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Machine learning techniques in multi-view settings face significant challenges, particularly when integrating heterogeneous data, aligning feature spaces, and managing view-specific biases. These issues are prominent in neuroscience, where data from multiple subjects exposed to the same stimuli are analyzed to uncover brain activity dynamics. In magnetoencephalography (MEG), where signals are captured at the scalp level, estimating the brain's underlying sources is crucial, especially in group studies where sources are assumed to be similar for all subjects. Common methods, such as Multi-View Independent Component Analysis (MVICA), assume identical sources across subjects, but this assumption is often too restrictive due to individual variability and age-related changes. Multi-View Independent Component Analysis with Delays (MVICAD) addresses this by allowing sources to differ up to a temporal delay. However, temporal dilation effects, particularly in auditory stimuli, are common in brain dynamics, making the estimation of time delays alone insufficient. To address this, we propose Multi-View Independent Component Analysis with Delays and Dilations (MVICAD2), which allows sources to differ across subjects in both temporal delays and dilations. We present a model with identifiable sources, derive an approximation of its likelihood in closed form, and use regularization and optimization techniques to enhance performance. Through simulations, we demonstrate that MVICAD2 outperforms existing multi-view ICA methods. We further validate its effectiveness using the Cam-CAN dataset, and showing how delays and dilations are related to aging., Comment: 19 pages, 8 figures

We develop a conformally invariant (CI) framework in $(1+3)$-dimensional de Sitter (dS) spacetime, that unifies the descriptions of graviton, ``partially massless'' graviton, and photon fields. This framework is grounded in a rigorous group-theoretical analysis in the Wigner sense and employs Dirac's six-cone formalism. Originally introduced by Dirac, the concept of conformal space and the six-cone formalism were used to derive the field equations for spinor and vector fields in $(1+3)$-dimensional Minkowski spacetime in a manifestly CI form. Our framework extends this approach to dS spacetime, unifying the treatment of massless and ``partially massless'' fields with integer spin $s>0$ under conformal symmetry. This unification enhances the understanding of fundamental aspects of gravitational theories in curved backgrounds., Comment: 20 pages

The Einstein Probe (EP) is an interdisciplinary mission of time-domain and X-ray astronomy. Equipped with a wide-field lobster-eye X-ray focusing imager, EP will discover cosmic X-ray transients and monitor the X-ray variability of known sources in 0.5-4 keV, at a combination of detecting sensitivity and cadence that is not accessible to the previous and current wide-field monitoring missions. EP can perform quick characterisation of transients or outbursts with a Wolter-I X-ray telescope onboard. In this paper, the science objectives of the Einstein Probe mission are presented. EP is expected to enlarge the sample of previously known or predicted but rare types of transients with a wide range of timescales. Among them, fast extragalactic transients will be surveyed systematically in soft X-rays, which include {\gamma}-ray bursts and their variants, supernova shock breakouts, and the predicted X-ray transients associated with binary neutron star mergers. EP will detect X-ray tidal disruption events and outbursts from active galactic nuclei, possibly at an early phase of the flares for some. EP will monitor the variability and outbursts of X-rays from white dwarfs, neutron stars and black holes in our and neighbouring galaxies at flux levels fainter than those detectable by the current instruments, and is expected to discover new objects. A large sample of stellar X-ray flares will also be detected and characterised. In the era of multi-messenger astronomy, EP has the potential of detecting the possible X-ray counterparts of gravitational wave events, neutrino sources, and ultra-high energy {\gamma}-ray and cosmic ray sources. EP is expected to help advance the studies of extreme objects/phenomena and their underlying physical processes revealed in the dynamic X-ray universe, as well as studies in other areas of X-ray astronomy., Comment: 67 pages, 24 figures, accepted for publication in SCIENCE CHINA Physics, Mechanics & Astronomy

Precision agriculture in general, and precision weeding in particular, have greatly benefited from the major advancements in deep learning and computer vision. A large variety of commercial robotic solutions are already available and deployed. However, the adoption by farmers of such solutions is still low for many reasons, an important one being the lack of trust in these systems. This is in great part due to the opaqueness and complexity of deep neural networks and the manufacturers' inability to provide valid guarantees on their performance. Conformal prediction, a well-established methodology in the machine learning community, is an efficient and reliable strategy for providing trustworthy guarantees on the predictions of any black-box model under very minimal constraints. Bridging the gap between the safe machine learning and precision agriculture communities, this article showcases conformal prediction in action on the task of precision weeding through deep learning-based image classification. After a detailed presentation of the conformal prediction methodology and the development of a precision spraying pipeline based on a ''conformalized'' neural network and well-defined spraying decision rules, the article evaluates this pipeline on two real-world scenarios: one under in-distribution conditions, the other reflecting a near out-of-distribution setting. The results show that we are able to provide formal, i.e. certifiable, guarantees on spraying at least 90% of the weeds.

Low-temperature luminescence spectra reveal the presence of two independant populations of GaN excitons within a $\mathrm{GaN/AlGaN/GaN/Al_2O_3}$ heterostructure in which a thick (1.5 $\mathrm{\mu m}$) AlGaN layer separates a thin (150 nm) top GaN layer and a thick (3.5 $\mathrm{\mu m}$) bottom GaN layer grown on sapphire. The presence of these two spectrally-distinct families of excitons in each GaN layer of the heterostructure is demonstrated using three different experimental methods: (i) low-power $\mathrm{\mu}$-photoluminescence ($\mathrm{\mu PL}$) using laser excitation sources with wavelengths above and below the AlGaN bandgap, (ii) $\mathrm{\mu PL}$ as a function of optically injected free carrier density, and (iii) quantitative numerical simulation of the $\mathrm{\mu}$-Reflectivity ($\mathrm{\mu R}$). One major impact of the built-in electric field is the reduction of the excitonic lifetime in the GaN surface layer, which transitions from less than 10 ps in the presence of the built-in electric field to the bulk lifetime (90 ps) when the field is screened. This increase in the excitonic lifetime is related to the modification of the band structure in the presence of optically injected free carriers. The effect of these lifetime variations on the luminescence spectra is analyzed. Lastly, we provide an estimate of the Mott density in GaN as $n_{\mathrm{Mott}} = 4\times 10^{17}\, \mathrm{cm^{-3}}$ at 130 K, consistent with values reported in the literature and accounting for the free carrier density required to screen the electric field.

Given a positive integer $k$, $k$-set agreement is the distributed task in which each process $i\in [n]$ in a group of $n$ processing nodes starts with an input value $x_i$ in the set $\{0,\dots,k\}$, and must output a value $y_i$ such that (1) for every $i \in [n]$, $y_i$ is the input value of some process, and (2)$|\{y_i : i\in [n]\}|\leq k$. That is, at most $k$ different values in total must be outputted by the processes. The case $k=1$ correspond to (binary) consensus, arguably the most studied problem in distributed computing. While lower bounds for consensus have been obtained for most of the standard distributed computing models, the design of lower bounds for $k$-set agreement with $k>1$ is notoriously known to be much more difficult, and remains open for many models. The main techniques for designing lower bounds for k-set agreement with $k>1$ use tools from algebraic topology. The algebraic topology tools are difficult to manipulate, and require a lot of care for avoiding mistakes. This difficulty increases when the communications are mediated by a network of arbitrary structure. Recently, the KNOWALL model has been specifically designed as a first attempt to understand the LOCAL model through the lens of algebraic topology, and Casta\~neda et al.(2021) have designed lower bounds for $k$-set agreement in the KNOWALL model, with applications to dynamic networks. In this work, we re-prove the same lower bound for $k$-set agreement in the KNOWALL model. This new proof stands out in its simplicity, which makes it accessible to a broader audience, and increases confidence in the result., Comment: 14 pages, 3 figures

Despite a century of use, the mechanism of nanoparticle-driven mechanical reinforcement of elastomers is unresolved. A major hypothesis attributes it to glassy interparticle bridges, supported by an observed inversion of the variation of the modulus E(T) on heating -- from entropic stiffening in elastomers to enthalpic softening in nanocomposites. Here, molecular simulations reveal that this instead arises from a bulk-modulus mediated competition between elastomer and nanoparticulate networks over preferred nonequilibrium densities under deformation. A theory accounting for softening of the bulk modulus on heating quantitatively predicts the simulated E(T) inversion, suggesting that reinforcement is driven by a volume-competition mechanism unique to co-continuous systems of soft and rigid networks.

Bourgain's slicing conjecture was recently resolved by Joseph Lehec and Bo'az Klartag. We present an alternative proof by establishing small ball probability estimates for isotropic log-concave measures. Our approach relies on the stochastic localization process and Guan's bound, techniques also used by Klartag and Lehec. The link between small ball probabilities and the slicing conjecture was first observed by Dafnis and Paouris and is established through Milman's theory of M-ellipsoids., Comment: 12 pages

The energy transition is profoundly reshaping electricity market dynamics. It makes it essential to understand how renewable energy generation actually impacts electricity prices, among all other market drivers. These insights are critical to design policies and market interventions that ensure affordable, reliable, and sustainable energy systems. However, identifying causal effects from observational data is a major challenge, requiring innovative causal inference approaches that go beyond conventional regression analysis only. We build upon the state of the art by developing and applying a local partially linear double machine learning approach. Its application yields the first robust causal evidence on the distinct and non-linear effects of wind and solar power generation on UK wholesale electricity prices, revealing key insights that have eluded previous analyses. We find that, over 2018-2024, wind power generation has a U-shaped effect on prices: at low penetration levels, a 1 GWh increase in energy generation reduces prices by up to 7 GBP/MWh, but this effect gets close to none at mid-penetration levels (20-30%) before intensifying again. Solar power places substantial downward pressure on prices at very low penetration levels (up to 9 GBP/MWh per 1 GWh increase in energy generation), though its impact weakens quite rapidly. We also uncover a critical trend where the price-reducing effects of both wind and solar power have become more pronounced over time (from 2018 to 2024), highlighting their growing influence on electricity markets amid rising penetration. Our study provides both novel analysis approaches and actionable insights to guide policymakers in appraising the way renewables impact electricity markets.

In this paper, we study a suitable experimental design of electrochemical impedance spectroscopy (EIS) to reduce the number of frequency points while not significantly affecting the uncertainties of the estimated cell's equivalent circuit model (ECM) parameters. It is based on an E-optimal experimental design that aims to maximize the information about the ECM parameters collected by EIS measurements and, at the same time, minimize the overall uncertainty. In a numerical experiment, we first analyze to which extent reducing the number of measurement points at low frequencies affects the uncertainty of the estimated parameters. Secondly, we show that applying the frequency adjustments can lead to the same or even improved global uncertainty of ECM parameter estimates as with a higher number of measurements. This is numerically verified through a case study using the ECM parameters of a commercial battery cell.

Many systems of partial differential equations have been proposed as simplified representations of complex collective behaviours in large networks of neurons. In this survey, we briefly discuss their derivations and then review the mathematical methods developed to handle the unique features of these models, which are often nonlinear and non-local. The first part focuses on parabolic Fokker-Planck equations: the Nonlinear Noisy Leaky Integrate and Fire neuron model, stochastic neural fields in PDE form with applications to grid cells, and rate-based models for decision-making. The second part concerns hyperbolic transport equations, namely the model of the Time Elapsed since the last discharge and the jump-based Leaky Integrate and Fire model. The last part covers some kinetic mesoscopic models, with particular attention to the kinetic Voltage-Conductance model and FitzHugh-Nagumo kinetic Fokker-Planck systems.

Glucose oxidation is a key reaction in biosensing and energy conversion, with anodized copper electrodes showing great potential as catalysts for its enhancement. This study explores the relationship between anodization parameters and glucose oxidation on thin copper films, providing valuable insights for optimization. By adjusting anodization parameters, specific species such as Cu2O, Cu(OH)2, and CuO can be selectively formed, each exhibiting different catalytic activities. Applying polarization in 1 M KOH at 0 V (vs Ag|AgCl) generates a highly active CuO surface layer, leading to significant performance improvements over bare copper electrodes. Specifically, sensitivity increases by 55% in the 0.1 mM-0.5 mM range and 73% in the 0.75 mM-2 mM range. Additionally, the electrodes demonstrate an exceptionally low detection limit of just 0.004 mM. These results are attributed to scalable, reproducible manufacturing on cleanroom-compatible platforms. This study not only sheds light on the effects of thin copper film anodization on glucose oxidation but also provides a practical approach for enhancing the efficiency of copper-based systems in biosensing and energy conversion applications.

Quantum algorithms can potentially solve a handful of problems more efficiently than their classical counterparts. In that context, it has been discussed that Markov chains problems could be solved significantly faster using quantum computing. Indeed, previous work suggests that quantum computers could accelerate sampling from the stationary distribution of reversible Markov chains. However, in practice, certain physical processes of interest are nonreversible in the probabilistic sense and reversible Markov chains can sometimes be replaced by more efficient nonreversible chains targeting the same stationary distribution. This study uses modern quantum algorithmic techniques and Markov chain theory to sample from the stationary distribution of nonreversible Markov chains with faster worst-case runtime and without requiring the stationary distribution to be computed up to a multiplicative constant. Such an up-to-exponential quantum speedup goes beyond the predicted quadratic quantum acceleration for reversible chains and is likely to have a decisive impact on many applications ranging from statistics and machine learning to computational modeling in physics, chemistry, biology and finance.

We report our findings on a spectroscopic survey of seven unresolved DA+DB binary white dwarf candidates. We have discovered extreme spectroscopic variations in one of these candidates, SDSS J084716.21+484220.40. Previous analysis failed to reproduce the optical spectrum using a single object with a homogeneous atmosphere. Our time-resolved spectroscopy reveals a double-faced white dwarf that switches between a DBA and DA spectral type over 6.5 or 8.9 hours due to varying surface abundances. We also provide time-series spectroscopy of the magnetic DBA, SDSS J085618.94+161103.6 (LB 8915), and confirm an inhomogeneous atmosphere. We employ an atmosphere model with hydrogen caps and a helium belt that yields excellent fits to our time-resolved spectra. We use the oblique rotator model to derive the system geometry for both targets. With the addition of these two objects, the emerging class of double-faced white dwarfs now consists of seven members. We summarize the properties of this new class of objects, and discuss how magnetism impacts the convective processes and leads to the formation of double-faced white dwarfs. We identify cooler versions of white dwarfs with inhomogeneous atmospheres among the cool magnetic DA white dwarf sample, where the H$\alpha$ line is shallower than expected based on pure hydrogen atmosphere models., Comment: 14 pages, 9 figures, Accepted for publication in ApJ

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

We introduce a novel framework to implement stochastic inflation on stochastic trees, modelling the inflationary expansion as a branching process. Combined with the $\delta N$ formalism, this allows us to generate real-space maps of the curvature perturbation that fully capture quantum diffusion and its non-perturbative backreaction during inflation. Unlike lattice methods, trees do not proceed on a fixed background since new spacetime units emerge dynamically as trees unfold, naturally incorporating metric fluctuations. The recursive structure of stochastic trees also offers remarkable numerical efficiency, and we develop the FOrtran Recursive Exploration of Stochastic Trees ($\texttt{FOREST}$) tool and demonstrate its performance. We show how primordial black holes blossom at unbalanced nodes of the trees, and how their mass distribution can be obtained while automatically accounting for the "cloud-in-cloud" effect. In the "quantum-well" toy model, we find broad mass distributions, with mild power laws terminated by exponential tails. We finally compare our results with existing approximations in the literature and discuss several prospects., Comment: 39 pages, 13 figures

Despite being the most common types of stars in the Galaxy, the physical properties of late M dwarfs are often poorly constrained. A trend of radius inflation compared to evolutionary models has been observed for earlier type M dwarfs in eclipsing binaries, possibly caused by magnetic activity. It is currently unclear whether this trend also extends to later type M dwarfs below the convective boundary. This makes the discovery of lower-mass, fully convective, M dwarfs in eclipsing binaries valuable for testing evolutionary models especially in longer-period binaries where tidal interaction between the primary and secondary is negligible. With this context, we present the discovery of the NGTS-EB-7 AB system, an eclipsing binary containing a late M dwarf secondary and an evolved G-type primary star. The secondary star has a radius of $0.125 \pm 0.006 R_\odot$ , a mass of $0.096 \pm 0.004 M_\odot$ and follows a highly eccentric $(e=0.71436 \pm 0.00085)$ orbit every $193.35875 \pm 0.00034$ days. This makes NGTS-EB-7 AB the third longest-period eclipsing binary system with a secondary smaller than $200 M_J$ with the mass and radius constrained to better than $5 \%$. In addition, NGTS-EB-7 is situated near the centre of the proposed LOPS2 southern field of the upcoming PLATO mission, allowing for detection of the secondary eclipse and measurement of the companion`s temperature. With its long-period and well-constrained physical properties - NGTS-EB-7 B will make a valuable addition to the sample of M dwarfs in eclipsing binaries and help in determining accurate empirical mass/radius relations for later M dwarf stars., Comment: Main body: 14 pages, 6 figures, 5 tables. Appendices: 7 pages, 5 figures, 5 tables

Endovascular navigation is a crucial aspect of minimally invasive procedures, where precise control of curvilinear instruments like guidewires is critical for successful interventions. A key challenge in this task is accurately predicting the evolving shape of the guidewire as it navigates through the vasculature, which presents complex deformations due to interactions with the vessel walls. Traditional segmentation methods often fail to provide accurate real-time shape predictions, limiting their effectiveness in highly dynamic environments. To address this, we propose SplineFormer, a new transformer-based architecture, designed specifically to predict the continuous, smooth shape of the guidewire in an explainable way. By leveraging the transformer's ability, our network effectively captures the intricate bending and twisting of the guidewire, representing it as a spline for greater accuracy and smoothness. We integrate our SplineFormer into an end-to-end robot navigation system by leveraging the condensed information. The experimental results demonstrate that our SplineFormer is able to perform endovascular navigation autonomously and achieves a 50% success rate when cannulating the brachiocephalic artery on the real robot., Comment: 8 pages

In this paper, we investigate on a bounded open set of $\mathbb{R}^N$ with smooth boundary, an eigenvalue problem involving the sum of nonlocal operators $(-\Delta)_p^{s_1}+ (-\Delta)_q^{s_2}$ with $s_1,s_2\in (0,1)$, $p,q\in (1,\infty)$ and subject to the corresponding homogeneous nonlocal $(p,q)$-Neumann boundary condition. A careful analysis of the considered problem leads us to a complete description of the set of eigenvalues as being the precise interval $\{0\}\cup(\lambda_{1}(s_2,q),\infty)$, where $\lambda_{1}(s_2,q)$ is the first nonzero eigenvalue of the homogeneous fractional $q$-Laplacian under nonlocal $q$-Neumann boundary condition. Furthermore, we establish that every eigenfunctions is globally bounded., Comment: 26 pages. arXiv admin note: text overlap with arXiv:2406.15825

Electromagnetic waves in the magnetosphere scatter electrons, causing them to precipitate deep into Earth's atmosphere, where they impart their temporal characteristics to diffuse aurorae. Using radar-observations of the ionospheric E-region and satellite observations from the magnetosphere, we demonstrate a close and unprecedented association between enhanced electrostatic cyclotron harmonic wave activity in the magnetosphere and the appearance of meter-scale ionospheric plasma turbulence observed a few seconds later in the lower ionosphere on nearby magnetic field lines., Comment: 10 pages, 6 figures

We report pattern formation in an otherwise non-uniform and unsteady flow arising in high-speed liquid entrainment conditions on the outer wall of a wide rotating drum. We show that the coating flow in this rotary drag-out undergoes axial modulations to form an array of roughly vertical thin liquid sheets which slowly drift from the middle of the drum towards its side walls. Thus, the number of sheets fluctuate in time such that the most probable rib spacing varies ever so slightly with the speed, and a little less weakly with the viscosity. We propose that these axial patterns are generated due to a primary instability driven by an adverse pressure gradient in the meniscus region of the rotary drag-out flow, similar to the directional Saffman-Taylor instability, as is well-known for ribbing in film-splitting flows. Rib spacing based on this mechanistic model turns out to be proportional to the capillary length, wherein the scaling factor can be determined based on existing models for film entrainment at both low and large Capillary numbers. In addition, we performed Direct Numerical Simulations, which reproduce the experimental phenomenology and the associated wavelength. We further include two numerical cases wherein either the liquid density, or the liquid surface tension are quadrupled while keeping all other parameters identical with experiments. The rib spacings of these cases are in agreement with the predictions of our model., Comment: in press for JFM

Large X-ray observatories such as Chandra and XMM-Newton have been delivering scientific breakthroughs in research fields as diverse as our Solar System, the astrophysics of stars, stellar explosions and compact objects, accreting super-massive black holes, and large-scale structures traced by the hot plasma permeating and surrounding galaxy groups and clusters. The recently launched observatory XRISM is opening in earnest the new observational window of non-dispersive high-resolution spectroscopy. However, several quests are left open, such as the effect of the stellar radiation field on the habitability of nearby planets, the Equation-of-State regulating matter in neutron stars, the origin and distribution of metals in the Universe, the processes driving the cosmological evolution of the baryons locked in the gravitational potential of Dark Matter and the impact of supermassive black hole growth on galaxy evolution, just to mention a few. Furthermore, X-ray astronomy is a key player in multi-messenger astrophysics. Addressing these quests experimentally requires an order-of-magnitude leap in sensitivity, spectroscopy and survey capabilities with respect to existing X-ray observatories. This paper succinctly summarizes the main areas where high-energy astrophysics is expected to contribute to our understanding of the Universe in the next decade and describes a new mission concept under study by the European Space Agency, the scientific community worldwide and two International Partners (JAXA and NASA), designed to enable transformational discoveries: NewAthena. This concept inherits its basic payload design from a previous study carried out until 2022, Athena., Comment: Published in Nature Astronomy. 9 pages, 4 figures

Charge transport in materials has an impact on a wide range of devices based on semiconductor, battery or superconductor technology. Charge transport in sliding Charge Density Waves (CDW) differs from all others in that the atomic lattice is directly involved in the transport process. To obtain an overall picture of the structural changes associated to the collective transport, the large coherent X-ray beam generated by an X-ray free-electron laser (XFEL) source was used. The CDW phase can be retrieved over the entire sample from diffracted intensities using a genetic algorithm. For currents below threshold, increasing shear deformation is observed in the central part of the sample while longitudinal deformation appears above threshold when shear relaxes. Shear thus precedes longitudinal deformation, with relaxation of one leading to the appearance of the other. Moreover, strain accumulates on surface steps in the sliding regime, demonstrating the strong pinning character of these surface discontinuities. The sliding process of nanometric CDW is based on an impressive spatial coherence involving the macroscopic sample dimensions., Comment: 20 pages, 5 figures

Accurate quantification of the extent of lung pathological patterns (fibrosis, ground-glass opacity, emphysema, consolidation) is prerequisite for diagnosis and follow-up of interstitial lung diseases. However, segmentation is challenging due to the significant class imbalance between healthy and pathological tissues. This paper addresses this issue by leveraging a diffusion model for data augmentation applied during training an AI model. Our approach generates synthetic pathological tissue patches while preserving essential shape characteristics and intricate details specific to each tissue type. This method enhances the segmentation process by increasing the occurence of underrepresented classes in the training data. We demonstrate that our diffusion-based augmentation technique improves segmentation accuracy across all pathological tissue types, particularly for the less common patterns. This advancement contributes to more reliable automated analysis of lung CT scans, potentially improving clinical decision-making and patient outcomes, Comment: accepted at ISBI 2025

During the 23 April 2023 geospace storm, we observed chorus wave-driven energetic particle precipitation on closed magnetic field lines in the dayside magnetosphere. Simultaneously and in the ionosphere's bottom-side, we observed signatures of impact ionization and strong enhancements in the ionospheric electric field strength, via radar-detection of meter-scale turbulence, and with matching temporal characteristics as that of the magnetospheric observations (correlation coefficient = 0.78). During geospace storms, then, turbulent structuring as well as fast electrodynamics in the dayside polar ionosphere can be driven by wave-particle interactions in the magnetosphere., Comment: 8 pages, 4 figures

This note studies phase transitions for the existence of unregularized M-estimators under proportional asymptotics where the sample size $n$ and feature dimension $p$ grow proportionally with $n/p \to \delta \in (0, \infty)$. We study the existence of M-estimators in single-index models where the response $y_i$ depends on covariates $x_i \sim N(0, I_p)$ through an unknown index $w \in R^p$ and an unknown link function. An explicit expression is derived for the critical threshold $\delta_\infty$ that determines the phase transition for the existence of the M-estimator, generalizing the results of Cand`es and Sur (2020) for binary logistic regression to other single-index linear models. Furthermore, we investigate the existence of a solution to the nonlinear system of equations governing the asymptotic behavior of the M-estimator when it exists. The existence of solution to this system for $\delta > \delta_\infty$ remains largely unproven outside the global null in binary logistic regression. We address this gap with a proof that the system admits a solution if and only if $\delta > \delta_\infty$, providing a comprehensive theoretical foundation for proportional asymptotic results that require as a prerequisite the existence of a solution to the system., Comment: 27 pages, 2 figures

Approximate Leave-One-Out Cross-Validation (ALO-CV) is a method that has been proposed to estimate the generalization error of a regularized estimator in the high-dimensional regime where dimension and sample size are of the same order, the so called ``proportional regime''. A new analysis is developed to derive the consistency of ALO-CV for non-differentiable regularizer under Gaussian covariates and strong-convexity of the regularizer. Using a conditioning argument, the difference between the ALO-CV weights and their counterparts in mean-field inference is shown to be small. Combined with upper bounds between the mean-field inference estimate and the leave-one-out quantity, this provides a proof that ALO-CV approximates the leave-one-out quantity as well up to negligible error terms. Linear models with square loss, robust linear regression and single-index models are explicitly treated.

This note develops an analysis of the Lasso \( \hat b\) in linear models without any sparsity or L1 assumption on the true regression vector, in the proportional regime where dimension \( p \) and sample \( n \) are of the same order. Under Gaussian design and covariance matrix with spectrum bounded away from 0 and $+\infty$, it is shown that the L2 risk is stochastically bounded if and only if the number of selected variables is bounded away from \( n \), in the sense that $$ (1-\|\hat b\|_0/n)^{-1} = O_P(1) \Longleftrightarrow \|\hat b- b^*\|_2 = O_P(1) $$ as \( n,p\to+\infty \). The right-to-left implication rules out constant risk for dense Lasso estimates (estimates with close to $n$ active variables), which can be used to discard tuning parameters leading to dense estimates. We then bring back sparsity in the picture, and revisit the precise phase transition characterizing the sparsity patterns of the true regression vector leading to unbounded Lasso risk -- or by the above equivalence to dense Lasso estimates. This precise phase transition was established by \citet{miolane2018distribution,celentano2020lasso} using fixed-point equations in an equivalent sequence model. An alternative proof of this phase transition is provided here using simple arguments without relying on the fixed-point equations or the equivalent sequence model. A modification of the well-known Restricted Eigenvalue argument allows to extend the analysis to any small tuning parameter of constant order, leading to a bounded risk on one side of the phase transition. On the other side of the phase transition, it is established the Lasso risk can be unbounded for a given sign pattern as soon as Basis Pursuit fails to recover that sign pattern in noiseless problems.

We construct and study new generalisations to rooted trees and forests of some properties of shuffles of words. First, we build a coproduct on rooted trees which, together with their shuffle, endow them with bialgebra structure. We then caracterize the coproduct dual to the shuffle product of rooted forests and build a product on rooted trees to obtain the bialgebra dual to the shuffle bialgebra. We then characterize and enumerate primitive trees for the dual coproduct. Finally, using modified shuffles of rooted forests, we prove a property in the category of Rota-Baxter algebras., Comment: 28 pages

We report on the electron spin resonance (ESR), heat capacity, magnetization, nuclear magnetic resonance (NMR), magnetic circular and linear dichroism (XMCD, XMLD), as well as the electrical resistivity of EuMn$_{2}$P$_{2}$ single crystals. Antiferromagnetic order of Eu was observed in several quantities at $T^{\rm Eu}_{\rm N}\,=\,18\,\rm K$. The temperature dependencies of ESR linewidth and resonance shift show, when approaching the Eu-ordered state, a divergence towards $T^{\rm Eu}_{\rm N}$, indicating the growing importance of magnetic correlations and the build-up of internal magnetic fields. An additional temperature scale of $\approx 47\,\rm K$ has considerable impact on linewidth, resonance field and intensity. This points to the presence of weak Mn-based ordering. The observed ESR line is interpreted as an Eu$^{2+}$ resonance, which probes the weak magnetic background of the Mn subsystem. Such picture is suggested by the lineshape which keeps to be Lorentzian across the $47\,\rm K$ scale and by the ESR intensity which can be described by the same Curie-Weiss temperature above and below $47\,\rm K$. In the same temperature range anomalies were observed at $48.5\,\rm K$ and $51\,\rm K$ in the heat capacity data as well as a pronounced broadening of the NMR signal of the EuMn$_{2}$P$_{2}$ samples. In XMCD and XMLD measurements, this weak magnetic order could not be detected in the same temperature range which might be due to the small magnetic moment, with a potential $c$-component or frustration.

End-to-end image and video codecs are becoming increasingly competitive, compared to traditional compression techniques that have been developed through decades of manual engineering efforts. These trainable codecs have many advantages over traditional techniques, such as their straightforward adaptation to perceptual distortion metrics and high performance in specific fields thanks to their learning ability. However, current state-of-the-art neural codecs do not fully exploit the benefits of vector quantization and the existence of the entropy gradient in decoding devices. In this paper, we propose to leverage these two properties (vector quantization and entropy gradient) to improve the performance of off-the-shelf codecs. Firstly, we demonstrate that using non-uniform scalar quantization cannot improve performance over uniform quantization. We thus suggest using predefined optimal uniform vector quantization to improve performance. Secondly, we show that the entropy gradient, available at the decoder, is correlated with the reconstruction error gradient, which is not available at the decoder. We therefore use the former as a proxy to enhance compression performance. Our experimental results show that these approaches save between 1 to 3% of the rate for the same quality across various pretrained methods. In addition, the entropy gradient based solution improves traditional codec performance significantly as well., Comment: Accepted in IEEE TRANSACTIONS ON IMAGE PROCESSING

Efforts towards endowing robots with the ability to speak have benefited from recent advancements in NLP, in particular large language models. However, as powerful as current models have become, they still operate on sentence or multi-sentence level input, not on the word-by-word input that humans operate on, affecting the degree of responsiveness that they offer, which is critical in situations where humans interact with robots using speech. In this paper, we review the literature on interactive systems that operate incrementally (i.e., at the word level or below it). We motivate the need for incremental systems, survey incremental modeling of important aspects of dialogue like speech recognition and language generation. Primary focus is on the part of the system that makes decisions, known as the dialogue manager. We find that there is very little research on incremental dialogue management, offer some requirements for practical incremental dialogue management, and the implications of incremental dialogue for embodied, robotic platforms., Comment: 16 pages

We introduce a solvable model of a measurement-induced phase transition (MIPT) in a deterministic but chaotic dynamical system with a positive Lyapunov exponent. In this setup, an observer only has a probabilistic description of the system but mitigates chaos-induced uncertainty through repeated measurements. Using a minimal representation via a branching tree, we map this problem to the directed polymer (DP) model on the Cayley tree, although in a regime dominated by rare events. By studying the Shannon entropy of the probability distribution estimated by the observer, we demonstrate a phase transition distinguishing a chaotic phase with reduced Lyapunov exponent from a strong-measurement phase where uncertainty remains bounded. Remarkably, the location of the MIPT transition coincides with the freezing transition of the DP, although the critical properties differ. We provide an exact universal scaling function describing entropy growth in the critical regime. Numerical simulations confirm our theoretical predictions, highlighting a simple yet powerful framework to explore measurement-induced transitions in classical chaotic systems., Comment: 14 pages, 5 figures

This paper describes RHP Friends, a social humanoid robot developed to enable assistive robotic deployments in human-coexisting environments. As a use-case application, we present its potential use in nursing by extending its capabilities to operate human devices and tools according to the task and by enabling remote assistance operations. To meet a wide variety of tasks and situations in environments designed by and for humans, we developed a system that seamlessly integrates the slim and lightweight robot and several technologies: locomanipulation, multi-contact motion, teleoperation, and object detection and tracking. We demonstrated the system's usage in a nursing application. The robot efficiently performed the daily task of patient transfer and a non-routine task, represented by a request to operate a circuit breaker. This demonstration, held at the 2023 International Robot Exhibition (IREX), conducted three times a day over three days., Comment: IEEE Robotics and Automation Magazine, In press

Molecular Dynamics simulations are essential tools for understanding the dynamic behavior of biomolecules, yet extracting meaningful molecular pathways from these simulations remains challenging due to the vast amount of generated data. In this work, we present Molecular Kinetics via Topology (MoKiTo), a novel approach that combines the ISOKANN algorithm to determine the reaction coordinate of a molecular system with a topological analysis inspired by the Mapper algorithm. Our strategy efficiently identifies and characterizes distinct molecular pathways, enabling the detection and visualization of critical conformational transitions and rare events. This method offers deeper insights into molecular mechanisms, facilitating the design of targeted interventions in drug discovery and protein engineering.

We Study versions of Cauchy formula in more general algebras than the complex case., Comment: 53 pages, in French language

The rapid expansion of citizen science initiatives has led to a significant growth of biodiversity databases, and particularly presence-only (PO) observations. PO data are invaluable for understanding species distributions and their dynamics, but their use in a Species Distribution Model (SDM) is curtailed by sampling biases and the lack of information on absences. Poisson point processes are widely used for SDMs, with Maxent being one of the most popular methods. Maxent maximises the entropy of a probability distribution across sites as a function of predefined transformations of variables, called features. In contrast, neural networks and deep learning have emerged as a promising technique for automatic feature extraction from complex input variables. Arbitrarily complex transformations of input variables can be learned from the data efficiently through backpropagation and stochastic gradient descent (SGD). In this paper, we propose DeepMaxent, which harnesses neural networks to automatically learn shared features among species, using the maximum entropy principle. To do so, it employs a normalised Poisson loss where for each species, presence probabilities across sites are modelled by a neural network. We evaluate DeepMaxent on a benchmark dataset known for its spatial sampling biases, using PO data for calibration and presence-absence (PA) data for validation across six regions with different biological groups and covariates. Our results indicate that DeepMaxent performs better than Maxent and other leading SDMs across all regions and taxonomic groups. The method performs particularly well in regions of uneven sampling, demonstrating substantial potential to increase SDM performances. In particular, our approach yields more accurate predictions than traditional single-species models, which opens up new possibilities for methodological enhancement., Comment: Submitted to Methods in Ecology and Evolution