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Ultralight bosonic fields can form condensates, or clouds, around spinning black holes. When this system is under the influence of a secondary massive body, its tidal response can be quantified in the tidal Love numbers (TLNs). Although TLNs vanish for black holes in vacuum, it has been shown that the same is not true for black holes immersed in matter environments. In this work, we compute the gravitational TLNs of black holes surrounded by scalar clouds, in the Newtonian limit. We show that they are non-vanishing, have a strong power-law dependence on the boson's mass, and are proportional to the scalar cloud's total mass. In particular, we find that, independently of the cloud's configuration, the TLNs from axisymmetric tides scale as $\propto r_c^{2l+1}$, for $r_c$ the cloud's "radius" and $l$ the multipole order of the external tidal field. This differs by a factor $r_c$ from previous estimates based on scalar and vector tidal perturbations but is in perfect agreement with the behavior of TLNs in other matter systems. Furthermore, we show that the adiabatic tides approximation we employ is, in general, not appropriate for non-axisymmetric tidal interactions., Comment: 14+23 pages, 2 figures. Auxiliary Mathematica package available at https://github.com/richbrito/Tidal_Grav_Atoms

Autonomous operations of robots in unknown environments are challenging due to the lack of knowledge of the dynamics of the interactions, such as the objects' movability. This work introduces a novel Causal Reinforcement Learning approach to enhancing robotics operations and applies it to an urban search and rescue (SAR) scenario. Our proposed machine learning architecture enables robots to learn the causal relationships between the visual characteristics of the objects, such as texture and shape, and the objects' dynamics upon interaction, such as their movability, significantly improving their decision-making processes. We conducted causal discovery and RL experiments demonstrating the Causal RL's superior performance, showing a notable reduction in learning times by over 24.5% in complex situations, compared to non-causal models., Comment: 6 pages, 12 figures, 3 tables. To be presented in 10th IEEE International Smart Cities Conference (ISC2-2024)

This paper introduces the Relative Inequality Index at the Maximum (IDRM), a novel and intuitive measure designed to capture inequality within a population, such as income inequality. The index is based on the idea that individuals experience varying levels of inequality depending on their position within the distribution, particularly with respect to those at the top. The key assumption is that for individuals in lower positions, inequalities referenced to the top positions have greater impact on their well-being and the inequality relative to maximum is the most critical. The IDRM fulfills desirable theoretical properties which were used for its evaluation and comparison against widely accepted measures in inequality literature. From this perspective, the IDRM is shown to be as robust as traditional measures and outperforms the Gini and Dalton indices by satisfying eight out of nine key properties, including decomposability across population subgroups. In a comparative analysis using income data from 58 countries and microdata from Mexico, with the Gini, Theil, and Atkinson indices as benchmarks, the IDRM demonstrates superior consistency, sensitivity to inequality, reduced bias in grouped data, and enhanced precision. This index reflects the varying forms of income distribution, showing heightened sensitivity to the magnitude of inequality., Comment: 38 pages, in Spanish language, 6 tables, 4 figures

This paper presents a machine learning approach to estimate the inertial parameters of a spacecraft in cases when those change during operations, e.g. multiple deployments of payloads, unfolding of appendages and booms, propellant consumption as well as during in-orbit servicing and active debris removal operations. The machine learning approach uses time series clustering together with an optimised actuation sequence generated by reinforcement learning to facilitate distinguishing among different inertial parameter sets. The performance of the proposed strategy is assessed against the case of a multi-satellite deployment system showing that the algorithm is resilient towards common disturbances in such kinds of operations., Comment: 6 pages, 3 figures, 1 table. To be presented in ESA - AI for Space (SPAICE)

Reservoir computing is a promising approach for harnessing the computational power of recurrent neural networks while dramatically simplifying training. This paper investigates the application of integrate-and-fire neurons within reservoir computing frameworks for two distinct tasks: capturing chaotic dynamics of the H\'enon map and forecasting the Mackey-Glass time series. Integrate-and-fire neurons can be implemented in low-power neuromorphic architectures such as Intel Loihi. We explore the impact of network topologies created through random interactions on the reservoir's performance. Our study reveals task-specific variations in network effectiveness, highlighting the importance of tailored architectures for distinct computational tasks. To identify optimal network configurations, we employ a meta-learning approach combined with simulated annealing. This method efficiently explores the space of possible network structures, identifying architectures that excel in different scenarios. The resulting networks demonstrate a range of behaviors, showcasing how inherent architectural features influence task-specific capabilities. We study the reservoir computing performance using a custom integrate-and-fire code, Intel's Lava neuromorphic computing software framework, and via an on-chip implementation in Loihi. We conclude with an analysis of the energy performance of the Loihi architecture., Comment: 19 pages, 9 figures; single column

In this work we demonstrate the possibility of estimating the wind environment of a UAV without specialised sensors, using only the UAV's trajectory, applying a causal machine learning approach. We implement the causal curiosity method which combines machine learning times series classification and clustering with a causal framework. We analyse three distinct wind environments: constant wind, shear wind, and turbulence, and explore different optimisation strategies for optimal UAV manoeuvres to estimate the wind conditions. The proposed approach can be used to design optimal trajectories in challenging weather conditions, and to avoid specialised sensors that add to the UAV's weight and compromise its functionality., Comment: 11 pages, 9 figures, 10 tables. To be presented in 15th International Conference on Mechanical and Aerospace Engineering (ICMAE)

The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. ML has previously been used to reliably 'screen' articles for review - that is, identify relevant articles based on reviewers' inclusion criteria. The application of ML techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy. We therefore set out to develop a series of tools that would assist in the profiling and analysis of 1,952 publications on the theme of 'outcomes-based contracting'. Tools were developed for the following tasks: assign publications into 'policy area' categories; identify and extract key information for evidence mapping, such as organisations, laws, and geographical information; connect the evidence base to an existing dataset on the same topic; and identify subgroups of articles that may share thematic content. An interactive tool using these techniques and a public dataset with their outputs have been released. Our results demonstrate the utility of ML techniques to enhance evidence accessibility and analysis within the systematic review processes. These efforts show promise in potentially yielding substantial efficiencies for future systematic reviewing and for broadening their analytical scope. Our work suggests that there may be implications for the ease with which policymakers and practitioners can access evidence. While ML techniques seem poised to play a significant role in bridging the gap between research and policy by offering innovative ways of gathering, accessing, and analysing data from systematic reviews, we also highlight their current limitations and the need to exercise caution in their application, particularly given the potential for errors and biases., Comment: 28 pages, 5 figures. To appear in Data & Policy journal

The design of cell-free massive MIMO (CF-mMIMO) systems requires accurate, measurement-based channel models. This paper provides the first results from the by far most extensive outdoor measurement campaign for CF-mMIMO channels in an urban environment. We measured impulse responses between over 20,000 potential access point (AP) locations and 80 user equipments (UEs) at 3.5 GHz with 350 MHz bandwidth (BW). Measurements use a "virtual array" approach at the AP and a hybrid switched/virtual approach at the UE. This paper describes the sounder design, measurement environment, data processing, and sample results, particularly the evolution of the power-delay profiles (PDPs) as a function of the AP locations, and its relation to the propagation environment., Comment: Submitted to: VTC 2024-Fall

Motivated by work of Dolgopyat and N\'andori, we establish a general method for upgrading limit theorems for Birkhoff sums and cocycles over dynamical systems to mixing limit theorems under mild ergodicity and hyperbolicity assumptions. Building on previous work of Al-Saqban and Forni, we apply this method to obtain mixing limit theorems for particular subbundles of the Kontsevich-Zorich cocycle. In forthcoming work of Arana-Herrera and Honaryar these results are applied to study the arithmetic/homological complexity of long simple closed geodesics on negatively curved surfaces., Comment: 45 pages

This letter presents a 360{\deg} phase detector cell for performing phase-shift measurements on multiple output systems. An analog phase detector, capable of detecting a maximum range of {\pm}90{\deg}, has been used to perform a double multiplication of two signals, both in-phase and phase-shifted. The proposed solution broadens the frequency range beyond other solutions that require to fulfill the quadrature condition. Subsequently, the possibility of reaching the theoretical limit of phase shift within a hybrid coupler ({\Phi} < 90{\deg} {\pm} 90{\deg}) is discussed by using four straight-line equations to characterize the phase detector response. The proposed solution allows to extend up to 360{\deg} the phase detection range and provide an increased immunity with respect to both impedance mismatching and phase deviations within the hybrid coupler. To demonstrate the feasibility of the proposed design, a phase detector cell prototype has been implemented using a commercial hybrid coupler with a phase shift of 92.5{\deg} {\pm} 0.5{\deg} at 3.1-5.9 GHz, an external switch and a microcontroller with 2 kB of memory. Measurements show a range of detection of 360{\deg} ({\pm}180{\deg}) across the tested frequency band of 2.7-6 GHz., Comment: Index Terms: Microwave analog multiplier phase detector, Phase detector array, 360{\deg} Phase Shift Detector

A new simulation technique to obtain the synchronized steady-state solutions existing in coupled oscillator systems is presented. The technique departs from a semi-analytical formulation presented in previous works. It extends the model of the admittance function describing each individual oscillator to a piecewise linear one. This provides a global formulation of the coupled system, considering the whole characteristic of each voltage-controlled oscillator (VCO) in the array. In comparison with the previous local formulation, the new formulation significantly improves the accuracy in the prediction of the system synchronization ranges. The technique has been tested by comparison with computationally demanding circuit-level Harmonic Balance simulations in an array of Van der Pol-type oscillators and then applied to a coupled system of FET based oscillators at 5 GHz, with very good agreement with measurements., Comment: Index Terms: Coupled-oscillator system, harmonic balance, phased arrays

Autonomous robotic arm manipulators have the potential to make planetary exploration and in-situ resource utilization missions more time efficient and productive, as the manipulator can handle the objects itself and perform goal-specific actions. We train a manipulator to autonomously study objects of which it has no prior knowledge, such as planetary rocks. This is achieved using causal machine learning in a simulated planetary environment. Here, the manipulator interacts with objects, and classifies them based on differing causal factors. These are parameters, such as mass or friction coefficient, that causally determine the outcomes of its interactions. Through reinforcement learning, the manipulator learns to interact in ways that reveal the underlying causal factors. We show that this method works even without any prior knowledge of the objects, or any previously-collected training data. We carry out the training in planetary exploration conditions, with realistic manipulator models., Comment: 8 pages, ASTRA 2023: 17th Symposium on Advanced Space Technologies in Robotics and Automation, 18-20 October 2023, Leiden, The Netherlands

This study investigates the acceptability of different artificial intelligence (AI) applications in education from a multi-stakeholder perspective, including students, teachers, and parents. Acknowledging the transformative potential of AI in education, it addresses concerns related to data privacy, AI agency, transparency, explainability and the ethical deployment of AI. Through a vignette methodology, participants were presented with four scenarios where AI's agency, transparency, explainability, and privacy were manipulated. After each scenario, participants completed a survey that captured their perceptions of AI's global utility, individual usefulness, justice, confidence, risk, and intention to use each scenario's AI if available. The data collection comprising a final sample of 1198 multi-stakeholder participants was distributed through a partner institution and social media campaigns and focused on individual responses to four AI use cases. A mediation analysis of the data indicated that acceptance and trust in AI varies significantly across stakeholder groups. We found that the key mediators between high and low levels of AI's agency, transparency, and explainability, as well as the intention to use the different educational AI, included perceived global utility, justice, and confidence. The study highlights that the acceptance of AI in education is a nuanced and multifaceted issue that requires careful consideration of specific AI applications and their characteristics, in addition to the diverse stakeholders' perceptions., Comment: 28 pages, 2 appendices, 3 figures, 5 tables, original research

Gas turbine superalloys experience hot corrosion, driven by factors including corrosive deposit flux, temperature, gas composition, and component material. The full mechanism still needs clarification and research often focuses on laboratory work. As such, there is interest in causal discovery to confirm the significance of factors and identify potential missing causal relationships or co-dependencies between these factors. The causal discovery algorithm Fast Causal Inference (FCI) has been trialled on a small set of laboratory data, with the outputs evaluated for their significance to corrosion propagation, and compared to existing mechanistic understanding. FCI identified the salt deposition flux as the most influential corrosion variable for this limited dataset. However, HCl was the second most influential for pitting regions, compared to temperature for more uniformly corroding regions. Thus FCI generated causal links aligned with literature from a randomised corrosion dataset, while also identifying the presence of two different degradation modes in operation., Comment: 12 pages, published in Materials and Corrosion

This article presents a radio frequency system that can be used to perform precise vertical landings of drones. The system is based on the three-way phase shift detection of a signal transmitted from the landing point. The antenna system is designed by taking into account parameters such as landing tracking area, analog-to-digital converter (ADC) resolution, phase detector output range, antenna polarization, and the effect of antenna axial ratio. The fabricated prototype consists of a landing point antenna that transmits a signal at 2.46 GHz, as well as a drone triantenna system that includes a phase shift detection circuitry, ADC, and a simple control program that provides the correction instructions for landing. The prototype provides an averaged output data rate (ODR) suitable for landing maneuvers (>300 Hz). A simple system calibration procedure (detector output zeroing) is performed by aligning the antenna system. The measurements performed at different altitudes demonstrate both the correct operation of the proposed solution and its viability as an instrument for precision vertical landings., Comment: The paper has been accepted by IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-8, 2022

This paper presents a circuit for precise vertical landing of drones based on a three phase-shifts detection of a single frequency transmitted from the landing point. The circuit can be considered as a new navigation sensor that assists in guidance corrections for landing at a specific point. The circuit has three inputs to which the signal transmitted from an oscillator located at the landing point arrives with different delays. The input signals are combined in pairs in each of the three analog phase detectors, after having passed through 3 dB@90 o hybrid couplers that guarantee a theoretical non-ambiguous phase-shift range of +-90 degree. Each output has a voltage that is proportional to the phase-shift between each of the input signals, which in turn depend on the position relative to the landing point. A simple landing algorithm based on phase-shift values is proposed, which could be integrated into the same flight control platform, thus avoiding the need to add additional processing components. To demonstrate the feasibility of the proposed design, a triangular phase-shift detector prototype has been implemented using commercial devices. Calibration and measurements at 2.46 GHz show a dynamic range of 30 dB and a non-ambiguous detection range of +-80 degree in the worst cases. Those specs let us to track the drone during the landing maneuver in an inverted cone formed by a surface with a +-4.19 m radius at 10m high and the landing point., Comment: The paper has been accepted by IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-8, 2021

This paper proposes a novel slacks-based interval DEA approach that computes interval targets, slacks, and crisp inefficiency scores. It uses interval arithmetic and requires solving a mixed-integer linear program. The corresponding super-efficiency formulation to discriminate among the efficient units is also presented. We also provide a case study of its application to sustainable tourism in the Mediterranean region, assessing the sustainable tourism efficiency of twelve Mediterranean regions to validate the proposed approach. The inputs and outputs cover the three sustainability dimensions and include GHG emissions as an undesirable output. Three regions were found inefficient, and the corresponding inputs and output improvements were computed. A total rank of the regions was also obtained using the super-efficiency model., Comment: 32 pages, 2 figures, 3 tables

Social media and user-generated content (UGC) have become increasingly important features of journalistic work in a number of different ways. However, the growth of misinformation means that news organisations have had devote more and more resources to determining its veracity and to publishing corrections if it is found to be misleading. In this work, we present the results of interviews with eight members of fact-checking teams from two organisations. Team members described their fact-checking processes and the challenges they currently face in completing a fact-check in a robust and timely way. The former reveals, inter alia, significant differences in fact-checking practices and the role played by collaboration between team members. We conclude with a discussion of the implications for the development and application of computational tools, including where computational tool support is currently lacking and the importance of being able to accommodate different fact-checking practices., Comment: 11 pages. International AAAI Conference on Web and Social Media, Mediate 2023: News Media and Computational Journalism Workshop

By work of Jenkins and Strebel, given a Riemann surface X and a simple closed multi-curve $\alpha$ on it, there exists a unique quadratic differential q on X whose horizontal foliation is measure equivalent to $\alpha$. We study the distribution of the critical graphs of these differentials in the moduli space of metric ribbon graphs as the extremal length of the multi-curves goes to infinity, showing they equidistribute to the Kontsevich measure regardless of the initial choice of X., Comment: 33 pages, 3 figures. Comments welcome!