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The development of Internet of Things (IoT) technologies has led to the widespread adoption of monitoring networks for a wide variety of applications, such as smart cities, environmental monitoring, and precision agriculture. A major research focus in recent years has been the development of graph-based techniques to improve the quality of data from sensor networks, a key aspect for the use of sensed data in decision-making processes, digital twins, and other applications. Emphasis has been placed on the development of machine learning and signal processing techniques over graphs, taking advantage of the benefits provided by the use of structured data through a graph topology. Many technologies such as the graph signal processing (GSP) or the successful graph neural networks (GNNs) have been used for data quality enhancement tasks. In this survey, we focus on graph-based models for data quality control in monitoring sensor networks. Furthermore, we delve into the technical details that are commonly leveraged for providing powerful graph-based solutions for data quality tasks in sensor networks, including missing value imputation, outlier detection, or virtual sensing. To conclude, we have identified future trends and challenges such as graph-based models for digital twins or model transferability and generalization., Comment: Paper submitted to Journal of Network and Computer Applications

Twisting two-dimensional van der Waals magnets allows the formation and control of different spin-textures, as skyrmions or magnetic domains. Beyond the rotation angle, different spin reversal processes can be engineered by increasing the number of magnetic layers forming the twisted van der Waals heterostructure. Here, we consider pristine monolayers and bilayers of the A-type antiferromagnet CrSBr as building blocks. By rotating 90 degrees these units, we fabricate symmetric (monolayer/monolayer and bilayer/bilayer) and asymmetric (monolayer/bilayer) heterostructures. The magneto-transport properties reveal the appearance of magnetic hysteresis, which is highly dependent upon the magnitude and direction of the applied magnetic field and is determined not only by the twist-angle but also by the number of layers forming the stack. This high tunability allows switching between volatile and non-volatile magnetic memory at zero-field and controlling the appearance of abrupt magnetic reversal processes at either negative or positive field values on demand. The phenomenology is rationalized based on the different spin-switching processes occurring in the layers, as supported by micromagnetic simulations. Our results highlight the combination between twist-angle and number of layers as key elements for engineering spin-switching reversals in twisted magnets, of interest towards the miniaturization of spintronic devices and realizing novel spin textures., Comment: 18 pages, 5 figures

This paper examines the interdependence between green financial instruments, represented by green bonds and green stocks, and a set of major conventional assets, such as Treasury, investment-grade and high-yield corporate bonds, general stocks, crude oil, and gold. To that end, a novel wavelet-based network approach that allows for assessing the degree of interconnection between green financial products and traditional asset classes across different investment horizons is applied. The~empirical results show that green bonds are tightly linked to Treasury and investment-grade corporate bonds, while green stocks are strongly tied to general stocks, regardless of the specific time period and investment horizon considered. However, despite their common climate-friendly nature, there is no a remarkable association between green bonds and green stocks. This means that these green investments constitute basically two independent asset classes, with a distinct risk-return profile and aimed at a different type of investor. Furthermore, green financial products have a weak connection with high-yield corporate bonds and crude oil. These findings can have important implications for investors and policy makers in terms of investment decision, hedging strategies, and sustainability and energy policies., Comment: 24 pages, 4 figures

Thermoelectric materials, enabling direct waste-heat to electricity conversion, need to be highly electrically conducting while simultaneously thermally insulating. This is fundamentally challenging since electrical and thermal conduction are usually coupled. Here, we discover that quasi-2D conjugated coordination polymer films exhibit this ideal mix of antithetical properties due to coexistence of defect-tolerant charge transport and defect-sensitive heat propagation. The former is highlighted by the highest conductivities > 2000 S cm-1 with metallic temperature dependence observed in disordered films with paracrystallinity > 10%, while the latter manifests in low, temperature-activated lattice thermal conductivities (< 0.38 W m-1 K-1) originating from small-amplitude, quasi-harmonic lattice dynamics with disorder-limited lifetimes and vibrational scattering length on the order of interatomic spacing. Based on temperature-dependent thermoelectric and magnetotransport experiments we identify a two-carrier (hole-electron), ambipolar metallic transport regime as the origin of relatively small Seebeck coefficients in these materials. Our findings identify conjugated coordination polymers as attractive materials for applications in thermoelectric energy harvesting, (bio)electronics and energy storage., Comment: 28 pages, 5 figures

We present GSLoc: a new visual localization method that performs dense camera alignment using 3D Gaussian Splatting as a map representation of the scene. GSLoc backpropagates pose gradients over the rendering pipeline to align the rendered and target images, while it adopts a coarse-to-fine strategy by utilizing blurring kernels to mitigate the non-convexity of the problem and improve the convergence. The results show that our approach succeeds at visual localization in challenging conditions of relatively small overlap between initial and target frames inside textureless environments when state-of-the-art neural sparse methods provide inferior results. Using the byproduct of realistic rendering from the 3DGS map representation, we show how to enhance localization results by mixing a set of observed and virtual reference keyframes when solving the image retrieval problem. We evaluate our method both on synthetic and real-world data, discussing its advantages and application potential.

Higher idempotent completion gives a formal inductive construction of the $n$-category of finite dimensional $n$-vector spaces starting with the complex numbers. We propose a manifestly unitary construction of low dimensional higher Hilbert spaces, formally constructing the $\mathrm{C}^*$-3-category of 3-Hilbert spaces from Baez's 2-Hilbert spaces, which itself forms a 3-Hilbert space. We prove that the forgetful functor from 3-Hilbert spaces to 3-vector spaces is fully faithful., Comment: 71 pages, 5 figures, many tikz diagrams. Comments welcome!

Red teaming assesses how large language models (LLMs) can produce content that violates norms, policies, and rules set during their safety training. However, most existing automated methods in the literature are not representative of the way humans tend to interact with AI models. Common users of AI models may not have advanced knowledge of adversarial machine learning methods or access to model internals, and they do not spend a lot of time crafting a single highly effective adversarial prompt. Instead, they are likely to make use of techniques commonly shared online and exploit the multiturn conversational nature of LLMs. While manual testing addresses this gap, it is an inefficient and often expensive process. To address these limitations, we introduce the Generative Offensive Agent Tester (GOAT), an automated agentic red teaming system that simulates plain language adversarial conversations while leveraging multiple adversarial prompting techniques to identify vulnerabilities in LLMs. We instantiate GOAT with 7 red teaming attacks by prompting a general-purpose model in a way that encourages reasoning through the choices of methods available, the current target model's response, and the next steps. Our approach is designed to be extensible and efficient, allowing human testers to focus on exploring new areas of risk while automation covers the scaled adversarial stress-testing of known risk territory. We present the design and evaluation of GOAT, demonstrating its effectiveness in identifying vulnerabilities in state-of-the-art LLMs, with an ASR@10 of 97% against Llama 3.1 and 88% against GPT-4 on the JailbreakBench dataset.

Defects in semiconductor crystals can have significant detrimental effects on their performance as radiation detectors. Defects cause charge trapping and recombination, leading to lower signal amplitudes and poor energy resolution. We have designed and built a modular 3D scanner for analyzing these defects in semiconductor samples using commercial off-the-shelf components. Previous solutions offer great spatial resolution, but have limited sample holding capacity and use continuum light sources which can cause difficulty differentiating between different materials within samples. Our design also includes a modular sample holder allowing for easy changing of samples. In this paper, we showcase first results achieved with this custom built scanner as well as planned developments., Comment: Submitted to Journal of Instrumentation for inclusion in the International Workshop on Radiation Imaging Detectors 2024 proceedings

Safety in industrial robotic environments is a hot research topic in the area of human-robot interaction (HRI). Up to now, a robotic arm on an assembly line interacts with other machines away from human workers. Nowadays, robotic arm manufactures are aimed to their robots could increasingly perform tasks collaborating with humans. One of the ways to improve this collaboration is by making the movement of robots more humanlike. This way, it would be easier for a human to foresee the movement of the robot and approach it without fear of contact. The main difference between the movement of a human and of a robotic arm is that the former has a bell-shaped speed profile while the latter has a uniform speed one. To generate this speed profile, the kinematic theory of rapid human movements and its Sigma-Lognormal model has been used. This model is widely used to explain most of the basic phenomena related to the control of human movements. Both human-like and robotic-like movements are transferred to the UR3 robot. In this paper we detail the how the UR3 robot was programmed to produce both kinds of movement. The dissimilarities result between the input motion and output motion to the robot confirm the possibility to develop human-like velocities in the UR3 robot., Comment: 6 pages, 5 figures

This paper investigates a critical aspect of wind energy research - the development of wind turbine wake and its significant impact on wind farm efficiency. The study focuses on the exploration and comparison of two mesh refinement strategies, h- and p-refinement, in their ability to accurately compute the development of wind turbine wake. The h-refinement method refines the mesh by reducing the size of the elements, while the p-refinement method increases the polynomial degree of the elements, potentially reducing the error exponentially for smooth flows. A comprehensive comparison of these methods is presented that evaluates their effectiveness, computational efficiency, and suitability for various scenarios in wind energy. The findings of this research could potentially guide future studies and applications in wind turbine wake modeling, thus contributing to the optimization of wind farms using high-order h/p methods. This study fills a gap in the literature by thoroughly investigating the application of these methods in the context of wind turbine wake development., Comment: 35 pages, 24 figures

Simulators are crucial during the development of a chip, like the RISC-V accelerator designed in the European Processor Initiative project. In this paper, we showcase the limitations of the current simulation solutions in the project and propose using QEMU with RAVE, a plugin we implement and describe in this document. This methodology can rapidly simulate and analyze applications running on the v1.0 and v0.7.1 RISC-V V-extension. Our plugin reports the vector and scalar instructions alongside useful information such as the vector-length being used, the single-element-width, and the register usage, among other vectorization metrics. We provide an API used from the simulated Application to control the RAVE plugin and the capability to generate vectorization traces that can be analyzed using Paraver. Finally, we demonstrate the efficiency of our solution between different evaluated machines and against other simulation methods used in the European Processor Accelerator (EPAC) project.

In this paper the neutral 2SC phase of color superconductivity is investigated in the presence of a magnetic field and for diquark coupling constants and baryonic densities that are expected to characterize neutron stars. Specifically, the behavior of the charged gluons Meissner masses is investigated in the parameter region of interest taking into account in addition the contribution of a rotated magnetic field. It is found that up to moderately-high diquark coupling constants the mentioned Meissner masses become tachyonic independently of the applied magnetic field amplitude, hence signalizing the chromomagnetic instability of this phase. To remove the instability, it is proposed the restructuring of the system ground state, which now will be formed by vortices of the rotated charged gluons. These vortices boost the applied magnetic field having the most significant increase for relatively low applied magnetic fields. Finally, considering that with the stellar rotational frequency observed for magnetars a field of order $10^8$ G can be generated by dynamo effect. Then, we show that by the boosting effect just described the field can be amplified to $10^{17}$ G that is in the range of inner core fields expected for magnetars. Thus, we conclude that the described mechanism could be the one responsible for the large fields characterizing magnetars if the core of this compact objects are formed by neutral 2SC matter., Comment: 14 pages, 7 figures

orbitize! is a package for Bayesian modeling of the orbital parameters of resolved binary objects from time series measurements. It was developed with the needs of the high-contrast imaging community in mind, and has since also become widely used in the binary star community. A generic orbitize! use case involves translating relative astrometric time series, optionally combined with radial velocity or astrometric time series, into a set of derived orbital posteriors. This paper is published alongside the release of orbitize! version 3.0, which has seen significant enhancements in functionality and accessibility since the release of version 1.0 (Blunt et al., 2020)., Comment: Published in JOSS

Automated speech analysis is a thriving approach to detect early markers of Alzheimer's disease (AD). Yet, recording conditions in most AD datasets are heterogeneous, with patients and controls often evaluated in different acoustic settings. While this is not a problem for analyses based on speech transcription or features obtained from manual alignment, it does cast serious doubts on the validity of acoustic features, which are strongly influenced by acquisition conditions. We examined this issue in the ADreSSo dataset, derived from the widely used Pitt corpus. We show that systems based on two acoustic features, MFCCs and Wav2vec 2.0 embeddings, can discriminate AD patients from controls with above-chance performance when using only the non-speech part of the audio signals. We replicated this finding in a separate dataset of Spanish speakers. Thus, in these datasets, the class can be partly predicted by recording conditions. Our results are a warning against the use of acoustic systems for identifying patients based on non-standardized recordings. We propose that acoustically heterogeneous datasets for dementia studies should be either (a) analyzed using only transcripts or other features derived from manual annotations, or (b) replaced by datasets collected with strictly controlled acoustic conditions., Comment: 5 pages, 1 figure, 1 table

We experimentally study the statistics of the transition path time taken by a submicron bead to successfully traverse an energy barrier created by two optical tweezers in two prototypical viscoelastic fluids, namely, aqueous polymer and micellar solutions. We find a very good agreement between our experimental distributions and a theoretical expression derived from the generalized Langevin equation for the particle motion. Our results reveal that the mean transition path time measured in such viscoelastic fluids have a non-trivial dependence on the barrier curvature and they can be significantly reduced when compared with those determined in Newtonian fluids of the same zero-shear viscosity. We verify that the decrease of the mean transition path time can be described in terms of an effective viscosity that quantitatively coincides with that measured by linear microrheology at a frequency determined by the reactive mode that gives rise to the unstable motion over the barrier. Therefore, our results uncover the linear response of the particle during its thermally activated escape from a metastable state even when taking place in a non-Markovian bath., Comment: 4 figures

This work extends our previous study from S. Shrestha et al. (2024) by introducing a new abstract framework for Variational Multiscale (VMS) methods at the discrete level. We introduce the concept of what we define as the optimal projector and present an approach where the infinite-dimensional unresolved scales are approximated in a finite-dimensional subspace using the numerically computed Fine-Scale Greens' function of the underlying symmetric problem. The proposed approach involves solving the VMS problem on two separate meshes: a coarse mesh for the full PDE and a fine mesh for the symmetric part of the continuous differential operator. We consider the 1D and 2D steady advection-diffusion problems in both direct and mixed formulations as the test cases in this paper. Moreover, we demonstrate the working of this method using the Mimetic Spectral Element Method (MSEM), however, it may be applied to other Finite/Spectral Element or Isogeometric frameworks. Furthermore, we propose that VMS should not be viewed as a stabilisation technique; instead, the base scheme should be inherently stable, with VMS enhancing the solution quality by supplementing the base scheme.

Single photons emitters (SPEs) are key components in quantum information applications and are commonly generated in 2D materials by inhomogeneous strain engineering. Here, we report an alternative approach that involves a 2D semiconductor/2D magnet heterostructure. The optical study of the WSe2/CrSBr heterostructures reveals several new emission lines at lower energies compared to characteristic WSe2 emissions, that are assigned to localized excitons. Further investigation demonstrates that one of these emergent lines is an SPE with a strong valley polarization response and large energy shift with the field-induced metamagnetic transition in CrSBr, linking it to the magnetic proximity effect of the adjacent CrSBr layer. In contrast to previous reports on WSe2 that only allow tuning of the SPEs by out-of-plane magnetic field, our emitter is sensitive to both in- and out-of-plane fields. Our findings demonstrate the potential of this approach for improved control and polarization of SPEs in 2D materials., Comment: 17 pages, 4 figures, plus suporting information

We address the linguistic problem of the sequential arrangement of a head and its dependents from an information theoretic perspective. In particular, we consider the optimal placement of a head that maximizes the predictability of the sequence. We assume that dependents are statistically independent given a head, in line with the open-choice principle and the core assumptions of dependency grammar. We demonstrate the optimality of harmonic order, i.e., placing the head last maximizes the predictability of the head whereas placing the head first maximizes the predictability of dependents. We also show that postponing the head is the optimal strategy to maximize its predictability while bringing it forward is the optimal strategy to maximize the predictability of dependents. We unravel the advantages of the strategy of maximizing the predictability of the head over maximizing the predictability of dependents. Our findings shed light on the placements of the head adopted by real languages or emerging in different kinds of experiments., Comment: Minor corrections; references added

A comparative vacuum ultraviolet spectroscopy study conducted at ISOLDE-CERN of the radiative decay of the $^{229m}$Th nuclear clock isomer embedded in different host materials is reported. The ratio of the number of radiative decay photons and the number of $^{229m}$Th embedded are determined for single crystalline CaF$_2$, MgF$_2$, LiSrAlF$_6$, AlN, and amorphous SiO$_2$. For the latter two materials, no radiative decay signal was observed and an upper limit of the ratio is reported. The radiative decay wavelength was determined in LiSrAlF$_6$ and CaF$_2$, reducing its uncertainty by a factor of 2.5 relative to our previous measurement. This value is in agreement with the recently reported improved values from laser excitation.

Multifactorial experimental designs allow us to assess the contribution of several factors, and potentially their interactions, to one or several responses of interests. Following the principles of the partition of the variance advocated by Sir R.A. Fisher, the experimental responses are factored into the quantitative contribution of main factors and interactions. A popular approach to perform this factorization in both ANOVA and ASCA(+) is through General Linear Models. Subsequently, different inferential approaches can be used to identify whether the contributions are statistically significant or not. Unfortunately, the performance of inferential approaches in terms of Type I and Type II errors can be heavily affected by missing data, outliers and/or the departure from normality of the distribution of the responses, which are commonplace problems in modern analytical experiments. In this paper, we study these problem and suggest good practices of application., Comment: Submitted to Chemometrics and Intelligent Laboratory Systems

Most machine learning classifiers are designed to output posterior probabilities for the classes given the input sample. These probabilities may be used to make the categorical decision on the class of the sample; provided as input to a downstream system; or provided to a human for interpretation. Evaluating the quality of the posteriors generated by these system is an essential problem which was addressed decades ago with the invention of proper scoring rules (PSRs). Unfortunately, much of the recent machine learning literature uses calibration metrics -- most commonly, the expected calibration error (ECE) -- as a proxy to assess posterior performance. The problem with this approach is that calibration metrics reflect only one aspect of the quality of the posteriors, ignoring the discrimination performance. For this reason, we argue that calibration metrics should play no role in the assessment of posterior quality. Expected PSRs should instead be used for this job, preferably normalized for ease of interpretation. In this work, we first give a brief review of PSRs from a practical perspective, motivating their definition using Bayes decision theory. We discuss why expected PSRs provide a principled measure of the quality of a system's posteriors and why calibration metrics are not the right tool for this job. We argue that calibration metrics, while not useful for performance assessment, may be used as diagnostic tools during system development. With this purpose in mind, we discuss a simple and practical calibration metric, called calibration loss, derived from a decomposition of expected PSRs. We compare this metric with the ECE and with the expected score divergence calibration metric from the PSR literature and argue, using theoretical and empirical evidence, that calibration loss is superior to these two metrics.

Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged as one suitable solution for transforming this flood of data into a simple yet robust data structure. ESDCs achieve this by organising data into an analysis-ready format aligned with a spatio-temporal grid, facilitating user-friendly analysis and diminishing the need for extensive technical data processing knowledge. Despite these significant benefits, the completion of the entire ESDC life cycle remains a challenging task. Obstacles are not only of a technical nature but also relate to domain-specific problems in Earth system research. There exist barriers to realising the full potential of data collections in light of novel cloud-based technologies, particularly in curating data tailored for specific application domains. These include transforming data to conform to a spatio-temporal grid with minimum distortions and managing complexities such as spatio-temporal autocorrelation issues. Addressing these challenges is pivotal for the effective application of Artificial Intelligence (AI) approaches. Furthermore, adhering to open science principles for data dissemination, reproducibility, visualisation, and reuse is crucial for fostering sustainable research. Overcoming these challenges offers a substantial opportunity to advance data-driven Earth system research, unlocking the full potential of an integrated, multidimensional view of Earth system processes. This is particularly true when such research is coupled with innovative research paradigms and technological progress.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

We present a novel approach to automate and optimize anisotropic p-adaptation in high-order h/p solvers using Reinforcement Learning (RL). The dynamic RL adaptation uses the evolving solution to adjust the high-order polynomials. We develop an offline training approach, decoupled from the main solver, which shows minimal overcost when performing simulations. In addition, we derive an inexpensive RL-based error estimation approach that enables the quantification of local discretization errors. The proposed methodology is agnostic to both the computational mesh and the partial differential equation to be solved. The application of RL to mesh adaptation offers several benefits. It enables automated and adaptive mesh refinement, reducing the need for manual intervention. It optimizes computational resources by dynamically allocating high-order polynomials where necessary and minimizing refinement in stable regions. This leads to computational cost savings while maintaining the accuracy of the solution. Furthermore, RL allows for the exploration of unconventional mesh adaptations, potentially enhancing the accuracy and robustness of simulations. This work extends our original research, offering a more robust, reproducible, and generalizable approach applicable to complex three-dimensional problems. We provide validation for laminar and turbulent cases: circular cylinders, Taylor Green Vortex and a 10MW wind turbine to illustrate the flexibility of the proposed approach., Comment: 38 pages, 18 figures, 5 tables

Optically read out gaseous detectors are used in track reconstruction and imaging applications requiring high granularity images. Among resolution-determining factors, the amplification stage plays a crucial role and optimisations of detector geometry are pursued to maximise spatial resolution. To compare MicroPattern Gaseous Detector (MPGD) technologies, focused low-energy X-ray beams at the SOLEIL synchrotron facility were used to record and extract point spread function widths with Micromegas and GEM detectors. Point spread function width of $\approx$108\,\microns for Micromegas and $\approx$127\,\microns for GEM foils were extracted. The scanning of the beam with different intensities, energies and across the detector active region can be used to quantify resolution-limiting factors and improve imaging detectors using MPGD amplification stages., Comment: 22 pages, 17 figures

We present a computational study to explore the potential of different experimental approaches to tune the magnetic interactions in two-dimensional van der Waals magnets. We selected CrGeSe$_3$, CrGeTe$_3$, and Janus Cr$_2$Ge$_2$(Se,Te)$_3$ monolayers as case studies and calculated the full exchange tensors among all relevant atomic pairs and analyze their dependence on different external parameters, such as biaxial and uniaxial strain, as well as gate voltage. We pay special attention to interactions that emerge or vanish due to changes of the symmetry of the system. We find that biaxial and uniaxial strains significantly modify isotropic exchange couplings, which can lead to a transition from a ferromagnetic to an antiferromagnetic phase, while a gate voltage induces Dzyaloshinskii-Moriya interactions, forming a vortex pattern whose chirality is determined by the sign of the electric field. The electric dipole moment of the Janus material is large, raising the possibility of multiferroic behaviour. The polarizability is similar for the three compounds., Comment: 10 pages 6 figures

We explore a reinforcement learning strategy to automate and accelerate h/p-multigrid methods in high-order solvers. Multigrid methods are very efficient but require fine-tuning of numerical parameters, such as the number of smoothing sweeps per level and the correction fraction (i.e., proportion of the corrected solution that is transferred from a coarser grid to a finer grid). The objective of this paper is to use a proximal policy optimization algorithm to automatically tune the multigrid parameters and, by doing so, improve stability and efficiency of the h/p-multigrid strategy. Our findings reveal that the proposed reinforcement learning h/p-multigrid approach significantly accelerates and improves the robustness of steady-state simulations for one dimensional advection-diffusion and nonlinear Burgers' equations, when discretized using high-order h/p methods, on uniform and nonuniform grids., Comment: 14 pages, 1 figure

We develop a torque-pitch control framework using deep reinforcement learning for wind turbines to optimize the generation of wind turbine energy while minimizing operational noise. We employ a double deep Q-learning, coupled to a blade element momentum solver, to enable precise control over wind turbine parameters. In addition to the blade element momentum, we use the wind turbine acoustic model of Brooks Pope and Marcolini. Through training with simple winds, the agent learns optimal control policies that allow efficient control for complex turbulent winds. Our experiments demonstrate that the reinforcement learning is able to find optima at the Pareto front, when maximizing energy while minimizing noise. In addition, the adaptability of the reinforcement learning agent to changing turbulent wind conditions, underscores its efficacy for real-world applications. We validate the methodology using a SWT2.3-93 wind turbine with a rated power of 2.3 MW. We compare the reinforcement learning control to classic controls to show that they are comparable when not taking into account noise emissions. When including a maximum limit of 45 dB to the noise produced (100 meters downwind of the turbine), the extracted yearly energy decreases by 22%. The methodology is flexible and allows for easy tuning of the objectives and constraints through the reward definitions, resulting in a flexible multi-objective optimization framework for wind turbine control. Overall, our findings highlight the potential of RL-based control strategies to improve wind turbine efficiency while mitigating noise pollution, thus advancing sustainable energy generation technologies

High-Energy Physics experiments are facing a multi-fold data increase with every new iteration. This is certainly the case for the upcoming High-Luminosity LHC upgrade. Such increased data processing requirements forces revisions to almost every step of the data processing pipeline. One such step in need of an overhaul is the task of particle track reconstruction, a.k.a., tracking. A Machine Learning-assisted solution is expected to provide significant improvements, since the most time-consuming step in tracking is the assignment of hits to particles or track candidates. This is the topic of this paper. We take inspiration from large language models. As such, we consider two approaches: the prediction of the next word in a sentence (next hit point in a track), as well as the one-shot prediction of all hits within an event. In an extensive design effort, we have experimented with three models based on the Transformer architecture and one model based on the U-Net architecture, performing track association predictions for collision event hit points. In our evaluation, we consider a spectrum of simple to complex representations of the problem, eliminating designs with lower metrics early on. We report extensive results, covering both prediction accuracy (score) and computational performance. We have made use of the REDVID simulation framework, as well as reductions applied to the TrackML data set, to compose five data sets from simple to complex, for our experiments. The results highlight distinct advantages among different designs in terms of prediction accuracy and computational performance, demonstrating the efficiency of our methodology. Most importantly, the results show the viability of a one-shot encoder-classifier based Transformer solution as a practical approach for the task of tracking.

Hypothetical axions provide a compelling explanation for dark matter and could be emitted from the hot solar interior. The CERN Axion Solar Telescope (CAST) has been searching for solar axions via their back conversion to X-ray photons in a 9-T 10-m long magnet directed towards the Sun. We report on an extended run with the IAXO (International Axion Observatory) pathfinder detector, doubling the previous exposure time. The detector was operated with a xenon-based gas mixture for part of the new run, providing technical insights for future detector configurations in IAXO. No counts are detected in the 95\% signal-encircling region during the new run, while one is expected. The new data improve the axion-photon coupling limit to 5.7$\times 10^{-11}\,$GeV$^{-1}$ at 95\% C.L., the most restrictive experimental limit to date., Comment: 8 pages, 4 figures (plus 6 pages, 10 figures of supplemental material) Corresponding authors: C. Margalejo (cmargalejo@unizar.es) and J. Ruz (Jaime.Ruz@cern.ch)

This paper compares two actuator line methodologies for modelling wind turbines employing high-order h/p solvers and large-eddy simulations. The methods combine the accuracy of high-order solvers (in this work the maximum order is 6) with the computational efficiency of actuator lines to capture the aerodynamic effects of wind turbine blades. Comparisons with experiments validate the actuator line methodologies. We explore the effects of the polynomial order and the smoothing parameter associated with the Gaussian regularization function, and use them to blend the actuator line forcing in the high-order computational mesh, to show that both parameters influence the distribution of forces along the blades and the turbine wake. The greatest impact is obtained when the polynomial order is increased, allowing one to better capture the physics without requiring new meshes. When comparing the actuator line methodologies, we show the advantages of performing weighted sums, over element averages, to compute the blade velocities and forces in high-order solvers. For low-order simulations (low polynomial orders), both methods provide similar results, but as the polynomial order is increased, the weighted sum method shows smoother thrust/torque distributions and better wake resolution. Furthermore, cell averaging introduces nonphysical oscillations when increasing the polynomial order beyond 3 (4th order accuracy).

To address issues of group-level fairness in machine learning, it is natural to adjust model parameters based on specific fairness objectives over a sensitive-attributed validation set. Such an adjustment procedure can be cast within a meta-learning framework. However, naive integration of fairness goals via meta-learning can cause hypergradient conflicts for subgroups, resulting in unstable convergence and compromising model performance and fairness. To navigate this issue, we frame the resolution of hypergradient conflicts as a multi-player cooperative bargaining game. We introduce a two-stage meta-learning framework in which the first stage involves the use of a Nash Bargaining Solution (NBS) to resolve hypergradient conflicts and steer the model toward the Pareto front, and the second stage optimizes with respect to specific fairness goals. Our method is supported by theoretical results, notably a proof of the NBS for gradient aggregation free from linear independence assumptions, a proof of Pareto improvement, and a proof of monotonic improvement in validation loss. We also show empirical effects across various fairness objectives in six key fairness datasets and two image classification tasks.

Aerodynamic noise is a limitation for further exploitation of wind energy resources. As this type of noise is caused by the interaction of turbulent flow with the airframe, a detailed resolution of the flow is necessary to obtain an accurate prediction of the far-field noise. Computational fluid dynamic (CFD) solvers simulate the flow field but only at a high computational cost, which is much increased when the acoustic field is resolved. Therefore, wind turbine noise predictions using numerical approaches remain a challenge. This paper presents a methodology that couples (relatively fast) wind turbine CFD simulations using actuator lines with a fast turn-around noise prediction method. The flow field is simulated using actuator lines and large eddy simulations. The noise prediction method is based on the Amiet-Schlinker's theory for rotatory noise sources, considering leading- and trailing-edge noise as unique noise sources. A 2D panel code (XFOIL) calculates the sectional forces and boundary layer quantities. The resulting methodology for the noise prediction method is of high fidelity since the wind turbine geometry is accounted for in both flow and acoustics predictions. Results are compared with field measurements of a full-scale wind turbine for two operational conditions, validating the results of this research., Comment: 13 pages, 12 figures

Handwriting is a complex task that involves the coordination of motor, perceptual and cognitive skills. It is a fundamental skill for the cognitive and academic development of children. However, the technological, and educational changes in recent decades have affected both the teaching and assessment of handwriting. This paper presents a literature review of handwriting analysis in children, including a bibliometric analysis of published articles, the study participants, and the methods of evaluating the graphonometric state of children. The aim is to synthesize the state of the art and provide an overview of the main study trends over the last decade. The review concludes that handwriting remains a fundamental tool for early estimation of cognitive problems and early intervention. The article analyzes graphonometric evaluation tools. Likewise, it reflects on the importance of graphonometric evaluation as a means to detect possible difficulties or disorders in learning to write. The article concludes by highlighting the need to agree on an evaluation methodology and to combine databases.

Satellite-based remote sensing has revolutionised the way we address global challenges. Huge quantities of Earth Observation (EO) data are generated by satellite sensors daily, but processing these large datasets for use in ML pipelines is technically and computationally challenging. While some preprocessed Earth observation datasets exist, their content is often limited to optical or near-optical wavelength data, which is ineffective at night or in adverse weather conditions. Synthetic Aperture Radar (SAR), an active sensing technique based on microwave length radiation, offers a viable alternative. However, the application of machine learning to SAR has been limited due to a lack of ML-ready data and pipelines, particularly for the full diversity of SAR data, including polarimetry, coherence and interferometry. In this work, we introduce M3LEO, a multi-modal, multi-label Earth observation dataset that includes polarimetric, interferometric, and coherence SAR data derived from Sentinel-1, alongside multispectral Sentinel-2 imagery and auxiliary data describing terrain properties such as land use. M3LEO spans approximately 17M 4x4 km data chips from six diverse geographic regions. The dataset is complemented by a flexible PyTorch Lightning framework configured using Hydra to accommodate its use across diverse ML applications in Earth observation. We provide tools to process any dataset available on popular platforms such as Google Earth Engine for seamless integration with our framework. We show that the distribution shift in self-supervised embeddings is substantial across geographic regions, even when controlling for terrain properties. Data: huggingface.co/M3LEO, Code: github.com/spaceml-org/M3LEO., Comment: 10 pages, 5 figures

The AEFishBIT tri-axial accelerometer was externally attached to the operculum to assess the divergent activity and respiratory patterns of two marine farmed fish, the gilthead sea bream (Sparus aurata) and European sea bass (Dicentrarchus labrax). Analysis of raw data from exercised fish highlighted the large amplitude of operculum aperture and body tail movements in European sea bass, which were overall more stable at low-medium exercise intensity levels. Cosinor analysis in free-swimming fish (on-board data processing) highlighted a pronounced daily rhythmicity of locomotor activity and respiratory frequency in both gilthead sea bream and European sea bass. Acrophases of activity and respiration were coupled in gilthead sea bream, acting feeding time (once daily at 11:00 h) as a main synchronizing factor. By contrast, locomotor activity and respiratory frequency were out of phase in European sea bass with activity acrophase on early morning and respiration acrophase on the afternoon. The daily range of activity and respiration variation was also higher in European sea bass, probably as part of the adaptation of this fish species to act as a fast swimming predator. In any case, lower locomotor activity and enhanced respiration were associated with larger body weight in both fish species. This agrees with the notion that selection for fast growth in farming conditions is accompanied by a lower activity profile, which may favor an efficient feed conversion for growth purposes. Therefore, the use of behavioral monitoring is becoming a reliable and large-scale promising tool for selecting more efficient farmed fish, allowing researchers and farmers to establish stricter criteria of welfare for more sustainable and ethical fish production.

The order in which the trajectory is executed is a powerful source of information for recognizers. However, there is still no general approach for recovering the trajectory of complex and long handwriting from static images. Complex specimens can result in multiple pen-downs and in a high number of trajectory crossings yielding agglomerations of pixels (also known as clusters). While the scientific literature describes a wide range of approaches for recovering the writing order in handwriting, these approaches nevertheless lack a common evaluation metric. In this paper, we introduce a new system to estimate the order recovery of thinned static trajectories, which allows to effectively resolve the clusters and select the order of the executed pen-downs. We evaluate how knowing the starting points of the pen-downs affects the quality of the recovered writing. Once the stability and sensitivity of the system is analyzed, we describe a series of experiments with three publicly available databases, showing competitive results in all cases. We expect the proposed system, whose code is made publicly available to the research community, to reduce potential confusion when the order of complex trajectories are recovered, and this will in turn make the trajectories recovered to be viable for further applications, such as velocity estimation.

Bilayers of NdCo$_5$/Ni$_8$Fe$_2$ can act as reconfigurable racetracks thanks to the parallel stripe domain configuration present in the hard magnetic material with weak perpendicular anisotropy (NdCo$_5$), and its imprint on the soft magnetic layer (Ni$_8$Fe$_2$). This pattern hosts spin textures with well defined topological charges and establishes paths for their deterministic propagation under the effect of pulsed currents, which has been studied as a function of externally applied fields by using Magnetic Transmission X-ray Microscopy. The experiments show guided vortex/antivortex propagation events within the Ni$_8$Fe$_2$ above a threshold current of $3\cdot10^{11}$ A/m$^2$. Opposite propagation senses have been observed depending on the topological charge of the spin texture, both in the remnant state and under an applied external field. Micromagnetic simulations of our multilayer reveal that the guiding effect and asymmetric propagation sense are due to the magnetic history of the hard magnetic layer. An exchange-bias-based memory effect acts as a magnetic spring and controls the propagation sense by favoring a specific orientation of the in plane magnetization, leading to a system which behaves as a hard-soft magnetic composite with reconfigurable capabilities for a controlled propagation of magnetic topological textures.

Aerial imagery and its direct application to visual localization is an essential problem for many Robotics and Computer Vision tasks. While Global Navigation Satellite Systems (GNSS) are the standard default solution for solving the aerial localization problem, it is subject to a number of limitations, such as, signal instability or solution unreliability that make this option not so desirable. Consequently, visual geolocalization is emerging as a viable alternative. However, adapting Visual Place Recognition (VPR) task to aerial imagery presents significant challenges, including weather variations and repetitive patterns. Current VPR reviews largely neglect the specific context of aerial data. This paper introduces a methodology tailored for evaluating VPR techniques specifically in the domain of aerial imagery, providing a comprehensive assessment of various methods and their performance. However, we not only compare various VPR methods, but also demonstrate the importance of selecting appropriate zoom and overlap levels when constructing map tiles to achieve maximum efficiency of VPR algorithms in the case of aerial imagery. The code is available on our GitHub repository -- https://github.com/prime-slam/aero-vloc.

Two-dimensional (2D) materials that exhibit spontaneous magnetization, polarization or strain (referred to as ferroics) have the potential to revolutionize nanotechnology by enhancing the multifunctionality of nanoscale devices. However, multiferroic order is difficult to achieve, requiring complicated coupling between electron and spin degrees of freedom. We propose a universal method to engineer multiferroics from van der Waals magnets by taking advantage of the fact that changing the stacking between 2D layers can break inversion symmetry, resulting in ferroelectricity and possibly magnetoelectric coupling. We illustrate this concept using first-principles calculations in bilayer NiI$_2$, which can be made ferroelectric upon rotating two adjacent layers by $180^{\circ}$ with respect to the bulk stacking. Furthermore, we discover a novel multiferroic order induced by interlayer charge transfer which couples the interlayer spin order and electronic polarization. Our approach is not only general but also systematic, and can enable the discovery of a wide variety of 2D multiferroics., Comment: Fixed a typo in the title

Collaborative robots or cobots interact with humans in a common work environment. In cobots, one under investigated but important issue is related to their movement and how it is perceived by humans. This paper tries to analyze whether humans prefer a robot moving in a human or in a robotic fashion. To this end, the present work lays out what differentiates the movement performed by an industrial robotic arm from that performed by a human one. The main difference lies in the fact that the robotic movement has a trapezoidal speed profile, while for the human arm, the speed profile is bell-shaped and during complex movements, it can be considered as a sum of superimposed bell-shaped movements. Based on the lognormality principle, a procedure was developed for a robotic arm to perform human-like movements. Both speed profiles were implemented in two industrial robots, namely, an ABB IRB 120 and a Universal Robot UR3. Three tests were used to study the subjects' preference when seeing both movements and another analyzed the same when interacting with the robot by touching its ends with their fingers.

Script identification plays a vital role in applications that involve handwriting and document analysis within a multi-script and multi-lingual environment. Moreover, it exhibits a profound connection with human cognition. This paper provides a new database for benchmarking script identification algorithms, which contains both printed and handwritten documents collected from a wide variety of scripts, such as Arabic, Bengali (Bangla), Gujarati, Gurmukhi, Devanagari, Japanese, Kannada, Malayalam, Oriya, Roman, Tamil, Telugu, and Thai. The dataset consists of 1,135 documents scanned from local newspaper and handwritten letters as well as notes from different native writers. Further, these documents are segmented into lines and words, comprising a total of 13,979 and 86,655 lines and words, respectively, in the dataset. Easy-to-go benchmarks are proposed with handcrafted and deep learning methods. The benchmark includes results at the document, line, and word levels with printed and handwritten documents. Results of script identification independent of the document/line/word level and independent of the printed/handwritten letters are also given. The new multi-lingual database is expected to create new script identifiers, present various challenges, including identifying handwritten and printed samples and serve as a foundation for future research in script identification based on the reported results of the three benchmarks.

Graphomotor and handwriting disabilities (GD and HD, respectively) could significantly reduce children's quality of life. Effective remediation depends on proper diagnosis; however, current approaches to diagnosis and assessment of GD and HD have several limitations and knowledge gaps, e.g. they are subjective, they do not facilitate identification of specific manifestations, etc. The aim of this work is to introduce a new scale (GHDRS Graphomotor and Handwriting Disabilities Rating Scale) that will enable experts to perform objective and complex computeraided diagnosis and assessment of GD and HD. The scale supports quantification of 17 manifestations associated with the process/product of drawing/ handwriting. The whole methodology of GHDRS design is made maximally transparent so that it could be adapted for other languages.

Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments. In this study, we delve into unexplored avenues for particle track reconstruction and hit clustering. Firstly, we enhance the algorithmic design effort by utilising a simplified simulator (REDVID) to generate training data that is specifically composed for simplicity. We demonstrate the effectiveness of this data in guiding the development of optimal network architectures. Additionally, we investigate the application of image segmentation networks for this task, exploring their potential for accurate track reconstruction. Moreover, we approach the task from a different perspective by treating it as a hit sequence to track sequence translation problem. Specifically, we explore the utilisation of Transformer architectures for tracking purposes. Our preliminary findings are covered in detail. By considering this novel approach, we aim to uncover new insights and potential advancements in track reconstruction. This research sheds light on previously unexplored methods and provides valuable insights for the field of particle track reconstruction and hit clustering in HEP.

Alzheimer's disease is one of the most incisive illnesses among the neurodegenerative ones, and it causes a progressive decline in cognitive abilities that, in the worst cases, becomes severe enough to interfere with daily life. Currently, there is no cure, so an early diagnosis is strongly needed to try and slow its progression through medical treatments. Handwriting analysis is considered a potential tool for detecting and understanding certain neurological conditions, including Alzheimer's disease. While handwriting analysis alone cannot provide a definitive diagnosis of Alzheimer's, it may offer some insights and be used for a comprehensive assessment. The Sigma-lognormal model is conceived for movement analysis and can also be applied to handwriting. This model returns a set of lognormal parameters as output, which forms the basis for the computation of novel and significant features. This paper presents a machine learning approach applied to handwriting features extracted through the sigma-lognormal model. The aim is to develop a support system to help doctors in the diagnosis and study of Alzheimer, evaluate the effectiveness of the extracted features and finally study the relation among them.

Lameness is one of the costliest pathological problems affecting dairy animals. It is usually assessed by trained veterinary clinicians who observe features such as gait symmetry or gait parameters as step counts in real-time. With the development of artificial intelligence, various modular systems have been proposed to minimize subjectivity in lameness assessment. However, the major limitation in their development is the unavailability of a public dataset which is currently either commercial or privately held. To tackle this limitation, we have introduced CowScreeningDB which was created using sensory data. This dataset was sourced from 43 cows at a dairy located in Gran Canaria, Spain. It consists of a multi-sensor dataset built on data collected using an Apple Watch 6 during the normal daily routine of a dairy cow. Thanks to the collection environment, sampling technique, information regarding the sensors, the applications used for data conversion and storage make the dataset a transparent one. This transparency of data can thus be used for further development of techniques for lameness detection for dairy cows which can be objectively compared. Aside from the public sharing of the dataset, we have also shared a machine-learning technique which classifies the caws in healthy and lame by using the raw sensory data. Hence validating the major objective which is to establish the relationship between sensor data and lameness.