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The cross-correlation of cosmic voids with the lensing convergence ($\kappa$) map of the Cosmic Microwave Background (CMB) fluctuations provides a powerful tool to refine our understanding of the cosmological model. However, several studies have reported a moderate tension between the lensing imprint of cosmic voids on the observed CMB and the simulated $\mathrm{\Lambda}$CDM signal. To address this "lensing-is-low" tension and to obtain new, precise measurements, we exploit the large DESI Legacy Survey Luminous Red Galaxy (LRG) dataset, covering approximately 19,500 $\deg^2$ of the sky and including about 10 million LRGs at $z < 1.05$. Our $\mathrm{\Lambda}$CDM template was created using the Buzzard mocks, which we specifically calibrated to match the clustering properties of the observed galaxy sample by exploiting more than one million DESI spectra. We identified our catalogs of 3D voids in the range $0.35 < z < 0.95$, dividing the sample into bins according to the redshift and $\lambda_\mathrm{v}$ values of the voids. We report a 14$\sigma$ detection of the lensing signal, with $A_\kappa = 1.016 \pm 0.054$, which increases to 17$\sigma$ when considering the void-in-void ($A_\kappa = 0.944 \pm 0.064$) and the void-in-cloud ($A_\kappa = 0.975 \pm 0.060$) populations individually, the highest detection significance for studies of this kind. We observe a full agreement between the observations and $\mathrm{\Lambda}$CDM predictions across all redshift bins, sky regions, and void populations considered. In addition to these findings, our analysis highlights the importance of matching sparseness and redshift error distributions between mocks and observations, as well as the role of $\lambda_\mathrm{v}$ in enhancing the signal-to-noise ratio., Comment: 19 pages, 13 figures

The fast advancement of Large Vision-Language Models (LVLMs) has shown immense potential. These models are increasingly capable of tackling abstract visual tasks. Geometric structures, particularly graphs with their inherent flexibility and complexity, serve as an excellent benchmark for evaluating these models' predictive capabilities. While human observers can readily identify subtle visual details and perform accurate analyses, our investigation reveals that state-of-the-art LVLMs exhibit consistent limitations in specific visual graph scenarios, especially when confronted with stylistic variations. In response to these challenges, we introduce VisGraphVar (Visual Graph Variability), a customizable benchmark generator able to produce graph images for seven distinct task categories (detection, classification, segmentation, pattern recognition, link prediction, reasoning, matching), designed to systematically evaluate the strengths and limitations of individual LVLMs. We use VisGraphVar to produce 990 graph images and evaluate six LVLMs, employing two distinct prompting strategies, namely zero-shot and chain-of-thought. The findings demonstrate that variations in visual attributes of images (e.g., node labeling and layout) and the deliberate inclusion of visual imperfections, such as overlapping nodes, significantly affect model performance. This research emphasizes the importance of a comprehensive evaluation across graph-related tasks, extending beyond reasoning alone. VisGraphVar offers valuable insights to guide the development of more reliable and robust systems capable of performing advanced visual graph analysis.

This paper addresses the problem of learning the impulse responses characterizing forward models by means of a regularized kernel-based Prediction Error Method (PEM). The common approach to accomplish that is to approximate the system with a high-order stable ARX model. However, such choice induces a certain undesired prior information in the system that we want to estimate. To overcome this issue, we propose a new kernel-based paradigm which is formulated directly in terms of the impulse responses of the forward model and leading to the identification of a high-order MAX model. The most challenging step is the estimation of the kernel hyperparameters optimizing the marginal likelihood. The latter, indeed, does not admit a closed form expression. We propose a method for evaluating the marginal likelihood which makes possible the hyperparameters estimation. Finally, some numerical results showing the effectiveness of the method are presented.

We present a modular toolkit to perform joint speaker diarization and speaker identification. The toolkit can leverage on multiple models and algorithms which are defined in a configuration file. Such flexibility allows our system to work properly in various conditions (e.g., multiple registered speakers' sets, acoustic conditions and languages) and across application domains (e.g. media monitoring, institutional, speech analytics). In this demonstration we show a practical use-case in which speaker-related information is used jointly with automatic speech recognition engines to generate speaker-attributed transcriptions. To achieve that, we employ a user-friendly web-based interface to process audio and video inputs with the chosen configuration., Comment: Show and Tell paper. Presented at Interspeech 2024

The Exame Nacional do Ensino M\'edio (ENEM) is a pivotal test for Brazilian students, required for admission to a significant number of universities in Brazil. The test consists of four objective high-school level tests on Math, Humanities, Natural Sciences and Languages, and one writing essay. Students' answers to the test and to the accompanying socioeconomic status questionnaire are made public every year (albeit anonymized) due to transparency policies from the Brazilian Government. In the context of large language models (LLMs), these data lend themselves nicely to comparing different groups of humans with AI, as we can have access to human and machine answer distributions. We leverage these characteristics of the ENEM dataset and compare GPT-3.5 and 4, and MariTalk, a model trained using Portuguese data, to humans, aiming to ascertain how their answers relate to real societal groups and what that may reveal about the model biases. We divide the human groups by using socioeconomic status (SES), and compare their answer distribution with LLMs for each question and for the essay. We find no significant biases when comparing LLM performance to humans on the multiple-choice Brazilian Portuguese tests, as the distance between model and human answers is mostly determined by the human accuracy. A similar conclusion is found by looking at the generated text as, when analyzing the essays, we observe that human and LLM essays differ in a few key factors, one being the choice of words where model essays were easily separable from human ones. The texts also differ syntactically, with LLM generated essays exhibiting, on average, smaller sentences and less thought units, among other differences. These results suggest that, for Brazilian Portuguese in the ENEM context, LLM outputs represent no group of humans, being significantly different from the answers from Brazilian students across all tests., Comment: Accepted at the Seventh AAAI/ACM Conference on AI, Ethics and Society (AIES 2024). 14 pages, 4 figures

The Southern Photometric Local Universe Survey (S-PLUS) is a project to map $\sim9300$ sq deg of the sky using twelve bands (seven narrow and five broadbands). Observations are performed with the T80-South telescope, a robotic telescope located at the Cerro Tololo Observatory in Chile. The survey footprint consists of several large contiguous areas, including fields at high and low galactic latitudes, and towards the Magellanic Clouds. S-PLUS uses fixed exposure times to reach point source depths of about $21$ mag in the $griz$ and $20$ mag in the $u$ and the narrow filters. This paper describes the S-PLUS Data Release 4 (DR4), which includes calibrated images and derived catalogues for over 3000 sq deg, covering the aforementioned area. The catalogues provide multi-band photometry performed with the tools \texttt{DoPHOT} and \texttt{SExtractor} -- point spread function (\PSF) and aperture photometry, respectively. In addition to the characterization, we also present the scientific potential of the data. We use statistical tools to present and compare the photometry obtained through different methods. Overall we find good agreement between the different methods, with a slight systematic offset of 0.05\,mag between our \PSF and aperture photometry. We show that the astrometry accuracy is equivalent to that obtained in previous S-PLUS data releases, even in very crowded fields where photometric extraction is challenging. The depths of main survey (MS) photometry for a minimum signal-to-noise ratio $S/N = 3$ reach from $\sim19.5$ for the bluer bands to $\sim21.5$ mag on the red. The range of magnitudes over which accurate \PSF photometry is obtained is shallower, reaching $\sim19$ to $\sim20.5$ mag depending on the filter. Based on these photometric data, we provide star-galaxy-quasar classification and photometric redshift for millions of objects., Comment: 26 pages, 17 figures, 14 tables, accepted for A&A

Wearable assistive robots (WR) for the lower extremity are extensively documented in literature. Various interfaces have been designed to control these devices during gait and balance activities. However, achieving seamless and intuitive control requires accurate modeling of the human neuromusculoskeletal (NMSK) system. Such modeling enables WR to anticipate user intentions and determine the necessary joint assistance. Despite the existence of controllers interfacing with the NMSK system, robust and generalizable techniques across different tasks remain scarce. Designing these novel controllers necessitates the combined expertise of neurophysiologists, who understand the physiology of movement initiation and generation, and biomechatronic engineers, who design and control devices that assist movement. This paper aims to bridge the gaps between these fields by presenting a primer on key concepts and the current state of the science in each area. We present three main sections: the neuromechanics of locomotion, neuromechanical models of movement, and existing neuromechanical controllers used in WR. Through these sections, we provide a comprehensive overview of seminal studies in the field, facilitating collaboration between neurophysiologists and biomechatronic engineers for future advances in wearable robotics for locomotion.

Context. The dissipation process responsible for the long gamma-ray burst (GRB) prompt emission and the kind of dynamics that drives the release of energy as a function of time are still key open issues. We recently found that the distribution of the number of peaks per GRB is described by a mixture of two exponentials, suggesting the existence of two behaviours that turn up as peak-rich and peak-poor time profiles. Aims. Our aims are to study the distribution of the number of peaks per GRB of the entire catalogue of about 3000 GRBs observed by the Fermi Gamma-ray Burst Monitor (GBM) and to make a comparison with previous results obtained from other catalogues. Methods. We identified GRB peaks using the MEPSA code and modelled the resulting distribution following the same procedure that was adopted in the previous analogous investigation. Results. We confirm that only a mixture of two exponentials can model the distribution satisfactorily, with model parameters that fully agree with those found from previous analyses. In particular, we confirm that (21 +- 4)% of the observed GRBs are peak-rich (8 +- 1 peaks per GRB on average), while the remaining 80% are peak-poor (2.12 +- 0.10 peaks per GRB on average). Conclusions. We confirm the existence of two different components, peak-poor and peak-rich GRBs, that make up the observed GRB populations. Together with previous analogous results from other GRB catalogues, these results provide compelling evidence that GRB prompt emission is governed by two distinct regimes., Comment: 6 pages, 3 figures, accepted as a letter by A&A

Like the letters in the alphabet forming words, reusing components of a heterogeneous mixture is an efficient strategy for assembling a large number of target structures. Examples range from synthetic DNA origami to proteins self-assembling into complexes. The standard self-assembly paradigm views target structures as free-energy minima of a mixture. While this is an appealing picture, at high speed structures may be kinetically trapped in local minima, reducing self-assembly accuracy. How then can high speed, high accuracy, and combinatorial usage of components coexist? We propose to reconcile these three concepts not by avoiding kinetic traps, but by exploiting them to encode target structures. This can be achieved by sculpting the kinetic pathways of the mixture, instead of its free-energy landscape. We formalize these ideas in a minimal toy model, for which we analytically estimate the encoding capacity and kinetic characteristics, in agreement with simulations. Our results may be generalized to other soft-matter systems capable of computation, such as liquid mixtures or elastic networks, and pave the way for high-dimensional information processing far from equilibrium., Comment: Main: 6 pages, 4 figures. SI: 3 pages, 3 figures

Since the rise of Large Language Models (LLMs) a couple of years ago, researchers in metaheuristics (MHs) have wondered how to use their power in a beneficial way within their algorithms. This paper introduces a novel approach that leverages LLMs as pattern recognition tools to improve MHs. The resulting hybrid method, tested in the context of a social network-based combinatorial optimization problem, outperforms existing state-of-the-art approaches that combine machine learning with MHs regarding the obtained solution quality. By carefully designing prompts, we demonstrate that the output obtained from LLMs can be used as problem knowledge, leading to improved results. Lastly, we acknowledge LLMs' potential drawbacks and limitations and consider it essential to examine them to advance this type of research further., Comment: Submitted for publication in an international journal

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'

Quantum computing represents a cutting-edge frontier in artificial intelligence. It makes use of hybrid quantum-classical computation which tries to leverage quantum mechanic principles that allow us to use a different approach to deep learning classification problems. The work presented here falls within the context of the AGILE space mission, launched in 2007 by the Italian Space Agency. We implement different Quantum Convolutional Neural Networks (QCNN) that analyze data acquired by the instruments onboard AGILE to detect Gamma-Ray Bursts from sky maps or light curves. We use several frameworks such as TensorFlow-Quantum, Qiskit and PennyLane to simulate a quantum computer. We achieved an accuracy of 95.1% on sky maps with QCNNs, while the classical counterpart achieved 98.8% on the same data, using however hundreds of thousands more parameters., Comment: 4 pages, 2 figures, proceedings of the ADASS XXXIII (2023) conference, to appear in ASP Conference Serie

Modern Web APIs allow developers to provide extensively customized experiences for website visitors, but the richness of the device information they provide also make them vulnerable to being abused to construct browser fingerprints, device-specific identifiers that enable covert tracking of users even when cookies are disabled. Previous research has established entropy, a measure of information, as the key metric for quantifying fingerprinting risk. However, earlier studies had two major limitations. First, their entropy estimates were based on either a single website or a very small sample of devices. Second, they did not adequately consider correlations among different Web APIs, potentially grossly overestimating their fingerprinting risk. We provide the first study of browser fingerprinting which addresses the limitations of prior work. Our study is based on actual visited pages and Web APIs reported by tens of millions of real Chrome browsers in-the-wild. We accounted for the dependencies and correlations among Web APIs, which is crucial for obtaining more realistic entropy estimates. We also developed a novel experimental design that accurately and efficiently estimates entropy while never observing too much information from any single user. Our results provide an understanding of the distribution of entropy for different website categories, confirm the utility of entropy as a fingerprinting proxy, and offer a method for evaluating browser enhancements which are intended to mitigate fingerprinting., Comment: A version of this report to appear in the proceedings of The Web Conference (WWW) 2024. This version contains additional material in the appendix

Testing the equality of mean vectors across $g$ different groups plays an important role in many scientific fields. In regular frameworks, likelihood-based statistics under the normality assumption offer a general solution to this task. However, the accuracy of standard asymptotic results is not reliable when the dimension $p$ of the data is large relative to the sample size $n_i$ of each group. We propose here an exact directional test for the equality of $g$ normal mean vectors with identical unknown covariance matrix, provided that $\sum_{i=1}^g n_i \ge p+g+1$. In the case of two groups ($g=2$), the directional test is equivalent to the Hotelling's $T^2$ test. In the more general situation where the $g$ independent groups may have different unknown covariance matrices, although exactness does not hold, simulation studies show that the directional test is more accurate than most commonly used likelihood based solutions. Robustness of the directional approach and its competitors under deviation from multivariate normality is also numerically investigated., Comment: 51 pages, 15 figures

The submillimeter and millimeter ((sub-)mm) sky contains a vast wealth of information that is both complementary and inaccessible to other wavelengths. Over half the light we receive is observable at (sub-)mm wavelengths, yet we have mapped only a small portion of the sky at sufficient spatial resolution and sensitivity to detect and resolve distant galaxies or star forming cores within their large-scale environments. For decades the astronomical community has highlighted the need for a large, high-throughput (sub-)mm ($\lambda\sim 0.35-10$ mm) single dish. The Atacama Large Aperture Submillimeter Telescope (AtLAST), with its 50-m aperture and $2^\circ$ maximal field of view, aims to be such a facility. We present here the preliminary design concept for AtLAST, developed through an EU Horizon 2020-funded design study. Our design approach begins with a long lineage of (sub-)mm telescopes, relies on calculations and simulations to realize the optics, and uses finite element analysis to optimize the designs for the mechanical structure and subsystems. The demanding technical requirements for AtLAST, set by transformative science goals, have motivated the design effort to combine novel concepts with lessons learned from the past experience of previous efforts. The result is an innovative rocking chair design with six instrument bays, two of which are mounted on Nasmyth platforms, inside a large receiver cabin. Ultimately, AtLAST aims to achieve a surface accuracy of $\leq 20~\mu$m root mean square half wavefront error, corresponding a Ruze efficiency $>50\%$ at 950~GHz. We conclude that closed-loop metrology of the active primary surface will likely be required to achieve our surface accuracy goal. In the next phase of the project, we will prototype and test such metrology on existing platforms, with a goal of delivering a mature, construction-ready design by the end of this decade., Comment: This version addresses the second round A&A referee reports

The variety of long duration gamma-ray burst (LGRB) light curves (LCs) encode a wealth of information on how LGRB engines release energy following the collapse of the progenitor star. Attempts to characterise GRB LCs focused on a number of properties, such as the minimum variability timescale, power density spectra (both ensemble average and individual), or with different definitions of variability. In parallel, a characterisation as a stochastic process was pursued by studying the distributions of waiting times, peak flux, fluence of individual peaks within GRB time profiles. Yet, the question remains as to whether the diversity of profiles can be described in terms of a common stochastic process. Here we address this issue by studying for the first time the distribution of the number of peaks in a GRB profile. We used four different GRB catalogues: CGRO/BATSE, Swift/BAT, BeppoSAX/GRBM, and Insight-HXMT. The statistically significant peaks were identified by means of well tested algorithm MEPSA and further selected by applying a set of thresholds on signal-to-noise ratio. We then extracted the corresponding distributions of number of peaks per GRB. Among the different models considered (power-law, simple or stretched exponential) only a mixture of two exponentials models all the observed distributions, suggesting the existence of two distinct behaviours: (i) an average number of 2.1+-0.1 peaks per GRB ("peak poor") and accounting for about 80% of the observed population of GRBs; (ii) an average number of 8.3+-1.0 peaks per GRB ("peak rich") and accounting for the remaining 20% of the observed population. We associate the class of peak-rich GRBs with the presence of sub-second variability, which seems to be absent among peak-poor GRBs. The two classes could result from two different regimes through which GRB engines release energy or through which energy is dissipated into gamma-rays., Comment: 7 pages, 5 figures, accepted by A&A

The ability of Large Language Models (LLMs) to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs within the realm of optimization algorithms by integrating them into STNWeb. This is a web-based tool for the generation of Search Trajectory Networks (STNs), which are visualizations of optimization algorithm behavior. Although visualizations produced by STNWeb can be very informative for algorithm designers, they often require a certain level of prior knowledge to be interpreted. In an attempt to bridge this knowledge gap, we have incorporated LLMs, specifically GPT-4, into STNWeb to produce extensive written reports, complemented by automatically generated plots, thereby enhancing the user experience and reducing the barriers to the adoption of this tool by the research community. Moreover, our approach can be expanded to other tools from the optimization community, showcasing the versatility and potential of LLMs in this field., Comment: Submitted to the GECCO 2024 conference

We present the S-PLUS Transient Extension Program (STEP): a supernova and fast transient survey conducted in the southern hemisphere using data from the Southern Photometric Local Universe Survey (S-PLUS) Main Survey and the T80-South telescope. Transient astrophysical phenomena have a range of interest that goes through different fields of astrophysics and cosmology. With the detection of an electromagnetic counterpart to the gravitational wave (GW) event GW170817 from a binary neutron stars merger, new techniques and resources to study fast astrophysical transients in the multi-messenger context have increased. In this paper, we present the STEP overview, the SN follow-up data obtained, data reduction, analysis of new transients and deep learning algorithms to optimize transient candidate selection. Additionally, we present prospects and optimized strategy for the search of Gravitational Wave counterparts in the current LIGO/Virgo/Kagra observational run (O4) in the context of T80-South telescope., Comment: Submitted to MNRAS

Politicization is a social phenomenon studied by political science characterized by the extent to which ideas and facts are given a political tone. A range of topics, such as climate change, religion and vaccines has been subject to increasing politicization in the media and social media platforms. In this work, we propose a computational method for assessing politicization in online conversations based on topic shifts, i.e., the degree to which people switch topics in online conversations. The intuition is that topic shifts from a non-political topic to politics are a direct measure of politicization -- making something political, and that the more people switch conversations to politics, the more they perceive politics as playing a vital role in their daily lives. A fundamental challenge that must be addressed when one studies politicization in social media is that, a priori, any topic may be politicized. Hence, any keyword-based method or even machine learning approaches that rely on topic labels to classify topics are expensive to run and potentially ineffective. Instead, we learn from a seed of political keywords and use Positive-Unlabeled (PU) Learning to detect political comments in reaction to non-political news articles posted on Twitter, YouTube, and TikTok during the 2022 Brazilian presidential elections. Our findings indicate that all platforms show evidence of politicization as discussion around topics adjacent to politics such as economy, crime and drugs tend to shift to politics. Even the least politicized topics had the rate in which their topics shift to politics increased in the lead up to the elections and after other political events in Brazil -- an evidence of politicization., Comment: 12 pages, 6 figures, accepted for the 18th AAAI International Conference on Web and Social Media (ICWSM-2024)

State of the art controllers for back exoskeletons largely rely on body kinematics. This results in control strategies which cannot provide adaptive support under unknown external loads. We developed a neuromechanical model-based controller (NMBC) for a soft back exosuit, wherein assistive forces were proportional to the active component of lumbosacral joint moments, derived from real-time electromyography-driven models. The exosuit provided adaptive assistance forces with no a priori information on the external loading conditions. Across 10 participants, who stoop-lifted 5 and 15 kg boxes, our NMBC was compared to a non-adaptive virtual spring-based control(VSBC), in which exosuit forces were proportional to trunk inclination. Peak cable assistive forces were modulated across weight conditions for NMBC (5kg: 2.13 N/kg; 15kg: 2.82 N/kg) but not for VSBC (5kg: 1.92 N/kg; 15kg: 2.00 N/kg). The proposed NMBC strategy resulted in larger reduction of cumulative compression forces for 5 kg (NMBC: 18.2%; VSBC: 10.7%) and 15 kg conditions (NMBC: 21.3%; VSBC: 10.2%). Our proposed methodology may facilitate the adoption of non-hindering wearable robotics in real-life scenarios., Comment: 17 pages, 8 figures