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We present an all-silicon source of near-infrared linearly-polarized single photons, fabricated by nanoscale positioning of a color center in a silicon-on-insulator microcavity. The color center consists of a single W center, created at a well-defined position by Si$^{+}$ ion implantation through a 150 nm-diameter nanohole in a mask. A circular Bragg grating cavity resonant with the W's zero-phonon line at 1217 nm is fabricated at the same location as the nanohole. Under above-gap continuous-wave excitation, a very clean photon antibunching behavior ($g{^2} \leq 0.06$) is observed over the entire power range, which highlights the absence of parasitic emitters. Purcell-enhancement of W's zero-phonon emission provides both a record-high photoluminescence count rate among Si color centers (ca $1.2 \times 10^{6}$ counts/s) and apparent Debye-Waller factor around 99%. We also demonstrate the triggered emission of single photons with 93% purity under weak pulsed laser excitation. At high pulsed laser power, we reveal a detrimental effect of repumping processes, that could be mitigated using selective pumping schemes in the future. These results represent a major step towards on-demand sources of indistinguishable near-infrared single photons within silicon photonics chips.

We performed a detailed spectroscopic analysis of three extremely metal-poor RR Lyrae stars, exploring uncharted territories at these low metallicities for this class of stars. Using high-resolution spectra acquired with HARPS-N at TNG, UVES at VLT, and PEPSI at LBT, and employing Non-Local Thermodynamic Equilibrium (NLTE) spectral synthesis calculations, we provide abundance measurements for Fe, Al, Mg, Ca, Ti, Mn, and Sr. Our findings indicate that the stars have metallicities of [Fe/H] = -3.40 \pm 0.05, -3.28 \pm 0.02, and -2.77 \pm 0.05 for HD 331986, DO Hya, and BPS CS 30317-056, respectively. Additionally, we derived their kinematic and dynamical properties to gain insights into their origins. Interestingly, the kinematics of one star (HD 331986) is consistent with the Galactic disc, while the others exhibit Galactic halo kinematics, albeit with distinct chemical signatures. We compared the [Al/Fe] and [Mg/Mn] ratios of the current targets with recent literature estimates to determine whether these stars were either accreted or formed in situ, finding that the adopted chemical diagnostics are ineffective at low metallicities ([Fe/H] $\lesssim -$1.5). Finally, the established horizontal branch evolutionary models, indicating that these stars arrive at hotter temperatures on the Zero-Age Horizontal Branch (ZAHB) and then transition into RR Lyrae stars as they evolve, fully support the existence of such low-metallicity RR Lyrae stars. As a consequence, we can anticipate detecting more of them when larger samples of spectra become available from upcoming extensive observational campaigns., Comment: Accepted for publication in Astronomy & Astrophysics; 13 pages. Appendices D-E available on Zenodo

In binary star systems, mass transfer can spin up the accretor, possibly leading to efficient chemical mixing and chemically quasi-homogeneous evolution (CHE). Here, we explore the effects of accretion-induced CHE on both stellar populations and their compact binary remnants with the state-of-the-art population synthesis code SEVN. We find that CHE efficiently enhances the formation of Wolf-Rayet stars (WRs) from secondary stars, which are spun-up by accretion, while simultaneously preventing their evolution into red supergiant stars (RSGs). Including CHE in our models increases the fraction of WRs in our stellar sample by nearly a factor of $\approx3$ at low metallicity ($Z=0.001$). WRs formed through CHE are, on average, more massive and luminous than those formed without CHE. Most WRs formed via CHE end their life as black holes. As a direct consequence, the CHE mechanism enhances the formation of binary black holes (BBHs) and black hole-neutron star (BHNS) systems, while simultaneously quenching the production of binary neutron stars (BNSs). However, CHE significantly quenches the merger rate of BBHs, BHNSs and BNSs at low metallicity ($Z\leq{}0.004$), because most binary compact objects formed via CHE have large orbital periods. For instance, the number of BBH and BHNS mergers decreases by one order of magnitude at $Z=0.004$ in the CHE model compared to the standard scenario. Finally, we find that secondary stars experiencing CHE frequently produce the most massive compact object in the binary system. In BHNSs, this implies that the black hole progenitor is the secondary star. Conversely, BBHs formed through accretion-induced CHE likely have asymmetric black hole components, but only a negligible fraction of these asymmetric systems ultimately merge within an Hubble time., Comment: A&A submitted, comments welcome

This study evaluates fine-tuning strategies for text classification using the DistilBERT model, specifically the distilbert-base-uncased-finetuned-sst-2-english variant. Through structured experiments, we examine the influence of hyperparameters such as learning rate, batch size, and epochs on accuracy, F1-score, and loss. Polynomial regression analyses capture foundational and incremental impacts of these hyperparameters, focusing on fine-tuning adjustments relative to a baseline model. Results reveal variability in metrics due to hyperparameter configurations, showing trade-offs among performance metrics. For example, a higher learning rate reduces loss in relative analysis (p=0.027) but challenges accuracy improvements. Meanwhile, batch size significantly impacts accuracy and F1-score in absolute regression (p=0.028 and p=0.005) but has limited influence on loss optimization (p=0.170). The interaction between epochs and batch size maximizes F1-score (p=0.001), underscoring the importance of hyperparameter interplay. These findings highlight the need for fine-tuning strategies addressing non-linear hyperparameter interactions to balance performance across metrics. Such variability and metric trade-offs are relevant for tasks beyond text classification, including NLP and computer vision. This analysis informs fine-tuning strategies for large language models and promotes adaptive designs for broader model applicability.

Depression has impacted millions of people worldwide and has become one of the most prevalent mental disorders. Early mental disorder detection can lead to cost savings for public health agencies and avoid the onset of other major comorbidities. Additionally, the shortage of specialized personnel is a critical issue because clinical depression diagnosis is highly dependent on expert professionals and is time consuming. In this study, we explore the use of GPT-4 for clinical depression assessment based on transcript analysis. We examine the model's ability to classify patient interviews into binary categories: depressed and not depressed. A comparative analysis is conducted considering prompt complexity (e.g., using both simple and complex prompts) as well as varied temperature settings to assess the impact of prompt complexity and randomness on the model's performance. Results indicate that GPT-4 exhibits considerable variability in accuracy and F1-Score across configurations, with optimal performance observed at lower temperature values (0.0-0.2) for complex prompts. However, beyond a certain threshold (temperature >= 0.3), the relationship between randomness and performance becomes unpredictable, diminishing the gains from prompt complexity. These findings suggest that, while GPT-4 shows promise for clinical assessment, the configuration of the prompts and model parameters requires careful calibration to ensure consistent results. This preliminary study contributes to understanding the dynamics between prompt engineering and large language models, offering insights for future development of AI-powered tools in clinical settings.

Thermal runaway in lithium ion batteries is a critical safety concern for the battery industry due to its potential to cause uncontrolled temperature rises and subsequent fires that can engulf the battery pack and its surroundings. Modeling and simulation offer cost effective tools for designing strategies to mitigate thermal runaway. Accurately simulating the chemical kinetics of thermal runaway,commonly represented by systems of Arrhenius based Ordinary Differential Equations (ODEs), requires fitting kinetic parameters to experimental calorimetry data, such as Accelerating Rate Calorimetry (ARC) measurements. However, existing fitting methods often rely on empirical assumptions and simplifications that compromise generality or require manual tuning during the fitting process. Particle Swarm Optimization (PSO) offers a promising approach for directly fitting kinetic parameters to experimental data. Yet, for systems involving large search spaces, such as those created by multiple Arrhenius ODEs, the computational cost of fitting can become prohibitive. This work introduces a divide and conquer approach based on PSO to fit N equation Arrhenius ODE models to ARC data. The proposed method achieves accurate parameter fitting while maintaining low computational costs. The resulting fit is analyzed using two distinct ARC datasets, highlighting the methods flexibility. The resulting models are further validated through simulations of 3D ARC and oven tests, showing excellent agreement with experimental data and alignment with expected trends.

We introduce partial group algebras with relations in a purely algebraic framework. Given a group and a set of relations, we define an algebraic partial action and prove that the resulting partial skew group ring is isomorphic to the associated partial group algebra with relations. Under suitable conditions - which always holds if the base ring is a field - we demonstrate that the partial skew group ring can also be described using a topological partial action. Furthermore, we show how subshift algebras can be realized as partial group algebras with relations. Using the topological partial action, we describe simplicity of subshift algebras in terms of the underlying dynamics of the subshift., Comment: 22 pages

The European project SCARLET aims to study and realize a demonstrator of a MVDC (Medium Voltage Direct Current) high-power superconducting cable. This device might be employed to connect offshore wind farms with land, expecting to significantly simplify the offshore platform by eliminating the need for its conversion function. For this purpose, windmill conversion chain must be modified to directly produce the MVDC export voltage. In this scenario, this paper presents the case of a 1GW offshore windmill superconducting link and outlines the design consideration for a 1 GW onshore converter. For this cable, a protection strategy that combines DC circuit breakers with a Resistive Superconducting Fault Current Limiter is proposed. Moreover, this works demonstrates how a superconducting cable can be modelled as an electrical circuit to be integrated into a network simulation tool, enabling the investigation of various fault scenarios and protection strategies. Finally, a specific result is discussed to exemplify how the proposed approach can benefit the design of both the electrical network and the superconducting cable itself.

Accurate and robust modelling of large deformation three dimensional contact interaction is an important area of engineering, but it is also challenging from a computational mechanics perspective. This is particularly the case when there is significant interpenetration and evolution of the contact surfaces, such as the case of a relatively rigid body interacting with a highly deformable body. The numerical challenges come from several non-linear sources: large deformation mechanics, history dependent material behaviour and slip/stick frictional contact. In this paper the Material Point Method (MPM) is adopted to represent the deformable material, combined with a discretised rigid body which provides an accurate representation of the contact surface. The three dimensional interaction between the bodies is detected though the use of domains associated with each material point. This provides a general and consistent representation of the extent of the deformable body without introducing boundary representation in the material point method. The dynamic governing equations allows the trajectory of the rigid body to evolve based on the interaction with the deformable body and the governing equations are solved within an efficient implicit framework. The performance of the method is demonstrated on a number of benchmark problems with analytical solutions. The method is also applied to the specific case of soil-structure interaction, using geotechnical centrifuge experimental data that confirms the veracity of the proposed approach.

The field of visual and physiological optics is undergoing continuous significant advancements, driven by a deeper understanding of the human visual system and the development of cutting-edge optical technologies. This Roadmap, authored by leading experts, delves into critical areas such as corneal biomechanical properties, keratoconus, and advancements in corneal imaging and elastography. It explores the intricate structure-function relationship within the eye lens, offering new perspectives through lens models and ray tracing techniques. The document also covers advancements in retinal imaging, highlighting the current state and future directions, and the role of adaptive optics in evaluating retinal structure and function in both healthy and diseased eyes. Furthermore, it addresses the modelling of ocular surfaces using different mathematical functions and examines the factors affecting peripheral image quality in the human eye, emphasizing the importance of these aspects in visual performance. Additional topics include schematic and functional models of the human eye, the impact of optical and chromatic aberrations, and the design of contact, and intraocular lenses. Finally, the Roadmap addresses the intersection of neurosciences with vision health, presenting a comprehensive overview of current research and future trends aimed at improving visual health and optical performance. Ultimately, this Roadmap aims to serve as a valuable resource for ophthalmologists, optometrists, vision scientists, and engineers dedicated to advancing the field of visual and physiological optics., Comment: This is the pre-peer-review and pre-editing version of the article, as submitted by the author to Journal of Optics

Jet observables at hadron colliders feature ''super-leading'' logarithms, double-logarithmic corrections resulting from a breakdown of color coherence due to complex phases in hard-scattering amplitudes. While these effects only arise in high orders of perturbation theory and are suppressed in the large-$N_c$ limit, they formally constitute leading logarithmic corrections to the cross sections. We present the first analysis of the corresponding contributions to a hadronic cross section, including all partonic channels and interference effects. Interestingly, some interference terms in partonic $q\bar q\to q\bar q$ scattering are only linearly suppressed in $1/N_c$. Our results for the $pp\to 2$ jets gap-between-jets cross section demonstrate the numerical importance of super-leading logarithms for small values of the veto scale $Q_0$, showing that these contributions should be accounted for in precision studies of such observables., Comment: 12 pages, 3 figures

Brillouin Light Scattering (BLS) spectroscopy is a non-invasive, non-contact, label-free optical technique that can provide information on the mechanical properties of a material on the sub-micron scale. Over the last decade it has seen increased applications in the life sciences, driven by the observed significance of mechanical properties in biological processes, the realization of more sensitive BLS spectrometers and its extension to an imaging modality. As with other spectroscopic techniques, BLS measurements not only detect signals characteristic of the investigated sample, but also of the experimental apparatus, and can be significantly affected by measurement conditions. The aim of this consensus statement is to improve the comparability of BLS studies by providing reporting recommendations for the measured parameters and detailing common artifacts. Given that most BLS studies of biological matter are still at proof-of-concept stages and use different--often self-built--spectrometers, a consensus statement is particularly timely to assure unified advancement., Comment: Main Text & Supplementary Text: 56 pages, 3 Figures, 2 Supplementary Figures, 1 Supplementary Table

We study the interplay between topology and disorder in the disodered Kitaev model with long range pairing, connected to two metallic leads exchanging particles with external Lindblad baths. We study how the phase diagram of the system is affected by the disorder by monitoring the subgap modes at increasing disorder, by computing the current flowing across the superconductor at a finite voltage bias between the baths, and by looking at the normal, single particle lead correlations across the Kitaev long range chain. In particular, we evidence the reentrant behavior of the massive, topological phase at limited values of the disorder strength, that has no analog in the disordered, short range pairing Kitaev model, thus rising the question of whether it is possible to recover a disorder triggered direct transition between the massive and the short range topological phase of the long range pairing Kitaev model., Comment: 16 pages, 10 .png figures

This paper presents an implementation of radio astronomy imaging algorithms on modern High Performance Computing (HPC) infrastructures, exploiting distributed memory parallelism and acceleration throughout multiple GPUs. Our code, called RICK (Radio Imaging Code Kernels), is capable of performing the major steps of the w-stacking algorithm presented in Offringa et al. (2014) both inter- and intra-node, and in particular has the possibility to run entirely on the GPU memory, minimising the number of data transfers between CPU and GPU. This feature, especially among multiple GPUs, is critical given the huge sizes of radio datasets involved. After a detailed description of the new implementations of the code with respect to the first version presented in Gheller et al. (2023), we analyse the performances of the code for each step involved in its execution. We also discuss the pros and cons related to an accelerated approach to this problem and its impact on the overall behaviour of the code. Such approach to the problem results in a significant improvement in terms of runtime with respect to the CPU version of the code, as long as the amount of computational resources does not exceed the one requested by the size of the problem: the code, in fact, is now limited by the communication costs, with the computation that gets heavily reduced by the capabilities of the accelerators., Comment: 12 pages, 4 figures, accepted for publication in Astronomy and Computing

The advent of digital neutral-atom quantum computers relies on the development of fast and robust protocols for high-fidelity quantum operations. In this work, we introduce a novel scheme for entangling gates using four atomic levels per atom: a ground state qubit and two Rydberg states. A laser field couples the qubit to one of the two Rydberg states, while a microwave field coupling the two Rydberg states enables a resonant dipole-dipole interaction between different atoms. We show that this interaction can mediate controlled-Z gates that are faster and less sensitive to Rydberg decay than state-of-the-art Rydberg gates based on van der Waals interactions. Moreover, we systematically stabilize our protocol against interatomic distance fluctuations and analyze its performance in realistic setups with rubidium or cesium atoms. Our results open up new avenues to the use of dipolar interactions for universal quantum computation with neutral atoms., Comment: 11 pages, 9 figures

In analogy with the 290-Theorem of Bhargava-Hanke, a criterion set is a finite subset $C$ of the totally positive integers in a given totally real number field such that if a quadratic form represents all elements of $C$, then it necessarily represents all totally positive integers, i.e., is universal. We use a novel characterization of minimal criterion sets to show that they always exist and are unique, and that they must contain certain explicit elements. We also extend the uniqueness result to the more general setting of representations of a given subset of the integers., Comment: 19 pages, comments are welcome!

As the number of gravitational-wave detections grows, the merger rate of binary black holes (BBHs) can help us to constrain their formation, the properties of their progenitors, and their birth environment. Here, we aim to address the impact of the metal-dependent star formation rate (SFR) on the BBH merger rate. To this end, we have developed a fully data-driven approach to model the metal-dependent SFR and coupled it to BBH evolution. We have adopted the most up-to-date scaling relations, based on recent observational results, and we have studied how the BBH merger rate density varies over a wide grid of galaxy and binary evolution parameters. Our results show that including a realistic metal-dependent SFR evolution yields a value of the merger rate density which is too high compared to the one inferred from GW data. Moreover, variations of the SFR in low-mass galaxies ($M_\ast \lesssim 10^8 \mathrm{M}_{\odot}$) do not contribute more than a factor $\sim 2$ to the overall merger rate density at redshift $z=0$. These results suggest that the discrepancy between the BBH merger rate density inferred from data and theoretical models is not caused by approximations in the treatment of the metal-dependent SFR, but rather stems from stellar evolution models and/or BBH formation channels., Comment: 14 pages, 9 figures. Submitted to A&A, comments are welcome

A key hurdle to the success of quantum computers is the ability to initialize qubits into a pure state, which can be achieved by cooling qubits down to very low temperatures. Computational cooling of qubits, whereby a subset of the qubits is cooled at the expense of heating the other qubits via the application of special sets of logic gates, offers a route to effectively cool qubits. Here, we present QuL, a programming library which can be used to generate, analyze, and test quantum circuits for various computational cooling protocols. In its most basic usage, QuL enables a novice user to easily produce cooling circuits with minimal input or knowledge required. The programming library, however, offers flexibility to more advanced users to finely tune the cooling protocol used to generate the quantum circuit. Finally, QuL offers methods to assess and compare various cooling protocols for users interested in studying optimal implementation of computational cooling in general, or on specific quantum backends. It is our hope that QuL will not only facilitate the execution of computational cooling on current quantum computers, but also serve as a tool to investigate open questions in the optimal implementation of computational cooling., Comment: 14 pages, 7 figures

Timeseria is an object-oriented time series processing library implemented in Python, which aims at making it easier to manipulate time series data and to build statistical and machine learning models on top of it. Unlike common data analysis frameworks, it builds up from well defined and reusable logical units (objects), which can be easily combined together in order to ensure a high level of consistency. Thanks to this approach, Timeseria can address by design several non-trivial issues which are often underestimated, such as handling data losses, non-uniform sampling rates, differences between aggregated data and punctual observations, time zones, daylight saving times, and more. Timeseria comes with a comprehensive set of base data structures, data transformations for resampling and aggregation, common data manipulation operations, and extensible models for data reconstruction, forecasting and anomaly detection. It also integrates a fully featured, interactive plotting engine capable of handling even millions of data points.

Continued fractions have been generalized over the field of $p$-adic numbers, where it is still not known an analogue of the famous Lagrange's Theorem. In general, the periodicity of $p$-adic continued fractions is well studied and addressed as a hard problem. In this paper, we show a strong connection between periodic $p$--adic continued fractions and the convergence to real quadratic irrationals. In particular, in the first part we prove that the convergence in $\mathbb{R}$ is a necessary condition for the periodicity of the continued fractions of a quadratic irrational in $\mathbb{Q}_p$. Moreover, we leave several conjectures on the converse, supported by experimental computations. In the second part of the paper, we exploit these results to develop a probabilistic argument for the non-periodicity of Browkin's $p$-adic continued fractions. The probabilistic results are conditioned under the assumption of uniform distribution of the $p$-adic digits of a quadratic irrational, that holds for almost all $p$-adic numbers.

Binaries with a Wolf-Rayet star and a compact object (WR-COs), either a black hole (BH) or a neutron star (NS), have been proposed as possible progenitors for the binary compact object mergers (BCOs) observed with the gravitational wave (GW) detectors. In this work, we use the open-source population synthesis code SEVN to investigate the role of WR-COs as BCO progenitors. We consider an initial population of $5 \times 10^6$ binaries and we evolve it across 96 combinations of metallicities, common envelope efficiencies, core-collapse supernova models and natal kick distributions. We find that WR-COs are the progenitors of most BCOs, especially at high and intermediate metallicity. At $Z=0.02,\,{}0.014,$ and $0.0014$, more than $\gtrsim 99 \%$ of all the BCOs in our simulations evolved as WR-COs. At $Z = 0.00014$, inefficient binary-stripping lowers the fraction of BCOs with WR-CO progenitors to $\approx 83-95 \%$. Despite their key role in BCO production, only $\approx 5-30 \%$ of WR-COs end their life as BCOs. We find that Cyg X-3, the only WR-CO candidate observed in the Milky Way, is a promising BCO progenitor, especially if it hosts a BH. In our simulations, about $\approx 70-100 \%$ of the Cyg X-3 - like systems in the WR-BH configuration (BH mass $ \leq 10 \rm ~ M_\odot$) are BCO progenitors, in agreement with the literature. Future observations of WR-COs similar to Cyg X-3 may be the Rosetta stone to interpret the formation of BCOs., Comment: 16 pages, 13 figures, 1 table. Submitted to A&A, comments welcome

We study how stronger noise can enhance the entanglement in inhomogeneously monitored quantum systems. We consider a free fermions model composed of two coupled chains - a system chain and an ancilla chain, each subject to its own different noise - and explore the dynamics of entanglement within the system chain under different noise intensities. Our results demonstrate that, contrary to the detrimental effects typically associated with noise, certain regimes of noise on the ancilla can significantly enhance entanglement within the system. Numerical simulations demonstrate the robustness of such entanglement enhancement across various system sizes and noise parameters. This enhancement is found to be highly dependent on the hopping strength in the ancilla, suggesting that the interplay between unitary dynamics and noise can be tuned to optimize the entanglement of a quantum system., Comment: 17 pages, 9 figures

First order phase transitions in the early universe may have left a variety of experimentally accessible imprints. The dynamics of such transitions is governed by the density perturbations caused by the propagation of the bubble wall in the false vacuum plasma, conveniently described by a Boltzmann equation. The determination of the bubble wall expansion velocity is crucial to determine the experimental signatures of the transition. We report on the first full (numerical) solution to the Boltzmann equation. Differently from traditional ones, our approach does not rely on any ansatz. The results significantly differ from the ones obtained within the fluid approximation and large differences for the friction acting on the bubble wall are found. The wall velocity is calculated in a singlet extension of the Standard Model, including out-of-equilibrium contributions from both the top quark and the electroweak gauge bosons., Comment: 7 pages, 3 figures, presented at QCD@Work International Workshop on QCD Theory and Experiment 2024

Recently, AI systems have made remarkable progress in various tasks. Deep Reinforcement Learning(DRL) is an effective tool for agents to learn policies in low-level state spaces to solve highly complex tasks. Researchers have introduced Intrinsic Motivation(IM) to the RL mechanism, which simulates the agent's curiosity, encouraging agents to explore interesting areas of the environment. This new feature has proved vital in enabling agents to learn policies without being given specific goals. However, even though DRL intelligence emerges through a sub-symbolic model, there is still a need for a sort of abstraction to understand the knowledge collected by the agent. To this end, the classical planning formalism has been used in recent research to explicitly represent the knowledge an autonomous agent acquires and effectively reach extrinsic goals. Despite classical planning usually presents limited expressive capabilities, PPDDL demonstrated usefulness in reviewing the knowledge gathered by an autonomous system, making explicit causal correlations, and can be exploited to find a plan to reach any state the agent faces during its experience. This work presents a new architecture implementing an open-ended learning system able to synthesize from scratch its experience into a PPDDL representation and update it over time. Without a predefined set of goals and tasks, the system integrates intrinsic motivations to explore the environment in a self-directed way, exploiting the high-level knowledge acquired during its experience. The system explores the environment and iteratively: (a) discover options, (b) explore the environment using options, (c) abstract the knowledge collected and (d) plan. This paper proposes an alternative approach to implementing open-ended learning architectures exploiting low-level and high-level representations to extend its knowledge in a virtuous loop.

Online platforms for social interactions are an essential part of modern society. With the advance of technology and the rise of algorithms and AI, content is now filtered systematically and facilitates the formation of filter bubbles. This work investigates the social consensus under limited visibility in a two-state majority-vote model on Barab\'asi-Albert scale-free networks. In the consensus evolution, each individual assimilates the opinion of the majority of their neighbors with probability $1-q$ and disagrees with chance $q$, known as the noise parameter. We define the visibility parameter $V$ as the probability of an individual considering the opinion of a neighbor at a given interaction. The parameter $V$ enables us to model the limited visibility phenomenon that produces synthetic neighborhoods in online interactions. We employ Monte Carlo simulations and finite-size scaling analysis to obtain the critical noise parameter as a function of the visibility $V$ and the growth parameter $z$. We find the critical exponents $\beta/\bar{\nu}$, $\gamma/\bar{\nu}$ and $1/\bar{\nu}$ of and validate their unitary relation for complex networks. Our analysis shows that installing and manipulating synthetic influence groups critically undermines consensus robustness., Comment: 10 pages, 9 figures, 1 table

Context. Computational astronomy has reached the stage where running a gravitational N-body simulation of a stellar system, such as a Milky Way star cluster, is computationally feasible, but a major limiting factor that remains is the ability to set up physically realistic initial conditions. Aims. We aim to obtain realistic initial conditions for N-body simulations by taking advantage of machine learning, with emphasis on reproducing small-scale interstellar distance distributions. Methods. The computational bottleneck for obtaining such distance distributions is the hydrodynamics of star formation, which ultimately determine the features of the stars, including positions, velocities, and masses. To mitigate this issue, we introduce a new method for sampling physically realistic initial conditions from a limited set of simulations using Gaussian processes. Results. We evaluated the resulting sets of initial conditions based on whether they meet tests for physical realism. We find that direct sampling based on the learned distribution of the star features fails to reproduce binary systems. Consequently, we show that physics-informed sampling algorithms solve this issue, as they are capable of generating realisations closer to reality., Comment: Accepted to A&A (4th Sep 2024)

To capture the dynamics of macroscopic non-relativistic fluids consisting of very many atoms, it is typically sufficient to truncate the gradient expansion at order zero, leading to ideal fluid dynamics, or at order one, leading to the Navier-Stokes theory. For mesoscopic fluids consisting of a small number of atoms, second-order corrections can become significant. We investigate here specifically superfluids at vanishing temperature, and identify relevant second-order terms of quantum origin that contribute already in a static situation. The general form of these terms arises from an extension of the Gross-Pitaevskii theory. In the context of density functional theory, they are named after C. von Weizs\"acker. We assess the influence of these terms on numerical solutions of second-order fluid dynamic equations for the expansion of a mesoscopic ultra-cold Fermi gas released from an anisotropic harmonic trap in two spatial dimensions.

Classical machine learning has proven remarkably useful in post-processing quantum data, yet typical learning algorithms often require prior training to be effective. In this work, we employ a tensorial kernel support vector machine (TK-SVM) to analyze experimental data produced by trapped-ion quantum computers. This unsupervised method benefits from directly interpretable training parameters, allowing it to identify the non-trivial string-order characterizing symmetry-protected topological (SPT) phases. We apply our technique to two examples: a spin-1/2 model and a spin-1 model, featuring the cluster state and the AKLT state as paradigmatic instances of SPT order, respectively. Using matrix product states, we generate a family of quantum circuits that host a trivial phase and an SPT phase, with a sharp phase transition between them. For the spin-1 case, we implement these circuits on two distinct trapped-ion machines based on qubits and qutrits. Our results demonstrate that the TK-SVM method successfully distinguishes the two phases across all noisy experimental datasets, highlighting its robustness and effectiveness in quantum data interpretation., Comment: 16 pages, 11 figures

This work utilizes the Immersed Boundary Conformal Method (IBCM) to analyze Kirchhoff-Love and Reissner-Mindlin shell structures within an immersed domain framework. Immersed boundary methods involve embedding complex geometries within a background grid, which allows for great flexibility in modeling intricate shapes and features despite the simplicity of the approach. The IBCM method introduces additional layers conformal to the boundaries, allowing for the strong imposition of Dirichlet boundary conditions and facilitating local refinement. In this study, the construction of boundary layers is combined with high-degree spline-based approximation spaces to further increase efficiency. The Nitsche method, employing non-symmetric average operators, is used to couple the boundary layers with the inner patch, while stabilizing the formulation with minimal penalty parameters. High-order quadrature rules are applied for integration over cut elements and patch interfaces. Numerical experiments demonstrate the efficiency and accuracy of the proposed formulation, highlighting its potential for complex shell structures modeled through Kirchhoff-Love and Reissner-Mindlin theories. These tests include the generation of conformal interfaces, the coupling of Kirchhoff-Love and Reissner-Mindlin theories, and the simulation of a damaged shell.

Large autoregressive generative models have emerged as the cornerstone for achieving the highest performance across several Natural Language Processing tasks. However, the urge to attain superior results has, at times, led to the premature replacement of carefully designed task-specific approaches without exhaustive experimentation. The Coreference Resolution task is no exception; all recent state-of-the-art solutions adopt large generative autoregressive models that outperform encoder-based discriminative systems. In this work,we challenge this recent trend by introducing Maverick, a carefully designed - yet simple - pipeline, which enables running a state-of-the-art Coreference Resolution system within the constraints of an academic budget, outperforming models with up to 13 billion parameters with as few as 500 million parameters. Maverick achieves state-of-the-art performance on the CoNLL-2012 benchmark, training with up to 0.006x the memory resources and obtaining a 170x faster inference compared to previous state-of-the-art systems. We extensively validate the robustness of the Maverick framework with an array of diverse experiments, reporting improvements over prior systems in data-scarce, long-document, and out-of-domain settings. We release our code and models for research purposes at https://github.com/SapienzaNLP/maverick-coref., Comment: Accepted at main conference of ACL 2024. 15 pages

Large Language Models (LLMs) show promising potential in Software Engineering, especially for code-related tasks like code completion and code generation. LLMs' evaluation is generally centred around general metrics computed over benchmarks. While painting a macroscopic view of the benchmarks and of the LLMs' capacity, it is unclear how each programming task in these benchmarks assesses the capabilities of the LLMs. In particular, the difficulty level of the tasks in the benchmarks is not reflected in the score used to report the performance of the model. Yet, a model achieving a 90% score on a benchmark of predominantly easy tasks is likely less capable than a model achieving a 90% score on a benchmark containing predominantly difficult tasks. This paper devises a framework, HardEval, for assessing task difficulty for LLMs and crafting new tasks based on identified hard tasks. The framework uses a diverse array of prompts for a single task across multiple LLMs to obtain a difficulty score for each task of a benchmark. Using two code generation benchmarks, HumanEval+ and ClassEval, we show that HardEval can reliably identify the hard tasks within those benchmarks, highlighting that only 21% of HumanEval+ and 27% of ClassEval tasks are hard for LLMs. Through our analysis of task difficulty, we also characterize 6 practical hard task topics which we used to generate new hard tasks. Orthogonal to current benchmarking evaluation efforts, HardEval can assist researchers and practitioners in fostering better assessments of LLMs. The difficulty score can be used to identify hard tasks within existing benchmarks. This, in turn, can be leveraged to generate more hard tasks centred around specific topics either for evaluation or improvement of LLMs. HardEval generalistic approach can be applied to other domains such as code completion or Q/A.

The proliferation of human-AI ecosystems involving human interaction with algorithms, such as assistants and recommenders, raises concerns about large-scale social behaviour. Despite evidence of such phenomena across several contexts, the collective impact of GPS navigation services remains unclear: while beneficial to the user, they can also cause chaos if too many vehicles are driven through the same few roads. Our study employs a simulation framework to assess navigation services' influence on road network usage and CO2 emissions. The results demonstrate a universal pattern of amplified conformity: increasing adoption rates of navigation services cause a reduction of route diversity of mobile travellers and increased concentration of traffic and emissions on fewer roads, thus exacerbating an unequal distribution of negative externalities on selected neighbourhoods. Although navigation services recommendations can help reduce CO2 emissions when their adoption rate is low, these benefits diminish or even disappear when the adoption rate is high and exceeds a certain city- and service-dependent threshold. We summarize these discoveries in a non-linear function that connects the marginal increase of conformity with the marginal reduction in CO2 emissions. Our simulation approach addresses the challenges posed by the complexity of transportation systems and the lack of data and algorithmic transparency.

Peculiar phenomena have been observed in analyses of anisotropic flow ($v_n$) fluctuations in ultracentral nucleus-nucleus collisions: The fourth-order cumulant of the elliptic flow ($v_2$) distribution changes sign. In addition, the ATLAS collaboration has shown that cumulants of $v_n$ fluctuations of all orders depend significantly on the centrality estimator. We show that these peculiarities are due to the fact that the impact parameter $b$ always spans a finite range for a fixed value of the centrality estimator. We provide a quantitative determination of this range through a simple Bayesian analysis. We obtain excellent fits of STAR and ATLAS data, with a few parameters, by assuming that the probability distribution of $v_n$ solely depends on $b$ at a given centrality. This probability distribution is almost Gaussian, and its parameters depend smoothly on $b$, in a way that is constrained by symmetry and scaling laws. We reconstruct, thus, the impact parameter dependence of the mean elliptic flow in the reaction plane in a model-independent manner, and assess the robustness of the extraction using Monte Carlo simulations of the collisions where the impact parameter is known. We argue that the non-Gaussianity of $v_n$ fluctuations gives direct information on the hydrodynamic response to initial anisotropies, ATLAS data being consistent with a smaller response for $n=4$ than for $n=2$ and $n=3$, in agreement with hydrodynamic calculations., Comment: 18 pages, 8 figures

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

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

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

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

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

We consider anyonic quasiparticles with charge e/2 described by the {\nu} = 1/2 chiral Luttinger liquid, which collide in a Hong-Ou-Mandel-like interferometer. These colliding anyonic channels can be formally viewed as hosting Laughlin-like fractional {\nu} = 1/2 quasiparticles. More specifically, two possible geometries are considered: (i) a two-edge-channel setup where anyons originate from equilibrium reservoirs; (ii) a four-edge-channel setup where nonequilibrium anyons arrive at the collider in the form of diluted beams. For both setups, we calculate the tunneling current and the current correlations. For setup (i), our results provide analytically exact expressions for the tunneling current, tunneling-current noise, and cross-correlation noise, The exact relation between conductance and noise is demonstrated. For setup (ii), we show that the tunneling current and the generalized Fano factor [defined in B. Rosenow et al. (2016)] are finite for diluted streams of {\nu} = 1/2 anyons. This is due to the processes where nonequilibrium anyons, supplied via either source edge, directly tunnel at the central QPC. Thus, to obtain meaningful results in this case, one should go beyond the so-called time-domain braiding processes, where nonequilibrium anyons do not tunnel at the collider, but rather indirectly influence the tunneling by braiding with the quasiparticle-quasihole pairs created at the collider. This suggests that the effect of direct tunneling and collisions of diluted anyons in the Hong-Ou-Mandel interferometer can be important for various observables in physical quantum-Hall edges at Laughlin filling fractions., Comment: 39 pages, 10 figures

The logico-algebraic study of Lewis's hierarchy of variably strict conditional logics has been essentially unexplored, hindering our understanding of their mathematical foundations, and the connections with other logical systems. This work aims to fill this gap by providing a comprehensive logico-algebraic analysis of Lewis's logics. We begin by introducing novel finite axiomatizations for varying strengths of Lewis's logics, distinguishing between global and local consequence relations on Lewisian sphere models. We then demonstrate that the global consequence relation is strongly algebraizable in terms of a specific class of Boolean algebras with a binary operator representing the counterfactual implication; in contrast, we show that the local consequence relation is generally not algebraizable, although it can be characterized as the degree-preserving logic over the same algebraic models. Further, we delve into the algebraic semantics of Lewis's logics, developing two dual equivalences with respect to particular topological spaces. In more details, we show a duality with respect to the topological version of Lewis's sphere models, and also with respect to Stone spaces with a selection function; using the latter, we demonstrate the strong completeness of Lewis's logics with respect to sphere models. Finally, we draw some considerations concerning the limit assumption over sphere models.

We introduce a cyclic quantum battery model, based on an interacting bipartite system, weakly coupled to a thermal bath. The working cycle of the battery consists of four strokes: system thermalization, disconnection of subsystems, ergotropy extraction, and reconnection. The thermal bath acts as a charger in the thermalization stroke, while ergotropy extraction is possible because the ensuing thermal state is no longer passive after the disconnection stroke. Focusing on the case of two interacting qubits, we show that phase coherence, in the presence of non-trivial correlations between the qubits, can be exploited to reach working regimes with efficiency higher than 50% while providing finite ergotropy. Our protocol is illustrated through a simple and feasible circuit model of a cyclic superconducting quantum battery. Furthermore, we simulate the considered cycle on superconducting IBM quantum machines. The good agreement between the theoretical and simulated results strongly suggests that our scheme for cyclic quantum batteries can be successfully realized in superconducting quantum hardware., Comment: This is the Accepted Manuscript version of an article accepted for publication in "Quantum Science and Technology". IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. This Accepted Manuscript is published under a CC BY licence. The Version of Record is available online at https://www.doi.org/10.1088/2058-9565/ad9ed4

The Fornax dwarf spheroidal galaxy (dSph) represents a challenge for some globular cluster (GC) formation models, because an exceptionally high fraction of its stellar mass is locked in its GC system. In order to shed light on our understanding of GC formation, we aim to constrain the amount of stellar mass that Fornax has lost via tidal interaction with the Milky Way (MW). Exploiting the flexibility of effective multi-component $N$-body simulations and relying on state-of-the-art estimates of Fornax's orbital parameters, we study the evolution of the mass distribution of the Fornax dSph in observationally justified orbits in the gravitational potential of the MW over 12 Gyr. We find that, though the dark-matter mass loss can be substantial, the fraction of stellar mass lost by Fornax to the MW is always negligible, even in the most eccentric orbit considered. We conclude that stellar-mass loss due to tidal stripping is not a plausible explanation for the unexpectedly high stellar mass of the GC system of the Fornax dSph and we discuss quantitatively the implications for GC formation scenarios., Comment: 15 pages, 12 figures. Version accepted in A&A

Recommendation systems and assistants (in short, recommenders) are ubiquitous in online platforms and influence most actions of our day-to-day lives, suggesting items or providing solutions based on users' preferences or requests. This survey analyses the impact of recommenders in four human-AI ecosystems: social media, online retail, urban mapping and generative AI ecosystems. Its scope is to systematise a fast-growing field in which terminologies employed to classify methodologies and outcomes are fragmented and unsystematic. We follow the customary steps of qualitative systematic review, gathering 144 articles from different disciplines to develop a parsimonious taxonomy of: methodologies employed (empirical, simulation, observational, controlled), outcomes observed (concentration, model collapse, diversity, echo chamber, filter bubble, inequality, polarisation, radicalisation, volume), and their level of analysis (individual, item, model, and systemic). We systematically discuss all findings of our survey substantively and methodologically, highlighting also potential avenues for future research. This survey is addressed to scholars and practitioners interested in different human-AI ecosystems, policymakers and institutional stakeholders who want to understand better the measurable outcomes of recommenders, and tech companies who wish to obtain a systematic view of the impact of their recommenders.

International trade and logistics are subject to factors including geopolitical instability, climate change, and black swan events such as the unforeseen closure of the Suez Canal. The problem of predicting local price change under modification of an underlying transport network or change in supply characteristics unites elements of game theory, network theory and transport. The Cournot Oligopoly models economic actors as rational players attempting to maximise profit by optimising supply quantities with analytical results now consolidated about equilibrium characteristics where transport conditions are fixed. Similarly, where supply and demand are fixed, the routing of goods in a transport network can be analytically solved through a traffic assignment problem. Hence we can solve the coupled Cournot-congestion problem by means of a 2-layer network. Where the layers are linked, inter-layer feedback wherein players attempt to maximise their utility occurs. In this respect we find players benefit from taking advantage of non-simultaneous responses to the market rather than moving to a new equilibrium. We draw conclusions about the nature of equilibria, finding that the concave utility curve property results in unique and stable equilibrium for each uncoupled layer, while linked layers have a non-unique stable equilibria for which general solutions are stated., Comment: 27 pages, 11 figures

We present the discovery and early observations of the nearby Type II supernova (SN) 2024ggi in NGC 3621 at 6.64 +/- 0.3 Mpc. The SN was caught 5.8 (+1.9 -2.9) hours after its explosion by the ATLAS survey. Early-phase, high-cadence, and multi-band photometric follow-up was performed by the Kinder (Kilonova Finder) project, collecting over 1000 photometric data points within a week. The combined o- and r-band light curves show a rapid rise of 3.3 magnitudes in 13.7 hours, much faster than SN 2023ixf (another recent, nearby, and well-observed SN II). Between 13.8 and 18.8 hours after explosion SN 2024ggi became bluer, with u-g colour dropping from 0.53 to 0.15 mag. The rapid blueward evolution indicates a wind shock breakout (SBO) scenario. No hour-long brightening expected for the SBO from a bare stellar surface was detected during our observations. The classification spectrum, taken 17 hours after the SN explosion, shows flash features of high-ionization species such as Balmer lines, He I, C III, and N III. Detailed light curve modeling reveals critical insights into the properties of the circumstellar material (CSM). Our favoured model has an explosion energy of 2 x 10^51 erg, a mass-loss rate of 10^-3 solar_mass/yr (with an assumed 10 km/s wind), and a confined CSM radius of 6 x 10^14 cm. The corresponding CSM mass is 0.4 solar_mass. Comparisons with SN 2023ixf highlight that SN 2024ggi has a smaller CSM density, resulting in a faster rise and fainter UV flux. The extensive dataset and the involvement of citizen astronomers underscore that a collaborative network is essential for SBO searches, leading to more precise and comprehensive SN characterizations., Comment: 11 pages, 5 figures in manuscript, 6 pages in appendix, submitted to ApJL

Alternative routing is crucial to minimize the environmental impact of urban transportation while enhancing road network efficiency and reducing traffic congestion. Existing methods neglect information about road popularity, possibly leading to unintended consequences such as increasing emissions and congestion. This paper introduces Polaris, an alternative routing algorithm that exploits road popularity to optimize traffic distribution and reduce CO2 emissions. Polaris leverages the novel concept of K-road layers, which mitigates the feedback loop effect where redirecting vehicles to less popular roads could increase their popularity in the future. We conduct experiments in three cities to evaluate Polaris against state-of-the-art alternative routing algorithms. Our results demonstrate that Polaris significantly reduces the overuse of highly popular road edges and traversed regulated intersections, showcasing its ability to generate efficient routes and distribute traffic more evenly. Furthermore, Polaris achieves substantial CO2 reductions, outperforming existing alternative routing strategies. Finally, we compare Polaris to an algorithm that coordinates vehicles centrally to distribute them more evenly on the road network. Our findings reveal that Polaris performs comparably well, even with much less information, highlighting its potential as an efficient and sustainable solution for urban traffic management.