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This paper establishes a rigorous connection between circuit representations and tensor factorizations, two seemingly distinct yet fundamentally related areas. By connecting these fields, we highlight a series of opportunities that can benefit both communities. Our work generalizes popular tensor factorizations within the circuit language, and unifies various circuit learning algorithms under a single, generalized hierarchical factorization framework. Specifically, we introduce a modular "Lego block" approach to build tensorized circuit architectures. This, in turn, allows us to systematically construct and explore various circuit and tensor factorization models while maintaining tractability. This connection not only clarifies similarities and differences in existing models, but also enables the development of a comprehensive pipeline for building and optimizing new circuit/tensor factorization architectures. We show the effectiveness of our framework through extensive empirical evaluations, and highlight new research opportunities for tensor factorizations in probabilistic modeling.

We provide a new existence result for abstract nonlinear operator systems in normed spaces, by means of topological methods. The solution is located within the product of annular regions and conical shells. The theoretical result possesses a wide range of applicability, which, for concreteness, we illustrate in the context of systems of nonlinear Poisson equations subject to homogeneous Dirichlet boundary conditions. For the latter problem we obtain existence and localization of solutions having all components nontrivial. This is also illustrated with an explicit example in which we also furnish a numerically approximated solution, consistent with the theoretical results., Comment: 18 pages, 2 figures

Coding camps bring together individuals from diverse backgrounds to tackle given challenges within a limited timeframe. Such camps create a rich learning environment for various skills, some of which are directly associated with the camp, and some of which are a result of working as a team during the camp. Unfortunately, coding camps often remain isolated from the broader educational curriculum or other bigger context, which downplays the opportunities they can offer to students. In this paper, we present the vision of the European initiative Oscar, which aims at connecting coding camps to the educational and professional context faced by the learners. In addition, we sketch a supporting platform and its features for connected coding camps., Comment: Accepted for publication at SIGCSE TS 2025, Proceedings of the 56th ACM Technical Symposium on Computer Science Education

Measuring distances in a multidimensional setting is a challenging problem, which appears in many fields of science and engineering. In this paper, to measure the distance between two multivariate distributions, we introduce a new measure of discrepancy which is scale invariant and which, in the case of two independent copies of the same distribution, and after normalization, coincides with the scaling invariant multidimensional version of the Gini index recently proposed in [34]. A byproduct of the analysis is an easy-to-handle discrepancy metric, obtained by application of the theory to a pair of Gaussian multidimensional densities. The obtained metric does improve the standard metrics, based on the mean squared error, as it is scale invariant. The importance of this theoretical finding is illustrated by means of a real problem that concerns measuring the importance of Environmental, Social and Governance factors for the growth of small and medium enterprises.

The top-quark mass is a fundamental parameter of the Standard Model, as it plays a crucial role in the electroweak precision tests, stability of the vacuum and inflation. I review the method and the main results contained in a recent ATLAS analysis which measures the top mass by using the invariant mass of the leptons coming from W and B-hadron decays. The extracted top mass turns out to be the most precise single measurement by ATLAS., Comment: 7 pages, 2 figures. Talk given at QCD@Work, International Workshop on QCD, theory and experiment, Trani (Italy), 18-21 June, 2024

We are considering the problem of optimal portfolio delegation between an investor and a portfolio manager under a random default time. We focus on a novel variation of the Principal-Agent problem adapted to this framework. We address the challenge of an uncertain investment horizon caused by an exogenous random default time, after which neither the agent nor the principal can access the market. This uncertainty introduces significant complexities in analyzing the problem, requiring distinct mathematical approaches for two cases: when the random default time falls within the initial time frame [0,T] and when it extends beyond this period. We develop a theoretical framework to model the stochastic dynamics of the investment process, incorporating the random default time. We then analyze the portfolio manager's investment decisions and compensation mechanisms for both scenarios. In the first case, where the default time could be unbounded, we apply traditional results from Backward Stochastic Differential Equations (BSDEs) and control theory to address the agent problem. In the second case, where the default time is within the interval [0,T], the problem becomes more intricate due to the degeneracy of the BSDE's driver. For both scenarios, we demonstrate that the contracting problem can be resolved by examining the existence of solutions to integro-partial Hamilton-Jacobi-Bellman (HJB) equations in both situations. We develop a deep-learning algorithm to solve the problem in high-dimension with no access to the optimizer of the Hamiltonian function.

We investigate the existence of nontrivial solutions of parameter-dependent elliptic equations with deviated argument in annular-like domains in $\mathbb{R}^{n}$, with $n\geq 2$, subject to functional boundary conditions. In particular we consider a boundary value problem that may be used to model heat-flow problems. We obtain an existence result by means of topological methods; in particular, we make use of a recent variant in affine cones of the celebrated Birkhoff--Kellogg theorem. Using an ODE argument, we illustrate in an example the applicability of our theoretical result., Comment: 11 pages, 1 figure

Verifying programs that manipulate tree data structures often requires complex, ad-hoc proofs that are hard to generalize and automate. This paper introduces an automatic technique for analyzing such programs. Our approach combines automata and logics to tackle the challenges posed by diverse tree data structures uniformly. At the core of our methodology is the knitted-tree encoding, which maps a program execution into a tree data structure encapsulating input, output, and intermediate configurations, within a single structure. By leveraging the compositional properties of knitted-trees, we characterize them using constrained Horn clauses (CHCs). This encoding reduces verification to solving CHC satisfiability, benefiting from ongoing advancements in CHC solvers. While we focus on the memory safety problem for illustration, our technique applies broadly to various verification tasks involving tree data structures.

Recent studies point out that quantum effects, referred to as "memory burden", may slow down the evaporation of black holes. As a result, a population of light primordial black holes could potentially survive to the present day, thus contributing to the energy density of dark matter. In this work, we focus on light primordial black holes with masses $M_{\rm PBH} \lesssim 10^{9}~{\rm g}$ that, due to the memory burden effect, are currently evaporating, emitting high-energy particles, among which neutrinos, in the local Universe. Analyzing the latest IceCube data, we place novel constraints on the combined parameter space of primordial black holes and the memory burden effect. We also study the projected reach of future neutrino telescopes such as IceCube-Gen2 and GRAND. We find that the neutrino observations are crucial to probe scenarios with highly-suppressed evaporation and light masses for primordial black holes., Comment: 7 pages, 2 figures. v2: references added

Rotation has been recognized for its ability to decrease the extent of separate flow regions on the inner sections ofhorizontal-axis wind turbine blades. This alteration is often linked to centrifugal pumping. Nevertheless, studies focused on insect flight conditions revealed a significant contribution of the Coriolis acceleration to the balance of vorticity within the leading-edge vortex (LEV). Despite this insight, it remains unexplored whether this phenomenon contributes to the observed rotational augmentation in wind turbines. Our study employed quasi-3D simulations to delve into the impact of Coriolis acceleration on the balance of span-wise vorticity within the region of separate flow along the upper surface of wind turbine blade sections. Our results allowed the identification of two different mechanisms of rotational augmentation, one associated to the height reduction of the region of separate flow and the other associated to the stabilization of the LEV. The former occurred when a trailing-edge separation was observed and the later when the flow featured a leading-edge separation. Within the range of conditions considered, the Coriolis acceleration neutralized up to about 40% of the span-wise vorticity flowing in from the boundary layer to feed the LEV.

Precision agriculture relies heavily on effective weed management to ensure robust crop yields. This study presents RoWeeder, an innovative framework for unsupervised weed mapping that combines crop-row detection with a noise-resilient deep learning model. By leveraging crop-row information to create a pseudo-ground truth, our method trains a lightweight deep learning model capable of distinguishing between crops and weeds, even in the presence of noisy data. Evaluated on the WeedMap dataset, RoWeeder achieves an F1 score of 75.3, outperforming several baselines. Comprehensive ablation studies further validated the model's performance. By integrating RoWeeder with drone technology, farmers can conduct real-time aerial surveys, enabling precise weed management across large fields. The code is available at: \url{https://github.com/pasqualedem/RoWeeder}., Comment: Computer Vision for Plant Phenotyping and Agriculture (CVPPA) workshop at ECCV 2024

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

Image manipulation is rapidly evolving, allowing the creation of credible content that can be used to bend reality. Although the results of deepfake detectors are promising, deepfakes can be made even more complicated to detect through adversarial attacks. They aim to further manipulate the image to camouflage deepfakes' artifacts or to insert signals making the image appear pristine. In this paper, we further explore the potential of super-resolution attacks based on different super-resolution techniques and with different scales that can impact the performance of deepfake detectors with more or less intensity. We also evaluated the impact of the attack on more diverse datasets discovering that the super-resolution process is effective in hiding the artifacts introduced by deepfake generation models but fails in hiding the traces contained in fully synthetic images. Finally, we propose some changes to the detectors' training process to improve their robustness to this kind of attack., Comment: Trust What You learN (TWYN) Workshop at European Conference on Computer Vision ECCV 2024

The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorithms to prioritize and optimize treatment options, thereby reducing costs and improving patient outcomes. However, the sensitive nature of healthcare data presents significant challenges in terms of privacy and data ownership, hindering data availability and the development of robust algorithms. Federated Learning (FL) addresses these challenges by enabling collaborative training of ML models without the exchange of local data. This paper introduces a novel FL platform designed to support the configuration, monitoring, and management of FL processes. This platform operates on Platform-as-a-Service (PaaS) principles and utilizes the Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol. Considering the production readiness and data sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture, addressing potential threats and proposing mitigation strategies to enhance the platform's trustworthiness. The platform has been successfully tested in various operational environments using a publicly available dataset, highlighting its benefits and confirming its efficacy.

In this paper, we introduce and study the Facility Location Problem with Aleatory Agents (FLPAA), where the facility accommodates n agents larger than the number of agents reporting their preferences, namely n_r. The spare capacity is used by n_u=n-n_r aleatory agents sampled from a probability distribution \mu. The goal of FLPAA is to find a location that minimizes the ex-ante social cost, which is the expected cost of the n_u agents sampled from \mu plus the cost incurred by the agents reporting their position. We investigate the mechanism design aspects of the FLPAA under the assumption that the Mechanism Designer (MD) lacks knowledge of the distribution $\mu$ but can query k quantiles of \mu. We explore the trade-off between acquiring more insights into the probability distribution and designing a better-performing mechanism, which we describe through the strong approximation ratio (SAR). The SAR of a mechanism measures the highest ratio between the cost of the mechanisms and the cost of the optimal solution on the worst-case input x and worst-case distribution \mu, offering a metric for efficiency that does not depend on \mu. We divide our study into four different information settings: the zero information case, in which the MD has access to no quantiles; the median information case, in which the MD has access to the median of \mu; the n_u-quantile information case, in which the MD has access to n_u quantiles of its choice, and the k-quantile information case, in which the MD has access to k

I review possible signals at hadron colliders of bileptons, namely doubly-charged vectors or scalars with lepton number L=+/- 2, as predicted by a 331 model, based on a SU(3)_c x SU(3)_L x U(1)_X symmetry. In particular, I account for a version of the 331 model, wherein the embedding of the hypercharge is obtained with the addition of 3 exotic quarks and vector bileptons. Furthermore, a sextet of SU(3)_L, necessary to give masses to leptons, yields an extra scalar sector, including a doubly-charged Higgs, i.e. scalar bileptons. As bileptons are mostly produced in pairs at hadron colliders, their main signal is given by two same-sign lepton pairs at high invariant mass. Nevertheless, they can also decay according to non-leptonic modes, such as a TeV-scale heavy quark, charged 4/3 or 5/3, plus a Standard Model quark. I explore both leptonic and non-leptonic decays and the sensitivity to such processes of present and future hadron colliders., Comment: 10 pages, 3 figures. Invited contribution to Entropy special issue 'Particle Theory and Theoretical Cosmology' dedicated to Paul Frampton on his 80th birthday. Typos corrected

Contrary to conventional massive MIMO cellular configurations plagued by inter-cell interference, cell-free massive MIMO systems distribute network resources across the coverage area, enabling users to connect with multiple access points (APs) and boosting both system capacity and fairness across user. In such systems, one critical functionality is the association between APs and users: determining the optimal association is indeed a combinatorial problem of prohibitive complexity. In this paper, a solution based on deep learning is thus proposed to solve the user clustering problem aimed at maximizing the sum spectral efficiency while controlling the number of active connections. The proposed solution can scale effectively with the number of users, leveraging long short-term memory cells to operate without the need for retraining. Numerical results show the effectiveness of the proposed solution, even in the presence of imperfect channel state information due to pilot contamination., Comment: Accepted to 25th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2024

Measuring the degree of inequality expressed by a multivariate statistical distribution is a challenging problem, which appears in many fields of science and engineering. In this paper, we propose to extend the well known univariate Gini coefficient to multivariate distributions, by maintaining most of its properties. Our extension is based on the application of whitening processes that possess the property of scale stability., Comment: 12 pages, 3 tables

The effects of kinetic-energy preservation errors due to Runge-Kutta (RK) temporal integrators have been analyzed for the case of large-eddy simulations of incompressible turbulent channel flow. Simulations have been run using the open-source solver Xcompact3D with an implicit spectral vanishing viscosity model and a variety of temporal Runge-Kutta integrators. Explicit pseudo-symplectic schemes, with improved energy preservation properties, have been compared to standard RK methods. The results show a marked decrease in the temporal error for higher-order pseudo-symplectic methods; on the other hand, an analysis of the energy spectra indicates that the dissipation introduced by the commonly used three-stage RK scheme can lead to significant distortion of the energy distribution within the inertial range. A cost-vs-accuracy analysis suggests that pseudo-symplectic schemes could be used to attain results comparable to traditional methods at a reduced computational cost., Comment: 13 pages, 11 figures. F.C. and G.C. contributed equally to this work

Metasurfaces are highly effective at manipulating classical light in the linear regime; however, effectively controlling the polarization of non-classical light generated from nonlinear resonant metasurfaces remains a challenge. Here, we present a solution by achieving polarization engineering of frequency-nondegenerate biphotons emitted via spontaneous parametric down-conversion (SPDC) in GaAs metasurfaces, where quasi-bound states in the continuum (qBIC) resonances were utilized for boosting the biphoton generation. By performing a comprehensive polarization tomography, we demonstrate that the polarization of the emitted photons directly reflects the qBIC mode's far-field properties. Furthermore, we show that both the type of qBIC mode and the symmetry of the meta-atoms can be tailored to control each single-photon polarization state, and that the subsequent two-photon polarization states are nearly separable, offering potential applications in the heralded generation of single photons with adjustable polarization. This work provides a significant step towards utilizing metasurfaces to not only generate quantum light but also engineer their polarization, a critical aspect for future quantum technologies.

Dirac particles have two intrinsic degrees-of-freedom, helicity and chirality. While helicity is conserved in time, chirality is not constant under time evolution for massive particles, yielding the phenomenon of chiral oscillations. So far, chiral oscillations have been mainly described in the framework of single particle relativistic quantum mechanics. In this paper, we present a quantum field theory approach to chiral oscillations in analogy with the one used to describe flavor mixing and oscillations. By taking the expectation value of chiral charges, we obtain the same chiral oscillation formula derived via standard relativistic quantum mechanics. We find that chiral charges are diagonalized by a Bogoliubov transformation: this implies that the vacuum for particles with definite chirality is orthogonal to the one for those with definite energy. In the case of neutrinos, our results can be further extended to include also flavor oscillations., Comment: 7 pages

In this paper, we introduce a novel post-processing algorithm that is both model-agnostic and does not require the sensitive attribute at test time. In addition, our algorithm is explicitly designed to enforce minimal changes between biased and debiased predictions; a property that, while highly desirable, is rarely prioritized as an explicit objective in fairness literature. Our approach leverages a multiplicative factor applied to the logit value of probability scores produced by a black-box classifier. We demonstrate the efficacy of our method through empirical evaluations, comparing its performance against other four debiasing algorithms on two widely used datasets in fairness research.

Superclusters are the largest-scale environments where a number of galaxy clusters interact with each other through minor/major mergers and grow via accretion along cosmic filaments. We focus on the A3528 complex in the core of the Shapley Supercluster. This system includes three clusters, A3528 (composed itself by two sub-clusters, namely A3528N and A3528S), A3532 and A3530, and presents a mildly active dynamical state. We study how minor mergers affect the evolution of radio galaxies and whether they are able to re-accelerate relativistic electrons in the ICM. We used observations from the uGMRT (Band 3, 4 and 5) and MeerKAT (L-band) telescopes to obtain images and spectral index maps over a wide frequency band and spatial resolutions. We compare these data with those from the SRG/eROSITA X-ray telescope. We detect faint diffuse radio emission associated with the radio galaxies. The BCGs in A3528S and A3532 show filaments of diffuse radio emission which extend for $\sim200-400$ kpc out of the radio galaxy. The spectral index of these filaments is extremely steep and almost constant ($\alpha\sim -2, -2.5$). Contrary to the radio tails in A3528N, the spectral properties of these radio filaments are not consistent with standard models of plasma ageing. We also detect roundish diffuse radio emission around the BCG in A3528S which could be classified as a radio mini-halo. The radio tail in this cluster appears longer that in earlier detections, being $\sim300$ kpc long at all frequencies. We linked the presence of extended radio emission in the form of filaments and threads in the A3528 complex with the effect of minor mergers. This is reinforced by the increasing X-ray fluctuations in correspondence with the radio extended emission in A3528S. Despite the less energy involved, our findings support the hypothesis that these events can re-energise plasma originating from radio galaxies., Comment: 28 pages, 23 figures and 5 tables; accepted for publication in Astronomy & Astrophysics

Quantum machine learning is one of the many potential applications of quantum computing, each of which is hoped to provide some novel computational advantage. However, quantum machine learning applications often fail to outperform classical approaches on real-world classical data. The ability of these models to generalize well from few training data points is typically considered one of the few definitive advantages of this approach. In this work, we will instead focus on encoding schemes and their effects on various machine learning metrics. Specifically, we focus on real-world data encoding to demonstrate differences between quantum encoding strategies for several real-world datasets and the classification model standard, LightGBM. In particular, we apply the following encoding strategies, including three standard approaches and two modified approaches: Angle, Amplitude, IQP, Entangled Angle, and Alternative IQP. As these approaches require either a significant number of qubits or gates to encode larger datasets, we perform feature selection to support the limited computing power of quantum simulators. This feature selection is performed through a quantum annealing enhanced approach that builds on a QUBO formulation of the problem. In this work, we provide a preliminary demonstration that quantum machine learning with the IQP encoding and LightGBM produce statistically equivalent results for a large majority of the assigned learning tasks.

We present the most comprehensive and deepest X-ray study to date of the properties of the richest Wolf-Rayet (WR) population observed in a single stellar cluster, Westerlund 1 (Wd1). This work is based on 36 Chandra observations obtained from the "Extended Westerlund 1 and 2 Open Clusters Survey" (EWOCS) project, plus 8 archival Chandra observations. The overall exposure depth (~1.1 Ms) and baseline of the EWOCS observations extending over more than one year enable us to perform a detailed photometric, colour, and spectral analysis, as well as to search for short- and long-term periodicity. In X-rays, we detect 20 out of the 24 known Wolf-Rayet stars in Wd1 down to an observed luminosity of ~7$\times10^{29}$erg s$^{-1}$ (assuming a distance of 4.23 kpc to Wd1), with 8 WR stars being detected in X-rays for the first time. Nine stars show clear evidence of variability over the year-long baseline, with clear signs of periodicity. The X-ray colours and spectral analysis reveal that the vast majority of the WR stars are hard X-ray sources (kT$\geq$2.0keV). The Fe XXV emission line at ~6.7 keV, which commonly originates from the wind-wind collision zone in binary systems, is detected for the first time in the spectra of 17 WR stars in Wd1. In addition the ~6.4 keV fluorescent line is observed in the spectra of three stars, indicating that dense cold material coexists with the hot gas in these systems. Overall, our X-ray results alone suggest a very high binary fraction ($\geq$80%) for the WR star population in Wd1. When combining our results with properties of the WR population from other wavelengths, we estimate a binary fraction of $\geq$92%, which could even reach unity. This suggests that either all the most massive stars are found in binary systems within Wd1, or that binarity is essential for the formation of such a rich population of WR stars., Comment: 20 pages, 11 figures, 6 tables, accepted for publication in A&A

We present a novel automata-based approach to address linear temporal logic modulo theory (LTL-MT) as a specification language for data words. LTL-MT extends LTL_f by replacing atomic propositions with quantifier-free multi-sorted first-order formulas interpreted over arbitrary theories. While standard LTL_f is reduced to finite automata, we reduce LTL-MT to symbolic data-word automata (SDWAs), whose transitions are guarded by constraints from underlying theories. Both the satisfiability of LTL-MT and the emptiness of SDWAs are undecidable, but the latter can be reduced to a system of constrained Horn clauses, which are supported by efficient solvers and ongoing research efforts. We discuss multiple applications of our approach beyond satisfiability, including model checking and runtime monitoring. Finally, a set of empirical experiments shows that our approach to satisfiability works at least as well as a previous custom solution., Comment: Published at ECAI'24 (Extended Version)

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

This study proposes a novel spatial discretization procedure for the compressible Euler equations which guarantees entropy conservation at a discrete level when an arbitrary equation of state is assumed. The proposed method, based on a locally-conservative discretization, guarantees also the spatial conservation of mass, momentum, and total energy and is kinetic energy-preserving. In order to achieve the entropy-conservation property for an arbitrary non-ideal gas, a general strategy is adopted based on the manipulation of discrete balance equations through the imposition of global entropy conservation and the use of a summation by parts rule. The procedure, which is extended to an arbitrary order of accuracy, conducts to a general form of the internal-energy numerical flux which results in a nonlinear function of thermodynamic and dynamic variables and still admits the mass flux as a residual degree of freedom. The effectiveness of the novel entropy-conservative formulation is demonstrated through numerical tests making use of some of the most popular cubic equations of state., Comment: 16 pages, 3 figures

We study the radio continuum emission of four galaxies experiencing ram-pressure stripping in four clusters of the Shapley supercluster at redshift z~0.05. Multi-band (235-1367 MHz) radio data, complemented by integral-field spectroscopy, allow us to detect and analyse in detail the non-thermal component both in the galaxy discs and the radio continuum tails. Three galaxies present radio continuum tails which are tens of kiloparsecs long. By deriving the radio spectral index in the inner and outer tails and comparing our findings with the distribution of the extraplanar ionised gas and the results of N-body/hydrodynamical simulations, we demonstrate that these tails are caused by the ram pressure which, together with the ionised gas, sweeps the magnetic field from the galaxy discs. We suggest that the radio continuum emission in these tails can be differently powered by (i) in situ star formation; (ii) relativistic electrons stripped from the disc; (iii) shock excitation or a combination of them. All the ram-pressure stripped galaxies are found in environments where cluster-cluster interactions occurred and/or are ongoing thus strongly supporting the thesis that cluster and group collisions and mergers may locally increase the ram pressure and trigger hydrodynamical interactions between the intracluster medium and the interstellar medium of galaxies., Comment: 18 pages, 14 figures, accepted for publication in MNRAS

Understanding how and when galaxies formed stars over the history of the Universe is fundamental to the study of galaxy evolution. The star formation histories (SFHs) of galaxies in the local Universe can be measured with high precision using deep imaging with space telescopes. Such resolved SFHs are based on modelling the observed color-magnitude diagram (CMD) with stellar evolution models and rely on age-sensitive features like the main sequence turn-off to measure a galaxy's star formation rate as a function of time. There are many other population-level parameters that factor into these measurements, such as the stellar initial mass function (IMF), binary fraction, and metallicity, to name a few. We present and release StarFormationHistories.jl, a modular, open-source Julia package for measuring resolved SFHs with a focus on model flexibility for these types of population parameters. The code can model unresolved photometric binaries and supports arbitrary IMFs. Random uncertainties in the SFH measurements can be quantified with Monte Carlo posterior sampling methods. We illustrate the performance of the package on JWST/NIRCAM data of the Local Group dwarf irregular galaxy WLM $\left(M_v\approx-14.2\right)$, which exhibits a complex, well-sampled CMD, and HST/ACS data of the ultra-faint Milky Way satellite dwarf galaxy Horologium I $\left(M_v\approx-3.7\right)$, which has a much simpler but sparser CMD., Comment: 28 pages, 11 figures, 1 appendix

This paper explores the Mechanism Design aspects of the $m$-Capacitated Facility Location Problem where the total facility capacity is less than the number of agents. Following the framework outlined by Aziz et al., the Social Welfare of the facility location is determined through a First-Come-First-Served (FCFS) game, in which agents compete once the facility positions are established. When the number of facilities is $m > 1$, the Nash Equilibrium (NE) of the FCFS game is not unique, making the utility of the agents and the concept of truthfulness unclear. To tackle these issues, we consider absolutely truthful mechanisms, i.e. mechanisms that prevent agents from misreporting regardless of the strategies used during the FCFS game. We combine this stricter truthfulness requirement with the notion of Equilibrium Stable (ES) mechanisms, which are mechanisms whose Social Welfare does not depend on the NE of the FCFS game. We demonstrate that the class of percentile mechanisms is absolutely truthful and identify the conditions under which they are ES. We also show that the approximation ratio of each ES percentile mechanism is bounded and determine its value. Notably, when all the facilities have the same capacity and the number of agents is sufficiently large, it is possible to achieve an approximation ratio smaller than $1+\frac{1}{2m-1}$. Finally, we extend our study to encompass higher-dimensional problems. Within this framework, we demonstrate that the class of ES percentile mechanisms is even more restricted and characterize the mechanisms that are both ES and absolutely truthful. We further support our findings by empirically evaluating the performance of the mechanisms when the agents are the samples of a distribution., Comment: 34 pages, 55 figures

In intertemporal settings, the multiattribute utility theory of Kihlstrom and Mirman suggests the application of a concave transform of the lifetime utility index. This construction, while allowing time and risk attitudes to be separated, leads to dynamically inconsistent preferences. We address this issue in a game-theoretic sense by formalizing an equilibrium control theory for continuous-time Markov processes. In these terms, we describe the equilibrium strategy and value function as the solution of an extended Hamilton-Jacobi-Bellman system of partial differential equations. We verify that (the solution of) this system is a sufficient condition for an equilibrium and examine some of its novel features. A consumption-investment problem for an agent with CRRA-CES utility showcases our approach.

NASA's Great Observatories Maturation Program (GOMAP) will advance the science definition, technology, and workforce needed for the Habitable Worlds Observatory (HWO) with the goal of a Phase A start by the end of the current decade. GOMAP offers long-term cost and schedule savings compared to the 'TRL 6 by Preliminary Design Review' paradigm historically adopted by large NASA missions. Many of the key technologies in the development queue for HWO require the combined activities of 1) facility and process development for validation of technologies at the scale required for HWO and 2) deployment in the 'real world' environment of mission Integration & Test prior to on-orbit operations. We present a concept for the Smallsat Technology Accelerated Maturation Platform (STAMP), an integrated facility, laboratory, and instrument prototype development program that could be supported through the GOMAP framework and applied to any of NASA's Future Great Observatories (FGOs). This brief describes the recommendation for the first entrant into this program, "STAMP-1", an ESPA Grande-class mission advancing key technologies to enable the ultraviolet capabilities of HWO. STAMP-1 would advance new broadband optical coatings, high-sensitivity ultraviolet detector systems, and multi-object target selection technology to TRL 6 with a flight demonstration. STAMP-1 advances HWO technology on an accelerated timescale, building on current ROSES SAT+APRA programs, reducing cost and schedule risk for HWO while conducting a compelling program of preparatory science and workforce development with direct benefits for HWO mission implementation in the 2030s., Comment: 14 pages, 4 figure. JATIS - accepted

In this paper, we investigate the $k$-Facility Location Problem ($k$-FLP) within the Bayesian Mechanism Design framework, in which agents' preferences are samples of a probability distributed on a line. Our primary contribution is characterising the asymptotic behavior of percentile mechanisms, which varies according to the distribution governing the agents' types. To achieve this, we connect the $k$-FLP and projection problems in the Wasserstein space. Owing to this relation, we show that the ratio between the expected cost of a percentile mechanism and the expected optimal cost is asymptotically bounded. Furthermore, we characterize the limit of this ratio and analyze its convergence speed. Our asymptotic study is complemented by deriving an upper bound on the Bayesian approximation ratio, applicable when the number of agents $n$ exceeds the number of facilities $k$. We also characterize the optimal percentile mechanism for a given agent's distribution through a system of $k$ equations. Finally, we estimate the optimality loss incurred when the optimal percentile mechanism is derived using an approximation of the agents' distribution rather than the actual distribution., Comment: 29 pages, 1 table, full version of a SAGT publication

Employing physically-consistent numerical methods is an important step towards attaining robust and accurate numerical simulations. When addressing compressible flows, in addition to preserving kinetic energy at a discrete level, as done in the incompressible case, additional properties are sought after, such as the ability to preserve the equilibrium of pressure that can be found at contact interfaces. This paper investigates the general conditions of the spatial numerical discretizations to achieve the pressure equilibrium preserving property (PEP). Schemes from the literature are analyzed in this respect, and procedures to impart the PEP property to existing discretizations are proposed. Additionally, new PEP numerical schemes are introduced through minor modifications of classical ones. Numerical tests confirmed the theory hereby presented and showed that the modifications, beyond the enforcement of the PEP property, have a generally positive impact on the performances of the original schemes., Comment: 15 pages, 2 figures

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.

Deepfake technology is rapidly advancing, posing significant challenges to the detection of manipulated media content. Parallel to that, some adversarial attack techniques have been developed to fool the deepfake detectors and make deepfakes even more difficult to be detected. This paper explores the application of super resolution techniques as a possible adversarial attack in deepfake detection. Through our experiments, we demonstrate that minimal changes made by these methods in the visual appearance of images can have a profound impact on the performance of deepfake detection systems. We propose a novel attack using super resolution as a quick, black-box and effective method to camouflage fake images and/or generate false alarms on pristine images. Our results indicate that the usage of super resolution can significantly impair the accuracy of deepfake detectors, thereby highlighting the vulnerability of such systems to adversarial attacks. The code to reproduce our experiments is available at: https://github.com/davide-coccomini/Adversarial-Magnification-to-Deceive-Deepfake-Detection-through-Super-Resolution

When the Bekenstein-Hawking entropy is modified, ambiguity often arises concerning whether the Hawking temperature or the thermodynamic mass should be modified. The common practice, however, is to keep the black hole solution the same as that in general relativity. On the other hand, if Jacobson's method of deriving Einstein equations from thermodynamic is valid in the general settings, then given a generalized entropy one should first derive the corresponding modified gravity, and then look for the compatible black hole solution before investigating its thermodynamics. We comment on some properties and subtleties in this approach. In particular, we point out that generically generalized entropy would lead to a varying effective gravitational "constant" theory, where $G_\text{eff}$ depends on the horizon area. We discuss in what ways such theories are discernible from general relativity despite its seemingly jarring differences, and how to make sense of area-dependent field equations. As a consequence we show that in the Jacobson's approach, the standard quantum gravitational logarithmic correction to Bekenstein-Hawking entropy is equivalent to a running gravitational "constant". A horizon area dependent $G_\text{eff}$ could also lead to a coupling between black hole masses and cosmological expansion, a scenario that has been studied recently in the literature, but so far lacks strong theoretical motivation. In the Tsallis case, we show that the thermodynamic mass for a Schwarzschild black hole is just a constant multiple of its ADM mass, which is considerably simpler than the approach not utilizing the Jacobson's method., Comment: Minor changes; also added/fixed some references

A fraction of galaxy clusters harbor diffuse radio sources known as radio halos. The currently adopted scenario for their formation is based on second-order Fermi re-acceleration of seed electrons that is driven by merger-driven turbulence in the intra-cluster medium. This mechanism is expected to be inefficient, which implies that a significant fraction of halos should have very steep ($\alpha < -1.5$) energy spectra. We start investigating the potential and current limitations of the combination of the two surveys conducted by LOFAR, LoTSS (144 MHz) and LoLSS (54 MHz), to probe the origin of radio halos. We follow up the 20 radio halos detected in the DR1 of LoTSS, which covers the HETDEX field, with the LoLSS survey, and we study their spectral properties between 54 and 144 MHz. After the removal of compact sources, 9 halos were excluded due to unreliable halo flux density measurements at 54 MHz. Our main finding is that 7 out of 11 ($\sim$ 64%) exhibit an ultra-steep spectrum ($\alpha < -1.5$), which is a key prediction of turbulent re-acceleration models. We also note a tentative trend for more massive systems to host flatter halos, although the currently poor statistics does not allow for a deeper analysis. Our sample suffers from low angular resolution at 54 MHz, which limits the accuracy of the compact-sources subtraction. Nevertheless, this study is the first step towards providing compelling evidence for the existence of a large fraction of radio halos with very steep spectrum, which is a fundamental prediction of turbulent re-acceleration models. In this regard, the forthcoming second data release of LoLSS, along with the integration of LOFAR international stations and the instrumental upgrade to LOFAR2.0, will improve both the statistics and the low-frequency angular resolution, allowing to conclusively determine the origin of radio halos in galaxy clusters., Comment: A&A Letter; 11 pages, 12 figures

Probabilistic integral circuits (PICs) have been recently introduced as probabilistic models enjoying the key ingredient behind expressive generative models: continuous latent variables (LVs). PICs are symbolic computational graphs defining continuous LV models as hierarchies of functions that are summed and multiplied together, or integrated over some LVs. They are tractable if LVs can be analytically integrated out, otherwise they can be approximated by tractable probabilistic circuits (PC) encoding a hierarchical numerical quadrature process, called QPCs. So far, only tree-shaped PICs have been explored, and training them via numerical quadrature requires memory-intensive processing at scale. In this paper, we address these issues, and present: (i) a pipeline for building DAG-shaped PICs out of arbitrary variable decompositions, (ii) a procedure for training PICs using tensorized circuit architectures, and (iii) neural functional sharing techniques to allow scalable training. In extensive experiments, we showcase the effectiveness of functional sharing and the superiority of QPCs over traditional PCs.

Cosmic strings radiate detectable gravitational waves in models featuring high-scale symmetry breaking, e.g., high-scale leptogenesis. In this Letter, for the first time, we show that different flavored regimes of high-scale leptogenesis can be tested distinctly with the spectral features in cosmic string-radiated gravitational waves. This is possible if the scalar field that makes right-handed neutrinos massive is feebly coupled to the Standard Model Higgs. Each flavored regime, sensitive to low-energy neutrino experiments, leaves a marked imprint on the gravitational waves spectrum. A three-flavor and a two-flavor regime could be probed by a characteristic fall-off of the gravitational wave spectrum at the LISA-DECIGO-ET frequency bands with preceding scale-invariant amplitudes bounded from above and below. We present Gravitational Waves windows for Flavored Regimes of Leptogenesis (GWFRL) falsifiable in the upcoming experiments., Comment: 5 pages, 2 figures, plus supplemental material

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care., Comment: 10 pages, 4 figures, 1 table

The advent of distributed computing systems will offer great flexibility for application workloads, while also imposing more attention to security, where the future advent and adoption of quantum technology can introduce new security threats. For this reason, the Multi-access Edge Computing (MEC) working group at ETSI has recently started delving into security aspects, especially motivated by the upcoming reality of the MEC federation, which involves services made of application instances belonging to different systems (thus, different trust domains). On the other side, Quantum Key Distribution (QKD) can help strengthen the level of security by enabling the exchange of secure keys through an unconditionally secure protocol, e.g., to secure communication between REST clients and servers in distributed computing systems at the edge. In this paper, we propose a technical solution to achieve this goal, building on standard specifications, namely ETSI MEC and ETSI QKD, and discussing the gaps and limitations of current technology, which hamper full-fledged in-field deployment and mass adoption. Furthermore, we provide our look-ahead view on the future of secure distributed computing through the enticing option of federating edge computing domains., Comment: Accepted for presentation at IEEE PIMRC 2024

We empirically assess estimates from v3.0 of the JWST NIRCam Exposure Time Calculator (ETC) using observations of resolved stars in Local Group targets taken as part of the Resolved Stellar Populations Early Release Science (ERS) Program. For bright stars, we find that: (i) purely Poissonian estimates of the signal-to-noise ratio (SNR) are in good agreement between the ETC and observations, but non-ideal effects (e.g., flat field uncertainties) are the current limiting factor in the photometric precision that can be achieved; (ii) source position offsets, relative to the detector pixels, have a large impact on the ETC saturation predictions and introducing sub-pixel dithers in the observation design can improve the saturation limits by up to ~1 mag. For faint stars, for which the sky dominates the error budget, we find that the choice in ETC extraction strategy (e.g., aperture size relative to point spread function size) can affect the exposure time estimates by up to a factor of 5. We provide guidelines for configuring the ETC aperture photometry to produce SNR predictions in line with the ERS data. Finally, we quantify the effects of crowding on the SNRs over a large dynamic range in stellar density and provide guidelines for approximating the effects of crowding on SNRs predicted by the ETC., Comment: Accepted for publication in ApJ. 18 pages, 9 Figures, 2 Tables

Computer aided diagnosis systems can provide non-invasive, low-cost tools to support clinicians. These systems have the potential to assist the diagnosis and monitoring of neurodegenerative disorders, in particular Parkinson's disease (PD). Handwriting plays a special role in the context of PD assessment. In this paper, the discriminating power of "dynamically enhanced" static images of handwriting is investigated. The enhanced images are synthetically generated by exploiting simultaneously the static and dynamic properties of handwriting. Specifically, we propose a static representation that embeds dynamic information based on: (i) drawing the points of the samples, instead of linking them, so as to retain temporal/velocity information; and (ii) adding pen-ups for the same purpose. To evaluate the effectiveness of the new handwriting representation, a fair comparison between this approach and state-of-the-art methods based on static and dynamic handwriting is conducted on the same dataset, i.e. PaHaW. The classification workflow employs transfer learning to extract meaningful features from multiple representations of the input data. An ensemble of different classifiers is used to achieve the final predictions. Dynamically enhanced static handwriting is able to outperform the results obtained by using static and dynamic handwriting separately.

Signature verification is a critical task in many applications, including forensic science, legal judgments, and financial markets. However, current signature verification systems are often difficult to explain, which can limit their acceptance in these applications. In this paper, we propose a novel explainable offline automatic signature verifier (ASV) to support forensic handwriting examiners. Our ASV is based on a universal background model (UBM) constructed from offline signature images. It allows us to assign a questioned signature to the UBM and to a reference set of known signatures using simple distance measures. This makes it possible to explain the verifier's decision in a way that is understandable to non experts. We evaluated our ASV on publicly available databases and found that it achieves competitive performance with state of the art ASVs, even when challenging 1 versus 1 comparison are considered. Our results demonstrate that it is possible to develop an explainable ASV that is also competitive in terms of performance. We believe that our ASV has the potential to improve the acceptance of signature verification in critical applications such as forensic science and legal judgments.