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We report on the results of performing computational gravitational backreaction on cosmic string loops taken from a network simulation. The principal effect of backreaction is to smooth out small-scale structure on loops, which we demonstrate by various measures including the average loop power spectrum and the distribution of kink angles on the loops. Backreaction does lead to self-intersections in most cases, but these are typically small. An important effect discussed in prior work is the rounding off of kinks to form cusps, but we find that the cusps produced by that process are very weak and do not significantly contribute to the total gravitational-wave radiation of the loop. We comment briefly on extrapolating our results to loops as they would be found in nature., Comment: 26 pages, 11 figures. Power spectrum data available via Zenodo: https://doi.org/10.5281/zenodo.14037540

We study the formation of gravitating global strings through quantum mechanical tunneling. The instantons that describe the nucleation process are characterized by two parameters: the string core thickness and its gravitational backreaction controlled by the string core energy density. We obtain solutions across a wide range of these parameters by carrying out numerical integration via multiple shooting methods. Our results are in agreement with previous findings on the nucleation of other topological defects; specifically, after reaching a certain threshold for the string core thickness or its gravitational backreaction, the configuration becomes homogeneous in a manner akin to Hawking-Moss solutions. Additionally, we analyze the global structure of the analytical continuation of the solutions to Lorentzian signature, revealing the emergence of a region of spacetime that describes an anisotropic universe. Finally, we also discuss the relevance of these instantons in the context of quantum cosmology., Comment: 36 pages, 17 figures

On-surface chemistry has emerged as a key technique for designing novel low-dimensional materials, enabling precise manipulation of their electronic and magnetic properties at the atomic scale. It also proves highly effective for the fabrication of heterostructures. Leveraging these benefits, herein, we perform a first principles study of the magnetic proximity effect (MPE) in a heterostructure formed by a monolayer of the two-dimensional carbon allotrope biphenylene network (BPN) deposited on the surface of the above-room-temperature ferrimagnet yttrium iron garnet (YIG). Our results reveal strong hybridization between BPN orbitals and YIG surface states, resulting in non-homogeneous electron transfer and robust MPE. The proposed methodology accurately describes YIG magnetic interactions, allowing us to study the tuning effects of BPN on the magnetic properties of the substrate for the first time. Additionally, we explore the impact of van der Waals (vdW) distance at the interface, finding enhanced spin splitting up to 30% under external pressure. These findings highlight a promising strategy for inducing spin polarization in BPN without chemical modifications, opening new possibilities for BPN-based spintronic devices through the creation of heterostructures with magnetic materials.

In this paper, we investigate the convergence of asymptotic systems in non-autonomous Cohen--Grossberg neural network models, which include both infinite discrete time-varying and distributed delays. We derive stability results under conditions where the non-delay terms asymptotically dominate the delay terms. Several examples and a numerical simulation are provided to illustrate the significance and novelty of the main result., Comment: 27 pages, 2 figures

Carbon chemistry has attracted a lot of attention by chemists, physicists and material scientists in the last decades. The recent discovery of graphullerene provides a promising platform for many applications due to its exceptional electronic properties and the possibility to host molecules or clusters inside the fullerene units. Herein, we introduce graphendofullerene, a novel molecular-based two-dimensional (2D) magnetic material formed by trimetallic nitrides clusters encapsulated on graphullerene. Through first-principles calculations, we demonstrate the successful incorporation of the molecules into the 2D network formed by C$_{80}$ fullerenes, which leads to a robust long-range ferromagnetic order with a Curie temperature (Tc) of 38 K. Additionally, we achieve a 45% increase in Tc by strain engineering. These findings open the way for a new family of molecular 2D magnets based on graphendofullerene for advanced technologies.

A variety of physical phenomena, such as amplification, absorption, and radiation, can be effectively described using non-Hermitian operators. However, the introduction of non-uniform non-Hermiticity can lead to the formation of exceptional points in a system's spectrum, where two or more eigenvalues become degenerate and their associated eigenvectors coallesce causing the underlying operator or matrix to become defective. Here, we explore extensions of the Clifford and quadratic $\epsilon$-pseudospectrum, previously defined for Hermitian operators, to accommodate non-Hermitian operators and matrices, including the possibility that the underlying operators may possess exceptional points in their spectra. In particular, we provide a framework for finding approximate joint eigenstates of a $d$-tuple of Hermitian operators $\mathbf{A}$ and non-Hermitian operators $\mathbf{B}$, and show that their Clifford and quadratic $\epsilon$-pseudospectra are still well-defined despite any non-normality. We prove that the non-Hermitian quadratic gap is local with respect to the probe location when there are perturbations to one or more of the underlying operators. Altogether, this framework enables the exploration of non-Hermitian physical systems' $\epsilon$-pseudospectra, including but not limited to photonic systems where gain, loss, and radiation are prominent physical phenomena.

In this work, we introduce a foundational definition of statistical virtual temperature, derived from the spectrum of the Gibbs Kubo-Martin-Schwinger (KMS) state and formulated using d-1 indices of purity (IP), where d represents the Hilbert space dimension within the C*-algebra framework. We demonstrate that the universal physical bounds between von Neumann entropy and statistical virtual temperature are constrained by these IPs, which may offer broader applications to quantum systems. Additionally, we geometrize classical optical polarization states of an arbitrary electromagnetic field and provide an interpretation of the quantum Mpemba effect, where a quantum system prepared at a higher statistical virtual temperature relaxes to equilibrium faster than one at a lower temperature. This behavior is explained through a novel concept of temperature-resolved entanglement asymmetry. Additionally, we present a geometric interpretation of the third law of thermodynamics using these entropy-temperature diagrams. Nevertheless, the defined statistical virtual temperature inherently exhibits the third law of thermodynamics. We believe that this work has the potential to significantly advance our understanding of classical polarization theory, quantum information theory, and quantum thermodynamics, and it may establish new connections and insights into these fields.

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

The radiance of nighttime artificial lights measured by the VIIRS-DNB instrument on board the satellite Suomi-NPP increases at an average rate ~2.2 %/yr worldwide, whereas the artificial radiance of the night sky deduced from the Globe at Night (GAN) unaided-eye observations of the number of visible stars is reported to increase at an average rate ~9.6 %/yr. The difference between these two estimates is remarkable. This raises the question of whether the diverging temporal evolution of these indicators could be due to changes in the spectral composition of outdoor artificial light, consequence of the current process of replacement of lighting technologies. This paper presents a model for evaluating the temporal rate of change of different light pollution indicators and applies it to the VIIRS-DNB vs GAN issue, based on available data. The results show that the reported difference could be explained by spectral changes alone, if the visual observations are made under scotopic adaptation in some specific transition conditions. In case of photopic adapted observers, however, reconciling these two measurement sets requires the existence of additional light sources that affect the Globe at Night observations but do not show up in the VIIRS-DNB data. The lumen emissions of these specific sources should increase at a rate 6 %/yr worlwide, adding to the estimated 3 %/yr of the remaining lights deduced from the VIIRS-DNB measurements corrected for spectral shift., Comment: 17 pages, 1 figure. Submitted for publication

The interest of oil industry in increasing heavy oil production has promoted the use of enhanced oil recovery techniques such as $CO_{2}$ injection, which produce a decrease of oil viscosity and displacement of heavy oil from reservoir to surface. The design of these processes requires accurate data of densities, viscosities or surface tensions of ($CO_{2}$ + hydrocarbon) mixtures in order to simulate the behaviour of these mixtures in the reservoir. An automated Anton Paar DMA HPM vibrating-tube densimeter was used to measure densities of this kind of mixtures, and a new mixture injection system, by means of two syringe pumps, was developed for the densimeter. The equipment operates at high pressure, which is controlled through a back pressure valve and a variable volume cylinder with a stepper motor. The estimated standard uncertainty of the density is $\mathrm{\pm0.9 \ kg \cdot m^{-3}}$ at temperatures below 373.15 K and pressure range (0.1-140) MPa.In this paper, the densities of the mixtures ($CO_{2}$ + $n$-decane), ($CO_{2}$ + $n$-dodecane) and ($CO_{2}$ + squalane) are reported at $T$ = (283.15-393.15) K and $p$ = (10-100) MPa.

The present work aims to measure speeds of sound $c$ in a biogas mixture of $CH_{4}$ + $N_{2}$ + $CO_{2}$ + $CO$, at $p$ = (1-12) MPa and $T$ = (273, 300 and 325) K, using a spherical acoustical resonator. The results are fitted to the virial acoustic equation of state, and the virial acoustic coefficients are obtained, $\beta_{a}$ and $\gamma_{a}$ and extrapolated to zero pressure, determining the adiabatic coefficient as perfect gas, $\gamma^{pg}$, and the isobaric and isochoric heat capacities as perfect gas, $C_{p}^{pg}$ and $C_{V}^{pg}$, respectively. The speeds of sound are acquired with a mean expanded relative uncertainty of 165 parts in $10^{6}$ ($k$ = 2) and are compared with the results predicted by the reference equation of state for this kind of mixture (natural gas-like mixtures), EoS GERG-2008. Relative deviations between experimental data and values estimated by this model were less than 700 parts in $10^{6}$ at $T$ = 325 K, and below 400 parts in $10^{6}$, and within measurement uncertainty of at $T$ = 300 K, although appreciably higher at isotherm $T$ = 273 K at the highest pressure data for this work, and even reaching values above 3 400 parts in $10^{6}$.

The development of a novel technique based on a cylindrical microwave resonator for high pressure phase equilibrium determination is described. Electric permittivity or dielectric constant is a physical property that depends on temperature and pressure $\epsilon$($p$,$T$). Based on this property, a measuring technique consisting of a cylindrical resonant cavity that works in the microwave spectrum has been developed. Equilibrium data of fluid mixtures are measured at high pressure using a synthetic method, where phase transition is determined under isothermal conditions due to the change of the dielectric constant. This technique may be a more accurate alternative to conventional visual synthetic methods. The technique was validated measuring pure $CO_{2}$, and phase behaviour was then determined for two binary mixtures [$CO_{2}$ (0.6) + $CH_{4}$ (0.4)] and [$CO_{2}$ (0.4) + $CH_{4}$ (0.6)], results for which are presented. These systems are interesting for the study of biogas-like mixtures. In addition, data were compared with the equation of state used for natural gas GERG-2008, and also, they were modelled using Peng-Robinson equation of state and Wong-Sandler mixing rules, which are widely employed in chemical industries and which give good results.

Density measurements from single-sinker magnetic suspension densimeters need to be corrected to compensate for the magnetic effects of the measuring cell materials and the fluid on the coupling transmission system. While the magnetic effect of the densimeter materials can be easily determined, the fluid effect requires the calculation of an apparatus-specific constant, $\epsilon_{\rho}$. In this work, the apparatus-specific constant of the single-sinker magnetic suspension densimeter at the University of Valladolid has been determined by using two alternative methods. The first method, which uses density data for the same fluid and conditions and different sinkers, yielded a value of $\epsilon_{\rho}$ = $4.6\cdot10^{-5}$. The second method, obtained from measurements with pure oxygen, yielded a value of $\epsilon_{\rho}$ = $8.822\cdot10^{-5}$. The second value is considered as more reliable, as the first method presents inherent limitations in this case.

This work aims to address the technical aspects related to the thermodynamic characterization of natural gas mixtures blended with hydrogen for the introduction of alternative energy sources within the Power-to-Gas framework. For that purpose, new experimental speed of sound data are presented in the pressure range between (0.1 up to 13) MPa and at temperatures of (260, 273.16, 300, 325, and 350) K for two mixtures qualified as primary calibration standards: a 11 component synthetic natural gas mixture (11 M), and another low-calorific $H_{2}$-enriched natural gas mixture with a nominal molar percentage $x_{H_{2}}$ = 3%. Measurements have been gathered using a spherical acoustic resonator with an experimental expanded ($k$ = 2) uncertainty better than 200 parts in $10^{6}$ (0.02%) in the speed of sound. The heat capacity ratio as perfect-gas $\gamma_{pg}$, the molar heat capacity as perfect-gas $C_{p,m}^{pg}$, and the second $\beta_{a}$ and third $\gamma_{a}$ acoustic virial coefficients are derived from the speed of sound values. All the results are compared with the reference mixture models for natural gas-like mixtures, the AGA8-DC92 EoS and the GERG-2008 EoS, with special attention to the impact of hydrogen on those properties. Data are found to be mostly consistent within the model uncertainty in the 11 M synthetic mixture as expected, but for the hydrogen-enriched mixture in the limit of the model uncertainty at the highest measuring pressures.

This work presents phase envelope and speed of sound data for the (0.60 $CO_{2}$ + 0.40 $C_{3}H_{8}$) and (0.80 $CO_{2}$ + 0.20 $C_{3}H_{8}$) binary mixtures. Phase equilibria was measured using a cylindrical resonator working in the microwave band whereas an acoustic resonator was used for speed of sound measurements. The experimental results were compared with GERG-2008 equation of state, obtaining average absolute deviations by 0.24% in pressure for phase equilibria data and 0.025% for speed of sound data. Speed of sound values were used to derive perfect-gas heat capacities, acoustic virial coefficients, and second density virial coefficients. In addition, AGA8-DC92 equation of state performance was checked for the results derived from speeds of sound.

This work aims to address the technical concerns related to the thermodynamic characterization of gas mixtures blended with hydrogen for the implementation of hydrogen as a new energy vector. For this purpose, new experimental speed of sound measurements have been done in gaseous and supercritical phases of two binary mixtures of nitrogen and hydrogen using the most accurate technique available, i.e., the spherical acoustic resonator, yielding an experimental expanded ($k$ = 2) uncertainty of only 220 parts in $10^{6}$ (0.022%). The measurements cover the pressure range between (0.5 and 20) MPa, the temperature range between (260 and 350) K, and the composition range with a nominal mole percentage of hydrogen of (5 and 10) mol%, respectively. From the speed of sound data sets, thermophysical properties that are relevant for the characterization of the mixture, namely the second $\beta_{a}$ and third $\gamma_{a}$ acoustic virial coefficients, are derived. These results are thoroughly compared and discussed with the established reference mixture models valid for mixtures of nitrogen and hydrogen, such as the AGA8-DC92 EoS, the GERG-2008 EoS, and the recently developed adaptation of the GERG-2008 EoS, here denoted GERG-$H_{2}$_improved EoS. Special attention has been given to the effect of hydrogen concentration on those properties, showing that only the GERG-$H_{2}$_improved EoS is consistent with the data sets within the experimental uncertainty in most measuring conditions.

Large Language Models (LLMs) are becoming ubiquitous to create intelligent virtual assistants that assist users in interacting with a system, as exemplified in marketing. Although LLMs have been discussed in Modeling & Simulation (M&S), the community has focused on generating code or explaining results. We examine the possibility of using LLMs to broaden access to simulations, by enabling non-simulation end-users to ask what-if questions in everyday language. Specifically, we discuss the opportunities and challenges in designing such an end-to-end system, divided into three broad phases. First, assuming the general case in which several simulation models are available, textual queries are mapped to the most relevant model. Second, if a mapping cannot be found, the query can be automatically reformulated and clarifying questions can be generated. Finally, simulation results are produced and contextualized for decision-making. Our vision for such system articulates long-term research opportunities spanning M&S, LLMs, information retrieval, and ethics., Comment: To appear in proceedings of the 2024 Winter Simulation Conference

We present the second data release of the MeerKAT Absorption Line Survey (MALS), consisting of wideband continuum catalogues of 391 pointings observed at L~band. The full wideband catalogue covers 4344 deg$^2$ of sky, reaches a depth of 10 $\mu$Jy beam$^{-1}$, and contains 971,980 sources. With its balance between survey depth and sky coverage, MALS DR2 covers five orders of magnitude of flux density, presenting a robust view of the extragalactic radio source population down to 200 $\mu$Jy. Using this catalogue, we perform a measurement of the cosmic radio dipole, an anisotropy in the number counts of radio sources with respect to the cosmic background, analogous to the dipole found in the cosmic microwave background (CMB). For this measurement, we present the characterisation of completeness and noise properties of the catalogue, and show that a declination-dependent systematic affects the number density of faint sources. In the dipole measurement on the MALS catalogue, we recover reasonable dipole measurements once we model the declination systematic with a linear fit between the size of the major axis of the restoring beam and the amount of sources of each pointing. The final results are consistent with the CMB dipole in terms of direction and amplitude, unlike many recent measurements of the cosmic radio dipole made with other centimetre wavelength catalogues, which generally show a significantly larger amplitude. This result demonstrates the value of dipole measurements with deeper and more sparse radio surveys, as the population of faint sources probed may have had a significant impact on the measured dipole., Comment: 25 pages, 14 figures. Accepted for publication in Astronomy & Astrophysics. The MALS wideband catalogues and images are publicly available at https://mals.iucaa.in

We present a detailed near-infrared study of an embedded cluster located in the hub of the giant molecular cloud G148.24+00.41 of mass $\sim$10$^5$ $M_\odot$, with the TANSPEC instrument mounted on the 3.6 m Devasthal Optical Telescope. The hub is located near the geometric center of the cloud and represents its most massive clump. We studied the central 2 pc $\times$ 2 pc area of the hub with 5$\sigma$ limiting magnitudes of 20.5, 20.1, and 18.6 mag in the $J$, $H$, and $K_s$ bands, respectively. Using the $K_s$-band luminosity function and comparing it with the synthetic clusters, we obtained the age of the cluster as $\sim$0.5 Myr, which was found to corroborate well with the visual extinction versus the age of nearby embedded clusters. We find that the present mass of the cluster is around $\sim$180 $M_\odot$, and the cluster is currently forming stars at a rate of $\sim$330 $M_\odot$ $\rm{Myr}^{-1}$, with an efficiency of $\sim$20%. The cluster is connected to an extended gas reservoir through a filamentary network; thus, we hypothesize that the cluster has the potential to become a richer cluster in a few Myr of time., Comment: 17 pages and 13 figures

Cygnus X-1 is a high mass X-ray binary where accretion onto the black hole is mediated by the stellar wind from the blue supergiant companion star HDE 226868. Depending on the position of the black hole along the orbit, X-ray observations can probe different layers of the stellar wind. Deeper wind layers can be investigated at superior conjunction (i.e. null orbital phases). We aim at characterising the stellar wind in the Cyg X-1/HDE 226868 system analysing one passage at superior conjunction covered by XMM-Newton during the CHOCBOX campaign via modelling of colour-colour diagrams. Since X-ray absorption is energy-dependent, colour indices provide information on the parameters of the stellar wind, such as the column density $N_{H,w}$ and the covering factor $f_c$. We fitted colour-colour diagrams with models that include both a continuum and a stellar wind component. We used the KDE method to infer the unknown probability distribution of the data points in the colour-colour diagram, and selected the model corresponding to the highest likelihood. In order to study the temporal evolution of the wind around superior conjunction, we extracted and fitted time-resolved colour-colour diagrams. We found that the model that best describes the shape of the colour-colour diagram of Cyg X-1 at superior conjunction requires the wind to be partially ionised. The shape of the colour-colour diagram strongly varies during the analysed observation, as due to concurrent changes of the mean $N_{H,w}$ and the $f_c$ of the wind. Our results suggest the existence of a linear scaling between the rapid variability amplitude of $N_{H,w}$ (on time scales between 10 s and 11 ks) and its long term variations (on time scales 11>ks). Using the inferred best-fit values, we estimated the stellar mass loss rate to be $\sim 7\times10^{-6} {\rm M_{\odot}yr^{-1}}$ and the clumps to have a mass of $\sim10^{17}$ g., Comment: Accepted for publication in A&A

Layered van der Waals two-dimensional (2D) magnets are a cornerstone of ultrathin spintronic and magnonic devices. The recent discovery of a 2D multiferroic with strong magnetoelectric coupling in NiI$_2$ offers a promising platform for the electrical control of spin-wave transport. In this work, using ab initio calculations, we investigate how the magnonic properties of monolayer NiI$_2$ can be controlled using an external electric field. We show that the emergence of a ferroelectric polarization leads to an energy splitting in the magnon spectrum, thus establishing a way to detect the electric polarization experimentally. We also show the modulation of the magnon splitting and the energy position of the singularities in magnon DOS by an electric field due to the strong magnetoelectric coupling. Our results highlight the interplay between ferroelectricity and magnons in van der Waals multiferroics and pave the way to design electrically tunable magnetic devices at the 2D limit.

Text Recognition (TR) refers to the research area that focuses on retrieving textual information from images, a topic that has seen significant advancements in the last decade due to the use of Deep Neural Networks (DNN). However, these solutions often necessitate vast amounts of manually labeled or synthetic data. Addressing this challenge, Self-Supervised Learning (SSL) has gained attention by utilizing large datasets of unlabeled data to train DNN, thereby generating meaningful and robust representations. Although SSL was initially overlooked in TR because of its unique characteristics, recent years have witnessed a surge in the development of SSL methods specifically for this field. This rapid development, however, has led to many methods being explored independently, without taking previous efforts in methodology or comparison into account, thereby hindering progress in the field of research. This paper, therefore, seeks to consolidate the use of SSL in the field of TR, offering a critical and comprehensive overview of the current state of the art. We will review and analyze the existing methods, compare their results, and highlight inconsistencies in the current literature. This thorough analysis aims to provide general insights into the field, propose standardizations, identify new research directions, and foster its proper development., Comment: This article is under revision

In this work, we uncover a collection of non invertible topological operators linked to the 0-, 2-, 4- and 6-form symmetries related to the type IIB superstring effective theory. By pinpointing the $\text{SL}(2,\mathbb{Z})$-covariant conserved currents corresponding to these symmetries, we first derive a set of $\text{SL}(2,\mathbb{Z})$ invariant invertible topological operators that encapsulate the integer BPS charges inherent to the theory. Moving forward, by incorporating fractional charges while maintaining gauge invariance, we introduce the non invertible topological operators for each generalized symmetry, and in particular for the $\text{SL}(2,\mathbb{Z})$ 0-form symmetry. Identifying them as a novel kind of symmetries reminiscent of fractional quantum Hall effect (FQHE)-like non invertible operators, we study their action on charged objects and their associated SymTFTs obtained via half (higher) gauging., Comment: are welcome. 16 pages, 1 figure. v2: References added, minor corrections in text and some equations. Main results unchanged

The recent discoveries of Neptune-sized ultra-short period planets (USPs) challenge existing planet formation theories. It is unclear whether these residents of the Hot Neptune Desert have similar origins to smaller, rocky USPs, or if this discrete population is evidence of a different formation pathway altogether. We report the discovery of TOI-3261b, an ultra-hot Neptune with an orbital period $P$ = 0.88 days. The host star is a $V = 13.2$ magnitude, slightly super-solar metallicity ([Fe/H] $\simeq$ 0.15), inactive K1.5 main sequence star at $d = 300$ pc. Using data from the Transiting Exoplanet Survey Satellite and the Las Cumbres Observatory Global Telescope, we find that TOI-3261b has a radius of $3.82_{-0.35}^{+0.42}$ $R_{\oplus}$. Moreover, radial velocities from ESPRESSO and HARPS reveal a mass of $30.3_{-2.4}^{+2.2}$ $M_{\oplus}$, more than twice the median mass of Neptune-sized planets on longer orbits. We investigate multiple mechanisms of mass loss that can reproduce the current-day properties of TOI-3261b, simulating the evolution of the planet via tidal stripping and photoevaporation. Thermal evolution models suggest that TOI-3261b should retain an envelope potentially enriched with volatiles constituting $\sim$5% of its total mass. This is the second highest envelope mass fraction among ultra-hot Neptunes discovered to date, making TOI-3261b an ideal candidate for atmospheric follow-up observations., Comment: 20 pages, 11 figures, accepted to AJ

We consider massive type IIA compactifications down to 4 dimensions in presence of O6 planes and D6 branes parallel to them, in order to preserve half-maximal supersymmetry in 4D. The dynamics of open strings living on the spacetime filling branes is taken into account, in the gauged supergravity description, by adding extra vector multiplets and embedding tensor components. The scalar potential gets new terms that can be matched with contributions coming from dimensional reduction of the non-Abelian DBI and WZ brane actions. In this setting, we analyze the vacuum structure of the theory and find novel AdS$_4$ vacua, both supersymmetric and non-supersymmetric ones. Furthermore, we address their perturbative stability by computing their mass spectra. Some of the vacua are found to be perturbatively stable, despite their being non-supersymmetric. We conclude by discussing the reliability of our setup in terms of higher-derivative corrections., Comment: 39 pages

Air pollution and greenhouse emissions are a significant problem across various sectors, urging the need for advanced technologies to detect and capture harmful gases. In recent years, two-dimensional (2D) materials have attracted an increasing attention due to their large surface-to-volume ratio and reactivity. Herein, we investigate the potential of single-layer CrSBr for gas sensing and capturing by means of first-principles calculations. We explore the adsorption behaviour of different pollutant gases (H$_2$S, NH$_3$, NO, NO$_2$, CO and CO$_2$) on this 2D ferromagnet and the impact of intrinsic defects on its magnetic properties. Interestingly, we find that Br vacancies enhance the adsorption of NH$_3$, NO and NO$_2$ and induces a selective frequency shift on the magnon dispersion. This work motivates the creation of novel magnonic gas sensing devices based on 2D van der Waals magnetic materials., Comment: 13 pages, 5 figures, 1 table

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.

We study the topological Weyl type-II semimetal WTe$_2$ via Scanning Tunneling Microscopy (STM) and Density Functional Theory calculations (DFT). We succesfully determine the band structure at zero field close to the Fermi level by comparing quasiparticle interference (QPI) experiments with DFT. Under magnetic fields, we observe Landau level quantization on atomic scale measurements and find a level sequence evidencing a linearly dispersing portion of the band structure. Our results establish the long sought connection between Weyl cones and Landau quantization in WTe$_2$. Atomic scale Landau quantization emerges as a powerful probe of topological surface states for semimetals, superconductors and topological insulators., Comment: 17 pages, 11 figures

Multiple imputation (MI) models can be improved by including auxiliary covariates (AC), but their performance in high-dimensional data is not well understood. We aimed to develop and compare high-dimensional MI (HDMI) approaches using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation study using data from opioid vs. non-steroidal anti-inflammatory drug (NSAID) initiators (X) with observed serum creatinine labs (Z2) and time-to-acute kidney injury as outcome. We simulated 100 cohorts with a null treatment effect, including X, Z2, atrial fibrillation (U), and 13 other investigator-derived confounders (Z1) in the outcome generation. We then imposed missingness (MZ2) on 50% of Z2 measurements as a function of Z2 and U and created different HDMI candidate AC using structured and NLP-derived features. We mimicked scenarios where U was unobserved by omitting it from all AC candidate sets. Using LASSO, we data-adaptively selected HDMI covariates associated with Z2 and MZ2 for MI, and with U to include in propensity score models. The treatment effect was estimated following propensity score matching in MI datasets and we benchmarked HDMI approaches against a baseline imputation and complete case analysis with Z1 only. HDMI using claims data showed the lowest bias (0.072). Combining claims and sentence embeddings led to an improvement in the efficiency displaying the lowest root-mean-squared-error (0.173) and coverage (94%). NLP-derived AC alone did not perform better than baseline MI. HDMI approaches may decrease bias in studies with partially observed confounders where missingness depends on unobserved factors.

The real estate market shows an inherent connection to space. Real estate agencies unevenly operate and specialize across space, price and type of properties, thereby segmenting the market into submarkets. We introduce here a methodology based on multipartite networks to detect the spatial segmentation emerging from data on housing online listings. Considering the spatial information of the listings, we build a bipartite network that connects agencies and spatial units. This bipartite network is projected into a network of spatial units, whose connections account for similarities in the agency ecosystem. We then apply clustering methods to this network to segment markets into spatially-coherent regions, which are found to be robust across different clustering detection algorithms, discretization of space and spatial scales, and across countries with case studies in France and Spain. This methodology addresses the long-standing issue of housing market segmentation, relevant in disciplines such as urban studies and spatial economics, and with implications for policymaking., Comment: 9 pages, 10 figures

In this paper we compute the Chermak-Delgado measure of the mod $p^{n}$ Heisenberg Group for any prime $p$. To achieve this we introduce the notion of the pseudocentralizer and prove various results about it., Comment: 40 pages

Sensors and Artificial Intelligence (AI) have revolutionized the analysis of human movement, but the scarcity of specific samples presents a significant challenge in training intelligent systems, particularly in the context of diagnosing neurodegenerative diseases. This study investigates the feasibility of utilizing robot-collected data to train classification systems traditionally trained with human-collected data. As a proof of concept, we recorded a database of numeric characters using an ABB robotic arm and an Apple Watch. We compare the classification performance of the trained systems using both human-recorded and robot-recorded data. Our primary objective is to determine the potential for accurate identification of human numeric characters wearing a smartwatch using robotic movement as training data. The findings of this study offer valuable insights into the feasibility of using robot-collected data for training classification systems. This research holds broad implications across various domains that require reliable identification, particularly in scenarios where access to human-specific data is limited.

We initiate the LISA template databank for stochastic gravitational wave backgrounds sourced by cosmic strings. We include two templates, an analytical template, which enables more flexible searches, and a numerical template derived directly from large Nambu-Goto simulations of string networks. Using searches based on these templates, we forecast the parameter space within the reach of the experiment and the precision with which their parameters will be reconstructed, provided a signal is observed. The reconstruction permits probing the Hubble expansion and new relativistic DoF in the early universe. We quantify the impact that astrophysical foregrounds can have on these searches. Finally, we discuss the impact that these observations would have on our understanding of the fundamental models behind the string networks. Overall, we prove that LISA has great potential for probing cosmic string models and may reach tensions as low as $G\mu =10^{-16} - 10^{-17} $, which translates into energy scales of the order $10^{11}~\text{GeV}$., Comment: 59 pages, 15 figures

In this paper we construct families of bit sequences using combinatorial methods. Each sequence is derived by con- verting a collection of numbers encoding certain combinatorial nu- merics from objects exhibiting symmetry in various dimensions. Using the algorithms first described in [1] we show that the NIST testing suite described in publication 800-22 does not detect these symmetries hidden within these sequences.

Visual electrophysiology is often used clinically to determine functional changes associated with retinal or neurological conditions. The full-field flash electroretinogram (ERG) assesses the global contribution of the outer and inner retinal layers initiated by the rods and cone pathways depending on the state of retinal adaptation. Within clinical centers reference normative data are used to compare with clinical cases that may be rare or underpowered within a specific demographic. To bolster either reference or case datasets the application of synthetic ERG waveforms may offer benefits to disease classification and case-control studies. In this study and as a proof of concept, artificial intelligence (AI) to generate synthetic signals using Generative Adversarial Networks is deployed to up-scale male participants within an ISCEV reference dataset containing 68 participants, with waveforms from the right and left eye. Random Forest Classifiers further improved classification for sex within the group from a balanced accuracy of 0.72 to 0.83 with the added synthetic male waveforms. This is the first study to demonstrate the generation of synthetic ERG waveforms to improve machine learning classification modelling with electroretinogram waveforms.

Handwritten Text Recognition (HTR) is a relevant problem in computer vision, and implies unique challenges owing to its inherent variability and the rich contextualization required for its interpretation. Despite the success of Self-Supervised Learning (SSL) in computer vision, its application to HTR has been rather scattered, leaving key SSL methodologies unexplored. This work focuses on one of them, namely Spatial Context-based SSL. We investigate how this family of approaches can be adapted and optimized for HTR and propose new workflows that leverage the unique features of handwritten text. Our experiments demonstrate that the methods considered lead to advancements in the state-of-the-art of SSL for HTR in a number of benchmark cases.

The ARS 548 RDI Radar is a premium model of the fifth generation of 77 GHz long range radar sensors with new RF antenna arrays, which offer digital beam forming. This radar measures independently the distance, speed and angle of objects without any reflectors in one measurement cycle based on Pulse Compression with New Frequency Modulation. Unfortunately, to the best of our knowledge, there are no open source drivers available for Linux systems to enable users to analyze the data acquired by the sensor. In this paper, we present a driver that can interpret the data from the ARS 548 RDI sensor and make it available over the Robot Operating System versions 1 and 2 (ROS and ROS2). Thus, these data can be stored, represented, and analyzed using the powerful tools offered by ROS. Besides, our driver offers advanced object features provided by the sensor, such as relative estimated velocity and acceleration of each object, its orientation and angular velocity. We focus on the configuration of the sensor and the use of our driver including its filtering and representation tools. Besides, we offer a video tutorial to help in its configuration process. Finally, a dataset acquired with this sensor and an Ouster OS1-32 LiDAR sensor, to have baseline measurements, is available, so that the user can check the correctness of our driver., Comment: 19 pages, 6 figures and 22 references

An outstanding mystery of dark matter physics is the lack of direct detection signals to date. We suggest that dark matter is scatophobic: due to a repulsive long-range interaction, it is repelled by objects with a large net scat charge, such as the Earth, and is therefore not able to reach direct detection experiments. This represents the first step in a broader theoretical paradigm that we dub the "anti-anthropic principle.", Comment: 8 pages, 4 of which are references, two figures. v2: added references

There are different proposals in the literature on how to protect pedestrians using warning systems to alert drivers of their presence. They can be based on onboard perception systems or wireless communications. The evaluation of these systems has been focused on testing their ability to detect pedestrians. A problem that has received much less attention is the possibility of generating too many alerts in the warning systems. In this paper, we propose and analyze four different algorithms to take the decision on generating alerts in a warning system that is based on direct wireless communications between vehicles and pedestrians. With the algorithms, we explore different strategies to reduce unnecessary alerts. The feasibility of the implementation of the algorithms was evaluated with a deployment using real equipment, and tests were carried out to verify their behavior in real scenarios. The ability of each algorithm to reduce unnecessary alerts was evaluated with realistic simulations in an urban scenario, using a traffic simulator with vehicular and pedestrian flows. The results show the importance of tackling the problem of driver overload in warning systems, and that it is not straightforward to predict the load of alerts generated by an algorithm in a large-scale deployment, in which there are multiple interactions between vehicles and pedestrians.

Recent advancements in 2D magnetic materials have attracted a growing interest driven by their unique properties and potential applications in spintronic devices. However, the scarcity of systems that exhibit magnetism at room-temperature has limited their practical implementation into functional devices. In this work we focus on the recently synthetised van der Waals (vdW) ferromagnet Fe$_3$GaTe$_2$, which exhibits above-room-temperature magnetism (T$_{\mathrm{c}}$ = 350-380 K) and strong perpendicular magnetic anisotropy. Through first-principles calculations, we examine the magnetic properties of Fe$_3$GaTe$_2$ and compare them with the widely known Fe$_3$GeTe$_2$ ferromagnet. Our calculations unveil the complex microscopic mechanisms governing their magnetic behaviour, emphasizing the pivotal role of the ferromagnetic in-plane exchange interactions in the stabilization of the elevated T$_{\mathrm{c}}$ in Fe$_3$GaTe$_2$. Additionally, we predict the stability, strong perpendicular anisotropy and high T$_{\mathrm{c}}$ of single-layer Fe$_3$GaTe$_2$. We also demonstrate the potential of strain engineering and electrostatic doping to modulate its magnetic exchange interactions and anisotropy. Our results incentivise the isolation of the monolayer and pave the way for the future optimization of Fe$_3$GaTe$_2$ in magnetic and spintronic nanodevices.

When considering airborne epidemic spreading in social systems, a natural connection arises between mobility and epidemic contacts. As individuals travel, possibilities to encounter new people either at the final destination or during the transportation process appear. Such contacts can lead to new contagion events. In fact, mobility has been a crucial target for early non-pharmaceutical containment measures against the recent COVID-19 pandemic, with a degree of intensity ranging from public transportation line closures to regional, city or even home confinements. Nonetheless, quantitative knowledge on the relationship between mobility-contagions and, consequently, on the efficiency of containment measures remains elusive. Here we introduce an agent-based model with a simple interaction between mobility and contacts. Despite its simplicity our model shows the emergence of a critical mobility level, inducing major outbreaks when surpassed. We explore the interplay between mobility restrictions and the infection in recent intervention policies seen across many countries, and how interventions in the form of closures triggered by incidence rates can guide the epidemic into an oscillatory regime with recurrent waves. We consider how the different interventions impact societal well-being, the economy and the population. Finally, we propose a mitigation framework based on the critical nature of mobility in an epidemic, able to suppress incidence and oscillations at will, preventing extreme incidence peaks with potential to saturate health care resources., Comment: 13 pages, 5 figures

Excitations between localized 3d states of transition metal ions within crystalline solids, commonly known as d-d transitions, play a pivotal role in diverse phenomena across solid state physics, materials science, and chemistry. These transitions contribute to the coloration in transition metal oxides, catalytic processes on oxide surfaces, and high-temperature superconductivity. They also couple optical excitation to quantized collective phenomena such as phonons and magnons in magnetic systems. Until now, an experimental method to unravel the complex quasiparticle dynamics associated with d-d transitions has remained elusive. We bridge this gap by demonstrating that d-d transitions can be distinctly traced in momentum space and time using time- and angle-resolved photoelectron spectroscopy (trARPES). Through this approach, we can assign specific momentum-dependent characteristics and elucidate the decay mechanisms of specific d-d transitions in FePS$_{3}$, a two-dimensional van der Waals antiferromagnet with a rich array of quantum phenomena stemming from d-d transitions. This study pioneers the use of ARPES in probing the dynamics of d-d transitions across a wide spectrum of solid-state systems., Comment: 15 pages, 4 figures

Catheter ablation (CA) remains the cornerstone alternative to cardioversion for sinus rhythm (SR) restoration in patients with atrial fibrillation (AF). Unfortunately, despite the last methodological and technological advances, this procedure is not consistently effective in treating persistent AF.

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.

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.