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Indirect dark matter detection methods are used to observe the products of dark matter annihilations or decays originating from astrophysical objects where large amounts of dark matter are thought to accumulate. With neutrino telescopes, an excess of neutrinos is searched for in nearby dark matter reservoirs, such as the Sun and the Galactic Centre, which could potentially produce a sizeable flux of Standard Model particles. The KM3NeT infrastructure, currently under construction, comprises the ARCA and ORCA undersea \v{C}erenkov neutrino detectors located at two different sites in the Mediterranean Sea, offshore of Italy and France, respectively. The two detector configurations are optimised for the detection of neutrinos of different energies, enabling the search for dark matter particles with masses ranging from a few GeV/c$^2$ to hundreds of TeV/c$^2$. In this work, searches for dark matter annihilations in the Galactic Centre and the Sun with data samples taken with the first configurations of both detectors are presented. No significant excess over the expected background was found in either of the two analyses. Limits on the velocity-averaged self-annihilation cross section of dark matter particles are computed for five different primary annihilation channels in the Galactic Centre. For the Sun, limits on the spin-dependent and spin-independent scattering cross sections of dark matter with nucleons are given for three annihilation channels.

Extensive research has been dedicated to optimizing the cyclist's position on the bike to enhance aerodynamic performance. This study aims to further investigate the aerobars position effect on cycling speed. Drawing from previous work (Fintelman et al., 2015), a relationship is established between position variations and hip angle, a critical determinant of power output. Based on a 3D scan of an elite athlete on his Time Trial (TT) bike, a digital twin with upper body mobility is created, utilizing inverse kinematics with aerobars as a root. Adjustments to the aerobars position translate into alterations in the cyclist's upper body posture. These changes influence both aerodynamic drag -- quantified by Computational Fluid Dynamics method (CFD) -- and hip angle -- computed by 3D software, directly affecting the athlete's capacity for power generation. The interplay between aerodynamic efficiency and power output is analyzed, with varying parameters such as speed and slope angle considered to ascertain the optimal aerobar position for individual athletes in a specific cycling context. Results show impactful variations in cycling speed as a function of the aerobars position, the latter having a strong influence on aerodynamic drag and theoretical power production.

In prototype-based federated learning, the exchange of model parameters between clients and the master server is replaced by transmission of prototypes or quantized versions of the data samples to the aggregation server. A fully decentralized deployment of prototype-based learning, without a central agregartor of prototypes, is more robust upon network failures and reacts faster to changes in the statistical distribution of the data, suggesting potential advantages and quick adaptation in dynamic learning tasks, e.g., when the data sources are IoT devices or when data is non-iid. In this paper, we consider the problem of designing a communication-efficient decentralized learning system based on prototypes. We address the challenge of prototype redundancy by leveraging on a twofold data compression technique, i.e., sending only update messages if the prototypes are informationtheoretically useful (via the Jensen-Shannon distance), and using clustering on the prototypes to compress the update messages used in the gossip protocol. We also use parallel instead of sequential gossiping, and present an analysis of its age-of-information (AoI). Our experimental results show that, with these improvements, the communications load can be substantially reduced without decreasing the convergence rate of the learning algorithm., Comment: 15 pages, 2 tables, 7 figures, 6 algorithms

Starting from a microscopic model for the particle-particle interactions in a Weyl semimetal, we analyzed the possibility for conventional as well as monopole Cooper pairing between quasiparticle excitations at the same (intra-nodal) or opposite (inter-nodal) Weyl nodes. We derived a coupled system of self-consistent BCS-like equations, where the angular dependence of the pairings is directly determined from the microscopic interaction symmetries. We studied the competition between conventional and monopole superconducting phases, thus obtaining explicitly the phase diagrams from the microscopic interaction model parameters. We determined the critical temperatures for both phases, and the low temperature critical behavior, including the specific heat, that we suggest as possible experimental probe for topological quantum criticality in Weyl semimetals., Comment: 14 pages, 3 figures, accepted for publication in Superconductivity

Experiments on chains of Rydberg atoms appear as a new playground to study quantum phase transitions in 1D. As a natural extension, we report a quantitative ground-state phase diagram of Rydberg atoms arranged in a two-leg ladder that interact via van der Waals potential. We address this problem numerically, using the Density Matrix Renormalization Group (DMRG) algorithm. Our results suggest that, surprisingly enough, $\mathbb{Z}_k$ crystalline phases, with the exception of the checkerboard phase, appear in pairs characterized by the same pattern of occupied rungs but distinguishable by a spontaneously broken $\tilde{\mathbb{Z}}_2$ symmetry between the two legs of the ladder. Within each pair, the two phases are separated by a continuous transition in the Ising universality class, which eventually fuses with the $\mathbb{Z}_k$ transition, whose nature depends on $k$. According to our results, the transition into the $\mathbb{Z}_2\otimes \tilde{\mathbb{Z}}_2$ phase changes its nature multiple of times and, over extended intervals, falls first into the Ashkin-Teller, latter into the $\mathbb{Z}_4$-chiral universality class and finally in a two step-process mediated by a floating phase. The transition into the $\mathbb{Z}_3$ phase with resonant states on the rungs belongs to the three-state Potts universality class at the commensurate point, to the $\mathbb{Z}_3$-chiral Huse-Fisher universality class away from it, and eventually it is through an intermediate floating phase. The Ising transition between $\mathbb{Z}_3$ and $\mathbb{Z}_3\otimes \tilde{\mathbb{Z}}_2$ phases, coming across the floating phase, opens the possibility to realize lattice supersymmetry in Rydberg quantum simulators.

We revisit the calculation of the soft gluon emission probability off a colour-singlet $q\bar q$ system that evolves in a quark-gluon plasma. The $q\bar q$ antenna is created in the presence of a medium and then emits a soft gluon outside. The gluon emission probability is modified with respect to the vacuum baseline due to interactions with the medium during the formation of the antenna and its propagation. Previous studies disregarded the former effect and found that the medium modification to the interference pattern of the antenna was controlled by the so-called critical angle $\theta_c$, that exclusively depends on medium properties. We find that accounting for medium interactions during the antenna formation enhances the total rate of emissions off the $q\bar q$ antenna. Interestingly, it also promotes the notion of a critical angle to a dynamic quantity, denoted $\tilde\theta_c$, that depends on both the medium and the antenna properties and is thus different for every splitting. As a consequence, depending on the region of parameter space, colour decoherence can either be delayed or accelerated with respect to previous estimates.

The KM3NeT Collaboration has tackled a common challenge faced by the astroparticle physics community, namely adapting the experiment-specific simulation software to work with the CORSIKA air shower simulation output. The proposed solution is an extension of the open-source code gSeaGen, allowing for the transport of muons generated by CORSIKA to a detector of any size at an arbitrary depth. The gSeaGen code was not only extended in terms of functionalities but also underwent a thorough redesign of the muon propagation routine, resulting in a more accurate and efficient simulation. This paper presents the capabilities of the new gSeaGen code as well as prospects for further developments., Comment: 27 pages, 13 figures

Large language models can produce highly fluent paraphrases while retaining much of the original meaning. While this capability has a variety of helpful applications, it may also be abused by bad actors, for example to plagiarize content or to conceal their identity. This motivates us to consider the problem of paraphrase inversion: given a paraphrased document, attempt to recover the original text. To explore the feasibility of this task, we fine-tune paraphrase inversion models, both with and without additional author-specific context to help guide the inversion process. We explore two approaches to author-specific inversion: one using in-context examples of the target author's writing, and another using learned style representations that capture distinctive features of the author's style. We show that, when starting from paraphrased machine-generated text, we can recover significant portions of the document using a learned inversion model. When starting from human-written text, the variety of source writing styles poses a greater challenge for invertability. However, even when the original tokens can't be recovered, we find the inverted text is stylistically similar to the original, which significantly improves the performance of plagiarism detectors and authorship identification systems that rely on stylistic markers.

In the present article, we study the integral aspects of the Fourier transform of an abelian variety $A$ over a field $k$, using \'etale motivic cohomology, following the ideas and theory given by Moonen, Polishchuk and later by Beckman and de Gaay Fortman. We prove that there exists a PD-structure over the positive degree part of the \'etale Chow ring $\text{CH}^{\text{\'et}}_{>0}(A)$ with respect to the Pontryagin product.

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

This research introduces a novel methodology for assigning quantifiable, controllable and psychometrically validated personalities to Large Language Models-Based Agents (Agents) using the Big Five personality framework. It seeks to overcome the constraints of human subject studies, proposing Agents as an accessible tool for social science inquiry. Through a series of four studies, this research demonstrates the feasibility of assigning psychometrically valid personality traits to Agents, enabling them to replicate complex human-like behaviors. The first study establishes an understanding of personality constructs and personality tests within the semantic space of an LLM. Two subsequent studies -- using empirical and simulated data -- illustrate the process of creating Agents and validate the results by showing strong correspondence between human and Agent answers to personality tests. The final study further corroborates this correspondence by using Agents to replicate known human correlations between personality traits and decision-making behaviors in scenarios involving risk-taking and ethical dilemmas, thereby validating the effectiveness of the psychometric approach to design Agents and its applicability to social and behavioral research.

Mass transport of impurities immersed in a confined quasi-two-dimensional moderately dense granular gas of inelastic hard spheres is studied. The effect of the confinement on granular particles is modeled through a collisional model (the so-called $\Delta$-model) that includes an effective mechanism to transfer the kinetic energy injected by vibration in the vertical direction to the horizontal degrees of freedom of grains. The impurity can differ in mass, diameter, inelasticity, or the energy injection at collisions, compared to the gas particles. The Enskog--Lorentz kinetic equation for the impurities is solved via the Chapman--Enskog method to first order in spatial gradients for states close to the homogeneous steady state. As usual, the three diffusion transport coefficients for tracer particles in a mixture are given in terms of the solutions of a set of coupled linear integral equations which are solved by considering the lowest Sonine approximation. The theoretical predictions for the tracer diffusion coefficient (relating the mass flux with the gradient of the number density of tracer particles) are compared with both direct simulation Monte Carlo and molecular dynamics simulations. The agreement is in general good, except for strong inelasticity and/or large contrast of energy injection at tracer-gas collisions compared to gas-gas collisions. Finally, as an application of our results, the segregation problem induced by both a thermal gradient and gravity is exhaustively analyzed., Comment: 24 pages, 12 figures

IRAS 22568+6141 has been classified as a low-ionisation planetary nebula (PN) and presents non-thermal radio continuum emission, which could be a signature of nascent PNe. We present intermediate-resolution long-slit spectra obtained in 2021 and 2023, high-resolution long-slit spectra taken in 2023, and a light curve at the $r$-filter between 1953 and 2019, that reveal changes in IRAS 22568+6141 with timescales of decades and a few years. The object underwent an energetic event around 1990 that suddenly increased its brightness which has been fading since then. A comparison with a published spectrum from 1988 shows an increase of the H$\beta$ flux in 2021 by factor of $\simeq$6 and the [O III] emission lines that were absent in 1988. Between 2021 and 2023 the H$\beta$ flux decreased by a factor of $\simeq$1.7, and the [O III] emission lines almost vanished. These results and the variability observed in other emission lines indicate that IRAS 22568+6141 is recombining and cooling down between 2021 and 2023, and probably since 2005, as suggested by archival radio continuum and mid-IR observations. The intermediate- and high-resolution spectra show that the excitation of the emission lines is dominated by shocks in 2021 and 2023, and, probably, also in 1988, which may be related to the non-thermal radio continuum emission from the object. Although the variability might be due to changes in the physical conditions in the shocks or in a nova-like eruption, it accommodates better to that expected from a late thermal pulse, which is further suggested by a comparison with other similar objects. New observations and monitoring in the coming years are crucial to corroborate the origin of the variability., Comment: 11 pages, 8 figures, 6 tables. Accepted for publication in A&A

Forecasting rail congestion is crucial for efficient mobility in transport systems. We present rail congestion forecasting using reports from passengers collected through a transit application. Although reports from passengers have received attention from researchers, ensuring a sufficient volume of reports is challenging due to passenger's reluctance. The limited number of reports results in the sparsity of the congestion label, which can be an issue in building a stable prediction model. To address this issue, we propose a semi-supervised method for congestion forecasting for trains, or SURCONFORT. Our key idea is twofold: firstly, we adopt semi-supervised learning to leverage sparsely labeled data and many unlabeled data. Secondly, in order to complement the unlabeled data from nearby stations, we design a railway network-oriented graph and apply the graph to semi-supervised graph regularization. Empirical experiments with actual reporting data show that SURCONFORT improved the forecasting performance by 14.9% over state-of-the-art methods under the label sparsity., Comment: Accepted in ACM SIGSPATIAL 2024

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance., Comment: 8 pages,14 figures, ICUAS 2024

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400~g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop kinematic chain, employs two deported servomotors. Each joint is actuated with a single tendon for active motion control in compression of the arm at the end-effector. Its elasto-mechanical design reduces weight and provides flexibility, allowing passive-compliant interactions without impacting the motors' integrity. Notably, the arm's damping can be adjusted based on the proposed inner frictional bulges. Experimental applications showcase the aerial system performance in both free-flight and physical interaction. The presented work may open safer applications for \ac{MAV} in real environments subject to perturbations during interaction., Comment: 6 pages, 7 figures, ROBOSOFT 2024

The UltraViolet imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) program provides deep HST F275W and F435W imaging over four CANDELS fields (GOODS-N, GOODS-S, COSMOS, and EGS). We combine this newly acquired UV imaging with existing HST imaging from CANDELS as well as existing ancillary data to obtain robust photometric redshifts and reliable estimates for galaxy physical properties for over 150,000 galaxies in the $\sim$430 arcmin$^2$ UVCANDELS area. Here, we leverage the power of the new UV photometry to not only improve the photometric redshift measurements in these fields, but also constrain the full redshift probability distribution combining multiple redshift fitting tools. Furthermore, using the full UV-to-IR photometric dataset, we measure the galaxy physical properties by fitting templates from population synthesis models with two different parameterizations (flexible and fixed-form) of the star-formation histories (SFHs). Compared to the flexible SFH parametrization, we find that the fixed-form SFHs systematically underestimate the galaxy stellar masses, both at the low- ($\lesssim10^9 M_\odot$) and high- ($\gtrsim10^{10} M_\odot$) mass end, by as much as $\sim0.5$ dex. This underestimation is primarily due the limited ability of fixed-form SFH parameterization to simultaneously capture the chaotic nature of star-formation in these galaxies., Comment: 22 pages, 6 figures; accepted to ApJS; catalogs available via MAST

Microquasars are laboratories for the study of jets of relativistic particles produced by accretion onto a spinning black hole. Microquasars are near enough to allow detailed imaging of spatial features across the multiwavelength spectrum. The recent extension of the spatial morphology of a microquasar, SS 433, to TeV gamma rays \cite{abeysekara2018very} localizes the acceleration of electrons at shocks in the jet far from the black hole \cite{hess2024ss433}. Here we report TeV gamma-ray emission from another microquasar, V4641~Sgr, which reveals particle acceleration at similar distances from the black hole as SS~433. Additionally, the gamma-ray spectrum of V4641 is among the hardest TeV spectra observed from any known gamma-ray source and is detected up to 200 TeV. Gamma rays are produced by particles, either electrons or hadrons, of higher energies. Because electrons lose energy more quickly the higher their energy, such a spectrum either very strongly constrains the electron production mechanism or points to the acceleration of high-energy hadrons. This observation suggests that large-scale jets from microquasars could be more common than previously expected and that microquasars could be a significant source of Galactic cosmic rays. high energy gamma-rays also provide unique constraints on the acceleration mechanisms of extra-Galactic cosmic rays postulated to be produced by the supermassive black holes and relativistic jets of quasars. The distance to quasars limits imaging studies due to insufficient angular resolution of gamma-rays and due to attenuation of the highest energy gamma-rays by the extragalactic background light.

In this paper we provide a complete characterisation of coisotropic embeddings of precosymplectic manifolds into cosymplectic manifolds. This result extends a theorem of Gotay about coisotropic embeddings of presymplectic manifolds. We also extend to the cosymplectic case some results of A. Weinstein which generalise the Darboux theorem. While symplectic geometry is the natural framework for developing Hamiltonian mechanics, cosymplectic geometry is the corresponding framework for time-dependent Hamiltonian mechanics. The motivation behind proving this theorem is to generalise known results for symplectic geometry to cosymplectic geometry, so that they can be used to study time-dependent systems, for instance for the regularization problem of singular Lagrangian systems.

This study investigates the application of an artificial neural network framework for analysing water pollution caused by solids. Water pollution by suspended solids poses significant environmental and health risks. Traditional methods for assessing and predicting pollution levels are often time-consuming and resource-intensive. To address these challenges, we developed a model that leverages a comprehensive dataset of water quality from total suspended solids. A convolutional neural network was trained under a transfer learning approach using data corresponding to different total suspended solids concentrations, with the goal of accurately predicting low, medium and high pollution levels based on various input variables. Our model demonstrated high predictive accuracy, outperforming conventional statistical methods in terms of both speed and reliability. The results suggest that the artificial neural network framework can serve as an effective tool for real-time monitoring and management of water pollution, facilitating proactive decision-making and policy formulation. This approach not only enhances our understanding of pollution dynamics but also underscores the potential of machine learning techniques in environmental science., Comment: 42 pages, 8 figures and 2 tables

We present a detailed derivation of the Poynting vector for Cauchy-Riemann beams propagating in free space considering a Gaussian modulation with $g \in \mathbb{C}$. The effect generated by this Gaussian modulation is a compression-expansion of the intensity distribution. It is shown that the parameter $g$ can reverse the direction of energy flux and eliminate the radial component, resulting in a purely azimuthal field. Additionally, we validate our analytical results through experimental verification.