We formalize the concept of the modular energy operator within the Page and Wootters timeless framework. As a result, this operator is elevated to the same status as the more studied modular operators of position and momentum. In analogy with dynamical nonlocality in space associated with the modular momentum, we introduce and analyze the nonlocality in time associated with the modular energy operator. Some applications of our formalization are provided through illustrative examples.

While transfer learning is an advantageous strategy, it overlooks the opportunity to leverage knowledge from numerous available models online. Addressing this multi-source transfer learning problem is a promising path to boost adaptability and cut re-training costs. However, existing approaches are inherently coarse-grained, lacking the necessary precision for granular knowledge extraction and the aggregation efficiency required to fuse knowledge from either a large number of source models or those with high parameter counts. We address these limitations by leveraging Singular Value Decomposition (SVD) to first decompose each source model into its elementary, rank-one components. A subsequent aggregation stage then selects only the most salient components from all sources, thereby overcoming the previous efficiency and precision limitations. To best preserve and leverage the synthesized knowledge base, our method adapts to the target task by fine-tuning only the principal singular values of the merged matrix. In essence, this process only recalibrates the importance of top SVD components. The proposed framework allows for efficient transfer learning, is robust to perturbations both at the input level and in the parameter space (e.g., noisy or pruned sources), and scales well computationally.

This paper introduces ExKLoP, a novel framework designed to evaluate how effectively Large Language Models (LLMs) integrate expert knowledge into logical reasoning systems. This capability is especially valuable in engineering, where expert knowledge-such as manufacturer-recommended operational ranges-can be directly embedded into automated monitoring systems. By mirroring expert verification steps, tasks like range checking and constraint validation help ensure system safety and reliability. Our approach systematically evaluates LLM-generated logical rules, assessing both syntactic fluency and logical correctness in these critical validation tasks. We also explore the models' capacity for self-correction via an iterative feedback loop based on code execution outcomes. ExKLoP presents an extensible dataset comprising 130 engineering premises, 950 prompts, and corresponding validation points. It enables comprehensive benchmarking while allowing control over task complexity and scalability of experiments. We leverage the synthetic data creation methodology to conduct extensive empirical evaluation on a diverse set of LLMs including Llama3, Gemma3, Codestral and QwenCoder. The results reveal that most models generate nearly perfect syntactically correct code and exhibit strong performance in translating expert knowledge into correct code. At the same time, while most LLMs produce nearly flawless syntactic output, their ability to correctly implement logical rules varies, as does their capacity for self-improvement. Overall, ExKLoP serves as a robust evaluation platform that streamlines the selection of effective models for self-correcting systems while clearly delineating the types of errors encountered.

We report the synthesis and physical properties of a polycrystalline, hexagonal boride YRu$_3$B$_2$. Our resistivity and heat capacity measurements indicate that YRu$_3$B$_2$ is a weakly coupled superconductor, with critical temperature $T_c$ = 0.63 K and upper critical field $\mu_0 H_{c2}$ (0)=0.11 T. Density functional theory calculations, together with chemical-bonding analysis, reveal that the electronic states at and near the Fermi energy level are dominated by the Ru kagome sublattice.

We present the results of a thorough investigation of the physical properties of EuAg$_4$Sb$_2$ single crystals using magnetization, heat capacity, and electrical resistivity measurements. High-quality single crystals, which crystallize in a trigonal structure with space group $R\bar{3}m$, were grown using a conventional flux method. Temperature-dependent magnetization measurements along different crystallographic orientations confirm two antiferromagnetic phase transitions around $T_{N1}$ = 10.5 K and $T_{N2}$ = 7.5 K. Isothermal magnetization data exhibit several metamagnetic transitions below these transition temperatures. Antiferromagnetic phase transitions in EuAg$_4$Sb$_2$ are further confirmed by two sharp peaks in the temperature-dependent heat capacity data at $T_{N1}$ and $T_{N2}$, which shift to the lower temperature in the presence of an external magnetic field. Our systematic heat capacity measurements utilizing a long-pulse and single-slope analysis technique allow us to detect a first-order phase transition in EuAg$_4$Sb$_2$ at 7.5 K. The temperature-dependent electrical resistivity data also manifest two features associated with magnetic order. The magnetoresistance exhibits a broad hump due to the field-induced metamagnetic transition. Remarkably, the magnetoresistance keeps increasing without showing any tendency to saturate as the applied magnetic field increases, and it reaches $\sim$20000\% at 1.6 K and 60 T. At high magnetic fields, several magnetic quantum oscillations are observed, indicating a complex Fermi surface. A large negative magnetoresistance of about -55\% is also observed near $T_{N1}$. Moreover, the $H$-$T$ phase diagram constructed using magnetization, heat capacity, and magnetotransport data indicates complex magnetic behavior in EuAg$_4$Sb$_2$.

This work presents a stabilized finite element formulation of the arbitrary Lagrangian-Eulerian (ALE) surface theory for Navier-Stokes flow on self-evolving manifolds developed in Sauer (2025). The formulation is physically frame-invariant, applicable to large deformations, and relevant to fluidic surfaces such as soap films, capillary menisci and lipid membranes, which are complex and inherently unstable physical systems. It is applied here to area-incompressible surface flows using a stabilized pressure-velocity (or surface tension-velocity) formulation based on quadratic finite elements and implicit time integration. The unknown ALE mesh motion is determined by membrane elasticity such that the in-plane mesh motion is stabilized without affecting the physical behavior of the system. The resulting three-field system is monolithically coupled, and fully linearized within the Newton-Rhapson solution method. The new formulation is demonstrated on several challenging examples including shear flow on self-evolving surfaces and inflating soap bubbles with partial inflow on evolving boundaries. Optimal convergence rates are obtained in all cases. Particularly advantageous are C1-continuous surface discretizations, for example based on NURBS.

We report a systematic investigation of the physical properties and Fermi-surface topology of layered single-crystal \ce{SrCu4As2} using electrical transport, magnetotransport, and quantum-oscillation experiments plus band-structure calculations. The temperature-dependent electrical resistivity reveals a hysteretic phase transition at $T_P$ = 59 K, most likely associated with a structural change. Hall resistivity data suggest a marked change in the average hole density resulting from the latter phase transition near $T_P$. A large, linear, and nonsaturating magnetoresistance is observed at low temperatures in \ce{SrCu4As2}, likely attributable to the multipocket Fermi surface. Quantum-oscillation data measured in magnetic fields of up to 60 T show several oscillation frequencies exhibiting low effective masses, indicating the presence of Dirac-like band dispersion in \ce{SrCu4As2}, as suggested by the band structure calculations.

This research investigates the potential of a sectoral Cylindrical Dielectric Resonator Antenna (CDRA) for biomedical telemetry. CDRAs are known for their low loss, ruggedness, and stability, but their limited bandwidth and size make them unsuitable for wearable devices. The research addresses these limitations by proposing a dual mode antenna that operates in EH110 and TE210 modes. The sectoral CDRA is a quarter segment with Perfect Electric Conductor boundaries, reducing its size by a factor of four. Mathematical derivations of the field components for both modes are derived to support the design. To minimize specific absorption rate (SAR), an Artificial Magnetic Conductor (AMC) surface is applied to the antennas backside, enhancing compatibility with the transverse electric modes. The antenna achieves a bandwidth of 0.7 GHz (5.2-5.9 GHz), suitable for biomedical applications, with a measured peak gain of 7.9 dBi and a SAR of 1.24 W/kg when applied to a human arm.

In quantum information theory, the evolution of an open quantum system -- a unitary evolution followed by a measurement -- is described by a quantum channel or, more generally, a quantum instrument. In this work, we formulate spin and flavour measurements in collider experiments as a quantum instrument. We demonstrate that the Choi matrix, which completely determines input-output transitions, can be both theoretically computed from a given model and experimentally reconstructed from a set of final state measurements (quantum state tomography) using varied input states. The reconstruction of the Choi matrix, known as quantum process tomography, offers a powerful new approach for probing potential extensions of the Standard Model within the quantum field theory framework and also provides a fundamental test of quantum mechanics itself. As an example, we outline a quantum process tomography approach applied to the $e^+ e^- \to t \bar{t}$ process at a polarized lepton collider.

The use of ChatGPT to analyze and classify evidence in criminal proceedings has been a topic of ongoing discussion. However, to the best of our knowledge, this issue has not been studied in the context of the Polish language. This study addresses this research gap by evaluating the effectiveness of ChatGPT in classifying legal cases under the Polish Penal Code. The results show excellent binary classification accuracy, with all positive and negative cases correctly categorized. In addition, a qualitative evaluation confirms that the legal basis provided for each case, along with the relevant legal content, was appropriate. The results obtained suggest that ChatGPT can effectively analyze and classify evidence while applying the appropriate legal rules. In conclusion, ChatGPT has the potential to assist interested parties in the analysis of evidence and serve as a valuable legal resource for individuals with less experience or knowledge in this area.

Fractionalized quasiparticles and their confinement into emergent bound states lie at the heart of modern quantum magnetism. While the evolution into magnonic bound states has been well characterized, experimental insight into the analogous transition to triplons remains limited. Here, using high-resolution neutron spectroscopy and state-of-the-art spin dynamics simulations, we uncover the transformation from weakly interacting spinons to tightly bound triplons in the spin-Peierls compound CuGeO3. Quantitative comparisons between the measured spectra and tensor network simulations reveal substantial next-nearest-neighbor frustration and weak external dimerization, placing the system deep within the spontaneously dimerized regime and near the exactly solvable Majumdar-Ghosh point. We further show an energy- and temperature-dependent evolution between two contrasting quasiparticle regimes: deconfined spinons with markedly suppressed interactions by frustration, and coherent triplonic bound states with no observable spinon degrees of freedom. Remarkably, triplon character persists into the two-particle regime, forming a structured two-triplon continuum with a spectral feature associated with a van Hove singularity at its lower boundary. These findings challenge the conventional view that robust triplons require strong external dimerization and demonstrate how the interplay between frustration and dimerization can reshape fractionalization and confinement.

Test-Time Adaptation (TTA) has recently emerged as a promising strategy for tackling the problem of machine learning model robustness under distribution shifts by adapting the model during inference without access to any labels. Because of task difficulty, hyperparameters strongly influence the effectiveness of adaptation. However, the literature has provided little exploration into optimal hyperparameter selection. In this work, we tackle this problem by evaluating existing TTA methods using surrogate-based hp-selection strategies (which do not assume access to the test labels) to obtain a more realistic evaluation of their performance. We show that some of the recent state-of-the-art methods exhibit inferior performance compared to the previous algorithms when using our more realistic evaluation setup. Further, we show that forgetting is still a problem in TTA as the only method that is robust to hp-selection resets the model to the initial state at every step. We analyze different types of unsupervised selection strategies, and while they work reasonably well in most scenarios, the only strategies that work consistently well use some kind of supervision (either by a limited number of annotated test samples or by using pretraining data). Our findings underscore the need for further research with more rigorous benchmarking by explicitly stating model selection strategies, to facilitate which we open-source our code.

Accurate segmentation of wounds and scale markers in clinical images remainsa significant challenge, crucial for effective wound management and automatedassessment. In this study, we propose a novel dual-attention U-Net++ archi-tecture, integrating channel-wise (SCSE) and spatial attention mechanisms toaddress severe class imbalance and variability in medical images this http URL, extensive benchmarking across diverse architectures and encoders via 5-fold cross-validation identified EfficientNet-B7 as the optimal encoder this http URL, we independently trained two class-specific models with tailoredpreprocessing, extensive data augmentation, and Bayesian hyperparameter tun-ing (WandB sweeps). The final model ensemble utilized Test Time Augmentationto further enhance prediction reliability. Our approach was evaluated on a bench-mark dataset from the NBC 2025 & PCBBE 2025 competition. Segmentationperformance was quantified using a weighted F1-score (75% wounds, 25% scalemarkers), calculated externally by competition organizers on undisclosed hard-ware. The proposed approach achieved an F1-score of 0.8640, underscoring itseffectiveness for complex medical segmentation tasks.

This study is motivated by two key considerations: the significant benefits mobile applications offer individuals and businesses, and the limited empirical research on usability challenges. To address this gap, we conducted structured interviews with twelve experts to identify common usability issues. Our findings highlight the top five concerns related to: information architecture, user interface design, performance, interaction patterns, and aesthetics. In addition, we identify five key directions for future research: usability in AI-powered mobile applications, augmented reality (AR) and virtual reality (VR), multimodal interactions, personalized mobile ecosystems, and accessibility. Our study provides insights into emerging usability challenges and trends, contributing to both the theory and practice of mobile human-computer interaction.

Reference frames are of special importance in physics. They are usually considered to be idealized entities. However, in most situations, e.g. in laboratories, physical processes are described within reference frames constituted by physical systems. As new technological developments make it possible to demonstrate quantum properties of complex objects an interesting conceptual problem arises: Could one use states of quantum systems to define reference frames? Recently such a framework has been introduced in [F. Giacomini, E. Castro-Ruiz, and \v{C}. Brukner, Nat Commun 10, 494 (2019)]. One of its consequences is the fact that quantum correlations depend on a physical state of an observers reference frame. The aim of this work is to examine the dynamical aspect of this phenomena and show that the same is true for correlations established during an evolution of a composite systems. Therefore, decoherence process is also relative: For some observers the reduced evolution of subsystems is unitary, whereas for others not. I also discuss implications of this results for modern developments of decoherence theory: Quantum Darwinism and Spectrum Broadcast Structures.

In this paper we state a fundamental question about the structure of correlations in time and analyze temporal monogamy relations. We show that the nature of temporal correlations is inherently different from the spatial ones but in similarity to quantum spatial correlations, we expose a phenomenon of monogamy of quantum entanglement in time. We perform this task applying the entangled histories framework as a modifcation of the consistent histories approach. These considerations are supported by introduction of necessary tools specific for the tensor algebra used for representation of spatial correlations. We show that Tsirelson bound on temporal Bell-like inequalities can be derived from the entangled histories approach. Finally, we point out that in a context of the tensor algebra used for linking states in different times further studies on mathematical structure of the state representing evolving systems are needed.

The spin-1/2 Heisenberg antiferromagnetic chain is ideal for realizing one of the simplest gapless quantum spin-liquids (QSLs), supporting a many-body ground state whose elementary excitations are fractional fermionic excitations called spinons. Here we report the discovery of such a 1D QSL in Cs4CuSb2Cl12. Compared to previously reported S = 1/2 1D chains, this material possesses a wider temperature range over which the QSL state is stabilized. We identify spinon excitations extending at T > 0.8 K, with a large T-linear contribution to the specific heat, gamma = 31.5(2) mJ mol-1 K-2 which contribute itinerantly to thermal transport up to temperatures as high as T = 35 K. At T = 0.7 K, we find a second-order phase transition, suggesting a weak spin-Peierls transition that is unchanged by a 5 T magnetic field. Cs4CuSb2Cl12 reveals new phenomenology deep in the 1D QSL regime, supporting a gapped QSL phase over a wide temperature range compared to many other experimental realizations.

Voting is a cornerstone of collective participatory decision-making in contexts ranging from political elections to decentralized autonomous organizations (DAOs). Despite the proliferation of internet voting protocols promising enhanced accessibility and efficiency, their evaluation and comparison are complicated by a lack of standardized criteria and unified definitions of security and maturity. Furthermore, socio-technical requirements by decision makers are not structurally taken into consideration when comparing internet voting systems. This paper addresses this gap by introducing a trust-centric maturity scoring framework to quantify the security and maturity of sixteen internet voting systems. A comprehensive trust model analysis is conducted for selected internet voting protocols, examining their security properties, trust assumptions, technical complexity, and practical usability. In this paper we propose the electronic voting maturity framework (EVMF) which supports nuanced assessment that reflects real-world deployment concerns and aids decision-makers in selecting appropriate systems tailored to their specific use-case requirements. The framework is general enough to be applied to other systems, where the aspects of decentralization, trust, and security are crucial, such as digital identity, Ethereum layer-two scaling solutions, and federated data infrastructures. Its objective is to provide an extendable toolkit for policy makers and technology experts alike that normalizes technical and non-technical requirements on a univariate scale.

Usability evaluation has received considerable attention from both the research and practice communities. While there are many evaluation tools available, the Software Usability Scale (SUS) is the most widely used. In this paper, we introduce and describe the SUS-Lib software package, which aims to compute SUS scores and generate graphical figures based on user input. SUS-Lib responds to the need for user-friendly software that requires only basic knowledge and skills of the Python environment and command line tools. By using open source solutions and low hardware resources, SUS-Lib is a cost-effective solution. In addition, due to its generic nature, SUS-Lib can also be used in different research setups and settings.