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A breakdown of Lorentz Invariance is predicted by some theories as a result of quantum physics applied to gravity or for the presence of vector/tensor fields, beside the metric tensor of General Relativity, mediating the gravitational interaction. A violation of Lorentz Invariance would manifest itself, in the gravitational sector, mainly through a non-zero value of the post-Newtonian parameter $\alpha_1$, that is null in General Relativity. In this paper we describe a test of Local Lorentz Invariance considering the effect of a possible preferred frame, that of the cosmic microwave background, on the orbit of the LAGEOS II satellite tracked by the Satellite Laser Ranging technique. We constrain this parameter down to the level of $3\times10^{-5}$, improving a previous limit obtained through the Lunar Laser Ranging technique., Comment: 6 pages, 4 figures. Under review in Physical Review Letters

Cerberus will be a new scientific instrument for the alt-az, 1m-class OARPAF telescope in Northern Italy. Currently, the telescope operates with a CCD camera used for imaging and photometry. One of the objectives of the project is to improve this observing mode with a tip-tilt lens for image stabilization up to 10Hz. Moreover, a long-slit spectroscopy at R 5900 and an optical fiber \'echelle spectroscopy at R 9300 observing modes will be included. These addtional two "heads" of Cerberus will be exclusively selected by moving flat 45 degree mirrors by means of a linear stage placed in a new custom interface flange. The flange will replace the existing one, recovering the included field flattener lens, to ensure optical correction of the imaging channel. We present the already procured COTS hardware, the opto-mechanical design of the interface flange, the results of the Zemax ray tracing and the design of the web-based instrument control software., Comment: 5 pages, 2 figures

Remote observations are often limited by user interfaces, which seem frozen to another computer era: low performances, outdated programming languages, command-line scripting, high version-dependent software. Instead, web-based tools are standard: using nothing more than a browser, astronomers can interact with a generic observatory in a native cross-platform, remote-born way. We used this approach while advancing in the remotization of the 1m-class OARPAF telescope, located in Northern Italy. The web-based control software provides easy and integrated management of its components. This solution can be exported not only to similar hardware/software facilities, but also to large instruments such as SHARK-NIR at LBT; not only for single operations, but also for procedure scripting. In this contribution we describe our best practices and present two recent, orthogonal use cases: an in-place professional use for exoplanetary transit follow-ups, and the first remote control of the telescope from a Japanese high school, allowing students to independently observe, in their daytime, globular clusters, Comment: 6 pages, 3 figures

This paper aims to describe the Pointing Reconstruction Model (PRM) and the prototype Star Tracker, which will be mounted on LSPE-Strip, a microwave Q- and W-band CMB telescope planned for installation at the "Observatorio del Teide" in Tenerife. The PRM integrates information on the instantaneous attitude provided by the telescope control system to determine the actual pointing direction and focal plane orientation of the telescope. It accounts for various non-idealities in the telescope setup, represented by eight configuration angles, which will be calibrated using the Star Tracker. Following the derivation of the PRM formalism and its implementation, we investigate the pointing errors caused by incorrect calibration of these configuration angles to validate the required 1 arcminute maximum systematic pointing error for the LSPE-Strip survey. This paper also describes the main structure and operations of the Star Tracker and presents the results of a campaign of actual sky observations conducted with a prototype. The results demonstrate a Star Tracker RMS accuracy of approximately 3 arcseconds, while systematic errors remain below 10 arcseconds. Based on these results, we analyzed the problem of reconstructing the PRM configuration angles. Two methods for intercalibrating the Star Tracker's pointing direction with respect to the focal plane's pointing direction were examined: (1) observations of planets and (2) observations of a drone carrying both an optical beacon and a radio beacon. In the first case, an intercalibration accuracy between 1/3 arcminute and 1 arcminute is achievable. In the second case, the expected intercalibration accuracy ranges from 0.25 arcminute to 1 arcminute., Comment: 37 pages; 16 figures; 5 tables; the limited length of the arXiv abstract required shortening the original abstract, which is presented in its full length in the PDF; paper accepted for pubblication 2025 jan 06 in the Journal of Instrumentation (JINST)

The study of regular black holes and black hole mimickers as alternatives to standard black holes has recently gained significant attention, driven both by the need to extend general relativity to describe black hole interiors, and by recent advances in observational technologies. Despite considerable progress in this field, significant challenges remain in identifying and characterizing physically well-motivated classes of regular black holes and black hole mimickers. This report provides an overview of these challenges, and outlines some of the promising research directions -- as discussed during a week-long focus programme held at the Institute for Fundamental Physics of the Universe (IFPU) in Trieste from November 11th to 15th, 2024., Comment: 39 pages, 3 figures

Early-type galaxies (ETGs) are reference systems to understand galaxy formation and evolution processes. The physics of their collapse and internal dynamics are codified in well-known scaling relations. Cosmological hydrodynamical simulations play an important role, providing insights into the 3D distribution of matter and galaxy formation mechanisms, as well as validating methods to infer the properties of real objects. In this work, we present the closest-to-reality sample of ETGs from the IllustrisTNG100-1 simulation, dubbed "virtual-ETGs," based on an observational-like algorithm that combines standard projected and three-dimensional galaxy structural parameters. We extract 2D photometric information by projecting the galaxies' light into three planes and modeling them via S\'ersic profiles. Aperture velocity dispersions, corrected for softened central dynamics, are calculated along the line-of-sight orthogonal to the photometric projection plane. Central mass density profiles assume a power-law model, while 3D masses remain unmodified from the IllustrisTNG catalogue. The final catalogue includes $10121$ galaxies at redshifts $z \leq 0.1$. By comparing the virtual properties with observations, we find that the virtual-ETG scaling relations (e.g., size-mass, size-central surface brightness, and Faber-Jackson), central density slopes, and scaling relations among total density slopes and galaxy structural parameters are generally consistent with observations. We make the virtual-ETG publicly available for galaxy formation studies and plan to use this sample as a training set for machine learning tools to infer galaxy properties in future imaging and spectroscopic surveys.

This paper broaches the peridynamic inverse problem of determining the horizon size of the kernel function in a one-dimensional model of a linear microelastic material. We explore different kernel functions, including V-shaped, distributed, and tent kernels. The paper presents numerical experiments using PINNs to learn the horizon parameter for problems in one and two spatial dimensions. The results demonstrate the effectiveness of PINNs in solving the peridynamic inverse problem, even in the presence of challenging kernel functions. We observe and prove a one-sided convergence behavior of the Stochastic Gradient Descent method towards a global minimum of the loss function, suggesting that the true value of the horizon parameter is an unstable equilibrium point for the PINN's gradient flow dynamics.

Recent innovations in light sheet microscopy, paired with developments in tissue clearing techniques, enable the 3D imaging of large mammalian tissues with cellular resolution. Combined with the progress in large-scale data analysis, driven by deep learning, these innovations empower researchers to rapidly investigate the morphological and functional properties of diverse biological samples. Segmentation, a crucial preliminary step in the analysis process, can be automated using domain-specific deep learning models with expert-level performance. However, these models exhibit high sensitivity to domain shifts, leading to a significant drop in accuracy when applied to data outside their training distribution. To address this limitation, and inspired by the recent success of self-supervised learning in training generalizable models, we organized the SELMA3D Challenge during the MICCAI 2024 conference. SELMA3D provides a vast collection of light-sheet images from cleared mice and human brains, comprising 35 large 3D images-each with over 1000^3 voxels-and 315 annotated small patches for finetuning, preliminary testing and final testing. The dataset encompasses diverse biological structures, including vessel-like and spot-like structures. Five teams participated in all phases of the challenge, and their proposed methods are reviewed in this paper. Quantitative and qualitative results from most participating teams demonstrate that self-supervised learning on large datasets improves segmentation model performance and generalization. We will continue to support and extend SELMA3D as an inaugural MICCAI challenge focused on self-supervised learning for 3D microscopy image segmentation., Comment: 2st version

This study aims to observe if the theorem prover Lean positively influences students' understanding of mathematical proving. To this end, we perform a pilot study concerning freshmen students at the University of Zurich (UZH). While doing so, we apply certain teaching methods and gather data from the volunteer students enrolled in the ``Foundations of Mathematics'' course. After eleven weeks of study covering some exercise questions implemented with Lean, we measure Lean students' performances in proving mathematical statements, compared to other students who are not engaged with Lean. For this measurement, we interview five Lean and four Non-Lean students and we analyze the scores of all students in the final exam. Finally, we check significance by performing a $t$-test for independent samples and the Mann-Whitney $U$-test.

SPARC4 is a new astronomical instrument developed entirely by Brazilian institutions, currently installed on the 1.6-m Perkin-Elmer telescope of the Pico dos Dias Observatory. It allows the user to perform photometric or polarimetric observations simultaneously in the four SDSS bands (g, r, i, and z). In this paper, we describe the control system developed for SPARC4. This system is composed of S4ACS, S4ICS, and S4GUI softwares and associated hardware. S4ACS is responsible for controlling the four EMCCD scientific cameras (one for each instrument band). S4ICS controls the sensors and motors responsible for the moving parts of SPARC4. Finally, S4GUI is the interface used to perform observations, which includes the choice of instrument configuration and image acquisition parameters. S4GUI communicates with the instrument subsystems and with some observatory facilities, needed during the observations. Bench tests were performed for the determination of the overheads added by SPARC4 control system in the acquisition of photometric and polarimetric series of images. In the photometric mode, SPARC4 allows the acquisition of a series of 1400 full-frame images, with a deadtime of 4.5 ms between images. Besides, several image series can be concatenated with a deadtime of 450 ms plus the readout time of the last image. For the polarimetric mode, measurements can be obtained with a deadtime of 1.41 s plus the image readout time between subsequent waveplate positions. For both photometric and polarimetric modes, the user can choose among operating modes with image readout times between 5.9 ms and 1.24 s, which ultimately defines the instrument temporal performance., Comment: 20 pages, 14 figures, peer-reviewed paper

Origami morphing, obtained with patches of piecewise smooth isometries separated by straight fold lines, is an exquisite art that has already received considerable attention in the mathematics and mechanics literature. Curved fold lines, leading to curved creases and curved pleated structures, introduce the additional complexity of mechanical coupling between the folds. This coupling can be exploited to obtain morphing structures with more robust folding pathways. We discuss one-degree-of-freedom mechanisms and folds actuated by spontaneous curvature (as in the case of hygromorphic multilayered composites), comparing the purely geometric approach to two approaches based on the mechanics of active shells and of active three-dimensional solids. Moreover, we discuss the cooperativity of multiple folds and demonstrate the energetic advantage of synchronous folding over sequential folding.

Models proposing a non-gravitational interaction between dark energy (DE) and dark matter (CDM) have been extensively studied as alternatives to the standard cosmological model. A common approach to describing the DE-CDM coupling assumes it to be linearly proportional to the dark energy density. In this work, we consider the model with interaction term $Q=3H\gamma{\rho_{x}^{2}}/{(\rho_{c}+\rho_{x})}$. We show that for positive values of $\gamma$ this model predicts a future violation of the Weak Energy Condition (WEC) for the dark matter component, and for a specific range of negative values of $\gamma$ the CDM energy density can be negative in the past. We perform a parameter selection analysis for this model using data from Type Ia supernovae, Cosmic Chronometers, Baryon Acoustic Oscillations, and CMB combined with the Hubble constant $H_0$ prior. Imposing a prior to ensure that the WEC is not violated, our model is consistent with $\Lambda$CDM in 2$\sigma$ C.L.. In reality, the WEC prior shifts the constraints towards smaller values of $H_0$, highlighting an increase in the tension on the Hubble parameter. However, it significantly improves the parameter constraints, with a preference for smaller values of $\sigma_8$, alleviating the $\sigma_8$ tension between the CMB results from Planck 2018 and the weak gravitational lensing observations from the KiDS-1000 cosmic shear survey. In the case without the WEC prior, our model seems to alleviate the $H_0$ tension, which is related to the positive value of the interaction parameter $\gamma$., Comment: 19 pages, 18 figures, 5 tables. Submitted for publication in EPJC

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024, building on the initial discussions during the inaugural workshop held at CERN in March 2023. Like the summary of the first workshop, this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions., Comment: Summary of the second Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at Imperial College London: https://indico.cern.ch/event/1369392/

We propose to explore the potential advantages of a new class of tracking algorithms loosely inspired by the Hough transform concept and where we include the time of arrival of each hit as an additional coordinate to be treated in the same way as a spatial coordinate. A remarkable property of this algorithm is that the execution time is proportional to the total number of hits to be processed, making it particularly attractive for high occupancy situations expected at future colliders. The particular structure of the algorithm also lends itself naturally to parallel hardware implementations which, combined to its intrinsic flexibility, should provide a powerful tool for triggering at future colliders. To probe the effectiveness of the algorithm, we apply it to a quasi-realistic simulated environment of a possible future muon collider experiment and report the performance.

Mushroom-shaped pillars have been extensively studied for their superior adhesive properties, often drawing inspiration from natural attachment systems observed in insects. Typically, pillars are modeled with linear elastic materials in the literature; in reality, the soft materials used for their fabrication exhibit a rate-dependent constitutive behavior. This study investigates the role of viscoelasticity in the adhesion between a mushroom-shaped pillar and a rigid flat countersurface. Interactions at the interface are assumed to be governed by van der Waals forces, and the material is modeled using a standard linear solid model. Normal push and release contact cycles are simulated at different approaching and retracting speeds. Results reveal that, in the presence of an interfacial defect, a monotonically increasing trend in the pull-off force with pulling speed is observed, and the corresponding change in the contact pressure distribution suggests a transition from short-range to long-range adhesion. This phenomenon corroborates recent experimental and theoretical investigations. Moreover, the pull-off force remains invariant to the loading history, due to our assumption of a flat-flat contact interface. Conversely, in absence of defects, detachment occurs after reaching the theoretical contact strength, and the corresponding pull-off force is found to be rate independent.

In this paper we prove general criticality criteria for operators $\Delta + V$ on manifolds with more than one end, where $V$ bounds the Ricci curvature, and a related spectral splitting theorem extending Cheeger-Gromoll's one. Our results give new insight on Li-Wang's theory of manifolds with a weighted Poincar\'e inequality. We apply them to study stable and $\delta$-stable minimal hypersurfaces in manifolds with non-negative bi-Ricci or sectional curvature, in ambient dimension up to $5$ and $6$, respectively. In the special case where the ambient space is $\mathbb{R}^4$, we prove that a $1/3$-stable minimal hypersurface must either have one end or be a catenoid, and that proper, $\delta$-stable minimal hypersurfaces with $\delta > 1/3$ must be hyperplanes., Comment: 36pp. We supplied the missing topological argument to prove Theorem 1.2 in its stronger version (that in v1), that is, without the assumption that $M$ has finite fundamental group. Package axessibility included to make the paper available to visually impaired people

The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunofluorescence imaging methods. The design of the Knowledge-Base is modeled after efforts in the open-source software community and includes three facets: a development platform (GitHub), static website, and service for data archiving. The Knowledge-Base facilitates the practice of open science throughout the research life cycle by providing validation data for recommended and non-recommended reagents, e.g., primary and secondary antibodies. In addition to reporting negative data, the Knowledge-Base empowers method adoption and evolution by providing a venue for sharing protocols, videos, datasets, software, and publications. A dedicated discussion forum fosters a sense of community among researchers while addressing questions not covered in published manuscripts. Together, scientists from around the world are advancing scientific discovery at a faster pace, reducing wasted time and effort, and instilling greater confidence in the resulting data.

In recent years, the application of machine learning to minimally invasive surgery (MIS) has attracted considerable interest. Datasets are critical to the use of such techniques. This paper presents a unique dataset recorded during ex vivo pseudo-cholecystectomy procedures on pig livers using the da Vinci Research Kit (dVRK). Unlike existing datasets, it addresses a critical gap by providing comprehensive kinematic data, recordings of all pedal inputs, and offers a time-stamped record of the endoscope's movements. This expanded version also includes segmentation and keypoint annotations of images, enhancing its utility for computer vision applications. Contributed by seven surgeons with varied backgrounds and experience levels that are provided as a part of this expanded version, the dataset is an important new resource for surgical robotics research. It enables the development of advanced methods for evaluating surgeon skills, tools for providing better context awareness, and automation of surgical tasks. Our work overcomes the limitations of incomplete recordings and imprecise kinematic data found in other datasets. To demonstrate the potential of the dataset for advancing automation in surgical robotics, we introduce two models that predict clutch usage and camera activation, a 3D scene reconstruction example, and the results from our keypoint and segmentation models., Comment: Preprint of an article accepted in Journal of Medical Robotics Research (2024). The metadata will be updated once it is published

The paper introduces a novel approach for estimating soil moisture in vegetated surfaces, specifically focusing on sugarcane crops throughout various growth stages in agriculture applications. While existing models typically address bare soil scenarios, this model utilizes data from P-, L-, and C-band Synthetic Aperture Radar (SAR) to estimate soil moisture. The semi-empirical Dubois model forms the basis of the proposed model, which has been adapted to accommodate multiband operation and crop height variations. Synthetic datasets are generated using the adjusted model to train two neural networks incorporated into the overall model. Additionally, a linear expression for estimating crop height is integrated into the model. The model is validated in an Experimental Site at the School of Agricultural Engineering, UNICAMP, and an independent area at the Sugarcane Technology Center in Piracicaba, Brazil. The model utilizes a multiband drone-borne SAR system with a 3-meter image resolution and radiometric accuracy of 0.5 dB. The results indicate that the model can estimate soil moisture with root-mean-square errors of 0.05 cm3.cm-3 (5 vol. %) across crop heights ranging from zero to 2.5 meters., Comment: 9 pages, 12 figures, 2 tables, 17 equations

Leaf-cutting ants, notorious for causing defoliation in commercial forest plantations, significantly contribute to biomass and productivity losses, impacting forest producers in Brazil. These ants construct complex underground nests, highlighting the need for advanced monitoring tools to extract subsurface information across large areas. Synthetic Aperture Radar (SAR) systems provide a powerful solution for this challenge. This study presents the results of electromagnetic simulations designed to detect leaf-cutting ant nests in industrial forests. The simulations modeled nests with 6 to 100 underground chambers, offering insights into their radar signatures. Following these simulations, a field study was conducted using a drone-borne SAR operating in the P-band. A helical flight pattern was employed to generate high-resolution ground tomography of a commercial eucalyptus forest. A convolutional neural network (CNN) was implemented to detect ant nests and estimate their sizes from tomographic data, delivering remarkable results. The method achieved an ant nest detection accuracy of 100%, a false alarm rate of 0%, and an average error of 21% in size estimation. These outcomes highlight the transformative potential of integrating Synthetic Aperture Radar (SAR) systems with machine learning to enhance monitoring and management practices in commercial forestry., Comment: 8 pages, 22 figures, 4 tables

We aim to investigate the energy-resolved pulse profile changes of the accreting X-ray pulsar V 0332+53, focusing in the cyclotron line energy range, using the full set of available NuSTAR observations. We applied a tailored pipeline to study the energy dependence of the pulse profiles and to build the pulsed fraction spectra (PFS) for the different observations. We studied the profile changes also using cross-correlation and lag spectra. We re-analysed the energy spectra to search for links between the local features observed in the PFS and spectral emission components associated with the shape of the fundamental cyclotron line. In the PFS data, with sufficiently high statistics, we observe a consistent behaviour around the cyclotron line energy. Specifically, two Gaussian-shaped features appear symmetrically on either side of the putative cyclotron line. These features exhibit minimal variation with source luminosity, and their peak positions consistently remain on the left and right of the cyclotron line energy. Associated with the cyclotron line-forming region, we interpret them as evidence for the resonant cyclotron absorption line wings, as predicted by theoretical models of how the cyclotron line profile should appear along the observer's line of sight. A phase-resolved analysis of the pulse in the energy bands surrounding these features enables us to determine both the spectral shape and the intensity of the photons responsible for these peaks in the PFS. Assuming these features correspond to a spectral component, we used their shapes as priors for the corresponding emission components, finding a statistically satisfactory description of the spectra. To explain these results, we propose that our line of sight is close to the direction of the spin axis, while the magnetic axis is likely orthogonal to it., Comment: 33 pages, 58 figures. Accepted for publication in A&A

In this paper we study the rigidity problem for sub-static systems with possibly non-empty boundary. First, we get local and global splitting theorems by assuming the existence of suitable compact minimal hypersurfaces, complementing recent results in the literature. Next, we prove some boundary integral inequalities that extend works by Chr\'usciel and Boucher-Gibbons-Horowitz to non-vacuum spaces. Even in the vacuum static case, the inequalities improve on known ones. Lastly, we consider the system arising from static solutions to the Einstein field equations coupled with a $\sigma$-model. The Liouville theorem we obtain allows for positively curved target manifolds, generalizing a result by Reiris., Comment: 29 pages. Comments are welcome!

The integration of artificial intelligence (AI) into the workplace is advancing rapidly, necessitating robust metrics to evaluate its tangible impact on the labour market. Existing measures of AI occupational exposure largely focus on AI's theoretical potential to substitute or complement human labour on the basis of technical feasibility, providing limited insight into actual adoption and offering inadequate guidance for policymakers. To address this gap, we introduce the AI Startup Exposure (AISE) index-a novel metric based on occupational descriptions from O*NET and AI applications developed by startups funded by the Y Combinator accelerator. Our findings indicate that while high-skilled professions are theoretically highly exposed according to conventional metrics, they are heterogeneously targeted by startups. Roles involving routine organizational tasks-such as data analysis and office management-display significant exposure, while occupations involving tasks that are less amenable to AI automation due to ethical or high-stakes, more than feasibility, considerations -- such as judges or surgeons -- present lower AISE scores. By focusing on venture-backed AI applications, our approach offers a nuanced perspective on how AI is reshaping the labour market. It challenges the conventional assumption that high-skilled jobs uniformly face high AI risks, highlighting instead the role of today's AI players' societal desirability-driven and market-oriented choices as critical determinants of AI exposure. Contrary to fears of widespread job displacement, our findings suggest that AI adoption will be gradual and shaped by social factors as much as by the technical feasibility of AI applications. This framework provides a dynamic, forward-looking tool for policymakers and stakeholders to monitor AI's evolving impact and navigate the changing labour landscape., Comment: 24 pages, 6 figures, + Supplementary information

Large Language Models (LLMs) have demonstrated excellent capabilities in composing various modules together to create programs that can perform complex reasoning tasks on images. In this paper, we propose TANGO, an approach that extends the program composition via LLMs already observed for images, aiming to integrate those capabilities into embodied agents capable of observing and acting in the world. Specifically, by employing a simple PointGoal Navigation model combined with a memory-based exploration policy as a foundational primitive for guiding an agent through the world, we show how a single model can address diverse tasks without additional training. We task an LLM with composing the provided primitives to solve a specific task, using only a few in-context examples in the prompt. We evaluate our approach on three key Embodied AI tasks: Open-Set ObjectGoal Navigation, Multi-Modal Lifelong Navigation, and Open Embodied Question Answering, achieving state-of-the-art results without any specific fine-tuning in challenging zero-shot scenarios.

The abundances resulting from $r$-process nucleosynthesis as predicted by simulations of binary neutron-star (BNS) mergers remain an open question as the current state-of-the-art is still restricted to three-species neutrino transport. We present the first BNS merger simulations employing a moment-based general-relativistic neutrino transport with five neutrino species, thus including (anti)muons and advanced muonic $\beta$-processes, and contrast them with traditional three neutrino-species simulations. Our results show that a muonic trapped-neutrino equilibrium is established, forming a different trapped-neutrino hierarchy akin to the electronic equilibrium. The formation of (anti)muons and the muonization via muonic $\beta$-processes enhance the neutrino luminosity, leading to rapid cooling in the early post-merger phase. Since muonic processes redirect part of the energy otherwise used for protonization by electronic processes, they yield a cooler remnant and disk, together with neutrino-driven winds that are more neutron-rich. Importantly, the unbound ejected mass is smaller than three-species simulations and, because of its comparatively smaller temperature and proton fraction, it can enhance lanthanide production and reduce the overproduction of light $r$-process elements for softer equations of state. This finding underlines the importance of muonic interactions and five neutrino species in long-lived BNS remnants.

Context. The accretion of magnetized plasma onto black holes is a complex and dynamic process, where the magnetic field plays a crucial role. The amount of magnetic flux accumulated near the event horizon significantly impacts the accretion flow behavior. Resistivity, a measure of how easily magnetic fields can dissipate, is thought to be a key factor influencing this process. This work explores the influence of resistivity on accretion flow variability. We investigate simulations reaching the magnetically arrested disk (MAD) limit and those with an initial multi-loop magnetic field configuration. Methods. We employ 3D resistive general relativistic magnetohydrodynamic (GRMHD) simulations to model the accretion process under various regimes, where resistivity has a global uniform value. Results. Our findings reveal distinct flow behaviors depending on resistivity. High resistivity simulations never achieve the MAD state, indicating a disturbed magnetic flux accumulation process. Conversely, low resistivity simulations converge towards the ideal MHD limit. The key results are: i) For the standard MAD model, resistivity plays a minimal role in flow variability, suggesting that flux eruption events dominate the dynamics. ii) High resistivity simulations exhibit strong magnetic field diffusion into the disk, rearranging efficient magnetic flux accumulation from the accretion flow. iii) In multi-loop simulations, resistivity significantly reduces flow variability, which was not expected. However, magnetic flux accumulation becomes more variable due to frequent reconnection events at very low resistivity values. Conclusions. This study shows that resistivity affects how much the flow is distorted due to magnetic field dissipation. Our findings provide new insights into the interplay between magnetic field accumulation, resistivity, variability and the dynamics of black hole accretion., Comment: 8 pages, Accepted in A&A

Recent advances in AI -- including generative approaches -- have resulted in technology that can support humans in scientific discovery and decision support but may also disrupt democracies and target individuals. The responsible use of AI increasingly shows the need for human-AI teaming, necessitating effective interaction between humans and machines. A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise. In cognitive science, human generalisation commonly involves abstraction and concept learning. In contrast, AI generalisation encompasses out-of-domain generalisation in machine learning, rule-based reasoning in symbolic AI, and abstraction in neuro-symbolic AI. In this perspective paper, we combine insights from AI and cognitive science to identify key commonalities and differences across three dimensions: notions of generalisation, methods for generalisation, and evaluation of generalisation. We map the different conceptualisations of generalisation in AI and cognitive science along these three dimensions and consider their role in human-AI teaming. This results in interdisciplinary challenges across AI and cognitive science that must be tackled to provide a foundation for effective and cognitively supported alignment in human-AI teaming scenarios.

This paper compares machine learning approaches with different input data formats for the classification of acoustic emission (AE) signals. AE signals are a promising monitoring technique in many structural health monitoring applications. Machine learning has been demonstrated as an effective data analysis method, classifying different AE signals according to the damage mechanism they represent. These classifications can be performed based on the entire AE waveform or specific features that have been extracted from it. However, it is currently unknown which of these approaches is preferred. With the goal of model deployment on resource-constrained embedded Internet of Things (IoT) systems, this work evaluates and compares both approaches in terms of classification accuracy, memory requirement, processing time, and energy consumption. To accomplish this, features are extracted and carefully selected, neural network models are designed and optimized for each input data scenario, and the models are deployed on a low-power IoT node. The comparative analysis reveals that all models can achieve high classification accuracies of over 99\%, but that embedded feature extraction is computationally expensive. Consequently, models utilizing the raw AE signal as input have the fastest processing speed and thus the lowest energy consumption, which comes at the cost of a larger memory requirement., Comment: Conference Presentations (Accepted) at IEEE 10th World Forum on Internet of Things. "https://wfiot2024.iot.ieee.org/program/technical-paper-program"

We explore the potential implications of Kaluza-Klein (KK) gravity in unifying the dark sector of the Universe. Through dimensional reduction in KK gravity, the 5D spacetime framework can be reformulated in terms of a 4D spacetime metric, along with additional scalar and vector fields. From the 4D perspective, this suggests the existence of a tower of particle states, including KK gravitons with massive spin-0 and spin-1 states, in addition to the massless spin-2 gravitons of general relativity (GR). By assuming a minimal coupling between the self-interacting scalar field and the gauge field, a "mass" term emerges for the spin-1 gravitons. This, in turn, leads to long-range gravitational effects that could modify Newton's law of gravity through Yukawa-type corrections. We draw an analogy with superconductivity theory, where the condensation of a scalar field results in the emergence of massive spin-1 particles producing repulsive forces, along with an increase of the gravitational force due the correction to Newton's constant. Assuming an environment-dependent mass for the spin-1 graviton, near the galactic center the repulsive force from this spin-1 graviton is suppressed by an additional attractive component from Newton's constant corrections, resulting in a Newtonian-like, attraction-dominated effect. In the galaxy's outer regions, the repulsive force fades due to its short range, making dark matter appear only as an effective outcome of the dominant attractive corrections. This approach also explains dark matter's emergence as an apparent effects on cosmological scales while our model is equivalent to the scalar-vector-tensor gravity theory. Finally, we examine the impact of dark matter on the primordial gravitational wave (PGW) spectrum and show that it is sensitive to dark matter effects, providing an opportunity to test this theory through future GW observatories., Comment: 13 pages, 4 figures

High-level synthesis (HLS) aims at democratizing custom hardware acceleration with highly abstracted software-like descriptions. However, efficient accelerators still require substantial low-level hardware optimizations, defeating the HLS intent. In the context of field-programmable gate arrays, digital signal processors (DSPs) are a crucial resource that typically requires a significant optimization effort for its efficient utilization, especially when used for sub-word vectorization. This work proposes SILVIA, an open-source LLVM transformation pass that automatically identifies superword-level parallelism within an HLS design and exploits it by packing multiple operations, such as additions, multiplications, and multiply-and-adds, into a single DSP. SILVIA is integrated in the flow of the commercial AMD Vitis HLS tool and proves its effectiveness by packing multiple operations on the DSPs without any manual source-code modifications on several diverse state-of-the-art HLS designs such as convolutional neural networks and basic linear algebra subprograms accelerators, reducing the DSP utilization for additions by 70 % and for multiplications and multiply-and-adds by 50 % on average.

Mining 4.0 leverages advancements in automation, digitalization, and interconnected technologies from Industry 4.0 to address the unique challenges of the mining sector, enhancing efficiency, safety, and sustainability. Conveyor belts are crucial in mining operations by enabling the continuous and efficient movement of bulk materials over long distances, which directly impacts productivity. While detecting anomalies in specific conveyor belt components, such as idlers, pulleys, and belt surfaces, has been widely studied, identifying the root causes of these failures remains critical due to factors like changing production conditions and operator errors. Continuous monitoring of mining conveyor belt work cycles for anomaly detection is still at an early stage and requires robust solutions. This study proposes two distinctive pattern recognition approaches for real-time anomaly detection in the operational cycles of mining conveyor belts, combining feature extraction, threshold-based cycle detection, and tiny machine-learning classification. Both approaches outperformed a state-of-the-art technique on two datasets for duty cycle classification in terms of F1-scores. The first approach, with 97.3% and 80.2% for normal and abnormal cycles, respectively, reaches the highest performance in the first dataset while the second approach excels on the second dataset, scoring 91.3% and 67.9%. Implemented on two low-power microcontrollers, the methods demonstrated efficient, real-time operation with energy consumption of 13.3 and 20.6 ${\mu}$J during inference. These results offer valuable insights for detecting mechanical failure sources, supporting targeted preventive maintenance, and optimizing production cycles., Comment: Preprint submitted to IEEE Transactions on Instrumentation and Measurement

Engineering technologies frequently draw inspiration from nature, as exemplified in bio-inspired adhesive surfaces. These surfaces present textures adorned by pillars, mimicking the topography found on the pads of certain animals renowned for their exceptional adhesive capabilities. The adhesive response is strongly influenced by the morphology of these pillars. In typical existing models, perfect bonding conditions are assumed between the pillar and the countersurface, and solely the detachment process of the pillar from the countersurface is investigated. The proposed model, based on the assumption that interactions at the interface are governed by van der Waals forces modeled by the Lennard-Jones potential law, enables the examination of the entire approach and retraction cycle, tracking the movement of the pillar towards and away from the countersurface. Our findings reveal that adhesive contact mechanics is primarily influenced by the geometry of the pillar and the potential presence of interfacial 'defects', which in turn affect the distribution of contact pressure. Furthermore, we show that the detachment process may simultaneously involve various modes of separation, such as crack propagation from outer edge, crack propagation from inner defects, and uniform decohesion. This suggests that existing theoretical models alone cannot fully elucidate the complexity of detachment phenomena. Additionally, we anticipate the occurrence of hysteretic losses during the approach-retraction cycle, attributed to pull-in and pull-off contact jumps. Adhesive hysteresis is a phenomenon consistently observed in experiments but frequently overlooked in existing models.

Mining machinery operating in variable environments faces high wear and unpredictable stress, challenging Predictive Maintenance (PdM). This paper introduces the Edge Sensor Network for Predictive Maintenance (ESN-PdM), a hierarchical inference framework across edge devices, gateways, and cloud services for real-time condition monitoring. The system dynamically adjusts inference locations--on-device, on-gateway, or on-cloud--based on trade-offs among accuracy, latency, and battery life, leveraging Tiny Machine Learning (TinyML) techniques for model optimization on resource-constrained devices. Performance evaluations showed that on-sensor and on-gateway inference modes achieved over 90\% classification accuracy, while cloud-based inference reached 99\%. On-sensor inference reduced power consumption by approximately 44\%, enabling up to 104 hours of operation. Latency was lowest for on-device inference (3.33 ms), increasing when offloading to the gateway (146.67 ms) or cloud (641.71 ms). The ESN-PdM framework provides a scalable, adaptive solution for reliable anomaly detection and PdM, crucial for maintaining machinery uptime in remote environments. By balancing accuracy, latency, and energy consumption, this approach advances PdM frameworks for industrial applications., Comment: This work has been submitted to the IEEE for possible publication

Astronomical observations and numerical simulations are providing increasing evidence that resistive effects in plasmas around black holes play an important role in determining the phenomenology observed from these objects. In this spirit, we present a general approach to the study of a Penrose process driven by plasmoids that are produced at reconnection sites along current sheets. Our formalism is meant to determine the physical conditions that make a plasmoid-driven Penrose process energetically viable and can be applied to scenarios that are matter- or magnetic-field-dominated, that is, in magnetohydrodynamical or force-free descriptions. Our approach is genuinely multidimensional and hence allows one to explore conditions that are beyond the ones explored so far and that have been restricted to the equatorial plane, thus providing a direct contact with numerical simulations exhibiting an intense reconnection activity outside the equatorial plane. Finally, our analysis does not resort to ad-hoc assumptions about the dynamics of the plasma or adopts oversimplified and possibly unrealistic models to describe the kinematics of the plasma. On the contrary, we study the dynamics of the plasma starting from a well-known configuration, that of an equilibrium torus with a purely toroidal magnetic field whose "ergobelt", i.e. the portion penetrating the ergosphere, naturally provides a site to compute, self-consistently, the occurrence of reconnection and estimate the energetics of a plasmoid-driven Penrose process., Comment: v1: 5 pages + 3 appendix, 4 figures

In this paper, we first show that current learning-based video codecs, specifically the SSF codec, are not suitable for real-world applications due to the mismatch between the encoder and decoder caused by floating-point round-off errors. To address this issue, we propose the static quantization of the hyper prior decoding path. The quantization parameters are determined through an exhaustive search of all possible combinations of observers and quantization schemes from PyTorch. For the SSF codec, when encoding and decoding on different machines, the proposed solution effectively mitigates the mismatch issue and enhances compression efficiency results by preventing severe image quality degradation. When encoding and decoding are performed on the same machine, it constrains the average BD-rate increase to 9.93% and 9.02% for UVG and HEVC-B sequences, respectively., Comment: 4 pages, submitted to ISCAS2025

This research presents an online path planner for Unmanned Aerial Vehicles (UAVs) that can handle dynamic obstacles and UAV motion constraints, including maximum curvature and desired orientations. Our proposed planner uses a NURBS path representation and a Differential Evolution algorithm, incorporating concepts from the Velocity Obstacle approach in a constraint function. Initial results show that our approach is feasible and provides a foundation for future extensions to three-dimensional (3D) environments., Comment: Accepted to the 40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40)

In this work we discuss how femtoscopic analysis can shed light on the nature of the two lightest axial charmed mesons, denominated as $D_1(2430)$ and $D_1(2420)$, whose masses are similar but widths are different. Their properties are reasonably described taking into account meson-meson coupled channel dynamics and a bare quark-model pole constituting the lowest-order amplitudes. Two different bare quark-model states are used in order to accommodate the different scattering lengths coming from the lattice QCD calculations for the $D\pi$ and $D^*\pi$ systems and the data from the ALICE Collaboration on the $D\pi$ system. The amplitudes are employed as inputs to determine the correlation functions for the $D^{*}\pi$ and $D\rho$ channels and identify the signatures associated with the lowest-lying axial charmed mesons., Comment: 6 pages; 2 figures; Proceedings of The 10th International Conference on Quarks and Nuclear Physics, (QNP 2024) Jul 8-12, 2024, Barcelona, Spain

A good deal of science and technology concepts and methods rely on comparing and relating entities in quantitative terms. Among the several possible approaches, similarity indices allow some interesting features, especially the ability to quantify how much two entities resemble one another. In this work, the Jaccard similarity for comparing non-zero real-valued vectors is modified so as to estimate similarity while focusing on the distinct parts of the signals. The resulting operator, which is called partially proportional similarity index, not only allows more strict comparisons, but also paves the way to develop an adaptive approach to similarity estimation in which the size and orientation of the comparisons adapt to those of a respective calibration field expressing how the observed features are related to original counterparts. Being a particularly relevant concept in data analysis and modeling, emphasis is placed on presenting and discussing the concept of calibration field and how they can be taken into account while performing similarity comparisons. Several results are described which illustrate the potential of the reported concepts and approaches for enhancing, and even simultaneously normalizing to some extent the representation of entities in terms of their features, as frequently required in scientific modeling and pattern recognition., Comment: 46 pages, 32 figures

The Jaccard similarity index has often been employed in science and technology as a means to quantify the similarity between two sets. When modified to operate on real-valued values, the Jaccard similarity index can be applied to compare vectors, an operation which plays a central role in visualization, classification, and modeling. The present work aims at developing an analytical approach for estimating the probability density of the Jaccard similarity values as implied by set of data elements characterized by specific statistical densities, with emphasis on the uniform and normal cases. Several theoretical and practical situations can benefit directly from such an approach, as it allows several of the properties of the similarity comparisons among a given dataset to be better understood and anticipated. Situations in which the described approach can be applied include the estimation and visualization of data interrelationships in terms of similarity networks, as well as diverse problems in data analysis, pattern recognition and scientific modeling. In addition to presenting the analytical developments and results, examples are also provided in order to illustrate the potential of the approach. The work also includes extension of the reported developments to modifications of the Jaccard index intended for regularization and control of the sharpness of the implemented comparisons.

Supermassive binary black holes in galactic centers are potential multimessenger sources in gravitational waves and electromagnetic radiation. To find such objects, isolating unique electromagnetic signatures of their accretion flow is key. With the aid of three-dimensional general-relativistic magnetohydrodynamic (GRMHD) simulations that utilize an approximate, semi-analytic, super-imposed spacetime metric, we identify two such signatures for merging binaries. Both involve magnetic reconnection and are analogous to plasma processes observed in the solar corona. The first, like colliding flux tubes that can cause solar flares, involves colliding jets that form an extended reconnection layer, dissipating magnetic energy and causing the two jets to merge. The second, akin to coronal mass ejection events, involves the accretion of magnetic field lines onto both black holes; these magnetic fields then twist, inflate, and form a trailing current sheet, ultimately reconnecting and driving a hot outflow. We provide estimates for the associated electromagnetic emission for both processes, showing that they likely accelerate electrons to high energies and are promising candidates for continuous, stochastic, and/or quasi-periodic higher energy electromagnetic emission. We also show that the accretion flows around each black hole can display features associated with the magnetically arrested state. However, simulations with black hole spins misaligned with the orbital plane and simulations with larger Bondi radii saturate at lower values of horizon-penetrating magnetic flux than standard magnetically arrested disks, leading to weaker, intermittent jets due to feedback from the weak jets or equatorial flux tubes ejected by reconnecting field lines near the horizon., Comment: 21 pages, 8 Figures, Submitted to ApJL. Animations available at https://www.youtube.com/playlist?list=PL3pLmTeUPcqThCLYhkSrkEBOUutrnSJWq

We explore the concept of negative pressure and its relevance in a variety of physical contexts: the expansion of the universe, mixture theory, cavitation, and the capillary effect in plants. Using thermodynamic arguments, we discuss the intricate connection between negative pressure and negative thermal expansion. We highlight the fact that metastable states and competing phases are often associated with the emergence of negative pressure. We also propose a new link between the effective Gr\"uneisen parameter and nucleation theory., Comment: 15 pages, 5 figs, comments are welcome

This thesis addresses the theory of topological spaces and the foundations of persistence theory. We will discuss chain complexes and the associated simplicial homology groups, as well as their relationship with singular homology theory. Moreover, we present the fundamental concepts of algebraic topology, including exact and short exact sequences and relative homology groups derived from quotienting with subspaces of a topological space. These tools are used to prove the Excision Theorem in algebraic topology. Subsequently, the theorem is applied to demonstrate the equivalence of simplicial and singular homology for triangulable topological spaces, i.e. those topological spaces which admit a simplicial structure. This enables a more general theory of homology to be adopted in the study of filtrations of point clouds. The chapter on homological persistence makes use of these tools throughout. We develop the theory of persistent homology, the homology of filtrations of topological spaces, and the corresponding dual concept of persistent cohomology. This work aims to provide mathematicians with a robust foundation for productive engagement with the aforementioned theories. The majority of the proofs have been rewritten to clarify the relationships between the techniques discussed. The novel aspect of this contribution is the canonical presentation of persistence theory and the associated ideas through a rigorous mathematical treatment for triangulable topological spaces and closing some gaps in the existing literature., Comment: Bachelor's thesis

We frame dynamic persuasion in a partial observation stochastic control Leader-Follower game with an ergodic criterion. The Receiver controls the dynamics of a multidimensional unobserved state process. Information is provided to the Receiver through a device designed by the Sender that generates the observation process. The commitment of the Sender is enforced. We develop this approach in the case where all dynamics are linear and the preferences of the Receiver are linear-quadratic. We prove a verification theorem for the existence and uniqueness of the solution of the HJB equation satisfied by the Receiver's value function. An extension to the case of persuasion of a mean field of interacting Receivers is also provided. We illustrate this approach in two applications: the provision of information to electricity consumers with a smart meter designed by an electricity producer; the information provided by carbon footprint accounting rules to companies engaged in a best-in-class emissions reduction effort. In the first application, we link the benefits of information provision to the mispricing of electricity production. In the latter, we show that information provision is a substitute for best-in-class objective., Comment: 2 figures

A significant interest has emerged recently in assessing whether collimated and ultra-relativistic outflows can be produced by a long-lived remnant from a binary neutron-star (BNS) merger, with different approaches leading to different outcomes. To clarify some of the aspect of this process, we report the results of long-term (\ie $\sim~110\,{\rm ms}$) state-of-the-art general-relativistic magnetohydrodynamics simulations of the inspiral and merger of a BNS system of magnetized stars. We find that after $\sim~50\,{\rm ms}$ from the merger, an $\alpha$-$\Omega$~dynamo driven by the magnetorotational instability (MRI) sets-in in the densest regions of the disk and leads to the breakout of the magnetic-field lines from the accretion disk around the remnant. The breakout, which can be associated with the violation of the Parker-stability criterion, is responsible for the generation of a collimated, magnetically-driven outflow with only mildly relativistic velocities that is responsible for a violent eruption of electromagnetic energy. We provide evidence that this outflow is partly collimated via a Blandford-Payne mechanism driven by the open field lines anchored in the inner disk regions. Finally, by including or not the radiative transport via neutrinos, we determine the role they play in the launching of the collimated wind. In this way, we conclude that the mechanism of magnetic-field breakout we observe is robust and takes place even without neutrinos. Contrary to previous expectations, the inclusion of neutrinos absorption and emission leads to a smaller baryon pollution in polar regions, and hence accelerates the occurrence of the breakout, yielding a larger electromagnetic luminosity. Given the mildly relativistic nature of these disk-driven breakout outflows, it is difficult to consider them responsible for the jet phenomenology observed in short gamma-ray bursts.

The Industrial Internet of Things (IIoT) paradigm has emerged as a transformative force, revolutionizing industrial processes by integrating advanced wireless technologies into traditional procedures to enhance their efficiency. The importance of this paradigm shift has produced a massive, yet heterogeneous, proliferation of scientific contributions. However, these works lack a standardized and cohesive characterization of the IIoT framework coming from different entities, like the 3rd Generation Partnership Project (3GPP) or the 5G Alliance for Connected Industries and Automation (5G-ACIA), resulting in divergent perspectives and potentially hindering interoperability. To bridge this gap, this article offers a unified characterization of (i) the main IIoT application domains, (ii) their respective requirements, (iii) the principal technological gaps existing in the current literature, and, most importantly, (iv) we propose a systematic approach for assessing and addressing the identified research challenges. Therefore, this article serves as a roadmap for future research endeavors, promoting a unified vision of the IIoT paradigm and fostering collaborative efforts to advance the field.

Large Language Models (LLMs) are poised to revolutionize healthcare. Ophthalmology-specific LLMs remain scarce and underexplored. We introduced an open-source, specialized LLM for ophthalmology, termed Language Enhanced Model for Eye (LEME). LEME was initially pre-trained on the Llama2 70B framework and further fine-tuned with a corpus of ~127,000 non-copyrighted training instances curated from ophthalmology-specific case reports, abstracts, and open-source study materials. We benchmarked LEME against eight other LLMs, namely, GPT-3.5, GPT-4, three Llama2 models (7B, 13B, 70B), PMC-LLAMA 13B, Meditron 70B, and EYE-Llama (another ophthalmology-specific LLM). Evaluations included four internal validation tasks: abstract completion, fill-in-the-blank, multiple-choice questions (MCQ), and short-answer QA. External validation tasks encompassed long-form QA, MCQ, patient EHR summarization, and clinical QA. Evaluation metrics included Rouge-L scores, accuracy, and expert evaluation of correctness, completeness, and readability. In internal validations, LEME consistently outperformed its counterparts, achieving Rouge-L scores of 0.20 in abstract completion (all p<0.05), 0.82 in fill-in-the-blank (all p<0.0001), and 0.22 in short-answer QA (all p<0.0001, except versus GPT-4). In external validations, LEME excelled in long-form QA with a Rouge-L of 0.19 (all p<0.0001), ranked second in MCQ accuracy (0.68; all p<0.0001), and scored highest in EHR summarization and clinical QA (ranging from 4.24 to 4.83 out of 5 for correctness, completeness, and readability). LEME's emphasis on robust fine-tuning and the use of non-copyrighted data represents a breakthrough in open-source ophthalmology-specific LLMs, offering the potential to revolutionize execution of clinical tasks while democratizing research collaboration.

In a temporal forest each edge has an associated set of time labels that specify the time instants in which the edges are available. A temporal path from vertex $u$ to vertex $v$ in the forest is a selection of a label for each edge in the unique path from $u$ to $v$, assuming it exists, such that the labels selected for any two consecutive edges are non-decreasing. We design linear-size data structures that maintain a temporal forest of rooted trees under addition and deletion of both edge labels and singleton vertices, insertion of root-to-node edges, and removal of edges with no labels. Such data structures can answer temporal reachability, earliest arrival, and latest departure queries. All queries and updates are handled in polylogarithmic worst-case time. Our results can be adapted to deal with latencies. More precisely, all the worst-case time bounds are asymptotically unaffected when latencies are uniform. For arbitrary latencies, the update time becomes amortized in the incremental case where only label additions and edge/singleton insertions are allowed as well as in the decremental case in which only label deletions and edge/singleton removals are allowed. To the best of our knowledge, the only previously known data structure supporting temporal reachability queries is due to Brito, Albertini, Casteigts, and Traven\c{c}olo [Social Network Analysis and Mining, 2021], which can handle general temporal graphs, answers queries in logarithmic time in the worst case, but requires an amortized update time that is quadratic in the number of vertices, up to polylogarithmic factors., Comment: ISAAC 2024

We construct and discuss generic N=1 and N=2 Carroll dilaton supergravity in two dimensions. We apply our general results to the supersymmetric Carroll-Jackiw-Teitelboim model, including a discussion of specific boundary conditions. For N=2 Carroll dilaton supergravity, we find two versions, dubbed "democratic" and "despotic"., Comment: 33pp