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This research explores the development and application of the High-Order Dynamic Integration Method for solving integro-differential equations, with a specific focus on turbulent fluid dynamics. Traditional numerical methods, such as the Finite Difference Method and the Finite Volume Method, have been widely employed in fluid dynamics but struggle to accurately capture the complexities of turbulence, particularly in high Reynolds number regimes. These methods often require significant computational resources and are prone to errors in nonlinear dynamic systems. The High-Order Dynamic Integration Method addresses these challenges by integrating higher-order interpolation techniques with dynamic adaptation strategies, significantly enhancing accuracy and computational efficiency. Through rigorous numerical analysis, this method demonstrates superior performance over the Finite Difference Method and the Finite Volume Method in handling the nonlinear behaviors characteristic of turbulent flows. Furthermore, the High-Order Dynamic Integration Method achieves this without a substantial increase in computational cost, making it a highly efficient tool for simulations in computational fluid dynamics. The research validates the capabilities of the High-Order Dynamic Integration Method through a series of benchmark tests and case studies. Results indicate a marked improvement in both accuracy and stability, particularly in simulations of high-Reynolds-number flows, where traditional methods often falter. This innovative approach offers a robust and efficient alternative for solving complex fluid dynamics problems, contributing to advances in the field of numerical methods and computational fluid dynamics., Comment: 7 pages

This work extends the study of mean field equations arising in two-dimensional (2D) turbulence by introducing generalized weighted Sobolev operators. Employing variational methods, particularly the mountain pass theorem and a refined blow-up analysis, we establish the existence of nontrivial solutions under broader boundary conditions than those considered in previous studies. In contrast to the unweighted approach developed by Ricciardi (2006), the incorporation of variable weights \(\rho(x)\) enables a more accurate representation of complex geometric domains. This generalization addresses cases where the geometry of the manifold or external factors induce local fluctuations, thereby enhancing the applicability of mean field models to real-world turbulence phenomena. The weighted Sobolev framework also strengthens control over the nonlinearity in the problem, reducing the risk of blow-up and ensuring the convergence of solutions in the \(H^1(M)\) space. The analysis reveals new conditions for the existence of solutions, including situations where classical methods may fail due to a lack of compactness. The significant advancement lies in the flexibility of the model; the inclusion of weights allows for a more precise description of physical systems in varied settings, representing a key advantage over the original unweighted problem. These results contribute to a deeper understanding of the role of geometric and physical constraints in turbulent behavior and pave new pathways for future research on nonlinear partial differential equations in complex domains., Comment: 11 pages

Autism Spectrum Disorder (ASD) is a neurodivergent condition with a wide range of characteristics and support levels. Individuals with ASD can exhibit various combinations of traits such as difficulties in social interaction, communication, and language, alongside restricted interests and repetitive activities. Many adults with ASD live independently due to increased awareness and late diagnoses, which help them manage long-standing challenges. Predictability, clarity, and minimized sensory stimuli are crucial for the daily comfort of autistic individuals. In mobile applications, autistic users face significant cognitive overload compared to neurotypicals, resulting in higher effort and time to complete tasks. Urban mobility apps, essential for daily routines, often overlook the needs of autistic users, leading to cognitive overload issues. This study investigates the accessibility of urban mobility apps for autistic individuals using the Interfaces Accessibility Guide for Autism (GAIA). By evaluating various apps, we have identified a common gap regarding accessibility for people with Autism Spectrum Disorder (ASD). This limitation relates to the absence of a functionality that allows users on the autism spectrum to customize the characteristics of the textual and visual elements of the software, such as changing the text font, altering the font type, and adjusting text colors, as well as native audio guidance within the applications themselves. Currently, the only function in this context is for visually impaired people, which completely changes the user experience in terms of navigation., Comment: Author pre-print of article published in the XXIII Brazilian Symposium on Human Factors in Computing Systems (IHC'24)

This paper presents an innovative framework for analyzing the regularity of solutions to the stochastic Navier-Stokes equations by integrating Sobolev-Besov hybrid spaces with fractional operators and quantum-inspired dynamics. We propose new regularity theorems that address the multiscale and chaotic nature of fluid flows, offering novel insights into energy dissipation mechanisms. The introduction of a Schr\"odinger-like operator into the fluid dynamics model captures quantum-scale turbulence effects, enhancing our understanding of energy redistribution across different scales. The results also include the development of anisotropic stochastic models that account for direction-dependent viscosity, improving the representation of real-world turbulent flows. These advances in stochastic modeling and regularity analysis provide a comprehensive toolset for tackling complex fluid dynamics problems. The findings are applicable to fields such as engineering, quantum turbulence, and environmental sciences. Future directions include the numerical implementation of the proposed models and the use of machine learning techniques to optimize parameters for enhanced simulation accuracy., Comment: 14 pages

We present a systematic study of the energy levels of twisted bilayer graphene (tBLG) quantum dots (QD) and rings (QR) under an external perpendicular magnetic field. The confinement structures are modeled by a circular dot-like- and ring-like-shaped site-dependent staggered potential, which prevents edge effects and leads to an energy gap between the electron and hole states. Results are obtained within the tight-binding model with interlayer hopping parameters defined by the Slater-Koster form for different interlayer twist angles $\theta$. Our findings show that, for $\theta$ around 0$^\circ$ or $60^\circ$, the energy spectra exhibit features resulting from the interplay between characteristics of the AA and AB/BA stacking orders that compose the moir\'e pattern of such tBLG, while the low-energy levels are shown to be nearly independent on the rotation angle for $10^\circ\lesssim \theta\lesssim 50^\circ$. In the absence of a magnetic field, the energy levels of the QR scale with its width $W$ according to a power law $W^{-\alpha}$, whose exponent $1 \lessapprox\alpha\lessapprox 2$ depends on the twist angle. Most interestingly, the lowest energy states of tBLG QRs oscillate as a function of its average radius, with the oscillation period matching half of the moir\'e period. In the presence of an intense magnetic field, the lowest energy levels for the tBLG QDs and QRs match almost perfectly, regardless of whether the external radius of the quantum confinement structure is smaller or on the order of the moir\'e period, which is due to the interplay of the trigonal warping effect and the preferential localization of the eigenstates. Our results reveal relevant information about the moir\'e pattern in tBLG and its role in charge particle confinement., Comment: 16 pages, 12 figures

This paper presents a rigorous study of advanced functional spaces, with a focus on Sobolev and Besov spaces, to investigate key aspects of fluid dynamics, including the regularity of solutions to the Navier-Stokes equations, hypercomplex bifurcations, and turbulence. We offer a comprehensive analysis of Sobolev embedding theorems in fractional spaces and apply bifurcation theory within quaternionic dynamical systems to better understand the complex behaviors in fluid systems. Additionally, the research delves into energy dissipation mechanisms in turbulent flows through the framework of Besov spaces. Key mathematical tools, such as interpolation theory, Littlewood-Paley decomposition, and energy cascade models, are integrated to develop a robust theoretical approach to these problems. By addressing challenges related to the existence and smoothness of solutions, this work contributes to the ongoing exploration of the open Navier-Stokes problem, providing new insights into the intricate relationship between fluid dynamics and functional spaces., Comment: 27 pages

We conducted systematic radiative transfer (RT) modeling of the Mg II doublet line profiles for 33 low-redshift Lyman continuum (LyC) leakers, and Ly$\alpha$ modeling for a subset of six objects, using a multiphase, clumpy circumgalactic medium (CGM) model. Our RT models successfully reproduced the Mg II line profiles for all 33 galaxies, revealing a necessary condition for strong LyC leakage: high maximum clump outflow velocity ($v_{\rm MgII,\,max} \gtrsim 390\,\rm km\,s^{-1}$) and low total Mg II column density ($N_{\rm MgII,\,tot} \lesssim 10^{14.3}\,\rm cm^{-2}$). We found that the clump outflow velocity and total Mg II column density have the most significant impact on Mg II spectra and emphasized the need for full RT modeling to accurately extract the CGM gas properties. In addition, using archival HST COS/G160M data, we modeled Ly$\alpha$ profiles for six objects and found that their spectral properties do not fully align with the conventional LyC leakage criteria, yet no clear correlation was identified between the modeled parameters and observed LyC escape fractions. We inferred LyC escape fractions based on HI properties from Ly$\alpha$ RT modeling and found that LyC leakage is primarily governed by the number of optically thick HI clumps per sightline ($f_{\rm cl}$). Intriguingly, two galaxies with relatively low observed LyC leakage exhibited the highest RT-inferred LyC escape fractions due to their lowest $f_{\rm cl}$ values, driven by the strong blue peaks of their Ly$\alpha$ emission. Future high-resolution, spatially resolved observations are crucial for resolving this puzzle. Overall, our results support a "picket fence" geometry over a "density-bounded" scenario for the CGM, where a combination of high Mg II outflow velocities and low Mg II column densities may be correlated with the presence of more low-density HI channels that facilitate LyC escape., Comment: 22 pages, 16 figures, comments are welcome

We analysed ALMA FIR (1.3 mm) dust continuum and CO emission of 12 starburst galaxies at $z\sim 2.1-3.6$, selected for their extreme brightness in the rest-UV with $M_{\rm UV} = -23.4$ to $-24.7$. We also analysed VLT HAWK-I $H$- and $K_{\rm s}$-band images. The galaxies are characterised by negligible dust attenuations with blue UV spectral slopes ($-2.62$ to $-1.84$), very young stellar populations of $\sim 10$ Myr, and powerful starbursts with a high mean specific star formation rate of $\rm 112~Gyr^{-1}$, placing them $\sim 1.5$~dex above the main sequence at similar redshifts and stellar masses ($M_{\rm stars} \sim (1.5-4.6)\times 10^9~M_{\odot}$). The FIR dust continuum emission revealed in 9 galaxies yields IR luminosities of $(5.9-28.3)\times 10^{11}~L_{\odot}$ and large dust masses barely produced by SNe within the 10~Myr timescale. The CO emission detected in 8 galaxies evidence large molecular gas masses with a mean molecular gas fraction of 82%. The corresponding star formation efficiencies reach $\gtrsim 40$\%, with amazingly short molecular gas depletion timescales between <13 Myr and 71 Myr. These unique properties, never reported in previously studied galaxies, highlight that these galaxies are likely caught at the very beginning of their stellar mass build-up and undergo a very efficient and fast conversion of gas into stars that can only result from the gas collapse within very short free-fall times. We find that the feedback-free starburst model seems to be able to explain the formation of these galaxies. To reconcile the co-spatial FIR dust emission with the UV-bright unattenuated emission, we speculate about radiation-driven outflows which can temporarily remove dust at the location of the starburst and expel dust at large distances in line with the measured large FIR effective radii ($\rm 1.7~kpc - 5~kpc$) in comparison to very compact stellar radii., Comment: 20 pages, 10 figures, 3 tables. Submitted to Astronomy & Astrophysics

Topological quantum field theories (TQFT) encode quantum correlations in topological features of spaces. In this work, we leverage this feature to explore how information encoded in TQFTs can be stored and retrieved in the presence of local decoherence affecting its physical carriers. TQFT states' inherent nonlocality, redundancy, and entanglement position them as natural error-correcting codes. We demonstrate that information recovery protocols can be derived from the principle that protected information must be uniformly distributed across the system and from interpreting correlations in terms of space connectivity. Specifically, we employ a topological framework to devise erasure error correction protocols, showing that information can be successfully recovered even when parts of the system are corrupted., Comment: 24 pages, 3 figures, color diagrams

On October 3, 2024, the "Em\'ilias Podcast -- Women in Computing" celebrates its 5th anniversary, standing out as a platform that promotes the participation of women in STEM (an acronym for "science, technology, engineering, and mathematics"). The podcast aims to provide a space for women in computing and related fields to share their experiences and highlight the various opportunities in Information and Communication Technology (ICT). The methodology included a feedback survey with interviewees, conducted via Google Forms, to assess their experience and determine whether they would recommend the podcast. In addition, we analyzed audience data, which showed consistent growth over the five years. The results revealed that 100% of the interviewees would recommend "Em\'ilias Podcast," reflecting a high level of satisfaction with the project. The average participation experience rating was 4.7 on a scale of 1 to 5, highlighting positive aspects such as the quality of the script, the interview conduction, and the networking opportunities. The audience data also underscore the podcast's impact: with over 10,000 accumulated downloads and plays, it is primarily listened to by people aged 23 to 44, with 50.9% of the audience being female, demonstrating its relevance and reach. In conclusion, the feedback from interviewees and the audience data reinforce the podcast's positive impact and its crucial role in the inclusion of women in technology. The results highlight the importance of promoting the field and its opportunities, contributing to a more inclusive and inspiring future. The data analysis demonstrates the podcast's effectiveness in engaging and expanding its audience, establishing it as a significant example of social impact in ICT., Comment: In Portuguese. Accepted for presentation at XIV SEMIN\'ARIO DE EXTENS\~AO E INOVA\c{C}\~AO. UTFPR 2024

Predictive queries over spatiotemporal (ST) stream data pose significant data processing and analysis challenges. ST data streams involve a set of time series whose data distributions may vary in space and time, exhibiting multiple distinct patterns. In this context, assuming a single machine learning model would adequately handle such variations is likely to lead to failure. To address this challenge, we propose StreamEnsemble, a novel approach to predictive queries over ST data that dynamically selects and allocates Machine Learning models according to the underlying time series distributions and model characteristics. Our experimental evaluation reveals that this method markedly outperforms traditional ensemble methods and single model approaches in terms of accuracy and time, demonstrating a significant reduction in prediction error of more than 10 times compared to traditional approaches., Comment: 13 pages

Strongly lensed supernovae (SNe) are a rare class of transient that can offer tight cosmological constraints that are complementary to methods from other astronomical events. We present a follow-up study of one recently-discovered strongly lensed SN, the quadruply-imaged Type Ia SN 2022qmx (aka, "SN Zwicky") at z = 0.3544. We measure updated, template-subtracted photometry for SN Zwicky and derive improved time delays and magnifications. This is possible because SNe are transient, fading away after reaching their peak brightness. Specifically, we measure point spread function (PSF) photometry for all four images of SN Zwicky in three Hubble Space Telescope WFC3/UVIS passbands (F475W, F625W, F814W) and one WFC3/IR passband (F160W), with template images taken $\sim 11$ months after the epoch in which the SN images appear. We find consistency to within $2\sigma$ between lens model predicted time delays ($\lesssim1$ day), and measured time delays with HST colors ($\lesssim2$ days), including the uncertainty from chromatic microlensing that may arise from stars in the lensing galaxy. The standardizable nature of SNe Ia allows us to estimate absolute magnifications for the four images, with images A and C being elevated in magnification compared to lens model predictions by about $6\sigma$ and $3\sigma$ respectively, confirming previous work. We show that millilensing or differential dust extinction is unable to explain these discrepancies and find evidence for the existence of microlensing in images A, C, and potentially D, that may contribute to the anomalous magnification., Comment: Submitted to ApJ

A geometrically nonlinear theory for field dislocation thermomechanics based entirely on measurable state variables is proposed. Instead of starting from an ordering-dependent multiplicative decomposition of the total deformation gradient tensor, the additive decomposition of the velocity gradient into elastic, plastic and thermal distortion rates is obtained as a natural consequence of the conservation of the Burgers vector. Based on this equation, the theory consistently captures the contribution of transient heterogeneous temperature fields on the evolution of the (polar) dislocation density. The governing equations of the model are obtained from the conservation of Burgers vector, mass, linear and angular momenta, and the First Law. The Second Law is used to deduce the thermodynamical driving forces for dislocation velocity. An evolution equation for temperature is obtained from the First Law and the Helmholtz free energy density, which is taken as a function of the following measurable quantities: elastic distortion, temperature and the dislocation density (the theory allows prescribing additional measurable quantities as internal state variables if needed). Furthermore, the theory allows one to compute the Taylor-Quinney factor, which is material and strain rate dependent. Accounting for the polar dislocation density as a state variable in the Helmholtz free energy of the system allows for temperature solutions in the form of dispersive waves with finite propagation speed, despite using Fourier's law of heat conduction as the constitutive assumption for the heat flux vector., Comment: 34 pages, 3 figures, preprint submitted to journal

One of the fundamental questions of cosmology is the origin and mechanism(s) responsible for the reionization of the Universe beyond $z\sim6$. To address this question, many studies over the past decade have focused on local ($z\sim0.3$) galaxies which leak ionizing radiation (Lyman continuum or LyC). However, line-of-sight effects and data quality have prohibited deeper insight into the nature of LyC escape. To circumvent these limitations, we analyze stacks of a consolidated sample of {\it HST}/COS observations of the LyC in 89 galaxies at $z\sim0.3$. From fitting of the continuum, we obtain information about the underlying stellar populations and neutral ISM geometry. We find that most LyC non-detections are not leaking appreciable LyC ($f_{esc}^{\rm LyC}<1$\%) but also that exceptional cases point to spatial variations in the LyC escape fraction $f_{esc}^{\rm LyC}$. Stellar populations younger than 3 Myr lead to an increase in ionizing feedback, which in turn increases the isotropy of LyC escape. Moreover, mechanical feedback from supernovae in 8-10 Myr stellar populations is important for anisotropic gas distributions needed for LyC escape. While mechanical feedback is necessary for any LyC escape, high $f_{esc}^{\rm LyC}$ ($>5$\%) also requires a confluence of young stars and ionizing feedback. A two-stage burst of star formation could facilitate this optimal LyC escape scenario., Comment: Submitted to AAS publications, 42 pages, 25 figures

We demonstrate chiral propagation of plasmon polaritons and show it is more efficient and easier to control than the recently observed chiral shear phonon polaritons. We consider plasmon polaritons created in an anisotropic two-dimensional (2D) material, twisted with respect to an anisotropic substrate, to best exploit the competition between anisotropic electron-electron interactions and the anisotropic electronic structure of the host material. Gate voltage and twist angle are then used for precise control of the chiral plasmon polaritons, overcoming the existing restrictions with chiral phonon polaritons. These findings open up feasible opportunities for efficient and tunable plasmon-based nanophotonics and compact high-performance on-chip optical devices.

On large scales, measurements of the Lyman-$\alpha$ forest offer insights into the expansion history of the Universe, while on small scales, these impose strict constraints on the growth history, the nature of dark matter, and the sum of neutrino masses. This work introduces ForestFlow, a cosmological emulator designed to bridge the gap between large- and small-scale Lyman-$\alpha$ forest analyses. Using conditional normalizing flows, ForestFlow emulates the 2 Lyman-$\alpha$ linear biases ($b_\delta$ and $b_\eta$) and 6 parameters describing small-scale deviations of the 3D flux power spectrum ($P_\mathrm{3D}$) from linear theory. These 8 parameters are modeled as a function of cosmology $\unicode{x2013}$ the small-scale amplitude and slope of the linear power spectrum $\unicode{x2013}$ and the physics of the intergalactic medium. Thus, in combination with a Boltzmann solver, ForestFlow can predict $P_\mathrm{3D}$ on arbitrarily large (linear) scales and the 1D flux power spectrum ($P_\mathrm{1D}$) $\unicode{x2013}$ the primary observable for small-scale analyses $\unicode{x2013}$ without the need for interpolation or extrapolation. Consequently, ForestFlow enables for the first time multiscale analyses. Trained on a suite of 30 fixed-and-paired cosmological hydrodynamical simulations spanning redshifts from $z=2$ to $4.5$, ForestFlow achieves $3$ and $1.5\%$ precision in describing $P_\mathrm{3D}$ and $P_\mathrm{1D}$ from linear scales to $k=5\,\mathrm{Mpc}^{-1}$ and $k_\parallel=4\,\mathrm{Mpc}^{-1}$, respectively. Thanks to its parameterization, the precision of the emulator is also similar for both ionization histories and two extensions to the $\Lambda$CDM model $\unicode{x2013}$ massive neutrinos and curvature $\unicode{x2013}$ not included in the training set. ForestFlow will be crucial for the cosmological analysis of Lyman-$\alpha$ forest measurements from the DESI survey., Comment: 17 pages, 11 figures. Submitted to A&A

Feedback is widely recognized as an essential condition for Lyman continuum (LyC) escape in star-forming galaxies. However, the mechanisms by which galactic outflows clear neutral gas and dust remain unclear. In this paper, we model the Mg II 2796\r{A}, 2804\r{A} absorption + emission lines in 29 galaxies taken from the Low-z LyC Survey (LzLCS) to investigate the impact of (radiation + mechanical) feedback on LyC escape. Using constraints on Mg$^+$ and photoionization models, we map the outflows' neutral hydrogen content and predict $f_{esc}^{LyC}$ with a multiphase wind model. We measure mass, momentum, and energy loading factors for the neutral winds, which carry up to 10% of the momentum and 1% of the energy in SFR-based deposition rates. We use SED template fitting to determine the relative ages of stellar populations, allowing us to identify radiation feedback dominant systems. We then examine feedback related properties (stellar age, loading factors, etc.) under conditions that optimize feedback efficiency, specifically high star formation rate surface density and compact UV half-light radii. Our findings indicate that the strongest leakers are radiation feedback dominant, lack Mg II outflows, but have extended broad components in higher ionization lines like [O III] 5007\r{A}, as observed by Amor\'in et al. (2024). In contrast, galaxies experiencing supernovae feedback typically exhibit weaker $f_{esc}^{LyC}$ and show evidence of outflows in both Mg II and higher ionization lines. We attribute these findings to rapid or "catastrophic" cooling in the radiation-dominant systems, which, given the low metallicities in our sample, are likely experiencing delayed supernovae., Comment: 34 pages, 16 figures, 7 tables

Increased usage of automated tools like deep learning in medical image segmentation has alleviated the bottleneck of manual contouring. This has shifted manual labour to quality assessment (QA) of automated contours which involves detecting errors and correcting them. A potential solution to semi-automated QA is to use deep Bayesian uncertainty to recommend potentially erroneous regions, thus reducing time spent on error detection. Previous work has investigated the correspondence between uncertainty and error, however, no work has been done on improving the "utility" of Bayesian uncertainty maps such that it is only present in inaccurate regions and not in the accurate ones. Our work trains the FlipOut model with the Accuracy-vs-Uncertainty (AvU) loss which promotes uncertainty to be present only in inaccurate regions. We apply this method on datasets of two radiotherapy body sites, c.f. head-and-neck CT and prostate MR scans. Uncertainty heatmaps (i.e. predictive entropy) are evaluated against voxel inaccuracies using Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves. Numerical results show that when compared to the Bayesian baseline the proposed method successfully suppresses uncertainty for accurate voxels, with similar presence of uncertainty for inaccurate voxels. Code to reproduce experiments is available at https://github.com/prerakmody/bayesuncertainty-error-correspondence, Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:018

Competition between magnetism and superconductivity can lead to unconventional and topological superconductivity. However, the experimental confirmation of the presence of Majorana edge states and unconventional pairing currently poses a major challenge. Here we consider a two-dimensional lattice model for a superconductor with spin-orbit coupling and exchange coupling to randomly distributed magnetic impurities. Depending on parameters of the model, this system may display topologically trivial or nontrivial edge states. We map out the phase diagram by computing the Bott index, a topological invariant defined in real space. We then use machine learning (ML) algorithms to predict the Bott index from the local density of states (LDOS) at zero energy, obtaining high-accuracy results. We also train ML models to predict the amplitude of odd-frequency pairing in the anomalous Green's function at zero energy. Once the ML models are trained using the LDOS, which is experimentally accessible via scanning tunneling spectroscopy, our method could be applied to predict the number of Majorana edge states and to estimate the magnitude of odd-frequency pairing in real materials., Comment: 10 pages, 8 figures

We investigate the electronic properties of nanoribbons made out of monolayer Lieb, transition, and kagome lattices using the tight-binding model with a generic Hamiltonian. It allows us to map the evolutionary stages of the interconvertibility process between Lieb and kagome nanoribbons by means of only one control parameter. Results for the energy spectra, the density of states, and spatial probability density distributions are discussed for nanoribbons with three types of edges: straight, bearded, and asymmetric. We explore for different nanoribbon terminations: (i) the semiconductor-metallic transition due to the interconvertibility of the Lieb and kagome lattices, (ii) the effect of both nanoribbon width and inclusion of the next-nearest-neighbor hopping term on the degeneracy of the quasi-flat states, (iii) the behavior of the energy gap versus the nanoribbon width, (iv) the existence and evolution of edge states, and (v) the nodal spatial distributions of the total probability densities of the non-dispersive states., Comment: 21 pages, 12 figures

Most complex diseases, including cancer and non-malignant diseases like asthma, have distinct molecular subtypes that require distinct clinical approaches. However, existing computational patient stratification methods have been benchmarked almost exclusively on cancer omics data and only perform well when mutually exclusive subtypes can be characterized by many biomarkers. Here, we contribute with a massive evaluation attempt, quantitatively exploring the power of 22 unsupervised patient stratification methods using both, simulated and real transcriptome data. From this experience, we developed UnPaSt (https://apps.cosy.bio/unpast/) optimizing unsupervised patient stratification, working even with only a limited number of subtype-predictive biomarkers. We evaluated all 23 methods on real-world breast cancer and asthma transcriptomics data. Although many methods reliably detected major breast cancer subtypes, only few identified Th2-high asthma, and UnPaSt significantly outperformed its closest competitors in both test datasets. Essentially, we showed that UnPaSt can detect many biologically insightful and reproducible patterns in omic datasets., Comment: The first two authors listed are joint first authors. The last two authors listed are joint last authors

Context. Mock galaxy catalogues are essential for correctly interpreting current and future generations of galaxy surveys. Despite their significance in galaxy formation and cosmology, little to no work has been done to validate the predictions of these mocks for high-order clustering statistics. Aims. We compare the predicting power of the latest generation of empirical models used in the creation of mock galaxy catalogues: a 13-parameter Halo Occupation Distribution (HOD) and an extension of the SubHalo Abundance Matching technique (SHAMe). Methods. We build GalaxyEmu-Planck, an emulator that makes precise predictions for the two-point correlation function, galaxy-galaxy lensing (restricted to distances greater than 1 $h^{-1} {\rm Mpc}$ to avoid baryonic effects), and other high-order statistics resulting from the evaluation of SHAMe and HOD models. Results. We evaluate the precision of GalaxyEmu-Planck using two galaxy samples extracted from the FLAMINGO hydrodynamical simulation that mimic the properties of DESI-BGS and BOSS galaxies, finding that the emulator reproduces all the predicted statistics precisely. The HOD showed comparable performance when fitting galaxy clustering and galaxy-galaxy lensing. In contrast, the SHAMe model showed better predictions for higher-order statistics, especially regarding the galaxy assembly bias. We also tested the performance of the models after removing some of their extensions, finding that we can withdraw two of the HOD parameters without a loss of performance. Conclusions. The results of this paper validate the current generation of empirical models as a way to reproduce galaxy clustering, galaxy-galaxy lensing and other high-order statistics. The excellent performance of the SHAMe model with a small number of free parameters suggests that it is a valid method to extract cosmological constraints from galaxy clustering., Comment: 22 pages, 12 figures, submitted to A&A. Comments welcomed!

J1316+2614 at z=3.613 is the UV-brightest ($M_{UV}$ = -24.7) and strongest Lyman continuum (LyC, $f_{esc}^{LyC} \approx$ 90%) emitting star-forming galaxy known, showing also signatures of inflowing gas from its blue-dominated Ly$\alpha$ profile. Here, we present high-resolution imaging with the HST and VLT of the LyC, Ly$\alpha$, rest-UV, and optical emission of J1316+2614. Detailed analysis of the LyC and UV light distributions reveals compact yet resolved profiles, with LyC and UV morphologies showing identical half-light radii of $\simeq$ 220 pc. The continuum-subtracted Ly$\alpha$ emission reveals an extended filamentary structure of $\simeq$ 6.0 kpc oriented south-north with only weak/residual flux within the stellar core, suggesting a Ly$\alpha$ "hole". J1316+2614 presents remarkably high SFR and stellar mass surface densities of log($\Sigma_{SFR}$ [$M_{\odot}$/yr/kpc^2]) = 3.47$\pm$0.11 and log($\Sigma_{M}$ [$M_{\odot}$/pc^2]) = 4.20$\pm$0.06, respectively, which are among the highest observed in star-forming galaxies. Our findings indicate that J1316+2614 is a powerful, young, and compact starburst, leaking significant LyC photons due to the lack of gas and dust within the starburst. We explore the conditions for gas expulsion using a simple energetic balance and find that, given the strong binding force in J1316+2614, a high star formation efficiency ($\epsilon_{SF} \geq 0.7$) is necessary to remove the gas and explain its exposed nature. Our results thus suggest a close link between high $\epsilon_{SF}$ and high $f_{esc}^{LyC}$. This high efficiency can also naturally explain the remarkably high SFR, UV-luminosity, and efficient mass growth of J1316+2614, where at least 62% of its mass formed in the last 6 Myr. J1316+2614 may exemplify an intense, feedback-free starburst with a high $\epsilon_{SF}$, similar to those proposed for UV-bright galaxies at high redshifts. (ABRIDGED), Comment: 15 pages and 9 figures. Accepted for publication in A&A

We report the discovery of robust spectroscopically confirmed Balmer break (BB) galaxies and candidates, with secure spectroscopic redshifts $7.1 \le z \le 9.6$ from publicly available JWST extra-galactic photometric and spectroscopic surveys. To do so, we used dedicated filters probing the Balmer break and inspected the objects with NIRSpec spectroscopy. We recover the previously known objects with strong Balmer breaks and reveal 10-11 new objects with clear BBs, thus tripling the number of spectroscopically confirmed galaxies with a BB at z >7. Approximately half of them show a pure BB and no signs of recent star formation, whereas the other half shows BB and emission lines, indicating most likely galaxies whose star formation ceased earlier and has restarted recently. Overall we find that ~10-20% of all galaxies from our sample show signatures of an evolved stellar population. Furthermore, we find that the strength of the BB does not significantly depend on the rest-UV and rest-optical brightness of these sources. In short, our work confirms that photometry alone has the potential to measure BB strengths and to identify evolved stellar populations at high redshift and that such objects may be more frequent than previously thought. The presence of galaxies with a range of break strengths and the joint presence of BB and emission lines indicate a bursty nature of the star formation in the early Universe., Comment: 10 pages, 5 figures, 1 table, Accepted for publication in A&A

The presence of very massive stars (VMS, masses $>100$ M$_{\odot}$) is now firmly established in the local group, nearby galaxies, and out to cosmological distances. If present, these stars could boost the UV luminosity and ionizing photon production of galaxies, helping thus to alleviate the overabundance of UV-bright galaxies found with the JWST at high-redshift. Combing consistent stellar evolution and atmosphere models tailored to VMS we compute spectral energy distributions (SEDs) for a large set of models. We find that VMS contribute significantly to the UV luminosity and Lyman continuum of young stellar populations, and they are characterized by strong stellar HeII1640 emission, with EW(HeII) up to 4-8 Ang at young ages or $\sim 2.5-4$ Ang for constant SFR. For IMFs with a Salpeter slope, the boost of the UV luminosity is relatively modest. However, small changes in the IMF slope (e.g.~from $\alpha_2=-2.35$ to $-2$) lead to large increases in $L_{UV}$ and the ionizing photon production $Q$. Emission line strengths and the ionizing photon efficiency $\xi_{\rm ion}$ are also increased with VMS. Interestingly, SEDs including VMS show smaller Lyman breaks, and the shape of the ionizing spectra remain unaltered up to $\sim 35$ eV, but becomes softer at higher energies. We derive and discuss the maximum values quantities such as $L_{UV}$ per stellar mass or unit SFR, and $\xi_{\rm ion}$, $Q$ can reach when VMS are included, and we show that these values become essentially independent of the IMF. We propose observational methods to test for VMS and constrain the IMF. Finally, using JWST observations, we examine if high-redshift galaxies show some evidence of the presence of VMS and signs of non-standard IMFs. Very top-heavy IMFs can be excluded on average, but the IMF could well extend into the regime of VMS and be flatter than Salpeter in the bulk of high-$z$ galaxies. (abridged), Comment: 16 pages, 11 figures. Submitted to Astronomy & Astrophysics. Comments welcome

Acoustic plasmons in graphene exhibit strong confinement induced by a proximate metal surface and hybridize with phonons of transition metal dichalcogenides (TMDs) when these materials are combined in a van der Waals heterostructure, thus forming screened graphene plasmon-phonon polaritons (SGPPPs), a type of acoustic mode. While SGPPPs are shown to be very sensitive to the dielectric properties of the environment, enhancing the SGPPPs coupling strength in realistic heterostructures is still challenging. Here we employ the quantum electrostatic heterostructure model, which builds upon the density functional theory calculations for monolayers, to show that the use of a metal as a substrate for graphene-TMD heterostructures (i) vigorously enhances the coupling strength between acoustic plasmons and the TMD phonons, and (ii) markedly improves the sensitivity of the plasmon wavelength on the structural details of the host platform in real space, thus allowing one to use the effect of environmental screening on acoustic plasmons to probe the structure and composition of a van der Waals heterostructure down to the monolayer resolution., Comment: 21 pages, 7 figures

JWST is uncovering the properties of ever increasing numbers of galaxies at z>6, during the epoch of reionization. Connecting these observed populations to the process of reionization requires understanding how efficiently they produce Lyman continuum (LyC) photons and what fraction (fesc) of these photons escape into the intergalactic medium. By applying the Cox proportional hazards model, a survival analysis technique, to the Low-redshift Lyman Continuum Survey (LzLCS), we develop new, empirical, multivariate predictions for fesc. The models developed from the LzLCS reproduce the observed fesc for z~3 samples, which suggests that LyC emitters may share similar properties at low and high redshift. Our best-performing models for the z~3 galaxies include information about dust attenuation, ionization, and/or morphology. We then apply these models to z$\gtrsim$6 galaxies. For large photometric samples, we find a median predicted fesc=0.047-0.14. For smaller spectroscopic samples, which may include stronger emission line galaxies, we find that $\geq$33% of the galaxies have fesc >0.2, and we identify several candidate extreme leakers with fesc $\geq$0.5. The current samples show no strong trend between predicted fesc and UV magnitude, but limited spectroscopic information makes this result uncertain. Multivariate predictions can give significantly different results from single variable predictions, and the predicted fesc for high-redshift galaxies can differ significantly depending on whether star formation rate surface density or radius is used as a measure of galaxy morphology. We provide all parameters necessary to predict fesc for additional samples of high-redshift galaxies using these models., Comment: Accepted for publication in ApJ. 33 pages, 9 figures, 10 tables, plus appendix

To understand how galaxies reionized the universe, we must determine how the escape fraction of Lyman Continuum (LyC) photons (fesc) depends on galaxy properties. Using the z~0.3 Low-redshift Lyman Continuum Survey (LzLCS), we develop and analyze new multivariate predictors of fesc. These predictions use the Cox proportional hazards model, a survival analysis technique that incorporates both detections and upper limits. Our best model predicts the LzLCS fesc detections with a root-mean-square (RMS) scatter of 0.31 dex, better than single-variable correlations. According to ranking techniques, the most important predictors of fesc are the equivalent width (EW) of Lyman-series absorption lines and the UV dust attenuation, which track line-of-sight absorption due to HI and dust. The HI absorption EW is uniquely crucial for predicting fesc for the strongest LyC emitters, which show properties similar to weaker LyC emitters and whose high fesc may therefore result from favorable orientation. In the absence of HI information, star formation rate surface density ($\Sigma_{\rm SFR}$) and [O III]/[O II] ratio are the most predictive variables and highlight the connection between feedback and fesc. We generate a model suitable for z>6, which uses only the UV slope, $\Sigma_{\rm SFR}$, and [O III]/[O II]. We find that $\Sigma_{\rm SFR}$ is more important in predicting fesc at higher stellar masses, whereas [O III]/[O II] plays a greater role at lower masses. We also analyze predictions for other parameters, such as the ionizing-to-non ionizing flux ratio and Ly=alpha escape fraction. These multivariate models represent a promising tool for predicting fesc at high redshift., Comment: Accepted for publication in ApJ. 34 pages + appendix, 12 figures

We report the discovery of a compact star-forming galaxy at $z=9.380$ in the GOODS-North field (named GN-z9p4) which shows numerous strong UV-optical emission lines and a single UV line, NIV] 1486. This makes GN-z9p4 the third-highest redshift N-emitter known to date. We determine the nebular abundances of H, C, N, O and Ne, size, and other physical properties of this object, and compare them to those of the other N-emitters known so far and to other star-forming galaxies. Using the direct method we find a metallicity 12+log(O/H)$=7.37 \pm 0.15$, one of the lowest among the N-emitters. The N/O abundance ratio is highly super-solar, and C/O and Ne/O normal compared to other galaxies at low metallicity. We show that the compactness of GN-z9p4 (with effective radius $118\pm16$ pc at 2 micron) and other N-emitters translates into very high stellar mass and SFR surface densities, which could be a criterium to identify other N-emitters. Future studies and larger samples are needed to understand these recently discovered, rare, and enigmatic objects., Comment: 6 pages, 4 figures. Astronomy & Astrophysics Letters, in press

Developing therapeutics is a lengthy and expensive process that requires the satisfaction of many different criteria, and AI models capable of expediting the process would be invaluable. However, the majority of current AI approaches address only a narrowly defined set of tasks, often circumscribed within a particular domain. To bridge this gap, we introduce Tx-LLM, a generalist large language model (LLM) fine-tuned from PaLM-2 which encodes knowledge about diverse therapeutic modalities. Tx-LLM is trained using a collection of 709 datasets that target 66 tasks spanning various stages of the drug discovery pipeline. Using a single set of weights, Tx-LLM simultaneously processes a wide variety of chemical or biological entities(small molecules, proteins, nucleic acids, cell lines, diseases) interleaved with free-text, allowing it to predict a broad range of associated properties, achieving competitive with state-of-the-art (SOTA) performance on 43 out of 66 tasks and exceeding SOTA on 22. Among these, Tx-LLM is particularly powerful and exceeds best-in-class performance on average for tasks combining molecular SMILES representations with text such as cell line names or disease names, likely due to context learned during pretraining. We observe evidence of positive transfer between tasks with diverse drug types (e.g.,tasks involving small molecules and tasks involving proteins), and we study the impact of model size, domain finetuning, and prompting strategies on performance. We believe Tx-LLM represents an important step towards LLMs encoding biochemical knowledge and could have a future role as an end-to-end tool across the drug discovery development pipeline.

With stunning clarity, JWST has revealed the Universe's first billion years. The scientific community is analyzing a wealth of JWST imaging and spectroscopic data from that era, and is in the process of rewriting the astronomy textbooks. Here, 1.5 years into the JWST science mission, we provide a snapshot of the great progress made towards understanding the initial chapters of our cosmic history. We highlight discoveries and breakthroughs, topics and issues that are not yet understood, and questions that will be addressed in the coming years, as JWST continues its revolutionary observations of the Early Universe. While this compendium is written by a small number of authors, invited to ISSI Bern in March 2024 as part of the 2024 ISSI Breakthrough Workshop, we acknowledge the work of a large community that is advancing our collective understanding of the evolution of the Early Universe., Comment: review article written by the attendees of the 2024 ISSI breakthrough workshop "The first billion year of the Universe", submitted. Comments welcome

In the evolving landscape of deep learning, selecting the best pre-trained models from a growing number of choices is a challenge. Transferability scorers propose alleviating this scenario, but their recent proliferation, ironically, poses the challenge of their own assessment. In this work, we propose both robust benchmark guidelines for transferability scorers, and a well-founded technique to combine multiple scorers, which we show consistently improves their results. We extensively evaluate 13 scorers from literature across 11 datasets, comprising generalist, fine-grained, and medical imaging datasets. We show that few scorers match the predictive performance of the simple raw metric of models on ImageNet, and that all predictors suffer on medical datasets. Our results highlight the potential of combining different information sources for reliably predicting transferability across varied domains., Comment: 15 pages, 3 figures, 2 tables

The understanding of open quantum systems is crucial for the development of quantum technologies. Of particular relevance is the characterisation of divisible quantum dynamics, seen as a generalisation of Markovian processes to the quantum setting. Here, we propose a way to detect divisibility and quantify how non-divisible a quantum channel is through the concept of channel discrimination. We ask how well we can distinguish generic dynamics from divisible dynamics. We show that this question can be answered efficiently through semidefinite programming, which provides us with an operational and efficient way to quantify divisibility., Comment: 13 pages. 5 figures. Several appendices. Suggestions are welcome!

This paper proposes a problem-independent GRASP metaheuristic using the random-key optimizer (RKO) paradigm. GRASP (greedy randomized adaptive search procedure) is a metaheuristic for combinatorial optimization that repeatedly applies a semi-greedy construction procedure followed by a local search procedure. The best solution found over all iterations is returned as the solution of the GRASP. Continuous GRASP (C-GRASP) is an extension of GRASP for continuous optimization in the unit hypercube. A random-key optimizer (RKO) uses a vector of random keys to encode a solution to a combinatorial optimization problem. It uses a decoder to evaluate a solution encoded by the vector of random keys. A random-key GRASP is a C-GRASP where points in the unit hypercube are evaluated employing a decoder. We describe random key GRASP consisting of a problem-independent component and a problem-dependent decoder. As a proof of concept, the random-key GRASP is tested on five NP-hard combinatorial optimization problems: traveling salesman problem, tree of hubs location problem, Steiner triple covering problem, node capacitated graph partitioning problem, and job sequencing and tool switching problem., Comment: 24 pages, 8 figures

The energy landscape of multiply connected superconducting structures is ruled by fluxoid quantization due to the implied single-valuedness of the complex wave function. The transitions and interaction between these energy states, each defined by a specific phase winding number, are governed by classical and/or quantum phase slips. Understanding these events requires the ability to probe, non-invasively, the state of the ring. Here, we employ a niobium resonator to examine the superconducting properties of an aluminum loop. By applying a magnetic field, adjusting temperature, and altering the loop's dimensions via focused ion beam milling, we correlate resonance frequency shifts with changes in the loop's kinetic inductance. This parameter is a unique indicator of the superconducting condensate's state, facilitating the detection of phase slips in nanodevices and providing insights into their dynamics. Our method presents a proof-of-principle spectroscopic technique with promising potential for investigating the Cooper pair density in inductively coupled superconducting nanostructures.

We study the impact of black hole nuclear activity on both the global and radial star formation rate (SFR) profiles in X-ray-selected active galactic nuclei (AGN) in the field of miniJPAS, the precursor of the much wider J-PAS project. Our sample includes 32 AGN with z < 0.3 detected via the XMM-Newton and Chandra surveys. For comparison, we assembled a control sample of 71 star-forming (SF) galaxies with similar magnitudes, sizes, and redshifts. To derive the global properties of both the AGN and the control SF sample, we used CIGALE to fit the spectral energy distributions derived from the 56 narrowband and 4 broadband filters from miniJPAS. We find that AGN tend to reside in more massive galaxies than their SF counterparts. After matching samples based on stellar mass and comparing their SFRs and specific SFRs (sSFRs), no significant differences appear. This suggests that the presence of AGN does not strongly influence overall star formation. However, when we used miniJPAS as an integral field unit (IFU) to dissect galaxies along their position angle, a different picture emerges. We find that AGN tend to be more centrally concentrated in mass with respect to SF galaxies. Moreover, we find a suppression of the sSFR up to 1Re and then an enhancement beyond 1Re , strongly contrasting with the decreasing radial profile of sSFRs in SF galaxies. This could point to an inside-out quenching of AGN host galaxies. These findings suggest that the reason we do not see differences on a global scale is because star formation is suppressed in the central regions and enhanced in the outer regions of AGN host galaxies. While limited in terms of sample size, this work highlights the potential of the upcoming J-PAS as a wide-field low-resolution IFU for thousands of nearby galaxies and AGN., Comment: 21 pages, 20 figures, Accepted for Publication in A&A

Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of complex multimodal data. Gemini models, with strong general capabilities in multimodal and long-context reasoning, offer exciting possibilities in medicine. Building on these core strengths of Gemini, we introduce Med-Gemini, a family of highly capable multimodal models that are specialized in medicine with the ability to seamlessly use web search, and that can be efficiently tailored to novel modalities using custom encoders. We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin. On the popular MedQA (USMLE) benchmark, our best-performing Med-Gemini model achieves SoTA performance of 91.1% accuracy, using a novel uncertainty-guided search strategy. On 7 multimodal benchmarks including NEJM Image Challenges and MMMU (health & medicine), Med-Gemini improves over GPT-4V by an average relative margin of 44.5%. We demonstrate the effectiveness of Med-Gemini's long-context capabilities through SoTA performance on a needle-in-a-haystack retrieval task from long de-identified health records and medical video question answering, surpassing prior bespoke methods using only in-context learning. Finally, Med-Gemini's performance suggests real-world utility by surpassing human experts on tasks such as medical text summarization, alongside demonstrations of promising potential for multimodal medical dialogue, medical research and education. Taken together, our results offer compelling evidence for Med-Gemini's potential, although further rigorous evaluation will be crucial before real-world deployment in this safety-critical domain.

Bell's theorem prompts us with a fundamental inquiry: what is the simplest scenario leading to the incompatibility between quantum correlations and the classical theory of causality? Here we demonstrate that quantum non-classicality is possible in a network consisting of only three dichotomic variables, without the need of the locality assumption neither external measurement choices. We also show that the use of interventions, a central tool in the field of causal inference, significantly improves the noise robustness of this new kind of non-classical behaviour, making it feasible for experimental tests with current technology., Comment: 4 pages, 2 figures

We propose a graph-based representation learning framework for video summarization. First, we convert an input video to a graph where nodes correspond to each of the video frames. Then, we impose sparsity on the graph by connecting only those pairs of nodes that are within a specified temporal distance. We then formulate the video summarization task as a binary node classification problem, precisely classifying video frames whether they should belong to the output summary video. A graph constructed this way aims to capture long-range interactions among video frames, and the sparsity ensures the model trains without hitting the memory and compute bottleneck. Experiments on two datasets(SumMe and TVSum) demonstrate the effectiveness of the proposed nimble model compared to existing state-of-the-art summarization approaches while being one order of magnitude more efficient in compute time and memory, Comment: arXiv admin note: text overlap with arXiv:2207.07783

The advent of the JWST has revolutionised our understanding of high-redshift galaxies. In particular, the NIRCam instrument on-board JWST has revealed a population of Hubble Space Telescope (HST)-dark galaxies that had previously evaded optical detection, potentially due to significant dust obscuration, quiescence, or simply extreme redshift. Here, we present the first NIRSpec spectra of 23 HST-dark galaxies ($\mathrm{H-F444W>1.75}$), unveiling their nature and physical properties. This sample includes both dusty and quiescent galaxies with spectroscopic data from NIRSpec/PRISM, providing accurate spectroscopic redshifts with $\mathrm{\overline{z}_{spec} = 4.1 \pm 0.7}$. The spectral features demonstrate that, while the majority of HST-dark galaxies are dusty, a substantial fraction, $\mathrm{13^{+9}_{-6} \%}$, are quiescent. For the dusty galaxies, we have quantified the dust attenuation using the Balmer decrement ($\mathrm{H\alpha / H\beta}$), finding attenuations $\mathrm{A_{V} > 2\ mag}$. We find that HST-dark dusty galaxies are $\mathrm{H\alpha}$ emitters with equivalent widths spanning the range $\mathrm{ 68 A < EW_{H\alpha} < 550 A }$, indicative of a wide range of recent star-formation activity. Whether dusty or quiescent, we find that HST-dark galaxies are predominantly massive, with 85\% of the galaxies in the sample having masses $\mathrm{log(M_{*}/M_{\odot}) > 9.8}$. This pilot NIRSpec program reveals the diverse nature of HST-dark galaxies and highlights the effectiveness of NIRSpec/PRISM spectroscopic follow-up in distinguishing between dusty and quiescent galaxies and properly quantifying their physical properties. Upcoming research utilising higher-resolution NIRSpec data and combining JWST with ALMA observations will enhance our understanding of these enigmatic and challenging sources., Comment: 13 pages, 11 figures

Generalizations of Bell's theorem, particularly within quantum networks, are now being analyzed through the causal inference lens. However, the exploration of interventions, a central concept in causality theory, remains significantly unexplored. In this work we give an initial step in this direction, by analyzing the instrumental scenario and proposing novel hybrid Bell inequalities integrating observational and interventional data. Focusing on binary outcomes with any number of inputs, we obtain the complete characterization of the observational-interventional polytope, equivalent to a Hardy-like Bell inequality albeit describing a distinct quantum experiment. To illustrate its applications, we show a significant enhancement regarding threshold detection efficiencies for quantum violations also showing the use of these hybrid approach in quantum steering scenarios.

We present cosmological results from the measurement of baryon acoustic oscillations (BAO) in galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations from the Dark Energy Spectroscopic Instrument (DESI), to be released in the DESI Data Release 1. DESI BAO provide robust measurements of the transverse comoving distance and Hubble rate, or their combination, relative to the sound horizon, in seven redshift bins from over 6 million extragalactic objects in the redshift range $0.1-1$ and $w_a<0$. This preference is 2.6$\sigma$ for the DESI+CMB combination, and persists or grows when SN~Ia are added in, giving results discrepant with the $\Lambda$CDM model at the $2.5\sigma$, $3.5\sigma$ or $3.9\sigma$ levels for the addition of Pantheon+, Union3, or DES-SN5YR datasets respectively. For the flat $\Lambda$CDM model with the sum of neutrino mass $\sum m_\nu$ free, combining the DESI and CMB data yields an upper limit $\sum m_\nu < 0.072$ $(0.113)$ eV at 95% confidence for a $\sum m_\nu>0$ $(\sum m_\nu>0.059)$ eV prior. These neutrino-mass constraints are substantially relaxed in models beyond $\Lambda$CDM. [Abridged.], Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers). Typos corrected and a new figure and discussion added to Appendix A

We present the measurement of Baryon Acoustic Oscillations (BAO) from the Lyman-$\alpha$ (Ly$\alpha$) forest of high-redshift quasars with the first-year dataset of the Dark Energy Spectroscopic Instrument (DESI). Our analysis uses over $420\,000$ Ly$\alpha$ forest spectra and their correlation with the spatial distribution of more than $700\,000$ quasars. An essential facet of this work is the development of a new analysis methodology on a blinded dataset. We conducted rigorous tests using synthetic data to ensure the reliability of our methodology and findings before unblinding. Additionally, we conducted multiple data splits to assess the consistency of the results and scrutinized various analysis approaches to confirm their robustness. For a given value of the sound horizon ($r_d$), we measure the expansion at $z_{\rm eff}=2.33$ with 2\% precision, $H(z_{\rm eff}) = (239.2 \pm 4.8) (147.09~{\rm Mpc} /r_d)$ km/s/Mpc. Similarly, we present a 2.4\% measurement of the transverse comoving distance to the same redshift, $D_M(z_{\rm eff}) = (5.84 \pm 0.14) (r_d/147.09~{\rm Mpc})$ Gpc. Together with other DESI BAO measurements at lower redshifts, these results are used in a companion paper to constrain cosmological parameters., Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers). Minor changes in v4, version accepted for publication in JCAP

We present the DESI 2024 galaxy and quasar baryon acoustic oscillations (BAO) measurements using over 5.7 million unique galaxy and quasar redshifts in the range 0.1