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The advent of new generation radio telescopes is opening new possibilities on the classification and study of extragalactic high-energy sources, specially the underrepresented ones like radio galaxies. Among these, Giant Radio Galaxies (GRG, larger than 0.7 Mpc) are among the most extreme manifestations of the accretion/ejection processes on supermassive black holes. Our recent studies have shown that GRG can be up to four times more abundant in hard X-ray selected (i.e. from INTEGRAL/IBIS and Swift/BAT at $>$20 keV) samples and, most interestingly, the majority of them present signs of restarted radio activity. This makes them the ideal test-bed to study the so far unknown duty cycle of jets in active galactic nuclei. Open questions in the field include: How and when jets are restarted? How jets evolve and what's their dynamic? What is the jet's duty cycle and what triggers them? Our group has recently collected a wealth of radio data on these high-energy selected GRGs, allowing us to study their jet formation and evolution from the pc to kpc scales, across different activity epochs. In particular, thanks to our EVN large programme, we were able to probe the new radio phase in the core of these giants. Furthermore, we are devoting an effort to the exploitation of new radio surveys data for the discovery of new classes of counterparts of Fermi/LAT catalogues. In particular, we are unveiling the hidden population of radio galaxies associated with gamma-ray sources., Comment: Proceedings of the 16th European VLBI Network Symposium Ros, E., Benke, P., Dzib, S., Rottmann, I., & Zensus, J.A. (eds.) September 2nd-6th 2024, Bonn, Germany

We measure for the first time the differential photoproduction cross section $d\sigma/dt$ of the $a_2(1320)$ meson at an average photon beam energy of 8.5~GeV, using data with an integrated luminosity of 104~pb$^{-1}$ collected by the GlueX experiment. We fully reconstruct the $\gamma p \to \eta\pi^0 p$ reaction and perform a partial-wave analysis in the $a_2(1320)$ mass region with amplitudes that incorporate the linear polarization of the beam. This allows us to separate for the first time the contributions of natural- and unnatural-parity exchanges. These measurements provide novel information about the photoproduction mechanism, which is critical for the search for spin-exotic states., Comment: 8 pages, 4 figures plus supplemental material

Content creators often use music to enhance their videos, from soundtracks in movies to background music in video blogs and social media content. However, identifying the best music for a video can be a difficult and time-consuming task. To address this challenge, we propose a novel framework for automatically retrieving a matching music clip for a given video, and vice versa. Our approach leverages annotated music labels, as well as the inherent artistic correspondence between visual and music elements. Distinct from previous cross-modal music retrieval works, our method combines both self-supervised and supervised training objectives. We use self-supervised and label-supervised contrastive learning to train a joint embedding space between music and video. We show the effectiveness of our approach by using music genre labels for the supervised training component, and our framework can be generalized to other music annotations (e.g., emotion, instrument, etc.). Furthermore, our method enables fine-grained control over how much the retrieval process focuses on self-supervised vs. label information at inference time. We evaluate the learned embeddings through a variety of video-to-music and music-to-video retrieval tasks. Our experiments show that the proposed approach successfully combines self-supervised and supervised objectives and is effective for controllable music-video retrieval., Comment: Accepted at ICASSP 2025

The main mathematical manifestation of the Stark effect in quantum mechanics is the shift and the formation of clusters of eigenvalues when a spherical Hamiltonian is perturbed by lower order terms. Understanding this mechanism turned out to be fundamental in the description of the large-time asymptotics of the associated Schr\"odinger groups and can be responsible for the lack of dispersion in Fanelli, Felli, Fontelos and Primo [Comm. Math. Phys., 324(2013), 1033-1067; 337(2015), 1515-1533]. Recently, Miao, Su, and Zheng introduced in [Tran. Amer. Math. Soc., 376(2023), 1739--1797] a family of spectrally projected intertwining operators, reminiscent of the Kato's wave operators, in the case of constant perturbations on the sphere (inverse-square potential), and also proved their boundedness in $L^p$. Our aim is to establish a general framework in which some suitable intertwining operators can be defined also for non constant spherical perturbations in space dimensions 2 and higher. In addition, we investigate the mapping properties between $L^p$-spaces of these operators. In 2D, we prove a complete result, for the Schr\"odinger Hamiltonian with a (fixed) magnetic potential an electric potential, both scaling critical, allowing us to prove dispersive estimates, uniform resolvent estimates, and $L^p$-bounds of Bochner--Riesz means. In higher dimensions, apart from recovering the example of inverse-square potential, we can conjecture a complete result in presence of some symmetries (zonal potentials), and open some interesting spectral problems concerning the asymptotics of eigenfunctions., Comment: 30 pages, comments are welcome

Inpainting focuses on filling missing or corrupted regions of an image to blend seamlessly with its surrounding content and style. While conditional diffusion models have proven effective for text-guided inpainting, we introduce the novel task of multi-mask inpainting, where multiple regions are simultaneously inpainted using distinct prompts. Furthermore, we design a fine-tuning procedure for multimodal LLMs, such as LLaVA, to generate multi-mask prompts automatically using corrupted images as inputs. These models can generate helpful and detailed prompt suggestions for filling the masked regions. The generated prompts are then fed to Stable Diffusion, which is fine-tuned for the multi-mask inpainting problem using rectified cross-attention, enforcing prompts onto their designated regions for filling. Experiments on digitized paintings from WikiArt and the Densely Captioned Images dataset demonstrate that our pipeline delivers creative and accurate inpainting results. Our code, data, and trained models are available at https://cilabuniba.github.io/i-dream-my-painting., Comment: Accepted at WACV 2025

In this paper, we prove a sharp, weighted Hardy-type inequality for the Dirac operator. A key feature of our result is that the inequality is not only sharp but also attained, and we construct explicit minimizers that satisfy the equality case. This extends previous work on the spectral properties of Dirac operators, especially in the context of relativistic quantum mechanics and Coulomb-like potentials.

Conventional histopathology has long been essential for disease diagnosis, relying on visual inspection of tissue sections. Immunohistochemistry aids in detecting specific biomarkers but is limited by its single-marker approach, restricting its ability to capture the full tissue environment. The advent of multiplexed imaging technologies, like multiplexed immunofluorescence and spatial transcriptomics, allows for simultaneous visualization of multiple biomarkers in a single section, enhancing morphological data with molecular and spatial information. This provides a more comprehensive view of the tissue microenvironment, cellular interactions, and disease mechanisms - crucial for understanding disease progression, prognosis, and treatment response. However, the extensive data from multiplexed imaging necessitates sophisticated computational methods for preprocessing, segmentation, feature extraction, and spatial analysis. These tools are vital for managing large, multidimensional datasets, converting raw imaging data into actionable insights. By automating labor-intensive tasks and enhancing reproducibility and accuracy, computational tools are pivotal in diagnostics and research. This review explores the current landscape of multiplexed imaging in pathology, detailing workflows and key technologies like PathML, an AI-powered platform that streamlines image analysis, making complex dataset interpretation accessible for clinical and research settings., Comment: 54 pages (39 manuscript + 14 supplementary), 3 figures (figure 1, 2 and supplementary figure 1), 6 Tables (Table 1, 2, 3 and supplementary table 1,2,3)

The distribution of urban services reveals critical patterns of human activity and accessibility. Proximity to amenities like restaurants, banks, and hospitals can reduce access barriers, but these services are often unevenly distributed, exacerbating spatial inequalities and socioeconomic disparities. In this study, we present a novel accessibility measure based on the spatial distribution of Points of Interest (POIs) within cities. Using the radial distribution function from statistical physics, we analyze the dispersion of services across different urban zones, combining local and remote access to services. This approach allows us to identify a city's central core, intermediate areas or secondary cores, and its periphery. Comparing the areas that we find with the resident population distribution highlights clusters of urban services and helps uncover disparities in access to opportunities., Comment: 9 pages of manuscript, 5 figures, 3 tables; 23 pages of supplementary material, 52 figures

Artificial Intelligence and generative models have revolutionized music creation, with many models leveraging textual or visual prompts for guidance. However, existing image-to-music models are limited to simple images, lacking the capability to generate music from complex digitized artworks. To address this gap, we introduce $\mathcal{A}\textit{rt2}\mathcal{M}\textit{us}$, a novel model designed to create music from digitized artworks or text inputs. $\mathcal{A}\textit{rt2}\mathcal{M}\textit{us}$ extends the AudioLDM~2 architecture, a text-to-audio model, and employs our newly curated datasets, created via ImageBind, which pair digitized artworks with music. Experimental results demonstrate that $\mathcal{A}\textit{rt2}\mathcal{M}\textit{us}$ can generate music that resonates with the input stimuli. These findings suggest promising applications in multimedia art, interactive installations, and AI-driven creative tools., Comment: Presented at the AI for Visual Arts (AI4VA) workshop at ECCV 2024

We introduce the first method of uncertainty quantification in the domain of Kolmogorov-Arnold Networks, specifically focusing on (Higher Order) ReLUKANs to enhance computational efficiency given the computational demands of Bayesian methods. The method we propose is general in nature, providing access to both epistemic and aleatoric uncertainties. It is also capable of generalization to other various basis functions. We validate our method through a series of closure tests, including simple one-dimensional functions and application to the domain of (Stochastic) Partial Differential Equations. Referring to the latter, we demonstrate the method's ability to correctly identify functional dependencies introduced through the inclusion of a stochastic term. The code supporting this work can be found at https://github.com/wmdataphys/Bayesian-HR-KAN, Comment: 13 pages, 7 Figures

The dataset collected at the Cape Rodney-Okakari Point Marine (CR-OPM) reserve on the North Island of New Zealand is rather unique. It describes the cyclic time evolution of a rocky intertidal community, with the relative abundances of the various coastal species that have been meticulously monitored for more than 20 years. Past theoretical studies, anchored on a deterministic description, required invoking ad hoc mechanisms to reproduce the observed dynamical paths. Following a maximum likelihood approach to interpolate individual stochastic trajectories, we here propose quasi-cycles as an alternative and simpler mechanism to explain the oscillations observed in the population numbers of the ecosystem. From a general standpoint, we also show that it is possible to return conclusive evidence on the existence of stochastic quasi-cycles, without resorting to global fitting strategies which necessitate handling a large collection of independent replicas of the dynamics, a possibility that is often precluded in real life applications.

We report on the first measurement of $J/\psi$ photoproduction from nuclei in the photon energy range of $7$ to $10.8$ GeV, extending above and below the photoproduction threshold in the free proton of $\sim8.2$ GeV. The experiment used a tagged photon beam incident on deuterium, helium, and carbon, and the GlueX detector at Jefferson Lab to measure the semi-inclusive $A(\gamma,e^+e^-p)$ reaction with a dilepton invariant mass $M(e^+e^-)\sim m_{J/\psi}=3.1$ GeV. The incoherent $J/\psi$ photoproduction cross sections in the measured nuclei are extracted as a function of the incident photon energy, momentum transfer, and proton reconstructed missing light-cone momentum fraction. Comparisons with theoretical predictions assuming a dipole form factor allow extracting a gluonic radius for bound protons of $\sqrt{\langle r^2\rangle}=0.85\pm0.14$ fm. The data also suggest an excess of the measured cross section for sub-threshold production and for interactions with high missing light-cone momentum fraction protons. The measured enhancement can be explained by modified gluon structure for high-virtuality bound-protons.

We consider the subcritical nonlinear Schr\"odinger (NLS) in dimension one posed on the unbounded real line. Several previous works have considered the deep neural network approximation of NLS solutions from the numerical and theoretical point of view in the case of bounded domains. In this paper, we introduce a new PINNs method to treat the case of unbounded domains and show rigorous bounds on the associated approximation error in terms of the energy and Strichartz norms, provided a reasonable integration scheme is available. Applications to traveling waves, breathers and solitons, as well as numerical experiments confirming the validity of the approximation are also presented as well., Comment: 24 pages

The Continuous-Variable Firing Rate (CVFR) model, widely used in neuroscience to describe the intertangled dynamics of excitatory biological neurons, is here trained and tested as a veritable dynamically assisted classifier. To this end the model is supplied with a set of planted attractors which are self-consistently embedded in the inter-nodes coupling matrix, via its spectral decomposition. Learning to classify amounts to sculp the basin of attraction of the imposed equilibria, directing different items towards the corresponding destination target, which reflects the class of respective pertinence. A stochastic variant of the CVFR model is also studied and found to be robust to aversarial random attacks, which corrupt the items to be classified. This remarkable finding is one of the very many surprising effects which arise when noise and dynamical attributes are made to mutually resonate.

We establish global bounds for solutions to stationary and time-dependent Schr\"odinger equations associated with the sublaplacian $\mathcal L$ on the Heisenberg group, as well as its pure fractional power $\mathcal L^s$ and conformally invariant fractional power $\mathcal L_s$. The main ingredient is a new abstract uniform weighted resolvent estimate which is proved by using the method of weakly conjugate operators -- a variant of Mourre's commutator method -- and Hardy's type inequalities on the Heisenberg group. As applications, we show Kato-type smoothing effects for the time-dependent Schr\"odinger equation, and spectral stability of the sublaplacian perturbed by complex-valued decaying potentials satisfying an explicit subordination condition. In the local case $s=1$, we obtain uniform estimates without any symmetry or derivative loss, which improve previous results., Comment: 28 pages

We present the first 3D kinematic analysis of multiple stellar populations (MPs) in a representative sample of 16 Galactic globular clusters (GCs). For each GC in the sample we studied the MP line-of-sight, plane-of-the-sky and 3D rotation as well as the velocity distribution anisotropy. The differences between first- (FP) and second-population (SP) kinematic patterns were constrained by means of parameters specifically defined to provide a global measure of the relevant physical quantities and to enable a meaningful comparison among different clusters. Our analysis provides the first observational description of the MP kinematic properties and of the path they follow during the long-term dynamical evolution. In particular, we find evidence of differences between the rotation of MPs along all velocity components with the SP preferentially rotating faster than the FP. The difference between the rotation strength of MPs is anti-correlated with the cluster dynamical age. We observe also that FPs are characterized by isotropic velocity distributions at any dynamical age probed by our sample. On the contrary, the velocity distribution of SP stars is found to be radially anisotropic in dynamically young clusters and isotropic at later evolutionary stages. The comparison with a set of numerical simulations shows that these observational results are consistent with the long-term evolution of clusters forming with an initially more centrally concentrated and more rapidly rotating SP subsystem. We discuss the possible implications these findings have on our understanding of MP formation and early evolution., Comment: 26 pages, 22 figures, 3 tables; accepted for publication in A&A on September 2 2024

Mapping stars and gas in nearby galaxies is fundamental for understanding their growth and the impact of their environment. This issue is addressed by comparing the stellar "edges" of galaxies $D_{\rm stellar}$, defined as the outermost diameter where in situ star formation significantly drops, with the gaseous distribution parameterized by the neutral atomic hydrogen diameter measured at 1 $M_{sun}$/pc$^2$, $D_{HI}$. By sampling a broad HI mass range $10^5 M_{sun} < M_{HI} < 10^{11} M_{sun}$, we find several dwarf galaxies with $M_{HI} < 10^9 M_{sun}$ from the field and Fornax Cluster which are distinguished by $D_{\rm stellar} >> D_{HI}$. For the cluster dwarfs, the average HI surface density near $D_{\rm stellar}$ is $\sim$0.3 $M_{sun}$/pc$^2$, reflecting the impact of quenching and outside-in gas removal from ram pressure and tidal interactions. In comparison, $D_{\rm stellar}/D_{HI}$ ranges between 0.5-2 in dwarf field galaxies, consistent with the expectations from stellar feedback. Only more massive disk galaxies in the field can thus be characterized by the common assumption that $D_{\rm stellar} \lesssim D_{HI}$. We discover a break in the $D_{\rm stellar}-M_{\rm stellar}$ relation at $m_{break} \sim 4\times10^8 M_{sun}$ that potentially differentiates the low mass regime where the influence of stellar feedback and environmental processes more prominently regulates the sizes of nearby galaxies. Our results highlight the importance of combining deep optical and HI imaging for understanding galaxy evolution., Comment: 19 pages, 10 figures, 2 tables. Key results in Figs. 4, 6, 7 and 8. Accepted for publication in The Astrophysical Journal

We present a high resolution chemical study of a representative sample of 21 luminous giant stars of Liller~1, a complex stellar system in the Galactic bulge, based on H band spectra acquired with the Near InfraRed Spectrograph at KeckII. 15 stars turn out to have a subsolar iron abundance and enhanced [$\alpha$/Fe] and [Al/Fe], likely old that formed early and quickly from gas mainly enriched by type~II supernovae, and 6 stars with supersolar iron and roughly solar-scaled [$\alpha$/Fe] and [Al/Fe], likely younger, thus formed at later epochs from gas also enriched by type~Ia supernovae. Moreover, both subpopulations show enhanced [N/Fe], as in the bulge field, about solar-scaled [V/Fe], and depletion of [C/Fe] and $^{12}$C/$^{13}$C with respect to the solar values, indicating the occurrence of significant mixing in the stellar interiors of these evolved stars. The current study has also made evident that the sub-solar subpopulation shows some structuring, and the presence of a third subcomponent with iron content and [$\alpha$/Fe] enhancement somewhat intermediate between the metal-poor and metal-rich main subpopulations, has been statistically assessed, providing the chemical signature of an extended star formation with multiple bursts and of some self-enrichment., Comment: Accepted for pubblication in A&A

Coalition formation over graphs is a well studied class of games whose players are vertices and feasible coalitions must be connected subgraphs. In this setting, the existence and computation of equilibria, under various notions of stability, has attracted a lot of attention. However, the natural process by which players, starting from any feasible state, strive to reach an equilibrium after a series of unilateral improving deviations, has been less studied. We investigate the convergence of dynamics towards individually stable outcomes under the following perspective: what are the most general classes of preferences and graph topologies guaranteeing convergence? To this aim, on the one hand, we cover a hierarchy of preferences, ranging from the most general to a subcase of additively separable preferences, including individually rational and monotone cases. On the other hand, given that convergence may fail in graphs admitting a cycle even in our most restrictive preference class, we analyze acyclic graph topologies such as trees, paths, and stars.

Congestion games are attractive because they can model many concrete situations where some competing entities interact through the use of some shared resources, and also because they always admit pure Nash equilibria which correspond to the local minima of a potential function. We explore the problem of computing a state of minimum potential in this setting. Using the maximum number of resources that a player can use at a time, and the possible symmetry in the players' strategy spaces, we settle the complexity of the problem for instances having monotone (i.e., either non-decreasing or non-increasing) latency functions on their resources. The picture, delineating polynomial and NP-hard cases, is complemented with tight approximation algorithms.

Neural networks are gaining widespread relevance for their versatility, holding the promise to yield a significant methodological shift in different domain of applied research. Here, we provide a simple pedagogical account of the basic functioning of a feedforward neural network. Then we move forward to reviewing two recent applications of machine learning to Earth and Materials Science. We will in particular begin by discussing a neural network based geothermobarometer, which returns reliable predictions of the pressure/temperature conditions of magma storage. Further, we will turn to illustrate how machine learning tools, tested on the list of minerals from the International Mineralogical Association, can help in the search for novel superconducting materials.

Combining Chandra, ALMA, EVLA, and Hubble Space Telescope archival data and newly acquired APO/DIS spectroscopy, we detect a double-lobed 17~kpc X-ray emission with plumes oriented approximately perpendicular and parallel to the galactic plane of the massive lenticular galaxy NGC\,5084 at 0.3--2.0~keV. We detect a highly inclined ($i=71.2^{+1.8\circ}_{-1.7}$), molecular circumnuclear disk ($D=304^{+10}_{-11}$ pc) in the core of the galaxy rotating (V$^{\rm (2-1) CO}_{\rm rot}=242.7^{+9.6}_{-6.4}$ km s$^{-1}$) in a direction perpendicular to that of the galactic disk, implying a total mass of $\log_{10}\left( \frac{M_{\rm BH}}{M_{\odot}} \right) = 7.66^{+0.21}_{-0.15}$ for NGC\,5084's supermassive black hole. Archival EVLA radio observations at 6 cm and 20 cm reveal two symmetric radio lobes aligned with the galactic plane, extending to a distance of $\overline{R}=4.6\pm0.6$ kpc from the core, oriented with the polar axis of the circumnuclear disk. The spectral energy distribution lacks strong emission lines in the optical range. Three formation scenarios are considered to explain these multi-wavelength archival observations: 1) AGN re-orientation caused by accretion of surrounding material, 2) AGN-driven hot gas outflow directed along the galactic minor axis, or 3) a starburst / supernovae driven outflow at the core of the galaxy. This discovery is enabled by new imaging analysis tools including \SAUNAS\ (Selective Amplification of Ultra Noisy Astronomical Signal), demonstrating the abundance of information still to be exploited in the vast and growing astronomical archives., Comment: Submitted to ApJ in May 21st 2024, in revision. 26 pages, 14 figures

A data fitting procedure is devised and thoroughly tested to provide self-consistent estimates of the relevant mechanokinetic parameters involved in a plausible scheme underpinning the output of an ensemble of myosin II molecular motors mimicking the muscle contraction. The method builds on a stochastic model accounting for the force exerted by the motor ensemble operated both in the low and high force-generating regimes corresponding to different temperature ranges. The proposed interpretative framework is successfully challenged against simulated data, meant to mimic the experimental output of a unidimensional synthetic nanomachine powered by pure muscle myosin isoforms.

Myosin II is the muscle molecular motor that works in two bipolar arrays in each thick filament of the striated (skeletal and cardiac) muscle, converting the chemical energy into steady force and shortening by cyclic ATP--driven interactions with the nearby actin filaments. Different isoforms of the myosin motor in the skeletal muscles account for the different functional requirements of the slow muscles (primarily responsible for the posture) and fast muscles (responsible for voluntary movements). To clarify the molecular basis of the differences, here the isoform--dependent mechanokinetic parameters underpinning the force of slow and fast muscles are defined with a unidimensional synthetic nanomachine powered by pure myosin isoforms from either slow or fast rabbit skeletal muscle. Data fitting with a stochastic model provides a self--consistent estimate of all the mechanokinetic properties of the motor ensemble including the motor force, the fraction of actin--attached motors and the rate of transition through the attachment--detachment cycle. The achievements in this paper set the stage for any future study on the emergent mechanokinetic properties of an ensemble of myosin molecules either engineered or purified from mutant animal models or human biopsies., Comment: Buonfiglio V, Pertici I, Marcello M, Morotti I, Caremani M, Reconditi M, Linari M, Fanelli D, Lombardi V, Bianco P. Force and kinetics of fast and slow muscle myosin determined with a synthetic sarcomere-like nanomachine. Commun Biol. 2024 Mar 23;7(1):361. doi: 10.1038/s42003-024-06033-8. PMID: 38521889; PMCID: PMC10960843

We investigate the asymptotic behaviour of fast rotating incompressible fluids with vanishing viscosity, in a {three dimensional} domain with topography including the case of land area. Assuming the initial data is well-prepared, we prove a convergence theorem of the velocity fields to a two-dimensional vector field solving a linear, damped ordinary differential equation.The proof is based on a weak-strong uniqueness argument, combinedwith an abstract result implying that the weak convergence of a familyof weak solutions to the Navier-Stokes-Coriolis system can be translated into a form of uniform-in-time convergence.This argument yields strong convergence of the velocity fields, without a precise rate though.

Social bots remain a major vector for spreading disinformation on social media and a menace to the public. Despite the progress made in developing multiple sophisticated social bot detection algorithms and tools, bot detection remains a challenging, unsolved problem that is fraught with uncertainty due to the heterogeneity of bot behaviors, training data, and detection algorithms. Detection models often disagree on whether to label the same account as bot or human-controlled. However, they do not provide any measure of uncertainty to indicate how much we should trust their results. We propose to address both bot detection and the quantification of uncertainty at the account level - a novel feature of this research. This dual focus is crucial as it allows us to leverage additional information related to the quantified uncertainty of each prediction, thereby enhancing decision-making and improving the reliability of bot classifications. Specifically, our approach facilitates targeted interventions for bots when predictions are made with high confidence and suggests caution (e.g., gathering more data) when predictions are uncertain., Comment: 11 pages, 5 figures

Nature is a blossoming of regular structures, signature of self-organization of the underlying microscopic interacting agents. Turing theory of pattern formation is one of the most studied mechanisms to address such phenomena and has been applied to a widespread gallery of disciplines. Turing himself used a spatial discretization of the hosting support to eventually deal with a set of ODEs. Such an idea contained the seeds of the theory on discrete support, which has been fully acknowledged with the birth of network science in the early 2000s. This approach allows us to tackle several settings not displaying a trivial continuous embedding, such as multiplex, temporal networks, and, recently, higher-order structures. This line of research has been mostly confined within the network science community, despite its inherent potential to transcend the conventional boundaries of the PDE-based approach to Turing patterns. Moreover, network topology allows for novel dynamics to be generated via a universal formalism that can be readily extended to account for higher-order structures. The interplay between continuous and discrete settings can pave the way for further developments in the field.

Imaging Cherenkov detectors are crucial for particle identification (PID) in nuclear and particle physics experiments. Fast reconstruction algorithms are essential for near real-time alignment, calibration, data quality control, and efficient analysis. At the future Electron-Ion Collider (EIC), the ePIC detector will feature a dual Ring Imaging Cherenkov (dual-RICH) detector in the hadron direction, a Detector of Internally Reflected Cherenkov (DIRC) in the barrel, and a proximity focus RICH in the electron direction. This paper focuses on the DIRC detector, which presents complex hit patterns and is also used for PID of pions and kaons in the GlueX experiment at JLab. We present Deep(er)RICH, an extension of the seminal DeepRICH work, offering improved and faster PID compared to traditional methods and, for the first time, fast and accurate simulation. This advancement addresses a major bottleneck in Cherenkov detector simulations involving photon tracking through complex optical elements. Our results leverage advancements in Vision Transformers, specifically hierarchical Swin Transformer and normalizing flows. These methods enable direct learning from real data and the reconstruction of complex topologies. We conclude by discussing the implications and future extensions of this work, which can offer capabilities for PID for multiple cutting-edge experiments like the future EIC., Comment: 19 pages, 7 figures

The CUbesat Solar Polarimeter (CUSP) project is a future CubeSat mission orbiting the Earth aimed to measure the linear polarization of solar flares in the hard X-ray band, by means of a Compton scattering polarimeter. CUSP will allow us to study the magnetic reconnection and particle acceleration in the flaring magnetic structures of our star. The project is in the framework of the Italian Space Agency Alcor Program, which aims to develop new CubeSat missions. CUSP is approved for a Phase B study that will last for 12 months, starting in mid-2024. We report on the current status of the CUSP mission project as the outcome of the Phase A., Comment: Proceeding of SPIE Conference "Astronomical Telescopes+ Instrumentation", Yokohama (Japan), 16-21 June 2024. arXiv admin note: substantial text overlap with arXiv:2208.06211

The CUbesat Solar Polarimeter (CUSP) project is a CubeSat mission orbiting the Earth aimed to measure the linear polarization of solar flares in the hard X-ray band by means of a Compton scattering polarimeter. CUSP will allow the study of the magnetic reconnection and particle acceleration in the flaring magnetic structures of our star. CUSP is a project in the framework of the Alcor Program of the Italian Space Agency aimed at developing new CubeSat missions. It is approved for a Phase B study. In this work, we report on the characterization of the Avalanche Photodiodes (APDs) that will be used as readout sensors of the absorption stage of the Compton polarimeter. We assessed the APDs gain and energy resolution as a function of temperature by irradiating the sensor with a \textsuperscript{55}Fe radioactive source. Moreover, the APDs were also characterized as being coupled to a GAGG scintillator., Comment: Proceeding of SPIE Conference "Astronomical Telescopes+ Instrumentation", Yokohama (Japan), 16-21 June 2024

The CUbesat Solar Polarimeter (CUSP) project aims to develop a constellation of two CubeSats orbiting the Earth to measure the linear polarization of solar flares in the hard X-ray band by means of a Compton scattering polarimeter on board of each satellite. CUSP will allow to study the magnetic reconnection and particle acceleration in the flaring magnetic structures. CUSP is a project approved for a Phase B study by the Italian Space Agency in the framework of the Alcor program aimed to develop CubeSat technologies and missions. In this paper we describe the a method for a multi-physical simulation analysis while analyzing some possible design optimization of the payload design solutions adopted. In particular, we report the mechanical design for each structural component, the results of static and dynamic finite element analysis, the preliminary thermo-mechanical analysis for two specific thermal cases (hot and cold orbit) and a topological optimization of the interface between the platform and the payload., Comment: Proceeding of SPIE Conference "Astronomical Telescopes+ Instrumentation", Yokohama (Japan), 16-21 June 2024

The CUbesat Solar Polarimeter (CUSP) project is a CubeSat mission orbiting the Earth aimed to measure the linear polarization of solar flares in the hard X-ray band by means of a Compton scattering polarimeter. CUSP will allow to study the magnetic reconnection and particle acceleration in the flaring magnetic structures of our star. CUSP is a project in the framework of the Alcor Program of the Italian Space Agency aimed to develop new CubeSat missions. It is approved for a Phase B study. In this work, we report on the accurate simulation of the detector's response to evaluate the scientific performance. A GEANT4 Monte Carlo simulation is used to assess the physical interactions of the source photons with the detector and the passive materials. Using this approach, we implemented a detailed CUSP Mass Model. In this work, we report on the evaluation of the detector's effective area as a function of the beam energy., Comment: Proceeding of SPIE Conference "Astronomical Telescopes+ Instrumentation", Yokohama (Japan), 16-21 June 2024

The spin-exotic hybrid meson $\pi_{1}(1600)$ is predicted to have a large decay rate to the $\omega\pi\pi$ final state. Using 76.6~pb$^{-1}$ of data collected with the GlueX detector, we measure the cross sections for the reactions $\gamma p \to \omega \pi^+ \pi^- p$, $\gamma p \to \omega \pi^0 \pi^0 p$, and $\gamma p\to\omega\pi^-\pi^0\Delta^{++}$ in the range $E_\gamma =$ 8-10 GeV. Using isospin conservation, we set the first upper limits on the photoproduction cross sections of the $\pi^{0}_{1}(1600)$ and $\pi^{-}_{1}(1600)$. We combine these limits with lattice calculations of decay widths and find that photoproduction of $\eta'\pi$ is the most sensitive two-body system to search for the $\pi_1(1600)$., Comment: 7 pages, 3 figures plus supplemental materials

We present Label Anything, an innovative neural network architecture designed for few-shot semantic segmentation (FSS) that demonstrates remarkable generalizability across multiple classes with minimal examples required per class. Diverging from traditional FSS methods that predominantly rely on masks for annotating support images, Label Anything introduces varied visual prompts -- points, bounding boxes, and masks -- thereby enhancing the framework's versatility and adaptability. Unique to our approach, Label Anything is engineered for end-to-end training across multi-class FSS scenarios, efficiently learning from diverse support set configurations without retraining. This approach enables a "universal" application to various FSS challenges, ranging from $1$-way $1$-shot to complex $N$-way $K$-shot configurations while remaining agnostic to the specific number of class examples. This innovative training strategy reduces computational requirements and substantially improves the model's adaptability and generalization across diverse segmentation tasks. Our comprehensive experimental validation, particularly achieving state-of-the-art results on the COCO-$20^i$ benchmark, underscores Label Anything's robust generalization and flexibility. The source code is publicly available at: https://github.com/pasqualedem/LabelAnything.

The Wilson-Cowan model for metapopulation, a Neural Mass Network Model, treats different subcortical regions of the brain as connected nodes, with connections representing various types of structural, functional, or effective neuronal connectivity between these regions. Each region comprises interacting populations of excitatory and inhibitory cells, consistent with the standard Wilson-Cowan model. By incorporating stable attractors into such a metapopulation model's dynamics, we transform it into a learning algorithm capable of achieving high image and text classification accuracy. We test it on MNIST and Fashion MNIST, in combination with convolutional neural networks, on CIFAR-10 and TF-FLOWERS, and, in combination with a transformer architecture (BERT), on IMDB, always showing high classification accuracy. These numerical evaluations illustrate that minimal modifications to the Wilson-Cowan model for metapopulation can reveal unique and previously unobserved dynamics., Comment: Paper Accepted in Neural Computation (in press)

We measure the spin-density matrix elements (SDMEs) of the $\Delta^{++}(1232)$ in the photoproduction reaction $\gamma p \to \pi^-\Delta^{++}(1232)$ with the GlueX experiment in Hall D at Jefferson Lab. The measurement uses a linearly--polarized photon beam with energies from $8.2$ to $8.8$~GeV and the statistical precision of the SDMEs exceeds the previous measurement by three orders of magnitude for the momentum transfer squared region below $1.4$ GeV$^2$. The data are sensitive to the previously undetermined relative sign between couplings in existing Regge-exchange models. Linear combinations of the extracted SDMEs allow for a decomposition into natural and unnatural--exchange amplitudes. We find that the unnatural exchange plays an important role in the low momentum transfer region.

We used near-infrared spectroscopy at medium-high resolution (R=8,000$-$25,000) to perform the first comprehensive chemical study of the intermediate luminosity bulge globular cluster Terzan~6. We derived detailed abundances and abundance patterns of 27 giant stars, likely members of Terzan~6, based on their accurate Hubble Space Telescope proper motions and line-of-sight radial velocities. From the spectral analysis of these stars, we determined an average heliocentric radial velocity of 143.3$\pm$1.0 km s$^{-1}$ with a velocity dispersion of 5.1$\pm$0.7 km s$^{-1}$ and an average [Fe/H]=$-0.65\pm0.01$ and a low 1$\sigma$ dispersion of 0.03 dex. We also measured some depletion of [Mn/Fe] with respect to the solar-scaled values and enhancement of for [Ca/Fe], [Si/Fe], [Mg/Fe], [Ti/Fe], [O/Fe], [Al/Fe], [Na/Fe], and, to a lower extent, for [K/Fe], consistent with previous measurements of other bulge globular clusters and favoring the scenario of a rapid bulge formation and chemical enrichment. Some spread in the light element abundances suggest the presence of first- and second-generation stars, typical of genuine globulars. Finally, we measured some depletion of carbon and low $\rm ^{12}C/^{13}C$ isotopic ratios, as in previous studies of field and cluster bulge giants, indicating that extra-mixing mechanisms should be at work during the post main sequence evolution in the high metallicity regime as well., Comment: 8 pages, 5 figures, accepted for publication in Astronomy and Astrophysics

Conversion of non-native accented speech to native (American) English has a wide range of applications such as improving intelligibility of non-native speech. Previous work on this domain has used phonetic posteriograms as the target speech representation to train an acoustic model which is then used to extract a compact representation of input speech for accent conversion. In this work, we introduce the idea of using an effective articulatory speech representation, extracted from an acoustic-to-articulatory speech inversion system, to improve the acoustic model used in accent conversion. The idea to incorporate articulatory representations originates from their ability to well characterize accents in speech. To incorporate articulatory representations with conventional phonetic posteriograms, a multi-task learning based acoustic model is proposed. Objective and subjective evaluations show that the use of articulatory representations can improve the effectiveness of accent conversion., Comment: Accepted at INTERSPEECH 2024

Working with high-dimensional data is a common practice, in the field of machine learning. Identifying relevant input features is thus crucial, so as to obtain compact dataset more prone for effective numerical handling. Further, by isolating pivotal elements that form the basis of decision making, one can contribute to elaborate on - ex post - models' interpretability, so far rather elusive. Here, we propose a novel method to estimate the relative importance of the input components for a Deep Neural Network. This is achieved by leveraging on a spectral re-parametrization of the optimization process. Eigenvalues associated to input nodes provide in fact a robust proxy to gauge the relevance of the supplied entry features. Unlike existing techniques, the spectral features ranking is carried out automatically, as a byproduct of the network training. The technique is successfully challenged against both synthetic and real data.

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like the ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using Geant4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process., Comment: 11 pages, 4 figures, AI4EIC 2023 proceeding

We present the results of a study aimed at characterizing the kinematics of the inner regions of the halo globular cluster M75 (NGC 6864) based on data acquired as part of the ESO-VLT Multi-Instrument Kinematic Survey (MIKiS) of Galactic globular clusters. Our analysis includes the first determination of the line-of-sight velocity dispersion profile in the core region of M75. By using MUSE/NFM observations, we obtained a sample of $\sim 1900$ radial velocity measurements from individual stars located within $16''$ (corresponding to about $r < 3 r_c$ where $r_c$ is the estimated core radius of the system) from the cluster center. After an appropriate selection of the most accurate velocity measures, we determined the innermost portion of the velocity dispersion profile, finding that it is characterized by a constant behavior and a central velocity dispersion of $\sigma_0\sim 9$ km s$^{-1}$. The simultaneous King model fitting to the projected velocity dispersion and density profiles allowed us to check and update previous determinations of the main structural parameters of the system. We also detected a mild hint of rotation in the central $\sim 7''$ from the center, with an amplitude of just $\sim 1.0$ km s$^{-1}$ and a position angle of the rotation axis of PA$_0 = 174\deg$. Intriguingly, the position angle is consistent with that previously quoted for the suspected rotation signal in the outer region of the cluster. Taking advantage of the high quality of the photometric catalog used for the analysis of the MUSE spectra, we also provide updated estimates of the cluster distance, age, and reddening., Comment: Accepted for publication in A&A

We present SAUNAS (Selective Amplification of Ultra Noisy Astronomical Signal), a pipeline designed for detecting diffuse X-ray emission in the data obtained with the Advanced CCD Imaging Spectrometer (ACIS) of the Chandra X-ray Observatory. SAUNAS queries the available observations in the Chandra archive, performs photometric calibration, PSF (point spread function) modeling, and deconvolution, point-source removal, adaptive smoothing, and background correction. This pipeline builds on existing and well-tested software including CIAO, VorBin, and LIRA. We characterize the performance of SAUNAS through several quality performance tests, and demonstrate the broad applications and capabilities of SAUNAS using two galaxies already known to show X-ray emitting structures. SAUNAS successfully detects the 30 kpc X-ray super-wind of NGC 3079 using Chandra/ACIS datasets, matching the spatial distribution detected with more sensitive XMM-Newton observations. The analysis performed by SAUNAS reveals an extended low surface brightness source in the field of UGC 5101 in the 0.3-1.0 keV and 1.0-2.0 keV bands. This source is potentially a background galaxy cluster or a hot gas plume associated with UGC 5101. SAUNAS demonstrates its ability to recover previously undetected structures in archival data, expanding exploration into the low surface brightness X-ray universe with Chandra/ACIS., Comment: Accepted for publication in The Astrophysical Journal. 27 pages, 19 figures. The SAUNAS X-ray surface brightness and signal-to-noise maps of NGC 3079 and UGC 5101 are publicly available in Zenodo in FITS format: https://zenodo.org/records/10892485. DOI: 10.5281/zenodo.10892485. The list of Chandra datasets, obtained by the Chandra X-ray Observatory, are contained in DOI: 10.25574/cdc.225