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The GPU-based High-order adaptive OpticS Testbench (GHOST) at the European Southern Observatory (ESO) is a new 2-stage extreme adaptive optics (XAO) testbench at ESO. The GHOST is designed to investigate and evaluate new control methods (machine learning, predictive control) for XAO which will be required for instruments such as the Planetary Camera and Spectrograph of ESOs Extremely Large Telescope. The first stage corrections are performed in simulation, with the residual wavefront error at each iteration saved. The residual wavefront errors from the first stage are then injected into the GHOST using a spatial light modulator. The second stage correction is made with a Boston Michromachines Corporation 492 actuator deformable mirror and a pyramid wavefront sensor. The flexibility of the bench also opens it up to other applications, one such application is investigating the flip-flop modulation method for the pyramid wavefront sensor., Comment: spie astronomical telescopes + instrumentation 2022

Finding correspondences between 3D shapes is an important and long-standing problem in computer vision, graphics and beyond. While approaches based on machine learning dominate modern 3D shape matching, almost all existing (learning-based) methods require that at least one of the involved shapes is complete. In contrast, the most challenging and arguably most practically relevant setting of matching partially observed shapes, is currently underexplored. One important factor is that existing datasets contain only a small number of shapes (typically below 100), which are unable to serve data-hungry machine learning approaches, particularly in the unsupervised regime. In addition, the type of partiality present in existing datasets is often artificial and far from realistic. To address these limitations and to encourage research on these relevant settings, we provide a generic and flexible framework for the procedural generation of challenging partial shape matching scenarios. Our framework allows for a virtually infinite generation of partial shape matching instances from a finite set of shapes with complete geometry. Further, we manually create cross-dataset correspondences between seven existing (complete geometry) shape matching datasets, leading to a total of 2543 shapes. Based on this, we propose several challenging partial benchmark settings, for which we evaluate respective state-of-the-art methods as baselines.

In causal inference, treatment effects are typically estimated under the ignorability, or unconfoundedness, assumption, which is often unrealistic in observational data. By relaxing this assumption and conducting a sensitivity analysis, we introduce novel bounds and derive confidence intervals for the Average Potential Outcome (APO) - a standard metric for evaluating continuous-valued treatment or exposure effects. We demonstrate that these bounds are sharp under a continuous sensitivity model, in the sense that they give the smallest possible interval under this model, and propose a doubly robust version of our estimators. In a comparative analysis with the method of Jesson et al. (2022) (arXiv:2204.10022), using both simulated and real datasets, we show that our approach not only yields sharper bounds but also achieves good coverage of the true APO, with significantly reduced computation times.

As two-dimensional (2D) materials continue to gain prominence, understanding their symmetries is critical for unlocking their full potential. In this work, we present comprehensive scanning tables that tabulate the rod group symmetries of all crystallographic lines in all 80 layer groups, which describe the symmetries of 2D materials. These tables are analogous to the scanning tables for space groups found in Volume E of the International Tables for Crystallography, but are specifically tailored for layer groups and their applications to 2D materials. This resource will aid in the analysis of line defects, such as domain walls and grain boundaries, which play a crucial role in determining the properties and functionality of 2D materials., Comment: 10 pages, 7 figures, 2 tables. Supplementary information: 37 pages, 80 tables

We describe observations of low energy excess (LEE) events (background events observed in all light dark matter direct detection calorimeters) and noise in a two-channel silicon athermal phonon detector with 375 meV baseline energy resolution. We measure two distinct LEE populations: ``shared'' multichannel events with a pulse shape consistent with athermal phonon events, and sub-eV events which couple nearly exclusively to a single channel with a significantly faster pulse shape. These ``singles'' are consistent with events occurring within the aluminum athermal phonon collection fins. Similarly, our measured detector noise is higher than the theoretical expectation. Measured noise can be split into an uncorrelated component, consistent with shot noise from small energy depositions within the athermal phonon sensor itself, and a correlated component, consistent with shot noise from energy depositions within the silicon crystal's phonon system., Comment: 6 pages, 5 figures

Material nonlinearities such as hyperelasticity, viscoelasticity, and plasticity have recently emerged as design paradigms for metamaterials based on buckling. These metamaterials exhibit properties such as shape morphing, transition waves, and sequential deformation. In particular, plasticity has been used in the design of sequential metamaterials which combine high stiffness, strength, and dissipation at low density and produce superior shock absorbing performances. However, the use of plasticity for tuning buckling sequences in metamaterials remains largely unexplored. In this work, we introduce yield area, yield criterion, and loading history as new design tools of plasticity in tuning the buckling load and sequence in metamaterials. We numerically and experimentally demonstrate a controllable buckling sequence in different metamaterial architectures with the above three strategies. Our findings enrich the toolbox of plasticity in the design of metamaterials with more controllable sequential deformations and leverage plasticity to broader applications in multi-functional metamaterials, high-performance soft robotics, and mechanical self-assembly.

A unified algebraic interpretation of both finite families of orthogonal polynomials and biorthogonal rational functions of $q$-Hahn type is provided. The approach relies on the meta $q$-Hahn algebra and its finite-dimensional bidiagonal representations. The functions of $q$-Hahn type are identified as overlaps (up to global factors) between bases solving ordinary or generalized eigenvalue problems in the representation of the meta $q$-Hahn algebra. Moreover, (bi)orthogonality relations, recurrence relations, difference equations and some contiguity relations satisfied by these functions are recovered algebraically using the actions of the generators of the meta $q$-Hahn algebra on various bases., Comment: 27 pages

This paper extends previous studies on the impact of tensor forces in fission dynamics of neutron-deficient Thorium isotopes to other isotopic chains of heavy actinides and low-mass super-heavy nuclei. Calculations are carried out within a mean-field framework based on the Gogny-D1S parametrization supplemented with the D1ST2a perturbative tensor term as driving force. Fission barrier heights and spontaneous fission half-lives are used as benchmarks to analyze the impact of the tensor term. A significant reduction of fission barrier heights and half-lives is associated to the tensor component of the force., Comment: 11 pages, 5 figures

This study provides the first comprehensive analysis of how ion mobility affects electron density and temperature in hypersonic flows. We compare two ion mobility models: one derived from Gupta-Yos cross-sections, and the other from swarm drift velocity experiments. The ion mobility model significantly alters the plasma density around a hypersonic waverider, with increases of more than twofold observed at low dynamic pressures and high Mach numbers. This is partly due to electron loss through surface catalysis, which depends on ambipolar diffusion scaling with ion mobility. We also derive novel scaling laws that highlight the strong dependence of electron cooling on ion mobility both within the quasi-neutral regions and the non-neutral plasma sheaths. Electron cooling influences the electron temperature across the plasma, leading to a previously unrecognized impact of ion mobility on plasma bulk temperature. This in turn affects plasma density via electron-ion recombination rates which are temperature-dependent. Accurately modeling ion mobility is critical for predicting hypersonic plasma behavior, with important implications for optimizing magnetohydrodynamic technologies and mitigating or exploiting plasma-induced interference with electromagnetic waves., Comment: 31 pages, 11 figures, 5 tables

We demonstrate efficient in-plane optical fiber collection of single photon emission from quantum dots embedded in photonic crystal cavities. This was achieved via adiabatic coupling between a tapered optical fiber and a tapered on-chip photonic waveguide coupled to the photonic crystal cavity. The collection efficiency of a dot in a photonic crystal cavity was measured to be 5 times greater via the tapered optical fiber compared to collection by a microscope objective lens above the cavity. The single photon source was also characterized by second order photon correlations measurements giving g(2)(0)=0.17 under non-resonant excitation. Numerical calculations demonstrate that the collection efficiency could be further increased by improving the dot-cavity coupling and by increasing the overlap length of the tapered fiber with the on-chip waveguide. An adiabatic coupling of near unity is predicted for an overlap length of 5 microns., Comment: 22 pages, 12 figures

Averaging dynamics drives countless processes in physics, biology, engineering, and the social sciences. In recent years, the $s$-energy has emerged as a useful tool for bounding the convergence rates of time-varying averaging systems. We derive new bounds on the $s$-energy, which we use to resolve a number of open questions in the areas of bird flocking, opinion dynamics, and distributed motion coordination. We also use our results to provide a theoretical validation for the idea of the "Overton Window" as an attracting manifold of viable group opinions. Our new bounds on the $s$-energy highlight its dependency on the connectivity of the underlying networks. In this vein, we use the $s$-energy to explain the exponential gap in the convergence rates of stationary and time-varying consensus systems., Comment: arXiv admin note: substantial text overlap with arXiv:2207.00213

Diffusion models have emerged as highly effective techniques for inpainting, however, they remain constrained by slow sampling rates. While recent advances have enhanced generation quality, they have also increased sampling time, thereby limiting scalability in real-world applications. We investigate the generative sampling process of diffusion-based inpainting models and observe that these models make minimal use of the input condition during the initial sampling steps. As a result, the sampling trajectory deviates from the data manifold, requiring complex synchronization mechanisms to realign the generation process. To address this, we propose Time-aware Diffusion Paint (TD-Paint), a novel approach that adapts the diffusion process by modeling variable noise levels at the pixel level. This technique allows the model to efficiently use known pixel values from the start, guiding the generation process toward the target manifold. By embedding this information early in the diffusion process, TD-Paint significantly accelerates sampling without compromising image quality. Unlike conventional diffusion-based inpainting models, which require a dedicated architecture or an expensive generation loop, TD-Paint achieves faster sampling times without architectural modifications. Experimental results across three datasets show that TD-Paint outperforms state-of-the-art diffusion models while maintaining lower complexity.

We investigate optimization of an algal-bacterial consortium, where an exogenous control input modulates bacterial resource allocation between growth and synthesis of a resource that is limiting for algal growth. Maximization of algal biomass synthesis is pursued in a continuous bioreactor, with dilution rate as an additional control variable. We formulate optimal control in the two variants of static and dynamic control problems, and address them by theoretical and numerical tools. We explore convexity of the static problem and uniqueness of its solution, and show that the dynamic problem displays a solution with bang-bang control actions and singular arcs that result in cyclic control actions. We finally discuss the relation among the two solutions and show the extent to which dynamic control can outperform static optimal solutions.

This article presents how ship trajectories have been built from historical sources dealing with maritime trade in the 18th century. It first summarizes the method for building the routes, and qualifying the uncertainty level linked to each of its segments. Then, it details how the geometries of these segments connecting two successive stopovers were automatically calculated in order to draw a map with maritime paths only. The algorithm, programmed with PL/SQL language, is available under an open-source licence (https://gitlab.huma-num.fr/portic/porticapi). Finally, an online tool for querying and mapping these routes with their associated level of uncertainty is exposed (http://shiproutes.portic.fr). We show its usefulness for historians, in particular for the control of the validity of built ship trajectories., Comment: in French language

Semiconductor quantum dots in microcavities are an excellent platform for the efficient generation of indistinguishable single photons. However, their use in a wide range of quantum technologies requires their controlled fabrication and integration in compact closed-cycle cryocoolers, with a key challenge being the efficient and stable extraction of the single photons into a single-mode fiber. Here we report on a novel method for fiber-pigtailing of deterministically fabricated single-photon sources. Our technique allows for nanometer-scale alignment accuracy between the source and a fiber, alignment that persists all the way from room temperature to 2.4 K. We demonstrate high performance of the device under near-resonant optical excitation with g$^{(2)}$(0) = 1.3 %, a photon indistinguishability of 97.5 % and a fibered brightness of 20.8 %. We show that the indistinguishability and single-photon rate are stable for over ten hours of continuous operation in a single cooldown. We further confirm that the device performance is not degraded by nine successive cooldown-warmup cycles., Comment: 7 pages, 5 figures

The maintenance of sewerage networks, with their millions of kilometers of pipe, heavily relies on efficient Closed-Circuit Television (CCTV) inspections. Many promising approaches based on multi-label image classification have leveraged databases of historical inspection reports to automate these inspections. However, the significant presence of label noise in these databases, although known, has not been addressed. While extensive research has explored the issue of label noise in singlelabel classification (SLC), little attention has been paid to label noise in multi-label classification (MLC). To address this, we first adapted three sample selection SLC methods (Co-teaching, CoSELFIE, and DISC) that have proven robust to label noise. Our findings revealed that sample selection based solely on the small-loss trick can handle complex label noise, but it is sub-optimal. Adapting hybrid sample selection methods to noisy MLC appeared to be a more promising approach. In light of this, we developed a novel method named MHSS (Multi-label Hybrid Sample Selection) based on CoSELFIE. Through an in-depth comparative study, we demonstrated the superior performance of our approach in dealing with both synthetic complex noise and real noise, thus contributing to the ongoing efforts towards effective automation of CCTV sewer pipe inspections.

The process of collecting blood from donors and making it available for transfusion requires a complex series of operations involving multiple actors and resources at each step. Ensuring hospitals receive adequate and safe blood for transfusion is a common challenge across low- and middle-income countries, but is rarely addressed from a system level. This paper presents the first use of discrete event simulation to study the blood system in Kenya and to explore the effect of variations and perturbations at different steps of the system on meeting patient blood demand. A process map of the Kenyan blood system was developed to capture critical steps from blood donation to transfusion using interviews with blood bank, hospital, and laboratory personnel at four public hospitals across three counties in Kenya. The blood system was simulated starting with blood collection, a blood bank where blood is tested and stored before it is issued, a major hospital attached to the blood bank, and several smaller hospitals served by the same blood bank. Values for supply-side parameters were based mainly on expert opinion; demand-side parameters were based on data from blood requisitions made in hospital wards, and dispatch of blood from the hospital laboratory. Illustrative examples demonstrate how the model can be used to explore the impacts of changes in blood collection (e.g., prioritising different donor types), blood demand (e.g., differing clinical case mix), and blood distribution (e.g., restocking strategies) on meeting demand at patient level. The model can reveal potential process impediments in the blood system and aid in choosing strategies for improving blood collection, distribution or use. Such a systems approach allows for interventions at different steps in the blood continuum to be tested on blood availability for different patients presenting at diverse hospitals across the country., Comment: 38 pages, 8 figures

Future space observatories dedicated to direct imaging and spectroscopy of extra-solar planets will require ultra-low-noise detectors that are sensitive over a broad range of wavelengths. Silicon charge-coupled devices (CCDs), such as EMCCDs, Skipper CCDs, and Multi-Amplifier Sensing CCDs, have demonstrated the ability to detect and measure single photons from ultra-violet to near-infrared wavelengths, making them candidate technologies for this application. In this context, we study a relatively unexplored source of low-energy background coming from Cherenkov radiation produced by energetic charged particles traversing a silicon detector. In the intense radiation environment of space, energetic cosmic rays produce high-energy tracks and more extended halos of low-energy Cherenkov photons, which are detectable with ultra-low-noise detectors. We present a model of this effect that is calibrated to laboratory data, and we use this model to characterize the residual background rate for ultra-low noise silicon detectors in space. We find that the rate of cosmic-ray-induced Cherenkov photon production is comparable to other detector and astrophysical backgrounds that have previously been considered., Comment: SPIE Proceeding; 9 pages, 6 figures

We study the problem of representing multivariate polynomials with rational coefficients, which are nonnegative and strictly positive on finite semialgebraic sets, using rational sums of squares. We focus on the case of finite semialgebraic sets S defined by equality constraints, generating a zero-dimensional ideal I, and by nonnegative sign constraints. First, we obtain existential results. We prove that a strictly positive polynomial f with coefficients in a subfield K of R has a representation in terms of weighted Sums-of-Squares with coefficients in this field, even if the ideal I is not radical. We generalize this result to the case where f is nonnegative on S and (f ) + (I : f ) = 1. We deduce that nonnegative polynomials with coefficients in K can be represented in terms of Sum-of-Squares of polynomials with coefficients in K, when the ideal is radical. Second, we obtain degree bounds for such Sums-of-Squares representations, which depend linearly on the regularity of the ideal and the degree of the defining equations, when they form a graded basis. Finally, we analyze the bit complexity of the Sums-of-Squares representations for polynomials with coefficients in Q, in the case the ideal is radical. The bitsize bounds are quadratic or cubic in the Bezout bound, and linear in the regularity, generalizing and improving previous results obtained for special zero dimensional ideals. As an application in the context of polynomial optimization, we retrieve and improve results on the finite convergence and exactness of the moment/Sums-of-Squares hierarchy.

Semi-structured networks (SSNs) merge the structures familiar from additive models with deep neural networks, allowing the modeling of interpretable partial feature effects while capturing higher-order non-linearities at the same time. A significant challenge in this integration is maintaining the interpretability of the additive model component. Inspired by large-scale biomechanics datasets, this paper explores extending SSNs to functional data. Existing methods in functional data analysis are promising but often not expressive enough to account for all interactions and non-linearities and do not scale well to large datasets. Although the SSN approach presents a compelling potential solution, its adaptation to functional data remains complex. In this work, we propose a functional SSN method that retains the advantageous properties of classical functional regression approaches while also improving scalability. Our numerical experiments demonstrate that this approach accurately recovers underlying signals, enhances predictive performance, and performs favorably compared to competing methods., Comment: Accepted at NeurIPS 2024

This study is the first to explore whether multi-modal large language models (LLMs) can align their behaviors with visual personas, addressing a significant gap in the literature that predominantly focuses on text-based personas. We developed a novel dataset of 5K fictional avatar images for assignment as visual personas to LLMs, and analyzed their negotiation behaviors based on the visual traits depicted in these images, with a particular focus on aggressiveness. The results indicate that LLMs assess the aggressiveness of images in a manner similar to humans and output more aggressive negotiation behaviors when prompted with an aggressive visual persona. Interestingly, the LLM exhibited more aggressive negotiation behaviors when the opponent's image appeared less aggressive than their own, and less aggressive behaviors when the opponents image appeared more aggressive., Comment: EMNLP 2024

We review our results on a new class of $2 \to 3$ exclusive processes, as a probe of both chiral-even and chiral-odd quark GPDs. We consider the exclusive photoproduction of a photon-meson pair, in the kinematics where the pair has a large invariant mass, described in the collinear factorization framework. We cover the whole kinematical range from medium energies in fixed target experiments to very large energies of colliders, by considering the experimental conditions of JLab 12-GeV, COMPASS, future EIC and LHC (in ultra-peripheral collisions) cases. Our analysis covers neutral and charged rho-mesons, as well as charged pions. The case of the rho-meson, depending on its polarization, provides access to either chiral-even or chiral-odd GPDs, at leading twist. We find that the order of magnitude of the obtained cross sections are sufficiently large for a dedicated experimental analysis to be performed, especially at JLab. Furthermore, we compute the linear photon beam polarization asymmetry, which we find to be sizeable, in the case of a longitudinally polarized $\rho$-meson or of a charged pion. These predictions are obtained for both asymptotic distribution amplitude (DA) and holographic DA., Comment: 7 pages, 1 figure, Presented by S. Wallon at the 31st International Workshop on Deep Inelastic Scattering (DIS2024). arXiv admin note: substantial text overlap with arXiv:2401.17656

The ComPair balloon instrument is a prototype gamma-ray telescope that aims to further develop technology for observing the gamma-ray sky in the MeV regime. ComPair combines four detector subsystems to enable parallel Compton scattering and pair-production detection, critical for observing in this energy range. This includes a 10 layer double-sided silicon strip detector tracker, a virtual Frisch grid low energy CZT calorimeter, a high energy CsI calorimeter, and a plastic scintillator anti-coincidence detector. The inaugural balloon flight successfully launched from the Columbia Scientific Balloon Facility site in Fort Sumner, New Mexico, in late August 2023, lasting approximately 6.5 hours in duration. In this proceeding, we discuss the development of the ComPair Since balloon payload, the performance during flight, and early results., Comment: 13 pages, 12 figures

Stochastic optimization naturally appear in many application areas, including machine learning. Our goal is to go further in the analysis of the Stochastic Average Gradient Accelerated (SAGA) algorithm. To achieve this, we introduce a new $\lambda$-SAGA algorithm which interpolates between the Stochastic Gradient Descent ($\lambda=0$) and the SAGA algorithm ($\lambda=1$). Firstly, we investigate the almost sure convergence of this new algorithm with decreasing step which allows us to avoid the restrictive strong convexity and Lipschitz gradient hypotheses associated to the objective function. Secondly, we establish a central limit theorem for the $\lambda$-SAGA algorithm. Finally, we provide the non-asymptotic $\mathbb{L}^p$ rates of convergence.

The task of action spotting consists in both identifying actions and precisely localizing them in time with a single timestamp in long, untrimmed video streams. Automatically extracting those actions is crucial for many sports applications, including sports analytics to produce extended statistics on game actions, coaching to provide support to video analysts, or fan engagement to automatically overlay content in the broadcast when specific actions occur. However, before 2018, no large-scale datasets for action spotting in sports were publicly available, which impeded benchmarking action spotting methods. In response, our team built the largest dataset and the most comprehensive benchmarks for sports video understanding, under the umbrella of SoccerNet. Particularly, our dataset contains a subset specifically dedicated to action spotting, called SoccerNet Action Spotting, containing more than 550 complete broadcast games annotated with almost all types of actions that can occur in a football game. This dataset is tailored to develop methods for automatic spotting of actions of interest, including deep learning approaches, by providing a large amount of manually annotated actions. To engage with the scientific community, the SoccerNet initiative organizes yearly challenges, during which participants from all around the world compete to achieve state-of-the-art performances. Thanks to our dataset and challenges, more than 60 methods were developed or published over the past five years, improving on the first baselines and making action spotting a viable option for the sports industry. This paper traces the history of action spotting in sports, from the creation of the task back in 2018, to the role it plays today in research and the sports industry., Comment: 31 pages, 2 figures, 5 tables

Superconductor-normal material-superconductor (SNS) junctions are being integrated into microwave circuits for fundamental and applied research goals. The short junction limit is a common simplifying assumption for experiments with SNS junctions, but this limit constrains how small the nonlinearity of the microwave circuit can be. Here, we show that a finite length of the weak link strongly suppresses the nonlinearity compared to its zero-length limit -- the suppression can be up to a factor of ten even when the length remains shorter than the induced coherence length. We tie this behavior to the nonanalytic dependence of nonlinearity on length, which the critical current does not exhibit. Further, we identify additional experimentally observable consequences of nonzero length, and we conjecture that anharmonicity is bounded between zero and a maximally negative value for any non-interacting Josephson junction in the presence of time-reversal symmetry. We promote SNS junction length as a useful parameter for designing weakly nonlinear microwave circuits.

Particle accelerators with superconducting magnets operating at cryogenic temperatures use a beam screen (BS) liner that extracts heat generated by the circulating bunched charge particle beam before it can reach the magnets. The BS surface, commonly made of high-conductivity copper, provides a low impedance for beam stability reasons, low secondary electron yield (SEY)to mitigate the electron-cloud (EC) effect, and low electron-stimulated desorption yield (ESD) to limit the dynamic pressure rise due to EC. Rare-earth barium copper oxide (REBCO) high-temperature superconductors (HTs) recently reached technical maturity, are produced as coated conductor tapes (REBCO-CCs), and will be considered for application in future colliders to decrease the BS impedance and enable operation at around 50 K, consequently relaxing the cryogenic requirements. Aside from HTS properties, industry-grade REBCO-CCs also need qualification for EC and dynamic vacuum compatibility under accelerator-like conditions. Hence, we report the SEY and ESD measured at cryogenic temperatures of 12 K under low-energy electron irradiation of 0 - 1.4 keV. We also verify the sample compositions and morphologies using XPS, SEM, and EDS methods. The energy and dose dependencies of ESD are comparable to those of technical-grade metals and one sample reached SEY$_{MAX}=$1.2 after electron conditioning.

Criteria for identifying optimal adjustment sets (i.e., yielding a consistent estimator with minimal asymptotic variance) for estimating average treatment effects in parametric and nonparametric models have recently been established. In a single treatment time point setting, it has been shown that the optimal adjustment set can be identified based on a causal directed acyclic graph alone. In a longitudinal treatment setting, previous work has established graphical rules to compare the asymptotic variance of estimators based on nested time-dependent adjustment sets. However, these rules do not always permit the identification of an optimal time-dependent adjustment set based on a causal graph alone. In this paper, we extend previous results by exploiting conditional independencies that can be read from the graph. We demonstrate theoretically and empirically that our results can yield estimators with even lower asymptotic variance than those allowed by previous results. We conjecture that our new results may even allow the identification of an optimal time-dependent adjustment set based on the causal graph and provide numerical examples supporting this conjecture., Comment: 24 pages, 3 figures

In the last decade, EU became aware of its dependency on mining countries for mineral resources. In that context in 2022, France launched new airborne geophysical surveys in the Limousin region (northwestern French Massif Central) which is historically renown for its gold, uranium, tungsten and tin production. Thanks to these new data, it has become possible to both explore the Saint-Goussaud - Auriat zone, centered on the Puy-les-Vignes tungsten deposit and to investigate the numerous W occurrences. To do so, direct geophysical modelling using gravimetric, airborne magnetic and electromagnetic data has allowed to create a 3D regional model. It mainly illustrates locations where evolved granites deepen, possibly associated with hidden granitic cupolas. Besides, electromagnetic data have been analysed to highlight conductive anomalies. The lineament analysis has contributed to identify a conductive corridor, interpreted as a regional structure. Conductive vertical conduits, which are spatially close to mineralized occurrences and stream-sediment anomalies have also been spotted. This new method has thus allowed generating new elements which could be combined and could have implications for granite-related tungsten exploration in this region.

A search for proton decay into $e^+/\mu^+$ and a $\eta$ meson has been performed using data from a 0.373 Mton$\cdot$year exposure (6050.3 live days) of Super-Kamiokande. Compared to previous searches this work introduces an improved model of the intranuclear $\eta$ interaction cross section, resulting in a factor of two reduction in uncertainties from this source and $\sim$10\% increase in signal efficiency. No significant data excess was found above the expected number of atmospheric neutrino background events resulting in no indication of proton decay into either mode. Lower limits on the proton partial lifetime of $1.4\times\mathrm{10^{34}~years}$ for $p\rightarrow e^+\eta$ and $7.3\times\mathrm{10^{33}~years}$ for $p\rightarrow \mu^+\eta$ at the 90$\%$ C.L. were set. These limits are around 1.5 times longer than our previous study and are the most stringent to date.

Scattering poses a significant challenge in optical imaging. In microscopy, it leads to progressive degradation of image quality preventing sample examination at increasing depths. As the thickness of the sample increases, the number of photons that can successfully pass through it decreases, leading to a reduced signal and preventing us from obtaining clear images. Imaging becomes even more difficult with dynamic scatterers like biological tissues that change over time. We demonstrate non-invasive imaging of fluorescent objects hidden behind dynamic diffusers. Our reconstruction uses a stack of images recorded for different diffuser realizations and works even when individual camera frames contain small photon numbers., Comment: Comments welcome; Main text: 12 pages, 4 figures, Supplementary Information: 9 pages, 3 figures

This paper is a report of the Workshop on Simulations for Information Access (Sim4IA) workshop at SIGIR 2024. The workshop had two keynotes, a panel discussion, nine lightning talks, and two breakout sessions. Key takeaways were user simulation's importance in academia and industry, the possible bridging of online and offline evaluation, and the issues of organizing a companion shared task around user simulations for information access. We report on how we organized the workshop, provide a brief overview of what happened at the workshop, and summarize the main topics and findings of the workshop and future work., Comment: Preprint of a SIGIR Forum submission for Vol. 58 No. 2 - December 2024

Christoffel polynomials are classical tools from approximation theory. They can be used to estimate the (compact) support of a measure $\mu$ on $\mathbb{R}^d$ based on its low-degree moments. Recently, they have been applied to problems in data science, including outlier detection and support inference. A major downside of Christoffel polynomials in such applications is the fact that, in order to compute their coefficients, one must invert a matrix whose size grows rapidly with the dimension $d$. In this paper, we propose a modification of the Christoffel polynomial which is significantly cheaper to compute, but retains many of its desirable properties. Our approach relies on sparsity of the underlying measure $\mu$, described by a graphical model. The complexity of our modification depends on the treewidth of this model., Comment: 21 pages, 1 figure

Plasma membranes appear as deformable systems wherein molecules are free to move and diffuse giving rise to condensed microdomains (composed of ordered lipids, transmembrane proteins and cholesterol) surrounded by disordered lipid molecules. Such denser and thicker regions, namely lipid rafts, are important communication hubs for cells. Indeed, recent experiments revealed how the most of active signaling proteins co-localize on such domains, thereby intensifying the biochemical trafficking of substances. From a material standpoint, it is reasonable to assume the bilayer as a visco-elastic body accounting for both in-plane fluidity and elasticity. Consequently, lipid rafts contribute to membrane heterogeneity by typically exhibiting higher stiffness and viscosity and by locally altering the bilayer dynamics and proteins activity. A chemo-mechanical model of lipid bilayer coupled with interspecific dynamics among the resident species (typically transmembrane receptors and trasporters) has been recently formulated to explain and predict how proteins regulate the dynamic heterogeneity of membrane. However, the explicit inclusion of the membrane viscosity in the model was not considered. To this aim, the present work enriches the constitutive description of the bilayer by modeling its visco-elastic behavior. This is done through a strain-level dependent viscosity able to theoretically trace back the alteration of membrane fluidity experimentally observed in lipid phase transitions. This provides new insights into how the quasi-solid and fluid components of lipid membrane response interact with the evolution of resident proteins by affecting the activity of raft domains, with effects on cell mechano-signaling., Comment: 26 pages, 7 figures

A tight $\ell$-cycle minus an edge $C_\ell^-$ is the $3$-graph on the vertex set $[\ell]$, where any three consecutive vertices in the string $123\ldots\ell 1$ form an edge. We show that for every $\ell\ge 5$, $\ell$ not divisible by $3$, the extremal number is $ ex\left(C_\ell^-,n\right)=\tfrac1{24}n^3+O(n\ln n)=\left(\tfrac14+o(1)\right){n\choose 3}. $ We determine the extremal graph up to $O(n)$ edge edits.

This paper addresses the problem of approximating an unknown probability distribution with density $f$ -- which can only be evaluated up to an unknown scaling factor -- with the help of a sequential algorithm that produces at each iteration $n\geq 1$ an estimated density $q_n$.The proposed method optimizes the Kullback-Leibler divergence using a mirror descent (MD) algorithm directly on the space of density functions, while a stochastic approximation technique helps to manage between algorithm complexity and variability. One of the key innovations of this work is the theoretical guarantee that is provided for an algorithm with a fixed MD learning rate $\eta \in (0,1 )$. The main result is that the sequence $q_n$ converges almost surely to the target density $f$ uniformly on compact sets. Through numerical experiments, we show that fixing the learning rate $\eta \in (0,1 )$ significantly improves the algorithm's performance, particularly in the context of multi-modal target distributions where a small value of $\eta$ allows to increase the chance of finding all modes. Additionally, we propose a particle subsampling method to enhance computational efficiency and compare our method against other approaches through numerical experiments.

Jupiter's icy moon Ganymede has a tenuous exosphere produced by sputtering and possibly sublimation of water ice. To date, only atomic hydrogen and oxygen have been directly detected in this exosphere. Here, we present observations of Ganymede's CO$_2$ exosphere obtained with the James Webb Space Telescope. CO$_2$ gas is observed over different terrain types, mainly over those exposed to intense Jovian plasma irradiation, as well as over some bright or dark terrains. Despite warm surface temperatures, the CO$_2$ abundance over equatorial subsolar regions is low. CO$_2$ vapor has the highest abundance over the north polar cap of the leading hemisphere, reaching a surface pressure of 1 pbar. From modeling we show that the local enhancement observed near 12 h local time in this region can be explained by the presence of cold traps enabling CO$_2$ adsorption. However, whether the release mechanism in this high-latitude region is sputtering or sublimation remains unclear. The north polar cap of the leading hemisphere also has unique surface-ice properties, probably linked to the presence of the large atmospheric CO2 excess over this region. These CO2 molecules might have been initially released in the atmosphere after the radiolysis of CO$_2$ precursors, or from the sputtering of CO$_2$ embedded in the H$_2$O ice bedrock. Dark terrains (regiones), more widespread on the north versus south polar regions, possibly harbor CO$_2$ precursors. CO$_2$ molecules would then be redistributed via cold trapping on ice-rich terrains of the polar cap and be diurnally released and redeposited on these terrains. Ganymede's CO$_2$ exosphere highlights the complexity of surface-atmosphere interactions on Jupiter's icy Galilean moons., Comment: 21 pages, 21 figures, Accepted as a Letter in Astronomy and Astrophysics

Efficient water management in agriculture is important for mitigating the growing freshwater scarcity crisis. Mixed-integer Model Predictive Control (MPC) has emerged as an effective approach for addressing the complex scheduling problems in agricultural irrigation. However, the computational complexity of mixed-integer MPC still poses a significant challenge, particularly in large-scale applications. This study proposes an approach to enhance the computational efficiency of mixed-integer MPC-based irrigation schedulers by employing ReLU surrogate models to describe the soil moisture dynamics of the agricultural field. By leveraging the mixed-integer linear representation of the ReLU operator, the proposed approach transforms the mixed-integer MPC-based scheduler with a quadratic cost function into a mixed-integer quadratic program, which is the simplest class of mixed-integer nonlinear programming problems that can be efficiently solved using global optimization solvers. The effectiveness of this approach is demonstrated through comparative studies conducted on a large-scale agricultural field across two growing seasons, involving other machine learning surrogate models, specifically Long Short-Term Memory (LSTM) networks, and the widely used triggered irrigation scheduling method. The ReLU-based approach significantly reduces solution times -- by up to 99.5\% -- while achieving comparable performance to the LSTM approach in terms of water savings and Irrigation Water Use Efficiency (IWUE). Moreover, the ReLU-based approach maintains enhanced performance in terms of total prescribed irrigation and IWUE compared to the widely-used triggered irrigation scheduling method.

ISO/IEC 17000:2020 defines conformity assessment as an "activity to determine whether specified requirements relating to a product, process, system, person or body are fulfilled". JCGM (2012) establishes a framework for accounting for measurement uncertainty in conformity assessment. The focus of JCGM (2012) is on the conformity assessment of individual units of product based on measurements on a cardinal continuous scale. However, the scheme can also be applied to composite assessment targets like finite lots of product or manufacturing processes, and to the evaluation of characteristics in discrete cardinal or nominal scales. We consider the application of the JCGM scheme in the conformity assessment of finite lots or processes of discrete units subject to a dichotomous quality classification as conforming and nonconforming. A lot or process is classified as conforming if the actual proportion nonconforming does not exceed a prescribed upper tolerance limit, otherwise the lot or process is classified as nonconforming. The measurement on the lot or process is a statistical estimation of the proportion nonconforming based on attributes or variables sampling, and meassurement uncertainty is sampling uncertainty. Following JCGM (2012), we analyse the effect of measurement uncertainty (sampling uncertainty) in attributes sampling, and we calculate key conformity assessment parameters, in particular the producer's and consumer's risk. We suggest to integrate such parameters as a useful add-on into ISO acceptance sampling standards such as the ISO 2859 series., Comment: 27 pages, 6 figures

Two $q$-analogs of the hypercube graph are introduced and shown to be related through a graph quotient. The roles of the subspace lattice graph, of a twisted primitive elements of $U_q(\mathfrak{su}(2))$ and of the dual $q$-Krawtchouk polynomials are elaborated upon. This paper is dedicated to Tom Koornwinder., Comment: 12 pages

The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding. This year, the challenges encompass four vision-based tasks. (1) Ball Action Spotting, focusing on precisely localizing when and which soccer actions related to the ball occur, (2) Dense Video Captioning, focusing on describing the broadcast with natural language and anchored timestamps, (3) Multi-View Foul Recognition, a novel task focusing on analyzing multiple viewpoints of a potential foul incident to classify whether a foul occurred and assess its severity, (4) Game State Reconstruction, another novel task focusing on reconstructing the game state from broadcast videos onto a 2D top-view map of the field. Detailed information about the tasks, challenges, and leaderboards can be found at https://www.soccer-net.org, with baselines and development kits available at https://github.com/SoccerNet., Comment: 7 pages, 1 figure

Contrary to fermions and bosons, anyons are quasiparticles that keep a robust memory of particle exchanges via a braiding phase factor. This provides them with unique dynamical properties so far unexplored. When an anyon excitation is emitted toward a quantum point contact (QPC) in a fractional quantum Hall (FQH) fluid, this memory translates into tunneling events that may occur long after the anyon excitation has exited the QPC. Here, we use triggered anyon pulses incident on a QPC in a $\nu= 1/3$ FQH fluid to investigate anyon tunneling in the time domain. We observe that braiding increases the tunneling timescale, which is set by the temperature and the anyon scaling dimension that characterizes the edge state dynamics. This contrasts with the electron behavior where braiding is absent and the tunneling timescale is set by the temporal width of the generated electron pulses. Our experiment introduces time-domain measurements for characterizing the braiding phase and scaling dimension of anyons.

Decarbonizing global energy systems requires extensive integration of renewable energy into the electric grid. However, the intermittency and variable nature of wind and other non-dispatchable renewable resources make integration a great challenge. Hybridizing renewables with energy storage to form integrated energy systems (IESs) helps mitigate these concerns by improving reliability and resiliency. This paper systematically studies the limitations of the prevailing price-taker assumption for techno-economic analysis (TEA) and optimization of hybrid energy systems. As an illustrative case study, we retrofit an existing wind farm in the RTS-GMLC test system (which loosely mimics the Southwest U.S.) with battery energy storage to form an IES. We show that the standard price-taker model overestimates the electricity revenue and the net present value (NPV) of the IES up to 178% and 50%, respectively, compared to our more rigorous multiscale optimization. These differences arise because introducing storage creates a more flexible resource that impacts the larger wholesale electricity market. Moreover, this work highlights the impact of the IES has on the market via various strategic bidding, and underscores the importance of moving beyond price-taker for optimal storage sizing and TEA of IESs. We conclude by discussing opportunities to generalize the proposed framework to other IESs, and highlight emerging research questions regarding the complex interactions between IESs and markets.

We present a plausible and coherent view of the evolution of the protosolar disk that is consistent with the cosmochemical constraints and compatible with observations of other protoplanetary disks and sophisticated numerical simulations. The evidence that high-temperature condensates, CAIs and AOAs, formed near the protosun before being transported to the outer disk can be explained by either an early phase of vigorous radial spreading of the disk, or fast transport of these condensates from the vicinity of the protosun towards large disk radii via the protostellar outflow. The assumption that the material accreted towards the end of the infall phase was isotopically distinct allows us to explain the observed dichotomy in nucleosynthetic isotopic anomalies of meteorites and leads to intriguing predictions on the isotopic composition of refractory elements in comets. When the infall of material waned, the disk started to evolve as an accretion disk. Initially, dust drifted inwards, shrinking the radius of the dust component to ~ 45 au, probably about 1/2 of the width of the gas component. Then structures must have emerged, producing a series of pressure maxima in the disk which trapped the dust on My timescales. This allowed planetesimals to form at radically distinct times without changing significantly of isotopic properties. There was no late accretion of material onto the disk via streamers. The disk disappeared in ~5 Myr, as indicated by paleomagnetic data in meteorites. In conclusion, the evolution of the protosolar disk seems to have been quite typical in terms of size, lifetime, and dust behavior, suggesting that the peculiarities of the Solar system with respect to extrasolar planetary system probably originate from the chaotic nature of planet formation and not at the level of the parental disk., Comment: In press in Astronomy and Astrophysics

In their comment (1), Hawreliak et al. claims that our observation of stacking fault formation and transonic dislocation propagation in diamond (2) is not valid as they interpret the observed features as cracks. In this response letter, we describe our rationale for interpreting the observed features as stacking faults. We also address other points raised in their comments, including the clarifications of how the results of Makarov et al. (3) are not in conflict with our study., Comment: Comment on arXiv:2401.04213. 10 pages, 2 figures

This application note discusses the working principle of spin-transfer torque magnetoresistive random access memory (STT-MRAM) and the impact that magnetic fields can have on STT-MRAM operation. Sources of magnetic field and typical magnitudes of magnetic fields are given. Based on the magnitude of commonly encountered external magnetic fields, we show below that magnetic immunity of STT-MRAM is sufficient for most uses once the chip is mounted on a printed circuit board (PCB) or inserted in its working environment. This statement is supported by the experience acquired during 60 years of use of magnetic hard disk drives (HDD) including 20 years of HDD with readers comprising magnetic tunnel junctions, 20+ years of use of magnetic field sensors as position encoders in automotive industry and 15+ years of use of MRAM. Mainly during chip handling does caution need to be exercised to avoid exposing the chip to excessively high magnetic fields.

This study applied machine learning models to estimate stellar rotation periods from corrected light curve data obtained by the NASA Kepler mission. Traditional methods often struggle to estimate rotation periods accurately due to noise and variability in the light curve data. The workflow involved using initial period estimates from the LS-Periodogram and Transit Least Squares techniques, followed by splitting the data into training, validation, and testing sets. We employed several machine learning algorithms, including Decision Tree, Random Forest, K-Nearest Neighbors, and Gradient Boosting, and also utilized a Voting Ensemble approach to improve prediction accuracy and robustness. The analysis included data from multiple Kepler IDs, providing detailed metrics on orbital periods and planet radii. Performance evaluation showed that the Voting Ensemble model yielded the most accurate results, with an RMSE approximately 50\% lower than the Decision Tree model and 17\% better than the K-Nearest Neighbors model. The Random Forest model performed comparably to the Voting Ensemble, indicating high accuracy. In contrast, the Gradient Boosting model exhibited a worse RMSE compared to the other approaches. Comparisons of the predicted rotation periods to the photometric reference periods showed close alignment, suggesting the machine learning models achieved high prediction accuracy. The results indicate that machine learning, particularly ensemble methods, can effectively solve the problem of accurately estimating stellar rotation periods, with significant implications for advancing the study of exoplanets and stellar astrophysics., Comment: 15 pages, 8 figures. Submitted for publication in A&A

In this paper, we study geostrophic turbulence without external forcing or dissipation, using a Casimir-preserving numerical method. The research examines the formation of large zonal jets, common in geophysical flows, especially in giant gas planets. These jets form due to the east-west stretching of vortices, influenced by the gradient of the Coriolis parameter, leading to a critical latitude beyond which jets do not form. Using a global quasi-geostrophic model with a fully latitude-dependent Coriolis parameter, we investigate this critical latitude, which is theorized to depend only on the product of the Rossby number and the Lamb parameter. By simulating random flow fields, the critical latitude was identified through zonally averaged zonal velocity profiles. Results align with geostrophic theory, especially near typical Rossby and Lamb parameter values for Earth's atmosphere. However, in the regime of weak rotation (high Rossby numbers) and strong stratification (high Lamb values), no clear critical latitude emerges; instead, zonal jet amplitude and width decrease gradually towards the poles. This research paves the way for further study of jet dynamics under a fully latitude-dependent Coriolis parameter.

Accurate long-term predictions of large-scale flow features on planets are crucial for understanding global atmospheric and oceanic systems, necessitating the development of numerical methods that can preserve essential physical structures over extended simulation periods without excessive computational costs. Recent advancements in the study of global single-layer barotropic models have led to novel numerical methods based on Lie-Poisson discretization that preserve energy, enstrophy and higher-order moments of potential vorticity. This work extends this approach to more complex stratified quasi-geostrophic (QG) systems on the sphere. In this work, we present a formulation of the multi-layer QG equations on the full globe. This allows for extending the Lie-Poisson discretization to multi-layer QG models, ensuring consistency with the underlying structure and enabling long-term simulations without additional regularization. The numerical method is benchmarked through simulations of forced geostrophic turbulence and the long-term behaviour of unforced multi-layered systems. These results demonstrate the structure-preserving properties and robustness of the proposed numerical method, paving the way for a better understanding of the role of high-order conserved quantities in large-scale geophysical flow dynamics.

We introduce a variant of the D-optimal design of experiments problem with a more general information matrix that takes into account the representation of the design space S. The main motivation is that if S $\subset$ R d is the unit ball, the unit box or the canonical simplex, then remarkably, for every dimension d and every degree n, the equilibrium measure of S (in pluripotential theory) is an optimal solution. Equivalently, for each degree n, the unique optimal solution is the vector of moments (up to degree 2n) of the equilibrium measure of S. Hence nding an optimal design reduces to nding a cubature for the equilibrium measure, with atoms in S, positive weights, and exact up to degree 2n. In addition, any resulting sequence of atomic D-optimal measures converges to the equilibrium measure of S for the weak-star topology, as n increases. Links with Fekete sets of points are also discussed. More general compact basic semialgebraic sets are also considered, and a previously developed two-step design algorithm is easily adapted to this new variant of D-optimal design problem.