Search

This research addresses the challenges of handling unbalanced datasets for binary classification tasks. In such scenarios, standard evaluation metrics are often biased by the disproportionate representation of the minority class. Conducting experiments across seven datasets, we uncovered inconsistencies in evaluation metrics when determining the model that outperforms others for each binary classification problem. This justifies the need for a metric that provides a more consistent and unbiased evaluation across unbalanced datasets, thereby supporting robust model selection. To mitigate this problem, we propose a novel metric, the Unbiased Integration Coefficients (UIC), which exhibits significantly reduced bias ($p < 10^{-4}$) towards the minority class compared to conventional metrics. The UIC is constructed by aggregating existing metrics while penalising those more prone to imbalance. In addition, we introduce the Identical Partitions for Imbalance Problems (IPIP) algorithm for imbalanced ML problems, an ensemble-based approach. Our experimental results show that IPIP outperforms other baseline imbalance-aware approaches using Random Forest and Logistic Regression models in three out of seven datasets as assessed by the UIC metric, demonstrating its effectiveness in addressing imbalanced data challenges in binary classification tasks. This new framework for dealing with imbalanced datasets is materialized in the FILM (Framework for Imbalanced Learning Machines) R Package, accessible at https://github.com/antoniogt/FILM.

Recently, exotic superconductivity emerging from a spin-and-valley-polarized metallic phase has been observed in rhombohedral tetralayer graphene. To explain this finding, we study the role of electron-electron interactions in determining flavor symmetry breaking, using the Hartree Fock (HF) approximation, and also superconductivity driven by repulsive interactions. Though mean field HF correctly predicts the isospin flavors and reproduces the experimental phase diagram, it overestimates the band renormalization near the Fermi energy and suppresses superconducting instabilities. To address this, we introduce a physically motivated scheme that includes internal screening in the HF calculation. Superconductivity arises in the spin-valley polarized phase for a range of electric fields and electron doping. Our findings reproduce the experimental observations and reveal a $p$-wave, finite-momentum, time-reversal symmetry broken superconducting state, encouraging further investigation into exotic phases in graphene multilayers., Comment: 7 pages, 4 figures, supplementary material. Comments are welcome

Reducing the environmental impact of cloud computing requires efficient workload distribution across geographically dispersed Data Center Clusters (DCCs) and simultaneously optimizing liquid and air (HVAC) cooling with time shift of workloads within individual data centers (DC). This paper introduces Green-DCC, which proposes a Reinforcement Learning (RL) based hierarchical controller to optimize both workload and liquid cooling dynamically in a DCC. By incorporating factors such as weather, carbon intensity, and resource availability, Green-DCC addresses realistic constraints and interdependencies. We demonstrate how the system optimizes multiple data centers synchronously, enabling the scope of digital twins, and compare the performance of various RL approaches based on carbon emissions and sustainability metrics while also offering a framework and benchmark simulation for broader ML research in sustainability.

We present a Reinforcement Learning Platform for Adversarial Black-box untargeted and targeted attacks, RLAB, that allows users to select from various distortion filters to create adversarial examples. The platform uses a Reinforcement Learning agent to add minimum distortion to input images while still causing misclassification by the target model. The agent uses a novel dual-action method to explore the input image at each step to identify sensitive regions for adding distortions while removing noises that have less impact on the target model. This dual action leads to faster and more efficient convergence of the attack. The platform can also be used to measure the robustness of image classification models against specific distortion types. Also, retraining the model with adversarial samples significantly improved robustness when evaluated on benchmark datasets. The proposed platform outperforms state-of-the-art methods in terms of the average number of queries required to cause misclassification. This advances trustworthiness with a positive social impact., Comment: Under Review for 2025 AAAI Conference on Artificial Intelligence Proceedings

In this note we show that the support of a locally $k$-uniform measure in $\mathbb R^{n+1}$ satisfies a kind of unique continuation property. As a consequence, we show that locally uniformly distributed measures satisfy a weaker unique continuation property. This continues work of Kirchheim and Preiss (Math. Scand. 2002) and David, Kenig and Toro (Comm. Pure Appl. Math. 2001) and lends additional evidence to the conjecture proposed by Kowalski and Preiss (J. Reine Angew. Math. 1987) that the support of a locally $n$-uniform measure in $\mathbb R^{n+1}$ is contained in the zero set of a quadratic polynomial.

This paper provides a dual domain derivation of the error exponent of maximum mutual information (MMI) decoding with constant composition codes, showing it coincides with that of maximum likelihood decoding for discrete memoryless channels. The analysis is further extended to joint source-channel coding, demonstrating that the generalized MMI decoder achieves the same random coding error exponent as the maximum a posteriori decoder., Comment: This paper has been submitted to ISIT 2025 for review

Charge density waves (CDW) in single-layer 1$T$-MTe$_2$ (M= Nb, Ta) recently raised large attention because of the contrasting structural and physical behavior with the sulfide and selenide analogues. A first-principles study of fourteen different 1$T$-type TaTe$_2$ single-layers is reported. The importance of Te to Ta electron transfer and multicenter metal-metal bonding in stabilizing different structural modulations is highlighted. Analysis of the electronic structure of the optimized structures provides a rationale for what distinguishes 1$T$-TaTe$_2$ from the related disulfide and diselenide, what are the more stable structural modulations for 1$T$-type TaTe$_2$ single-layers, the possible role of Fermi surface nesting on some of these CDW instabilities, how the CDW affects the metallic properties of the non-distorted lattice and the possibility that some of these CDW phases exhibit exotic properties. All CDW phases studied exhibit band structures typical of metallic systems although some exhibit both very flat and dispersive bands at the Fermi level so that Mott effects could develop; one of the (4$\times$4) phases studied exhibits a Dirac cone at the Fermi level.

Aims. Our goals are to characterise the kinematic properties and to identify young and old stars among the M dwarfs of the CARMENES input catalogue. Methods. We compiled the spectral types, proper motions, distances, and radial velocities for 2187 M dwarfs. We used the public code SteParKin to derive their galactic space velocities and identify members in the different galactic populations. We also identified candidate members in young stellar kinematic groups, with ages ranging from 1 Ma to 800 Ma with SteParKin, LACEwING, and BANYAN {\Sigma}. We removed known close binaries and perform an analysis of kinematic, rotation, and activity indicators (rotational periods and projected velocities, Halpha, X-rays, and UV emission) for 1546 M dwarfs. We defined five rotation-activity-colour relations satisfied by young ({\tau} <= 800 Ma) stars. Results. We identified 191 young M dwarf candidates (~12%), 113 of which are newly recognised in this work. In this young sample, there are 118 very active stars based on H{\alpha} emission, fast rotation, and X-ray and UV emission excess. Of them, 27 have also strong magnetic fields, 9 of which are likely younger than 50 Ma. Additionally, there are 87 potentially young stars and 99 stars with a dubious youth classification, which may increase the fraction of young stars to an astounding 24%. Only one star out of the 2187 exhibits kinematics typical of the old Galactic halo. Conclusions. A combined analysis of kinematic and rotation-activity properties provides a robust method for identifying young M dwarfs from archival data. However, more observational efforts are needed to ascertain the true nature of numerous young star candidates in the field and, perhaps more importantly, to precisely quantify their age.

IRAS 22568+6141 has been classified as a low-ionisation planetary nebula (PN) and presents non-thermal radio continuum emission, which could be a signature of nascent PNe. We present intermediate-resolution long-slit spectra obtained in 2021 and 2023, high-resolution long-slit spectra taken in 2023, and a light curve at the $r$-filter between 1953 and 2019, that reveal changes in IRAS 22568+6141 with timescales of decades and a few years. The object underwent an energetic event around 1990 that suddenly increased its brightness which has been fading since then. A comparison with a published spectrum from 1988 shows an increase of the H$\beta$ flux in 2021 by factor of $\simeq$6 and the [O III] emission lines that were absent in 1988. Between 2021 and 2023 the H$\beta$ flux decreased by a factor of $\simeq$1.7, and the [O III] emission lines almost vanished. These results and the variability observed in other emission lines indicate that IRAS 22568+6141 is recombining and cooling down between 2021 and 2023, and probably since 2005, as suggested by archival radio continuum and mid-IR observations. The intermediate- and high-resolution spectra show that the excitation of the emission lines is dominated by shocks in 2021 and 2023, and, probably, also in 1988, which may be related to the non-thermal radio continuum emission from the object. Although the variability might be due to changes in the physical conditions in the shocks or in a nova-like eruption, it accommodates better to that expected from a late thermal pulse, which is further suggested by a comparison with other similar objects. New observations and monitoring in the coming years are crucial to corroborate the origin of the variability., Comment: 11 pages, 8 figures, 6 tables. Accepted for publication in A&A

Imaging atmospheric Cherenkov telescopes (IACTs) are used to observe very high-energy photons from the ground. Gamma rays are indirectly detected through the Cherenkov light emitted by the air showers they induce. The new generation of experiments, in particular the Cherenkov Telescope Array Observatory (CTAO), sets ambitious goals for discoveries of new gamma-ray sources and precise measurements of the already discovered ones. To achieve these goals, both hardware and data analysis must employ cutting-edge techniques. This also applies to the LST-1, the first IACT built for the CTAO, which is currently taking data on the Canary island of La Palma. This paper introduces a new event reconstruction technique for IACT data, aiming to improve the image reconstruction quality and the discrimination between the signal and the background from misidentified hadrons and electrons. The technique models the development of the extensive air shower signal, recorded as a waveform per pixel, seen by CTAO telescopes' cameras. Model parameters are subsequently passed to random forest regressors and classifiers to extract information on the primary particle. The new reconstruction was applied to simulated data and to data from observations of the Crab Nebula performed by the LST-1. The event reconstruction method presented here shows promising performance improvements. The angular and energy resolution, and the sensitivity, are improved by 10 to 20% over most of the energy range. At low energy, improvements reach up to 22%, 47%, and 50%, respectively. A future extension of the method to stereoscopic analysis for telescope arrays will be the next important step., Comment: Accepted in A&A

As the use of Generative AI (Gen-AI) in scholarly writing and peer reviews continues to rise, it is essential for the computing field to establish and adopt clear Gen-AI policies. This study examines the landscape of Gen-AI policies across 64 major Computer Science conferences and offers recommendations for promoting more effective and responsible use of Gen-AI in the field., Comment: Accepted and to appear in Communications of the ACM (CACM) in 2025

We make a careful analysis of Bohr's inequality, in the line started by Kayumov and Ponnusamy, where some extra summand (depending on the function) is added in the right-hand side of the inequality. We analyse the inequality when smaller radius are taken, giving sharp constants. As a result of this point of view, some previous results are improved., Comment: 12 pages, 10 figures

Gamma/hadron discrimination in ground-based gamma-ray observatories at the sub-TeV energy range is challenging as traditional muon-based methods become less effective at lower energies. This work explores a novel gamma/hadron discrimination method for Extensive Air Shower arrays that analyzes the shower signal footprint patterns using a state-of-the-art pre-trained Vision Transformer (ViT). The resilience of the method to background noise, such as atmospheric muons and low-energy proton showers, along with its adaptability to different zenith angles and array configurations, demonstrates its potential for application in current and future ground-based gamma-ray observatories., Comment: 13 pages, 13 figures

We study the isotropic and anisotropic Hamiltonian of two coupled harmonic oscillators from an algebraic approach of the $SU(1,1)$ and $SU(2)$ groups. In order to obtain the energy spectrum and eigenfunctions of this problem, we write its Hamiltonian in terms of the boson generators of the $SU(1,1)$ and $SU(2)$ groups. We use the one boson and two boson realizations of the $su(1,1)$ Lie algebra, and the one boson realization of the $su(2)$ Lie algebra to apply three tilting transformations to diagonalize the original Hamiltonian. These transformations let us to obtain the exact solutions of the isotropic and the anisotropic cases, from which the particular expected results are obtained for the cases where the coupling is neglected., Comment: 13 pages. arXiv admin note: substantial text overlap with arXiv:2409.08179

We study the Hamiltonian of two isotropic oscillators with weak coupling from an algebraic approach. We write the Hamiltonian of this problem in terms of the boson generators of the $SU(1,1)$ and $SU(2)$ groups. This allows us to apply two tilting transformations based on both group similarity transformations to obtain its energy spectrum and eigenfunctions. Then, we obtain the Mandel $Q-$parameter of the photon numbers $n_a$ and $n_b$. It is important to note that in our procedure we consider the case of weak coupling., Comment: 11 pages

The TESS mission searches for transiting exoplanets by monitoring the brightness of hundreds of thousands of stars across the entire sky. M-type planet hosts are ideal targets for this mission due to their smaller size and cooler temperatures, which makes it easier to detect smaller planets near or within their habitable zones. Additionally, M~dwarfs have a smaller contrast ratio between the planet and the star, making it easier to measure the planet's properties accurately. Here, we report the validation analysis of 13 TESS exoplanet candidates orbiting around M dwarfs. We studied the nature of these candidates through a multi-colour transit photometry transit analysis using several ground-based instruments (MuSCAT2, MuSCAT3, and LCO-SINISTRO), high-spatial resolution observations, and TESS light curves. We present the validation of five new planetary systems: TOI-1883b, TOI-2274b, TOI2768b, TOI-4438b, and TOI-5319b, along with compelling evidence of a planetary nature for TOIs 2781b and 5486b. We also present an empirical definition for the Neptune desert boundaries. The remaining six systems could not be validated due to large true radius values overlapping with the brown dwarf regime or, alternatively, the presence of chromaticity in the MuSCAT2 light curves.

Foundation models (FMs) are a popular topic of research in AI. Their ability to generalize to new tasks and datasets without retraining or needing an abundance of data makes them an appealing candidate for applications on specialist datasets. In this work, we compare the performance of FMs to finetuned pre-trained supervised models in the task of semantic segmentation on an entirely new dataset. We see that finetuned models consistently outperform the FMs tested, even in cases were data is scarce. We release the code and dataset for this work on GitHub., Comment: Accepted at ECCV 2024 Green Foundation Models workshop

We report VI CCD photometry of the globular cluster cluster NGC 1851. We aim to study the membership of the variable stars detected in the field of the cluster as listed in the Catalogue of Variable stars in Globular Clusters (CVSGC; Clement et al. 2001) and reported by the Gaia mission. We cross match the two sets of variables to produce light curves that lead to the estimation of physical parameters. The resulting colour-magnitude diagram (CMD), free of likely field stars, enables to confirm the position of the variables, their type and evolutionary stage. We provide new estimations of the period using data acquired on a long timebase. The Fourier decomposition of cluster member RR Lyrae light curves lead to a mean metalicity and distance of \([Fe/H]_{ZW} = -1.35 \pm 0.22\) dex and \(11.9 \pm 0.6\) kpc. The variability and membership of stars reported by Gaia-DR3 as variables in the field of the cluster is discussed., Comment: 16 pages,10 figures, 5 table

Machine learning has driven an exponential increase in computational demand, leading to massive data centers that consume significant amounts of energy and contribute to climate change. This makes sustainable data center control a priority. In this paper, we introduce SustainDC, a set of Python environments for benchmarking multi-agent reinforcement learning (MARL) algorithms for data centers (DC). SustainDC supports custom DC configurations and tasks such as workload scheduling, cooling optimization, and auxiliary battery management, with multiple agents managing these operations while accounting for the effects of each other. We evaluate various MARL algorithms on SustainDC, showing their performance across diverse DC designs, locations, weather conditions, grid carbon intensity, and workload requirements. Our results highlight significant opportunities for improvement of data center operations using MARL algorithms. Given the increasing use of DC due to AI, SustainDC provides a crucial platform for the development and benchmarking of advanced algorithms essential for achieving sustainable computing and addressing other heterogeneous real-world challenges., Comment: Under review at Advances in Neural Information Processing Systems 2024 (NeurIPS 2024)

The existence of superconductivity (SC) in graphene appears to be established in both twisted and non-twisted multilayers. However, whether their building block, single-layer graphene (SLG), can also host SC remains an open question. Earlier theoretical works predicted that SLG could become a chiral d-wave superconductor driven by electronic interactions when doped to its van Hove singularity, but questions such as whether the d-wave SC survives the strong band renormalizations seen in experiments, its robustness against the source of doping, or if it will occur at any reasonable critical temperature (Tc) have remained difficult to answer, in part due to uncertainties in model parameters. In this study, we adopt a random-phase approximation framework based on a Kohn-Luttinger-like mechanism to investigate SC in heavily-doped SLG. We predict that robust d+id topological SC could arise in SLG doped by Tb, with a Tc up to 600 mK. We also investigate the possibility of realizing d-wave SC by employing other dopants, such as Li or Cs. The structural models have been derived from angle-resolved photoemission spectroscopy measurements on Tb-doped graphene and first-principles calculations for Cs and Li doping. We find that dopants that change the lattice symmetry of SLG are detrimental to the d-wave state. The stability of the d-wave SC predicted here in Tb-doped SLG could provide a valuable insight for guiding future experimental efforts aimed at exploring topological superconductivity in monolayer graphene., Comment: 14 pages, 6 figures. Comments are very welcome

In this paper, we obtain new bounds for the Hausdorff dimension of planar elliptic measure via the application of quasiconformal mappings, with these bounds depending solely on the ellipticity constant of the matrix. In fact, in our case studies, we find a quasiconformal mapping that relates the elliptic measure in a domain to the harmonic measure in its image domain, allowing us to deduce bounds for the dimension of the elliptic measure from the known results on the harmonic side. This extends previous works of Makarov, Jones and Wolff., Comment: Updated abstract, introduction and references

Context: There are currently three pulsars firmly detected by imaging atmospheric Cherenkov telescopes (IACTs), two of them reaching TeV energies, challenging models of very-high-energy (VHE) emission in pulsars. More precise observations are needed to better characterize pulsar emission at these energies. The LST-1 is the prototype of the Large-Sized Telescope, that will be part of the Cherenkov Telescope Array Observatory (CTAO). Its improved performance over previous IACTs makes it well suited for studying pulsars. Aims: To study the Crab pulsar emission with the LST-1, improving and complementing the results from other telescopes. These observations can also be used to characterize the potential of the LST-1 to study other pulsars and detect new ones. Methods: We analyzed a total of $\sim$103 hours of gamma-ray observations of the Crab pulsar conducted with the LST-1 in the period from September 2020 to January 2023. The observations were carried out at zenith angles less than 50 degrees. A new analysis of the Fermi-LAT data was also performed, including $\sim$14 years of observations. Results: The Crab pulsar phaseogram, long-term light-curve, and phase-resolved spectra are reconstructed with the LST-1 from 20 GeV to 450 GeV for P1 and up to 700 GeV for P2. The pulsed emission is detected with a significance of 15.2$\sigma$. The two characteristic emission peaks of the Crab pulsar are clearly detected (>10$\sigma$), as well as the so-called bridge emission (5.7$\sigma$). We find that both peaks are well described by power laws, with spectral indices of $\sim$3.44 and $\sim$3.03 respectively. The joint analysis of Fermi-LAT and LST-1 data shows a good agreement between both instruments in the overlapping energy range. The detailed results obtained in the first observations of the Crab pulsar with LST-1 show the potential that CTAO will have to study this type of sources., Comment: Accepted by A&A

We made a survey of the variable stars in a $13.2 \times 13.2$ arcmin$^2$ centered on the field of the Galactic bulge cluster NGC 6558. A total of 78 variables was found in the field of the cluster. Many of these variables are included in the Catalogue of Variable Stars in Galactic Globular Clusters (Clement et al. 2001), OGLE or Gaia-DR3 data releases. A membership analysis based on the proper motions of Gaia-DR3 revealed that many of these variables do not belong to the cluster. We employed the data from the aforementioned surveys and our own data in the VI photometric system to estimate the periods, which along with the light curves morphology and position in a deferentially dereddened colour-magnitude diagram(CMD), help classifying the variable types. Two new member variables were found; an eclipsing binary (V18) and a semi-regular SR/L (V19). In the end we conclude that only 9 variables are likely cluster members. Member variables were used to discuss the mean metallicity and distance of the parental cluster and find the average values.

Constructing prediction intervals for time series forecasting is challenging, particularly when practitioners rely solely on point forecasts. While previous research has focused on creating increasingly efficient intervals, we argue that standard measures alone are inadequate. Beyond efficiency, prediction intervals must adapt their width based on the difficulty of the prediction while preserving coverage regardless of complexity. To address these issues, we propose combining Heteroscedastic Quantile Regression (HQR) with Width-Adaptive Conformal Inference (WACI). This integrated procedure guarantees theoretical coverage and enables interval widths to vary with predictive uncertainty. We assess its performance using both a synthetic example and a real world Electricity Price Forecasting scenario. Our results show that this combined approach meets or surpasses typical benchmarks for validity and efficiency, while also fulfilling important yet often overlooked practical requirements.

This research introduces STAR, a sociotechnical framework that improves on current best practices for red teaming safety of large language models. STAR makes two key contributions: it enhances steerability by generating parameterised instructions for human red teamers, leading to improved coverage of the risk surface. Parameterised instructions also provide more detailed insights into model failures at no increased cost. Second, STAR improves signal quality by matching demographics to assess harms for specific groups, resulting in more sensitive annotations. STAR further employs a novel step of arbitration to leverage diverse viewpoints and improve label reliability, treating disagreement not as noise but as a valuable contribution to signal quality., Comment: 8 pages, 5 figures, 5 pages appendix. * denotes equal contribution

In this paper we show that, given a planar Reifenberg flat domain with small constant and a divergence form operator associated to a real (not necessarily symmetric) uniformly elliptic matrix with Lipschitz coefficients, the Hausdorff dimension of its elliptic measure is at most 1. More precisely, we prove that there exists a subset of the boundary with full elliptic measure and with $\sigma$-finite one-dimensional Hausdorff measure. For Reifenberg flat domains, this result extends a previous work of Thomas H. Wolff for the harmonic measure.

In this work we present two new numerical schemes to approximate the Navier-Stokes-Cahn-Hilliard system with degenerate mobility using finite differences in time and finite elements in space. The proposed schemes are conservative, energy-stable and preserve the maximum principle approximately (the amount of the phase variable being outside of the interval [0,1] goes to zero in terms of a truncation parameter). Additionally, we present several numerical results to illustrate the accuracy and the well behavior of the proposed schemes, as well as a comparison with the behavior of the Navier-Stokes-Cahn-Hilliard model with constant mobility.

Sub-Neptunes with $2-3R_\oplus$ are intermediate in size between rocky planets and Neptune-sized planets. The orbital properties and bulk compositions of transiting sub-Neptunes provide clues to the formation and evolution of close-in small planets. In this paper, we present the discovery and follow-up of four sub-Neptunes orbiting M dwarfs (TOI-782, TOI-1448, TOI-2120, and TOI-2406), three of which were newly validated by ground-based follow-up observations and statistical analyses. TOI-782 b, TOI-1448 b, TOI-2120 b, and TOI-2406 b have radii of $R_\mathrm{p} = 2.740^{+0.082}_{-0.079}\,R_\oplus$, $2.769^{+0.073}_{-0.068}\,R_\oplus$, $2.120\pm0.067\,R_\oplus$, and $2.830^{+0.068}_{-0.066}\,R_\oplus$ and orbital periods of $P = 8.02$, $8.11$, $5.80$, and $3.08$\,days, respectively. Doppler monitoring with Subaru/InfraRed Doppler instrument led to 2$\sigma$ upper limits on the masses of $<19.1\ M_\oplus$, $<19.5\ M_\oplus$, $<6.8\ M_\oplus$, and $<15.6\ M_\oplus$ for TOI-782 b, TOI-1448 b, TOI-2120 b, and TOI-2406 b, respectively. The mass-radius relationship of these four sub-Neptunes testifies to the existence of volatile material in their interiors. These four sub-Neptunes, which are located above the so-called ``radius valley'', are likely to retain a significant atmosphere and/or an icy mantle on the core, such as a water world. We find that at least three of the four sub-Neptunes (TOI-782 b, TOI-2120 b, and TOI-2406 b) orbiting M dwarfs older than 1 Gyr, are likely to have eccentricities of $e \sim 0.2-0.3$. The fact that tidal circularization of their orbits is not achieved over 1 Gyr suggests inefficient tidal dissipation in their interiors., Comment: Accepted for publication in AJ, 32 pages, 17 figures, 6 tables

In this article we investigate and solve exactly the modified Dirac oscillator in curved spacetime with spin and pseudospin symmetries through an algebraic approach. By focusing on the radial part of this problem, we use the Schr\"odinger factorization method to show that this problem possesses an SU(1; 1) symmetry. This symmetry allowed us to obtain the wave functions and their corresponding energy spectrum. From these results, we calculate the radial coherent states of the modified Dirac oscillator and their temporal evolution in the spin and pseudospin limits, respectively.

(Abridged) We present narrow-band images in several emission lines, and high- and intermediate-resolution long-slit spectra of Me2-1 to investigate its morphology and 3D structure, its physical parameters and chemical abundances. We identified in Me2-1: an elliptical ring; two elongated, curved structures (caps) that contain three pairs of bright point-symmetric (PS) knots; a shell interior of the ring; and a faint halo or attached shell. The caps are observed in all images, the PS knots only in the low-excitation emission line ones. These structures are also identified in the high-resolution long-slit spectra. The 3D reconstruction shows that Me2-1 consists of a ring seen almost pole-on, and a virtually spherical shell, to which the caps and PS knots are attached. Caps and PS knots most probably trace the sites where high-velocity collimated bipolar outflows, ejected along a wobbling axis, collide with the spherical shell, are slowed down, and remain attached to it. Although the main excitation mechanism in Me2-1 is found to be photoionization, a contribution of shocks in the PS knots is suggested by their emission line ratios. The combination of collimated outflows and a ring with a spherical shell is unusual among planetary nebulae. We speculate that two planets, each with less than one Jupiter mass, could be involved in the formation of Me2-1 if both enter a common envelope evolution during the asymptotic giant branch phase of the progenitor. One planet is tidally disrupted, forming an accretion disk around the central star, from which collimated bipolar outflows are ejected; the other planet survives, causing wobbling of the accretion disk. The derived physical parameters and chemical abundances are similar to those obtained in previous analyses, with the abundances also pointing to a low-mass progenitor of Me2-1., Comment: Accepted by A&A. 12 pages, 6 figures, 5 tables, an Appendix with 3 figures

New generation networks, based on Cognitive Radio technology, allow dynamic allocation of the spectrum, alleviating spectrum scarcity. These networks also have a resilient potential for dynamic operation for energy saving. In this paper, we present a novel wireless network optimization algorithm for cognitive radio networks based on a cloud sharing-decision mechanism. Three Key Performance Indicators (KPIs) were optimized: spectrum usage, power consumption, and exposure of human beings. For a realistic suburban scenario in Ghent city, Belgium, we determine the optimality among the KPIs. Compared to a traditional Cognitive Radio network design, our optimization algorithm for the cloud-based architecture reduced the network power consumption by 27.5%, the average global exposure by 34.3%, and spectrum usage by 34.5% at the same time. Even for the worst optimization case, our solution performs better than the traditional architecture by 4.8% in terms of network power consumption, 7.3% in terms of spectrum usage and 4.3% in terms of global exposure.

We perform a detailed study of (supersymmetric) moduli stabilisation in type IIB toroidal orientifolds with fluxes. We provide strong evidence towards exhaustion of the finite number of inequivalent vacua for a given total 3-form flux charge $N_{\rm flux}$. We also find that the minimal value of the string coupling $g_s$ is given in terms of $N_{\rm flux}$ and present strong evidence for the asymptotic relation $g_{s,\,\rm{min}}\sim c/N_{\rm flux}^\alpha$, with $\alpha=1,2$, valid for not too small $N_{\rm flux}$, where $c$ is an order 1 coefficient and $\alpha$ depends on the orbifold. Imposing tadpole cancellation, $N_{\rm flux}$ is bounded from the number of orientifold O3-planes. Combined with the flux quantisation, this forbids orientifold vacua without anti-brane charge. On the other hand, the presence of negative D3-brane charge induced by magnetised D7-branes breaks supersymmetry and relaxes the bound, allowing significantly smaller values for $g_s$., Comment: 54 pages, 3 figures. v2: minor corrections, added references. v3: published version, minor corrections + correction factor 1/2 in tadpole conditions

There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers. The design and intelligent control for data centers have an important impact on data center carbon footprint. In this paper, we showcase PyDCM, a Python library that enables extremely fast prototyping of data center design and applies reinforcement learning-enabled control with the purpose of evaluating key sustainability metrics including carbon footprint, energy consumption, and observing temperature hotspots. We demonstrate these capabilities of PyDCM and compare them to existing works in EnergyPlus for modeling data centers. PyDCM can also be used as a standalone Gymnasium environment for demonstrating sustainability-focused data center control., Comment: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning https://www.climatechange.ai/papers/neurips2023/15. arXiv admin note: substantial text overlap with arXiv:2310.03906

The rapid growth of machine learning (ML) has led to an increased demand for computational power, resulting in larger data centers (DCs) and higher energy consumption. To address this issue and reduce carbon emissions, intelligent design and control of DC components such as IT servers, cabinets, HVAC cooling, flexible load shifting, and battery energy storage are essential. However, the complexity of designing and controlling them in tandem presents a significant challenge. While some individual components like CFD-based design and Reinforcement Learning (RL) based HVAC control have been researched, there's a gap in the holistic design and optimization covering all elements simultaneously. To tackle this, we've developed DCRL-Green, a multi-agent RL environment that empowers the ML community to design data centers and research, develop, and refine RL controllers for carbon footprint reduction in DCs. It is a flexible, modular, scalable, and configurable platform that can handle large High Performance Computing (HPC) clusters. Furthermore, in its default setup, DCRL-Green provides a benchmark for evaluating single as well as multi-agent RL algorithms. It easily allows users to subclass the default implementations and design their own control approaches, encouraging community development for sustainable data centers. Open Source Link: https://github.com/HewlettPackard/dc-rl, Comment: 2024 Proceedings of the AAAI Conference on Artificial Intelligence

As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide. This requires a paradigm shift in optimizing power consumption in cooling and IT loads, shifting flexible loads based on the availability of renewable energy in the power grid, and leveraging battery storage from the uninterrupted power supply in data centers, using collaborative agents. The complex association between these optimization strategies and their dependencies on variable external factors like weather and the power grid carbon intensity makes this a hard problem. Currently, a real-time controller to optimize all these goals simultaneously in a dynamic real-world setting is lacking. We propose a Data Center Carbon Footprint Reduction (DC-CFR) multi-agent Reinforcement Learning (MARL) framework that optimizes data centers for the multiple objectives of carbon footprint reduction, energy consumption, and energy cost. The results show that the DC-CFR MARL agents effectively resolved the complex interdependencies in optimizing cooling, load shifting, and energy storage in real-time for various locations under real-world dynamic weather and grid carbon intensity conditions. DC-CFR significantly outperformed the industry standard ASHRAE controller with a considerable reduction in carbon emissions (14.5%), energy usage (14.4%), and energy cost (13.7%) when evaluated over one year across multiple geographical regions.

In the present review we focus on the chemotaxis-consumption model $\partial_t u - \Delta u = - \nabla \cdot (u \nabla v)$ and $\partial_t v - \Delta v = - u^s v$ in $(0,T) \times \Omega$, for any fixed $s \geq 1$, endowed with isolated boundary conditions and nonnegative initial conditions, where $(u,v)$ model cell density and chemical signal concentration. Our objective is to present an overview of the related literature and latest results on the aforementioned model concerning the following three distinct research lines we have obtained in [12,24-26]: the mathematical analysis, the numerical analysis and the related optimal control theory with a bilinear control acting on the chemical equation.

Early wildfire detection is of the utmost importance to enable rapid response efforts, and thus minimize the negative impacts of wildfire spreads. To this end, we present PyroNear-2024, a new dataset composed of both images and videos, allowing for the training and evaluation of smoke plume detection models, including sequential models. The data is sourced from: \textit{(i)} web-scraped videos of wildfires from public networks of cameras for wildfire detection in-the-wild, \text{(ii)} videos from our in-house network of cameras, and \textit{(iii)} a small portion of synthetic and real images. This dataset includes around 150,000 manual annotations on 50,000 images, covering 400 wildfires, \Pyro surpasses existing datasets in size and diversity. It includes data from France, Spain, and the United States. Finally, it is composed of both images and videos, allowing for the training and evaluation of smoke plume detection models, including sequential models. We ran cross-dataset experiments using a lightweight state-of-the-art object detection model and found out the proposed dataset is particularly challenging, with F1 score of around 60%, but more stable than existing datasets. The video part of the dataset can be used to train a lightweight sequential model, improving global recall while maintaining precision. Finally, its use in concordance with other public dataset helps to reach higher results overall. We will make both our code and data available., Comment: Preprint of ongoing work

The high-pressure crystal structure evolution of CH3NH3PbBr3 (MAPbBr3) perovskite has been investigated by single-crystal X-ray diffraction and synchrotron-based powder X-ray diffraction. Single-crystal X-ray diffraction reveals that the crystal structure of MAPbBr3 undergoes two phase transitions following the space-group sequence: Pm-3m-to-Im3-to-Pmn21. The transitions take place at around 0.8 and 1.8 GPa, respectively. This result is contradicting the previously reported phase transition sequence: Pm-3m-to-Im3-to-Pnma. In this work the crystal structures of each of the three phases are determined from single-crystal X-ray diffraction analysis which is later supported by Rietveld refinement of powder X-ray diffraction patterns. The pressure dependence of the crystal lattice parameters and unit-cell volumes are determined from the two aforementioned techniques, as well as the bulk moduli for each phase. The bandgap behaviour of MAPbBr3 has been studied up to around 4 GPa, by the means of single-crystal optical-absorption experiments. The evolution of the bandgap has been well explained using the pressure dependence of the Pb-Br bond distance and Pb-Br-Pb angles as determined from single-crystal X-ray diffraction experiments., Comment: 20 pages, 5 pages

The main purpose of this work is the modelling of large single populations of cells under different interactions among themselves, in balance with natural random diffusion. We focus on cell-cell adhesion mechanisms for one single population confined in isolated domains. Our most relevant contribution is the proposal of a mathematical model, that includes a nonlocal saturation coefficient multiplying an appropriate nonlocal drift term, that can even induce repulsion effects from saturated areas.

We study the non-degenerate parametric amplifier problem from an algebraic approach of the $SU(1,1)$ group. We write the Hamiltonian of this problem in terms of the boson generators of the $SU(1,1)$ group and the difference operator. We apply the tilting transformation to our results to exactly solve this Hamiltonian and obtain its energy spectrum and eigenfunctions. Then, by assuming that our Hamiltonian is an explicit function of time we calculate its Berry phase. Finally we obtain the Mandel $Q-$parameter of the photon numbers $n_a$ and $n_b$., Comment: 11 pages

It was recently proposed that five-dimensional inflation can relate the causal size of the observable universe to the present weakness of gravitational interactions by blowing up an extra compact dimension from the microscopic fundamental length of gravity to a large size in the micron range, as required in the Dark Dimension proposal. Here, we compute the power spectrum of all primordial fluctuations emerging from a 5-dimensional inflaton in a slow-roll region of its potential, showing an interesting change of behaviour at large scales corresponding to angles larger than about 10 degrees in the sky., Comment: 21 pages, 1 figure, journal version (JHEP)

Powder metallurgy with hot isostatic pressing (PM-HIP) is an advanced manufacturing process that is envisioned to replace forging for heavy nuclear components, including the reactor pressure vessel (RPV). But PM-HIP products must at least demonstrate comparable irradiation tolerance than forgings in order to be qualified for nuclear applications. The objective of this study is to directly compare PM-HIP to forged SA508 Grade 3 Class 1 low-alloy RPV steel at two neutron irradiation conditions: ~0.5-1.0 displacements per atom (dpa) at ~270C and ~370C. PM-HIP SA508 experiences greater irradiation hardening and embrittlement (total elongation) than forged SA508. However, uniform elongation and approximate toughness are comparable across all irradiated materials, suggesting irradiated PM-HIP SA508 exhibits superior ductility at maximum load-bearing capacity. The irradiation hardening mechanism is linked to composition rather than fabrication method. Since PM-HIP SA508 has higher Mn and Ni concentration, it is more susceptible to irradiation-induced nucleation of Mn-Ni-Si-P (MNSP) nanoprecipitates and dislocation loops, which both contribute to hardening. Conversely, the forged material nucleates fewer MNSPs, causing dislocation loops to control irradiation hardening. These results show promise for the irradiation performance of PM-HIP SA508 and can motivate future nuclear code qualification of PM-HIP fabrication for RPVs.

We consider solutions to the space-homogeneous Landau equation with a general family of interaction potentials. We prove that their Fisher information is monotone decreasing in time. The class of interaction potentials covered by our result includes the case of the Landau equation with Coulomb interactions. As a consequence of the global boundedness of the Fisher information, we deduce that solutions to the space-homogeneous Landau equation never blow up., Comment: In the second version, we added some references and fixed some trivial typos

The study of Day-Ahead prices in the electricity market is one of the most popular problems in time series forecasting. Previous research has focused on employing increasingly complex learning algorithms to capture the sophisticated dynamics of the market. However, there is a threshold where increased complexity fails to yield substantial improvements. In this work, we propose an alternative approach by introducing an adaptive standardisation to mitigate the effects of dataset shifts that commonly occur in the market. By doing so, learning algorithms can prioritize uncovering the true relationship between the target variable and the explanatory variables. We investigate five distinct markets, including two novel datasets, previously unexplored in the literature. These datasets provide a more realistic representation of the current market context, that conventional datasets do not show. The results demonstrate a significant improvement across all five markets using the widely accepted learning algorithms in the literature (LEAR and DNN). In particular, the combination of the proposed methodology with the methodology previously presented in the literature obtains the best results. This significant advancement unveils new lines of research in this field, highlighting the potential of adaptive transformations in enhancing the performance of forecasting models.

We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate issues such as toxicity and fact hallucination. This method involves refining model outputs through an ensemble of critics and the model's own feedback. Drawing inspiration from human behavior, we explore whether LLMs can emulate the self-correction process observed in humans who often engage in self-reflection and seek input from others to refine their understanding of complex topics. Our approach is model-agnostic and can be applied across various domains to enhance trustworthiness by addressing fairness, bias, and robustness concerns. We consistently observe performance improvements in LLMs for reducing toxicity and correcting factual errors.

We present a novel framework for generating adversarial benchmarks to evaluate the robustness of image classification models. Our framework allows users to customize the types of distortions to be optimally applied to images, which helps address the specific distortions relevant to their deployment. The benchmark can generate datasets at various distortion levels to assess the robustness of different image classifiers. Our results show that the adversarial samples generated by our framework with any of the image classification models, like ResNet-50, Inception-V3, and VGG-16, are effective and transferable to other models causing them to fail. These failures happen even when these models are adversarially retrained using state-of-the-art techniques, demonstrating the generalizability of our adversarial samples. We achieve competitive performance in terms of net $L_2$ distortion compared to state-of-the-art benchmark techniques on CIFAR-10 and ImageNet; however, we demonstrate our framework achieves such results with simple distortions like Gaussian noise without introducing unnatural artifacts or color bleeds. This is made possible by a model-based reinforcement learning (RL) agent and a technique that reduces a deep tree search of the image for model sensitivity to perturbations, to a one-level analysis and action. The flexibility of choosing distortions and setting classification probability thresholds for multiple classes makes our framework suitable for algorithmic audits., Comment: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

The Landau-Coulomb equation is an important model in plasma physics featuring both nonlinear diffusion and reaction terms. In this manuscript we focus on the diffusion operator within the equation by dropping the potentially nefarious reaction term altogether. We show that the diffusion operator in the Landau-Coulomb equation provides a much stronger L^1 to L^\infty rate of regularization than its linear counterpart, the Laplace operator. The result is made possible by a nonlinear functional inequality of Gressman, Krieger, and Strain together with a De Giorgi iteration. This stronger regularization rate illustrates the importance of the nonlinear nature of the diffusion in the analysis of the Landau equation and raises the question of determining whether this rate also happens for the Landau-Coulomb equation itself., Comment: Updated the introduction to include two new references: arXiv:2410.10765 and arXiv:2410.09035

The supernova remnant (SNR) G288.8-6.3 was recently discovered as a faint radio shell at large Galactic latitude using observations with ASKAP in the EMU survey. Here, we make the first detailed investigation of the $\gamma$-ray emission from the G288.8-6.3 region, aiming to characterise the high-energy emission in the GeV regime from the newly discovered SNR, dubbed Ancora. 15 years of Fermi-Large Area Telescope (LAT) data were analysed at energies between 400 MeV and 1 TeV and the excess seen in the region was modelled using different spatial and spectral models. We detect spatially extended $\gamma$-ray emission coinciding with the radio SNR, with detection significance up to 8.8 $\sigma$. A radial disk spatial model in combination with a power-law spectral model with an energy flux of $(4.80 \pm 0.91) \times 10^{-6}$ $\text{MeV}$ $\text{cm}^{-2}$ $\text{s}^{-1}$, with the spectrum extending up to around 5 GeV was found to be the preferred model. Morphologically, hotspots seen above 1 GeV are well-correlated with the bright western part of the radio shell. The emission is more likely to be of leptonic origin given the estimated gas density in the region and the estimated distance and age of the SNR, but a hadronic scenario cannot be ruled out. Ancora is the eighth SNR detected at high Galactic latitude with Fermi-LAT. This new population of remnants has the potential to constrain the physics of particle diffusion and escape from SNRs into the Galaxy., Comment: 11 pages, 6 figures, 4 tables. Accepted for publication in Astronomy & Astrophysics

In this work we introduce two different generalizations of the Fokker-Planck equation in (1+1) dimensions by replacing the spatial derivatives in terms of generalized Dunkl-type derivatives involving reflection operators. As applications of these results, we solve exactly the generalized Fokker-Planck equations for the harmonic oscillator and the centrifugal-type potentials., Comment: 14 pages, 2 figures, 2 tables. arXiv admin note: text overlap with arXiv:2310.05016

By replacing the spatial derivative with the Dunkl derivative, we generalize the Fokker-Planck equation in (1+1) dimensions. We obtain the Dunkl-Fokker-Planck eigenvalues equation and solve it for the harmonic oscillator plus a centrifugal-type potential. Furthermore, when the drift function is odd, we reduce our results to those of the recently developed Wigner-Dunkl supersymmetry., Comment: 10 pages