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Context. Transient sky astronomy is entering a new era with the advent of the SVOM mission (Space Variable Objects Monitor), which was successfully launched on the 26th of June, 2024. The primary goal of SVOM is to monitor the hard X-ray sky searching for gamma-ray bursts (GRBs). On top of its on-board follow-up capabilities, SVOM will be backed by its ground segment composed of several facilities, of which the near-infrared imager CAGIRE. Mounted on the robotic telescope COLIBRI, it will be a unique instrument, able to perform fast follow-up of GRB afterglows in J and H bands, an ideal combination to catch high-redshift (z>6) and/or obscured GRBs. Aims. This paper aims at estimating the performances of CAGIRE for GRB near-infrared afterglow detection based on the characteristics of the detector and the specificities of the COLIBRI telescope. Quickly fading GRB afterglows pose challenges that should be addressed by adapting observing strategies to the capabilities of CAGIRE. Methods. We use an end-to-end image simulator to produce realistic CAGIRE images, taking into account results from the characterization of the ALFA detector used by CAGIRE. We implemented a GRB afterglow generator that simulates infrared lightcurves and spectra based on published observation of distant GRBs (z>6). Results. We retrieved the photometry of 9 GRB afterglows in various scenarios covered by CAGIRE. Catching afterglows as early as two minutes after burst allows the identification of a nIR counterpart in the brightest 4 events. When artificially redshifted even further away, these events remain detectable by CAGIRE up to z=9.6 in J band, and z=13.3 in H band, indicating the potential of CAGIRE to be a pioneer in the identification of the most distant GRBs to date., Comment: 8 pages, 5 figures, 3 tables, accepted for publication in A&A

The notion of double depth associated with quasi-Jacobi forms allows distinguishing, within the algebra of quasi-Jacobi singular forms of index zero, certain significant subalgebras (modular-type forms, elliptic-type forms, Jacobi forms). We study the stability of these subalgebras under the derivations of this algebra and through certain sequences of bidifferential operators constituting analogs of Rankin-Cohen brackets or transvectants

A dual-operation mode SNSPD is demonstrated. In the conventional Geiger SNSPD mode the sensor operates at temperatures well below the critical temperature, Tc, working as an event counter without sensitivity to the number of photons impinging the sensor. In the calorimetric mode, the detector is operated at temperatures just below Tc and displays photon-number sensitivity for wavelengths in the optical spectrum. In this energy sensitive mode, photon absorption causes Joule heating of the SNSPD that becomes partially resistive without the presence of latching. Depending on the application, by tuning the sample temperature and bias current using the same readout system, the SNSPD can readily switch between the two modes. In the calorimetric mode, SNSPD recovery times shorter than the ones in the Geiger mode are observed, reaching values as low as 580ps. Dual-mode SNSPD's may provide significant advancements in spectroscopy and calorimetry, where precise timing, photon counting and energy resolution are required., Comment: Manuscript prepared for APL

This study introduces a novel frequency-based data-driven controller for adaptive optics, using power spectral density for optimization while ensuring stability criteria. It addresses disturbance rejection, command amplitude constraints and system transfer functions through convex optimization to obtain an optimal control in an infinite input response filter form. Evaluated within the SAXO+ project, it demonstrates efficacy under diverse atmospheric conditions and operational scenarios. The proposed controller is tested in both standard and disentangled adaptive optics schemes, showcasing its adaptability and performance. Experimental validation is conducted using the COMPASS simulation tool, affirming the controller's promise for enhancing adaptive optics systems in real-world applications.

Task-aware robotic grasping is a challenging problem that requires the integration of semantic understanding and geometric reasoning. Traditional grasp planning approaches focus on stable or feasible grasps, often disregarding the specific tasks the robot needs to accomplish. This paper proposes a novel framework that leverages Large Language Models (LLMs) and Quality Diversity (QD) algorithms to enable zero-shot task-conditioned grasp selection. The framework segments objects into meaningful subparts and labels each subpart semantically, creating structured representations that can be used to prompt an LLM. By coupling semantic and geometric representations of an object's structure, the LLM's knowledge about tasks and which parts to grasp can be applied in the physical world. The QD-generated grasp archive provides a diverse set of grasps, allowing us to select the most suitable grasp based on the task. We evaluate the proposed method on a subset of the YCB dataset, where a Franka Emika robot is assigned to perform various actions based on object-specific task requirements. We created a ground truth by conducting a survey with six participants to determine the best grasp region for each task-object combination according to human intuition. The model was evaluated on 12 different objects across 4--7 object-specific tasks, achieving a weighted intersection over union (IoU) of 76.4% when compared to the survey data., Comment: 8 pages, 5 figures

Regular unsupervised domain adaptive person re-identification (ReID) focuses on adapting a model from a source domain to a fixed target domain. However, an adapted ReID model can hardly retain previously-acquired knowledge and generalize to unseen data. In this paper, we propose a Dual-level Joint Adaptation and Anti-forgetting (DJAA) framework, which incrementally adapts a model to new domains without forgetting source domain and each adapted target domain. We explore the possibility of using prototype and instance-level consistency to mitigate the forgetting during the adaptation. Specifically, we store a small number of representative image samples and corresponding cluster prototypes in a memory buffer, which is updated at each adaptation step. With the buffered images and prototypes, we regularize the image-to-image similarity and image-to-prototype similarity to rehearse old knowledge. After the multi-step adaptation, the model is tested on all seen domains and several unseen domains to validate the generalization ability of our method. Extensive experiments demonstrate that our proposed method significantly improves the anti-forgetting, generalization and backward-compatible ability of an unsupervised person ReID model., Comment: Accepted to TPAMI

Graphite, conventionally regarded as a gapless material, exhibits a bandgap of ~100 meV in nano-scale patterned highly oriented pyrolytic graphite (HOPG), as revealed by angle-resolved photoemission spectroscopy (ARPES). Our advanced first-principles calculations, incorporating photoemission matrix element effects, predict this bandgap with remarkable accuracy and attribute it to mechanical distortions introduced during patterning. This work bridges theory and experiment, providing the direct evidence of a tunable bandgap in HOPG. Beyond its fundamental significance, this finding opens new possibilities for designing materials with tailored electronic properties, enabling advancements in terahertz devices and optoelectronics., Comment: 33 pages, 9 figures

The discovery of in-plane anisotropic excitons in two-dimensional layered semiconductors enables state-of-the-art nanophotonic applications. A fundamental yet unknown parameter of these quasiparticles is the coherence time (T_2 ), which governs the quantum dephasing timescale, over which the coherent superposition of excitons can be maintained and manipulated. Here, we report the direct measurement of T_2 within the sub-picosecond range, along with multiple population decay timescales (T_1 ) at resonance for anisotropic excitons in pristine layered rhenium disulfide (ReS2). We observe a notable weak dependence on layer thickness for T_2 , and a quasi-independence for T_1 . The excitonic coherence in few-layer ReS2 exhibits exceptional robustness against optical density and temperature compared to other two-dimensional semiconductors, enabling quantum features even at room temperature. No photon echo fingerprints were observed in pristine ReS2, highlighting the homogeneous character of the anisotropic excitonic transitions and a particularly low level of disorder in exfoliated flakes. Lastly, our results for mono- to bulk-like ReS2 support a direct gap band structure regardless their layer thickness, addressing the ongoing discussion about its nature.

We expand the theoretical framework by Gottlieb el al. (2023), which connects binary merger populations with long and short binary gamma-ray bursts (lbGRBs and sbGRBs), incorporating kilonovae as a key diagnostic tool. We show that lbGRBs, powered by massive accretion disks around black holes (BHs), should be accompanied by bright, red kilonovae. In contrast, sbGRBs - if also powered by BHs - would produce fainter, red kilonovae, potentially biasing against their detection. However, magnetized hypermassive neutron star (HMNS) remnants that precede BH formation can produce jets with power ($P_{\rm NS} \approx 10^{51}\,{\rm erg\,s^{-1}}$) and Lorentz factor ($\Gamma>10$), likely compatible with sbGRB observations, and would result in distinctly bluer kilonovae, offering a pathway to identifying the sbGRB central engine. Recent modeling by Rastinejad et al. (2024) found luminous red kilonovae consistently accompany lbGRBs, supporting lbGRB originating from BH-massive disk systems, likely following a short-lived HMNS phase. The preferential association of sbGRBs with comparably luminous kilonovae argues against the BH engine hypothesis for sbGRBs, while the bluer hue of these KNe provides additional support for an HMNS-driven mechanism. Within this framework, BH-NS mergers likely contribute exclusively to the lbGRB population with red kilonovae. Our findings suggest that GW170817 may, in fact, have been an lbGRB to on-axis observers. Finally, we discuss major challenges faced by alternative lbGRB progenitor models, such as white dwarf-NS or white dwarf-BH mergers and accretion-induced collapse forming magnetars, which fail to align with observed GRB timescales, energies, and kilonova properties.

In this work, we propose a new approach based on positron annihilation spectroscopy to estimate the concentration of vacancy-type defects induced by self-ion irradiation in tungsten at room temperature, 500, and 700{\deg}C. Using experimental and Two-component density functional theory calculated annihilation characteristics of various vacancy clusters V$_{n}$ ($n$=1-65) and a positron trapping model associated with the simulated annealing algorithm, vacancy cluster concentration distribution could be extracted from experimental data. The method was validated against simulation results for room-temperature irradiation and transmission electron microscopy observations for higher temperatures. After irradiation at 500 and 700{\deg}C, small clusters (<20 vacancies, ~0.85 nm) undetectable by TEM were unveiled, with concentrations exceeding 10$^{25}$ m$^{-3}$, significantly higher than the concentration of TEM-visible defects (10$^{24}$ m$^{-3}$). Moreover, incorporating an oxygen-vacancy complex is deemed necessary to accurately replicate experimental data in samples subjected to high-temperature irradiation.

The universal dependence of hadron suppression, $R_{\rm{AA}}(p_\perp)$, observed at large-$p_\perp$ in heavy ion collisions at RHIC and LHC allows for a systematic determination of the average parton energy loss $\langle \epsilon \rangle$ in quark-gluon plasma (QGP). A simple relation between $\langle \epsilon \rangle$ and the soft particle multiplicity allows for probing the dependence of parton energy loss on the medium path-length. We find that all the available measurements are consistent with $\langle \epsilon \rangle \propto L^\beta$ with $\beta=1.02\pm^{0.09}_{0.06}$, consistent with the pQCD expectation of parton energy loss in a longitudinally expanding QGP. We then show, based on the model predictions, that the data on the azimuthal anisotropy coefficient divided by the collision eccentricity, $v_2/\rm{e}$, follows the same scaling property as $R_{\rm{AA}}$. Finally, a linear relationship between $v_2/\rm{e}$ and the logarithmic derivative of $R_{\rm{AA}}$ at large $p_\perp$ offers a purely data-driven access to the $L$ dependence of parton energy loss. Quite remarkably, both hadron and jet measurements obey this latter relationship, moreover with consistent values of $\beta$. This points to the same parametric path-length dependence of parton and jet energy loss in QGP., Comment: 6 pages, 4 figures. Talk given at the 42nd International Conference on High Energy Physics (ICHEP2024)

The Bethe-Salpeter equation has been extensively employed to compute the two-body electron-hole propagator and its poles which correspond to the neutral excitation energies of the system. Through a different time-ordering, the two-body Green's function can also describe the propagation of two electrons or two holes. The corresponding poles are the double ionization potentials and double electron affinities of the system. In this work, a Bethe-Salpeter equation for the two-body particle-particle propagator is derived within the linear-response formalism using a pairing field and anomalous propagators. This framework allows us to compute kernels corresponding to different self-energy approximations ($GW$, $T$-matrix, and second-Born) as in the usual electron-hole case. The performance of these various kernels is gauged for singlet and triplet valence double ionization potentials using a set of 23 small molecules. The description of double core hole states is also analyzed., Comment: 19 pages, 8 figures

Context: The small- to intermediate-mass ($M <0.8 M_\odot$), most metal-poor stars that formed in the infancy of the Universe are still shining today in the sky. They are very rare, but their discovery and investigation brings new knowledge on the formation of the first stellar generations. Aims: SDSS J102915.14+172927.9 is one of the most metal-poor star known to date. Since no carbon can be detected in its spectrum, a careful upper limit is important, both to classify this star and to distinguish it from the carbon-enhanced stars that represent the majority at these metallicities. Methods: We undertook a new observational campaign to acquire high-resolution UVES spectra. The new spectra were combined with archival spectra in order to increase the signal-to-noise ratio. From the combined spectrum, we derived abundances for seven elements (Mg, Si, Ca, Ti, Fe, Ni, and a tentative Li) and five significant upper limits (C, Na, Al, Sr, and Ba). Results: The star has a carbon abundance A(C) <4.68 and therefore is not enhanced in carbon, at variance with the majority of the stars at this Fe regime, which typically show A(C)> 6.0. A feature compatible with the Li doublet at 670.7 nm is tentatively detected. Conclusions: The upper limit on carbon implies $Z<1.915 \times 10^{-6}$, more than 20 times lower than the most iron-poor star known. Therefore, the gas cloud out of which the star was formed did not cool via atomic lines but probably through dust. Fragmentation of the primordial cloud is another possibility for the formation of a star with a metallicity this low.

In this article we study the spectral, linear and nonlinear stability of stationary shock profile solutions to the Lax-Wendroff scheme for hyperbolic conservation laws. We first clarify the spectral stability of such solutions depending on the convexity of the flux for the underlying conservation law. The main contribution of this article is a detailed study of the so-called Green's function for the linearized numerical scheme. As evidenced on numerical simulations, the Green's function exhibits a highly oscillating behavior ahead of the leading wave before this wave reaches the shock location. One of our main results gives a quantitative description of this behavior. Because of the existence of a one-parameter family of stationary shock profiles, the linearized numerical scheme admits the eigenvalue 1 that is embedded in its continuous spectrum, which gives rise to several contributions in the Green's function. Our detailed analysis of the Green's function describes these contributions by means of a so-called activation function. For large times, the activation function describes how the mass of the initial condition accumulates along the eigenvector associated with the eigenvalue 1 of the linearized numerical scheme. We can then obtain sharp decay estimates for the linearized numerical scheme in polynomially weighted spaces, which in turn yield a nonlinear orbital stability result for spectrally stable stationary shock profiles. This nonlinear result is obtained despite the lack of uniform ${\ell}$ 1 estimates for the Green's function of the linearized numerical scheme, the lack of such estimates being linked with the dispersive nature of the numerical scheme. This dispersive feature is in sharp contrast with previous results on the orbital stability of traveling waves or discrete shock profiles for parabolic perturbations of conservation laws.

The present contribution is in the field of quantum modelling of macroscopic phenomena. The focus is on one enigmatic aspect of quantum physics, namely the Einstein-Podolsky-Rosen paradox and entanglement. After a review of the state of the art concerning macroscopic quantum effects and quantum interaction, this contribution proposes a link between embryology and acupuncture in the framework of macroscopic intricate states induced by quantum mechanics.

We present a numerical model of fractal-structured aggregates in low-Reynolds-number flows. Assuming that aggregates are made of cubic particles, we first use a boundary integral method to compute the stresses acting on the boundary of the aggregates. From these external stresses, we compute the stresses within the aggregates in order to gain insights on their breakup, or disaggregation. We focus on systems in which aggregates are either settling under gravity or subjected to a background shear flow and study two types of aggregates, one with fractal dimension slightly less than two and one with fractal dimension slightly above two. We partition the aggregates into multiple shells based on the distance between the individual cubes in the aggregates and their center of mass and observe the distribution of internal stresses in each shell. Our findings indicate that large stresses are least likely to occur near the far edges of the aggregates. We also find that, for settling aggregates, the maximum internal stress scales as about 7.5% of the ratio of an aggregate's apparent weight to the area of the thinnest connection, here a single square. For aggregates exposed to a shear flow, we find that the maximum internal stress scales roughly quadratically with the aggregate radius. In addition, after breaking aggregates at the face with the maximum internal stress, we compute the mass distribution of sub-aggregates and observe significant differences between the settling and shear setups for the two types of aggregates, with the low-fractal-dimension aggregates being more likely to split approximately evenly. Information obtained by our numerical model can be used to develop more refined dynamical models that incorporate disaggregation.

Deciding bank interest rates has been a long-standing challenge in finance. It is crucial to ensure that the selected rates balance market share and profitability. However, traditional approaches typically focus on the interest rate changes of individual banks, often neglecting the interactions with other banks in the market. This work proposes a novel framework that models the interest rate problem as a major-minor mean field game within the context of an interbank game. To incorporate the complex interactions between banks, we utilize mean-field theory and employ impulsive control to model the overhead in rate adjustments. Ultimately, we solve this optimal control problem using a new deep Q-network method, which iterates the parameterized action value functions for major and minor players and updates the networks in a Fictitious Play way. Our proposed algorithm converges, offering a solution that enables the analysis of strategies for major and minor players in the market under the Nash Equilibrium., Comment: 8 pages, 4 figures, Oral Paper of Simulation of Financial Markets and Economic Systems(SFMES), ICAIF 2024 Workshop

Large Language Models (LLMs) have shown impressive performance in complex reasoning tasks through Chain-of-Thought (CoT) reasoning, allowing models to break down problems into manageable sub-tasks. However, existing CoT evaluation techniques either require annotated CoT data or fall short in accurately assessing intermediate reasoning steps, leading to high rates of false positives. In this paper, we formalize CoT reasoning in LLMs through an information-theoretic lens. Specifically, our framework quantifies the `information gain' at each reasoning step, enabling the identification of failure modes in LLMs without the need for expensive annotated datasets. We demonstrate the efficacy of our approach through extensive experiments on toy and GSM-8K data, where it significantly outperforms existing outcome-based methods by providing more accurate insights into model performance on individual tasks.

In this contribution, we construct a connection between two quantum voting models presented previously. We propose to try to determine the result of a vote from associated given opinion polls. We introduce a density operator relative to the family of all candidates to a particular election. From an hypothesis of proportionality between a family of coefficients which characterize the density matrix and the probabilities of vote for all the candidates, we propose a numerical method for the entire determination of the density operator. This approach is a direct consequence of the Perron-Frobenius theorem for irreductible positive matrices. We applyour algorithm to synthetic data and to operational results issued from the French presidential election of April 2012., Comment: arXiv admin note: substantial text overlap with arXiv:2411.10041

To take full advantage of twisted bilayers of graphene or other two-dimensional materials, it is essential to precisely control the twist angle between the stacked layers, as this parameter determines the properties of the heterostructure. In this context, a growth routine using borazine as a surfactant molecule on SiC(0001) surfaces has been reported, leading to the formation of high-quality epitaxial graphene layers that are unconventionally oriented, i.e., aligned with the substrate lattice (G-$R0^\circ$) [Bocquet et al. Phys. Rev. Lett. 125, 106102 (2020)]. Since the G-$R0^\circ$ layer sits on a buffer layer, also known as zeroth-layer graphene (ZLG), which is rotated $30^\circ$ with respect to the SiC substrate and still covalently bonded to it, decoupling the ZLG-$R30^\circ$ from the substrate can lead to high-quality twisted bilayer graphene (tBLG). Here we report the decoupling of ZLG-$R30^\circ$ by increasing the temperature during annealing in a borazine atmosphere. While this converts ZLG-$R30^\circ$ to G-$R30^\circ$ and thus produces tBLG, the growth process at elevated temperature is no longer self-limiting, so that the surface is covered by a patchwork of graphene multilayers of different thicknesses. We find a 20% coverage of tBLG on ZLG, while on the rest of the surface tBLG sits on one or more additional graphene layers. In order to achieve complete coverage with tBLG only, alternative ways of decoupling the ZLG, e.g., by intercalation with suitable atoms, may be advantageous., Comment: 14 + 1 pages, 9 + 1 figures

This document summarizes the prospective physics plans of the French QCD and Heavy-Ion community, including the experimental programs at the LHC Run 5 and beyond and future colliders at CERN, within the context of the French contribution to the update of the European Strategy in Particle Physics (ESPPU 2025), as discussed in the workshop on European Strategy for Particle Physics Update 2025 organised by the QCD GdR in Oct. 2024., Comment: 6.5 pages without title page and without references, no figures. Keywords: QCD, Heavy Ions, Quark Gluon Plasma, LHC, FCC

Condensation processes, which are responsible for the main chemical differences between gas and solids in the Galaxy, are the major mechanisms that control the cycle of dust from evolved stars to planetary systems. However, they are still poorly understood, mainly because the thermodynamics and kinetic models of nucleation or grain growth lack experimental data. To bridge this gap, we used a large-volume three-phase alternating-current plasma torch to obtain a full high-temperature condensation sequence at an elevated carbon-to-oxygen ratio from a fluxed chondritic gas composition. We show that the crystallized suites of carbides, silicides, nitrides, sulfides, oxides and silicates and the bulk composition of the condensates are properly modelled by a kinetically inhibited condensation scenario controlled by gas flow. This validates the thermodynamic predictions of the condensation sequence at a high carbon-to-oxygen ratio. On this basis and using appropriate optical properties, we also demonstrate the influence of pressure on dust chemistry as well as the low probability of forming and detecting iron silicides in asymptotic giant branch C-rich circumstellar environments as well as in our chondritic meteorites. By demonstrating the potential of predicting dust mineralogy in these environments, this approach holds high promise for quantitatively characterizing dust composition and formation in diverse astrophysical settings., Comment: 19 pages, 10 figures, paper published in Nature Astronomy

Motivated by a case study of vegetation patterns, we introduce a mixture model with concomitant variables to examine the association between the orientation of vegetation stripes and wind direction. The proposal relies on a novel copula-based bivariate distribution for mixed axial and circular observations and provides a parsimonious and computationally tractable approach to examine the dependence of two environmental variables observed in a complex manifold. The findings suggest that dominant winds shape the orientation of vegetation stripes through a mechanism of neighbouring plants providing wind shelter to downwind individuals.

Modeling observations as random distributions embedded within Wasserstein spaces is becoming increasingly popular across scientific fields, as it captures the variability and geometric structure of the data more effectively. However, the distinct geometry and unique properties of Wasserstein space pose challenges to the application of conventional statistical tools, which are primarily designed for Euclidean spaces. Consequently, adapting and developing new methodologies for analysis within Wasserstein spaces has become essential. The space of distributions on $\mathbb{R}^d$ with $d>1$ is not linear, and ''mimic'' the geometry of a Riemannian manifold. In this paper, we extend the concept of statistical depth to distribution-valued data, introducing the notion of {\it Wasserstein spatial depth}. This new measure provides a way to rank and order distributions, enabling the development of order-based clustering techniques and inferential tools. We show that Wasserstein spatial depth (WSD) preserves critical properties of conventional statistical depths, notably, ranging within $[0,1]$, transformation invariance, vanishing at infinity, reaching a maximum at the geometric median, and continuity. Additionally, the population WSD has a straightforward plug-in estimator based on sampled empirical distributions. We establish the estimator's consistency and asymptotic normality. Extensive simulation and real-data application showcase the practical efficacy of WSD.

Federated Learning (FL) enables collaborative, personalized model training across multiple devices without sharing raw data, making it ideal for pervasive computing applications that optimize user-centric performances in diverse environments. However, data heterogeneity among clients poses a significant challenge, leading to inconsistencies among trained client models and reduced performance. To address this, we introduce the Alignment with Prototypes (ALP) layers, which align incoming embeddings closer to learnable prototypes through an optimal transport plan. During local training, the ALP layer updates local prototypes and aligns embeddings toward global prototypes aggregated from all clients using our novel FL framework, Federated Alignment (FedAli). For model inferences, embeddings are guided toward local prototypes to better reflect the client's local data distribution. We evaluate FedAli on heterogeneous sensor-based human activity recognition and vision benchmark datasets, demonstrating that it outperforms existing FL strategies. We publicly release our source code to facilitate reproducibility and furthered research., Comment: Pre-print version 1

Models and observations have demonstrated that Twisted Flux Ropes (TFRs) play a significant role in the structure and eruptive dynamics of active regions. Their role in the dynamics of the quiet Sun atmosphere on has remained elusive, their fundamental relevance emerging mainly from theoretical models (Amari et al. 2015), showing that they form and erupt as a result of flux cancellation. Here HINODE high-resolution photospheric vector magnetic field measurements are integrated with advanced environment reconstruction models: TFRs develop on various scales and are associated with the appearance of mesospots. The developing TFRs contain sufficient free magnetic energy to match the requirements of the recently observed "campfires" discovered by Solar Orbiter in the quiet Sun. The free magnetic energy is found to be large enough to trigger eruptions while the magnetic twist large enough to trigger confined eruptions, heating the atmosphere. TFRs are also connected to larger scale magnetic fields such as supergranulation loops, allowing the generation of Alfv\'en waves at the top of the chromosphere that can propagate along them. High-resolution magnetohydrodynamic simulations, incorporating subsurface dynamo activity at an unprecedented 30 km spatial resolution, confirm that TFRs are ubiquitous products of the permanent small scale dynamo engine that feeds their formation, destabilization, eruption via flux emergence, submergence and cancellation of their chromospheric feet, similar to the dynamics driving large scale eruptive events. Future investigations, especially with the Daniel K. Inouye Solar Telescope (DKIST) and Solar Orbiter will deepen our understanding of TFRs in the context of atmospheric heating.

A well-known result of Shalom says that lattices in SO$(n,1)$ are $L^p$ measure equivalent for all $p

We propose to define a notion of state of the opinion in order to link politician popularity estimations and voting intentions. We present two ways of modelling: a classical approach and quantum modelling. We test these ideas on data obtained during the French presidential election of April 2012.

Lattice Boltzmann models are briefly introduced together with references to methods used to predict their ability for simulations of systems described by partial differential equations that are first order in time and low order in space derivatives. Several previous works have been devoted to analyzing the accuracy of these models with special emphasis on deviations from pure Newtonian viscous behaviour, related to higher order space derivatives of even order. The presentcontribution concentrates on possible inaccuracies of the advection behaviour linked to space derivatives of odd order. Detailed properties of advection-diffusion and athermal fluids are presented for two-dimensional situations allowing to propose situations that are accurate to third order in space derivatives. Simulations of the advection of a gaussian dot or vortex are presented. Similar results are discussed in appendices for three-dimensional advection-diffusion.

Quasi-static time series (QSTS) simulations have great potential for evaluating the grid's ability to accommodate the large-scale integration of distributed energy resources. However, as grids expand and operate closer to their limits, iterative power flow solvers, central to QSTS simulations, become computationally prohibitive and face increasing convergence issues. Neural power flow solvers provide a promising alternative, speeding up power flow computations by 3 to 4 orders of magnitude, though they are costly to train. In this paper, we envision how recently introduced grid foundation models could improve the economic viability of neural power flow solvers. Conceptually, these models amortize training costs by serving as a foundation for a range of grid operation and planning tasks beyond power flow solving, with only minimal fine-tuning required. We call for collaboration between the AI and power grid communities to develop and open-source these models, enabling all operators, even those with limited resources, to benefit from AI without building solutions from scratch., Comment: Equal contributors: A.P. and F.M.; Lead contact: A.P

Pick $n$ independent and uniform random points $U_1,\ldots,U_n$ in a compact convex set $K$ of $\mathbb{R}^d$ with volume 1, and let $P^{(d)}_K(n)$ be the probability that these points are in convex position. The Sylvester conjecture in $\mathbb{R}^d$ is that $\min_K P^{(d)}_K(d+2)$ is achieved by the $d$-dimensional simplices $K$ (only). In this paper, we focus on a companion model, already studied in the $2d$ case, which we define in any dimension $d$: we say that $K$ has $F$ as a flat floor, if $F$ is a subset of $K$, contained in a hyperplan $P$, such that $K$ lies in one of the half-spaces defined by $P$. We define $Q_K^F(n)$ as the probability that $U_1,\cdots,U_n$ together with $F$ are in convex position (i.e., the $U_i$ are on the boundary of the convex hull ${\sf CH}(\{U_1,\cdots,U_n\}\cup F\})$). We prove that, for all fixed $F$, $K\mapsto Q_K^F(2)$ reaches its minimum on the "mountains" with floor $F$ (mountains are convex hull of $F$ union an additional vertex), while the maximum is not reached, but $K\mapsto Q_K^F(2)$ has values arbitrary close to 1. If the optimisation is done on the set of $K$ contained in $F\times[0,d]$ (the "subprism case"), then the minimum is also reached by the mountains, and the maximum by the "prism" $F\times[0,1]$. Since again, $Q_K^F{(2)}$ relies on the expected volume (of ${\sf CH}(\{V_1,V_2\}\cup F\})$), this result can be seen as a proof of the Sylvester problem in the floor case. In $2d$, where $F$ can essentially be the segment $[0,1],$ we give a general decomposition formula for $Q_K^F(n)$ so to compute several formulas and bounds for different $K$. In 3D, we give some bounds for $Q_K^F(n)$ for various floors $F$ and special cases of $K$., Comment: 28 pages, 8 figures

The structured deformation theory is used within the thermodynamics of irreversible processes framework in order to build a damage model relevant for quasi-brittle materials. The cracks are supposed smeared in the body and their shape is assumed to be sinusoidal. The convex of elasticity supposes a limitation of the thermodynamic force associated to the relative sliding of the crack lips. This limitation consists of both some equivalent to the Coulomb's friction and a cohesive force issued from the Barenblatt approach. This leads to an initial yield surface that can be expressed as the Mohr-Coulomb criterion, known to be relevant for such materials in term of initial yield surface and prediction of the crack orientation. The evolution law introduces an hardening and softening function that, combined to the rest of the framework, allows the description of the stress to strain evolution. The results are identified and compared to the Kupfer's database on concrete.

We prove a formula which gives the number of occurrences of certain labels and local configurations inside two-step puzzles introduced by Buch, Kresch, Purbhoo and Tamvakis from the work of Knutson. Puzzles are tilings of the triangular lattice by edge labeled tiles and are known to compute the Schubert structure constants of the cohomology of two-step flag varieties. The formula that we obtain depends only on the boundary conditions of the puzzle. The proof is based on the study of color maps which are tilings of the triangular lattice by edge labeled tiles obtained from puzzles.

In the last few decades planet search surveys have been focusing on solar type stars, and only recently the high-mass regimes. This is mostly due to challenges arising from the lack of instrumental precision, and more importantly, the inherent active nature of fast rotating massive stars. Here we report NGTS-33b (TOI-6442b), a super-Jupiter planet with mass, radius and orbital period of 3.6 $\pm$ 0.3 M$_{\rm jup}$, 1.64 $\pm$ 0.07 R$_{\rm jup}$ and $2.827972 \pm 0.000001$ days, respectively. The host is a fast rotating ($0.6654 \pm 0.0006$ day) and hot (T$_{\rm eff}$ = 7437 $\pm$ 72 K) A9V type star, with a mass and radius of 1.60 $\pm$ 0.11 M$_{\odot}$ and 1.47 $\pm$ 0.06 R$_{\odot}$, respectively. Planet structure and Gyrochronology models shows that NGTS-33 is also very young with age limits of 10-50 Myr. In addition, membership analysis points towards the star being part of the Vela OB2 association, which has an age of $\sim$ 20-35 Myr, thus providing further evidences about the young nature of NGTS-33. Its low bulk density of 0.19$\pm$0.03 g cm$^{-3}$ is 13$\%$ smaller than expected when compared to transiting hot Jupiters with similar masses. Such cannot be solely explained by its age, where an up to 15$\%$ inflated atmosphere is expected from planet structure models. Finally, we found that its emission spectroscopy metric is similar to JWST community targets, making the planet an interesting target for atmospheric follow-up. Therefore, NGTS-33b's discovery will not only add to the scarce population of young, massive and hot Jupiters, but will also help place further strong constraints on current formation and evolution models for such planetary systems., Comment: 19 pages, 17 figures, 7 tables, accepted for publication in MNRAS

The SMT (Satisfiability Modulo Theories) theory of arrays is well-established and widely used, with variousdecision procedures and extensions developed for it. However, recent works suggest that developing tailoredreasoning for some theories, such as sequences and strings, is more efficient than reasoning over them throughaxiomatization over the theory of arrays. In this paper, we are interested in reasoning over n-indexed sequences asthey are found in some programming languages, such as Ada. We propose an SMT theory of n-indexed sequencesand explore different ways to represent and reason over n-indexed sequences using existing theories, as well astailored calculi for the theory., Comment: 22nd International Workshop on Satisfiability Modulo Theories, Jul 2024, Montreal, Canada, France

Exploiting the spin-valley degree of freedom of electrons in materials is a promising avenue for energy-efficient information storage and quantum computing. A key challenge in utilizing spin-valley polarization is the realization of spin-valley locking in bulk systems. Here, we report a comprehensive study of the noncentrosymmetric bulk misfit compound (PbS)$_{1.13}$(TaS$_2$), showing a strong spin-valley locking. Our investigation reveals Ising superconductivity with a transition temperature of 3.14 K, closely matching that of a monolayer of TaS$_2$. Notably, the absence of charge density wave (CDW) signatures in transport measurements suggests that the PbS layers primarily act as spacers between the dichalcogenide monolayers. This is further supported by angle-resolved photoemission spectroscopy (ARPES), which shows negligible interlayer coupling, a lack of dispersion along the $k_{\perp}$ direction and significant charge transfer from the PbS to the TaS$_2$ layers. Spin resolved ARPES shows strong spin-valley locking of the electronic bands. Muon spin rotation experiments conducted in the vortex phase reveal an isotropic superconducting gap. However, the temperature dependence of the upper critical field and low-temperature specific heat measurements suggest the possibility of multigap superconductivity. These findings underscore the potential of misfit compounds as robust platforms for both realizing and utilizing spin-valley locking in bulk materials, as well as exploring proximity effects in two-dimensional structures.

Hand-face interactions play a key role in many everyday tasks, providing insights into user habits, behaviors, intentions, and expressions. However, existing wearable sensing systems often struggle to track these interactions in daily settings due to their reliance on multiple sensors or privacy-sensitive, vision-based approaches. To address these challenges, we propose WristSonic, a wrist-worn active acoustic sensing system that uses speakers and microphones to capture ultrasonic reflections from hand, arm, and face movements, enabling fine-grained detection of hand-face interactions with minimal intrusion. By transmitting and analyzing ultrasonic waves, WristSonic distinguishes a wide range of gestures, such as tapping the temple, brushing teeth, and nodding, using a Transformer-based neural network architecture. This approach achieves robust recognition of 21 distinct actions with a single, low-power, privacy-conscious wearable. Through two user studies with 15 participants in controlled and semi-in-the-wild settings, WristSonic demonstrates high efficacy, achieving macro F1-scores of 93.08% and 82.65%, respectively., Comment: 21 Pages, 10 Figures, Under Review

In this paper, we first propose a novel method for transferring material transformations across different scenes. Building on disentangled Neural Radiance Field (NeRF) representations, our approach learns to map Bidirectional Reflectance Distribution Functions (BRDF) from pairs of scenes observed in varying conditions, such as dry and wet. The learned transformations can then be applied to unseen scenes with similar materials, therefore effectively rendering the transformation learned with an arbitrary level of intensity. Extensive experiments on synthetic scenes and real-world objects validate the effectiveness of our approach, showing that it can learn various transformations such as wetness, painting, coating, etc. Our results highlight not only the versatility of our method but also its potential for practical applications in computer graphics. We publish our method implementation, along with our synthetic/real datasets on https://github.com/astra-vision/BRDFTransform

In the context of particle therapy monitoring, we are developing a gamma-ray detector to determine the ion range in vivo from the measurement of particle time-of-flight. For this application, a beam monitor capable to tag in time the incident ion with a time resolution below 235 ps FWHM (100 ps rms) is required to provide a start signal for the acquisition. We have therefore developed a dedicated detector based on a fast organic scintillator (EJ-204) of 25x25x1 mm3 coupled to four SiPM strips that allow measuring the particle incident position by scintillation light sharing. The prototype was characterised with single protons of energies between 63 and 225 MeV at the MEDICYC and ProteusONE facilities of the Antoine Lacassagne proton therapy centre in Nice. We obtained a time resolution of 120 ps FWHM at 63 MeV, and a spatial resolution of ~2 mm rms for single particles. Two identical detectors also allowed to measure the MEDICYC proton energy with 0.3% accuracy.

We extend the methodology introduced by Jahandar et al. (2024) to determine the effective temperature and chemical abundances of 31 slowly-rotating solar neighborhood M dwarfs (M1-M5) using high-resolution spectra from CFHT/SPIRou. This group includes 10 M dwarfs in binary systems with FGK primaries of known metallicity from optical measurements. By testing our $T_{\rm eff}$ method on various synthetic models, we find a consistent inherent synthetic uncertainty of $\sim$10 K at a signal-to-noise ratio greater than 100. Additionally, we find that our results align with interferometric measurements, showing a consistent residual of $-$29 $\pm$ 31 K. Taking the inherent uncertainties into account, we infer the $T_{\rm eff}$ values of our targets and find an excellent agreement with previous optical and NIR studies. Our high-resolution chemical analysis examines hundreds of absorption lines using $\chi^2$ minimization using PHOENIX-ACES stellar atmosphere models. We present elemental abundances for up to 10 different elements, including refractory elements such as Si, Mg, and Fe, which are important for modelling the interior structure of exoplanets. In binary systems, we find an average [Fe/H] of $-$0.15 $\pm$ 0.08 for M dwarfs, marginally lower than the reported metallicity of $-$0.06 $\pm$ 0.18 for the FGK primaries from Mann et al. (2013a). We also observe slightly sub-solar chemistry for various elements in our non-binary M dwarfs, most notably for O, C, and K abundances. In particular, we find an average metallicity of $-$0.11 $\pm$ 0.16 lower but still consistent with the typical solar metallicity of FGK stars (e.g. [Fe/H] = 0.04 $\pm$ 0.20 from Brewer et al. 2016). This study highlights significant discrepancies in various major M dwarf surveys likely related to differences in the methodologies employed., Comment: 24 pages, 9 figures, accepted for publication in ApJ

The transition matrix of a Markov chain $(X_k,k\geq 0)$ on a finite or infinite rooted tree is said to be almost upper-directed if, given $X_k$, the node $X_{k+1}$ is either a descendant of $X_k$ or the parent of $X_k$. It is said to be almost lower-directed if given $X_k$, $X_{k+1}$ is either an ancestor of $X_k$ or a child of $X_k$. These models include nearest neighbor Markov chains on trees. Under an irreducibility assumption, we show that every almost upper-directed transition matrix on infinite (locally finite) trees has some invariant measures. An invariant measure $\pi$ is expressed thanks to a determinantal formula. We give general explicit criteria for recurrence and positive recurrence. An efficient algorithm (the leaf addition algorithm) of independent interest allows $\pi$ to be computed on many trees, without resorting to linear algebra considerations. Flajolet, in a series of papers, provided some relations between continuous fractions, generating functions of weighted M\"otzkin paths, and used them in connection with the analysis of birth and death processes. These fruitful representations made it possible to establish many formulae for continuous fractions. Analogous considerations appear here: this type of study can be extended to weighted paths on trees, whose generating functions can also be expressed, this time in terms of multicontinuous fractions.

Quadrupedal locomotion is a complex, open-ended problem vital to expanding autonomous vehicle reach. Traditional reinforcement learning approaches often fall short due to training instability and sample inefficiency. We propose a novel method leveraging multi-objective evolutionary algorithms as an automatic curriculum learning mechanism, which we named Multi-Objective Learning (MOL). Our approach significantly enhances the learning process by projecting velocity commands into an objective space and optimizing for both performance and diversity. Tested within the MuJoCo physics simulator, our method demonstrates superior stability and adaptability compared to baseline approaches. As such, it achieved 19\% and 44\% fewer errors against our best baseline algorithm in difficult scenarios based on a uniform and tailored evaluation respectively. This work introduces a robust framework for training quadrupedal robots, promising significant advancements in robotic locomotion and open-ended robotic problems.

Large language models are increasingly relied upon as sources of information, but their propensity for generating false or misleading statements with high confidence poses risks for users and society. In this paper, we confront the critical problem of epistemic miscalibration $\unicode{x2013}$ where a model's linguistic assertiveness fails to reflect its true internal certainty. We introduce a new human-labeled dataset and a novel method for measuring the linguistic assertiveness of Large Language Models (LLMs) which cuts error rates by over 50% relative to previous benchmarks. Validated across multiple datasets, our method reveals a stark misalignment between how confidently models linguistically present information and their actual accuracy. Further human evaluations confirm the severity of this miscalibration. This evidence underscores the urgent risk of the overstated certainty LLMs hold which may mislead users on a massive scale. Our framework provides a crucial step forward in diagnosing this miscalibration, offering a path towards correcting it and more trustworthy AI across domains.

Portfolio diversification, traditionally measured through asset correlations and volatilitybased metrics, is fundamental to managing financial risk. However, existing diversification metrics often overlook non-numerical relationships between assets that can impact portfolio stability, particularly during market stresses. This paper introduces the lexical ratio (LR), a novel metric that leverages textual data to capture diversification dimensions absent in standard approaches. By treating each asset as a unique document composed of sectorspecific and financial keywords, the LR evaluates portfolio diversification by distributing these terms across assets, incorporating entropy-based insights from information theory. We thoroughly analyze LR's properties, including scale invariance, concavity, and maximality, demonstrating its theoretical robustness and ability to enhance risk-adjusted portfolio returns. Using empirical tests on S&P 500 portfolios, we compare LR's performance to established metrics such as Markowitz's volatility-based measures and diversification ratios. Our tests reveal LR's superiority in optimizing portfolio returns, especially under varied market conditions. Our findings show that LR aligns with conventional metrics and captures unique diversification aspects, suggesting it is a viable tool for portfolio managers.

This paper addresses the critical issue of psychological safety in the design and operation of autonomous vehicles, which are increasingly integrated with artificial intelligence technologies. While traditional safety standards focus primarily on physical safety, this paper emphasizes the psychological implications that arise from human interactions with autonomous vehicles, highlighting the importance of trust and perceived risk as significant factors influencing user acceptance. Through a review of existing safety techniques, the paper defines psychological safety in the context of autonomous vehicles, proposes a risk model to identify and assess psychological risks, and adopts a system-theoretic analysis method. The paper illustrates the potential psychological hazards using a scenario involving a family's experience with an autonomous vehicle, aiming to systematically evaluate situations that could lead to psychological harm. By establishing a framework that incorporates psychological safety alongside physical safety, the paper contributes to the broader discourse on the safe deployment of autonomous vehicle and aims to guide future developments in user-cantered design and regulatory practices.

HeH$^+$ was the first heteronuclear molecule to form in the metal-free Universe after the Big Bang. The molecule gained significant attention following its first circumstellar detection in the young and dense planetary nebula NGC 7027. We target some hydride ions associated with the noble gases (HeH$^+$, ArH$^+$, and NeH$^+$) to investigate their formation in a harsh environment like the novae outburst region. We use a photoionization modeling (based on the earlier published best-fitted physical parameters) of the moderately fast ONe type nova: QU Vulpeculae 1984 and CO type novae: RS Ophiuchi and V1716 Scorpii. Our steady-state modeling reveals a convincing amount of HeH$^+$, especially in the dense clump of RS Ophiuchi and V1716 Scorpii. The calculated upper limit of the surface brightness of HeH$^+$ transitions suggests that the James Webb Space Telescope (JWST) could detect some of them, particularly in sources like RS Ophiuchi and V1716 Scorpii with similar physical and chemical conditions and evolution. It is to be clearly noted that the sources studied are used as templates, not as targets for observations. The detection of these lines could be useful for determining physical conditions in similar types of systems and validating our predictions based on new electron-impact rovibrational collisional data at temperatures up to 20,000 K., Comment: 15 pages, 12 figures, accepted for publication in Astronomy & Astrophysics (A&A) Journal

We establish a logarithmic version of the classical result of Artin-Furw{\"a}ngler on the principalization ofideal classes in the Hilbert class-field by applying the group theoretic description of the transfert map to logarithmic class-groups of degree 0., Comment: in French language

In this communication, we propose a tentative to set the fundamental problem of measuring process done by a large structure on a microsopic one. We consider the example of voting when an entire society tries to measure globally opinions of all social actors in order to elect a delegate. We present a quantum model to interpret an operational voting system and propose a quantum approach for grading step of Range Voting, developed by M. Balinski and R. Laraki in 2007.

Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this problem, we have curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of all of the 36 datasets that consist of statements or claims. We assess these datasets to identify those with solid foundations for empirical work and those with flaws that could result in misleading and non-generalizable results, such as insufficient label quality, spurious correlations, or political bias. We further provide state-of-the-art baselines on all these datasets, but show that regardless of label quality, categorical labels may no longer give an accurate evaluation of detection model performance. We discuss alternatives to mitigate this problem. Overall, this guide aims to provide a roadmap for obtaining higher quality data and conducting more effective evaluations, ultimately improving research in misinformation detection. All datasets and other artifacts are available at https://misinfo-datasets.complexdatalab.com/.

The Bradley-Terry (BT) model is a common and successful practice in reward modeling for Large Language Model (LLM) alignment. However, it remains unclear why this model -- originally developed for multi-player stochastic game matching -- can be adopted to convert pairwise response comparisons to reward values and make predictions. Especially given the fact that only a limited number of prompt-response pairs are sparsely compared with others. In this paper, we first revisit the foundations of using BT models in reward modeling, and establish the convergence rate of BT reward models based on deep neural networks using embeddings, providing a theoretical foundation for their use. Despite theoretically sound, we argue that the BT model is not a necessary choice from the perspective of downstream optimization. This is because a reward model only needs to preserve the correct ranking predictions through a monotonic transformation of the true reward. We highlight the critical concept of order consistency in reward modeling and demonstrate that the BT model possesses this property. Consequently, we propose a simple and straightforward upper-bound algorithm, compatible with off-the-shelf binary classifiers, as an alternative order-consistent reward modeling objective. To offer practical insights, we empirically evaluate the performance of these different reward modeling approaches across more than 12,000 experimental setups, using $6$ base LLMs, $2$ datasets, and diverse annotation designs that vary in quantity, quality, and pairing choices in preference annotations.