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This study analyzed Pennsylvania School Climate Survey data from students and staff in the 2021/22 school year to assess the validity and reliability of the elementary school student version of the survey; approaches to scoring the survey in individual schools at all grade levels; and perceptions of school climate across student, staff, and school groups. The survey encourages data-informed efforts in participating Pennsylvania schools to foster supportive learning environments that promote social and emotional wellness for students and staff. The study validated the elementary school student survey but found that one domain--safe and respectful school climate--did not meet the reliability threshold and thus suggests that revisions are needed. At all grade levels noninstructional staff had the most positive perceptions of school climate, followed by classroom teachers then students. The study found that different approaches to combining the school climate scores of students, teachers, and noninstructional staff within schools yielded slightly different distributions of school climate summary index scores. It also found that different performance category thresholds resulted in similar distributions of schools across categories. Scores calculated using simple averages were strongly and positively correlated with scores calculated using a more complex approach (Rasch models), suggesting that both approaches deliver similar information. School climate scores varied across student groups (defined by race/ethnicity, gender, and grade level) within schools and across school groups. Larger schools and schools with higher percentages of Black students tended to have lower school climate scores than other schools. The findings can inform the Pennsylvania Department of Education's decisionmaking on revisions to the elementary school student survey, approaches to scoring and reporting survey results, and efforts to increase participation in future survey administrations.

The quality of open-weight LLMs has seen significant improvement, yet they remain predominantly focused on English. In this paper, we introduce the EuroLLM project, aimed at developing a suite of open-weight multilingual LLMs capable of understanding and generating text in all official European Union languages, as well as several additional relevant languages. We outline the progress made to date, detailing our data collection and filtering process, the development of scaling laws, the creation of our multilingual tokenizer, and the data mix and modeling configurations. Additionally, we release our initial models: EuroLLM-1.7B and EuroLLM-1.7B-Instruct and report their performance on multilingual general benchmarks and machine translation.

This paper presents the second-placed solution for task 8 and the participation solution for task 7 of BraTS 2024. The adoption of automated brain analysis algorithms to support clinical practice is increasing. However, many of these algorithms struggle with the presence of brain lesions or the absence of certain MRI modalities. The alterations in the brain's morphology leads to high variability and thus poor performance of predictive models that were trained only on healthy brains. The lack of information that is usually provided by some of the missing MRI modalities also reduces the reliability of the prediction models trained with all modalities. In order to improve the performance of these models, we propose the use of conditional 3D wavelet diffusion models. The wavelet transform enabled full-resolution image training and prediction on a GPU with 48 GB VRAM, without patching or downsampling, preserving all information for prediction. For the inpainting task of BraTS 2024, the use of a large and variable number of healthy masks and the stability and efficiency of the 3D wavelet diffusion model resulted in 0.007, 22.61 and 0.842 in the validation set and 0.07 , 22.8 and 0.91 in the testing set (MSE, PSNR and SSIM respectively). The code for these tasks is available at https://github.com/ShadowTwin41/BraTS_2023_2024_solutions.

This paper presents the winning solution of task 1 and the third-placed solution of task 3 of the BraTS challenge. The use of automated tools in clinical practice has increased due to the development of more and more sophisticated and reliable algorithms. However, achieving clinical standards and developing tools for real-life scenarios is a major challenge. To this end, BraTS has organised tasks to find the most advanced solutions for specific purposes. In this paper, we propose the use of synthetic data to train state-of-the-art frameworks in order to improve the segmentation of adult gliomas in a post-treatment scenario, and the segmentation of meningioma for radiotherapy planning. Our results suggest that the use of synthetic data leads to more robust algorithms, although the synthetic data generation pipeline is not directly suited to the meningioma task. The code for these tasks is available at https://github.com/ShadowTwin41/BraTS_2023_2024_solutions.

Let $B\subset \mathbb{P}^3$ be an slc quartic surface. The existence of an embedding $\mathbb{G}_m^3\hookrightarrow \mathbb{P}^3\setminus B$ implies that $B$ has coregularity zero. In this article, we initiate the classification of coregularity zero slc quartic surfaces $B\subset \mathbb{P}^3$ for which $\mathbb{P}^3\setminus B$ contains an algebraic torus $\mathbb{G}_m^3$. Equivalently, the classification of cluster type pairs $(\mathbb{P}^3,B)$. Along the way, we give criteria for a log Calabi--Yau pair $(X,B)$ over a toric variety $T$ to be of cluster type., Comment: 24 pages

Given a Furstenberg family F and a subset {\Gamma} of C, we introduce and explore the notions of F_{\Gamma}-hypercyclic operator and F-hypercyclic scalar set. First, the study of F_C-hypercyclic operators yields new interesting information about frequently supercyclic, U-frequently supercyclic, reiteratively supercyclic and supercyclic operators. Then we provide a criterion for identifying F_{\Gamma}-hypercyclic operators. As applications of this criterion, we show that any unilateral pseudo-shift operator on c_0(N) or l_p(N) is F_{\Gamma}-hypercyclic for every unbounded subset {\Gamma} of C. Moreover, under the same condition on {\Gamma}, we show that any separable infinite-dimensional Banach space supports an F_{\Gamma}-hypercyclic operator. Finally, our study provides sufficient and necessary conditions for a subset {\Gamma} of C to be a hypercyclic scalar set. These results give partial answers to a question raised by Charpentier, Ernst, and Menet in 2016., Comment: 22 pages

The KM3NeT Collaboration has tackled a common challenge faced by the astroparticle physics community, namely adapting the experiment-specific simulation software to work with the CORSIKA air shower simulation output. The proposed solution is an extension of the open-source code gSeaGen, allowing for the transport of muons generated by CORSIKA to a detector of any size at an arbitrary depth. The gSeaGen code was not only extended in terms of functionalities but also underwent a thorough redesign of the muon propagation routine, resulting in a more accurate and efficient simulation. This paper presents the capabilities of the new gSeaGen code as well as prospects for further developments., Comment: 27 pages, 13 figures

We consider the space of functions almost in $L_p$ with the topology of asymptotic $L_p$-convergence. We prove that this space forms a completely metrizable topological vector space, which extends the space of measurable functions with the topology of convergence in measure to infinite measure spaces. We investigate classical properties such as dominated convergence, Vitali convergence theorems, and Marcinkiewicz interpolation. For the case where the underlying measure space is $\mathbb{R}^d$, we establish results on approximation by smooth functions, separability, and the boundedness of maximal functions. Additionally, we explore key topological features, such as local boundedness, local convexity, and duality, which fundamentally distinguish these spaces from the standard $L_p$ spaces.

Modeling the intrinsic alignment (IA) of galaxies poses a challenge to weak lensing analyses. The Dark Energy Survey is expected to be less impacted by IA when limited to blue, star-forming galaxies. The cosmological parameter constraints from this blue cosmic shear sample are stable to IA model choice, unlike passive galaxies in the full DES Y3 sample, the goodness-of-fit is improved and the $\Omega_{m}$ and $S_8$ better agree with the cosmic microwave background. Mitigating IA with sample selection, instead of flexible model choices, can reduce uncertainty in $S_8$ by a factor of 1.5., Comment: Data access available at https://jamiemccullough.github.io/data/blueshear/

Dengue is a climate-sensitive mosquito-borne disease with a complex transmission dynamic. Data related to climate, environmental and sociodemographic characteristics of the target population are important for project scenarios. Different datasets and methodologies have been applied to build complex models for dengue forecast, stressing the need to evaluate these models and their relative accuracy grounded on a reproducible methodology. The goal of this work is to describe and present Mosqlimate, a web-based platform composed by a dashboard, a data store, model and rediction registries and support for a community of practice in arbovirus forecasting. Multiple API endpoints give access to data for development, open registration of predictive models from different approaches and sharing of predictive models for arboviruses incidence, facilitating interaction between modellers and allowing for proper comparison of the performance of different registered models, by means of probabilistic scores. Epidemiological, entomological, climatic and sociodemographic datasets related to arboviruses in Brazil, are freely available for download, alongside full documentation., Comment: 10 pages, 2 figures, 6 tables

The way we look at the sky is connected to the cosmological paradigm embraced by the society we live in. On the other hand, several astronomical concepts reinforce the idea of a common humanity. Yet, scientific outreach is frequenty reaching out only to a specific part of the world population, often excluding people living in extreme social vulnerability, victims of violence and prejudice, fighting for their lives and for the right of living according to their traditions. We present two outreach projects, developed in Brazil, funded by the Office of Astronomy for Development (OAD) of the International Astronomical Union (IAU), i.e. 'Under Other Skies' and 'OruMbya', which tackle the importance of ethno-astronomy, and the collaboration with leaders and cultural agents of marginalised communities. We also describe an educational project born in the favela of Cantagalo Pav\~ao Pava\~azinho (PPG), in Rio de Janeiro, during the COVID19 pandemic, which started a collaboration with local educators and artists to offer classes of astronomy and English language to children in the favela, Comment: 13 pages, 7 figures, conference proceedings

We explore the kinematics and star formation history of the Scorpius Centaurus (Sco-Cen) OB association following the initial identification of sequential, linearly aligned chains of clusters. Building upon our characterization of the Corona Australis (CrA) chain, we now analyze two additional major cluster chains that exhibit similar characteristics: the Lower Centaurus Crux (LCC) and Upper Scorpius (Upper Sco) chains. All three cluster chains display distinct sequential patterns in 1) the 3D spatial distribution, 2) age, 3) velocity, and 4) mass. The Upper-Sco chain is the most massive and complex cluster chain, possibly consisting of two or more overlapping subchains. We discuss the possible formation of cluster chains and argue for a scenario where feedback from the most massive star formation episode 15 Myr ago initiated the formation of these spatio-temporal cluster sequences. Our results identify cluster chains as a distinct type of stellar structure with well-defined physical properties, formed in environments capable of sustaining stellar feedback over timescales of 5-10 Myr. We find that around 40% of the stellar population in Sco-Cen formed due to triggered star formation, with 35% forming along the three cluster chains. We conclude that cluster chains could be common structures in OB associations, particularly in regions that have similar natal environments as Sco-Cen. Beyond their significance for star formation and stellar feedback, they appear to be promising laboratories for chemical enrichment and the transport of elements from one generation to the next in the same star-forming region., Comment: accepted for publication in A&A (October 22, 2024)

Phylogenetic trees constitute an interesting class of objects for stochastic processes due to the non-standard nature of the space they inhabit. In particular, many statistical applications require the construction of Markov processes on the space of trees, whose cardinality grows superexponentially with the number of leaves considered. We investigate whether certain lower-dimensional projections of tree space preserve the Markov property in tree-valued Markov processes. We study exact lumpability of tree shapes and $\varepsilon$-lumpability of clades, exploiting the combinatorial structure of the SPR graph to obtain bounds on the lumping error under the random walk and Metropolis-Hastings processes. Finally, we show how to use these results in empirical investigation, leveraging exact and $\varepsilon$-lumpability to improve Monte Carlo estimation of tree-related quantities.

We have used the multiobject mode of the Near-Infrared Spectrograph (NIRSpec) on board the James Webb Space Telescope (JWST) to obtain low-resolution 1-5um spectra of 22 brown dwarf candidates in the Orion Nebula Cluster, which were selected with archival images from the Hubble Space Telescope. One of the targets was previously classified as a Herbig-Haro (HH) object and exhibits strong emission in H I, H2, and the fundamental band of CO, further demonstrating that HH objects can have bright emission in that CO band. The remaining targets have late spectral types (M6.5 to early L) and are young based on gravity sensitive features, as expected for low-mass members of the cluster. According to theoretical evolutionary models, these objects should have masses that range from the hydrogen burning limit to 0.003-0.007 Msun. Two of the NIRSpec targets were identified as proplyds in earlier analysis of Hubble images. They have spectral types of M6.5 and M7.5, making them two of the coolest and least massive known proplyds. Another brown dwarf shows absorption bands at 3-5um from ices containing H2O, CO2, OCN-, and CO, indicating that it is either an edge-on class II system or a class I protostar. It is the coolest and least massive object that has detections of these ice features. In addition, it appears to be the first candidate for a protostellar brown dwarf that has spectroscopy confirming its late spectral type., Comment: Astronomical Journal, in press

Fej\'er monotonicity is a well-established property commonly observed in sequences generated by optimization algorithms. In this paper, we introduce an extension of this property, called Fej\'er* monotonicity, which was initially proposed in [SIAM J. Optim., 34(3), 2535-2556 (2024)]. We discuss and build upon the concept by exploring its behavior within Hilbert spaces, presenting an illustrative example and insightful results regarding weak and strong convergence. We also compare Fej\'er* monotonicity with other weak notions of Fej\'er-like monotonicity, to better establish the role of Fej\'er* monotonicity in optimization algorithms.

When deploying large-scale machine learning models for smart city applications, such as image-based parking lot monitoring, data often must be sent to a central server to perform classification tasks. This is challenging for the city's infrastructure, where image-based applications require transmitting large volumes of data, necessitating complex network and hardware infrastructures to process the data. To address this issue in image-based parking space classification, we propose creating a robust ensemble of classifiers to serve as Teacher models. These Teacher models are distilled into lightweight and specialized Student models that can be deployed directly on edge devices. The knowledge is distilled to the Student models through pseudo-labeled samples generated by the Teacher model, which are utilized to fine-tune the Student models on the target scenario. Our results show that the Student models, with 26 times fewer parameters than the Teacher models, achieved an average accuracy of 96.6% on the target test datasets, surpassing the Teacher models, which attained an average accuracy of 95.3%., Comment: Accepted for presentation at the International Conference on Machine Learning and Applications (ICMLA) 2024

This proposal outlines the future plans of the Brazilian High-Energy Physics (HEP) community for upcoming collider experiments. With the construction of new particle colliders on the horizon and the ongoing operation of the High-Luminosity LHC, several research groups in Brazil have put forward technical proposals, covering both hardware and software contributions, as part of the Brazilian contribution to the global effort. The primary goal remains to foster a unified effort within the Brazilian HEP community, optimizing resources and expertise to deliver a high-impact contribution to the international HEP community., Comment: 24 pages, submitted to the Latin American Strategy Forum for Research Infrastructure

The Circular Electron Positron Collider (CEPC) is a large-scale particle accelerator designed to collide electrons and positrons at high energies. One of the primary goals of the CEPC is to achieve high-precision measurements of the properties of the Higgs boson, facilitated by the large number of Higgs bosons that can be produced with significantly low contamination. The measurements of Higgs boson branching fractions into $b\overline{b} /c\overline{c} /gg$ and $WW^{*} /ZZ^{*} $, where the $W$ or $Z$ bosons decay hadronically, are presented in the context of the CEPC experiment, assuming a scenario with 5600 fb$^{-1}$ of collision data at a center-of-mass energy of 240 GeV. In this study the Higgs bosons are produced in association with a $Z$ boson, with the $Z$ boson decaying into a pair of muons $(\mu^{+}\mu^{-})$, which have high efficiency and high resolution. In order to separate all decay channels simultaneously with high accuracy, the Particle Flow Network (PFN), a graph-based machine learning model, is considered. The precise classification provided by the PFN is employed in measuring the branching fractions using the migration matrix method, which accurately corrects for detector effects in each decay channel. The statistical uncertainty of the measured branching ratio is estimated to be 0.58% in $H\to b\overline{b}$ final state, and approximately 2%-10% in $H\to c\overline{c} /gg/WW^{*} /ZZ^{*} $ final states. In addition, the main sources of systematic uncertainties to the measurement of the branching fractions are discussed., Comment: 8 pages, 7 figures

The miniaturisation of transistors is approaching its limits due to challenges in heat management and information transfer speed. To overcome these obstacles, emerging technologies such as spintronics are being developed, which leverage the electron's spin in addition to its charge. Local phenomena at interfaces or structural defects will greatly influence the efficiency of spin-based devices, making the ability to study and control spin wave propagation at the nano- and atomic scales a key challenge. The development of high-spatial-resolution tools to probe spin waves, also called magnons, at relevant lengthscales is thus essential to understand how their properties are affected by such local features. Here, we show the first experimental detection of bulk magnons at the nanoscale using scanning transmission electron microscopy. By employing high-resolution electron energy loss spectroscopy with hybrid-pixel direct electron detectors optimized for low acceleration voltages, we successfully overcome the challenges posed by weak signals and identify magnon excitations in a thin NiO nanocrystal. Advanced inelastic electron scattering simulations corroborate our findings. These results open new avenues for detecting magnons, exploring their dispersions and their modifications arising from nanoscale structural or chemical defects. This marks an important milestone in magnonics and presents exciting opportunities for the future development of spintronic devices.

Neutrinos described as an open quantum system may interact with the environment which introduces stochastic perturbations to their quantum phase. This mechanism leads to a loss of coherence along the propagation of the neutrino $-$ a phenomenon commonly referred to as decoherence $-$ and ultimately, to a modification of the oscillation probabilities. Fluctuations in space-time, as envisaged by various theories of quantum gravity, are a potential candidate for a decoherence-inducing environment. Consequently, the search for decoherence provides a rare opportunity to investigate quantum gravitational effects which are usually beyond the reach of current experiments. In this work, quantum decoherence effects are searched for in neutrino data collected by the KM3NeT/ORCA detector from January 2020 to November 2021. The analysis focuses on atmospheric neutrinos within the energy range of a few GeV to $100\,\mathrm{GeV}$. Adopting the open quantum system framework, decoherence is described in a phenomenological manner with the strength of the effect given by the parameters $\Gamma_{21}$ and $\Gamma_{31}$. Following previous studies, a dependence of the type $\Gamma_{ij} \propto (E/E_0)^n$ on the neutrino energy is assumed and the cases $n = -2,-1$ are explored. No significant deviation with respect to the standard oscillation hypothesis is observed. Therefore, $90\,\%$ CL upper limits are estimated as $\Gamma_{21} < 4.6\cdot 10^{-21}\,$GeV and $\Gamma_{31} < 8.4\cdot 10^{-21}\,$GeV for $n = -2$, and $\Gamma_{21} < 1.9\cdot 10^{-22}\,$GeV and $\Gamma_{31} < 2.7\cdot 10^{-22}\,$GeV for $n = -1$, respectively., Comment: 17 pages, 5 figures

Analyzing and finding anomalies in multi-dimensional datasets is a cumbersome but vital task across different domains. In the context of financial fraud detection, analysts must quickly identify suspicious activity among transactional data. This is an iterative process made of complex exploratory tasks such as recognizing patterns, grouping, and comparing. To mitigate the information overload inherent to these steps, we present a tool combining automated information highlights, Large Language Model generated textual insights, and visual analytics, facilitating exploration at different levels of detail. We perform a segmentation of the data per analysis area and visually represent each one, making use of automated visual cues to signal which require more attention. Upon user selection of an area, our system provides textual and graphical summaries. The text, acting as a link between the high-level and detailed views of the chosen segment, allows for a quick understanding of relevant details. A thorough exploration of the data comprising the selection can be done through graphical representations. The feedback gathered in a study performed with seven domain experts suggests our tool effectively supports and guides exploratory analysis, easing the identification of suspicious information.

When we want to simulate the realization of a symmetric simple random walk on $\mathbb Z^d$, we use $(2d)$-side fair dice to decide to which neighbor it jumps at each step if $d\geq 2$ or we simply use a fair coin when $d=1$. Assume that instead of using a dice or a coin we want to do a simulation using a well shuffled deck with $K$ cards of each of the $2d$ suits. In the first step the probability of jumping to each neighbor is $(2d)^{-1}$, but from the second step it becomes biased. Of course if we continue performing this simulation, the total variation distance between its law and the law of the random walk will increase until all cards are used. In this paper we investigate the minimum number of cards $N=2d K$ that a deck must contain so that the total variation distance between the law of a $n$-step simulation and the law of a $n$-step realization of the random walk is smaller than a chosen threshold $\varepsilon \in (0,1)$. More generally, we prove that when $N=cn$ this distance converges, as $n \to \infty$, to a Gaussian profile which depends on $c\geq 2d$. Furthermore, our analysis shows that this Gaussian profile vanishes as $c \to \infty$, proving the convergence of a multivariate hypergeometric distribution to a multinomial distribution in total variation., Comment: 12 pages, 2 figures

This volume contains the proceedings of DCM 2023, the 13th International Workshop on Developments in Computational Models held on 2 July 2023 in Rome, Italy. DCM 2023 was organised as a one-day satellite event of FSCD 2023, the 8th International Conference on Formal Structures for Computation and Deduction. The aim of this workshop is to bring together researchers who are currently developing new computation models or new features for traditional computation models, in order to foster their interaction, to provide a forum for presenting new ideas and work in progress, and to enable newcomers to learn about current activities in this area.

Using an abstract perturbation result established by Keller and Liverani, we obtain the H\"older continuity of the invariant density and entropy of the physical measures for some families of piecewise expanding maps. We apply these results to a family of two-dimensional tent maps., Comment: 16 pages, 1 figure

This paper introduces the hypothetical track-length fitting algorithm, a novel method for measuring the kinetic energies of ionizing particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

We establish a uniform estimate for a bilinear fractional integral operator via restricted weak-type endpoint estimates and Marcinkiewicz interpolation. This estimate is crucial in the integrability analysis of a tensor-valued bilinear fractional integral operator associated with Euler-Riesz systems modeling mean-field interactions induced by a singular kernel. The tensorial operator arises from a reformulation of the Euler-Riesz system that yields a gain in integrability for finite energy solutions through compensated integrability. Additionally, for smooth periodic solutions of the reformulated system, we derive a stability result.

This paper introduces the minimum error entropy (MEE) criterion as an advanced information-theoretic loss function tailored for deep learning applications in wireless communications. The MEE criterion leverages higher-order statistical properties, offering robustness in noisy scenarios like Rayleigh fading and impulsive interference. In addition, we propose a less computationally complex version of the MEE function to enhance practical usability in wireless communications. The method is evaluated through simulations on two critical applications: over-the-air regression and indoor localization. Results indicate that the MEE criterion outperforms conventional loss functions, such as mean squared error (MSE) and mean absolute error (MAE), achieving significant performance improvements in terms of accuracy, over $20 \%$ gain over traditional methods, and convergence speed across various channel conditions. This work establishes MEE as a promising alternative for wireless communication tasks in deep learning models, enabling better resilience and adaptability.

The recent rise of deep learning has led to numerous applications, including solving partial differential equations using Physics-Informed Neural Networks. This approach has proven highly effective in several academic cases. However, their lack of physical invariances, coupled with other significant weaknesses, such as an inability to handle complex geometries or their lack of generalization capabilities, make them unable to compete with classical numerical solvers in industrial settings. In this work, a limitation regarding the use of automatic differentiation in the context of physics-informed learning is highlighted. A hybrid approach combining physics-informed graph neural networks with numerical kernels from finite elements is introduced. After studying the theoretical properties of our model, we apply it to complex geometries, in two and three dimensions. Our choices are supported by an ablation study, and we evaluate the generalisation capacity of the proposed approach.

We show that under natural growth conditions on the entropy function, convergence in relative entropy is equivalent to $L_p$-convergence. The main tool is the theory of Young measures, in a form that accounts for the formation of concentrations in weak limits.

Reinforcement learning (RL) has proved to have a promising role in future intelligent wireless networks. Online RL has been adopted for radio resource management (RRM), taking over traditional schemes. However, due to its reliance on online interaction with the environment, its role becomes limited in practical, real-world problems where online interaction is not feasible. In addition, traditional RL stands short in front of the uncertainties and risks in real-world stochastic environments. In this manner, we propose an offline and distributional RL scheme for the RRM problem, enabling offline training using a static dataset without any interaction with the environment and considering the sources of uncertainties using the distributions of the return. Simulation results demonstrate that the proposed scheme outperforms conventional resource management models. In addition, it is the only scheme that surpasses online RL and achieves a $16 \%$ gain over online RL.

CORSIKA 8 is a new framework for simulations of particle cascades in air and dense media implemented in modern C++17, based on past experience with existing codes, in particular CORSIKA 7. The flexible and modular structure of the project allows the development of independent modules that can produce a fully customizable particle shower simulation. The radio module in particular is designed to treat the electric field calculation and its propagation through complex media to each observer location in an autonomous and flexible way. It already allows for the simultaneous simulation of the radio emission calculated with two independent time-domain formalisms, the "Endpoint formalism" as previously implemented in CoREAS and the "ZHS" algorithm as ported from ZHAireS. The design acts as the baseline interface for current and future development for the simulation of radio emission from particle showers in standard and complex scenarios, such as cross-media showers penetrating from air into ice. In this work, we present the design and implementation of the radio module in CORSIKA 8, along with validation studies and a direct comparison of the radio emission from air showers simulated with CORSIKA 8, CORSIKA 7 and ZHAireS. We also present the impact of simulation details such as the step size of simulated particle tracks on radio-emission simulations and perform a direct comparison of the "Endpoints" and "ZHS" formalisms for the same underlying air showers. Finally, we present an in-depth comparison of CORSIKA 8 and CORSIKA 7 for optimum simulation settings and discuss the relevance of observed differences in light of reconstruction efforts for the energy and mass of cosmic rays., Comment: 17 pages

Contemporary wireless communications systems adopt the Multi-User Multiple-Input Multiple-Output (MU-MIMO) technique: a single base station or Access Point (AP) equipped with multiple antenna elements serves multiple active users simultaneously. Aiming at providing a more uniform wireless coverage, industry and academia have been working towards the evolution from centralized MIMO to Distributed-MIMO. That is, instead of having all the antenna elements co-located at a single AP, multiple APs, each equipped with a few or a single antenna element, jointly cooperate to serve the active users in the coverage area. In this work, we evaluate the performance of different D-MIMO setups under Rician fading, and considering different receive combining schemes. Note that the Rician fading model is convenient for MU-MIMO performance assessment, as it encompasses a wide variety of scenarios. Our numerical results show that the correlation among the channel vectors of different users increases with the Rician factor, which leads to a reduction on the achievable Spectral Efficiency (SE). Moreover, given a total number of antenna elements, there is an optimal number of APs and antenna elements per AP that provides the best performance. This "sweet spot" depends on the Rician factor and on the adopted receive combining scheme., Comment: 6 pages, 6 figures. Manuscript submitted to the IEEE Wireless Communications and Networking Conference (WCNC), Milan, Italy, 2025. arXiv admin note: text overlap with arXiv:2406.19078

Soccer attracts the attention of many researchers and professionals in the sports industry. Therefore, the incorporation of science into the sport is constantly growing, with increasing investments in performance analysis and sports prediction industries. This study aims to (i) highlight the use of complex networks as an alternative tool for predicting soccer match outcomes, and (ii) show how the combination of structural analysis of passing networks with match statistical data can provide deeper insights into the game patterns and strategies used by teams. In order to do so, complex network metrics and match statistics were used to build machine learning models that predict the wins and losses of soccer teams in different leagues. The results showed that models based on passing networks were as effective as ``traditional'' models, which use general match statistics. Another finding was that by combining both approaches, more accurate models were obtained than when they were used separately, demonstrating that the fusion of such approaches can offer a deeper understanding of game patterns, allowing the comprehension of tactics employed by teams relationships between players, their positions, and interactions during matches. It is worth mentioning that both network metrics and match statistics were important and impactful for the mixed model. Furthermore, the use of networks with a lower granularity of temporal evolution (such as creating a network for each half of the match) performed better than a single network for the entire game., Comment: To appear in Journal of Complex Networks

We propose a new relative-error inexact version of the alternating direction method of multipliers (ADMM) for convex optimization. We prove the asymptotic convergence of our main algorithm as well as pointwise and ergodic iteration-complexities for residuals. We also justify the effectiveness of the proposed algorithm through some preliminary numerical experiments on regression problems., Comment: To appear in Communications in Optimization Theory; 28 pages

Understanding faint dwarf galaxies is fundamental to the development of a robust theory of galaxy formation on small scales. Since the discovery of a population of ultra diffuse galaxies (UDGs) rich in globular clusters (GCs) in Coma, an increasing number of studies on low surface brightness dwarf galaxies (LSBds) have been published in recent years. The most massive LSBds have been observed predominantly in groups and clusters, with properties displaying dependence on the environment. In this work, we use deep DECam imaging to systematically identify LSBds and their GC populations around the low-density environment of NGC 3115. We carefully analyse the structure and morphology of 24 candidates, 18 of which are reported for the first time. Most candidates exhibit red colours suggesting a connection between their colour and distance to NGC 3115. We followed up with Gemini GMOS imaging 9 LSBds to properly identify their GC populations. We derive lower limits for the number of GCs associated with each galaxy. Our analysis reveals that they occur around of the same loci of Fornax LSB dwarf GC systems. The relationship between the number of GCs and total mass provides a tool in which, by counting the GCs in these galaxies, we estimate an upper limit for the total mass of these LSB dwarfs, obtaining the mean value of $\sim 3.3\times10^{10}$ M$_{\odot}$. Our results align with expectations for dwarf-sized galaxies, particularly regarding the distribution and specific frequency of their GC systems., Comment: 25 pages, 22 figures, accepted for publication in MNRAS

The weak mixing angle provides a sensitive test of the Standard Model. We study SBND's sensitivity to the weak mixing angle using neutrino-electron scattering events. We perform a detailed simulation, paying particular attention to background rejection and estimating the detector response. We find that SBND can provide a reasonable constraint on the weak mixing angle, achieving 8% precision for $10^{21}$ protons on target, assuming an overall flux normalization uncertainty of 10%. This result is superior to those of current neutrino experiments and is relatively competitive with other low-energy measurements., Comment: 9 pages, 9 figures

Strong collective interaction of atoms with an optical cavity causes normal mode splitting of the cavity's resonances, whose width is given by the collective coupling strength. At low optical density of the atomic cloud the intensity distribution of light in the cavity is ruled by the cavity's mode function, which is solely determined by its geometry. In this regime the dynamics of the coupled atom-cavity system is conveniently described by the open Dicke model, which we apply to calculating normal mode splitting generated by periodically ordered clouds in linear and ring cavities. We also show how to use normal mode splitting as witness for Wannier-Bloch oscillations in the tight-binding limit. At high optical density the atomic distribution contributes to shaping the mode function. This regime escapes the open Dicke model, but can be treated by a transfer matrix model provided the saturation parameter is low. Applying this latter model to an atomic cloud periodically ordered into a one-dimensional lattice, we observe the formation of photonic bands gaps competing with the normal mode splitting. We discuss the limitations of both models and point out possible pathways to generalized theories., Comment: 10 pages, 14 figures

User interface (UI) personalization can improve usability and user experience. However, current systems offer limited opportunities for customization, and third-party solutions often require significant effort and technical skills beyond the reach of most users, impeding the future adoption of interface personalization. In our research, we explore the concept of UI customization for the self and others. We performed a two-week study where nine participants used a custom-designed tool that allows websites' UI customization for oneself and to create and reply to customization assistance requests from others. Results suggest that people enjoy customizing for others more than for themselves. They see requests as challenges to solve and are motivated by the positive feeling of helping others. To customize for themselves, people need help with the creative process. We discuss challenges and opportunities for future research seeking to democratize access to personalized UIs, particularly through community-based approaches.

Low-temperature plasmas are partially ionized gases, where ions and neutrals coexist in a highly reactive environment. This creates a rich chemistry, which is often difficult to understand in its full complexity. In this work, we develop a machine learning model to identify the most important reactions in a given chemical scheme. The training data are an initial distribution of species and a final distribution of species, which can be obtained from either experiments or simulations. The model is trained to provide a set of reaction weights, which become the basis for reducing the chemical scheme. The approach is applied to N$_2$-H$_2$ plasmas, created by an electric discharge at low pressure, where the main goal is to produce NH$_3$. The interplay of multiple species, as well as of volume and surface reactions, make this chemistry especially challenging to understand. Reducing the chemical scheme via the proposed model helps identify the main chemical pathways.

Heavy neutral leptons (HNLs), depending on their mass and mixing, can be efficiently produced in meson decays from the target or absorber in short- to medium-baseline accelerator neutrino experiments, leaving detectable signals through their decays inside the neutrino detectors. We show that the currently running ICARUS experiment at Fermilab can reconstruct the HNL mass and explore new HNL parameter space in the mass range of 70-190 MeV. The mass reconstruction is enabled by two ingredients: (i) simple two-body kinematics of HNL production from stopped kaon decays at the NuMI absorber, followed by HNL decay into a charged-lepton pair and neutrino at the detector, and (ii) high resolution of Liquid Argon Time Projection Chamber (LArTPC) detectors in reconstructing final state particles. Our mass reconstruction method is robust under realistic energy resolution and angular smearing of the charged leptons, and is applicable to any LArTPC detector. We also discuss the synergy between ICARUS and future facilities like DUNE near detector and PIP-II beam dump in probing the HNL parameter space., Comment: 14 pages, 6 figures, 1 table

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

Although fundamental to the advancement of Machine Learning, the classic evaluation metrics extracted from the confusion matrix, such as precision and F1, are limited. Such metrics only offer a quantitative view of the models' performance, without considering the complexity of the data or the quality of the hit. To overcome these limitations, recent research has introduced the use of psychometric metrics such as Item Response Theory (IRT), which allows an assessment at the level of latent characteristics of instances. This work investigates how IRT concepts can enrich a confusion matrix in order to identify which model is the most appropriate among options with similar performance. In the study carried out, IRT does not replace, but complements classical metrics by offering a new layer of evaluation and observation of the fine behavior of models in specific instances. It was also observed that there is 97% confidence that the score from the IRT has different contributions from 66% of the classical metrics analyzed., Comment: 15 pages, 8 figures, 3 tables, submitted for the BRACIS'24 conference

Semantic and goal-oriented (SGO) communication is an emerging technology that only transmits significant information for a given task. Semantic communication encounters many challenges, such as computational complexity at end users, availability of data, and privacy-preserving. This work presents a TinyML-based semantic communication framework for few-shot wireless image classification that integrates split-learning and meta-learning. We exploit split-learning to limit the computations performed by the end-users while ensuring privacy-preserving. In addition, meta-learning overcomes data availability concerns and speeds up training by utilizing similarly trained tasks. The proposed algorithm is tested using a data set of images of hand-written letters. In addition, we present an uncertainty analysis of the predictions using conformal prediction (CP) techniques. Simulation results show that the proposed Semantic-MSL outperforms conventional schemes by achieving 20 % gain on classification accuracy using fewer data points, yet less training energy consumption.

Neutrinos at ultrahigh energies can originate both from interactions of cosmic rays at their acceleration sites and through cosmic-ray interactions as they propagate through the universe. These neutrinos are expected to have a low flux which drives the need for instruments with large effective areas. Radio observations of the inclined air showers induced by tau neutrino interactions in rock can achieve this, because radio waves can propagate essentially unattenuated through the hundreds of kilometers of atmosphere. Proposed arrays for radio detection of tau neutrinos focus on either arrays of inexpensive receivers distributed over a large area, the GRAND concept, or compact phased arrays on elevated mountains, the BEACON concept, to build up a large detector area with a low trigger threshold. We present a concept that combines the advantages of these two approaches with a trigger driven by phased arrays at a moderate altitude (1 km) and sparse, high-gain outrigger receivers for reconstruction and background rejection. We show that this design has enhanced sensitivity at 100 PeV over the two prior designs with fewer required antennas and discuss the need for optimized antenna designs., Comment: ARENA2024 Conference Proceeding PoS(ARENA2024)058

The inference and training stages of Graph Neural Networks (GNNs) are often dominated by the time required to compute a long sequence of matrix multiplications between the sparse graph adjacency matrix and its embedding. To accelerate these stages, we first propose the Compressed Binary Matrix (CBM) storage format to succinctly represent the binary adjacency matrix of an unweighted graph. Then, we show how to generalize this representation to normalized adjacency matrices of unweighted graphs which arise in the context of GNNs. Finally, we develop efficient matrix multiplication kernels based on this compressed representation. The matrix multiplication kernels proposed in this work never require more scalar operations than classic sparse matrix multiplication algorithms. Experimental evaluation shows that the matrix multiplication strategies proposed outperform the current state-of-the-art implementations provided by Intel MKL, achieving speedups close to 5$\times$. Furthermore, our optimized matrix-multiplication strategies accelerated the inference time of a GNN by up to $3\times$.

We revisit two evolutionary game theory models, namely the Prisoner and the Snowdrift dilemmas, on top of small-world networks. These dynamics on networked populations (individuals occupying nodes of a graph) are mainly concerning on the competition between to cooperate or to defect, by allowing some process of revision of strategies. Cooperators avoid defectors by forming clusters in a process known as network reciprocity. This defense strategy is based on the fact that any individual interact only with its nearest neighbors. The minimum cluster, in turn, is formed by a set of three completely connected nodes and the bulk of these triplets is associated with the transitivity property of a network. Particularly, we show that the transitivity increases eventually assuming a constant behavior when observed as a function of the number of contacts of an individual. We investigate the influence of the network reciprocity on that transitivity increasing regime on the promotion of a cooperative behavior. The dynamics on small-world networks are compared with those random regular, and annealed networks, the later typically studied as the well-mixed approach. We observe that the Snowdrift Game converge to an annealed scenario as randonness and coordination number increase, whereas the Prisoner's Dilemma becomes more severe against the cooperative behavior under the regime of an increasing network reciprocity., Comment: 10 pages, 7 figures

We introduce the generalized diffusive epidemic process, which is a metapopulation model for an epidemic outbreak where a non-sedentary population of walkers can jump along lattice edges with diffusion rates $D_S$ or $D_I$ if they are susceptible or infected, respectively, and recovered individuals possess permanent immunity. Individuals can be contaminated with rate $\mu_c$ if they share the same lattice node with an infected individual and recover with rate $\mu_r$, being removed from the dynamics. Therefore, the model does not have the conservation of the active particles composed of susceptible and infected individuals. The reaction-diffusion dynamics are separated into two stages: (i) Brownian diffusion, where the particles can jump to neighboring nodes, and (ii) contamination and recovery reactions. The dynamics are mapped into a growing process by activating lattice nodes with successful contaminations where activated nodes are interpreted as infection sources. In all simulations, the epidemic starts with one infected individual in a lattice filled with susceptibles. Our results indicate a phase transition in the dynamic percolation universality class controlled by the population size, irrespective of diffusion rates $D_S$ and $D_I$ and a subexponential growth of the epidemics in the percolation threshold., Comment: 8 pages, 4 figures

The development of magnetic resonance imaging (MRI) for medical imaging has provided a leap forward in diagnosis, providing a safe, non-invasive alternative to techniques involving ionising radiation exposure for diagnostic purposes. It was described by Block and Purcel in 1946, and it was not until 1980 that the first clinical application of MRI became available. Since that time the MRI has gone through many advances and has altered the way diagnosing procedures are performed. Due to its ability to improve constantly, MRI has become a commonly used practice among several specialisations in medicine. Particularly starting 0.55T and 7T MRI technologies have pointed out enhanced preservation of image detail and advanced tissue characterisation. This review examines the integration of deep learning (DL) techniques into these MRI modalities, disseminating and exploring the study applications. It highlights how DL contributes to 0.55T and 7T MRI data, showcasing the potential of DL in improving and refining these technologies. The review ends with a brief overview of how MRI technology will evolve in the coming years.