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Challenging to capture, and challenging to display on a cellphone screen, the panorama paradoxically remains both a staple and underused feature of modern mobile camera applications. In this work we address both of these challenges with a spherical neural light field model for implicit panoramic image stitching and re-rendering; able to accommodate for depth parallax, view-dependent lighting, and local scene motion and color changes during capture. Fit during test-time to an arbitrary path panoramic video capture -- vertical, horizontal, random-walk -- these neural light spheres jointly estimate the camera path and a high-resolution scene reconstruction to produce novel wide field-of-view projections of the environment. Our single-layer model avoids expensive volumetric sampling, and decomposes the scene into compact view-dependent ray offset and color components, with a total model size of 80 MB per scene, and real-time (50 FPS) rendering at 1080p resolution. We demonstrate improved reconstruction quality over traditional image stitching and radiance field methods, with significantly higher tolerance to scene motion and non-ideal capture settings., Comment: Project site: https://light.princeton.edu/publication/neuls/

Recent studies have shown that post-aligning multilingual pretrained language models (mPLMs) using alignment objectives on both original and transliterated data can improve crosslingual alignment. This improvement further leads to better crosslingual transfer performance. However, it remains unclear how and why a better crosslingual alignment is achieved, as this technique only involves transliterations, and does not use any parallel data. This paper attempts to explicitly evaluate the crosslingual alignment and identify the key elements in transliteration-based approaches that contribute to better performance. For this, we train multiple models under varying setups for two pairs of related languages: (1) Polish and Ukrainian and (2) Hindi and Urdu. To assess alignment, we define four types of similarities based on sentence representations. Our experiments show that adding transliterations alone improves the overall similarities, even for random sentence pairs. With the help of auxiliary alignment objectives, especially the contrastive objective, the model learns to distinguish matched from random pairs, leading to better alignments. However, we also show that better alignment does not always yield better downstream performance, suggesting that further research is needed to clarify the connection between alignment and performance., Comment: preprint

There have long been questions about the limits to the validity of relativistic fluid dynamics, and whether it is being used outside its regime of validity in modern simulations of relativistic heavy-ion collisions. An important new tool for answering this question is a causality analysis in the nonlinear regime -- if the solutions of the evolution equations do not respect relativistic causality, they are not a faithful representation of the underlying relativistic theory (in this case, quantum chromodynamics). Using this non-linear criterion, it has recently been shown that hydrodynamics is indeed being used outside its regime of validity in simulations, at least sometimes. Here we explore the phenomenological implications, particularly the quantitative effects of demanding limits on acausality in modern Bayesian parameter estimation. We find that, while typically only a small fraction of the system's energy is initially in an acausal regime, placing strict limits on the allowed energy fraction significantly changes the preferred properties of the initial condition, which in turn alters the extracted medium properties such as bulk viscosity, where large values are no longer favored. These findings highlight the importance of developing better theoretical descriptions of the early-time, out-of-equilibrium dynamics of relativistic heavy-ion collisions., Comment: 13 pages, 9 figures

This study introduces i-GRIP, an innovative movement goal estimator designed to facilitate the control of assistive devices for grasping tasks in individuals with upperlimb impairments. The algorithm operates within a collaborative control paradigm, eliminating the need for specific user actions apart from naturally moving their hand toward a desired object. i-GRIP analyzes the hand's movement in an object-populated scene to determine its target and select an appropriate grip. In an experimental study involving 11 healthy participants, i-GRIP showed promising goal estimation performances and responsiveness and the potential to facilitate the daily use of assistive devices for individuals with upper-limb impairments in the future.

We study in detail the pressure stabilizing effects of the non-iterated fixed-stress splitting in poromechanical problems which are nearly undrained and incompressible. When applied in conjunction with a spatial discretization which does not satisfy the discrete inf-sup condition, namely a mixed piecewise linear - piecewise constant spatial discretization, the explicit fixed-stress scheme can have a pressure stabilizing effect in transient problems. This effect disappears, however, upon time step refinement or the attainment of steady state. The interpretation of the scheme as an Augmented Lagrangian method similar to Uzawa iteration for incompressible flow helps explain these results. Moreover, due to the slowly evolving solution within undrained seal regions, we show that the explicit fixed-stress scheme requires very large time steps to reveal its pressure stabilizing effect in examples of geologic CO$_2$ sequestration. We note that large time steps can result in large errors in drained regions, such as the aquifer or reservoir regions of these examples, and can prevent convergence of nonlinear solvers in the case of multiphase flows, which can make the explicit scheme an unreliable source of pressure stabilization. We conclude by demonstrating that pressure jump stabilization is as effective in the explicit fixed-stress setting as in the fully implicit setting for undrained problems, while maintaining the stability and convergence of the fixed-stress split for drained problems.

The model underlying physics of guiding light in single-mode fibers -- the one-dimensional nonlinear Schr\"odinger equation (NLSE), reveals a remarkable balance of the fiber dispersion and nonlinearity, leading to the existence of optical solitons. With the Inverse Scattering Transform (IST) method and its perturbation theory extension one can go beyond single-soliton physics and investigate nonlinear dynamics of complex optical pulses driven by soliton interactions. Here, advancing the IST perturbation theory approach, we introduce the eigenvalue response functions, which provide an intuitively clear way to manipulate individual characteristics of solitons even in the case of their entire overlapping, i.e. very strong interactions. The response functions reveal the spatial sensitivity of the multi-soliton pulse concerning its instantaneous perturbations, allowing one to manipulate the velocities and amplitudes of each soliton. Our experimental setup, based on a recirculating optical fiber loop system and homodyne measurement, enables observation of long-distance NLSE dynamics and complete characterization of optical pulses containing several solitons. Adding a localized phase perturbation on a box-shaped wavefield, we change individual soliton characteristics and can detach solitons selectively from the whole pulse. The detached solitons exhibit velocities very close to the theoretical predictions, thereby demonstrating the efficiency and robustness of the response functions approach., Comment: 11 pages, 7 figures

Dwarf spheroidal galaxies are known to be dominated by old stellar populations. This has led to the assumption that their gas-rich progenitors lost their gas during their infall in the Milky Way (MW) halo at distant look-back times. Here, we report a discovery of a tiny but robustly detected population of possibly young ($\sim$ 1 Gyr old) and intermediate-mass ($\rm 1.8 M_{\odot} \le M < 3 M_{\odot}$) stars in MW dwarf spheroidal galaxies. This was established on the basis of their positions in color-magnitude diagrams, after filtering out the bulk of the foreground MW using Gaia DR3 proper motions. We have considered the possibility that this population is made of evolved blue stragglers. For Sculptor, it seems unlikely, because 95.5% of its stars are older than 8 Gyr, leading to masses smaller than 0.9 M$_{\odot}$. This would only allow blue straggler masses of less than 1.8 M$_{\odot}$, which is much lower than what we observed. Alternatively, it would require the merger of three turnoff stars, which appears even more unlikely. On the other hand, the recent Gaia proper motion measurements of MW dwarf galaxies infer their low binding energies and large angular momenta, pointing to a more recent, $\le$ 3 Gyr, infall. Although the nature of the newly discovered stars still needs further confirmation, we find that they are consistent with the recent infall of the dwarf galaxies into the MW halo, when star formation occurred from the ram pressurization of their gas content before its removal by the hot Galactic corona. The abundance of this plausibly young population of stars is similar to the expectations drawn from hydrodynamical simulations. These results point to a novel origin for MW dwarf spheroidal galaxies., Comment: A&A accepted. 20 pages, 11 figures

Human navigation is facilitated through the association of actions with landmarks, tapping into our ability to recognize salient features in our environment. Consequently, navigational instructions for humans can be extremely concise, such as short verbal descriptions, indicating a small memory requirement and no reliance on complex and overly accurate navigation tools. Conversely, current autonomous navigation schemes rely on accurate positioning devices and algorithms as well as extensive streams of sensory data collected from the environment. Inspired by this human capability and motivated by the associated technological gap, in this work we propose a hierarchical end-to-end meta-learning scheme that enables a mobile robot to navigate in a previously unknown environment upon presentation of only a few sample images of a set of landmarks along with their corresponding high-level navigation actions. This dramatically simplifies the wayfinding process and enables easy adoption to new environments. For few-shot waypoint detection, we implement a metric-based few-shot learning technique through distribution embedding. Waypoint detection triggers the multi-task low-level maneuver controller module to execute the corresponding high-level navigation action. We demonstrate the effectiveness of the scheme using a small-scale autonomous vehicle on novel indoor navigation tasks in several previously unseen environments., Comment: Appeared at the 40th Anniversary of the IEEE International Conference on Robotics and Automation (ICRA@40), 23-26 September, 2024, Rotterdam, The Netherlands. 9 pages, 5 figures

Multi-object tracking (MOT) involves identifying and consistently tracking objects across video sequences. Traditional tracking-by-detection methods, while effective, often require extensive tuning and lack generalizability. On the other hand, segmentation mask-based methods are more generic but struggle with tracking management, making them unsuitable for MOT. We propose a novel approach, McByte, which incorporates a temporally propagated segmentation mask as a strong association cue within a tracking-by-detection framework. By combining bounding box and mask information, McByte enhances robustness and generalizability without per-sequence tuning. Evaluated on four benchmark datasets - DanceTrack, MOT17, SoccerNet-tracking 2022, and KITTI-tracking - McByte demonstrates performance gain in all cases examined. At the same time, it outperforms existing mask-based methods. Implementation code will be provided upon acceptance.

We consider de Finetti's stochastic control problem for a spectrally negative L\'evy process in an Omega model. In such a model, the (controlled) process is allowed to spend time under the critical level but is then subject to a level-dependent intensity of bankruptcy. First, before considering the control problem, we derive some analytical properties of the corresponding Omega scale functions. Second, we prove that exists a barrier strategy that is optimal for this control problem under a mild assumption on the L\'evy measure. Finally, we analyse numerically the impact of the bankruptcy rate function on the optimal strategy.

We develop a Lie group geometric framework for the motion of fluids with permeable boundaries that extends Arnold's geometric description of fluid in closed domains. Our setting is based on the classical Hamilton principle applied to fluid trajectories, appropriately amended to incorporate bulk and boundary forces via a Lagrange-d'Alembert approach, and to take into account only the fluid particles present in the fluid domain at a given time. By applying a reduction by relabelling symmetries, we deduce a variational formulation in the Eulerian description that extends the Euler-Poincar\'e framework to open fluids. On the Hamiltonian side, our approach yields a bracket formulation that consistently extends the Lie-Poisson bracket of fluid dynamics and contains as a particular case a bulk+boundary bracket formulation proposed earlier. We illustrate the geometric framework with several examples, and also consider the extension of this setting to include multicomponent fluids, the consideration of general advected quantities, the analysis of higher-order fluids, and the incorporation of boundary stresses. Open fluids are found in a wide range of physical systems, including geophysical fluid dynamics, porous media, incompressible flow and magnetohydrodynamics. This new formulation permits a description based on geometric mechanics that can be applied to a broad class of models.

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

The goal of text-queried target sound extraction (TSE) is to extract from a mixture a sound source specified with a natural-language caption. While it is preferable to have access to large-scale text-audio pairs to address a variety of text prompts, the limited number of available high-quality text-audio pairs hinders the data scaling. To this end, this work explores how to leverage audio-only data without any captions for the text-queried TSE task to potentially scale up the data amount. A straightforward way to do so is to use a joint audio-text embedding model, such as the contrastive language-audio pre-training (CLAP) model, as a query encoder and train a TSE model using audio embeddings obtained from the ground-truth audio. The TSE model can then accept text queries at inference time by switching to the text encoder. While this approach should work if the audio and text embedding spaces in CLAP were well aligned, in practice, the embeddings have domain-specific information that causes the TSE model to overfit to audio queries. We investigate several methods to avoid overfitting and show that simple embedding-manipulation methods such as dropout can effectively alleviate this issue. Extensive experiments demonstrate that using audio-only data with embedding dropout is as effective as using text captions during training, and audio-only data can be effectively leveraged to improve text-queried TSE models., Comment: Submitted to ICASSP 2025

We present recent laboratory results demonstrating high-contrast coronagraphy for the future space-based large IR/Optical/Ultraviolet telescope recommended by the Decadal Survey. The High-contrast Imager for Complex Aperture Telescopes (HiCAT) testbed aims to implement a system-level hardware demonstration for segmented aperture coronagraphs with wavefront control. The telescope hardware simulator employs a segmented deformable mirror with 37 hexagonal segments that can be controlled in piston, tip, and tilt. In addition, two continuous deformable mirrors are used for high-order wavefront sensing and control. The low-order sensing subsystem includes a dedicated tip-tilt stage, a coronagraphic target acquisition camera, and a Zernike wavefront sensor that is used to measure and correct low-order aberration drifts. We explore the performance of a segmented aperture coronagraph both in static operations (limited by natural drifts and instabilities) and in dynamic operations (in the presence of artificial wavefront drifts added to the deformable mirrors), and discuss the estimation and control strategies used to reach and maintain the dark-zone contrast using our low-order wavefront sensing and control. We summarize experimental results that quantify the performance of the testbed in terms of contrast, inner/outer working angle and bandpass, and analyze limiting factors., Comment: 17 pages, 14 figures, SPIE Astronomical Telescopes + Instrumentation, 2022, Montr\'eal, Qu\'ebec, Canada

We present an experimental study on the freezing of alkane drops impacted on a liquid bath. More specifically, for drops of hexadecane and tetradecane on brine, we found a morphological transition of the solid between a flat disk and a cupped shape. We show that this transition depends mainly on melting temperature and thermal shock, and varies weakly with impact velocity. We observed that the impact dynamics does not depend on the thermal shock before the drop starts to solid ify, which allows a rationalization of the solid size by models established for impact without phase change. Finally, we show that the relevant timescale setting the onset of solidification is associated with the formation of a thin solid layer between the drop and the bath, a timescale much shorter than the total solidification time. These findings offer the possibility to collapse the data for both liquids in a single phase diagram.

In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for high-energy physics. In MPM, a model is trained to recover the missing elements of a set, a learning objective that requires no labels and can be applied directly to experimental data. We achieve significant performance improvements over previous work on MPM by addressing inefficiencies in the implementation and incorporating a more powerful decoder. We compare several pre-training tasks and introduce new reconstruction methods that utilize conditional generative models without data tokenization or discretization. We show that these new methods outperform the tokenized learning objective from the original MPM on a new test bed for foundation models for jets, which includes using a wide variety of downstream tasks relevant to jet physics, such as classification, secondary vertex finding, and track identification.

The task of content-type detection -- which entails identifying the data encoded in an arbitrary byte sequence -- is critical for operating systems, development, reverse engineering environments, and a variety of security applications. In this paper, we introduce Magika, a novel AI-powered content-type detection tool. Under the hood, Magika employs a deep learning model that can execute on a single CPU with just 1MB of memory to store the model's weights. We show that Magika achieves an average F1 score of 99% across over a hundred content types and a test set of more than 1M files, outperforming all existing content-type detection tools today. In order to foster adoption and improvements, we open source Magika under an Apache 2 license on GitHub and make our model and training pipeline publicly available. Our tool has already seen adoption by the Gmail email provider for attachment scanning, and it has been integrated with VirusTotal to aid with malware analysis. We note that this paper discusses the first iteration of Magika, and a more recent version already supports more than 200 content types. The interested reader can see the latest development on the Magika GitHub repository, available at https://github.com/google/magika.

In this work, we introduce an explicit expression for the inverse of the symmetric bilinear form of Virasoro Verma modules, the so-called Shapovalov form, in terms of singular vector operators and their conformal dimensions. Our proposed expression also determines the resolution of the identity for Verma modules of the Virasoro algebra, and can be thus employed in the computation of Virasoro conformal blocks via the sewing procedure., Comment: 8 pages, no figures

Variational Quantum Imaginary Time Evolution (VQITE) is a leading technique for ground state preparation on quantum computers. A significant computational challenge of VQITE is the determination of the quantum geometric tensor. We show that requiring the imaginary-time evolution to be correct only when projected onto a chosen set of operators allows to achieve a twofold reduction in circuit depth by bypassing fidelity estimations, and reduces measurement complexity from quadratic to linear in the number of parameters. We demonstrate by a simulation of the transverse-field Ising model that our algorithm achieves a several orders of magnitude improvement in the number of measurements required for the same accuracy.

We study various formulations of the boundary conditions for the Euler equations of gas dynamics from a mathematical and numerical point of view. In the case of one space dimension, we recall the classical results, based on an analysis of the linearized problem. Then we present a more recent formulation of the problem, which allows for nonlinear effects at the boundary of the study domain. This formulation fits naturally into a finite volume discretization, and we present a significant one-dimensional test case., Comment: in French language

We compare the high-harmonic-generation (HHG) yield driven by a mid-infrared laser in three organic ring-shaped molecules, calculated using time-dependent density-functional theory (TDDFT). We average the yield over the relative orientation of the molecules and the linearly-polarized, 1825 nm driving laser pulse in order to compare to experimental spectra obtained by Alharbi et al., Phys. Rev. A 92, 041801 (2015). We find that the raw TDDFT-calculated HHG yield in cyclohexane (CHA) is strongly overestimated compared to those of benzene and cyclohexene, and that this can be attributed to unphysically large contributions from CHA orbitals lying well below the highest-occupied molecular orbital. We show that implementing a simple orbital-resolved scaling factor, which corrects the yield of the tunneling ionization contribution to the first step in the HHG process, leads to much better comparisons with experimental results. Our results are encouraging for the use of TDDFT in systematic computations of HHG in large molecules., Comment: 11 pages, 8 figures

We develop a transformer-based conditional generative model for discrete point objects and their properties. We use it to build a model for populating cosmological simulations with gravitationally collapsed structures called dark matter halos. Specifically, we condition our model with dark matter distribution obtained from fast, approximate simulations to recover the correct three-dimensional positions and masses of individual halos. This leads to a first model that can recover the statistical properties of the halos at small scales to better than 3% level using an accelerated dark matter simulation. This trained model can then be applied to simulations with significantly larger volumes which would otherwise be computationally prohibitive with traditional simulations, and also provides a crucial missing link in making end-to-end differentiable cosmological simulations. The code, named GOTHAM (Generative cOnditional Transformer for Halo's Auto-regressive Modeling) is publicly available at \url{https://github.com/shivampcosmo/GOTHAM}., Comment: 6 pages, 2 figures. Accepted by the Structured Probabilistic Inference & Generative Modeling workshop of ICML 2024

In this paper, a definition of the gain and added noise of impedance matching networks for scanning microwave microscopy is given. This definition can be used to compare different impedance matching techniques independently of the instrument used to measure the S-parameter. As a demonstration, impedance matching devices consisting of a Beatty line, a tuner, and interferometric setups with and without amplifiers have been investigated. Measurement frequencies up to 28 GHz are used, and the maximal resulting gain found was 9504.7 per Siemens., Comment: IEEE Transactions on Instrumentation and Measurement (2024)

Relighting radiance fields is severely underconstrained for multi-view data, which is most often captured under a single illumination condition; It is especially hard for full scenes containing multiple objects. We introduce a method to create relightable radiance fields using such single-illumination data by exploiting priors extracted from 2D image diffusion models. We first fine-tune a 2D diffusion model on a multi-illumination dataset conditioned by light direction, allowing us to augment a single-illumination capture into a realistic -- but possibly inconsistent -- multi-illumination dataset from directly defined light directions. We use this augmented data to create a relightable radiance field represented by 3D Gaussian splats. To allow direct control of light direction for low-frequency lighting, we represent appearance with a multi-layer perceptron parameterized on light direction. To enforce multi-view consistency and overcome inaccuracies we optimize a per-image auxiliary feature vector. We show results on synthetic and real multi-view data under single illumination, demonstrating that our method successfully exploits 2D diffusion model priors to allow realistic 3D relighting for complete scenes. Project site https://repo-sam.inria.fr/fungraph/generative-radiance-field-relighting/, Comment: Project site https://repo-sam.inria.fr/fungraph/generative-radiance-field-relighting/

Under high perpendicular magnetic field and at low temperatures, graphene develops an insulating state at the charge neutrality point. This state, dubbed $\nu=0$, is due to the interplay between electronic interactions and the four-fold spin and valley degeneracies in the flat band formed by the $n=0$ Landau level. Determining the ground state of $\nu=0$, including its spin and valley polarization, has been a theoretical and experimental undertaking for almost two decades. Here, we present experiments probing the bulk thermal transport properties of monolayer graphene at $\nu=0$, which directly probe its ground state and collective excitations. We observe a vanishing bulk thermal transport, in contradiction with the expected ground state, predicted to have a finite thermal conductance even at very low temperature. Our result highlight the need for further investigations on the nature of $\nu=0$., Comment: Contains Supplementary Information

The dissemination of Large Language Models (LLMs), trained at scale, and endowed with powerful text-generating abilities has vastly increased the threats posed by generative AI technologies by reducing the cost of producing harmful, toxic, faked or forged content. In response, various proposals have been made to automatically discriminate artificially generated from human-written texts, typically framing the problem as a classification problem. Most approaches evaluate an input document by a well-chosen detector LLM, assuming that low-perplexity scores reliably signal machine-made content. As using one single detector can induce brittleness of performance, we instead consider several and derive a new, theoretically grounded approach to combine their respective strengths. Our experiments, using a variety of generator LLMs, suggest that our method effectively increases the robustness of detection., Comment: Preprint, work in progress

The detection of stellar variability often relies on the measurement of selected activity indicators such as coronal emission lines and non-thermal emissions. On the flip side, the effective stellar temperature is normally seen as one of the key fundamental parameters (with mass and radius) to understanding the basic physical nature of a star and its relation with its environment (e.g., planetary instellation). We present a novel approach for measuring disk-averaged temperature variations to sub-Kelvin accuracy inspired by algorithms developed for precision radial velocity. This framework uses the entire content of the spectrum, not just pre-identified lines, and can be applied to existing data obtained with high-resolution spectrographs. We demonstrate the framework by recovering the known rotation periods and temperature modulation of Barnard star and AU Mic in datasets obtained in the infrared with SPIRou at CHFT and at optical wavelengths on $\epsilon$ Eridani with HARPS at ESO 3.6-m telescope. We use observations of the transiting hot Jupiter HD189733\,b, obtained with SPIRou, to show that this method can unveil the minute temperature variation signature expected during the transit event, an effect analogous to the Rossiter-McLaughlin effect but in temperature space. This method is a powerful new tool for characterizing stellar activity, and in particular temperature and magnetic features at the surfaces of cool stars, affecting both precision radial velocity and transit spectroscopic observations. We demonstrate the method in the context of high-resolution spectroscopy but the method could be used at lower resolution., Comment: accepted for publication in AJ

When using unitary gate sequences, the growth in depth of many quantum circuits with output size poses significant obstacles to practical quantum computation. The quantum fan-out operation, which reduces the circuit depth of quantum algorithms such as the quantum Fourier transform and Shor's algorithm, is an example that can be realized in constant depth independent of the output size. Here, we demonstrate a quantum fan-out gate with real-time feedforward on up to four output qubits using a superconducting quantum processor. By performing quantum state tomography on the output states, we benchmark our gate with input states spanning the entire Bloch sphere. We decompose the output-state error into a set of independently characterized error contributions. We extrapolate our constant-depth circuit to offer a scaling advantage compared to the unitary fan-out sequence beyond 25 output qubits with feedforward control, or beyond 17 output qubits if the classical feedforward latency is negligible. Our work highlights the potential of mid-circuit measurements combined with real-time conditional operations to improve the efficiency of complex quantum algorithms.

We prove that $\Theta$-positive representations of fundamental groups of surfaces (possibly cusped or of infinite type) satisfy a collar lemma, and their associated cross-ratios are positive. As a consequence we deduce that $\Theta$-positive representations form closed subsets of the representation variety.

Ganymede's aurora are the product of complex interactions between its intrinsic magnetosphere and the surrounding Jovian plasma environment and can be used to derive both atmospheric composition and density. In this study, we analyzed a time-series of Ganymede's optical aurora taken with Keck I/HIRES during eclipse by Jupiter on 2021-06-08 UTC, one day after the Juno flyby of Ganymede. The data had sufficient signal-to-noise in individual 5-minute observations to allow for the first high cadence analysis of the spatial distribution of the aurora brightness and the ratio between the 630.0 and 557.7 nm disk-integrated auroral brightnesses -- a quantity diagnostic of the relative abundances of O, O$_2$ and H$_2$O in Ganymede's atmosphere. We found that the hemisphere closer to the centrifugal equator of Jupiter's magnetosphere (where electron number density is highest) was up to twice as bright as the opposing hemisphere. The dusk (trailing) hemisphere, subjected to the highest flux of charged particles from Jupiter's magnetosphere, was also consistently almost twice as bright as the dawn (leading) hemisphere. We modeled emission from simulated O$_2$ and H$_2$O atmospheres during eclipse and found that if Ganymede hosts an H$_2$O sublimation atmosphere in sunlight, it must collapse on a faster timescale than expected to explain its absence in our data given our current understanding of Ganymede's surface properties., Comment: 20 pages, 13 figures, 7 tables

The multicommodity capacitated fixed-charge network design problem has been extensively studied in the literature due to its wide range of applications. Despite the fact that many sophisticated solution methods exist today, finding high-quality solutions to large-scale instances remains challenging. In this paper, we explore how a data-driven approach can help improve upon the state of the art. By leveraging machine learning models, we attempt to reveal patterns hidden in the data that might be difficult to capture with traditional optimization methods. For scalability, we propose a prediction method where the machine learning model is called at the level of each arc of the graph. We take advantage of off-the-shelf models trained via supervised learning to predict near-optimal solutions. Our experimental results include an algorithm design analysis that compares various integration strategies of predictions within local search algorithms. We benchmark the ML-based approach with respect to the state-of-the-art heuristic for this problem. The findings indicate that our method can outperform the leading heuristic on sets of instances sampled from a uniform distribution.

In the Naive Bayes classification model the class conditional densities are estimated as the products of their marginal densities along the cardinal basis directions. We study the problem of obtaining an alternative basis for this factorisation with the objective of enhancing the discriminatory power of the associated classification model. We formulate the problem as a projection pursuit to find the optimal linear projection on which to perform classification. Optimality is determined based on the multinomial likelihood within which probabilities are estimated using the Naive Bayes factorisation of the projected data. Projection pursuit offers the added benefits of dimension reduction and visualisation. We discuss an intuitive connection with class conditional independent components analysis, and show how this is realised visually in practical applications. The performance of the resulting classification models is investigated using a large collection of (162) publicly available benchmark data sets and in comparison with relevant alternatives. We find that the proposed approach substantially outperforms other popular probabilistic discriminant analysis models and is highly competitive with Support Vector Machines.

The Interplanetary Network (IPN) emerges as the backbone for communication between various spacecraft and satellites orbiting distant celestial bodies. This paper introduces the Interplanetary Network Visualizer (IPN-V), a software platform that integrates interplanetary communications planning support, education, and outreach. IPN-V bridges the gap between the complexities of astrodynamics and network engineering by enabling the generation and assessment of dynamic, realistic network topologies that encapsulate the inherent challenges of space communication, such as time-evolving latencies and planetary occlusions. Leveraging the power of Unity 3D and C#, IPN-V provides a user-friendly 3D interface for the interactive visualization of interplanetary networks, incorporating contact tracing models to represent line-of-sight communication constraints accurately. IPN-V supports importing and exporting contact plans compatible with established space communication standards, including NASA's ION and HDTN formats. This paper delineates the conception, architecture, and operational framework of IPN-V while evaluating its performance metrics.

Neutrino flavor instabilities appear to be omnipresent in dense astrophysical environments, thus presenting a challenge to large-scale simulations of core-collapse supernovae and neutron star mergers (NSMs). Subgrid models offer a path forward, but require an accurate determination of the local outcome of such conversion phenomena. Focusing on "fast" instabilities, related to the existence of a crossing between neutrino and antineutrino angular distributions, we consider a range of analytical mixing schemes, including a new, fully three-dimensional one, and also introduce a new machine learning (ML) model. We compare the accuracy of these models with the results of several thousands of local dynamical calculations of neutrino evolution from the conditions extracted from classical NSM simulations. Our ML model shows good overall performance, but struggles to generalize to conditions from a NSM simulation not used for training. The multidimensional analytic model performs and generalizes even better, while other analytic models (which assume axisymmetric neutrino distributions) do not have reliably high performances, as they notably fail as expected to account for effects resulting from strong anisotropies. The ML and analytic subgrid models extensively tested here are both promising, with different computational requirements and sources of systematic errors., Comment: Comments welcome

Score-based diffusion methods provide a powerful strategy to solve image restoration tasks by flexibly combining a pre-trained foundational prior model with a likelihood function specified during test time. Such methods are predominantly derived from two stochastic processes: reversing Ornstein-Uhlenbeck, which underpins the celebrated denoising diffusion probabilistic models (DDPM) and denoising diffusion implicit models (DDIM), and the Langevin diffusion process. The solutions delivered by DDPM and DDIM are often remarkably realistic, but they are not always consistent with measurements because of likelihood intractability issues and the associated required approximations. Alternatively, using a Langevin process circumvents the intractable likelihood issue, but usually leads to restoration results of inferior quality and longer computing times. This paper presents a novel and highly computationally efficient image restoration method that carefully embeds a foundational DDPM denoiser within an empirical Bayesian Langevin algorithm, which jointly calibrates key model hyper-parameters as it estimates the model's posterior mean. Extensive experimental results on three canonical tasks (image deblurring, super-resolution, and inpainting) demonstrate that the proposed approach improves on state-of-the-art strategies both in image estimation accuracy and computing time., Comment: 24 pages

In the near-infrared wavelength regime, atmospheric turbulence fluctuates at a scale of a few milliseconds, and its precise control requires the use of extreme adaptive optics (XAO) systems equipped with fast and sensitive detectors operating at kHz speeds. The C-RED One cameras developed by First Light Imaging (FLI), based on SAPHIRA detectors made of HgCdTe e-APD array sensitive to 0.8-2.5 $\mu$m light, featuring a 320x256 pixels with 24 $\mu$m pitch, offering sub-electron readout noise and the ability to read subarrays, at frame-rates of up to few 10-kHz, are state-of-the-art for XAO wavefront sensing. The Observatory of Geneva purchased two C-RED One cameras identified as necessary for RISTRETTO (a proposed high-contrast high-resolution spectrograph for the VLT) and SAXO+ (an upgrade of the VLT/SPHERE XAO system) projects. We present a comprehensive characterization and comparative analysis of both the cameras. We present test results examining key noise contributors, including readout noise, detector bias, etc. And we also study their temporal variability. Additionally, we assess the conversion gain and the avalanche gain calibration of the detector. We also study the evolution some of these parameters over time., Comment: 14 pages, 16 figures, SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Temporal Action Detection (TAD), the task of localizing and classifying actions in untrimmed video, remains challenging due to action overlaps and variable action durations. Recent findings suggest that TAD performance is dependent on the structural design of transformers rather than on the self-attention mechanism. Building on this insight, we propose a refined feature extraction process through lightweight, yet effective operations. First, we employ a local branch that employs parallel convolutions with varying window sizes to capture both fine-grained and coarse-grained temporal features. This branch incorporates a gating mechanism to select the most relevant features. Second, we introduce a context branch that uses boundary frames as key-value pairs to analyze their relationship with the central frame through cross-attention. The proposed method captures temporal dependencies and improves contextual understanding. Evaluations of the gating mechanism and context branch on challenging datasets (THUMOS14 and EPIC-KITCHEN 100) show a consistent improvement over the baseline and existing methods., Comment: Accepted for publication at the ECCV 2024 ABAW Workshop

The stability and non-linearity of a commercial AH 2700A capacitance bridge were studied beyond its specified capabilities using the Thompson-Lampard Calculable Capacitor (TLCC) at LNE. The TLCC allows for continuous variation of measured capacitance between 0.4 pF and 1.2 pF with a resolution of 2 parts in $10^{7}$ and stability better than 1 part in $10^{9}$ over 2 days. The study aimed to determine root cause of the saw-tooth non-linearity pattern observed in the AH 2700A capacitance bridge. This pattern becomes apparent when the internal calibration is no longer valid, indicating deviations in the bridge circuit. Additionally, the dependence of capacitance non-linearity on various factors such as frequency and capacitance value are described. This work enables automatic calibration of the commercial bridge with an uncertainty of sub-ppm level and allows for quick evaluation of TLCC's non-linearity and monitoring of any changes over time through in-situ measurements.

Traditionally, inapproximability results for $\mathsf{Max\,k\,CSP\!-\!q}$ have been established using balanced $t$-wise independent distributions, which are related to orthogonal arrays of strength $t$. We contribute to the field by exploring such combinatorial designs in the context of the differential approximation measure. First, we establish a connection between the average differential ratio and orthogonal arrays. We deduce a differential approximation ratio of $1/q^k$ on $(k +1)$-partite instances of $\mathsf{k\,CSP\!-\!q}$, $\Omega(1/n^{k/2})$ on Boolean instances, $\Omega(1/n)$ when $k =2$, and $\Omega(1/\nu^{k -\lceil\log_{p^\kappa} k\rceil})$ when $k\geq 3$ and $q\geq 3$, where $p^\kappa$ is the smallest prime power greater than $q$. Secondly, by considering pairs of arrays related to balanced $k$-wise independence, we establish a reduction from $\mathsf{k\,CSP\!-\!q}$ to $\mathsf{k\,CSP\!-\!k}$ (with $q>k$), with an expansion factor of $1/(q -k/2)^k$ on the differential approximation guarantee. This, combined with the result of Yuri Nesterov, implies a lower differential approximability bound of $0.429/(q -1)^2$ for $\mathsf{2\,CSP\!-\!q}$. Finally, we prove that every Hamming ball with radius $k$ provides a $\Omega(1/n^k)$-approximation of the instance diameter. Our work highlights the utility of combinatorial designs in establishing approximation results., Comment: Preliminary versions of this work have been presented or published at the ISCO 2012, ISCO 2018 and IWOCA 2018 conferences

This paper focuses on studying a model for molecular motors responsible for the bending of the axoneme in the flagella of microorganisms. The model is a coupled system of partial differential equations inspired by J\"ulicher et al. or Camalet, incorporating two rows of molecular motors between microtubules filaments. Existence and uniqueness of a solution is proved, together with the presence of a supercritical Hopf bifurcation. Additionally, numerical simulations are provided to illustrate the theoretical results. A brief study on the generalization to N-rows is also included.

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

Fluid injections can induce aseismic slip, resulting in stress changes that may propagate faster than pore pressure diffusion, potentially triggering seismicity at significant distances from injection wells. Constraining the maximum extent of these aseismic ruptures is thus important for better delineating the influence zone of injections concerning their seismic hazard. Here we derive a scaling relation based on rupture physics for the maximum size of aseismic ruptures, accounting for fluid injections with arbitrary flow rate histories. Moreover, based on mounting evidence that the moment release during these operations is often predominantly aseismic, we derive a scaling relation for the maximum magnitude of aseismic slip events. Our theoretical predictions are consistent with observations over a broad spectrum of event sizes, from laboratory to real-world cases, indicating that fault-zone storativity, background stress state, and injected fluid volume are key determinants of the maximum size and magnitude of injection-induced slow slip events., Comment: 29 pages, 6 figures

RISTRETTO is a visible high-resolution spectrograph fed by an extreme adaptive optics (AO) system, to be proposed as a visitor instrument on ESO VLT. The main science goal of RISTRETTO is to pioneer the detection and atmospheric characterisation of exoplanets in reflected light, in particular the temperate rocky planet Proxima b. RISTRETTO will be able to measure albedos and detect atmospheric features in a number of exoplanets orbiting nearby stars for the first time. It will do so by combining a high-contrast AO system working at the diffraction limit of the telescope to a high-resolution spectrograph, via a 7-spaxel integral-field unit (IFU) feeding single-mode fibers. Further science cases for RISTRETTO include the study of accreting protoplanets such as PDS70b/c through spectrally-resolved H-alpha emission, and spatially-resolved studies of Solar System objects such as icy moons and the ice giants Uranus and Neptune. The project is in the manufacturing phase for the spectrograph sub-system, and the preliminary design phase for the AO front-end. Specific developments for RISTRETTO include a novel coronagraphic IFU combining a phase-induced amplitude apodizer (PIAA) to a 3D-printed microlens array feeding a bundle of single-mode fibers. It also features an XAO system with a dual wavefront sensor aiming at high robustness and sensitivity, including to pupil fragmentation. RISTRETTO is a pathfinder instrument in view of similar developments at the ELT, in particular the SCAO-IFU mode of ELT-ANDES and the future ELT-PCS instrument., Comment: 6 pages, 1 figure, SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

We study a non-reversible random walk advected by the symmetric simple exclusion process, so that the walk has a local drift of opposite sign when sitting atop an occupied or an empty site. We prove that the back-tracking probability of the walk exhibits a sharp transition as the density $\rho$ of particles in the underlying exclusion process varies across a critical density $\rho_c$. Our results imply that the speed $v=v(\rho)$ of the walk is a strictly monotone function and that the zero-speed regime is either absent or collapses to a single point, $\rho_c$, thus solving a conjecture of arXiv:1906.03167. The proof proceeds by exhibiting a quantitative monotonicity result for the speed of a truncated model, in which the environment is renewed after a finite time horizon $L$. The truncation parameter $L$ is subsequently pitted against the density $\rho$ to carry estimates over to the full model. Our strategy is somewhat reminiscent of certain techniques recently used to prove sharpness results in percolation problems. A key instrument is a combination of renormalisation arguments with refined couplings of environments at slightly different densities, which we develop in this article. Our results hold in fact in greater generality and apply to a class of environments with possibly egregious features, outside perturbative regimes., Comment: 69 pages, 7 figures

Controlling quantum spins using electric rather than magnetic fields promises significant architectural advantages for developing quantum technologies. In this context, spins in molecular nanomagnets offer tunability of spin-electric couplings (SEC) by rational chemical design. Here we demonstrate systematic control of SECs in a family of Mn(II)-containing molecules via chemical engineering. The trigonal bipyramidal (tbp) molecular structure with C3 symmetry leads to a significant molecular electric dipole moment that is directly connected to its magnetic anisotropy. The interplay between these two features gives rise to significant experimentally observed SECs, which can be rationalised by wavefunction theoretical calculations. Our findings guide strategies for the development of electrically controllable molecular spin qubits for quantum technologies., Comment: 8 pages, 3 figures, 1 table

Triton and Pluto are believed to share a common origin, both forming initially in the Kuiper Belt but Triton being later captured by Neptune. Both objects display similar sizes, densities, and atmospheric and surface ice composition, with the presence of volatile ices N2, CH4 and CO. Yet their appearance, including their surface albedo and ice distribution strongly differ. What can explain these different appearances? A first disparity is that Triton is experiencing significant tidal heating due to its orbit around Neptune, with subsequent resurfacing and a relatively flat surface, while Pluto is not tidally activated and displays a pronounced topography. Here we present long-term volatile transport simulations of Pluto and Triton, using the same initial conditions and volatile inventory, but with the known orbit and rotation of each object. The model reproduces, to first order, the observed volatile ice surface distribution on Pluto and Triton. Our results unambiguously demonstrate that obliquity is the main driver of the differences in surface appearance and in climate properties on Pluto and Triton, and give further support to the hypothesis that both objects had a common origin followed by a different dynamical history., Comment: 16 pages, 3 figures

Kagome materials with magnetic frustration in two-dimensional networks are known for their exotic properties, such as the anomalous Hall effect (AHE) with non-collinear spin textures. However, the effects of one-dimensional (1D) spin chains within these networks are less understood. Here, we report a distinctive AHE in the bilayer-distorted kagome material GdTi$_3$Bi$_4$, featuring 1D Gd zigzag spin chains, a one-third magnetization plateau, and two successive metamagnetic transitions. At these metamagnetic transitions, Hall resistivity shows abrupt jumps linked to the formation of stripe domain walls, while within the plateau, the absence of detectable domain walls suggests possible presence of skyrmion phase. Reducing the sample size to a few microns reveals additional Hall resistivity spikes, indicating domain wall skew scattering contributions. Magnetic atomistic spin dynamics simulations reveal that the magnetic textures at these transitions have reverse chirality, explaining the evolution of AHE and domain walls with fields. These results underscore the potential of magnetic and crystal symmetry interplay, and magnetic field-engineered spin chirality, for controlling domain walls and tuning transverse properties, advancing spintronic applications.

Recent experimental and theoretical results have demonstrated the possibility of accelerating electrons in the MeV range by focusing tightly a few-cycle laser beam in ambient air. Using Particle-In-Cell (PIC) simulations, this configuration is revisited within a more accurate modelling approach to analyze and optimize the mechanism responsible for electron acceleration. In particular, an analytical model for a linearly polarized tightly-focused ultrashort laser field is derived and coupled to a PIC code, allowing us to model the interaction of laser beams reflected by high-numerical aperture mirrors with laser-induced plasmas. A set of 3D PIC simulations is performed where the laser wavelength is varied from 800 nm to 7.0 $\mu$m while the normalized amplitude of the electric field is varied from $a_{0} = 3.6$ to $a_{0} = 7.0$. The preferential forward acceleration of electrons, as well as the analysis of the laser intensity evolution in the plasma and data on electron number density, confirm that the relativistic ponderomotive force is responsible for the acceleration. We also demonstrate that the electron kinetic energy reaches a maximum of $\approx1.6$ MeV when the central wavelength is of 2.5 $\mu$m., Comment: 16 pages, 17 figures

We present a discontinuous Galerkin-finite difference hybrid scheme that allows high-order shock capturing with the discontinuous Galerkin method for general relativistic magnetohydrodynamics in dynamical spacetimes. We present several optimizations and stability improvements to our algorithm that allow the hybrid method to successfully simulate single, rotating, and binary neutron stars. The hybrid method achieves the efficiency of discontinuous Galerkin methods throughout almost the entire spacetime during the inspiral phase, while being able to robustly capture shocks and resolve the stellar surfaces. We also use Cauchy-Characteristic evolution to compute the first gravitational waveforms at future null infinity from binary neutron star mergers. The simulations presented here are the first successful binary neutron star inspiral and merger simulations using discontinuous Galerkin methods., Comment: 31 pages, 8 figures, minor wording updates

SPHERE, operating at the VLT since 2014, is currently one of the high-contrast instruments with a higher performance. Its adaptive optics system, known as SAXO, will be upgraded to SAXO+, which features the addition of a second stage of adaptive optics. This stage will use a near-infrared pyramid wavefront sensor to record images of fainter exoplanets around redder stars. In this work, we compare the performance of SAXO and SAXO+. We look for the optimal values of the key system parameters of SAXO+ for various science cases and turbulence conditions. We performed numerical simulations using COMPASS, an end-to-end adaptive optics simulation tool. We simulated perfect coronagraph images of an on-axis point source, and we minimized the residual starlight intensity between 3 and 5 ${\lambda}/D$ as a performance criterion. The explored parameter space includes science cases, turbulence conditions, and key system parameters. In every science case and turbulence condition, SAXO+ reduces the residual starlight intensity inside the correction zone of the second stage by a factor of ten compared to SAXO. The optimal first stage gain is lower for SAXO+ than for SAXO alone. We quantified the gain in performance of SAXO+ when changing the second stage frequency from 2 kHz to 3 kHz, and we conclude that 2 kHz may be sufficient for most realistic conditions. We give the optimal first stage gain as well as the first and second stage frequencies for every seeing, coherence time, and science case. Finally, we find that a 2 ${\lambda_{\mathrm{WFS}}}/D$ pyramid modulation radius is a good trade-off between performance and robustness against varying turbulence conditions. This study shows that the future SAXO+ system will outperform the current SAXO system in all studied cases., Comment: 14 pages, 11 figures