The centrosymmetric cubic compound EuTi$_2$Al$_{20}$, in which magnetic Eu ions form a diamond network, undergoes an antiferromagnetic transition at T$_N$ = 3.3 K and exhibits metamagnetic transitions at H$_{m1}$ = 1.7 T and H$_{m2}$ = 2.8 T for H || [100] at 1.9 K. Between these fields, the magnetization shows a step-like behavior, defining an intermediate field-induced phase (Phase~II). We investigated the electronic transport in Phase~II and found that both the resistivity and Hall resistivity are markedly enhanced, while remaining nearly field independent within the phase. Phase~II appears for all field directions, although its transport response shows moderate directional dependence. These features differ from the strongly orientation-selective behavior often observed in skyrmion-lattice phases of several 4f-electron compounds, suggesting that Phase II may host a field-induced spin texture with a topological character distinct from that of a conventional skyrmion lattice.
The exploration of magnetic topological insulators is instrumental in exploring axion electrodynamics and intriguing transport phenomena, such as the quantum anomalous Hall effect. Here, we report that a family of magnetic compounds Eu$_{2n+1}$In$_{2}$(As,Sb)$_{2n+2}$ ($n=0,1,2$) exhibit both gapless Dirac surface states and chiral hinge modes. Such a hybrid-order topology hatches surface-dependent quantum geometry. By mapping the responses into real space, we demonstrate the existence of chiral hinge modes along the $c$ direction, which originate from the half-quantized anomalous Hall effect on two gapped $ac$/$bc$ facets due to Berry curvature, while the unpinned Dirac surface states on the gapless $ab$ facet generate an intrinsic nonlinear anomalous Hall effect due to the quantum metric. When Eu$_{3}$In$_{2}$As$_{4}$ is polarized to the ferromagnetic phase by an external magnetic field, it becomes an ideal Weyl semimetal with a single pair of type-I Weyl points and no extra Fermi pocket. Our work predicts rich topological states sensitive to magnetic structures, quantum geometry-induced transport and topological superconductivity if proximitized with a superconductor.
An underestimation of the fundamental band gap values by the density functional theory within the local density approximation and associated approaches is a well-known challenge of ab-initio electronic structure computations. Motivated by recent optical experiments [D. Santos-Cottin et al., arXiv:2301.08014], we have revisited first-principle results obtained earlier for EuCd2As2 and extended the computational studies to the whole class of systems EuCd2X2 (X = P, As, Sb, Bi), to EuIn2X2 (X = P, As, Sb), and to nonmagnetic AEIn2As2 (AE= Ca, Sr, Ba) employing a hybrid functional method. We find that our approach provides the magnitude of the energy gap for EuCd2As2 in agreement with the experimental value. Actually, our results indicate that EuSn2As2, BaIn2As2, EuCd2Bi2 and EuCd2SbBi are robust topological insulators, while all other compounds are topologically trivial semiconductors. The trivial band gaps of EuCd2P2, EuCd2As2 and EuCd2Sb2 are in the range of 1.38-1.48 eV, 0.72-0.79 eV and 0.46-0.49 eV, respectively. The topologically trivial Eu-based systems are antiferromagnetic semiconductors with a strong red shift of the energy gap in a magnetic field caused by the exchange coup
Recently, a colossal magnetoresistance (CMR) was observed in EuCd$_2$P$_2$ -- a compound that does not fit the conventional mixed-valence paradigm. Instead, experimental evidence points at a resistance driven by strong magnetic fluctuations within the two-dimensional ($2d$) ferromagnetic (FM) planes of the layered antiferromagnetic (AFM) structure. While the experimental results have not yet been fully understood, a recent theory relates the CMR to a topological vortex-antivortex unbinding, i.e., Berezinskii-Kosterlitz-Thouless (BKT), phase transition. Motivated by these observations, in this work we explore the magnetic phases hosted by a microscopic classical magnetic model for EuCd$_2$P$_2$, which easily generalizes to other Eu A-type antiferromagnetic compounds. Using Monte Carlo techniques to probe the specific heat and the helicity modulus, we show that our model can exhibit a vortex-antivortex unbinding phase transition. We find that this phase transition displays the same sensitivity to in-plane magnetization, interlayer coupling, and easy-plane anisotropy that is observed experimentally in the CMR signal, providing qualitative numerical evidence that the effect is related
Understanding how data quality aligns with regulatory requirements in machine learning (ML) systems presents a critical challenge for practitioners navigating the evolving EU regulatory landscape. To address this, we first propose a practical framework aligning established data quality dimensions with specific EU regulatory requirements. Second, we conducted a comprehensive online survey with over 180 EU-based data practitioners, investigating their approaches, key challenges, and unmet needs when ensuring data quality in ML systems that align with regulatory requirements. Our findings highlight crucial gaps between current practices and regulatory expectations, underscoring practitioners' need for more integrated data quality tools and better collaboration between technical and legal practitioners. These insights inform recommendations for bridging technical expertise and regulatory compliance, ultimately fostering responsible and trustworthy ML deployments.
The EU AI Act constitutes an important development in shaping the Union's digital regulatory architecture. The Act places fundamental rights at the heart of a risk-based governance framework. The article examines how the AI Act institutionalises a human-centric approach to AI and how the AI Act's provisions explicitly and implicitly embed the protection of rights enshrined in the EU Charter of Fundamental Rights. It argues that fundamental rights function not merely as aspirational goals, but as legal thresholds and procedural triggers across the lifecycle of an AI system. The analysis suggests that the AI Act has the potential to serve as a model for rights-preserving AI systems, while acknowledging that challenges will emerge at the level of implementation.
In the EU project MARE, a novel plane was proposed and used in combination with intent-based networking (IBN), allowing the operator to focus on what, rather than on how. Recently, LLMs have been successfully employed to translate the high-level intents into low-level actions. The open challenge is to understand how IBN can be effectively enhanced with LLM and the emerging agentic AI for security purposes. Enhancing IBN with an agentic AI paradigm introduces significant challenges that existing solutions do not fully address. This paper proposes an enhanced IBN framework with a strong security focus toward agentic AI. We address the architectural and security requirements for a multi-agent intent-based system (IBS) architecture, including a multi-domain IBN. We propose a hierarchical multi-agent and multi-vendor architecture that can also be applied more broadly in 6G architectures and beyond, beyond the security architecture proposed in MARE. The architecture incorporates an interactive intent-processing pipeline using LLMs, and it also allows the IBS to connect to external security knowledge bases, such as MITRE ATT\&CK, MITRE FiGHT, and NIST.
The emergence of colossal magnetoresistance in a new generation of Eu$^{2+}$-based antiferromagnets is intriguing given stark contrasts to the archetypal perovskite manganites and doped Eu-chalcogenides. In this study the thermal conductivity and magnetostriction of Eu$_5$Sn$_2$As$_6$ -- one such representative -- have been measured to better understand the role of the crystal lattice. Both properties are strongly field-dependent and mirror the magnetization, saturating once the Eu$^{2+}$ moments are polarized. The field-enhancement of the phonon-dominated thermal conductivity is interpreted through the lifting of a degeneracy of spin configurations, and the subsequent saturation due to quenched magnetostrain in high field. Comparison with spin-glass insulators suggests that this phenomenon is not a byproduct but rather the driver of electron delocalization due to the suppression of strong phonon scattering arising from exchange frustration.
Business cycle synchronization between EU and Western Balkan candidate economies is usually modeled with aggregate time-domain correlations that mix short-run and long-run dynamics. This paper addresses that limitation by combining wavelet-based time-frequency decomposition with Bayesian zero-inflated beta regression. Using annual dyad-year data for 2001--2021, we estimate synchronization separately at shorter (1.5--4.5 years) and longer (4.5--8.5 years) horizons and relate each horizon to its correlates. The results show that EU--WB dyads are less synchronized than EU--EU dyads in the short run, and that trade deepening over time is more positively associated with short-run synchronization in EU--WB pairs. At longer horizons, the positive association between shared EU/EMU membership and synchronization weakens or reverses when the same country pair moves into deeper institutional integration, while differences across country pairs in average EU/EMU status become negligible. Over the same horizon, trade deepening within a pair is more consistently associated with synchronization, and more persistent structural dissimilarity is associated with lower synchronization. EU--WB dyads are
In solid-state compounds, the valence of europium can sometimes be mixed -- which is especially favored in structures with several positions for the europium atoms. In this work, we study the Eu-based intermetallic noncentrosymmetric system Eu$_{10}$Hg$_{55}$ which has 65 atoms per unit cell and 4 distinct crystallographic positions for europium and 17 positions for mercury. Our detailed analysis of magnetism of large single crystals suggests that europium in Eu$_{10}$Hg$_{55}$ might be present in two valence states, resulting in a fragile magnetic ground state. Due to the cage-like structure with a large distance between the Eu atoms, those atoms are weakly ferromagnetically coupled and Eu$_{10}$Hg$_{55}$ orders at low temperatures, below $T_{1} = 5.5$ K, with a subsequent spin re-orientation at $T_{2} = 4.3$ K. There is no sign of magnetic frustration. Interestingly, the magnetic ordering of europium sub-lattices results in a magnetization pole reversal with a weak ferrimagnetic ground state. Additional magnetic phases can be induced by application of a modest external magnetic field.
The European Union (EU) has long favored a risk-based approach to regulation. Such an approach is also used in recent cyber security legislation enacted in the EU. Risks are also inherently related to compliance with the new legislation. Objective: The paper investigates how risks are framed in the EU's five core cyber security legislative acts, whether the framings indicate convergence or divergence between the acts and their risk concepts, and what qualifying words and terms are used when describing the legal notions of risks. Method : The paper's methodology is based on qualitative legal interpretation and taxonomy-building. Results: The five acts have an encompassing coverage of different cyber security risks, including but not limited to risks related to technical, organizational, and human security as well as those not originating from man-made actions. Both technical aspects and assets are used to frame the legal risk notions in many of the legislative acts. A threat-centric viewpoint is also present in one of the acts. Notable gaps are related to acceptable risks, non-probabilistic risks, and residual risks. Conclusion: The EU's new cyber security legislation has significan
In the EU, the General Data Protection Regulation and the ePrivacy Directive mandate consent for the use of personal data for the purpose of behavioural advertising and tracking technologies. However, the ubiquity of consent banners has led to widespread consent fatigue and questions about the effectiveness of these mechanisms in protecting data subjects' data. To simplify digital laws and make the EU more competitive, the EU Commission recently proposed the Digital Omnibus, introducing a new Article 88b GDPR to express data subjects' choices in a technical way. While the Digital Omnibus is under legislative negotiation, California residents and residents of other US states can already exercise their rights via Global Privacy Control (GPC), a privacy signal to automatically broadcast a legally binding opt-out request to websites. In light of the Digital Omnibus, we evaluate to which extent GPC can be adapted to the EU legal framework to reduce consent banners, mitigate consent fatigue, and improve data protection for EU users. GPC is based on a technical specification, currently being standardised at the World Wide Web Consortium. By sending a GPC signal, data subjects can express
Tungstate-based oxides have attracted significant attention owing to their excellent structural stability, chemical robustness, and versatile optical properties, making them suitable for next-generation optoelectronic and phosphor applications. Among these, ZnWO$_4$ has emerged as a promising host matrix; however, the role of europium (Eu) substitution in modulating its optoelectronic behavior remains underexplored. In this work, we employ spin-polarized density functional theory (DFT) within the GGA+U framework to investigate the structural, electronic, and optical properties of pristine ZnWO$_4$ and Eu-doped ZnWO4 systems. Phonon dispersion analysis confirms dynamical stability for both pristine and doped structures. Eu doping reduces the bandgap, introduces new localized states near the Fermi level, and significantly alters the density of states, thereby enhancing electronic transitions. The optical response reveals a broadened dielectric function, red-shifted absorption edge, and intensified extinction coefficient, consistent with the presence of Eu 4f states. Additionally, reflectivity and energy-loss spectra indicate improved photon-phonon coupling and optical tunability upon
The EU AI Act represents the world's first transnational AI regulation with concrete enforcement measures. It builds on existing EU mechanisms for regulating health and safety of products but extends them to protect fundamental rights and to address AI as a horizontal technology across multiple application sectors. We argue that this will lead to multiple uncertainties in the enforcement of the AI Act, which coupled with the fast-changing nature of AI technology, will require a strong emphasis on comprehensive and rapid regulatory learning for the Act. We define a parametrised regulatory learning space based on the provisions of the Act and describe a layered system of different learning arenas where the population of oversight authorities, value chain participants, and affected stakeholders may interact to apply and learn from technical, organisational and legal implementation measures. We conclude by exploring how existing open data policies and practices in the EU can be adapted to support rapid and effective regulatory learning.
Pancreatic cancer carries a poor prognosis and relies on endoscopic ultrasound (EUS) for targeted biopsy and radiotherapy. However, the speckle noise, low contrast, and unintuitive appearance of EUS make segmentation of pancreatic tumors with fully supervised deep learning (DL) models both error-prone and dependent on large, expert-curated annotation datasets. To address these challenges, we present TextSAM-EUS, a novel, lightweight, text-driven adaptation of the Segment Anything Model (SAM) that requires no manual geometric prompts at inference. Our approach leverages text prompt learning (context optimization) through the BiomedCLIP text encoder in conjunction with a LoRA-based adaptation of SAM's architecture to enable automatic pancreatic tumor segmentation in EUS, tuning only 0.86% of the total parameters. On the public Endoscopic Ultrasound Database of the Pancreas, TextSAM-EUS with automatic prompts attains 82.69% Dice and 85.28% normalized surface distance (NSD), and with manual geometric prompts reaches 83.10% Dice and 85.70% NSD, outperforming both existing state-of-the-art (SOTA) supervised DL models and foundation models (e.g., SAM and its variants). As the first attemp
In the context of the new mandatory labor compliance in the European Union (EU), which will be implemented in 2027, supply chain enterprises face stringent working hour management requirements and compliance risks. In order to scientifically predict the enterprises' coping behaviors and performance outcomes under the policy impact, this paper constructs a methodological framework that integrates the AI synthetic data generation mechanism and structural path regression modeling to simulate the enterprises' strategic transition paths under the new regulations. In terms of research methodology, this paper adopts high-quality simulation data generated based on Monte Carlo mechanism and NIST synthetic data standards to construct a structural path analysis model that includes multiple linear regression, logistic regression, mediation effect and moderating effect. The variable system covers 14 indicators such as enterprise working hours, compliance investment, response speed, automation level, policy dependence, etc. The variable set with explanatory power is screened out through exploratory data analysis (EDA) and VIF multicollinearity elimination. The findings show that compliance inves
We report the design, synthesis, crystal structure, and physical properties of a layered intergrowth compound, Eu$_2$CuMn$_2$P$_3$. The structure of Eu$_2$CuMn$_2$P$_3$ features an alternating arrangement of hexagonal EuCuP block layers and trigonal EuMn$_2$P$_2$ block layers, interconnected through shared Eu planes. This structural hybridization leads to multiple magnetic orderings in Eu$_2$CuMn$_2$P$_3$: weak antiferromagnetic (AFM) ordering of Mn at $T_\mathrm{N}^\mathrm{Mn}$ = 80 K, AFM ordering of Eu at $T_\mathrm{N}^\mathrm{Eu}$ = 29 K, a spin-reorientation transition at $T_\mathrm{SR}$ = 14.5 K, and weak ferromagnetism below $T_\mathrm{N}^\mathrm{Mn}$. The spin configurations at different temperature regions were discussed based on the calculations of magnetic energies for various collinear arrangements. Resistivity measurements reveal a pronounced transition peak at $T_\mathrm{N}^\mathrm{Eu}$, which is suppressed in the presence of a magnetic field, resulting in a significant negative magnetoresistance effect. The computed semimetallic band structure, characterized by a small density of states at the Fermi level, aligns well with experimental observations. The successful sy
As deep learning (DL) technologies advance, their application in automated visual inspection for Class III medical devices offers significant potential to enhance quality assurance and reduce human error. However, the adoption of such AI-based systems introduces new regulatory complexities-particularly under the EU Artificial Intelligence (AI) Act, which imposes high-risk system obligations that differ in scope and depth from established regulatory frameworks such as the Medical Device Regulation (MDR) and the U.S. FDA Quality System Regulation (QSR). This paper presents a high-level technical assessment of the foreseeable challenges that manufacturers are likely to encounter when qualifying DL-based automated inspections -- specifically static models -- within the existing medical device compliance landscape. It examines divergences in risk management principles, dataset governance, model validation, explainability requirements, and post-deployment monitoring obligations. The discussion also explores potential implementation strategies and highlights areas of uncertainty, including data retention burdens, global compliance implications, and the practical difficulties of achieving
Identifying regulatory statements in legislation is useful for developing metrics to measure the regulatory density and strictness of legislation. A computational method is valuable for scaling the identification of such statements from a growing body of EU legislation, constituting approximately 180,000 published legal acts between 1952 and 2023. Past work on extraction of these statements varies in the permissiveness of their definitions for what constitutes a regulatory statement. In this work, we provide a specific definition for our purposes based on the institutional grammar tool. We develop and compare two contrasting approaches for automatically identifying such statements in EU legislation, one based on dependency parsing, and the other on a transformer-based machine learning model. We found both approaches performed similarly well with accuracies of 80% and 84% respectively and a K alpha of 0.58. The high accuracies and not exceedingly high agreement suggests potential for combining strengths of both approaches.
Accurate measurements of europium abundances in cool stars are essential for an enhanced understanding of the r-process mechanisms. We measure the abundance of Eu in solar spectra and a sample of metal-poor stars in the Galactic halo and metal-poor disk, with the metallicities ranging from \GG{$-2.4$} to $-0.5$ dex, using non-local thermodynamic equilibrium (NLTE) line formation. We compare these measurements with Galactic Chemical Evolution (GCE) models to \GG{explore the impact of the NLTE corrections on the contribution of r-process site in Galactic chemical evolution. In this work, we use NLTE line formation, as well as one-dimensional (1D) hydrostatic and spatial averages of 3D hydrodynamical ($<$3D$>$) model atmospheres to measure the abundance of Eu based on both the Eu II 4129 Å and Eu II 6645 Å lines for solar spectra and metal-poor stars. We find that \GG{for Eu II 4129 Å line the NLTE modelling leads to higher (0.04 dex) solar Eu abundance in 1D and higher (0.07 dex) in \GG{$<$3D$>$} NLTE while} NLTE modelling leads to higher (0.01 dex) solar Eu abundance in 1D and lower (0.03 dex) in \GG{$<$3D$>$} NLTE for Eu II 6645 Å line. Although the NLTE correctio