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Stellar feedback is an essential step in the baryon cycle of galaxies, but it remains unconstrained beyond Cosmic Noon. We study the ionized gas kinematics, dynamical mass and gas-flow properties of a sample of 16 sub-$L^{\star}$ star-forming galaxies at $4\leq z\leq7.6$, using high-resolution JWST/NIRSpec observations. The emission lines are resolved, with velocity dispersions ($\sigma_{\rm gas}{\rm~(km~s^{-1})}\simeq38-96$) comparable to more massive galaxies at Cosmic Noon. From $\sigma_{\rm gas}$ and the galaxy size ($r_e=400-960~$pc), we estimate the dynamical mass to be $\log M_{\rm dyn}/M_{\odot}=9.25-10.25$. Stellar-to-dynamical mass ratios are low ($\log M_{\star}/M_{\rm dyn}\in[-0.5,-2]$) and decrease with increasing SFR surface density ($\Sigma_{\rm SFR}$). We estimate the gas surface densities assuming a star-formation law, but the gas masses do not balance the baryon-to-dynamical mass ratios, which would require a decrease in the star-formation efficiency. We find evidence of ionized outflows in five out of the sixteen galaxies, based on the need of broad components to reproduce the emission-line wings. We only observe outflows from galaxies undergoing recent bursts of star formation ${\rm SFR_{10}/SFR_{100}\geq1}$, with elevated $\Sigma_{\rm SFR}$ and low $M_{\star}/M_{\rm dyn}$. This links high gas surface densities to increased outflow incidence and lower $M_{\star}/M_{\rm dyn}$. With moderate outflow velocities ($v_{\rm flow}{\rm~(km~s^{-1})}=150-250$) and mass outflow rates ($\dot{M}_{\rm flow}/{\rm M_{\odot} yr^{-1}}=0.2-5$), these high-redshift galaxies appear more efficient at removing baryons than low-redshift galaxies with similar $M_{\star}$, showing mass loading-factors of $\dot{M}_{\rm flow}/{\rm SFR}=0.04-0.4$. For their given dynamical mass, the outflow velocities exceed the escape velocities, meaning that they may eventually enrich the Circumgalactic Medium., Comment: 15 pages, 11 figures, 3 tables; submitted to MNRAS

In this paper we present a unified proof of the fact that the category of modules over a ring and the category of near-vector spaces in the sense of J. Andr\'e, over an appropriate scalar system (a 'scalar group'), are both abelian categories. The unification is possible by viewing each of these categories as subcategories of the (abelian) category of modules over a multiplicative monoid $M$. Although in the case of near-vector spaces all elements of $M$ except one (the 'zero' element) are invertible, we show that this requirement is not necessary for the corresponding category to be abelian in analogy to the well-known fact that modules over a ring form an abelian category even if the ring is not a field (i.e., modules over it are not vector spaces).

Materials play a critical role in various technological applications. Identifying and enumerating stable compounds, those near the convex hull, is therefore essential. Despite recent progress, generative models either have a relatively low rate of stable compounds, are computationally expensive, or lack symmetry. In this work we present Matra-Genoa, an autoregressive transformer model built on invertible tokenized representations of symmetrized crystals, including free coordinates. This approach enables sampling from a hybrid action space. The model is trained across the periodic table and space groups and can be conditioned on specific properties. We demonstrate its ability to generate stable, novel, and unique crystal structures by conditioning on the distance to the convex hull. Resulting structures are 8 times more likely to be stable than baselines using PyXtal with charge compensation, while maintaining high computational efficiency. We also release a dataset of 3 million unique crystals generated by our method, including 4,000 compounds verified by density-functional theory to be within 0.001 eV/atom of the convex hull., Comment: 14 pages, 10 figures

This paper examines the recursive sequence of polynomials $p_n(x)$, defined by $p_0(x) = x^2 - 2$ and $p_n(x) = p_{n-1}(x)^2 - 2$ for $n \geq 1$. It describes the field-theoretic motivations behind this sequence, derives a recursive formula for its coefficients, and identifies invariants that uncover combinatorial connections, including links to weighted Catalan numbers., Comment: 15 pages

Recent work demonstrated the existence of critical flaws in the current use of Shapley values in explainable AI (XAI), i.e. the so-called SHAP scores. These flaws are significant in that the scores provided to a human decision-maker can be misleading. Although these negative results might appear to indicate that Shapley values ought not be used in XAI, this paper argues otherwise. Concretely, this paper proposes a novel definition of SHAP scores that overcomes existing flaws. Furthermore, the paper outlines a practically efficient solution for the rigorous estimation of the novel SHAP scores. Preliminary experimental results confirm our claims, and further underscore the flaws of the current SHAP scores.

Detecting the first generation of stars, Population III (PopIII), has been a long-standing goal in astrophysics, yet they remain elusive even in the JWST era. Here we present a novel NIRCam-based selection method for PopIII galaxies, and carefully validate it through completeness and contamination simulations. We systematically search ~500 arcmin$^{2}$ across JWST legacy fields for PopIII candidates, including GLIMPSE which, assisted by gravitational lensing, has produced JWST's deepest NIRCam imaging thus far. We discover one promising PopIII galaxy candidate (GLIMPSE-16043) at $z=6.50^{+0.03}_{-0.24}$, a moderately lensed galaxy (mu=2.9) with an intrinsic UV magnitude of $M_{UV}$=-15.89. It exhibits key PopIII features: strong H$\alpha$ emission (rest-frame EW $2810\pm550$\AA); a Balmer jump; no dust (UV slope $\beta=-2.34\pm0.36$); and undetectable metal lines (e.g., [OIII]; [OIII]/H$\beta$<0.44) implying a gas-phase metallicity of Zgas/Zsun<0.5%. These properties indicate the presence of a nascent, metal-deficient young stellar population (<5Myr) with a stellar mass of $\simeq10^{5}M_{\odot}$. Intriguingly, this source deviates significantly from the extrapolated UV-metallicity relation derived from recent JWST observations at $z=4-10$, consistent with UV enhancement by a top-heavy PopIII initial mass function or the presence of an extremely metal-poor AGN. We also derive the first observational constraints on the PopIII UV luminosity function at z~6-7. The volume density of GLIMPSE-16043 ($\approx10^{-4}$ cMpc$^{-3}$) is in excellent agreement with theoretical predictions, independently reinforcing its plausibility. This study demonstrates the power of our novel NIRCam method to finally reveal distant galaxies even more pristine than the Milky Way's most metal-poor satellites, thereby promising to bring us closer to the first generation of stars than we have ever been before., Comment: 41 pages, 20 figures, and 10 Tables. Submitted to ApJ. Comments are welcome!

In this work we investigate the two-neutron decay of 13Li and of the excited states of 11Li populated via one-proton removal from 14Be and 12Be, respectively. A phenomenological model is used to describe the decay of 11Li and 13Li. While the first one displays important sequential components, the second one appears dominated by the direct two-neutron decay. A microscopic three-body model is used to extract information on the spatial configuration of the emitted neutrons before the decay and shows that the average distance between the neutrons increases going from 11Li to 13Li.

We study the problem of learning optimal policies in finite-horizon Markov Decision Processes (MDPs) using low-rank reinforcement learning (RL) methods. In finite-horizon MDPs, the policies, and therefore the value functions (VFs) are not stationary. This aggravates the challenges of high-dimensional MDPs, as they suffer from the curse of dimensionality and high sample complexity. To address these issues, we propose modeling the VFs of finite-horizon MDPs as low-rank tensors, enabling a scalable representation that renders the problem of learning optimal policies tractable. We introduce an optimization-based framework for solving the Bellman equations with low-rank constraints, along with block-coordinate descent (BCD) and block-coordinate gradient descent (BCGD) algorithms, both with theoretical convergence guarantees. For scenarios where the system dynamics are unknown, we adapt the proposed BCGD method to estimate the VFs using sampled trajectories. Numerical experiments further demonstrate that the proposed framework reduces computational demands in controlled synthetic scenarios and more realistic resource allocation problems.

In pursuit of reinforcement learning systems that could train in physical environments, we investigate multi-task approaches as a means to alleviate the need for massive data acquisition. In a tabular scenario where the Q-functions are collected across tasks, we model our learning problem as optimizing a higher order tensor structure. Recognizing that close-related tasks may require similar actions, our proposed method imposes a low-rank condition on this aggregated Q-tensor. The rationale behind this approach to multi-task learning is that the low-rank structure enforces the notion of similarity, without the need to explicitly prescribe which tasks are similar, but inferring this information from a reduced amount of data simultaneously with the stochastic optimization of the Q-tensor. The efficiency of our low-rank tensor approach to multi-task learning is demonstrated in two numerical experiments, first in a benchmark environment formed by a collection of inverted pendulums, and then into a practical scenario involving multiple wireless communication devices.

Reinforcement learning (RL) aims to estimate the action to take given a (time-varying) state, with the goal of maximizing a cumulative reward function. Predominantly, there are two families of algorithms to solve RL problems: value-based and policy-based methods, with the latter designed to learn a probabilistic parametric policy from states to actions. Most contemporary approaches implement this policy using a neural network (NN). However, NNs usually face issues related to convergence, architectural suitability, hyper-parameter selection, and underutilization of the redundancies of the state-action representations (e.g. locally similar states). This paper postulates multi-linear mappings to efficiently estimate the parameters of the RL policy. More precisely, we leverage the PARAFAC decomposition to design tensor low-rank policies. The key idea involves collecting the policy parameters into a tensor and leveraging tensor-completion techniques to enforce low rank. We establish theoretical guarantees of the proposed methods for various policy classes and validate their efficacy through numerical experiments. Specifically, we demonstrate that tensor low-rank policy models reduce computational and sample complexities in comparison to NN models while achieving similar rewards.

With today's multi-petawatt lasers, testing quantum electrodynamics (QED) in the strong field regime, where the electric field exceeds the Schwinger critical field in the rest frame of an electron, becomes within reach. Inverse Compton scattering of an intense laser pulse off a high-energy electron beam is the mainstream approach, resulting in the emission of high-energy photons that can decay into Breit-Wheeler electron-positron pairs. Here, we demonstrate experimentally that very high energy photons can be generated in a self-aligned single-laser Compton scattering setup, combining a laser-plasma accelerator and a plasma mirror. Reaching up to the GeV scale, photon emission via nonlinear Compton scattering exhibits a nonclassical scaling in the experiment that is consistent with electric fields reaching up to a fraction $\chi\simeq0.3$ of the Schwinger field in the electron rest frame. These foolproof collisions guaranteed by automatic laser-electron overlap provide a new approach for precise investigations of strong-field QED processes.

Recent work revealed a tight connection between adversarial robustness and restricted forms of symbolic explanations, namely distance-based (formal) explanations. This connection is significant because it represents a first step towards making the computation of symbolic explanations as efficient as deciding the existence of adversarial examples, especially for highly complex machine learning (ML) models. However, a major performance bottleneck remains, because of the very large number of features that ML models may possess, in particular for deep neural networks. This paper proposes novel algorithms to compute the so-called contrastive explanations for ML models with a large number of features, by leveraging on adversarial robustness. Furthermore, the paper also proposes novel algorithms for listing explanations and finding smallest contrastive explanations. The experimental results demonstrate the performance gains achieved by the novel algorithms proposed in this paper., Comment: arXiv admin note: substantial text overlap with arXiv:2405.08297

There has been an ongoing race for the past couple of years to develop the best universal machine learning interatomic potential. This rapid growth has driven researchers to create increasingly accurate models for predicting energy, forces, and stresses, combining innovative architectures with big data. Here, these models are benchmarked for their performance in predicting phonon properties, which are critical for understanding the vibrational and thermal behavior of materials. Our analysis is based on around 10 000 ab initio phonon calculations, enabling us to assess performance across a range of phonon-related parameters while testing the universal applicability of these models. The results reveal that some models are already capable of predicting phonon properties with a high level of accuracy. However, others still exhibit substantial inaccuracies, even if they excel in the prediction of the energy and the forces for materials close to dynamical equilibrium. These findings highlight the importance of considering phonon-related properties in the development of universal machine learning interatomic potentials.

The biological brain has inspired multiple advances in machine learning. However, most state-of-the-art models in computer vision do not operate like the human brain, simply because they are not capable of changing or improving their decisions/outputs based on a deeper analysis. The brain is recurrent, while these models are not. It is therefore relevant to explore what would be the impact of adding recurrent mechanisms to existing state-of-the-art architectures and to answer the question of whether recurrency can improve existing architectures. To this end, we build on a feed-forward segmentation model and explore multiple types of recurrency for image segmentation. We explore self-organizing, relational, and memory retrieval types of recurrency that minimize a specific energy function. In our experiments, we tested these models on artificial and medical imaging data, while analyzing the impact of high levels of noise and few-shot learning settings. Our results do not validate our initial hypothesis that recurrent models should perform better in these settings, suggesting that these recurrent architectures, by themselves, are not sufficient to surpass state-of-the-art feed-forward versions and that additional work needs to be done on the topic., Comment: 24 pages

We identified the Twitter accounts of 941 climate change policy actors across nine countries, and collected their activities from 2017--2022, totalling 48 million activities from 17,700 accounts at different organizational levels. There is considerable temporal and cross-national variation in how prominent climate-related activities were, but all national policy systems generally responded to climate-related events, such as climate protests, in a similar manner. Examining patterns of interaction within and across countries, we find that these national policy systems rarely directly interact with one another, but are connected through consistently engaging with the same content produced by accounts of international organizations, climate activists, and researchers., Comment: working paper, 16 pages, 6 figures

The ubiquitous use of Shapley values in eXplainable AI (XAI) has been triggered by the tool SHAP, and as a result are commonly referred to as SHAP scores. Recent work devised examples of machine learning (ML) classifiers for which the computed SHAP scores are thoroughly unsatisfactory, by allowing human decision-makers to be misled. Nevertheless, such examples could be perceived as somewhat artificial, since the selected classes must be interpreted as numeric. Furthermore, it was unclear how general were the issues identified with SHAP scores. This paper answers these criticisms. First, the paper shows that for Boolean classifiers there are arbitrarily many examples for which the SHAP scores must be deemed unsatisfactory. Second, the paper shows that the issues with SHAP scores are also observed in the case of regression models. In addition, the paper studies the class of regression models that respect Lipschitz continuity, a measure of a function's rate of change that finds important recent uses in ML, including model robustness. Concretely, the paper shows that the issues with SHAP scores occur even for regression models that respect Lipschitz continuity. Finally, the paper shows that the same issues are guaranteed to exist for arbitrarily differentiable regression models., Comment: arXiv admin note: text overlap with arXiv:2405.00076

We report the discovery of an ultra-massive grand-design red spiral galaxy, named Zh\'ul\'ong (Torch Dragon), at $z_{\rm phot} = 5.2^{+0.3}_{-0.2}$ in the JWST PANORAMIC survey, identified as the most distant bulge+disk galaxy with spiral arms known to date. Zh\'ul\'ong displays an extraordinary combination of properties: 1) a classical bulge centered in a large, face-on exponential stellar disk (half-light radius of $R_{\rm e} = 3.7 \pm 0.1 \, \mathrm{kpc}$), with spiral arms extending across 19 kpc; 2) a clear transition from the red, quiescent core ($F150W-F444W=3.1$ mag) with high stellar mass surface density ($\log(\Sigma M_{\star}/M_{\odot} \, \mathrm{kpc}^{-2}) = 9.91_{-0.09}^{+0.11}$) to the star-forming outer regions, as revealed by spatially resolved SED analysis, which indicates significant inside-out galaxy growth; 3) an extremely high stellar mass at its redshift, with $\log (M_{\star}/M_{\odot})=11.03_{-0.08}^{+0.10}$ comparable to the Milky Way, and an implied baryon-to-star conversion efficiency ($\epsilon \sim 0.3$) that is 1.5 times higher than even the most efficient galaxies at later epochs; 4) despite an active disk, a relatively modest overall star formation rate ($\mathrm{SFR} =66_{-46}^{+89} ~M_{\odot} \, \mathrm{yr}^{-1}$), which is $>$0.5 dex below the star formation main sequence at $z \sim 5.2$ and $>$10 times lower than ultra-massive dusty galaxies at $z=5-6$. Altogether, Zh\'ul\'ong shows that mature galaxies emerged much earlier than expected in the first billion years after the Big Bang through rapid galaxy formation and morphological evolution. Our finding offers key constraints for models of massive galaxy formation and the origin of spiral structures in the early universe., Comment: 11 pages, 5 main figures, 2 extended figures, 1 table; submitted to A&A

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

This paper presents a deep MIRI/JWST medium resolution spectroscopy (MRS) covering the rest-frame optical spectrum of the GN-z11 galaxy. The [OIII]5008 and H$\alpha$ emission lines are detected and spectroscopically resolved. The line profiles are well-modeled by a narrow Gaussian component with intrinsic FWHMs of 189$\pm$25 and 231$\pm$52 kms$^{-1}$, respectively. We do not find any evidence of a dominant broad H$\alpha$ emission line component tracing a Broad Line Region in a type 1 active galactic nuclei (AGN). However, a broad ($\sim$430-470 kms$^{-1}$) and weak ($<$ 20-30%) H$\alpha$ line component, tracing a minor AGN contribution in the optical, cannot be ruled out completely with the sensitivity of the present data. The physical and excitation properties of the ionized gas are consistent with a low-metallicity starburst forming stars at a rate of SFR(H$\alpha$)$=$24 $\pm$3$M_{\odot}$yr$^{-1}$. The electron temperature of the ionized gas is $T_{\mathrm{e}}$(O$^{++}$)$=$14000$\pm$2100K, while the direct-$T_{\mathrm{e}}$ gas-phase metallicity is 12+$\log$(O/H)$=$7.91$\pm$0.07 (Z=0.17$\pm$0.03Z$_{\odot}$). The optical line ratios locate GN-z11 in the starburst or AGN region but more consistent with those of local low-metallicity starbursts and high-$z$ luminous galaxies detected at redshifts similar to GN-z11. We conclude that the MRS optical spectrum of GN-z11 is consistent with that of a massive, compact, and low-metallicity starburst galaxy. Due to its high SFR and stellar mass surface densities, close to that of the densest stellar clusters, we speculate that GN-z11 could be undergoing a feedback-free, highly efficient starburst phase. Additional JWST data are needed to validate this scenario, and other recently proposed alternatives, to explain the existence of bright compact galaxies in the early Universe., Comment: accepted for publication in A&A (17 pages, 6 figures, 2 tables)

Cycling is critical for cities to transition to more sustainable transport modes. Yet, safety concerns remain a critical deterrent for individuals to cycle. If individuals perceive an environment as unsafe for cycling, it is likely that they will prefer other means of transportation. Yet, capturing and understanding how individuals perceive cycling risk is complex and often slow, with researchers defaulting to traditional surveys and in-loco interviews. In this study, we tackle this problem. We base our approach on using pairwise comparisons of real-world images, repeatedly presenting respondents with pairs of road environments and asking them to select the one they perceive as safer for cycling, if any. Using the collected data, we train a siamese-convolutional neural network using a multi-loss framework that learns from individuals' responses, learns preferences directly from images, and includes ties (often discarded in the literature). Effectively, this model learns to predict human-style perceptions, evaluating which cycling environments are perceived as safer. Our model achieves good results, showcasing this approach has a real-life impact, such as improving interventions' effectiveness. Furthermore, it facilitates the continuous assessment of changing cycling environments, permitting short-term evaluations of measures to enhance perceived cycling safety. Finally, our method can be efficiently deployed in different locations with a growing number of openly available street-view images., Comment: \copyright 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

We assess the sensitivity of future lepton colliders to weakly coupled vector dark portals (aka ``$ Z' $ bosons'') with masses ranging from tens of GeV to a few TeV. Our analysis focuses on dark photons and $ L_{\mu} - L_{\tau} $ gauge bosons. We consider both visible and invisible decay channels. We demonstrate that both high energy $\mu$ colliders and future $ e^+e^- $ colliders, using the FCC-ee $Z$-pole and $ZH$ operation modes as a benchmark, offer significant improvements in sensitivity. We find that both colliders can enhance the sensitivity to $ L_{\mu} - L_{\tau} $ bosons (for both visible and invisible decays) and to invisibly decaying dark photons by 1--2 orders of magnitude across the relevant mass range. Furthermore, we study the impact of forward $ \mu $ detectors at the $ \mu $-collider on the sensitivity to both models., Comment: 20 pages, 8 figures

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.

The redshift drift of objects following the cosmological expansion is a unique model-independent probe of background cosmology, detectable by astrophysical facilities presently under construction. Previous forecasts for such measurements assume flat universes. We explore the impact of relaxing this assumption on the constraining power of the redshift drift, focusing on the two most promising routes for its measurement: the SKA at low redshifts, and the Golden Sample for the ELT's ANDES spectrograph at higher redshifts. We also discuss the cosmological sensitivity of possible differential redshift drift measurements, both on their own and, for the specific case of the Golden Sample, in combination with the standard method. Overall, we find that the sensitivity of the redshift drift to curvature is comparable to that of matter (especially at low redshifts) and higher than the sensitivity to the dark energy equation of state. We also show that the sensitivity of redshift drift measurements to these cosmological parameters is asymmetric with respect to the curvature parameter, being different for open and closed universes with the same absolute value of the curvature parameter $\Omega_k$., Comment: 19 pages, 10 figures, 5 tables; Physics of the Dark Universe (in press)

In this paper, we present the design of Owi, a symbolic interpreter for WebAssembly written in OCaml, and how we used it to create a state-of-the-art tool to find bugs in programs combining C and Rust code. WebAssembly (Wasm) is a binary format for executable programs. Originally intended for web applications, Wasm is also considered a serious alternative for server-side runtimes and embedded systems due to its performance and security benefits. Despite its security guarantees and sandboxing capabilities, Wasm code is still vulnerable to buffer overflows and memory leaks, which can lead to exploits on production software. To help prevent those, different techniques can be used, including symbolic execution. Owi is built around a modular, monadic interpreter capable of both normal and symbolic execution of Wasm programs. Monads have been identified as a way to write modular interpreters since 1995 and this strategy has allowed us to build a robust and performant symbolic execution tool which our evaluation shows to be the best currently available for Wasm. Moreover, because WebAssembly is a compilation target for multiple languages (such as Rust and C), Owi can be used to find bugs in C and Rust code, as well as in codebases mixing the two. We demonstrate this flexibility through illustrative examples and evaluate its scalability via comprehensive experiments using the 2024 Test-Comp benchmarks. Results show that Owi achieves comparable performance to state-of-the-art tools like KLEE and Symbiotic, and exhibits advantages in specific scenarios where KLEE's approximations could lead to false negatives.

Hollow-core photonic crystal fibers (HCPCFs) have become a key enabling technology for addressing a broad spectrum of fundamental and applied needs. Indeed, recent advancements achieved by the HCPCF research community have led to significant progress, establishing these fibers as the lowest-loss optical fibers currently available for use in the visible and ultraviolet ranges. However, the fabrication process of HCPCFs demands costly infrastructure, and achieving ultralow-loss fibers remains a complex technical challenge as numerous fabrication attempts are typically required to optimize their performances. Therefore, predicting these fibers' performances before experimental fabrication is highly desirable. In this work, we tackle this task by analytically assessing the total loss in tubular-lattice HCPCFs during their fabrication process. By considering the variation in the microstructure's geometrical parameters during fabrication and the different sources of loss, we estimate expected loss levels and identify the conditions for loss minimization. We understand that our research provides valuable insights into the fabrication process of hollow-core fibers, offering a predictive approach to evaluate the fibers' performance before their experimental realization. By determining optimal conditions considering geometry, fabrication constraints, and loss figures, we believe that our work contributes to the ongoing efforts to further reduce the loss levels in HCPCFs.

Graph Neural Networks (GNNs) have emerged as a promising tool to handle data exhibiting an irregular structure. However, most GNN architectures perform well on homophilic datasets, where the labels of neighboring nodes are likely to be the same. In recent years, an increasing body of work has been devoted to the development of GNN architectures for heterophilic datasets, where labels do not exhibit this low-pass behavior. In this work, we create a new graph in which nodes are connected if they share structural characteristics, meaning a higher chance of sharing their labels, and then use this new graph in the GNN architecture. To do this, we compute the k-nearest neighbors graph according to distances between structural features, which are either (i) role-based, such as degree, or (ii) global, such as centrality measures. Experiments show that the labels are smoother in this newly defined graph and that the performance of GNN architectures improves when using this alternative structure., Comment: Presented as a conference paper in the Asilomar Conference on Signals, Systems, and Computers 2024

We obtain a class of solutions corresponding to a generalization of the Hayward black hole by solving the Einstein equations coupled to a particular nonlinear electromagnetic field. The generalization is realized by considering, additionally, the presence of the cosmological constant and a source corresponding to an anisotropic fluid, namely, a fluid of strings, that surrounds the black hole. We show that the obtained class of solutions preserves or does not the regularity of the original Hayward black hole solution, depending on the values of the parameter $\beta$ which labels the different solutions. We discuss the characteristics of the solutions, from the point of view of the singularities of spacetime, by examining the behavior of the Kretschmann scalar as well as of the geodesics concerning their completeness. We analyze some aspects of thermodynamics, particularizing one of the solutions obtained, namely, for $\beta =-1/2$, in which case the regularity of the Hayward black hole is preserved. Some thermodynamic quantities are obtained and analyzed, for example, pressure, heat capacity, and the critical points, and we show how these quantities change for different values of the parameter $q$ associated with the original Hayward solution, as well as with the parameter $b$ associated with the presence of the fluid of strings. The phase transitions are also analyzed by using the equation of state and the Gibbs free energy.

The framework of templicial vector spaces was put forth in arXiv:2302.02484v2 as a suitable generalization of simplicial sets in order to develop a theory of enriched quasi-categories, called quasi-categories in vector spaces. We construct a lift of Faonte's $A_{\infty}$-nerve arXiv:1312.2127v2 which lands in templicial vector spaces. Further, we show that when restricted to dg-categories, this nerve recovers the templicial dg-nerve of arXiv:2005.04778v4, and that the nerve of any $A_{\infty}$-category is a quasi-category in vector spaces., Comment: 26 pages; comments welcome

Hydrogen-rich superconductors are promising candidates to achieve room-temperature superconductivity. However, the extreme pressures needed to stabilize these structures significantly limit their practical applications. An effective strategy to reduce the external pressure is to add a light element M that binds with H to form MHx units, acting as a chemical precompressor. We exemplify this idea by performing ab initio calculations of the Ac-Be-H phase diagram, proving that the metallization pressure of Ac-H binaries, for which critical temperatures as high as 200 K were predicted at 200 GPa, can be significantly reduced via beryllium incorporation. We identify three thermodynamically stable (AcBe2H10, AcBeH8, and AcBe2H14) and four metastable compounds (fcc AcBeH8, AcBeH10, AcBeH12 and AcBe2H16). All of them are superconductors. In particular, fcc AcBeH8 remains dynamically stable down to 10 GPa, where it exhibits a superconducting transition temperature Tc of 181 K. The Be-H bonds are responsible for the exceptional properties of these ternary compounds and allow them to remain dynamically stable close to ambient pressure. Our results suggest that high-Tc superconductivity in hydrides is achievable at low pressure and may stimulate experimental synthesis of ternary hydrides.

The stellar disc is the dominant luminous component of the Milky Way (MW). Although our understanding of its structure is rapidly expanding due to advances in large-scale stellar surveys, our picture of the MW disc remains substantially obscured by selection functions and incomplete spatial coverage of observational data. In this work, we present the comprehensive chrono-chemo-kinematic structure of the MW disc, recovered using a novel orbit superposition approach combined with data from APOGEE DR 17. We detect periodic azimuthal metallicity variations within 6-8 kpc with an amplitude of 0.05-0.1 dex peaking along the bar major axis. The radial metallicity profile of the MW also varies with azimuth, displaying a pattern typical among other disc galaxies: a decline outside the solar radius and an almost flat profile in the inner region, attributed to the presence of old, metal-poor high-{\alpha} populations, which comprise about 40% of the total stellar mass. The geometrically defined thick disc and the high-{\alpha} populations have comparable masses, with differences in their stellar population content, which we quantify using the reconstructed 3D MW structure. The well-known [{\alpha}/Fe]-bimodality in the MW disc, once weighted by stellar mass, is less pronounced at a given metallicity for the whole galaxy but distinctly visible in a narrow range of galactic radii (5-9 kpc), explaining its relative lack of prominence in external galaxies and galaxy formation simulations. Analysing a more evident double age-abundance sequence, we construct a scenario for the MW disc formation, advocating for an inner/outer disc dichotomy genetically linked to the MW's evolutionary stages. In this picture, the extended solar vicinity is a transition zone that shares chemical properties of both the inner (old age-metallicity sequence) and outer discs (young age-metallicity sequence)., Comment: 36 pages, 34 figures; submitted to A&A

We introduce a novel orbit superposition method designed to reconstruct the stellar density structure, kinematics, and chemical abundance distribution of the entire Milky Way by leveraging 6D phase-space information from its resolved stellar populations, limited by the spatial coverage of APOGEE DR17., Comment: 17 pages, 13 figures, submitted to A&A

BabyIAXO is the intermediate stage of the International Axion Observatory (IAXO) to be hosted at DESY. Its primary goal is the detection of solar axions following the axion helioscope technique. Axions are converted into photons in a large magnet that is pointing to the sun. The resulting X-rays are focused by appropriate X-ray optics and detected by sensitive low-background detectors placed at the focal spot. The aim of this article is to provide an accurate quantitative description of the different components (such as the magnet, optics, and X-ray detectors) involved in the detection of axions. Our efforts have focused on developing robust and integrated software tools to model these helioscope components, enabling future assessments of modifications or upgrades to any part of the IAXO axion helioscope and evaluating the potential impact on the experiment's sensitivity. In this manuscript, we demonstrate the application of these tools by presenting a precise signal calculation and response analysis of BabyIAXO's sensitivity to the axion-photon coupling. Though focusing on the Primakoff solar flux component, our virtual helioscope model can be used to test different production mechanisms, allowing for direct comparisons within a unified framework., Comment: 36 pages, 18 figures, 4 tables, Submitted to JHEP

We report the discovery of five galaxy candidates at redshifts between $15.9

We present a new class of interacting dark sector models that can address the Hubble tension. Interacting dark radiation (DR) has previously been put forward as a solution to the problem, but this proposal is disfavored by the high-$\ell$ cosmic microwave background (CMB) data. We modify this basic framework by introducing a subcomponent of dark matter (DM) that interacts strongly with the DR, so that together they constitute a tightly coupled fluid at early times. We show that if this subcomponent decouples from the interacting DR during the CMB epoch, the $\ell$ modes of the CMB that entered the horizon before decoupling are impacted differently from those that entered after, allowing a solution to the problem. We present a model that realizes this framework, which we dub "New Atomic Dark Matter", or nuADaM, in which the interacting dark matter (iDM) subcomponent is composed of dark atoms, and dark "neutrinos" with long-range interactions contribute to the DR, hence the name of the model. This iDM subcomponent is acoustic at early times but decouples from the DR following dark recombination. In contrast to conventional atomic dark matter (ADM) models, the dark photon is part of a richer DR sector, which ensures that it continues to be self-interacting even after recombination. We show that this model admits a fit to the available cosmological data that is significantly better than both $\Lambda$CDM and conventional ADM., Comment: 47 pages, 15 figures

We give an efficient algorithmic method to calculate minimal projective resolutions of simple modules for a finite dimensional incidence $k$-algebra $\Lambda$, where $k$ is a field. We apply the method to the calculation of $Ext$-groups between simple $\Lambda$-modules, Hochschild cohomology groups $HH^i(\Lambda, \Lambda)$, and singular cohomology groups of finite $T_0$ topological spaces with coefficients in $k$., Comment: 14 pages

Graph neural networks (GNNs) have emerged as a promising solution to deal with unstructured data, outperforming traditional deep learning architectures. However, most of the current GNN models are designed to work with a single graph, which limits their applicability in many real-world scenarios where multiple graphs may be involved. To address this limitation, we propose a novel graph-based deep learning architecture to handle tasks where two sets of signals exist, each defined on a different graph. First we consider the setting where the input is represented as a signal on top of one graph (input graph) and the output is a graph signal defined over a different graph (output graph). For this setup, we propose a three-block architecture where we first process the input data using a GNN that operates over the input graph, then apply a transformation function that operates in a latent space and maps the signals from the input to the output graph, and finally implement a second GNN that operates over the output graph. Our goal is not to propose a single specific definition for each of the three blocks, but rather to provide a flexible approach to solve tasks involving data defined on two graphs. The second part of the paper addresses a self-supervised setup, where the focus is not on the output space but on the underlying latent space and, inspired by Canonical Correlation Analysis, we seek informative representations of the data that can be leveraged to solve a downstream task. By leveraging information from multiple graphs, the proposed architecture can capture more intricate relationships between different entities in the data. We test this in several experimental setups using synthetic and real world datasets, and observe that the proposed architecture works better than traditional deep learning architectures, showcasing the importance of leveraging the information of the two graphs.

This study investigates the influence of Herbig-Haro jets on initiating star formation in dense environments. When molecular clouds are nearing gravitational instability, the impact of a protostellar jet could provide the impetus needed to catalyse star formation. A high-energy-density experiment was carried out at the LULI2000 laser facility, where a supersonic jet generated by a nanosecond laser was used to compress a foam or plastic ball, mimicking the interaction of a Herbig-Haro jet with a molecular cloud. Simulations using the 3D radiation hydrodynamics code TROLL provided comprehensive data for analysing ball compression and calculating jet characteristics. After applying scaling laws, similarities between stellar and experimental jets were explored. Diagnostic simulations, including density gradient, emission and X-ray radiographies, showed strong agreement with experimental data. The results of the experiment, supported by simulations, demonstrated that the impact of a protostellar jet on a molecular cloud could reduce the Bonnor-Ebert mass by approximately 9%, thereby initiating collapse., Comment: Submitted to the Astrophysical Journal

Recent predictions of metastable high-temperature hydride superconductors give hope that superconductivity at ambient conditions is within reach. In this work, we predict RbPH$_3$ as a new compound with a superconducting critical temperature around 100 K at ambient pressure, dynamically stabilized thanks to ionic quantum anharmonic effects. RbPH$_3$ is thermodynamically stable at 30 GPa in a perovskite $Pm\bar{3}m$ phase, allowing its experimental synthesis at moderate pressures far from the megabar regime. With lowering pressure it is expected to transform to a $R3m$ phase that should stay dynamically stable thanks to quantum fluctuations down to ambient pressures. Both phases are metallic, with the $R3m$ phase having three distinct Fermi surfaces, composed mostly of states with phosphorus and hydrogen character. The structures are held together by strong P-H covalent bonds, resembling the pattern observed in the high-temperature superconducting H$_3$S, with extra electrons donated by rubidium. These results demonstrate that quantum ionic fluctuations, neglected thus far in high-throughput calculations, can stabilize at ambient pressure hydride superconductors with a high critical temperature.

Gigahertz Peaked Spectrum (GPS) and Compact Steep Spectrum (CSS) sources are compact radio galaxies (RGs), with jets extending up to 20 kpc and ages <10^3 years. They are considered to evolve to Fanaroff-Riley RGs, but the real scenario to explain the compact sources remains unsolved. The young compact jets make GPS/CSS ideal for studying feedback in the nuclear region of AGNs because the jets are just starting to leave this region. Numerical simulations and jet power estimates suggest that compact sources can drive outflows on scales several times larger than the radio source itself, but the lack of suitable data limits comparisons between theory and observation. We carried out an optical spectroscopic study of 82 CSS/GPS with SDSS-DR12 data to investigate the influence of compact jets in the gas. We found outflowing gas components in the [OIII]\lambda5007 emission lines in half of our sample. The kinetic energy of the outflowing gas in compact sources is comparable to that observed in extended RGs, indicating that the compact jets can drive powerful outflows similar to those in FR RGs. The observed anti-correlation between the kinetic power of the outflow and the radio luminosity suggests an interaction between the young jet and the interstellar medium (ISM). This finding provides significant observational support for previous simulations of jet-ISM interactions and supports the evolutionary scenario for RGs. However, the lack of sources with high kinetic efficiency indicates that some compact galaxies may be frustrated sources., Comment: Accepted for publication in ApJ, 49 pages, 37 figures. Some typos corrected

Despite the popularity of large language model (LLM) quantization for inference acceleration, significant uncertainty remains regarding the accuracy-performance trade-offs associated with various quantization formats. We present a comprehensive empirical study of quantized accuracy, evaluating popular quantization formats (FP8, INT8, INT4) across academic benchmarks and real-world tasks, on the entire Llama-3.1 model family. Additionally, our study examines the difference in text generated by quantized models versus their uncompressed counterparts. Beyond benchmarks, we also present a couple of quantization improvements which allowed us to obtain state-of-the-art accuracy recovery results. Our investigation, encompassing over 500,000 individual evaluations, yields several key findings: (1) FP8 weight and activation quantization (W8A8-FP) is lossless across all model scales, (2) INT8 weight and activation quantization (W8A8-INT), when properly tuned, incurs surprisingly low 1-3% accuracy degradation, and (3) INT4 weight-only quantization (W4A16-INT) is competitive with 8-bit integer weight and activation quantization. To address the question of the "best" format for a given deployment environment, we conduct inference performance analysis using the popular open-source vLLM framework on various GPU architectures. We find that W4A16 offers the best cost-efficiency for synchronous deployments, and for asynchronous deployment on mid-tier GPUs. At the same time, W8A8 formats excel in asynchronous "continuous batching" deployment of mid- and large-size models on high-end GPUs. Our results provide a set of practical guidelines for deploying quantized LLMs across scales and performance requirements.

This work presents a low-rank tensor model for multi-dimensional Markov chains. A common approach to simplify the dynamical behavior of a Markov chain is to impose low-rankness on the transition probability matrix. Inspired by the success of these matrix techniques, we present low-rank tensors for representing transition probabilities on multi-dimensional state spaces. Through tensor decomposition, we provide a connection between our method and classical probabilistic models. Moreover, our proposed model yields a parsimonious representation with fewer parameters than matrix-based approaches. Unlike these methods, which impose low-rankness uniformly across all states, our tensor method accounts for the multi-dimensionality of the state space. We also propose an optimization-based approach to estimate a Markov model as a low-rank tensor. Our optimization problem can be solved by the alternating direction method of multipliers (ADMM), which enjoys convergence to a stationary solution. We empirically demonstrate that our tensor model estimates Markov chains more efficiently than conventional techniques, requiring both fewer samples and parameters. We perform numerical simulations for both a synthetic low-rank Markov chain and a real-world example with New York City taxi data, showcasing the advantages of multi-dimensionality for modeling state spaces.

In this paper, we present XST-GCNN (eXplainable Spatio-Temporal Graph Convolutional Neural Network), a novel architecture for processing heterogeneous and irregular Multivariate Time Series (MTS) data. Our approach captures temporal and feature dependencies within a unified spatio-temporal pipeline by leveraging a GCNN that uses a spatio-temporal graph aimed at optimizing predictive accuracy and interoperability. For graph estimation, we introduce techniques, including one based on the (heterogeneous) Gower distance. Once estimated, we propose two methods for graph construction: one based on the Cartesian product, treating temporal instants homogeneously, and another spatio-temporal approach with distinct graphs per time step. We also propose two GCNN architectures: a standard GCNN with a normalized adjacency matrix and a higher-order polynomial GCNN. In addition to accuracy, we emphasize explainability by designing an inherently interpretable model and performing a thorough interpretability analysis, identifying key feature-time combinations that drive predictions. We evaluate XST-GCNN using real-world Electronic Health Record data from University Hospital of Fuenlabrada to predict Multidrug Resistance (MDR) in ICU patients, a critical healthcare challenge linked to high mortality and complex treatments. Our architecture outperforms traditional models, achieving a mean ROC-AUC score of 81.03 +- 2.43. Furthermore, the interpretability analysis provides actionable insights into clinical factors driving MDR predictions, enhancing model transparency. This work sets a benchmark for tackling complex inference tasks with heterogeneous MTS, offering a versatile, interpretable solution for real-world applications.

Background: Brain maturation and aging involve significant microstructural changes, resulting in functional and cognitive alterations. Quantitative MRI (qMRI) can measure this evolution, distinguishing the physiological effects of normal aging from pathological deviations. Methods: We conducted a multicentre study using qMRI metrics (R1, R2*, and Quantitative Susceptibility Mapping) to model age trajectories across brain structures, including tractography-based white matter bundles (TWMB), superficial white matter (SWM), and cortical grey matter (CGM). MRI data from 537 healthy subjects, aged 8 to 79 years, were harmonized using two independent methods. We modeled age trajectories and performed regional analyses to capture maturation patterns and aging effects across the lifespan. Findings: Our findings revealed a distinct brain maturation gradient, with early qMRI peak values in TWMB, followed by SWM, and culminating in CGM regions. This gradient was observed as a posterior-to-anterior maturation pattern in the cortex and an inferior-to-superior maturation pattern in white matter tracts. R1 demonstrated the most robust age trajectories, while R2* and susceptibility exhibited greater variability and different patterns. The normative modeling framework confirmed the reliability of our age-modelled trajectories across datasets. Interpretation: Our study highlights the potential of multiparametric qMRI to capture complex, region-specific brain development patterns, addressing the need for comprehensive, age-spanning studies across multiple brain structures. Various harmonization strategies can merge qMRI cohorts, improving the robustness of qMRI-based age models and facilitating the understanding of normal patterns and disease-associated deviations.

In smart cities, it is common practice to define a maximum length of stay for a given parking space to increase the space's rotativity and discourage the usage of individual transportation solutions. However, automatically determining individual car dwell times from images faces challenges, such as images collected from low-resolution cameras, lighting variations, and weather effects. In this work, we propose a method that combines two deep neural networks to compute the dwell time of each car in a parking lot. The proposed method first defines the parking space status between occupied and empty using a deep classification network. Then, it uses a Siamese network to check if the parked car is the same as the previous image. Using an experimental protocol that focuses on a cross-dataset scenario, we show that if a perfect classifier is used, the proposed system generates 75% of perfect dwell time predictions, where the predicted value matched exactly the time the car stayed parked. Nevertheless, our experiments show a drop in prediction quality when a real-world classifier is used to predict the parking space statuses, reaching 49% of perfect predictions, showing that the proposed Siamese network is promising but impacted by the quality of the classifier used at the beginning of the pipeline., Comment: Paper accepted to the 2024 International Conference on Machine Learning and Applications

We apply split conformal prediction techniques to regression problems with circular responses by introducing a suitable conformity score, leading to prediction sets with adaptive arc length and finite-sample coverage guarantees for any circular predictive model under exchangeable data. Leveraging the high performance of existing predictive models designed for linear responses, we analyze a general projection procedure that converts any linear response regression model into one suitable for circular responses. When random forests serve as basis models in this projection procedure, we harness the out-of-bag dynamics to eliminate the necessity for a separate calibration sample in the construction of prediction sets. For synthetic and real datasets the resulting projected random forests model produces more efficient out-of-bag conformal prediction sets, with shorter median arc length, when compared to the split conformal prediction sets generated by two existing alternative models., Comment: 7 pages; 4 figures

In this work we investigate the presence of scalar field models supporting kink solutions with logarithmic tails, which we call super long-range structures. We first consider models with a single real scalar field and associate the long-range profile to the orders of vanishing derivatives of the potential at its minima. We then present a model whose derivatives are null in all orders and obtain analytical solutions with logarithmic falloff. We also show that these solutions are stable under small fluctuations. To investigate the forces between super long-range structures, we consider three methods and compare them. Next, we study two-field models in which the additional field is used to modify the kinetic term of the other. By using a first-order formalism based on the minimization of the energy, we explore the situation in which one of the fields can be obtained independently from the other. Within this framework, we unveil how to smoothly go from long- or short- to super long-range structures., Comment: 14 pages, 13 figures