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We apply methods of derived and non-commutative algebraic geometry to understand ramification phenomena on arithmetic schemes. As an application, we prove the Deligne-Milnor conjecture and, in the pure characteristic case, a generalization of Bloch conductor conjecture., Comment: Comments welcome

Overcoming both modeling and computational challenges, we present, for the first time, the extended surface-brightness distribution model of a strongly-lensed source in a complex galaxy-cluster-scale system. We exploit the high-resolution Hubble Space Telescope (HST) imaging and extensive Multi Unit Spectroscopic Explorer spectroscopy to build an extended strong-lensing model, in a full multi-plane formalism, of SDSS J1029+2623, a lens cluster at $z = 0.588$ with three multiple images of a background quasar ($z = 2.1992$). Going beyond typical cluster strong-lensing modeling techniques, we include as observables both the positions of 26 pointlike multiple images from seven background sources, spanning a wide redshift range between 1.02 and 5.06, and the extended surface-brightness distribution of the strongly-lensed quasar host galaxy, over $\sim78000$ HST pixels. In addition, we model the light distribution of seven objects, angularly close to the strongly-lensed quasar host, over $\sim9300$ HST pixels. Our extended lens model reproduces well both the observed intensity and morphology of the quasar host galaxy in the HST F160W band (with a 0''.03 pixel scale). The reconstructed source shows a single, compact, and smooth surface-brightness distribution, for which we estimate an intrinsic magnitude of 23.3 $\pm$ 0.1 in the F160W band and a half-light radius of (2.39 $\pm$ 0.03) kpc. The increased number of observables enables the accurate determination of the total mass of line-of-sight halos lying angularly close to the extended arc. This work paves the way for a new generation of galaxy cluster strong-lens models, where additional, complementary lensing observables are directly incorporated as model constraints., Comment: 19 pages, 9 Figures, 2 Tables. Accepted for publication in ApJ

Altermagnetism, a new phase of collinear spin-order sharing similarities with antiferromagnets and ferromagnets, has introduced a new guiding principle for spintronic/thermoelectric applications due to its direction-dependent magnetic properties. Fulfilling the promise to exploit altermagnetism for device design depends on identifying materials with tuneable transport properties. The search for intrinsic altermagnets has so far focused on the role of anisotropy in the crystallographic symmetries and in the bandstructure. Here, we present a different mechanism that approaches this goal by leveraging the interplay between a Hubbard local repulsion and the itinerant magnetism given by the presence of van Hove singularities. We show that altermagnetism is stable for a broad range of interactions and dopings and we focus on tunability of the spin-charge conversion ratio., Comment: 5 pages, 3 figures

We present Timber, the first white-box poisoning attack targeting decision trees. Timber is based on a greedy attack strategy leveraging sub-tree retraining to efficiently estimate the damage performed by poisoning a given training instance. The attack relies on a tree annotation procedure which enables sorting training instances so that they are processed in increasing order of computational cost of sub-tree retraining. This sorting yields a variant of Timber supporting an early stopping criterion designed to make poisoning attacks more efficient and feasible on larger datasets. We also discuss an extension of Timber to traditional random forest models, which is useful because decision trees are normally combined into ensembles to improve their predictive power. Our experimental evaluation on public datasets shows that our attacks outperform existing baselines in terms of effectiveness, efficiency or both. Moreover, we show that two representative defenses can mitigate the effect of our attacks, but fail at effectively thwarting them., Comment: 18 pages, 7 figures, 5 tables

Phonons in Bose-Einstein condensates propagate as massless scalar particles on top of an emergent acoustic metric. This hydrodynamics/gravity analogy can be exploited to realize acoustic black holes, featuring an event horizon that traps phonons. We show that by appropriately perturbing the background fluid, gravitational wave-like fluctuations of the acoustic metric can be produced. Such fluctuations can be used to excite an acoustic black hole, which should then relax by phonon emission.

According to the microscopic formulation of Landauer's principle, when information is deleted the Von Neumann entropy of the system gets reduced with a corresponding energy cost. Although within the same perspective the growth of the entropy should remain unconstrained we show that during quantum amplification the heat flow does restrict the increase of the Von Neumann entropy. When applied to the case of relic gravitons (with frequencies between the aHz region and the THz domain) the bounds obtained here set a limit on initial thermal gravitons and on the total duration of inflation., Comment: 4 pages, 2 included figures

The behavior of many complex systems, from nanostructured materials to animal colonies, is governed by local transitions that, while involving a restricted number of interacting units, may generate collective cascade phenomena. Tracking such local events and understanding how they emerge and propagate throughout these systems represent often a challenge. Common strategies monitor specific parameters, tailored ad hoc to describe certain systems, over time. However, such approaches typically require prior knowledge of the underpinning physics and are poorly transferable to different systems. Here we present LEAP, a general, transferable, agnostic analysis approach that can reveal precious information on the physics of a variety of complex dynamical systems simply starting from the trajectory of their constitutive units. Built on a bivariate combination of two abstract descriptors, LENS and {\tau}SOAP, the LEAP analysis allows (i) detecting the emergence of local fluctuations in simulation or experimentally-acquired trajectories of any type of multicomponent system, (ii) classifying fluctuations into categories, and (iii) correlating them in space and time. We demonstrate how LEAP, just building on the abstract concepts of local fluctuations and their spatiotemporal correlation, efficiently reveals precious insights on the emergence and propagation of local and collective phenomena in a variety of complex dynamical systems ranging from the atomic- to the microscopic-scale. Given its abstract character, we expect that LEAP will offer an important tool to understand and predict the behavior of systems whose physics is unknown a priori, as well as to revisit a variety of known complex physical phenomena under a new perspective.

In processes such as embryo shaping, wound healing, and malignant cell invasion, epithelial cells transition between dispersed phases, where the cells move independently, and condensed phases, where they aggregate and deform to close gaps, forming confluent tissues. Understanding how cells regulate these transitions and how these transitions differ from those of inert particles remains an open challenge. Addressing these questions requires linking the macroscopic properties of tissues to the mechanical characteristics and active responses of individual cells, driven by sub-cellular processes. Here, we introduce a computational model that incorporates key factors such as cell deformability, lamellipodium-driven dynamics, cell-junction-mediated adhesion, and contact inhibition of locomotion (CIL)-a process where cells alter their motion upon contact with others. We demonstrate how these factors, along with cell density, regulate the dynamical and mechanical properties of tissues. We show that CIL imparts unique living-like behaviors to cells and tissues by reducing density fluctuations. This reduction in fluctuations affects the dynamics: it inhibits cell motion in steady states but promotes it in the presence of gaps, accelerating wound healing. Furthermore, the stabilization of tensile states by CIL, which would otherwise fracture, enables the formation of tensile force chains., Comment: SM included

Efficient network management in optical backbone networks is crucial for handling continuous traffic growth. In this work, we address the challenges of managing dynamic traffic in C- and C+L-band optical backbone networks while exploring application flexibility, namely the compressibility and delayability metrics. We propose a strategy, named Delay-Aware and Compression-Aware (DACA) provisioning algorithm, which reduces blocking probability, thereby increasing information-carrying capacity of the network compared to baseline strategies.

A new generation of gravitational wave detectors is currently being designed with the likely use of a different laser wavelength compared to current instruments. The estimation of the optical losses for this new wavelength is particularly relevant to derive the detector sensitivity and also to anticipate the optical performances of future instruments. In this article, we measured the absorption and angle-resolved scattering of several mirror samples in order to compare optical losses at a wavelength of 1064 and 1550\ nm. In addition, we have carried out simulations of the Einstein Telescope arm cavities at 1064 and 1550\ nm taking into account losses due to surface low-spatial frequency flatness. Our results suggest that optical losses as measured at 1064\ nm are about twice as large as those at 1550\ nm as predicted with a simple model., Comment: 12 pages, 3 figures

The vibrational density of states is key to understanding the mechanical, thermal, and transport properties of materials. In amorphous solids, this density shows an excess of vibrational modes compared to the Debye model, known as the boson peak, whose origin remains poorly understood. Previous studies have suggested a link between the boson peak and quasi-localized nonphononic vibrations, or "defects." However, it has been difficult to clearly identify these defects, possibly because they hybridize with extended phonons, casting doubt on their existence and connection to the boson peak. In this work, we introduce a simple and practical method for separating hybridized phonons from localized vibrations. We show that phonons at the boson peak frequency hybridize with localized defects. These defects are anisotropic, compact, and exhibit oscillatory pure shear deformations. Their density correlates with the excess of vibrational modes at the boson peak frequency across various two- and three-dimensional systems, confirming that they are the microscopic origin of the boson peak.

A multitude of JWST studies reveal a surprising over-abundance of over-massive accreting super-massive blackholes (SMBHs) -- leading to a deepening tension between theory and observation in the first billion years of cosmic time. Across X-ray to infrared wavelengths, models built off of pre-JWST predictions fail to easily reproduce observed AGN signatures (or lack thereof), driving uncertainty around the true nature of these sources. Using a sample of JWST AGN identified via their broadened Halpha emission and covered by the deepest X-ray surveys, we find neither any measurable X-ray emission nor any detection of high-ionization emission lines frequently associated with accreting SMBHs. We propose that these sources are accreting at or beyond the Eddington limit, which reduces the need for efficient production of heavy SMBH seeds at cosmic dawn. Using a theoretical model of super-Eddington accretion, we can produce the observed relative dearth of both X-ray and ultraviolet emission, as well as the high Balmer decrements, without the need for significant dust attenuation. This work indicates that super-Eddington accretion is easily achieved through-out the early Universe, and further study is required to determine what environments are required to trigger this mode of black hole growth., Comment: Submitted

Generating images from brain waves is gaining increasing attention due to its potential to advance brain-computer interface (BCI) systems by understanding how brain signals encode visual cues. Most of the literature has focused on fMRI-to-Image tasks as fMRI is characterized by high spatial resolution. However, fMRI is an expensive neuroimaging modality and does not allow for real-time BCI. On the other hand, electroencephalography (EEG) is a low-cost, non-invasive, and portable neuroimaging technique, making it an attractive option for future real-time applications. Nevertheless, EEG presents inherent challenges due to its low spatial resolution and susceptibility to noise and artifacts, which makes generating images from EEG more difficult. In this paper, we address these problems with a streamlined framework based on the ControlNet adapter for conditioning a latent diffusion model (LDM) through EEG signals. We conduct experiments and ablation studies on popular benchmarks to demonstrate that the proposed method beats other state-of-the-art models. Unlike these methods, which often require extensive preprocessing, pretraining, different losses, and captioning models, our approach is efficient and straightforward, requiring only minimal preprocessing and a few components. The code is available at https://github.com/LuigiSigillo/GWIT., Comment: Accepted at ICASSP 2025

Vision Based Navigation consists in utilizing cameras as precision sensors for GNC after extracting information from images. To enable the adoption of machine learning for space applications, one of obstacles is the demonstration that available training datasets are adequate to validate the algorithms. The objective of the study is to generate datasets of images and metadata suitable for training machine learning algorithms. Two use cases were selected and a robust methodology was developed to validate the datasets including the ground truth. The first use case is in-orbit rendezvous with a man-made object: a mockup of satellite ENVISAT. The second use case is a Lunar landing scenario. Datasets were produced from archival datasets (Chang'e 3), from the laboratory at DLR TRON facility and at Airbus Robotic laboratory, from SurRender software high fidelity image simulator using Model Capture and from Generative Adversarial Networks. The use case definition included the selection of algorithms as benchmark: an AI-based pose estimation algorithm and a dense optical flow algorithm were selected. Eventually it is demonstrated that datasets produced with SurRender and selected laboratory facilities are adequate to train machine learning algorithms., Comment: 6 pages, 4 figures, preprint of the proceedings of ESA SPAICE conference 2024

In turbulent flows, tracking the source of a passive scalar cue requires exploiting the limited information that can be gleaned from rare, stochastic encounters with the cue. When crafting a search policy, the most challenging and important decision is what to do in the absence of an encounter. In this work, we perform high-fidelity direct numerical simulations of a turbulent flow with a stationary source of tracer particles, and obtain quasi-optimal policies (in the sense of minimal average search time) with respect to the empirical encounter statistics. We study the trajectories under such policies and compare the results to those of the infotaxis heuristic. In the presence of a strong mean wind, the optimal motion in the absence of an encounter is zigzagging (akin to the well-known insect behavior "casting") followed by a return to the starting location. The zigzag motion generates characteristic $t^{1/2}$ scaling of the rms displacement envelope. By passing to the limit where the probability of detection vanishes, we connect these results to the classical linear search problem and derive an estimate of the tail of the arrival time pdf as a stretched exponential, which agrees with Monte Carlo results. We also discuss what happens as the wind speed decreases., Comment: 7 pages, 6 figures, plus supplement

Blockchain technology, which was introduced for supporting cryptocurrencies, today provides a decentralized infrastructure for general information storage and execution of algorithms, thus enabling the conversion of many applications and services from a centralized and intermediated model to a decentralized and disintermediated one. In this paper we focus on biometric authentication, which is classically performed using centralized systems, and could hence benefit from decentralization. For such a purpose, however, an inherent contradiction between biometric applications and blockchain technology must be overcome, as the former require keeping biometric features private, while blockchain is a public infrastructure. We propose a blockchain-based biometric authentication protocol that enables decentralization and resilience while protecting the privacy, personal data, and, in particular, biometric features of users. The protocol we propose leverages fuzzy commitment schemes to allow biometric authentication to be performed without disclosing biometric data. We also analyze the security of the protocol we propose by considering some relevant attacks.

The aim of this paper is to prove a theorem of C.~Miranda for the single and double layer potential corresponding to the fundamental solution of a second order differential operator with constant coefficients in Schauder spaces in the limiting case in which the open set is of class $C^{m,1}$ and the densities are of class $C^{m-1,1}$ for the single layer potential and of class $C^{m,1}$ for the double layer potential for some nonzero natural number $m$. The treatment of the limiting case requires generalized Schauder spaces., Comment: arXiv admin note: substantial text overlap with arXiv:2408.17192; text overlap with arXiv:2309.00393, arXiv:2307.04775, arXiv:2305.19672

The goal of style transfer is, given a content image and a style source, generating a new image preserving the content but with the artistic representation of the style source. Most of the state-of-the-art architectures use transformers or diffusion-based models to perform this task, despite the heavy computational burden that they require. In particular, transformers use self- and cross-attention layers which have large memory footprint, while diffusion models require high inference time. To overcome the above, this paper explores a novel design of Mamba, an emergent State-Space Model (SSM), called Mamba-ST, to perform style transfer. To do so, we adapt Mamba linear equation to simulate the behavior of cross-attention layers, which are able to combine two separate embeddings into a single output, but drastically reducing memory usage and time complexity. We modified the Mamba's inner equations so to accept inputs from, and combine, two separate data streams. To the best of our knowledge, this is the first attempt to adapt the equations of SSMs to a vision task like style transfer without requiring any other module like cross-attention or custom normalization layers. An extensive set of experiments demonstrates the superiority and efficiency of our method in performing style transfer compared to transformers and diffusion models. Results show improved quality in terms of both ArtFID and FID metrics. Code is available at https://github.com/FilippoBotti/MambaST.

Deep learning has revolutionized the field of hyperspectral image (HSI) analysis, enabling the extraction of complex and hierarchical features. While convolutional neural networks (CNNs) have been the backbone of HSI classification, their limitations in capturing global contextual features have led to the exploration of Vision Transformers (ViTs). This paper introduces AMBER, an advanced SegFormer specifically designed for multi-band image segmentation. AMBER enhances the original SegFormer by incorporating three-dimensional convolutions to handle hyperspectral data. Our experiments, conducted on the Indian Pines, Pavia University, and PRISMA datasets, show that AMBER outperforms traditional CNN-based methods in terms of Overall Accuracy, Kappa coefficient, and Average Accuracy on the first two datasets, and achieves state-of-the-art performance on the PRISMA dataset., Comment: submitted to Neural Computing & Applications (Springer). Currently under review

We constrain dark energy and modified gravity within the effective field theory of dark energy framework using the full-shape BOSS galaxy power spectrum, combined with Planck cosmic microwave background (CMB) data and recent baryon acoustic oscillations (BAO) measurements from DESI. Specifically, we focus on a varying braiding parameter $\alpha_{\rm B}$, a running of the ``effective'' Planck mass $\alpha_{\rm M}$, and a constant dark energy equation of state $w$. The analysis is performed with two of these parameters at a time, including all the other standard cosmological parameters and marginalizing over bias and nuisance parameters. The full-shape galaxy power spectrum is modeled using the effective field theory of large-scale structure up to 1-loop order in perturbation theory. We find that the CMB data is most sensitive to $\alpha_{\rm B}$, and that adding large-scale structure information only slightly changes the parameter constraints. However, the large-scale structure data significantly improve the bounds on $\alpha_{\rm M}$ and $w$ by a factor of two. This improvement is driven by background information contained in the BAO, which breaks the degeneracy with $H_0$ in the CMB. We confirm this by comparing the BOSS full-shape information with BOSS BAO, finding no significant differences. This is likely to change with future high-precision full-shape data from Euclid and DESI however, to which the pipeline developed here is immediately applicable., Comment: 32 pages, 9 figures

Low-mass X-ray binaries hosting weakly magnetized neutron stars (NS-LMXBs) are among the brightest sources in the X-ray sky. Since 2021, the Imaging X-ray Polarimetry Explorer (IXPE) has provided new measurements of the X-ray polarization of these sources. IXPE observations have revealed that most NS-LMXBs are significantly polarized in the X-rays, providing unprecedented insight into the geometry of their accretion flow. In this review paper, we summarize the first results obtained by IXPE on NS-LMXBs, the emerging trends within each class of sources (atoll/Z), and possible physical interpretations., Comment: 8 pages, 3 figures, invited review for the Special Issue X-ray Polarization: A New Era Begins

Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to efficiently process information encoded in input quantum states, avoiding the high computational cost of training more complicated nonlinear models. On the other hand, quantum information scrambling (QIS) quantifies how the spread of quantum information into correlations makes it irretrievable from local measurements. Here, we explore the tight relation between QIS and the predictive power of QELMs. In particular, we show efficient state estimation is possible even beyond the scrambling time, for many different types of dynamics -- in fact, we show that in all the cases we studied, the reconstruction efficiency at long interaction times matches the optimal one offered by random global unitary dynamics. These results offer promising venues for robust experimental QELM-based state estimation protocols, as well as providing novel insights into the nature of QIS from a state estimation perspective.

In this paper we prove the uniqueness of the critical point for stable solutions of the Robin problem \[ \begin{cases} -\Delta u=f(u)&\text{in }\Omega\\ u>0&\text{in }\Omega\\ \partial_\nu u+\beta u=0&\text{on }\partial\Omega, \end{cases} \] where $\Omega\subseteq\mathbb{R}^2$ is a smooth and bounded domain with strictly positive curvature of the boundary, $f\ge0$ is a smooth function and $\beta>0$. Moreover, for $\beta$ large the result fails as soon as the domain is no more convex, even if it is very close to be: indeed, in this case it is possible to find solutions with an arbitrary large number of critical points.

Ensemble methods such as random forests have transformed the landscape of supervised learning, offering highly accurate prediction through the aggregation of multiple weak learners. However, despite their effectiveness, these methods often lack transparency, impeding users' comprehension of how RF models arrive at their predictions. Explainable ensemble trees (E2Tree) is a novel methodology for explaining random forests, that provides a graphical representation of the relationship between response variables and predictors. A striking characteristic of E2Tree is that it not only accounts for the effects of predictor variables on the response but also accounts for associations between the predictor variables through the computation and use of dissimilarity measures. The E2Tree methodology was initially proposed for use in classification tasks. In this paper, we extend the methodology to encompass regression contexts. To demonstrate the explanatory power of the proposed algorithm, we illustrate its use on real-world datasets.

The design of absorbing boundary conditions (ABC) in a numerical simulation is a challenging task. In the best cases, spurious reflections remain for some angles of incidence or at certain wave lengths. In the worst, ABC are not even possible for the set of equations and/or numerical schemes used in the simulation and reflections can not be avoided at all. Perflectly Matched Layer (PML) are layers of absorbing medium which can be added at the simulation edges in order to significantly damp both outgoing and reflected waves, thus effectively playing the role of an ABC. They are able to absorb waves and prevent reflections for all angles and frequencies at a modest computational cost. It increases the simulation accuracy and negates the need of oversizing the simulation usually imposed by ABC and leading to a waste of computational resources and power. PML for finite-difference time-domain (FDTD) schemes in Particle-In-cell (PIC) codes are presented for both Maxwell's equations and, for the first time, the envelope wave equation. Being of the second order, the latter requires significant evolutions with respect to the former. It applies in particular to simulations of lasers propagating in plasmas using the reduced Complex Envelope model. The implementation is done in the open source code Smilei for both Cartesian and azimuthal modes (AM) decomposition geometries.

We introduce ClarQ-LLM, an evaluation framework consisting of bilingual English-Chinese conversation tasks, conversational agents and evaluation metrics, designed to serve as a strong benchmark for assessing agents' ability to ask clarification questions in task-oriented dialogues. The benchmark includes 31 different task types, each with 10 unique dialogue scenarios between information seeker and provider agents. The scenarios require the seeker to ask questions to resolve uncertainty and gather necessary information to complete tasks. Unlike traditional benchmarks that evaluate agents based on fixed dialogue content, ClarQ-LLM includes a provider conversational agent to replicate the original human provider in the benchmark. This allows both current and future seeker agents to test their ability to complete information gathering tasks through dialogue by directly interacting with our provider agent. In tests, LLAMA3.1 405B seeker agent managed a maximum success rate of only 60.05\%, showing that ClarQ-LLM presents a strong challenge for future research.

Generating random bit streams is required in various applications, most notably cyber-security. Ensuring high-quality and robust randomness is crucial to mitigate risks associated with predictability and system compromise. True random numbers provide the highest unpredictability levels. However, potential biases in the processes exploited for the random number generation must be carefully monitored. This paper reports the implementation and characterization of an on-line procedure for the detection of anomalies in a true random bit stream. It is based on the NIST Adaptive Proportion and Repetition Count tests, complemented by statistical analysis relying on the Monobit and RUNS. The procedure is firmware implemented and performed simultaneously with the bit stream generation, and providing as well an estimate of the entropy of the source. The experimental validation of the approach is performed upon the bit streams generated by a quantum, silicon-based entropy source., Comment: 9 pages, 12 figures, 3 tables, submitted at IEEE CSR 2024

Harnessing the wave-nature of charge carriers in solid state devices, electron optics investigates and exploits coherent phenomena, in analogy with optics and photonics. Typically, this requires complex electronic devices leveraging macroscopically coherent charge transport in two-dimensional electron gases and superconductors. Here, collective coherent effects are induced in a simple counterintuitive architecture by defect engineering. Deterministically introduced lattice defects in graphene enable the phase coherent charge transport by playing the role of potential barriers, instead of scattering centres as conventionally considered. Thus, graphene preserves its quasi-ballistic quantum transport and can support phase-matched charge carrier waves. Based on this approach, multiple electronic Fabry-P\`erot cavities are formed by creating periodically alternating defective and pristine nano-stripes through low energy electron-beam irradiation. Indeed, defective stripes behave as partially reflecting mirrors and resonantly confine the charge carrier waves within the pristine areas, giving rise to Fabry-P\`erot resonant modes. These modes experimentally manifest as sheet resistance oscillations, as also confirmed by Landauer-B\"uttiker simulations. Moreover, these coherent phenomena survive up to 30 K for both polarities of charge carriers, contrarily to traditional monopolar electrostatically created Fabry-P\`erot interferometers. Our study positions defective graphene as an innovative platform for coherent electronic devices, with potential applications in nano and quantum technologies., Comment: 29 pages, 4 figures main text, 8 figures supplementary

A successful theory of star formation should predict the number of objects as a function of their mass produced through star-forming events. Previous studies in star-forming regions and the solar neighborhood identify a mass function increasing from the hydrogen-burning limit down to about 10 M$_{J}$. Theory predicts a limit to the fragmentation process, providing a natural turnover in the mass function down to the opacity limit of turbulent fragmentation thought to be 2-10 M$_{J}$. Programs to date have not been sensitive enough to probe the hypothesized opacity limit of fragmentation. Here we present the first identification of a turnover in the initial mass function below 12 M$_{J}$ within NGC 2024, a young star-forming region. With JWST/NIRCam deep exposures across 0.7-5 {\mu}m, we identified several free floating objects down to ~ 3 M$_{J}$ with sensitivity to 0.5 M$_{J}$. We present evidence for a double power law model increasing from about 60 M$_{J}$ to roughly 12 M$_{J}$, consistent with previous studies, followed by a decrease down to 0.5 M$_{J}$. Our results support the predictions of star and brown dwarf formation theory, identifying the theoretical turnover in the mass function and suggest the fundamental limit of turbulent fragmentation near 3 M$_{J}$., Comment: 16 pages, 4 figures, submitted 6 September 2024

We study anomalous dissipation in the context of passive scalars and we construct a two-dimensional autonomous divergence-free velocity field in $C^\alpha$ (with $\alpha \in (0,1)$ arbitrary but fixed) which exhibits anomalous dissipation. Our proof employs the fluctuation-dissipation formula, which links spontaneous stochasticity with anomalous dissipation. Therefore, we address the issue of anomalous dissipation by showing that the variance of stochastic trajectories, in the zero noise limit, remains positive. Based on this result, we answer Question 2.2 and Question 2.3 in [Bru\`{e} & De Lellis '22] regarding anomalous dissipation for the forced three-dimensional Navier-Stokes equations., Comment: 39 pages, 8 figures

We report the observation of Shapiro steps in a periodically driven Josephson junction between strongly-interacting Fermi superfluids of ultracold atoms. We observe quantized plateaus in the current-potential characteristics, the height and width of which mirror the external drive frequency and the junction nonlinear response. Direct measurements of the current-phase relationship showcase how Shapiro steps arise from the synchronization between the relative phase of the two reservoirs and the external drive. Such mechanism is further supported by the detection of periodic phase-slippage processes, in the form of vortex-antivortex pairs. Our results are corroborated by a circuital model and numerical simulations, overall providing a clear understanding of Shapiro dynamics in atomic Fermi superfluids. Our work demonstrates phase-coherent and synchronization effects in driven strongly-interacting superfluids, opening prospects for studying emergent non-equilibrium dynamics in quantum many-body systems under external drives.

Most domains of science are experiencing a paradigm shift due to the advent of a new generation of instruments and detectors which produce data and data streams at an unprecedented rate. The scientific exploitation of these data, namely Data Driven Discovery, requires interoperability, massive and optimal use of Artificial Intelligence methods in all steps of the data acquisition, processing and analysis, the access to large and distributed computing HPC facilities, the implementation and access to large simulations and interdisciplinary skills that usually are not provided by standard academic curricula. Furthermore, to cope with this data deluge, most communities have leveraged solutions and tools originally developed by large corporations for purposes other than scientific research and accepted compromises to adapt them to their specific needs. Through the presentation of several astrophysical use cases, we show how the Data Driven based solutions could represent the optimal playground to achieve the multi-disciplinary methodological approach., Comment: To be published by World Scientific as a proceeding of the 17th Marcel Grossmann meeting. Editors: Remo Ruffini and Gregory Vereshchagin. 20 pages, 13 figures

PPG-based Blood Pressure (BP) estimation is a challenging biosignal processing task for low-power devices such as wearables. State-of-the-art Deep Neural Networks (DNNs) trained for this task implement either a PPG-to-BP signal-to-signal reconstruction or a scalar BP value regression and have been shown to outperform classic methods on the largest and most complex public datasets. However, these models often require excessive parameter storage or computational effort for wearable deployment, exceeding the available memory or incurring too high latency and energy consumption. In this work, we describe a fully-automated DNN design pipeline, encompassing HW-aware Neural Architecture Search (NAS) and Quantization, thanks to which we derive accurate yet lightweight models, that can be deployed on an ultra-low-power multicore System-on-Chip (SoC), GAP8. Starting from both regression and signal-to-signal state-of-the-art models on four public datasets, we obtain optimized versions that achieve up to 4.99% lower error or 73.36% lower size at iso-error. Noteworthy, while the most accurate SoA network on the largest dataset can not fit the GAP8 memory, all our optimized models can; our most accurate DNN consumes as little as 0.37 mJ while reaching the lowest MAE of 8.08 on Diastolic BP estimation.

Euclid will cover over 14000 $deg^{2}$ with two optical and near-infrared spectro-photometric instruments, and is expected to detect around ten million active galactic nuclei (AGN). This unique data set will make a considerable impact on our understanding of galaxy evolution and AGN. In this work we identify the best colour selection criteria for AGN, based only on Euclid photometry or including ancillary photometric observations, such as the data that will be available with the Rubin legacy survey of space and time (LSST) and observations already available from Spitzer/IRAC. The analysis is performed for unobscured AGN, obscured AGN, and composite (AGN and star-forming) objects. We make use of the spectro-photometric realisations of infrared-selected targets at all-z (SPRITZ) to create mock catalogues mimicking both the Euclid Wide Survey (EWS) and the Euclid Deep Survey (EDS). Using these catalogues we estimate the best colour selection, maximising the harmonic mean (F1) of completeness and purity. The selection of unobscured AGN in both Euclid surveys is possible with Euclid photometry alone with F1=0.22-0.23, which can increase to F1=0.43-0.38 if we limit at z>0.7. Such selection is improved once the Rubin/LSST filters (a combination of the u, g, r, or z filters) are considered, reaching F1=0.84 and 0.86 for the EDS and EWS, respectively. The combination of a Euclid colour with the [3.6]-[4.5] colour, which is possible only in the EDS, results in an F1-score of 0.59, improving the results using only Euclid filters, but worse than the selection combining Euclid and LSST. The selection of composite ($f_{{\rm AGN}}$=0.05-0.65 at 8-40 $\mu m$) and obscured AGN is challenging, with F1<0.3 even when including ancillary data. This is driven by the similarities between the broad-band spectral energy distribution of these AGN and star-forming galaxies in the wavelength range 0.3-5 $\mu m$., Comment: 25 pages, 28 figures, accepted for publication on A&A

Quantum Key Distribution (QKD) promises to revolutionize the field of security in communication, with applications ranging from state secrets to personal data, making it a key player in the ongoing battle against cyber threats. Reference-Frame-Independent (RFI) QKD aims to simplify QKD implementations by allowing to reduce the requirements of alignment on a shared reference frame. This is done by performing two mutually unbiased measurements on the control states. In this work, we present a novel fully passive receiver for time-bin encoded RFI-QKD. Conversion of time-bin to polarization is employed to perform the required quantum measurement in a fully passive manner. Furthermore, to overcome experimental errors, we retrieved a complete description of our measurement apparatus by employing a recently introduced Quantum Detector Self-Characterization technique, without performing tomographic studies on the detection stage. In fact, the security analysis carried out in this work uses experimentally retrieved Positive Operator Valued Measurements, which consider our receiver defects, substituting the ideal expected operators and thus increasing the overall level of secrecy. Lastly, we conducted a proof-of-principle experiment that validated the feasibility of our method and its applicability to QKD applications., Comment: 13 pages, 6 figures, 1 table

Vision-language models (VLMs) have demonstrated remarkable performance across various visual tasks, leveraging joint learning of visual and textual representations. While these models excel in zero-shot image tasks, their application to zero-shot video action recognition (ZSVAR) remains challenging due to the dynamic and temporal nature of actions. Existing methods for ZS-VAR typically require extensive training on specific datasets, which can be resource-intensive and may introduce domain biases. In this work, we propose Text-Enhanced Action Recognition (TEAR), a simple approach to ZS-VAR that is training-free and does not require the availability of training data or extensive computational resources. Drawing inspiration from recent findings in vision and language literature, we utilize action descriptors for decomposition and contextual information to enhance zero-shot action recognition. Through experiments on UCF101, HMDB51, and Kinetics-600 datasets, we showcase the effectiveness and applicability of our proposed approach in addressing the challenges of ZS-VAR., Comment: accepted to ICPR 2024

The low-frequency component of the upcoming Square Kilometre Array Observatory (SKA-Low) will be sensitive enough to construct 3D tomographic images of the 21-cm signal distribution during reionization. However, foreground contamination poses challenges for detecting this signal, and image recovery will heavily rely on effective mitigation methods. We introduce \texttt{SERENEt}, a deep-learning framework designed to recover the 21-cm signal from SKA-Low's foreground-contaminated observations, enabling the detection of ionized (HII) and neutral (HI) regions during reionization. \texttt{SERENEt} can recover the signal distribution with an average accuracy of 75 per cent at the early stages ($\overline{x}_\mathrm{HI}\simeq0.9$) and up to 90 per cent at the late stages of reionization ($\overline{x}_\mathrm{HI}\simeq0.1$). Conversely, HI region detection starts at 92 per cent accuracy, decreasing to 73 per cent as reionization progresses. Beyond improving image recovery, \texttt{SERENEt} provides cylindrical power spectra with an average accuracy exceeding 93 per cent throughout the reionization period. We tested \texttt{SERENEt} on a 10-degree field-of-view simulation, consistently achieving better and more stable results when prior maps were provided. Notably, including prior information about HII region locations improved 21-cm signal recovery by approximately 10 per cent. This capability was demonstrated by supplying \texttt{SERENEt} with ionizing source distribution measurements, showing that high-redshift galaxy surveys of similar observation fields can optimize foreground mitigation and enhance 21-cm image construction., Comment: 16 pages, 14 figures, 1 table

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

The optimisation of the plasma target design for high quality beam laser-driven plasma injector electron source relies on numerical parametric studies using Particle in Cell (PIC) codes. The common input parameters to explore are laser characteristics and plasma density profiles extracted from computational fluid dynamic studies compatible with experimental measurements of target plasma density profiles. We demonstrate the construction of surrogate models using machine learning technique for a laser-plasma injector (LPI) electron source based on more than 12000 simulations of a laser wakefield acceleration performed for sparsely spaced input parameters [1]. Surrogate models are very interesting for LPI design and optimisation because they are much faster than PIC simulations. We develop and compare the performance of three surrogate models, namely, Gaussian processes (GP), multilayer perceptron (MLP), and decision trees (DT). We then use the best surrogate model to quickly find optimal working points to get a selected electron beam energy, charge and energy spread using different methods, namely random search, Bayesian optimisation and multi-objective Bayesian optimisation, Comment: 10 pages, 13 figures

We investigate the Maximum Cut (MaxCut) problem on different graph classes with the Quantum Approximate Optimization Algorithm (QAOA) using symmetries. In particular, heuristics on the relationship between graph symmetries and the approximation ratio achieved by a QAOA simulation are considered. To do so, we first solve the MaxCut problem on well-known graphs, then we consider a simple and controllable perturbation of the graph and find again the approximate MaxCut with the QAOA. Through an analysis of the spectrum of the graphs and their perturbations, as well as a careful study of the associated automorphism groups, we aim to extract valuable insights into how symmetry impacts the performance of QAOA. These insights can then be leveraged to heuristically reduce the quantum circuit complexity, the number of training steps, or the number of parameters involved, thus enhancing the efficiency and effectiveness of QAOA-based solutions., Comment: 39 pages, 11 figures, 2 tables

The physical trigger powering supernovae following the core collapse of massive stars is believed to involve a neutron star (NS) or a black hole (BH), depending largely on progenitor mass. A potentially distinct signature is a long-duration gravitational wave (GW) burst from BH central engines by their ample energy reservoir $E_J$ in angular momentum, far more so than an NS can provide. A natural catalyst for this radiation is surrounding high-density matter in the form of a non-axisymmetric disk or torus. Here, we derive a detailed outlook on LVK probes of core-collapse supernovae CC-SNe during the present observational run O4 based on their event rate, an association with normal long GRBs and mass-scaling of GW170817B/GRB170817A. For BH central engines of mass $M$, GW170817B predicts a descending GW-chirp of energy ${\cal E}_{GW}\simeq 3.5\% M_\odot c^2 \left(M/M_0\right)$ at frequency $f_{GW}\lesssim 700\,{\rm Hz}\left(M_0/M\right)$, where $M_0\simeq 2.8\,M_\odot$. For a few tens of events per year well into the Local Universe within 50-100Mpc, probes at the detector-limited sensitivity are expected to break the degeneracy between their NS or BH central engines {by GW calorimetry., Comment: 20 pages, 9 figures, to appear in ApJL

Detecting a compact subsolar object would have profound implications in physics, the reach of which depends on the nature of the object. Here we explore such consequences for a putative subsolar-mass gravitational wave event detected by the LIGO-Virgo-KAGRA Collaboration. We forecast that the nature of a subsolar binary (made of light neutron stars, primordial black holes, or more exotic compact objects) can be inferred with a great statistical confidence level already during the ongoing fourth observing run, based on the large tidal deformability effects on the signal. The detection of a primordial black hole would have implications for cosmology and dark matter scenarios, while the measurement of the tidal deformability of a subsolar neutron star could rule out or confirm the existence of strange stars made of quarks., Comment: 5+4 pages, 3 figures

We report the identification of two galaxy overdensities at $z\sim5.7$ in the sightline of the galaxy cluster Abell 2744. These overdensities consist of 25 and 17 member galaxies, spectroscopically confirmed with JWST NIRSpec/MSA and NIRCam/WFSS. Each overdensity has a total stellar mass of $\sim2\times10^{10} M_\odot$ and a star formation rate of $\sim200 M_\odot$/yr within a central region of radius $R=2$ Mpc (physical). The sensitive PRISM spectra allow us to identify six galaxies that show weak Ha+[NII] emissions within the overdensities ($27\pm6\%$), whereas the fraction of such galaxies is found significantly lower ($6\pm2\%$) in field samples of the equivalent redshift range. These weak emission line galaxies, dubbed as wELGs, exhibit a strong continuum break at $4000$AA rest-frame, a characteristic feature of evolved stellar populations. The high observed fraction of wELGs in the two overdensities is consistent with the idea that high-density environments are an ideal site where galaxies can accelerate their evolutionary pace compared to field analogs. Our study pinpoints an early onset of environmental effects, already important within one billion years after the Big Bang, and provides a complementary perspective on the emergence of quenched, massive galaxies at lower redshifts. Potential contributions from black hole accretion feedback to the reduction of star formation activity are discussed, but the connection to the local environments remains unclear., Comment: Submitted to ApJ

Coupling is an essential mechanism that drives complexity in natural systems, transforming single, non-interacting elements into intricate networks with rich physical properties. Here, we demonstrate a chip-scale coupled laser system that exhibits complex optical states impossible to achieve in an uncoupled system. We show that a pair of coupled semiconductor ring lasers spontaneously forms a frequency comb consisting of the hybridized modes of its coupled cavity, exhibiting a large number of phase-locked tones that anticross with one another. Experimental coherent waveform reconstruction reveals that the hybridized frequency comb manifests itself as pairs of bright and dark picosecond-long solitons circulating simultaneously. The dark and bright solitons exit the coupled cavity at the same time, leading to breathing bright solitons temporally overlapped with their dark soliton counterparts - a state inaccessible for a single, free-running laser. Our results demonstrate that the rules that govern allowable states of light can be broken by simply coupling elements together, paving the way for the design of more complex networks of coupled on-chip lasers., Comment: 13 pages, 3 figures

Deep-learning-based identity management systems, such as face authentication systems, are vulnerable to adversarial attacks. However, existing attacks are typically designed for single-task purposes, which means they are tailored to exploit vulnerabilities unique to the individual target rather than being adaptable for multiple users or systems. This limitation makes them unsuitable for certain attack scenarios, such as morphing, universal, transferable, and counter attacks. In this paper, we propose a multi-task adversarial attack algorithm called MTADV that are adaptable for multiple users or systems. By interpreting these scenarios as multi-task attacks, MTADV is applicable to both single- and multi-task attacks, and feasible in the white- and gray-box settings. Furthermore, MTADV is effective against various face datasets, including LFW, CelebA, and CelebA-HQ, and can work with different deep learning models, such as FaceNet, InsightFace, and CurricularFace. Importantly, MTADV retains its feasibility as a single-task attack targeting a single user/system. To the best of our knowledge, MTADV is the first adversarial attack method that can target all of the aforementioned scenarios in one algorithm., Comment: Accepted by ACM Transactions on Multimedia Computing, Communications, and Applications

We present a comprehensive study of the X-ray spectral properties of the accreting millisecond pulsar IGR J17498$-$2921 during its 2023 outburst. Similar to other accreting millisecond pulsars, the broad-band spectral emission observed quasi-simultaneously by NICER and NuSTAR is well described by an absorbed Comptonized emission with an electron temperature of $\sim$17 keV plus a disk reflection component. The broadening of the disk reflection spectral features, such as a prominent iron emission line at 6.4-6.7 keV, is consistent with the relativistic motion of matter in a disk truncated at $\sim$$21 \, \mathrm{R_g}$ from the source, near the Keplerian co-rotation radius. From the high-cadence monitoring data obtained with NICER, we observe that the evolution of the photon index and the temperature of seed photons tracks variations in the X-ray flux. This is particularly evident close to a sudden $\sim$-0.25 cycles jump in the pulse phase, which occurs immediately following an X-ray flux flare and a drop in the pulse amplitude below the $3\sigma$ detection threshold. We also report on the non-detection of optical pulsations with TNG/SiFAP2 from the highly absorbed optical counterpart., Comment: 14 pages, 5 figures. Submitted to A&A

Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs), aimed at overcoming the computational and scalability challenges inherent in classical GNNs that are powerful tools for analyzing data with complex relational structures but suffer from limitations such as high computational complexity and over-smoothing in large-scale applications. Quantum computing, leveraging principles like superposition and entanglement, offers a pathway to enhanced computational capabilities. This paper critically reviews the state-of-the-art in QGNNs, exploring various architectures. We discuss their applications across diverse fields such as high-energy physics, molecular chemistry, finance and earth sciences, highlighting the potential for quantum advantage. Additionally, we address the significant challenges faced by QGNNs, including noise, decoherence, and scalability issues, proposing potential strategies to mitigate these problems. This comprehensive review aims to provide a foundational understanding of QGNNs, fostering further research and development in this promising interdisciplinary field., Comment: 21 pages, 9 figures, 2 tables. arXiv admin note: text overlap with arXiv:1909.12264 by other authors

The cognitive essence of humans is deeply intertwined with the concept of animacy, which plays an essential role in shaping their memory, vision, and multi-layered language understanding. Although animacy appears in language via nuanced constraints on verbs and adjectives, it is also learned and refined through extralinguistic information. Similarly, we assume that the LLMs' limited abilities to understand natural language when processing animacy are motivated by the fact that these models are trained exclusively on text. Hence, the question this paper aims to answer arises: can LLMs, in their digital wisdom, process animacy in a similar way to what humans would do? We then propose a systematic analysis via prompting approaches. In particular, we probe different LLMs by prompting them using animate, inanimate, usual, and stranger contexts. Results reveal that, although LLMs have been trained predominantly on textual data, they exhibit human-like behavior when faced with typical animate and inanimate entities in alignment with earlier studies. Hence, LLMs can adapt to understand unconventional situations by recognizing oddities as animated without needing to interface with unspoken cognitive triggers humans rely on to break down animations.

Large Language Models (LLMs) represent a significant advancement in artificial intelligence, finding applications across various domains. However, their reliance on massive internet-sourced datasets for training brings notable privacy issues, which are exacerbated in critical domains (e.g., healthcare). Moreover, certain application-specific scenarios may require fine-tuning these models on private data. This survey critically examines the privacy threats associated with LLMs, emphasizing the potential for these models to memorize and inadvertently reveal sensitive information. We explore current threats by reviewing privacy attacks on LLMs and propose comprehensive solutions for integrating privacy mechanisms throughout the entire learning pipeline. These solutions range from anonymizing training datasets to implementing differential privacy during training or inference and machine unlearning after training. Our comprehensive review of existing literature highlights ongoing challenges, available tools, and future directions for preserving privacy in LLMs. This work aims to guide the development of more secure and trustworthy AI systems by providing a thorough understanding of privacy preservation methods and their effectiveness in mitigating risks., Comment: GitHub repository: https://github.com/michele17284/Awesome-Privacy-Preserving-LLMs

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer parameters, resulting in an extremely interpretable architecture rooted in the underlying physics of the problem. The architectural novelties arise from three pivotal aspects. Firstly, we employ an embedding updating method that is analogous to classical Graph Neural Networks, therefore bridging the classical-quantum gap. Secondly, each layer is devoted to capturing interactions of distinct orders, aligning with the physical properties of the system. Lastly, we harness SWAP gates to emulate the problem's inherent symmetry, a novel strategy not found currently in the literature. The obtained results in the considered experiments are encouraging to lay the foundation for continued research in this field., Comment: 20 pages, 10 figures, 3 tables