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Cross-assignment of directional wave spectra is a critical task in wave data assimilation. Traditionally, most methods rely on two-parameter spectral distances or energy ranking approaches, which often fail to account for the complexities of the wave field, leading to inaccuracies. To address these limitations, we propose the Controlled Four-Parameter Method (C4PM), which independently considers four integrated wave parameters. This method enhances the accuracy and robustness of cross-assignment by offering flexibility in assigning weights and controls to each wave parameter. We compare C4PM with a two-parameter spectral distance method using data from two buoys moored 13 km apart in deep water. Although both methods produce negligible bias and high correlation, C4PM demonstrates superior performance by preventing the occurrence of outliers and achieving a lower root mean square error across all parameters. The negligible computational cost and customization make C4PM a valuable tool for wave data assimilation, improving the reliability of forecasts and model validations.

We introduce separability properties corresponding to generalized versions of the conjugacy, twisted conjugacy, Brinkmann and Brinkmann's conjugacy problems and how they relate when finite and cyclic extensions of groups are taken. In particular, we prove that some (concrete) generalizations of twisted conjugacy separability of a group $G$ with respect to virtually inner automorphisms are equivalent to some (concrete) generalizations of the conjugacy problem in finite extensions of $G$. Similarly, (generalized) conjugacy separability in cyclic extensions of $G$ implies (generalized) twisted conjugacy and Brinkmann's conjugacy separability in $G$. Applications include results in free, virtually abelian, virtually polycyclic groups and a proof that virtually free times free groups are conjugacy separable., Comment: 22 pages, comments are very welcome!

The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems. The report was mandated by the nations attending the AI Safety Summit in Bletchley, UK. Thirty nations, the UN, the OECD, and the EU each nominated a representative to the report's Expert Advisory Panel. A total of 100 AI experts contributed, representing diverse perspectives and disciplines. Led by the report's Chair, these independent experts collectively had full discretion over the report's content.

Spin engineering of advanced pulse sequences has had a transformative impact on the development of nuclear magnetic resonance (NMR) spectroscopy, to an extending degree also electron paramagnetic resonance (EPR), and the hybrid between the two, dynamic nuclear polarization (DNP). Based on a simple formalism, we demonstrate that (i) single-crystal static-sample optimisations may tremendously ease design of experiments for rotating powders and (ii) pulse sequences may readily be exchanged between these distinct spectroscopies. Specifically, we design broadband heteronuclear solid-state NMR magic-angle-spinning (MAS) dipolar recoupling experiments based on the recently developed PLATO (PoLarizAtion Transfer via non-linear Optimization) microwave (MW) pulse sequence optimized on a single crystal for powder static-sample DNP. Using this concept, we demonstrate design of ultra-broadband 13C-15N and 2H-13C cross-polarization experiments, using PLATO on the 13C radio-frequency (RF) channel and square/ramped or RESPIRATION (Rotor Echo Short Pulse IRrAdiaTION) RF irradiation on the 15N and 2H RF channels, respectively.

The time dynamics of spin-injected, electrically contacted quantum dots were investigated with a focus on the time evolution of photon statistics. Photon statistics can provide insights into whether the device functions as an effective single-photon emitter or exhibits higher-order emissions. Through these investigations, we found that the shape of the electrical excitation pulse has a direct impact on photon statistics. Specifically, the rising edge of the pulse corresponds to a significantly higher number of higher-order photon states, which decay much faster than single photons associated with the falling edge of the electrical pulse. This relationship implies that the pulse shape can be tailored to optimize the device as either a better single-photon source or a generator of higher-order photon states, with potential applications in creating deterministic higher-order photon Fock states. The ability to easily modify the pulse shape is a unique feature of electrically excited quantum dots., Comment: significant overhaul of the interpretations needs to be further investigated

The 2-point correlation function of the galaxy spatial distribution is a major cosmological observable that enables constraints on the dynamics and geometry of the Universe. The Euclid mission aims at performing an extensive spectroscopic survey of approximately 20--30 million H$\alpha$-emitting galaxies up to about redshift two. This ambitious project seeks to elucidate the nature of dark energy by mapping the 3-dimensional clustering of galaxies over a significant portion of the sky. This paper presents the methodology and software developed for estimating the 3-dimensional 2-point correlation function within the Euclid Science Ground Segment. The software is designed to overcome the significant challenges posed by the large and complex Euclid data set, which involves millions of galaxies. Key challenges include efficient pair counting, managing computational resources, and ensuring the accuracy of the correlation function estimation. The software leverages advanced algorithms, including kd-tree, octree, and linked-list data partitioning strategies, to optimise the pair-counting process. The implementation also includes parallel processing capabilities using shared-memory open multi-processing to further enhance performance and reduce computation times. Extensive validation and performance testing of the software are presented. The results indicate that the software is robust and can reliably estimate the 2-point correlation function, which is essential for deriving cosmological parameters with high precision. Furthermore, the paper discusses the expected performance of the software during different stages of the Euclid Wide Survey observations and forecasts how the precision of the correlation function measurements will improve over the mission's timeline, highlighting the software's capability to handle large data sets efficiently., Comment: 17 pages, 13 figures, submitted to A&A

Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains. A common approach merges domain-specific sequences into cross-domain sequences, serving as bridges to connect domains. One key challenge is to correctly extract the shared knowledge among these sequences and appropriately transfer it. Most existing works directly transfer unfiltered cross-domain knowledge rather than extracting domain-invariant components and adaptively integrating them into domain-specific modelings. Another challenge lies in aligning the domain-specific and cross-domain sequences. Existing methods align these sequences based on timestamps, but this approach can cause prediction mismatches when the current tokens and their targets belong to different domains. In such cases, the domain-specific knowledge carried by the current tokens may degrade performance. To address these challenges, we propose the A-B-Cross-to-Invariant Learning Recommender (ABXI). Specifically, leveraging LoRA's effectiveness for efficient adaptation, ABXI incorporates two types of LoRAs to facilitate knowledge adaptation. First, all sequences are processed through a shared encoder that employs a domain LoRA for each sequence, thereby preserving unique domain characteristics. Next, we introduce an invariant projector that extracts domain-invariant interests from cross-domain representations, utilizing an invariant LoRA to adapt these interests into modeling each specific domain. Besides, to avoid prediction mismatches, all domain-specific sequences are aligned to match the domains of the cross-domain ground truths. Experimental results on three datasets demonstrate that our approach outperforms other CDSR counterparts by a large margin. The codes are available in \url{https://github.com/DiMarzioBian/ABXI}., Comment: Accepted by WebConf '25 (WWW '25)

The Euclid mission is generating a vast amount of imaging data in four broadband filters at high angular resolution. This will allow the detailed study of mass, metallicity, and stellar populations across galaxies, which will constrain their formation and evolutionary pathways. Transforming the Euclid imaging for large samples of galaxies into maps of physical parameters in an efficient and reliable manner is an outstanding challenge. We investigate the power and reliability of machine learning techniques to extract the distribution of physical parameters within well-resolved galaxies. We focus on estimating stellar mass surface density, mass-averaged stellar metallicity and age. We generate noise-free, synthetic high-resolution imaging data in the Euclid photometric bands for a set of 1154 galaxies from the TNG50 cosmological simulation. The images are generated with the SKIRT radiative transfer code, taking into account the complex 3D distribution of stellar populations and interstellar dust attenuation. We use a machine learning framework to map the idealised mock observational data to the physical parameters on a pixel-by-pixel basis. We find that stellar mass surface density can be accurately recovered with a $\leq 0.130 {\rm \,dex}$ scatter. Conversely, stellar metallicity and age estimates are, as expected, less robust, but still contain significant information which originates from underlying correlations at a sub-kpc scale between stellar mass surface density and stellar population properties.

In this paper, we show that the ground-state density of any non-interacting Schr\"odinger operator on the one-dimensional torus with potentials in a certain class of distributions is strictly positive. This result together with recent results from [Sutter el al (2024), J. Phys. A: Math. Theor. 57 475202] provides a complete characterization of the set of non-interacting v-representable densities on the torus. Moreover, we prove that, for said class of non-interacting Schr\"odinger operators with distributional potentials, the Hohenberg-Kohn theorem holds, i.e., the external potential is uniquely determined by the ground-state density. In particular, the density-to-potential Kohn-Sham map is single-valued, and the non-interacting Lieb functional is differentiable at every point in this space of $v$-representable densities. These results contribute to establishing a solid mathematical foundation for the Kohn-Sham scheme in this simplified setting., Comment: Fixed some typos and corrected some arguments in the proof of Theorem 2.5

This paper reports an innovative process to fabricate $\beta$-Ga$_{2}$O$_{3}$ microtubes and nanomembranes based on ion implantation in (100)-oriented single-crystals. We show that, under specific flux and fluence conditions, the irradiation-induced strain profile promotes the detachment and rolling-up of a thin surface layer, forming a microtube. The strain-disorder interplay was investigated in detail for Cr-implanted $\beta$-Ga$_{2}$O$_{3}$ with a range of complementary methods, showing an excellent agreement between experimental and simulation data, and suggesting an exfoliation mechanism that is correlated with the anisotropic nature of the $\beta$-Ga$_{2}$O$_{3}$ monoclinic system and its easy-cleavage planes. Moreover, these microtubes can be unrolled upon a subsequent annealing step, resulting in nanomembranes with bulk-like crystalline quality that can be transferred to other substrates. The recovery of the implantation-induced damage under thermal annealing has also been studied, showing a remarkable recovery at moderate temperatures (~500 {\deg}C). This observation underscores the potential of this method for the scalable production of nanomembranes with improved reproducibility compared to conventional mechanical exfoliation techniques. Importantly, such exfoliation can be done employing different ions, providing simultaneous $\beta$-Ga$_{2}$O$_{3}$ doping, chosen to control the structural, optical, magnetic and electrical properties of the nanomembranes, thus tailoring them to fit the desired applications., Comment: 35 pages, 16 figures, 4 tables

Data from the Euclid space telescope will enable cosmic shear measurements with very small statistical errors, requiring corresponding systematic error control level. A common approach to correct for shear biases involves calibrating shape measurement methods using image simulations with known input shear. Given their high resolution, Hubble Space Telescope (HST) galaxies can, in principle, be utilised to emulate Euclid observations. In this work, we employ a GalSim-based testing environment to investigate whether uncertainties in the HST point spread function (PSF) model or in data processing techniques introduce significant biases in weak-lensing (WL) shear calibration. We used single S\'ersic galaxy models to simulate both HST and Euclid observations. We then `Euclidised' our HST simulations and compared the results with the directly simulated Euclid-like images. For this comparison, we utilised a moment-based shape measurement algorithm and galaxy model fits. Through the Euclidisation procedure, we effectively reduced the residual multiplicative biases in shear measurements to sub-percent levels. This achievement was made possible by employing either the native pixel scales of the instruments, utilising the Lanczos15 interpolation kernel, correcting for noise correlations, and ensuring consistent galaxy signal-to-noise ratios between simulation branches. However, the Euclidisation procedure requires further analysis on the impact of the correlated noise, to estimate calibration bias. Additionally, we conducted an in-depth analysis of the accuracy of TinyTim HST PSF models using star fields observed in the F606W and F814W filters. We observe that F606W images exhibit a broader scatter in the recovered best-fit focus, compared to those in the F814W filter., Comment: Accepted by A&A. 23 pages, 20 figures, Euclid pre-launch key paper

In energy markets, joint historical and implied calibration is of paramount importance for practitioners yet notoriously challenging due to the need to align historical correlations of futures contracts with implied volatility smiles from the option market. We address this crucial problem with a parsimonious multiplicative multi-factor Heath-Jarrow-Morton (HJM) model for forward curves, combined with a stochastic volatility factor coming from the Lifted Heston model. We develop a sequential fast calibration procedure leveraging the Kemna-Vorst approximation of futures contracts: (i) historical correlations and the Variance Swap (VS) volatility term structure are captured through Level, Slope, and Curvature factors, (ii) the VS volatility term structure can then be corrected for a perfect match via a fixed-point algorithm, (iii) implied volatility smiles are calibrated using Fourier-based techniques. Our model displays remarkable joint historical and implied calibration fits - to both German power and TTF gas markets - and enables realistic interpolation within the implied volatility hypercube.

The success of social media platforms has facilitated the emergence of various forms of online abuse within digital communities. This abuse manifests in multiple ways, including hate speech, cyberbullying, emotional abuse, grooming, and sexting. In this paper, we present a comprehensive analysis of the different forms of abuse prevalent in social media, with a particular focus on how emerging technologies, such as Language Models (LMs) and Large Language Models (LLMs), are reshaping both the detection and generation of abusive content within these networks. We delve into the mechanisms through which social media abuse is perpetuated, exploring the psychological and social impact. Additionally, we examine the dual role of advanced language models-highlighting their potential to enhance automated detection systems for abusive behavior while also acknowledging their capacity to generate harmful content. This paper aims to contribute to the ongoing discourse on online safety and ethics, offering insights into the evolving landscape of cyberabuse and the technological innovations that both mitigate and exacerbate it., Comment: 37 pages, under review in WIREs Data Mining and Knowledge Discovery

Vision-Language Models (VLMs) have recently experienced significant advancements. However, challenges persist in the accurate recognition of fine details within high resolution images, which limits performance in multiple tasks. This work introduces Pheye, a novel architecture that efficiently processes high-resolution images while training fewer parameters than similarly sized VLMs. Notably, Pheye achieves a high efficiency while maintaining strong performance, particularly in tasks that demand fine-grained image understanding and/or the handling of scene-text., Comment: Accepted to COLING 2025

The Doer Effect states that completing more active learning activities, like practice questions, is more strongly related to positive learning outcomes than passive learning activities, like reading, watching, or listening to course materials. Although broad, most evidence has emerged from practice with tutoring systems in Western, Industrialized, Rich, Educated, and Democratic (WEIRD) populations in North America and Europe. Does the Doer Effect generalize beyond WEIRD populations, where learners may practice in remote locales through different technologies? Through learning analytics, we provide evidence from N = 234 Ugandan students answering multiple-choice questions via phones and listening to lectures via community radio. Our findings support the hypothesis that active learning is more associated with learning outcomes than passive learning. We find this relationship is weaker for learners with higher prior educational attainment. Our findings motivate further study of the Doer Effect in diverse populations. We offer considerations for future research in designing and evaluating contextually relevant active and passive learning opportunities including leveraging familiar technology, increasing the number of practice opportunities, and aligning multiple data sources., Comment: Accepted to Learning Analytics and Knowledge (LAK 2025)

Model-based reinforcement learning aims to increase sample efficiency, but the accuracy of dynamics models and the resulting compounding errors are often seen as key limitations. This paper empirically investigates potential sample efficiency gains from improved dynamics models in model-based value expansion methods. Our study reveals two key findings when using oracle dynamics models to eliminate compounding errors. First, longer rollout horizons enhance sample efficiency, but the improvements quickly diminish with each additional expansion step. Second, increased model accuracy only marginally improves sample efficiency compared to learned models with identical horizons. These diminishing returns in sample efficiency are particularly noteworthy when compared to model-free value expansion methods. These model-free algorithms achieve comparable performance without the computational overhead. Our results suggest that the limitation of model-based value expansion methods cannot be attributed to model accuracy. Although higher accuracy is beneficial, even perfect models do not provide unrivaled sample efficiency. Therefore, the bottleneck exists elsewhere. These results challenge the common assumption that model accuracy is the primary constraint in model-based reinforcement learning., Comment: arXiv admin note: substantial text overlap with arXiv:2303.03955

We investigate the single-site von Neumann entropy along a harmonically confined superfluid chain, as described by the one-dimensional fermionic Hubbard model with strongly attractive interactions. We find that by increasing the confinement (or equivalently the particle filling) the system undergoes a quantum phase transition from a superfluid (SF) to a non-trivial topological insulator (TI) phase, which is characterized by an insulating bulk surrounded by highly entangled superfluid edges. These highly entangled states are found to be robust against perturbations and topologically protected by the particle-hole symmetry, which is locally preserved. We also find a semi-quantitative agreement between entanglement and superconducting order parameter profiles, confirming then that entanglement can be used as a topological marker and an order parameter in these systems. The charge gap not only confirms the SF-TI transition, but also shows that this transition is mediated by a metallic intermediate regime within the bulk. Using the gap we could depict a phase diagram for which the non-trivial topological insulator can be found in such harmonically confined attractive systems.

The performance of optimization-based robot motion planning algorithms is highly dependent on the initial solutions, commonly obtained by running a sampling-based planner to obtain a collision-free path. However, these methods can be slow in high-dimensional and complex scenes and produce non-smooth solutions. Given previously solved path-planning problems, it is highly desirable to learn their distribution and use it as a prior for new similar problems. Several works propose utilizing this prior to bootstrap the motion planning problem, either by sampling initial solutions from it, or using its distribution in a maximum-a-posterior formulation for trajectory optimization. In this work, we introduce Motion Planning Diffusion (MPD), an algorithm that learns trajectory distribution priors with diffusion models. These generative models have shown increasing success in encoding multimodal data and have desirable properties for gradient-based motion planning, such as cost guidance. Given a motion planning problem, we construct a cost function and sample from the posterior distribution using the learned prior combined with the cost function gradients during the denoising process. Instead of learning the prior on all trajectory waypoints, we propose learning a lower-dimensional representation of a trajectory using linear motion primitives, particularly B-spline curves. This parametrization guarantees that the generated trajectory is smooth, can be interpolated at higher frequencies, and needs fewer parameters than a dense waypoint representation. We demonstrate the results of our method ranging from simple 2D to more complex tasks using a 7-dof robot arm manipulator. In addition to learning from simulated data, we also use human demonstrations on a real-world pick-and-place task.

We study Schr\"odinger operators $H:= -\Delta + V$ with potentials $V$ that have power-law growth (not necessarily polynomial) at 0 and at $\infty$ using methods of Lie theory (Lie-Rinehart algebras) and microlocal analysis. More precisely, we show that $H$ is ''generated'' in a certain sense by an explicit Lie-Rinehart algebra. This allows then to construct a suitable (microlocal) calculus of pseudodifferential operators that provides further properties of $H$. Classically, this microlocal analysis method was used to study $H$ when the power-laws describing the potential $V$ have integer exponents. Thus, the main point of this paper is that this integrality condition on the exponents is not really necessary for the microlocal analysis method to work. While we consider potentials following (possibly non-integer) power-laws both at the origin and at infinity, our results extend right away to potentials having power-law singularities at several points. The extension of the classical microlocal analysis results to potentials with non-integer power-laws is achieved by considering the setting of Lie-Rinehart algebras and of the continuous family groupoids integrating them. (The classical case relies instead on Lie algebroids and Lie groupoids.)

As is widely known, the flavour and mass eigenbasis in neutrino sector can be related by a reduced unitary matrix, described by 3 real parameters and one phase for Dirac neutrinos, and 2 extra phases if neutrinos are Majorana particles. Neutrino flavour oscillation experiments are insensitive to Majorana phases, which leads to using the PMNS matrix with only one phase to describe flavour transitions. In this paper, we point out one feature of such choice that is commonly ignored: when neutrinos propagate in matter, the reduced unitary matrix PMNS does not properly diagonalize the neutrino system. We present some numerical calculations and analytical estimations to illustrate such statement, and discuss some scenarios where this effect can be probed., Comment: 8 pages, 2 figures

Modern network architectures have shaped market segments, governments, and communities with intelligent and pervasive applications. Ongoing digital transformation through technologies such as softwarization, network slicing, and AI drives this evolution, along with research into Beyond 5G (B5G) and 6G architectures. Network slices require seamless management, observability, and intelligent-native resource allocation, considering user satisfaction, cost efficiency, security, and energy. Slicing orchestration architectures have been extensively studied to accommodate these requirements, particularly in resource allocation for network slices. This study explored the observability of resource allocation regarding network slice performance in two nationwide testbeds. We examined their allocation effects on slicing connectivity latency using a partial factorial experimental method with Central Processing Unit (CPU) and memory combinations. The results reveal different resource impacts across the testbeds, indicating a non-uniform influence on the CPU and memory within the same network slice., Comment: Paper already published at Anais do XV Workshop de Pesquisa Experimental da Internet do Futuro (WPEIF)

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://gregorconsortium.org/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics.

In this work, we analyze the scattering of fermionic quasiparticles in the presence of radial disgyrations and symmetric vortices in the superfluid 3He-A. We consider a Volovik analog model for the description of these defects and investigate the scattering of fermionic quasiparticles in this background. Furthermore, we solve the massless Dirac equation employing this methodology to gain comprehensive insights into the scattering phenomena and its dependence on the geometric properties. These results validate the optical theorem and highlight the role of defect topology in the scattering process., Comment: 14 pages, 2 figures

Thanks to Integral Field Unit survey data it is possible to explore in detail the link between the formation of the stellar content in galaxies and the drivers of evolution. Traditionally, scaling relations have connected galaxy-wide parameters such as stellar mass (M$_s$), morphology or average velocity dispersion ($\sigma$) to the star formation histories (SFHs). We study a high quality sample of SDSS-MaNGA spectra to test the possibility that sub-galaxy ($\sim$2\,kpc) scales are dominant, instead of galaxy-wide parameters. We find a strong correlation between local velocity dispersion and key line strengths that depend on the SFHs, allowing us to make the ansatz that this indicator - that maps the local gravitational potential - is the major driver of star formation in galaxies, whereas larger scales play a role of a secondary nature. Galactocentric distance has a weaker correlation, suggesting that the observed radial gradients effectively reflect local variations of velocity dispersion. In our quest for a cause, instead of a correlation, we contrast $\sigma$ with local stellar mass, that appears less correlated with population properties. We conclude that the inherently higher uncertainty in M$_s$ may explain its lower correlation with respect to $\sigma$, but the extra uncertainty needed for $\sigma$ to have similar correlations as M$_s$ is rather high. Therefore we posit local velocity dispersion as the major driver of evolution, a result that should be reproduced by hydrodynamical models at the proper resolution., Comment: 14 pages, 8+2 figures. Submitted for publication in MNRAS. Comments welcome

Grasping objects successfully from a single-view camera is crucial in many robot manipulation tasks. An approach to solve this problem is to leverage simulation to create large datasets of pairs of objects and grasp poses, and then learn a conditional generative model that can be prompted quickly during deployment. However, the grasp pose data is highly multimodal since there are several ways to grasp an object. Hence, in this work, we learn a grasp generative model with diffusion models to sample candidate grasp poses given a partial point cloud of an object. A novel aspect of our method is to consider diffusion in the manifold space of rotations and to propose a collision-avoidance cost guidance to improve the grasp success rate during inference. To accelerate grasp sampling we use recent techniques from the diffusion literature to achieve faster inference times. We show in simulation and real-world experiments that our approach can grasp several objects from raw depth images with $90\%$ success rate and benchmark it against several baselines.

Morphological classification of galaxies becomes increasingly challenging with redshift. We apply a hybrid supervised-unsupervised method to classify $\sim 14,000$ galaxies in the CANDELS fields at $0.2 \leq z \leq 2.4$ into spheroid, disk, and irregular systems. Unlike previous works, our method is applied to redshift bins of width 0.2. Comparison between models applied to a wide redshift range versus bin-specific models reveals significant differences in galaxy morphology beyond $z \geq 1$ and a consistent $\sim 25\%$ disagreement. This suggests that using a single model across wide redshift ranges may introduce biases due to the large time intervals involved compared to galaxy evolution timescales. Using the FERENGI code to assess the impact of cosmological effects, we find that flux dimming and smaller angular scales may lead to the misclassification of up to $18\%$ of disk galaxies as spheroids or irregulars. Contrary to previous studies, we find an almost constant fraction of disks ($\sim 60\%$) and spheroids ($\sim 30\%$) across redshifts. We attribute discrepancies with earlier works, which suggest a decreasing fraction of disks beyond $z \sim 1$, to the biases introduced by visual classification. Our claim is further strengthened by the striking agreement to the results reported by Lee et al. (2024) using an objective, unsupervised method applied to James Webb Space Telescope data. Exploring mass dependence, we observe a $\sim 40\%$ increase in the fraction of massive ($M_{\rm stellar} \geq 10^{10.5}{\rm M}_{\odot}$) spheroids with decreasing redshift, well balanced with a decrease of $\sim 20\%$ in the fraction of $M_{\rm stellar} \geq 10^{10.5}{\rm M}_{\odot}$ disks, suggesting that merging massive disk galaxies may form spheroidal systems., Comment: 15 pages, 11 figures, 3 tables

Magnetic bimerons are potential information carriers in spintronic devices. Bimerons, topologically equivalent to skyrmions, manifest in chiral magnetic systems with in-plane magnetization due to anisotropies or external magnetic fields. Applications demanding their current-driven motion face significant challenges, notably the bimeron Hall effect, which causes transverse movement and annihilation at nanomagnet borders. This study addresses the problem of stabilizing bimeron propagation under current-driven conditions. We demonstrate that bimerons can propagate through thin ferromagnetic strips without annihilation when the easy-axis anisotropy and the electric current are orthogonal. Our findings indicate that below a threshold value of current, the repulsion between the bimeron and the strip boundary allows for stable soliton propagation, even in bent regions. This phenomenon extends to bimeron chains, which propagate parallel to the current flow. By enabling stable long-distance propagation, our results open new avenues for developing bimeron-based racetrack memory devices, enhancing the efficiency and reliability of future spintronic applications.

Surgical treatment for prostate cancer often involves organ removal, i.e., prostatectomy. Pathology reports on these specimens convey treatment-relevant information. Beyond these reports, the diagnostic process generates extensive and complex information that is difficult to represent in reports, although it is of significant interest to the other medical specialties involved. 3D tissue reconstruction would allow for better spatial visualization, as well as combinations with other imaging modalities. Existing approaches in this area have proven labor-intensive and challenging to integrate into clinical workflows. 3D-SLIVER provides a simplified solution, implemented as an open-source 3DSlicer extension. We outline three specific real-world scenarios to illustrate its potential to improve transparency in diagnostic workflows and contribute to multi-modal research endeavors. Implementing the 3D reconstruction process involved four sub-modules of 3D-SLIVER: digitization of slicing protocol, virtual slicing of arbitrary 3D models based on that protocol, registration of slides with virtual slices using the Coherent Point Drift algorithm, and 3D reconstruction of registered information using convex hulls, Gaussian splatter and linear extrusion. Three use cases to employ 3D-SLIVER are presented: a low-effort approach to pathology workflow integration and two research-related use cases illustrating how to perform retrospective evaluations of PI-RADS predictions and statistically model 3D distributions of morphological patterns. 3D-SLIVER allows for improved interdisciplinary communication among specialties. It is designed for simplicity in application, allowing for flexible integration into various workflows and use cases. Here we focused on the clinical care of prostate cancer patients, but future possibilities are extensive with other neoplasms and in education and research.

Cluster cosmology can benefit from combining multi-wavelength studies, which can benefit from characterising the correlation coefficients between different mass-observable relations. In this work, we aim to provide information on the scatter, the skewness, and the covariance of various mass-observable relations in galaxy clusters in cosmological hydrodynamic simulations. This information will help future analyses to better tackle accretion histories and projection effects and model mass observable relations for cosmology studies.We identify galaxy clusters in Magneticum Box2b simulations with mass $M_{\rm 200c}>10^{14} {\rm M}_\odot$ at redshift $z=0.24$ and $z=0.90$. Our analysis includes \Euclid-derived properties such as richness, stellar mass, lensing mass, and concentration. Additionally, we investigate complementary multi-wavelength data, including X-ray luminosity, integrated Compton-$y$ parameter, gas mass, and temperature. The impact of projection effects on mass-observable residuals and correlations is then examined. At intermediate redshift ($z=0.24$), projection effects impact lensing concentration, richness, and gas mass the most in terms of scatter and skewness of log-residuals of scaling relations. The contribution of projection effects can be significant enough to boost a spurious hot- vs. cold-baryons correlation and consequently hide underlying correlations due to halo accretion histories. At high redshift ($z=0.9$), the richness has a much lower scatter (of log-residuals), and the quantity that is most impacted by projection effects is the lensing mass. Lensing concentration reconstruction, in particular, is affected by deviations of the reduced-shear profile shape from the one derived by an NFW profile rather than interlopers in the line of sight., Comment: 26 pages, 22 figures, submitted to A&A, replied to referee comments

Batch planning is increasingly necessary to quickly produce diverse and high-quality motion plans for downstream learning applications, such as distillation and imitation learning. This paper presents Global Tensor Motion Planning (GTMP) -- a sampling-based motion planning algorithm comprising only tensor operations. We introduce a novel discretization structure represented as a random multipartite graph, enabling efficient vectorized sampling, collision checking, and search. We provide a theoretical investigation showing that GTMP exhibits probabilistic completeness while supporting modern GPU/TPU. Additionally, by incorporating smooth structures into the multipartite graph, GTMP directly plans smooth splines without requiring gradient-based optimization. Experiments on lidar-scanned occupancy maps and the MotionBenchMarker dataset demonstrate GTMP's computation efficiency in batch planning compared to baselines, underscoring GTMP's potential as a robust, scalable planner for diverse applications and large-scale robot learning tasks., Comment: 8 pages, 4 figures

This work explores the use of Role Playing Games (RPG) as an active methodology in teaching Modern Physics, focusing on a game called Newton's Revenge. The game was developed with the aim of engaging students in collaborative and investigative learning processes, using gamification elements to increase motivation and involvement. Based on the constructivist theories of Piaget and Vygotsky, the RPG stimu-\newline lates cognitive and social development by placing students in the roles of historical science figures. Through contextualized physical challenges, such as understanding the Photoelectric Effect, participants actively construct knowledge. This study presents preliminary learning data obtained through pre- and post-tests, as well as evaluates students' perceptions of using educational games in science education. The results indicate that the use of RPG can be an effective tool for teaching Modern Physics, promoting greater engagement and understanding of scientific concepts., Comment: This article aims to detail and contextualize part of one of the authors' presentations at the 20th Matogrossense School of Physics, held in October 2024. In Portuguese. Accepted in "Jornal Mato-Grossense de F\'isica"

Fog Computing provides computational resources close to the end user, supporting low-latency and high-bandwidth communications. It supports IoT applications, enabling real-time data processing, analytics, and decision-making at the edge of the network. However, the high distribution of its constituent nodes and resource-restricted devices interconnected by heterogeneous and unreliable networks makes it challenging to execute service maintenance and troubleshooting, increasing the time to restore the application after failures and not guaranteeing the service level agreements. In such a scenario, increasing the observability of Fog applications and services may speed up troubleshooting and increase their availability. An observability system is a data-intensive service, and Fog Computing could have its nodes and channels saturated with an additional load. In this work, we detail the three pillars of observability (metrics, log, and traces), discuss the challenges, and clarify the approaches for increasing the observability of services in Fog environments. Furthermore, the system architecture that supports observability in Fog, related tools, and technologies are presented, providing a comprehensive discussion on this subject. An example of a solution shows how a real-world application can benefit from increased observability in this environment. Finally, there is a discussion about the future directions of Fog observability.

Metal organic frameworks (MOFs) are nanoporous materials with high surface-to-volume ratio that have potential applications as gas sorbents. Sample quality is, however, often compromised and it is unclear how defects and surface contaminants affect the spectral properties of single MOF crystals. Raman micro-spectroscopy is a powerful tool for characterizing MOFs, yet spatial spectral heterogeneity distributions of single MOF crystals have not been reported so far. In this work, we use Raman micro-spectroscopy to characterize spatially isolated, single crystals of the MOF species HKUST-1. In a first step, we validate HKUST-1's Raman spectrum based on DFT simulations and we identify a previously unreported vibrational feature. In a second step, we acquire diffraction-limited, mode-selective Raman images of a single HKUST-1 crystals that reveal how the spectral variations are distributed across the crystal surface. In a third step, we statistically analyze the measured spectral peak positions and line widths for quantifying the variability occurring within the same crystal as well as between different crystals taken from the same batch. Finally, we explore how multivariate data analysis can aid feature identification in Raman images of single MOF crystals. For enabling validation and reuse, we have made the spectroscopic data and simulation code publicly available.

In face of climate change and increasing urbanization, the predictive mosquito-borne diseases (MBD) transmission models require constant updates. Thus, is urgent to comprehend the driving forces of this non stationary behavior, observed through spatial and incidence expansion. We observed that temperature is a critical driver in predictive models for MBD transmission, also being consistently used in multiple reviewed papers with considerable incidence predictive capacity. Rainfall, however, have more subtle importance as moderate precipitation creates breeding sites for mosquitoes, but excessive rainfall can reduce larvae populations. We highlight the frequent use of mechanistic models, particularly those that integrate temperature-dependent biological parameters of disease transmission in incidence proxies as the Vectorial Capacity (VC) and temperature-based basic reproduction number $R_0(t)$, for example. These models show the importance of climate variables, but the socio-demographic factors are often not considered. This gap is a significant opportunity for future research to incorporate socio-demographic data into long-term predictive models for more comprehensive and reliable forecasts. With this survey, we outline the most promising paths to be followed by long-term MBD transmission research and highlighting the potential facing challenges. Thus, we offer a valuable foundation for enhancing disease forecasting models and supporting more effective public health interventions, specially in the long term.

When several intruders move in a granular medium, coupling effects are observed, the motion of one intruder affecting that of others. In this paper, we investigate experimentally how the drag forces acting on a pair of spherical intruders moving amid grains at constant velocity vary with the transverse separation between them and their depth. When intruders are sufficiently far apart, they do not influence each other, and the average drag felt by each of them matches that of a single intruder. However, for small distances between intruders and at a given depth, the average drag per intruder decreases, highlighting a collaborative effect that facilitates motion. This collaboration effect is amplified when the depth of the intruders increases. We propose a model for the drag reduction of a pair of intruders based on the breakup of contact chains, caused by the perturbation generated by the neighbor intruder. Our findings provide new insights into the interaction effects on the motion of solids in sand, such as those observed in animal locomotion, root growth, and soil survey.

The use of 3D modelling in medical education is a revolutionary tool during the learning process. In fact, this type of technology enables a more interactive teaching approach, making information retention more effective and enhancing students' understanding. 3D modelling allows for the creation of precise representations of the human body, as well as interaction with three-dimensional models, giving students a better spatial understanding of the different organs and systems and enabling simulations of surgical and technical procedures. This way, medical education is enriched with a more realistic and safe educational experience. The goal is to understand whether, when students and schools are challenged, they play an important role in addressing health issues in their community. School-led projects are directed towards educational scenarios that emphasize STEM education, tackling relevant public health problems through open-school initiatives. By implementing an educational scenario focused on 3D modelling and leveraging technology, we aim to raise community awareness on public health issues.

Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to the traditional lithium-ion batteries with liquid electrolytes. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. Recently, universal interatomic potential models have been developed using equivariant graph neural networks. These models are trained on extensive datasets of first-principles force and energy calculations. One can achieve significant computational advantages by leveraging them as the foundation for traditional methods of assessing the ionic conductivity, such as molecular dynamics or nudged elastic band techniques. However, the generalization error from model inference on diverse atomic structures arising in such calculations can compromise the reliability of the results. In this work, we propose an approach for the quick and reliable evaluation of ionic conductivity through the analysis of a universal interatomic potential. Our method incorporates a set of heuristic structure descriptors that effectively employ the rich knowledge of the underlying model while requiring minimal generalization capabilities. Using our descriptors, we rank lithium-containing materials in the Materials Project database according to their expected ionic conductivity. Eight out of the ten highest-ranked materials are confirmed to be superionic at room temperature in first-principles calculations. Notably, our method achieves a speed-up factor of approximately 50 compared to molecular dynamics driven by a machine-learning potential, and is at least 3,000 times faster compared to first-principles molecular dynamics.

Quantum mechanics started out as a theory to describe the smallest scales of energy in Nature. After hundred years of development it is now routinely employed to describe, for example, quantum computers with thousands of qubits. This tremendous progress turns the debate of foundational questions into a technological imperative. In what follows we introduce a model of a quantum measurement process that consistently includes the impact of having access only to finite resources when describing a macroscopic system, like a measurement apparatus. Leveraging modern tools from equilibration of closed systems and typicality, we show how the collapse can be seen as an effective description of a closed dynamics, of which we do not know all its details. Our model is then exploited to address the ``Wigner Friend Scenario'', and we observe that an agreement is reached when both Wigner and his friend acknowledge their finite resources perspective and describe the measurement process accordingly., Comment: 7+2 pages, 2 figures. Comments are welcome

We studied the characteristics, regions of existence and stability of different types of solitons for a distributed model of a mode-locked laser whose dispersion is purely quartic and normal. Among the different types of solitons, we identified three main branches that are named according to their different amplitude: low, medium and high amplitude solitons. It was found that the first solitons are always unstable while the latter two exist and are stable in relatively large regions of the parameter space. Moreover, the stability regions of medium and high amplitude solitons overlap over a certain range of parameters, manifesting effects of bistability. The energy of high amplitude solitons increases quadratically with their width, whereas the energy of medium amplitude solitons may decrease or increase with the width depending on the parameter region. Furthermore, we have investigated the long term evolution of the continuous wave solutions under modulational instability, showing that medium amplitude solitons can arise in this scenario. Additionally, we assessed the effects of second and third order dispersion on medium and high amplitude solitons and found that both remain stable in the presence of these terms.

In the present work, we consider existence and multiplicity of positive solutions for nonlocal elliptic problems driven by the Stein-Weiss problem with concave-convex nonlinearities defined in the whole space $\mathbb{R}^N$. More precisely, we consider the following nonlocal elliptic problem: \begin{equation*} - \Delta u + V(x)u = \lambda a(x) |u|^{q-2} u + \displaystyle \int \limits_{\mathbb{R}^N}\frac{b(y)\vert u(y) \vert^p dy}{\vert x\vert^\alpha\vert x-y\vert^\mu \vert y\vert^\alpha} b(x)\vert u\vert^{p-2}u, \,\, \hbox{in}\ \mathbb{R}^N, \,\, u\in H^1(\mathbb{R}^N), \end{equation*} where $\lambda >0, \alpha \in (0,N), N\geq3, 0<\mu 0$ in $\mathbb{R}^N$ and $b\in{L}^{t}(\mathbb{R}^N), b>0$ in $\mathbb{R}^N$ for some specific $r, t > 1$. We assume also that $1\leq q<2$ and $2_{\alpha,\mu} < p<2_{\alpha,\mu}^*$ where $2_{\alpha ,\mu}=(2N-2\alpha-\mu)/N$ and $2_{\alpha,\mu}^*= (2N-2\alpha-\mu)/(N-2)$. Our main contribution is to find the largest $\lambda^* > 0$ in such way that our main problem admits at least two positive solutions for each $\lambda \in (0, \lambda^*)$. In order to do that we apply the nonlinear Rayleigh quotient together with the Nehari method. Moreover, we prove a Brezis-Lieb type Lemma and a regularity result taking into account our setting due to the potentials $a, b : \mathbb{R}^N \to \mathbb{R}$., Comment: In the present work, we consider existence and multiplicity of positive solutions for nonlocal elliptic problems driven by the Stein-Weiss problem with concave-convex nonlinearities defined in the whole space $\mathbb{R}^N$

The synchronization of motor responses to rhythmic auditory cues is a fundamental biological phenomenon observed across various species. While the importance of temporal alignment varies across different contexts, achieving precise temporal synchronization is a prominent goal in musical performances. Musicians often incorporate expressive timing variations, which require precise control over timing and synchronization, particularly in ensemble performance. This is crucial because both deliberate expressive nuances and accidental timing deviations can affect the overall timing of a performance. This discussion prompts the question of how musicians adjust their temporal dynamics to achieve synchronization within an ensemble. This paper introduces a novel feedback correction model based on the Kalman Filter, aimed at improving the understanding of interpersonal timing in ensemble music performances. The proposed model performs similarly to other linear correction models in the literature, with the advantage of low computational cost and good performance even in scenarios where the underlying tempo varies., Comment: 7 pages, 1 figure. Accepted for publication on the 25th International Society for Music Information Retrieval (ISMIR 2024)

Automated knowledge extraction from scientific literature can potentially accelerate materials discovery. We have investigated an approach for extracting synthesis protocols for reticular materials from scientific literature using large language models (LLMs). To that end, we introduce a Knowledge Extraction Pipeline (KEP) that automatizes LLM-assisted paragraph classification and information extraction. By applying prompt engineering with in-context learning (ICL) to a set of open-source LLMs, we demonstrate that LLMs can retrieve chemical information from PDF documents, without the need for fine-tuning or training and at a reduced risk of hallucination. By comparing the performance of five open-source families of LLMs in both paragraph classification and information extraction tasks, we observe excellent model performance even if only few example paragraphs are included in the ICL prompts. The results show the potential of the KEP approach for reducing human annotations and data curation efforts in automated scientific knowledge extraction., Comment: 16 pages

Segment Anything Model (SAM), a new AI model from Meta AI released in April 2023, is an ambitious tool designed to identify and separate individual objects within a given image through semantic interpretation. The advanced capabilities of SAM are the result of its training with millions of images and masks, and a few days after its release, several researchers began testing the model on medical images to evaluate its performance in this domain. With this perspective in focus -- i.e., optimizing work in the healthcare field -- this work proposes the use of this new technology to evaluate and study chest X-ray images. The approach adopted for this work, with the aim of improving the model's performance for lung segmentation, involved a transfer learning process, specifically the fine-tuning technique. After applying this adjustment, a substantial improvement was observed in the evaluation metrics used to assess SAM's performance compared to the masks provided by the datasets. The results obtained by the model after the adjustments were satisfactory and similar to cutting-edge neural networks, such as U-Net.

HIV is known for causing the destruction of the immune system by affecting different types of cells, while SARS-CoV-2 is an extremely contagious virus that leads to the development of COVID-19. In this study, we propose a mathematical model to investigate the interaction between HIV and SARS-CoV-2 under highly active antiretroviral therapy (HAART). We determine the conditions for the endemic equilibria of both viruses, showing that transcritical bifurcations occur when the basic reproduction numbers of HIV and SARS-CoV-2 pass through 1. We set the condition for the stability of the disease-free equilibrium point of the model with coinfection as a function of the basic reproduction number $\mathcal{R}_0$. Through numerical simulations, we conclude that HAART, used to control HIV, also reduces the proliferation of SARS-CoV-2-infected cells in coinfected hosts. These findings provide important insights into the epidemiological dynamics of HIV and SARS-CoV-2 coinfection.