual place recognition (VPRPre).

We demonstrate that a three dimensional time-periodically driven (Floquet) lattice can exhibit chiral hinge states and describe their interplay with Weyl physics. A peculiar type of the hinge states are enforced by the repeated boundary reflections with lateral Goos-Hänchen like shifts occurring at the second-order boundaries of our system. Such chiral hinge modes coexist in a wide range of parameters regimes with Fermi arc surface states connecting a pair of Weyl points in a two-band model. We find numerically that these modes still preserve their locality along the hinge and their chiral nature in the presence of local defects and other parameter changes. We trace the robustness of such chiral hinge modes to special band structure unique in a Floquet system allowing all the eigenstates to be localized in quasi-one-dimensional regions parallel to each other when open hinge boundaries are introduced. The implementation of a model featuring both the second-order Floquet skin effect and the Weyl physics is straightforward with ultracold atoms in optical superlattices.

Spin squeezing protocols successfully generate entangled many-body quantum states, the key pillars of the second quantum revolution. In our recent work [Phys. Rev. Lett. 129, 090403 (2022)] we showed that spin squeezing described by the one-axis twisting model could be generated in the Heisenberg spin-1/2 chain with periodic boundary conditions when accompanied by a position-dependent spin-flip coupling induced by a single laser field. This work shows analytically that the change of boundary conditions from the periodic to the open ones significantly modifies spin squeezing dynamics. A broad family of twisting models can be simulated by the system in the weak coupling regime, including the one- and two-axis twisting under specific conditions, providing the Heisenberg level of squeezing and acceleration of the dynamics. Full numerical simulations confirm our analytical findings.

Accurate atmospheric parameters and chemical composition of planet-hosting stars are crucial for characterising exoplanets and understanding their formation and evolution. Our objective is to uniformly determine the atmospheric parameters and chemical abundances of carbon, nitrogen, oxygen, and the $\alpha$-elements, magnesium and silicon, along with C/O, N/O and Mg/Si abundance ratios in planet-hosts. We aim to investigate the potential links between stellar chemistry and the presence of planets using high-resolution spectra of 149 F, G, and K dwarf and giant stars hosting planets or planetary systems. The spectra were obtained with the Vilnius University Echelle Spectrograph on the 1.65 m Molėtai Observatory telescope. Stellar parameters were determined through standard analysis using equivalent widths and one-dimensional, plane-parallel model atmospheres calculated under the assumption of local thermodynamical equilibrium. The differential synthetic spectrum method was used to uniformly determine carbon C(C2), nitrogen N(CN), oxygen [O I], magnesium Mg I, and silicon Si I elemental abundances as well as the C/O, N/O, and Mg/Si ratios. We found that [C/Fe], [O/Fe], and [Mg/Fe] are lower in metal-rich dwarf hosts; whereas [N/Fe] is close to the Solar ratio. Giants show smaller scatter in [C/Fe] and [O/Fe] and lower than the Solar average [C/Fe] and C/O ratios. The (C+N+O) abundances increase with [Fe/H] in giant stars, with a minimal scatter. We also noted an overabundance of Mg and Si in planet hosting stars, particularly at lower metallicities, and a lower Mg/Si ratio in stars with planets. In giants hosting high-mass planets, nitrogen shows a moderate positive relationship with planet mass. C/O and N/O ratios show moderate negative and positive slopes in giant stars, respectively. The Mg/Si ratio shows a negative correlation with planet mass across the entire stellar sample.

Neural quantum states (NQS) have emerged as powerful tools for simulating many-body quantum systems, but their practical use is often hindered by limitations of current sampling techniques. Markov chain Monte Carlo (MCMC) methods suffer from slow mixing and require manual tuning, while autoregressive NQS impose restrictive architectural constraints that complicate the enforcement of symmetries and the construction of determinant-based multi-state wave functions. In this work, we introduce Neural Importance Resampling (NIR), a new sampling algorithm that combines importance resampling with a separately trained autoregressive proposal network. This approach enables efficient and unbiased sampling without constraining the NQS architecture. We demonstrate that NIR supports stable and scalable training, including for multi-state NQS, and mitigates issues faced by MCMC and autoregressive approaches. Numerical experiments on the 2D transverse-field Ising and $J_1$-$J_2$ Heisenberg models show that NIR outperforms MCMC in challenging regimes and yields results competitive with state of the art methods. Our results establish NIR as a robust alternative for sampling in variational NQS algorithms.

We show how the renormalization constant of the Higgs vacuum expectation value, fixed by a tadpole condition, is responsible for gauge dependences in various definitions of parameters in the $R_{\xi}$-gauge. Then we show the relationship of this renormalization constant to the Fleischer-Jegerlehner (FJ) scheme, which is used to avoid these gauge dependences. In this way, we also present a viewpoint on the FJ-scheme complementary to the ones already existing in the literature. Additionally, we compare and discuss different approaches to the renormalization of tadpoles by identifying the similarities and relations between them. The relationship to the Higgs background field renormalization is also discussed.

Nonlinear propagation of intense femtosecond laser pulses in bulk transparent media leads to a specific propagation regime, termed femtosecond filamentation, which in turn produces dramatic spectral broadening, or superbroadening, termed supercontinuum generation. Femtosecond supercontinuum generation in transparent solids represents a compact, efficient and alignment-insensitive technique for generation of coherent broadband radiation at various parts of the optical spectrum, which finds numerous applications in diverse fields of modern ultrafast science. During recent years, this research field has reached a high level of maturity, both in understanding of the underlying physics and in achievement of exciting practical results. In this paper we overview the state of the art of femtosecond supercontinuum generation in various transparent solid-state media, ranging from wide-bandgap dielectrics to semiconductor materials and in various parts of the optical spectrum, from the ultraviolet to the mid-infrared. A particular emphasis is given to the most recent experimental developments: multioctave supercontinuum generation with pumping in the mid-infrared spectral range, spectral control, power and energy scaling of broadband radiation and the development of simple, flexible and robust pulse compression techniques, which deliver few optical cycle pulses and which could be readily implemented in a variety of modern ultrafast laser systems.

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the upcoming v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development.

As frontier artificial intelligence (AI) models rapidly advance, benchmarks are integral to comparing different models and measuring their progress in different task-specific domains. However, there is a lack of guidance on when and how benchmarks should be deprecated once they cease to effectively perform their purpose. This risks benchmark scores over-valuing model capabilities, or worse, obscuring capabilities and safety-washing. Based on a review of benchmarking practices, we propose criteria to decide when to fully or partially deprecate benchmarks, and a framework for deprecating benchmarks. Our work aims to advance the state of benchmarking towards rigorous and quality evaluations, especially for frontier models, and our recommendations are aimed to benefit benchmark developers, benchmark users, AI governance actors (across governments, academia, and industry panels), and policy makers.

Aims: We carried out a detailed investigation of Lithium and CNO abundances, including carbon isotope ratios, in RS CVn stars to assess the role of magnetic activity in the mixing of stellar atmospheres. Methods: We obtained high-resolution spectra at the Moletai Astronomical Observatory. Lithium abundances were determined by spectral synthesis of the 6707 A line and the CNO abundances using the C2 band heads at 5135 and 5635.5 A CN bands at 6470- 6490 A and 7980 to 8005 A, and the [O I] line at 6300 A. By fitting the 13CN band at 8004.7 A, we determined the carbon isotope this http URL. We determined the main atmospheric parameters and investigated the chemical composition of 32 RS CVn stars. Lithium abundances were determined for 13 additional stars using archival spectra. We report that *iot Gem and HD 179094 have carbon isotope ratios already affected by extra-mixing, even though they are in the evolutionary stage below the red giant branch luminosity bump. About half of the low-mass giants, for which the lithium abundance was determined, follow the first dredge-up predictions; however, other stars show reduced Lithium abundances, as predicted by thermohaline-induced mixing. The intermediate-mass stars show reduced Lithium abundances reduced, as predicted by rotation-induced mixing. Conclusions. In low-mass, chromospherically active RS CVn stars, extra-mixing of lithium and carbon isotopes may begin earlier than in normal giants. The Li-rich RS CVn giant V*OP And has large C/N and carbon isotope ratios and raises questions about the origin of its lithium enhancement.

There is an urgent need to identify both short and long-term risks from newly emerging types of Artificial Intelligence (AI), as well as available risk management measures. In response, and to support global efforts in regulating AI and writing safety standards, we compile an extensive catalog of risk sources and risk management measures for general-purpose AI (GPAI) systems, complete with descriptions and supporting examples where relevant. This work involves identifying technical, operational, and societal risks across model development, training, and deployment stages, as well as surveying established and experimental methods for managing these risks. To the best of our knowledge, this paper is the first of its kind to provide extensive documentation of both GPAI risk sources and risk management measures that are descriptive, self-contained and neutral with respect to any existing regulatory framework. This work intends to help AI providers, standards experts, researchers, policymakers, and regulators in identifying and mitigating systemic risks from GPAI systems. For this reason, the catalog is released under a public domain license for ease of direct use by stakeholders in AI governance and standards.

We present a study of neutron-capture element abundances (Sr, Y, Zr, Ba, La, Ce, Nd, Pr, and Eu) in a large and homogeneous sample of 160 F-, G-, and K-type planet-host stars located in the northern hemisphere, including 32 stars in multi-planetary systems. The sample hosts a total of 175 high-mass planets and 47 Neptunian and super-Earth planets. High-resolution spectra were obtained with the 1.65-metre telescope at the Molėtai Astronomical Observatory using a fibre-fed spectrograph covering 4000-8500 Å. Elemental abundances were determined by differential line-by-line spectrum synthesis with the TURBOSPECTRUM code and MARCS model atmospheres. The analysis of $[\mathrm{El}/\mathrm{Fe}]$ ratios shows that most elements in PHSs follow the Galactic chemical evolution, but $[\mathrm{Zr}/\mathrm{Fe}]$, $[\mathrm{La}/\mathrm{Fe}]$, and $[\mathrm{Ce}/\mathrm{Fe}]$ are overabundant in PHSs relative to reference stars at a given $[\mathrm{Fe}/\mathrm{H}]$. Correlations between $[\mathrm{El}/\mathrm{Fe}]$ and planet mass are generally positive, except for Sr, Y, and Ba, which show no significant trends. The distribution of $\Delta[\mathrm{El}/\mathrm{H}]$ versus condensation temperature ($T_{\mathrm{cond}}$) slopes is positively skewed for PHSs, indicating enrichment in refractory elements compared to analogues. While no strong correlations are found between $\Delta[\mathrm{El}/\mathrm{H}]$-$T_{\mathrm{cond}}$ slopes and stellar or planetary parameters, older dwarf stars with multiple planets tend to have smaller or negative slopes, whereas younger dwarf stars exhibit larger positive slopes. Our results also confirm that multi-planetary systems are more frequent around metal-rich stars.

This paper examines the impact of US monetary policy tightening on emerging markets, distinguishing between direct and indirect spillover effects using the global vector autoregression with stochastic volatility covering 32 countries. The paper demonstrates that an increase in the US interest rate significantly reduces output for emerging markets, leading to larger, more prolonged, and persistent declines. Such an impact is further intensified by global trade integration, causing a sharper yet slightly quicker rebounding output drop. The spillover effects are significantly amplified when US monetary policy tightening is accompanied by an increase in monetary policy uncertainty. Finally, emerging markets exhibit considerable heterogeneity in their responses to US monetary policy shocks.