Search

This manuscript explores novel complexity results for the feasibility problem over $p$-order cones, extending the foundational work of Porkolab and Khachiyan. By leveraging the intrinsic structure of $p$-order cones, we derive refined complexity bounds that surpass those obtained via standard semidefinite programming reformulations. Our analysis not only improves theoretical bounds but also provides practical insights into the computational efficiency of solving such problems. In addition to establishing complexity results, we derive explicit bounds for solutions when the feasibility problem admits one. For infeasible instances, we analyze their discrepancy quantifying the degree of infeasibility. Finally, we examine specific cases of interest, highlighting scenarios where the geometry of $p$-order cones or problem structure yields further computational simplifications. These findings contribute to both the theoretical understanding and practical tractability of optimization problems involving $p$-order cones., Comment: 21 pages, 2 tables

In this work we explore readout architectures for the simultaneous high-resolution timing and bidimensional tracking of charged particles with Resistive Plate Chambers (TOF-tracker) and for the accurate detection of gamma rays for PET imaging. On 625 $cm^2$ of active area we obtained a time resolution of 61 ps $\sigma$ and bidimensional position resolution below 150 $\mu$m $\sigma$ for the tracking and timing of charged particles from cosmic rays. An intrinsic precision of 0.49 mm FWHM was determined for the localization of a small $\beta^+$ source via the detection of its annihilation radiation., Comment: Presented at the 5th Jagiellonian Symposium on Advances in Particle Physics and Medicine (JS:2024), June 29 - July 7, 2024, Krak\'ow, Poland

We introduce a scissors congruence $K$-theory spectrum which lifts the equivariant scissors congruence groups for compact $G$-manifolds with boundary, and we show that on $\pi_0$ this is the source of a spectrum level lift of the Burnside ring valued equivariant Euler characteristic of a compact $G$-manifold. We also show that the equivariant scissors congruence groups for varying subgroups assemble into a Mackey functor, which is a shadow of a conjectural higher genuine equivariant structure., Comment: 20 pages, 1 figure

We utilize the cosmological volume simulation, FIREbox, to investigate how a galaxy's environment influences its size and dark matter content. Our study focuses on approximately 1,200 galaxies (886 central and 332 satellite halos) in the low-mass regime, with stellar masses between $10^6$ to $10^9$ $M_{\odot}$. We analyze the size-mass relation ($r_{50} - M_{\star}$), inner dark matter mass-stellar mass ($M^{50}_{\rm DM} - M_{\star}$) relation, and the halo mass-stellar mass ($M_{\rm halo} - M_{\star}$) relation. At fixed stellar mass, we find the galaxies experiencing stronger tidal influences, indicated by higher Perturbation Indices (PI $>$ 1) are generally larger and have lower masses relative to their counterparts with lower Perturbation Indices (PI $<$ 1). Applying a Random Forest regression model, we show that both the environment (PI) and halo mass ($M_{rm halo}$) are significant predictors of a galaxy's relative size and dark matter content. Notably, because $M_{\rm halo}$ is also strongly affected by the environment, our findings indicate that environmental conditions not only influence galactic sizes and relative inner dark matter content directly, but also indirectly through their impact on halo mass. Our results highlight a critical interplay between environmental factors and halo mass in shaping galaxy properties, affirming the environment as a fundamental driver in galaxy formation and evolution., Comment: 17 pages, 9 figures; submitted to ApJ

Motivated by the definition of Freiman homomorphism we explore the possibilities of formulating some basic notions and techniques of additive combinatorics in a categorical language. We show that additive sets and Freiman homomorphisms form a category and we study several limit and colimit constructions in this, and in an interesting subcategory of this category. Moreover, we study the additive structure of these (co)limit objects using additive doubling constant. We relate this category to that of finite sets and mappings, and that of abelian groups and group homomorphisms. We show that the Konyagin \& Lev result on universal ambient groups is an instance of adjunction., Comment: 16 pages, 4 diagrams

Reward machines (RMs) are an effective approach for addressing non-Markovian rewards in reinforcement learning (RL) through finite-state machines. Traditional RMs, which label edges with propositional logic formulae, inherit the limited expressivity of propositional logic. This limitation hinders the learnability and transferability of RMs since complex tasks will require numerous states and edges. To overcome these challenges, we propose First-Order Reward Machines ($\texttt{FORM}$s), which use first-order logic to label edges, resulting in more compact and transferable RMs. We introduce a novel method for $\textbf{learning}$ $\texttt{FORM}$s and a multi-agent formulation for $\textbf{exploiting}$ them and facilitate their transferability, where multiple agents collaboratively learn policies for a shared $\texttt{FORM}$. Our experimental results demonstrate the scalability of $\texttt{FORM}$s with respect to traditional RMs. Specifically, we show that $\texttt{FORM}$s can be effectively learnt for tasks where traditional RM learning approaches fail. We also show significant improvements in learning speed and task transferability thanks to the multi-agent learning framework and the abstraction provided by the first-order language., Comment: AAMAS'25

Robotic grasp and manipulation taxonomies, inspired by observing human manipulation strategies, can provide key guidance for tasks ranging from robotic gripper design to the development of manipulation algorithms. The existing grasp and manipulation taxonomies, however, often assume object rigidity, which limits their ability to reason about the complex interactions in the robotic manipulation of deformable objects. Hence, to assist in tasks involving deformable objects, taxonomies need to capture more comprehensively the interactions inherent in deformable object manipulation. To this end, we introduce T-DOM, a taxonomy that analyses key aspects involved in the manipulation of deformable objects, such as robot motion, forces, prehensile and non-prehensile interactions and, for the first time, a detailed classification of object deformations. To evaluate T-DOM, we curate a dataset of ten tasks involving a variety of deformable objects, such as garments, ropes, and surgical gloves, as well as diverse types of deformations. We analyse the proposed tasks comparing the T-DOM taxonomy with previous well established manipulation taxonomies. Our analysis demonstrates that T-DOM can effectively distinguish between manipulation skills that were not identified in other taxonomies, across different deformable objects and manipulation actions, offering new categories to characterize a skill. The proposed taxonomy significantly extends past work, providing a more fine-grained classification that can be used to describe the robotic manipulation of deformable objects. This work establishes a foundation for advancing deformable object manipulation, bridging theoretical understanding and practical implementation in robotic systems., Comment: 18 pages, 5 figures, 2 tables, Submitted to the International Journal of Robotics Research (IJRR). Project website: https://sites.google.com/view/t-dom

Direct searches for sub-GeV dark matter are limited by the intrinsic quantum properties of the target material. In this proof-of-concept study, we argue that this problem is particularly well suited for machine learning. We demonstrate that a simple neural architecture consisting of a variational autoencoder and a multi-layer perceptron can efficiently generate unique molecules with desired properties. In specific, the energy threshold and signal (quantum) efficiency determine the minimum mass and cross section to which a detector can be sensitive. Organic molecules present a particularly interesting class of materials with intrinsically anisotropic electronic responses and $\mathcal{O}$(few) eV excitation energies. However, the space of possible organic compounds is intractably large, which makes traditional database screening challenging. We adopt excitation energies and proxy transition matrix elements as target properties learned by our network. Our model is able to generate molecules that are not in even the most expansive quantum chemistry databases and predict their relevant properties for high-throughput and efficient screening. Following a massive generation of novel molecules, we use clustering analysis to identify some of the most promising molecular structures that optimise the desired molecular properties for dark matter detection.

This paper studies a practical regional demand continuous multifacility location problems whose main goal is to locate a given number of services and entry points in each region to distribute certain products to the users at minimum transportation cost. Additionally, a minimum satisfaction level is required for the customers in each region. This satisfaction is measured through continuous preference functions that reflect the satisfaction degree of each location in the region. We provide a mathematical optimization-based framework for the problem and derive suitable Mixed Integer Second Order Cone optimization models for some interesting situations: norm-based transportation costs for the services to the entry points, and different families of preference functions. Among these preference functions, we highlight those derived from economic production models and distance-based preferences. We conduct an extensive computational study along two main lines: a computational approach, where we provide optimal solutions for up to 500 demand regions in the single-facility case and up to $50$ for the p-facility case; and a qualitative approach, where we analyze whether the incorporation of preferences is statistically significant compared to the case without preferences.

We compute the entanglement entropy of an interval for a chiral scalar on a circle at an arbitrary temperature. We use the resolvent method, which involves expressing the entropy in terms of the resolvent of a certain operator, and we compute that resolvent by solving a problem that entails finding an analytic function on the complex torus with certain jump conditions at the interval. The resolvent is relevant by itself, since it can be used to compute any function of the reduced density matrix. We illustrate that by also computing all the R\'enyi entropies for the model., Comment: 19 pages, 3 figures

Among the various implications of the spiral arms, it has been observed in the Milky Way disc that the distribution of radial actions from the Gaia Data Release 3 exhibits structures that may be related to the spiral arms. Our goal is to investigate the relationship between regions of low radial action identified in simulated discs and the location of the spiral arms, such as that suggested in Palicio et al. (2023) for the Galaxy. For a sample of 23 simulated spiral galaxies, we modelled the axisymmetric component of their gravitational potential to compute the radial action of their stellar particles using the St\"ackel fudge. The spatial distribution of the radial action was then compared to the location of the spiral arms, identified as overdensities in the stellar surface density using a kernel density estimator. Our analysis reveals a strong correlation between the radial action distribution and the spiral arms in 18 of 23 simulated galaxies. However, notable discrepancies are observed in the remaining five, since they are profoundly out-of-equilibrium systems, such as galaxies influenced by external interactions or spiral arms still in the process of winding up. We have confirmed that, in general, there is a tendency of spatial correlation between spiral arms and stellar populations featuring low values of the radial action observed in Gaia DR3 data by Palicio et al. (2023). However, discrepancies between features in the radial action distribution and the spiral structure can be interpreted as signatures of recent disturbances, a scenario applicable to the Milky Way. Furthermore, populations at least as old as 3 Gyr trace the spiral arms with no significant misalignment across age bins. A linear relation between the maximum value of the radial action of the spiral arms and the vertical scale-length is found, which is also satisfied by the Milky Way., Comment: 25 pages, 18 figures

Chemical reaction networks (CRNs) are essential for modeling and analyzing complex systems across fields, from biochemistry to economics. Autocatalytic reaction network -- networks where certain species catalyze their own production -- are particularly significant for understanding self-replication dynamics in biological systems and serve as foundational elements in formalizing the concept of a circular economy. In a previous study, we developed a mixed-integer linear optimization-based procedure to enumerate all minimal autocatalytic subnetworks within a network. In this work, we define the maximum growth factor (MGF) of an autocatalytic subnetwork, develop mathematical optimization approaches to compute this metric, and explore its implications in the field of economics and dynamical systems. We develop exact approaches to determine the MGF of any subnetwork based on an iterative procedure with guaranteed convergence, which allows for identifying autocatalytic subnetworks with the highest MGF. We report the results of computational experiments on synthetic CRNs and two well-known datasets, namely the Formose and E. coli reaction networks, identifying their autocatalytic subnetworks and exploring their scientific ramifications. Using advanced optimization techniques and interdisciplinary applications, our framework adds an essential resource to analyze complex systems modeled as reaction networks.

Compounds based on Cr have been found to be among the first single-layer magnets. In addition, transition metal dichalcogenides are promising candidates to show long-range ferromagnetic order down to the two-dimensional limit. We use ab initio calculations to provide a description of the various Cr$_x$Se$_{x+1}$ stoichiometries that may occur by analyzing from the bulk materials to the monolayer limit. We study the different structural distortions, including charge density waves that each system can present by analyzing their phonon spectra and dynamic stability. We provide a description of their basic electronic structure and study their magnetic properties, including the magnetocrystalline anisotropy energy. The evolution of all these properties with the dimensionality of the systems is discussed. This intends to be a comprehensive view of the broad family of Cr selenides.

We study dijet production in pA collisions at forward rapidities at next-to-eikonal accuracy. We restrict ourselves to the next-to-eikonal corrections that are induced by the quark background field of the target. We consider all possible channels, compute scattering amplitudes both in general kinematics and in the back-to-back limit. By using these results, we compute the back-to-back production cross section and obtain a factorized expression with a quark TMD times associated hard factor for each channel., Comment: 76 pages, 17 figures

Many drift-diffusion transport models rely on a coupling with a sub-model of the drift velocity. In this letter we extend Feynman-Kac's theory to provide probabilistic representations of such velocity-coupled models, so far remained out of reach. Hence a single embedded stochastic process is built, enabling such representations in a single branching path-space. To address this, we propose renewed physical insights in terms of propagative pictures to non-linear physics such as Navier-Stokes, Keller-Segel and Poisson-Nernst-Planck equations in confined and complex geometries.

Coherent technologies have revolutionized optical communications, driving the capacity per fiber to multi-terabit per second (Tb/s) in combination with wavelength division multiplexing (WDM). With an ever-increasing deployment density of coherent systems, the demand for highly integrated WDM coherent transceivers has been rising. While tremendous progress has been made on silicon photonics compatible high-speed modulation and photodetection on chip, a solution for monolithically integrable amplifier with high gain and output power remains a challenge. Recently, an erbium doped waveguide amplifier based on ultra-low loss silicon nitride waveguides has demonstrated gain and output power levels potentially suitable for Terabit class coherent communications. Here, we demonstrate a WDM coherent system enabled by this integrated photonic amplification solution. The system uses the waveguide amplifier as a booster amplifier of 16 WDM signals each carrying a net data rate of 1.6 Tb/s, achieving 25.6-Tb/s net capacity over 81-km fiber transmission. Our results highlight a fully integrated solution for highly parallel coherent transceivers including amplification, that has the potential to transform future optical communications., Comment: Added acknowledgements

The nano-meter beam size in future linear colliders requires very high resolution beam position monitor since higher resolution allows more accurate position measurement in the interaction point. We developed and tested a low-Q C-band beam position monitor with position resolution of nanometer. The C-band BPM was tested for the fast beam feedback system at the interaction point of ATF2 in KEK, in which C-band beam position monitor is called to IPBPM (Interaction Point Beam Position Monitor). The average position resolution of the developed IPBPMs was measured to be 10.1 nm at a nominal beam charge of $87\%$ of ATF2. From the measured beam position resolution, we can expect beam position resolution of around 8.8 nm and 4.4 nm with nominal ATF2 and ILC beam charge conditions, respectively, in which the position resolution is below the vertical beam size in ILC. In this paper, we describe the development of the IPBPM and the beam test results at the nanometer level in beam position resolution, Comment: 12 pages, 13 figures. To be published in the Physical Review Accelerators and Beams (PRAB). This article introduces the development of a low-Q cavity beam position monitor (BPM) with nanometer-scale position resolution for future colliders. For further details or associated files, please contact the corresponding author via the provided email address

In this paper we provide a novel mathematical optimization based methodology to perturb the features of a given observation to be re-classified, by a tree ensemble classification rule, to a certain desired class. The method is based on these facts: the most viable changes for an observation to reach the desired class do not always coincide with the closest distance point (in the feature space) of the target class; individuals put effort on a few number of features to reach the desired class; and each individual is endowed with a probability to change each of its features to a given value, which determines the overall probability of changing to the target class. Putting all together, we provide different methods to find the features where the individuals must exert effort to maximize the probability to reach the target class. Our method also allows us to rank the most important features in the tree-ensemble. The proposed methodology is tested on a real dataset, validating the proposal., Comment: 30 pages, 4 Figures, 4 Tables

Path Planning methods for autonomously controlling swarms of unmanned aerial vehicles (UAVs) are gaining momentum due to their operational advantages. An increasing number of scenarios now require autonomous control of multiple UAVs, as autonomous operation can significantly reduce labor costs. Additionally, obtaining optimal flight paths can lower energy consumption, thereby extending battery life for other critical operations. Many of these scenarios, however, involve obstacles such as power lines and trees, which complicate Path Planning. This paper presents an evolutionary computation-based system employing genetic algorithms to address this problem in environments with obstacles. The proposed approach aims to ensure complete coverage of areas with fixed obstacles, such as in field exploration tasks, while minimizing flight time regardless of map size or the number of UAVs in the swarm. No specific goal points or prior information beyond the provided map is required. The experiments conducted in this study used five maps of varying sizes and obstacle densities, as well as a control map without obstacles, with different numbers of UAVs. The results demonstrate that this method can determine optimal paths for all UAVs during full map traversal, thus minimizing resource consumption. A comparative analysis with other state-of-the-art approach is presented to highlight the advantages and potential limitations of the proposed method.

Federated Learning (FL) has emerged as a solution for distributed model training across decentralized, privacy-preserving devices, but the different energy capacities of participating devices (system heterogeneity) constrain real-world implementations. These energy limitations not only reduce model accuracy but also increase dropout rates, impacting on convergence in practical FL deployments. In this work, we propose LeanFed, an energy-aware FL framework designed to optimize client selection and training workloads on battery-constrained devices. LeanFed leverages adaptive data usage by dynamically adjusting the fraction of local data each device utilizes during training, thereby maximizing device participation across communication rounds while ensuring they do not run out of battery during the process. We rigorously evaluate LeanFed against traditional FedAvg on CIFAR-10 and CIFAR-100 datasets, simulating various levels of data heterogeneity and device participation rates. Results show that LeanFed consistently enhances model accuracy and stability, particularly in settings with high data heterogeneity and limited battery life, by mitigating client dropout and extending device availability. This approach demonstrates the potential of energy-efficient, privacy-preserving FL in real-world, large-scale applications, setting a foundation for robust and sustainable pervasive AI on resource-constrained networks.

This paper focuses on the task of measuring and forecasting incivility in conversations following replies to hate speech. Identifying replies that steer conversations away from hatred and elicit civil follow-up conversations sheds light into effective strategies to engage with hate speech and proactively avoid further escalation. We propose new metrics that take into account various dimensions of antisocial and prosocial behaviors to measure the conversation incivility following replies to hate speech. Our best metric aligns with human perceptions better than prior work. Additionally, we present analyses on a) the language of antisocial and prosocial posts, b) the relationship between antisocial or prosocial posts and user interactions, and c) the language of replies to hate speech that elicit follow-up conversations with different incivility levels. We show that forecasting the incivility level of conversations following a reply to hate speech is a challenging task. We also present qualitative analyses to identify the most common errors made by our best model., Comment: The 19th International AAAI Conference on Web and Social Media (ICWSM 2025) Accepted

We derive a general procedure for calculating the gravitational wave background (GWB) from cosmic string loops whose typical shape evolves over time, as in gravitational backreaction. Using the results of a large-scale study of numerical gravitational backreaction on Nambu-Goto cosmic string loops, we construct GWBs of backreacted cosmic strings for a range of tensions and frequencies of cosmological interest, and compare them to current and upcoming gravitational wave detectors. The GWBs are lower than prior predictions by anywhere from a few percent to around 30\%, depending on the frequency and tension in question., Comment: 21 pages, 4 figures. Data for GWB available at: https://doi.org/10.5281/zenodo.14036934

Research on Reinforcement Learning (RL) approaches for discrete optimization problems has increased considerably, extending RL to an area classically dominated by Operations Research (OR). Vehicle routing problems are a good example of discrete optimization problems with high practical relevance where RL techniques have had considerable success. Despite these advances, open-source development frameworks remain scarce, hampering both the testing of algorithms and the ability to objectively compare results. This ultimately slows down progress in the field and limits the exchange of ideas between the RL and OR communities. Here we propose a library composed of multi-agent environments that simulates classic vehicle routing problems. The library, built on PyTorch, provides a flexible modular architecture design that allows easy customization and incorporation of new routing problems. It follows the Agent Environment Cycle ("AEC") games model and has an intuitive API, enabling rapid adoption and easy integration into existing reinforcement learning frameworks. The library allows for a straightforward use of classical OR benchmark instances in order to narrow the gap between the test beds for algorithm benchmarking used by the RL and OR communities. Additionally, we provide benchmark instance sets for each environment, as well as baseline RL models and training code.

The PANDAX-4T and XENONnT experiments present indications of Coherent Elastic Neutrino Nucleus Scattering (CE$\nu$NS) from ${}^{8}$B solar neutrinos at 2.6$\sigma$ and 2.7$\sigma$, respectively. This constitutes the first observation of the neutrino "floor" or "fog", an irreducible background that future dark matter searches in terrestrial detectors will have to contend with. Here, we first discuss the contributions from neutrino-electron scattering and from the Migdal effect in the region of interest of these experiments, and we argue that they are non-negligible. Second, we make use of the recent PANDAX-4T and XENONnT data to derive novel constraints on light scalar and vector mediators coupling to neutrinos and quarks. We demonstrate that these experiments already provide world-leading laboratory constraints on new light mediators in some regions of parameter space., Comment: 11 pages, 4 figures

We study the problem of collaborative best-arm identification in stochastic linear bandits under a fixed-budget scenario. In our learning model, we consider multiple agents connected through a star network or a generic network, interacting with a linear bandit instance in parallel. The objective of the agents is to collaboratively learn the best arm of the given bandit instance with the help of a central server while minimizing the probability of error in best arm estimation. For this purpose, we devise the algorithms MaLinBAI-Star and MaLinBAI-Gen for star networks and generic networks respectively. Both algorithms employ an Upper-Confidence-Bound approach where agents share their knowledge through the central server during each communication round. We demonstrate, both theoretically and empirically, that our algorithms enjoy exponentially decaying probability of error in the allocated time budget. Furthermore, experimental results based on synthetic and real-world data validate the effectiveness of our algorithms over the existing multi-agent algorithms.

We introduce a systematic method to derive the effective action for domain walls directly from the scalar field theory that gives rise to their solitonic solutions. The effective action for the Goldstone mode, which characterizes the soliton's position, is shown to consist of the Nambu-Goto action supplemented by higher-order curvature invariants associated to its worldvolume metric. Our approach constrains the corrections to a finite set of Galileon terms, specifying both their functional forms and the procedure to compute their coefficients. We do a collection of tests across various models in $2+1$ and $3+1$ dimensions that confirm the validity of this framework. Additionally, the method is extended to include bound scalar fields living on the worldsheet, along with their couplings to the Goldstone mode. These interactions reveal a universal non-minimal coupling of these scalar fields to the Ricci scalar on the worldsheet. A significant consequence of this coupling is the emergence of a parametric instability, driven by interactions between the bound states and the Goldstone mode., Comment: 46 pages, 10 figures

Shock-generated transients, such as hot flow anomalies (HFAs), upstream of planetary bow shocks, play a critical role in electron acceleration. Using multi-mission data from NASA's Magnetospheric Multiscale (MMS) and ESA's Cluster missions, we demonstrate the transmission of HFAs through Earth's quasi-parallel bow shock, associated with acceleration of electrons up to relativistic energies. Energetic electrons, initially accelerated upstream, are shown to remain broadly confined within the transmitted transient structures downstream, where betatron acceleration further boosts their energy due to elevated compression levels. Additionally, high-speed jets form at the compressive edges of HFAs, exhibiting a significant increase in dynamic pressure and potentially contributing to driving further localized compression. Our findings emphasize the efficiency of quasi-parallel shocks in driving particle acceleration far beyond the immediate shock transition region, expanding the acceleration region to a larger spatial domain. Finally, this study underscores the importance of multi-scale observational approach in understanding the convoluted processes behind collisionless shock physics and their broader implications.

We report on the results of performing computational gravitational backreaction on cosmic string loops taken from a network simulation. The principal effect of backreaction is to smooth out small-scale structure on loops, which we demonstrate by various measures including the average loop power spectrum and the distribution of kink angles on the loops. Backreaction does lead to self-intersections in most cases, but these are typically small. An important effect discussed in prior work is the rounding off of kinks to form cusps, but we find that the cusps produced by that process are very weak and do not significantly contribute to the total gravitational-wave radiation of the loop. We comment briefly on extrapolating our results to loops as they would be found in nature., Comment: 26 pages, 11 figures. Power spectrum data available via Zenodo: https://doi.org/10.5281/zenodo.14037540

The distribution of chemical elements in the Galactic disc can reveal fundamental clues on the physical processes that led to the current configuration of our Galaxy. We map chemical azimuthal variations in the disc using individual stellar chemical abundances and discuss their possible connection with the spiral arms and other perturbing mechanisms. Using Gaia Data Release 3, we examine [Ca/Fe] and [Mg/Fe] fluctuations in a ~4 kpc region around the Sun, focusing on bright giant stars. We implemented a kernel density estimator technique to enhance the chemical inhomogeneities. We observe radial gradients and azimuthal fluctuations in [alpha/Fe] for young (<150 Myr) and old (>2 Gyr) stars, with amplitudes varying according to the studied element. In young stars, those within spiral arms (e.g., Sagittarius-Carina and Local arms) are generally more metal and calcium-rich (~0-0.19 dex) but show lower [Ca/Fe] (~0.06 dex) and [Mg/Fe] (~0.05 dex) compared to inter-arm regions, suggesting enhanced iron production in spiral arms. These [alpha/Fe] depletions are analysed in light of theoretical scenarios and compared to a 2D chemical evolution model with multiple spiral patterns. For the old sample, [Ca/Fe] maps reveal deficiencies along a segment of the Local arm identified by young stars. We caution that, for this old sample, the quality of the obtained maps might be limited along a specific line-of-sight, due to the Gaia scanning law. This study transitions our understanding of disc chemical evolution from a 1D radial view to a more detailed 2D framework incorporating radial, azimuthal, and small-scale variations. Individual chemical abundances prove essential for tracing spiral arms in disc galaxies. We recommend models and simulations incorporate alpha-abundance trends to better address spiral arm lifetimes.

Understanding the interplay of non-Hermiticity and topology is crucial given the intrinsic openness of most natural and engineered systems and it has important ramifications in topological lasers and sensors. Intense efforts have been devoted to unveiling how non-Hermiticity may impact the most significant features of topological systems, but only recently it has been theoretically proposed that topological features could originate solely from the system's non-Hermiticity in photonic systems. In this work, we experimentally demonstrate the appearance of non-Hermitian topology exclusively from loss modulation in a photonic system that is topologically trivial in the absence of loss. We do this by implementing a non-Hermitian generalization of an Aubry-Andre-Harper model with purely imaginary potential in a programmable integrated photonics platform, which allows us to investigate different periodic and quasi-periodic configurations of the model. In both cases, we show the emergence of topological edge modes and explore their resilience to different kinds of disorder. Our work highlights loss engineering as a mechanism to generate topological properties.

In recent years, it has become increasingly clear that space weather disturbances can be triggered by transient upstream mesoscale structures (TUMS), independently of the occurrence of large-scale solar wind (SW) structures, such as interplanetary coronal mass ejections and stream interaction regions. Different types of magnetospheric pulsations, transient perturbations of the geomagnetic field and auroral structures are often observed during times when SW monitors indicate quiet conditions, and have been found to be associated to TUMS. In this mini-review we describe the space weather phenomena that have been associated with four of the largest-scale and the most energetic TUMS, namely hot flow anomalies, foreshock bubbles, travelling foreshocks and foreshock compressional boundaries. The space weather phenomena associated with TUMS tend to be more localized and less intense compared to geomagnetic storms. However, the quiet time space weather may occur more often since, especially during solar minima, quiet SW periods prevail over the perturbed times., Comment: 1 figure, 1 table

The demand for real-time Indoor Air Quality (IAQ) management has increased recently, since low-cost and modern sensors such as Particulate Matter (PM), Volatile Organic Compounds (VOCs), Carbon Monoxide (CO), Carbon Dioxide (CO2), Radon (Rn), among others, have been put forward with considerable accuracy. Although these low-cost sensors cannot be considered measurement instruments, they are very useful for a vast number of application domains, such as home automation, smart building management, IAQ management, risk exposure assessment, to name a few. This paper presents a literature review and a prospective analysis and discussion regarding Internet of Things (IoT) technologies adopted to deal with scenarios that present known indoor Radon gas problems. Specifically, the main requirements for developing IoT-enabled radon management solutions are reviewed. Thus, a traditional IoT architecture is described, its main components are analyzed and some of the most recent academic solutions are reviewed. Finally, novel approaches for deploying IoT radon management architectures are presented together with the most relevant open challenges. In this way, this article presents a holistic review of the past, present, and future of indoor radon management in order to provide guidelines for future designers and developers., Comment: Paper accepted in Edge-IoT 2022

Modern digital applications extensively integrate Artificial Intelligence models into their core systems, offering significant advantages for automated decision-making. However, these AI-based systems encounter reliability and safety challenges when handling continuously generated data streams in complex and dynamic scenarios. This work explores the concept of resilient AI systems, which must operate in the face of unexpected events, including instances that belong to patterns that have not been seen during the training process. This is an issue that regular closed-set classifiers commonly encounter in streaming scenarios, as they are designed to compulsory classify any new observation into one of the training patterns (i.e., the so-called \textit{over-occupied space} problem). In batch learning, the Open Set Recognition research area has consistently confronted this issue by requiring models to robustly uphold their classification performance when processing query instances from unknown patterns. In this context, this work investigates the application of an Open Set Recognition framework that combines classification and clustering to address the \textit{over-occupied space} problem in streaming scenarios. Specifically, we systematically devise a benchmark comprising different classification datasets with varying ratios of known to unknown classes. Experiments are presented on this benchmark to compare the performance of the proposed hybrid framework with that of individual incremental classifiers. Discussions held over the obtained results highlight situations where the proposed framework performs best, and delineate the limitations and hurdles encountered by incremental classifiers in effectively resolving the challenges posed by open-world streaming environments., Comment: 12 pages, 3 figures, an updated version of this article is published in LNAI,volume 14857 as part of the conference proceedings HAIS 2024

We study the formation of gravitating global strings through quantum mechanical tunneling. The instantons that describe the nucleation process are characterized by two parameters: the string core thickness and its gravitational backreaction controlled by the string core energy density. We obtain solutions across a wide range of these parameters by carrying out numerical integration via multiple shooting methods. Our results are in agreement with previous findings on the nucleation of other topological defects; specifically, after reaching a certain threshold for the string core thickness or its gravitational backreaction, the configuration becomes homogeneous in a manner akin to Hawking-Moss solutions. Additionally, we analyze the global structure of the analytical continuation of the solutions to Lorentzian signature, revealing the emergence of a region of spacetime that describes an anisotropic universe. Finally, we also discuss the relevance of these instantons in the context of quantum cosmology., Comment: 36 pages, 17 figures

Interplanetary shocks are fundamental constituents of the heliosphere, where they form as a result of solar activity. We use previously unavailable measurements of interplanetary shocks in the inner heliosphere provided by Solar Orbiter, and present a survey of the first 100 shocks observed in situ at different heliocentric distances during the rising phase of solar cycle 25. The fundamental shock parameters (shock normals, shock normal angles, shock speeds, compression ratios, Mach numbers) have been estimated and studied as a function of heliocentric distance, revealing a rich scenario of configurations. Comparison with large surveys of shocks at 1~au show that shocks in the quasi-parallel regime and with high speed are more commonly observed in the inner heliosphere. The wave environment of the shocks has also been addressed, with about 50\% of the events exhibiting clear shock-induced upstream fluctuations. We characterize energetic particle responses to the passage of IP shocks at different energies, often revealing complex features arising from the interaction between IP shocks and pre-existing fluctuations, including solar wind structures being processed upon shock crossing. Finally, we give details and guidance on the access use of the present survey, available on the EU-project ``solar energetic particle analysis platform for the inner heliosphere'' (SERPENTINE) website. The algorithm used to identify shocks in large datasets, now publicly available, is also described.

Blockchain has been widely criticized due to the use of inefficient consensus protocols and energy-intensive mechanisms that derived into a global enormous power consumption. Fortunately, since the first blockchain was conceived in 2008 (the one that supports Bitcoin), hardware and consensus protocols have evolved, decreasing energy consumption significantly. This article describes a green blockchain solution and quantifies energy savings when deploying the system on traditional computers and embedded Single-Board Computers (SBCs). To illustrate such savings, it is proposed a solution for tackling the problem of academic certificate forgery, which has a significant cost to society, since it harms the trustworthiness of certificates and academic institutions. The proposed solution is aimed at recording and verifying academic records (ARs) through a decentralized application (DApp) that is supported by a smart contract deployed in the Ethereum blockchain. The application stores the raw data (i.e., the data that are not managed by the blockchain) on a decentralized storage system based on Inter-Planetary File System (IPFS). To demonstrate the efficiency of the developed solution, it is evaluated in terms of performance (transaction latency and throughput) and efficiency (CPU usage and energy consumption), comparing the results obtained with a traditional Proof-of-Work (PoW) consensus protocol and the new Proof-of-Authority (PoA) protocol. The results shown in this paper indicate that the latter is clearly greener and demands less CPU load. Moreover, this article compares the performance of a traditional computer and two SBCs (a Raspberry Pi 4 and an Orange Pi One), showing that is possible to make use of the latter low-power devices to implement blockchain nodes for proposed DApp, but at the cost of higher response latency that varies greatly depending on the used SBCs [...]

Counterspeech is a targeted response to counteract and challenge abusive or hateful content. It effectively curbs the spread of hatred and fosters constructive online communication. Previous studies have proposed different strategies for automatically generated counterspeech. Evaluations, however, focus on relevance, surface form, and other shallow linguistic characteristics. This paper investigates the human likeness of AI-generated counterspeech, a critical factor influencing effectiveness. We implement and evaluate several LLM-based generation strategies, and discover that AI-generated and human-written counterspeech can be easily distinguished by both simple classifiers and humans. Further, we reveal differences in linguistic characteristics, politeness, and specificity. The dataset used in this study is publicly available for further research., Comment: Accepted for presentation at the COLING 2025

We implement a simulation environment on top of NetSquid that is specifically designed for estimating the end-to-end fidelity across a path of quantum repeaters or quantum switches. The switch model includes several generalizations which are not currently available in other tools, and are useful for gaining insight into practical and realistic quantum network engineering problems: an arbitrary number of memory registers at the switches, simplicity in including entanglement distillation mechanisms, arbitrary switching topologies, and more accurate models for the depolarization noise. An illustrative case study is presented, namely a comparison in terms of performance between a repeater chain where repeaters can only swap sequentially, and a single switch equipped with multiple memory registers, able to handle multiple swapping requests.

The relationship between UV Bursts and solar surges is complex, with these events sometimes being observed together and sometimes being observed independently. Why this sporadic association exists is unknown, however, it likely relates to the physical conditions at the site of the energy release that drives these events. Here, we aim to better understand the relationship between UV Bursts and solar surges through a multi-instrument analysis of several associated events that occurred around the trailing sunspot in AR 12957. We use data from Solar Orbiter, the Solar Dynamics Observatory (SDO), and the Interface Region Imaging Spectrograph (IRIS) to achieve our aims. These data were sampled on 3rd March 2022 between 09:30:30 UT and 11:00:00 UT, during which time a coordinated observing campaign associated with the Slow Solar Wind Connection Solar Orbiter Observing Plan took place. Numerous small-scale negative polarity magnetic magnetic features (MMFs) are observed to move quickly (potentially up to 3.3 km/s) away from a sunspot until they collide with a more stable positive polarity plage region around 7 Mm away. Several UV Bursts are identified in IRIS slit-jaw imager (SJI) 1400 {\AA} data co-spatial to where these opposite polarity fields interact, with spatial scales (2 Mm<) and lifetimes (20< min) larger than typical values for such events. Two surges are also observed to occur at these locations, with one being short (5 Mm) and hot (bright in IRIS SJI images), whilst the other is a cooler (dark in coronal imaging channels), longer surge that appears to fill an active region loop. Magnetic reconnection between the negative polarity MMFs around the sunspot and the positive polarity plage region appears to be the driver of these events. Both the speed of the MMFs and the locally open magnetic topology of the plage region could possibly be important for forming the surges., Comment: 13 pages, 9 figures, accepted in A&A

We present the Merian Survey, an optical imaging survey optimized for studying the physical properties of bright star-forming dwarf galaxies. Merian is carried out with two medium-band filters ($N708$ and $N540$, centered at $708$ and $540$ nm), custom-built for the Dark Energy Camera (DECam) on the Blanco telescope. Merian covers $\sim 750\,\mathrm{deg}^2$ of equatorial fields, overlapping with the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) wide, deep, and ultra-deep fields. When combined with the HSC-SSP imaging data ($grizy$), the new Merian DECam medium-band imaging allows for photometric redshift measurements via the detection of H$\rm\alpha$ and [OIII] line emission flux excess in the $N708$ and $N540$ filters, respectively, at $0.06

The Polynomial Learning With Errors problem (PLWE) serves as the background of two of the three cryptosystems standardized in August 2024 by the National Institute of Standards and Technology to replace non-quantum resistant current primitives like those based on RSA, Diffie-Hellman or its elliptic curve analogue. Although PLWE is highly believed to be quantum resistant, this fact has not yet been established, contrariwise to other post-quantum proposals like multivariate and some code based ones. Moreover, several vulnerabilities have been encountered for a number of specific instances. In a search for more flexibility, it becomes fully relevant to study the robustness of PLWE based on other polynomials, not necessarily cyclotomic. In 2015, Elias et al found a good number of attacks based on different features of the roots of the polynomial. In the present work we present an overview of the approximations made against PLWE derived from this and subsequent works, along with several new attacks which refine those by Elias et al. exploiting the order of the trace of roots over finite extensions of the finite field under the three scenarios laid out by Elias et al., allowing to generalize the setting in which the attacks can be carried out.

We prove the equivalence between the Ring Learning With Errors (RLWE) and the Polynomial Learning With Errors (PLWE) problems for the maximal totally real subfield of the $2^r 3^s$-th cyclotomic field for $r \geq 3$ and $s \geq 1$. Moreover, we describe a fast algorithm for computing the product of two elements in the ring of integers of these subfields. This multiplication algorithm has quasilinear complexity in the dimension of the field, as it makes use of the fast Discrete Cosine Transform (DCT). Our approach assumes that the two input polynomials are given in a basis of Chebyshev-like polynomials, in contrast to the customary power basis. To validate this assumption, we prove that the change of basis from the power basis to the Chebyshev-like basis can be computed with $\mathcal{O}(n \log n)$ arithmetic operations, where $n$ is the problem dimension. Finally, we provide a heuristic and theoretical comparison of the vulnerability to some attacks for the $p$-th cyclotomic field versus the maximal totally real subextension of the $4p$-th cyclotomic field for a reasonable set of parameters of cryptographic size.

Even though a few initial works have shown on small sets of data some level of bias in the performance of fingerprint recognition technology with respect to certain demographic groups, there is still not sufficient evidence to understand the impact that certain factors such as gender, age or finger-type may have on fingerprint quality and, in turn, also on fingerprint matching accuracy. The present work addresses this still under researched topic, on a large-scale database of operational data containing 10-print impressions of almost 16,000 subjects. The results reached provide further insight into the dependency of fingerprint quality and demographics, and show that there in fact exists a certain degree of performance variability in fingerprint-based recognition systems for different segments of the population. Based on the experimental evaluation, the work points out new observations based on data-driven evidence, provides plausible hypotheses to explain such observations, and concludes with potential follow-up actions that can help to reduce the observed fingerprint quality differences. This way, the current paper can be considered as a contribution to further increase the algorithmic fairness and equality of biometric technology., Comment: Extended journal version submitted to IET Biometrics. 10 pages, 5 figures Reference conference paper: J. Galbally, A. Cepilovs, R. Blanco-Gonzalo, G. Ormiston, O. Miguel-Hurtado, and I. S. Racz, 'Fingerprint quality per individual finger type: A large-scale study on real operational data' in Proc. IEEE Intl. Workshop on Biometrics and Forensics 2023 (IWBF 2023)

Frequency domain sweeps of array antennas are well-known to be time-intensive, and different surrogate models have been used to improve the performance. Data-driven model order reduction algorithms, such as the Loewner framework and vector fitting, can be integrated with these adaptive error estimates, in an iterative algorithm, to reduce the number of full-wave simulations required to accurately capture the requested frequency behavior of multiport array antennas. In this work, we propose two novel adaptive methods exploiting a block matrix function which is a key part of the Loewner framework generating system approach. The first algorithm leverages an inherent matrix parameter freedom in the block matrix function to identify frequency points with large errors, whereas the second utilizes the condition number of the block matrix function. Both methods effectively provide frequency domain error estimates, which are essential for improved performance. Numerical experiments on multiport array antenna S-parameters demonstrate the effectiveness of our proposed algorithms within the Loewner framework., Comment: 10 pages, 12 figures