Search

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

Due to the fundamental transition to a power electronic dominated power system, the increasing diversity of dynamic elements underscores the need to assess their similarity to mature electrical engineering models. This article addresses the concept of the Dynamic Similarity Index (DSI) for its use in, power electronics-dominated networks. The DSI is a multipurpose tool developed to be used by different stakeholders (e.g., converter manufacturers and system operators). Such an index is calculated per frequency, which serves to anticipate potential differences in particular frequency ranges of interest between the model under study and the reference model. Within the scope of this study, the dynamic similarity of inverter-based generators to an ideal voltage source behind an impedance is assessed, due to the relevance of this fundamental circuit in the representation of generation units in power system studies. The article presents two potential applications based on this mathematical framework. First, for manufacturers to evaluate control performance compared to a reference model and second, it enables operators to diagnose buses with voltage vulnerability based on a user-defined reference Short-Circuit Ratio (SCR) value. The DSI results for these two case studies are validated using Matlab Simulink simulations.

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem that aims to find the shortest possible route that visits each city exactly once and returns to the starting point. This paper explores the application of Particle Swarm Optimization (PSO), a population-based optimization algorithm, to solve TSP. Although PSO was originally designed for continuous optimization problems, this work adapts PSO for the discrete nature of TSP by treating the order of cities as a permutation. A local search strategy, including 2-opt and 3-opt techniques, is applied to improve the solution after updating the particle positions. The performance of the proposed PSO algorithm is evaluated using benchmark TSP instances and compared to other popular optimization algorithms, such as Genetic Algorithms (GA) and Simulated Annealing (SA). Results show that PSO performs well for small to medium-sized problems, though its performance diminishes for larger instances due to difficulties in escaping local optima. This paper concludes that PSO is a promising approach for solving TSP, with potential for further improvement through hybridization with other optimization techniques., Comment: 6 pages, 1 figure

The HAWC Observatory collected 6 years of extensive data, providing an ideal platform for long-term monitoring of blazars in the Very High Energy (VHE) band, without bias towards specific flux states. HAWC continuously monitors blazar activity at TeV energies, focusing on sources with a redshift of {z \lt 0.3}, based on the Third Fermi-LAT Catalog of High-Energy sources. We specifically focused our analysis on Mrk 421 and Mrk 501, as they are the brightest blazars observed by the HAWC Observatory. With a dataset of 2143 days, this work significantly extends the monitoring previously published, which was based on 511 days of observation. By utilizing HAWC data for the VHE {\gamma}-ray emission in the 300 GeV to 100 TeV energy range, in conjunction with Swift-XRT data for the 0.3 to 10 keV X-ray emission, we aim to explore potential correlations between these two bands. For Mrk 501, we found evidence of a long-term correlation. Additionally, we identified a period in the light curve where the flux was very low for more than two years. On the other hand, our analysis of Mrk 421 measured a strong linear correlation for quasi-simultaneous observations collected by HAWC and Swift-XRT. This result is consistent with a linear dependence and a multiple-zone synchrotron self-Compton model to explain the X-ray and the {\gamma}-ray emission. Finally, as suggested by previous findings, we confirm a harder-when-brighter behavior in the spectral evolution of the flux properties for Mrk 421. These findings contribute to the understanding of blazar emissions and their underlying mechanisms.

Neovascular age-related macular degeneration (nAMD) is a leading cause of vision loss among older adults, where disease activity detection and progression prediction are critical for nAMD management in terms of timely drug administration and improving patient outcomes. Recent advancements in deep learning offer a promising solution for predicting changes in AMD from optical coherence tomography (OCT) retinal volumes. In this work, we proposed deep learning models for the two tasks of the public MARIO Challenge at MICCAI 2024, designed to detect and forecast changes in nAMD severity with longitudinal retinal OCT. For the first task, we employ a Vision Transformer (ViT) based Siamese Network to detect changes in AMD severity by comparing scan embeddings of a patient from different time points. To train a model to forecast the change after 3 months, we exploit, for the first time, an Earth Mover (Wasserstein) Distance-based loss to harness the ordinal relation within the severity change classes. Both models ranked high on the preliminary leaderboard, demonstrating that their predictive capabilities could facilitate nAMD treatment management., Comment: Solution to the MICCAI 2024 MARIO Challange. First 3 authors contributed equally. Models can be found at https://github.com/EmreTaha/Siamese-EMD-for-AMD-Change

Context. Supernova remnants are one potential source class considered a PeVatron (i.e. capable of accelerating cosmic rays above PeV energies). The shock fronts produced after the explosion of the supernova are ideal regions for particle acceleration. IC 443 is a supernova remnant that has been studied extensively at different wavelengths. We study this region using very-high-energy gamma-ray data. Aims. We explore the region of IC 443 using 2966 days of gamma-ray data from the HAWC observatory. We study the emission of this supernova remnant and search for signatures that would show acceleration of (hadronic) cosmic rays at the PeV range. Methods. We use the maximum likelihood estimation and a likelihood ratio test to perform a multi-source fitting search. We find the best-fit morphology and spectrum of the IC 443 region above $\sim$300 GeV that best describes the HAWC data. Results. We observe a point source located at ($\alpha$=94.42$^{\circ}$, $\delta$=22.35$^{\circ}$) that we associate with IC 443. The measured spectrum is a simple power law with an index of -3.14$\pm$0.18, which is consistent with previous TeV observations. We also find a new extended component in the region whose emission is described by a simple power law with an index of -2.49$\pm$0.08 and which we call HAWC J0615+2213. Conclusions. Although we cannot confirm that IC 443 is a hadronic PeVatron, we do not find any sign that the spectrum has a cut off at tens of TeV energies, with the spectrum extending to $\sim$30 TeV. Furthermore, we find a new extended source in the region. While we show evidence that this new source might be a new TeV halo, we defer a detailed analysis of this new source to another publication.

The rise of smart contracts has expanded blockchain's capabilities, enabling the development of innovative decentralized applications (dApps). However, this advancement brings its own challenges, including the management of distributed architectures and immutable data. Addressing these complexities requires a specialized approach to software engineering, with blockchain-oriented practices emerging to support development in this domain. Developer Experience (DEx) is central to this effort, focusing on the usability, productivity, and overall satisfaction of tools and frameworks from the engineers' perspective. Despite its importance, research on Blockchain Developer Experience (BcDEx) remains limited, with no systematic mapping of academic and industry efforts. To bridge this gap, we conducted a Multivocal Literature Review analyzing 62 to understand the distribution of BcDEx sources, practical implementations, and their impact. Our findings revealed that academic focus on BcDEx is limited compared to the coverage in gray literature, which primarily includes blogs (41.8%) and corporate sources (21.8%). Particularly, development efficiency, multi-network support, and usability are the most addressed aspects in tools and frameworks. In addition, we found that BcDEx is being shaped through five key perspectives: complexity abstraction, adoption facilitation, productivity enhancement, developer education, and BcDEx evaluation., Comment: 12 pages, 5 figures, 18th Conference on Cooperative and Human Aspects of Software Engineering (CHASE)

In this work, we study the $\Xi_c$ baryon electromagnetic decay widths within the constituent quark model formalism through an analysis of the transitions from $P-$wave states to ground states. We use the non-relativistic limit of the Hamiltonian of the electromagnetic interaction on keeping all the terms up to the order $m_j^{-1}$. We calculate the electromagnetic decay widths analytically, for the first time, without any further approximation. Specifically, our theoretical results for the $\Xi_c(2790)^{+/0}$ and $\Xi_c(2815)^{+/0}$ radiative decay widths, without the introduction of any additional parameters, display a significant agreement with the recent experimental values obtained by the Belle experiment. The agreement is due to the fact that we have not introduced further approximation to simplify the calculation of the difficult convective term, unlike what was done in previous studies. Our predictions may be useful for future experiments at the Belle, BABAR, and LHC experiments., Comment: 5 pages

The research related to digital twins has been increasing in recent years. Besides the mirroring of the physical word into the digital, there is the need of providing services related to the data collected and transferred to the virtual world. One of these services is the forecasting of physical part future behavior, that could lead to applications, like preventing harmful events or designing improvements to get better performance. One strategy used to predict any system operation it is the use of time series models like ARIMA or LSTM, and improvements were implemented using these algorithms. Recently, deep learning techniques based on generative models such as Generative Adversarial Networks (GANs) have been proposed to create time series and the use of LSTM has gained more relevance in time series forecasting, but both have limitations that restrict the forecasting results. Another issue found in the literature is the challenge of handling multivariate environments/applications in time series generation. Therefore, new methods need to be studied in order to fill these gaps and, consequently, provide better resources for creating useful digital twins. In this proposal, it is going to be studied the integration of a BiLSTM layer with a time series obtained by GAN in order to improve the forecasting of all the features provided by the dataset in terms of accuracy and, consequently, improving behaviour prediction.

This paper proposes SAT-based techniques to calculate a specific normal form of a given finite mathematical structure (model). The normal form is obtained by permuting the domain elements so that the representation of the structure is lexicographically smallest possible. Such a normal form is of interest to mathematicians as it enables easy cataloging of algebraic structures. In particular, two structures are isomorphic precisely when their normal forms are the same. This form is also natural to inspect as mathematicians have been using it routinely for many decades. We develop a novel approach where a SAT solver is used in a black-box fashion to compute the smallest representative. The approach constructs the representative gradually and searches the space of possible isomorphisms, requiring a small number of variables. However, the approach may lead to a large number of SAT calls and therefore we devise propagation techniques to reduce this number. The paper focuses on finite structures with a single binary operation (encompassing groups, semigroups, etc.). However, the approach is generalizable to arbitrary finite structures. We provide an implementation of the proposed algorithm and evaluate it on a variety of algebraic structures.

Complete enumeration of finite models of first-order logic (FOL) formulas is pivotal to universal algebra, which studies and catalogs algebraic structures. Efficient finite model enumeration is highly challenging because the number of models grows rapidly with their size but at the same time, we are only interested in models modulo isomorphism. While isomorphism cuts down the number of models of interest, it is nontrivial to take that into account computationally. This paper develops a novel algorithm that achieves isomorphism-free enumeration by employing isomorphic graph detection algorithm nauty, cube-based search space splitting, and compact model representations. We name our algorithm cube-based isomorph-free finite model finding algorithm (CBIF). Our approach contrasts with the traditional two-step algorithms, which first enumerate (possibly isomorphic) models and then filter the isomorphic ones out in the second stage. The experimental results show that CBIF is many orders of magnitude faster than the traditional two-step algorithms. CBIF enables us to calculate new results that are not found in the literature, including the extension of two existing OEIS sequences, thereby advancing the state of the art.

The theory of tensor categories has found applications across various fields, including representation theory, quantum field theory (conformal in 2 dimensions, and topological in 3 and 4 dimensions), quantum invariants of low-dimensional objects, topological phases of matter, and topological quantum computation. In essence, it is a categorification of the classical theory of algebras and modules. In this analogy, the Deligne tensor product $\boxtimes$ is to the linear tensor $\otimes_{\mathbb{C}}$ as the balanced tensor product $\boxtimes_C$ is to the tensor over algebra $\otimes_A$, where $\mathbb{C}$ is a field, $A$ is a $\mathbb{C}$-algebra, and $C$ is a tensor category. Before this work, several algebraic constructions for balanced tensor products were known, including categories of modules, internal Hom spaces, and generalized categorical centers. In this paper, we introduce a topological construction based on skein theory that offers a better mix of algebra and topology. This approach not only works for products of multiple module categories, but also provides the missing key to proving that the Turaev-Viro state sum model naturally arises from the 3-functor in the classification of fully extended field theories. Building on this result, we establish this long-anticipated proof in an upcoming work., Comment: 25 pages

In this work, we discuss several results concerning Serrin's problem in convex cones in Riemannian manifolds. First, we present a rigidity result for an overdetermined problem in a class of warped products with Ricci curvature bounded below. As a consequence, we obtain a rigidity result for Einstein warped products. Next, we derive a soap bubble result and a Heintze-Karcher inequality that characterize the intersection of geodesic balls with cones in these spaces. Finally, we analyze the analogous ovedetermined problem for the drift Laplacian, where the ambient space is a cone in the Euclidean space., Comment: Comments are welcome!

Early-type galaxies (ETGs) are reference systems to understand galaxy formation and evolution processes. The physics of their collapse and internal dynamics are codified in well-known scaling relations. Cosmological hydrodynamical simulations play an important role, providing insights into the 3D distribution of matter and galaxy formation mechanisms, as well as validating methods to infer the properties of real objects. In this work, we present the closest-to-reality sample of ETGs from the IllustrisTNG100-1 simulation, dubbed "virtual-ETGs," based on an observational-like algorithm that combines standard projected and three-dimensional galaxy structural parameters. We extract 2D photometric information by projecting the galaxies' light into three planes and modeling them via S\'ersic profiles. Aperture velocity dispersions, corrected for softened central dynamics, are calculated along the line-of-sight orthogonal to the photometric projection plane. Central mass density profiles assume a power-law model, while 3D masses remain unmodified from the IllustrisTNG catalogue. The final catalogue includes $10121$ galaxies at redshifts $z \leq 0.1$. By comparing the virtual properties with observations, we find that the virtual-ETG scaling relations (e.g., size-mass, size-central surface brightness, and Faber-Jackson), central density slopes, and scaling relations among total density slopes and galaxy structural parameters are generally consistent with observations. We make the virtual-ETG publicly available for galaxy formation studies and plan to use this sample as a training set for machine learning tools to infer galaxy properties in future imaging and spectroscopic surveys.

We study obstacle problems governed by two distinct types of diffusion operators involving interacting free boundaries. We obtain a somewhat surprising coupling property, leading to a comprehensive analysis of the free boundary. More precisely, we show that near regular points of a coordinate function, the free boundary is analytic, whereas singular points lie on a smooth manifold. Additionally, we prove that uncoupled free boundary points are singular, indicating that regular points lie exclusively on the coupled free boundary. Furthermore, optimal regularity, non-degeneracy, and lower dimensional Hausdorff measure estimates are obtained. Explicit examples illustrate the sharpness of assumptions., Comment: 19

Calibration, the alignment between model confidence and prediction accuracy, is critical for the reliable deployment of large language models (LLMs). Existing works neglect to measure the generalization of their methods to other prompt styles and different sizes of LLMs. To address this, we define a controlled experimental setting covering 12 LLMs and four prompt styles. We additionally investigate if incorporating the response agreement of multiple LLMs and an appropriate loss function can improve calibration performance. Concretely, we build Calib-n, a novel framework that trains an auxiliary model for confidence estimation that aggregates responses from multiple LLMs to capture inter-model agreement. To optimize calibration, we integrate focal and AUC surrogate losses alongside binary cross-entropy. Experiments across four datasets demonstrate that both response agreement and focal loss improve calibration from baselines. We find that few-shot prompts are the most effective for auxiliary model-based methods, and auxiliary models demonstrate robust calibration performance across accuracy variations, outperforming LLMs' internal probabilities and verbalized confidences. These insights deepen the understanding of influence factors in LLM calibration, supporting their reliable deployment in diverse applications., Comment: 24 pages, 11 figures, 8 tables

Large language models and vision transformers have demonstrated impressive zero-shot capabilities, enabling significant transferability in downstream tasks. The fusion of these models has resulted in multi-modal architectures with enhanced instructional capabilities. Despite incorporating vast image and language pre-training, these multi-modal architectures often generate responses that deviate from the ground truth in the image data. These failure cases are known as hallucinations. Current methods for mitigating hallucinations generally focus on regularizing the language component, improving the fusion module, or ensembling multiple visual encoders to improve visual representation. In this paper, we address the hallucination issue by directly enhancing the capabilities of the visual component. Our approach, named EAGLE, is fully agnostic to the LLM or fusion module and works as a post-pretraining approach that improves the grounding and language alignment of the visual encoder. We show that a straightforward reformulation of the original contrastive pre-training task results in an improved visual encoder that can be incorporated into the instructional multi-modal architecture without additional instructional training. As a result, EAGLE achieves a significant reduction in hallucinations across multiple challenging benchmarks and tasks., Comment: 12 pages, 4 figures, 8 tables

Animal welfare has become a critical issue in contemporary society, emphasizing our ethical responsibilities toward animals, particularly within livestock farming. The advent of Artificial Intelligence (AI) technologies, specifically computer vision, offers an innovative approach to monitoring and enhancing animal welfare. Cows, as essential contributors to sustainable agriculture, are central to this effort. However, existing cow detection algorithms face challenges in real-world farming environments, such as complex lighting, occlusions, pose variations, and background interference, hindering detection. Model generalization is crucial for adaptation across contexts beyond the training dataset. This study addresses these challenges using a diverse cow dataset from six environments, including indoor and outdoor scenarios. We propose a detection model combining YOLOv8 with the CBAM (Convolutional Block Attention Module) and assess its performance against baseline models, including Mask R-CNN, YOLOv5, and YOLOv8. Our findings show baseline models degrade in complex conditions, while our approach improves using CBAM. YOLOv8-CBAM outperformed YOLOv8 by 2.3% in mAP, achieving 95.2% precision and an mAP@0.5:0.95 of 82.6%, demonstrating superior accuracy. Contributions include (1) analyzing detection limitations, (2) proposing a robust model, and (3) benchmarking state-of-the-art algorithms. Applications include health monitoring, behavioral analysis, and tracking in smart farms, enabling precise detection in challenging settings. This study advances AI-driven livestock monitoring, improving animal welfare and smart agriculture.

The property of $*$-cleanness in group rings has been studied for some groups considering the classical involution, given by $g^*=g^{-1}$. A group is called an SLC-group if its quotient by its center is isomorphic to the Klein group; these groups are equipped with its own canonical involution, which usually does not coincide with the classical one. In this paper we study the $*$-cleanness of $RG$ when $G$ is an SLC-group, considering $*$ as its canonical involution. In that context, we prove that if $RG$ is $*$-clean then $G$ is the direct product of $Q_8$ and an abelian group with some extra properties and we find a converse for some specific cases, generalizing a result by Gao, Chen and Li for $Q_8$.

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

In this work, we analyze the impact of non-metricity on particle creation and the evaporation process of black holes within the framework of bumblebee gravity. In general lines, we compare black holes in the metric formalism [1] and the metric-affine approach [2]. Initially, we focus on bosonic particle modes to investigate Hawking radiation. Using the Klein-Gordon equation, we compute the Bogoliubov coefficients and derive the Hawking temperature. Subsequently, we examine Hawking radiation as a tunneling process, resolving divergent integrals through the residue method. The analysis is then extended to fermionic particle modes, also within the tunneling framework. Particle creation densities are calculated for both bosonic and fermionic cases. Additionally, greybody bounds are estimated for bosonic and fermionic particles. Finally, we explore the evaporation process, considering the final state of the black holes. In a general panorama, non-metricity in bumblebee gravity raises particle density for bosons while reducing it for fermions, increases greybody factors (for both bosons and fermions), amplifies the emission rate, and accelerates the evaporation process., Comment: 24 pages (in two columns) and 28 figures

In this paper, we study a Bhabha-like scattering in a massive Rarita-Schwinger model at finite temperature. The analysis is conducted at the tree level and addresses temperature effects through the thermofield dynamics formalism. We consider the usual fermion-antifermion into fermion-antifermion scattering and compute the cross-section in order to investigate the influence of the finite temperature effects., Comment: 21 pages, 7 figures

The dynamic magnetic properties of Full Heusler alloy thin films of Co$_2$FeGe, grown on MgO (001) substrates under different thermal conditions, were investigated. Brillouin light scattering and ferromagnetic resonance measurements revealed that depositing at room temperature followed by annealing at 300 deg C for 1 hour produces the best results for maximizing magnetization, exchange stiffness, and minimizing spin-dynamic dissipation in the films, which are desirable characteristics for high-speed spintronic devices. Additionally, strong hybridization of spin waves in the Damon-Eshbach geometry was observed, which is attractive for applications in magnonic signal processing circuits., Comment: 13 pages, 6 figures

Image processing has always been a topic of significant importance to society. Recently, this field has gained considerable prominence due to the development of intelligent systems. In this work, we present a new method of image processing that utilizes statistical analysis, specifically designed for applications in intelligent systems. We tested our method on a large collection of images to assess its effectiveness.

The radio pulsar PSR J0742-2822 is known to exhibit rapid changes between different pulse profile states that correlate with changes in its spin-down rate. However, the connection between these variations and the glitch activity of the pulsar remains unclear. We aim to study the evolution of the pulse profile and spin-down rate of PSR J0742-2822 in the period MJD 58810-60149 (November 2019 to July 2023), which includes the glitch on MJD 59839 (September 2022). In particular, we look for pulse profile or spin-down changes associated with the 2022 glitch. We observed PSR J0742-2822 with high cadence from the Argentine Institute of Radio astronomy (IAR) between November 2019 and July 2023. We used standard timing tools to characterize the times of arrival of the pulses and study the pulsar rotation, and particularly, the oscillations of $\dot \nu$. We also study the evolution of the pulse profile. For both of them, we compare their behavior before and after the 2022 glitch. With respect to $\dot \nu$, we found oscillations diminished in amplitude after the glitch. We found four different components contributing to the pre-glitch $\dot \nu$ oscillations, and only one component after the glitch. About the emission, we found the pulse profile has two main peaks. We detected an increase in the $W_{50}$ of the total pulse profile of $\sim$12% after the glitch and we found the amplitude of the trailing peak increased with respect to the amplitude of the leading one after the glitch. We found significant changes in the pulse profile and the spin-down rate of PSR J0742-2822 after its 2022 glitch. These results suggest that there is a strong coupling between the internal superfluid of the neutron star and its magnetosphere, and that pulse profile changes may be led by this coupling instead of being led purely by magnetospheric effects., Comment: 8 pages, 8 figures, submitted to A&A. Comments are welcome

We study a general class of elliptic free boundary problems equipped with a Dirichlet boundary condition. Our primary result establishes an optimal $C^{1,1}$-regularity estimate for $L^p$-strong solutions at points where the free and fixed boundaries intersect. A key novelty is that no convexity or concavity assumptions are imposed on the fully nonlinear operator governing the system. Our analysis derives BMO estimates in a universal neighbourhood of the fixed boundary. It relies solely on a differentiability assumption. Once those estimates are available, applying by now standard methods yields the optimal regularity.

This work introduces the Schmidt quantum compressor, an innovative approach to quantum data compression that leverages the principles of Schmidt decomposition to encode quantum information efficiently. In contrast to traditional variational quantum autoencoders, which depend on stochastic optimization and face challenges such as shot noise, barren plateaus, and non-convex optimization landscapes, our deterministic method substantially reduces the complexity and computational overhead of quantum data compression. We evaluate the performance of the compressor through numerical experiments, demonstrating its ability to achieve high fidelity in quantum state reconstruction compared to variational quantum algorithms. Furthermore, we demonstrate the practical utility of the Schmidt quantum compressor in one-class classification tasks.

Boundaries in gauge theory and gravity give rise to symmetries and charges at both finite and asymptotic distance. Due to their structural similarities, it is often held that soft modes are some kind of asymptotic limit of edge modes. Here, we show in Maxwell theory that there is an arguably more interesting relationship between the \emph{asymptotic} symmetries and their charges, on one hand, and their \emph{finite-distance} counterparts, on the other, without the need of a limit. Key to this observation is to embed the finite region in the global spacetime and identify edge modes as dynamical $\rm{U}(1)$-reference frames for dressing subregion variables. Distinguishing \emph{intrinsic} and \emph{extrinsic} frames, according to whether they are built from field content in- or outside the region, we find that non-trivial corner symmetries arise only for extrinsic frames. Further, the asymptotic-to-finite relation requires asymptotically charged ones (like Wilson lines). Such frames, called \emph{soft edges}, extend to asymptotia and realize the corner charge algebra by ``pulling in'' the asymptotic one from infinity. Realizing an infinite-dimensional algebra requires a new set of \emph{soft boundary conditions}, relying on the distinction between extrinsic and intrinsic data. We identify the subregion Goldstone mode as the relational observable between extrinsic and intrinsic frames and clarify the meaning of vacuum degeneracy. We also connect the asymptotic memory effect with a more operational \emph{quasi-local} one. A main conclusion is that the relationship between asymptotia and finite distance is \emph{frame-dependent}; each choice of soft edge mode probes distinct cross-boundary data of the global theory. Our work combines the study of boundary symmetries with the program of dynamical reference frames and we anticipate that core insights extend to Yang-Mills theory and gravity., Comment: 69 pages, 13 figures

This work presents a novel methodology for deriving stationary and axially symmetric solutions to Einstein field equations using the 1+3 tetrad formalism. This approach reformulates the Einstein equations into first order scalar equations, enabling systematic resolution in vacuum scenarios. We derive two distinct solutions in polar and hyperbolic geometries by assuming the separability of a key metric function. Our method reproduces well known solutions such as Schwarzschild and Kerr metrics and extends the case of rotating spacetimes to hyperbolic configurations. Additionally, we explore the role of Killing tensors in enabling separable metric components, simplifying analyses of geodesic motion and physical phenomena. This framework demonstrates robustness and adaptability for addressing the complexities of axially symmetric spacetimes, paving the way for further applications to Kerr like solutions in General Relativity.

This paper explores gravitational phenomena associated with a non-commutative black hole. Geodesic equations are derived, and a thin accretion disk is analyzed to model the black hole shadow image, considering an optically thin, radiating, and infalling gas. Retrolensing effects are examined to trace photon emission configurations, while gravitational lensing is investigated through weak and strong deflection limits, with lensing equations and observables applied to Sagittarius A*. The study also includes calculations of time delay, energy deposition rate from neutrino annihilation, phase and probability of neutrino oscillation, and neutrino gravitational lensing., Comment: 26 pages, 9 figures and 2 tables

The particles of synthetic colloids are usually treated with chemical techniques to prevent the loss of colloidal stability caused by van der Waals and magnetic dipolar attractive interactions. However, understanding the counterbalance between the attractive and repulsive interactions is challenging due to the limitations of the conventional mesoscopic models at short nanoparticle separations. In this study, we examined three models that describe short-range interactions by proposing different corrections to the van der Waals energy for short distances. The three models show only a minimal deviation from energy extensivity, as expected of a system with a comparatively short interaction range. Our analysis shows that a more detailed microscopic model at short-range separations is crucial for proper sampling, which is necessary to estimate physical quantities adequately. The same model predicts that polydispersity can lead to an overall decrease in mean particle distance for a configuration with 5% colloidal volume fraction. The other, simpler models make the opposite prediction, which opens an interesting venue for experimental exploration that could shed light on the validity of this model. The predicted decrease in particle distance could lead to coagulation, suggesting a preference for ferrofluids with more uniform particle sizes, leading to lower attraction, but still responding to applied fields, as needed in most applications., Comment: 17 pages (11 main text + 6 supplementary), 10 figures (5 main text + 5 supplementary)

3D human motion generation has seen substantial advancement in recent years. While state-of-the-art approaches have improved performance significantly, they still struggle with complex and detailed motions unseen in training data, largely due to the scarcity of motion datasets and the prohibitive cost of generating new training examples. To address these challenges, we introduce CoMA, an agent-based solution for complex human motion generation, editing, and comprehension. CoMA leverages multiple collaborative agents powered by large language and vision models, alongside a mask transformer-based motion generator featuring body part-specific encoders and codebooks for fine-grained control. Our framework enables generation of both short and long motion sequences with detailed instructions, text-guided motion editing, and self-correction for improved quality. Evaluations on the HumanML3D dataset demonstrate competitive performance against state-of-the-art methods. Additionally, we create a set of context-rich, compositional, and long text prompts, where user studies show our method significantly outperforms existing approaches., Comment: Project Page: https://gabrie-l.github.io/coma-page/

This work examines the implications of a black hole featuring a de Sitter core. We begin by analyzing the spacetime and event horizon in the presence of de Sitter core. Then the partial wave equation necessary for calculating quasinormal modes is derived and the relation of scalar quasinormal modes with the de Sitter core parameter is explored. Subsequently, we explore the greybody factors and their correspondence with the gravitational quasinormal modes. We also analyze the emission rate. Finally, variations in the thin accretion disks and the influence of de Sitter core spacetime on the optical appearance of the black hole are discussed as well.

We report on a search for millicharged particles (mCPs) produced in cosmic ray proton atmospheric interactions using data collected during the first science run of the LUX-ZEPLIN experiment. The mCPs produced by two processes -- meson decay and proton bremsstrahlung -- are considered in this study. This search utilized a novel signature unique to liquid xenon (LXe) time projection chambers (TPCs), allowing sensitivity to mCPs with masses ranging from 10 to 1000 MeV/c$^2$ and fractional charges between 0.001 and 0.02 of the electron charge e. With an exposure of 60 live days and a 5.5 tonne fiducial mass, we observed no significant excess over background. This represents the first experimental search for atmospheric mCPs and the first search for mCPs using an underground LXe experiment.

The properties of the entire neutron star population can be inferred by modeling their evolution, from birth to the present, through pulsar population synthesis. This involves simulating a mock population, applying observational filters, and comparing the resulting sources to the limited subset of detected pulsars. We specifically focus on the magneto-rotational properties of Galactic isolated neutron stars and provide new insights into the intrinsic radio luminosity law by combining pulsar population synthesis with a simulation-based inference (SBI) technique called truncated sequential neural posterior estimation (TSNPE). We employ TSNPE to train a neural density estimator on simulated pulsar populations to approximate the posterior distribution of the underlying parameters. This technique efficiently explores the parameter space by concentrating on regions that are most likely to match the observed data thus allowing a significant reduction in training dataset size. We demonstrate the efficiency of TSNPE over standard neural posterior estimation (NPE), achieving robust inferences of magneto-rotational parameters consistent with previous studies using only around 4% of the simulations required by NPE approaches. Moreover, for the first time, we incorporate data from the Thousand Pulsar Array (TPA) program on MeerKAT, the largest unified sample of neutron stars with consistent fluxes measurement to date, to help constrain the stars' intrinsic radio luminosity. We find that adding flux information as an input to the neural network largely improves the constraints on the pulsars' radio luminosity, as well as improving the estimates on other input parameters.

The channel estimation problem has been widely discussed in traditional reconfigurable intelligent surface assisted multiple-input multiple-output. However, solutions for channel estimation adapted to beyond diagonal RIS need further study, and few recent works have been proposed to tackle this problem. Moreover, methods that avoid or minimize the use of pilot sequences are of interest. This work formulates a data-driven (semi-blind) joint channel and symbol estimation algorithm for beyond diagonal RIS that avoids a prior pilot-assisted stage while providing decoupled estimates of the involved communication channels. The proposed receiver builds upon a PARATUCK tensor model for the received signal, from which a trilinear alternating estimation scheme is derived. Preliminary numerical results demonstrate the proposed method's performance for selected system setups. The symbol error rate performance is also compared with that of a linear receiver operating with perfect knowledge of the cascaded channel.

The propagation of light that undergoes multiple-scattering by resonant atomic vapor can be described as a L\'evy flight. L\'evy flight is a random walk with heavy tailed step-size (r) distribution, decaying asymptotically as $P(r)\sim r^{-1-\alpha}$, with $\alpha<2$. The large steps, typical of L\'evy flights, have its origins in frequency redistribution of the light scattered by the vapor. We calculate the frequency redistribution function and the step-size distribution for light diffusion in atomic vapor. From the step-size distribution we extract a L\'evy parameter $\alpha$ that depends on the step's size. We investigate how the frequency redistribution function and step-size distribution are influenced by the finite size of the vapor and the many-level structure typical for alkali vapors. Finite size of the vapor introduces cutoff on the light scattered spectrum and thus in the size of steps. Multi-level structure introduces oscillations in $P(r)$ slope. Both effects might have an impact on measurables related to the L\'evy flight random walk., Comment: 37 pages, 12 figures

Liquid argon detectors rely on wavelength shifters for efficient detection of scintillation light. The current standard is tetraphenyl butadiene (TPB), but it is challenging to instrument on a large scale. Poly(ethylene 2,6-naphthalate) (PEN), a polyester easily manufactured as thin sheets, could simplify the coverage of large surfaces with wavelength shifters. Previous measurements have shown that commercial grades of PEN have approximately 50% light conversion efficiency relative to TPB. Encouraged by these results, we conducted a large-scale measurement using $4~m^2$ combined PEN and specular reflector foils in a two-tonne liquid argon dewar to assess its stability over approximately two weeks. This test is crucial for validating PEN as a viable substitute for TPB. The setup used for the measurement of the stability of PEN as a wavelength shifter is described, together with the first results, showing no evidence of performance deterioration over a period of 12 days., Comment: 10 pages, 13 figures

Fog Computing provides computational resources close to the end user, supporting low-latency and high-bandwidth communications. It supports IoT applications, enabling real-time data processing, analytics, and decision-making at the edge of the network. However, the high distribution of its constituent nodes and resource-restricted devices interconnected by heterogeneous and unreliable networks makes it challenging to execute service maintenance and troubleshooting, increasing the time to restore the application after failures and not guaranteeing the service level agreements. In such a scenario, increasing the observability of Fog applications and services may speed up troubleshooting and increase their availability. An observability system is a data-intensive service, and Fog Computing could have its nodes and channels saturated with an additional load. In this work, we detail the three pillars of observability (metrics, log, and traces), discuss the challenges, and clarify the approaches for increasing the observability of services in Fog environments. Furthermore, the system architecture that supports observability in Fog, related tools, and technologies are presented, providing a comprehensive discussion on this subject. An example of a solution shows how a real-world application can benefit from increased observability in this environment. Finally, there is a discussion about the future directions of Fog observability.

We propose a $U(1)_{B-L}$ gauge extension to the SM, in which the dark sector is stabilized through a matter parity symmetry preserved after spontaneous symmetry breaking. The fermion spectrum includes three neutral right-handed fields with $B-L$ charges $(-4,-4, 5)$, that make the model free of gauge anomalies. Two of these neutral fermion fields serve as mediators in a scotogenic mechanism for light-active Majorana neutrino masses. The corresponding neutrino mass matrix has rank 2, predicting a massless state and a lower bound for neutrinoless double beta decay. Regions in the parameter space consistent with dark matter relic abundance are accomplished by the lightest neutral mediator.

In face of climate change and increasing urbanization, the predictive mosquito-borne diseases (MBD) transmission models require constant updates. Thus, is urgent to comprehend the driving forces of this non stationary behavior, observed through spatial and incidence expansion. We observed that temperature is a critical driver in predictive models for MBD transmission, also being consistently used in multiple reviewed papers with considerable incidence predictive capacity. Rainfall, however, have more subtle importance as moderate precipitation creates breeding sites for mosquitoes, but excessive rainfall can reduce larvae populations. We highlight the frequent use of mechanistic models, particularly those that integrate temperature-dependent biological parameters of disease transmission in incidence proxies as the Vectorial Capacity (VC) and temperature-based basic reproduction number $R_0(t)$, for example. These models show the importance of climate variables, but the socio-demographic factors are often not considered. This gap is a significant opportunity for future research to incorporate socio-demographic data into long-term predictive models for more comprehensive and reliable forecasts. With this survey, we outline the most promising paths to be followed by long-term MBD transmission research and highlighting the potential facing challenges. Thus, we offer a valuable foundation for enhancing disease forecasting models and supporting more effective public health interventions, specially in the long term.

This work investigates the application of the Newton's method for the numerical solution of a nonlinear boundary value problem formulated through an ordinary differential equation (ODE). Nonlinear ODEs arise in various mathematical modeling contexts, where an exact solution is often unfeasible due to the intrinsic complexity of these equations. Thus, a numerical approach is employed, using Newton's method to solve the system resulting from the discretization of the original problem. The procedure involves the iterative formulation of the method, which enables the approximation of solutions and the evaluation of convergence with respect to the problem parameters. The results demonstrate that Newton's method provides a robust and efficient solution, highlighting its applicability to complex boundary value problems and reinforcing its relevance for the numerical analysis of nonlinear systems. It is concluded that the methodology discussed is suitable for solving a wide range of boundary value problems, ensuring precision and stability in the results., Comment: 9 pages, 2 figures

In this paper, frequency dynamics in modern power systems with a high penetration of converter-based generation is analysed. A fundamental analysis of the frequency dynamics is performed to identify the limitations and challenges when the converter penetration is increased. The voltage-source behaviour is found as an essential characteristic of converters to improve the initial frequency derivative of Synchronous Generators (SGs). A detailed small-signal analysis, based on the system's eigenvalues, participation factors and mode shapes, is then performed in a reduced system for different converter penetrations, showing that the flexibility of grid-forming (GFOR) converters as well as the system's inertia reduction may lead to have a more controllable system frequency. First-order frequency responses can be programmed for high converter penetrations, when GFOR operation can impose their dominance over SGs. These results have been validated in the IEEE 118-bus system simulated in PSCAD., Comment: 11 pages, 17 figures

In this work, we examine particle creation and the evaporation process in the context of Kalb-Ramond gravity. Specifically, we build upon two existing solutions from the literature \cite{yang2023static} and \cite{Liu:2024oas}, both addressing a static, spherically symmetric configuration. For this study, we focus on the scenario in which the cosmological constant vanishes. To begin, the corrections to Hawking radiation for bosonic modes are examined by studying the Klein-Gordon equation in curved spacetime. Through the calculation of Bogoliubov coefficients, we identify how the parameter $\ell$, which governs Lorentz symmetry breaking, contributes a correction to the amplitude associated with particle production. Within this approach, the power spectrum and Hawking temperature are derived. Additionally, we obtain expressions for the power spectrum and particle number density by analyzing Hawking radiation from a tunneling viewpoint. A parallel approach is applied to fermionic modes. Finally, we examine black hole evaporation, finding that the black hole lifetime in these cases can be determined analytically., Comment: 14 pages, and 11 figures