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Utilizing course grade data from 22 four-year higher education institutions, this study highlights the trends in first-year grade point average (FYGPA) between the 2017-2018 and 2021-2022 academic years, the period immediately before and after the pandemic disrupted both K12 and higher education. Results showed that while FYGPAs generally increased at institutions with more selective admission standards, especially private, more selective institutions, FYGPAs remained relatively unchanged at the less selective institutions. Over these five years, high school grade point average (HSGPA) generally increased among incoming students at nearly every institution while SAT® scores generally decreased. The results of institution-level logistic regression analyses indicated that students with the same pre-college academic achievement levels as measured by SAT scores and HSGPA were more likely to earn a FYGPA of 3.0 or higher in later cohort years than were students in the 2017 cohort. Faculty survey results, based on more than 3,000 respondents from over 1,200 higher education institutions, complement the administrative data analyses in this study. The survey findings suggest college faculty believe that the characteristics of incoming students as well as their academic performance in college are weaker now than in the past. Implications for future higher education research and facilitating nuanced K12 and higher education conversations on more effectively preparing students for college are discussed.

The dynamical screening of the electron--phonon vertex is caused by the retarded oscillations of the electronic charge following the electron--hole scattering with a phonon mode. This retardation induces a frequency dependence of the electron--phonon interaction. Model Hamiltonians and {\em ab--initio} approaches have instilled the idea that this retardation is, in most of the cases, negligible. In this work I demonstrate that the dynamical screening of the electron--phonon vertex cannot be neglected {\em a priori}. By using a perturbative expansion I introduce a controllable and physically sound method to evaluate and include dynamical screening effects. Based on the exact results of the homogeneous electron gas I propose a dynamical vertex correction function $\Gamma_{e-p}(\omega)$ designed to screen the commonly used adiabatic electron--phonon interaction. This function is expressed in terms of adiabatic quantities, that can be easily calculated and used to evaluate the strength of the dynamical corrections, even in realistic materials.

We study a two-dimensional chiral active crystal composed of underdamped chiral active particles. These particles, characterized by intrinsic handedness and persistence, interact via linear forces derived from harmonic potentials. Chirality plays a pivotal role in shaping the system's behavior: it reduces displacement and velocity fluctuations while inducing cross-spatial correlations among different Cartesian components of velocity. These features distinguish chiral crystals from their non-chiral counterparts, leading to the emergence of net angular momentum, as predicted analytically. This angular momentum, driven by the torque generated by the chiral active force, exhibits a non-monotonic dependence on the degree of chirality. Additionally, it contributes to the entropy production rate, as revealed through a path-integral analysis. We investigate the dynamic properties of the crystal in both Fourier and real space. Chirality induces a non-dispersive peak in the displacement spectrum, which underlies the generation of angular momentum and oscillations in time-dependent autocorrelation functions or mean-square displacement, all of which are analytically predicted.

In this paper, we propose a new multilevel stochastic framework for the solution of optimization problems. The proposed approach uses random regularized first-order models that exploit an available hierarchical description of the problem, being either in the classical variable space or in the function space, meaning that different levels of accuracy for the objective function are available. The converge analysis of the method is conducted and its numerical behavior is tested on the solution of finite-sum minimization problems. Indeed, the multilevel framework is tailored to the solution of such problems resulting in fact in a nontrivial variance reduction technique with adaptive step-size that outperforms standard approaches when solving nonconvex problems. Differently from classical deterministic multilevel methods, our stochastic method does not require the finest approximation to coincide with the original objective function. This allows to avoid the evaluation of the full sum in finite-sum minimization problems, opening at the solution of classification problems with large data sets.

This study examines the average X-ray properties of massive halos at z< 0.2, covering the largest halo mass range to date, from Milky Way-like halos to massive clusters. The analysis is based on stacking in the eFEDS area of the GAMA galaxy group sample, validated with synthetic data that mimic observed eROSITA X-ray and GAMA optical data using Magneticum lightcones. Stacking was conducted in halo mass bins and tested for AGN and X-ray binary contamination, systematics in the halo mass proxy, and uncertainties in optical group centers. The study provides average X-ray surface brightness profiles in six mass bins, spanning Milky Way-like systems to poor clusters. The scatter in the X-ray luminosity-mass (LX-M) relation is attributed to gas concentration: low X-ray luminosity systems at fixed halo mass exhibit lower central gas concentrations than high-luminosity systems, consistent with Magneticum predictions. However, discrepancies in dark matter concentration arise, with Magneticum predicting undetected groups as older and more relaxed, while observations suggest the opposite. New LX-M relations are presented covering three decades of halo mass. These relations fit a single power law, aligning with previous studies. Magneticum matches observed gas distributions across all masses, whereas IllustrisTNG, EAGLE, Simba, and FLAMINGO exhibit significant discrepancies at various mass scales. Simulations calibrated on local galaxy properties accurately reproduce central galaxies but fail to capture gas properties. Conversely, simulations like Magneticum excel in gas predictions but produce overly massive central galaxies. Further exploration of gas and dark matter distributions and their effects on galaxy properties is critical to comprehending the role of gravitational forces and feedback in shaping large-scale structure and galaxy evolution., Comment: Submitted to A&A, 15 pages, 9 figures. Abstract adapted to arxiv submission. arXiv admin note: text overlap with arXiv:2411.16546

By using eROSITA data in the eFEDS area, we provide a measure of the hot gas fraction vs. halo mass relation over the largest halo mass range, from Milky Way-sized halos to massive clusters, and to the largest radii ever probed so far in local systems. To cope with the incompleteness and selection biases of the X-ray selection, we apply the stacking technique in eROSITA data of a highly complete and tested sample of optically selected groups. The method has been extensively tested on mock observations. In massive clusters, the hot gas alone provides a baryon budget within $R_{200}$ consistent with the cosmic value. At the same time, at the group mass scale, it accounts only for 20-40% of it. The hot gas fraction vs. halo mass relation is well-fitted by a power law, with a consistent shape and a normalization varying at maximum by a factor of 2 from $r_{500}$ to $r_{200}$. Such a relation is consistent with other works in the literature that consider X-ray survey data at the same depth as eFEDS. However, it provides a lower average gas fraction in the group regime than works based on X-ray bright group samples. The comparison of the observed relation with the predictions of several hydrodynamical simulations (BAHAMAS, FLAMINGO, SIMBA, Illustris, IllustrisTNG, MillenniumTNG, and Magneticum) shows that all simulations but Magneticum and SIMBA overpredict the gas fraction, with the largest discrepancy (up to a factor of 3) in the massive group-poor cluster halo mass range. We emphasize the need for mechanisms that can effectively expel gas to larger radii in galaxy groups without excessively quenching star formation in their member galaxies. Current hydrodynamical simulations face a significant challenge in balancing their subgrid physics: none can sufficiently evacuate gas from the halo virial region without negatively impacting the properties of the resident galaxy population., Comment: submitted to A&A, 11 pages, 8 figures. Abstract reduced for ArXiv submission

Hydrodynamical simulation predictions are often compared with observational data without fully accounting for systematics and biases specific to observational techniques. Using the magnetohydrodynamical simulation Magneticum, we generate mock eROSITA eRASS:4 data, combined with GAMA-like spectroscopic surveys and optically selected galaxy catalogs from the same light-cone, to analyze hot gas properties in galaxy groups via a stacking technique. This study aims to (i) incorporate observational systematics into predictions and (ii) evaluate the reliability of stacking techniques for determining average X-ray properties of galaxy groups. Our analysis provides X-ray emission predictions from Magneticum, including contributions from AGN, X-ray binaries (XRBs), and the Intra-Group Medium (IGM) as a function of halo mass, covering Milky Way (MW)-like groups to poor clusters. We find that AGN and XRBs dominate the X-ray surface brightness profiles of low-mass halos. The reliability of stacking techniques is tested by reproducing input X-ray surface brightness and electron density profiles, accounting for completeness and contamination of prior samples, miscentering of optical group centers, uncertainties in X-ray emissivity due to gas temperature and metallicity assumption, and systematics in halo mass proxies. The halo mass proxy emerges as the primary source of systematics, affecting X-ray surface brightness and scaling relations. We show that stacked X-ray luminosity-mass relations are flatter than input relations but consistent with observations. Additionally, the retrieved hot gas fraction-mass relation aligns well with observational data. These results highlight the need to account for systematic errors when comparing stacking techniques to other methods using different prior catalogs or predictions., Comment: Submitted to A&A. 18 pages and 19 figures, Abstract reduced for ArXiv submission

Wide-field cosmological surveys provide hundreds of thousands of spectroscopically confirmed galaxy groups and clusters, valuable for tracing baryonic matter distribution. However, controlling systematics in identifying host dark matter halos and estimating their properties is crucial. We evaluate three group detection methods on a simulated dataset replicating the GAMA selection to understand systematics and selection effects. This is key for interpreting data from SDSS, GAMA, DESI, WAVES, and leveraging optical catalogues in the (X-ray) eROSITA era to quantify baryonic mass in galaxy groups. Using a lightcone from the Magneticum hydrodynamical simulation, we simulate a spectroscopic galaxy survey in the local Universe (down to $z<0.2$ and stellar mass completeness $M_{\star}\geq10^{9.8} M_{\odot}$). We assess completeness and contamination of reconstructed halo catalogues, evaluate membership accuracy, and analyse the halo mass recovery rate of group finders. All three group finders achieve high completeness ($>80\%$) at group and cluster scales, confirming optical selection's suitability for dense regions. Contamination at low masses ($M_{200}<10^{13} M_{\odot}$) arises from interlopers and fragmentation. Membership is at least 70\% accurate above the group mass scale, but inaccuracies bias halo mass estimates using galaxy velocity dispersion. Alternative proxies, like total stellar luminosity or mass, yield more accurate halo masses. The cumulative luminosity function of galaxy members matches predictions, showing the group finders' accuracy in identifying galaxy populations. These results confirm the reliability and completeness of spectroscopic catalogues produced by state-of-the-art group finders. This supports studies requiring large spectroscopic samples of galaxy groups and clusters, as well as investigations into galaxy evolution across diverse environments., Comment: submitted to A&A, 17 pages, 9 figures. Abstract reduced for ArXiv submission

Maximally localized Wannier functions use the gauge freedom of Bloch wavefunctions to define the optimally smooth subspace with matrix elements that depend smoothly on crystal momentum. The associated Wannier functions are real-space localized, a feature often used to Fourier interpolate periodic observables in reciprocal space on ultradense momentum grids. However, Fourier interpolation cannot handle non-periodic quantities in reciprocal space, such as the oscillator strength matrix elements, which are crucial for the evaluation of optical properties. We show that a direct multidimensional interpolation in the optimally smooth subspace yields comparable accuracy with respect to Fourier interpolation at a similar or lower computational cost. This approach can also interpolate and extrapolate non-periodic observables, enabling the calculation of optical properties on ultradense momentum grids. Finally, we underline that direct interpolation in the optimally smooth subspace can be employed for periodic and non-periodic tensors of any order without any information on the position of the Wannier centers in real space.

Biological organisms often have elongated, flexible structures with some degree of chirality in their bodies or movements. In nature, these organisms frequently take advantage of self-encapsulation mechanisms that create folded configurations, changing their functionality, such as for defensive purposes. Here, we explore the role of chirality in polymeric structures composed of chiral active monomers exhibiting circular motion. Through a combination of experiments and numerical simulations, we demonstrate a spontaneous unfolding-folding transition uniquely induced by chirality, a phenomenon not observed in passive polymers. This transition is driven by a self-wrapping mechanism, resulting in dynamic polymer collapse even without attractive interactions. Our findings, based on chiral polymers made from chiral active granular particles, present new opportunities in robotic applications taking advantage of the interplay between chirality and deformability.

The hot phase of the circumgalactic medium (CGM) allows us to probe the inflow and outflow of gas within a galaxy, which is responsible for dictating the evolution of the galaxy. Studying the hot CGM sheds light on a better understanding of gas physics, which is crucial to inform and constrain simulation models. With the recent advances in observational measurements probing the hot CGM in X-rays and tSZ, we have a new avenue for widening our knowledge of gas physics and feedback by exploiting the information from current/future observations. In this paper, we use the TNG300 hydrodynamical simulations to build a fully self-consistent forward model for the hot CGM. We construct a lightcone and generate mock X-ray observations. We quantify the projection effects, namely the locally correlated large-scale structure in X-rays and the effect due to satellite galaxies misclassified as centrals which affects the measured hot CGM galactocentric profiles in stacking experiments. We present an analytical model that describes the intrinsic X-ray surface brightness profile across the stellar and halo mass bins. The increasing stellar mass bins result in decreasing values of $\beta$, the exponent quantifying the slope of the intrinsic galactocentric profiles. We carry forward the current state-of-the-art by also showing the impact of the locally correlated environment on the measured X-ray surface brightness profiles. We also present, for the first time, the effect of misclassified centrals in stacking experiments for three stellar mass bins: $10^{10.5-11}\ M_\odot$, $10^{11-11.2}\ M_\odot$, and $10^{11.2-11.5}\ M_\odot$. We find that the contaminating effect of the misclassified centrals on the stacked profiles increases when the stellar mass decreases., Comment: 14 pages, 10 figures, Submitted to A&A, comments welcome

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.

This paper presents an energy resolution study of the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3\% at 1~MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of the liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The results of study reveal an energy resolution of 2.95\% at 1~MeV. Furthermore, this study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data collection. Moreover, it provides a guideline for comprehending the energy resolution characteristics of liquid scintillator-based detectors.