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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

Split conformal prediction techniques are applied 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. Remarkably, by investigating the forces between super long-range structures, we show that the kink-antikink force is stronger than the kink-kink one. 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: 12 pages, 12 figures

The fabrication of well-defined, low-dimensional diamondoid-based materials is a promising approach for tailoring diamond properties such as superconductivity. On-surface self-assembly of halogenated diamondoids under ultrahigh vacuum conditions represents an effective strategy in this direction, enabling reactivity exploration and on-surface synthesis approaches. Here we demonstrate through scanning probe microscopy, time-of-flight mass spectrometry and photoelectron spectroscopy, that self-assembled layers of dibromodiamantanes on gold can be debrominated at atomic wavelengths (Al K$\alpha$ at 8.87 $\mathring{A}$ and Mg K$\alpha$ at 9.89 $\mathring{A}$) and low temperatures without affecting their well-defined arrangement. The resulting 'in-architecture' debromination enables the fabrication of diamantane chains from self-assembled precursors in close proximity, which is otherwise inaccessible through annealing on metal surfaces. Our work introduces a novel approach for the fabrication of nanodiamond chains, with significant implications for in-architecture and layer-by-layer synthesis.

Recent works identified a way to recover the time evolution of a galaxy's disk metallicity gradient from the shape of its age--metallicity relation. However, the success of the method is dependent on how the width of the star-forming region evolves over time, which in turn is dependent on a galaxy's present-day bar strength. In this paper, we account for the time variation in the width of the star-forming region when deriving the interstellar medium (ISM) metallicity gradient evolution over time ($\rm \nabla [Fe/H](\tau)$), which provides more realistic birth radii estimates of Milky Way (MW) disk stars. Using MW/Andromeda analogues from the TNG50 simulation, we quantified the disk growth of newly born stars as a function of present-day bar strength to provide a correction that improves recovery of $\rm \nabla [Fe/H](\tau)$. In TNG50, we find that our correction reduces the median absolute error in recovering $\rm \nabla [Fe/H] (\tau)$ by over 30%. To confirm its universality, we test our correction on two galaxies from NIHAO-UHD and find the median absolute error is over 3 times smaller even in the presence of observational uncertainties for the barred, MW-like galaxy. Applying our correction to APOGEE DR17 red giant MW disk stars suggests the effects of merger events on $\rm \nabla [Fe/H](\tau)$ are less significant than originally found, and the corresponding estimated birth radii expose epochs when different migration mechanisms dominated. Our correction to account for the growth of the star-forming region in the disk allows for better recovery of the evolution of the MW disk's ISM metallicity gradient and, thus, more meaningful stellar birth radii estimates. With our results, we are able to suggest the evolution of not only the ISM gradient, but also the total stellar disk radial metallicity gradient, providing key constraints to select MW analogues across redshift., Comment: submitted to A&A

A triangular optical cavity can be used to distinguish between two beams with the same intensity profile but different wavefronts. This is what we show in this paper, both theoretically and experimentally, in the case of beams with a doughnut-like intensity profile: one of them having a helical wavefront (vortex beam with orbital angular momentum) and the other with no orbital angular momentum at all (which we call pseudovortex beam). We write the mode decomposition of such beams in the Hermite-Gaussian basis and in the the Laguerre-Gauss basis, respectively, and study how they interact with a triangular cavity in terms of their resonance peaks. The experimental results corroborate the theory, showing that each beam displays its own resonance pattern. Therefore, such cavity may be used to identify beams with orbital angular momentum, distinguishing them from pseudovortices., Comment: 5 pages, 3 figures

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 that 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: 21 pages, 8 figures, 4 tables, 1 Appendix. Astronomy & Astrophysics in press

This paper presents a theoretical investigation of vortex modes in acoustofluidic cylindrical resonators with rigid boundaries and viscous fluids. By solving the Helmholtz equation for linear pressure, incorporating boundary conditions that account for no-slip surfaces and vortex and nonvortex excitation at the base, we analyze both single- and dual-eigenfunction modes near system resonance. The results demonstrate that single vortex modes generate spin angular momentum exclusively along the axial direction, while dual modes introduce a transverse spin component due to the nonlinear interaction between axial and transverse ultrasonic waves, even in the absence of vortex excitation. We find that nonlinear acoustic fields, including energy density, radiation force potential, and spin, scale with the square of the shear wave number, defined as the ratio of the cavity radius to the boundary layer depth. Theoretical predictions align closely with finite element simulations based on a thermoviscous model for an acoustofluidic cavity with adiabatic and rigid walls. These findings hold particular significance for acoustofluidic systems, offering potential applications in the precise control of cells and microparticles., Comment: 31 pages, 7 figures

Measures of voting power have been the subject of extensive research since the mid 1940s. More recently, similar measures of relative importance have been studied in other domains that include inconsistent knowledge bases, intensity of attacks in argumentation, different problems in the analysis of database management, and explainability. This paper demonstrates that all these examples are instantiations of computing measures of importance for a rather more general problem domain. The paper then shows that the best-known measures of importance can be computed for any reference set whenever one is given a monotonically increasing predicate that partitions the subsets of that reference set. As a consequence, the paper also proves that measures of importance can be devised in several domains, for some of which such measures have not yet been studied nor proposed. Furthermore, the paper highlights several research directions related with computing measures of importance.

Sloshing of cryogenic liquid propellants can significantly impact a spacecraft's mission safety and performance by unpredictably altering the center of mass and producing large pressure fluctuations due to the increased heat and mass transfer within the tanks. This study, conducted as part of the NT-SPARGE (Non-isoThermal Sloshing PARabolic FliGht Experiment) project, provides a detailed experimental investigation of the thermodynamic evolution of a partially filled upright cylindrical tank undergoing non-isothermal sloshing in microgravity. Sloshing was induced by a step reduction in gravity during the 83rd European Space Agency (ESA) parabolic flight, resulting in a chaotic reorientation of the free surface under inertia-dominated conditions. To investigate the impact of heat and mass transfer on the sloshing dynamics, two identical test cells operating with a representative fluid, HFE-7000, in single-species were considered simultaneously. One cell was maintained in isothermal conditions, while the other started with initially thermally stratified conditions. Flow visualization, pressure, and temperature measurements were acquired for both cells. The results highlight the impact of thermal mixing on liquid dynamics coupled with the significant pressure and temperature fluctuations produced by the destratification. The comprehensive experimental data gathered provide a unique opportunity to validate numerical simulations and simplified models for non-isothermal sloshing in microgravity, thus contributing to improved cryogenic fluid management technologies.

In Kaluza-Klein compactifications, some symmetries of the higher dimensional theory are preserved in lower dimensions, others are broken, and occasionally, there are symmetry enhancements. The symmetries that are enhanced by toroidal compactifications were recently shown to define a symmetry principle with constrained parameters that fixes the action before dimensional reduction. Here we show the opposite: symmetries of the higher dimensional theory that are broken in the reduction process, can actually be realized after dimensional reduction as a global symmetry principle with constrained parameters that fixes couplings in the lower dimensional theory. We implement this principle in pure gravity, half-maximal supergravity and the circle reduction of 11 dimensional supergravity to Type IIA superstring theory. As a further application, we show that it can be used to constrain the quartic Ramond-Ramond couplings in Type IIA superstring theory from the four-point $\zeta(3)\, \alpha'{}^3\, t_8 t_8 R^{(-) 4}$ interactions., Comment: 36p

Two-dimensional heterostructures have been crucial in advancing optoelectronic devices utilizing van der Waals materials. Semiconducting transition metal dichalcogenide monolayers, known for their unique optical properties, offer extensive possibilities for light-emitting devices. Recently, a memory-driven optical device, termed a Mem-emitter, was proposed using these monolayers atop dielectric substrates. The successful realization of such devices heavily depends on selecting the optimal substrate. Here, we report a pronounced memory effect in a MoSe$_2$/clinochlore device, evidenced by electric hysteresis in the intensity and energy of MoSe$_2$ monolayer emissions. This demonstrates both population-driven and transition-rate-driven Mem-emitter abilities. Our theoretical approach correlates these memory effects with internal state variables of the substrate, emphasizing that clinochlore layered structure is crucial for a robust and rich memory response. This work introduces a novel two-dimensional device with promising applications in memory functionalities, highlighting the importance of alternative insulators in fabricating van der Waals heterostructures.

We bridge sheaves of rings over a topological space with common meadows (algebraic structures where the inverse for multiplication is a total operation). More specifically, we show that the subclass of pre-meadows with $\mathbf{a}$, coming from the lattice of open sets of a topological space $X$, and presheaves over $X$ are the same structure. Furthermore, we provide a construction that, given a sheaf of rings $\mathcal{F}$ on $X$ produces a common meadow as a disjoint union of elements of the form $\mathcal{F}(U)$ indexed over the open subsets of $X$. We also establish a correspondence between the process of going from a presheaf to a sheaf (called sheafification) and the process of going from a pre-meadow with $\mathbf{a}$ to a common meadow.

The goal of cross-speaker style transfer in TTS is to transfer a speech style from a source speaker with expressive data to a target speaker with only neutral data. In this context, we propose using a pre-trained singing voice conversion (SVC) model to convert the expressive data into the target speaker's voice. In the conversion process, we apply a fundamental frequency (F0) matching technique to mitigate tonal variances between speakers with significant timbral differences. A style classifier filter is proposed to select the most expressive output audios for the TTS training. Our approach is comparable to state-of-the-art with only a few minutes of neutral data from the target speaker, while other methods require hours. A perceptual assessment showed improvements brought by the SVC and the style filter in naturalness and style intensity for the styles that display more vocal effort. Also, increased speaker similarity is obtained with the proposed F0 matching algorithm., Comment: Submitted to INTERSPEECH 2024

Accurate state estimation is crucial for legged robot locomotion, as it provides the necessary information to allow control and navigation. However, it is also challenging, especially in scenarios with uneven and slippery terrain. This paper presents a new Invariant Extended Kalman filter for legged robot state estimation using only proprioceptive sensors. We formulate the methodology by combining recent advances in state estimation theory with the use of robust cost functions in the measurement update. We tested our methodology on quadruped robots through experiments and public datasets, showing that we can obtain a pose drift up to 40% lower in trajectories covering a distance of over 450m, in comparison with a state-of-the-art Invariant Extended Kalman filter., Comment: Accepted to the IEEE-RAS International Conference on Humanoid Robots 2024

Trans-Neptunian objects (TNOs) are considered to be among the most primitive objects in our Solar System. Knowledge of their primary physical properties is essential for understanding their origin and the evolution of the outer Solar System. We predicted a stellar occultation by this TNO for 2020 October 23 UT and ran a specific campaign to investigate this event. We derived the projected profile shape and size from the occultation observations by means of an elliptical fit to the occultation chords. We also performed photometric observations of (143707) 2003 UY117 to obtain the absolute magnitude and the rotational period from the observed rotational light curve. Finally, we combined these results to derive the three-dimensional shape, volume-equivalent diameter, and geometric albedo for this TNO. From the stellar occultation, we obtained a projected ellipse with axes of $(282 \pm 18) \times (184 \pm 32)$ km. The area-equivalent diameter for this ellipse is $D_\textrm{eq,A} = 228 \pm 21$ km. From our photometric $R$ band observations, we derived an absolute magnitude of $H_V = 5.97 \pm 0.07$ mag using $V-R = 0.46 \pm 0.07$ mag, which was derived from a $V$ band subset of these data. The rotational light curve has a peak-to-valley amplitude of $\Delta m = 0.36 \pm 0.13$ mag. We find the most likely rotation period to be $P = 12.376 \pm 0.0033$ hours. By combining the occultation with the rotational light curve results and assuming a triaxial ellipsoid, we derived axes of $a \times b \times c = (332 \pm 24)$ km $\times$ $(216 \pm 24)$ km $\times$ $(180\substack{+28\\-24})$ km for this ellipsoid, and therefore a volume-equivalent diameter of $D_\textrm{eq,V} = 235 \pm 25$ km. Finally, the values for the absolute magnitude and for the area-equivalent diameter yield a geometric albedo of $p_V = 0.139 \pm 0.027$., Comment: 9 pages, 7 figures, accepted for publication in Astronomy and Astrophysics on Sept 13, 2024

We use Minkowski functionals to analyse weak lensing convergence maps from the first-year data release of the Subaru Hyper Suprime-Cam (HSC-Y1) survey. Minkowski functionals provide a description of the morphological properties of a field, capturing the non-Gaussian features of the Universe matter-density distribution. Using simulated catalogs that reproduce survey conditions and encode cosmological information, we emulate Minkowski functionals predictions across a range of cosmological parameters to derive the best-fit from the data. By applying multiple scales cuts, we rigorously mitigate systematic effects, including baryonic feedback and intrinsic alignments. From the analysis, combining constraints of the angular power spectrum and Minkowski functionals, we obtain $S_8 \equiv \sigma_8\sqrt{\Omega_{{\rm m}}/0.3} = {0.808}_{-0.046}^{+0.033}$ and $\Omega_{\rm m} = {0.293}_{-0.043}^{+0.157}$. These results represent a $40\%$ improvement on the $S_8$ constraints compared to using power spectrum only, and are consistent with previous non-Gaussian statistics analyses of the same dataset. Our study demonstrates the power of Minkowski functionals beyond two-point statistics for constraining and breaking the degeneracy between $\Omega_{\rm m}$ and $\sigma_8$., Comment: 8 pages, 5 figures. Submitted to MNRAS

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

The first generation of ELT instruments includes an optical-infrared high-resolution spectrograph, indicated as ELT-HIRES and recently christened ANDES (ArmazoNes high Dispersion Echelle Spectrograph). ANDES consists of three fibre-fed spectrographs ([U]BV, RIZ, YJH) providing a spectral resolution of $\sim$100,000 with a minimum simultaneous wavelength coverage of 0.4-1.8 $\mu$m with the goal of extending it to 0.35-2.4 $\mu$m with the addition of a U arm to the BV spectrograph and a separate K band spectrograph. It operates both in seeing- and diffraction-limited conditions and the fibre feeding allows several, interchangeable observing modes including a single conjugated adaptive optics module and a small diffraction-limited integral field unit in the NIR. Modularity and fibre-feeding allow ANDES to be placed partly on the ELT Nasmyth platform and partly in the Coud\'e room. ANDES has a wide range of groundbreaking science cases spanning nearly all areas of research in astrophysics and even fundamental physics. Among the top science cases, there are the detection of biosignatures from exoplanet atmospheres, finding the fingerprints of the first generation of stars, tests on the stability of Nature's fundamental couplings, and the direct detection of the cosmic acceleration. The ANDES project is carried forward by a large international consortium, composed of 35 Institutes from 13 countries, forming a team of almost 300 scientists and engineers which include the majority of the scientific and technical expertise in the field that can be found in ESO member states., Comment: SPIE astronomical telescope and instrumentation 2024, in press

The introduction of Peierls phases in open tight-binding chains without closed paths in either real or synthetic dimensions is understood to be physically inconsequential, as one assumes they can always be gauged away. Here, we show that this assumption does not necessarily hold for all systems in open chains, as closed paths may appear in the Fock space where these phases can lead to the creation of magnetic flux analogs with physical effects. This idea is first illustrated in the quadratic Kitaev model, where different patterns for the Peierls phases are studied and their independent manipulation is seen to be able to drive the appearance of topological states and Majorana flat bands. We then consider a system with quartic interactions, namely an extended Bose-Hubbard (EBH) open chain with a finite Peierls phase associated with the hopping terms. Focusing on the strong interactions limit of the two-body sector, we show the decisive influence of these phases on the two-band spectrum of the higher energy subspace, which behaves as an effective sawtooth chain with magnetic flux at each plaquette. In particular, both the width of the energy gap between the two bands and the position of the in-gap edge states can be controlled by the Peierls phase. Finally, by translating this two-particle one-dimensional (1D) system into a single-particle two-dimensional (2D) one, and subsequently mapping it onto an equivalent electrical LC circuit, we identify a parameter set for which in-gap states are only present for certain finite phase values. Tuning the circuit to these parameters generates the corresponding boundary voltage states, providing an experimentally detectable signature of the effects induced by manipulating Peierls phases in a model built on an open linear chain., Comment: 15 pages, 7 figures

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

Incorporating cross-speaker style transfer in text-to-speech (TTS) models is challenging due to the need to disentangle speaker and style information in audio. In low-resource expressive data scenarios, voice conversion (VC) can generate expressive speech for target speakers, which can then be used to train the TTS model. However, the quality and style transfer ability of the VC model are crucial for the overall TTS model quality. In this work, we explore the use of synthetic data generated by a VC model to assist the TTS model in cross-speaker style transfer tasks. Additionally, we employ pre-training of the style encoder using timbre perturbation and prototypical angular loss to mitigate speaker leakage. Our results show that using VC synthetic data can improve the naturalness and speaker similarity of TTS in cross-speaker scenarios. Furthermore, we extend this approach to a cross-language scenario, enhancing accent transfer., Comment: Accepted at SynData4GenAI 2024

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

While convolutional neural networks (CNNs) excel at clean image classification, they struggle to classify images corrupted with different common corruptions, limiting their real-world applicability. Recent work has shown that incorporating a CNN front-end block that simulates some features of the primate primary visual cortex (V1) can improve overall model robustness. Here, we expand on this approach by introducing two novel biologically-inspired CNN model families that incorporate a new front-end block designed to simulate pre-cortical visual processing. RetinaNet, a hybrid architecture containing the novel front-end followed by a standard CNN back-end, shows a relative robustness improvement of 12.3% when compared to the standard model; and EVNet, which further adds a V1 block after the pre-cortical front-end, shows a relative gain of 18.5%. The improvement in robustness was observed for all the different corruption categories, though accompanied by a small decrease in clean image accuracy, and generalized to a different back-end architecture. These findings show that simulating multiple stages of early visual processing in CNN early layers provides cumulative benefits for model robustness.

The Problem-oriented AutoML in Clustering (PoAC) framework introduces a novel, flexible approach to automating clustering tasks by addressing the shortcomings of traditional AutoML solutions. Conventional methods often rely on predefined internal Clustering Validity Indexes (CVIs) and static meta-features, limiting their adaptability and effectiveness across diverse clustering tasks. In contrast, PoAC establishes a dynamic connection between the clustering problem, CVIs, and meta-features, allowing users to customize these components based on the specific context and goals of their task. At its core, PoAC employs a surrogate model trained on a large meta-knowledge base of previous clustering datasets and solutions, enabling it to infer the quality of new clustering pipelines and synthesize optimal solutions for unseen datasets. Unlike many AutoML frameworks that are constrained by fixed evaluation metrics and algorithm sets, PoAC is algorithm-agnostic, adapting seamlessly to different clustering problems without requiring additional data or retraining. Experimental results demonstrate that PoAC not only outperforms state-of-the-art frameworks on a variety of datasets but also excels in specific tasks such as data visualization, and highlight its ability to dynamically adjust pipeline configurations based on dataset complexity.

We present the first measurement of the $^{47}$K($d,p\gamma$)$^{48}$K transfer reaction, performed in inverse kinematics using a reaccelerated beam of $^{47}$K. The level scheme of $^{48}$K has been greatly extended with nine new bound excited states identified and spectroscopic factors deduced. Detailed comparisons with SDPF-U and SDPF-MU shell-model calculations reveal a number of discrepancies with these results, and a preference for SDPF-MU is found. Intriguingly, an apparent systematic overestimation of spectroscopic factors and a poor reproduction of the energies for 1$^-$ states suggests that the mixing between the $\pi s^{\,\,\,1}_{1/2} d^{\,\,\,4}_{3/2}$ and $\pi s^{\,\,\,2}_{1/2} d^{\,\,\,3}_{3/2}$ proton configurations in $^{48}$K is not correctly described using current interactions, challenging our descriptions of light $N=28$ nuclei., Comment: 7 pages, 3 figures, 1 table

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

Graph learning is the fundamental task of estimating unknown graph connectivity from available data. Typical approaches assume that not only is all information available simultaneously but also that all nodes can be observed. However, in many real-world scenarios, data can neither be known completely nor obtained all at once. We present a novel method for online graph estimation that accounts for the presence of hidden nodes. We consider signals that are stationary on the underlying graph, which provides a model for the unknown connections to hidden nodes. We then formulate a convex optimization problem for graph learning from streaming, incomplete graph signals. We solve the proposed problem through an efficient proximal gradient algorithm that can run in real-time as data arrives sequentially. Additionally, we provide theoretical conditions under which our online algorithm is similar to batch-wise solutions. Through experimental results on synthetic and real-world data, we demonstrate the viability of our approach for online graph learning in the presence of missing observations.

Graph neural networks (GNNs) have become a workhorse approach for learning from data defined over irregular domains, typically by implicitly assuming that the data structure is represented by a homophilic graph. However, recent works have revealed that many relevant applications involve heterophilic data where the performance of GNNs can be notably compromised. To address this challenge, we present a simple yet effective architecture designed to mitigate the limitations of the homophily assumption. The proposed architecture reinterprets the role of graph filters in convolutional GNNs, resulting in a more general architecture while incorporating a stronger inductive bias than GNNs based on filter banks. The proposed convolutional layer enhances the expressive capacity of the architecture enabling it to learn from both homophilic and heterophilic data and preventing the issue of oversmoothing. From a theoretical standpoint, we show that the proposed architecture is permutation equivariant. Finally, we show that the proposed GNNs compares favorably relative to several state-of-the-art baselines in both homophilic and heterophilic datasets, showcasing its promising potential.

Whether learned, simulated, or analytical, approximations of a robot's dynamics can be inaccurate when encountering novel environments. Many approaches have been proposed to quantify the aleatoric uncertainty of such methods, i.e. uncertainty resulting from stochasticity, however these estimates alone are not enough to properly estimate the uncertainty of a model in a novel environment, where the actual dynamics can change. Such changes can induce epistemic uncertainty, i.e. uncertainty due to a lack of information/data. Accounting for both epistemic and aleatoric dynamics uncertainty in a theoretically-grounded way remains an open problem. We introduce Local Uncertainty Conformal Calibration (LUCCa), a conformal prediction-based approach that calibrates the aleatoric uncertainty estimates provided by dynamics models to generate probabilistically-valid prediction regions of the system's state. We account for both epistemic and aleatoric uncertainty non-asymptotically, without strong assumptions about the form of the true dynamics or how it changes. The calibration is performed locally in the state-action space, leading to uncertainty estimates that are useful for planning. We validate our method by constructing probabilistically-safe plans for a double-integrator under significant changes in dynamics., Comment: Accepted to the 16th International Workshop on the Algorithmic Foundations of Robotics (WAFR) 2024

This paper introduces a probabilistic approach for tracking the dynamics of unweighted and directed graphs using state-space models (SSMs). Unlike conventional topology inference methods that assume static graphs and generate point-wise estimates, our method accounts for dynamic changes in the network structure over time. We model the network at each timestep as the state of the SSM, and use observations to update beliefs that quantify the probability of the network being in a particular state. Then, by considering the dynamics of transition and observation models through the update and prediction steps, respectively, the proposed method can incorporate the information of real-time graph signals into the beliefs. These beliefs provide a probability distribution of the network at each timestep, being able to provide both an estimate for the network and the uncertainty it entails. Our approach is evaluated through experiments with synthetic and real-world networks. The results demonstrate that our method effectively estimates network states and accounts for the uncertainty in the data, outperforming traditional techniques such as recursive least squares., Comment: Submitted to the 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025)