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This paper investigates the performance of 16 Large Language Models (LLMs) in automating LoRaWAN-related engineering tasks involving optimal placement of drones and received power calculation under progressively complex zero-shot, natural language prompts. The primary research question is whether lightweight, locally executed LLMs can generate correct Python code for these tasks. To assess this, we compared locally run models against state-of-the-art alternatives, such as GPT-4 and DeepSeek-V3, which served as reference points. By extracting and executing the Python functions generated by each model, we evaluated their outputs on a zero-to-five scale. Results show that while DeepSeek-V3 and GPT-4 consistently provided accurate solutions, certain smaller models-particularly Phi-4 and LLaMA-3.3-also demonstrated strong performance, underscoring the viability of lightweight alternatives. Other models exhibited errors stemming from incomplete understanding or syntactic issues. These findings illustrate the potential of LLM-based approaches for specialized engineering applications while highlighting the need for careful model selection, rigorous prompt design, and targeted domain fine-tuning to achieve reliable outcomes.

This study presents an automated method for objectively measuring rock heterogeneity via raw X-ray micro-computed tomography (micro-CT) images, thereby addressing the limitations of traditional methods, which are time-consuming, costly, and subjective. Unlike approaches that rely on image segmentation, the proposed method processes micro-CT images directly, identifying textural heterogeneity. The image is partitioned into subvolumes, where attributes are calculated for each one, with entropy serving as a measure of uncertainty. This method adapts to varying sample characteristics and enables meaningful comparisons across distinct sets of samples. It was applied to a dataset consisting of 4,935 images of cylindrical plug samples derived from Brazilian reservoirs. The results showed that the selected attributes play a key role in producing desirable outcomes, such as strong correlations with structural heterogeneity. To assess the effectiveness of our method, we used evaluations provided by four experts who classified 175 samples as either heterogeneous or homogeneous, where each expert assessed a different number of samples. One of the presented attributes demonstrated a statistically significant difference between the homogeneous and heterogeneous samples labelled by all the experts, whereas the other two attributes yielded nonsignificant differences for three out of the four experts. The method was shown to better align with the expert choices than traditional textural attributes known for extracting heterogeneous properties from images. This textural heterogeneity measure provides an additional parameter that can assist in rock characterization, and the automated approach ensures easy reproduction and high cost-effectiveness., Comment: 26 pages, 11 figures

Let f = 0 be an implicit singular plane curve. When deforming f = 0, inflections and vertex emerge from the singularities. In this papper, we classify the deformations of f = 0 with respect to the inflections and the vertices in the cases of codimension less than or equal to 2, that is, in the cases that occur generically in families of implicit curves with 2 parameters.

In loop-current states, interacting electronic degrees of freedom collectively establish interatomic currents, in a rare example of magnetism in which spin degrees of freedom do not play the primary role. The main impact of such states on the electronic spectrum is not via the standard Zeeman term, but via the kinetic energy, in which hopping parameters develop non-trivial phases that break time-reversal symmetry. The recent proposal of loop-current states in kagome superconductors has stimulated renewed interest in this exotic type of magnetism. In this perspective, we use kagome materials as a scaffolding to frame the basic phenomenology of loop-current states. We provide an overview of the group-theoretical properties of loop currents, as well as of relevant microscopic models and ab initio methods. Particular emphasis is given to the comparison with spin-density waves in the presence of spin-orbit coupling, as well as to the anharmonic coupling with charge-density waves, which is present in systems with threefold rotational symmetry. We also provide a brief overview of the current status of loop-current order in kagome metals and discuss open challenges including their experimental detection and interplay with other orders., Comment: perspective paper

We explore purely metric theories of gravity with second-order equations of motion and a single additional, purely gravitational, propagating, scalar degree of freedom. We identify a subclass of these theories in which this scalar causes a phenomenon similar to black-hole scalarization, which we call curvaturization: around small enough Kerr black holes, the scalar induces a tachyonic instability. This triggers a sudden growth of Ricci curvature and results in a new branch of vacuum black-hole solutions. We study the properties of these black holes both in the static as well as the spinning case., Comment: 12 pages + references, 3 figures

We investigate the performance of Opticks, an NVIDIA OptiX API 7.5 GPU-accelerated photon propagation compared with a single-threaded Geant4 simulation. We compare the simulations using an improved model of the NEXT-CRAB-0 gaseous time projection chamber. Performance results suggest that Opticks improves simulation speeds by between $58.47\pm{0.02}$ and $181.39\pm{0.28}$ times relative to a CPU-only Geant4 simulation and these results vary between different types of GPU and CPU. A detailed comparison shows that the number of detected photons, along with their times and wavelengths, are in good agreement between Opticks and Geant4., Comment: 12 pages, 8 Figures

Detecting anomalies in datasets is a longstanding problem in machine learning. In this context, anomalies are defined as a sample that significantly deviates from the remaining data. Meanwhile, optimal transport (OT) is a field of mathematics concerned with the transportation, between two probability measures, at least effort. In classical OT, the optimal transportation strategy of a measure to itself is the identity. In this paper, we tackle anomaly detection by forcing samples to displace its mass, while keeping the least effort objective. We call this new transportation problem Mass Repulsing Optimal Transport (MROT). Naturally, samples lying in low density regions of space will be forced to displace mass very far, incurring a higher transportation cost. We use these concepts to design a new anomaly score. Through a series of experiments in existing benchmarks, and fault detection problems, we show that our algorithm improves over existing methods., Comment: 15 pages, 9 figures, 1 table, under review

Due to their high computational cost, geophysical applications are typically designed to run in large computing systems. Because of that, such applications must implement several high-performance techniques to use the computational resources better. In this paper, we present Mamute, a software that delivers wave equation-based geophysical methods. Mamute implements two geophysical methods: seismic modeling and full waveform inversion (FWI). It also supports high-performance strategies such as fault tolerance, automatic parallel looping scheduling, and distributed systems workload balancing. We demonstrate Mamute's operation using both seismic modeling and FWI. Mamute is a C++ software readily available under the MIT license., Comment: 24 pages, 6 figures, Journal

If neutrinoless double beta decay is discovered, the next natural step would be understanding the lepton number violating physics responsible for it. Several alternatives exist beyond the exchange of light neutrinos. Some of these mechanisms can be distinguished by measuring phase-space observables, namely the opening angle $\cos\theta$ among the two decay electrons, and the electron energy spectra, $T_1$ and $T_2$. In this work, we study the statistical accuracy and precision in measuring these kinematic observables in a future xenon gas detector with the added capability to precisely locate the decay vertex. For realistic detector conditions (a gas pressure of 10 bar and spatial resolution of 4 mm), we find that the average $\overline{\cos\theta}$ and $\overline{T_1}$ values can be reconstructed with a precision of 0.19 and 110 keV, respectively, assuming that only 10 neutrinoless double beta decay events are detected., Comment: 19 pages, 8 figures

Upcoming telescopes like the Vera Rubin Observatory (VRO) and the Argus Array will image large fractions of the sky multiple times per night yielding numerous Near Earth Object (NEO) discoveries. When asteroids are measured with short observation time windows, the dominant uncertainty in orbit construction is due to distance uncertainty to the NEO. One approach to recover distances is from topocentric parallax, which is a technique that leverages the rotation of the Earth, causing a small but detectable sinusoidal additive signal to the Right Ascension (RA) of the NEO following a period of 1 day. In this paper, we further develop and evaluate this technique to recover distances in as quickly as a single night. We first test the technique on synthetic data of 19 different asteroids ranging from $\sim0.05 \,\text{AU}$ to $\sim2.4 \,\text{AU}$. We modify previous algorithms and quantify the limitations of the method, recovering distances with uncertainties as low as the $\sim1.3\%$ level for more nearby objects ($\lesssim$ 0.3 AU) assuming typical astrometric uncertainties. We then acquire our own observations of two asteroids within a single night with $\sim0.1''$ uncertainties on RA, and we find we are able to recover distances to the $3\%$ level. We forecast likely scenarios with the VRO and the Argus Array with varying levels of astrometric precision and expected pointings per night. Our analysis indicates that distances to NEOs on the scale of $\sim0.5$ AU can be constrained to below the percent level within a single night, depending on spacing of observations from one observatory. In a follow-up paper, we will compare these constraints with synchronous and asynchronous observations from two separate observatories to measure parallax even more efficiently, an exciting and likely possibility over the upcoming decade., Comment: Submitting to the Astronomical Journal, comments welcome

In a system of charged chiral fermions driven out of equilibrium, an electric current parallel to the magnetic field can generate a dynamic instability by which electromagnetic waves become amplified. Whether a similar instability can occur in chiral solid-state systems remains an open question. Using time-domain terahertz (THz) emission spectroscopy, we detect signatures of what we dub a ``dynamic magneto-chiral instability" in elemental tellurium, a structurally chiral crystal. Upon transient photoexcitation in a moderate external magnetic field, tellurium emits THz radiation consisting of coherent modes that amplify over time. An explanation for this amplification is proposed using a theoretical model based on a dynamic instability of electromagnetic waves interacting with infrared-active oscillators of impurity acceptor states in tellurium to form an amplifying polariton. Our work not only uncovers the presence of a magneto-chiral instability but also highlights its promise for THz-wave amplification in chiral materials., Comment: Supplementary Information (SI) available as a PDF in the TeX source

A dedicated search for upward-going air showers at zenith angles exceeding $110^\circ$ and energies $E>0.1$ EeV has been performed using the Fluorescence Detector of the Pierre Auger Observatory. The search is motivated by two "anomalous" radio pulses observed by the ANITA flights I and III which appear inconsistent with the Standard Model of particle physics. Using simulations of both regular cosmic ray showers and upward-going events, a selection procedure has been defined to separate potential upward-going candidate events and the corresponding exposure has been calculated in the energy range [0.1-33] EeV. One event has been found in the search period between 1 Jan 2004 and 31 Dec 2018, consistent with an expected background of $0.27 \pm 0.12$ events from mis-reconstructed cosmic ray showers. This translates to an upper bound on the integral flux of $(7.2 \pm 0.2) \times 10^{-21}$ cm$^{-2}$ sr$^{-1}$ y$^{-1}$ and $(3.6 \pm 0.2) \times 10^{-20}$ cm$^{-2}$ sr$^{-1}$ y$^{-1}$ for an $E^{-1}$ and $E^{-2}$ spectrum, respectively. An upward-going flux of showers normalized to the ANITA observations is shown to predict over 34 events for an $E^{-3}$ spectrum and over 8.1 events for a conservative $E^{-5}$ spectrum, in strong disagreement with the interpretation of the anomalous events as upward-going showers., Comment: 10+5 Pages of Manuscript plus Supplemental Material, 3+8 figures, Accepted for publication in Physical Review Letters

Within the machine learning community, reconstruction attacks are a principal concern and have been identified even in federated learning (FL), which was designed with privacy preservation in mind. In response to these threats, the privacy community recommends the use of differential privacy (DP) in the stochastic gradient descent algorithm, termed DP-SGD. However, the proliferation of variants of DP in recent years\textemdash such as metric privacy\textemdash has made it challenging to conduct a fair comparison between different mechanisms due to the different meanings of the privacy parameters $\epsilon$ and $\delta$ across different variants. Thus, interpreting the practical implications of $\epsilon$ and $\delta$ in the FL context and amongst variants of DP remains ambiguous. In this paper, we lay a foundational framework for comparing mechanisms with differing notions of privacy guarantees, namely $(\epsilon,\delta)$-DP and metric privacy. We provide two foundational means of comparison: firstly, via the well-established $(\epsilon,\delta)$-DP guarantees, made possible through the R\'enyi differential privacy framework; and secondly, via Bayes' capacity, which we identify as an appropriate measure for reconstruction threats.

Empirical evidence shows that typing on touchscreen devices is prone to errors and that correcting them poses a major detriment to users' performance. Design of text entry systems that better serve users, across their broad capability range, necessitates understanding the cognitive mechanisms that underpin these errors. However, prior models of typing cover only motor slips. The paper reports on extending the scope of computational modeling of typing to cover the cognitive mechanisms behind the three main types of error: slips (inaccurate execution), lapses (forgetting), and mistakes (incorrect knowledge). Given a phrase, a keyboard, and user parameters, Typoist simulates eye and finger movements while making human-like insertion, omission, substitution, and transposition errors. Its main technical contribution is the formulation of a supervisory control problem wherein the controller allocates cognitive resources to detect and fix errors generated by the various mechanisms. The model generates predictions of typing performance that can inform design, for better text entry systems.

Altermagnets break time-reversal symmetry, preserve the crystal translation invariance, and have a spin density with $d$-wave, $g$-wave, etc. momentum dependencies which do not contribute to the magnetization. When an $s$-wave spin-density contribution cannot be excluded by symmetry a small magnetization and an anomalous Hall effect (AHE) emerge. However, for so-called "pure" altermagnets, where the $s$-wave component is symmetry forbidden even in the presence of SOC, both the zero-field magnetization and the AHE vanish. We show that altermagnets generally exhibit a non-zero elasto-Hall-conductivity, by which application of strain leads to a non-zero AHE. For pure altermagnets it is the only contribution to the AHE. This elasto-Hall-conductivity is caused by strain coupling to the Berry curvature quadrupole that characterizes altermagnets and allows for the determination of the altermagnetic order using transport measurements that are linear in the electrical field. We further show that the emergence of a non-zero magnetization in the presence of strain arises from a different response function: piezomagnetism. While this magnetization gives rise to an additional contribution to the elasto-Hall conductivity, the corresponding Berry curvature is qualitatively different from the distorted Berry curvature quadrupole originating from the altermagnetic order parameter. This insight also helps to disentangle AHE and weak ferromagnetism for systems with symmetry-allowed $s$-wave contribution. Quantitatively, the elasto-Hall conductivity is particularly pronounced for systems with a Dirac spectrum in the altermagnetic state. The same mechanism gives rise to anomalous elasto-thermal Hall, Nernst, and Ettinghausen effects., Comment: 17 pages, 12 figures

Diffuse photons of energy above 0.1 PeV, produced through the interactions between cosmic rays and either interstellar matter or background radiation fields, are powerful tracers of the distribution of cosmic rays in the Galaxy. Furthermore, the measurement of a diffuse photon flux would be an important probe to test models of super-heavy dark matter decaying into gamma-rays. In this work, we search for a diffuse photon flux in the energy range between 50 PeV and 200 PeV using data from the Pierre Auger Observatory. For the first time, we combine the air-shower measurements from a 2 km$^2$ surface array consisting of 19 water-Cherenkov surface detectors, spaced at 433 m, with the muon measurements from an array of buried scintillators placed in the same area. Using 15 months of data, collected while the array was still under construction, we derive upper limits to the integral photon flux ranging from 13.3 to 13.8 km$^{-2}$ sr$^{-1}$ yr$^{-1}$ above tens of PeV. We extend the Pierre Auger Observatory photon search program towards lower energies, covering more than three decades of cosmic-ray energy. This work lays the foundation for future diffuse photon searches: with the data from the next 10 years of operation of the Observatory, this limit is expected to improve by a factor of $\sim$20., Comment: Submitted to JCAP

Multi-band photometric surveys provide a straightforward way to discover and classify astrophysical objects systematically, enabling the study of a large number of targets at relatively low cost. Here we introduce an alternative approach to select Accreting White Dwarf (AWD) candidates following their spectral energy distribution, entirely supported by the twelve photometric bands of the Southern Photometric Local Universe Survey (S-PLUS). The method was validated with optical spectroscopic follow-up with the Gemini South telescope which unambiguously established ten systems as cataclysmic variables (CVs), alongside Swift X-ray observations of four of them. Among the ten CVs presented here are those that may be low-luminosity intermediate polars or WZ Sge-type dwarf novae with rare outbursts, two subclasses that can be easily missed in time-domain and X-ray surveys, the two methods currently dominating the discovery of new CVs. Our approach based on S-PLUS provides an important, complementary tool to uncover the total population of CVs and the complete set of its subclasses, which is an important step towards a full understanding of close binary evolution, including the origin of magnetic fields in white dwarfs and the physics of accretion. Finally, we highlight the potential of S-PLUS beyond AWDs, serving other surveys in the characterization of their sources., Comment: Accepted for publication in ApJL

Context. We use the Southern Photometric Local Universe Survey (S-PLUS) Fourth Data Release (DR4) to identify and classify H$\alpha$-excess point sources in the Southern Sky, combining photometric data from 12 S-PLUS filters with machine learning to improve classification of H$\alpha$-related phenomena. Aims. Our goal is to classify H$\alpha$-excess point sources by distinguishing Galactic and extragalactic objects, particularly those with redshifted emission lines, and identifying variability phenomena like RR Lyrae stars. Methods. We selected H$\alpha$-excess candidates using the ($r - J0660$) vs. ($r - i$) colour-colour diagram from the S-PLUS main survey (MS) and Galactic Disk Survey (GDS). UMAP for dimensionality reduction and HDBSCAN clustering were used to separate source types. Infrared data was incorporated, and a Random Forest model was trained on clustering results to identify key colour features. New colour-colour diagrams from S-PLUS MS and infrared data offer a preliminary classification. Results. Combining multiwavelength data with machine learning significantly improved H$\alpha$-excess source classification. We identified 6956 sources with excess in the $J0660$ filter. Cross-matching with SIMBAD explored object types, including emission-line stars, young stellar objects, nebulae, stellar binaries, cataclysmic variables, QSOs, AGNs, and galaxies. Using S-PLUS colours and machine learning, we separated RR Lyrae stars from other sources. The separation of Galactic and extragalactic sources was clearer, but distinguishing cataclysmic variables from QSOs at certain redshifts remained challenging. Infrared data refined the classification, and the Random Forest model highlighted key colour features for future follow-up spectroscopy., Comment: 23 pages, 20 figures, 3 tables, accepted to be published in A&A

Recently, optimal transport-based approaches have gained attention for deriving counterfactuals, e.g., to quantify algorithmic discrimination. However, in the general multivariate setting, these methods are often opaque and difficult to interpret. To address this, alternative methodologies have been proposed, using causal graphs combined with iterative quantile regressions (Ple\v{c}ko and Meinshausen (2020)) or sequential transport (Fernandes Machado et al. (2025)) to examine fairness at the individual level, often referred to as ``counterfactual fairness.'' Despite these advancements, transporting categorical variables remains a significant challenge in practical applications with real datasets. In this paper, we propose a novel approach to address this issue. Our method involves (1) converting categorical variables into compositional data and (2) transporting these compositions within the probabilistic simplex of $\mathbb{R}^d$. We demonstrate the applicability and effectiveness of this approach through an illustration on real-world data, and discuss limitations.

Studying the interstellar medium in nearby starbursts is essential for understanding the physical mechanisms driving these objects, thought to resemble young star-forming galaxies. This study aims to analyze the physical properties of the first spatially-resolved multi-wavelength SED of an extragalactic source, spanning six decades in frequency (from near-UV to cm wavelengths) at an angular resolution of 3$^{\prime\prime}$ (51 pc at the distance of NGC,253). We focus on the central molecular zone (CMZ) of NGC,253, which contains giant molecular clouds (GMCs) responsible for half of the galaxy's star formation. We use archival data, spanning optical to centimeter wavelengths, to compute SEDs with the GalaPy and CIGALE codes for validation, and analyze stellar optical spectra with the \textsc{starlight} code. Our results show significant differences between central and external GMCs in terms of stellar and dust masses, star formation rates (SFRs), and bolometric luminosities. We identify the best SFR tracers as radio continuum bands at 33 GHz, radio recombination lines, and the total infrared luminosity (L$_{\rm IR}$; 8-1000$\mu$m), as well as 60$\mu$m IR emission. BPT and WHAN diagrams indicate shock signatures in NGC~253's nuclear region, associating it with AGN/star-forming hybrids, though the AGN fraction is negligible ($\leq$7.5%). Our findings show significant heterogeneity in the CMZ, with central GMCs exhibiting higher densities, SFRs, and dust masses compared to external GMCs. We confirm that certain centimeter photometric bands can reliably estimate global SFR at GMC scales., Comment: Submitted to A&A. 33 pages (20 main text), 20 figures (13 main text)

Advanced multimodal AI agents can now collaborate with users to solve challenges in the world. We explore eye tracking's role in such interaction to convey a user's attention relative to the physical environment. We hypothesize that this knowledge improves contextual understanding for AI agents. By observing hours of human-object interactions, we first measure the relationship between an eye tracker's signal quality and its ability to reliably place gaze on nearby physical objects. We then conduct experiments which relay the user's scanpath history as additional context querying multimodal agents. Our results show that eye tracking provides high value as a user attention signal and can convey information about the user's current task and interests to the agent.

Analyzing relationships between objects is a pivotal problem within data science. In this context, Dimensionality reduction (DR) techniques are employed to generate smaller and more manageable data representations. This paper proposes a new method for dimensionality reduction, based on optimal transportation theory and the Gromov-Wasserstein distance. We offer a new probabilistic view of the classical Multidimensional Scaling (MDS) algorithm and the nonlinear dimensionality reduction algorithm, Isomap (Isometric Mapping or Isometric Feature Mapping) that extends the classical MDS, in which we use the Gromov-Wasserstein distance between the probability measure of high-dimensional data, and its low-dimensional representation. Through gradient descent, our method embeds high-dimensional data into a lower-dimensional space, providing a robust and efficient solution for analyzing complex high-dimensional datasets.

We establish new sharp asymmetric Poincare inequalities in one-dimension with the computation of optimal constants and characterization of extremizers. Using the one-dimensional theory we develop a comprehensive study on fundamental frequencies in the plane and related spectral optimization in the very general setting of positively homogeneous anisotropies., Comment: 38 pages, comments are welcome

The paper is concerned with the Dirichlet spectrum $\Lambda^{a,b}_p(0,L)$ of the asymmetric $p$-Laplace operator $- \Delta^{a,b}_{p}$ on an interval $(0,L)$ where \[ \Delta^{a,b}_p u:= \left(a^{p}[(u')^{+}]^{p-1}-b^{p}[(u')^{-}]^{p-1}\right)'. \] We characterize completely the set $\Lambda^{a,b}_p(0,L)$ and the respective eigenfunctions in terms of the corresponding ones for the classical one-dimensional $p$-Laplace operator and establish that only the eigenfunctions associated to the first Dirichlet eigenvalue of $- \Delta^{a,b}_{p}$ on $(0,L)$ have definite sign and, furthermore, its eigenspace is formed precisely by two non-collinear half-lines in $C^1[0,L]$ if and only if $a \neq b$. Our approach also allows to characterize the Fu\v c\'ik spectrum $\Sigma^{a,b}_p(0,L)$ of $- \Delta^{a,b}_{p}$ on $(0,L)$ from a disjoint enumerate union of hyperboles closely related to the classical counterpart and corresponding solutions. All results are novelty even for $p = 2$., Comment: 17 pages, comments are welcome

We surface a new threat to closed-weight Large Language Models (LLMs) that enables an attacker to compute optimization-based prompt injections. Specifically, we characterize how an attacker can leverage the loss-like information returned from the remote fine-tuning interface to guide the search for adversarial prompts. The fine-tuning interface is hosted by an LLM vendor and allows developers to fine-tune LLMs for their tasks, thus providing utility, but also exposes enough information for an attacker to compute adversarial prompts. Through an experimental analysis, we characterize the loss-like values returned by the Gemini fine-tuning API and demonstrate that they provide a useful signal for discrete optimization of adversarial prompts using a greedy search algorithm. Using the PurpleLlama prompt injection benchmark, we demonstrate attack success rates between 65% and 82% on Google's Gemini family of LLMs. These attacks exploit the classic utility-security tradeoff - the fine-tuning interface provides a useful feature for developers but also exposes the LLMs to powerful attacks.

In this work, we revisited the Ziff-Gullari-Barshad (ZGB) model in order to investigate its critical behavior when carbon monoxide (CO) molecules are allowed to desorb from the catalytic surface. As shown by several authors, when this kind of desorption takes place, the first-order phase transition of the standard model disappears, and an Ising-like critical point is found for a very small value of the desorption rate. However, our time-dependent Monte Carlo simulations reveal that, instead of a single critical point, there exists a critical line that encompasses multiple universality classes, passing through the three- and four-state Potts points, as well as, the Ising one, resulting in an unprecedented critical line of weak universality., Comment: 8 pages, 7 figures

In this paper, we continue the quest to understand the interplay between wrapped Floer homology barcode and topological entropy. Wrapped Floer homology barcode entropy is defined as the exponential growth, with respect to the left endpoints, of the number of not-too-short bars in its barcode. We prove that, in the presence of a topologically transitive, locally maximal hyperbolic set for the Reeb flow on the boundary of a Liouville domain, the barcode entropy is bounded from below by the topological entropy restricted to the hyperbolic set., Comment: 23 pages

Glucose oxidation is a key reaction in biosensing and energy conversion, with anodized copper electrodes showing great potential as catalysts for its enhancement. This study explores the relationship between anodization parameters and glucose oxidation on thin copper films, providing valuable insights for optimization. By adjusting anodization parameters, specific species such as Cu2O, Cu(OH)2, and CuO can be selectively formed, each exhibiting different catalytic activities. Applying polarization in 1 M KOH at 0 V (vs Ag|AgCl) generates a highly active CuO surface layer, leading to significant performance improvements over bare copper electrodes. Specifically, sensitivity increases by 55% in the 0.1 mM-0.5 mM range and 73% in the 0.75 mM-2 mM range. Additionally, the electrodes demonstrate an exceptionally low detection limit of just 0.004 mM. These results are attributed to scalable, reproducible manufacturing on cleanroom-compatible platforms. This study not only sheds light on the effects of thin copper film anodization on glucose oxidation but also provides a practical approach for enhancing the efficiency of copper-based systems in biosensing and energy conversion applications.

Incoherent Diffraction Imaging - IDI - is a diffraction-based imaging technique that has been recently proposed to exploit the partial coherence of incoherently scattered light to retrieve structural information from the scattering centers. Similar to the stellar intensity interferometry of Hanbury Brown and Twiss, the signal builds up on the second-order spatial correlations of the emitted light. The complex spatial distribution of the target is thereby encoded in the spatial intensity fluctuations of the scattered light. The first experimental realisations of this imaging technique have been realised using the fluorescence excited by an ultra-short X-ray pulse at Free Electron Laser (FEL) facilities. Here, we propose an alternative set-up based on a table-top Pseudo-Thermal Light Source. This set-up allows us to explore IDI under a wide range of physically relevant conditions as well as to benchmark numerical and analytical models currently used to determine the imaging capabilities of this technique.

Music genres are shaped by both the stylistic features of songs and the cultural preferences of artists' audiences. Automatic classification of music genres using lyrics can be useful in several applications such as recommendation systems, playlist creation, and library organization. We present a multi-label, cross-lingual genre classification system based on multilingual sentence embeddings generated by sBERT. Using a bilingual Portuguese-English dataset with eight overlapping genres, we demonstrate the system's ability to train on lyrics in one language and predict genres in another. Our approach outperforms the baseline approach of translating lyrics and using a bag-of-words representation, improving the genrewise average F1-Score from 0.35 to 0.69. The classifier uses a one-vs-all architecture, enabling it to assign multiple genre labels to a single lyric. Experimental results reveal that dataset centralization notably improves cross-lingual performance. This approach offers a scalable solution for genre classification across underrepresented languages and cultural domains, advancing the capabilities of music information retrieval systems., Comment: 5 pages

Systems such as Microsoft Songsmith automatically assign chords and harmony to a melody by minimizing the dissonance across all chord changes. Although this produces harmonious music, it is not what practicing musicians do. In this paper, I describe Harmonizer, a prototype system for melodic harmonization. Harmonizer uses Schoenberg's chart of regions as the underlying data structure that allows harmonization using several different methods. Because the chart reveals inter-chordal relationships, the harmonizations may be programmed to emphasize desired relationships. In the prototype Harmonizer, I also explore recent signal-processing methods that enable songwriters to easily input a melody by singing or by playing a musical instrument. The prototype Harmonizer is available on GitHub and a video demonstrating its distinctive harmonizations is on YouTube as explained in the Results section of the paper.

Context. Spectroscopic surveys like APOGEE, GALAH, and LAMOST have significantly advanced our understanding of the Milky Way by providing extensive stellar parameters and chemical abundances. Complementing these, photometric surveys with narrow/medium-band filters, such as the Southern Photometric Local Universe Survey (S-PLUS), offer the potential to estimate stellar parameters and abundances for a much larger number of stars. Aims. This work develops methodologies to extract stellar atmospheric parameters and selected chemical abundances from S-PLUS photometric data, which spans ~3000 square degrees using seven narrowband and five broadband filters. Methods. Using 66 S-PLUS colors, we estimated parameters based on training samples from LAMOST, APOGEE, and GALAH, applying Cost-Sensitive Neural Networks (NN) and Random Forests (RF). We tested for spurious correlations by including abundances not covered by the S-PLUS filters and evaluated NN and RF performance, with NN consistently outperforming RF. Including Teff and log g as features improved accuracy by ~3%. We retained only parameters with a goodness-of-fit above 50%. Results. Our approach provides reliable estimates of fundamental parameters (Teff, log g, [Fe/H]) and abundance ratios such as [{\alpha}/Fe], [Al/Fe], [C/Fe], [Li/Fe], and [Mg/Fe] for ~5 million stars, with goodness-of-fit >60%. Additional ratios like [Cu/Fe], [O/Fe], and [Si/Fe] were derived but are less accurate. Validation using star clusters, TESS, and J-PLUS data confirmed the robustness of our methodology. Conclusions. By leveraging S-PLUS photometry and machine learning, we present a cost-effective alternative to high-resolution spectroscopy for deriving stellar parameters and abundances, enabling insights into Milky Way stellar populations and supporting future classification efforts., Comment: 23 pages, 14 Figures