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Local gravitational instability (LGI) is considered crucial for regulating star formation and gas turbulence in galaxy discs, especially at high redshift. Instability criteria usually assume infinitesimally thin discs or rely on approximations to include the stabilising effect of the gas disc thickness. We test a new 3D instability criterion for rotating gas discs that are vertically stratified in an external potential. This criterion reads $Q_{\rm3D}<1$, where $Q_{\rm3D}$ is the 3D analogue of the Toomre parameter $Q$. The advantage of $Q_{\rm3D}$ is that it allows us to study LGI in and above the galaxy midplane in a rigorous and self-consistent way. We apply the criterion to a sample of 44 star-forming galaxies at $0\lesssim\mathrm{z}\lesssim5$ hosting rotating discs of cold gas. The sample is representative of galaxies on the main sequence at $\mathrm{z}\approx 0$ and includes massive star-forming and starburst galaxies at $1\lesssim\mathrm{z}\lesssim5$. For each galaxy, we first apply the Toomre criterion for infinitesimally thin discs, finding 10 unstable systems. We then obtain maps of $Q_{\rm 3D}$ from a 3D model of the gas disc derived in the combined potential of dark matter, stars and the gas itself. According to the 3D criterion, two galaxies with $Q<1$ show no evidence of instability and the unstable regions that are 20% smaller than those where $Q<1$. No unstable disc is found at $0\lesssim\mathrm{z}\lesssim 1$, while $\approx 60$% of the systems at $2\lesssim\mathrm{z}\lesssim5$ are locally unstable. In these latter, a relatively small fraction of the total gas ($\approx 30$%) is potentially affected by the instability. Our results disfavour LGI as the main regulator of star formation and turbulence in moderately star-forming galaxies in the present-day Universe. LGI likely becomes important at high redshift, but the input by other mechanisms seems required [abridged], Comment: 19 pages (6 figures, 2 tables) + appendixes, accepted by Astronomy & Astrophysics

In this article, we investigate some issues related to the quantification of uncertainties associated with the electrical properties of graphene nanoribbons. The approach is suited to understand the effects of missing information linked to the difficulty of fixing some material parameters, such as the band gap, and the strength of the applied electric field. In particular, we focus on the extension of particle Galerkin methods for kinetic equations in the case of the semiclassical Boltzmann equation for charge transport in graphene nanoribbons with uncertainties. To this end, we develop an efficient particle scheme which allows us to parallelize the computation and then, after a suitable generalization of the scheme to the case of random inputs, we present a Galerkin reformulation of the particle dynamics, obtained by means of a generalized polynomial chaos approach, which allows the reconstruction of the kinetic distribution. As a consequence, the proposed particle-based scheme preserves the physical properties and the positivity of the distribution function also in the presence of a complex scattering in the transport equation of electrons. The impact of the uncertainty of the band gap and applied field on the electrical current is analyzed., Comment: 26 pages, 6 Figures, 4 Tables

We study the measure-valued death state and local sensitivity analysis of the Winfree model and its mean-field counterpart with uncertain high-order couplings. The Winfree model is the first mathematical model for synchronization, and it can cast as the effective approximation of the pulse-coupled model for synchronization, and it exhibits diverse asymptotic patterns depending on system parameters and initial data. For the proposed models, we present several frameworks leading to oscillator death in terms of system parameters and initial data, and the propagation of regularity in random space. We also present several numerical tests and compare them with analytical results.

The emergence of 6G-enabled Internet of Vehicles (IoV) promises to revolutionize mobility and connectivity, integrating vehicles into a mobile Internet-of-Things (IoT)-oriented wireless sensor network (WSN). 5G technologies and mobile edge computing further support this vision by facilitating real-time connectivity and empowering massive access to the Internet. In this context, IoT-oriented WSNs play a crucial role in intelligent transportation systems, offering affordable alternatives for traffic monitoring and management. This paper's contribution is twofold: (i) surveying state-of-the-art model-based techniques for efficient sensor selection in traffic flow monitoring, emphasizing challenges of sensor placement; and (ii) advocating for data-driven methodologies to enhance sensor deployment efficacy and traffic modeling accuracy. Further considerations underscore the importance of data-driven approaches for adaptive transportation systems aligned with the IoV paradigm., Comment: 5 pages, 0 figures, submitted to the EuCNC & 6G Summit, Antwerp, Belgium, 3-6 June, 2024

Video anomaly detection (VAD) aims to temporally locate abnormal events in a video. Existing works mostly rely on training deep models to learn the distribution of normality with either video-level supervision, one-class supervision, or in an unsupervised setting. Training-based methods are prone to be domain-specific, thus being costly for practical deployment as any domain change will involve data collection and model training. In this paper, we radically depart from previous efforts and propose LAnguage-based VAD (LAVAD), a method tackling VAD in a novel, training-free paradigm, exploiting the capabilities of pre-trained large language models (LLMs) and existing vision-language models (VLMs). We leverage VLM-based captioning models to generate textual descriptions for each frame of any test video. With the textual scene description, we then devise a prompting mechanism to unlock the capability of LLMs in terms of temporal aggregation and anomaly score estimation, turning LLMs into an effective video anomaly detector. We further leverage modality-aligned VLMs and propose effective techniques based on cross-modal similarity for cleaning noisy captions and refining the LLM-based anomaly scores. We evaluate LAVAD on two large datasets featuring real-world surveillance scenarios (UCF-Crime and XD-Violence), showing that it outperforms both unsupervised and one-class methods without requiring any training or data collection., Comment: CVPR 2024. Project website at https://lucazanella.github.io/lavad/

Heterogeneous networks have emerged as a popular solution for accommodating the growing number of connected devices and increasing traffic demands in cellular networks. While offering broader coverage, higher capacity, and lower latency, the escalating energy consumption poses sustainability challenges. In this paper a novel optimization approach for OFDMA heterogeneous networks is proposed to minimize transmission power while respecting individual users throughput constraints. The problem is formulated as a mixed integer geometric program, and optimizes at once multiple system variables such as user association, working bandwidth, and base stations transmission powers. Crucially, the proposed approach becomes a convex optimization problem when user-base station associations are provided. Evaluations in multiple realistic scenarios from the production mobile network of a major European operator and based on precise channel gains and throughput requirements from measured data validate the effectiveness of the proposed approach. Overall, our original solution paves the road for greener connectivity by reducing the energy footprint of heterogeneous mobile networks, hence fostering more sustainable communication systems.

In this work we propose and investigate a strategy to prevent consensus in kinetic models for opinion formation. We consider a large interacting agent system, and assume that agent interactions are driven by compromise as well as self-thinking dynamics and also modulated by an underlying static social network. This network structure is included using so-called graphons, which modulate the interaction frequency in the corresponding kinetic formulation. We then derive the corresponding limiting Fokker Planck equation, and analyze its large time behavior. This microscopic setting serves as a starting point for the proposed control strategy, which steers agents away from mean opinion and is characterised by a suitable penalization depending on the properties of the graphon. We show that this minimalist approach is very effective by analyzing the quasi-stationary solutions mean-field model in a plurality of graphon structures. Several numerical experiments are also provided to show the effectiveness of the approach in preventing the formation of consensus steering the system towards a declustered state., Comment: Changed to align to the in-print version

We study general coordinate-wise MCMC schemes (such as Metropolis-within-Gibbs samplers), which are commonly used to fit Bayesian non-conjugate hierarchical models. We relate their convergence properties to the ones of the corresponding (potentially not implementable) Gibbs sampler through the notion of conditional conductance. This allows us to study the performances of popular Metropolis-within-Gibbs schemes for non-conjugate hierarchical models, in high-dimensional regimes where both number of datapoints and parameters increase. Given random data-generating assumptions, we establish dimension-free convergence results, which are in close accordance with numerical evidences. Applications to Bayesian models for binary regression with unknown hyperparameters and discretely observed diffusions are also discussed. Motivated by such statistical applications, auxiliary results of independent interest on approximate conductances and perturbation of Markov operators are provided.

Clusters and their progenitors (protoclusters) at z = 2-4, the peak epoch of star formation, are ideal laboratories to study the formation process of both the clusters themselves and their member galaxies. However, a complete census of their member galaxies has been challenging due to observational difficulties. Here we present new JWST/NIRCam observations targeting the distant cluster CLJ1001 at z = 2.51 from the COSMOS-Web program, which, in combination with previous narrowband imaging targeting H-alpha emitters and deep millimeter surveys of CO emitters, provide a complete view of massive galaxy assembly in CLJ1001. In particular, JWST reveals a population of massive, extremely red cluster members in the long-wavelength bands that were invisible in previous Hubble Space Telescope (HST)/F160W imaging (HST-dark members). Based on this highly complete spectroscopic sample of member galaxies, we show that the spatial distribution of galaxies in CLJ1001 exhibits a strong central concentration, with the central galaxy density already resembling that of low-z clusters. Moreover, we reveal a "top-heavy" stellar mass function for the star-forming galaxies (SFGs), with an overabundance of massive SFGs piled up in the cluster core. These features strongly suggest that CLJ1001 is caught in a rapid transition, with many of its massive SFGs likely soon becoming quiescent. In the context of cluster formation, these findings suggest that the earliest clusters form from the inside out and top to bottom, with the massive galaxies in the core assembling first, followed by the less massive ones in the outskirts., Comment: 14 pages, 9 figures, 1 table, published by ApJL

Observations at UV and optical wavelengths have revealed that galaxies at z~1-4 host star-forming regions, dubbed "clumps", which are believed to form due to the fragmentation of gravitationally unstable, gas-rich disks. However, the detection of the parent molecular clouds that give birth to such clumps is still possible only in a minority of galaxies, mostly at z~1. We investigated the [CII] and dust morphology of a z~3.4 lensed galaxy hosting four clumps detected in the UV continuum. We aimed to observe the [CII] emission of individual clumps that, unlike the UV, is not affected by dust extinction, to probe their nature and cold gas content. We conducted ALMA observations probing scales down to ~300 pc and detected three [CII] clumps. One (dubbed "NE") coincides with the brightest UV clump, while the other two ("SW" and "C") are not detected in the UV continuum. We do not detect the dust continuum. We converted the [CII] luminosity of individual clumps into molecular gas mass and found Mmol~10^8 Msun. By complementing it with the star formation rate (SFR) estimate from the UV continuum, we estimated the gas depletion time (tdep) of clumps and investigated their location in the Schmidt-Kennicutt plane. While the NE clump has a short tdep=0.16 Gyr, comparable with high-redshift starbursts, the SW and C clumps instead have longer tdep>0.65 Gyr and are likely probing the initial phases of star formation. The lack of dust continuum detection is consistent with the blue UV continuum slope estimated for this galaxy (beta~-2.5) and it indicates that dust inhomogeneities do not significantly affect the detection of UV clumps in this target. We pushed the observation of the cold gas content of individual clumps up to z~3.4 and showed that the [C II] line emission is a promising tracer of molecular clouds at high redshift, allowing the detection of clumps with a large range of depletion times., Comment: Accepted for publication in A&A

Let $18$ only three families of scattered polynomials in $\mathbb F_{q^n}[X]$ are known: $(i)$~monomials of pseudoregulus type, $(ii)$~binomials of Lunardon-Polverino type, and $(iii)$~a family of quadrinomials defined in [1,10] and extended in [8,13]. In this paper we prove that the polynomial $\varphi_{m,q^J}=X^{q^{J(t-1)}}+X^{q^{J(2t-1)}}+m(X^{q^J}-X^{q^{J(t+1)}})\in\mathbb F_{q^{2t}}[X]$, $q$ odd, $t\ge3$ is R-$q^t$-partially scattered for every value of $m\in\mathbb F_{q^t}^*$ and $J$ coprime with $2t$. Moreover, for every $t>4$ and $q>5$ there exist values of $m$ for which $\varphi_{m,q}$ is scattered and new with respect to the polynomials mentioned in $(i)$, $(ii)$ and $(iii)$ above. The related linear sets are of $\Gamma L$-class at least two.

Machine learning models trained with differentially-private (DP) algorithms such as DP-SGD enjoy resilience against a wide range of privacy attacks. Although it is possible to derive bounds for some attacks based solely on an $(\varepsilon,\delta)$-DP guarantee, meaningful bounds require a small enough privacy budget (i.e., injecting a large amount of noise), which results in a large loss in utility. This paper presents a new approach to evaluate the privacy of machine learning models against specific record-level threats, such as membership and attribute inference, without the indirection through DP. We focus on the popular DP-SGD algorithm, and derive simple closed-form bounds. Our proofs model DP-SGD as an information theoretic channel whose inputs are the secrets that an attacker wants to infer (e.g., membership of a data record) and whose outputs are the intermediate model parameters produced by iterative optimization. We obtain bounds for membership inference that match state-of-the-art techniques, whilst being orders of magnitude faster to compute. Additionally, we present a novel data-dependent bound against attribute inference. Our results provide a direct, interpretable, and practical way to evaluate the privacy of trained models against specific inference threats without sacrificing utility.

Astronomy is a discipline primarily reliant on visual data. However, alternative data representation techniques are being explored, in particular ''sonification'', namely, the representation of data into sound. While there is increasing interest in the astronomical community in using sonification in research and educational contexts, its full potential is still to be explored. This study measured the performance of astronomers and non-astronomers to detect a transit-like feature in time series data (i.e., light curves), that were represented visually or auditorily, adopting different data-to-sound mappings. We also assessed the bias that participants exhibited in the different conditions. We simulated the data of 160 light curves with different signal-to-noise ratios (SNR). We represented them as visual plots or auditory streams with different sound parameters to represent brightness: pitch, duration, or the redundant duration & pitch. We asked the participants to identify the presence of transit-like features in these four conditions in a session that included an equal number of stimuli with and without transit-like features. With auditory stimuli, participants detected transits with performances above the chance level. However, visual stimuli led to overall better performances compared to auditory stimuli and astronomers outperformed non-astronomers. Visualisations led to a conservative response bias (reluctance to answer ''yes, there is a transit''), whereas sonifications led to more liberal responses (proneness to respond ''yes, there is a transit''). Overall, this study contributes to understanding how different representations (visual or auditory) and sound mappings (pitch, duration, duration & pitch) of time series data affect detection accuracy and biases., Comment: Accepted for publication in AAJ. 10 pages, 4 figures

In recent years, groundbreaking advancements in Generative Artificial Intelligence (GenAI) have triggered a transformative paradigm shift, significantly influencing various domains. In this work, we specifically explore an integrated approach, leveraging advanced techniques in GenAI and computer vision emphasizing image manipulation. The methodology unfolds through several stages, including depth estimation, the creation of inpaint masks based on depth information, the generation and replacement of backgrounds utilizing Stable Diffusion in conjunction with Latent Consistency Models (LCMs), and the subsequent replacement of clothes and application of aesthetic changes through an inpainting pipeline. Experiments conducted in this study underscore the methodology's efficacy, highlighting its potential to produce visually captivating content. The convergence of these advanced techniques allows users to input photographs of individuals and manipulate them to modify clothing and background based on specific prompts without manually input inpainting masks, effectively placing the subjects within the vast landscape of creative imagination.

The remote wireless control of industrial systems is one of the major use cases for 5G and beyond systems: in these cases, the massive amounts of sensory information that need to be shared over the wireless medium may overload even high-capacity connections. Consequently, solving the effective communication problem by optimizing the transmission strategy to discard irrelevant information can provide a significant advantage, but is often a very complex task. In this work, we consider a prototypal system in which an observer must communicate its sensory data to a robot controlling a task (e.g., a mobile robot in a factory). We then model it as a remote Partially Observable Markov Decision Process (POMDP), considering the effect of adopting semantic and effective communication-oriented solutions on the overall system performance. We split the communication problem by considering an ensemble Vector Quantized Variational Autoencoder (VQ-VAE) encoding, and train a Deep Reinforcement Learning (DRL) agent to dynamically adapt the quantization level, considering both the current state of the environment and the memory of past messages. We tested the proposed approach on the well-known CartPole reference control problem, obtaining a significant performance increase over traditional approaches., Comment: Submitted to the IEEE Transactions on Communications (under review). arXiv admin note: substantial text overlap with arXiv:2301.05901

In Cyber Physical Systems (CPSs), two groups of actors interact toward the maximization of system performance: the sensors, observing and disseminating the system state, and the actuators, performing physical decisions based on the received information. While it is generally assumed that sensors periodically transmit updates, returning the feedback signal only when necessary, and consequently adapting the physical decisions to the communication policy, can significantly improve the efficiency of the system. In particular, the choice between push-based communication, in which updates are initiated autonomously by the sensors, and pull-based communication, in which they are requested by the actuators, is a key design step. In this work, we propose an analytical model for optimizing push- and pull-based communication in CPSs, observing that the policy optimality coincides with Value of Information (VoI) maximization. Our results also highlight that, despite providing a better optimal solution, implementable push-based communication strategies may underperform even in relatively simple scenarios.

In this paper we will study the action of $\mathbb{F}_{q^n}^{2 \times 2}$ on the graph of an $\mathbb{F}_q$-linear function of $\mathbb{F}_{q^n}$ into itself. In particular we will see that, under certain combinatorial assumptions, its stabilizer (together with the sum and product of matrices) is a field. We will also see some examples for which this does not happen. Moreover, we will establish a connection between such a stabilizer and the right idealizer of the rank-metric code defined by the linear function and give some structural results in the case in which the polynomials are partially scattered.

Generative AI has become pervasive in society, witnessing significant advancements in various domains. Particularly in the realm of Text-to-Image (TTI) models, Latent Diffusion Models (LDMs), showcase remarkable capabilities in generating visual content based on textual prompts. This paper addresses the potential of LDMs in representing local cultural concepts, historical figures, and endangered species. In this study, we use the cultural heritage of Rio Grande do Sul (RS), Brazil, as an illustrative case. Our objective is to contribute to the broader understanding of how generative models can help to capture and preserve the cultural and historical identity of regions. The paper outlines the methodology, including subject selection, dataset creation, and the fine-tuning process. The results showcase the images generated, alongside the challenges and feasibility of each concept. In conclusion, this work shows the power of these models to represent and preserve unique aspects of diverse regions and communities.

Audio Universe: Tour of the Solar System is an audio-visual show for planetariums and flatscreen viewing. It is designed in collaboration with members of the blind and vision impaired (BVI) community, BVI specialist teachers and their pupils. It aims to be suitable for audiences with all sight levels by representing key concepts through sound and using a carefully constructed narration. We present results from 291 audience evaluations from online viewers and audience members of several planetarium showings in the UK and Italy. We find a strong appreciation from BVI and non-BVI audiences, with ~90% scoring 4 or 5 (out of 5) for both how useful and enjoyable the sounds are. We also present results from surveying planetariums and communication leaders known to have downloaded the show. We find international success for special events, for BVI audiences and for those with other special educational needs and disabilities (SEND; including sensory needs and learning difficulties). Feedback suggests this is due to its multi-sensory, clearly narrated, and low sensory load (calm) production. However, we also describe limitations identified during this evaluation exercise, including the show's limited incorporation into regular (non-special) planetarium programmes. This highlights an ongoing challenge of creating a fully inclusive planetarium experience., Comment: Published in Communicating Astronomy with the Public Journal: CAPj, 33, 6, (2023). Available here: https://www.capjournal.org/issues/33/index.php

Random Batch Methods (RBM) for mean-field interacting particle systems enable the reduction of the quadratic computational cost associated with particle interactions to a near-linear cost. The essence of these algorithms lies in the random partitioning of the particle ensemble into smaller batches at each time step. The interaction of each particle within these batches is then evolved until the subsequent time step. This approach effectively decreases the computational cost by an order of magnitude while increasing the amount of fluctuations due to the random partitioning. In this work, we propose a variance reduction technique for RBM applied to nonlocal PDEs of Fokker-Planck type based on a control variate strategy. The core idea is to construct a surrogate model that can be computed on the full set of particles at a linear cost while maintaining enough correlations with the original particle dynamics. Examples from models of collective behavior in opinion spreading and swarming dynamics demonstrate the great potential of the present approach.

While generalized linear mixed models are a fundamental tool in applied statistics, many specifications, such as those involving categorical factors with many levels or interaction terms, can be computationally challenging to estimate due to the need to compute or approximate high-dimensional integrals. Variational inference is a popular way to perform such computations, especially in the Bayesian context. However, naive use of such methods can provide unreliable uncertainty quantification. We show that this is indeed the case for mixed models, proving that standard mean-field variational inference dramatically underestimates posterior uncertainty in high-dimensions. We then show how appropriately relaxing the mean-field assumption leads to methods whose uncertainty quantification does not deteriorate in high-dimensions, and whose total computational cost scales linearly with the number of parameters and observations. Our theoretical and numerical results focus on mixed models with Gaussian or binomial likelihoods, and rely on connections to random graph theory to obtain sharp high-dimensional asymptotic analysis. We also provide generic results, which are of independent interest, relating the accuracy of variational inference to the convergence rate of the corresponding coordinate ascent algorithm that is used to find it. Our proposed methodology is implemented in the R package, see https://github.com/mgoplerud/vglmer . Numerical results with simulated and real data examples illustrate the favourable computation cost versus accuracy trade-off of our approach compared to various alternatives., Comment: Accepted version available at DOI below; major revision to earlier version

We design a deterministic particle method for the solution of the spatially homogeneous Landau equation with uncertainty. The deterministic particle approximation is based on the reformulation of the Landau equation as a formal gradient flow on the set of probability measures, whereas the propagation of uncertain quantities is computed by means of a sg representation of each particle. This approach guarantees spectral accuracy in uncertainty space while preserving the fundamental structural properties of the model: the positivity of the solution, the conservation of invariant quantities, and the entropy production. We provide a regularity results for the particle method in the random space. We perform the numerical validation of the particle method in a wealth of test cases., Comment: 23 pages, 13 figures

Using images collected with the WFC3 camera on board of the Hubble Space Telescope, we detect stellar clumps in continuum-subtracted $H\alpha$ and ultraviolet (F275W filter), such clumps are often embedded in larger regions (star-forming complexes) detected in the optical (F606W filter). We model the photometry of these objects using BAGPIPES to obtain their stellar population parameters. The median mass-weighted stellar ages are 27 Myr for $H\alpha$ clumps and 39 Myr for F275W clumps and star-forming complexes, the oldest stars in the complexes can be older than $\sim$300 Myr which indicates that star-formation is sustained for long periods of time. Stellar masses vary from 10$^{3.5}$ to 10$^{7.1}$ $M_\odot$, with star-forming complexes being more massive objects in the sample. Clumps and complexes found further away from the host galaxy are younger, less massive and less obscured by dust. We interpret these trends as due to the effect of ram-pressure in different phases of the interstellar medium. $H\alpha$ clumps form a well-defined sequence in the stellar mass--SFR plane with slope 0.73. Some F275W clumps and star-forming complexes follow the same sequence while others stray away from it and passively age. The difference in stellar age between a complex and its youngest embedded clump scales with the distance between the clump and the center of the complex, with the most displaced clumps being hosted by the most elongated complexes. This is consistent with a fireball-like morphology, where star-formation proceeds in a small portion of the complex while older stars are left behind producing a linear stellar population gradient. The stellar masses of star-forming complexes are consistent with the ones of globular clusters, but stellar mass surface densities are lower by 2 dex, and their properties are more consistent with the population of dwarf galaxies in clusters., Comment: Accepted for publication in A&A

The use of Wake-Up Radio (WUR) in Internet of Things (IoT) networks can significantly improve their energy efficiency: battery-powered sensors can remain in a low-power (sleep) mode while listening for wake-up messages using their WUR and reactivate only when polled. However, polling-based WUR may still lead to wasted energy if values sensed by the polled sensors provide no new information to the receiver, or in general have a low Value of Information (VoI). In this paper, we design a content-based WUR that tracks the process observed by the sensors and only wakes up the sensor if its estimated update's VoI is higher than a threshold communicated through the poll. If the sensor does not reply to the polling request, the Gateway (GW) can make a Bayesian update, knowing that either the sensor value substantially confirms its current estimate or the transmission failed due to the wireless channel. We analyze the trade-off between the tracking error and the battery lifetime of the sensors, showing that content-based WUR can provide fine-grained control of this trade-off and significantly increase the battery lifetime of the node with a minimal Mean Squared Error (MSE) increase.

Context: From redshift 6 to redshift $\approx$ 4 galaxies grow rapidly from low mass galaxies towards the more mature massive galaxies we see at the cosmic noon. Growth via gas accretion and mergers undoubtedly shape this evolution - however, there currently exists much uncertainty over the contribution of each of these processes to the overall evolution of galaxies. Furthermore, previous characterisations of the morphology of galaxies in the molecular gas phase has been limited by the coarse resolution of previous observations. Aims: The goal of this paper is to derive the morpho-kinematic properties of 3 main-sequence systems at $z\sim4.5$, drawn from the ALPINE survey, using brand new high-resolution ALMA data in band 7. The objects were previously characterised as one merger with three components, and and two dispersion-dominated galaxies. Methods: We use intensity and velocity maps, position-velocity diagrams and radial profiles of [CII], in combination with dust continuum maps, to analyse the morphology and kinematics of the 3 systems.} Results: In general, we find that the high-resolution ALMA data reveal more complex morpho-kinematic properties. We identify in one galaxy interaction-induced clumps, showing the profound effect that mergers have on the molecular gas in galaxies, consistent with what is suggested in recent simulations. A galaxy that was previously classified as dispersion dominated turned out to show two bright [CII] emission regions, that could either be merging galaxies or massive star-forming regions within the galaxy itself. The high resolution data for the other dispersion dominated object also revealed clumps of [CII] that were not previously identified. Within the sample, we might also detect star-formation powered outflows (or outflows from Active Galactic Nuclei) which appear to be fuelling diffuse gas regions and enriching the circumgalactic medium., Comment: Submitted to A&A, 13 pages

The Kennicutt-Schmidt (KS) relation between the gas and the star formation rate (SFR) surface density ($\Sigma_{\rm gas}$-$\Sigma_{\rm SFR}$) is essential to understand star formation processes in galaxies. So far, it has been measured up to z~2.5 in main-sequence galaxies. In this letter, we aim to put constraints at z~4.5 using a sample of four massive main-sequence galaxies observed by ALMA at high resolution. We obtained ~0.3"-resolution [CII] and continuum maps of our objects, which we then converted into gas and obscured SFR surface density maps. In addition, we produced unobscured SFR surface density maps by convolving Hubble ancillary data in the rest-frame UV. We then derived the average $\Sigma_{\rm SFR}$ in various $\Sigma_{\rm gas}$ bins, and estimated the uncertainties using a Monte Carlo sampling. Our galaxy sample follows the KS relation measured in main-sequence galaxies at lower redshift and is slightly lower than predictions from simulations. Our data points probe the high end both in terms of $\Sigma_{\rm gas}$ and $\Sigma_{\rm gas}$, and gas depletion timescales (285-843 Myr) remain similar to z~2 objects. However, three of our objects are clearly morphologically disturbed, and we could have expected shorter gas depletion timescales (~100 Myr) similar to merger-driven starbursts at lower redshifts. This suggests that the mechanisms triggering starbursts at high redshift may be different than in the low- and intermediate-z Universe., Comment: 9 pages, 7 figures, 2 tables, accepted by A&A (letter)

In this work we define a kinetic model for understanding the impact of heterogeneous opinion formation dynamics on epidemics. The considered many-agent system is characterized by nonsymmetric interactions which define a coupled system of kinetic equations for the evolution of the opinion density in each compartment. In the quasi-invariant limit we may show positivity and uniqueness of the solution of the problem together with its convergence towards an equilibrium distribution exhibiting bimodal shape. The tendency of the system towards opinion clusters is further analyzed by means of numerical methods, which confirm the consistency of the kinetic model with its moment system whose evolution is approximated in several regimes of parameters.

Theoretical models struggle to reproduce dynamically cold disks with significant rotation-to-dispersion support($V_{\rm{rot}}/\sigma$) observed in star-forming galaxies in the early Universe, at redshift $z>4$. We aim to explore the possible emergence of dynamically cold disks in cosmological simulations and to understand if different kinematic tracers can help reconcile the tension between theory and observations. We use 3218 galaxies from the SERRA suite of zoom-in simulations, with $8<\log(M_*/M_{\odot})<10.3$ and SFR$<128\,M_{\odot}{yr}^{-1}$, within $410^9\,M_{\odot}$ galaxies, for which $\sigma_{H\alpha}>2\sigma_{CII}$ for a significant fraction of the sample. Regardless of the tracer, our predictions suggest the existence of massive ($M_*>10^{10}M_{\odot}$) galaxies with $V_{rot}/\sigma>10$ at $z>4$, maintaining cold disks for >10 orbital periods (200Myr). Furthermore, we do not find any significant redshift dependence for $V_{rot}/\sigma$ ratio in our sample. Our simulations predict the existence of dynamically cold disks in the early Universe. However, different tracers are sensitive to different kinematic properties. While [CII] effectively traces the thin, gaseous disk of galaxies, H$\alpha$ includes the contribution from ionized gas beyond the disk, characterized by prevalent vertical or radial motions that may be associated with outflows. The presence of H$\alpha$ halos could be a signature of such galactic outflows. This emphasizes the importance of combining ALMA and JWST/NIRspec studies of high-z galaxies., Comment: submitted to A&A