Search

The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables

This paper analyses the centralized fusion linear estimation problem in multi-sensor systems with multiple packet dropouts and correlated noises. Packet dropouts are modeled by independent Bernoulli distributed random variables. This problem is addressed in the tessarine domain under conditions of T1 and T2-properness, which entails a reduction in the dimension of the problem and, consequently, computational savings. The methodology proposed enables us to provide an optimal (in the least-mean-squares sense) linear fusion filtering algorithm for estimating the tessarine state with a lower computational cost than the conventional one devised in the real field. Simulation results illustrate the performance and advantages of the solution proposed in different settings.

Aquaporins play a key role for the regulation of water transport and solute selectivity in biological cells and tissues. Due to their unique properties, during the last years aquaporins (AQPs) have attracted increasing interest for their use in the development of biomimetic membranes for water filtration. Motivated by this fascinating application, and the need for reliable methodologies to rationalize the design of AQPs or other biomimetic molecules with targeted filtration properties, we conduct in this work an all-round analysis of their structural features to provide crucial insights into the key determinants of functionality of these relevant biomolecules. Particularly, we analyze -- on a selected set of experimentally-resolved AQP structures -- the composition and peculiarities of their NPA and Selectivity Filter (SF) motifs, the overall structural and sequence similarities between them, their pore shape and the residue-level pore-lining composition. Then, AQPs' pore structural and physico-chemical descriptors are put into correlation with available data on experimental permeation rate, within the context of a pore-level QSAR modeling. The approach, presented here as a methodological proof of concept, enables the identification of key -- potentially novel -- features related to water permeability, which could contribute to a deeper residue-level understanding of AQP filtering properties. Potential beneficiary areas include the rational design of optimized AQP-based biomimetic membranes and research into the molecular mechanisms underlying AQP-related diseases.

We present a novel method for automatically detecting and characterising semi-resolved star clusters: clusters where the observational point-spread function (PSF) is smaller than the cluster's radius, but larger than the separations between individual stars. We apply our method to a 1.77 deg$^2$ field located in the Large Magellanic Cloud (LMC) using the VISTA survey of the Magellanic Clouds (VMC), which surveyed the LMC in the $YJK_\text{s}$ bands. Our approach first models the position-dependent PSF to detect and remove point sources from deep $K_\text{s}$ images; this leaves behind extended objects such as star clusters and background galaxies. We then analyse the isophotes of these extended objects to characterise their properties, perform integrated photometry, and finally remove any spurious objects this procedure identifies. We demonstrate our approach in practice on a deep VMC $K_\text{s}$ tile that contains the most active star-forming regions in the LMC: 30 Doradus, N158, N159, and N160. We select this tile because it is the most challenging for automated techniques due both to crowding and nebular emission. We detect 682 candidate star clusters, with an estimated contamination rate of 13% from background galaxies and chance blends of physically unrelated stars. We compare our candidates to publicly available James Webb Space Telescope data and find that at least 80% of our detections appear to be star clusters., Comment: 26 pages, 17 figures, submitted to MNRAS

The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine Learning (ML) techniques to explore WIMP radiative decays into a Dark Matter (DM) candidate in a supersymmetric framework. The minimal supersymmetric WIMP sector includes the lightest neutralino that can provide the observed DM relic density through its co-annihilation with the second lightest neutralino and lightest chargino. Moreover, the direct DM detection cross section rates fulfill current experimental bounds and provide discovery targets for the same region of model parameters in which the radiative decay of the second lightest neutralino into a photon and the lightest neutralino is enhanced. This strongly motivates the search for radiatively decaying neutralinos which, however, suffers from strong backgrounds. We investigate the LHC reach in the search for these radiatively decaying particles by means of cut-based and ML methods and estimate its discovery potential in this well-motivated, new physics scenario., Comment: 32 pages, 9 figures, 3 tables, 4 appendices

Let $\Omega$ be a bounded, smooth domain of $\mathbb R^N$, $N\ge 2$. In this paper, we prove some inequalities involving the first Robin eigenvalue of the $p$-laplacian operator. In particular, we prove an upper bound for the first Robin eigenvalue of nonlinear elliptic operators in terms of the first Dirichlet eigenvalue.

Contemporary self-tracking technologies (STTs), such as smartwatches and smartphone apps, allow people to become self-aware through the datafication of their everyday lives. However, concerns are emerging over the global north/Western portrayal of the self in the envisionment of STTs. Given the call to diversify participant samples in HCI knowledge building, we see it timely in understanding the influence of ubiquitous STTs in global south societies. We conduct a between-group analysis of 156 and 121 participants from Global North and South through two iterative surveys, respectively. We uncover significant differences in perceived trust with their STTs and reflection practices between the groups. We provide an empirical understanding on advocating for inclusive design strategies that recognize diverse interpretations of STTs and highlight the need to prioritize local values and flexibility in tracking to foster deeper reflection across cultures. Lastly, we discuss our findings in relation to the existing literature and highlight design recommendations for future research., Comment: 12 pages

Alzheimer's disease (AD) poses significant global health challenges due to its increasing prevalence and associated societal costs. Early detection and diagnosis of AD are critical for delaying progression and improving patient outcomes. Traditional diagnostic methods and single-modality data often fall short in identifying early-stage AD and distinguishing it from Mild Cognitive Impairment (MCI). This study addresses these challenges by introducing a novel approach: multImodal enseMble via class BALancing diversity for iMbalancEd Data (IMBALMED). IMBALMED integrates multimodal data from the Alzheimer's Disease Neuroimaging Initiative database, including clinical assessments, neuroimaging phenotypes, biospecimen and subject characteristics data. It employs an ensemble of model classifiers, each trained with different class balancing techniques, to overcome class imbalance and enhance model accuracy. We evaluate IMBALMED on two diagnostic tasks (binary and ternary classification) and four binary early detection tasks (at 12, 24, 36, and 48 months), comparing its performance with state-of-the-art algorithms and an unbalanced dataset method. IMBALMED demonstrates superior diagnostic accuracy and predictive performance in both binary and ternary classification tasks, significantly improving early detection of MCI at 48-month time point. The method shows improved classification performance and robustness, offering a promising solution for early detection and management of AD.

We propose an inflation scenario with three independent stages of cold, warm and thermal inflation, respectively, driven by different scalar fields, motivated by the large number of such fields predicted in most extensions of the Standard Model. We show, in particular, that the intermediate period of warm inflation naturally leads to large density fluctuations on small scales, which can lead to primordial black hole formation in the mass window where they may account for all dark matter. This type of scenario yields a distinctive primordial black hole mass function with a mass gap, with the final period of thermal inflation diluting the abundance of very light black holes., Comment: 26 pages, 5 figures

Fault-tolerant routing allows the selection of alternative routes to the destination after the route being used fails. Fast Reroute (FRR) is a proactive strategy through which the protocol pre-configures backup routes that are activated when needed. In this work, we propose the MaxFlowRouting algorithm that employs maximum flow evaluation as well as the route size to select routes that are highly connected. The main advantage of the proposed algorithm is that if any component of such a route fails, there are more alternative paths to the destination in comparison with the route computed with Dijkstra's shortest path algorithm. Simulation results are presented in which we compare the two algorithms (Dijkstra's and MaxFlowRouting) for multiple different random graphs (including Erdos-Renyi, Bar\'abasi-Albert, and Watts-Strogatz) and also for the topologies of some of the most important Internet backbones of the U.S.A., Europe, Brazil, and Japan: Internet2, Geant, RNP, and Wide.

Online platforms have transformed the formal job market but continue to struggle with effectively engaging passive candidates-individuals not actively seeking employment but open to compelling opportunities. We introduce the Independent Halting Cascade (IHC) model, a novel framework that integrates complex network diffusion dynamics with economic game theory to address this challenge. Unlike traditional models that focus solely on information propagation, the IHC model empowers network agents to either disseminate a job posting or halt its spread by applying for the position themselves. By embedding economic incentives into agent decision-making processes, the model creates a dynamic interplay between maximizing information spread and promoting application. Our analysis uncovers distinct behavioral regimes within the IHC model, characterized by critical thresholds in recommendation and application probabilities. Extensive simulations on both synthetic and real-world network topologies demonstrate that the IHC model significantly outperforms traditional direct-recommendation systems in recruiting suitable passive candidates. Specifically, the model achieves up to a 30% higher hiring success rate compared to baseline methods. These findings offer strategic insights into leveraging economic incentives and network structures to enhance recruitment efficiency. The IHC model thus provides a robust framework for modernizing recruitment strategies, particularly in engaging the vast pool of passive candidates in the job market., Comment: 15 pages, 6 figures

The High Energy Modular Array (HEMA) is one of three instruments that compose the STROBE-X mission concept. The HEMA is a large-area, high-throughput non-imaging pointed instrument based on the Large Area Detector developed as part of the LOFT mission concept. It is designed for spectral timing measurements of a broad range of sources and provides a transformative increase in sensitivity to X-rays in the energy range of 2--30 keV compared to previous instruments, with an effective area of 3.4 m$^{2}$ at 8.5 keV and an energy resolution of better than 300 eV at 6 keV in its nominal field of regard., Comment: 16 pages, 10 figures

The Calar Alto Void Integral-field Treasury surveY (CAVITY) is a legacy project aimed at characterising the population of galaxies inhabiting voids, which are the most under-dense regions of the cosmic web, located in the Local Universe. This paper describes the first public data release (DR1) of CAVITY, comprising science-grade optical data cubes for the initial 100 out of a total of $\sim$300 galaxies in the Local Universe ($0.005 < z < 0.050$). These data were acquired using the integral-field spectrograph PMAS/PPak mounted on the 3.5m telescope at the Calar Alto observatory. The DR1 galaxy sample encompasses diverse characteristics in the color-magnitude space, morphological type, stellar mass, and gas ionisation conditions, providing a rich resource for addressing key questions in galaxy evolution through spatially resolved spectroscopy. The galaxies in this study were observed with the low-resolution V500 set-up, spanning the wavelength range 3745-7500 \AA, with a spectral resolution of 6.0 \AA (FWHM). Here, we describe the data reduction and characteristics and data structure of the CAVITY datasets essential for their scientific utilisation, highlighting such concerns as vignetting effects, as well as the identification of bad pixels and management of spatially correlated noise. We also provide instructions for accessing the CAVITY datasets and associated ancillary data through the project's dedicated database., Comment: 17 pages, 9 figures, 5 tables. Accepted for publication in A&A

Recent critiques of Artificial-intelligence (AI)-generated visual content highlight concerns about the erosion of artistic originality, as these systems often replicate patterns from their training datasets, leading to significant uniformity and reduced diversity. Our research adopts a novel approach by focusing on user behavior during interactions with Text-to-Image models. Instead of solely analyzing training data patterns, we examine how users' tendencies to create original prompts or rely on common templates influence content homogenization. We developed three originality metrics -- lexical, thematic, and word-sequence originality -- and applied them to user-generated prompts from two datasets, DiffusionDB and Civiverse. Additionally, we explored how characteristics such as topic choice, language originality, and the presence of NSFW content affect image popularity, using a linear regression model to predict user engagement. Our research enhances the discourse on AI's impact on creativity by emphasizing the critical role of user behavior in shaping the diversity of AI-generated visual content.

We propose a new predictive theory for the analysis of common envelope (CE) events which incorporates the effects of relevant hydrodynamical processes into a simple analytical framework. We introduce the ejection and dynamical parameters $\xi$ and $\beta$, which define if envelope ejection is energetically or hydrodynamically favorable, respectively, during CE inspiral. When combined, these parameters offer a detailed narrative of how inspiral begins, proceeds, and ends that is consistent with preliminary comparisons to 3D hydrodynamical models. This physically-motivated framework impacts predictions for CE outcomes, especially for systems that have energy excess, and offers promise as a potential alternative for the treatment of CE in binary population synthesis., Comment: 11 pages, 3 figures, submitted to ApJL

Massive stars experience strong mass-loss, producing a dense, H-rich circumstellar medium (CSM). After the explosion, the collision and continued interaction of the supernova (SN) ejecta with the CSM power the light curve through the conversion of kinetic energy into radiation. When the interaction is strong, the light curve shows a broad peak and high luminosity lasting for a relatively long time. Also the spectral evolution is slower, compared to non-interacting SNe. Energetic shocks between the ejecta and the CSM create the ideal conditions for particle acceleration and production of high-energy (HE) neutrinos above 1 TeV. In this paper, we study four strongly-interacting Type IIn SNe: 2021acya, 2021adxl, 2022qml, and 2022wed to highlight their peculiar characteristics, derive the kinetic energy of the explosion and the characteristics of the CSM, infer clues on the possible progenitors and their environment and relate them to the production of HE neutrinos. The SNe analysed in this sample exploded in dwarf, star-forming galaxies and they are consistent with energetic explosions and strong interaction with the surrounding CSM. For SNe 2021acya and 2022wed, we find high CSM masses and mass-loss rates, linking them to very massive progenitors. For SN 2021adxl, the spectral analysis and less extreme CSM mass suggest a stripped-envelope massive star as possible progenitor. SN 2022qml is marginally consistent with being a Type Ia thermonuclear explosion embedded in a dense CSM. The mass-loss rates for all SNe are consistent with the expulsion of several solar masses of material during eruptive episodes in the last few decades before the explosion. Finally, we find that the SNe in our sample are marginally consistent with HE neutrino production., Comment: Submitted to A&A

HERMES-Pathfinder is a space-borne mission based on a constellation of six nano-satellites flying in a low-Earth orbit. The 3U CubeSats, to be launched in early 2025, host miniaturized instruments with a hybrid Silicon Drift Detector/scintillator photodetector system, sensitive to both X-rays and gamma-rays. A seventh payload unit is installed onboard SpIRIT, an Australian-Italian nano-satellite developed by a consortium led by the University of Melbourne and launched in December 2023. The project aims at demonstrating the feasibility of Gamma-Ray Burst detection and localization using miniaturized instruments onboard nano-satellites. The HERMES flight model payloads were exposed to multiple well-known radioactive sources for spectroscopic calibration under controlled laboratory conditions. The analysis of the calibration data allows both to determine the detector parameters, necessary to map instrumental units to accurate energy measurements, and to assess the performance of the instruments. We report on these efforts and quantify features such as spectroscopic resolution and energy thresholds, at different temperatures and for all payloads of the constellation. Finally we review the performance of the HERMES payload as a photon counter, and discuss the strengths and the limitations of the architecture.

We use Gaia DR3 to explore how well equilibrium dynamical models based on the Jeans equations and the Schwarzschild orbit superposition method are able to describe LMC's 5-dimensional phase-space distribution and line-of-sight (LOS) velocity distribution, respectively. In the latter model we incorporate a triaxial bar component and derive LMC's bar pattern speed. We fit Jeans dynamical models to all Gaia DR3 stars with proper motion and LOS velocity measurements found in the VMC VISTA survey of the LMC using a discrete maximum likelihood approach. These models are very efficient at discriminating genuine LMC member stars from Milky Way foreground stars and background galaxies. They constrain the shape, orientation, and enclosed mass of the galaxy under the assumption for axisymmetry. We use the Jeans model results as a stepping stone to more complex 2-component Schwarzschild models, which include an axisymmetric disc and a co-centric triaxial bar, which we fit to the LMC Gaia DR3 LOS velocity field, using a chi^2 minimisation approach. The Jeans models describe well the rotation and velocity dispersion of the LMC disc and we find an inclination angle 25.5 deg, line of nodes orientation 124 deg, and an intrinsic thickness of the disc 0.23 (minor to major axis ratio). However, bound to axisymmetry, these models fail to properly describe the kinematics in the central region of the galaxy, dominated by the bar. We use the derived disc orientation and the Gaia DR3 density image of the LMC to obtain the intrinsic shape of the bar. Using these two components as an input to our Schwarzschild models, we perform orbit integration and weighting in a rotating reference frame fixed to the bar, deriving an independent measurement of the LMC bar pattern speed 11+/-4 km/s/kpc. Both the Jeans and Schwarzschild models predict the same enclosed mass distribution within a radius of 6.2 kpc of ~1.4e10 Msun., Comment: Accepted for publication in A&A

The local (z=0.0315) AGN Mrk 817, was monitored over more than 500 days with space-borne and ground-based instruments as part of a large international campaign AGN STORM 2. Here, we present a comprehensive analysis of the broad-band continuum variations using detailed modeling of the broad line region (BLR), several types of disk winds classified by their optical depth, and new numerical simulations. We find that diffuse continuum (DC) emission, with additional contributions from strong and broad emission lines, can explain the continuum lags observed in this source during high and low luminosity phases. Disk illumination by the variable X-ray corona contributes only a small fraction of the observed continuum lags. Our BLR models assume radiation pressure-confined clouds distributed over a distance of 2-122 light days. We present calculated mean-emissivity radii of many emission lines, and DC emission, and suggest a simple, transfer-function-dependent method that ties them to cross-correlation lag determinations. We do not find clear indications for large optical depth winds but identify the signature of lower column density winds. In particular, we associate the shortest observed continuum lags with a combination of tau(1 Ryd) approx. 2 wind and a partly shielded BLR. Even smaller optical depth winds may be associated with X-ray absorption features and with noticeable variations in the width and lags of several high ionization lines like HeII and CIV. Finally, we demonstrate the effect of torus dust emission on the observed lags in the i and z bands., Comment: 23 pages, 18 figures. Corrected typographical error in the title of the paper as it appeared in the Metadata

Massive stars end their life as core-collapse supernovae, amongst which some extremes are Type Ic broad-lined supernovae associated with long-duration gamma-ray bursts (LGRBs) having powerful relativistic jets. Their less-extreme brethren make unsuccessful jets that are choked inside the stars, appearing as X-ray flashes or low-luminosity GRBs. On the other hand, there exists a population of extragalactic fast X-ray transients (EFXTs) with timescales ranging from seconds to thousands of seconds, whose origins remain obscure. Known sources that contribute to the observed EFXT population include the softer analogs of LGRBs, shock breakouts of supernovae, or unsuccessful jets. Here, we report the discovery of the bright X-ray transient EP240414a detected by the Einstein Probe (EP), which is associated with the Type Ic supernova SN 2024gsa at a redshift of 0.401. The X-ray emission evolution is characterised by a very soft energy spectrum peaking at < 1.3 keV, which makes it distinct from known LGRBs, X-ray flashes, or low-luminosity GRBs. Follow-up observations at optical and radio bands revealed the existence of a weak relativistic jet that interacts with an extended shell surrounding the progenitor star. Located on the outskirts of a massive galaxy, this event reveals a new population of explosions of Wolf-Rayet stars characterised by a less powerful engine that drives a successful but weak jet, possibly owing to a progenitor star with a smaller core angular momentum than in traditional LGRB progenitors., Comment: 43 pages, 9 figures, 4 tables, submitted. Comments are welcome

We present the results from spectroscopic and photometric analysis of 17 globular cluster (GC) candidates in the Irr II galaxy NGC 3077. The GC candidates were selected on the Hubble Space Telescope (HST) images and were cleaned of foreground Galactic stars using the GAIA parameters. We carried out aperture photometry using the multi-band archival images from SDSS, and 2MASS of all candidates, and low resolution (R= 1000) spectroscopic observations of 12 GC candidates and three suspected foreground stars using the OSIRIS/MOS mode at the Gran Telescopio Canarias (GTC). Age, metallicity and extinction values were determined both using spectroscopic and photometric data, independently. We find three of the 17 candidates are old (age >10 Gyr), metal-poor ([Fe/H]<-1.0 dex), massive (mass>10^5 Msun) GCs with characteristics similar to the classical GCs in the Milky Way. The rest are intermediate-age clusters (IACs) with typical ages of 3 to 4 Gyr, and in general metal-rich clusters. The radial velocities of both populations are within 100 km/s of the recessional velocity of the host galaxy. A relatively large population of IACs and low value of GC specific frequency (Sn=0.7) suggest that the pre-interaction galaxy was actively forming stars and star clusters, and is unlikely to be a dE as suggested in some previous works., Comment: Accepted in Astronomical Journal

Motivated by the growing interest in the nonmetricity-matter couplings, we develop the scalar-tensor formulation of recently introduced $f(Q,T)$ gravity, where $Q$ is the nonmetricity and $T$ is the trace of the energy-momentum tensor. The main properties of the scalar-tensor formalism for the Friedmann-Lema{\^ i}tre-Robertson-Walker (FLRW) Universe are discussed, and we introduce an appropriate set of dynamical variables to analyze the cosmic evolution of the scalar-tensor $f(Q,T)$ cosmology as a dynamical system. By considering two distinct cosmic fluids, namely matter and radiation, we have demonstrated that the cosmological phase space exhibits the typical curvature-dominated, radiation-dominated, matter-dominated, and exponentially accelerated fixed points. Furthermore, under an appropriate set of initial conditions compatible with the current observations from the Planck satellite, our analysis shows that the scalar-tensor $f(Q,T)$ successfully yields models indistinguishable from the $\Lambda$CDM cosmology and compatible with the weak-field solar system dynamics, without the inclusion of a cosmological constant $\Lambda$. Thus, the theory introduced herein may be regarded as a suitable candidate to describe the cosmological dynamics of the Universe., Comment: 13 pages, 2 figures, 1 table

Real-time supercomputing performance analysis is a critical aspect of evaluating and optimizing computational systems in a dynamic user environment. The operation of supercomputers produce vast quantities of analytic data from multiple sources and of varying types so compiling this data in an efficient matter is critical to the process. MIT Lincoln Laboratory Supercomputing Center has been utilizing the Unity 3D game engine to create a Digital Twin of our supercomputing systems for several years to perform system monitoring. Unity offers robust visualization capabilities making it ideal for creating a sophisticated representation of the computational processes. As we scale the systems to include a diversity of resources such as accelerators and the addition of more users, we need to implement new analysis tools for the monitoring system. The workloads in research continuously change, as does the capability of Unity, and this allows us to adapt our monitoring tools to scale and incorporate features enabling efficient replay of system wide events, user isolation, and machine level granularity. Our system fully takes advantage of the modern capabilities of the Unity Engine in a way that intuitively represents the real time workload performed on a supercomputer. It allows HPC system engineers to quickly diagnose usage related errors with its responsive user interface which scales efficiently with large data sets.

We introduce a dynamic benchmarking system for conversational agents that evaluates their performance through a single, simulated, and lengthy user$\leftrightarrow$agent interaction. The interaction is a conversation between the user and agent, where multiple tasks are introduced and then undertaken concurrently. We context switch regularly to interleave the tasks, which constructs a realistic testing scenario in which we assess the Long-Term Memory, Continual Learning, and Information Integration capabilities of the agents. Results from both proprietary and open-source Large-Language Models show that LLMs in general perform well on single-task interactions, but they struggle on the same tasks when they are interleaved. Notably, short-context LLMs supplemented with an LTM system perform as well as or better than those with larger contexts. Our benchmark suggests that there are other challenges for LLMs responding to more natural interactions that contemporary benchmarks have heretofore not been able to capture., Comment: Accepted as a poster at NeurIPS D&B Track 2024

A search for proton decay into $e^+/\mu^+$ and a $\eta$ meson has been performed using data from a 0.373 Mton$\cdot$year exposure (6050.3 live days) of Super-Kamiokande. Compared to previous searches this work introduces an improved model of the intranuclear $\eta$ interaction cross section, resulting in a factor of two reduction in uncertainties from this source and $\sim$10\% increase in signal efficiency. No significant data excess was found above the expected number of atmospheric neutrino background events resulting in no indication of proton decay into either mode. Lower limits on the proton partial lifetime of $1.4\times\mathrm{10^{34}~years}$ for $p\rightarrow e^+\eta$ and $7.3\times\mathrm{10^{33}~years}$ for $p\rightarrow \mu^+\eta$ at the 90$\%$ C.L. were set. These limits are around 1.5 times longer than our previous study and are the most stringent to date.

This paper presents a statistical analysis of air temperature data from 32 stations in Italy and the UK up to 2000 m above sea level, from 2002 to 2021. The data came from both highland and lowland areas, in order to evaluate both the differences due to location, and elevation. The analysis focused on detecting trends at annual and monthly time scales, employing both ordinary least squares, robust S-estimator regression, and Mann-Kendall and Sen's slope methods. Then hierarchical clustering using Dynamic Time Warping (DTW) was applied to the monthly data to analyze the intra-annual pattern similarity of trends within and across the groups. Two different regions of Europe were chosen because of the different climate and temperature trends, namely the Northern UK (smaller trends) and the North-West Italian Alps (larger trends). The main novelty of the work is to show that stations having similar locations and altitudes have similar monthly slopes by quantifying them using DTW and clustering. These results reveal the nonrandomness of different trends along the year and among different parts of Europe, with a modest influence of altitude in wintertime. The findings revealed that group average trends were close to the NOAA values for the areas in Italy and the UK, confirming the validity of analyzing a small number of stations. More interestingly, intra-annual patterns were detected commonly at the stations of each of the groups, and clearly different between them Hierarchical clustering in combination with DTW showed consistent similarity between monthly patterns of means and trends within the group of stations and inconsistent similarity between patterns across groups. Distance correlation matrices also contributes to what is the main result of the paper, which is to clearly show the different temporal patterns in relation to location and (in some months) altitude., Comment: The final version of this paper is in press on International journal of Climatology

We present an analysis of the photometric and spectroscopic dataset of the Type II supernova (SN) 2018ivc in the nearby (10 Mpc) galaxy Messier 77. Thanks to the high cadence of the CHASE survey, we observed the SN rising very rapidly by nearly three magnitudes in five hours (or 18 mag d$^{-1}$). The $r$-band light curve presents four distinct phases: the maximum light is reached in just one day, then a first, rapid linear decline precedes a short-duration plateau. Finally, a long, slower linear decline lasted for one year. Following a radio rebrightening, we detected SN 2018ivc four years after the explosion. The early spectra show a blue, nearly featureless continuum, but the spectra evolve rapidly: after about 10 days a prominent H$\alpha$ line starts to emerge, with a peculiar profile, but the spectra are heavily contaminated by emission lines from the host galaxy. He I lines, namely $\lambda\lambda$5876,7065, are also strong. On top of the former, a strong absorption from the Na I doublet is visible, indicative of a non-negligible internal reddening. From its equivalent width, we derive a lower limit on the host reddening of $A_V\simeq1.5$ mag, while from the Balmer decrement and a match of the $B-V$ colour curve of SN 2018ivc to that of the comparison objects, a host reddening of $A_V\simeq3.0$ mag is obtained. The spectra are similar to those of SNe II, but with strong He lines. Given the peculiar light curve and spectral features, we suggest SN 2018ivc could be a transitional object between the Type IIL and Type IIb SNe classes. In addition, we found signs of interaction with circumstellar medium in the light curve, making SN 2018ivc also an interacting event. Finally, we modelled the early multi-band light curves and photospheric velocity of SN 2018ivc to estimate the explosion and CSM physical parameters., Comment: 19 pages, 22 figures, 3 tables, 3 appendices, accepted for publication on A&A

In any closed smooth Riemannian manifold of dimension at least three, we use the min-max construction to find anisotropic minimal hyper-surfaces with respect to elliptic integrands, with a singular set of codimension~$2$ vanishing Hausdorff measure. In particular, in a closed $3$-manifold, we obtain a smooth anisotropic minimal surface. The critical step is to obtain a uniform upper bound for density ratios in the anisotropic min-max construction. This confirms a conjecture by Allard [Invent. Math., 1983]., Comment: 40 pages

The spatial organization of chromatin within the nucleus plays a crucial role in gene expression and genome function. However, the quantitative relationship between this organization and nuclear biochemical processes remains under debate. In this study, we present a graph-based generative model, bioSBM, designed to capture long-range chromatin interaction patterns from Hi-C data and, importantly, simultaneously, link these patterns to biochemical features. Applying bioSBM to Hi-C maps of the GM12878 lymphoblastoid cell line, we identified a latent structure of chromatin interactions, revealing 12 distinct communities that strongly align with known biological annotations. Additionally, we infer a linear transformation that maps biochemical observables, such as histone marks, to the parameters of the generative graph model, enabling accurate genome-wide predictions of chromatin contact maps on out-of-sample data, both within the same cell line, and on the completely unseen HCT116 cell line under RAD21 depletion. These findings highlight bioSBM's potential as a powerful tool for elucidating the relationship between biochemistry and chromatin architecture and predicting long-range genome organization from independent biochemical data.

Groundwater resources are one of the most relevant elements in the water cycle, therefore developing models to accurately predict them is a pivotal task in the sustainable resources management framework. Deep Learning (DL) models have been revealed very effective in hydrology, especially by feeding spatially distributed data (e.g. raster data). In many regions, hydrological measurements are difficult to obtain regularly or periodically in time, and in some cases, last available data are not up to date. Reversely, weather data, which significantly impacts water resources, are usually more available and with higher quality. More specifically, we have proposed two different DL models to predict the water table depth in the Grana-Maira catchment (Piemonte, IT) using only exogenous weather image time series. To deal with the image time series, both models are made of a first Time Distributed Convolutional Neural Network (TDC) which encodes the image available at each time step into a vectorial representation. The first model, TDC-LSTM uses then a Sequential Module based on an LSTM layer to learn temporal relations and output the predictions. The second model, TDC-UnPWaveNet uses instead a new version of the WaveNet architecture, adapted here to output a sequence shorter and completely shifted in the future with respect to the input one. To this aim, and to deal with the different sequence lengths in the UnPWaveNet, we have designed a new Channel Distributed layer, that acts like a Time Distributed one but on the channel dimension, i.e. applying the same set of operations to each channel of the input. TDC-LSTM and TDC-UnPWaveNet have shown both remarkable results. However, the two models have focused on different learnable information: TDC-LSTM has focused more on lowering the bias, while the TDC-UnPWaveNet has focused more on the temporal dynamics maximising correlation and KGE.

Currently, the Morpho-Constitutional Analysis (MCA) is the de facto approach for the etiological diagnosis of kidney stone formation, and it is an important step for establishing personalized treatment to avoid relapses. More recently, research has focused on performing such tasks intra-operatively, an approach known as Endoscopic Stone Recognition (ESR). Both methods rely on features observed in the surface and the section of kidney stones to separate the analyzed samples into several sub-groups. However, given the high intra-observer variability and the complex operating conditions found in ESR, there is a lot of interest in using AI for computer-aided diagnosis. However, current AI models require large datasets to attain a good performance and for generalizing to unseen distributions. This is a major problem as large labeled datasets are very difficult to acquire, and some classes of kidney stones are very rare. Thus, in this paper, we present a method based on diffusion as a way of augmenting pre-existing ex-vivo kidney stone datasets. Our aim is to create plausible diverse kidney stone images that can be used for pre-training models using ex-vivo data. We show that by mixing natural and synthetic images of CCD images, it is possible to train models capable of performing very well on unseen intra-operative data. Our results show that is possible to attain an improvement of 10% in terms of accuracy compared to a baseline model pre-trained only on ImageNet. Moreover, our results show an improvement of 6% for surface images and 10% for section images compared to a model train on CCD images only, which demonstrates the effectiveness of using synthetic images., Comment: 8 pages, 6 figures, 1 table, conference paper

A commutator of unipotent matrices of index 2 is a matrix of the form $XYX^{-1}Y^{-1}$, where $X$ and $Y$ are unipotent matrices of index 2, that is, $X\ne I_n$, $Y\ne I_n$, and $(X-I_n)^2=(Y-I_n)^2=0_n$. If $n>2$ and $\mathbb F$ is a field with $|\mathbb F|\geq 4$, then it is shown that every $n\times n$ matrix over $\mathbb F$ with determinant 1 is a product of at most four commutators of unipotent matrices of index 2. Consequently, every $n\times n$ matrix over $\mathbb F$ with determinant 1 is a product of at most eight unipotent matrices of index 2. Conditions on $\mathbb F$ are given that improve the upper bound on the commutator factors from four to three or two. The situation for $n=2$ is also considered. This study reveals a connection between factorability into commutators of unipotent matrices and properties of $\mathbb F$ such as its characteristic or its set of perfect squares., Comment: 23 pages

Architected materials achieve unique mechanical properties through precisely engineered microstructures that minimize material usage. However, a key challenge of low-density materials is balancing high stiffness with stable deformability up to large strains. Current microstructures, which employ slender elements such as thin beams and plates arranged in periodic patterns to optimize stiffness, are largely prone to instabilities, including buckling and brittle collapse at low strains. This challenge is here addressed by introducing a new class of aperiodic architected materials inspired by quasicrystalline lattices. Beam networks derived from canonical quasicrystalline patterns, such as the Penrose tiling in 2D and icosahedral quasicrystals in 3D, are shown to create stiff, stretching-dominated topologies with non-uniform force chain distributions, effectively mitigating the global instabilities observed in periodic designs. Numerical and experimental results confirm the effectiveness of these designs in combining stiffness and stable deformability at large strains, representing a significant advancement in the development of low-density metamaterials for applications requiring high impact resistance and energy absorption. Our results demonstrate the potential of deterministic quasi-periodic topologies to bridge the gap between periodic and random structures, while branching towards uncharted territory in the property space of architected materials.

Band gap energy of an organic molecule can be reduced by intermolecular interaction enhancement, and thus, certain polycyclic aromatic hydrocarbons (PAHs), which are insulators with wide band gaps, are expected to undergo insulator-metal transitions by simple compression. Such a pressure-induced electronic transition can be exploited to transform non-metallic organic materials into states featuring intriguing electronic characteristics such as high-temperature superconductivity. Numerous attempts have been made to metalize various small PAHs, but so far only pressure-induced amorphization well below the megabar region was observed. The wide band gap energy of the small PAHs and low chemical stability under simple compression are the bottlenecks. We have investigated the band gap energy evolution and the crystal structural compression of the large PAH molecules, where the band gap energy is significantly reduced by increasing the number of {\pi}-electrons and improved chemical stability with fully benzenoid molecular structure. Herein, we present a pressure-induced transition in dicoronylene, C48H20, an insulator at ambient conditions that transforms into a semi-metallic state above 23.0 GPa with a three-order-of-magnitude reduction in resistivity. In-situ UV-visible absorption, transport property measurement, Raman spectroscopy, X-ray diffraction and density functional theory calculations were performed to provide tentative explanations to the alterations in its electronic structure at high pressure. The discovery of an electronic transition at pressures well below the megabar is a promising step towards realization of a single component purely hydrocarbon molecular metal in the near future., Comment: 22 pages, 5 figures

Recently, AI systems have made remarkable progress in various tasks. Deep Reinforcement Learning(DRL) is an effective tool for agents to learn policies in low-level state spaces to solve highly complex tasks. Researchers have introduced Intrinsic Motivation(IM) to the RL mechanism, which simulates the agent's curiosity, encouraging agents to explore interesting areas of the environment. This new feature has proved vital in enabling agents to learn policies without being given specific goals. However, even though DRL intelligence emerges through a sub-symbolic model, there is still a need for a sort of abstraction to understand the knowledge collected by the agent. To this end, the classical planning formalism has been used in recent research to explicitly represent the knowledge an autonomous agent acquires and effectively reach extrinsic goals. Despite classical planning usually presents limited expressive capabilities, PPDDL demonstrated usefulness in reviewing the knowledge gathered by an autonomous system, making explicit causal correlations, and can be exploited to find a plan to reach any state the agent faces during its experience. This work presents a new architecture implementing an open-ended learning system able to synthesize from scratch its experience into a PPDDL representation and update it over time. Without a predefined set of goals and tasks, the system integrates intrinsic motivations to explore the environment in a self-directed way, exploiting the high-level knowledge acquired during its experience. The system explores the environment and iteratively: (a) discover options, (b) explore the environment using options, (c) abstract the knowledge collected and (d) plan. This paper proposes an alternative approach to implementing open-ended learning architectures exploiting low-level and high-level representations to extend its knowledge in a virtuous loop.

We developed a jitted compiler for training Artificial Neural Networks using C++, LLVM and Cuda. It features object-oriented characteristics, strong typing, parallel workers for data pre-processing, pythonic syntax for expressions, PyTorch like model declaration and Automatic Differentiation. We implement the mechanisms of cache and pooling in order to manage VRAM, cuBLAS for high performance matrix multiplication and cuDNN for convolutional layers. Our experiments with Residual Convolutional Neural Networks on ImageNet, we reach similar speed but degraded performance. Also, the GRU network experiments show similar accuracy, but our compiler have degraded speed in that task. However, our compiler demonstrates promising results at the CIFAR-10 benchmark, in which we reach the same performance and about the same speed as PyTorch. We make the code publicly available at: https://github.com/NoSavedDATA/NoSavedKaleidoscope, Comment: 12 pages, 3 figures and 3 tables

orbitize! is a package for Bayesian modeling of the orbital parameters of resolved binary objects from time series measurements. It was developed with the needs of the high-contrast imaging community in mind, and has since also become widely used in the binary star community. A generic orbitize! use case involves translating relative astrometric time series, optionally combined with radial velocity or astrometric time series, into a set of derived orbital posteriors. This paper is published alongside the release of orbitize! version 3.0, which has seen significant enhancements in functionality and accessibility since the release of version 1.0 (Blunt et al., 2020)., Comment: Published in JOSS

We examine a century of radial velocity, visual magnitude, and astrometric observations of the nearest red supergiant, Betelgeuse, in order to reexamine the century-old assertion that Betelgeuse might be a spectroscopic binary. These data reveal Betelgeuse varying stochastically over years and decades due to its boiling, convective envelope, periodically with a $ 5.78$~yr long secondary period, and quasi-periodically from pulsations with periods of several hundred days. We show that the long secondary period is consistent between astrometric and RV datasets, and argue that it indicates a low-mass companion to Betelgeuse, less than a solar mass, orbiting in a 2,110 day period at a separation of just over twice Betelgeuse's radius. The companion star would be nearly twenty times less massive and a million times fainter than Betelgeuse, with similar effective temperature, effectively hiding it in plain sight near one of the best-studied stars in the night sky. The astrometric data favor an edge-on binary with orbital plane aligned with Betelgeuse's measured spin axis. Tidal spin-orbit interaction drains angular momentum from the orbit and spins up Betelgeuse, explaining the spin--orbit alignment and Betelgeuse's anomalously rapid spin. In the future, the orbit will decay until the companion is swallowed by Betelgeuse in the next 10,000 years., Comment: Submitted to AAS Journals, comments welcome

Deforestation is gaining an increasingly importance due to its strong influence on the sorrounding environment, especially in developing countries where population has a disadvantaged economic condition and agriculture is the main source of income. In Ivory Coast, for instance, where the cocoa production is the most remunerative activity, it is not rare to assist to the replacement of portion of ancient forests with new cocoa plantations. In order to monitor this type of deleterious activities, satellites can be employed to recognize the disappearance of the forest to prevent it from expand its area of interest. In this study, Forest-Non-Forest map (FNF) has been used as ground truth for models based on Sentinel images input. State-of-the-art models U-Net, Attention U-Net, Segnet and FCN32 are compared over different years combining Sentinel-1, Sentinel-2 and cloud probability to create forest/non-forest segmentation. Although Ivory Coast lacks of forest coverage datasets and is partially covered by Sentinel images, it is demonstrated the feasibility to create models classifying forest and non-forests pixels over the area using open datasets to predict where deforestation could have occurred. Although a significant portion of the deforestation research is carried out on visible bands, SAR acquisitions are employed to overcome the limits of RGB images over areas often covered by clouds. Finally, the most promising model is employed to estimate the hectares of forest has been cut between 2019 and 2020.

HPC systems used for research run a wide variety of software and workflows. This software is often written or modified by users to meet the needs of their research projects, and rarely is built with security in mind. In this paper we explore several of the key techniques that MIT Lincoln Laboratory Supercomputing Center has deployed on its systems to manage the security implications of these workflows by providing enforced separation for processes, filesystem access, network traffic, and accelerators to make every user feel like they are running on a personal HPC.

X-ray reverberation mapping is a powerful technique for probing the innermost accretion disk, whereas continuum reverberation mapping in the UV, optical, and infrared (UVOIR) reveals reprocessing by the rest of the accretion disk and broad-line region (BLR). We present the time lags of Mrk 817 as a function of temporal frequency measured from 14 months of high-cadence monitoring from Swift and ground-based telescopes, in addition to an XMM-Newton observation, as part of the AGN STORM 2 campaign. The XMM-Newton lags reveal the first detection of a soft lag in this source, consistent with reverberation from the innermost accretion flow. These results mark the first simultaneous measurement of X-ray reverberation and UVOIR disk reprocessing lags$\unicode{x2013}$effectively allowing us to map the entire accretion disk surrounding the black hole. Similar to previous continuum reverberation mapping campaigns, the UVOIR time lags arising at low temporal frequencies are longer than those expected from standard disk reprocessing by a factor of 2-3. The lags agree with the anticipated disk reverberation lags when isolating short-timescale variability, namely timescales shorter than the H$\beta$ lag. Modeling the lags requires additional reprocessing constrained at a radius consistent with the BLR size scale inferred from contemporaneous H$\beta$-lag measurements. When we divide the campaign light curves, the UVOIR lags show substantial variations, with longer lags measured when obscuration from an ionized outflow is greatest. We suggest that, when the obscurer is strongest, reprocessing by the BLR elongates the lags most significantly. As the wind weakens, the lags are dominated by shorter accretion disk lags., Comment: 28 pages, 11 figures, 3 tables. Accepted for publication in the Astrophysical Journal

The MIT/IEEE/Amazon GraphChallenge encourages community approaches to developing new solutions for analyzing graphs and sparse data derived from social media, sensor feeds, and scientific data to discover relationships between events as they unfold in the field. The anonymized network sensing Graph Challenge seeks to enable large, open, community-based approaches to protecting networks. Many large-scale networking problems can only be solved with community access to very broad data sets with the highest regard for privacy and strong community buy-in. Such approaches often require community-based data sharing. In the broader networking community (commercial, federal, and academia) anonymized source-to-destination traffic matrices with standard data sharing agreements have emerged as a data product that can meet many of these requirements. This challenge provides an opportunity to highlight novel approaches for optimizing the construction and analysis of anonymized traffic matrices using over 100 billion network packets derived from the largest Internet telescope in the world (CAIDA). This challenge specifies the anonymization, construction, and analysis of these traffic matrices. A GraphBLAS reference implementation is provided, but the use of GraphBLAS is not required in this Graph Challenge. As with prior Graph Challenges the goal is to provide a well-defined context for demonstrating innovation. Graph Challenge participants are free to select (with accompanying explanation) the Graph Challenge elements that are appropriate for highlighting their innovations., Comment: Accepted to IEEE HPEC 2024

Chiral superconductors are theorized to exhibit spontaneous edge currents. Here, we found magnetic fields at the edges of UTe$_2$, a candidate odd-parity chiral superconductor, that seem to agree with predictions for a chiral order parameter. However, we did not detect the chiral domains that would be expected, and recent polar Kerr and muon spin relaxation data in nominally clean samples argue against chiral superconductivity. Our results show that hidden sources of magnetism must be carefully ruled out when using spontaneous edge currents to identify chiral superconductivity., Comment: 19 pages, 12 figures

The integration of Reduced Order Models (ROMs) with High-Performance Computing (HPC) is critical for developing digital twins, particularly for real-time monitoring and predictive maintenance of industrial systems. This paper describes a comprehensive, HPC-enabled workflow for developing and deploying projection-based ROMs (PROMs). We use PyCOMPSs' parallel framework to efficiently execute ROM training simulations, employing parallel Singular Value Decomposition (SVD) algorithms such as randomized SVD, Lanczos SVD, and full SVD based on Tall-Skinny QR. In addition, we introduce a partitioned version of the hyper-reduction scheme known as the Empirical Cubature Method. Despite the widespread use of HPC for PROMs, there is a significant lack of publications detailing comprehensive workflows for building and deploying end-to-end PROMs in HPC environments. Our workflow is validated through a case study focusing on the thermal dynamics of a motor. The PROM is designed to deliver a real-time prognosis tool that could enable rapid and safe motor restarts post-emergency shutdowns under different operating conditions for further integration into digital twins or control systems. To facilitate deployment, we use the HPC Workflow as a Service strategy and Functional Mock-Up Units to ensure compatibility and ease of integration across HPC, edge, and cloud environments. The outcomes illustrate the efficacy of combining PROMs and HPC, establishing a precedent for scalable, real-time digital twin applications across multiple industries.

In pursuit of a full-fledged theory of quantum gravity, operational approaches offer insights into quantum-gravitational effects produced by quantum superposition of different spacetimes not diffeomorphic to one another. Recent work applies this approach to superpose cylindrically identified Minkowski spacetimes (i.e. periodic boundary conditions) with different characteristic circumferences, where a two-level detector coupled to a quantum field residing in the spacetime exhibits resonance peaks in response at certain values of the superposed length ratios. Here, we extend this analysis to a superposition of cylindrically identified Rindler spacetimes, considering a two-level detector constantly accelerated in the direction orthogonal to the compact dimension. Similarly to previous work, we find resonance peaks in the detector response at rational ratios of the superposed compactified lengths, which we observe to be accentuated by the acceleration of the detector. Furthermore, for the first time we confirm the detailed balance condition due to acceleration in a superposition of spacetimes, commensurate with the Unruh effect in a single spacetime state. The resonant structure of detector response in the presence of event horizons, for the first time observed in 3+1 dimensions, may offer clues to the nature of black hole entropy in the full theory of quantum gravity., Comment: 8 + 12 pages, 7 + 2 figures

The tetragonal $4f$-electron intermetallic ErB$_4$ is characterized by strong Ising anisotropy along the tetragonal $c$ axis. The magnetic moments on the erbium sites can be mapped onto a Shastry-Sutherland (SSL) lattice resulting in geometrical frustration. At zero magnetic field ErB$_4$ exhibits collinear columnar antiferromagnetic (CAFM) order below $T_\text{N} = 15.4$ K. In the presence of a magnetic field parallel to the $c$ axis, ErB$_4$ exhibits a plateau at $1/2$ of the saturation magnetization $M_\text{S}$, which arises at a spin flip transition at $H_1$ $=$ 1.9 T. Fractional magnetization plateaus and other exotic spin phases are a well-established characteristic feature of frustrated spin systems. Monte Carlo simulations propose that ErB$_4$ is an ideal candidate to feature a spin supersolid phase in close vicinity of $H_1$ between the CAFM and $M/M_\text{S}=1/2$ plateau (HP) phase. Here we combine single-crystal neutron diffraction and inelastic neutron scattering to study the magnetic phase diagram and the crystal electric field (CEF) ground state of ErB$_4$. Our measurements as a function of magnetic field find no signature of the spin supersolid phase but allow us to determine the magnetic structure of the HP phase to be of the uuud type consistent with an Ising material. The magnetic moment $\mu_{\mathrm{CEF}}$ $=$ 8.96 $\mu_B$ expected from the CEF configuration determined by our inelastic neutron scattering measurements is also consistent with the ordered moment observed in neutron diffraction showing that the moments are fully ordered and close to the Er$^{3+}$ free ion moment (9.6 $\mu_B$).

SPIDER (Source for the Production of Ions of Deuterium Extracted from Rf plasma) is a full-scale prototype of the ITER NBI source. It is based on the concept of inductive coupling between radio-frequency current drive and plasma. Present three-dimensional (3D) electromagnetic calculations of stationary RF fields in SPIDER permit an evaluation of the power dissipation in its main constituents. Taking experimental plasma parameters as input, we concentrate on the power dissipation in the copper-made Faraday shield lateral wall (FSLW) of the source for discharges with and without a static magnetic filter field. In agreement with our previous results and a first comparison with calorimetry data from the FSLW cooling circuit, the FSLW cylinder alone absorbs around 50\% of the available power for the studied plasma parameters. A hypothesized improvement of transport confinement may increase significantly the efficiency., Comment: 14 pages, 8 figures