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This paper presents and evaluates a set of methods to classify individual Scopus publications using their references back to the second generation, where each publication can be assigned fractionally into up to five ASJC (All Science Journal Classifications) categories, excluding the Multidisciplinary area and the miscellaneous categories. Based on proposals by Glanzel et al. (1999a, 1999b, 2021), some additional parameters are established that allow different results to be obtained depending on how category membership is weighted or how the acceptance thresholds for multiple assignments are established. Various classifications are obtained, and then compared with each other, with the original ASJC Scopus journal classification, and with the AAC (Authors Assignation Collection) classification of a previous study (Alvarez-Llorente et al., 2023) in which the papers corresponding authors assign them the most appropriate categories. Classifications in which a high threshold is set for allowing assignments to multiple categories, combined with the use of first- and second-generation references and averaging over the number of references, provide the most promising results, improving over other reference-based reclassification proposals in terms of granularity, and over the Scopus classification itself in such aspects as the homogeneity of the publications assigned to a category. They also show greater coincidence with the AAC classification.

Sulphur vacancies in MoS$_2$ on Au(111) have been shown to be negatively charged as reflected by a Kondo resonance. Here, we use scanning tunneling microscopy to show that these vacancies serve as anchoring sites for thiol-based molecules (CF$_3$-3P-SH) with two distinct reaction products, one of them showing a Kondo resonance. Based on comparisons with density-functional theory (DFT) calculations, including a random structure search and computation of energies and electronic properties at a hybrid exchange-correlation functional level, we conclude that both anchored molecules are charge neutral. One of them is an anchored intact CF$_3$-3P-SH molecule while the other one is the result of catalytically activated dehydrogenation to CF$_3$-3P-S with subsequent anchoring. Our investigations highlight a perspective of functionalizing defects with thiol-terminated molecules that can be equipped with additional functional groups, such as charge donor- or acceptor-moieties, switching units or magnetic centers.

A common practice in deep learning involves training large neural networks on massive datasets to achieve high accuracy across various domains and tasks. While this approach works well in many application areas, it often fails drastically when processing data from a new modality with a significant distribution shift from the data used to pre-train the model. This paper focuses on adapting a large object detection model trained on RGB images to new data extracted from IR images with a substantial modality shift. We propose Modality Translator (ModTr) as an alternative to the common approach of fine-tuning a large model to the new modality. ModTr adapts the IR input image with a small transformation network trained to directly minimize the detection loss. The original RGB model can then work on the translated inputs without any further changes or fine-tuning to its parameters. Experimental results on translating from IR to RGB images on two well-known datasets show that our simple approach provides detectors that perform comparably or better than standard fine-tuning, without forgetting the knowledge of the original model. This opens the door to a more flexible and efficient service-based detection pipeline, where a unique and unaltered server, such as an RGB detector, runs constantly while being queried by different modalities, such as IR with the corresponding translations model. Our code is available at: https://github.com/heitorrapela/ModTr., Comment: ECCV 2024: European Conference on Computer Vision, Milan Italy

Finding obstacle-free paths in unknown environments is a big navigation issue for visually impaired people and autonomous robots. Previous works focus on obstacle avoidance, however they do not have a general view of the environment they are moving in. New devices based on computer vision systems can help impaired people to overcome the difficulties of navigating in unknown environments in safe conditions. In this work it is proposed a combination of sensors and algorithms that can lead to the building of a navigation system for visually impaired people. Based on traditional systems that use RGB-D cameras for obstacle avoidance, it is included and combined the information of a fish-eye camera, which will give a better understanding of the user's surroundings. The combination gives robustness and reliability to the system as well as a wide field of view that allows to obtain many information from the environment. This combination of sensors is inspired by human vision where the center of the retina (fovea) provides more accurate information than the periphery, where humans have a wider field of view. The proposed system is mounted on a wearable device that provides the obstacle-free zones of the scene, allowing the planning of trajectories for people guidance.

Reference Evapotranspiration (ET0) is a key parameter for designing smart irrigation scheduling, since it is related by a coefficient to the water needs of a crop. The United Nations Food and Agriculture Organization, proposed a standard method for ET0 computation (FAO56PM), based on the parameterization of the Penman-Monteith equation, that is widely adopted in the literature. To compute ET0 using the FAO56-PM method, four main weather parameters are needed: temperature, humidity, wind, and solar radiation (SR). One way to make daily ET0 estimations for future days is to use freely available weather forecast services (WFSs), where many meteorological parameters are estimated up to the next 15 days. A problem with this method is that currently, SR is not provided as a free forecast parameter on most of those online services or, normally, such forecasts present a financial cost penalty. For this reason, several ET0 estimation models using machine and deep learning were developed and presented in the literature, that use as input features a reduced set of carefully selected weather parameters, that are compatible with common freely available WFSs. However, most studies on this topic have only evaluated model performance using data from weather stations (WSs), without considering the effect of using weather forecast data. In this study, the performance of authors' previous models is evaluated when using weather forecast data from two online WFSs, in the following scenarios: (i) direct ET0 estimation by an ANN model, and (ii) estimate SR by ANN model, and then use that estimation for ET0 computation, using the FAO56-PM method. Employing data collected from two WFSs and a WS located in Vale do Lobo, Portugal, the latter approach achieved the best result, with a coefficient of determination (R2) ranging between 0.893 and 0.667, when considering forecasts up to 15 days., Comment: A partial version of the work submitted to ESRE/INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCES AND RENEWABLE ENERGY

Estimating perceptual attributes of materials directly from images is a challenging task due to their complex, not fully-understood interactions with external factors, such as geometry and lighting. Supervised deep learning models have recently been shown to outperform traditional approaches, but rely on large datasets of human-annotated images for accurate perception predictions. Obtaining reliable annotations is a costly endeavor, aggravated by the limited ability of these models to generalise to different aspects of appearance. In this work, we show how a much smaller set of human annotations ("strong labels") can be effectively augmented with automatically derived "weak labels" in the context of learning a low-dimensional image-computable gloss metric. We evaluate three alternative weak labels for predicting human gloss perception from limited annotated data. Incorporating weak labels enhances our gloss prediction beyond the current state of the art. Moreover, it enables a substantial reduction in human annotation costs without sacrificing accuracy, whether working with rendered images or real photographs., Comment: Computer Graphics Forum (Eurographics 2024)

In an era where autonomous robots increasingly inhabit public spaces, the imperative for transparency and interpretability in their decision-making processes becomes paramount. This paper presents the overview of a Robotic eXplanation and Interpretability Engine (ROXIE), which addresses this critical need, aiming to demystify the opaque nature of complex robotic behaviors. This paper elucidates the key features and requirements needed for providing information and explanations about robot decision-making processes. It also overviews the suite of software components and libraries available for deployment with ROS 2, empowering users to provide comprehensive explanations and interpretations of robot processes and behaviors, thereby fostering trust and collaboration in human-robot interactions., Comment: 7 pages, 3 figures, 1 tables, Submitted to IROS 2024

Domain Randomization (DR) is commonly used for sim2real transfer of reinforcement learning (RL) policies in robotics. Most DR approaches require a simulator with a fixed set of tunable parameters from the start of the training, from which the parameters are randomized simultaneously to train a robust model for use in the real world. However, the combined randomization of many parameters increases the task difficulty and might result in sub-optimal policies. To address this problem and to provide a more flexible training process, we propose Continual Domain Randomization (CDR) for RL that combines domain randomization with continual learning to enable sequential training in simulation on a subset of randomization parameters at a time. Starting from a model trained in a non-randomized simulation where the task is easier to solve, the model is trained on a sequence of randomizations, and continual learning is employed to remember the effects of previous randomizations. Our robotic reaching and grasping tasks experiments show that the model trained in this fashion learns effectively in simulation and performs robustly on the real robot while matching or outperforming baselines that employ combined randomization or sequential randomization without continual learning. Our code and videos are available at https://continual-dr.github.io/., Comment: Accepted at IROS 2024. Equal contribution from first two authors

The inflated radii observed in hundreds of Hot Jupiters represent a long-standing open issue. The observed correlation between radii and irradiation strength, and the occasional extreme cases, nearly double the size of Jupiter, remain without a comprehensive quantitative explanation. In this investigation, we delve into this issue within the framework of Ohmic dissipation, one of the most promising mechanisms for explaining the radius anomaly. Using the evolutionary code MESA, we simulate the evolution of irradiated giant planets, spanning the range 1 to 8 Jupiter masses, incorporating an internal source of Ohmic dissipation located beneath the radiative-convective boundary. Our modeling is based on physical parameters, and accounts for the approximated conductivity and the evolution of the magnetic fields, utilizing widely-used scaling laws. We compute the radius evolution across a spectrum of masses and equilibrium temperatures, considering varying amounts of Ohmic dissipation, calculated with the internal conductivity profile and an effective parametrization of the currents, based on the typical radius of curvature of the field lines. Our analysis reveals that this internal Ohmic dissipation can broadly reproduce the range of observed radii using values of radius of curvature up to about one order of magnitude lower than what we estimate from the Juno measurements of the Jovian magnetosphere and from MHD dynamo simulations presented herein. The observed trend with equilibrium temperature can be explained if the highly-irradiated planets have more intense and more small-scale magnetic fields. This suggests the possibility of an interplay between atmospherically induced currents and the interior, via turbulence, in agreement with recent box simulations of turbulent MHD in atmospheric columns., Comment: 13 pages, 14 figures

The deployment of autonomous agents in environments involving human interaction has increasingly raised security concerns. Consequently, understanding the circumstances behind an event becomes critical, requiring the development of capabilities to justify their behaviors to non-expert users. Such explanations are essential in enhancing trustworthiness and safety, acting as a preventive measure against failures, errors, and misunderstandings. Additionally, they contribute to improving communication, bridging the gap between the agent and the user, thereby improving the effectiveness of their interactions. This work presents an accountability and explainability architecture implemented for ROS-based mobile robots. The proposed solution consists of two main components. Firstly, a black box-like element to provide accountability, featuring anti-tampering properties achieved through blockchain technology. Secondly, a component in charge of generating natural language explanations by harnessing the capabilities of Large Language Models (LLMs) over the data contained within the previously mentioned black box. The study evaluates the performance of our solution in three different scenarios, each involving autonomous agent navigation functionalities. This evaluation includes a thorough examination of accountability and explainability metrics, demonstrating the effectiveness of our approach in using accountable data from robot actions to obtain coherent, accurate and understandable explanations, even when facing challenges inherent in the use of autonomous agents in real-world scenarios., Comment: 23 pages, 13 figures

We describe the results of a haloscope axion search performed with an 11.7 T dipole magnet at CERN. The search used a custom-made radio-frequency cavity coated with high-temperature superconducting tape. A set of 27 h of data at a resonant frequency of around 8.84 GHz was analysed. In the range of axion mass 36.5676 $\mu$eV to 36.5699 $\mu$eV, corresponding to a width of 554 kHz, no signal excess hinting at an axion-like particle was found. Correspondingly, in this mass range, a limit on the axion to photon coupling-strength was set in the range between g$_{a\gamma}\gtrsim$ 6.2e-13 GeV$^{-1}$ and g$_{a\gamma}\gtrsim$ 1.54e-13 GeV$^{-1}$ with a 95% confidence level., Comment: 19 pages, 8 figures

We propose a data-driven methodology to learn a low-dimensional actuation manifold of controlled flows. The starting point is resolving snapshot flow data for a representative ensemble of actuations. Key enablers for the actuation manifold are isometric mapping as encoder and k-nearest neighbour regression as a decoder. This methodology is tested for the fluidic pinball, a cluster of three parallel cylinders perpendicular to the oncoming uniform flow. The centers of these cylinders are the vertices of an equilateral triangle pointing upstream. The flow is manipulated by constant rotation of the cylinders, i.e. described by three actuation parameters. The Reynolds number based on a cylinder diameter is chosen to be 30. The unforced flow yields statistically symmetric unforced periodic shedding represented by a one-dimensional limit cycle. The proposed methodology yields a five-dimensional manifold describing a wide range of dynamics with small representation error. Interestingly, the manifold coordinates automatically unveil physically meaningful parameters. Two of them describe the downstream periodic vortex shedding. The other three ones describe the near-field actuation, i.e. the strength of boat-tailing, the Magnus effect and forward stagnation point. The manifold is shown to be a key enabler for control-oriented flow estimation., Comment: 10 pages, 5 figures

The advancement of Parkinson's Disease (PD) diagnosis through speech analysis is hindered by a notable lack of publicly available, diverse language datasets, limiting the reproducibility and further exploration of existing research. In response to this gap, we introduce a comprehensive corpus from 108 native Castilian Spanish speakers, comprising 55 healthy controls and 53 individuals diagnosed with PD, all of whom were under pharmacological treatment and recorded in their medication-optimized state. This unique dataset features a wide array of speech tasks, including sustained phonation of the five Spanish vowels, diadochokinetic tests, 16 listen-and-repeat utterances, and free monologues. The dataset emphasizes accuracy and reliability through specialist manual transcriptions of the listen-and-repeat tasks and utilizes Whisper for automated monologue transcriptions, making it the most complete public corpus of Parkinsonian speech, and the first in Castillian Spanish. NeuroVoz is composed by 2,903 audio recordings averaging $26.88 \pm 3.35$ recordings per participant, offering a substantial resource for the scientific exploration of PD's impact on speech. This dataset has already underpinned several studies, achieving a benchmark accuracy of 89% in PD speech pattern identification, indicating marked speech alterations attributable to PD. Despite these advances, the broader challenge of conducting a language-agnostic, cross-corpora analysis of Parkinsonian speech patterns remains an open area for future research. This contribution not only fills a critical void in PD speech analysis resources but also sets a new standard for the global research community in leveraging speech as a diagnostic tool for neurodegenerative diseases., Comment: Preprint version

We numerically investigate the driving of MHD turbulence by gravitational contraction using simulations of an initially spherical, magnetically supercritical cloud core with initially transonic and trans-Alfv\'enic turbulence. We perform a Helmholtz decomposition of the velocity field, and investigate the evolution of its solenoidal and compressible parts, as well as of the velocity component along the gravitational acceleration vector, a proxy for the infall component of the velocity field. We find that: 1) In spite of being supercritical, the core first contracts to a sheet perpendicular to the mean field, and the sheet itself collapses. 2) The solenoidal component of the turbulence remains at roughly its initial level throughout the simulation, while the compressible component increases continuously. This implies that turbulence does {\it not} dissipate towards the center of the core. 3) The distribution of simulation cells in the $B$-$\rho$ plane occupies a wide triangular region at low densities, bounded below by the expected trend for fast MHD waves ($B \propto \rho$, applicable for high local Alfv\'enic Mach number $\Ma$) and above by the trend expected for slow waves ($B \sim$ constant, applicable for low local $\Ma$). At high densities, the distribution follows a single trend $B \propto \rho^{\gamef}$, with $1/2 < \gamef < 2/3$, as expected for gravitational compression. 4) The measured mass-to-magnetic flux ratio $\lambda$ increases with radius $r$, due to the different scalings of the mass and magnetic flux with $r$. At a fixed radius, $\lambda$ increases with time due to the accretion of material along field lines. 5) The solenoidal energy fraction is much smaller than the total turbulent component, indicating that the collapse drives the turbulence mainly compressibly, even in directions orthogonal to that of the collapse., Comment: Resubmitted to MNRAS after first set of reviewer's recommendations. Comments welcome

We report spectroscopic surveys of planetary nebulae (PNe) in the Milky Way and Andromeda (M31), using the 10.4-m Gran Telescopio Canarias (GTC). The spectra are of high quality and cover the whole optical range, mostly from 3650 \r{A} to beyond 1 micron, enabling detection of nebular emission lines critical for spectral analysis as well as photoionization modeling. We obtained GTC spectra of 24 compact (angular diameter <5 arcsec) PNe located in the Galactic disk, ~3-20 kpc from the Galactic centre, and can be used to constrain stellar evolution models and derive radial abundance gradients of the Milky Way. We have observed 30 PNe in the outer halo of M31 using the GTC. These halo PNe are uniformly metal-rich and probably all evolved from low-mass stars, consistent with the conjecture that they all formed from the metal-rich gas in M31 disk but displaced to their present locations due to galaxy interactions., Comment: 6 pages, 4 figures, in production; Proceedings of the IAUS384: "Planetary Nebulae: a Universal Toolbox in the Era of Precision Astrophysics", Krakow, Poland, September 4-8, 2023

We present the analysis of archival Very Large Telescope (VLT) MUSE and multi-epoch Hubble Space Telescope (HST) WFPC2 and WFC3/UVIS narrow-band observations of the remnant associated with the ejecta of the mid-nineteenth century outburst of the recurrent nova T Pyx. These data sets are used to investigate its true 3D physical structure and the nebular expansion patterns along the line of sight and on the plane of the sky. The VLT MUSE emission line maps and 3D visualisations based on position-position-velocity diagrams reveal T Pyx as a bipolar nebula, with a knotty toroidal structure at its waist best seen in H$\beta$ and two open bowl-shaped bipolar lobes (a diabolo) best revealed by the [OIII] emission lines. The comparison of multi-epoch HST WFPC2 and WFC3/UVIS narrow-band images and VLT MUSE emission line maps of T Pyx reveals the angular expansion of the remnant through the proper motion of individual knots and nebular features. The angular expansion is confirmed to be homologous in the period from 1994.2 to 2007.4 before the recent 2011 outburst, but there is suggestive evidence that the inner knots have experienced a higher expansion rate since then., Comment: 18 pages, 15 figures. Accepted for publication in MNRAS

We report the characterization of two planet candidates detected by the Transiting Exoplanet Survey Satellite (TESS), TOI-1199 b and TOI-1273 b, with periods of 3.7 and 4.6 days, respectively. Follow-up observations for both targets, which include several ground-based light curves, confirmed the transit events. High-precision radial velocities from the SOPHIE spectrograph revealed signals at the expected frequencies and phases of the transiting candidates and allowed mass determinations with a precision of $8.4\%$ and $6.7\%$ for TOI-1199 b and TOI-1273 b, respectively. The planetary and orbital parameters were derived from a joint analysis of the radial velocities and photometric data. We find that the planets have masses of $0.239\,\pm\,0.020\,M_{\mathrm{J}}$ and $0.222\,\pm\,0.015\,M_{\mathrm{J}}$ and radii of $0.938\,\pm\,0.025\,R_{\mathrm{J}}$ and $0.99\,\pm\,0.22\,R_{\mathrm{J}}$, respectively. The grazing transit of TOI-1273 b translates to a larger uncertainty in its radius, and hence also in its bulk density, compared to TOI-1199 b. The inferred bulk densities of $0.358\,\pm\,0.041\,\mathrm{g}\,\mathrm{cm}^{-3}$ and $0.28\,\pm\,0.11\,\mathrm{g}\,\mathrm{cm}^{-3}$ are among the lowest known for exoplanets in this mass range, which, considering the brightness of the host stars ($V \approx 11\,\mathrm{mag}$), render them particularly amenable to atmospheric characterization via the transit spectroscopy technique. The better constraints on the parameters of TOI-1199 b provide a transmission spectroscopy metric of $134\,\pm\,17$, making it the better suited of the two planets for atmospheric studies., Comment: Accepted for publication in A&A. 20 pages, 7 tables, and 15 figures

In this study, we present a perturbative analysis of the three-gluon vertex for a kinematical symmetric configuration in dimension $n=4-2\epsilon$ and different covariant gauges.Our study can describe the form factors of the three gluon vertex in a wide range of momentum. We employ a momentum subtraction (MOM) scheme to define the renormalized vertex. We give an in-depth review of three commonly used vector basis for the vertex, and explicitly show the expressions to change from one basis to the other. Although our estimates are valid only in the perturbative regime, we extend our numerical predictions to the infrared domain and show that in $n=4$ some nonperturbative properties are qualitatively present already at perturbation theory. In particular, we find a critical gauge above which the leading form factor displays the so-called zero crossing. We contrast our findings to those of other models and observe a fairly good agreement., Comment: 9 figures, 28 pages

A new and straightforward single-step route to decorate melamine foams with silver nanoparticles (ME/Ag) is proposed.

This paper introduces a system designed to generate explanations for the actions performed by an autonomous robot in Human-Robot Interaction (HRI). Explainability in robotics, encapsulated within the concept of an eXplainable Autonomous Robot (XAR), is a growing research area. The work described in this paper aims to take advantage of the capabilities of Large Language Models (LLMs) in performing natural language processing tasks. This study focuses on the possibility of generating explanations using such models in combination with a Retrieval Augmented Generation (RAG) method to interpret data gathered from the logs of autonomous systems. In addition, this work also presents a formalization of the proposed explanation system. It has been evaluated through a navigation test from the European Robotics League (ERL), a Europe-wide social robotics competition. Regarding the obtained results, a validation questionnaire has been conducted to measure the quality of the explanations from the perspective of technical users. The results obtained during the experiment highlight the potential utility of LLMs in achieving explanatory capabilities in robots., Comment: 26 pages, 15 Figures, 11 Tables. This paper is a preprint of an article submitted to the International Journal of Social Robotics

The growth of distributed renewable energy in the electrical grid presents challenges to its stability and quality. To address this at the local level, flexibility energy strategies emerge as an innovative technique. However, managing these strategies in residential areas becomes complex due to the unique characteristics of each prosumer. A major challenge lies in managing communication among diverse devices with different protocols. To address these issues, a comprehensive framework is designed and implemented to facilitate prosumers' integration in flexibility strategies, addressing communication at various levels. The effectiveness of the proposed framework is demonstrated through its implementation in a real smart home environment with diverse devices. The framework enables seamless integration and communication between IoT devices and IEC 61,850-compliant power devices. This research presents a novel approach to address the challenges of managing flexibility strategies in residential areas, providing a practical solution for prosumers to actively participate in optimizing energy consumption and enhancing the stability and quality of the electricity system amidst the growing integration of distributed renewable energy.

From a non-central panorama, 3D lines can be recovered by geometric reasoning. However, their sensitivity to noise and the complex geometric modeling required has led these panoramas being very little investigated. In this work we present a novel approach for 3D layout recovery of indoor environments using single non-central panoramas. We obtain the boundaries of the structural lines of the room from a non-central panorama using deep learning and exploit the properties of non-central projection systems in a new geometrical processing to recover the scaled layout. We solve the problem for Manhattan environments, handling occlusions, and also for Atlanta environments in an unified method. The experiments performed improve the state-of-the-art methods for 3D layout recovery from a single panorama. Our approach is the first work using deep learning with non-central panoramas and recovering the scale of single panorama layouts., Comment: arXiv admin note: substantial text overlap with arXiv:2401.17058

In this article we present a visual gyroscope based on equirectangular panoramas. We propose a new pipeline where we take advantage of combining three different methods to obtain a robust and accurate estimation of the attitude of the camera. We quantitatively and qualitatively validate our method on two image sequences taken with a $360^\circ$ dual-fisheye camera mounted on different aerial vehicles.

Convolution kernels are the basic structural component of convolutional neural networks (CNNs). In the last years there has been a growing interest in fisheye cameras for many applications. However, the radially symmetric projection model of these cameras produces high distortions that affect the performance of CNNs, especially when the field of view is very large. In this work, we tackle this problem by proposing a method that leverages the calibration of cameras to deform the convolution kernel accordingly and adapt to the distortion. That way, the receptive field of the convolution is similar to standard convolutions in perspective images, allowing us to take advantage of pre-trained networks in large perspective datasets. We show how, with just a brief fine-tuning stage in a small dataset, we improve the performance of the network for the calibrated fisheye with respect to standard convolutions in depth estimation and semantic segmentation., Comment: Previously presented at BMVC: https://proceedings.bmvc2023.org/721/

Omnidirectional images are one of the main sources of information for learning based scene understanding algorithms. However, annotated datasets of omnidirectional images cannot keep the pace of these learning based algorithms development. Among the different panoramas and in contrast to standard central ones, non-central panoramas provide geometrical information in the distortion of the image from which we can retrieve 3D information of the environment [2]. However, due to the lack of commercial non-central devices, up until now there was no dataset of these kinds of panoramas. In this data paper, we present the first dataset of non-central panoramas for indoor scene understanding. The dataset is composed by {\bf 2574} RGB non-central panoramas taken in around 650 different rooms. Each panorama has associated a depth map and annotations to obtain the layout of the room from the image as a structural edge map, list of corners in the image, the 3D corners of the room and the camera pose. The images are taken from photorealistic virtual environments and pixel-wise automatically annotated.

Omnidirectional and 360{\deg} images are becoming widespread in industry and in consumer society, causing omnidirectional computer vision to gain attention. Their wide field of view allows the gathering of a great amount of information about the environment from only an image. However, the distortion of these images requires the development of specific algorithms for their treatment and interpretation. Moreover, a high number of images is essential for the correct training of computer vision algorithms based on learning. In this paper, we present a tool for generating datasets of omnidirectional images with semantic and depth information. These images are synthesized from a set of captures that are acquired in a realistic virtual environment for Unreal Engine 4 through an interface plugin. We gather a variety of well-known projection models such as equirectangular and cylindrical panoramas, different fish-eye lenses, catadioptric systems, and empiric models. Furthermore, we include in our tool photorealistic non-central-projection systems as non-central panoramas and non-central catadioptric systems. As far as we know, this is the first reported tool for generating photorealistic non-central images in the literature. Moreover, since the omnidirectional images are made virtually, we provide pixel-wise information about semantics and depth as well as perfect knowledge of the calibration parameters of the cameras. This allows the creation of ground-truth information with pixel precision for training learning algorithms and testing 3D vision approaches. To validate the proposed tool, different computer vision algorithms are tested as line extractions from dioptric and catadioptric central images, 3D Layout recovery and SLAM using equirectangular panoramas, and 3D reconstruction from non-central panoramas.

In this work we present a novel approach for 3D layout recovery of indoor environments using a non-central acquisition system. From a non-central panorama, full and scaled 3D lines can be independently recovered by geometry reasoning without geometric nor scale assumptions. However, their sensitivity to noise and complex geometric modeling has led these panoramas being little investigated. Our new pipeline aims to extract the boundaries of the structural lines of an indoor environment with a neural network and exploit the properties of non-central projection systems in a new geometrical processing to recover an scaled 3D layout. The results of our experiments show that we improve state-of-the-art methods for layout reconstruction and line extraction in non-central projection systems. We completely solve the problem in Manhattan and Atlanta environments, handling occlusions and retrieving the metric scale of the room without extra measurements. As far as the authors knowledge goes, our approach is the first work using deep learning on non-central panoramas and recovering scaled layouts from single panoramas.

Understanding the magnetic fields of the Sun is essential for unraveling the underlying mechanisms driving solar activity. Integrating topological data analysis techniques into these investigations can provide valuable insights into the intricate structures of magnetic fields, enhancing our comprehension of solar activity and its implications. In this study, we explore what persistent homology can offer in the analysis of solar magnetograms, with the objective of introducing a novel tool that will serve as the foundation for further studies of magnetic structures at the solar surface. By combining various filtration methods of the persistent homology analysis, we conduct an analysis of solar magnetograms that captures the broad magnetic scene, involving a mixture of positive and negative polarities. This analysis is applied to observations of both quiet Sun and active regions, taken with Hinode/SOT and SDO/HMI, respectively. Our primary focus is on analyzing the properties of the spatial structures and features of the magnetic fields identified through these techniques. The results show that persistent diagrams can encode the spatial structural complexity of the magnetic flux of active regions by identifying the isolated, connected, and interacting features. They facilitate the classification of active regions based on their morphology and the detection and quantification of interacting structures of opposing polarities, such as $\delta$-spots. The small-scale events in the quiet Sun, such as magnetic flux cancellation and emergence, are also revealed in persistent diagrams and can be studied by observing the evolution of the plots and tracking the relevant features.

Peer-to-peer (P2P) lending has emerged as a distinctive financing mechanism, linking borrowers with lenders through online platforms. However, P2P lending faces the challenge of information asymmetry, as lenders often lack sufficient data to assess the creditworthiness of borrowers. This paper proposes a novel approach to address this issue by leveraging the textual descriptions provided by borrowers during the loan application process. Our methodology involves processing these textual descriptions using a Large Language Model (LLM), a powerful tool capable of discerning patterns and semantics within the text. Transfer learning is applied to adapt the LLM to the specific task at hand. Our results derived from the analysis of the Lending Club dataset show that the risk score generated by BERT, a widely used LLM, significantly improves the performance of credit risk classifiers. However, the inherent opacity of LLM-based systems, coupled with uncertainties about potential biases, underscores critical considerations for regulatory frameworks and engenders trust-related concerns among end-users, opening new avenues for future research in the dynamic landscape of P2P lending and artificial intelligence.

In this manuscript, we illustrate how to use the newly proposed $\tau$ re-clustering algorithm to select jets with different degrees of quenching without biasing their initial transverse momentum spectrum. Our study is based on Z+jet simulated events using the JEWEL Monte Carlo event generator to account for jet quenching effects. We apply the $\tau$ re-clustering algorithm to extract a proxy for a time axis (formation time) within the evolving medium. This information allows us to label jets according to their fragmentation pattern and select populations with enhanced sensitivity to quenching effects. Our results illustrate the potential of jets as precision tools for QGP tomography. Further, we show that the discussed method minimizes the biases stemming from $p_{T}$-, $dR$- or mass-based jet selection., Comment: 13 pages. Added references and further discussion of method and results. Figure 15 corrected. Results unchanged

Plasmonic lattice nanostructures are of technological interest because of their capacity to manipulate light below the diffraction limit. Here, we present a detailed study of dark and bright modes in the visible and near-infrared energy regime of an inverted plasmonic honeycomb lattice by a combination of Au+ focused ion beam lithography with nanometric resolution, optical and electron spectroscopy, and finite-difference time-domain simulations. The lattice consists of slits carved in a gold thin film, exhibiting hotspots and a set of bright and dark modes. We proposed that some of the dark modes detected by electron energy-loss spectroscopy are caused by antiferroelectric arrangements of the slit polarizations with two times the size of the hexagonal unit cell. The plasmonic resonances take place within the 0.5_2 eV energy range, indicating that they could be suitable for a synergistic coupling with excitons in two-dimensional transition metal dichalcogenides materials or for designing nanoscale sensing platforms based on near-field enhancement over a metallic surface.

A decentralized optimization policy for service placement in fog computing is presented. The optimization is addressed to place most popular services as closer to the users as possible. The experimental validation is done in the iFogSim simulator and by comparing our algorithm with the simulator's built-in policy. The simulation is characterized by modeling a microservice-based application for different experiment sizes. Results showed that our decentralized algorithm places most popular services closer to users, improving network usage and service latency of the most requested applications, at the expense of a latency increment for the less requested services and a greater number of service migrations.

The use of containers in cloud architectures has become widespread because of advantages such as limited overhead, easier and faster deployment and higher portability. Moreover, they are a suitable architectural solution for deployment of applications created using a microservices development pattern. Despite the large number of solutions and implementations, open issues have not been addressed in container automation and management. Container resource allocation influences system performance and resource consumption so it is a key factor for cloud providers. We propose a genetic algorithm approach, using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), to optimize container allocation and elasticity management due to the good results obtained with this algorithm in other resource management optimization problems in cloud architectures. The optimization has been focused on a tight use of the resources and a reduction of the network overhead and system failure rate. A model for cloud cluster, containers, microservices and four optimization objectives is presented. Experimental results have shown that our approach is a suitable solution to address the problem of container allocation and elasticity and it obtains better objectives values than the container management policies implemented in Kubernetes.

This paper presents a policy for service placement of fog applications inspired on complex networks and graph theory. We propose a twofold partition process based on communities for the partition of the fog devices and based on transitive closures for the application services partition. The allocation of the services is performed sequentially by, firstly, mapping applications to device communities and, secondly, mapping service transitive closures to fog devices in the community. The underlying idea is to place as many inter-related services as possible in the most nearby devices to the users. The optimization objectives are the availability of the applications and the Quality of Service (QoS) of the system, measured as the number of requests that are executed before the application deadlines. We compared our solution with an Integer Linear Programming approach, and the simulation results showed that our proposal obtains higher QoS and availability when fails in the nodes are considered.