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We investigate the mechanisms by which a student's age relative to classmates (i.e., relative age) influences risky health behaviors among European adolescents. Using a two-stage least squares approach, we show that relatively young students are more prone to engage in risky behaviors. These results hold after controlling for absolute age, country fixed effects, and birth season effects. In the second part of the paper, we conduct two sets of analyses on possible mechanisms. First, causal mediation analyses reveal that students' perceived academic performance is the primary mediator. Second, additional analyses suggest that perceptions of substance risks and peer usage prevalence may also play a significant role.

We explore the impact of loop effects on positivity in effective field theories emerging in the infrared from unitary and causal microscopic dynamics. Focusing on massless particles coupled to gravity, we address the treatment of forward-limit divergences from loop discontinuities and establish necessary conditions for maintaining computational control in perturbation theory. While loop effects remain small, ensuring consistency in our approach leads to a significant impact on bounds, even at tree level., Comment: 38 pages, 4 appendices, 15 figures

Reconfigurable photonic integrated circuits (PICs) can implement arbitrary operations and signal processing functionalities directly in the optical domain. Run-time configuration of these circuits requires an electronic control layer to adjust the working point of their building elements and make them compensate for thermal drifts or degradations of the input signal. As the advancement of photonic foundries enables the fabrication of chips of increasing complexity, developing scalable electronic controllers becomes crucial for the operation of complex PICs. In this paper, we present an electronic application-specific integrated circuit (ASIC) designed for reconfiguration of PICs featuring numerous tuneable elements. Each channel of the ASIC controller independently addresses one optical component of the PIC, as multiple parallel local feedback loops are operated to achieve full control. The proposed design is validated through real-time reconfiguration of a 16-channel silicon photonics adaptive beam coupler. Results demonstrate automatic coupling of an arbitrary input beam to a single-mode waveguide, dynamic compensation of beam wavefront distortions and successful transmission of a 50 Gbit/s signal through an optical free-space link. The low power consumption and compactness of the electronic chip provide a scalable paradigm that can be seamlessly extended to larger photonic architectures., Comment: Manuscript contains both main text and supplementary information

Glass-forming liquids have only a modest tendency to crystallize and hence their dynamics can be studied even below the melting temperature. The relaxation dynamics of most of these liquids shows at a temperature $T_c$, somewhat above the glass-transition temperature $T_g$, a crossover, which indicates the conjunction of two different dynamical regimes. For temperatures slightly above $T_c$, experiments and computer simulations have extensively probed this dynamics on the particle level and identified several universal scaling laws that are often compatible with theoretical predictions. Using large scale computer simulations we extend these studies to temperatures below $T_c$ and find that the relaxation mechanism is qualitatively different from the one found at higher temperatures. We identify new scaling laws that allow to give a simple description of the relaxation dynamics at very low $T$s. Specifically we reveal that the cage-escape process is related to rare but large particle displacements that give rise to a distinctive sub-diffusive power-law in the time correlation functions. This insight helps to advance our understanding on the relaxation dynamics of glass-forming systems at temperatures that are close to the experimental glass transition.

This paper aims to describe the Pointing Reconstruction Model (PRM) and the prototype Star Tracker, which will be mounted on LSPE-Strip, a microwave Q- and W-band CMB telescope planned for installation at the "Observatorio del Teide" in Tenerife. The PRM integrates information on the instantaneous attitude provided by the telescope control system to determine the actual pointing direction and focal plane orientation of the telescope. It accounts for various non-idealities in the telescope setup, represented by eight configuration angles, which will be calibrated using the Star Tracker. Following the derivation of the PRM formalism and its implementation, we investigate the pointing errors caused by incorrect calibration of these configuration angles to validate the required 1 arcminute maximum systematic pointing error for the LSPE-Strip survey. This paper also describes the main structure and operations of the Star Tracker and presents the results of a campaign of actual sky observations conducted with a prototype. The results demonstrate a Star Tracker RMS accuracy of approximately 3 arcseconds, while systematic errors remain below 10 arcseconds. Based on these results, we analyzed the problem of reconstructing the PRM configuration angles. Two methods for intercalibrating the Star Tracker's pointing direction with respect to the focal plane's pointing direction were examined: (1) observations of planets and (2) observations of a drone carrying both an optical beacon and a radio beacon. In the first case, an intercalibration accuracy between 1/3 arcminute and 1 arcminute is achievable. In the second case, the expected intercalibration accuracy ranges from 0.25 arcminute to 1 arcminute., Comment: 37 pages; 16 figures; 5 tables; the limited length of the arXiv abstract required shortening the original abstract, which is presented in its full length in the PDF; paper accepted for pubblication 2025 jan 06 in the Journal of Instrumentation (JINST)

The study of regular black holes and black hole mimickers as alternatives to standard black holes has recently gained significant attention, driven both by the need to extend general relativity to describe black hole interiors, and by recent advances in observational technologies. Despite considerable progress in this field, significant challenges remain in identifying and characterizing physically well-motivated classes of regular black holes and black hole mimickers. This report provides an overview of these challenges, and outlines some of the promising research directions -- as discussed during a week-long focus programme held at the Institute for Fundamental Physics of the Universe (IFPU) in Trieste from November 11th to 15th, 2024., Comment: 39 pages, 3 figures

Studying the isotopic composition of cosmic-rays (CRs) provides crucial insights into the galactic environment and helps improve existing propagation models. Special attention is given to the secondary-to-primary ratios of light isotopic components in CRs, as these measurements can offer complementary data compared to traditional secondary-to-primary ratios like B/C. Recently, a precision measurement of the Deuterium (D) abundance in CR in the 2-21 GV rigidity range provided by the AMS02 experiment unexpectedly detected an excess of D with respect to its expected secondary nature, opening the field for new measurements at high rigidity to determine how the spectrum evolves and whether there is confirmation of a primary or primary plus secondary origin. While there are theoretical models that attempt to explain this excess, the experimental uncertainties on D production cross-sections and on CR propagation models remain significant, and only new and precise measurements can dissipate existing doubts. In this work we review the current experimental scenario and we propose a dedicated experiment able to extend the D abundance measurement up to 100 GeV/nucl without the need of a magnetic spectrometer, using a multiple scattering based technique for the measurement of particle momentum. The expected performances of the proposed detector were assessed through a dedicated simulation using the GEANT4 package, and its role in the current particle physics scenario is discussed.

Mechanical and elastic properties of materials are among the most fundamental quantities for many engineering and industrial applications. Here, we present a formulation that is efficient and accurate for calculating the elastic and bending rigidity tensors of crystalline solids, leveraging interatomic force constants and long-wavelength perturbation theory. Crucially, in the long-wavelength limit, lattice vibrations induce macroscopic electric fields which further couple with the propagation of elastic waves, and a separate treatment on the long-range electrostatic interactions is thereby required to obtain elastic properties under the appropriate electrical boundary conditions. A cluster expansion of the charge density response and dielectric screening function in the long-wavelength limit has been developed to efficiently extract multipole and dielectric tensors of arbitrarily high order. We implement the proposed method in a first-principles framework and perform extensive validations on silicon, NaCl, GaAs and rhombohedral BaTiO$_3$ as well as monolayer graphene, hexagonal BN, MoS$_2$ and InSe, obtaining good to excellent agreement with other theoretical approaches and experimental measurements. Notably, we establish that multipolar interactions up to at least octupoles are necessary to obtain the accurate short-circuit elastic tensor of bulk materials, while higher orders beyond octupole interactions are required to converge the bending rigidity tensor of 2D crystals. The present approach greatly simplifies the calculations of bending rigidities and will enable the automated characterization of the mechanical properties of novel functional materials., Comment: 35 pages, 7 figures and 7 tables for main text; 43 pages with supplementary material

DLV2 is an AI tool for Knowledge Representation and Reasoning which supports Answer Set Programming (ASP) - a logic-based declarative formalism, successfully used in both academic and industrial applications. Given a logic program modelling a computational problem, an execution of DLV2 produces the so-called answer sets that correspond one-to-one to the solutions to the problem at hand. The computational process of DLV2 relies on the typical Ground & Solve approach where the grounding step transforms the input program into a new, equivalent ground program, and the subsequent solving step applies propositional algorithms to search for the answer sets. Recently, emerging applications in contexts such as stream reasoning and event processing created a demand for multi-shot reasoning: here, the system is expected to be reactive while repeatedly executed over rapidly changing data. In this work, we present a new incremental reasoner obtained from the evolution of DLV2 towards iterated reasoning. Rather than restarting the computation from scratch, the system remains alive across repeated shots, and it incrementally handles the internal grounding process. At each shot, the system reuses previous computations for building and maintaining a large, more general ground program, from which a smaller yet equivalent portion is determined and used for computing answer sets. Notably, the incremental process is performed in a completely transparent fashion for the user. We describe the system, its usage, its applicability and performance in some practically relevant domains. Under consideration in Theory and Practice of Logic Programming (TPLP)., Comment: 22 pages, 4 figures

Answer Set Programming (ASP), a well-known declarative logic programming paradigm, has recently found practical application in Process Mining. In particular, ASP has been used to model tasks involving declarative specifications of business processes. In this area, Declare stands out as the most widely adopted declarative process modeling language, offering a means to model processes through sets of constraints valid traces must satisfy, that can be expressed in Linear Temporal Logic over Finite Traces (LTLf). Existing ASP-based solutions encode Declare constraints by modeling the corresponding LTLf formula or its equivalent automaton which can be obtained using established techniques. In this paper, we introduce a novel encoding for Declare constraints that directly models their semantics as ASP rules, eliminating the need for intermediate representations. We assess the effectiveness of this novel approach on two Process Mining tasks by comparing it with alternative ASP encodings and a Python library for Declare. Under consideration in Theory and Practice of Logic Programming (TPLP)., Comment: Under consideration in Theory and Practice of Logic Programming (TPLP)

This paper presents a novel control algorithm for robotic manipulators in unstructured environments using proximity sensors partially distributed on the platform. The proposed approach exploits arrays of multi zone Time-of-Flight (ToF) sensors to generate a sparse point cloud representation of the robot surroundings. By employing computational geometry techniques, we fuse the knowledge of robot geometric model with ToFs sensory feedback to generate whole-body motion tasks, allowing to move both sensorized and non-sensorized links in response to unpredictable events such as human motion. In particular, the proposed algorithm computes the pair of closest points between the environment cloud and the robot links, generating a dynamic avoidance motion that is implemented as the highest priority task in a two-level hierarchical architecture. Such a design choice allows the robot to work safely alongside humans even without a complete sensorization over the whole surface. Experimental validation demonstrates the algorithm effectiveness both in static and dynamic scenarios, achieving comparable performances with respect to well established control techniques that aim to move the sensors mounting positions on the robot body. The presented algorithm exploits any arbitrary point on the robot surface to perform avoidance motion, showing improvements in the distance margin up to 100 mm, due to the rendering of virtual avoidance tasks on non-sensorized links., Comment: Submitted for evaluation at the IEEE/ASME Transactions on Mechatronics Journal

Efficiency and emission rate are two traditionally conflicting parameters in radiation detection, and achieving their simultaneous maximization could significantly advance ultrafast time-of-flight (ToF) technologies. In this study, we demonstrate that this goal is attainable by harnessing the giant oscillator strength (GOS) inherent to weakly confined perovskite nanocrystals, which enables superradiant scintillation under mildly cryogenic conditions that align seamlessly with ToF technologies. We show that the radiative acceleration due to GOS encompasses both single and multiple exciton dynamics arising from ionizing interactions, further enhanced by suppressed non-radiative losses and Auger recombination at 80 K. The outcome is ultrafast scintillation with 420 ps lifetime and light yield of ~10'000 photons/MeV for diluted NC solutions, all without non-radiative losses. Temperature-dependent light-guiding experiments on test-bed nanocomposite scintillators finally indicate that the light-transport capability remains unaffected by the accumulation of band-edge oscillator strength due to GOS. These findings suggest a promising pathway toward developing ultrafast nanotech scintillators with optimized light output and timing performance.

We study from an algebraic and geometric viewpoint Hamiltonian operators which are sum of a non-degenerate first-order homogeneous operator and a Poisson tensor. In flat coordinates, also known as Darboux coordinates, these operators are uniquely determined by a triple composed by a Lie algebra, its most general non-degenerate quadratic Casimir and a 2-cocycle. We present some classes of operators associated to Lie algebras with non-degenerate quadratic Casimirs and we give a description of such operators in low dimensions. Finally, motivated by the example of the KdV equation we discuss the conditions of bi-Hamiltonianity of such operators.

Virtual reality enables users to experience real-life situations in immersive environments. Interaction methods significantly shape user experience, particularly in high fidelity simulations mimicking real world tasks. This study evaluates two primary VR interaction techniques, hand based and controller based, through virtual shopping tasks in a simulated supermarket with 40 participants. Hand-based interaction was preferred for its natural, immersive qualities and alignment with real-world gestures but faced usability challenges, including limited haptic feedback and grasping inefficiencies. In contrast, controller-based interaction offered greater precision and reliability, making it more suitable for tasks requiring fine motor skills., Comment: 3 pages (2+references), 1 figure

In contrast to the case of non-zero baryon chemical potential, the isospin chemical potential does not introduce a sign problem and can be simulated on the lattice. When the isospin chemical potential is large enough, a phase transition to a Bose-Einstein condensate of pions takes place. Currently available results in the literature on the phase diagram and the equation of state in this setup employ staggered fermions. We present preliminary results on the onset of the pion condensation phase in simulations with Wilson fermions., Comment: talk presented at the 41st International Symposium on Lattice Field Theory (LATTICE2024), July 28th - August 3rd, 2024, The University of Liverpool

We study the light meson - charmonium - glueball mixing using flavor-singlet meson operators built from optimal distillation profiles together with purely gluonic operators in different $J^{PC}$ channels at two different pion masses ($\approx$ $420$, $800$ MeV) in two $N_{\rm f} = 3 + 1$ ensembles at close to physical charm quark mass. We observe non-zero mixing correlations between the different types of operators and quantify the overlaps between states created by them and the energy eigenstates by means of a GEVP formulation. We are particularly interested in the scalar glueball and its possible decay into two pions so we also include two-pion operators in our calculation., Comment: 11 pages, 6 figures, 1 table. Proceedings of the 41st International Symposium on Lattice Field Theory (Lattice 2024), University of Liverpool, UK, 2024

In today's digital age, conspiracies and information campaigns can emerge rapidly and erode social and democratic cohesion. While recent deep learning approaches have made progress in modeling engagement through language and propagation models, they struggle with irregularly sampled data and early trajectory assessment. We present IC-Mamba, a novel state space model that forecasts social media engagement by modeling interval-censored data with integrated temporal embeddings. Our model excels at predicting engagement patterns within the crucial first 15-30 minutes of posting (RMSE 0.118-0.143), enabling rapid assessment of content reach. By incorporating interval-censored modeling into the state space framework, IC-Mamba captures fine-grained temporal dynamics of engagement growth, achieving a 4.72% improvement over state-of-the-art across multiple engagement metrics (likes, shares, comments, and emojis). Our experiments demonstrate IC-Mamba's effectiveness in forecasting both post-level dynamics and broader narrative patterns (F1 0.508-0.751 for narrative-level predictions). The model maintains strong predictive performance across extended time horizons, successfully forecasting opinion-level engagement up to 28 days ahead using observation windows of 3-10 days. These capabilities enable earlier identification of potentially problematic content, providing crucial lead time for designing and implementing countermeasures. Code is available at: https://github.com/ltian678/ic-mamba. An interactive dashboard demonstrating our results is available at: https://ic-mamba.behavioral-ds.science., Comment: 11 pages, 5 figures, 10 tables, Accepted by the Web Conference 2025 (WWW2025)

Oversmoothing is a fundamental challenge in graph neural networks (GNNs): as the number of layers increases, node embeddings become increasingly similar, and model performance drops sharply. Traditionally, oversmoothing has been quantified using metrics that measure the similarity of neighbouring node features, such as the Dirichlet energy. While these metrics are related to oversmoothing, we argue they have critical limitations and fail to reliably capture oversmoothing in realistic scenarios. For instance, they provide meaningful insights only for very deep networks and under somewhat strict conditions on the norm of network weights and feature representations. As an alternative, we propose measuring oversmoothing by examining the numerical or effective rank of the feature representations. We provide theoretical support for this approach, demonstrating that the numerical rank of feature representations converges to one for a broad family of nonlinear activation functions under the assumption of nonnegative trained weights. To the best of our knowledge, this is the first result that proves the occurrence of oversmoothing without assumptions on the boundedness of the weight matrices. Along with the theoretical findings, we provide extensive numerical evaluation across diverse graph architectures. Our results show that rank-based metrics consistently capture oversmoothing, whereas energy-based metrics often fail. Notably, we reveal that a significant drop in the rank aligns closely with performance degradation, even in scenarios where energy metrics remain unchanged.

Analog in-memory computing is an emerging paradigm designed to efficiently accelerate deep neural network workloads. Recent advancements have demonstrated significant improvements in throughput and efficiency, focusing independently on either inference or training acceleration. However, a unified analog in-memory technology platform-capable of performing on-chip training, retaining the weights, and sustaining long-term inference acceleration-has yet to be reported. In this work, an all-in-one analog AI accelerator is presented and benchmarked, combining these capabilities to enable autonomous, energy-efficient, and continuously adaptable AI systems. The platform leverages an array of filamentary conductive-metal-oxide (CMO)/HfOx redox-based resistive switching memory cells (ReRAM) in one-transistor one-ReRAM (1T1R) configuration, integrated into the back-end-of-line (BEOL) of a 130 nm technology node. The array characterization demonstrates reliable and optimized resistive switching with voltage amplitudes of less than 1.5 V, enabling compatibility with advanced technology nodes. The multi-bit capability of over 32 stable states (5 bits) and record-low programming noise down to 10 nS enable an almost ideal weight transfer process, more than an order of magnitude better than other memristive technologies. The array's inference performance is validated through realistic matrix-vector multiplication simulations on a 64x64 array, achieving a record-low root-mean-square error ranging from 0.06 at 1 second to 0.2 at 10 years after programming, compared to the ideal floating-point case. The array is then measured under the same conditions as those used for on-chip training. Training accuracy closely matching the software equivalent is achieved across different datasets, with high-fidelity modelling of the device response based on experimental-only data.

Large language models have revolutionized natural language processing through self-supervised pretraining on massive datasets. Inspired by this success, researchers have explored adapting these methods to speech by discretizing continuous audio into tokens using neural audio codecs. However, existing approaches face limitations, including high bitrates, the loss of either semantic or acoustic information, and the reliance on multi-codebook designs when trying to capture both, which increases architectural complexity for downstream tasks. To address these challenges, we introduce FocalCodec, an efficient low-bitrate codec based on focal modulation that utilizes a single binary codebook to compress speech between 0.16 and 0.65 kbps. FocalCodec delivers competitive performance in speech resynthesis and voice conversion at lower bitrates than the current state-of-the-art, while effectively handling multilingual speech and noisy environments. Evaluation on downstream tasks shows that FocalCodec successfully preserves sufficient semantic and acoustic information, while also being well-suited for generative modeling. Demo samples, code and checkpoints are available at https://lucadellalib.github.io/focalcodec-web/., Comment: 18 pages

A photonic heat amplifier (PHA) designed for cryogenic operations is introduced and analyzed. This device comprises two Anderson insulator reservoirs connected by lossless lines, allowing them to exchange heat through photonic modes. This configuration enables negative differential thermal conductance (NDTC), which can be harnessed to amplify thermal signals. To achieve this, we maintain one reservoir at a high temperature, serving as the source terminal of a thermal transistor. Concurrently, in the other one, we establish tunnel contacts to metallic reservoirs, which function as the gate and drain terminals. With this arrangement, it is possible to control the heat flux exchange between the source and drain by adjusting the gate temperature. We present two distinct parameter choices that yield different performances: the first emphasizes modulating the source-drain heat current, while the second focuses on the temperature modulation of the colder Anderson insulator. Lastly, we present a potential design variation in which all electronic reservoirs are thermally connected through only photonic modes, allowing interactions between distant elements. The proposal of the PHA addresses the lack of thermal transistors and amplifiers in the mK range while being compatible with the rich toolbox of circuit quantum electrodynamics. It can be adapted to various applications, including sensing and developing thermal circuits and control devices at sub-Kelvin temperatures, which are relevant to quantum technologies., Comment: 13 pages, 6 figures

The asymptotic properties of Bayesian Neural Networks (BNNs) have been extensively studied, particularly regarding their approximations by Gaussian processes in the infinite-width limit. We extend these results by showing that posterior BNNs can be approximated by Student-t processes, which offer greater flexibility in modeling uncertainty. Specifically, we show that, if the parameters of a BNN follow a Gaussian prior distribution, and the variance of both the last hidden layer and the Gaussian likelihood function follows an Inverse-Gamma prior distribution, then the resulting posterior BNN converges to a Student-t process in the infinite-width limit. Our proof leverages the Wasserstein metric to establish control over the convergence rate of the Student-t process approximation.

In the application domain of fleet management and driver monitoring, it is very challenging to obtain relevant driving events and activities from dashcam footage while minimizing the amount of information stored and analyzed. In this paper, we address the identification of overtake and lane change maneuvers with a novel object detection approach applied to motion profiles, a compact representation of driving video footage into a single image. To train and test our model we created an internal dataset of motion profile images obtained from a heterogeneous set of dashcam videos, manually labeled with overtake and lane change maneuvers by the ego-vehicle. In addition to a standard object-detection approach, we show how the inclusion of CoordConvolution layers further improves the model performance, in terms of mAP and F1 score, yielding state-of-the art performance when compared to other baselines from the literature. The extremely low computational requirements of the proposed solution make it especially suitable to run in device., Comment: 6 pages, 3 figures

In most anisotropic compounds such as bismuth-based layered cuprate perovskites, the supercurrent across the blocking layer is of Josephson type, and a single crystal forms a natural stack of Josephson junctions. Here, we report on the evidence of Josephson-like transport in an artificial cuprate superlattice composed of 10 LaSrCuO-LaCuO repeats, creating a superlattice of junctions, where LCO is a superconducting Mott insulator and LSCO an overdoped metal, respectively. The superlattice has been designed with a long period d = L+W = 5.28 nm, with L and W the thickness of LCO and LSCO units, respectively, and is in the underdoped regime with an average doping level < {\delta} >= 0.11. Quantum-size effects and Rashba spin-orbit coupling are controlled by L/d = 0.75, with a quasi-2D superconducting transition temperature of 41 K and a c-axis coherence length of about 1.5 nm. Measurements at very low temperatures show evidence of Josephson phase dynamics consistent with very low Josephson coupling and a phase diffusion regime, thus explaining why Josephson coupling in LSCO superlattices has been so elusive. The tuning of LSCO superlattices in the Josephson regime enriches the phase diagram of HTS.

Varying the curvature, quantum phase transitions are investigated in holographic confining QFTs defined on a fixed constant positive curvature background. We find a competition between two branches of solutions and a phase transition as one varies the space-time curvature. The low-curvature phase has the same kind of IR geometry as the flat-space solution, while the high-curvature phase has a regular interior. We argue that, depending on the leading asymptotic exponent of the scalar potential, the transition may be first-order or higher-order., Comment: 83 pages including references, 18 figures, 6 appendices

We present POPACheck, the first full-fledged model checking tool for pPDA. POPACheck provides a user-friendly probabilistic modeling language with recursion that automatically translates into pOPA. pOPA are a class of pPDA that can express all the behaviors of probabilistic programs. On pOPA, POPACheck can solve reachability queries as well as qualitative and quantitative model checking queries for specifications in LTL and a fragment of POTL, a logic for context-free properties such as pre/post-conditioning., Comment: 13 pages, 8 figures

Robots with very limited capabilities are placed on the vertices of a graph and are required to move toward a single, common vertex, where they remain stationary once they arrive. This task is referred to as the GATHERING problem. Most of the research on this topic has focused on feasibility challenges in the asynchronous setting, where robots operate independently of each other. A common assumption in these studies is that robots are equipped with multiplicity detection, the ability to recognize whether a vertex is occupied by more than one robot. Additionally, initial configurations are often restricted to ensure that no vertex hosts more than one robot. A key difficulty arises from the possible symmetries in the robots' placement relative to the graph's topology. This paper investigates the GATHERING problem on Rings under a sequential scheduler, where only one robot at a time is active. While this sequential activation helps to break symmetries, we remove two common assumptions: robots do not have multiplicity detection, and in initial configurations, vertices can be occupied by multiplicities. We prove that such a generalized GATHERING problem cannot be solved under general sequential schedulers. However, we provide a complete characterization of the problem when a sequential Round Robin scheduler is used, where robots are activated one at a time in a fixed cyclic order that repeats indefinitely. Furthermore, we fully characterize the DISTINCT GATHERING problem, the most used variant of GATHERING, in which the initial configurations do not admit multiplicities.

Imaginary baryon number chemical potential simulations are a popular workaround for the (in)famous sign problem plaguing finite density QCD studies on the lattice. One is necessarily left with the problem of analytically continuing results to real values of $\mu_B$. In the framework of the Bielefeld Parma Collaboration, we have in recent years studied a multi-point Pad\'e description of the net baryon number density computed as a function of imaginary baryon number chemical potential. While our main emphasis has till now been on the determination of Lee-Yang singularities, the method is per se a natural tool to analytically continue results. We report on the status of our projects with this respect, comparing the Pad\'e approach to analytic continuation to another, new strategy, which is an application of the Cauchy integral formula in the sense of an inverse problem., Comment: 9 pages, 4 figures. Talk presented at the 41th International Symposium on Lattice Field Theory (Lattice 2024), July 28th to August 3rd, 2024, the University of Liverpool (UK)

Low Mode Average (LMA) is a technique to improve the quality of the signal-to-noise ratio in the long time separation of Euclidean correlation functions. We report on its beneficial impact in computing the vector-vector light connected two-point correlation functions and derived physical quantities in the mixed action lattice setup adopted by ETM collaboration. We focus on preliminary results of the computation within isospin symmetric QCD (isoQCD) of the $R$-ratio smeared with Gaussian kernels of widths down to $\sigma\sim250$ MeV, which is enough to appreciate the $\rho$ resonance around 770 MeV, using the Hansen-Lupo-Tantatlo (HLT) spectral-density reconstruction method., Comment: 12 pages, 5 figures

We prove existence of solutions to the following problem \begin{equation*} \begin{cases} -\Delta_1 u +g(u)|Du|=h(u)f & \text{in $\Omega$,} \\ u=0 & \text{on $\partial\Omega$,} \end{cases} \end{equation*} where $\Omega \subset \mathbb{R}^N$, with $N\ge2$, is an open and bounded set with Lipschitz boundary, $g$ is a continuous and positive function which possibly blows up at the origin and bounded at infinity and $h$ is a continuous and nonnegative function bounded at infinity (possibly blowing up at the origin) and finally $0 \le f \in L^N(\Omega)$. As a by-product, this paper extends the results found where $g$ is a continuous and bounded function. \\We investigate the interplay between $g$ and $h$ in order to have existence of solutions.

We present the stellar properties of 2908 galaxies at 0.6 < z < 1.0 from the LEGA-C survey. We emphasize the importance of high signal-to-noise, high spectral resolution spectroscopy in the inference of stellar population properties of galaxies. We estimate the galaxy properties with the SED fitting code Prospector, by fitting spectroscopy and broadband photometry together, drawn from the LEGA-C DR3 and UltraVISTA catalogs respectively. We report a positive correlation between light-weighted ages and stellar velocity dispersion ($\sigma_\star$). The trend with $\sigma_\star$ is weaker for the mass-weighted ages and stellar metallicity ($Z_\star$). On average, quiescent galaxies are characterized by high $Z_\star$, they are \sim 1.1 Gyr older, less dusty, with steeper dust attenuation slopes compared to star-forming galaxies. Conversely, star-forming galaxies are characterized by significantly higher dust optical depths and shallower (grayer) attenuation slopes. Low mass (high mass) star-forming galaxies have lower (higher) $Z_\star$, while their stellar populations are on average younger (older). A key pragmatic result of our study is that a linear-space metallicity prior is preferable to a logarithmic-space one when using photometry alone, as the latter biases the posteriors downward. Spectroscopy greatly improves stellar population measurements and is required to provide meaningful constraints on age, metallicity, and other properties. Pairing spectroscopy with photometry helps resolving the dust-age-metallicity degeneracy, yielding more accurate mass- and light-weighted ages, with ages inferred from photometry alone suffering such large uncertainties. Stellar metallicities are constrained by our spectroscopy, but precise measurements remain challenging (and impossible with photometry alone), particularly in the absence of Mg and Fe lines redward of 5000 $\AA$ in the observed spectrum., Comment: Accepted, 25 pages, 17 figures

Spherically symmetric effective dust collapse inspired by effective loop quantum cosmology predicts a bounce when the stellar energy density becomes planckian, which in turn inevitably leads to shell-crossing singularity formation. An extension of the spacetime beyond such singularities is possible through weak solutions of the equations of motion in integral form, leading to the shockwave model. In this work, we show explicitly that such an extension is not unique, and that relevant features like the black hole life-time strongly depend on the choice of the integral form of the equation of motion.

Context. The combination between non-thermal and thermal emission in $\gamma$-ray blazars pushes them to a specific region of the mid-infrared three-dimensional color diagram, the so-called blazar locus, built based on observations performed with the Wide-field Infrared Survey Explorer. The selection of blazar candidates based on these mid-infrared colors has been extensively used in the past decade in the hunt for the counterparts of unassociated $\gamma$-ray sources observed with the Fermi Large Area Telescope and in the search for new blazars in optical spectroscopic campaigns. Aims. In this work, we provide a theoretical description of the origin of the blazar locus and show how we can reasonably reproduce it with a model consisting of only three spectral components: a log-parabola accounting for the non-thermal emission, and an elliptical host and dust torus accounting for the thermal emission. Methods. We simulate spectral energy distributions for blazars, starting with a pure log-parabola model and then increasing its complexity by adding a template elliptical galaxy and dust torus. From these simulations, we compute the mid-infrared magnitudes and corresponding colors to create our own version of the blazar locus. Results. Our modeling allows for the selection of spectral parameters that better characterize the mid-infrared emission of $\gamma$-ray blazars, such as the log-parabola curvature ($\beta < 0.04$ for 50\% of our sample) and an average spectral peak around $E_p \approx 1.5 \times 10^{-13}$ erg. We also find that the log-parabola is the main spectral component behind the observed mid-infrared blazar colors, although additional components such as a host galaxy and a dust torus are crucial to obtain a precise reconstruction of the blazar locus., Comment: 9 pages, 7 figures

Online planning and execution of minimum-time maneuvers on three-dimensional (3D) circuits is an open challenge in autonomous vehicle racing. In this paper, we present an artificial race driver (ARD) to learn the vehicle dynamics, plan and execute minimum-time maneuvers on a 3D track. ARD integrates a novel kineto-dynamical (KD) vehicle model for trajectory planning with economic nonlinear model predictive control (E-NMPC). We use a high-fidelity vehicle simulator (VS) to compare the closed-loop ARD results with a minimum-lap-time optimal control problem (MLT-VS), solved offline with the same VS. Our ARD sets lap times close to the MLT-VS, and the new KD model outperforms a literature benchmark. Finally, we study the vehicle trajectories, to assess the re-planning capabilities of ARD under execution errors. A video with the main results is available as supplementary material., Comment: This paper will be presented at the 2025 IEEE International Conference on Robotics & Automation (ICRA)

This work presents the computation of real corrections to the impact factor for forward Higgs boson production, preserving the full dependence on the top-quark mass. The results are shown to align with the BFKL factorization framework, particularly in reproducing the expected rapidity divergence. Additionally, the subtraction of this divergence has been demonstrated using the appropriate counterterm within the BFKL scheme. In the infinite-top-mass limit, our findings reproduce the previously established result., Comment: 6 pages, 2 figures. Presented by Gabriele Gatto at "Diffraction and Low-$x$ 2024'', Trabia (Palermo, Italy), September 8-14, 2024

Integrated Development Environments increasingly implement AI-powered code completion tools (CCTs), which promise to enhance developer efficiency, accuracy, and productivity. However, interaction challenges with CCTs persist, mainly due to mismatches between developers' mental models and the unpredictable behavior of AI-generated suggestions. This is an aspect underexplored in the literature. To address this gap, we conducted an elicitation study with 56 developers using focus groups, to elicit their mental models when interacting with CCTs. The study findings provide actionable insights for designing human-centered CCTs that align with user expectations, enhance satisfaction and productivity, and foster trust in AI-powered development tools. To demonstrate the feasibility of these guidelines, we also developed ATHENA, a proof-of-concept CCT that dynamically adapts to developers' coding preferences and environments, ensuring seamless integration into diverse

As multiple crises threaten the sustainability of our societies and pose at risk the planetary boundaries, complex challenges require timely, updated, and usable information. Natural-language processing (NLP) tools enhance and expand data collection and processing and knowledge utilization capabilities to support the definition of an inclusive, sustainable future. In this work, we apply different NLP techniques, tools and approaches to climate and sustainability documents to derive policy-relevant and actionable measures. We focus on general and domain-specific large language models (LLMs) using a combination of static and prompt-based methods. We find that the use of LLMs is successful at processing, classifying and summarizing heterogeneous text-based data. However, we also encounter challenges related to human intervention across different workflow stages and knowledge utilization for policy processes. Our work presents a critical but empirically grounded application of LLMs to complex policy problems and suggests avenues to further expand Artificial Intelligence-powered computational social sciences., Comment: 15 pages; 4 figures

Context. Binary systems can be born surrounded by circumbinary discs. The gaseous discs surrounding either of the two stellar companions can have their life extended by the supply of mass arriving from the circumbinary disc. Aims. The objective of this study is to investigate the gravitational interactions exerted by a compact and eccentric binary system on the circumbinary and circumprimary discs, and the resulting transport of gas and solids between the disc components. Methods. We assume that the gas in the system behaves as a fluid and model its evolution by means of high resolution hydrodynamical simulations. Dust grains are modeled as Lagrangian particles that interact with the gas and the stars. Results. Models indicate that significant fluxes of gas and dust proceed from the circumbinary disc toward the circumprimary disc. For the applied system parameters, grains of certain sizes are segregated outside the tidal gap generated by the stars. Consequently, the size distribution of the transported dust is not continuous but it presents a gap in the mm size range. In close binaries, the lifetime of an isolated circumprimary disc is found to be short, approximately 10 5 years, because of its small mass. However, because of the influx of gas from beyond the tidal gap, the disc around the primary star can survive much longer, about 10 6 years, as long as gas accretion from the circumbinary disc continues. The supply of solids and the extended lifetime of a circumbinary disc also aids in the possible formation of giant planets. Compared to close binary systems without a circumbinary disc, we expect a higher frequency of single- or multiple- planet systems. Additionally, a planetesimal or debris belt can form in proximity of the truncation radius of the circumprimary disc and/or around the location of the exterior edge of the tidal gap., Comment: Accepted for publication on Astronomy and Astrophysics

The interplay between electronic correlations, density wave orders, and magnetism gives rise to several fascinating phenomena. In recent years, kagome metals have emerged as an excellent platform for investigating these unique properties, which stem from their itinerant carriers arranged in a kagome lattice. Here, we show that electronic structure of the prototypical kagome metal, Fe$_3$Sn$_2$, can be tailored by manipulating the breathing distortion of its kagome lattice with external pressure. The breathing distortion is suppressed around 15 GPa and reversed at higher pressures. These changes lead to a series of Lifshitz transitions that we detect using broadband and transient optical spectroscopy. Remarkably, the strength of the electronic correlations and the tendency to carrier localization are enhanced as the kagome network becomes more regular, suggesting that breathing distortion can be a unique control parameter for the microscopic regime of the kagome metals and their electron dynamics., Comment: Main text (4 figures) + Supplementary Materials (4 figures)

Machine Learning (ML) has proven to be a promising solution to provide novel scalable and efficient fault management solutions in modern 5G-and-beyond communication networks. In the context of microwave networks, ML-based solutions have received significant attention. However, current solutions can only be applied to monolithic scenarios in which a single entity (e.g., an operator) manages the entire network. As current network architectures move towards disaggregated communication platforms in which multiple operators and vendors collaborate to achieve cost-efficient and reliable network management, new ML-based approaches for fault management must tackle the challenges of sharing business-critical information due to potential conflicts of interest. In this study, we explore the application of Federated Learning in disaggregated microwave networks for failure-cause identification using a real microwave hardware failure dataset. In particular, we investigate the application of two Vertical Federated Learning (VFL), namely using Split Neural Networks (SplitNNs) and Federated Learning based on Gradient Boosting Decision Trees (FedTree), on different multi-vendor deployment scenarios, and we compare them to a centralized scenario where data is managed by a single entity. Our experimental results show that VFL-based scenarios can achieve F1-Scores consistently within at most a 1% gap with respect to a centralized scenario, regardless of the deployment strategies or model types, while also ensuring minimal leakage of sensitive-data., Comment: 6 pages, 7 figure, IEEE ICC 2025

We extend the recently introduced Clifford dressed Time-Dependent Variational Principle (TDVP) to efficiently compute many-body wavefunction amplitudes in the computational basis. This advancement enhances the study of Loschmidt echoes, which generally require accurate calculations of the overlap between the evolved state and the initial wavefunction. By incorporating Clifford disentangling gates during TDVP evolution, our method effectively controls entanglement growth while keeping the computation of these amplitudes accessible. Specifically, it reduces the problem to evaluating the overlap between a Matrix Product State (MPS) and a stabilizer state, a task that remains computationally feasible within the proposed framework. To demonstrate the effectiveness of this approach, we first benchmark it on the one-dimensional transverse-field Ising model. We then apply it to more challenging scenarios, including a non-integrable next-to-nearest-neighbor Ising chain and a two-dimensional Ising model. Our results highlight the versatility and efficiency of the Clifford-augmented MPS, showcasing its capability to go beyond the evaluation of simple expectation values. This makes it a powerful tool for exploring various aspects of many-body quantum dynamics., Comment: 7 pages, 5 figures

Join-the-shortest queue (JSQ) and its variants have often been used in solving load balancing problems. JSQ minimizes the average system occupation, e.g., the customer's system time. In this paper, we extend the load balancing setting to include constraints that may be imposed due to the communication network. In particular, we cast the problem in the framework of constrained MDPs: this permit us to address both action-dependent constraints, such as, e.g, bandwidth constraints, and state-dependent constraints, such as, e.g., minimum queue utilization constraints. Unlike the state-of-the-art approaches in load balancing, our policies satisfy the constraints while delivering favorable results in terms of system occupancy. We derive policies that provably satisfy the constraints and evaluate their performance through extensive simulations.

We present a comprehensive analysis of galaxy close-pair fractions and major merger rates to evaluate the importance of mergers in the hierarchical growth of galaxies over cosmic time. This study focuses on the previously poorly understood redshift range of $z \approx 3-9$ using JADES observations. Our mass-complete sample includes primary galaxies with stellar masses of ${\rm log}(M_\star/{\rm M_\odot}) = [8, 10]$, having major companions (mass ratio $\geq 1/4$) selected by $5-30$ pkpc projected separation and redshift proximity criteria. Pair fractions are measured using a statistically robust method incorporating photometric redshift posteriors and available spectroscopic data. The pair fraction evolves steeply with redshift, peaking at $z \sim 5-6$, followed by a turnover, and shows dependence on the stellar mass: the pair fraction peaks at later cosmic times (lower redshifts) for more massive galaxies. Similarly, the derived galaxy major merger rate increases and flattens beyond $z \sim 6$ to $2-10~{\rm Gyr^{-1}}$ per galaxy, showing a weak scaling with stellar mass, driven by the evolution of the galaxy stellar mass function. A comparison between the cumulative mass accretion from major mergers and the mass assembled through star formation indicates that major mergers contribute approximately $5-14\%$ to the total mass growth over the studied redshift range, which is in agreement with the ex-situ mass fraction estimated from our simple numerical model. These results highlight that major mergers contribute little to the direct stellar mass growth compared to in-situ star formation but could still play an indirect role by driving star formation itself., Comment: 32 pages, 24 figures, 4 tables. Submitted to MNRAS. Comments are welcome

Asynchronous methods are fundamental for parallelizing computations in distributed machine learning. They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be fast and resource-efficient by assigning more tasks to faster workers. The challenge lies in achieving this optimal allocation without prior knowledge of the computation time distributions. In this paper, we propose ATA (Adaptive Task Allocation), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times. Experimental results further demonstrate that ATA is resource-efficient, significantly reducing costs compared to the greedy approach, which can be arbitrarily expensive depending on the number of workers.

As digital technologies advance, communication networks face challenges in handling the vast data generated by intelligent devices. Autonomous vehicles, smart sensors, and IoT systems necessitate new paradigms. This thesis addresses these challenges by integrating semantic communication and generative models for optimized image compression and edge network resource allocation. Unlike bit-centric systems, semantic communication prioritizes transmitting meaningful data specifically selected to convey the meaning rather than obtain a faithful representation of the original data. The communication infrastructure can benefit to significant improvements in bandwidth efficiency and latency reduction. Central to this work is the design of semantic-preserving image compression using Generative Adversarial Networks and Denoising Diffusion Probabilistic Models. These models compress images by encoding only semantically relevant features, allowing for high-quality reconstruction with minimal transmission. Additionally, a Goal-Oriented edge network optimization framework is introduced, leveraging the Information Bottleneck principle and stochastic optimization to dynamically allocate resources and enhance efficiency. By integrating semantic communication into edge networks, this approach balances computational efficiency and communication effectiveness, making it suitable for real-time applications. The thesis compares semantic-aware models with conventional image compression techniques using classical and semantic evaluation metrics. Results demonstrate the potential of combining generative AI and semantic communication to create more efficient semantic-goal-oriented communication networks that meet the demands of modern data-driven applications., Comment: PhD thesis

Radiation pressure is a key mechanism by which stellar feedback disrupts molecular clouds and drives HII region expansion. This includes direct radiation pressure exerted by UV photons on dust grains, pressure associated with photoionization, and infrared (IR) radiation pressure on grains due to dust-reprocessed IR photons. We present a new method that combines high resolution mid-IR luminosities from JWST-MIRI, optical attenuation and nebular line measurements from VLT-MUSE, and HST H$\alpha$-based region sizes to estimate the strength of radiation pressure in $\approx 18,000$ HII regions across 19 nearby star-forming galaxies. This is the most extensive and direct estimate of these terms beyond the Local Group to date. In the disks of galaxies, we find that the total reprocessed IR pressure is on average 5% of the direct UV radiation pressure. This fraction rises to 10% in galaxy centers. We expect reprocessed IR radiation pressure to dominate over UV radiation pressure in regions where $L_{\rm F2100W}/L_{\rm H\alpha}^{\rm corr} \gtrsim 75$. Radiation pressure due to H ionizations is lower than pressure on dust in our sample, but appears likely to dominate the radiation pressure budget in dwarf galaxies similar to the Small Magellanic Cloud. The contribution from all radiation pressure terms appears to be subdominant compared to thermal pressure from ionized gas, reinforcing the view that radiation pressure is most important in compact, heavily embedded, and young regions., Comment: main text: 21 pages and 11 figures; accepted for publication in ApJ

The improved sensitivity of interferometric facilities to the 21-cm line of atomic hydrogen (HI) enables studies of its properties in galaxies beyond the local Universe. In this work, we perform a 21 cm line spectral stacking analysis combining the MIGHTEE and CHILES surveys in the COSMOS field to derive a robust HI-stellar mass relation at z=0.36. In particular, by stacking thousands of star-forming galaxies subdivided into stellar mass bins, we optimize the signal-to-noise ratio of targets and derive mean HI masses in the different stellar mass intervals for the investigated galaxy population. We combine spectra from the two surveys, estimate HI masses, and derive the scaling relation log10(MHI) = (0.32 +- 0.04)log10(M*) + (6.65 +- 0.36). Our findings indicate that galaxies at z=0.36 are HI richer than those at z=0, but HI poorer than those at z=1, with a slope consistent across redshift, suggesting that stellar mass does not significantly affect HI exchange mechanisms. We also observe a slower growth rate HI relative to the molecular gas, supporting the idea that the accretion of cold gas is slower than the rate of consumption of molecular gas to form stars. This study contributes to understanding the role of atomic gas in galaxy evolution and sets the stage for future development of the field in the upcoming SKA era., Comment: 23 pages, 16 figures, accepted for publication in ApJ

By employing the RM123 approach to QCD+QED, we computed the valence quark-connected isospin-breaking corrections to the light, strange and charm contributions at leading order in $\alpha_{\mathrm{em}}$ and $\left(\mu_d-\mu_u\right) / \Lambda_{\mathrm{QCD}}$. Here we report the preliminary results on two different volumes ($L \sim 3.8$ fm and $L \sim 5.1$ fm) and a fixed lattice spacing (corresponding to $a_{\text {isoQCD }} \sim 0.07951(4)$ fm), obtained in the framework of the ongoing computation by ETMC of the leading-order hadronic vacuum polarization (HVP) contribution to the muon anomalous magnetic moment $a_\mu^{\mathrm{HVP}}$ in QCD+QED., Comment: 10 pages, 5 figures, Contribution to Lattice 2024

We present preliminary results obtained using a new code for $SU (N_c)$ Yang-Mills theory which performs a 2-level sampling of glueball correlators obtained from a suitably chosen basis of (APE) smeared and unsmeared operators. The code builds loop operators of any shape and length and classifies them according to the irreducible representations of the cubic group. We report on the performances of the algorithm and on the computation of the first low-lying glueball states choosing $N_c = 3$ as a reference to compare our results with the literature.

Reinforcement Learning from Human Feedback (RLHF), the standard for aligning Large Language Models (LLMs) with human values, is known to fail to satisfy properties that are intuitively desirable, such as respecting the preferences of the majority \cite{ge2024axioms}. To overcome these issues, we propose the use of a probabilistic Social Choice rule called \emph{maximal lotteries} as a replacement for RLHF. We show that a family of alignment techniques, namely Nash Learning from Human Feedback (NLHF) \cite{munos2023nash} and variants, approximate maximal lottery outcomes and thus inherit its beneficial properties. We confirm experimentally that our proposed methodology handles situations that arise when working with preferences more robustly than standard RLHF, including supporting the preferences of the majority, providing principled ways of handling non-transitivities in the preference data, and robustness to irrelevant alternatives. This results in systems that better incorporate human values and respect human intentions.