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The GAia Map of the Brightness Of the Natural Sky (GAMBONS) is a model to map the natural night brightness of the sky in cloudless and moonless nights. It computes the star radiance from the photometric data in Gaia and Hipparcos catalogues, adding the contributions of the diffuse galactic and extragalactic light, zodiacal light and airglow, and taking into account the effects of atmospheric attenuation and scattering. The model allows computing the natural sky brightness in any given photometric band for a ground-based observer, if appropriate transformations from the Gaia bands are available. In this work we present the most recent improvements of the model, including the use of Gaia EDR3 data, the inclusion of a wide set of photometric bands and the derivation of additional sky brightness indicators, as the horizontal irradiance and the average hemispheric radiance, Comment: 12 pages, 3 figures

The Dirac Scotogenic model provides an elegant mechanism for generating small Dirac neutrino masses at the one loop level. A single abelian discrete $Z_{6}$ symmetry, simultaneously protects the ``Diracness'' of the neutrinos and the stability of the dark matter candidate. This symmetry originates as an unbroken subgroup of the so-called 445 $U(1)_{B-L}$ symmetry. Here we thoroughly explore the phenomenological implications of such a construction including analysis of electroweak vacuum stability, charged lepton flavor violation and the dark matter phenomenology. After taking all the constraints into account, we also show that the model allows for the possibility of novel low mass scalar as well as fermionic dark matter, a feature not shared by its canonical Majorana counterpart., Comment: 49 pages, 29 figures, 5 tables

The necessary and sufficient conditions for a perfect fluid solution to define a spatially-homogeneous cosmology are achieved. These conditions are Intrinsic, Deductive, Explicit and ALgorithmic, and they offer an IDEAL labeling of these geometries. When a three-dimensional group acts on the three-dimensional space-like orbits, the Bianchi type of the model is also obtained., Comment: 25 pages, 4 figures, 1 supplementary documentation

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 in-vivo identification of the kidney stone types during an ureteroscopy would be a major medical advance in urology, as it could reduce the time of the tedious renal calculi extraction process, while diminishing infection risks. Furthermore, such an automated procedure would make possible to prescribe anti-recurrence treatments immediately. Nowadays, only few experienced urologists are able to recognize the kidney stone types in the images of the videos displayed on a screen during the endoscopy. Thus, several deep learning (DL) models have recently been proposed to automatically recognize the kidney stone types using ureteroscopic images. However, these DL models are of black box nature whicl limits their applicability in clinical settings. This contribution proposes a case-based reasoning DL model which uses prototypical parts (PPs) and generates local and global descriptors. The PPs encode for each class (i.e., kidney stone type) visual feature information (hue, saturation, intensity and textures) similar to that used by biologists. The PPs are optimally generated due a new loss function used during the model training. Moreover, the local and global descriptors of PPs allow to explain the decisions ("what" information, "where in the images") in an understandable way for biologists and urologists. The proposed DL model has been tested on a database including images of the six most widespread kidney stone types. The overall average classification accuracy was 90.37. When comparing this results with that of the eight other DL models of the kidney stone state-of-the-art, it can be seen that the valuable gain in explanability was not reached at the expense of accuracy which was even slightly increased with respect to that (88.2) of the best method of the literature. These promising and interpretable results also encourage urologists to put their trust in AI-based solutions., Comment: Paper submitted to Artificial Intelligence in Medicine. (AIIM), Elsevier

We investigate the etching characteristics of PIN-PMN-PT crystals, exploring various wet etching solutions, reactive ion etching gases, and ion beam etching. The etch rate and surface roughness are systematically measured for each method. A process flow to obtain PIN-PMN-PT thin films is described and performed to later build a single mode rib waveguide. An integrated electro-optic phase modulator is fabricated by depositing electrodes on the sides of the waveguides. The electro-optic coefficient implied in our phase modulators is the $r_{51}/r_{42}$. The performances are tested achieving a $V_\pi L$ of 19.2 V$\cdot$cm.

The thermodynamic interpretation of the Stephani Universes is studied in detail. The general expression of the speed of sound and of the thermodynamic schemes associated with a thermodynamic solution is obtained. The constraints imposed on the solutions by considering some significant physical properties are analyzed. We focus on the models where the cosmological observer measures isotropic radiation. We consider some examples, and a solution that models an ultrarelativistic gas is analyzed in detail., Comment: 16 pages, 7 figures

The main purpose of this work is the derivation of a functional partial differential equation (FPDE) for the calculations of equity-linked insurance policies, where the payment stream may depend on the whole past history of the financial asset. To this end, we employ variational techniques from the theory of functional It\^o calculus.

According to the Tidal Torque Theory (TTT), the angular momentum of haloes arises from the tidal torque produced on protohaloes by the mass fluctuations around them. That approach, initially developed assuming protohaloes as random overdense regions in the linear density field, was extended to the more realistic scenario that protohaloes are collapsing patches around peaks in the Gaussian-smoothed linear density field. But that extension faced two fundamental issues: 1) the unknown mass of collapsing patches marked by Gaussian peaks, and 2) the unknown ellipsoidal collapse time of those triaxial patches. Furthermore, the TTT strictly holds in linear regime only. This Paper is the first of a series of two devoted to revisiting the TTT and accurately calculating the halo angular momentum. In the present Paper we use the CUSP formalism fixing all those issues to deal with the TTT from a full peak model viewpoint, i.e. not only is the protohalo suffering the tidal torque identified to a peak, but the main mass fluctuation causing the tidal torque is also seen as a peak or a hole. This way we obtain a simple analytic expression for the Lagrangian protohalo AM, which can be readily implemented in galaxy formation models and be compared to the results of simulations., Comment: 11 pages, 2 figures, submitted to MNRAS. There was an error in an average performed in Appendix B. It has been corrected, as well as the subsequent expressions

The clustering of dark matter halos depends not only on their mass, the so-called primary bias, but also on their internal properties, the so-called secondary bias. While the former effect is well-understood within the Press-Schechter (PS) and excursion set (ES) models of structure formation, the latter is not. In those models, protohalos are fully characterised by their height and scale, which determine the halo mass and collapse time, so there is no room for any other halo property. This is why the secondary bias was believed not to be innate but due to the distinct merger rate of halos lying in different backgrounds, and dubbed assembly bias. However, it is now admitted that mergers leave no imprint in the inner halo properties. In fact, the innate origin of the secondary bias cannot be discarded because, in the more realistic peak model of structure formation, halo seeds are characterized by one additional property: the peak curvature. Here we use the confluent system of peak trajectories (CUSP) formalism to show that peaks lying in different backgrounds have different mean curvatures, which in turn cause them to evolve in halos with different typical inner properties. The dependence we find of the properties on halo background (or halo clustering) reproduces the results of simulations., Comment: 14 pages, 4 figures, accepted for publication in The Astrophysical Journal

The Press-Schechter (PS) and excursion set (ES) models of structure formation fail in reproducing the halo bias found in simulations, while the excursion set-peaks (ESP) formalism built in the peak model reproduces it only at high masses and does not address in a fully satisfactory manner peak nesting and the mass and time of ellipsoidal collapse of triaxial peaks in the Gaussian-smoothed density field. Here we apply the CUSP formalism fixing all these issues from first principles and with no free parameters to infer the Lagrangian local peak bias parameters, which adopt very simple analytic expressions similar to those found in the PS and ES models. The predicted Eulerian linear halo bias recovers the results of simulations. More specifically, we show that the only small departure observed at intermediate and low masses can be due to the spurious halo splitting and grouping caused by the Spherical Overdensity halo-finding algorithm used in simulations., Comment: 19 pages, 8 figures, accepted for publication in The Astrophysical Journal. It includes minor changes to match the published version

Experiments in Agricultural Sciences often involve the analysis of longitudinal nominal polytomous variables, both in individual and grouped structures. Marginal and mixed-effects models are two common approaches. The distributional assumptions induce specific mean-variance relationships, however, in many instances, the observed variability is greater than assumed by the model. This characterizes overdispersion, whose identification is crucial for choosing an appropriate modeling framework to make inferences reliable. We propose an initial exploration of constructing a longitudinal multinomial dispersion index as a descriptive and diagnostic tool. This index is calculated as the ratio between the observed and assumed variances. The performance of this index was evaluated through a simulation study, employing statistical techniques to assess its initial performance in different scenarios. We identified that as the index approaches one, it is more likely that this corresponds to a high degree of overdispersion. Conversely, values closer to zero indicate a low degree of overdispersion. As a case study, we present an application in animal science, in which the behaviour of pigs (grouped in stalls) is evaluated, considering three response categories., Comment: 22 pages

Scientific Machine Learning (ML) is gaining momentum as a cost-effective alternative to physics-based numerical solvers in many engineering applications. In fact, scientific ML is currently being used to build accurate and efficient surrogate models starting from high-fidelity numerical simulations, effectively encoding the parameterized temporal dynamics underlying Ordinary Differential Equations (ODEs), or even the spatio-temporal behavior underlying Partial Differential Equations (PDEs), in appropriately designed neural networks. We propose an extension of Latent Dynamics Networks (LDNets), namely Liquid Fourier LDNets (LFLDNets), to create parameterized space-time surrogate models for multiscale and multiphysics sets of highly nonlinear differential equations on complex geometries. LFLDNets employ a neurologically-inspired, sparse, liquid neural network for temporal dynamics, relaxing the requirement of a numerical solver for time advancement and leading to superior performance in terms of tunable parameters, accuracy, efficiency and learned trajectories with respect to neural ODEs based on feedforward fully-connected neural networks. Furthermore, in our implementation of LFLDNets, we use a Fourier embedding with a tunable kernel in the reconstruction network to learn high-frequency functions better and faster than using space coordinates directly as input. We challenge LFLDNets in the framework of computational cardiology and evaluate their capabilities on two 3-dimensional test cases arising from multiscale cardiac electrophysiology and cardiovascular hemodynamics. This paper illustrates the capability to run Artificial Intelligence-based numerical simulations on single or multiple GPUs in a matter of minutes and represents a significant step forward in the development of physics-informed digital twins.

Recent and unremitting capability advances have been accompanied by calls for comprehensive, rather than patchwork, regulation of frontier artificial intelligence (AI). Approval regulation is emerging as a promising candidate. An approval regulation scheme is one in which a firm cannot legally market, or in some cases develop, a product without explicit approval from a regulator on the basis of experiments performed upon the product that demonstrate its safety. This approach is used successfully by the FDA and FAA. Further, its application to frontier AI has been publicly supported by many prominent stakeholders. This report proposes an approval regulation schematic for only the largest AI projects in which scrutiny begins before training and continues through to post-deployment monitoring. The centerpieces of the schematic are two major approval gates, the first requiring approval for large-scale training and the second for deployment. Five main challenges make implementation difficult: noncompliance through unsanctioned deployment, specification of deployment readiness requirements, reliable model experimentation, filtering out safe models before the process, and minimizing regulatory overhead. This report makes a number of crucial recommendations to increase the feasibility of approval regulation, some of which must be followed urgently if such a regime is to succeed in the near future. Further recommendations, produced by this report's analysis, may improve the effectiveness of any regulatory regime for frontier AI.

This article investigates the qualitative aspects of dark solitons of one-dimensional Gross-Pitaevskii equations with general nonlocal interactions, which correspond to traveling waves with subsonic speeds. Under general conditions on the potential interaction term, we provide uniform bounds, demonstrate the existence of symmetric solitons, and identify conditions under which monotonicity is lost. Additionally, we present new properties of black solitons. Moreover, we establish the nonlocal-to-local convergence, i.e. the convergence of the soliton of the nonlocal model toward the explicit dark solitons of the local Gross-Pitaevskii equation.

We present a new approach for nonlinear dimensionality reduction, specifically designed for computationally expensive mathematical models. We leverage autoencoders to discover a one-dimensional neural active manifold (NeurAM) capturing the model output variability, plus a simultaneously learnt surrogate model with inputs on this manifold. The proposed dimensionality reduction framework can then be applied to perform outer loop many-query tasks, like sensitivity analysis and uncertainty propagation. In particular, we prove, both theoretically under idealized conditions, and numerically in challenging test cases, how NeurAM can be used to obtain multifidelity sampling estimators with reduced variance by sampling the models on the discovered low-dimensional and shared manifold among models. Several numerical examples illustrate the main features of the proposed dimensionality reduction strategy, and highlight its advantages with respect to existing approaches in the literature.

The AC optimal power flow (AC-OPF) problem is essential for power system operations, but its non-convex nature makes it challenging to solve. A widely used simplification is the linearized DC optimal power flow (DC-OPF) problem, which can be solved to global optimality, but whose optimal solution is always infeasible in the original AC-OPF problem. Recently, neural networks (NN) have been introduced for solving the AC-OPF problem at significantly faster computation times. However, these methods necessitate extensive datasets, are difficult to train, and are often viewed as black boxes, leading to resistance from operators who prefer more transparent and interpretable solutions. In this paper, we introduce a novel learning-based approach that merges simplicity and interpretability, providing a bridge between traditional approximation methods and black-box learning techniques. Our approach not only provides transparency for operators but also achieves competitive accuracy. Numerical results across various power networks demonstrate that our method provides accuracy comparable to, and often surpassing, that of neural networks, particularly when training datasets are limited.

We derive an intuitive and novel method to represent nodes in a graph with special unitary operators, or quantum operators, which does not require parameter training and is competitive with classical methods on scoring similarity between nodes. This method opens up future possibilities to apply quantum algorithms for NLP or other applications that need to detect anomalies within a network structure. Specifically, this technique leverages the advantage of quantum computation, representing nodes in higher dimensional Hilbert spaces. To create the representations, the local topology around each node with a predetermined number of hops is calculated and the respective adjacency matrix is used to derive the Hamiltonian. While using the local topology of a node to derive a Hamiltonian is a natural extension of a graph into a quantum circuit, our method differs by not assuming the quantum operators in the representation a priori, but letting the adjacency matrix dictate the representation. As a consequence of this simplicity, the set of adjacency matrices of size $2^n \times 2^n$ generates a sub-vector space of the Lie algebra of the special unitary operators, $\mathfrak{su}(2^n)$. This sub-vector space in turn generates a subgroup of the Lie group of special unitary operators, $\mathrm{SU}(2^n)$. Applications of our quantum embedding method, in comparison with the classical algorithms GloVe (a natural language processing embedding method) and FastRP (a general graph embedding method, display superior performance in measuring similarity between nodes in graph structures.

In this work we characterize all the static and spherically symmetric vacuum solutions in $f(R)$ gravity when the principal null directions of the Weyl tensor are non-expanding. In contrast to General Relativity, we show that the Nariai spacetime is not the only solution of this type when general $f(R)$ theories are considered. In particular, we find four different solutions for the non-constant Ricci scalar case, all of them corresponding to the same theory, given by $f(R) = r_0^{-1}\left\lvert R-3/r_0^2\right\rvert^{1/2}$, where $r_0$ is a non-null constant. Finally, we briefly present some geometric properties of these solutions., Comment: 7 pages, 4 figures, published in EPJC

The domain of speech emotion recognition (SER) has persistently been a frontier within the landscape of machine learning. It is an active field that has been revolutionized in the last few decades and whose implementations are remarkable in multiple applications that could affect daily life. Consequently, the Iberian Languages Evaluation Forum (IberLEF) of 2024 held a competitive challenge to leverage the SER results with a Spanish corpus. This paper presents the approach followed with the goal of participating in this competition. The main architecture consists of different pre-trained speech and text models to extract features from both modalities, utilizing an attention pooling mechanism. The proposed system has achieved the first position in the challenge with an 86.69% in Macro F1-Score.

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

Social Robots need to be safe and reliable to share their space with humans. This paper reports on the first results of a research project that aims to create more safe and reliable, intelligent autonomous robots by investigating the implications and interactions between cybersecurity and safety. We propose creating a robotic intrusion prevention system (RIPS) that follows a novel approach to detect and mitigate intrusions in cognitive social robot systems and other cyber-physical systems. The RIPS detects threats at the robotic communication level and enables mitigation of the cyber-physical threats by using System Modes to define what part of the robotic system reduces or limits its functionality while the system is compromised. We demonstrate the validity of our approach by applying it to a cognitive architecture running in a real social robot that preserves the privacy and safety of humans while facing several cyber attack situations.

Viscous electron flow exhibits exotic signatures such as superballistic conduction. Bending the geometry of the device is a must to observe hydrodynamic effects. To this end, we build three antidot graphene superlattices with different hole diameters. We measure their electrical properties at various temperatures and under the effect of a perpendicular magnetic field. We find an enhanced superballistic effect, suggesting the effectiveness of the geometry at bending the electron flow. In addition, superballistic conduction behaves non-monotonically with the magnetic field, which is related with the ballistic-hydrodynamic transition. We also analyze the device resistance as a function of the size of the antidot superlattice to find characteristic scaling laws describing the different transport regimes. We prove that the antidot superlattice is a convenient geometry for realizing hydrodynamic flow, and the experiment provides valuable explanations for the technologically relevant effects of superballistic conduction and scaling laws.

We present a spatially resolved study of the kinematical properties of known supernova remnants (SNRs) in the nearest galaxies of the SIGNALS survey, namely NGC 6822 (one object) and M33 (163 objects), based on data obtained with the SITELLE Imaging Fourier Transform Spectrometer (iFTS) at the Canada-France-Hawaii Telescope. The purpose of this paper is to provide a better scheme of identification for extragalactic SNRs and, in particular, to distinguish between HII regions and SNRs. For that we have used diagrams which involve both the [SII]/H$\alpha$ ratio and the velocity dispersion ($\sigma$). We also introduce a new parameter, $\xi = {[SII] \over H\alpha} \times \sigma$, which enhances still the contrast between SNRs and the rest of the ionised gas. More than 90\% of the SNRs in our entire sample show an integrated [SII]/H$\alpha$ ratio larger than the canonical value (0.4). 86\% of the SNRs present in our field show a significant velocity dispersion. The spectral resolution of our observations allows us to observe the complex velocity structure of some SNRs., Comment: Accepted for publication in MNRAS, 31 pages, 34 figures, and 6 tables

This research addresses the challenge of integrating forecasting and optimization in energy management systems, focusing on the impacts of switching costs, forecast accuracy, and stability. It proposes a novel framework for analyzing online optimization problems with switching costs and enabled by deterministic and probabilistic forecasts. Through empirical evaluation and theoretical analysis, the research reveals the balance between forecast accuracy, stability, and switching costs in shaping policy performance. Conducted in the context of battery scheduling within energy management applications, it introduces a metric for evaluating probabilistic forecast stability and examines the effects of forecast accuracy and stability on optimization outcomes using the real-world case of the Citylearn 2022 competition. Findings indicate that switching costs significantly influence the trade-off between forecast accuracy and stability, highlighting the importance of integrated systems that enable collaboration between forecasting and operational units for improved decision-making. The study shows that committing to a policy for longer periods can be advantageous over frequent updates. Results also show a correlation between forecast stability and policy performance, suggesting that stable forecasts can mitigate switching costs. The proposed framework provides valuable insights for energy sector decision-makers and forecast practitioners when designing the operation of an energy management system., Comment: 14 pages, contains the Online Appendix with a comment on KPIs, MPC formulation, Theoretical analysis of the MPC performance bounds and extra results on the in-sample performance

We present PoliFormer (Policy Transformer), an RGB-only indoor navigation agent trained end-to-end with reinforcement learning at scale that generalizes to the real-world without adaptation despite being trained purely in simulation. PoliFormer uses a foundational vision transformer encoder with a causal transformer decoder enabling long-term memory and reasoning. It is trained for hundreds of millions of interactions across diverse environments, leveraging parallelized, multi-machine rollouts for efficient training with high throughput. PoliFormer is a masterful navigator, producing state-of-the-art results across two distinct embodiments, the LoCoBot and Stretch RE-1 robots, and four navigation benchmarks. It breaks through the plateaus of previous work, achieving an unprecedented 85.5% success rate in object goal navigation on the CHORES-S benchmark, a 28.5% absolute improvement. PoliFormer can also be trivially extended to a variety of downstream applications such as object tracking, multi-object navigation, and open-vocabulary navigation with no finetuning.

In this paper, we study the dynamic formation of transients when plane waves impinge on a dispersive slab that abruptly changes its electrical properties in time. The time-varying slab alternates between air and metal-like states, whose frequency dispersion is described by the Drude model. It is shown how the physics of this complex system can be well described with the joint combination of two terms: one associated with temporal refractions and the other associated with spatial refractions. To test the validity of the approach, some analytical results are compared with a self-implemented finite-difference time-domain (FDTD) method. Results show how the transients that occurred after the abrupt temporal changes can shape the overall steady-state response of the space-time system. In fact, far from always being detrimental, these transient states can be conveniently used to perform frequency conversion or to amplify/attenuate the electromagnetic fields.

Survey strategies for upcoming exoplanet direct imaging missions have considered varying assumptions of prior knowledge. Precursor radial velocity surveys could have detected nearby exo-Earths and provided prior orbit and mass constraints. Alternatively, a direct imaging mission performing astrometry could yield constraints on orbit and phase angle of target planets. Understanding the impact of prior mass and orbit information on planetary characterization is crucial for efficiently recognizing habitable exoplanets. To address this question, we use a reflected-light retrieval tool to infer the atmospheric and bulk properties of directly imaged Earth-analogs while considering varying levels of prior information and signal-to-noise ratio (SNR). Because of the strong correlation between the orbit-related parameters and the planetary radius, prior information on the orbital distance and planetary phase yield tight constraints on the planetary radius: from $R_{\rm{p}}=2.95^{+2.69}_{-1.95}~R_{\oplus}$ without prior knowledge, to $R_{\rm{p}}=1.01^{+0.33}_{-0.19}~R_{\oplus}$ with prior determination of the orbit for $\rm{SNR}=20$ in the visible/near-infrared spectral range, thus allowing size determination from reflected light observations. However, additional knowledge of planet mass does not notably enhance radius ($R_{\rm{p}}=0.98^{+0.17}_{-0.14}~R_{\oplus}$) or atmospheric characterization. Also, prior knowledge of the mass alone does not yield a tight radius constraint ($R_{\rm{p}}=1.64^{+1.29}_{-0.80}~R_{\oplus}$) nor improves atmospheric composition inference. By contrast, because of its sensitivity to gas column abundance, detecting a Rayleigh scattering slope or bounding Rayleigh opacity helps to refine gas mixing ratio inferences without requiring prior mass knowledge. Overall, apart from radius determination, increasing the SNR is more beneficial than additional prior observations., Comment: Accepted for publication in ApJL

We analyze IceCube public data from its IC86 configuration, namely PSTracks event selection, to search for pseudo-Dirac signatures in high-energy neutrinos from astrophysical sources. Neutrino flux from astrophysical sources is reduced in the pseudo-Dirac scenario due to conversion of active-to-sterile neutrinos as compared to the neutrino oscillation scenario of only three active neutrinos over astrophysical distances. We fit IceCube data using astrophysical flux models for three point-like sources in both scenarios and constrain the active-sterile mass-square-difference in the absence of any evidence for pseudo-Dirac scenario. We find that a common mass-squared-difference $\delta m^2$ for all three flavors can be constrained as $\delta m^2 \lesssim 2.2 \times 10^{-19} eV^2$ for the source NGC 1068, $\delta m^2 \lesssim 1.2 \times 10^{-20} eV^2$ for the source TXS 0506+056, and $\delta m^2 \lesssim 1.5 \times 10^{-21} eV^2$ for the source PKS 1424+240 at 90% C.L. A stacking analysis gives a constraint on $\delta m^2 \lesssim 1.5 \times 10^{-21} eV^2$ at 90% CL which is dominated by the constraint obtained from PKS 1424+240., Comment: 11 pages, 8 figures, Updated analysis and constraints on mass splitting for pseudo-Dirac scenario

The rise of Large Language Models (LLMs) such as ChatGPT and Gemini has posed new challenges for the academic community. With the help of these models, students can easily complete their assignments and exams, while educators struggle to detect AI-generated content. This has led to a surge in academic misconduct, as students present work generated by LLMs as their own, without putting in the effort required for learning. As AI tools become more advanced and produce increasingly human-like text, detecting such content becomes more challenging. This development has significantly impacted the academic world, where many educators are finding it difficult to adapt their assessment methods to this challenge. This research first demonstrates how LLMs have increased academic dishonesty, and then reviews state-of-the-art solutions for academic plagiarism in detail. A survey of datasets, algorithms, tools, and evasion strategies for plagiarism detection has been conducted, focusing on how LLMs and AI-generated content (AIGC) detection have affected this area. The survey aims to identify the gaps in existing solutions. Lastly, potential long-term solutions are presented to address the issue of academic plagiarism using LLMs based on AI tools and educational approaches in an ever-changing world.

In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems devised to cope with a single task. The recent emergence of General-Purpose Artificial Intelligence Systems (GPAIS) poses model configuration and adaptability challenges at far greater complexity scales than the optimal design of traditional Machine Learning models. Evolutionary Computation (EC) has been a useful tool for both the design and optimization of Machine Learning models, endowing them with the capability to configure and/or adapt themselves to the task under consideration. Therefore, their application to GPAIS is a natural choice. This paper aims to analyze the role of EC in the field of GPAIS, exploring the use of EC for their design or enrichment. We also match GPAIS properties to Machine Learning areas in which EC has had a notable contribution, highlighting recent milestones of EC for GPAIS. Furthermore, we discuss the challenges of harnessing the benefits of EC for GPAIS, presenting different strategies to both design and improve GPAIS with EC, covering tangential areas, identifying research niches, and outlining potential research directions for EC and GPAIS.

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

This study assessed the effectiveness of machine learning models in predicting poverty levels in the Philippines using five boosting algorithms: Adaptive Boosting (AdaBoost), CatBoosting (CatBoost), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). CatBoost emerged as the superior model and achieved the highest scores across accuracy, precision, recall, and F1-score at 91 percent, while XGBoost and GBM followed closely with 89 percent and 88 percent respectively. Additionally, the research examined the computational efficiency of these models to analyze the balance between training time, testing speed, and model size factors crucial for real-world applications. Despite its longer training duration, CatBoost demonstrated high testing efficiency. These results indicate that machine learning can aid in poverty prediction and in the development of targeted policy interventions. Future studies should focus on incorporating a wider variety of data to enhance the predictive accuracy and policy utility of these models., Comment: 9 pages, 6 figures, 5 tables

We propose a modified version of the quantum walk-based search algorithm created by Shenvi, Kempe and Whaley, also known as the SKW algorithm. In our version of the algorithm, we modified the evolution operator of the system so that it is composed by the product of the shift operator associated to the $2^n$-complete graph with self-loops and a perturbed coin operator based on the Hadamard operator that works as an oracle for the search. The modified evolution operator leads the opposite behavior as in the original algorithm, that is, the probability to measure the target state is reduced. We call this new behavior the $\textit{search complement}$. Taking a multigraph and matrix approach, we were able to explain that the new algorithm decreases the probability of the target state given that there are less paths that lead towards the node that is associated to the target state in a Unitary Coined Discrete-Time Quantum Walk. The search complement algorithm was executed experimentally on IBM quantum processor $\textit{ibmq_manila}$ obtaining statistical distances $\ell_1\leq 0.0895$ when decreasing the probability of one state out of four.

The Rank Pricing Problem (RPP) is a challenging bilevel optimization problem with binary variables whose objective is to determine the optimal pricing strategy for a set of products to maximize the total benefit, given that customer preferences influence the price for each product. Traditional methods for solving RPP are based on exact approaches which may be computationally expensive. In contrast, this paper presents a novel approach utilizing Variable Neighborhood Search (VNS), a popular heuristic known for its effectiveness in solving combinatorial optimization problems. Our proposed VNS algorithm introduces problem-specific neighborhood operators designed to effectively explore the solution space of the RPP. Even though our methodology does not have optimality guarantees, our computational experiments show that it outperforms Mixed Integer Program solvers regarding solution quality and computational burden.

Bent crystal are widely used as optics for X-rays, but via the phenomenon of planar channeling they may act as waveguide for relativistic charged particles beam as well, outperforming some of the traditional technologies currently employed. A physical description of the phenomenon and the resulting potential for applications in a particle accelerator is reported. The elastic properties of the anisotropic crystal lattice medium are discussed, introducing different types of curvature which can enable a wide array of bending schemes optimized for each different case features. The technological development of machining strategy and bending solutions useful for the fabrication of crystals suitable in high energy particle manipulations are described. As well as the high precision characterization processes developed in order to satisfy the strict requirements for installation in an accelerator. Finally, the characterization of channeling phenomenon in bent crystal is described, pointing out several experimental setups suitable to comply each specific case constrains.

Cardiocirculatory mathematical models serve as valuable tools for investigating physiological and pathological conditions of the circulatory system. To investigate the clinical condition of an individual, cardiocirculatory models need to be personalized by means of calibration methods. In this study we propose a new calibration method for a lumped-parameter cardiocirculatory model. This calibration method utilizes the correlation matrix between parameters and model outputs to calibrate the latter according to data. We test this calibration method and its combination with L-BFGS-B (Limited memory Broyden - Fletcher - Goldfarb - Shanno with Bound constraints) comparing them with the performances of L-BFGS-B alone. We show that the correlation matrix calibration method and the combined one effectively reduce the loss function of the associated optimization problem. In the case of in silico generated data, we show that the two new calibration methods are robust with respect to the initial guess of parameters and to the presence of noise in the data. Notably, the correlation matrix calibration method achieves the best results in estimating the parameters in the case of noisy data and it is faster than the combined calibration method and L-BFGS-B. Finally, we present real test case where the two new calibration methods yield results comparable to those obtained using L-BFGS-B in terms of minimizing the loss function and estimating the clinical data. This highlights the effectiveness of the new calibration methods for clinical applications., Comment: 33 pages, 7 figures, submitted to International Journal for Numerical Methods for Engineering

Recent studies have shown that the electromagnetic shower induced by a high-energy electron, positron or photon incident along the axis of an oriented crystal develops in a space more compact than the ordinary. On the other hand, the properties of the hadronic interactions are not affected by the lattice structure. This means that, inside an oriented crystal, the natural difference between the hadronic and the electromagnetic shower profile is strongly accentuated. Thus, a calorimeter composed of oriented crystals could be intrinsically capable of identifying more accurately the nature of the incident particles, with respect to a detector composed only of non-aligned crystals. Since no oriented calorimeter has ever been developed, this possibility remains largely unexplored and can be investigated only by mean of numerical simulations. In this work, we report the first quantitative evaluation of the particle identification capability of such a calorimeter, focusing on the case of neutron-gamma discrimination. We demonstrate through Geant4 simulations that the use of oriented crystals significantly improves the performance of a Random Forest classifier trained on the detector data. This work is a proof that oriented calorimeters could be a viable option for all the environments where particle identification must be performed with a very high accuracy, such as future high-intensity particle physics experiments and satellite-based gamma-ray telescopes.

Monitoring the evolution of the anthropogenic light emissions is a priority task in light pollution research. Among the complementary approaches that can be adopted to achieve this goal stand out those based on measuring the direct radiance of the sources at ground level or from low Earth orbit satellites, and on measuring the scattered radiance (known as artificial night sky brightness or skyglow) using networks of ground-based sensors. The terrestrial atmosphere is a variable medium interposed between the sources and the measuring instruments, and the fluctuation of its optical parameters sets a lower limit for the actual source emission changes that can be confidently detected. In this paper we analyze the effect of the fluctuations of the molecular and aerosol optical depths. It is shown that for reliably detecting changes in the anthropogenic light emissions of order ~1% per year, the inter-annual variability of the annual means of these atmospheric parameters in the measurement datasets must be carefully controlled or efficiently corrected for., Comment: 20 pages, 2 figures