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Cell-free massive MIMO (CF-mMIMO) networks have recently emerged as a promising solution to tackle the challenges arising from next-generation massive machine-type communications. In this paper, a fully grant-free deep learning (DL)-based method for user activity detection in CF-mMIMO networks is proposed. Initially, the known non-orthogonal pilot sequences are used to estimate the channel coefficients between each user and the access points. Then, a deep convolutional neural network is used to estimate the activity status of the users. The proposed method is "blind", i.e., it is fully data-driven and does not require prior large-scale fading coefficients estimation. Numerical results show how the proposed DL-based algorithm is able to merge the information gathered by the distributed antennas to estimate the user activity status, yet outperforming a state-of-the-art covariance-based method., Comment: Accepted for publication at IEEE RTSI 2024, Lecco, Italy

Knowledge Organization Systems (KOSs), such as term lists, thesauri, taxonomies, and ontologies, play a fundamental role in categorising, managing, and retrieving information. In the academic domain, KOSs are often adopted for representing research areas and their relationships, primarily aiming to classify research articles, academic courses, patents, books, scientific venues, domain experts, grants, software, experiment materials, and several other relevant products and agents. These structured representations of research areas, widely embraced by many academic fields, have proven effective in empowering AI-based systems to i) enhance retrievability of relevant documents, ii) enable advanced analytic solutions to quantify the impact of academic research, and iii) analyse and forecast research dynamics. This paper aims to present a comprehensive survey of the current KOS for academic disciplines. We analysed and compared 45 KOSs according to five main dimensions: scope, structure, curation, usage, and links to other KOSs. Our results reveal a very heterogeneous scenario in terms of scope, scale, quality, and usage, highlighting the need for more integrated solutions for representing research knowledge across academic fields. We conclude by discussing the main challenges and the most promising future directions.

Delineating and classifying individual cells in microscopy tissue images is a complex task, yet it is a pivotal endeavor in various medical and biological investigations. We propose a new deep learning framework (CISCA) for automatic cell instance segmentation and classification in histological slices to support detailed morphological and structural analysis or straightforward cell counting in digital pathology workflows and brain cytoarchitecture studies. At the core of CISCA lies a network architecture featuring a lightweight U-Net with three heads in the decoder. The first head classifies pixels into boundaries between neighboring cells, cell bodies, and background, while the second head regresses four distance maps along four directions. The network outputs from the first and second heads are integrated through a tailored post-processing step, which ultimately yields the segmentation of individual cells. A third head enables simultaneous classification of cells into relevant classes, if required. We showcase the effectiveness of our method using four datasets, including CoNIC, PanNuke, and MoNuSeg, which are publicly available H\&E datasets. Additionally, we introduce CytoDArk0, a novel dataset consisting of Nissl-stained images of the cortex, cerebellum, and hippocampus from mammals belonging to the orders Cetartiodactyla and Primates. We evaluate CISCA in comparison to other state-of-the-art methods, demonstrating CISCA's robustness and accuracy in segmenting and classifying cells across diverse tissue types, magnifications, and staining techniques.

Since Parker Solar Probe's (Parker's) first perihelion pass at the Sun, large amplitude Alfv\'en waves grouped in patches have been observed near the Sun throughout the mission. Several formation processes for these magnetic switchback patches have been suggested with no definitive consensus. To provide insight to their formation, we examine the heavy ion properties of several adjacent magnetic switchback patches around Parker's 11th perihelion pass capitalizing on a spacecraft lineup with Solar Orbiter where each samples the same solar wind streams over a large range of longitudes. Heavy ion properties (Fe/O, C$^{6+}$/C$^{5+}$, O$^{7+}$/O$^{6+}$) related to the wind's coronal origin, measured with Solar Orbiter can be linked to switchback patch structures identified near the Sun with Parker. We find that switchback patches do not contain distinctive ion and elemental compositional signatures different than the surrounding non-switchback solar wind. Both the patches and ambient wind exhibit a range of fast and slow wind qualities, indicating coronal sources with open and closed field lines in close proximity. These observations and modeling indicate switchback patches form in coronal hole boundary wind and with a range of source region magnetic and thermal properties. Furthermore, the heavy ion signatures suggest interchange reconnection and/or shear driven processes may play a role in their creation., Comment: Accepted for publication in ApJ on September 4th, 2024

Data-driven RANS modeling is emerging as a promising methodology to exploit the information provided by high-fidelity data. However, its widespread application is limited by challenges in generalization and robustness to inconsistencies between input data of varying fidelity levels. This is especially true for thermal turbulent closures, which inherently depend on momentum statistics provided by low or high fidelity turbulence momentum models. This work investigates the impact of momentum modeling inconsistencies on a data-driven thermal closure trained with a dataset with multiple fidelity (DNS and RANS). The analysis of the model inputs shows that the two fidelity levels correspond to separate regions in the input space. It is here shown that such separation can be exploited by a training with heterogeneous data, allowing the model to detect the level of fidelity in its inputs and adjust its prediction accordingly. In particular, a sensitivity analysis and verification shows that such a model can leverage the data inconsistencies to increase its robustness. Finally, the verification with a CFD simulation shows the potential of this multi-fidelity training approach for flows in which momentum statistics provided by traditional models are affected by model uncertainties.

High-quality (Q)-factor optical resonators with extreme temporal coherence are of both technological and fundamental importance in optical metrology, continuous-wave lasing, and semiconductor quantum optics. Despite extensive efforts in designing high-Q resonators across different spectral regimes, the experimental realization of very large Q-factors at visible wavelengths remains challenging due to the small feature size that is sensitive to fabrication imperfections, and thus is typically implemented in integrated photonics. In the pursuit of free-space optics with the benefits of large space-bandwidth product and massive parallel operations, here we design and fabricate a visible-wavelength etch-free metasurface with minimized fabrication defects and experimentally demonstrate a million-scale ultrahigh-Q resonance. A new laser-scanning momentum-space-resolved spectroscopy technique with extremely high spectral and angular resolution is developed to characterize the record-high Q-factor as well as the dispersion of the million-Q resonance in free space. By integrating monolayer WSe2 into our ultrahigh-Q meta-resonator, we further demonstrate laser-like highly unidirectional and narrow-linewidth exciton emission, albeit without any operating power density threshold. Under continuous-wave laser pumping, we observe pump-power-dependent linewidth narrowing at room temperature, indicating the potential of our meta-optics platform in controlling coherent quantum light-sources. Our result also holds great promise for applications like optical sensing, spectral filtering, and few-photon nonlinear optics.

Research conducted previously has focused on either attitudes toward or behaviors associated with autonomous driving. In this paper, we bridge these two dimensions by exploring how attitudes towards autonomous driving influence behavior in an autonomous car. We conducted a field experiment with twelve participants engaged in non-driving related tasks. Our findings indicate that attitudes towards autonomous driving do not affect participants' driving interventions in vehicle control and eye glance behavior. Therefore, studies on autonomous driving technology lacking field tests might be unreliable for assessing the potential behaviors, attitudes, and acceptance of autonomous vehicles., Comment: 12 pages

We study the effect of Gaussian perturbations on a class of model hyperbolic partial differential equations with double symplectic characteristics in low spatial dimensions, extending some recent work in [5]. The coefficients of our partial differential operators contain harmonic oscillators in the space variables, while the noise is additive, white in time and colored in space. We provide sufficient conditions on the spectral measure of the covariance functional describing the noise that allows for the existence of a random field solution for the resulting stochastic partial differential equation. Furthermore we show how the symplectic structure of the set of multiple points affects the regularity of the noise needed to build a measurable process solution. Our approach is based on some explicit computations for the fundamental solutions of several model partial differential operators together with their explicit Fourier transforms.

The goal of this note is to prove that every real-valued Lipschitz function on a Banach space can be pointwise approximated on a given $\sigma$-compact set by smooth cylindrical functions whose asymptotic Lipschitz constants are controlled. This result has applications in the study of metric Sobolev and BV spaces: it implies that smooth cylindrical functions are dense in energy in these kinds of functional spaces defined over any weighted Banach space., Comment: 6 pages

We investigate equivariant localization of the gravitational on-shell action in odd dimensions, focusing on five-dimensional ungauged supergravity. We analyze the conditions for cancellation of boundary terms, so that the full action integral is given in terms of the odd-dimensional analog of the nuts and bolts of Gibbons-Hawking. We specialize to supersymmetric configurations with an additional ${\rm U}(1)$ symmetry preserving the supercharge and provide a formula for the localized on-shell action. We construct asymptotically flat Euclidean supersymmetric non-extremal solutions with two independent rotations and an arbitrary number of electric charges, providing black hole saddles of the gravitational path integral that computes a supersymmetric index, and evaluate their action equivariantly. We find that these Euclidean saddles smoothly interpolate between the supersymmetric extremal black holes and two-center horizonless microstate geometries. The interpolating parameter is the formal temperature, which does not appear in the action; hence the two limiting solutions give the same contribution to the index., Comment: 63 pages

We apply the variational Monte Carlo method based on neural network quantum states, using a neural autoregressive flow architecture as our ansatz, to determine the ground state wave function of the bosonic SU($N$) Yang-Mills-type two-matrix model at strong coupling. Previous literature hinted at the inaccuracy of such an approach at strong coupling. In this work, the accuracy of the results is tested using lattice Monte Carlo simulations: we benchmark the expectation value of the energy of the ground state for system sizes $N$ that are beyond brute-force exact diagonalization methods. We observe that the variational method with neural network states reproduces the right ground state energy when the width of the network employed in this work is sufficiently large. We confirm that the correct result is obtained for $N=2$ and $3$, while obtaining a precise value for $N=4$ requires more resources than the amount available for this work., Comment: 27 pages

In observational studies, the recorded treatment assignment is not purely random, but it is influenced by external factors such as patient characteristics, reimbursement policies, and existing guidelines. Therefore, the treatment effect can be estimated only after accounting for confounding factors. Propensity score (PS) methods are a family of methods that is widely used for this purpose. Although they are all based on the estimation of the a posteriori probability of treatment assignment given patient covariates, they estimate the treatment effect from different statistical points of view and are, thus, relatively hard to compare. In this work, we propose a simulation experiment in which a hypothetical cohort of subjects is simulated in seven scenarios of increasing complexity of the associations between covariates and treatment, but where the two main definitions of treatment effect (average treatment effect, ATE, and average effect of the treatment on the treated, ATT) coincide. Our purpose is to compare the performance of a wide array of PS-based methods (matching, stratification, and inverse probability weighting) in estimating the treatment effect and their robustness in different scenarios. We find that inverse probability weighting provides estimates of the treatment effect that are closer to the expected value by weighting all subjects of the starting population. Conversely, matching and stratification ensure that the subpopulation that generated the final estimate is made up of real instances drawn from the starting population, and, thus, provide a higher degree of control on the validity domain of the estimates., Comment: 19th conference on Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB), September 4-6th 2024, Benevento (Italy)

Multiple Sclerosis (MS) is a chronic autoimmune and inflammatory neurological disorder characterised by episodes of symptom exacerbation, known as relapses. In this study, we investigate the role of environmental factors in relapse occurrence among MS patients, using data from the H2020 BRAINTEASER project. We employed predictive models, including Random Forest (RF) and Logistic Regression (LR), with varying sets of input features to predict the occurrence of relapses based on clinical and pollutant data collected over a week. The RF yielded the best result, with an AUC-ROC score of 0.713. Environmental variables, such as precipitation, NO2, PM2.5, humidity, and temperature, were found to be relevant to the prediction.

Geometric effects in curvilinear nanomagnets can enable chiral, anisotropic and even magnetoelectric responses. Here, we study the effects of magnetic frustration in curvilinear (quasi-)1D magnets represented by spin chains arranged along closed space curves of constant torsion. Considering the cases of easy- and hard-axis anisotropy in ferro- and antiferromagnetic samples, we determine their ground states and analyze the related magnetoelectric multipoles. A constant torsion along the chain results in alternating regions of high and low curvature, facilitating the spin spiral state perturbed by the (anti)periodic boundary conditions on the magnetic order parameter. While easy-axis ferromagnetic chains develop a purely toroidal configuration with the magnetic toroidal moment oriented along the geometry symmetry axis, hard-axis antiferromagnetic chains support multiple magnetic toroidal domains. Our findings suggest that tailoring curvature and torsion of a spin chain enables a new physical mechanism for magnetic frustration, which can be observed in the inhomogeneity of the magnetic order parameter and in the local ferrotoroidic responses.

Recent observations have begun probing the early phases of disc formation, but little data yet exists on disc structure and morphology of Class 0 objects. Using simulations, we are able to lay out predictions of disc morphologies expected in future surveys of young discs. Based on detailed simulations of ab initio star formation by core collapse, we predict that early discs must be eccentric. In this letter, we study the morphology and, in particular, the eccentricity of discs formed in non-ideal magnetohydrodynamic (MHD) collapse simulations. We attempt to show that discs formed by cloud collapse are likely to be eccentric. We ran non-ideal MHD collapse simulations in the adaptive mesh refinement code RAMSES with radiative transfer. We used state-of-the-art analysis methods to measure the disc eccentricity. We find that despite no asymmetry in the initial conditions, the discs formed are eccentric, with eccentricities on the order of 0.1. These results may have important implications for protoplanetary disc dynamics and planet formation. The presence of eccentricity in young discs that is not seen at later stages of disc evolution is in tension with current viscous eccentricity damping models. This implies that there may be an as-yet undiscovered circularisation mechanism in circumstellar discs., Comment: accepted for publication in Astronomy & Astrophysics, 7 pages, 8 figures

Value factorization is a popular paradigm for designing scalable multi-agent reinforcement learning algorithms. However, current factorization methods make choices without full justification that may limit their performance. For example, the theory in prior work uses stateless (i.e., history) functions, while the practical implementations use state information -- making the motivating theory a mismatch for the implementation. Also, methods have built off of previous approaches, inheriting their architectures without exploring other, potentially better ones. To address these concerns, we formally analyze the theory of using the state instead of the history in current methods -- reconnecting theory and practice. We then introduce DuelMIX, a factorization algorithm that learns distinct per-agent utility estimators to improve performance and achieve full expressiveness. Experiments on StarCraft II micromanagement and Box Pushing tasks demonstrate the benefits of our intuitions., Comment: 22 pages, 9 figures, 4 tables

We analyze the effect of acoustic phonons on the photocurrent and the spectral characteristics of a simplified photovoltaic setup made of Mott insulating layers between two metallic leads among which a bias voltage is applied. This paper extends and completes the study made in a previous work by the authors [Gazzaneo et al., Phys. Rev. B 109, 235134]. We include acoustic phonons via the Migdal approximation and we use real-space Floquet dynamical mean-field theory to address the nonequilibrium Floquet steady-state. The so-called auxiliary master equation approach is employed as impurity solver. We find that impact ionization is only weakly affected by dissipation by acoustic phonons at low bias voltages. For higher biases instead, the Hartree shift considerably alters the on-site energies of the Hubbard bands and suppresses the photocurrent for intermediate electron-phonon coupling strengths. Impact ionization processes play a fundamental role in enhancing the electrical output power, which decreases when electron-phonon interaction is considered., Comment: 13 pages, 9 figures, comments are welcome

The tidal Love numbers of self-gravitating compact objects describe their response to external tidal perturbations, such as those from a companion in a binary system, offering valuable insights into their internal structure. For static tidal fields, asymptotically flat black holes in vacuum exhibit vanishing Love numbers in general relativity, even though this property is sensitive to the presence of an external environment. In this work we study the tidal deformability of black holes surrounded by thin accretion disks, showing that the Love numbers could be large enough to mask any effect of modified gravity and to intrinsically limit tidal tests of black-hole mimickers. Furthermore, we investigate the measurability of the tidal parameters with next-generation gravitational wave experiments, like LISA and Einstein Telescope. Our findings suggest that these parameters could be measured with high precision, providing a powerful tool to probe the environment around coalescing binary systems., Comment: 13 pages, 4 figures

We consider the continuous superposition of operators of the form \[ \iint_{[0, 1]\times (1, N)} (-\Delta)_p^s \,u\,d\mu(s,p), \] where $\mu$ denotes a signed measure over the set $[0, 1]\times (1, N)$, joined to a nonlinearity satisfying a proper subcritical growth. The novelty of the paper relies in the fact that, differently from the existing literature, the superposition occurs in both $s$ and $p$. Here we introduce a new framework which is so broad to include, for example, the scenarios of the finite sum of different (in both $s$ and $p$) Laplacians, or of a fractional $p$-Laplacian plus a $p$-Laplacian, or even combinations involving some fractional Laplacians with the "wrong" sign. The development of this new setting comes with two applications, which are related to the Weierstrass Theorem and a Mountain Pass technique. The results obtained contribute to the existing literature with several specific cases of interest which are entirely new.

Dislocations in ductile ceramics offer the potential for robust mechanical performance while unlocking versatile functional properties. Previous studies have been limited by small volumes with dislocations and/or low dislocation densities in ceramics. Here, we use Brinell ball scratching to create crack-free, large plastic zones, offering a simple and effective method for dislocation engineering at room temperature. Using MgO, we tailor high dislocation densities up to ~10^15 m^-2. We characterize the plastic zones by chemical etching, electron channeling contrast imaging, and scanning transmission electron microscopy, and further demonstrate that crack initiation and propagation in the plastic zones with high-density dislocations can be completely suppressed. The residual stresses in the plastic zones were analyzed using high-resolution electron backscatter diffraction. With the residual stress being subsequently relieved via thermal annealing while retaining the high-density dislocations, we observe the cracks are no longer completely suppressed, but the pure toughening effect of the dislocations remains evident.

The advent of quantum computers has justified the development of quantum machine learning algorithms , based on the adaptation of the principles of machine learning to the formalism of qubits. Among such quantum algorithms, anomaly detection represents an important problem crossing several disciplines from cybersecurity, to fraud detection to particle physics. We summarize the key concepts involved in quantum computing, introducing the formal concept of quantum speed up. The survey provides a structured map of anomaly detection based on quantum machine learning. We have grouped existing algorithms according to the different learning methods, namely quantum supervised, quantum unsupervised and quantum reinforcement learning, respectively. We provide an estimate of the hardware resources to provide sufficient computational power in the future. The survey provides a systematic and compact understanding of the techniques belonging to each category. We eventually provide a discussion on the computational complexity of the learning methods in real application domains.

As autonomous vehicle technology advances, the precise assessment of safety in complex traffic scenarios becomes crucial, especially in mixed-vehicle environments where human perception of safety must be taken into account. This paper presents a framework designed for assessing traffic safety in multi-vehicle situations, facilitating the simultaneous utilization of diverse objective safety metrics. Additionally, it allows the integration of subjective perception of safety by adjusting model parameters. The framework was applied to evaluate various model configurations in car-following scenarios on a highway, utilizing naturalistic driving datasets. The evaluation of the model showed an outstanding performance, particularly when integrating multiple objective safety measures. Furthermore, the performance was significantly enhanced when considering all surrounding vehicles.

There is growing interest towards the use of AI directly onboard satellites for quick analysis and rapid response to critical events such as natural disasters. This paper presents a blueprint to the mission designer for the development of a modular and efficient deep learning payload to address multiple onboard inference tasks. In particular, we design a self-supervised lightweight backbone that provides features to efficient task-specific heads. The latter can be developed independently and with reduced data labeling requirements thanks to the frozen backbone. Experiments on three sample tasks of cloud segmentation, flood detection, and marine debris classification on a 7W embedded system show competitive results with inference quality close to high-complexity state-of-the-art models and high throughput in excess of 8 Mpx/s.

Several Pulsar Timing Array (PTA) collaborations have recently found evidence for a Gravitational Wave Background (GWB) by measuring the perturbations that this background induces in the time-of-arrivals of pulsar signals. These perturbations are expected to be correlated across different pulsars and, for isotropic GWBs, the expected values of these correlations (obtained by averaging over different GWB realizations) are a simple function of the pulsars' angular separations, known as the Hellings-Downs (HD) correlation function. On the other hand, anisotropic GWBs would induce deviations from these HD correlations in a way that can be used to search for anisotropic distributions of the GWB power. However, even for isotropic GWBs, interference between GW sources radiating at overlapping frequencies induces deviations from the HD correlation pattern, an effect known in the literature as "cosmic variance". In this work, we study the impact of cosmic variance on PTA anisotropy searches. We find that the fluctuations in cross-correlations related to cosmic variance can lead to the miss-classification of isotropic GWBs as anisotropic, leading to a false detection rate of $\sim 50\%$ for frequentist anisotropy searches. We also observe that cosmic variance complicates the reconstruction of the GWB sky map, making it more challenging to resolve bright GW hotspots, like the ones expected to be produced from a Supermassive Black Hole Binaries population. These findings highlight the need to refine anisotropy search techniques to improve our ability to reconstruct the GWB sky map and accurately assess the significance of any isotropy deviations we might find in it., Comment: 15 pages, 8 figures

We introduce a constructive method for defining a global loop-integrand basis for scattering amplitudes, encompassing both planar and nonplanar contributions. Our approach utilizes a graph-based framework to establish a well-defined, non-redundant basis of integrands. This basis, constructed from a chosen set of non-redundant graphs together with a selection of irreducible scalar products, provides clear insights into various physical properties of scattering amplitudes and proves useful in multiple contexts, such as on-shell Ward identities and manifesting gauge-choice independence. A key advantage of our integrand basis is its ability to streamline the generalized unitarity method. Specifically, we can directly read off the coefficients of basis elements without resorting to ansatze or solving linear equations. This novel approach allows us to lift generalized unitarity cuts -- expressed as products of tree amplitudes -- to loop-level integrands, facilitating the use of the tree-level double copy to generate complete gravitational integrands at any loop order. This method circumvents the difficulties in identifying complete higher-loop-order gauge-theory integrands that adhere to the color-kinematics duality. Additionally, our cut-based organization is well-suited for expansion in hard or soft limits, aiding in the exploration of ultraviolet or classical limits of scattering amplitudes., Comment: 56 pages, 15 figures

Proximally-sensed laser scanning offers significant potential for automated forest data capture, but challenges remain in automatically identifying tree species without additional ground data. Deep learning (DL) shows promise for automation, yet progress is slowed by the lack of large, diverse, openly available labeled datasets of single tree point clouds. This has impacted the robustness of DL models and the ability to establish best practices for species classification. To overcome these challenges, the FOR-species20K benchmark dataset was created, comprising over 20,000 tree point clouds from 33 species, captured using terrestrial (TLS), mobile (MLS), and drone laser scanning (ULS) across various European forests, with some data from other regions. This dataset enables the benchmarking of DL models for tree species classification, including both point cloud-based (PointNet++, MinkNet, MLP-Mixer, DGCNNs) and multi-view image-based methods (SimpleView, DetailView, YOLOv5). 2D image-based models generally performed better (average OA = 0.77) than 3D point cloud-based models (average OA = 0.72), with consistent results across different scanning platforms and sensors. The top model, DetailView, was particularly robust, handling data imbalances well and generalizing effectively across tree sizes. The FOR-species20K dataset, available at https://zenodo.org/records/13255198, is a key resource for developing and benchmarking DL models for tree species classification using laser scanning data, providing a foundation for future advancements in the field.

The solar-type subgiant $\beta$ Hyi has long been studied as an old analog of the Sun. Although the rotation period has never been measured directly, it was estimated to be near 27 days. As a southern hemisphere target it was not monitored by long-term stellar activity surveys, but archival International Ultraviolet Explorer data revealed a 12 year activity cycle. Previous ground-based asteroseismology suggested that the star is slightly more massive and substantially larger and older than the Sun, so the similarity of both the rotation rate and the activity cycle period to solar values is perplexing. We use two months of precise time-series photometry from the Transiting Exoplanet Survey Satellite (TESS) to detect solar-like oscillations in $\beta$ Hyi and determine the fundamental stellar properties from asteroseismic modeling. We also obtain a direct measurement of the rotation period, which was previously estimated from an ultraviolet activity-rotation relation. We then use rotational evolution modeling to predict the rotation period expected from either standard spin-down or weakened magnetic braking (WMB). We conclude that the rotation period of $\beta$ Hyi is consistent with WMB, and that changes in stellar structure on the subgiant branch can reinvigorate the large-scale dynamo and briefly sustain magnetic activity cycles. Our results support the existence of a "born-again" dynamo in evolved subgiants -- previously suggested to explain the cycle in 94 Aqr Aa -- which can best be understood within the WMB scenario., Comment: ApJ accepted. 8 pages including 6 figures and 2 tables

Ultra-Wideband (UWB) technology re-emerges as a groundbreaking ranging technology with its precise micro-location capabilities and robustness. This paper highlights the security dimensions of UWB technology, focusing in particular on the intricacies of device fingerprinting for authentication, examined through the lens of state-of-the-art deep learning techniques. Furthermore, we explore various potential enhancements to the UWB standard that could realize a sovereign UWB data network. We argue that UWB data communication holds significant potential in healthcare and ultra-secure environments, where the use of the common unlicensed 2.4~GHz band-centric wireless technology is limited or prohibited. A sovereign UWB network could serve as an alternative, providing secure localization and short-range data communication in such environments., Comment: NetAISys Workshop at ACM Mobisys 2024

In the strong magnetic field of a neutron star's magnetosphere, axions coupled to electromagnetism develop a nonzero probability to convert into photons. Past studies have revealed that the axion-photon conversion can be resonantly enhanced. We recognize that the axion-photon resonance admits two parametrically distinct resonant solutions, which we call the mass-matched resonance and the Euler-Heisenberg assisted resonance. The mass-matched resonance occurs at a point in the magnetosphere where the radially-varying plasma frequency crosses the axion mass $\omega_\mathrm{pl} \approx m_a$. The Euler-Heisenberg assisted resonance occurs where the axion energy satisfies $\omega \approx (2 \omega_\mathrm{pl}^2 / 7 g_{\gamma\gamma\gamma\gamma} \bar{B}^2 )^{1/2}$. This second resonance is made possible though the strong background magnetic field $\bar{B}$ as well as the nonzero Euler-Heisenberg four-photon self interaction, which has the coupling $g_{\gamma\gamma\gamma\gamma} = 8 \alpha^2 / 45 m_e^4$. We study the resonant conversion of relativistic axion dark radiation into photons via the Euler-Heisenberg assisted resonance, and we calculate the expected electromagnetic radiation assuming different values for the axion-photon coupling $g_{a\gamma\gamma}$ and different amplitudes for the axion flux onto the neutron star $\Phi_a$. We briefly discuss several possible sources of axion dark radiation. Achieving a sufficiently strong axion flux to induce a detectable electromagnetic signal seems unlikely., Comment: 11 pages + appendices, 8 figures; v2 - references added

The seismocardiographic signal is a promising alternative to the traditional ECG in the analysis of the cardiac activity. In particular, the systolic complex is known to be the most informative part of the seismocardiogram, thus requiring further analysis. State-of-art solutions to detect the systolic complex are based on Deep Learning models, which have been proven effective in pioneering studies. However, these solutions have only been tested in a controlled scenario considering only clean signals acquired from users maintained still in supine position. On top of that, all these studies consider data coming from a single dataset, ignoring the benefits and challenges related to a cross-dataset scenario. In this work, a cross-dataset experimental analysis was performed considering also data from a real-world scenario. Our findings prove the effectiveness of a deep learning solution, while showing the importance of a personalization step to contrast the domain shift, namely a change in data distribution between training and testing data. Finally, we demonstrate the benefits of a multi-channels approach, leveraging the information extracted from both accelerometers and gyroscopes data.

We prove a quantitative version of a Gidas-Ni-Nirenberg-type symmetry result involving the $p$-Laplacian. Quantitative stability is achieved here via integral identities based on the proof of rigidity established by J. Serra in 2013, which extended to general dimension and the $p$-Laplacian operator an argument proposed by P. L. Lions in dimension $2$ for the classical Laplacian. Stability results for the classical Gidas-Ni-Nirenberg symmetry theorem (involving the classical Laplacian) via the method of moving planes were established by Rosset in 1994 and by Ciraolo, Cozzi, Perugini, Pollastro in 2024. To the authors' knowledge, the present paper provides the first quantitative Gidas-Ni-Nirenberg-type result involving the $p$-Laplacian for $p \neq 2$. Even for the classical Laplacian (i.e., for $p=2$), this is the first time that integral identities are used to achieve stability for a Gidas-Ni-Nirenberg-type result. In passing, we obtain a quantitative estimate for the measure of the singular set and an explicit uniform gradient bound.

We present a phase-dependent analysis of the polarized emission from the Crab pulsar based on three sets of observations by the Imaging X-ray Polarimetry Explorer (IXPE). We found that a phenomenological model involving a simple linear transformation of the Stokes parameters adequately describes the IXPE data. This model enables us to establish, for the first time, a connection between the polarization properties of the Crab pulsar in the optical and soft X-ray bands, suggesting a common underlying emission mechanism across these bands. In particular, the phase-dependent polarization degree in X-rays for pure pulsar emission shows similar features but is reduced by a factor $\approx (0.46-0.56)$ compared to the optical band, implying an energy-dependent polarized emission. In addition, using this model, we study the polarization angle swing in X-rays and identify a potentially variable phase-shift at the interpulse relative to the optical band, alongside a phase-shift marginally consistent with zero persisting at the main pulse. While the variability presents a new challenge for theoretical interpretation, our findings suggest that the emission mechanism for the main pulse is likely located far from the neutron star surface, perhaps near or beyond the light cylinder, rather than operating in the inner magnetosphere where vacuum birefringence is expected to be at work. Ignoring the phase-shifts would result in identical phase-dependent polarization angles between the optical and X-ray bands for pure pulsar emission., Comment: 16 pages, 11 figures, submitted to A&A

The properties of energy transfer in the kinetic range of plasma turbulence have fundamental implications on the turbulent heating of space and astrophysical plasmas. It was recently suggested that magnetic reconnection may be responsible for driving the sub-ion scale cascade, and that this process would be characterized by a direct energy transfer towards even smaller scales (until dissipation), and a simultaneous inverse transfer of energy towards larger scales, until the ion break. Here we employ the space-filter technique on high-resolution 2D3V hybrid-Vlasov simulations of continuously driven turbulence providing for the first time quantitative evidence that magnetic reconnection is indeed able to trigger a dual energy transfer originating at sub-ion scales.

We present a pragmatic approach to the sparse identification of nonlinear dynamics for systems with discrete delays. It relies on approximating the underlying delay model with a system of ordinary differential equations via pseudospectral collocation. To minimize the reconstruction error, the new strategy avoids optimizing all possible multiple unknown delays, identifying only the maximum one. The computational burden is thus greatly reduced, improving the performance of recent implementations that work directly on the delay system.

Quantum computers have proven to be effective in simulating many quantum systems. Simulating nuclear processes and state preparation poses significant challenges, even for traditional supercomputers. This study demonstrates the feasibility of a complete simulation of a nuclear transition, including the preparation of both ground and first excited states. To tackle the complexity of strong interactions between two and three nucleons, the states are modeled on the tritium nucleus. Both the initial and final states are represented using quantum circuits with variational quantum algorithms and inductive biases. Describing the spin-isospin states requires four qubits, and a parameterized quantum circuit that exploits a total of 16 parameters is initialized. The estimated energy has a relative error of approximately 2% for the ground state and about 10% for the first excited state of the system. The quantum computer simulation estimates the transition probability between the two states as a function of the dipole polarization angle. This work marks a first step towards leveraging digital quantum computers to simulate nuclear physics., Comment: 22 pages, 7 figures

In this paper, we analyse the light distribution in the Hydra I cluster of galaxies to explore their low surface brightness features, measure the intra-cluster light, and address the assembly history of the cluster. For this purpose, we used deep wide-field g- and r-band images obtained with the VST as part of the VEGAS project. The VST mosaic covers ~0.4 times the virial radius around the core of the cluster, which enabled us to map the light distribution down to faint surface brightness levels of mu_g ~ 28 mag/arcsec^2. In this region of the cluster, 44 cluster members are brighter than m_B<16 mag, and the region includes more than 300 dwarf galaxies. Similar to the projected distribution of all cluster members (bright galaxies and dwarfs), we find that the bulk of the galaxy light is concentrated in the cluster core, which also emits in the X-rays, and there are two overdensities: in the north (N) and south-east (SE) with respect to the cluster core. We present the analysis of the light distribution of all the bright cluster members. After removing foreground stars and other objects, we measured the diffuse intra-cluster light and compared its distribution with that of the globular clusters and dwarf galaxies in the cluster. We find that most of the diffuse light low surface brightness features, and signs of possible gravitational interaction between galaxies reside in the core and in the group in the N, while ram-pressure stripping is frequently found to affect galaxies within the SE group. All these features confirm that the mass assembly in this cluster is still ongoing. By combining the projected phase-space with these observed properties, we trace the different stages of the assembly history. We also address the main formation channels for the intra-cluster light detected in the cluster, which contributes ~ 12% to the total luminosity of the cluster., Comment: 18 pages, 13 figures, accepted for publication in A&A

As a signature of competing correlations, stripes occur in a variety of strongly correlated systems, such as high temperature superconductors (SCs) and quantum Hall effect. We study a double layer SC in the presence of a parallel magnetic field $B$ within the Bogoliubov-de Gennes framework. We find that for low $B$ the system remains in the ``Bardeen-Cooper-Schrieffer (BCS) phase" with a spatially uniform gap, but with increasing $B$, a transition occurs into a phase which contains stripes of the BCS phase separated by regions where the interlayer phase difference rotates by $2\pi$ due to the presence of inter-layer vortices. This stripe phase is predicted to manifest through oscillations in the amplitude of the SC gap and an alternating pattern of supercurrents. We will comment on the relation to previous works based on the Landau-Ginzburg formalism as well as on the possible experimental realization and signature of this phase., Comment: 6 pages, 2 figures

This document aims at describing, in a suitably precise and unambiguous though informal way, the execution semantics of Behavior Trees as used in Robotics applications, with particular attention to the Halt semantics., Comment: 13 pages, 9 figures

Quantum many-body systems are of great interest for many research areas, including physics, biology and chemistry. However, their simulation is extremely challenging, due to the exponential growth of the Hilbert space with the system size, making it exceedingly difficult to parameterize the wave functions of large systems by using exact methods. Neural networks and machine learning in general are a way to face this challenge. For instance, methods like Tensor networks and Neural Quantum States are being investigated as promising tools to obtain the wave function of a quantum mechanical system. In this tutorial, we focus on a particularly promising class of deep learning algorithms. We explain how to construct a Physics-Informed Neural Network (PINN) able to solve the Schr\"odinger equation for a given potential, by finding its eigenvalues and eigenfunctions. This technique is unsupervised, and utilizes a novel computational method in a manner that is barely explored. PINNs are a deep learning method that exploits Automatic Differentiation to solve Integro-Differential Equations in a mesh-free way. We show how to find both the ground and the excited states. The method discovers the states progressively by starting from the ground state. We explain how to introduce inductive biases in the loss to exploit further knowledge of the physical system. Such additional constraints allow for a faster and more accurate convergence. This technique can then be enhanced by a smart choice of collocation points in order to take advantage of the mesh-free nature of the PINN. The methods are made explicit by applying them to the infinite potential well and the particle in a ring, a challenging problem to be learned by an AI agent due to the presence of complex-valued eigenfunctions and degenerate states., Comment: 17 pages, 4 figures

We consider the minimization property of a Gagliardo-Slobodeckij seminorm which can be seen as the fractional counterpart of the classical problem of functions of least gradient and which is related to the minimization of the nonlocal perimeter functional. We discuss continuity properties for this kind of problem. In particular, we show that, under natural structural assumptions, the minimizers are bounded and continuous in the interior of the ambient domain (and, in fact, also continuous up to the boundary under some mild additional hypothesis). We show that these results are also essentially optimal, since in general the minimizer is not necessarily continuous across the boundary., Comment: 15 pages, 1 figure

Generative models based on variational autoencoders are a popular technique for detecting anomalies in images in a semi-supervised context. A common approach employs the anomaly score to detect the presence of anomalies, and it is known to reach high level of accuracy on benchmark datasets. However, since anomaly scores are computed from reconstruction disparities, they often obscure the detection of various spurious features, raising concerns regarding their actual efficacy. This case study explores the robustness of an anomaly detection system based on variational autoencoder generative models through the use of eXplainable AI methods. The goal is to get a different perspective on the real performances of anomaly detectors that use reconstruction differences. In our case study we discovered that, in many cases, samples are detected as anomalous for the wrong or misleading factors., Comment: World Conference on eXplainable Artificial Intelligence

Symmetric Multi-Processing (SMP) based on cache coherency is crucial for high-end embedded systems like automotive applications. RISC-V is gaining traction, and open-source hardware (OSH) platforms offer solutions to issues such as IP costs and vendor dependency. Existing multi-core cache-coherent RISC-V platforms are complex and not efficient for small embedded core clusters. We propose an open-source SystemVerilog implementation of a lightweight snoop-based cache-coherent cluster of Linux-capable CVA6 cores. Our design uses the MOESI protocol via the Arm's AMBA ACE protocol. Evaluated with Splash-3 benchmarks, our solution shows up to 32.87% faster performance in a dual-core setup and an average improvement of 15.8% over OpenPiton. Synthesized using GF 22nm FDSOI technology, the Cache Coherency Unit occupies only 1.6% of the system area., Comment: 4 pages, 4 figures, DSD2024 and SEAA2024 Works in Progress Session AUG 2024; Updated the acknowledgments

Disease mapping analyses the distribution of several diseases within a territory. Primary goals include identifying areas with unexpected changes in mortality rates, studying the relation among multiple diseases, and dividing the analysed territory into clusters based on the observed levels of disease incidence or mortality. In this work, we focus on detecting spatial mortality clusters, that occur when neighbouring areas within a territory exhibit similar mortality levels due to one or more diseases. When multiple death causes are examined together, it is relevant to identify both the spatial boundaries of the clusters and the diseases that lead to their formation. However, existing methods in literature struggle to address this dual problem effectively and simultaneously. To overcome these limitations, we introduce Perla, a multivariate Bayesian model that clusters areas in a territory according to the observed mortality rates of multiple death causes, also exploiting the information of external covariates. Our model incorporates the spatial data structure directly into the clustering probabilities by leveraging the stick-breaking formulation of the multinomial distribution. Additionally, it exploits suitable global-local shrinkage priors to ensure that the detection of clusters is driven by concrete differences across mortality levels while excluding spurious differences. We propose an MCMC algorithm for posterior inference that consists of closed-form Gibbs sampling moves for nearly every model parameter, without requiring complex tuning operations. This work is primarily motivated by a case study on the territory of the local unit ULSS6 Euganea within the Italian public healthcare system. To demonstrate the flexibility and effectiveness of our methodology, we also validate Perla with a series of simulation experiments and an extensive case study on mortality levels in U.S. counties., Comment: Figures and tables at the end of the manuscript

Fault zones mature through the accumulation of earthquakes and the wearing of contact asperities at multiple scales. This study examines how wear-induced gouge production affects the evolution of fault seismicity, focusing on earthquake nucleation, recurrence, and moment partitioning. Using 2D quasi-dynamic simulations integrating rate-and-state friction with Archard's law of wear, we model the space-time distribution of gouge and its effect on the critical slip distance. The study reveals a shift from single to multi-rupture nucleation, marked by increased foreshock activity. The recurrence interval undergoes two separate phases: an initial phase of steady increase followed by a secondary phase of unpredictable behavior. Finally, we observe a transition in the moment partitioning from faster to slower slip rates and a decrease in the moment released per cycle relative to the case where no gouge formation is simulated. This research sheds light on wear-driven mechanisms affecting fault slip behavior, offering valuable insights into how the evolution of gouge along a fault affects its seismic potential.

SOXS (Son Of X-Shooter) is the new single object spectrograph for the ESO New Technology Telescope (NTT) at the La Silla Observatory, able to cover simultaneously both the UV-VIS and NIR bands (350-2000 nm). The instrument is currently in the integration and test phase, approaching the Preliminary Acceptance in Europe (PAE) before shipment to Chile for commissioning. After the assembly and preliminary test of the control electronics at INAF - Astronomical Observatory of Capodimonte (Napoli), the two main control cabinets of SOXS are now hosted in Padova, connected to the real hardware. This contribution describes the final electronic cabinets layout, the control strategy and the different integration phases, waiting for the Preliminary Acceptance in Europe and the installation of the instrument in Chile.

The Son Of X-Shooter (SOXS) will be the specialized facility to observe any transient event with a flexible scheduler at the ESO New Technology Telescope (NTT) at La Silla, Chile. SOXS is a single object spectrograph offering simultaneous spectral coverage in UV-VIS (350-850 nm) and NIR (800-2000 nm) wavelength regimes with an average of R~4500 for a 1arcsec slit. SOXS also has imaging capabilities in the visible wavelength regime. Currently, SOXS is being integrated at the INAF-Astronomical Observatory of Padova. Subsystem- and system-level tests and verification are ongoing to ensure and confirm that every requirement and performance are met. In this paper, we report on the integration and verification of SOXS as the team and the instrument prepare for the Preliminary Acceptance Europe (PAE).

The Son Of X-Shooter (SOXS) is a single object spectrograph, built by an international consortium for the 3.58-m ESO New Technology Telescope at the La Silla Observatory [1]. It offers a simultaneous spectral coverage over 350-2000 nm, with two separate spectrographs. In this paper we present the status of the Near InfraRed (NIR) cryogenic echelle cross-dispersed spectrograph [1], in the range 0.80-2.00 {\mu}m with 15 orders, equipped with an 2k x 2k Hawaii H2RG IR array from Teledyne, working at 40K, that is currently assembled and tested on the SOXS instrument, in the premises of INAF in Padova. We describe the different tests and results of the cryo, vacuum, opto-mechanics and detector subsystems that finally will be part of the PAE by ESO., Comment: 8 Pages, 7 Figures, Astronomical Telescopes and Instrumentation, SPIE Proceedings 2024

We present the advancements in the development of the scheduler for the Son Of X-shooter instrument at the ESO-NTT 3.58-m telescope in La Silla, Chile. SOXS is designed as a single-object spectroscopic facility and features a high-efficiency spectrograph with two arms covering the spectral range of 350-2000 nm and a mean resolving power of approximately R=4500. It will conduct UV-visible and near-infrared follow-up observations of astrophysical transients, drawing from a broad pool of targets accessible through the streaming services of wide-field telescopes, both current and future, as well as high-energy satellites. The instrument will cater to various scientific objectives within the astrophysical community, each entailing specific requirements for observation planning. SOXS will operate at the European Southern Observatory (ESO) in La Silla, without the presence of astronomers on the mountain. This poses a unique challenge for the scheduling process, demanding a fully automated algorithm that is autonomously interacting with the appropriate databases and the La Silla Weather API, and is capable of presenting the operator not only with an ordered list of optimal targets (in terms of observing constraints) but also with optimal backups in the event of changing weather conditions. This imposes the necessity for a scheduler with rapid-response capabilities without compromising the optimization process, ensuring the high quality of observations and best use of the time at the telescope. We thus developed a new highly available and scalable architecture, implementing API Restful applications like Docker Containers, API Gateway, and Python-based Flask frameworks. We provide an overview of the current state of the scheduler, which is now ready for the approaching on-site testing during Commissioning phase, along with insights into its web interface and preliminary performance tests.

The Instrument Control Software of SOXS (Son Of X-Shooter), the forthcoming spectrograph for the ESO New Technology Telescope at the La Silla Observatory, has reached a mature state of development and is approaching the crucial Preliminary Acceptance in Europe phase. Now that all the subsystems have been integrated in the laboratories of the Padova Astronomical Observatory, the team operates for testing purposes with the whole instrument at both engineering and scientific level. These activities will make use of a set of software peculiarities that will be discussed in this contribution. In particular, we focus on the synoptic panel, the co-rotator system special device, on the Active Flexure Compensation system which controls two separate piezo tip-tilt devices., Comment: 6 pages, 3 figures, SPIE conference

The ultrafast conversion of coherent excitons into incoherent excitons, as well as the subsequent exciton diffusion and thermalization, are central topics in current scientific research due to their relevance in optoelectronics, photovoltaics and photocatalysis. Current approaches to the exciton dynamics rely on {\em model} Hamiltonians that depend on already screened electron-electron and electron-phonon couplings. In this work, we subject the state-of-the-art methods to scrutiny using the {\em ab initio} Hamiltonian for electrons and phonons. We offer a rigorous and intuitive proof demonstrating that the exciton dynamics governed by model Hamiltonians is affected by an overscreening of the electron-phonon interaction. The introduction of an auxiliary exciton species, termed the irreducible exciton, enables us to formulate a theory free from overscreening and derive the excitonic Bloch equations. These equations describe the time-evolution of coherent, irreducible, and incoherent excitons during and after the optical excitation. They are applicable beyond the linear regime, and predict that the total number of excitons is preserved when the external fields are switched off., Comment: 29 pages