Search

A charge-density-wave (CDW) is characterized by a dynamical order parameter consisting of a time-dependent amplitude and phase, which manifest as optically-active collective modes of the CDW phase. Studying the behaviour of such collective modes in the time-domain, and their coupling with electronic and lattice order, provides important insight into the underlying mechanisms behind CDW formation. In this work, we report on femtosecond broadband transient reflectivity experiments on bulk 1$T$-VSe$_2$ using near-infrared excitation. At low temperature, we observe coherent oscillations associated with the CDW amplitude mode and phonons of the distorted lattice. Across the expected transition temperature at 110 K, we confirm signatures of a rearrangement of the electronic structure evident in the quasiparticle dynamics. However, we find that the amplitude mode instead softens to zero frequency at 80 K, possibly indicating an additional phase transition at this temperature. In addition, we demonstrate photoinduced CDW melting, associated with a collapse of the electronic and lattice order, which occurs at moderate excitation densities, consistent with a dominant electron-phonon CDW mechanism., Comment: 13 pages, 6 figures, supporting information

Mercury telluride is an intriguing compound that is well known for the first realization of topological states. Despite being known for a long time, a quantitative understanding of its electronic properties remains challenging due to the presence of many concomitant subtle effects. By combining accurate first principles calculations and $k.p$ modelling, we investigate the topological phase diagram of mercury telluride as a function of strain. Our research demonstrates the significance of including the linearly $k$-dependent higher-order $C_4$ strain terms into the usual $k.p$ treatment. In particular, we report a unique $k$-dependence of the sub-band splitting, which arises from the interplay between strain and the standard bulk inversion asymmetry terms within our $k.p$ model. The impact of this phenomenon is explored in relation to the camel's back shape during the tensile strain phase and its implications for the Weyl semimetal state in the compressive strain case are discussed., Comment: 12 pages, 7 figures

Screening, a ubiquitous phenomenon associated with the shielding of electric fields by surrounding charges, has been widely adopted as a means to modify a material's properties. While so far most studies have relied on static changes of screening through doping or gating, here we demonstrate that screening can also drive the onset of distinct quantum states on the ultrafast timescale. By using time and angle-resolved photoemission spectroscopy we show that intense optical excitation can drive 1T-TiSe$_2$, a prototypical charge density wave material, almost instantly from a gapped into a semimetallic state. By systematically comparing changes in bandstructure over time and excitation strength with theoretical calculations we find that the appearance of this state is likely caused by a dramatic reduction of the screening length. In summary, this work showcases how optical excitation enables the screening driven design of a non-equilibrium semimetallic phase in TiSe$_2$, possibly providing a general pathway into highly screened phases in other strongly correlated materials.

The field of single-cell biology is growing rapidly and is generating large amounts of data from a variety of species, disease conditions, tissues, and organs. Coordinated efforts such as CZI CELLxGENE, HuBMAP, Broad Institute Single Cell Portal, and DISCO, allow researchers to access large volumes of curated datasets. Although the majority of the data is from scRNAseq experiments, a wide range of other modalities are represented as well. These resources have created an opportunity to build and expand the computational biology ecosystem to develop tools necessary for data reuse, and for extracting novel biological insights. Here, we highlight achievements made so far, areas where further development is needed, and specific challenges that need to be overcome.

Fix integers $r\geq 4$ and $i\geq 2$. Let $C$ be a non-degenerate, reduced and irreducible complex projective curve in $\mathbb P^r$, of degree $d$, not contained in a hypersurface of degree $\leq i$. Let $p_a(C)$ be the arithmetic genus of $C$. Continuing previous research, under the assumption $d\gg \max\{r,i\}$, in the present paper we exhibit a Castelnuovo bound $G_0(r;d,i)$ for $p_a(C)$. In general, we do not know whether this bound is sharp. However, we are able to prove it is sharp when $i=2$, $r=6$ and $d\equiv 0,3,6$ (mod $9$). Moreover, when $i=2$, $r\geq 9$, $r$ is divisible by $3$, and $d\equiv 0$ (mod $r(r+3)/6$), we prove that if $G_0(r;d,i)$ is not sharp, then for the maximal value of $p_a(C)$ there are only three possibilities. The case in which $i=2$ and $r$ is not divisible by $3$ has already been examined in the literature. We give some information on the extremal curves., Comment: 19 pages

We study a repulsive thermal system governed by odd interactions. The interplay between oddness and inertia induces a non-equilibrium phase transition from a homogeneous to a non-homogeneous phase, characterized by bubbles induced by odd interactions. This phenomenon occurs in the absence of attractions and is generated by the competition between the standard pressure contribution due to particle repulsion and an effective surface tension generated by odd-induced centrifugal forces. As a signature of the phase transition, the system exhibits vortex structures and oscillating spatial velocity correlations, which emerge close to the analytically predicted transition point. Our findings can be verified in granular experiments governed by odd interactions, such as spinners and colloidal magnets, and could be key to characterizing the emerging properties of metamaterials.

Disentangling the stellar population in the central galaxy from the intrahalo light can help us shed light on the formation history of the host halo, as the properties of the stellar components are expected to retain traces of its formation history. Many approaches are adopted, depending on different physical assumptions (e.g. the light profile, chemical composition, and kinematical differences) and on whether the full six-dimensional phase-space information is known (much like in simulations) or whether one analyses projected quantities (i.e. observations). This paper paves the way for a new approach to bridge the gap between observational and simulation methods. We propose the use of projected kinematical information from stars in simulations in combination with deep learning to create a robust method for identifying intrahalo light in observational data to enhance understanding and consistency in studying the process of galaxy formation. Using a U-Net, we developed a methodology for predicting these contributions from a sample of mock images from hydrodynamical simulations to train, validate and test the network. Reinforced training (Attention U-Net) was used to improve the first results, as the innermost central regions of the mock images consistently overestimate the stellar intrahalo contribution. Our work shows that adequate training over a representative sample of mock images can lead to good predictions of the intrahalo light distribution. The model is mildly dependent on the training size and its predictions are less accurate when applied to mock images from different simulations. However, the main features (spatial scales and gradients of the stellar fractions) are recovered for all tests. While the method presented here should be considered as a proof of concept, future work is required to enable the application of the proposed model to observational data., Comment: Accepted for publication in A&A. Abstract reduced for ArXiv. 12 pages, 11 figures

The increasing availability of biomedical data is helping to design more robust deep learning (DL) algorithms to analyze biomedical samples. Currently, one of the main limitations to train DL algorithms to perform a specific task is the need for medical experts to label data. Automatic methods to label data exist, however automatic labels can be noisy and it is not completely clear when automatic labels can be adopted to train DL models. This paper aims to investigate under which circumstances automatic labels can be adopted to train a DL model on the classification of Whole Slide Images (WSI). The analysis involves multiple architectures, such as Convolutional Neural Networks (CNN) and Vision Transformer (ViT), and over 10000 WSIs, collected from three use cases: celiac disease, lung cancer and colon cancer, which one including respectively binary, multiclass and multilabel data. The results allow identifying 10% as the percentage of noisy labels that lead to train competitive models for the classification of WSIs. Therefore, an algorithm generating automatic labels needs to fit this criterion to be adopted. The application of the Semantic Knowledge Extractor Tool (SKET) algorithm to generate automatic labels leads to performance comparable to the one obtained with manual labels, since it generates a percentage of noisy labels between 2-5%. Automatic labels are as effective as manual ones, reaching solid performance comparable to the one obtained training models with manual labels., Comment: pre-print of the journal paper

The Cryogenic Underground Observatory for Rare Events (CUORE) is a detector array comprised by 988 5$\;$cm$\times$5$\;$cm$\times$5$\;$cm TeO$_2$ crystals held below 20 mK, primarily searching for neutrinoless double-beta decay in $^{130}$Te. Unprecedented in size amongst cryogenic calorimetric experiments, CUORE provides a promising setting for the study of exotic through-going particles. Using the first tonne-year of CUORE's exposure, we perform a search for hypothesized fractionally charged particles (FCPs), which are well-motivated by various Standard Model extensions and would have suppressed interactions with matter. No excess of FCP candidate tracks is observed over background, setting leading limits on the underground FCP flux with charges between $e/24-e/5$ at 90\% confidence level. Using the low background environment and segmented geometry of CUORE, we establish the sensitivity of tonne-scale sub-Kelvin detectors to diverse signatures of new physics., Comment: 7 pages, 5 figures

In games, and more generally in the field of software development, early detection of bugs is vital to maintain a high quality of the final product. Automated tests are a powerful tool that can catch a problem earlier in development by executing periodically. As an example, when new code is submitted to the code base, a new automated test verifies these changes. However, identifying the specific change responsible for a test failure becomes harder when dealing with batches of changes -- especially in the case of a large-scale project such as a AAA game, where thousands of people contribute to a single code base. This paper proposes a new approach to automatically identify which change in the code caused a test to fail. The method leverages Large Language Models (LLMs) to associate error messages with the corresponding code changes causing the failure. We investigate the effectiveness of our approach with quantitative and qualitative evaluations. Our approach reaches an accuracy of 71% in our newly created dataset, which comprises issues reported by developers at EA over a period of one year. We further evaluated our model through a user study to assess the utility and usability of the tool from a developer perspective, resulting in a significant reduction in time -- up to 60% -- spent investigating issues., Comment: Published at CoG 2024

Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models, the need for real-time detection demands greater efficiency. With this in mind, unlike previous work, we introduce a novel deepfake detection approach on images using Binary Neural Networks (BNNs) for fast inference with minimal accuracy loss. Moreover, our method incorporates Fast Fourier Transform (FFT) and Local Binary Pattern (LBP) as additional channel features to uncover manipulation traces in frequency and texture domains. Evaluations on COCOFake, DFFD, and CIFAKE datasets demonstrate our method's state-of-the-art performance in most scenarios with a significant efficiency gain of up to a $20\times$ reduction in FLOPs during inference. Finally, by exploring BNNs in deepfake detection to balance accuracy and efficiency, this work paves the way for future research on efficient deepfake detection., Comment: Accepted at CVPR24 DFAD Workshop

We present the first results from a proof-of-concept search for dark sectors via invisible decays of pseudoscalar $\eta$ and $\eta'$ mesons in the NA64h experiment at the CERN SPS. Our novel technique uses the charge-exchange reaction of 50 GeV $\pi^-$ on nuclei of an active target as the source of neutral mesons. The $\eta, \eta' \to invisible$ events would exhibit themselves via a striking signature - the complete disappearance of the incoming beam energy in the detector. No evidence for such events has been found with $2.9\times10^{9}$ pions on target accumulated during one day of data taking. This allows us to set a stringent limit on the branching ratio ${\rm Br}(\eta' \to invisible) < 2.1 \times 10^{-4}$ improving the current bound by a factor of $\simeq3$. We also set a limit on ${\rm Br}(\eta \to invisible) < 1.1 \times 10^{-4}$ comparable with the existing one. These results demonstrate the great potential of our approach and provide clear guidance on how to enhance and extend the sensitivity for dark sector physics from future searches for invisible neutral meson decays., Comment: 5 pages, 3 figures

Purpose: Analysis of [18F]-Fluorodeoxyglucose (FDG) kinetics in cancer has been most often limited to the evaluation of the average uptake over relatively large volumes. Nevertheless, tumor lesion almost contains inflammatory infiltrates whose cells are characterized by a significant radioactivity washout due to the hydrolysis of FDG-6P catalyzed by glucose-6P phosphatase. The present study aimed to verify whether voxel-wise compartmental analysis of dynamic imaging can identify tumor regions characterized by tracer washout. Materials & Methods: The study included 11 patients with lung cancer submitted to PET/CT imaging for staging purposes. Tumor was defined by drawing a volume of interest loosely surrounding the lesion and considering all inside voxels with standardized uptake value (SUV) >40% of the maximum. After 20 minutes dynamic imaging centered on the heart, eight whole body scans were repeated. Six parametric maps were progressively generated by computing six regression lines that considered all eight frames, the last seven ones, and so on, up to the last three. Results: Progressively delaying the starting point of regression line computation identified a progressive increase in the prevalence of voxels with a negative slope. Conclusions: The voxel-wise parametric maps provided by compartmental analysis permits to identify a measurable volume characterized by radioactivity washout. The spatial localization of this pattern is compatible with the recognized preferential site of inflammatory infiltrates populating the tumor stroma and might improve the power of FDG imaging in monitoring the effectiveness of treatments aimed to empower the host immune response against the cancer.

Deep learning, particularly Convolutional Neural Networks (CNNs), has gained significant attention for its effectiveness in computer vision, especially in agricultural tasks. Recent advancements in instance segmentation have improved image classification accuracy. In this work, we introduce a comprehensive dataset for training neural networks to detect weeds and soy plants through instance segmentation. Our dataset covers various stages of soy growth, offering a chronological perspective on weed invasion's impact, with 1,000 meticulously annotated images. We also provide 6 state of the art models, trained in this dataset, that can understand and detect soy and weed in every stage of the plantation process. By using this dataset for weed and soy segmentation, we achieved a segmentation average precision of 79.1% and an average recall of 69.2% across all plant classes, with the YOLOv8X model. Moreover, the YOLOv8M model attained 78.7% mean average precision (mAp-50) in caruru weed segmentation, 69.7% in grassy weed segmentation, and 90.1% in soy plant segmentation., Comment: 11th IEEE International Conference on Cybernetics and Intelligent Systems (CIS)

We consider a system of $N$ interacting particles, described by SDEs driven by Poisson random measures, where the coefficients depend on the empirical measure of the system. Every particle jumps with a jump rate depending on its position. When this happens, all the other particles of the system receive a small random kick which is distributed according to a heavy tailed random variable belonging to the domain of attraction of an $\alpha-$ stable law and scaled by $N^{-1/\alpha},$ where $0 < \alpha <2 .$ We call these jumps collateral jumps. Moreover, in case $ 0 < \alpha < 1, $ the jumping particle itself undergoes a macroscopic, main jump. Such systems appear in the modeling of large neural networks, such as the human brain. The particular scaling of the collateral jumps implies that the limit of the empirical measures of the system is random and equals the conditional distribution of one typical particle in the limit system, given the source of common noise. Thus the system exhibits the conditional propagation of chaos property. The limit system turns out to be solution of a non-linear SDE, driven by an $ \alpha-$stable process. We prove strong unique existence of the limit system and introduce a suitable coupling to obtain the strong convergence of the finite to the limit system, together with precise error bounds for finite time marginals., Comment: arXiv admin note: text overlap with arXiv:2306.12275

Misfit layer compounds, heterostructures composed by a regular alternating stacking of rocksalt monochalcogenides bilayers and few-layer transition metal dichalchogenides, are an emergent platform to investigate highly doped transition metal dichalcogenides. Among them, (LaSe)$_{1.14}$(NbSe$_2$)$_2$ displays Ising superconductivity, while the presence of a charge density wave (CDW) in the material is still under debate. Here, by using polarized Raman spectroscopy and first-principles calculations, we show that NbSe$_2$ undergoes a doping-driven collapse of the CDW ordering within the misfit, and no signature of the CDW is detected down to 8~K. We provide a complete experimental and theoretical description of the lattice dynamics of this misfit compound. We show that the vibrational properties are obtained from those of the two subunits, namely the LaSe unit and the NbSe$_2$ bilayer, in the presence of a suitable field-effect doping, and then highlight the 2D nature of the lattice dynamics of NbSe$_2$ within the (LaSe)$_{1.14}$(NbSe$_2$)$_2$ 3D structure., Comment: First version of the manuscript (before revisions)

We present the model we developed to reconstruct the CUORE radioactive background based on the analysis of an experimental exposure of 1038.4 kg yr. The data reconstruction relies on a simultaneous Bayesian fit applied to energy spectra over a broad energy range. The high granularity of the CUORE detector, together with the large exposure and extended stable operations, allow for an in-depth exploration of both spatial and time dependence of backgrounds. We achieve high sensitivity to both bulk and surface activities of the materials of the setup, detecting levels as low as 10 nBq kg$^{-1}$ and 0.1 nBq cm$^{-2}$, respectively. We compare the contamination levels we extract from the background model with prior radio-assay data, which informs future background risk mitigation strategies. The results of this background model play a crucial role in constructing the background budget for the CUPID experiment as it will exploit the same CUORE infrastructure.

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $ n \rightarrow { inv} $ and $ nn \rightarrow { inv} $. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$., Comment: 28 pages, 7 figures, 4 tables

The current experiments searching for neutrinoless double-$\beta$ ($0\nu\beta\beta$) decay also collect large statistics of Standard Model allowed two-neutrino double-$\beta$ ($2\nu\beta\beta$) decay events. These can be used to search for Beyond Standard Model (BSM) physics via $2\nu\beta\beta$ decay spectral distortions. $^{100}$Mo has a natural advantage due to its relatively short half-life, allowing higher $2\nu\beta\beta$ decay statistics at equal exposures compared to the other isotopes. We demonstrate the potential of the dual read-out bolometric technique exploiting a $^{100}$Mo exposure of 1.47 kg $\times$ y, acquired in the CUPID-Mo experiment at the Modane underground laboratory (France). We set limits on $0\nu\beta\beta$ decays with the emission of one or more Majorons, on $2\nu\beta\beta$ decay with Lorentz violation, and $2\nu\beta\beta$ decay with a sterile neutrino emission. In this analysis, we investigate the systematic uncertainty induced by modeling the $2\nu\beta\beta$ decay spectral shape parameterized through an improved model, an effect never considered before. This work motivates searches for BSM processes in the upcoming CUPID experiment, which will collect the largest amount of $2\nu\beta\beta$ decay events among the next-generation experiments.

The main goal of the CRESST-III experiment is the direct detection of dark matter particles via their scattering off target nuclei in cryogenic detectors. In this work we present the results of a Silicon-On-Sapphire (SOS) detector with a mass of 0.6$\,$g and an energy threshold of (6.7$\, \pm \,$0.2)$\,$eV with a baseline energy resolution of (1.0$\, \pm \,$0.2)$\,$eV. This allowed for a calibration via the detection of single luminescence photons in the eV-range, which could be observed in CRESST for the first time. We present new exclusion limits on the spin-independent and spin-dependent dark matter-nucleon cross section that extend to dark matter particle masses of less than 100$\,$MeV/c$^{2}$., Comment: 10 pages, 8 figures

Photoexcited GeTe undergoes a non-thermal phase transition from a rhombohedral to a rocksalt crystalline phase. The microscopic mechanism and the nature of the transition are unclear. By using constrained density functional perturbation theory and by accounting for quantum anharmonicity within the stochastic self-consistent harmonic approximation, we show that the non-thermal phase transition is strongly first order and does not involve phonon softening, at odd with the thermal one. The transition is driven by the closure of the single particle gap in the photoexcited rhombohedral phase. Finally, we show that ultrafast X-ray diffraction data are consistent with a coexistence of the two phases, as expected in a first order transition. Our results are relevant for the understanding of phase transitions and bonding in phase change materials., Comment: 5 pages, 6 figures To be published in Phys. Rev. Lett

Experiments aimed at direct searches for WIMP dark matter require highly effective reduction of backgrounds and control of any residual radioactive contamination. In particular, neutrons interacting with atomic nuclei represent an important class of backgrounds due to the expected similarity of a WIMP-nucleon interaction, so that such experiments often feature a dedicated neutron detector surrounding the active target volume. In the context of the development of DarkSide-20k detector at INFN Gran Sasso National Laboratory (LNGS), several R&D projects were conceived and developed for the creation of a new hybrid material rich in both hydrogen and gadolinium nuclei to be employed as an essential element of the neutron detector. Thanks to its very high cross-section for neutron capture, gadolinium is one of the most widely used elements in neutron detectors, while the hydrogen-rich material is instrumental in efficiently moderating the neutrons. In this paper results from one of the R&Ds are presented. In this effort the new hybrid material was obtained as a poly(methyl methacrylate) (PMMA) matrix, loaded with gadolinium oxide in the form of nanoparticles. We describe its realization, including all phases of design, purification, construction, characterization, and determination of mechanical properties of the new material.

Many of the most sensitive physics experiments searching for rare events, like neutrinoless double beta ($0\nu\beta\beta$) decay and dark matter interactions, rely on cryogenic macro-calorimeters operating at the mK-scale. Located underground at the Gran Sasso National Laboratory (LNGS), in central Italy, CUORE (Cryogenic Underground Observatory for Rare Events) is one of the leading experiments for the search of $0\nu\beta\beta$ decay, implementing the low-temperature calorimetric technology. We present a novel multi-detector analysis to correlate environmental phenomena with the low-frequency noise of low-temperature calorimeters. Indeed, the correlation of marine and seismic data with data from a pair of CUORE detectors indicates that cryogenic detectors are sensitive not only to intense vibrations generated by earthquakes, but also to the much fainter vibrations induced by marine microseisms in the Mediterranean Sea due to the motion of sea waves. Proving that cryogenic macro-calorimeters are sensitive to such environmental sources of noise opens the possibility of studying their impact on the detectors physics-case sensitivity. Moreover, this study could pave the road for technology developments dedicated to the mitigation of the noise induced by marine microseisms, from which the entire community of cryogenic calorimeters can benefit.

DeeperHistReg is a software framework dedicated to registering whole slide images (WSIs) acquired using multiple stains. It allows one to perform the preprocessing, initial alignment, and nonrigid registration of WSIs acquired using multiple stains (e.g. hematoxylin \& eosin, immunochemistry). The framework implements several state-of-the-art registration algorithms and provides an interface to operate on arbitrary resolution of the WSIs (up to 200k x 200k). The framework is extensible and new algorithms can be easily integrated by other researchers. The framework is available both as a PyPI package and as a Docker container.

The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful to quickly transfer annotations between consecutive or restained slides, thus significantly reducing the annotation time and associated costs. Nevertheless, the slide preparation is different for each stain and the tissue undergoes complex and large deformations. Therefore, a robust, efficient, and accurate registration method is highly desired by the scientific community and hospitals specializing in digital pathology. We propose a two-step hybrid method consisting of (i) deep learning- and feature-based initial alignment algorithm, and (ii) intensity-based nonrigid registration using the instance optimization. The proposed method does not require any fine-tuning to a particular dataset and can be used directly for any desired tissue type and stain. The method scored 1st place in the ACROBAT 2023 challenge. We evaluated using three open datasets: (i) ANHIR, (ii) ACROBAT, and (iii) HyReCo, and performed several ablation studies concerning the resolution used for registration and the initial alignment robustness and stability. The method achieves the most accurate results for the ACROBAT dataset, the cell-level registration accuracy for the restained slides from the HyReCo dataset, and is among the best methods evaluated on the ANHIR dataset. The method does not require any fine-tuning to a new datasets and can be used out-of-the-box for other types of microscopic images. The method is incorporated into the DeeperHistReg framework, allowing others to directly use it to register, transform, and save the WSIs at any desired pyramid level. The proposed method is a significant contribution to the WSI registration, thus advancing the field of digital pathology.

The eROSITA will deliver an unprecedented volume of X-ray survey observations, 20-30 times more sensitive than ROSAT in the soft band (0.5-2 keV) and for the first time imaging in the hard band (2-10 keV) including galaxy clusters and groups along with obscured and unobscured AGNs. This calls for a powerful theoretical effort to control the systematics and biases that may affect the data analysis. We investigate the detection technique and selection effects in the galaxy group and AGN populations of a mock eROSITA survey at the depth of eRASS:4. We create a $30\times 30$ deg$^{2}$ mock observation based on the cosmological hydrodynamical simulation Magneticum Pathfinder within z=0-0.2. We combine a physical background extracted from the real eFEDS background analysis with realistic simulations of X-ray emission for the hot gas, AGNs and X-ray binaries. We apply a detection procedure equivalent to the reduction done on eRASS data and evaluate the completeness and contamination to reconstruct the luminosity functions of the extended and point sources in the catalogue. We assess the completeness of extended detections as a function of the input X-ray flux and halo. We achieve full recovery of the brightest (most massive) clusters and AGNs. However, a significant fraction of galaxy groups remains undetected. Examining the gas properties between the detected and undetected galaxy groups at fixed halo mass, we observe that the detected population exhibits, on average, higher X-ray brightness compared to the undetected ones. Moreover, we find that X-ray luminosity primarily correlates with the hot gas fraction, rather than temperature or metallicity. Our simulation suggests the presence of a systematic selection effect in current surveys, resulting in X-ray survey catalogues predominantly composed of the lowest-entropy, gas-richest, and highest surface brightness halos on galaxy group scales., Comment: 18 pages, 14 figures, submitted to A&A

The inclusion of an additional $U(1)$ gauge $L_\mu-L_\tau$ symmetry would release the tension between the measured and the predicted value of the anomalous muon magnetic moment: this paradigm assumes the existence of a new, light $Z^\prime$ vector boson, with dominant coupling to $\mu$ and $\tau$ leptons and interacting with electrons via a loop mechanism. The $L_\mu-L_\tau$ model can also explain the Dark Matter relic abundance, by assuming that the $Z'$ boson acts as a "portal" to a new Dark Sector of particles in Nature, not charged under known interactions. In this work we present the results of the $Z'$ search performed by the NA64-$e$ experiment at CERN SPS, that collected $\sim 9\times10^{11}$ 100 GeV electrons impinging on an active thick target. Despite the suppressed $Z'$ production yield with an electron beam, NA64-$e$ provides the first accelerator-based results excluding the $g-2$ preferred band of the $Z'$ parameter space in the 1 keV $ < m_{Z'} \lesssim 2$ MeV range, in complementarity with the limits recently obtained by the NA64-$\mu$ experiment with a muon beam., Comment: 15 pages, 5 figures. New version with extended exclusion limits down to 1 keV in Z' mass

The matter-antimatter asymmetry underlines the incompleteness of the current understanding of particle physics. Neutrinoless double-beta ($0\nu \beta\beta$) decay may help explain this asymmetry, while unveiling the Majorana nature of the neutrino. The CUORE experiment searches for $0\nu \beta\beta$ decay of $^{130}$Te using a tonne-scale cryogenic calorimeter operated at milli-kelvin temperatures. We report no evidence for $0\nu \beta\beta$ decay and place a lower limit on the half-life of T$_{1/2}$ $>$ 3.8 $\times$ 10$^{25}$ years (90% C.I.) with over 2 tonne$\cdot$year TeO$_2$ exposure. The tools and techniques developed for this result and the 5 year stable operation of nearly 1000 detectors demonstrate the infrastructure for a next-generation experiment capable of searching for $0\nu \beta\beta$ decay across multiple isotopes.

In recent times, the sensitivity of low-mass direct dark matter searches has been limited by unknown low energy backgrounds close to the energy threshold of the experiments known as the low energy excess (LEE). The CRESST experiment utilises advanced cryogenic detectors constructed with different types of crystals equipped with Transition Edge Sensors (TESs) to measure signals of nuclear recoils induced by the scattering of dark matter particles in the detector. In CRESST, this low energy background manifests itself as a steeply rising population of events below 200 eV. A novel detector design named doubleTES using two identical TESs on the target crystal was studied to investigate the hypothesis that the events are sensor-related. We present the first results from two such modules, demonstrating their ability to differentiate between events originating from the crystal's bulk and those occurring in the sensor or in its close proximity., Comment: 10 pages, 13 figures

On March 6 2023, the Gaia telescope has alerted a 2-magnitude burst from Gaia23bab, a Young Stellar Object in the Galactic plane. We observed Gaia23bab with the Large Binocular Telescope obtaining optical and near-infrared spectra close in time to the peak of the burst, and collected all public multi-band photometry to reconstruct the historical light curve. This latter shows three bursts in ten years (2013, 2017 and 2023), whose duration and amplitude are typical of EXor variables. We estimate that, due to the bursts, the mass accumulated on the star is about twice greater than if the source had remained quiescent for the same period of time. Photometric analysis indicates that Gaia23bab is a Class,II source with age < 1 Myr, spectral type G3-K0, stellar luminosity 4.0 L_sun, and mass 1.6 M_sun. The optical/near infrared spectrum is rich in emission lines. From the analysis of these lines we measured the accretion luminosity and the mass accretion rate L_acc(burst)=3.7 L_sun, M_acc(burst) 2.0 10 $^(-7) M_sun/yr, consistent with those of EXors. More generally, we derive the relationships between accretion and stellar parameters in a sample of EXors. We find that, when in burst, the accretion parameters become almost independent of the stellar parameters and that EXors, even in quiescence, are more efficient than classical T Tauri stars in assembling mass., Comment: Accepted by The Astrophysical Journal

Traditionally, a college grade point average (GPA) of 2.00 or higher has signified that a student has made acceptable academic progress and avoided academic probation. However, having a 3.00 or higher has signified a level of success that is often required for admission to graduate school, maintaining a scholarship or enrollment in an honors program, and for consideration by corporate recruiters. Focusing on students with a first-year GPA (FYGPA) between 2.00 and 2.99, this study examined degree completion rates and fourth-year cumulative grade point average (CGPA) across a sample of 97,282 students enrolled at 73 four-year institutions. Results showed that for students with FYGPAs between 2.50 and 2.99, just 48% graduated within four years and only 45% had a fourth-year CGPA of 3.00 or higher. For students with FYGPAs between 2.00 and 2.49, just 28% graduated within four years and only 18% had a fourth-year CGPA of 3.00 or higher. These students with a 2.00 to 2.99 FYGPA were also more likely to be first-generation college students, underrepresented minority students, and students from more challenging neighborhood environments. Logistic regression analyses showed that students' chances of graduating within four years and pulling their CGPAs above 3.00 given a FYGPA below 3.00 were quite low. Given the implications of a low but acceptable FYGPA, early identification of students who may benefit from particular academic advising initiatives as they transition to college may be key to keeping all doors open for as many students as possible, doors encountered both throughout and after college.

This pilot study examines digital SAT® score relationships with first-year college performance. Results show that digital SAT scores predict college performance as well as paper and pencil SAT scores, and that digital SAT scores meaningfully improve our understanding of a student's readiness for college above high school grade point average (HSGPA) alone. In this study, there was a 22% improvement in the prediction of college performance when the SAT and HSGPA were used together, instead of using the HSGPA alone. For STEM majors, the added SAT value was 38%. Similar results were found when the outcome examined was course credits earned in the first year, a metric for understanding student progress toward degree completion. Findings from this study show that the SAT remains a powerful tool for understanding students' readiness for college, for course placement and academic major field decisions, scholarship and honors program decisions, and identifying students who may need academic support.

This article introduces a method for adjusting macro-particle weights within a particle distribution while preserving statistical and physical properties. The method allows the weights of the new macro-particle distribution to be determined by any continuous function, including uniform. Computational simulations validate the presented approach.

We study a dissipative version of the contact process, with mean-field interaction, which admits a simple epidemiological interpretation. The propagation of chaos and the corresponding normal fluctuations reveal that the noise present in the finite-size system induces oscillations with a nearly deterministic period and a randomly varying amplitude. This is reminiscent of the emergence of pandemic waves in real epidemics.

Cryogenic scintillating calorimeters are ultrasensitive particle detectors for rare event searches, particularly for the search for dark matter and the measurement of neutrino properties. These detectors are made from scintillating target crystals generating two signals for each particle interaction. The phonon (heat) signal precisely measures the deposited energy independent of the type of interacting particle. The scintillation light signal yields particle discrimination on an event-by-event basis. This paper presents a likelihood framework modeling backgrounds and a potential dark matter signal in the two-dimensional plane spanned by phonon and scintillation light energies. We apply the framework to data from CaWO$_4$-based detectors operated in the CRESST dark matter search. For the first time, a single likelihood framework is used in CRESST to model the data and extract results on dark matter in one step by using a profile likelihood ratio test. Our framework simultaneously fits (neutron) calibration data and physics (background) data and allows combining data from multiple detectors. Although tailored to CaWO$_4$-targets and the CRESST experiment, the framework can easily be expanded to other materials and experiments using scintillating cryogenic calorimeters for dark matter search and neutrino physics., Comment: 18 pages, 11 figures, additional figures and data in ancillary files

Orientation: The study explores the connections among servant leadership, burnout, and work ethic culture in organizations. It aims to provide a detailed understanding of how servant leadership influences work ethic culture, especially by considering the role of burnout. Research Purpose: This study aims to understand how servant leadership influences work ethic culture and explore the mediating role of burnout in this relationship. Motivation for the Study: This study wants to fill gaps in our understanding of how servant leadership, burnout, and work ethic culture are connected. It seeks to add useful insights to what we already know from previous research. Research Approach/Design and Method: The study, using surveys and statistics, examines the links between servant leadership, burnout, and work ethic culture in 113 hotels in Bandung, Indonesia, with 339 participants. A 183-sample, chosen with a 0.05 margin of error, underwent SEM-PLS analysis using SmartPLS 3.0. Main Findings: The key findings underscore that servant leadership exerts a positive influence on work ethic culture, and burnout plays a pivotal mediating role in this dynamic. The results shed light on the intricate dynamics shaping organizational cultures. Practical/Managerial Implications: The findings aid organizations in forming supportive leadership policies, promoting employee well-being, and fostering ethical work culture. Managers can apply these insights to enhance leadership practices and reduce burnout impact. Contribution/Value-Add: This study clarifies the connection between servant leadership, burnout, and work ethic culture. The findings offer insights for future research and practical actions in organizational leadership.

Extreme intensity contrast is considered essential for ultraintense, femtosecond laser excitation of solid targets, in particular for studies with structured or ultra-thin targets. Second-harmonic generation has been used to maximize the contrast in the nanosecond and picosecond timescales but the resulting pulses can have intense broad femtosecond structures in the rising edge of the pulse. We show that femtosecond scale structures that arise in this process critically modify the interaction, by altering the local field structures and hence redirecting the electron trajectories and distributions, especially concerning resonant phenomena such as surface plasmon excitation in structured targets. Particle-in-cell (PIC) simulations fully support and give further insight into our experimental results. Our findings have important implications not only for the use of harmonic pulses on solid targets but also for two-color schemes based on second harmonic pulses., Comment: 6 pages, 4 figures

The photoluminescence spectrum of a single-layer boron nitride remains elusive, marked by enigmatic satellites that hint at a significant but unidentified exciton-phonon coupling.Here, by employing a first principles approach based on the many-body cumulant expansion of the charge response, we calculate the optical absorption and photoluminescence of a single layer boron nitride. We identify the specific exciton-phonon scattering channels and unravel their impact on the optical absorption and photoluminescence spectra thereby providing an interpretation of the experimental features. Finally, we show that, even in a strongly polar material such as h-BN monolayer, the electron-hole interaction responsible for the excitonic effect results in the cancellation of the Fr\"olich interaction at small phonon momenta. This effect is captured only if the invariance of the exciton-phonon matrix elements under unitary transformations in the Bloch functions manifold is preserved in the calculation.

The search for new comparably light (well below the electroweak scale) feebly interacting particles is an exciting possibility to explain some mysterious phenomena in physics, among them the origin of Dark Matter. The sensitivity study through detailed simulation of projected experiments is a key point in estimating their potential for discovery. Several years ago we created the DMG4 package for the simulation of DM (Dark Matter) particles in fixed target experiments. The natural approach is to integrate this simulation into the same program that performs the full simulation of particles in the experiment setup. The Geant4 toolkit framework was chosen as the most popular and versatile solution nowadays. The simulation of DM particles production by this package accommodates several possible scenarios, employing electron, muon or photon beams and involving various mediators, such as vector, axial vector, scalar, pseudoscalar, or spin 2 particles. The bremsstrahlung, annihilation or Primakoff processes can be simulated. The package DMG4 contains a subpackage DarkMatter with cross section methods weakly connected to Geant4. It can be used in different frameworks. In this paper, we present the latest developments of the package, such as extending the list of possible mediator particle types, refining formulas for the simulation and extending the mediator mass range. The user interface is also made more flexible and convenient. In this work, we also demonstrate the usage of the package, the improvements in the simulation accuracy and some cross check validations., Comment: 17 pages, 11 figures, 1 table

In upcoming 6G networks, unmanned aerial vehicles (UAVs) are expected to play a fundamental role by acting as mobile base stations, particularly for demanding vehicle-to-everything (V2X) applications. In this scenario, one of the most challenging problems is the design of trajectories for multiple UAVs, cooperatively serving the same area. Such joint trajectory design can be performed using multi-agent deep reinforcement learning (MADRL) algorithms, but ensuring collision-free paths among UAVs becomes a critical challenge. Traditional methods involve imposing high penalties during training to discourage unsafe conditions, but these can be proven to be ineffective, whereas binary masks can be used to restrict unsafe actions, but naively applying them to all agents can lead to suboptimal solutions and inefficiencies. To address these issues, we propose a rank-based binary masking approach. Higher-ranked UAVs move optimally, while lower-ranked UAVs use this information to define improved binary masks, reducing the number of unsafe actions. This approach allows to obtain a good trade-off between exploration and exploitation, resulting in enhanced training performance, while maintaining safety constraints., Comment: Accepted for the 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)