Search

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

Several recurrent X-class flares from Active Region (AR) 13664 have triggered a severe G5-class geomagnetic storm between May 10 and 11, 2024. The morphology and compactness of this AR closely resemble the active region responsible for the famous Carrington Event of 1859. Although the induced geomagnetic currents produced a value of the Dst index, probably, an order of magnitude weaker than that of the Carrington Event, the characteristics of AR 13664 warrant special attention. Understanding the mechanisms of magnetic field emergence and transformation in the solar atmosphere that lead to the formation of such an extensive, compact and complex AR is crucial. Our analysis of the emerging flux and horizontal motions of the magnetic structures observed in the photosphere reveals the fundamental role of a sequence of emerging bipoles at the same latitude and longitude, followed by converging and shear motions. This temporal order of processes frequently invoked in magnetohydrodynamic models - emergence, converging motions, and shear motions - is critical for the storage of magnetic energy preceding strong solar eruptions that, under the right timing, location and direction conditions, can trigger severe space weather events at Earth., Comment: 25 pages, 5 figures

We study K-stability on smooth complex Fano 4-folds having large Lefschetz defect, that is greater or equal then 3, with a special focus on the case of Lefschetz defect 3. In particular, we determine whether these Fano 4-folds are K-polystable or not, and show that there are 5 families (out of 19) of K-polystable smooth Fano 4-folds with Lefschetz defect 3.

As pulsar timing arrays (PTAs) transition into the detection era of the stochastic gravitational wave background (GWB), it is important for PTA collaborations to review, and possibly revise, their observing campaigns. The source of the GWB is unknown, and it may take years to pin down its nature. An astrophysical ensemble of supermassive binary black holes is one very likely source for the GWB. Evidence for such a background should come in the form of detectable anisotropies in the GWB and resolvable binary signals. A ``single source'' would be a boon for gravitational astrophysics, as such a source would emit gravitational waves for millions of years in the PTA frequency band. Earlier studies have shown that the observational strategies for finding single sources are somewhat different than for finding the statistical correlations needed for the detection of a stochastic background. Here we present generic methods for studying the effects of various observational strategies, taking advantage of detector sensitivity curves, i.e., noise-averaged, frequency-domain detection statistics. The statistical basis for these methods is presented along with myriad examples of how to tune a detector towards single, deterministic signals or a stochastic background. The importance of the uncorrelated half of the GWB, i.e. the pulsar-term, will be discussed as one of the most important sources of noise in the observational era of PTAs.

The magnetite/water interface is commonly found in nature and plays a crucial role in various technological applications. However, our understanding of its structural and dynamical properties at the molecular scale remains still limited. In this study, we develop an efficient Behler-Parrinello neural network potential (NNP) for the magnetite/water system, paying particular attention to the accurate generation of reference data with density functional theory. Using this NNP, we performed extensive molecular dynamics simulations of the magnetite (001) surface across a wide range of water coverages, from the single molecule to bulk water. Our simulations revealed several new ground states of low coverage water on the Subsurface Cation Vacancy (SCV) model and yielded a density profile of water at the surface that exhibits marked layering. By calculating mean square displacements, we obtained quantitative information on the diffusion of water molecules on the SCV for different coverages, revealing significant anisotropy. Additionally, our simulations provided qualitative insights into the dissociation mechanisms of water molecules at the surface.

The NANOGrav 15-year data provides compelling evidence for a stochastic gravitational-wave (GW) background at nanohertz frequencies. The simplest model-independent approach to characterizing the frequency spectrum of this signal consists in a simple power-law fit involving two parameters: an amplitude A and a spectral index \gamma. In this paper, we consider the next logical step beyond this minimal spectral model, allowing for a running (i.e., logarithmic frequency dependence) of the spectral index, \gamma_run(f) = \gamma + \beta \ln(f/f_ref). We fit this running-power-law (RPL) model to the NANOGrav 15-year data and perform a Bayesian model comparison with the minimal constant-power-law (CPL) model, which results in a 95% credible interval for the parameter \beta consistent with no running, \beta \in [-0.80,2.96], and an inconclusive Bayes factor, B(RPL vs. CPL) = 0.69 +- 0.01. We thus conclude that, at present, the minimal CPL model still suffices to adequately describe the NANOGrav signal; however, future data sets may well lead to a measurement of nonzero \beta. Finally, we interpret the RPL model as a description of primordial GWs generated during cosmic inflation, which allows us to combine our results with upper limits from big-bang nucleosynthesis, the cosmic microwave background, and LIGO-Virgo-KAGRA., Comment: 17 pages, 4 figures, 2 tables

The coupling between two ferromagnets separated by a superconductor has been mostly investigated for the case of Cooper pairs with spin-singlet symmetry. Here, we consider a spin-triplet superconductor with chiral pairing. By full self-consistent analysis of the spatial dependent superconducting order parameter, we determine the magnetic ground state of the superconducting spin valve. The study is performed by investigating the role of the orientation and strength of the magnetization in the ferromagnets including spin-valve asymmetries in the magnetic configurations. Due to the nonvanishing angular momentum of the spin-triplet Cooper pairs we demonstrate that the induced magnetic coupling has an anisotropic character and a structure that can favor collinear or noncollinear magnetic orientations, thus mimicking a magnetic interaction of the Heisenberg or Dzyaloshinskii-Moriya type, respectively. We investigate the role of the physical parameters controlling the character of the magnetic exchange: the amplitude of the magnetization in the ferromagnets and the length of the superconducting spacer in the spin valve. Our study demonstrates that spin-triplet superconductors can be employed to devise anisotropic magnetic exchange and to allow for transitions in the spin-valve state from a collinear to noncollinear magnetic configuration., Comment: 13 pages, 8 figures

We present a novel approach for test-time adaptation via online self-training, consisting of two components. First, we introduce a statistical framework that detects distribution shifts in the classifier's entropy values obtained on a stream of unlabeled samples. Second, we devise an online adaptation mechanism that utilizes the evidence of distribution shifts captured by the detection tool to dynamically update the classifier's parameters. The resulting adaptation process drives the distribution of test entropy values obtained from the self-trained classifier to match those of the source domain, building invariance to distribution shifts. This approach departs from the conventional self-training method, which focuses on minimizing the classifier's entropy. Our approach combines concepts in betting martingales and online learning to form a detection tool capable of quickly reacting to distribution shifts. We then reveal a tight relation between our adaptation scheme and optimal transport, which forms the basis of our novel self-supervised loss. Experimental results demonstrate that our approach improves test-time accuracy under distribution shifts while maintaining accuracy and calibration in their absence, outperforming leading entropy minimization methods across various scenarios.

We give a generalisation of the Lenstra-Lenstra-Lov\'asz (LLL) lattice-reduction algorithm that is valid for an arbitrary (split, semisimple) reductive group $G$. This can be regarded as `lattice reduction with symmetries'. We make this algorithm explicit for the classical groups $G = \mathrm{Sp}_{2g}$, $\mathrm{SO}_{2g}$, and for the exceptional group $G = G_2$.

Metal-assisted chemical etching of silicon is a promising method for fabricating nanostructures with a high aspect ratio. To define a pattern for the catalyst, lift-off processes are commonly used. The lift-off step however is often a process bottle neck due to low yield, especially for smaller structures. To bypass the lift-off process, other methods such as electroplating can be utilized. In this paper, we suggest an electroplated bi-layer catalyst for vapour phase metal-assisted chemical etching as an alternative to the commonly utilised lift-off process. Samples were successfully etched in vapour, and resulting structures had feature sizes down to 10 nm.

This study examines the pressure exerted by a cavitation bubble collapsing near a rigid wall. A laser-generated bubble in a water basin undergoes growth, collapse, second growth, and final collapse. Shock waves and liquid jets from non-spherical collapses are influenced by the stand-off ratio ${\gamma}$, defined as the bubble centroid distance from the wall divided by the bubble radius. We detail shock mechanisms, such as tip or torus collapse, for various ${\gamma}$ values. High-speed and Schlieren imaging visualize the microjet and shock waves. The microjet's evolution is tracked for large ${\gamma}$, while shock waves are captured in composite images showing multiple shock positions. Quantitative analyses of the microjet interface, shock wave velocities, and impact times are reported. Wall-mounted sensors and a needle hydrophone measure pressure and compare with high-speed observations to assess the dominant contributions to pressure changes with ${\gamma}$, revealing implications for cavitation erosion mechanisms.

Protection of data-in-use is a key priority, for which Trusted Execution Environment (TEE) technology has unarguably emerged as a, possibly the most, promising solution. Multiple server-side TEE offerings have been released over the years, exhibiting substantial differences with respect to several aspects. The first comer was Intel SGX, which featured Process-based TEE protection, an efficient yet difficult to use approach. Some SGX limitations were (partially) overcome by runtimes, notably: Gramine, Scone, and Occlum. A major paradigm shift was later brought by AMD SEV, with VM-based TEE protection, which enabled lift-and-shift deployment of legacy applications. This new paradigm has been implemented by Intel only recently, in TDX. While the threat model of the aforementioned TEE solutions has been widely discussed, a thorough performance comparison is still lacking in the literature. This paper provides a comparative evaluation of TDX, SEV, Gramine-SGX, and Occlum-SGX. We study computational overhead and resource usage, under different operational scenarios and using a diverse suite of legacy applications. By doing so, we provide a reliable performance assessment under realistic conditions. We explicitly emphasize that, at the time of writing, TDX was not yet available to the public. Thus, the evaluation of TDX is a unique feature of this study., Comment: Under review at IEEE Transactions on Dependable and Secure Computing

Pulsar-timing-array experiments have reported evidence for a stochastic background of nanohertz gravitational waves consistent with the signal expected from a population of supermassive--black-hole binaries. Those analyses assume power-law spectra for intrinsic pulsar noise and for the background, as well as a Hellings--Downs cross-correlation pattern among the gravitational-wave--induced residuals across pulsars. These assumptions are idealizations that may not be realized in actuality. We test them in the NANOGrav 15 yr data set using Bayesian posterior predictive checks: after fitting our fiducial model to real data, we generate a population of simulated data-set replications, and use them to assess whether the optimal-statistic significance, inter-pulsar correlations, and spectral coefficients assume extreme values for the real data when compared to the replications. We confirm that the NANOGrav 15 yr data set is consistent with power-law and Hellings--Downs assumptions. We also evaluate the evidence for the stochastic background using posterior-predictive versions of the frequentist optimal statistic and of Bayesian model comparison, and find comparable significance (3.2\ $\sigma$ and 3\ $\sigma$ respectively) to what was previously reported for the standard statistics. We conclude with novel visualizations of the reconstructed gravitational waveforms that enter the residuals for each pulsar. Our analysis strengthens confidence in the identification and characterization of the gravitational-wave background as reported by NANOGrav., Comment: 20 pages, 14 Figures

This paper introduces a Random Survival Forest (RSF) method for functional data. The focus is specifically on defining a new functional data structure, the Censored Functional Data (CFD), for dealing with temporal observations that are censored due to study limitations or incomplete data collection. This approach allows for precise modelling of functional survival trajectories, leading to improved interpretation and prediction of survival dynamics across different groups. A medical survival study on the benchmark SOFA data set is presented. Results show good performance of the proposed approach, particularly in ranking the importance of predicting variables, as captured through dynamic changes in SOFA scores and patient mortality rates., Comment: 18 pages

The first generation of ELT instruments includes an optical-infrared high-resolution spectrograph, indicated as ELT-HIRES and recently christened ANDES (ArmazoNes high Dispersion Echelle Spectrograph). ANDES consists of three fibre-fed spectrographs ([U]BV, RIZ, YJH) providing a spectral resolution of $\sim$100,000 with a minimum simultaneous wavelength coverage of 0.4-1.8 $\mu$m with the goal of extending it to 0.35-2.4 $\mu$m with the addition of a U arm to the BV spectrograph and a separate K band spectrograph. It operates both in seeing- and diffraction-limited conditions and the fibre feeding allows several, interchangeable observing modes including a single conjugated adaptive optics module and a small diffraction-limited integral field unit in the NIR. Modularity and fibre-feeding allow ANDES to be placed partly on the ELT Nasmyth platform and partly in the Coud\'e room. ANDES has a wide range of groundbreaking science cases spanning nearly all areas of research in astrophysics and even fundamental physics. Among the top science cases, there are the detection of biosignatures from exoplanet atmospheres, finding the fingerprints of the first generation of stars, tests on the stability of Nature's fundamental couplings, and the direct detection of the cosmic acceleration. The ANDES project is carried forward by a large international consortium, composed of 35 Institutes from 13 countries, forming a team of almost 300 scientists and engineers which include the majority of the scientific and technical expertise in the field that can be found in ESO member states., Comment: SPIE astronomical telescope and instrumentation 2024, in press

We present new JWST/NIRSpec IFU observations of the J1000+0234 system at $z=4.54$, the dense core of a galaxy protocluster hosting a massive, dusty star forming galaxy (DSFG) with a low luminosity radio counterpart. The new data reveals two extended, high equivalent width (EW$_0 > 1000$ {\AA}) nebulae at each side of the DSFG disk along its minor axis (namely O3-N and O3-S). On one hand, O3-N's spectrum shows a prominent FWHM $\sim1300$ km s$^{-1}$ broad and blueshifted component, suggesting an outflow origin. On the other hand, O3-S stretches over parsec and has a velocity gradient that spans $800$ km s$^{-1}$ but no evidence of a broad component. Both sources, however, seem to be powered at least partially by an active galactic nucleus (AGN), so we classify them as extended emission-line regions (EELRs). The strongest evidence comes from the detection of the high-ionization [Ne V] $\lambda3427$ line toward O3-N, which paired with the non-detection of hard X-rays implies an obscuring column density above the Compton-thick regime. In O3-S, the [Ne V] line is not detected, but we measure a He II well above the expectation for star formation. We interpret this as O3-S being externally irradiated by the AGN, akin to the famous Hanny's Voorwerp object in the local Universe. In addition, more classical line ratio diagnostics (e.g. [O III]/H$\beta$ vs [N II]/H$\alpha$) put the DSFG itself in the AGN region of the diagrams, and hence the most probable host of the AGN. These results showcase the ability of JWST of unveiling highly obscured AGN at high redshifts., Comment: 5 pages, 4 figures plus 5 appendices (incl. 3 extra figures and one table). Submitted to A&A on July 17th 2024

WebAssembly is revolutionizing the approach to developing modern applications. Although this technology was born to create portable and performant modules in web browsers, currently, its capabilities are extensively exploited in multiple and heterogeneous use-case scenarios. With the extensive effort of the community, new toolkits make the use of this technology more suitable for real-world applications. In this context, it is crucial to study the liaisons between the WebAssembly ecosystem and software security. Indeed, WebAssembly can be a medium for improving the security of a system, but it can also be exploited to evade detection systems or for performing cryptomining activities. In addition, programs developed in low-level languages such as C can be compiled in WebAssembly binaries, and it is interesting to evaluate the security impacts of executing programs vulnerable to attacks against memory in the WebAssembly sandboxed environment. Also, WebAssembly has been designed to provide a secure and isolated environment, but such capabilities should be assessed in order to analyze their weaknesses and propose new mechanisms for addressing them. Although some research works have provided surveys of the most relevant solutions aimed at discovering WebAssembly vulnerabilities or detecting attacks, at the time of writing, there is no comprehensive review of security-related literature in the WebAssembly ecosystem. We aim to fill this gap by proposing a comprehensive review of research works dealing with security in WebAssembly. We analyze 121 papers by identifying seven different security categories. We hope that our work will provide insights into the complex landscape of WebAssembly and guide researchers, developers, and security professionals towards novel avenues in the realm of the WebAssembly ecosystem.

Pulsar timing arrays (PTAs) detect gravitational waves (GWs) via the correlations they create in the arrival times of pulses from different pulsars. The mean correlation, a function of the angle between the directions to two pulsars, was predicted in 1983 by Hellings and Downs (HD). Observation of this angular pattern is the ``smoking gun'' that GWs are present, so PTAs ``reconstruct the HD curve'' by estimating the correlation using pulsar pairs separated by similar angles. Several studies have examined the amount by which this curve is expected to differ from the HD mean. The variance arises because (a) a finite set of pulsars at specific sky locations is used, (b) the GW sources interfere, and (c) the data are contaminated by noise. Here, for a Gaussian ensemble of sources, we predict that variance by constructing an optimal estimator of the HD correlation, taking into account the pulsar sky locations and the frequency distribution of the GWs and the pulsar noise. The variance is a ratio: the numerator depends upon the pulsar sky locations, and the denominator is the (effective) number of frequency bins for which the GW signal dominates the noise. In effect, after suitable combination, each frequency bin gives an independent estimate of the HD correlation., Comment: 6 pages, 2 figures, revised to explicitly include and illustrate the effects of pulsar timing and measurement noise

Open clusters are ideal tools for tracing the abundances of different elements because their stars are expected to have the same age, distance, and metallicity. Therefore, they serve as very powerful tracers for investigating the cosmic origins of elements. This paper expands on a recent study by us, where the element Fluorine was studied in seven previously open clusters, adding six open clusters as well as eight field stars. The primary objective is to determine the abundance of fluorine (F) to gain insight into its production and evolution. The magnesium (Mg) abundances were derived to categorize the field stars into high and low alpha disk populations. Additionally, cerium (Ce) abundances are determined to better understand the interplay between F and s-process elements. The spectra were obtained from the high-resolution near-infra-red GIANO-B instrument at the Telescopio Nazionale Galileo (TNG). For the derivation of the stellar parameters and abundances, the Python version of Spectroscopy Made Easy (PySME) was used. OH, CN, and CO molecular lines and band heads along with Fe I lines were used to determine the stellar parameters in the H-band region. Two HF lines in the K-band ({\lambda}{\lambda} 2.28, 2.33 {\mu}m), three K-band Mg I lines ({\lambda}{\lambda} 2.10, 2.11, 2.15 {\mu}m), and two Ce II lines in the H-band ({\lambda}{\lambda} 1.66, and 1.71 {\mu}m) were used to derive the abundances of F, Mg, and Ce, respectively. F, Mg, and Ce abundances were derived for 14 stars from 6 OCs, as well as 8 field stars. The F and Ce abundances were investigated as a function of metallicity, age, and Galactocentric distances. Our results indicate that asymptotic giant branch stars and massive stars, including a subset of fast rotators (whose rotation speed likely increases as metallicity decreases), are necessary to explain the cosmic origin of F., Comment: 12 pages, 4 figures, accepted by Astronomy and Astrophysics(A&A)

In the era of big data, an ever-growing volume of information is recorded, either continuously over time or sporadically, at distinct time intervals. Functional Data Analysis (FDA) stands at the cutting edge of this data revolution, offering a powerful framework for handling and extracting meaningful insights from such complex datasets. The currently proposed FDA me\-thods can often encounter challenges, especially when dealing with curves of varying shapes. This can largely be attributed to the method's strong dependence on data approximation as a key aspect of the analysis process. In this work, we propose a free knots spline estimation method for functional data with two penalty terms and demonstrate its performance by comparing the results of several clustering methods on simulated and real data., Comment: 12 pages, 8 figures

Primordial black holes (PBH) can form in the early Universe from peaks of the primordial curvature perturbations. The statistics of the peaks determines the abundance of PBHs, and is related to the amplitude of the primordial curvature spectrum. We study single field inflationary models with local features of the inflaton potential which induce a growth of curvature perturbations on super-horizon scales, which can increase the spectrum, and consequently the predicted PBHs abundance. We compute the effects of the different parameters of the features on the PBHs abundance, giving some example of models compatible with observational constraints. The mechanism is general, and can induce the PBH production in diffent mass ranges by appropriately tuning the parameters, and we give a specific example producing PBHs with abundance compatible with asteroid mass constraints.

We report the observation of a coalescing compact binary with component masses $2.5\text{-}4.5~M_\odot$ and $1.2\text{-}2.0~M_\odot$ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO-Virgo-KAGRA detector network on 2023 May 29 by the LIGO Livingston Observatory. The primary component of the source has a mass less than $5~M_\odot$ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of $55^{+127}_{-47}~\text{Gpc}^{-3}\,\text{yr}^{-1}$ for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star-black hole merger, GW230529_181500-like sources constitute about 60% of the total merger rate inferred for neutron star-black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star-black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap., Comment: 45 pages (10 pages author list, 13 pages main text, 1 page acknowledgements, 13 pages appendices, 8 pages bibliography), 17 figures, 16 tables. Update to match version published in The Astrophysical Journal Letters. Data products available from https://zenodo.org/records/10845779

Gyrotrons are the only sources used for electron cyclotron resonance heating (ECRH). Past gyrotron experiments have highlighted some instability issues, leading to restricted operating ranges. One major cause is the possible presence of trapped electrons within the gyrotron's magnetron injection gun (MIG) region, resulting in undesired currents and subsequent operational failures. Hereby, we present the design and initial experimental findings of the TRapped Electrons eXperiment (T-REX), a novel and unique basic plasma experiment constructed at the Swiss Plasma Center to investigate the underlying physics of trapped electron clouds, therefore entering the realm of non-neutral plasmas. The primary objective is to explore the formation and evolution of electron clouds in gyrotron MIG designs. Typical MIG conditions are replicated in T-REX in terms of geometries, electric and magnetic fields, and supported by kinetic simulations via the 2D Particle-in-Cell (PIC) code FENNECS. T-REX is characterized by two coaxial electrodes, placed in a vacuum chamber on top of a superconducting magnet, with an applied external radial electric field (0.1 - 2 MV/m), and a nearly axial magnetic field (B<0.4T) inducing azimuthal drifts and confining electrons with energies between 0.1-1 keV. The experimental diagnostics include current, voltage, and Faraday probes, and in future plans imaging via phosphor screen, optical emission spectroscopy, streak camera imaging, and, potentially, electric field distribution via the Stark effect. Moreover, T-REX is also designed to be capable of detecting potential growth of diocotron modes via such diagnostics in combination with a segmented outer electrode. Through T-REX, we aim to gain valuable insights into the trapping and dynamics of electrons within MIG regions, paving the way for improved gyrotron performance and reliability in magnetic fusion reactors.

We study on-shell scattering amplitudes for continuous-spin particles. Poincar\'e invariance, little-group $ISO(2)$ covariance, analyticity, and on-shell factorization (unitarity) impose stringent conditions on these amplitudes. We solve them by realizing a non-trivial representation for all little-group generators on the space of functions of bi-spinors. The three-point amplitudes are uniquely determined by matching their high-energy limit to that of definite-helicity (ordinary) massless particles. Four-point amplitudes are then bootstrapped using consistency conditions, allowing us to analyze the theory in a very transparent way, without relying on any off-shell Lagrangian formulation. We present several examples that highlight the main features of the resulting scattering amplitudes. Finally, we explore under which conditions it is possible to relax some assumptions, such as strict on-shell factorization, analyticity, or others. We show that continuous-spin particle dynamics may approximate gravity and electromagnetism in a loose version of $S$-matrix principles., Comment: 31 pages, 7 figures

Sparse auto-encoders are useful for extracting low-dimensional representations from high-dimensional data. However, their performance degrades sharply when the input noise at test time differs from the noise employed during training. This limitation hinders the applicability of auto-encoders in real-world scenarios where the level of noise in the input is unpredictable. In this paper, we formalize single hidden layer sparse auto-encoders as a transform learning problem. Leveraging the transform modeling interpretation, we propose an optimization problem that leads to a predictive model invariant to the noise level at test time. In other words, the same pre-trained model is able to generalize to different noise levels. The proposed optimization algorithm, derived from the square root lasso, is translated into a new, computationally efficient auto-encoding architecture. After proving that our new method is invariant to the noise level, we evaluate our approach by training networks using the proposed architecture for denoising tasks. Our experimental results demonstrate that the trained models yield a significant improvement in stability against varying types of noise compared to commonly used architectures.

Pulsar timing arrays (PTA) hunt for gravitational waves (GW) by searching for the correlations that GWs induce in the time-of-arrival residuals from different pulsars. If the GW sources are of astrophysical origin, then they are located at discrete points on the sky. However, PTA data are often modeled, and subsequently analyzed, via a "standard Gaussian ensemble". That ensemble is obtained in the limit of an infinite density of vanishingly weak, Poisson-distributed sources. In this paper, we move away from that ensemble, to study the effects of two types of "source anisotropy". The first (a), which is often called "shot noise", arises because there are $N$ discrete GW sources at specific sky locations. The second (b) arises because the GW source positions are not a Poisson process, for example, because galaxy locations are clustered. Here, we quantify the impact of (a) and (b) on the mean and variance of the pulsar-averaged Hellings and Downs correlation. For conventional PTA sources, we show that the effects of shot noise (a) are much larger than the effects of clustering (b)., Comment: 16 pages, 3 figures; revised with h=0.68 in Fig. 3 and more detail added about expected number density of PTA sources

Context. The recent detection of nitrogen-enhanced, metal-poor galaxies at high redshift by the James Webb Space Telescope has sparked renewed interest in exploring the chemical evolution of carbon, nitrogen, and oxygen (the CNO elements) at early times, prompting fresh inquiries into their origins. Aims. The main goal of this paper is to shed light onto the early evolution of the main CNO isotopes in our Galaxy and in young distant systems, such as GN-z11 at z=10.6. Methods. To this aim, we incorporate a stochastic star-formation component into a chemical evolution model calibrated with high-quality Milky Way (MW) data, focusing on the contribution of Population III (Pop III) stars to the early chemical enrichment. Results. By comparing the model predictions with CNO abundance measurements from high-resolution spectroscopy of an homogeneous sample of Galactic halo stars, we first demonstrate that the scatter observed in the metallicity range -4.5 < [Fe/H] <-1.5 can be explained by pre-enrichment from Pop III stars that explode as supernovae (SNe) with different initial masses and energies. Then, by exploiting the chemical evolution model, we provide testable predictions for log(C/N), log(N/O), and log(C/O) vs. log(O/H)+12 in MW-like galaxies observed at different cosmic epochs/redshifts. Finally, by calibrating the chemical evolution model to replicate the observed properties of GN-z11, we provide an alternative interpretation of its log(N/O) abundance ratio, demonstrating that its high N content can be reproduced through enrichment from high-mass faint Pop III SNe. Conclusions. Stochastic chemical enrichment from primordial stars explains both the observed scatter in CNO abundances in MW halo stars and the exceptionally high N/O ratios in some distant galaxies. These findings emphasize the critical role of Pop III stars in shaping early chemical evolution.

We present several algorithms for evaluating point containment in constructive solid geometry (CSG) trees with unbounded primitives. Three algorithms are presented based on postfix, prefix, and infix notations of the CSG binary expression tree. We show that prefix and infix notations enable short-circuiting logic, which reduces the number of primitives that must be checked during point containment. To evaluate the performance of the algorithms, each algorithm was implemented in the OpenMC Monte Carlo particle transport code, which relies on CSG to represent solid bodies through which subatomic particles travel. Two sets of tests were carried out. First, the execution time to generate a high-resolution rasterized image of a 2D slice of a detailed CSG model of the ITER tokamak was measured. Use of both prefix and infix notations offered significant speedup over the postfix notation that has traditionally been used in particle transport codes, with infix resulting in a 6$\times$ reduction in execution time relative to postfix. We then measured the execution time of neutron transport simulations of the same ITER model using each of the algorithms. The results and performance improvements reveal the same trends as for the rasterization test, with a 4.59$\times$ overall speedup using the infix notation relative to the original postfix notation in OpenMC.

Pulsar timing arrays (PTAs) have made tremendous progress and are now showing strong evidence for the gravitational-wave background (GWB). Further probing the origin and characteristics of the GWB will require more generalized analysis techniques. Bayesian methods are most often used but can be computationally expensive. On the other hand, frequentist methods, like the PTA Optimal Statistic (OS), are more computationally efficient and can produce results that are complementary to Bayesian methods, allowing for stronger statistical cases to be built from a confluence of different approaches. In this work we expand the capabilities of the OS through a technique we call the Per-Frequency Optimal Statistic (PFOS). The PFOS removes the underlying power-law assumption inherent in previous implementations of the OS, and allows one to estimate the GWB spectrum in a frequency-by-frequency manner. We have also adapted a recent generalization from the OS pipeline into the PFOS, making it capable of accurately characterizing the spectrum in the intermediate and strong GW signal regimes using only a small fraction of the necessary computational resources when compared with fully-correlated Bayesian methods, while also empowering many new types of analyses not possible before. We find that even in the strong GW signal regime, where the GWB dominates over noise in all frequencies, the injected value of the signal lies within the 50th-percentile of the PFOS uncertainty distribution in 41-45% of simulations, remaining 3$\sigma$-consistent with unbiased estimation., Comment: 23 pages, 9 figures,submitted to Physical Review D

We review approaches to statistical inference based on randomization. Permutation tests are treated as an important special case. Under a certain group invariance property, referred to as the ``randomization hypothesis,'' randomization tests achieve exact control of the Type I error rate in finite samples. Although this unequivocal precision is very appealing, the range of problems that satisfy the randomization hypothesis is somewhat limited. We show that randomization tests are often asymptotically, or approximately, valid and efficient in settings that deviate from the conditions required for finite-sample error control. When randomization tests fail to offer even asymptotic Type 1 error control, their asymptotic validity may be restored by constructing an asymptotically pivotal test statistic. Randomization tests can then provide exact error control for tests of highly structured hypotheses with good performance in a wider class of problems. We give a detailed overview of several prominent applications of randomization tests, including two-sample permutation tests, regression, and conformal inference.

In this work we compute the rates and numbers of different types of stars and phenomena (SNe, novae, white dwarfs, merging neutron stars, black holes) that contributed to the chemical composition of the Solar System. Stars die and restore the newly formed elements into the interstellar gas. This process is called "chemical evolution". In particular, we analyse the death rates of stars of all masses, dying either quiescently or explosively. These rates and total star numbers are computed in the context of a revised version of the two-infall model for the chemical evolution of the Milky Way, which reproduces fairly well the observed abundance patterns of several chemical species, as well as the global solar metallicity. We compute also the total number of stars ever born and still alive as well as the number of stars born up to the formation of the Solar System and with a mass and metallicity like the Sun. This latter number will account for all the possible existing Solar Systems which can host life in the solar vicinity. Among all the stars (from 0.8 to 100 M$_{\odot}$) born and died from the beginning up to the Solar System formation epoch, which contributed to its chemical composition, 93.00$\%$ are represented by stars dying as single white dwarfs (without interacting significantly with a companion star) and originating in the mass range 0.8-8 M$_{\odot}$, while 5.24$\%$ are neutron stars and 0.73$\%$ are black holes, both originating from SNe core-collapse (M>8 M$_{\odot}$); 0.64$\%$ are Type Ia SNe and 0.40$\%$ are nova systems, both originating from the same mass range as the white dwarfs. The number of stars similar to the Sun born from the beginning up to the Solar System formation, with metallicity in the range 12+log(Fe/H)= 7.50 $\pm$ 0.04 dex is 3.1732$\cdot$ 10$^{7}$, and in particular our Sun is the 2.6092$\cdot$ 10$^7$-th star of this kind, born in the solar vicinity., Comment: Submitted to A&A, 11 pages, 9 figures

In this paper, an optimized version of classical Bombelli's algorithm for computing integer square roots is presented. In particular, floating-point arithmetic is used to compute the initial guess of each digit of the root, following similar ideas to those used in "The Art of Computer Programming" Vol. 2, p. 4.3.1 for division. A program with an implementation of the algorithm in Java is also presented, and its running time is compared with that of the algorithm provided by the Java standard library, which uses the Newton's method. From tests, the algorithm presented here turns out to be much faster., Comment: 28 pages

A well known procedure to get quantum hydrodynamical models for charge transport is to resort to the Wigner equations and deduce the hierarchy of the moment equations as in the semiclassical approach. If one truncates the moment hierarchy to a finite order, the resulting set of balance equations requires some closure assumption because the number of unknowns exceed the number of equations. In the classical and semiclassical kinetic theory a sound approach to get the desired closure relations is that based on the Maximum Entropy Principle (MEP) [13] (see[20] for charge transport in semiconductors). In [9] a quantum MEP hydrodynamical model has been devised for charge transport in the parabolic band approximation by introducing quantum correction based on the equilibrium Wigner function [30]. An extension to electron moving in pristine graphene has been obtained in [29]. Here we present a quantum hydrodynamical model which is valid for a general energy band considering a closure of the moment system deduced by the Wigner equation resorting to a quantum version of MEP. Explicit formulas for quantum correction at order \hbar^2 are obtained with the aid of the Moyal calculus for silicon and graphene removing the limitation that the quantum corrections are based on the equilibrium Wigner function as in [9, 29]. As an application, quantum correction to the mobilities are deduced., Comment: arXiv admin note: text overlap with arXiv:2301.00445

The testing of atmosphere-breathing electric propulsion intakes is an important step in the development of functional propulsion systems which provide sustained drag compensation in very low Earth orbits. To make satellite operations more sustainable, it is necessary to develop new materials which withstand erosion, long-lasting propulsion systems to overcome drag, and tools that allow for ground-based testing. Among the tools to enable these innovations is the Rarefied Orbital Aerodynamics Research facility at the University of Manchester. Here, a description of the facility is provided together with two different methodologies for testing sub-scaled intake designs for atmosphere-breathing electric propulsion systems. The first methodology is based on measurements of the pressure difference between the two extremities of the intake, while the second uses a gas sensor to measure the collection efficiency of the intake. Direct Simulation Monte Carlo models have been used to assess the viability of the proposed testing methodologies. The results of this analysis indicate that either methodology or a combination of both can provide suitable measurements to assess the performance of future intake designs.

We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data, such as missing or noisy variables. Our approach builds on conformal prediction, a powerful framework to construct prediction sets that are valid under the i.i.d assumption. Importantly, naively applying conformal prediction does not provide reliable predictions in this setting, due to the distribution shift induced by the corruptions. To account for the distribution shift, we assume access to privileged information (PI). The PI is formulated as additional features that explain the distribution shift, however, they are only available during training and absent at test time. We approach this problem by introducing a novel generalization of weighted conformal prediction and support our method with theoretical coverage guarantees. Empirical experiments on both real and synthetic datasets indicate that our approach achieves a valid coverage rate and constructs more informative predictions compared to existing methods, which are not supported by theoretical guarantees.

Recent hydrodynamical simulations of the late stages of supernova remnant (SNR) evolution have revealed that as they merge with the ambient medium, SNRs implode, leading to the formation of dense clouds in their center. While being highly chemically enriched by their host SNR, these clouds appear to have similar properties as giant molecular clouds, which are believed to be the main site of star formation. Here, we develop a simple model, in order to estimate the efficiency of the star formation that might be triggered by the implosion of SNRs. We separately consider two cases, cyclic star formation, maintained by the episodic driving of feedback from new generations of stars; and a single burst of star formation, triggered by a single explosion. We find that in the cyclic case, star formation is inefficient, with implosion-triggered star-formation contributing a few percent of the observed star-formation efficiency per free-fall timescale. In the single-burst case, higher star-formation efficiencies can be obtained. However, while the implosion-triggered process might not contribute much to the overall star-formation, due to the high chemical enrichment of the birth clouds, it can explain the formation of a significant fraction of metal-rich stars., Comment: 9 pages, 3 figures, accepted for publication in ApJL

Nanomechanical resonators can serve as ultrasensitive, miniaturized force probes. While vertical structures like nanopillars are ideal for this purpose, transducing their motion is challenging. Pillar-based photonic crystals (PhCs) offer a potential solution by integrating optical transduction within the pillars. However, achieving high-quality PhCs is hindered by inefficient vertical light confinement. Here, we present a full-silicon 1D photonic crystal cavity based on nanopillars as a new platform with great potential for applications in force sensing and biosensing areas. Its unit cell consists of a silicon pillar with larger diameter at its top portion than at the bottom, which allows vertical light confinement and an energy bandgap in the near infrared range for transverse-magnetic (TM) polarization. We experimentally demonstrate optical cavities with Q-factors exceeding 1e3 constructed by inserting a defect within a periodic arrangement of this type of pillars. Given the fact that that each nanopillar naturally behaves as a nanomechanical cantilever, the fabricated geometries are excellent optomechanical (OM) photonic crystal cavities in which the mechanical motion of each nanopillar composing the cavity can be optically transduced. These novel geometries display enhanced mechanical properties, cost-effectiveness, integration possibilities, and scalability, and opens and new path in front of the widely used suspended Si beam OM cavities made on silicon-on-insulator., Comment: Main text: 15 pages, 5 figures Supplementary Information: 8 pages, 6 figures

Predictive uncertainty quantification is crucial in decision-making problems. We investigate how to adequately quantify predictive uncertainty with missing covariates. A bottleneck is that missing values induce heteroskedasticity on the response's predictive distribution given the observed covariates. Thus, we focus on building predictive sets for the response that are valid conditionally to the missing values pattern. We show that this goal is impossible to achieve informatively in a distribution-free fashion, and we propose useful restrictions on the distribution class. Motivated by these hardness results, we characterize how missing values and predictive uncertainty intertwine. Particularly, we rigorously formalize the idea that the more missing values, the higher the predictive uncertainty. Then, we introduce a generalized framework, coined CP-MDA-Nested*, outputting predictive sets in both regression and classification. Under independence between the missing value pattern and both the features and the response (an assumption justified by our hardness results), these predictive sets are valid conditionally to any pattern of missing values. Moreover, it provides great flexibility in the trade-off between statistical variability and efficiency. Finally, we experimentally assess the performances of CP-MDA-Nested* beyond its scope of theoretical validity, demonstrating promising outcomes in more challenging configurations than independence.

Animal behavior serves as a reliable indicator of the adaptation of organisms to their environment and their overall well-being. Through rigorous observation of animal actions and interactions, researchers and observers can glean valuable insights into diverse facets of their lives, encompassing health, social dynamics, ecological relationships, and neuroethological dimensions. Although state-of-the-art deep learning models have demonstrated remarkable accuracy in classifying various forms of animal data, their adoption in animal behavior studies remains limited. This survey article endeavors to comprehensively explore deep learning architectures and strategies applied to the identification of animal behavior, spanning auditory, visual, and audiovisual methodologies. Furthermore, the manuscript scrutinizes extant animal behavior datasets, offering a detailed examination of the principal challenges confronting this research domain. The article culminates in a comprehensive discussion of key research directions within deep learning that hold potential for advancing the field of animal behavior studies.

Auctions are key for maximizing sellers' revenue and ensuring truthful bidding among buyers. Recently, an approach known as differentiable economics based on deep learning shows promise in learning optimal auction mechanisms for multiple items and participants. However, this approach has no guarantee of strategy-proofness at test time. Strategy-proofness is crucial as it ensures that buyers are incentivized to bid their true valuations, leading to optimal and fair auction outcomes without the risk of manipulation. Building on conformal prediction, we introduce a novel approach to achieve strategy-proofness with rigorous statistical guarantees. The key novelties of our method are: (i) the formulation of a regret prediction model, used to quantify at test time violations of strategy-proofness; and (ii) an auction acceptance rule that leverages the predicted regret to ensure that for a new auction, the data-driven mechanism meets the strategy-proofness requirement with high probability (e.g., 99\%). Numerical experiments demonstrate the necessity for rigorous guarantees, the validity of our theoretical results, and the applicability of our proposed method.

1$T$-TaSe$_2$ is a prototypical charge density wave (CDW) material for which electron-phonon coupling and associated lattice distortion play an important role in driving and stabilizing the CDW phase. Here, we investigate the lattice dynamics of bulk 1$T$-TaSe$_2$ using angle-resolved ultralow wavenumber Raman spectroscopy down to 10 cm$^{-1}$. Our high-resolution spectra allow us to identify at least 27 Raman-active modes in the commensurate (CCDW) phase. Contrary to other layered materials, we do not find evidence of interlayer breathing or shear modes, suggestive of $AA$ stacking in the bulk, or sufficiently weak interlayer coupling. Polarization dependence of the mode intensities allows the assignment of their symmetry, which is supported by first-principles calculations of the phonons for the bulk structure using density functional theory. A detailed temperature dependence in the range $T$ = 80 - 500 K allows us to identify soft modes associated with the CDW superlattice. Upon entering the incommensurate (ICCDW) phase above 473 K, we observe a dramatic loss of resolution of all modes, and significant linewidth broadening associated with a reduced phonon lifetime as the charge-order becomes incommensurate with the lattice., Comment: 8 pages, 3 figures, supplemental material

The Ornstein-Uhlenbeck (OU) process describes the dynamics of Brownian particles in a confining harmonic potential, thereby constituting the paradigmatic model of overdamped, mean-reverting Langevin dynamics. Despite its widespread applicability, this model falls short when describing physical systems where the confining potential is itself subjected to stochastic fluctuations. However, such stochastic fluctuations generically emerge in numerous situations, including in the context of colloidal manipulation by optical tweezers, leading to inherently out-of-equilibrium trapped dynamics. To explore the consequences of stochasticity at this level, we introduce a natural extension of the OU process, in which the stiffness of the harmonic potential is itself subjected to OU-like fluctuations. We call this model the OU$^2$ process. We examine its statistical, dynamic, and thermodynamic properties through a combination of analytical and numerical methods. Importantly, we show that the probability density for the particle position presents power-law tails, in contrast to the Gaussian decay of the standard OU process. In turn, this causes the trapping behavior, extreme value statistics, first passage statistics, and entropy production of the OU$^2$ process to differ qualitatively from their standard OU counterpart. Due to the wide applicability of the standard OU process and of the proposed OU$^2$ generalisation, our study sheds light on the peculiar properties of stochastic dynamics in random potentials and lays the foundation for the refined analysis of the dynamics and thermodynamics of numerous experimental systems., Comment: 32 pages, 7 figures

Large general-purpose transformer models have recently become the mainstay in the realm of speech analysis. In particular, Whisper achieves state-of-the-art results in relevant tasks such as speech recognition, translation, language identification, and voice activity detection. However, Whisper models are not designed to be used in real-time conditions, and this limitation makes them unsuitable for a vast plethora of practical applications. In this paper, we introduce Whispy, a system intended to bring live capabilities to the Whisper pretrained models. As a result of a number of architectural optimisations, Whispy is able to consume live audio streams and generate high level, coherent voice transcriptions, while still maintaining a low computational cost. We evaluate the performance of our system on a large repository of publicly available speech datasets, investigating how the transcription mechanism introduced by Whispy impacts on the Whisper output. Experimental results show how Whispy excels in robustness, promptness, and accuracy.

Neural networks are often overconfident about their predictions, which undermines their reliability and trustworthiness. In this work, we present a novel technique, named Error-Driven Uncertainty Aware Training (EUAT), which aims to enhance the ability of neural models to estimate their uncertainty correctly, namely to be highly uncertain when they output inaccurate predictions and low uncertain when their output is accurate. The EUAT approach operates during the model's training phase by selectively employing two loss functions depending on whether the training examples are correctly or incorrectly predicted by the model. This allows for pursuing the twofold goal of i) minimizing model uncertainty for correctly predicted inputs and ii) maximizing uncertainty for mispredicted inputs, while preserving the model's misprediction rate. We evaluate EUAT using diverse neural models and datasets in the image recognition domains considering both non-adversarial and adversarial settings. The results show that EUAT outperforms existing approaches for uncertainty estimation (including other uncertainty-aware training techniques, calibration, ensembles, and DEUP) by providing uncertainty estimates that not only have higher quality when evaluated via statistical metrics (e.g., correlation with residuals) but also when employed to build binary classifiers that decide whether the model's output can be trusted or not and under distributional data shifts.

Conformal prediction is a powerful tool to generate uncertainty sets with guaranteed coverage using any predictive model, under the assumption that the training and test data are i.i.d.. Recently, it has been shown that adversarial examples are able to manipulate conformal methods to construct prediction sets with invalid coverage rates, as the i.i.d. assumption is violated. To address this issue, a recent work, Randomized Smoothed Conformal Prediction (RSCP), was first proposed to certify the robustness of conformal prediction methods to adversarial noise. However, RSCP has two major limitations: (i) its robustness guarantee is flawed when used in practice and (ii) it tends to produce large uncertainty sets. To address these limitations, we first propose a novel framework called RSCP+ to provide provable robustness guarantee in evaluation, which fixes the issues in the original RSCP method. Next, we propose two novel methods, Post-Training Transformation (PTT) and Robust Conformal Training (RCT), to effectively reduce prediction set size with little computation overhead. Experimental results in CIFAR10, CIFAR100, and ImageNet suggest the baseline method only yields trivial predictions including full label set, while our methods could boost the efficiency by up to $4.36\times$, $5.46\times$, and $16.9\times$ respectively and provide practical robustness guarantee. Our codes are available at https://github.com/Trustworthy-ML-Lab/Provably-Robust-Conformal-Prediction.

In this article, we investigate some issues related to the quantification of uncertainties associated with the electrical properties of graphene nanoribbons. The approach is suited to understand the effects of missing information linked to the difficulty of fixing some material parameters, such as the band gap, and the strength of the applied electric field. In particular, we focus on the extension of particle Galerkin methods for kinetic equations in the case of the semiclassical Boltzmann equation for charge transport in graphene nanoribbons with uncertainties. To this end, we develop an efficient particle scheme which allows us to parallelize the computation and then, after a suitable generalization of the scheme to the case of random inputs, we present a Galerkin reformulation of the particle dynamics, obtained by means of a generalized polynomial chaos approach, which allows the reconstruction of the kinetic distribution. As a consequence, the proposed particle-based scheme preserves the physical properties and the positivity of the distribution function also in the presence of a complex scattering in the transport equation of electrons. The impact of the uncertainty of the band gap and applied field on the electrical current is analyzed., Comment: 26 pages, 6 Figures, 4 Tables

The design and optimization of Reconfigurable Intelligent Surfaces (RISs) are key challenges for future wireless communication systems. RISs are devices that can manipulate electromagnetic (EM) waves in a programmable way, thus enhancing the performance and efficiency of wireless links. To achieve this goal, it is essential to have reliable EM models that can capture the behavior of RISs in different scenarios. This work demonstrates that the Partial Elements Equivalent Circuit (PEEC) method is a powerful tool for EM analysis of RIS-aided wireless links. It might also be integrated with optimization algorithms in order to optimize wireless communication networks.

Reconfigurable Intelligent Surface (RIS) modeling and optimization are a crucial steps in developing the next generation of wireless communications. To this aim, the availability of accurate electromagnetic (EM) models is of paramount important for the design of RIS-assisted communication links. In this work, we validate a widely-used analytical multiport network for RISs by means of a well-established full-wave numerical method based on the Partial Elements Equivalent Circuit (PEEC) approach. Numerical results show good agreement between the two methods, thus demonstrating i) the considered multiport network model being effective and ii) the PEEC method being appropriate for EM modeling of RIS-assisted wireless links.

Optical Bound States in the Continuum are peculiar localized states within the continuous spectrum that are unaffected by any far-field radiation and intrinsic absorption, therefore possessing infinite mode lifetime and Q-factor. To date they have been widely studied in dielectric structures whereas their exploitation in lossy media, i.e. plasmonic nanostructures, still remains a challenge. Here, we show the emergence of a hybrid BIC state in a 2D system of silver-filled dimers, quasi-embedded in a high-index dielectric waveguide. The hybrid BIC onset is found to be highly dependent on the bare modes' spectral and spatial overlap, but particularly on the plasmonic field's intensity. By tailoring the hybridizing plasmonic/photonic fractions we select an ideal coupling regime for which the mode exhibits both, high Q-factor values and strong near-field enhancement tightly confined in the nanogap and a consequently extremely small modal volume. We demonstrate that this optical layout can be exploited in a proof-of-concept experiment for the detection of TAR DNA-binding protein 43, which outperforms the sensitivity of current label-free biosensing platforms, reaching the zeptomolar range of concentration.