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With the expanding integration of infrared instruments in astronomical missions, accurate per-pixel flux estimation for near-infrared hybrid detectors has become critical to the success of these missions. Based on CPPM's involvement in both SVOM/Colibri and Euclid missions, this study introduces universally applicable methods and framework for characterizing IR hybrid detectors and decorrelating their intrinsic properties. The characterization framework, applied to the ALFA detector and \Euclid's H2RG, not only validates the proposed methods but also points out subtle behaviors inherent to each detector.

Realizing tunable functional materials with built-in nanoscale heat flow directionality represents a significant challenge with the potential to enable novel thermal management strategies. Here we use spatiotemporally-resolved thermoreflectance to visualize lateral thermal transport anisotropy in self-assembled supercrystals of anisotropic Au nanocrystals. Correlative electron and thermoreflectance microscopy reveal that heat predominantly flows along the long-axis of the anisotropic nanocrystals, and does so across grain boundaries and curved assemblies while voids disrupt heat flow. We finely control the anisotropy via the aspect ratio of constituent nanorods, and it exceeds the aspect ratio for nano-bipyramid supercrystals and certain nanorod arrangements. Finite element simulations and effective medium modeling rationalize the emergent anisotropic behavior in terms of a simple series resistance model, further providing a framework for estimating thermal anisotropy as a function of material and structural parameters. Self-assembly of colloidal nanocrystals promises a novel route to direct heat flow in a wide range of applications that utilize this important class of materials.

Hidden Markov Models (HMM) model a sequence of observations that are dependent on a hidden (or latent) state that follow a Markov chain. These models are widely used in diverse fields including ecology, speech recognition, and genetics.Parameter estimation in HMM is typically performed using the Baum-Welch algorithm, a special case of the Expectation-Maximisation (EM) algorithm. While this method guarantee the convergence to a local maximum, its convergence rates is usually slow.Alternative methods, such as the direct maximisation of the likelihood using quasi-Newton methods (such as L-BFGS-B) can offer faster convergence but can be more complicated to implement due to challenges to deal with the presence of bounds on the space of parameters.We propose a novel hybrid algorithm, QNEM, that combines the Baum-Welch and the quasi-Newton algorithms. QNEM aims to leverage the strength of both algorithms by switching from one method to the other based on the convexity of the likelihood function.We conducted a comparative analysis between QNEM, the Baum-Welch algorithm, an EM acceleration algorithm called SQUAREM (Varadhan, 2008, Scand J Statist), and the L-BFGS-B quasi-Newton method by applying these algorithms to four examples built on different models. We estimated the parameters of each model using the different algorithms and evaluated their performances.Our results show that the best-performing algorithm depends on the model considered. QNEM performs well overall, always being faster or equivalent to L-BFGS-B. The Baum-Welch and SQUAREM algorithms are faster than the quasi-Newton and QNEM algorithms in certain scenarios with multiple optimum. In conclusion, QNEM offers a promising alternative to existing algorithms.

Precise wave manipulation has undoubtedly forged the technological landscape we thrive in today. Although our understanding of wave phenomena has come a long way since the earliest observations of desert dunes or ocean waves, the unimpeded development of mathematics has enabled ever more complex and exotic physical phenomena to be comprehensively described. Here, we take wave manipulation a step further by introducing an unprecedented synthetic acoustic crystal capable of realizing simultaneous linear broadband conduction and nonlinear topological insulation, depicting a robust amplitude-dependent mode localized deep within - i.e. an amplitude-driven topological confinement of sound. The latter is achieved by means of an open acoustic waveguide lined with a chain of nonlocally and nonlinearly coupled active electroacoustic resonators. Starting from a comprehensive topological model for classical waves, we demonstrate that different topological regimes can be accessed by increasing driving amplitude and that topological robustness against coupling disorder is a direct consequence of symmetric and simultaneous response between coupled resonators. Theoretical predictions are validated by a fully programmable experimental apparatus capable of realizing the real-time manipulation of metacrystal properties. In all, our results provide a solid foundation for future research in the design and manipulation of classical waves in artificial materials involving nonlinearity, nonlocality, and non-hermiticity.

Flow cytometry is a cornerstone technique in medical and biological research, providing crucial information about cell size and granularity through forward scatter (FSC) and side scatter (SSC) signals. Despite its widespread use, the precise relationship between these scatter signals and corresponding microscopic images remains underexplored. Here, we investigate this intrinsic relationship by utilizing scattering theory and holotomography, a three-dimensional quantitative phase imaging (QPI) technique. We demonstrate the extraction of FSC and SSC signals from individual, unlabeled cells by analyzing their three-dimensional refractive index distributions obtained through holotomography. Additionally, we introduce a method for digitally windowing SSC signals to facilitate effective segmentation and morphology-based cell type classification. Our approach bridges the gap between flow cytometry and microscopic imaging, offering a new perspective on analyzing cellular characteristics with high accuracy and without the need for labeling.

Mathematical modeling in systems toxicology enables a comprehensive understanding of the effects of pharmaceutical substances on cardiac health. However, the complexity of these models limits their widespread application in early drug discovery. In this paper, we introduce a novel approach to solving parameterized models of cardiac action potentials by combining meta-learning techniques with Systems Biology-Informed Neural Networks (SBINNs). The proposed method, HyperSBINN, effectively addresses the challenge of predicting the effects of various compounds at different concentrations on cardiac action potentials, outperforming traditional differential equation solvers in speed. Our model efficiently handles scenarios with limited data and complex parameterized differential equations. The HyperSBINN model demonstrates robust performance in predicting APD90 values, indicating its potential as a reliable tool for modeling cardiac electrophysiology and aiding in preclinical drug development. This framework represents an advancement in computational modeling, offering a scalable and efficient solution for simulating and understanding complex biological systems.

In this paper, we detail the high-performance implementation of our spaceborne radar simulator for satellite oceanography. Our software simulates the sea surface and the signal to imitate, as far as possible, the measurement process, starting from its lowest level mechanisms. In this perspective, raw data are computed as the sum of many illuminated scatterers, whose time-evolving properties are related to the surface roughness, topography, and kinematics. To achieve efficient performance, we intensively use GPU computing. Moreover, we propose a fast simulation mode based on the assumption that the instantaneous Doppler spectrum within a range gate varies on a timescale significantly larger than the PRI. The sea surface can then be updated at a frequency much smaller than the PRF, drastically reducing the computational cost. When the surface is updated, Doppler spectra are computed for all range gates. Signals segments are then obtained through 1D inverse Fourier transforms and pondered to ensure a smooth time evolution between surface updates. We validate this fast simulation mode with a radar altimeter simulation case of the Sentinel-3 SRAL instrument, showing that simulated raw data can be focused and retrieved using state-of-the-art algorithms. Finally, we show that, using a modest hardware configuration, our simulator can generate enough data in one day to compute the SWH and SSH spectra of a scene. This demonstrate that we achieved an important state-of-the-art speed-up.

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models (conv5-FC3, ResNet and SECNN) as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM/QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the conv5-FC3 network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:016

We show the existence and uniqueness of a continuous solution to a path-dependent volatility model introduced by Guyon and Lekeufack (2023) to model the price of an equity index and its spot volatility. The considered model for the trend and activity features can be written as a Stochastic Volterra Equation (SVE) with non-convolutional and non-bounded kernels as well as non-Lipschitz coefficients. We first prove the existence and uniqueness of a solution to the SVE under integrability and regularity assumptions on the two kernels and under a condition on the second kernel weighting the past squared returns which ensures that the activity feature is bounded from below by a positive constant. Then, assuming in addition that the kernel weighting the past returns is of exponential type and that an inequality relating the logarithmic derivatives of the two kernels with respect to their second variables is satisfied, we show the positivity of the volatility process which is obtained as a non-linear function of the SVE's solution. We show numerically that the choice of an exponential kernel for the kernel weighting the past returns has little impact on the quality of model calibration compared to other choices and the inequality involving the logarithmic derivatives is satisfied by the calibrated kernels. These results extend those of Nutz and Valdevenito (2023).

Die studies are fundamental to quantifying ancient monetary production, providing insights into the relationship between coinage, politics, and history. The process requires tedious manual work, which limits the size of the corpora that can be studied. Few works have attempted to automate this task, and none have been properly released and evaluated from a computer vision perspective. We propose a fully automatic approach that introduces several innovations compared to previous methods. We rely on fast and robust local descriptors matching that is set automatically. Second, the core of our proposal is a clustering-based approach that uses an intrinsic metric (that does not need the ground truth labels) to determine its critical hyper-parameters. We validate the approach on two corpora of Greek coins, propose an automatic implementation and evaluation of previous baselines, and show that our approach significantly outperforms them., Comment: code: https://cea-list-lasti.github.io/projects/studies/studies.html

We report the discovery and characterization of a small planet, TOI-1408 c, on a 2.2-day orbit located interior to a previously known hot Jupiter, TOI-1408 b ($P=4.42$ d, $M=1.86\pm0.02\,M_\mathrm{Jup}$, $R=2.4\pm0.5\,R_\mathrm{Jup}$) that exhibits grazing transits. The two planets are near 2:1 period commensurability, resulting in significant transit timing variations (TTVs) for both planets and transit duration variations (TDVs) for the inner planet. The TTV amplitude for TOI-1408 c is 15% of the planet's orbital period, marking the largest TTV amplitude relative to the orbital period measured to date. Photodynamical modeling of ground-based radial velocity (RV) observations and transit light curves obtained with the Transiting Exoplanet Survey Satellite (TESS) and ground-based facilities leads to an inner planet radius of $2.22\pm0.06\,R_\oplus$ and mass of $7.6\pm0.2\,M_\oplus$ that locates the planet into the Sub-Neptune regime. The proximity to the 2:1 period commensurability leads to the libration of the resonant argument of the inner planet. The RV measurements support the existence of a third body with an orbital period of several thousand days. This discovery places the system among the rare systems featuring a hot Jupiter accompanied by an inner low-mass planet., Comment: Accepted to ApJL, 17 pages, 6 figures, 4 tables

3D object detection plays a crucial role in various applications such as autonomous vehicles, robotics and augmented reality. However, training 3D detectors requires a costly precise annotation, which is a hindrance to scaling annotation to large datasets. To address this challenge, we propose a weakly supervised 3D annotator that relies solely on 2D bounding box annotations from images, along with size priors. One major problem is that supervising a 3D detection model using only 2D boxes is not reliable due to ambiguities between different 3D poses and their identical 2D projection. We introduce a simple yet effective and generic solution: we build 3D proxy objects with annotations by construction and add them to the training dataset. Our method requires only size priors to adapt to new classes. To better align 2D supervision with 3D detection, our method ensures depth invariance with a novel expression of the 2D losses. Finally, to detect more challenging instances, our annotator follows an offline pseudo-labelling scheme which gradually improves its 3D pseudo-labels. Extensive experiments on the KITTI dataset demonstrate that our method not only performs on-par or above previous works on the Car category, but also achieves performance close to fully supervised methods on more challenging classes. We further demonstrate the effectiveness and robustness of our method by being the first to experiment on the more challenging nuScenes dataset. We additionally propose a setting where weak labels are obtained from a 2D detector pre-trained on MS-COCO instead of human annotations.

We study the boundary behavior of the Kobayashi-Royden metric and the Kobayashi hyperbolicity of domains in Riemannian manifolds. As an application, we prove a Fatou type theorem on the existence, almost everywhere, of non tangential limits for bounded conformal harmonic immersed discs., Comment: 12 pages

This paper introduces a computational framework designed to delineate gender distribution biases in topics covered by French TV and radio news. We transcribe a dataset of 11.7k hours, broadcasted in 2023 on 21 French channels. A Large Language Model (LLM) is used in few-shot conversation mode to obtain a topic classification on those transcriptions. Using the generated LLM annotations, we explore the finetuning of a specialized smaller classification model, to reduce the computational cost. To evaluate the performances of these models, we construct and annotate a dataset of 804 dialogues. This dataset is made available free of charge for research purposes. We show that women are notably underrepresented in subjects such as sports, politics and conflicts. Conversely, on topics such as weather, commercials and health, women have more speaking time than their overall average across all subjects. We also observe representations differences between private and public service channels., Comment: Accepted to Interspeech 2024

The rapid rise of air pollution events necessitates accurate, real-time monitoring for informed mitigation strategies. Data Assimilation (DA) methods provide promising solutions, but their effectiveness hinges heavily on optimal measurement locations. This paper presents a novel approach for air quality mapping where autonomous drones, guided by a collaborative multi-agent reinforcement learning (MARL) framework, act as airborne detectives. Ditching the limitations of static sensor networks, the drones engage in a synergistic interaction, adapting their flight paths in real time to gather optimal data for Data Assimilation (DA). Our approach employs a tailored reward function with dynamic credit assignment, enabling drones to prioritize informative measurements without requiring unavailable ground truth data, making it practical for real-world deployments. Extensive experiments using a real-world dataset demonstrate that our solution achieves significantly improved pollution estimates, even with limited drone resources or limited prior knowledge of the pollution plume. Beyond air quality, this solution unlocks possibilities for tackling diverse environmental challenges like wildfire detection and management through scalable and autonomous drone cooperation., Comment: 8 pages, 4 figures

This paper describes the submissions of team TalTech-IRIT-LIS to the DISPLACE 2024 challenge. Our team participated in the speaker diarization and language diarization tracks of the challenge. In the speaker diarization track, our best submission was an ensemble of systems based on the pyannote.audio speaker diarization pipeline utilizing powerset training and our recently proposed PixIT method that performs joint diarization and speech separation. We improve upon PixIT by using the separation outputs for speaker embedding extraction. Our ensemble achieved a diarization error rate of 27.1% on the evaluation dataset. In the language diarization track, we fine-tuned a pre-trained Wav2Vec2-BERT language embedding model on in-domain data, and clustered short segments using AHC and VBx, based on similarity scores from LDA/PLDA. This led to a language diarization error rate of 27.6% on the evaluation data. Both results were ranked first in their respective challenge tracks., Comment: accepted at Interspeech 2024

Retrieval-Augmented Generation (RAG) allows overcoming the limited knowledge of LLMs by extending the input with external information. As a consequence, the contextual inputs to the model become much longer which slows down decoding time directly translating to the time a user has to wait for an answer. We address this challenge by presenting COCOM, an effective context compression method, reducing long contexts to only a handful of Context Embeddings speeding up the generation time by a large margin. Our method allows for different compression rates trading off decoding time for answer quality. Compared to earlier methods, COCOM allows for handling multiple contexts more effectively, significantly reducing decoding time for long inputs. Our method demonstrates a speed-up of up to 5.69 $\times$ while achieving higher performance compared to existing efficient context compression methods., Comment: 10 pages

How can technical advice on operating experience feedback be managed efficiently in an organization that has never used knowledge engineering techniques and methods? This article explains how an industrial company in the nuclear and defense sectors adopted such an approach, adapted to its "TA KM" organizational context and falls within the ISO30401 framework, to build a complete system with a "SARBACANES" application to support its business processes and perpetuate its know-how and expertise in a knowledge base. Over and above the classic transfer of knowledge between experts and business specialists, SARBACANES also reveals the ability of this type of engineering to deliver multi-functional operation. Modeling was accelerated by the use of a tool adapted to this type of operation: the Ardans Knowledge Maker platform., Comment: in French language. 35es Journ{\'e}es francophones d'Ing{\'e}nierie des Connaissances (IC 2024) @ Plate-Forme Intelligence Artificielle (PFIA 2024), Association Fran\c{c}aise pour l'Intelligence Artificielle; Laboratoire L3i La Rochelle Universit{\'e}, Jul 2024, La Rochelle, France

We present an experimental study on the perturbed evolution of Korteweg-deVries soliton gases in a weakly dissipative nonlinear electrical transmission line. The system's dynamics reveal that an initially dense, fully randomized, soliton gas evolves into a coherent macroscopic state identified as a soliton condensate through nonlinear spectral analysis. The emergence of the soliton condensate is driven by the spatial rearrangement of the systems's eigenmodes and by the proliferation of new solitonic states due to nonadiabatic effects, a phenomenon not accounted for by the existing hydrodynamic theories.

The accelerating growth of the Internet of Things (IoT) and its integration with Low-Earth Orbit (LEO) satellites demand efficient, reliable, and scalable communication protocols. Among these, the Long-Range Frequency Hopping Spread Spectrum (LR-FHSS) modulation, tailored for LEO satellite IoT communications, sparks keen interest. This work presents a joint approach to enhancing the scalability of LR-FHSS, addressing the demand for massive connectivity. We deepen into Frequency Hopping Sequence (FHS) mechanisms within LR-FHSS, spotlighting the potential of leveraging Wide-Gap sequences. Concurrently, we introduce two novel demodulator allocation strategies, namely, ``Early-Decode" and ``Early-Drop," to optimize the utilization of LoRa-specific gateway decoding resources. Our research further validates these findings with extensive simulations, offering a comprehensive look into the future potential of LR-FHSS scalability in IoT settings.

Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge. In response to the recent popularity of generative LLMs, many RAG approaches have been proposed, which involve an intricate number of different configurations such as evaluation datasets, collections, metrics, retrievers, and LLMs. Inconsistent benchmarking poses a major challenge in comparing approaches and understanding the impact of each component in the pipeline. In this work, we study best practices that lay the groundwork for a systematic evaluation of RAG and present BERGEN, an end-to-end library for reproducible research standardizing RAG experiments. In an extensive study focusing on QA, we benchmark different state-of-the-art retrievers, rerankers, and LLMs. Additionally, we analyze existing RAG metrics and datasets. Our open-source library BERGEN is available under \url{https://github.com/naver/bergen}., Comment: 29 pages

Retrieval-augmented generation (RAG) has recently emerged as a promising solution for incorporating up-to-date or domain-specific knowledge into large language models (LLMs) and improving LLM factuality, but is predominantly studied in English-only settings. In this work, we consider RAG in the multilingual setting (mRAG), i.e. with user queries and the datastore in 13 languages, and investigate which components and with which adjustments are needed to build a well-performing mRAG pipeline, that can be used as a strong baseline in future works. Our findings highlight that despite the availability of high-quality off-the-shelf multilingual retrievers and generators, task-specific prompt engineering is needed to enable generation in user languages. Moreover, current evaluation metrics need adjustments for multilingual setting, to account for variations in spelling named entities. The main limitations to be addressed in future works include frequent code-switching in non-Latin alphabet languages, occasional fluency errors, wrong reading of the provided documents, or irrelevant retrieval. We release the code for the resulting mRAG baseline pipeline at https://github.com/naver/bergen.

Face recognition and verification are two computer vision tasks whose performance has progressed with the introduction of deep representations. However, ethical, legal, and technical challenges due to the sensitive character of face data and biases in real training datasets hinder their development. Generative AI addresses privacy by creating fictitious identities, but fairness problems persist. We promote fairness by introducing a demographic attributes balancing mechanism in generated training datasets. We experiment with an existing real dataset, three generated training datasets, and the balanced versions of a diffusion-based dataset. We propose a comprehensive evaluation that considers accuracy and fairness equally and includes a rigorous regression-based statistical analysis of attributes. The analysis shows that balancing reduces demographic unfairness. Also, a performance gap persists despite generation becoming more accurate with time. The proposed balancing method and comprehensive verification evaluation promote fairer and transparent face recognition and verification.

In a general fair division model with transferable utilities we discuss endogenous lower and upper guarantees on individual shares of benefits or costs. Like the more familiar exogenous bounds on individual shares described by an outside option or a stand alone utility, these guarantees depend on my type but not on others' types, only on their number and the range of types. Keeping the range from worst share to best share as narrow as permitted by the physical constraints of the model still leaves a large menu of tight guarantee functions. We describe in detail these design options in several iconic problems where each tight pair of guarantees has a clear normative meaning: the allocation of indivisible goods or costly chores, cost sharing of a public facility and the exploitation of a commons with substitute or complementary inputs. The corresponding benefit or cost functions are all sub- or super-modular, and for this class we characterise the set of minimal upper and maximal lower guarantees in all two agent problems.

Methylglyoxal (MGO) is an endogenous, highly reactive dicarbonyl metabolite generated under hyperglycaemic conditions. MGO plays a role in developing pathophysiological conditions, including diabetic cardiomyopathy. However, the mechanisms involved and the molecular targets of MGO in the heart have not been elucidated. In this work, we studied the exposure-related effects of MGO on cardiac function in an isolated perfused rat heart ex vivo model. The effect of MGO on calcium homeostasis in cardiomyocytes was studied in vitro by the fluorescence indicator of intracellular calcium Fluo-4. We demonstrated that MGO induced cardiac dysfunction, both in contractility and diastolic function. In rat heart, the effects of MGO treatment were significantly limited by aminoguanidine, a scavenger of MGO, ruthenium red, a general cation channel blocker, and verapamil, an L-type voltage-dependent calcium channel blocker, demonstrating that this dysfunction involved alteration of calcium regulation. MGO induced a significant concentration-dependent increase of intracellular calcium in neonatal rat cardiomyocytes, which was limited by aminoguanidine and verapamil. These results suggest that the functionality of various calcium channels is altered by MGO, particularly the L-type calcium channel, thus explaining its cardiac toxicity. Therefore, MGO could participate in the development of diabetic cardiomyopathy through its impact on calcium homeostasis in cardiac cells.

Efficiently quantifying predictive uncertainty in medical images remains a challenge. While Bayesian neural networks (BNN) offer predictive uncertainty, they require substantial computational resources to train. Although Bayesian approximations such as ensembles have shown promise, they still suffer from high training and inference costs. Existing approaches mainly address the costs of BNN inference post-training, with little focus on improving training efficiency and reducing parameter complexity. This study introduces a training procedure for a sparse (partial) Bayesian network. Our method selectively assigns a subset of parameters as Bayesian by assessing their deterministic saliency through gradient sensitivity analysis. The resulting network combines deterministic and Bayesian parameters, exploiting the advantages of both representations to achieve high task-specific performance and minimize predictive uncertainty. Demonstrated on multi-label ChestMNIST for classification and ISIC, LIDC-IDRI for segmentation, our approach achieves competitive performance and predictive uncertainty estimation by reducing Bayesian parameters by over 95\%, significantly reducing computational expenses compared to fully Bayesian and ensemble methods.

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

To obtain high-resolution images of subsurface structures from seismic data, seismic imaging techniques such as Full Waveform Inversion (FWI) serve as crucial tools. However, FWI involves solving a nonlinear and often non-unique inverse problem, presenting challenges such as local minima trapping and inadequate handling of inherent uncertainties. In addressing these challenges, we propose leveraging deep generative models as the prior distribution of geophysical parameters for stochastic Bayesian inversion. This approach integrates the adjoint state gradient for efficient back-propagation from the numerical solution of partial differential equations. Additionally, we introduce explicit and implicit variational Bayesian inference methods. The explicit method computes variational distribution density using a normalizing flow-based neural network, enabling computation of the Bayesian posterior of parameters. Conversely, the implicit method employs an inference network attached to a pretrained generative model to estimate density, incorporating an entropy estimator. Furthermore, we also experimented with the Stein Variational Gradient Descent (SVGD) method as another variational inference technique, using particles. We compare these variational Bayesian inference methods with conventional Markov chain Monte Carlo (McMC) sampling. Each method is able to quantify uncertainties and to generate seismic data-conditioned realizations of subsurface geophysical parameters. This framework provides insights into subsurface structures while accounting for inherent uncertainties.

New JWST/NIRCam wide-field slitless spectroscopy provides redshifts for four z>8 galaxies located behind the lensing cluster MACS J0416.1-2403. Two of them, "Y1" and "JD", have previously reported spectroscopic redshifts based on ALMA measurements of [OIII] 88 $\mu$m and/or [CII] 157.7 $\mu$m lines. Y1 is a merging system of three components, and the existing redshift z=8.31 is confirmed. However, JD is at z=8.34 instead of the previously claimed z=9.28. JD's close companion, "JD-N", which was a previously discovered z>8 candidate, is now identified at the same redshift as JD. JD and JD-N form an interacting pair. A new candidate at z>8, "f090d_018", is also confirmed and is at z=8.49. These four objects are likely part of an overdensity that signposts a large structure extending ~165 kpc in projected distance and ~48.7 Mpc in radial distance. They are magnified by less than one magnitude and have intrinsic $M_{UV}$ ranging from -19.57 to -20.83 mag. Their spectral energy distributions show that the galaxies are all very young with ages ~ 4-18 Myr and stellar masses about $10^{7-8}$ ${\rm M_\odot}$. These infant galaxies have very different star formation rates ranging from a few to over a hundred $\rm{M_\odot}$ yr$^{-1}$, but only two of them (JD and f090d_018) have blue rest-frame UV slopes $\beta<-2.0$ indicative of a high Lyman-continuum photon escape fraction that could contribute significantly to the cosmic hydrogen-reionizing background. Interestingly, these two galaxies are the least massive and least active ones among the four. The other two systems have much flatter UV slopes largely because of their high dust extinction ($A_{\rm V}$=0.9-1.0 mag). Their much lower indicated escape fractions show that even very young, actively star-forming galaxies can have negligible contribution to reionization when they quickly form dust throughout their bodies., Comment: 18 pages, 6 figures, after addressing the referee report

Deep learning models for plant species identification rely on large annotated datasets. The PlantNet system enables global data collection by allowing users to upload and annotate plant observations, leading to noisy labels due to diverse user skills. Achieving consensus is crucial for training, but the vast scale of collected data makes traditional label aggregation strategies challenging. Existing methods either retain all observations, resulting in noisy training data or selectively keep those with sufficient votes, discarding valuable information. Additionally, as many species are rarely observed, user expertise can not be evaluated as an inter-user agreement: otherwise, botanical experts would have a lower weight in the AI training step than the average user. Our proposed label aggregation strategy aims to cooperatively train plant identification AI models. This strategy estimates user expertise as a trust score per user based on their ability to identify plant species from crowdsourced data. The trust score is recursively estimated from correctly identified species given the current estimated labels. This interpretable score exploits botanical experts' knowledge and the heterogeneity of users. Subsequently, our strategy removes unreliable observations but retains those with limited trusted annotations, unlike other approaches. We evaluate PlantNet's strategy on a released large subset of the PlantNet database focused on European flora, comprising over 6M observations and 800K users. We demonstrate that estimating users' skills based on the diversity of their expertise enhances labeling performance. Our findings emphasize the synergy of human annotation and data filtering in improving AI performance for a refined dataset. We explore incorporating AI-based votes alongside human input. This can further enhance human-AI interactions to detect unreliable observations.

In this paper we describe our search for galaxy protocluster candidates at $4.5< z < 10$ and explore the environmental and physical properties of their member galaxies identified through JWST wide-field surveys within the CEERS, JADES, and PEARLS NEP-TDF fields. Combining with HST data, we identify 2948 robust $z>4.5$ candidates within an area of 185.4 arcmin$^2$. We determine nearest neighbour statistics and galaxy environments. We find that high-$z$ galaxies in overdense environments exhibit higher star formation activity compared to those in underdense regions. Galaxies in dense environments have a slightly increased SFR at a given mass compared with galaxies in the lower density environments. At the high mass end we also find a gradual flattening of the $M_{\star}$-SFR slope. We find that galaxies in high-density regions often have redder UV slopes than those in low-density regions, suggesting more dust extinction, weaker Lyman-alpha emission and / or a higher damped Lyman-alpha absorption. We also find that the mass-size relation remains consistent and statistically similar across all environments. Furthermore, we quantitatively assess the probability of a galaxy belonging to a protocluster candidate. In total, we identified 26 overdensities at $z=5-7$ and estimate their dark matter halo masses. We find that all protocluster candidates could evolve into clusters with $M_{\rm halo} > 10^{14}M_{\odot}$ at $z = 0$, thereby supporting the theoretical and simulation predictions of cluster formation. Notably, this marks an early search for protocluster candidates in JWST wide field based on photometric data, providing valuable candidates to study cosmic structure formation at the early stages., Comment: 23 pages, 14 figures and 7 table, submitted to MNRAS

Self-supervised features are the cornerstone of modern machine learning systems. They are typically pre-trained on data collections whose construction and curation typically require extensive human effort. This manual process has some limitations similar to those encountered in supervised learning, e.g., the crowd-sourced selection of data is costly and time-consuming, preventing scaling the dataset size. In this work, we consider the problem of automatic curation of high-quality datasets for self-supervised pre-training. We posit that such datasets should be large, diverse and balanced, and propose a clustering-based approach for building ones satisfying all these criteria. Our method involves successive and hierarchical applications of $k$-means on a large and diverse data repository to obtain clusters that distribute uniformly among data concepts, followed by a hierarchical, balanced sampling step from these clusters. Extensive experiments on three different data domains including web-based images, satellite images and text show that features trained on our automatically curated datasets outperform those trained on uncurated data while being on par or better than ones trained on manually curated data. Code is available at https://github.com/facebookresearch/ssl-data-curation.

We describe laboratory experiments in a 2D wave tank that aim at building up and monitor 2D shallow water soliton gas. The water surface elevation is obtained over a large ($\sim 100\,\text{m}^2$) domain, with centimetre-resolution, by stereoscopic vision using two cameras. Floating particles are seeded to get surface texture and determine the wave field by image correlation. With this set-up, soliton propagation and multiple interactions can be measured with a previously unreachable level of detail. The propagation of an oblique soliton is analysed, the amplitude decay and local incidence are compared to analytical predictions. We further present two cases of 2D soliton gas, emerging from multiple line solitons with random incidence ($|\theta|<30^\circ$) and from irregular random waves forced with a {\sc jonswap} spectrum ($|\theta|<45^\circ$). To our knowledge, those are the first observations of random 2D soliton gas for gravity waves. In both cases Mach reflections and Mach expansions result in solitons that mainly propagate in directions perpendicular to the wave-makers., Comment: Accepted for publication in Experiments in Fluids

Semi-Supervised Learning (SSL) seeks to leverage large amounts of non-annotated data along with the smallest amount possible of annotated data in order to achieve the same level of performance as if all data were annotated. A fruitful method in SSL is Pseudo-Labeling (PL), which, however, suffers from the important drawback that the associated loss function has discontinuities in its derivatives, which cause instabilities in performance when labels are very scarce. In the present work, we address this drawback with the introduction of a Smooth Pseudo-Labeling (SP L) loss function. It consists in adding a multiplicative factor in the loss function that smooths out the discontinuities in the derivative due to thresholding. In our experiments, we test our improvements on FixMatch and show that it significantly improves the performance in the regime of scarce labels, without addition of any modules, hyperparameters, or computational overhead. In the more stable regime of abundant labels, performance remains at the same level. Robustness with respect to variation of hyperparameters and training parameters is also significantly improved. Moreover, we introduce a new benchmark, where labeled images are selected randomly from the whole dataset, without imposing representation of each class proportional to its frequency in the dataset. We see that the smooth version of FixMatch does appear to perform better than the original, non-smooth implementation. However, more importantly, we notice that both implementations do not necessarily see their performance improve when labeled images are added, an important issue in the design of SSL algorithms that should be addressed so that Active Learning algorithms become more reliable and explainable.

In this paper, we introduce HEEPS/MARE, the end-to-end simulator developed for the SAR oceanographic products of ESA Earth Explorer 10 mission, Harmony, expected to launch in Decembre 2029. Harmony is primarily dedicated to the observation of small-scale motion and deformation fields of the Earth surface (oceans, glaciers and ice sheets, solid Earth), thanks to passive SAR/ATI receivers carried by two companion satellites for Sentinel-1. The paper focuses on the raw data generator designed to efficiently simulate large, heterogeneous, moving oceanic areas and produce the acquired SAR/ATI bistatic IQ signals. The heterogeneous sea-surface model, bistatic scattering model, multi-GPU implementation and achieved performance are emphasized. Finally, sample results are presented, to illustrate the ability of Harmony to map wind and surface current vectors at kilometric scale.

We present an exploration of the unpolarized isovector proton generalized parton distributions (GPDs) $H^{u-d}(x, \xi, t)$ and $E^{u-d}(x, \xi, t)$ in the pseudo-distribution formalism using distillation. Taking advantage of the large kinematic coverage made possible by this approach, we present results on the moments of GPDs up to the order $x^3$ -- including their skewness dependence -- at a pion mass $m_\pi = 358$ MeV and a lattice spacing $a = 0.094$ fm., Comment: added discussion on isovector D-term and stability of fits with respect to the range in non-local separation of the operator

We propose a new method to investigate signatures of a quantum gravity phase in the primordial state of cosmological perturbations. We formulate and study a quantum model of a perturbed Friedmann-Lemaitre-Robertson-Walker universe beyond a tensor-product Born-Oppenheimer-like factorization, that is, without restricting the wave function of the universe to the product of the background and perturbation wave functions. We show that the quantum dynamics generically does not preserve the product form of the universe's wave function, which spontaneously evolves into a more general entangled state. Upon expanding this state in a suitable basis of background wave functions and setting Gaussian initial conditions for the perturbations, we numerically find that each of these wave functions becomes associated with a non-Gaussian state of an inhomogeneous perturbation., Comment: 10 pages, 7 figures

Two-dimensional and van der Waals ferromagnets are ideal platform to study low dimensional magnetism and proximity effects in van der Waals heterostructures. Their ultimate two dimensional character offers also the opportunity to easily adjust their magnetic properties using strain or electric fields. Among 2D ferromagnets, the Cr$_{1+x}$Te$_2$ compounds with $x$=0-1 are very promising because their magnetic properties depend on the amount of self-intercalated Cr atoms between pure CrTe$_2$ layers and the Curie temperature (T$_C$) can reach room temperature for certain compositions. Here, we investigate the evolution of the composition, structural and magnetic properties of thin Cr$_{1.33}$Te$_2$ (Cr$_2$Te$_3$) films epitaxially grown on graphene upon annealing. We observe a transition above 450{\deg}C from the Cr$_{1.33}$Te$_2$ phase with perpendicular magnetic anisotropy and a T$_C$ of 180 K to a composition close to Cr$_{1.39}$Te$_2$ with in-plane magnetic anisotropy and a T$_C$ of 240-250 K. This phase remains stable up to 650{\deg}C above which a pure Cr film starts to form. This work demonstrates the complex interplay between intercalated Cr, lattice parameters and magnetic properties in Cr$_{1+x}$Te$_2$ compounds., Comment: 5 pages, 5 figures

We introduce a novel technique to mitigate the adverse effects of atmospheric turbulence on astronomical imaging. Utilizing a video-to-image neural network trained on simulated data, our method processes a sliding sequence of short-exposure ($\sim$0.2s) stellar field images to reconstruct an image devoid of both turbulence and noise. We demonstrate the method with simulated and observed stellar fields, and show that the brief exposure sequence allows the network to accurately associate speckles to their originating stars and effectively disentangle light from adjacent sources across a range of seeing conditions, all while preserving flux to a lower signal-to-noise ratio than an average stack. This approach results in a marked improvement in angular resolution without compromising the astrometric stability of the final image., Comment: Accepted for publication in MNRAS (advance copy available at https://doi.org/10.1093/mnras/stae1018). Project website available at https://dancecam.info/ . 20 pages, 17 figures

Analyzing 3D anisotropic materials presents significant challenges, especially when assessing 3D orientations, material distributions, and anisotropies through scattered light, due to the inherently vectorial nature of light-matter interactions. In this study, we formulate a scattering theory based on the Rytov approximation, commonly employed in scalar wave tomography, tailored to accommodate vector waves by modifying the scattering matrix. Using this formulation, we investigate the intricate 3D structure of liquid crystals with multiple topological defects exploiting dielectric tensor tomography. By leveraging dielectric tensor tomography, we successfully visualize these topological defects in three dimensions, a task that conventional 2D imaging techniques fail to achieve.

The late interaction paradigm introduced with ColBERT stands out in the neural Information Retrieval space, offering a compelling effectiveness-efficiency trade-off across many benchmarks. Efficient late interaction retrieval is based on an optimized multi-step strategy, where an approximate search first identifies a set of candidate documents to re-rank exactly. In this work, we introduce SPLATE, a simple and lightweight adaptation of the ColBERTv2 model which learns an ``MLM adapter'', mapping its frozen token embeddings to a sparse vocabulary space with a partially learned SPLADE module. This allows us to perform the candidate generation step in late interaction pipelines with traditional sparse retrieval techniques, making it particularly appealing for running ColBERT in CPU environments. Our SPLATE ColBERTv2 pipeline achieves the same effectiveness as the PLAID ColBERTv2 engine by re-ranking 50 documents that can be retrieved under 10ms., Comment: To appear at SIGIR'24 (short paper track)

Learned sparse models such as SPLADE have successfully shown how to incorporate the benefits of state-of-the-art neural information retrieval models into the classical inverted index data structure. Despite their improvements in effectiveness, learned sparse models are not as efficient as classical sparse model such as BM25. The problem has been investigated and addressed by recently developed strategies, such as guided traversal query processing and static pruning, with different degrees of success on in-domain and out-of-domain datasets. In this work, we propose a new query processing strategy for SPLADE based on a two-step cascade. The first step uses a pruned and reweighted version of the SPLADE sparse vectors, and the second step uses the original SPLADE vectors to re-score a sample of documents retrieved in the first stage. Our extensive experiments, performed on 30 different in-domain and out-of-domain datasets, show that our proposed strategy is able to improve mean and tail response times over the original single-stage SPLADE processing by up to $30\times$ and $40\times$, respectively, for in-domain datasets, and by 12x to 25x, for mean response on out-of-domain datasets, while not incurring in statistical significant difference in 60\% of datasets., Comment: published in Findings at ECIR'24

French and American participants listened to new music stimuli and evaluated the stimuli using either adjectives or quantitative musical dimensions. Results were analyzed using correspondence analysis (CA), hierarchical cluster analysis (HCA), multiple factor analysis (MFA), and partial least squares correlation (PLSC). French and American listeners differed when they described the musical stimuli using adjectives, but not when using the quantitative dimensions. The present work serves as a case study in research methodology that allows for a balance between relaxing experimental control and maintaining statistical rigor.

The LG cryptosystem is a public-key encryption scheme in the rank metric using the recent family of $\lambdav-$Gabidulin codes and introduced in 2019 by Lau and Tan. In this paper, we present a cryptanalysis showing that the security of several parameters of the scheme have been overestimated. We also show the existence of some weak keys allowing an attacker to find in polynomial time an alternative private key.

Topological defects are ubiquitous, they manifest in a wide variety of systems such as liquid crystals, magnets or superconductors. The recent quest for nonabelian anyons in condensed matter physics stimulates the interest for topological defects since they can be hosted in vortices in quantum magnets or topological superconductors. In addition to these vortex defects, in this study we propose to investigate edge dislocations in 2D magnets as new building blocks for topological physics since they can be described as vortices in the structural phase field. Here we demonstrate the existence of higher order topological dislocations within the higher order moir\'e pattern of the van der Waals 2D magnet CrCl3 deposited on Au(111). Surprizingly, these higher order dislocations arise from ordinary simple edge dislocations in the atomic lattice of CrCl3. We provide a theoretical framework explaining the higher order dislocations as vortex with a winding Chern number of 2. We expect that these original defects could stabilize some anyons either in a 2D quantum magnet or within a 2D superconductor coupled to it.

In two recent papers Khrennikov uses what he calls Ozawa intersubjectivity theorem to claim that intersubjectivity is necessarily verified in quantum mechanics and to criticize QBism and more generally all interpretations that are perspectival. In agreement with two previous QBist papers, I explain here why Khrennikov proof is not valid but in contrast with one of these papers, I criticize the way intersubjectivity is dealt with in QBism.

Cross-modality image segmentation aims to segment the target modalities using a method designed in the source modality. Deep generative models can translate the target modality images into the source modality, thus enabling cross-modality segmentation. However, a vast body of existing cross-modality image translation methods relies on supervised learning. In this work, we aim to address the challenge of zero-shot learning-based image translation tasks (extreme scenarios in the target modality is unseen in the training phase). To leverage generative learning for zero-shot cross-modality image segmentation, we propose a novel unsupervised image translation method. The framework learns to translate the unseen source image to the target modality for image segmentation by leveraging the inherent statistical consistency between different modalities for diffusion guidance. Our framework captures identical cross-modality features in the statistical domain, offering diffusion guidance without relying on direct mappings between the source and target domains. This advantage allows our method to adapt to changing source domains without the need for retraining, making it highly practical when sufficient labeled source domain data is not available. The proposed framework is validated in zero-shot cross-modality image segmentation tasks through empirical comparisons with influential generative models, including adversarial-based and diffusion-based models., Comment: Neurips 2023 Diffusion Workshop