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Palm-sized autonomous nano-drones, i.e., sub-50g in weight, recently entered the drone racing scenario, where they are tasked to avoid obstacles and navigate as fast as possible through gates. However, in contrast with their bigger counterparts, i.e., kg-scale drones, nano-drones expose three orders of magnitude less onboard memory and compute power, demanding more efficient and lightweight vision-based pipelines to win the race. This work presents a map-free vision-based (using only a monocular camera) autonomous nano-drone that combines a real-time deep learning gate detection front-end with a classic yet elegant and effective visual servoing control back-end, only relying on onboard resources. Starting from two state-of-the-art tiny deep learning models, we adapt them for our specific task, and after a mixed simulator-real-world training, we integrate and deploy them aboard our nano-drone. Our best-performing pipeline costs of only 24M multiply-accumulate operations per frame, resulting in a closed-loop control performance of 30 Hz, while achieving a gate detection root mean square error of 1.4 pixels, on our ~20k real-world image dataset. In-field experiments highlight the capability of our nano-drone to successfully navigate through 15 gates in 4 min, never crashing and covering a total travel distance of ~100m, with a peak flight speed of 1.9 m/s. Finally, to stress the generalization capability of our system, we also test it in a never-seen-before environment, where it navigates through gates for more than 4 min., Comment: \c{opyright}2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Localizing humans is a key prerequisite for any service robot operating in proximity to people. In these scenarios, robots rely on a multitude of state-of-the-art detectors usually designed to operate with RGB-D cameras or expensive 3D LiDARs. However, most commercially available service robots are equipped with cameras with a narrow field of view, making them blind when a user is approaching from other directions, or inexpensive 1D LiDARs whose readings are difficult to interpret. To address these limitations, we propose a self-supervised approach to detect humans and estimate their 2D pose from 1D LiDAR data, using detections from an RGB-D camera as a supervision source. Our approach aims to provide service robots with spatial awareness of nearby humans. After training on 70 minutes of data autonomously collected in two environments, our model is capable of detecting humans omnidirectionally from 1D LiDAR data in a novel environment, with 71% precision and 80% recall, while retaining an average absolute error of 13 cm in distance and 44{\deg} in orientation.

Context: This paper focuses on a class of galaxies characterised by an extremely low surface brightness: the ultra-diffuse galaxies (UDGs). We used new integral-field spectroscopic data from the ESO Large Programme Looking into the faintEst WIth MUSE (LEWIS) project. Aims: Our main goals are addressing the formation channels and investigating possible correlations of their observational properties. In particular, we derive their stellar kinematics and dynamical properties. Methods: We extract the 1D stacked spectrum inside the effective radius to obtain an unbiased measure of $\sigma_{\rm eff}$. To derive the spatially-resolved stellar kinematics, we first apply the Voronoi tessellation algorithm to bin the spaxels in the datacube and then follow the same prescription adopted for the 1D case. In addition, we extract the velocity profiles along the galaxy's major and minor axes. Results: We find that 7 out of 18 UDGs in LEWIS show a mild rotation, 5 do not have evidence of any rotation, and the remaining 6 UDGs are unconstrained cases. This is the first large census of velocity profiles for UDGs. On average, UDGs in LEWIS are characterised by low values of $\sigma_{\rm eff}$, comparable with available values from the literature. In the Faber-Jackson relation plane, we found a group of UDGs consistent with the relation within the errorbars, whereas outliers are objects with non-negligible rotation components. UDGs and LSBs in LEWIS have larger dark matter content than dwarf galaxies with similar total luminosity. We do not find clear correlations between the derived properties and the local environment. Conclusions: Based on the stellar kinematics, two classes of UDGs are found in the Hydra I cluster: the rotating and non-rotating systems. This result, combined with other structural properties, can help to discriminate between the several formation scenarios proposed for UDGs., Comment: Accepted for publication in Astronomy and Astrophysics

The ability to detect coordinated activity in communication networks is an ongoing challenge. Prior approaches emphasize considering any activity exceeding a specific threshold of similarity to be coordinated. However, identifying such a threshold is often arbitrary and can be difficult to distinguish from grassroots organized behavior. In this paper, we investigate a set of Twitter retweeting data collected around the 2022 US midterm elections, using a latent sharing-space model, in which we identify the main components of an association network, thresholded with a k-nearest neighbor criterion. This approach identifies a distribution of association values with different roles in the network at different ranges, where the shape of the distribution suggests a natural place to threshold for coordinated user candidates. We find coordination candidates belonging to two broad categories, one involving music awards and promotion of Korean pop or Taylor Swift, the other being users engaged in political mobilization. In addition, the latent space suggests common motivations for different coordinated groups otherwise fragmented by using an appropriately high threshold criterion for coordination.

The exponential growth of astronomical data from large-scale surveys has created both opportunities and challenges for the astrophysics community. This paper explores the possibilities offered by transfer learning techniques in addressing these challenges across various domains of astronomical research. We present a set of recent applications of transfer learning methods for astronomical tasks based on the usage of a pre-trained convolutional neural networks. The examples shortly discussed include the detection of candidate active galactic nuclei (AGN), the possibility of deriving physical parameters for galaxies directly from images, the identification of artifacts in time series images, and the detection of strong lensing candidates and outliers. We demonstrate how transfer learning enables efficient analysis of complex astronomical phenomena, particularly in scenarios where labeled data is scarce. This kind of method will be very helpful for upcoming large-scale surveys like the Rubin Legacy Survey of Space and Time (LSST). By showcasing successful implementations and discussing methodological approaches, we highlight the versatility and effectiveness of such techniques., Comment: proceeding of the Seventeenth Marcel Grossmann Meeting

Social robots are required not only to understand human intentions but also to effectively communicate their intentions or own internal states to users. This study explores the use of sonification to provide explicit auditory feedback, enhancing mutual understanding in HRI. We introduce a novel sonification approach that conveys the robot's internal state, linked to its perception of nearby individuals and their interaction intentions. The approach is evaluated through a two-fold user study: an online video-based survey with $26$ participants and live experiments with $10$ participants. Results indicate that while sonification improves the robot's expressivity and communication effectiveness, the design of the auditory feedback needs refinement to enhance user experience. Participants found the auditory cues useful but described the sounds as uninteresting and unpleasant. These findings underscore the importance of carefully designed auditory feedback in developing more effective and engaging HRI systems.

We propose a machine learning-based estimator of the hand state for rehabilitation purposes, using light exoskeletons. These devices are easy to use and useful for delivering domestic and frequent therapies. We build a supervised approach using information from the muscular activity of the forearm and the motion of the exoskeleton to reconstruct the hand's opening degree and compliance level. Such information can be used to evaluate the therapy progress and develop adaptive control behaviors. Our approach is validated with a real light exoskeleton. The experiments demonstrate good predictive performance of our approach when trained on data coming from a single user and tested on the same user, even across different sessions. This generalization capability makes our system promising for practical use in real rehabilitation.

We aim at characterizing the X-ray-to-optical/near-infrared broad-band emission of luminous QSOs in the first Gyr of cosmic evolution to understand whether they exhibit differences compared to the lower-\textit{z} QSO population. Our goal is also to provide for these objects a reliable and uniform catalog of SED fitting derivable properties such as bolometric and monochromatic luminosities, Eddington ratios, dust extinction, strength of the hot dust emission. We characterize the X-ray/UV emission of each QSO using average SEDs from luminous Type 1 sources and calculate bolometric and monochromatic luminosities. Finally we construct a mean SED extending from the X-rays to the NIR bands. We find that the UV-optical emission of these QSOs can be modelled with templates of $z\sim$2 luminous QSOs. We observe that the bolometric luminosities derived adopting some bolometric corrections at 3000 \AA\ ($BC_{3000\text{\AA}}$) largely used in the literature are slightly overestimated by 0.13 dex as they also include reprocessed IR emission. We estimate a revised value, i.e. $BC_{3000\text{\AA}}=3.3 $ which can be used for deriving $L_\text{bol}$ in \textit{z} $\geq$ 6 QSOs. A sub-sample of 11 QSOs is provided with rest-frame NIR photometry, showing a broad range of hot dust emission strength, with two sources exhibiting low levels of emission. Despite potential observational biases arising from non-uniform photometric coverage and selection biases, we produce a X-ray-to-NIR mean SED for QSOs at \textit{z} $\gtrsim$ 6, revealing a good match with templates of lower-redshift, luminous QSOs up to the UV-optical range, with a slightly enhanced contribution from hot dust in the NIR., Comment: All photometric data and the derived properties (luminosities, spectral slopes) are available on Zenodo at https://zenodo.org/records/14867157 in machine-readable format. The repository also includes the mean SED template for QSOs at the Epoch of Reionization, as shown in Figure 13 of the paper

This work addresses the problem of exact schedulability assessment in uniprocessor mixed-criticality real-time systems with sporadic task sets. We model the problem by means of a finite automaton that has to be explored in order to check for schedulability. To mitigate the state explosion problem, we provide a generic algorithm which is parameterised by several techniques called oracles and simulation relations. These techniques leverage results from the scheduling literature as "plug-ins" that make the algorithm more efficient in practice. Our approach achieves up to a 99.998% reduction in the search space required for exact schedulability testing, making it practical for a range of task sets, up to 8 tasks or maximum periods of 350. This method enables to challenge the pessimism of an existing schedulability test and to derive a new dynamic-priority scheduler, demonstrating its good performance. This is the full version of an RTNS 2024 paper., Comment: In the proceedings of 32nd International Conference on Real-Time Networks and Systems, RTNS24, ACM, 2024

We consider a service robot that offers chocolate treats to people passing in its proximity: it has the capability of predicting in advance a person's intention to interact, and to actuate an "offering" gesture, subtly extending the tray of chocolates towards a given target. We run the system for more than 5 hours across 3 days and two different crowded public locations; the system implements three possible behaviors that are randomly toggled every few minutes: passive (e.g. never performing the offering gesture); or active, triggered by either a naive distance-based rule, or a smart approach that relies on various behavioral cues of the user. We collect a real-world dataset that includes information on 1777 users with several spontaneous human-robot interactions and study the influence of robot actions on people's behavior. Our comprehensive analysis suggests that users are more prone to engage with the robot when it proactively starts the interaction. We release the dataset and provide insights to make our work reproducible for the community. Also, we report qualitative observations collected during the acquisition campaign and identify future challenges and research directions in the domain of social human-robot interaction.

In modern society, service robots are increasingly recognized for their wide range of practical applications. In large and crowded social spaces, such as museums and hospitals, these robots are required to safely move in the environment while exhibiting user-friendly behavior. Ensuring the safe and socially acceptable operation of robots in such settings presents several challenges. To enhance the social acceptance in the design process of service robots, we present a systematic analysis of requirements, categorized into functional and non-functional. These requirements are further classified into different categories, with a single requirement potentially belonging to multiple categories. Finally, considering the specific case of a receptionist robotic agent, we discuss the requirements it should possess to ensure social acceptance.

Globular clusters (GCs) are among the oldest stellar systems in the universe. As such, GCs population are valuable fossil tracers of galaxy formation and interaction history. We use the multi-band wide-field images obtained with the VST to study the properties of the GC population in an interacting pair of galaxies. We derived ugri photometry over $1.5x1.5deg^2$ centred on the galaxy group composed by two elliptical galaxies: NGC3640 and its fainter companion NGC3641. We studied the GC system properties from both the ugri and gri matched catalogs GC candidates were identified based on a combination of photometric properties (colors, magnitudes) and morphometric criteria (concentration index, elongation, FWHM), using sources with well-defined classifications from spectroscopic or imaging data available in the literature and numerical simulations as references. The selection criteria were also applied to empty fields to determine a statistical background correction for the number of identified GC candidates. The 2D density maps of GCs appear to align with the diffuse light patches resulting from merging events of the galaxies. The highest density peak of GCs is observed to be on NGC3641 rather than NGC3640, despite the latter being the more massive galaxy. The azimuthal averaged radial density profiles in both galaxies reveal that the GC population extends beyond the galaxy light profile and indicate the likely presence of an intra-group GC component. A color bimodality in (u-r) and (g-i) is observed for NGC3641, whereas NGC3640 shows a broad unimodal distribution. Analysis of the GC Luminosity Function indicates that both galaxies are roughly located at the same distance (~27Mpc). We provide an estimate of the total number of GCs, and determine the specific frequency for NGC3640, $S_N =2.0\pm0.6$, which aligns with expectations, while for NGC3641 we find a large $S_N=4.5\pm1.6$., Comment: 37 pages, 23 figures, 13 tables. Accepted for publication in A&A

Our understanding of highly accreting AGNs is hampered by the lack of a complete systematic investigation in terms of their main spectral and variability properties, and by the relative paucity of them in the local Universe, especially those powered by supermassive black holes with $M_\mathrm{BH} > 10^8\,M_\odot$. To overcome this limitation, we present here the X-ray spectral analysis of a new, large sample of 61 highly accreting AGNs named as the \emph{XMM-Newton} High-Eddington Serendipitous AGN Sample, or X-HESS, obtained by cross-correlating the 11th release of the \emph{XMM-Newton} serendipitous catalogue and the catalogue of spectral properties of quasars from the SDSS DR14. The X-HESS AGNs are spread across wide intervals with a redshift of $0.06

Empirical studies of online disinformation emphasize matters of public concern such as the COVID-19 pandemic, foreign election interference, and the Russo-Ukraine war, largely in studies that treat the topics separately. Comparatively fewer studies attempt to relate such disparate topics and address the extent to which they share behaviors. In this study, we compare three samples of Twitter data on COVID-19 vaccination, the Ukraine war and the 2022 midterm elections, to ascertain how distinct ideological stances of users across the three samples might be related. Our results indicate the emergence of a broad contrarian stance that is defined by its opposition to public health narratives/policies along with the Biden administration's foreign policy stances. Sharing activity within the contrarian position falls on a spectrum with outright conspiratorial content on one end. We confirm the existence of ideologically coherent cross-subject stances among Twitter users, but in a manner not squarely aligned with right-left political orientations.

Today robots must be safe, versatile, and user-friendly to operate in unstructured and human-populated environments. Dynamical system-based imitation learning enables robots to perform complex tasks stably and without explicit programming, greatly simplifying their real-world deployment. To exploit the full potential of these systems it is crucial to implement closed loops that use visual feedback. Vision permits to cope with environmental changes, but is complex to handle due to the high dimension of the image space. This study introduces a dynamical system-based imitation learning for direct visual servoing. It leverages off-the-shelf deep learning-based perception backbones to extract robust features from the raw input image, and an imitation learning strategy to execute sophisticated robot motions. The learning blocks are integrated using the large projection task priority formulation. As demonstrated through extensive experimental analysis, the proposed method realizes complex tasks with a robotic manipulator., Comment: First two authors contributed equally

Extragalactic globular clusters (EGCs) are an abundant and powerful tracer of galaxy dynamics and formation, and their own formation and evolution is also a matter of extensive debate. The compact nature of globular clusters means that they are hard to spatially resolve and thus study outside the Local Group. In this work we have examined how well EGCs will be detectable in images from the Euclid telescope, using both simulated pre-launch images and the first early-release observations of the Fornax galaxy cluster. The Euclid Wide Survey will provide high-spatial resolution VIS imaging in the broad IE band as well as near-infrared photometry (YE, JE, and HE). We estimate that the galaxies within 100 Mpc in the footprint of the Euclid survey host around 830 000 EGCs of which about 350 000 are within the survey's detection limits. For about half of these EGCs, three infrared colours will be available as well. For any galaxy within 50Mpc the brighter half of its GC luminosity function will be detectable by the Euclid Wide Survey. The detectability of EGCs is mainly driven by the residual surface brightness of their host galaxy. We find that an automated machine-learning EGC-classification method based on real Euclid data of the Fornax galaxy cluster provides an efficient method to generate high purity and high completeness GC candidate catalogues. We confirm that EGCs are spatially resolved compared to pure point sources in VIS images of Fornax. Our analysis of both simulated and first on-sky data show that Euclid will increase the number of GCs accessible with high-resolution imaging substantially compared to previous surveys, and will permit the study of GCs in the outskirts of their hosts. Euclid is unique in enabling systematic studies of EGCs in a spatially unbiased and homogeneous manner and is primed to improve our understanding of many understudied aspects of GC astrophysics., Comment: Accepted in A&A

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'

We present a novel method for detecting outliers in astronomical time series based on the combination of a deep neural network and a k-nearest neighbor algorithm with the aim of identifying and removing problematic epochs in the light curves of astronomical objects. We use an EfficientNet network pre-trained on ImageNet as a feature extractor and perform a k-nearest neighbor search in the resulting feature space to measure the distance from the first neighbor for each image. If the distance is above the one obtained for a stacked image, we flag the image as a potential outlier. We apply our method to time series obtained from the VLT Survey Telescope (VST) monitoring campaign of the Deep Drilling Fields of the Vera C. Rubin Legacy Survey of Space and Time (LSST). We show that our method can effectively identify and remove artifacts from the VST time series and improve the quality and reliability of the data. This approach may prove very useful in sight of the amount of data that will be provided by the LSST, which will prevent the inspection of individual light curves. We also discuss the advantages and limitations of our method and suggest possible directions for future work., Comment: accepted for publication in A&A

Context: Quasar variability has proven to be a powerful tool to constrain the properties of their inner engine and the accretion process onto supermassive black holes. Correlations between UV/optical variability and physical properties have been long studied with a plethora of different approaches and time-domain surveys, although the detailed picture is not yet clear. Aims: We analysed archival data from the SDSS Stripe-82 region to study how the quasar power spectral density (PSD) depends on the black hole mass, bolometric luminosity, accretion rate, redshift, and rest-frame wavelength. We developed a model-independent analysis framework that could be easily applied to upcoming large surveys such as the Legacy Survey of Space and Time (LSST). Methods: We used light curves of 8042 spectroscopically confirmed quasars, observed in at least six yearly seasons in five filters ugriz. We split the sample into bins of similar physical properties containing at least 50 sources, and we measured the ensemble PSD in each of them. Results: We find that a simple power law is a good fit to the power spectra in the frequency range explored. Variability does not depend on redshift at a fixed wavelength. Instead, both PSD amplitude and slope depend on the black hole mass, accretion rate, and rest-frame wavelength. We provide scaling relations to model the observed variability as a function of the physical properties, and discuss the possibility of a universal PSD shape for all quasars, where frequencies scale with the black hole mass, while normalization and slope(s) are fixed (at any given wavelength and accretion rate)., Comment: Accepted for publication in A&A

For a service robot, it is crucial to perceive as early as possible that an approaching person intends to interact: in this case, it can proactively enact friendly behaviors that lead to an improved user experience. We solve this perception task with a sequence-to-sequence classifier of a potential user intention to interact, which can be trained in a self-supervised way. Our main contribution is a study of the benefit of features representing the person's gaze in this context. Extensive experiments on a novel dataset show that the inclusion of gaze cues significantly improves the classifier performance (AUROC increases from 84.5% to 91.2%); the distance at which an accurate classification can be achieved improves from 2.4 m to 3.2 m. We also quantify the system's ability to adapt to new environments without external supervision. Qualitative experiments show practical applications with a waiter robot.

The Legacy Survey of Space and Time (LSST) will revolutionize Time Domain Astronomy by detecting millions of transients. In particular, it is expected to increment the number of type Ia supernovae (SNIa) of a factor of 100 compared to existing samples up to z~1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of SNIa properties and rates. However, the impact of all other sources of uncertainty on the measurement must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SNIa rate can put constraints on the evolutionary scenarios of different SNIa progenitors. We use simulated data from the DESC Data Challenge 2 (DC2) and LSST Data Preview 0 (DP0) to measure the SNIa rate on a 15 deg2 region of the Wide-Fast-Deep area. We select a sample of SN candidates detected on difference images, associate them to the host galaxy, and retrieve their photometric redshifts (z-phot). Then, we test different light curves classification methods, with and without redshift priors. We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. We measure the SNIa rate analyzing the impact of uncertainties due to z-phot, host galaxy association and classification on the distribution in redshift of the starting sample. We found a 17% average lost fraction of SNIa with respect to the simulated sample. As 10% of the bias is due to the uncertainty on the z-phot alone (which also affects classification when used as a prior), it results to be the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SNIa rate, including synergies with other surveys, which may help using the rate to discriminate different progenitor models., Comment: Accepted for publication in A&A

Although flux variability is one of the defining properties of accretion flows onto supermassive black holes, its dependence on physical parameters such as the mass of the compact object and the Eddington ratio remain under discussion. In this paper we address this issue using the structure function statistic to measure the variability at X-ray wavelengths of a sample of optically selected QSOs with available black hole masses and Eddington ratios. We present a new Bayesian methodology for estimating the structure function tailored to the Poisson nature of the X-ray data. This is applied to 15,548 SDSS DRQ16 QSOs with repeat observations in the XMM-Newton archive and/or the SRG/eROSITA All Sky Survey. The X-ray structure function monotonically increases to time intervals of about 10-15 years, suggesting a preference for scenarios in which instabilities of the accretion disk contribute to the X-ray variability on long timescales. Additionally, there is evidence that the amplitude of the stochastic X-ray flux variations rises with decreasing black hole mass and Eddington ratio. This finding imposes stringent constraints on empirical models of Active Galactic Nuclei variability derived from local samples, emphasizing the significance of high-redshift population studies for comprehending the stochastic flux variations in active black holes., Comment: 14 pages, 13 figure, MNRAS accepted, revision following refereeing process. The code used in this paper is available at https://github.com/ageorgakakis/StructureFunction

We present an improved study of the relation between supermassive black hole growth and their host galaxy properties in the local Universe (z < 0.33). To this end, we build an extensive sample combining spectroscopic measurements of star-formation rate (SFR) and stellar mass from Sloan Digital Sky Survey, with specific Black Hole accretion rate (sBHAR, $\lambda_{\mathrm{sBHAR}} \propto L_{\mathrm{X}}/\mathcal{M}_{\ast}$) derived from the XMM-Newton Serendipitous Source Catalogue (3XMM-DR8) and the Chandra Source Catalogue (CSC 2.0). We find that the sBHAR probability distribution for both star-forming and quiescent galaxies has a power-law shape peaking at $\log\lambda_{\mathrm{sBHAR}}\sim -3.5$ and declining toward lower sBHAR in all stellar mass ranges. This finding confirms the decrease of AGN activity in the local Universe compared to higher redshifts. We observe a significant correlation between $\log\,\lambda_{\mathrm{sBHAR}}$ and $\log\,{\mathrm{SFR}}$ in almost all stellar mass ranges, but the relation is shallower compared to higher redshifts, indicating a reduced availability of accreting material in the local Universe. At the same time, the BHAR-to-SFR ratio for star-forming galaxies strongly correlates with stellar mass, supporting the scenario where both AGN activity and stellar formation primarily depend on the stellar mass via fuelling by a common gas reservoir. Conversely, this ratio remains constant for quiescent galaxies, possibly indicating the existence of the different physical mechanisms responsible for AGN fuelling or different accretion mode in quiescent galaxies., Comment: 18 pages, 12 figures, accepted for publication in MNRAS

In everyday life collaboration tasks between human operators and robots, the former necessitate simple ways for programming new skills, the latter have to show adaptive capabilities to cope with environmental changes. The joint use of visual servoing and imitation learning allows us to pursue the objective of realizing friendly robotic interfaces that (i) are able to adapt to the environment thanks to the use of visual perception and (ii) avoid explicit programming thanks to the emulation of previous demonstrations. This work aims to exploit imitation learning for the visual servoing paradigm to address the specific problem of tracking moving objects. In particular, we show that it is possible to infer from data the compensation term required for realizing the tracking controller, avoiding the explicit implementation of estimators or observers. The effectiveness of the proposed method has been validated through simulations with a robotic manipulator., Comment: International Workshop on Human-Friendly Robotics (HFR), 2023

A service robot can provide a smoother interaction experience if it has the ability to proactively detect whether a nearby user intends to interact, in order to adapt its behavior e.g. by explicitly showing that it is available to provide a service. In this work, we propose a learning-based approach to predict the probability that a human user will interact with a robot before the interaction actually begins; the approach is self-supervised because after each encounter with a human, the robot can automatically label it depending on whether it resulted in an interaction or not. We explore different classification approaches, using different sets of features considering the pose and the motion of the user. We validate and deploy the approach in three scenarios. The first collects $3442$ natural sequences (both interacting and non-interacting) representing employees in an office break area: a real-world, challenging setting, where we consider a coffee machine in place of a service robot. The other two scenarios represent researchers interacting with service robots ($200$ and $72$ sequences, respectively). Results show that, even in challenging real-world settings, our approach can learn without external supervision, and can achieve accurate classification (i.e. AUROC greater than $0.9$) of the user's intention to interact with an advance of more than $3$s before the interaction actually occurs., Comment: Paper under revision for Robotics and Autonomous Systems journal

We present measurements of the relation between X-ray luminosity and star formation activity for a sample of normal galaxies spanning the redshift range between 0 and 0.25. We use data acquired by SRG/eROSITA for the performance and verification phase program called eROSITA Final Equatorial Depth Survey (eFEDS). The eFEDS galaxies are observed in the 0.2-2.3 keV band. Making use of a wide range of ancillary data, spanning from the ultraviolet (UV) to mid-infrared wavelengths (MIR), we estimated the star formation rate (SFR) and stellar mass ($M_{star}$) of 888 galaxies, using Code Investigating GALaxy Emission (CIGALE). We divided our sample of normal galaxies in star-forming (SFGs) and quiescent galaxies according to their position on the main sequence. We confirm a linear correlation between the X-ray luminosity and the SFR for our sample of SFGs, as shown previously in the literature. However, we find this relation to be strongly biased by the completeness limit of the eFEDS survey. Correcting for completeness, we find the fitted relation to be consistent with the literature. We also investigated the relation between X-ray emission from both LMXBs and HMXBs populations with $M_{star}$ and SFR, respectively. Correcting for completeness, we find our fitted relation to considerably scatter from the literature relation at high specific SFR ($SFR/M_{star}$). We conclude that without accounting for X-ray non-detections, it is not possible to employ eFEDS data to study the redshift evolution of the LMXBs and HMXBs contributions due to completeness issues. Furthermore, we find our sources to largely scatter from the expected Lx/SFR vs specific SFR relation at high redshift. We discuss the dependence of the scatter on the stellar mass, metallicity, or the globular cluster content of the galaxy., Comment: Accepted for publication in A&A

Looking into the faintEst WIth MUSE (LEWIS) is an ESO large observing programme aimed at obtaining the first homogeneous integral-field spectroscopic survey of 30 extremely low-surface brightness (LSB) galaxies in the Hydra I cluster of galaxies, with MUSE at ESO-VLT. The majority of LSB galaxies in the sample (22 in total) are ultra-diffuse galaxies (UDGs). The distribution of systemic velocities Vsys ranges between 2317 km/s and 5198 km/s and is centred on the mean velocity of Hydra I (Vsys = 3683 $\pm$ 46 km/s). Considering the mean velocity and the velocity dispersion of the cluster, 17 out of 20 targets are confirmed cluster members. To assess the quality of the data and demonstrate the feasibility of the science goals, we report the preliminary results obtained for one of the sample galaxies, UDG11. For this target, we derived the stellar kinematics, including the 2-dimensional maps of line-of-sight velocity and velocity dispersion, constrained age and metallicity, and studied the globular cluster (GC) population hosted by the UDG. Results are compared with the available measurements for UDGs and dwarf galaxies in literature. By fitting the stacked spectrum inside one effective radius, we find that UDG11 has a velocity dispersion $\sigma = 20 \pm 8$ km/s, it is old ($10\pm1$ Gyr), metal-poor ([M/H]=-1.17$\pm$0.11 dex) and has a total dynamical mass-to-light ratio M$/L_V\sim 14$, comparable to those observed for classical dwarf galaxies. The spatially resolved stellar kinematics maps suggest that UDG11 does not show a significant velocity gradient along either major or minor photometric axes. We find two GCs kinematically associated with UDG11. The estimated total number of GCs in UDG11, corrected for the spectroscopic completeness limit, is $N_{GC}= 5.9^{+2.2}_ {-1.8}$, which corresponds to a GC specific frequency of $S_N = 8.4^{+3.2}_{-2.7}$., Comment: Accepted for publication in Astronomy and Astrophysics

Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DC) arranged by various LSST Scientific Collaborations (SC) that are taking place during the projects preoperational phase. The AGN Science Collaboration Data Challenge (AGNSCDC) is a partial prototype of the expected LSST AGN data, aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took part in 2021 focusing on accuracy, robustness, and scalability. The training and the blinded datasets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift and class label with the addition of variability features and images. We present the results of four DC submitted solutions using both classical and machine learning methods. We systematically test the performance of supervised (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised (deep embedding clustering) models when applied to the problem of classifying/clustering sources as stars, galaxies or AGNs. We obtained classification accuracy 97.5% for supervised and clustering accuracy 96.0% for unsupervised models and 95.0% with a classic approach for a blinded dataset. We find that variability features significantly improve the accuracy of the trained models and correlation analysis among different bands enables a fast and inexpensive first order selection of quasar candidates, Comment: Accepted by ApJ. 21 pages, 14 figures, 5 tables