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Sarcopenia is an age-related progressive loss of muscle mass and strength that significantly impacts daily life. A commonly studied criterion for characterizing the muscle mass has been the combination of 3D imaging and manual segmentations. In this paper, we instead study the muscles' shape. We rely on an implicit neural representation (INR) to model normal muscle shapes. We then introduce an unsupervised anomaly detection method to identify sarcopenic muscles based on the reconstruction error of the implicit model. Relying on a conditional INR with an auto-decoding strategy, we also learn a latent representation of the muscles that clearly separates normal from abnormal muscles in an unsupervised fashion. Experimental results on a dataset of 103 segmented volumes indicate that our double anomaly detection strategy effectively discriminates sarcopenic and non-sarcopenic muscles.

Dissipative cat-qubits are a promising architecture for quantum processors due to their built-in quantum error correction. By leveraging two-photon stabilization, they achieve an exponentially suppressed bit-flip error rate as the distance in phase-space between their basis states increases, incurring only a linear increase in phase-flip rate. This property substantially reduces the number of qubits required for fault-tolerant quantum computation. Here, we implement a squeezing deformation of the cat qubit basis states, further extending the bit-flip time while minimally affecting the phase-flip rate. We demonstrate a steep reduction in the bit-flip error rate with increasing mean photon number, characterized by a scaling exponent $\gamma=4.3$, rising by a factor of 74 per added photon. Specifically, we measure bit-flip times of 22 seconds for a phase-flip time of 1.3 $\mu$s in a squeezed cat qubit with an average photon number $\bar{n}=4.1$, a 160-fold improvement in bit-flip time compared to a standard cat. Moreover, we demonstrate a two-fold reduction in $Z$-gate infidelity, with an estimated phase-flip probability of $\epsilon_X = 0.085$ and a bit-flip probability of $\epsilon_Z = 2.65 \cdot 10^{-9}$ which confirms the gate bias-preserving property. This simple yet effective technique enhances cat qubit performances without requiring design modification, moving multi-cat architectures closer to fault-tolerant quantum computation.

This paper introduces a differentiable ray-tracing based model that incorporates aberrations and distortions to render realistic coded hyperspectral acquisitions using Coded-Aperture Spectral Snapshot Imagers (CASSI). CASSI systems can now be optimized in order to fulfill simultaneously several optical design constraints as well as processing constraints. Four comparable CASSI systems with varying degree of optical aberrations have been designed and modeled. The resulting rendered hyperspectral acquisitions from each of these systems are combined with five state-of-the-art hyperspectral cube reconstruction processes. These reconstruction processes encompass a mapping function created from each system's propagation model to account for distortions and aberrations during the reconstruction process. Our analyses show that if properly modeled, the effects of geometric distortions of the system and misalignments of the dispersive elements have a marginal impact on the overall quality of the reconstructed hyperspectral data cubes. Therefore, relaxing traditional constraints on measurement conformity and fidelity to the scene enables the development of novel imaging instruments, guided by performance metrics applied to the design or the processing of acquisitions. By providing a complete framework for design, simulation and evaluation, this work contributes to the optimization and exploration of new CASSI systems, and more generally to the computational imaging community.

We use the Milky Way neutral hydrogen (HI) absorption and emission spectra from the Galactic Australian Square Kilometre Array Pathfinder (GASKAP) Phase II Pilot survey along with toy models to investigate the effects of stacking multicomponent spectra on measurements of peak optical depth and spin temperature. Shifting spectra by the peak in emission, 'primary' components shifted to 0 km s$^{-1}$ are correctly averaged. Additional components on individual sightlines are averaged with non-centred velocities, producing a broader and shallower 'secondary' component in the resulting stack. Peak optical depths and brightness temperatures of the secondary components from stacks are lower limits of their true average values due to the velocity offset of each component. The spin temperature however is well correlated with the truth since the velocity offset of components affects the emission and absorption spectra equally. Stacking 462 GASKAP absorption-emission spectral pairs, we detect a component with a spin temperature of 1320 $\pm$ 263 K, consistent with gas from the unstable neutral medium and higher than any previous GASKAP detection in this region. We also stack 2240 pilot survey spectra containing no Milky Way absorption, revealing a primary narrow and secondary broad component, with spin temperatures belonging to the cold neutral medium (CNM). Spatially binning and stacking the non-detections across the plane-of-sky by their distance from CNM absorption detections, the primary component's optical depth decreases with distance from known locations of cold gas. The spin temperature however remains stable in both components, over an approximate physical plane-of-sky distance of $\sim$ 100 pc., Comment: This paper has 16 pages, 17 figures. This paper has been accepted for publication in Monthly Notices of the Royal Astronomical Society Main Journal

In Precision Livestock Farming, detecting deviations from optimal or baseline values - i.e. anomalies in time series - is essential to allow undertaking corrective actions rapidly. Here we aim at detecting anomalies in 24h time series of cow activity, with a view to detect cases of disease or oestrus. Deviations must be distinguished from noise which can be very high in case of biological data. It is also important to detect the anomaly early, e.g. before a farmer would notice it visually. Here, we investigate the benefit of using wavelet transforms to denoise data and we assess the performance of an anomaly detection algorithm considering the timing of the detection. We developed features based on the comparisons between the wavelet transforms of the mean of the time series and the wavelet transforms of individual time series instances. We hypothesized that these features contribute to the detection of anomalies in periodic time series using a feature-based algorithm. We tested this hypothesis with two datasets representing cow activity, which typically follows a daily pattern but can deviate due to specific physiological or pathological conditions. We applied features derived from wavelet transform as well as statistical features in an Isolation Forest algorithm. We measured the distance of detection between the days annotated abnormal by animal caretakers days and the days predicted abnormal by the algorithm. The results show that wavelet-based features are among the features most contributing to anomaly detection. They also show that detections are close to the annotated days, and often precede it. In conclusion, using wavelet transforms on time series of cow activity data helps to detect anomalies related to specific cow states. The detection is often obtained on days that precede the day annotated by caretakers, which offer possibility to take corrective actions at an early stage., Comment: 17 pages, 8 figures, 4 tables, 1 algorithm

We propose two formulations to leverage the geometric properties of bivariate signals for dealing with the denoising problem. In doing so, we use the instantaneous Stokes parameters to incorporate the polarization state of the signal. While the first formulation exploits the statistics of the Stokes representation in a Bayesian setting, the second uses a kernel regression formulation to impose locally smooth time-varying polarization properties. In turn, we obtain two formulations that allow us to use both signal and polarization domain regularization for denoising a bivariate signal. The solutions to them exploit the polarization information efficiently as demonstrated in the numerical simulations

We introduce a hierarchical framework that combines ab initio many-body calculations with a Bayesian neural network, developing emulators capable of accurately predicting nuclear properties across the nuclear chart, including multiple isotopes simultaneously. We benchmark our developments using the oxygen isotopic chain, achieving accurate results for ground-state energies and nuclear charge radii, while providing robust uncertainty quantification. Our framework enables global sensitivity analysis of nuclear binding energies and charge radii with respect to the low-energy constants that describe the nuclear force.

Deploying digital pathology models across medical centers is challenging due to distribution shifts. Recent advances in domain generalization improve model transferability in terms of aggregated performance measured by the Area Under Curve (AUC). However, clinical regulations often require to control the transferability of other metrics, such as prescribed sensitivity levels. We introduce a novel approach to control the sensitivity of whole slide image (WSI) classification models, based on optimal transport and Multiple Instance Learning (MIL). Validated across multiple cohorts and tasks, our method enables robust sensitivity control with only a handful of calibration samples, providing a practical solution for reliable deployment of computational pathology systems., Comment: Preprint

It is shown that the smallest possible distance between two disjoint lattice polytopes contained in the cube $[0,k]^3$ is exactly $$ \frac{1}{\sqrt{2(2k^2-4k+5)(2k^2-2k+1)}} $$ for every integer $k$ at least $4$. The proof relies on modeling this as a minimization problem over a subset of the lattice points in the hypercube $[-k,k]^9$. A precise characterization of this subset allows to reduce the problem to computing the roots of a finite number of degree at most $4$ polynomials, which is done using symbolic computation., Comment: 17 pages, 1 figure

The recent astrometric discovery of the nearby (590 pc) massive ($33 M_\odot$) dormant black hole candidate Gaia BH3 offers the possibility to angularly resolve the black hole from its companion star by using optical interferometry. Our aim is to detect emission in the near-infrared K band from the close-in environment of Gaia BH3 caused by accretion. Gaia BH3 was observed with the GRAVITY instrument using the four 8-meter Unit Telescopes of the VLT Interferometer. We searched for the signature of emission from the black hole in the interferometric data using the CANDID, PMOIRED, and exoGravity tools. With a present separation of 18 mas, the Gaia BH3 system can be well resolved angularly by GRAVITY. We did not detect emission from the black hole at a contrast level of $\Delta m = 6.8$ mag with respect to the companion star, that is, $f_\mathrm{BH}/f_* < 0.2\%$. This corresponds to an upper limit on the continuum flux density of $f_\mathrm{BH} < 1.9 \times 10^{-16}$ W m$^{-2}$ ${\mu}$m$^{-1}$ in the K band. In addition, we did not detect emission from the black hole in the hydrogen Br{\gamma} line. The non-detection of near-infrared emission from the black hole in Gaia BH3 indicates that its accretion of the giant star wind is presently occurring at most at a very low rate. This is consistent with the limit of $f_\mathrm{Edd} < 4.9 \times 10^{-7}$ derived previously on the Eddington ratio for an advection-dominated accretion flow. Deeper observations with GRAVITY may be able to detect the black hole as the companion star approaches periastron around 2030., Comment: 9 pages, 12 figures, published as a Letter in A&A, 695, L1 (2025)

In large language models (LLMs), code and reasoning reinforce each other: code offers an abstract, modular, and logic-driven structure that supports reasoning, while reasoning translates high-level goals into smaller, executable steps that drive more advanced code intelligence. In this study, we examine how code serves as a structured medium for enhancing reasoning: it provides verifiable execution paths, enforces logical decomposition, and enables runtime validation. We also explore how improvements in reasoning have transformed code intelligence from basic completion to advanced capabilities, enabling models to address complex software engineering tasks through planning and debugging. Finally, we identify key challenges and propose future research directions to strengthen this synergy, ultimately improving LLM's performance in both areas., Comment: Project Repo: https://github.com/dayuyang1999/Awesome-Code-Reasoning

This paper studies balance properties for billiard words. Billiard words generalize Sturmian words by coding trajectories in hypercubic billiards. In the setting of aperiodic order, they also provide the simplest examples of quasicrystals, as tilings of the line obtained via cut and project sets with a cubical canonical window. By construction, the number of occurrences of each letter in a factor (i.e., a string of consecutive letters) of a hypercubic billiard word only depends on the length of the factor, up to an additive constant. In other words, the difference of the number of occurrences of each letter in factors of the same length is bounded. In contrast with the behaviour of letters, we prove the existence of words that are not balanced in billiard words: the difference of the number of occurrences of such unbalanced factors in longer factors of the same length is unbounded. The proof relies both on topological methods inspired by tiling cohomology and on arithmetic results on bounded remainder sets for toral translations.

Predicting the binding affinity between small molecules and target macromolecules while combining both speed and accuracy, is a cornerstone of modern computational drug discovery which is critical for accelerating therapeutic development. Despite recent progresses in molecular dynamics (MD) simulations, such as advanced polarizable force fields and enhanced-sampling techniques, estimating absolute binding free energies (ABFE) remains computationally challenging. To overcome these difficulties, we introduce a highly efficient, hybrid methodology that couples the Lambda-Adaptive Biasing Force (Lambda-ABF) scheme with On-the-fly Probability Enhanced Sampling (OPES). This approach achieves up to a nine-fold improvement in sampling efficiency and computational speed compared to the original Lambda-ABF when used in conjunction with the AMOEBA polarizable force field, yielding converged results at a fraction of the cost of standard techniques.

We study the action of local conformal transformations on several measures related to the Gaussian free field and Schramm--Loewner evolutions. The main novelty of our work is a Cameron--Martin-type formula for the welding homeomorphism of the SLE loop measure ($\kappa\in(0,4]$); its proof relies on a rigorous interpretation (and computation) of the "Jacobian" of the conformal welding map, which we relate to the "$bc$-ghost system" from bosonic string theory. We also give an intrinsic definition of the trace of the GFF on SLE, and prove a characterisation of the free boundary GFF in $\mathbb{D}$. As an application, we introduce a new and intrinsic approach to the conformal welding of quantum surfaces., Comment: 41 pages

Optical Character Recognition (OCR) plays a crucial role in digitizing historical and multilingual documents, yet OCR errors -- imperfect extraction of the text, including character insertion, deletion and permutation -- can significantly impact downstream tasks like question-answering (QA). In this work, we introduce a multilingual QA dataset MultiOCR-QA, designed to analyze the effects of OCR noise on QA systems' performance. The MultiOCR-QA dataset comprises 60K question-answer pairs covering three languages, English, French, and German. The dataset is curated from OCR-ed old documents, allowing for the evaluation of OCR-induced challenges on question answering. We evaluate MultiOCR-QA on various levels and types of OCR errors to access the robustness of LLMs in handling real-world digitization errors. Our findings show that QA systems are highly prone to OCR induced errors and exhibit performance degradation on noisy OCR text.

Monocular visual inertial odometry (VIO) has facilitated a wide range of real-time motion tracking applications, thanks to the small size of the sensor suite and low power consumption. To successfully bootstrap VIO algorithms, the initialization module is extremely important. Most initialization methods rely on the reconstruction of 3D visual point clouds. These methods suffer from high computational cost as state vector contains both motion states and 3D feature points. To address this issue, some researchers recently proposed a structureless initialization method, which can solve the initial state without recovering 3D structure. However, this method potentially compromises performance due to the decoupled estimation of rotation and translation, as well as linear constraints. To improve its accuracy, we propose novel structureless visual-inertial bundle adjustment to further refine previous structureless solution. Extensive experiments on real-world datasets show our method significantly improves the VIO initialization accuracy, while maintaining real-time performance., Comment: Accepted by ICRA 2025

The visible and infrared Moon And Jupiter Imaging Spectrometer (MAJIS), aboard the JUpiter ICy Moons Explorer (JUICE) spacecraft, will characterize the composition of the surfaces and atmospheres of the Jupiter system. Prior to the launch, a campaign was carried out to obtain the measurements needed to calibrate the instrument. The aim was not only to produce data for the calculation of the radiometric, spectral, and spatial transfer functions, but also to evaluate MAJIS performance, such as signal-to-noise ratio and amount of straylight, under near-flight conditions. Here, we first describe the setup implemented to obtain these measurements, based on five optical channels. We notably emphasize the concepts used to mitigate thermal infrared emissions generated at ambient temperatures, since the MAJIS spectral range extends up to 5.6 $\mu$m. Then, we characterize the performance of the setup by detailing the validation measurements obtained before the campaign. In particular, the radiometric, geometric, and spectral properties of the setup needed for the inversion of collected data and the calculation of the instrument's calibration functions are presented and discussed. Finally, we provide an overview of conducted measurements with MAJIS, and we discuss unforeseen events encountered during the on-ground calibration campaign.

The leading AI companies are increasingly focused on building generalist AI agents -- systems that can autonomously plan, act, and pursue goals across almost all tasks that humans can perform. Despite how useful these systems might be, unchecked AI agency poses significant risks to public safety and security, ranging from misuse by malicious actors to a potentially irreversible loss of human control. We discuss how these risks arise from current AI training methods. Indeed, various scenarios and experiments have demonstrated the possibility of AI agents engaging in deception or pursuing goals that were not specified by human operators and that conflict with human interests, such as self-preservation. Following the precautionary principle, we see a strong need for safer, yet still useful, alternatives to the current agency-driven trajectory. Accordingly, we propose as a core building block for further advances the development of a non-agentic AI system that is trustworthy and safe by design, which we call Scientist AI. This system is designed to explain the world from observations, as opposed to taking actions in it to imitate or please humans. It comprises a world model that generates theories to explain data and a question-answering inference machine. Both components operate with an explicit notion of uncertainty to mitigate the risks of overconfident predictions. In light of these considerations, a Scientist AI could be used to assist human researchers in accelerating scientific progress, including in AI safety. In particular, our system can be employed as a guardrail against AI agents that might be created despite the risks involved. Ultimately, focusing on non-agentic AI may enable the benefits of AI innovation while avoiding the risks associated with the current trajectory. We hope these arguments will motivate researchers, developers, and policymakers to favor this safer path., Comment: v2 with fixed formatting for URLs and hyperlinks

Time-harmonic wave propagation problems, especially those governed by Maxwell's equations, pose significant computational challenges due to the non-self-adjoint nature of the operators and the large, non-Hermitian linear systems resulting from discretization. Domain decomposition methods, particularly one-level Schwarz methods, offer a promising framework to tackle these challenges, with recent advancements showing the potential for weak scalability under certain conditions. In this paper, we analyze the weak scalability of one-level Schwarz methods for Maxwell's equations in strip-wise domain decompositions, focusing on waveguides with general cross sections and different types of transmission conditions such as impedance or perfectly matched layers (PMLs). By combining techniques from the limiting spectrum analysis of Toeplitz matrices and the modal decomposition of Maxwell's solutions, we provide a novel theoretical framework that extends previous work to more complex geometries and transmission conditions. Numerical experiments confirm that the limiting spectrum effectively predicts practical behavior even with a modest number of subdomains. Furthermore, we demonstrate that the one-level Schwarz method can achieve robustness with respect to the wave number under specific domain decomposition parameters, offering new insights into its applicability for large-scale electromagnetic wave problems.

We present the design of an automated current-voltage (IV) multiplexer (MUX) that enables accurate measurement of the dark current in cryogenic silicon photomultipliers (SiPMs), achieving a sensitivity equivalent to detecting less than one avalanche per second. Dynamic pulse-counting measurements were used as a benchmark for reconstructing the dark current in static IV measurements. The IV-MUX features 15 channels on a single board and up to seven boards can be connected in parallel under the control of one Arduino microcontroller. To minimize leakage and enhance performance, the layout includes guard rings and high-isolation relays, enabling resolution of currents as small as 49 fA. The IV-MUX can be integrated into systems designed for IV or pulse-counting measurements, enabling seamless switching between IV and pulse-counting modes. Moreover, the IV-MUX is vacuum-compatible, validated by testing an SiPM array in a cryostat. This feature reduces the need for multiple feedthroughs when testing sensor arrays in vacuum. The design is open source and can be used to facilitate rapid and automated testing of SiPMs or similar low-current devices in one measurement cycle., Comment: 10 pages, 10 figures. Updated on 2/24/2025 to include missing axis label on figure and added a sentence with reference regarding sipm iv measurements at low current for cryogenic sipms. Updated article template package from elsa-artice to ieeetran

In this paper, we investigate how weakening the classical hydrostatic balance hypothesis impacts the well-posedness of the stochastic LU primitive equations. The models we consider are intermediate between the incompressible 3D LU Navier-Stokes equations and the LU primitive equations with standard hydrostatic balance. As such, they are expected to be numerically tractable, while accounting well for phenomena within the grey zone between hydrostatic balance and non-hydrostatic processes. Our main result is the well-posedness of a low-pass filtering-based stochastic interpretation of the LU primitive equations, with rigid-lid type boundary conditions, in the limit of ``quasi-barotropic'' flow. This assumption is linked to the structure assumption proposed in the work of Agresti et al., which can be related to the dynamical regime where the primitive equations remain valid. Furthermore, we present and study two eddy-(hyper)viscosity-based models.

While machine learning on graphs has demonstrated promise in drug design and molecular property prediction, significant benchmarking challenges hinder its further progress and relevance. Current benchmarking practices often lack focus on transformative, real-world applications, favoring narrow domains like two-dimensional molecular graphs over broader, impactful areas such as combinatorial optimization, relational databases, or chip design. Additionally, many benchmark datasets poorly represent the underlying data, leading to inadequate abstractions and misaligned use cases. Fragmented evaluations and an excessive focus on accuracy further exacerbate these issues, incentivizing overfitting rather than fostering generalizable insights. These limitations have prevented the development of truly useful graph foundation models. This position paper calls for a paradigm shift toward more meaningful benchmarks, rigorous evaluation protocols, and stronger collaboration with domain experts to drive impactful and reliable advances in graph learning research, unlocking the potential of graph learning.

We study the problem of reinforcement learning in infinite-horizon discounted linear Markov decision processes (MDPs), and propose the first computationally efficient algorithm achieving near-optimal regret guarantees in this setting. Our main idea is to combine two classic techniques for optimistic exploration: additive exploration bonuses applied to the reward function, and artificial transitions made to an absorbing state with maximal return. We show that, combined with a regularized approximate dynamic-programming scheme, the resulting algorithm achieves a regret of order $\tilde{\mathcal{O}} (\sqrt{d^3 (1 - \gamma)^{- 7 / 2} T})$, where $T$ is the total number of sample transitions, $\gamma \in (0,1)$ is the discount factor, and $d$ is the feature dimensionality. The results continue to hold against adversarial reward sequences, enabling application of our method to the problem of imitation learning in linear MDPs, where we achieve state-of-the-art results.

Two-sample hypothesis testing-determining whether two sets of data are drawn from the same distribution-is a fundamental problem in statistics and machine learning with broad scientific applications. In the context of nonparametric testing, maximum mean discrepancy (MMD) has gained popularity as a test statistic due to its flexibility and strong theoretical foundations. However, its use in large-scale scenarios is plagued by high computational costs. In this work, we use a Nystr\"om approximation of the MMD to design a computationally efficient and practical testing algorithm while preserving statistical guarantees. Our main result is a finite-sample bound on the power of the proposed test for distributions that are sufficiently separated with respect to the MMD. The derived separation rate matches the known minimax optimal rate in this setting. We support our findings with a series of numerical experiments, emphasizing realistic scientific data., Comment: 23 pages, 2 figures

Designing nanophotonic structures traditionally grapples with the complexities of discrete parameters, such as real materials, often resorting to costly global optimization methods. This paper introduces an approach that leverages generative deep learning to map discrete parameter sets into a continuous latent space, enabling direct gradient-based optimization. For scenarios with non-differentiable physics evaluation functions, a neural network is employed as a differentiable surrogate model. The efficacy of this methodology is demonstrated by optimizing the directional scattering properties of core-shell nanoparticles composed of a selection of realistic materials. We derive suggestions for core-shell geometries with strong forward scattering and minimized backscattering. Our findings reveal significant improvements in computational efficiency and performance when compared to global optimization techniques. Beyond nanophotonics design problems, this framework holds promise for broad applications across all types of inverse problems constrained by discrete variables., Comment: 7 pages, 7 figures

We report a comprehensive theoretical analysis of the instability achievable by using phase modulation spectroscopy to lock a terahertz local oscillator to an absorptive reference consisting of the rotational transition of molecules at room temperature. We find that the signal-to-noise ratio of the THz detector provides the limitation to the instability that can be achieved and analyze a number of viable candidate molecules, identifying several as being of particular interest, including OCS and HI. We find that a one-second instability in the $10^{-13}$ decade is achievable for molecules confined to waveguide, while instability at the $10^{-14}$ level can be reached for molecules in free space. We also present calculations of the intermodulation effect for spectroscopy taking place far outside the quasistatic regime and find that this source of noise presents constraints on which THz local oscillators are appropriate to be used for such frequency references., Comment: 8 pages, 4 figures

The rampdown in tokamak operations is a difficult to simulate phase during which the plasma is often pushed towards multiple instability limits. To address this challenge, and reduce the risk of disrupting operations, we leverage recent advances in Scientific Machine Learning (SciML) to develop a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak \`a Configuration Variable (TCV) rampdowns. By integrating simple physics structure and data-driven models, the NSSM efficiently learns plasma dynamics during the rampdown from a modest dataset of 311 pulses with only five pulses in the reactor relevant high performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid multiple instability limits with high probability. Experiments at TCV ramping down high performance plasmas show statistically significant improvements in current and energy at plasma termination, with improvements in speed through continuous re-training. A predict-first experiment, increasing plasma current by 20\% from baseline, demonstrates the NSSM's ability to make small extrapolations with sufficient accuracy to design trajectories that successfully terminate the pulse. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty, and demonstrates the relevance of the SciML approach to learning plasma dynamics for rapidly developing robust trajectories and controls during the incremental campaigns of upcoming burning plasma tokamaks.

Hard carbons show significant promise as anode materials for sodium-ion batteries. However, monitoring the sodiation process in the hard carbon electrode during cycling and understanding the sodiation mechanism remain challenging. This article reports on operando 2D scanning small- and wide-angle X-ray scattering (SWAXS) and ex situ 3D SAXS tomography of hard carbon electrodes during the sodiation process. Structural changes are monitored with spatial and temporal resolution during the electrochemical process and shows that sodiation through micropore filling is the more dominating mechanism in the later stages of sodiation, i.e. in the plateau region of the voltage profile, while intercalation occurs continuously. Spatial inhomogeneities are resolved over the electrode and reveal an increased level of inhomogeneity at higher degree of sodiation with regions of different degrees of micropore filling. Resolving the processes spatially enables us to correlate plating, starting from the interface between the electrode and the current collector, to a higher degree of micropore filling. The work demonstrates how SWAXS imaging can contribute to understanding the sodiation of hard carbon anodes, not only by spatially resolved analysis, but also as a method to decouple contributions from different components in a cell, enabling more accurate scattering analysis in in situ environments.

Although mobile devices benefit users in their daily lives in numerous ways, they also raise several privacy concerns. For instance, they can reveal sensitive information that can be inferred from location data. This location data is shared through service providers as well as mobile applications. Understanding how and with whom users share their location data -- as well as users' perception of the underlying privacy risks --, are important notions to grasp in order to design usable privacy-enhancing technologies. In this work, we perform a quantitative and qualitative analysis of smartphone users' awareness, perception and self-reported behavior towards location data-sharing through a survey of n=99 young adult participants (i.e., digital natives). We compare stated practices with actual behaviors to better understand their mental models, and survey participants' understanding of privacy risks before and after the inspection of location traces and the information that can be inferred therefrom. Our empirical results show that participants have risky privacy practices: about 54% of participants underestimate the number of mobile applications to which they have granted access to their data, and 33% forget or do not think of revoking access to their data. Also, by using a demonstrator to perform inferences from location data, we observe that slightly more than half of participants (57%) are surprised by the extent of potentially inferred information, and that 47% intend to reduce access to their data via permissions as a result of using the demonstrator. Last, a majority of participants have little knowledge of the tools to better protect themselves, but are nonetheless willing to follow suggestions to improve privacy (51%). Educating people, including digital natives, about privacy risks through transparency tools seems a promising approach., Comment: Submitted to ICWSM on January 15, 2025

We characterize exactness of a countable group $\Gamma$ in terms of invariant random equivalence relations (IREs) on $\Gamma$. Specifically, we show that $\Gamma$ is exact if and only if every weak limit of finite IREs is an amenable IRE. In particular, for exact groups this implies amenability of the restricted rerooting relation associated to the ideal Bernoulli Voronoi tessellation, the discrete analog of the ideal Poisson Voronoi tesselation., Comment: 20 pages

Effective decision-making in complex systems requires synthesizing diverse perspectives to address multifaceted challenges under uncertainty. This study introduces a real-world inspired agentic Large Language Models (LLMs) framework, to simulate and enhance decision discourse-the deliberative process through which actionable strategies are collaboratively developed. Unlike traditional decision-support tools, the framework emphasizes dialogue, trade-off exploration, and the emergent synergies generated by interactions among agents embodying distinct personas. These personas simulate diverse stakeholder roles, each bringing unique priorities, expertise, and value-driven reasoning to the table. The framework incorporates adaptive and self-governing mechanisms, enabling agents to dynamically summon additional expertise and refine their assembly to address evolving challenges. An illustrative hypothetical example focused on extreme flooding in a Midwestern township demonstrates the framework's ability to navigate uncertainty, balance competing priorities, and propose mitigation and adaptation strategies by considering social, economic, and environmental dimensions. Results reveal how the breadth-first exploration of alternatives fosters robust and equitable recommendation pathways. This framework transforms how decisions are approached in high-stakes scenarios and can be incorporated in digital environments. It not only augments decision-makers' capacity to tackle complexity but also sets a foundation for scalable and context-aware AI-driven recommendations. This research explores novel and alternate routes leveraging agentic LLMs for adaptive, collaborative, and equitable recommendation processes, with implications across domains where uncertainty and complexity converge., Comment: 24 pages, 4 figures, 1 appendix

The Athena mission entered a redefinition phase in July 2022, driven by the imperative to reduce the mission cost at completion for the European Space Agency below an acceptable target, while maintaining the flagship nature of its science return. This notably called for a complete redesign of the X-ray Integral Field Unit (X-IFU) cryogenic architecture towards a simpler active cooling chain. Passive cooling via successive radiative panels at spacecraft level is now used to provide a 50 K thermal environment to an X-IFU owned cryostat. 4.5 K cooling is achieved via a single remote active cryocooler unit, while a multi-stage Adiabatic Demagnetization Refrigerator ensures heat lift down to the 50 mK required by the detectors. Amidst these changes, the core concept of the readout chain remains robust, employing Transition Edge Sensor microcalorimeters and a SQUID-based Time-Division Multiplexing scheme. Noteworthy is the introduction of a slower pixel. This enables an increase in the multiplexing factor (from 34 to 48) without compromising the instrument energy resolution, hence keeping significant system margins to the new 4 eV resolution requirement. This allows reducing the number of channels by more than a factor two, and thus the resource demands on the system, while keeping a 4' field of view (compared to 5' before). In this article, we will give an overview of this new architecture, before detailing its anticipated performances. Finally, we will present the new X-IFU schedule, with its short term focus on demonstration activities towards a mission adoption in early 2027., Comment: 44 pages, 14 figures, accepted for publication in Experimental Astronomy

This paper studies rational functions $\mathfrak{J}_\alpha(q)$, which depend on a positive element $\alpha$ of the root lattice of a root system. These functions arise as Shapovalov pairings of Whittaker vectors in Verma modules of highest weight $-\rho$ for quantum groups and as Hilbert series of Zastava spaces, and are related to the Toda system. They are specializations of multivariate functions more commonly studied in the literature. We investigate the denominator of these rational functions and give an explicit combinatorial formula for the numerator in type A., Comment: 12 pages

There is now a large sample of stars observed by the Kepler satellite with measured rotation periods and photometric activity index $S_{\rm ph}$. We use this data, in conjunction with stellar interiors models, to explore the interplay of magnetism, rotation, and convection. Stellar activity proxies other than $S_{\rm ph}$ are correlated with the Rossby number, $Ro$, or ratio of rotation period to convective overturn timescale. We compute the latter using the Yale Rotating Evolution Code stellar models. We observe different $S_{\rm ph}$-$Ro$ relationships for different stellar spectral types. Though the overall trend of decreasing magnetic activity versus $Ro$ is recovered, we find a localized dip in $S_{\rm ph}$ around $Ro/Ro_{\odot} \sim$\,0.3 for the G and K dwarfs. F dwarfs show little to no dependence of $S_{\rm ph}$ on $Ro$ due to their shallow convective zones; further accentuated as $T_{\rm eff}$ increases. The dip in activity for the G and K dwarfs corresponds to the intermediate rotation period gap, suggesting that the dip in $S_{\rm ph}$ could be associated with the redistribution of angular momentum between the core and convective envelope inside stars. For G-type stars, we observe enhanced magnetic activity above solar $Ro$. Compared to other Sun-like stars with similar effective temperature and metallicity, we find that the Sun's current level of magnetic activity is comparable to its peers and lies near the transition to increasing magnetic activity at high $Ro$. We confirm that metal-rich stars have a systematically larger $S_{\rm ph}$ level than metal-poor stars, which is likely a consequence of their deeper convective zones., Comment: 21 pages, 10 figures, including 7 pages of Appendix. Accepted for publication in ApJ

We study the Euler-Korteweg equations with a weak capillarity tensor. It formally converges to the Euler equations in the zero capillarity limit. Our aim is two-fold : first we prove rigorously this limit in R d , d $\ge$ 1, and obtain a more precise BKW expansion of the solution, second we initiate the study of the problem on the half space. In this case we obtain a priori estimates for the solutions that degenerate as the capillary coefficient converges to zero, and we explain this degeneracy with the construction of a (formal) BKW expansion that exhibits boundary layers. The results on the full space extend and improve a classical result of Grenier (1998) on the semi-classical limit of nonlinear Schr{\"o}dinger equations. The analysis on the half space is restricted to the case of quantum fluids with irrotational velocity.

Abdominal aortic aneurysm (AAA) is a life-threatening condition involving the permanent dilation of the aorta, often detected incidentally through imaging for some other condition. The standard clinical approach to managing AAA follows a one-size-fits-all model based on aneurysm size and growth rate, leading to underestimation or overestimation of rupture risk in individual patients. The widely studied stress-based rupture risk estimation using computational biomechanics requires wall strength information. However, non-invasive methods for local patient-specific wall strength measurement have not yet been developed. Recently, we introduced an image-based approach for patient-specific, in vivo, non-invasive AAA kinematic analysis using time-resolved 3D computed tomography angiography (4D-CTA) images to measure wall strain throughout the cardiac cycle. In the present study, we integrated wall tension computation and strain measurement to develop a novel measure of local structural integrity of AAA wall - Relative Structural Integrity Index (RSII), independent of material properties and thickness of the wall and conditions of blood pressure measurement. Our methods provide a visual map of AAA wall structural integrity for individual patients using only their medical images and blood pressure data. We applied our methods to twelve patients. Additionally, we compared our measure of structural integrity of aneurysmal and non-aneurysmal aortas. Our results show similar values of the wall structural integrity measure across the patients, indicating the reliability of our methods. In line with experimental observations reported in the literature, our analysis revealed that localized low stiffness areas are primarily found in the most dilated AAA regions. Our results clearly demonstrate that the AAA wall is stiffer than the non-aneurysmal aorta., Comment: 25 Pages

We compare three methods of deriving the local Galactic star formation history, using as a benchmark the Gaia-defined 40 pc white dwarf sample, currently the largest volume complete sample of stellar remnants with medium-resolution spectroscopy. We create a population synthesis model to 1) reproduce the observed white dwarf luminosity function, 2) reproduce the observed absolute Gaia G magnitude distribution, and 3) directly calculate the ages of all individual white dwarfs in the 40 pc volume. We then compare the star formation histories determined from each method. Previous studies using these methods were based on different white dwarf samples and as such were difficult to compare. Uncertainties in each method such as the initial mass function, initial-final mass relation, main sequence lifetimes, stellar metallicity, white dwarf cooling ages and binary evolution are accounted for to estimate the precision and accuracy of each method. We conclude that no method is quantitatively better at determining the star formation history and all three produce star formation histories that agree within uncertainties of current external astrophysical relations., Comment: Submitted to MNRAS. Comments are welcome

In "chemostat"-type population models that incorporate substrate (nutrient) dynamics, the dependence of the birth (or growth) rate on the substrate concentration introduces nonlinear coupling that creates a challenge for stabilization that is global, namely, for all positive concentrations of the biomass and nutrients. This challenge for global stabilization has been overcome in the literature using relatively simple feedback when natural mortality of the biomass is absent. However, under natural mortality, it takes fortified, more complex feedback, outside of the existing nonlinear control design toolbox, to avoid biomass extinction from nutrient-depleted initial conditions. Such fortified feedback, the associated control Laypunov function design, and Lyapunov analysis of global stability are provided in this paper. We achieve global stabilization for two different chemostat models: (i) a lumped model, with two state variables, and (ii) a three-state model derived from an age-structured infinite-dimensional model. The proposed feedback stabilizers are explicit, applicable to both the lumped and the age-structured models, and coincide with simple feedback laws proposed in the literature when the mortality rate is zero. Global stabilization means subject to constraints: all positive biomass and nutrient concentrations are within the region of attraction of the desired equilibrium, and, additionally, this is achieved with a dilution input that is guaranteed to remain positive. For the lumped case with Haldane kinetics, we show that the reproduction rate dominating the mortality (excluding the reproduction and mortality being in balance) is not only sufficient but also necessary for global stabilization. The obtained results are illustrated with simple examples.

The loss of close-in planetary atmospheres is influenced by various physical processes, such as photoionisation, which could potentially affect the atmosphere survivability on a secular timescale. The amount of stellar radiation converted into heat depends on the energy of the primary electrons produced by photoionisation and the local ionisation fraction. The Lyman-alpha line is an excellent probe for atmospheric escape. We study the interaction between the planetary and the stellar wind, the difference of the predicted mass-loss rates between 1D and 2D models, the signal of Ly-a and the impact of stellar flares. Using the PLUTO code, we perform 2D hydrodynamics simulations for four different planets. We consider planets in the size range from Neptune to Jupiter. We produce synthetic Ly-a profiles to comprehend the origin of the signal, and in particular its high velocity Doppler shift. Our results indicate a trend similar to the 1D models, with a decrease in the planetary mass-loss rate for all systems when secondary ionisation is taken into account. The mass-loss rates are found to decrease by 48% for the least massive planet when secondary ionisation is accounted for. We find nevertheless a decrease that is less pronounced in 2D than in 1D. We observe differences in the Ly-a profile between the different cases and significant asymmetries in all of them, especially for the lower mass planets. Finally, we observe that stellar flares do not affect the mass-loss rate because they act, in general, on a timescale that is too short. We find velocities in the escaping atmosphere up to 100 km/s, with the gas moving away from the star, which could be the result of the interaction with the stellar wind. Furthermore, we find that stellar flares generally occur on a timescale that is too short to have a visible impact on the mass-loss rate of the atmosphere., Comment: 13 Figures, 11 pages

Chaotic systems are characterised by exponential separation between close-by trajectories, which in particular leads to deterministic unpredictability over an infinite time-window. It is now believed, that such butterfly effect is not fully relevant to account for the type of randomness observed in turbulence. For example, tracers in homogeneous isotropic flows are observed to separate algebraically, following a universal cubic growth, independent from the initial separation. This regime, known as Richardon's regime, suggests that at the level of trajectories, and unlike in chaos theory, randomness may in fact emerge in finite-time. This phenomenon called 'spontaneous stochasticity' originates from the singular nature of the underlying dynamics, and provides a candidate framework for turbulent randomness and transport. While spontaneous stochasticity has been mathematically formalised in simplified turbulence models, a precise and systematic tool for quantifying the various facets of this phenomenon is to this day missing. In particular, it is still unclear whether chaos is important for that behaviour to appear. In this paper we introduce a 3d rough flow that can be tuned to present Lagrangian chaos. The flow is inspired by the Weierstrass function and is entitled 'the WABC model'. After analysing its properties, we define what is spontaneous stochasticity in this context. The provided formal definition is then adapted to better suit for numerical analysis. We present the results from Monte-Carlo simulations of Lagrangian particles in this flow. Within the numerical precision, we quantitatively observe the appearance of spontaneous stochasticity in this model. We investigate the influence of noise type and find that the observed spontaneous stochasticity does not depend on the chosen stochastic regularisations., Comment: 23 pages, 15 figures

Electronic flat bands associated with quenched kinetic energy and heavy electron mass have attracted great interest for promoting strong electronic correlations and emergent phenomena such as high-temperature charge fractionalization and superconductivity. Intense experimental and theoretical research has been devoted to establishing the rich non-trivial metallic and heavy fermion phases intertwined with such localized electronic states. Here, we investigate the transition metal oxide spinel LiV2O4, an enigmatic heavy fermion compound lacking localized f orbital states. We use angle-resolved photoemission spectroscopy and dynamical mean field theory to reveal a new kind of correlation-induced flat band with suppressed inter-atomic electron hopping arising from intra-atomic Hund coupling. The appearance of heavy quasiparticles is ascribed to a proximate orbital-selective Mott state characterized by fluctuating local moments as evidenced by complementary magnetotransport measurements. The spectroscopic fingerprints of long-lived quasiparticles and their disappearance with increasing temperature further support the emergence of a high-temperature bad metal state observed in transport data. This work resolves a long-standing puzzle on the origin of heavy fermion behavior and unconventional transport in LiV2O4. Simultaneously, it opens a new path to achieving flat bands through electronic interactions in d-orbital systems with geometrical frustration, potentially enabling the realization of exotic phases of matter such as the fractionalized Fermi liquids., Comment: 21 pages, 4 figures

Larrauri and \v{Z}ivn\'y recently established a complete complexity classification of the problem of solving a system of equations over a monoid $N$ assuming that a solution exists over a monoid $M$, where both monoids are finite and $M$ admits a homomorphism to $N$. Using the algebraic approach to promise constraint satisfaction problems, we extend their complexity classification in two directions: we obtain a complexity dichotomy in the case where arbitrary relations are added to the monoids, and we moreover allow the monoid $M$ to be finitely generated.

In order to both learn and protect sensitive training data, there has been a growing interest in privacy preserving machine learning methods. Differential privacy has emerged as an important measure of privacy. We are interested in the federated setting where a group of parties each have one or more training instances and want to learn collaboratively without revealing their data. In this paper, we propose strategies to compute differentially private empirical distribution functions. While revealing complete functions is more expensive from the point of view of privacy budget, it may also provide richer and more valuable information to the learner. We prove privacy guarantees and discuss the computational cost, both for a generic strategy fitting any security model and a special-purpose strategy based on secret sharing. We survey a number of applications and present experiments.

A recent developing trend in clustering is the advancement of algorithms that not only identify clusters within data, but also express and capture the uncertainty of cluster membership. Evidential clustering addresses this by using the Dempster-Shafer theory of belief functions, a framework designed to manage and represent uncertainty. This approach results in a credal partition, a structured set of mass functions that quantify the uncertain assignment of each object to potential groups. The Python framework evclust, presented in this paper, offers a suite of efficient evidence clustering algorithms as well as tools for visualizing, evaluating and analyzing credal partitions., Comment: 13 pages, 2 figures, Preprint

Early Classification of Time Series (ECTS) has been recognized as an important problem in many areas where decisions have to be taken as soon as possible, before the full data availability, while time pressure increases. Numerous ECTS approaches have been proposed, based on different triggering functions, each taking into account various pieces of information related to the incoming time series and/or the output of a classifier. Although their performances have been empirically compared in the literature, no studies have been carried out on the optimality of these triggering functions that involve ``man-tailored'' decision rules. Based on the same information, could there be better triggering functions? This paper presents one way to investigate this question by showing first how to translate ECTS problems into Reinforcement Learning (RL) ones, where the very same information is used in the state space. A thorough comparison of the performance obtained by ``handmade'' approaches and their ``RL-based'' counterparts has been carried out. A second question investigated in this paper is whether a different combination of information, defining the state space in RL systems, can achieve even better performance. Experiments show that the system we describe, called \textsc{Alert}, significantly outperforms its state-of-the-art competitors on a large number of datasets.

Quality-Diversity (QD) has demonstrated potential in discovering collections of diverse solutions to optimisation problems. Originally designed for deterministic environments, QD has been extended to noisy, stochastic, or uncertain domains through various Uncertain-QD (UQD) methods. However, the large number of UQD methods, each with unique constraints, makes selecting the most suitable one challenging. To remedy this situation, we present two contributions: first, the Extract-QD Framework (EQD Framework), and second, Extract-ME (EME), a new method derived from it. The EQD Framework unifies existing approaches within a modular view, and facilitates developing novel methods by interchanging modules. We use it to derive EME, a novel method that consistently outperforms or matches the best existing methods on standard benchmarks, while previous methods show varying performance. In a second experiment, we show how our EQD Framework can be used to augment existing QD algorithms and in particular the well-established Policy-Gradient-Assisted-MAP-Elites method, and demonstrate improved performance in uncertain domains at no additional evaluation cost. For any new uncertain task, our contributions now provide EME as a reliable "first guess" method, and the EQD Framework as a tool for developing task-specific approaches. Together, these contributions aim to lower the cost of adopting UQD insights in QD applications.

This paper describes the problem of coordination of an autonomous Multi-Agent System which aims to solve the coverage planning problem in a complex environment. The considered applications are the detection and identification of objects of interest while covering an area. These tasks, which are highly relevant for space applications, are also of interest among various domains including the underwater context, which is the focus of this study. In this context, coverage planning is traditionally modelled as a Markov Decision Process where a coordinated MAS, a swarm of heterogeneous autonomous underwater vehicles, is required to survey an area and search for objects. This MDP is associated with several challenges: environment uncertainties, communication constraints, and an ensemble of hazards, including time-varying and unpredictable changes in the underwater environment. MARL algorithms can solve highly non-linear problems using deep neural networks and display great scalability against an increased number of agents. Nevertheless, most of the current results in the underwater domain are limited to simulation due to the high learning time of MARL algorithms. For this reason, a novel strategy is introduced to accelerate this convergence rate by incorporating biologically inspired heuristics to guide the policy during training. The PSO method, which is inspired by the behaviour of a group of animals, is selected as a heuristic. It allows the policy to explore the highest quality regions of the action and state spaces, from the beginning of the training, optimizing the exploration/exploitation trade-off. The resulting agent requires fewer interactions to reach optimal performance. The method is applied to the MSAC algorithm and evaluated for a 2D covering area mission in a continuous control environment., Comment: i-SAIRAS 2024 Conference

This work addresses the problem of active 3D mapping, where an agent must find an efficient trajectory to exhaustively reconstruct a new scene. Previous approaches mainly predict the next best view near the agent's location, which is prone to getting stuck in local areas. Additionally, existing indoor datasets are insufficient due to limited geometric complexity and inaccurate ground truth meshes. To overcome these limitations, we introduce a novel dataset AiMDoom with a map generator for the Doom video game, enabling to better benchmark active 3D mapping in diverse indoor environments. Moreover, we propose a new method we call next-best-path (NBP), which predicts long-term goals rather than focusing solely on short-sighted views. The model jointly predicts accumulated surface coverage gains for long-term goals and obstacle maps, allowing it to efficiently plan optimal paths with a unified model. By leveraging online data collection, data augmentation and curriculum learning, NBP significantly outperforms state-of-the-art methods on both the existing MP3D dataset and our AiMDoom dataset, achieving more efficient mapping in indoor environments of varying complexity., Comment: To appear at ICLR 2025. Project webpage: https://shiyao-li.github.io/nbp/

The companion GL229B was recently resolved by Xuan et al. (2024) as a tight binary of two brown dwarfs (Ba and Bb) through VLTI-GRAVITY interferometry and VLT-CRIRES+ RV measurements. Here, we present Bayesian models of the interferometric and RV data in additional detail, along with an updated outer orbit of the brown dwarf pair about the primary. To create a model of the inner orbit with robust uncertainties, we apply kernel phases to the GRAVITY data to address baseline redundancy in the raw closure phases. Using parallel tempering, we constrain the binary's orbit using only VLTI-GRAVITY data, despite each epoch having low visibility-plane coverage and/or SNR. We demonstrate very agreement the VLTI-GRAVITY and CRIRES+ datasets and find that the inner binary has a period of 12.1346$\pm$0.0011 days, eccentricity of 0.2317$\pm$0.0025, and total mass of 71.0$\pm$0.4 Mjup, with Ba and Bb having masses of 37.7$\pm$1.1Mjup and 33.4$\pm$1.0Mjup respectively. With new Keck/NIRC2 astrometry, we update the outer orbit GL229B around the primary. We find a semi-major axis of 42.9+3.0-2.4AU, eccentricity of 0.736$\pm$0.014, and a total mass for B of 71.7$\pm$0.6Mjup, consistent with that derived from the inner orbit. We find a mutual inclination of 31$\pm$2.5deg, below the threshold for Kozai-Lidov oscillations. The agreement on the mass of Ba+Bb between the inner and outer orbits is an important test of our ability to model RV, astrometry, and Hipparcos-Gaia proper motion anomaly. Our methodological advances in handling interferometric data with low SNR and sparse UV-coverage will benefit future observations of rapidly-orbiting companions with VLTI-GRAVITY., Comment: Resubmitted to AJ

We consider the problem of approximating a subset $M$ of a Hilbert space $X$ by a low-dimensional manifold $M_n$, using samples from $M$. We propose a nonlinear approximation method where $M_n $ is defined as the range of a smooth nonlinear decoder $D$ defined on $\mathbb{R}^n$ with values in a possibly high-dimensional linear space $X_N$, and a linear encoder $E$ which associates to an element from $ M$ its coefficients $E(u)$ on a basis of a $n$-dimensional subspace $X_n \subset X_N$, where $X_n$ and $X_N$ are optimal or near to optimal linear spaces, depending on the selected error measure. The linearity of the encoder allows to easily obtain the parameters $E(u)$ associated with a given element $u$ in $M$. The proposed decoder is a polynomial map from $\mathbb{R}^n$ to $X_N$ which is obtained by a tree-structured composition of polynomial maps, estimated sequentially from samples in $M$. Rigorous error and stability analyses are provided, as well as an adaptive strategy for constructing a decoder that guarantees an approximation of the set $M$ with controlled mean-squared or wort-case errors, and a controlled stability (Lipschitz continuity) of the encoder and decoder pair. We demonstrate the performance of our method through numerical experiments., Comment: 26 pages, 9 figures