Spatially-resolved images of debris disks are necessary to determine disk morphological properties and the scattering phase function (SPF) which quantifies the brightness of scattered light as a function of phase angle. Current high-contrast imaging instruments have successfully resolved several dozens of debris disks around other stars, but few studies have investigated trends in the scattered-light, resolved population of debris disks in a uniform and consistent manner. We have combined Karhunen-Loeve Image Projection (KLIP) with radiative-transfer disk forward modeling in order to obtain the highest quality image reductions and constrain disk morphological properties of eight debris disks imaged by the Gemini Planet Imager at H-band with a consistent and uniformly-applied approach. In describing the scattering properties of our models, we assume a common SPF informed from solar system dust scattering measurements and apply it to all systems. We identify a diverse range of dust density properties among the sample, including critical radius, radial width, and vertical width. We also identify radially narrow and vertically extended disks that may have resulted from substellar companion perturbations, along with a tentative positive trend in disk eccentricity with relative disk width. We also find that using a common SPF can achieve reasonable model fits for disks that are axisymmetric and asymmetric when fitting models to each side of the disk independently, suggesting that scattering behavior from debris disks may be similar to Solar System dust.

Inference from tabular data, collections of continuous and categorical variables organized into matrices, is a foundation for modern technology and science. Yet, in contrast to the explosive changes in the rest of AI, the best practice for these predictive tasks has been relatively unchanged and is still primarily based on variations of Gradient Boosted Decision Trees (GBDTs). Very recently, there has been renewed interest in developing state-of-the-art methods for tabular data based on recent developments in neural networks and feature learning methods. In this work, we introduce xRFM, an algorithm that combines feature learning kernel machines with a tree structure to both adapt to the local structure of the data and scale to essentially unlimited amounts of training data. We show that compared to $31$ other methods, including recently introduced tabular foundation models (TabPFNv2) and GBDTs, xRFM achieves best performance across $100$ regression datasets and is competitive to the best methods across $200$ classification datasets outperforming GBDTs. Additionally, xRFM provides interpretability natively through the Average Gradient Outer Product.

Addressing real-world optimization problems becomes particularly challenging when analytic objective functions or constraints are unavailable. While numerous studies have addressed the issue of unknown objectives, limited research has focused on scenarios where feasibility constraints are not given explicitly. Overlooking these constraints can lead to spurious solutions that are unrealistic in practice. To deal with such unknown constraints, we propose to perform optimization within the data manifold using diffusion models. To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model. Depending on the differentiability of the objective function, we propose two different sampling methods. For differentiable objectives, we propose a two-stage framework that begins with a guided diffusion process for warm-up, followed by a Langevin dynamics stage for further correction. For non-differentiable objectives, we propose an iterative importance sampling strategy using the diffusion model as the proposal distribution. Comprehensive experiments on a synthetic dataset, six real-world black-box optimization datasets, and a multi-objective molecule optimization dataset show that our method achieves better or comparable performance with previous state-of-the-art baselines.

Observations of exoplanet transits by small satellites have gained increasing attention for reducing detection biases. However, no unambiguous detection of an exoplanet has yet been demonstrated using optics with apertures smaller than 60 mm. Here, we investigated the detectability of exoplanet transits using the telescopic Optical Navigation Camera (ONC-T) onboard the Hayabusa2 spacecraft, which has an effective aperture of only 15 mm. We conducted transit observations of the hot Jupiters WASP-189 b and MASCARA-1 b, collecting data for ten and four events, respectively. The transit signal was detected with a signal-to-noise ratio (SNR) of 13 for WASP-189 b and 8 for MASCARA-1 b for each event. Stacking all events improved the SNR to 40 and 16, respectively. The transit mid-times of each event were measured with a precision of 6 minutes and were consistent with Transiting Exoplanet Survey Satellite (TESS) data to within 2 minutes. The planet-to-star radius ratio was determined with an absolute precision of 0.004 (6% relative) and agreed with TESS results to within 0.002 (3% relative). The recent ONC-T and TESS data enabled an update to the planetary ephemerides. We report a 4 sigma discrepancy between the updated orbital period of MASCARA-1 b and previously reported values. ONC-T sets a new record for the smallest-aperture instrument to detect an exoplanet transit from space, advancing the frontier of exoplanet science with miniature instrumentation. Our results suggest that optics as small as ONC-T may be capable of detecting transiting long-period Jupiters: a population that remains underrepresented in current surveys.

Child-centered long-form recordings are essential for studying early language development, but existing speech models trained on clean adult data perform poorly due to acoustic and linguistic differences. We introduce BabyHuBERT, the first self-supervised speech representation model trained on 13,000 hours of multilingual child-centered long-form recordings spanning over 40 languages. We evaluate BabyHuBERT on speaker segmentation, identifying when target children speak versus female adults, male adults, or other children -- a fundamental preprocessing step for analyzing naturalistic language experiences. BabyHuBERT achieves F1-scores from 52.1% to 74.4% across six diverse datasets, consistently outperforming W2V2-LL4300 (trained on English long-forms) and standard HuBERT (trained on clean adult speech). Notable improvements include 13.2 absolute F1 points over HuBERT on Vanuatu and 15.9 points on Solomon Islands corpora, demonstrating effectiveness on underrepresented languages. By sharing code and models, BabyHuBERT serves as a foundation model for child speech research, enabling fine-tuning on diverse downstream tasks.

We consider the problem of sampling distributions stemming from non-convex potentials with Unadjusted Langevin Algorithm (ULA). We prove the stability of the discrete-time ULA to drift approximations under the assumption that the potential is strongly convex at infinity. In many context, e.g. imaging inverse problems, potentials are non-convex and non-smooth. Proximal Stochastic Gradient Langevin Algorithm (PSGLA) is a popular algorithm to handle such potentials. It combines the forward-backward optimization algorithm with a ULA step. Our main stability result combined with properties of the Moreau envelope allows us to derive the first proof of convergence of the PSGLA for non-convex potentials. We empirically validate our methodology on synthetic data and in the context of imaging inverse problems. In particular, we observe that PSGLA exhibits faster convergence rates than Stochastic Gradient Langevin Algorithm for posterior sampling while preserving its restoration properties.

Model and hyperparameter selection are critical but challenging in machine learning, typically requiring expert intuition or expensive automated search. We investigate whether large language models (LLMs) can act as in-context meta-learners for this task. By converting each dataset into interpretable metadata, we prompt an LLM to recommend both model families and hyperparameters. We study two prompting strategies: (1) a zero-shot mode relying solely on pretrained knowledge, and (2) a meta-informed mode augmented with examples of models and their performance on past tasks. Across synthetic and real-world benchmarks, we show that LLMs can exploit dataset metadata to recommend competitive models and hyperparameters without search, and that improvements from meta-informed prompting demonstrate their capacity for in-context meta-learning. These results highlight a promising new role for LLMs as lightweight, general-purpose assistants for model selection and hyperparameter optimization.

Mitigation of the threat from airbursting asteroids requires an understanding of the potential risk they pose for the ground. How asteroids release their kinetic energy in the atmosphere is not well understood due to the rarity of significant impacts. Ordinary chondrites, in particular L chondrites, represent a frequent type of Earth-impacting asteroids. Here, we present the first comprehensive, space-to-lab characterization of an L chondrite impact. Small asteroid 2023 CX1 was detected in space and predicted to impact over Normandy, France, on 13 February 2023. Observations from multiple independent sensors and reduction techniques revealed an unusual but potentially high-risk fragmentation behavior. The nearly spherical 650 $\pm$ 160 kg (72 $\pm$ 6 cm diameter) asteroid catastrophically fragmented around 28 km altitude, releasing 98% of its total energy in a concentrated region of the atmosphere. The resulting shockwave was spherical, not cylindrical, and released more energy closer to the ground. This type of fragmentation increases the risk of significant damage at ground level. These results warrant consideration for a planetary defense strategy for cases where a >3-4 MPa dynamic pressure is expected, including planning for evacuation of areas beneath anticipated disruption locations.

Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare in a geometrically faithful way point clouds and more generally probability distributions. The wide adoption of OT into existing data analysis and machine learning pipelines is however plagued by several shortcomings. This includes its lack of robustness to outliers, its high computational costs, the need for a large number of samples in high dimension and the difficulty to handle data in distinct spaces. In this review, we detail several recently proposed approaches to mitigate these issues. We insist in particular on unbalanced OT, which compares arbitrary positive measures, not restricted to probability distributions (i.e. their total mass can vary). This generalization of OT makes it robust to outliers and missing data. The second workhorse of modern computational OT is entropic regularization, which leads to scalable algorithms while lowering the sample complexity in high dimension. The last point presented in this review is the Gromov-Wasserstein (GW) distance, which extends OT to cope with distributions belonging to different metric spaces. The main motivation for this review is to explain how unbalanced OT, entropic regularization and GW can work hand-in-hand to turn OT into efficient geometric loss functions for data sciences.

Efficient learning of quantum state properties is both a fundamental and practical problem in quantum information theory. Classical shadows have emerged as an efficient method for estimating properties of unknown quantum states, with rigorous statistical guarantees, by performing randomized measurement on a few number of copies. With the advent of photonic technologies, formulating efficient learning algorithms for such platforms comes out as a natural problem. Here, we introduce a classical shadow protocol for learning photonic quantum states via randomized passive linear optical transformations and photon-number measurement. We show that this scheme is efficient for a large class of observables of interest. We experimentally demonstrate our findings on a twelve-mode photonic integrated quantum processing unit. Our protocol allows for scalable learning of a wide range of photonic state properties and paves the way to applying the already rich variety of applications of classical shadows to photonic platforms.

LiDAR semantic segmentation is crucial for autonomous vehicles and mobile robots, requiring high accuracy and real-time processing, especially on resource-constrained embedded systems. Previous state-of-the-art methods often face a trade-off between accuracy and speed. Point-based and sparse convolution-based methods are accurate but slow due to the complexity of neighbor searching and 3D convolutions. Projection-based methods are faster but lose critical geometric information during the 2D projection. Additionally, many recent methods rely on test-time augmentation (TTA) to improve performance, which further slows the inference. Moreover, the pre-processing phase across all methods increases execution time and is demanding on embedded platforms. Therefore, we introduce HARP-NeXt, a high-speed and accurate LiDAR semantic segmentation network. We first propose a novel pre-processing methodology that significantly reduces computational overhead. Then, we design the Conv-SE-NeXt feature extraction block to efficiently capture representations without deep layer stacking per network stage. We also employ a multi-scale range-point fusion backbone that leverages information at multiple abstraction levels to preserve essential geometric details, thereby enhancing accuracy. Experiments on the nuScenes and SemanticKITTI benchmarks show that HARP-NeXt achieves a superior speed-accuracy trade-off compared to all state-of-the-art methods, and, without relying on ensemble models or TTA, is comparable to the top-ranked PTv3, while running 24$\times$ faster. The code is available at this https URL

We present new VLTI/GRAVITY astrometry and updated orbit fits for the directly imaged companions YSES 1 b and HR 2562 B, substellar objects straddling the planet-brown dwarf boundary. Using high-precision astrometry, radial velocity (RV) data, and proper motions, we derive revised orbital parameters with orbitize! arXiv:1910.01756. For YSES 1 b, the inclusion of GRAVITY astrometry and a relative radial velocity measurement from arXiv:2409.16660 overcomes the traditional challenge of constraining eccentricities for distant companions, enabling the first orbit fit and yielding a constrained eccentricity of 0.44 (0.20). This represents the first full orbit fit for the system. Additionally, we calculate a median line-of-sight stellar obliquity of 12 (+11, -8) degrees, providing further insight into the system's dynamical architecture. For HR 2562 B, our analysis agrees with arXiv:2302.04893, confirming a low-eccentricity orbit (0.34 (0.20)) and an inclination of 87 (1) degrees. We find HR 2562 B's orbit to be nearly coplanar with the debris disk, with a mutual inclination of 3.7 (0.3) degrees. For both YSES 1 b and HR 2562 B the lower eccentricities favor an in situ formation scenario over extreme scattering or cloud fragmentation.

We present a multiphase, resolved study of the galactic wind extending from the nearby starburst galaxy NGC 4666. For this we use VLT/MUSE observations from the GECKOS program and HI data from the WALLABY survey. We identify both ionised and HI gas in a biconical structure extending to at least $z\sim$8 kpc from the galaxy disk, with increasing velocity offsets above the midplane in both phases, consistent with a multiphase wind. The measured electron density, using [SII], differs significantly from standard expectations of galactic winds. We find electron density declines from the galaxy centre to $\sim2$ kpc, then rises again, remaining high ($\sim100-300$ cm$^{-3}$) out to $\sim$5 kpc. We find that HI dominates the mass loading. The total HI mass outflow rate (above $z~>2$ kpc) is between $5-13~M_{\odot}~\rm yr^{-1}$, accounting for uncertainties from disk-blurring and group interactions. The total ionised mass outflow rate (traced by H$\alpha$) is between $0.5~M_{\odot}~\rm yr^{-1}$ and $5~M_{\odot}~\rm yr^{-1}$, depending on $n_e(z)$ assumptions. From ALMA/ACA observations, we place an upper-limit on CO flux in the outflow which correlates to $\lesssim2.9~M_{\odot}~\rm yr^{-1}$. We also show that the entire outflow is not limited to the bicone, but a secondary starburst at the edge generates a more widespread outflow, which should be included in simulations. The cool gas in NGC 4666 wind has insufficient velocity to escape the halo of a galaxy of its mass, especially because most of the mass is present in the slower atomic phase. This strong biconical wind contributes to gas cycling around the galaxy.

The future space-based gravitational wave observatory LISA is expected to detect massive black hole binaries (MBHBs) with high signal-to-noise ratios (SNRs), ranging up to thousands. Such high-precision observations require accurate modeling of the detector response. However, current derivations of the response function neglect the motion of the spacecraft during light travel time, omitting velocity-dependent terms of order $\beta = v/c \sim 10^{-4}$. In this work, we derive the velocity-dependent corrections to the gravitational wave response. We analyze the contribution of the velocity-terms for MBHBs in the mass range $[10^6,10^8]\:\mathrm{M}_{\odot}$ using a modified version of the state-of-the-art response simulator lisagwresponse. We find that corrections introduce residual SNRs up to $\sim 2$ for the loudest events and fractional differences up to $0.04\%$, compared to lisagwresponse. While small, these effects are comparable to current waveform modeling uncertainties and imprint distinctive sky-localization signatures, making them potentially relevant for parameter estimation of high-mass MBHBs and simulation of mock datasets.

Scaling relations between galactic parameters represent key pieces of evidence for investigating the processes of galaxy formation and evolution. In most studies, these relations have been obtained for large portions of the galaxies (i.e., on kpc scales), but it is also important to evaluate these relations in smaller scales. In this work, we used optical data cubes of a subsample of nearby galaxies of the DIVING 3D survey. These allowed us to analyze the scaling relations involving stellar velocity dispersion, stellar population age, and stellar population metallicity at the nuclear and circumnuclear regions of galaxies. We detected correlations between the stellar velocity dispersion and the age, metallicity, and total stellar mass. These correlations are independent of galaxy inclinations, considering all morphological types, nuclear activity, and the presence or absence of galactic bars. We detected, for the first time, a correlation between the stellar velocity dispersion and stellar metallicity in the nuclear regions of galaxies. It is found to be qualitatively consistent with the well-known stellar mass-metallicity relation. We also noted that barred galaxies tend to show younger and less metal-rich stellar populations than unbarred galaxies in the central regions, which may be a consequence of the bar triggering star formation in the nuclear regions of these objects. Some active galactic nuclei (AGNs) in our sample are positioned above the observed correlation between stellar velocity dispersion and stellar population age, suggesting that their nuclear stellar populations are younger than expected. This may be a consequence of positive AGN feedback, triggering star formation. Conversely, starburst galaxies do not show nuclear stellar populations at ages over one billion years.

One of the first exoplanet hosts discovered thirty years ago, the star 55 Cnc has been constantly observed ever since. It is now known to host at least five planets with orbital periods ranging from 17 hours to 15 years. It is also one of the most extreme metal rich stars in the neighbourhood and it has a low-mass secondary star. In this article, we present data obtained at the Canada-France-Hawai'i Telescope with the SPIRou spectropolarimeter on both components of the 55 Cnc stellar system. We revisit the long-period radial-velocity signals of 55 Cnc A, with a focus on the role of the magnetic cycle, and propose the existence of a sixth planet candidate, whose period falls close to that of the magnetic cycle, or half of it. The other massive outer planet has a revised period of 13.15 years and a minimum mass of 3.8 MJup. Although some uncertainty remains on these outer planets, the characterization of the four inner planets is very robust through the combination of many different data sets, and all signals are consistent in the nIR and optical domains. In addition, the magnetic topology of the solar-type primary component of the system is observed by SPIRou at the minimum of its activity cycle, characterized by an amplitude ten times smaller than observed during its maximum in 2017. For the low-mass component 55 Cnc B, we report the discovery of two exoplanets in the system, with a period of 6.799+-0.0014 and 33.75+-0.04 days and a minimum mass of 3.5+-0.8 and 5.3+-1.4 MEarth, respectively. The secondary magnetic field is very weak and the current data set does not allow its precise characterization, setting an upper limit of 10 G. The system 55 Cnc stands out as the sixth binary system with planetary systems around both components, and the first one with non equal-mass stellar components.

We present an overview of the JWST GLIMPSE program, highlighting its survey design, primary science goals, gravitational lensing models, and first results. GLIMPSE provides ultra-deep JWST/NIRCam imaging across seven broadband filters (F090W, F115W, F200W, F277W, F356W, F444W) and two medium-band filters (F410M, F480M), with exposure times ranging from 20 to 40 hours per filter. This yields a 5$\sigma$ limiting magnitude of 30.9 AB (measured in a 0.2 arcsec diameter aperture). The field is supported by extensive ancillary data, including deep HST imaging from the Hubble Frontier Fields program, VLT/MUSE spectroscopy, and deep JWST/NIRSpec medium-resolution multi-object spectroscopy. Exploiting the strong gravitational lensing of the galaxy cluster Abell S1063, GLIMPSE probes intrinsic depths beyond 33 AB magnitudes and covers an effective source-plane area of approximately 4.4 arcmin$^2$ at $z \sim 6$. The program's central aim is to constrain the abundance of the faintest galaxies from $z \sim 6$ up to the highest redshifts, providing crucial benchmarks for galaxy formation models, which have so far been tested primarily on relatively bright systems. We present an initial sample of $\sim 540$ galaxy candidates identified at 6 < z < 16, with intrinsic UV magnitudes spanning $M_{\mathrm UV}$ = $-$20 to $-$12. This enables unprecedented constraints on the extreme faint end of the UV luminosity function at these epochs. In addition, GLIMPSE opens new windows for spatially resolved studies of star clusters in early galaxies and the detection and characterization of faint high-$z$ active galactic nuclei. This paper accompanies the first public data release, which includes reduced JWST and HST mosaics, photometric catalogs, and gravitational lensing models.