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The activity of neurons within brain circuits has been ubiquitously reported to be correlated. The impact of these correlations on brain function has been extensively investigated. Correlations can in principle increase or decrease the information that neural populations carry about sensory stimuli, but experiments in cortical areas have mostly reported information-limiting correlations, which decrease the information encoded in the population. However, a second stream of evidence suggests that temporal correlations between the spiking activity of different neurons may increase the impact of neural activity downstream, implying that temporal correlations affect both the encoding of information and its downstream readout. The principle of how encoding and readout combine are still unclear. Here, we consider a model of transmission of stimulus information encoded in pre-synaptic input spike trains with information-limiting time-correlations to the output firing of a post-synaptic biologicaly-plausible leaky integrate and fire (LIF) readout neuron. We derive an analytical solution of the model in the diffusion approximation, in which the encoding spiking activity is treated as a continuous-time stochastic variable. An ansatz based on a separation of timescales allows us compute the stimulus information transmitted to the readout over a broad range of parameters. Our analytical results reveal that, for sufficiently low input firing rates, large enough difference in input stimulus-specific activity, and moderately large input temporal correlations, the stimulus discriminability of the firing of the LIF readout neuron can be enhanced by the presence of input time correlations, despite they decrease the stimulus information encoded in its inputs., Comment: 17 pages, 10 figures (with appendices: 41 pages and 25 figures)

We construct decompositions of: (1) the cohomology of smooth stacks, (2) the Borel--Moore homology of $0$-shifted symplectic stacks, and (3) the vanishing cycle cohomology of $(-1)$-shifted symplectic stacks, under the assumption that a good moduli space exists and the tangent space has a point-wise orthogonal structure. This holds for many moduli stacks of interest, such as the moduli stack of semistable $G$-bundles and (twisted) $G$-Higgs bundles on a curve and the $G$-character stacks of oriented closed 2-manifolds and various 3-manifolds. For smooth stacks, we show that their cohomology decomposes into the intersection cohomology of the good moduli space of the stack of graded points via the cohomological Hall induction map. This generalizes and provides a new proof of Meinhardt--Reineke's theorem identifying the cohomological BPS invariant for symmetric quivers with the intersection cohomology of the good moduli space. For $0$-shifted symplectic stacks, we define a pure Hodge module on the good moduli space, the BPS sheaf, and prove that the Borel--Moore homology of the stack decomposes into the cohomology of the BPS sheaves on the good moduli space of the stack of graded points. As an application, we propose a formulation of the topological mirror symmetry conjecture for $G$-Higgs bundles for semisimple groups $G$ in the style of Hausel--Thaddeus. For $(-1)$-shifted symplectic stacks, we introduce a mixed Hodge module, also referred to as the BPS sheaf, and prove a decomposition theorem analogous to the one for $0$-shifted symplectic stacks, but now for the vanishing cycle cohomology. As a particular case, we establish the cohomological integrality theorem for $3$-Calabi--Yau categories admitting a certain orientation data, generalizing the result of Davison and Meinhardt for quivers with potentials., Comment: 119 pages. Comments are welcome!

Previous computations of feebly interacting particle production have encountered issues with unphysical (negative) interaction rates at soft momenta. We address this problem by studying the production of Axion-Like Particles (ALPs) coupled to $U(1)$-gauge fields, employing the full form of 1PI-resummed gauge boson propagators. This approach avoids the need for matching or subtraction procedures, ensuring physically consistent results. We find that the ALP production rate remains positive across all momentum scales and identify the dominant production mechanisms. At soft ALP momenta ($p \lesssim g^2 T$), interactions involving two spacelike gauge bosons dominate the production rate, surpassing other channels by an order of magnitude. In particular, using the full gauge boson propagator suggests that at even softer momenta ($p \lesssim g^4 T$), production involving two timelike gauge bosons becomes significant, potentially exceeding other contributions by another order of magnitude. Using these insights, we update the thermal ALP abundance and refine the estimate of the average ALP momentum, providing important input for structure formation constraints on ALP dark matter in the keV mass range., Comment: 20 pages + appendices, 9 figures

We introduce a novel framework that integrates Hodge decomposition with Filtered Average Short-Term (FAST) functional connectivity to analyze dynamic functional connectivity (DFC) in EEG signals. This method leverages graph-based topology and simplicial analysis to explore transient connectivity patterns at multiple scales, addressing noise, sparsity, and computational efficiency. The temporal EEG data are first sparsified by keeping only the most globally important connections, instantaneous connectivity at these connections is then filtered by global long-term stable correlations. This tensor is then decomposed into three orthogonal components to study signal flows over higher-order structures such as triangle and loop structures. Our analysis of Alzheimer-related MCI patients show significant temporal differences related to higher-order interactions that a pairwise analysis on its own does not implicate. This allows us for the first time to capture higher-dimensional interactions at high temporal resolution in noisy EEG signal recordings.

Surrogate models provide efficient alternatives to computationally demanding real-world processes but often require large datasets for effective training. A promising solution to this limitation is the transfer of pre-trained surrogate models to new tasks. Previous studies have investigated the transfer of differentiable and non-differentiable surrogate models, typically assuming an affine transformation between the source and target functions. This paper extends previous research by addressing a broader range of transformations, including linear and nonlinear variations. Specifically, we consider the combination of an unknown input warping, such as one modelled by the beta cumulative distribution function, with an unspecified affine transformation. Our approach achieves transfer learning by employing a limited number of data points from the target task to optimize these transformations, minimizing empirical loss on the transfer dataset. We validate the proposed method on the widely used Black-Box Optimization Benchmark (BBOB) testbed and a real-world transfer learning task from the automobile industry. The results underscore the significant advantages of the approach, revealing that the transferred surrogate significantly outperforms both the original surrogate and the one built from scratch using the transfer dataset, particularly in data-scarce scenarios.

This paper presents an integrated modelling assessment that estimated the sensitivities of five endogenous factors in commercial rangelands, i.e. number of active farmers, profits, stocking rate, standing herbage biomass, and soil erosion, to the same percentage variation in 70 factors, including economic and climate drivers. The assessment utilised a system dynamics model (107 equations) which represents an area of extensive private farms, its farmers, the main local markets on which they trade, and key ecosystem services involved. The assessment procedure consisted in analysing the behaviours of 288,000 variants of this system during 300 years, each under a different economic and climate scenario. Our key findings were as follows: 1) It is likely that at least annual grasslands will suffer environmental degradation in the future, and that such degradation will be primarily caused by climate change, not by the increasing demand for livestock products; 2) Private farming systems provide social and economic security to farmers against the effects of climate change, especially in a scenario of rising prices of animal products. However, this research will remain incomplete until its methods and results can be contrasted with other similar assessments., Comment: Original draft sent to journal

Legal texts, characterized by complex and specialized terminology, present a significant challenge for Language Models. Adding an underrepresented language, such as Spanish, to the mix makes it even more challenging. While pre-trained models like XLM-RoBERTa have shown capabilities in handling multilingual corpora, their performance on domain specific documents remains underexplored. This paper presents the development and evaluation of MEL, a legal language model based on XLM-RoBERTa-large, fine-tuned on legal documents such as BOE (Bolet\'in Oficial del Estado, the Spanish oficial report of laws) and congress texts. We detail the data collection, processing, training, and evaluation processes. Evaluation benchmarks show a significant improvement over baseline models in understanding the legal Spanish language. We also present case studies demonstrating the model's application to new legal texts, highlighting its potential to perform top results over different NLP tasks., Comment: 8 pages, 6 figures, 3 tables

Modern power grids are transitioning towards power electronics-dominated grids (PEDG) due to the increasing integration of renewable energy sources and energy storage systems. This shift introduces complexities in grid operation and increases vulnerability to cyberattacks. This research explores the application of digital twin (DT) technology and machine learning (ML) techniques for anomaly detection in PEDGs. A DT can accurately track and simulate the behavior of the physical grid in real-time, providing a platform for monitoring and analyzing grid operations, with extended amount of data about dynamic power flow along the whole power system. By integrating ML algorithms, the DT can learn normal grid behavior and effectively identify anomalies that deviate from established patterns, enabling early detection of potential cyberattacks or system faults. This approach offers a comprehensive and proactive strategy for enhancing cybersecurity and ensuring the stability and reliability of PEDGs., Comment: preprint accepted to 2025 Conference of Young Researchers in Electrical and Electronic Engineering (2025 ElCon)

Surrogate models are frequently employed as efficient substitutes for the costly execution of real-world processes. However, constructing a high-quality surrogate model often demands extensive data acquisition. A solution to this issue is to transfer pre-trained surrogate models for new tasks, provided that certain invariances exist between tasks. This study focuses on transferring non-differentiable surrogate models (e.g., random forest) from a source function to a target function, where we assume their domains are related by an unknown affine transformation, using only a limited amount of transfer data points evaluated on the target. Previous research attempts to tackle this challenge for differentiable models, e.g., Gaussian process regression, which minimizes the empirical loss on the transfer data by tuning the affine transformations. In this paper, we extend the previous work to the random forest model and assess its effectiveness on a widely-used artificial problem set - Black-Box Optimization Benchmark (BBOB) testbed, and on four real-world transfer learning problems. The results highlight the significant practical advantages of the proposed method, particularly in reducing both the data requirements and computational costs of training surrogate models for complex real-world scenarios.

Traditional cosmic ray simulations make use of the Montecarlo method in a very naive way to randomise energy and direction for each simulated particle. The flux of cosmic rays is modelled as a rain coming from a plane above the object of interest (detectors in particle physics applications, planes in dosimetry studies, etc.) with an experimental angular and energy distributions. This strategy is very inefficient because many of the particles never touch the detector. Here a refined way of implementing the Montecarlo method is proposed in order to generate a sample of events that hit the target volume whose angular distribution coincides with the one from the naive implementation. It is based on the projection of a sphere containing the target volume onto a plane tangent to it with a fixed angle, we call it the secant method. This configuration allows to compute the probability of a cosmic particle hitting the sphere with this incoming angle as proportional to the area of the corresponding section of a cylinder. The performance of this method is faster in terms of computing time and identical physical results are achieved. It has been implemented in REST-for-Physics framework and it is tested with the geometry of a real detector, the IAXO-D0 Micromegas X-ray detector for the future axion helioscope BabyIAXO. Our method is 37 times more efficient than the traditional Montecarlo schema for the same accuracy, being more useful when the target volume departs from spherical shape

Background: we develop the concept of a Micromegas (MICRO-MEsh GAseous Structure) readout plane with an additional GEM (Gas Electron Multiplier) preamplification stage placed a few mm above it, to increase the maximum effective gain of the combined readout. We implement it and test it in realistic conditions for its application to low-background dark matter searches like the TREX-DM experiment. Methods: for this, we use a Micromegas of microbulk type, built with radiopure materials. A small test chamber allowing for systematic scanning of voltages and pressures is used. In addition, a TREX-DM full-scale set-up has also been built and tested, featuring a replica of the fully-patterned TREX-DM microbulk readout. Results: we report on GEM effective extra gain factors of about 90, 50 and 20 in 1, 4 and 10 bar of Ar-1%iC$_{4}$H$_{10}$. Conclusions: the results here obtained show promise to lower the threshold of the experiment down to 50 eV$_{ee}$, corresponding to substantially enhanced sensitivity to low-mass WIMPs (Weakly Interacting Massive Particles)., Comment: 14 pages, 8 figures, corresponding authors: \'Oscar P\'erez (oscarperlaz@unizar.es), H\'ector Mirallas (mirallas@unizar.es)

The mechanical properties of minerals in planetary materials are not only interesting from a fundamental point of view but also critical to the development of future space missions. Here we present nanoindentation experiments to evaluate the hardness and reduced elastic modulus of olivine, (Mg, Fe)2SiO4, in meteorite NWA 12008, a lunar basalt. Our experiments suggest that the olivine grains in this lunaite are softer and more elastic than their terrestrial counterparts. Also, we have performed synchrotron-based high-pressure X-ray diffraction (HP-XRD) measurements to probe the compressibility properties of this meteorite and, for comparison purposes, of three ordinary chondrites. The HP-XRD results suggest that the axial compressibility of the orthorhombic $b$ lattice parameter of olivine relative to terrestrial olivine is higher in NWA 12008 and also in the highly-shocked Chelyabinsk meteorite. The origin of the observed differences is discussed. A simple model combining the results of both our nanoindentation and HP-XRD measurements allows us to describe the contribution of macroscopic and chemical-bond related effects, both of which are necessary to reproduce the observed elastic modulus softening. Such joint analysis of the mechanical and elastic properties of meteorites and returned samples opens up a new avenue for characterizing these highly interesting materials., Comment: 22 pages, 9 figures, 2 tables

Benchmarking is one of the key ways in which we can gain insight into the strengths and weaknesses of optimization algorithms. In sampling-based optimization, considering the anytime behavior of an algorithm can provide valuable insights for further developments. In the context of multi-objective optimization, this anytime perspective is not as widely adopted as in the single-objective context. In this paper, we propose a new software tool which uses principles from unbounded archiving as a logging structure. This leads to a clearer separation between experimental design and subsequent analysis decisions. We integrate this approach as a new Python module into the IOHprofiler framework and demonstrate the benefits of this approach by showcasing the ability to change indicators, aggregations, and ranking procedures during the analysis pipeline.

Negative index metamaterials (NIMs) can be achieved with uniaxial hyperbolic metamaterials (HMMs) featuring $\epsilon_{parallel}>0$ and $\epsilon_{perpendicular}<0$. This type of approach has been traditionally realized using stacked doped/undoped semiconductor layers. Only recently surface phonon polaritons (SPhPs) have emerged as a promising low-loss alternative to surface plasmon polaritons (SPPs). Despite this advantage, the SPhP-based approach has been underexplored due to the challenges associated with ensuring high crystal quality in the heterostructure when using alloys with different phonon frequencies. In this work, we design a phononic-driven NIM using a ZnO/(Zn,Mg)O heterostructure, demonstrating control over its hyperbolic behavior through the precise selection of the Mg content and the relative layer thicknesses. Our study shows that increasing the Mg content in the ternary layers enhances the type I behavior, and that the optimal layer thickness varies depending on the Mg content. After analyzing the conditions for achieving type I hyperbolic dispersion, we experimentally demonstrate this concept with a sample featuring equal layer thicknesses and a 32% Mg concentration. We characterize the structure by means of polarized reflectance spectroscopy and use attenuated total reflectance spectroscopy to report the presence of a SPhP mode located within the type I hyperbolic region. By employing the transfer matrix method, we demonstrate that this mode exhibits negative frequency dispersion, a hallmark of type I hyperbolic modes, and isofrequency curve calculations further confirm this behavior. Controlling the design of a phononic hyperbolic type I metamaterial lays the groundwork for exploring its potential applications in attaining low-loss, sub-diffraction-limited optical modes using SPhP excitations., Comment: 13 pages, 6 figures

Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Previous efforts focus on black-to-grey-box models, thus neglecting the potential benefit from internal LLM information. To address this, we propose the use of Linear Probes (LPs) as a method to detect Membership Inference Attacks (MIAs) by examining internal activations of LLMs. Our approach, dubbed LUMIA, applies LPs layer-by-layer to get fine-grained data on the model inner workings. We test this method across several model architectures, sizes and datasets, including unimodal and multimodal tasks. In unimodal MIA, LUMIA achieves an average gain of 15.71 % in Area Under the Curve (AUC) over previous techniques. Remarkably, LUMIA reaches AUC>60% in 65.33% of cases -- an increment of 46.80% against the state of the art. Furthermore, our approach reveals key insights, such as the model layers where MIAs are most detectable. In multimodal models, LPs indicate that visual inputs can significantly contribute to detect MIAs -- AUC>60% is reached in 85.90% of experiments.

This paper explores the behavior of the torsional rigidity of a precompact domain as the ambient manifold evolves under a geometric flow. Specifically, we derive bounds on torsional rigidity under the Ricci Flow for Heisenberg spaces and homogeneous spheres. Additionally, we establish bounds under the Inverse Mean Curvature Flow for strictly convex, free-boundary, disk-type hypersurfaces within a ball. In this latter case, by extending the analysis to the maximal existence time of the flow, we obtain inequalities of comparison with the flat disk for both volume and torsional rigidity.

BabyIAXO is the intermediate stage of the International Axion Observatory (IAXO) to be hosted at DESY. Its primary goal is the detection of solar axions following the axion helioscope technique. Axions are converted into photons in a large magnet that is pointing to the sun. The resulting X-rays are focused by appropriate X-ray optics and detected by sensitive low-background detectors placed at the focal spot. The aim of this article is to provide an accurate quantitative description of the different components (such as the magnet, optics, and X-ray detectors) involved in the detection of axions. Our efforts have focused on developing robust and integrated software tools to model these helioscope components, enabling future assessments of modifications or upgrades to any part of the IAXO axion helioscope and evaluating the potential impact on the experiment's sensitivity. In this manuscript, we demonstrate the application of these tools by presenting a precise signal calculation and response analysis of BabyIAXO's sensitivity to the axion-photon coupling. Though focusing on the Primakoff solar flux component, our virtual helioscope model can be used to test different production mechanisms, allowing for direct comparisons within a unified framework., Comment: 36 pages, 18 figures, 4 tables, Submitted to JHEP

Autonomous systems use independent decision-making with only limited human intervention to accomplish goals in complex and unpredictable environments. As the autonomy technologies that underpin them continue to advance, these systems will find their way into an increasing number of applications in an ever wider range of settings. If we are to deploy them to perform safety-critical or mission-critical roles, it is imperative that we have justified confidence in their safe and correct operation. Verification is the process by which such confidence is established. However, autonomous systems pose challenges to existing verification practices. This paper highlights viewpoints of the Roadmap Working Group of the IEEE Robotics and Automation Society Technical Committee for Verification of Autonomous Systems, identifying these grand challenges, and providing a vision for future research efforts that will be needed to address them.

This paper reports on a comprehensive study of an ultra-relativistic positron beam deflection by an oriented quasi-mosaic crystal. The analysis was carried out for the positron energy of 530 MeV incident on the quasi-mosaic bent Si(111) crystal. This particular case was chosen because it has recently been studied experimentally at the Mainz Microtron MAMI. The results of the relativistic molecular dynamics simulations were compared with the experimental observations and a good agreement was found. The presence of planar channeling, de-channeling, volume reflection and volume capture processes in the angular distribution of deflected positrons for different beam-crystal alignments has been studied. Predictions have been made for certain crystal orientations for which experimental data are lacking.

Large-scale synthesis of DNA and RNA is a crucial technology for modern biological research ranging from genomics to nucleic acid therapeutics and for technological research ranging from nanofabrication of materials to molecular-level writing of digital data. Maskless Array Synthesis (MAS) is a versatile and efficient approach for creating the required complex microarrays and libraries of DNA and other nucleic acids for these applications and, more generally, for the synthesis of sequence-defined engineered and biological oligomers. MAS uses digital photomasks displayed by a digital micromirror device (DMD) illuminated by an appropriate light source and imaged into a photochemical reaction chamber with an optical relay system. Previously, Offner relay systems were used for imaging, but modern DMD formats with more and smaller micromirrors favor a different solution. We present a desktop MAS optical system with the larger numerical aperture and larger field of view required by 1080p and other large-format DMDs. The resulting catadioptric relay is well suited to modern DMDs in this application, and is corrected for first order axial and lateral color, enabling the use of high-power LEDs as inexpensive and long-lasting light sources spanning the ultraviolet-to-violet to perform the required photochemistry. Additional characteristics of the system, including high contrast and low scatter, make it ideal for reducing the error rates in photochemical synthesis of biomolecules., Comment: 17 pages, 10 figures

The environmental impact of Bitcoin mining has become a significant concern, prompting several governments to consider or implement bans on cryptocurrency mining. However, these well-intentioned policies may lead to unintended consequences, notably the redirection of mining activities to regions with higher carbon intensities. This study aims to quantify the environmental effectiveness of Bitcoin mining bans by estimating the resultant carbon emissions from displaced mining operations. Our findings indicate that, contrary to policy goals, Bitcoin mining bans in low-emission countries can result in a net increase in global carbon emissions, a form of aggravated carbon leakage. We further explore the policy implications of these results, suggesting that more nuanced approaches may be required to mitigate the environmental impact of cryptocurrency mining effectively. This research contributes to the broader discourse on sustainable cryptocurrency regulation and provides a data-driven foundation for evaluating the true environmental costs of Bitcoin regulatory policies., Comment: 16 pages, 3 figures, 4 tables

Accurate and robust recording and decoding from the central nervous system (CNS) is essential for advances in human-machine interfacing. However, technologies used to directly measure CNS activity are limited by their resolution, sensitivity to interferences, and invasiveness. Advances in muscle recordings and deep learning allow us to decode the spiking activity of spinal motor neurons (MNs) in real time and with high accuracy. MNs represent the motor output layer of the CNS, receiving and sampling signals originating in different regions in the nervous system, and generating the neural commands that control muscles. The input signals to MNs can be estimated from the MN outputs. Here we argue that peripheral neural interfaces using muscle sensors represent a promising, non-invasive approach to estimate some neural activity from the CNS that reaches the MNs but does not directly modulate force production. We also discuss the evidence supporting this concept, and the necessary advances to consolidate and test MN-based CNS interfaces in controlled and real-world settings.

The decomposition of high-density surface electromyography (HD-sEMG) signals into motor unit discharge patterns has become a powerful tool for investigating the neural control of movement, providing insights into motor neuron recruitment and discharge behavior. However, current algorithms, while very effective under certain conditions, face significant challenges in complex scenarios, as their accuracy and motor unit yield are highly dependent on anatomical differences among individuals. This can limit the number of decomposed motor units, particularly in challenging conditions. To address this issue, we recently introduced Swarm-Contrastive Decomposition (SCD), which dynamically adjusts the separation function based on the distribution of the data and prevents convergence to the same source. Initially applied to intramuscular EMG signals, SCD is here adapted for HD-sEMG signals. We demonstrated its ability to address key challenges faced by existing methods, particularly in identifying low-amplitude motor unit action potentials and effectively handling complex decomposition scenarios, like high-interference signals. We extensively validated SCD using simulated and experimental HD-sEMG recordings and compared it with current state-of-the-art decomposition methods under varying conditions, including different excitation levels, noise intensities, force profiles, sexes, and muscle groups. The proposed method consistently outperformed existing techniques in both the quantity of decoded motor units and the precision of their firing time identification. For instance, under certain experimental conditions, SCD detected more than three times as many motor units compared to previous methods, while also significantly improving accuracy. These advancements represent a major step forward in non-invasive EMG technology for studying motor unit activity in complex scenarios.

Decoding nervous system activity is a key challenge in neuroscience and neural interfacing. In this study, we propose a novel neural decoding system that enables unprecedented large-scale sampling of muscle activity. Using micro-electrode arrays with more than 100 channels embedded within the forearm muscles, we recorded high-density signals that captured multi-unit motor neuron activity. This extensive sampling was complemented by advanced methods for neural decomposition, analysis, and classification, allowing us to accurately detect and interpret the spiking activity of spinal motor neurons that innervate hand muscles. We evaluated this system in two healthy participants, each implanted with three electromyogram (EMG) micro-electrode arrays (comprising 40 electrodes each) in the forearm. These arrays recorded muscle activity during both single- and multi-digit isometric contractions. For the first time under controlled conditions, we demonstrate that multi-digit tasks elicit unique patterns of motor neuron recruitment specific to each task, rather than employing combinations of recruitment patterns from single-digit tasks. This observation led us to hypothesize that hand tasks could be classified with high precision based on the decoded neural activity. We achieved perfect classification accuracy (100%) across 12 distinct single- and multi-digit tasks, and consistently high accuracy (>96\%) across all conditions and subjects, for up to 16 task classes. These results significantly outperformed conventional EMG classification methods. The exceptional performance of this system paves the way for developing advanced neural interfaces based on invasive high-density EMG technology. This innovation could greatly enhance human-computer interaction and lead to substantial improvements in assistive technologies, offering new possibilities for restoring motor function in clinical applications.

The long-term solar magnetic activity and its cyclical behaviour, which is maintained by a dynamo mechanism, are both still challenging for the astrophysics. In particular, an atypical event occurred between 1645 and 1715 when the solar activity was remarkably decreased and the number of sunspots got extremely reduced. However, it is still unclear what happened to the solar cycle. The discovery of longer activity minima in cool stars may shed light on the nature of the complex mechanisms involved in the long-term behaviour of the solar-stellar dynamo. Our aim is to explore if the G5V solar-like star HD 4915, which showed a striking chromospheric activity pattern in a previous study performed with HIRES data, could be considered a bona fide Maunder Minimum (hereafter MM) candidate. We have analyzed over 380 spectra acquired between 2003 and 2022 using HARPS and HIRES spectrographs. We carried out a detailed search of activity signatures in HD 4915 by using the Mount Wilson and the Balmer H$_{\alpha}$ activity indexes. This task was performed by means of the GLS periodogram. The new HARPS data show that the chromospheric activity of HD 4915 is not decreasing. In fact, the rise of the activity after the broad minimum in three years gets to the level of activity before that phase, suggesting that it is not entering into a MM phase. HD 4915 shows a distinctive activity behaviour initially attributed to a possible and incipient MM phase. The additional HARPS data allow us to discard a MM in the star. Our analysis shows that the complex activity pattern of HD 4915 could be ruled by a multiple activity cycle, being a shorter cycle of 4.8-yr modulated by a potential longer one. More activity surveys with extensive records and suitable cadence are crucial for accurate identification of stars in Magnetic Grand Minima., Comment: (5 pages, 4 figures)

Strategies aimed at reducing the negative effects of long-term uncertainty and risk are common in biology, game theory, and finance, even if they entail a cost in terms of mean benefit. Here, we focus on the single mutant's invasion of a finite resident population subject to fluctuating environmental conditions. Thus, the game-theoretical model we analyze integrates environmental and demographic randomness, i.e., the two leading sources of stochasticity and uncertainty. We use simulations and mathematical analysis to study if and when strategies that either increase or reduce payoff variance across environmental states can enhance the mutant fixation probability. Variance aversion implies that the mutant pays insurance in terms of mean payoff to avoid worst-case scenarios. Variance-prone or gambling strategies, on the other hand, entail specialization, allowing the mutant to capitalize on transient favorable conditions, leading to a series of ``boom-and-bust'' cycles. Our analyses elucidate how the rate of change of environmental conditions and the shape of the probability distribution of possible states affect the possible most convenient strategies. We discuss how our results relate to the bet-hedging theory, which aims to reduce fitness variance rather than payoff variance. We also describe the analogies and differences between these similar yet distinct approaches.

The challenge of imaging low-density objects in an electron microscope without causing beam damage is significant in modern TEM. This is especially true for life science imaging, where the sample, rather than the instrument, still determines the resolution limit. Here, we explore whether we have to accept this or can progress further in this area. To do this, we use numerical simulations to see how much information we can obtain from a weak phase object at different electron doses. Starting from a model with four phase values, we compare Zernike phase contrast with measuring diffracted intensity under multiple random phase illuminations to solve the inverse problem. Our simulations have shown that diffraction-based methods perform better than the Zernike method, as we have found and addressed a normalization issue that, in some other studies, led to an overly optimistic representation of the Zernike setup. We further validated this using more realistic 2D objects and found that random phase illuminated diffraction can be up to five times more efficient than an ideal Zernike implementation. These findings suggest that diffraction-based methods could be a promising approach for imaging beam-sensitive materials and that current low-dose imaging methods are not yet at the quantum limit., Comment: 25 pages, 7 figures

In this paper, we present ASPEN, an answer set programming (ASP) implementation of a recently proposed declarative framework for collective entity resolution (ER). While an ASP encoding had been previously suggested, several practical issues had been neglected, most notably, the question of how to efficiently compute the (externally defined) similarity facts that are used in rule bodies. This leads us to propose new variants of the encodings (including Datalog approximations) and show how to employ different functionalities of ASP solvers to compute (maximal) solutions, and (approximations of) the sets of possible and certain merges. A comprehensive experimental evaluation of ASPEN on real-world datasets shows that the approach is promising, achieving high accuracy in real-life ER scenarios. Our experiments also yield useful insights into the relative merits of different types of (approximate) ER solutions, the impact of recursion, and factors influencing performance., Comment: Extended version of a paper accepted at KR 2024

Generalized disjunctive programming (GDP) models with bilinear and concave constraints, often seen in water network design, are challenging optimization problems. This work proposes quadratic and piecewise linear approximations for nonlinear terms to reformulate GDP models into quadratic GDP (QGDP) models that suitable solvers may solve more efficiently. We illustrate the benefits of the quadratic reformulation with a water treatment network design problem in which nonconvexities arise from bilinear terms in the mixers' mass balances and concave investment cost functions of treatment units. Given the similarities with water network design problems, we suggest quadratic approximation for the GDP model for the optimal design of a large-scale reverse electrodialysis (RED) process. This power technology can recover energy from salinity differences between by-product streams of the water sector, such as desalination brine mixed with regenerated wastewater effluents. The solver Gurobi excels in handling QGDP problems, but weighing the problem's precision and tractability balance is crucial. The piecewise linear approximation yields more accurate, yet larger QGDP models that may require longer optimization times in large-scale process synthesis problems., Comment: 7 pages, Proceedings of the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering (ESCAPE34/PSE24), June 2-6, 2024, Florence, Italy

The virtualization of cultural events in the metaverse creates opportunities to generate valuable and innovative experiences that replicate and extend in-person events; but the process faces associated challenges. In the absence of relevant empirical studies, the aim of this article is to analyze the positive and negative aspects of the user experience in a cultural event held in the metaverse. A mixed-methods approach is employed to test the proposed hypotheses. The results from three focus groups demonstrated the difficulty that users face in focusing their attention on the main elements of the metaverse, and the inability of this virtual sphere to convey the authenticity of a cultural event. Based on these findings, a metaverse-focused quantitative study was conducted to examine whether perceived gamification mitigate the negative effects of users failing to pay attention in their metaverse experiences. When users increased their attention levels, their ability to imagine the real experience and their perceptions of the authenticity of the cultural event increased, which produced positive behavioral intentions. This is one of the first studies to empirically analyze the tourist experience in the metaverse; managers and policymakers can benefit from the results to hold valuable virtual cultural events., Comment: Published research article

Potentials in cosmological inflation often involve scalars with trans-Planckian ranges. As a result, towers of states become massless and their presence pushes the fundamental scale not to coincide with $M_{\rm P}$ but rather with the $species\, scale$, $\Lambda$. This scale transforms as an automorphic form of the theory's duality symmetries. We propose that the inflaton potential should be 1) an automorphic invariant form, non-singular over all moduli space, 2) depending only on $\Lambda$ and its field derivatives, and 3) approaching constant values in the large moduli to ensure a long period of inflation. These conditions lead to the proposal $V \sim \lambda(\phi, \phi^*)$, with $\lambda = G^{i\bar{j}} (\partial_i \Lambda)(\partial_{\bar{j}} \Lambda) / \Lambda^2$, determining the 'species scale convex hull'. For a single elliptic complex modulus with $SL(2, Z)$ symmetry, this results in an inflaton potential $V \simeq (\text{Im} \tau)^2 |\tilde{G}_2|^2 / N^2$, with $N \simeq -\log(\text{Im} \tau |\eta(\tau)|^4)$, where $\eta$ is the Dedekind function and $\tilde{G}_2$ the Eisenstein modular form of weight 2. Surprisingly, this potential at large modulus resembles that of the Starobinsky model. We compute inflation parameters yielding results similar to Starobinsky's, but extended to modular invariant expressions. Interestingly, the number of e-folds is proportional to the number of species in the tower, $N_e \simeq N$, and $\epsilon \simeq \Lambda^4$ at large moduli, suggesting that the tower of states plays an important role in the inflation process., Comment: 21 pages + appendices, 3 figures. v2: references added, minor changes

A proven methodology to solve multiphase flows is based on the one-fluid formulation of the governing equations, which treats the phase transition across the interface as a single fluid with varying properties and adds additional source terms to satisfy interface jump conditions, e.g., surface tension and mass transfer. Used interchangeably in the limit of non-evaporative flows, recent literature has formalized the inconsistencies that arise in the momentum balance of the non-conservative one-fluid formulation compared to its conservative counterpart when phase change is involved. This translates into an increased sensitivity of the numerical solution to the choice of formulation. Motivated by the fact that many legacy codes using the non-conservative one-fluid formulation have been extended to phase-change simulations, the inclusion of two corrective forces at the interface and a modification of the pressure-velocity solver with an additional predictor-projection step are shown to recover the exact momentum balance in the evaporative non-conservative one-fluid framework for low-viscosity incompressible flows. This has direct implications for obtaining a physically meaningful pressure jump across the interface and is seen to affect the dynamics of two-phase flows. In the high-viscosity domain, the discretization of the viscous term introduces a momentum imbalance which is highly dependent on the chosen method to model the phase transition. In the context of film boiling, this imbalance affects the time scales for the instability growth. Lastly, the need to develop sub-models for heat and mass transfer and for surface tension becomes evident since typical grid resolutions defined as ``resolved" in the literature may not be enough to capture interfacial phenomena., Comment: 43 pages, 27 figures

We present a novel analysis of the redshift-space power spectrum of galaxies in the SDSS-III BOSS survey. Our methodology improves upon previous analyses by using a theoretical model based on cosmological simulations coupled with a perturbative description of the galaxy-matter connection and a phenomenological prescription of Fingers of God. This enables a very robust analysis down to mildly non-linear scales, $k\sim 0.4\,h\,{\rm Mpc}^{-1}$. We carried out a number of tests on mock data, different subsets of BOSS, and using model variations, all of which support the robustness of our analysis. Our results provide constraints on $\sigma_8$, $\Omega_m$, $h$, and $S_8 \equiv \sigma_8 \sqrt{\Omega_{\rm m}/0.3}$. Specifically, we measure $\Omega_m=0.301\pm 0.011$, $\sigma_8=0.745^{+0.028}_{-0.035}$, $h=0.705\pm 0.015$, and $ S_8 = 0.747^{+0.032}_{-0.039}$ when all the nuisance parameters of our model are left free. By adopting relationships among bias parameters measured in galaxy formation simulations, the value of $S_8$ remains consistent whereas uncertainties are reduced by $\sim20\%$. Our cosmological constraints are some of the strongest obtained with the BOSS power spectrum alone: they exhibit a $2.5-3.5\sigma$ tension with the results of the {\it Planck\/} satellite, agreeing with the lower values of $S_8$ derived from gravitational lensing. However, the cosmological model preferred by {\it Planck\/} is still a good fit to the BOSS data, assuming small departures from physical bias priors and, therefore, cannot be excluded at high significance. We conclude that, at the present, the BOSS data alone does not show strong evidence for a tension between the predictions of $\Lambda$CDM for the high- and low-redshift Universe., Comment: 17 pages, 12 figures

The concept of spin symmetries has gained renewed interest as a valuable tool for classifying novel magnetic phases, including altermagnets and recently identified unconventional p-wave magnets. In this work, we show that in certain cases, the photoresponse is determined by spin group rather than the conventional magnetic group symmetry, a finding with implications for compounds with weak spin-orbit coupling. As a concrete realization we consider the nonlinear shift photocurrent in Mn$_5$Si$_3$, a material that features the two possible classes of unconventional p-wave magnetism in the form of two competing spin structures, a coplanar and non-coplanar one. While both are predicted to generate shift currents based on magnetic symmetry considerations, only the non-coplanar configuration survives the spin symmetry requirements. This is numerically confirmed by our $\textit{ab-initio}$ calculations, providing a protocol to experimentally identify the spin configuration of this promising material via linear photogalvanic measurements.

Hypothetical axions provide a compelling explanation for dark matter and could be emitted from the hot solar interior. The CERN Axion Solar Telescope (CAST) has been searching for solar axions via their back conversion to X-ray photons in a 9-T 10-m long magnet directed towards the Sun. We report on an extended run with the IAXO (International Axion Observatory) pathfinder detector, doubling the previous exposure time. The detector was operated with a xenon-based gas mixture for part of the new run, providing technical insights for future detector configurations in IAXO. No counts are detected in the 95% signal-encircling region during the new run, while 0.75 are expected. The new data improve the axion-photon coupling limit to 5.8$\times 10^{-11}\,$GeV$^{-1}$ at 95% C.L. (for $m_a \lesssim 0.02$ eV), the most restrictive experimental limit to date., Comment: 9 pages, 4 figures (plus 8 pages, 10 figures of supplemental material) Corresponding authors: C. Margalejo (cmargalejo@unizar.es) and J. Ruz (Jaime.Ruz@cern.ch)

We study how tiny Dirac neutrino masses consistent with experimental constraints can arise in string theory SM-like vacua. We use as a laboratory 4d ${\cal N}=1$ type IIA Calabi--Yau orientifold compactifications, and in particular recent results on Yukawa couplings at infinite field-space distance. In this regime we find Dirac neutrino masses of the form $m_\nu \simeq g_\nu\langle H\rangle$, with $g_{\nu}$ the gauge coupling of the massive $U(1)$ under which the right-handed neutrinos $\nu_R$ are charged, and which should be in the range $g_{\nu}\simeq 10^{-14}-10^{-12}$ to reproduce neutrino data. The neutrino mass suppression occurs because the right-handed neutrino kinetic term behaves as $K_{\nu\nu} \simeq 1 /g_{\nu}^2 $. At the same time a tower of $\nu_R$-like states appears with characteristic scale $m_0\simeq g_{\nu}^2M_{\rm P}\simeq 0.1-500$ eV, in agreement with Swampland expectations. Two large hidden dimensions only felt by the $\nu_R$ sector arise at the same scale, while the string scale is around $M_s\simeq g_\nu M_{\rm P}\simeq 10-700$ TeV. Some phenomenological implications and model building challenges are described. As a byproduct, independently of the neutrino issue, we argue that a single large dimension in the context of SM-like type IIA Calabi--Yau orientifolds leads to too small Yukawa couplings for quarks and charged leptons., Comment: 31 pages + appendices, 4 figures, 4 tables. v2: references added; Appendix updated

Large language models (LLMs) often lack culture-specific knowledge of daily life, especially across diverse regions and non-English languages. Existing benchmarks for evaluating LLMs' cultural sensitivities are limited to a single language or collected from online sources such as Wikipedia, which do not reflect the mundane everyday lifestyles of diverse regions. That is, information about the food people eat for their birthday celebrations, spices they typically use, musical instruments youngsters play, or the sports they practice in school is common cultural knowledge but uncommon in easily collected online sources, especially for underrepresented cultures. To address this issue, we introduce BLEnD, a hand-crafted benchmark designed to evaluate LLMs' everyday knowledge across diverse cultures and languages. BLEnD comprises 52.6k question-answer pairs from 16 countries/regions, in 13 different languages, including low-resource ones such as Amharic, Assamese, Azerbaijani, Hausa, and Sundanese. We construct the benchmark to include two formats of questions: short-answer and multiple-choice. We show that LLMs perform better for cultures that are highly represented online, with a maximum 57.34% difference in GPT-4, the best-performing model, in the short-answer format. For cultures represented by mid-to-high-resource languages, LLMs perform better in their local languages, but for cultures represented by low-resource languages, LLMs perform better in English than the local languages. We make our dataset publicly available at: https://github.com/nlee0212/BLEnD., Comment: Accepted to NeurIPS 2024 Datasets & Benchmark Track

Dielectric breakdown of physical vacuum (Schwinger effect) is the textbook demonstration of compatibility of Relativity and Quantum theory. Although, the observation of this effect in its original static formulation is practically unachievable, it has been shown that the requirements on field strengths can be significantly reduced for dynamical generalizations of Schwinger effect. Here, we report on observation of an analog dynamical Schwinger effect in gapped Dirac semiconductor lead-halide perovskite MAPbBr$_3$. Specifically, we observe strong photoluminescence of a lead-halide perovskite driven by deep sub-gap irradiation, and use the quasi-adiabatic Landau-Dykhne approach to interpret our data in terms of the dynamical Schwinger effect. Further, the exponential sensitivity of the Schwinger effect to driving fields allows us to measure the local frozen-in fields in a nominally cubic single perovskite crystal at room temperature. Finally, we demonstrate an AC analogue of biasing in our system -- the non-perturbative cooperation between two time-dependent fields simultaneously driving the sample. Our results establish lead-halide perovskites as an excellent platform for simulating effects of strong fields on Dirac fields. In addition, they contribute to the on-going discussion about inversion-breaking in MAPbBr$_3$ single crystal and pave the way for a mid-infrared light detection with lead-halide perovskites.

We present an approach to compute the Floquet quasienergy spectrum of time-periodic systems. The method allows to characterize the light-matter interaction in finite and extended structures by carefully addressing the resolution of the position operator. In periodic systems we discuss the role of the quantum-geometric gauge freedom of Bloch states and employ a Wannier-based scheme to compute the required matrix elements. As a consequence, the method is accurate and applicable to a broad range of systems, from atoms and molecules to cold atomic gases and materials described by density functional theory, as well as model systems. We demonstrate the applicability of the approach by studying two cases: a particle trapped in a one-dimensional box and the semiconducting material BC$_2$N. We employ the first example to provide a numerical proof of the invariance of the Floquet quasienergy spectrum with respect to the choice of electromagnetic gauge. The analysis of BC$_2$N then serves to illustrate the physical effects described by the quasienergies, such as multiphoton resonances, and their expected range of occurrence in real materials in terms of external electric field and frequency of the drive pulse., Comment: 16 pages, 6 figures