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In social sciences, researchers often face challenges when conducting large-scale experiments, particularly due to the simulations' complexity and the lack of technical expertise required to develop such frameworks. Agent-Based Modeling (ABM) is a computational approach that simulates agents' actions and interactions to evaluate how their behaviors influence the outcomes. However, the traditional implementation of ABM can be demanding and complex. Generative Agent-Based Modeling (GABM) offers a solution by enabling scholars to create simulations where AI-driven agents can generate complex behaviors based on underlying rules and interactions. This paper introduces a framework for designing reliable experiments using GABM, making sophisticated simulation techniques more accessible to researchers across various fields. We provide a step-by-step guide for selecting appropriate tools, designing the model, establishing experimentation protocols, and validating results.

Layered van der Waals (vdW) materials have emerged as a promising platform for nanophotonics due to large refractive indexes and giant optical anisotropy. Unlike conventional dielectrics and semiconductors, the absence of covalent bonds between layers allows for novel degrees of freedom in designing optically resonant nanophotonic structures down to the atomic scale, from the precise stacking of vertical heterostructures to controlling the twist angle between crystallographic axes. Specifically, while transition metal dichalcogenides monolayers exhibit giant second order nonlinear responses, their bulk counterparts with 2H stacking have zero second order response. In this work, we show second harmonic generation (SHG) arising from the interface of WS$_2$/MoS$_2$ hetero-bilayer thin films with an additional SHG enhancement in nanostructured optical antennas mediated by both the excitonic resonances and the anapole condition. When both conditions are met, we observe up to $10^2$ SHG signal enhancement. Our results highlights vdW materials as a platform for designing unique multilayer optical nanostructures and metamaterial, paving the way for advanced applications in nanophotonics and nonlinear optics., Comment: Manuscript + Supplementary (21 pages, 3 Main figures 8 supplementary figures)

This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory. We evaluate several state-of-the-art LLMs across a spectrum of complete-information and incomplete-information games. Our findings reveal that LLMs frequently deviate from rational strategies, particularly as the complexity of the game increases with larger payoff matrices or deeper sequential trees. To address these limitations, we design multiple game-theoretic workflows that guide the reasoning and decision-making processes of LLMs. These workflows aim to enhance the models' ability to compute Nash Equilibria and make rational choices, even under conditions of uncertainty and incomplete information. Experimental results demonstrate that the adoption of these workflows significantly improves the rationality and robustness of LLMs in game-theoretic tasks. Specifically, with the workflow, LLMs exhibit marked improvements in identifying optimal strategies, achieving near-optimal allocations in negotiation scenarios, and reducing susceptibility to exploitation during negotiations. Furthermore, we explore the meta-strategic considerations of whether it is rational for agents to adopt such workflows, recognizing that the decision to use or forgo the workflow constitutes a game-theoretic issue in itself. Our research contributes to a deeper understanding of LLMs' decision-making capabilities in strategic contexts and provides insights into enhancing their rationality through structured workflows. The findings have implications for the development of more robust and strategically sound AI agents capable of navigating complex interactive environments. Code and data supporting this study are available at \url{https://github.com/Wenyueh/game_theory}., Comment: 45 pages, 12 figures

We address the orbital distribution of stars in merging nuclear star clusters (NSCs) and the subsequent effects on supermassive black hole binary (SMBHB) evolution. We ran direct-summation $N$-body simulations with different initial conditions to do a detailed study of the resulting NSC after their progenitors had merged. Our findings reveal that prograde stars form a flattened structure, while retrograde stars have a more spherical distribution. The axial ratios of the prograde component vary based on the presence and mass ratio of the SMBHs. The fraction of prograde and retrograde stars depends on the merger orbital properties and the SMBH mass ratio. The interactions of retrograde stars with the SMBHB affect the eccentricity and separation evolution of the binary. Our analysis reveals a strong correlation between the angular momentum and eccentricity of the SMBH binary. This relationship could serve as a means to infer information about the stellar dynamics surrounding the binary. We find that prograde orbits are particularly close to the binary of SMBHs, a promising fact regarding extreme mass ratio inspiral (EMRI) production. Moreover, prograde and retrograde stars have different kinematic structures, with the prograde stars typically rotating faster than the retrograde ones. The line-of-sight velocity and velocity dispersion, as well as the velocity anisotropy of each NSC, depend on the initial merger orbital properties and SMBH mass ratios. The prograde and retrograde stars always show different behaviours. The distribution of stellar orbits and the dynamical properties of each kinematic population can potentially be used as a way to tell the properties of the parent nuclei apart, and has an important impact on expected rates of EMRIs, which will be detected by future gravitational wave observatories such as the Laser Interferometer Space Antenna (LISA). [abridged], Comment: 14 pages, 8 figures, 2 tables. Two appendices on zenodo (https://zenodo.org/records/13986686). Accepted for publication in A&A

Smartphones, equipped with an array of sensors, have become valuable tools for personal sensing. Particularly in digital health, smartphones facilitate the tracking of health-related behaviors and contexts, contributing significantly to digital phenotyping, a process where data from digital interactions is analyzed to infer behaviors and assess mental health. Traditional methods process raw sensor data into information features for statistical and machine learning analyses. In this paper, we introduce a novel approach that systematically converts smartphone-collected data into structured, chronological narratives. The AWARE Narrator translates quantitative smartphone sensing data into English language descriptions, forming comprehensive narratives of an individual's activities. We apply the framework to the data collected from university students over a week, demonstrating the potential of utilizing the narratives to summarize individual behavior, and analyzing psychological states by leveraging large language models.

We prove a homogenization result for a family of time-dependent Hamilton-Jacobi equations, rescaled by a parameter $\varepsilon$ tending to zero, posed on a periodic network, with a suitable notion of periodicity that will be defined. As $\varepsilon$ becomes infinitesimal, we derive a limiting Hamilton-Jacobi equation in a Euclidean space, whose dimension is determined by the topological complexity of the network and is independent of the ambient space in which the network is embedded. Among the key contributions of our analysis, we extend to the setting of networks and graphs Mather's result on the asymptotic behavior of the average minimal action functional, as time tends to infinity. Additionally, we establish the well-posedness of the approximating problems, representing a nontrivial generalization of existing results for finite networks to a non-compact setting.

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Defining a distance in a mixed setting requires the quantification of observed differences of variables of different types and of variables that are measured on different scales. There exist several proposals for mixed variable distances, however, such distances tend to be biased towards specific variable types and measurement units. That is, the variable types and scales influence the contribution of individual variables to the overall distance. In this paper, we define unbiased mixed variable distances for which the contributions of individual variables to the overall distance are not influenced by measurement types or scales. We define the relevant concepts to quantify such biases and we provide a general formulation that can be used to construct unbiased mixed variable distances., Comment: 40 pages, 9 figures

Classification is a pivotal task in deep learning not only because of its intrinsic importance, but also for providing embeddings with desirable properties in other tasks. To optimize these properties, a wide variety of loss functions have been proposed that attempt to minimize the intra-class distance and maximize the inter-class distance in the embeddings space. In this paper we argue that, in addition to these two, eliminating hierarchies within and among classes are two other desirable properties for classification embeddings. Furthermore, we propose the Angular Distance Distribution (ADD) Loss, which aims to enhance the four previous properties jointly. For this purpose, it imposes conditions on the first and second order statistical moments of the angular distance between embeddings. Finally, we perform experiments showing that our loss function improves all four properties and, consequently, performs better than other loss functions in audio classification tasks.

Context. Mass is the most critical physical parameter in the evolution of a star. Since stars form in clusters their Initial Mass Function (IMF) is decisive in their evolution. Aims. Use Gaia DR3-based stellar masses_mass flame and the stellar members found for fifteen nearby open clusters from Paper I, to estimate their mass segregation and distribution. Methods. For each cluster, the single stars' main sequence was fitted with a moving straight line weighted fit to the Color-Magnitude Diagram, stars brighter than the residuals dispersion were taken as binaries. Single stars masses were obtained from a cubic spline fit to the mass_flame vs. G magnitude data. For binary stars, the individual masses of each component were estimated using simulated-based inference. We used the minimum spanning tree concept to measure the mass segregation of each cluster. From the stellar mass distribution, an estimate of the power-law coefficient that best describes it was used to characterize the IMF. Results. Mass segregation is visible in all the clusters, the older ones have about 50% of their most massive stars segregated, while younger ones extend from about 30% to 55%. The IMF of the studied clusters is well described by a power-law of index 2.09. Conclusions. Significant mass segregation, from one-third to one-half of its most massive population is present in open clusters as young as > 10 Myr. Mass segregation may be strong for only a few of the most massive stars or less intense but extended to a larger fraction of those, it may start as early as 0.20 of the relaxation time of the cluster and progress over time by increasing both the number of the most massive stars affected and their amount of segregation. Older open clusters show evidence of binary disruption as time progresses., Comment: Submitted to A&A. 11 pages, 6 figures

We describe the chiral transition for the quark condensate and the melting of scalar and vector mesons in two-flavor holographic QCD. This is done by extending the improved holographic soft wall models proposed in [1] to finite temperature, by means of introducing an asymptotically AdS black brane. We find that the chiral transition is second order in the chiral limit and a crossover for physical quark masses, as expected in two-flavor QCD. We investigate the melting of vector and scalar mesons in the deconfined plasma through the calculation of hadronic spectral functions. Fixing the model parameters by the meson spectrum at zero temperature, we find that the mesons melt at temperatures between $90$ and $110$ MeV and the chiral transition occurs around $129$ MeV. We also provide a prediction for the hydrodynamic diffusion constant associated with a flavor current in the deconfined plasma., Comment: 36 pages, 12 figures

Magnetoencephalography (MEG) allows the non-invasive detection of interictal epileptiform discharges (IEDs). Clinical MEG analysis in epileptic patients traditionally relies on the visual identification of IEDs, which is time consuming and partially subjective. Automatic, data-driven detection methods exist but show limited performance. Still, the rise of deep learning (DL)-with its ability to reproduce human-like abilities-could revolutionize clinical MEG practice. Here, we developed and validated STIED, a simple yet powerful supervised DL algorithm combining two convolutional neural networks with temporal (1D time-course) and spatial (2D topography) features of MEG signals inspired from current clinical guidelines. Our DL model enabled both temporal and spatial localization of IEDs in patients suffering from focal epilepsy with frequent and high amplitude spikes (FE group), with high-performance metrics-accuracy, specificity, and sensitivity all exceeding 85%-when learning from spatiotemporal features of IEDs. This performance can be attributed to our handling of input data, which mimics established clinical MEG practice. Reverse engineering further revealed that STIED encodes fine spatiotemporal features of IEDs rather than their mere amplitude. The model trained on the FE group also showed promising results when applied to a separate group of presurgical patients with different types of refractory focal epilepsy, though further work is needed to distinguish IEDs from physiological transients. This study paves the way of incorporating STIED and DL algorithms into the routine clinical MEG evaluation of epilepsy., Comment: 10 pages, 7 figures

In altermagnets, time-reversal symmetry breaking spin-polarizes electronic states, while total magnetization remains zero. In addition, at altermagnetic surfaces Rashba-spin orbit coupling is activated due to broken inversion symmetry, introducing a competing spin-momentum locking interaction. Here we show that their interplay leads to the formation of complex, chiral spin textures that offer novel, non-linear spin-to-charge conversion properties. Whereas altermagnetic order suppresses the canonical linear in-plane Rashba-Edelstein response, we establish the presence of an anomalous transversal Edelstein effect for planar applied electric and magnetic field, or alternatively, an in-plane magnetization. Moreover the non-linear Edelstein response resulting purely from electric fields also triggers the anomalous out-of-plane magnetization. We determine the anomalous response with a model based on the ab-initio electronic structure of RuO$_2$ bilayers, ultimately opening experimental avenues to explore spin-charge conversion phenomena at altermagnetic interfaces., Comment: 11 pages, 15 figures

Material Girl was a two-stage sounding rocket designed and flown during the 2022-2023 academic year. Weighing a total of 197 lbs and measuring 176 inches in length, the vehicle incorporated many improvements from the team's previous rocket, Mr. Blue Sky. The rocket used a Student Researched and Developed (SRAD) P-class motor on each stage, a mechanical staging system, and recovery systems for the booster, sustainer, and nose cone. Flight events were controlled by a commercial off-the-shelf (COTS) avionics flight computer, with a custom flight computer carried as a payload. The motor grain geometry and fin dimensions were optimized using an in-house software developed from RASAero and RocketPy.

We study superfluid helium droplets multiply charged with ions. When stable, the charges are found to reside in equilibrium close to the droplet surface, thus representing a physical realization of Thomson's model. We find the minimum radius of the helium droplet that can host a given number of ions using a model whose physical ingredients are the solvation energy of the cations, calculated within the He-DFT approach, and their mutual Coulomb repulsion energy. Our model goes beyond the often used liquid drop model, where charges are smeared out either within the droplet or on its surface, and which neglects the solid-like helium shell around the ions. We find that below a threshold droplet radius R_0, the total energy of the system becomes higher than that of the separated system of the pristine helium droplet and the charges embedded in their solvation microcluster ("snowball"). However, the ions are still kept within the droplet by the presence of energy barriers which hinder Coulomb explosion. A further reduction of the droplet radius below a value R_expl eventually results in the disappearance of such barrier, leading to Coulomb explosion. Surprisingly, our results are rather insensitive to the ion atomic species. This makes room to discuss them in the context of intrinsic multicharged helium droplets, where the charges are triatomic He3+ ions. Our calculated values for R_expl display the correct scaling with the number of cations compared to available experimental results, at variance with other estimates for the critical radii., Comment: 12 pages, 10 figures

Symplectic billiards were introduced by Albers and Tabachnikov as billiards in strictly convex bounded domains of the plane with smooth boundary having a specific law of reflection. This paper proves a rigidity result for symplectic billiards which is similar to a previous result on classical billiards formulated by De Simoi, Kaloshin and Wei. Namely, it states that close to an ellipse, a sufficiently smooth one-parameter family of axially symmetric domains either contains domains with different area-spectra or is trivial, in a sense that the domains differ by area-preserving affine transformations of the plane. The paper also prove that in the general setting - that is even if the domains are not close to an ellipse - any sufficiently smooth one-parameter family of axially symmetric domains which preserves the area-spectrum is tangent to a finite dimensionnal space., Comment: 29 pages, 2 figures

Web application pentesting is a crucial component in the offensive cybersecurity area, whose aim is to safeguard web applications and web services as the majority of the web applications are mounted in publicly accessible web environments. This method requires that the cybersecurity experts pretend and act as real attackers to identify all the errors and vulnerabilities in web applications with the objective of preventing and reducing damages. As this process may be quite complex and the amount of information pentesters need may be big, being able to automate it will help them to easily discover the vulnerabilities given. This project is the direct continuation of the previous initiative called PThelper: An open source tool to support the Penetration Testing process. This continuation is focused on expanding PThelper with the functionality to detect and later report web vulnerabilities in order to address emerging threats and strengthen the ability of the organizations to protect their web applications against potential cyber-attacks.

Osteoarthritis is a degenerative condition affecting bones and cartilage, often leading to osteophyte formation, bone density loss, and joint space narrowing. Treatment options to restore normal joint function vary depending on the severity of the condition. This work introduces an innovative deep-learning framework processing shoulder CT scans. It features the semantic segmentation of the proximal humerus and scapula, the 3D reconstruction of bone surfaces, the identification of the glenohumeral (GH) joint region, and the staging of three common osteoarthritic-related pathologies: osteophyte formation (OS), GH space reduction (JS), and humeroscapular alignment (HSA). The pipeline comprises two cascaded CNN architectures: 3D CEL-UNet for segmentation and 3D Arthro-Net for threefold classification. A retrospective dataset of 571 CT scans featuring patients with various degrees of GH osteoarthritic-related pathologies was used to train, validate, and test the pipeline. Root mean squared error and Hausdorff distance median values for 3D reconstruction were 0.22mm and 1.48mm for the humerus and 0.24mm and 1.48mm for the scapula, outperforming state-of-the-art architectures and making it potentially suitable for a PSI-based shoulder arthroplasty preoperative plan context. The classification accuracy for OS, JS, and HSA consistently reached around 90% across all three categories. The computational time for the inference pipeline was less than 15s, showcasing the framework's efficiency and compatibility with orthopedic radiology practice. The outcomes represent a promising advancement toward the medical translation of artificial intelligence tools. This progress aims to streamline the preoperative planning pipeline delivering high-quality bone surfaces and supporting surgeons in selecting the most suitable surgical approach according to the unique patient joint conditions.

In recent years, $b$-symplectic manifolds have become important structures in the study of symplectic geometry, serving as Poisson manifolds that retain symplectic properties away from a hypersurface. Inspired by this rich landscape, $E$-structures were introduced by Nest and Tsygan in \cite{NT2} as a comprehensive framework for exploring generalizations of $b$-structures. This paper initiates a deeper investigation into their Poisson facets, building on foundational work by \cite{MS21}. We also examine the closely related concept of almost regular Poisson manifolds, as studied in \cite{AZ17}, which reveals a natural Poisson groupoid associated with these structures. In this article, we investigate the intricate relationship between $E$-structures and almost regular Poisson structures. Our comparative analysis not only scrutinizes their Poisson properties but also offers explicit formulae for the Poisson structure on the Poisson groupoid associated to the $E$-structures as both Poisson manifolds and singular foliations. In doing so, we reveal an interesting link between the existence of commutative frames and Darboux-Carath\'eodory-type expressions for the relevant structures., Comment: 26 pages

Flatback airfoils, characterized by a blunt trailing edge, are used at the root of large wind turbine blades. A low-drag pocket has recently been identified in the flow past these airfoils at high angles of attack, potentially offering opportunities for enhanced energy extraction. This study uses three-dimensional Detached Eddy Simulations (DES) combined with statistical and data-driven modal analysis techniques to explore the aerodynamics and coherent structures of a flatback airfoil in these conditions. Two angles of attack - one inside $\left(12^{\circ}\right)$ and one outside $\left(0^{\circ}\right)$ of the low-drag pocket - are examined more thoroughly. The spanwise correlation length of secondary instability is analyzed in terms of autocorrelation of the $\Gamma_{1}$ vortex identification criterion, while coherent structures were extracted via the multiscale Proper Orthogonal Decomposition (mPOD). The results show increased base pressure, BL thickness, vortex formation length, and more organized wake structures inside the low-drag regime. While the primary instability (B\'enard-von K\'arm\'an vortex street) dominates in both cases, the secondary instability is distinguishable only for the $12^{\circ}$ case and is identified as a Mode S$^{\prime}$ instability., Comment: 27 pages, 25 figures, submitted to Physical Review Fluids

Sloshing of cryogenic liquid propellants can significantly impact a spacecraft's mission safety and performance by unpredictably altering the center of mass and producing large pressure fluctuations due to the increased heat and mass transfer within the tanks. This study, conducted as part of the NT-SPARGE (Non-isoThermal Sloshing PARabolic FliGht Experiment) project, provides a detailed experimental investigation of the thermodynamic evolution of a partially filled upright cylindrical tank undergoing non-isothermal sloshing in microgravity. Sloshing was induced by a step reduction in gravity during the 83rd European Space Agency (ESA) parabolic flight, resulting in a chaotic reorientation of the free surface under inertia-dominated conditions. To investigate the impact of heat and mass transfer on the sloshing dynamics, two identical test cells operating with a representative fluid, HFE-7000, in single-species were considered simultaneously. One cell was maintained in isothermal conditions, while the other started with initially thermally stratified conditions. Flow visualization, pressure, and temperature measurements were acquired for both cells. The results highlight the impact of thermal mixing on liquid dynamics coupled with the significant pressure and temperature fluctuations produced by the destratification. The comprehensive experimental data gathered provide a unique opportunity to validate numerical simulations and simplified models for non-isothermal sloshing in microgravity, thus contributing to improved cryogenic fluid management technologies.

In realistic radar tracking, target measurement uncertainty (TMU) in terms of both detection probability and measurement error covariance is significantly affected by the target-to-radar (T2R) geometry. However, existing posterior Cramer-Rao Lower Bounds (PCRLBs) rarely investigate the fundamental impact of T2R geometry on target measurement uncertainty and eventually on mean square error (MSE) of state estimate, inevitably resulting in over-conservative lower bound. To address this issue, this paper firstly derives the generalized model of target measurement error covariance for bistatic radar with moving receiver and transmitter illuminating any type of signal, along with its approximated solution to specify the impact of T2R geometry on error covariance. Based upon formulated TMU model, an improved PCRLB (IPCRLB) fully accounting for both measurement origin uncertainty and geometry-dependent TMU is then re-derived, both detection probability and measurement error covariance are treated as state-dependent parameters when differentiating log-likelihood with respect to target state. Compared to existing PCRLBs that partially or completely ignore the dependence of target measurement uncertainty on T2R geometry, proposed IPCRLB provides a much accurate (less-conservative) lower bound for radar tracking in clutter with geometry-dependent TMU. The new bound is then applied to radar trajectory control to effectively optimize T2R geometry and exhibits least uncertainty of acquired target measurement and more accurate state estimate for bistatic radar tracking in clutter, compared to state-of-the-art trajectory control methods., Comment: 15 pages,12 figures

When making treatment selection decisions, it is essential to include a causal effect estimation analysis to compare potential outcomes under different treatments or controls, assisting in optimal selection. However, merely estimating individual treatment effects may not suffice for truly optimal decisions. Our study addressed this issue by incorporating additional criteria, such as the estimations' uncertainty, measured by the conditional value-at-risk, commonly used in portfolio and insurance management. For continuous outcomes observable before and after treatment, we incorporated a specific prediction condition. We prioritized treatments that could yield optimal treatment effect results and lead to post-treatment outcomes more desirable than pretreatment levels, with the latter condition being called the prediction criterion. With these considerations, we propose a comprehensive methodology for multitreatment selection. Our approach ensures satisfaction of the overlap assumption, crucial for comparing outcomes for treated and control groups, by training propensity score models as a preliminary step before employing traditional causal models. To illustrate a practical application of our methodology, we applied it to the credit card limit adjustment problem. Analyzing a fintech company's historical data, we found that relying solely on counterfactual predictions was inadequate for appropriate credit line modifications. Incorporating our proposed additional criteria significantly enhanced policy performance., Comment: 34 pages, 4 figures

A device filled with pure xenon first demonstrated the ability to operate simultaneously as a bubble chamber and scintillation detector in 2017. Initial results from data taken at thermodynamic thresholds down to ~4 keV showed sensitivity to ~20 keV nuclear recoils with no observable bubble nucleation by $\gamma$-ray interactions. This paper presents results from further operation of the same device at thermodynamic thresholds as low as 0.50 keV, hardware limited. The bubble chamber has now been shown to have sensitivity to ~1 keV nuclear recoils while remaining insensitive to bubble nucleation by $\gamma$-rays. A robust calibration of the chamber's nuclear recoil nucleation response, as a function of nuclear recoil energy and thermodynamic state, is presented. Stringent upper limits are established for the probability of bubble nucleation by $\gamma$-ray-induced Auger cascades, with a limit of $<1.1\times10^{-6}$ set at 0.50 keV, the lowest thermodynamic threshold explored., Comment: 10 pages, 7 figures

This project focuses on the design and construction of a prototype mouse based on the Arduino platform, intended for individuals without upper limbs to use computers more effectively. The prototype comprises a microcontroller responsible for processing signals from the MPU-6050 sensor, used as a reference for cursor position, and foot-operated buttons for right and left-click functions. Its design enables cursor control through head movements, providing users with an easy and intuitive way to interact with the computer's graphical interface. Feasibility testing was conducted through experimental trials, resulting in ideal accuracy and precision. These trials indicate that the device is viable for use in individuals without upper limbs., Comment: 6 pages, 9 figures

We introduce distinct definitions of local stable/unstable sets for flows without fixed points, namely, kinematic, geometric, and sectionally geometric, and discuss relations between them. We prove the existence of continua with a uniform diameter within each sectionally geometric local stable/unstable set for cw-expansive flows defined on Peano continua.

This paper proposes a variance-based measure of importance for coherent systems with dependent and heterogeneous components. The particular cases of independent components and homogeneous components are also considered. We model the dependence structure among the components by the concept of copula. The proposed measure allows us to provide the best estimation of the system lifetime, in terms of the mean squared error, under the assumption that the lifetime of one of its components is known. We include theoretical results that are useful to calculate a closed-form of our measure and to compare two components of a system. We also provide some procedures to approximate the importance measure by Monte Carlo simulation methods. Finally, we illustrate the main results with several examples.

The article is focused on studying how to predict the failure times of coherent systems from the early failure times of their components. Both the cases of independent and dependent components are considered by assuming that they are identically distributed (homogeneous components). The heterogeneous components' case can be addressed similarly but more complexly. The present study is for non-repairable systems, but the information obtained could be used to decide if a maintenance action should be carried out at time t. Different cases are considered regarding the information available at time t. We use quantile regression techniques to predict the system failure times and to provide prediction intervals. The theoretical results are applied to specific system structures in some illustrative examples.

We introduce a new family of operators in 4-dimensional pseudo-Riemannian manifolds with a non-vanishing Weyl scalar (non-degenerate spaces) that keep the conformal covariance of \emph{conformally covariant tensor concomitants}. A particular case that arises naturally is the $\mathcal{C}$-connection that is a Weyl connection that keeps \emph{conformal invariance}. Using the $\mathcal{C}-$connection we give a new characterization of non-degenerate spaces that are conformal to an Einstein space., Comment: 14 pages, no figures

Discontinuous reception (DRX) is a key technology for reducing the energy consumption of industrial Internet of Things (IIoT) devices. Specifically, DRX allows the devices to operate in a low-power mode when no data reception is scheduled, and its effectiveness depends on the proper configuration of the DRX parameters. In this paper, we characterize the DRX process departing from a semi-Markov chain modeling. We detail two ways to set DRX parameters to minimize the device power consumption while meeting a mean delay constraint. The first method exhaustively searches for the optimal configuration. In contrast, the second method uses a low-complexity metaheuristic to find a sub-optimal configuration, thus considering ideal and practical DRX configurations. Notably, within the DRX parameters, the inactivity timer (IT) is a caution time that specifies how long a device remains active after the last information exchange. Traditionally, a device implementing DRX will restart the IT after each data reception as a precedent to a low-power mode. The usual approach lies in restarting the IT whenever new data is received during this cautious period, which might sometimes needlessly extend the active time. Herein, we propose a more efficient method in which the transmit base station (BS) explicitly indicates restarting the timer through the control channel only when appropriate. The decision is taken based on the BS's knowledge about its buffer status. We consider Poisson and bursty traffic models, which are typical in IIoT setups, and verify the suitability of our proposal for reducing the energy consumption of the devices without significantly compromising the communication latency through extensive numerical simulations. Specifically, energy-saving gains of up to 30% can be obtained regardless of the arrival rate and delay constraints., Comment: IEEE Transactions on Industrial Informatics (2024)

Superconductors exhibit remarkable properties such as zero resistivity and diamagnetism at the boiling temperature of liquid hydrogen (20 K) and even aboven the boiling temperature of liquid nitrogen (77 K), making them promising candidates for various applications including electrical engines, energy generation, storage, and high-tech devices like single photon detectors. In this overview, we explore the correlation between ceramic superconductors and the United Nations (UN) Development Goals, emphasizing their potential impact on sustainable development. Through bibliometric analysis, we underscore the significance of ceramic superconductors in addressing global challenges outlined by the UN. Additionally, we discuss the application of supermagnets and second-generation tapes in healthcare systems, particularly in magnetic resonance imaging (MRI) devices for diagnostic imaging. Electric superconducting motors offer a clean alternative to highly polluting diesel engines in maritime transportation and superconducting wires/cables enable effective transport of energy on large scale as well as in industrial structures. Magnetic levitation technology holds promise for developing zero-emission public transportation systems, and magnetic separation with strong magnets will contribute to solve the microplastic pollution. The combination of superconductivity with the planned hydrogen economy further offers new possibilities to bring superconductivity to common applications. At the nanoscale, superconducting nanowire single photon detectors (SNSPDs) enable real-time monitoring of environmental health, exemplified by applications in plant physiology, and superconducting qubits provide the best-engineered structures for quantum computers. However it is still crordwork to works with superconductors UN develepoment goals., Comment: 51 pages, 17 figures, 7 tables, Review paper based on bibliometry

Mult-layered meta-optics have enabled complex wavefront shaping beyond their single layer counterpart owing to the additional design variables afforded by each plane. For instance, complex amplitude modulation, generalized polarization transformations, and wide field of view are key attributes that fundamentally require multi-plane wavefront matching. Nevertheless, existing embodiments of bilayer metasurfaces have relied on configurations which suffer from Fresnel reflections, low mode confinement, or undesired resonances which compromise the intended response. Here, we introduce bilayer metasurfaces made of free-standing meta-atoms working in the visible spectrum. We demonstrate their use in wavefront shaping of linearly polarized light using pure geometric phase with diffraction efficiency of 80 % expanding previous literature on Pancharatnam-Berry phase metasurfaces which rely on circularly or elliptically polarized illumination. The fabrication relies on a two-step lithography and selective development processes which yield free standing, bilayer stacked metasurfaces, of 1200 nm total thickness. The metasurfaces comprise TiO2 nanofins with vertical side walls. Our work advances the nanofabrication of compound meta-optics and inspires new directions in wavefront shaping, metasurface integration, and polarization control., Comment: A. H. Dorrah, J. -S. Park, and A. Palmieri contributed equally to this work

Given a smooth Tonelli Hamiltonian on the torus $\mathbb{T}^{n}$ and a $C^{2}$ Lagrangian graph $W \subset T^{*}\mathbb{T}^{n}$ that is invariant under the Hamiltonian flow and contained within a Ma\~n\'e supercritical energy level, we demonstrate the existence of a proper cone in the first real homology group $H_1(\mathbb{T}^n,\mathbb{R})$ that contains the asymptotic homologies of the canonical projections of recurrent orbits in $W$. Additionally, for invariant Lagrangian graphs on $\mathbb{T}^{3}$, drawing on Franks' theory of the rotation set of homeomorphisms of $\mathbb{T}^{2}$ homotopic to the identity, we show that under certain assumptions for an invariant Lagrangian graph on $\mathbb{T}^{3}$, if there exists a rational vector in homology contained in the set of asymptotic homologies of orbits on the Lagrangian graph, then the graph contains a Mather measure supported on a periodic orbit. This result generalizes a well-known fact for Lagrangian graphs on $\mathbb{T}^{2}$. Finally, we exploit these results for three dimensional tori to give a partial answer to a conjecture by Carneiro-Ruggiero about the non-existence of Hedlund Lagrangian tori at supercritical energy levels., Comment: 32 pages, 2 figures

This work focuses on the numerical solution of the dynamics of a poroelastic material in the frequency domain. We provide a detailed stability analysis based on the application of the Fredholm alternative in the continuous case, considering a total pressure formulation of the Biot's equations. In the discrete setting, we propose a stabilized equal order finite element method complemented by an additional pressure stabilization to enhance the robustness of the numerical scheme with respect to the fluid permeability. Utilizing the Fredholm alternative, we extend the well-posedness results to the discrete setting, obtaining theoretical optimal convergence for the case of linear finite elements. We present different numerical experiments to validate the proposed method. First, we consider model problems with known analytic solutions in two and three dimensions. As next, we show that the method is robust for a wide range of permeabilities, including the case of discontinuous coefficients. Lastly, we show the application for the simulation of brain elastography on a realistic brain geometry obtained from medical imaging.

Journaling offers significant benefits, including fostering self-reflection, enhancing writing skills, and aiding in mood monitoring. However, many people abandon the practice because traditional journaling is time-consuming, and detailed life events may be overlooked if not recorded promptly. Given that smartphones are the most widely used devices for entertainment, work, and socialization, they present an ideal platform for innovative approaches to journaling. Despite their ubiquity, the potential of using digital phenotyping, a method of unobtrusively collecting data from digital devices to gain insights into psychological and behavioral patterns, for automated journal generation has been largely underexplored. In this study, we propose AutoJournaling, the first-of-its-kind system that automatically generates journals by collecting and analyzing screenshots from smartphones. This system captures life events and corresponding emotions, offering a novel approach to digital phenotyping. We evaluated AutoJournaling by collecting screenshots every 3 seconds from three students over five days, demonstrating its feasibility and accuracy. AutoJournaling is the first framework to utilize seamlessly collected screenshots for journal generation, providing new insights into psychological states through digital phenotyping.

Galaxy properties are known to be affected by their environment. This is well established for the extremes of the density scales, between the high-density cluster environment and the low-density field. It is however not fully understood how the intermediate-density regime of cosmic web filaments affects galaxy evolution. We investigate this environmental effect using a mass complete sample of 23,441 galaxies in the Sloan Digital Sky Survey DR8 Main Galaxy Sample (${M}_{\text{Stellar}} > 10^{9.91} \text{M}_{\odot}$). We define 6 environments, probing different density regimes and representing unique stages in the structure formation process, comparing the differences in star formation activity and morphology between them. We find that galaxies in filaments tend to be less star forming and favour more early-type morphologies than those in the field. These differences persist when considering stellar mass-matched samples, suggesting that this is a consequence of the environment. We further investigate whether these trends are a result of the large scale or local environment through constructing samples matched both in stellar mass and local galaxy density. We find that when also matching in local galaxy density, the differences observed between the filament and field population vanishes, concluding that the environmental effect of filaments can be entirely parameterised by a local galaxy density index. We find that differences can still be seen in comparisons with the interiors of clusters, suggesting these are unique environments which can impart additional physical processes not characterised by local galaxy density., Comment: 19 pages, 10 figures, Accepted for publication in MNRAS

With the advent of billion-parameter foundation models, efficient fine-tuning has become increasingly important for the adaptation of models to downstream tasks. However, especially in computer vision, it can be hard to achieve good performance when access to quality labeled data is lacking. In this work, we propose a method adapting pretrained generalist models in a self-supervised manner by learning binary masks. These self-supervised masking networks (SMNs) are up to 79x more efficient to store and significantly improve performance on label-efficient downstream tasks. We validate the usefulness of learning binary masks as a fine-tuning method on 8 datasets and 3 model architectures, and we demonstrate the effectiveness of SMNs in 3 label-efficient settings., Comment: Oral at GCPR'24, code at https://github.com/alvitawa/UnsupervisedMasking

Digital agriculture leverages technology to enhance crop yield, disease resilience, and soil health, playing a critical role in agricultural research. However, it raises privacy concerns such as adverse pricing, price discrimination, higher insurance costs, and manipulation of resources, deterring farm operators from sharing data due to potential misuse. This study introduces a privacy-preserving framework that addresses these risks while allowing secure data sharing for digital agriculture. Our framework enables comprehensive data analysis while protecting privacy. It allows stakeholders to harness research-driven policies that link public and private datasets. The proposed algorithm achieves this by: (1) identifying similar farmers based on private datasets, (2) providing aggregate information like time and location, (3) determining trends in price and product availability, and (4) correlating trends with public policy data, such as food insecurity statistics. We validate the framework with real-world Farmer's Market datasets, demonstrating its efficacy through machine learning models trained on linked privacy-preserved data. The results support policymakers and researchers in addressing food insecurity and pricing issues. This work significantly contributes to digital agriculture by providing a secure method for integrating and analyzing data, driving advancements in agricultural technology and development.

Nowadays, the large amount of audio-visual content available has fostered the need to develop new robust automatic speaker diarization systems to analyse and characterise it. This kind of system helps to reduce the cost of doing this process manually and allows the use of the speaker information for different applications, as a huge quantity of information is present, for example, images of faces, or audio recordings. Therefore, this paper aims to address a critical area in the field of speaker diarization systems, the integration of audio-visual content of different domains. This paper seeks to push beyond current state-of-the-art practices by developing a robust audio-visual speaker diarization framework adaptable to various data domains, including TV scenarios, meetings, and daily activities. Unlike most of the existing audio-visual speaker diarization systems, this framework will also include the proposal of an approach to lead the precise assignment of specific identities in TV scenarios where celebrities appear. In addition, in this work, we have conducted an extensive compilation of the current state-of-the-art approaches and the existing databases for developing audio-visual speaker diarization.

Nonlinear disordered media uniquely combine multiple scattering and second-harmonic generation. Here, we investigate the statistical properties of the nonlinear light generated within such media. We report super-Rayleigh statistics of the second-harmonic speckle intensity, and demonstrate that it is caused by the mesoscopic correlations arising in extreme scattering conditions. The measured conductance is the lowest ever observed in an isotropically scattering 3D medium, with applications in broadband second-harmonic generation, wavefront shaping in nonlinear disordered media, and photonic computing., Comment: 13 pages, 10 figures

Data-driven RANS modeling is emerging as a promising methodology to exploit the information provided by high-fidelity data. However, its widespread application is limited by challenges in generalization and robustness to inconsistencies between input data of varying fidelity levels. This is especially true for thermal turbulent closures, which inherently depend on momentum statistics provided by low or high fidelity turbulence momentum models. This work investigates the impact of momentum modeling inconsistencies on a data-driven thermal closure trained with a dataset with multiple fidelity (DNS and RANS). The analysis of the model inputs shows that the two fidelity levels correspond to separate regions in the input space. It is here shown that such separation can be exploited by a training with heterogeneous data, allowing the model to detect the level of fidelity in its inputs and adjust its prediction accordingly. In particular, a sensitivity analysis and verification shows that such a model can leverage the data inconsistencies to increase its robustness. Finally, the verification with a CFD simulation shows the potential of this multi-fidelity training approach for flows in which momentum statistics provided by traditional models are affected by model uncertainties.

The injection of hydrogen into the natural-gas grid is an alternative during the process of a gradual decarbonization of the heat and power supply. When dealing with hydrogen-enriched natural gas mixtures, the performance of the reference equations of state habitually used for natural gas should be validated by using high-precision experimental thermophysical data from multicomponent reference mixtures prepared with the lowest possible uncertainty in composition. In this work, we present experimental density data for an 11-compound high-calorific (hydrogen-free) natural gas mixture and for two derived hydrogen-enriched natural gas mixtures prepared by adding (10 and 20) mol-% of hydrogen to the original standard natural gas mixture. The three mixtures were prepared gravimetrically according to ISO 6142-1 for maximum precision in their composition and thus qualify for reference materials. A single-sinker densimeter was used to determine the density of the mixtures from (250-350) K and up to 20 MPa. The experimental density results of this work have been compared to the densities calculated by three different reference equations of state for natural gas related mixtures: the AGA8-DC92 EoS, the GERG-2008 EoS, and an improved version of the GERG-2008 EoS. While relative deviations of the experimental density data for the hydrogen-free natural gas mixture are always within the claimed uncertainty of the three considered equations of state, larger deviations can be observed for the hydrogen-enriched natural gas mixtures from any of the three equations of state, especially for the lowest temperature and the highest pressures.

We study the 3.4-4.4$\mu$m fundamental rovibrational band of H3+, a key tracer of the ionization of the molecular interstellar medium (ISM), in a sample of 12 local (d< 400 Mpc) ultra/luminous infrared galaxies (U/LIRGs) observed with JWST/NIRSpec. The P, Q, and R branches of the band are detected in 13 out of 20 analyzed regions within these U/LIRGs, which increases the number of extragalactic H3+ detections by a factor of 6. For the first time in the ISM, the H3+ band is observed in emission in 3 of these regions. In the remaining 10 regions, the band is seen in absorption. The absorptions are produced toward the 3.4-4.4$\mu$m hot dust continuum rather than toward the stellar continuum, indicating that they likely originate in clouds associated with the dust continuum source. The H3+ band is undetected in Seyfert-like U/LIRGs where the mildly obscured X-ray radiation from the AGN might limit the abundance of this molecule. For the detections, the H3+ abundances, N(H3+)/N_H = (0.5-5.5)x10^-7, imply relatively high ionization rates between 3x10^-16 and >4x10^-15 s^-1, which are likely associated with high-energy cosmic rays. In half of the targets the absorptions are blue-shifted by 50-180 km/s, which are lower than the molecular outflow velocities measured using other tracers such as OH 119$\mu$m or rotational CO lines. This suggests that H3+ traces gas close to the outflow launching sites before it has been fully accelerated. We used nonlocal thermodynamic equilibrium models to investigate the physical conditions of these clouds. In 7 out of 10 objects, the H3+ excitation is consistent with inelastic collisions with H2 in warm translucent molecular clouds (T_kin ~ 250-500 K and n(H2) ~ 10^(2-3) cm^-3). In three objects, dominant infrared pumping excitation is required to explain the absorptions from the (3,0) and (2,1) levels of H3+ detected for the first time in the ISM., Comment: Accepted for publication in A&A Letters. 6 pages, 5 figures. Appendix: 5 pages, 4 figures