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We present a novel statistical algorithm, Stellar Ages, which currently infers the age, metallicity, and extinction posterior distributions of stellar populations from their magnitudes. While this paper focuses on these parameters, the framework is readily adaptable to include additional properties, such as rotation, in future work. Historical age-dating techniques either model individual stars or populations of stars, often sacrificing population context or precision for individual estimates. Stellar Ages does both, combining the strengths of these approaches to provide precise individual ages for stars while leveraging population-level constraints. We verify the algorithm's capabilities by determining the age of synthetic stellar populations and actual stellar populations surrounding a nearby supernova, SN 2004dj. In addition to inferring an age, we infer a progenitor mass consistent with direct observations of the precursor star. The median age inferred from the brightest nearby stars is $\log_{10}$(Age/yr) = $7.19^{+0.10}_{-0.13}$, and its corresponding progenitor mass is $13.95^{+3.33}_{-1.96}$ $\text{M}_{\odot}$., Comment: 15 pages, 12 figures, figures 7 and 12 are most important

High-mass prestellar cores are extremely rare. The search for such objects has long been hindered by small sample sizes, leading to large uncertainties in their lifetimes and the conditions in which high-mass stars ($> 8\,M_{\odot}$) form. We leverage the large sample ($\sim 580$ cores) detected in the ALMA-IMF survey to identify both protostellar and prestellar cores and estimate their relative lifetimes. We use CO and SiO outflows to identify protostellar cores and introduce a new automated method based on aperture line emission and background subtraction to systematically detect outflows associated with each of the 141 most massive cores. Massive cores that do not drive an outflow in either tracer are classified as prestellar. Our method enables efficient outflow detection with performance comparable to more traditional techniques. We identify 30 likely prestellar cores with $M > 8\,M_{\odot}$, including 12 with $M > 16\,M_{\odot}$, the best candidates for high-mass star precursors. Most of these 12 cores reside in the crowded central regions of protoclusters, where high-mass stars are expected to form. Using prestellar-to-protostellar core ratios and a 300 kyr protostellar lifetime, we estimate prestellar lifetimes of 120 to 240 kyr for $8\,M_{\odot} < M < 16\,M_{\odot}$ and 50 to 100 kyr for $30\,M_{\odot} < M < 55\,M_{\odot}$. These timescales, which depend on different mass reservoir evolution scenarios, significantly exceed the 4 to 15 kyr free-fall time of the cores, suggesting that high-mass cores persist for 10 to 30 free-fall times. This indicates that collapse is slowed by turbulence, magnetic fields, or rotation at or below the observed scale.

The strong coupling between the spatial and polarisation degrees of freedom (DoF) in vector modes enables a diverse array of exotic, inhomogeneous polarisation distributions through a non-separable superposition, which are conventionally generated in circular-cylindrical symmetry. Here, we theoretically and experimentally demonstrate a generalized class of vector modes specified in elliptical spatial coordinates and elliptical polarisation. This generalisation gives rise to an even larger set of vector beams with more intricate polarisation distributions. Crucially, controlling the beam parameters allows engineering of vector beams with predefined polarisation trajectories on the Poincar\'e sphere. This capability offers potential applications, for example in optical communications, where precise polarisation control can significantly enhance data transmission and security.

Let $Y$ be a simple $3$-manifold, and let $A$ be a finitely generated, freely indecomposable subgroup of $\pi_1(Y)$. Set $\eta=\dim H_1(A;{\bf F}_2)$. Suppose that either (a) $\partial Y\ne\emptyset$ or (b) $\dim H_1(Y;{\bf F}_2)\ge3\eta^2-4\eta+4$. Under these hypotheses, we show that $A$ is carried by some compact, connected three-dimensional submanifold $Z$ of $\text{int} \;Y$ such that (1) $\partial Z$ is non-empty, and each of its components is incompressible in $Y$; (2) the Euler characteristic of $Z$ is bounded below by $1-\eta$; and (3) $\dim H_1(Z;{\bf F}_2)\le 3\eta^2-4\eta+1$. The conclusion implies that any boundary component of $Z$ is an incompressible surface of genus at most $\eta$. In Case (b), this should be compared with earlier results proved by Agol-Culler-Shalen and Culler-Shalen, which provide a surface of genus at most $\eta$ under weaker hypotheses (the lower bound on $\dim H_1(Y; {\bf F}_2)$ being linear in $\eta$ rather than quadratic), but do not give any relationship between the given subgroup $A$ and this surface. In a forthcoming paper we will apply the result to give a new upper bound for the ratio of the rank of the mod 2 homology of a closed, orientable hyperbolic $3$-manifold to the volume of the manifold., Comment: 83 pages

Recent discoveries of transiting giant exoplanets around M dwarfs (GEMS) present an opportunity to investigate their atmospheric compositions and explore how such massive planets can form around low-mass stars contrary to canonical formation models. Here, we present the first transmission spectra of TOI-5205b, a short-period ($P=1.63~\mathrm{days}$) Jupiter-like planet ($M_p=1.08~\mathrm{M_J}$ and $R_p=0.94~\mathrm{R_J}$) orbiting an M4 dwarf. We obtained three transits using the PRISM mode of the JWST Near Infrared Spectrograph (NIRSpec) spanning $0.6-5.3$ um. Our data reveal significant stellar contamination that is evident in the light curves as spot-crossing events and in the transmission spectra as a larger transit depth at bluer wavelengths. Atmospheric retrievals demonstrate that stellar contamination from unocculted star spots is the dominant component of the transmission spectrum at wavelengths $\lambda\lesssim3.0$ um, which reduced the sensitivity to the presence of clouds or hazes in our models. The degree of stellar contamination also prevented the definitive detection of any $\mathrm{H_2O}$, which has primary absorption features at these shorter wavelengths. The broad wavelength coverage of NIRSpec PRISM enabled a robust detection of $\mathrm{CH_4}$ and $\mathrm{H_2S}$, which have detectable molecular features between $3.0-5.0$ um. Our gridded and Bayesian retrievals consistently favored an atmosphere with both sub-solar metallicity ($\log\mathrm{[M/H]}\sim-2$ for a clear atmosphere) and super-solar C/O ratio ($\log\mathrm{[C/O]}\sim3$ for a clear or cloudy atmosphere). This contrasts with estimates from planetary interior models that predict a bulk metallicity of 10--20%, which is $\sim100\times$ the atmospheric metallicity, and suggests that the planetary interior for TOI-5205b is decoupled from its atmosphere and not well mixed., Comment: 27 pages + appendix, includes figure sets that will appear in journal, submitted to AAS. Comments welcome

The emergence and stability of static hexadecapole deformations as well as the impact in the development of dynamic deformation due to collective motion considering quadrupole-hexadecapole coupling are studied for a selected set of radium, thorium, uranium and plutonium isotopes, using the Gogny Hartree-Fock-Bogoliubov and Generator Coordinate Method frameworks. Sizable hexadecapole deformations are found to play a significant role in the ground and excited states of nuclei in the neighborhood of $^{238}$U. For each of the studied isotopic chains, it is shown that a region with small negative hexadecapole deformation, just below the neutron magic number $N =184$, remains stable once zero-point quadrupole-hexadecapole fluctuations are taken into account. A transition is predicted, with increasing mass number, from a regime in which the quadrupole and hexadecapole degrees of freedom are interwoven to a regime in which they are decoupled, accompanied by an enhanced shape coexistence in the more neutron-rich sectors of the isotopic chains. It is also shown, that quadrupole-hexadecapole configuration mixing brings a nontrivial additional correlation energy gain comparable to the quadrupole correlation energy itself., Comment: 14 pages, 8 figures

We construct projections onto the classical finite element spaces based on Lagrange, N\'ed\'elec, Raviart--Thomas, and discontinuous elements on shape-regular simplicial meshes. Our projections are defined locally, are bounded in the L2-norm, and commute with the corresponding differential operators. The cornerstone of the construction are local weight functions which are piecewise polynomials built using the Alfeld split of local patches from the original simplicial mesh. This way, the L2-stability of the projections is established by invoking discrete Poincar\'e inequalities on these local stars, for which we provide constructive proofs. We also show how to modify the construction to preserve homogeneous boundary conditions. The material is presented using the language of vector calculus, and links to the formalism of finite element exterior calculus are provided.

In this work a method for using accelerometers for the determination of angular velocity and acceleration is presented. Minimum sensor requirements and insights into how an array of accelerometers can be configured to maximize estimator performance are considered. The framework presented utilizes linear least squares to estimate functions that are quadratic in angular velocity. Simple methods for determining the sign of the spin axis and the linearized covariance approximation are presented and found to perform quite effectively when compared to results obtained by Monte Carlo., Comment: Attitude estimation paper, 10 pages

Higher-order networks are used to model complex contagion processes in social groups of varying sizes, where heterogeneity and microscopic group arrangements can critically influence the dynamics. However, existing frameworks fail to fully capture the interplay between these features. Here, we introduce group-based compartmental modeling (GBCM), a mean-field framework for irreversible contagion that incorporates heterogeneity and captures correlations across group sizes. Validated through numerical simulations, GBCM analytically disentangles the contributions of different interaction orders to global epidemic dynamics. Our results reveal how heterogeneity and inter-order correlations shape epidemic thresholds and demonstrate that high heterogeneity in group membership drives rapid infection growth, leading to abrupt phase transitions. This provides an explanation for the emergence of explosive contagion in higher-order networks.

One of the primary objectives of this paper is to establish a generalized Cauchy-Kovalevskaya extension for axially harmonic functions. We demonstrate that the result can be expressed as a power series involving Bessel-type functions of specific differential operators acting on two initial functions. Additionally, we analyze the decomposition of the harmonic CK extension in terms of integrals over the sphere $ \mathbb{S}^{m-1} $ involving functions of plane wave type. Another key goal of this paper is to explore the relationship between the harmonic Cauchy-Kovalevskaya extension and the Fueter-Sce theorem. The Fueter-Sce theorem outlines a two-step process for constructing axially monogenic functions in $ \mathbb{R}^{m+1}$ starting from holomorphic functions in one complex variable. The first step generates the class of slice monogenic functions, while the second step produces axially monogenic functions by applying the pointwise differential operator $ \Delta_{\mathbb{R}{^{m+1}}}^{\frac{m-1}{2}} $ with $m$ being odd, known as the Fueter-Sce map, to a slice monogenic function. By suitably factorizing the Fueter-Sce map, we introduce the set of axially harmonic functions, which serves as an intermediate class between slice monogenic and axially monogenic functions. In this paper, we establish a connection between the harmonic CK extension and the factorization of the Fueter-Sce map. This connection leads to a new notion of harmonic polynomials, which we show to form a basis for the Riesz potential. Finally, we also construct a basis for the space of axially harmonic functions.

This study investigates teamwork dynamics in student software development projects through a mixed-method approach combining quantitative analysis of GitLab commit logs and qualitative survey data. We analyzed individual contributions across six project phases, comparing self-reported and actual contributions to measure discrepancies. Additionally, a survey captured insights on team leadership, conflict resolution, communication practices, and workload perceptions. Findings reveal that teams with minimal contribution discrepancies achieved higher project grades and exam pass rates. In contrast, teams with more significant discrepancies experienced lower performance, potentially due to role clarity and communication issues. These results underscore the value of shared leadership, structured conflict resolution, and regular feedback in fostering effective teamwork, offering educators strategies to enhance collaboration in software engineering education through self-reflection and balanced workload allocation., Comment: Paper accepted in The 16th IEEE Global Engineering Education Conference (IEEE EDUCON 2025)

The effective design of patrol strategies is a difficult and complex problem, especially in medium and large areas. The objective is to plan, in a coordinated manner, the optimal routes for a set of patrols in a given area, in order to achieve maximum coverage of the area, while also trying to minimize the number of patrols. In this paper, we propose a multi-agent reinforcement learning (MARL) model, based on a decentralized partially observable Markov decision process, to plan unpredictable patrol routes within an urban environment represented as an undirected graph. The model attempts to maximize a target function that characterizes the environment within a given time frame. Our model has been tested to optimize police patrol routes in three medium-sized districts of the city of Malaga. The aim was to maximize surveillance coverage of the most crime-prone areas, based on actual crime data in the city. To address this problem, several MARL algorithms have been studied, and among these the Value Decomposition Proximal Policy Optimization (VDPPO) algorithm exhibited the best performance. We also introduce a novel metric, the coverage index, for the evaluation of the coverage performance of the routes generated by our model. This metric is inspired by the predictive accuracy index (PAI), which is commonly used in criminology to detect hotspots. Using this metric, we have evaluated the model under various scenarios in which the number of agents (or patrols), their starting positions, and the level of information they can observe in the environment have been modified. Results show that the coordinated routes generated by our model achieve a coverage of more than $90\%$ of the $3\%$ of graph nodes with the highest crime incidence, and $65\%$ for $20\%$ of these nodes; $3\%$ and $20\%$ represent the coverage standards for police resource allocation.

Crime is one of the greatest threats to urban security. Around 80 percent of the world's population lives in countries with high levels of criminality. Most of the crimes committed in the cities take place in their urban environments. This paper presents the development and validation of a digital shadow platform for modeling and simulating urban crime. This digital shadow has been constructed using data-driven agent-based modeling and simulation techniques, which are suitable for capturing dynamic interactions among individuals and with their environment. Our approach transforms and integrates well-known criminological theories and the expert knowledge of law enforcement agencies (LEA), policy makers, and other stakeholders under a theoretical model, which is in turn combined with real crime, spatial (cartographic) and socio-economic data into an urban model characterizing the daily behavior of citizens. The digital shadow has also been instantiated for the city of Malaga, for which we had over 300,000 complaints available. This instance has been calibrated with those complaints and other geographic and socio-economic information of the city. To the best of our knowledge, our digital shadow is the first for large urban areas that has been calibrated with a large dataset of real crime reports and with an accurate representation of the urban environment. The performance indicators of the model after being calibrated, in terms of the metrics widely used in predictive policing, suggest that our simulated crime generation matches the general pattern of crime in the city according to historical data. Our digital shadow platform could be an interesting tool for modeling and predicting criminal behavior in an urban environment on a daily basis and, thus, a useful tool for policy makers, criminologists, sociologists, LEAs, etc. to study and prevent urban crime.

We study the mixing time of the projected Langevin algorithm (LA) and the privacy curve of noisy Stochastic Gradient Descent (SGD), beyond nonexpansive iterations. Specifically, we derive new mixing time bounds for the projected LA which are, in some important cases, dimension-free and poly-logarithmic on the accuracy, closely matching the existing results in the smooth convex case. Additionally, we establish new upper bounds for the privacy curve of the subsampled noisy SGD algorithm. These bounds show a crucial dependency on the regularity of gradients, and are useful for a wide range of convex losses beyond the smooth case. Our analysis relies on a suitable extension of the Privacy Amplification by Iteration (PABI) framework (Feldman et al., 2018; Altschuler and Talwar, 2022, 2023) to noisy iterations whose gradient map is not necessarily nonexpansive. This extension is achieved by designing an optimization problem which accounts for the best possible R\'enyi divergence bound obtained by an application of PABI, where the tractability of the problem is crucially related to the modulus of continuity of the associated gradient mapping. We show that, in several interesting cases -- including the nonsmooth convex, weakly smooth and (strongly) dissipative -- such optimization problem can be solved exactly and explicitly. This yields the tightest possible PABI-based bounds, where our results are either new or substantially sharper than those in previous works., Comment: 40 pages, 2 figures

Charged black holes are known to suffer from an interior instability associated with the presence of the Cauchy horizon. Recently, a hairy charged black hole was proposed that avoids the formation of a Cauchy horizon. It is natural to question whether this instability might manifest in the exterior solution. In this paper, we have analyzed the stability of this black hole. Our results show that vector perturbations are stable, along with the scalar sector for $l=0$. We have also computed the corresponding quasinormal modes (QNMs) and quasibound states (QBSs). Among the six degrees of freedom identified, one exhibits a ghost-like behavior. However, this mode decouples from the other perturbations, rendering the associated instability irrelevant in the linear regime. Nonetheless, this feature could indicate a more significant issue in the nonlinear regime., Comment: 14 pages, 8 figures

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

The unpredictable nature of outdoor settings introduces numerous safety concerns, making hazard detection crucial for safe navigation. This paper introduces a novel system for sidewalk safety navigation utilizing a hybrid approach that combines a Variational Autoencoder (VAE) with a One-Class Support Vector Machine (OCSVM). The system is designed to detect anomalies on sidewalks that could potentially pose walking hazards. A dataset comprising over 15,000 training frames and 5,000 testing frames was collected using video recordings, capturing various sidewalk scenarios, including normal and hazardous conditions. During deployment, the VAE utilizes its reconstruction mechanism to detect anomalies within a frame. Poor reconstruction by the VAE implies the presence of an anomaly, after which the OCSVM is used to confirm whether the anomaly is hazardous or non-hazardous. The proposed VAE model demonstrated strong performance, with a high Area Under the Curve (AUC) of 0.94, effectively distinguishing anomalies that could be potential hazards. The OCSVM is employed to reduce the detection of false hazard anomalies, such as manhole or water valve covers. This approach achieves an accuracy of 91.4%, providing a highly reliable system for distinguishing between hazardous and non-hazardous scenarios. These results suggest that the proposed system offers a robust solution for hazard detection in uncertain environments., Comment: 7 pages

In physical networks trained using supervised learning, physical parameters are adjusted to produce desired responses to inputs. An example is electrical contrastive local learning networks of nodes connected by edges that are resistors that adjust their conductances during training. When an edge conductance changes, it upsets the current balance of every node. In response, physics adjusts the node voltages to minimize the dissipated power. Learning in these systems is therefore a coupled double-optimization process, in which the network descends both a cost landscape in the high-dimensional space of edge conductances, and a physical landscape -- the power -- in the high-dimensional space of node voltages. Because of this coupling, the physical landscape of a trained network contains information about the learned task. Here we demonstrate that all the physical information relevant to the trained input-output relation can be captured by a susceptibility, an experimentally measurable quantity. We supplement our theoretical results with simulations to show that the susceptibility is positively correlated with functional importance and that we can extract physical insight into how the system performs the task from the conductances of highly susceptible edges., Comment: 14 pages, 12 figures

What is appropriateness? Humans navigate a multi-scale mosaic of interlocking notions of what is appropriate for different situations. We act one way with our friends, another with our family, and yet another in the office. Likewise for AI, appropriate behavior for a comedy-writing assistant is not the same as appropriate behavior for a customer-service representative. What determines which actions are appropriate in which contexts? And what causes these standards to change over time? Since all judgments of AI appropriateness are ultimately made by humans, we need to understand how appropriateness guides human decision making in order to properly evaluate AI decision making and improve it. This paper presents a theory of appropriateness: how it functions in human society, how it may be implemented in the brain, and what it means for responsible deployment of generative AI technology., Comment: 115 pages, 2 figures

Antiferromagnets are promising candidates for ultrafast spintronic applications, leveraging current-induced spin-orbit torques. However, experimentally distinguishing between different switching mechanisms of the staggered magnetization (N\'eel vector) driven by current pulses remains a challenge. In an exemplary study of the collinear antiferromagnetic compound Mn$_2$Au, we demonstrate that slower thermomagnetoelastic effects predominantly govern switching over a wide parameter range. In the regime of short current pulses in the nanosecond range, however, we observe fully N\'eel spin-orbit torque driven switching. We show that this ultrafast mechanism enables the complete directional alignment of the N\'eel vector by current pulses in device structures., Comment: 8 pages, 6 figures

The NAHU$^2$ project is a Franco-Mexican collaboration aimed at building the $\pi$-YALLI corpus adapted to machine learning, which will subsequently be used to develop computer resources for the Nahuatl language. Nahuatl is a language with few computational resources, even though it is a living language spoken by around 2 million people. We have decided to build $\pi$-YALLI, a corpus that will enable to carry out research on Nahuatl in order to develop Language Models (LM), whether dynamic or not, which will make it possible to in turn enable the development of Natural Language Processing (NLP) tools such as: a) a grapheme unifier, b) a word segmenter, c) a POS grammatical analyser, d) a content-based Automatic Text Summarization; and possibly, e) a translator translator (probabilistic or learning-based)., Comment: 9 pages, in French language, 2 figures

We simulate the formation of Fuzzy Dark Matter (FDM) cores in the presence of a Black Hole (BH) to explore whether BHs can serve as seeds for FDM core condensation. Our analysis is based on the core-condensation via the kinetic relaxation process for random initial conditions of the FDM. We show that in general the BH merges with pre-collapsed mini-clusters and once they share location the BH oscillates within the core. The condensation takes place around the black hole and the FDM acquires a density profile consistent with the density of the stationary solution of the FDM+BH eigenvalue problem in average. The central density of the resulting core depends on the mass of the BH, which due to its motion relative to the FDM cloud produces a smaller time-averaged densities for bigger BH masses, which lead to a new diversity of central FDM core densities. Our results indicate that BHs can indeed act as focal points for FDM core condensation. As a collateral result, for our analysis we revised the construction of stationary solutions of FDM+BH and found a phenomenological formula for the FDM density that can be used to fit FDM cores around BHs., Comment: 8 pages, 8 figures

In the context of Fuzzy Dark Matter (FDM) we study the core formation in the presence of an Ideal Gas (IG). Our analysis is based on the solution of the Schr\"odinger-Poisson-Euler system of equations that drives the evolution of FDM together with a compressible IG, both coupled through the gravitational potential they produce. Starting from random initial conditions for both FDM and IG, with dominant FDM, we study the evolution of the system until it forms a nearly relaxed, virialized and close to hydrostatic equilibrium core, surrounded by an envelope of the two components. We find that the core corresponds to Newtonian Fermion-Boson Stars (FBS). If the IG is used to model luminous matter, our results indicate that FBS behave as attractor core solutions of structure formation of FDM along with baryonic matter., Comment: 6 pages, 5 figures

In this paper we explain how to use the Fast Fourier Transform (FFT) to solve partial differential equations (PDEs). We start by defining appropriate discrete domains in coordinate and frequency domains. Then describe the main limitation of the method arising from the Sampling Theorem, which defines the critical Nyquist frequency and the aliasing effect. We then define the Fourier Transform (FT) and the FFT in a way that can be implemented in one and more dimensions. Finally, we show how to apply the FFT in the solution of PDEs related to problems involving two spatial dimensions, specifically the Poisson equation, the diffusion equation and the wave equation for elliptic, parabolic and hyperbolic cases respectively., Comment: Prepared for educational purposes, 9 pages, 9 figures. Accepted for publication in the educative section of Revista Mexicana de Fisica

We investigate discrete Poincar\'e inequalities on piecewise polynomial subspaces of the Sobolev spaces H(curl) and H(div) in three space dimensions. We characterize the dependence of the constants on the continuous-level constants, the shape regularity and cardinality of the underlying tetrahedral mesh, and the polynomial degree. One important focus is on meshes being local patches (stars) of tetrahedra from a larger tetrahedral mesh. We also review various equivalent results to the discrete Poincar\'e inequalities, namely stability of discrete constrained minimization problems, discrete inf-sup conditions, bounds on operator norms of piecewise polynomial vector potential operators (Poincar\'e maps), and existence of graph-stable commuting projections.

Intellectual Disability (ID) is characterized by deficits in intellectual functioning and adaptive behavior, necessitating customized therapeutic interventions to improve daily life skills. This paper presents the development and evaluation of Space Exodus, a task-based role-playing Virtual Reality (VR) game designed to support therapy for children with ID. The game integrates everyday life scenarios into an immersive environment to enhance skill acquisition and transfer. Functional tests and preliminary experiments demonstrated the system's stability, usability, and adaptability, with 70--80\% of participants demonstrating successful skill transfer to new challenges. Challenges, such as VR discomfort, controller misoperation, and task complexity, were identified, emphasizing the need for ergonomic improvements and adaptive guidance. The results provide empirical evidence supporting VR as a promising tool in ID therapy. Future work will focus on refining gameplay mechanics, enhancing user guidance, and expanding accessibility to broader populations., Comment: Accepted paper at the 7th IEEE International Conference on Artificial Intelligence & eXtended and Virtual Reality

Atmospheric models demand a lot of computational power and solving the chemical processes is one of its most computationally intensive components. This work shows how to improve the computational performance of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH), a chemical weather prediction system developed by the Barcelona Supercomputing Center. The model implements the new flexible external package Chemistry Across Multiple Phases (CAMP) for the solving of gas- and aerosol-phase chemical processes, that allows multiple chemical processes to be solved simultaneously as a single system. We introduce a novel strategy to simultaneously solve multiple instances of a chemical mechanism, represented in the model as grid-cells, obtaining a speedup up to 9x using thousands of cells. In addition, we present a GPU strategy for the most time-consuming function of CAMP. The GPU version achieves up to 1.2x speedup compared to CPU. Also, we optimize the memory access in the GPU to increase its speedup up to 1.7x.

We use effective string theory to study mesons with large spin $J$ in large $N_c$ QCD as rotating open strings. In the first part of this work, we formulate a consistent effective field theory (EFT) for open spinning strings with light quarks. Our EFT provides a consistent treatment of the endpoints' singularities that arise in the massless limit. We obtain results, in a systematic $1/J$ expansion, for the spectrum of the leading and daughter Regge trajectories. Interestingly, we find that the redshift factor associated with the quarks' acceleration implies that the applicability regime of the EFT is narrower compared to that of static flux tubes. In the second part of this work, we discuss several extensions of phenomenological interests, including mesons with heavy quarks, the quarks' spin and the daughter Regge trajectories associated with the worldsheet axion, a massive string mode identified in lattice simulations of $4d$ flux tubes. We compare our predictions with $4d$ QCD spectroscopy data, and suggest potential $stringy$ interpretations of the observed mesons. We finally comment on the relation between the EFT spectrum and the Axionic String Ansatz, a recently proposed characterization of the spectrum of Yang-Mills glueballs., Comment: 66 pages+appendices, 9 figures; v2 typos fixed and ref.s updated

This paper introduces Mayfly, a federated analytics approach enabling aggregate queries over ephemeral on-device data streams without central persistence of sensitive user data. Mayfly minimizes data via on-device windowing and contribution bounding through SQL-programmability, anonymizes user data via streaming differential privacy (DP), and mandates immediate in-memory cross-device aggregation on the server -- ensuring only privatized aggregates are revealed to data analysts. Deployed for a sustainability use case estimating transportation carbon emissions from private location data, Mayfly computed over 4 million statistics across more than 500 million devices with a per-device, per-week DP $\varepsilon = 2$ while meeting strict data utility requirements. To achieve this, we designed a new DP mechanism for Group-By-Sum workloads leveraging statistical properties of location data, with potential applicability to other domains., Comment: 22 pages, 7 figures

The paper ``Tropical Geometry of Deep Neural Networks'' by L. Zhang et al. introduces an equivalence between integer-valued neural networks (IVNN) with activation $\text{ReLU}_{t}$ and tropical rational functions, which come with a map to polytopes. Here, IVNN refers to a network with integer weights but real biases, and $\text{ReLU}_{t}$ is defined as $\text{ReLU}_{t}(x)=\max(x,t)$ for $t\in\mathbb{R}\cup\{-\infty\}$. For every poset with $n$ points, there exists a corresponding order polytope, i.e., a convex polytope in the unit cube $[0,1]^n$ whose coordinates obey the inequalities of the poset. We study neural networks whose associated polytope is an order polytope. We then explain how posets with four points induce neural networks that can be interpreted as $2\times 2$ convolutional filters. These poset filters can be added to any neural network, not only IVNN. Similarly to maxout, poset convolutional filters update the weights of the neural network during backpropagation with more precision than average pooling, max pooling, or mixed pooling, without the need to train extra parameters. We report experiments that support our statements. We also prove that the assignment from a poset to an order polytope (and to certain tropical polynomials) is one to one, and we define the structure of algebra over the operad of posets on tropical polynomials., Comment: Poster presentation in NeuroIPS WIML 2024

TiniScript is an intermediate programming language designed for educational robotics, aligned with STEM principles to foster integrative learning experiences. With its minimalist single-line syntax, such as F(2, 80) , TiniScript simplifies robotic programming, allowing users to bypass complex code uploading processes and enabling realtime direct instruction transmission. Thanks to its preloaded interpreter, TiniScript decouples programming from hardware, significantly reducing wait times. Instructions can be sent wirelessly from any Bluetooth enabled device, making TiniScript adaptable to various robots. This adaptability optimizes iterative and collaborative learning, allowing students to focus on the creative aspects of robotics. This paper explores TiniScripts design principles, syntax, and practical applications, highlighting its potential to make robotics programming more accessible and effective in developing critical thinking skills., Comment: 10 pages, 5 figures. For associated resources and block-based programming interface, see tinibot.pe. This work explores TiniScripts design for simplified, real-time robotics programming aimed at educational environments, emphasizing accessibility and creative engagement in STEM learning