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We propose a general framework for end-to-end learning of data structures. Our framework adapts to the underlying data distribution and provides fine-grained control over query and space complexity. Crucially, the data structure is learned from scratch, and does not require careful initialization or seeding with candidate data structures/algorithms. We first apply this framework to the problem of nearest neighbor search. In several settings, we are able to reverse-engineer the learned data structures and query algorithms. For 1D nearest neighbor search, the model discovers optimal distribution (in)dependent algorithms such as binary search and variants of interpolation search. In higher dimensions, the model learns solutions that resemble k-d trees in some regimes, while in others, they have elements of locality-sensitive hashing. The model can also learn useful representations of high-dimensional data and exploit them to design effective data structures. We also adapt our framework to the problem of estimating frequencies over a data stream, and believe it could also be a powerful discovery tool for new problems.

The Atomic Clock Ensemble in Space (ACES) mission is developing high performance clocks and links for space to test Einstein's theory of general relativity. From the International Space Station, the ACES payload will distribute a clock signal with fractional frequency stability and accuracy of 1E-16 establishing a worldwide network to compare clocks in space and on the ground. ACES will provide an absolute measurement of Einstein's gravitational redshift, it will search for time variations of fundamental constants, contribute to test topological dark matter models, and perform Standard Model Extension tests. Moreover, the ground clocks connected to the ACES network will be compared over different continents and used to measure geopotential differences at the clock locations. After solving some technical problems, the ACES flight model is now approaching its completion. System tests involving the laser-cooled Cs clock PHARAO, the active H-maser SHM and the on-board frequency comparator FCDP have measured the performance of the clock signal delivered by ACES. The ACES microwave link MWL is currently under test. The single-photon avalanche detector of the optical link ELT has been tested and will now be integrated in the ACES payload. The ACES mission concept, its scientific objectives, and the recent test results are discussed here together with the major milestones that will lead us to the ACES launch., Comment: 12 pages, 8 figures

The coronavirus crisis has increased medical teleconsultations (in France) by 10 times between 2019 and the first 3 months of 2020. However, the consultation and teleconsultation process have remained much the same. To improve the patient pathway and the efficiency of a session, we have developed a conversational assistant to optimize the time spent with the patient, the quality of the information transmitted to the physicians and thus the accuracy of diagnosis. One of the chatbot's interests is to give the physicians at the beginning of the consultation key elements for further inquiry. In this way, the system aims to help the patients concentrate and well describe their problem, which leads to more meaningful interactions with physicians and helps to enable them to operate more efficiently.

We present two cases of Ly$\alpha$ changing-look (CL) quasars (J1306 and J1512) along with two additional candidates (J1511 and J1602), all discovered serendipitously at $z >2$ through the Dark Energy Spectroscopic Instrument (DESI) and the Sloan Digital Sky Survey (SDSS). It is the first time to capture CL events in Ly$\alpha$ at high redshift, which is crucial for understanding underlying mechanisms driving the CL phenomenon and the evolution of high-redshift quasars and galaxies. The variability of all four sources is confirmed by the significant change of amplitude in the $r$ band ($|r_{\rm DESI}-r_{\rm SDSS}| >0.5 \ \rm mag$). We find that the accretion rate in the dim state for these CL objects corresponds to a relatively low value ($\mathscr{\dot M} \approx 2\times10^{-3}$), which suggests that the inner region of the accretion disk might be in transition between the Advection Dominated Accretion Flow ($\mathscr{\dot M}<10^{-3}\sim 10^{-2}$) and the canonical accretion disk (optically thick, geometrically thin). However, unlike in C {\sc iv} CL quasars in which broad Ly$\alpha$ remained, the broad C {\sc iv} may still persist after a CL event occurs in Ly$\alpha$, making the physical origin of the CL and ionization mechanism event more puzzling and interesting.

Models of how things spread often assume that transmission mechanisms are fixed over time. However, social contagions--the spread of ideas, beliefs, innovations--can lose or gain in momentum as they spread: ideas can get reinforced, beliefs strengthened, products refined. We study the impacts of such self-reinforcement mechanisms in cascade dynamics. We use different mathematical modeling techniques to capture the recursive, yet changing nature of the process. We find a critical regime with a range of power-law cascade size distributions with varying scaling exponents. This regime clashes with classic models, where criticality requires fine tuning at a precise critical point. Self-reinforced cascades produce critical-like behavior over a wide range of parameters, which may help explain the ubiquity of power-law distributions in empirical social data.

The democratization of fabrication equipment has spurred recent interest in maskless grayscale lithography for both 2D and 3D microfabrication. However, the design of suitable template images remains a challenge. This work presents a simplified method for encoding 3D objects into grayscale image files optimized for grayscale lithography. Leveraging the widely used, open-source 3D modeling software Blender, we developed a robust approach to convert geometric heights into grayscale levels and generate image files through top-view rendering. Our method accurately reproduced the overall shape of simple structures like stairs and ramps compared to the original designs. We extended this approach to complex 3D sinusoidal surfaces, achieving similar results. Given the increasing accessibility and user-friendliness of digital rendering tools, this study offers a promising strategy for rapid prototyping of initial designs with minimal effort., Comment: 12 pages, 6 figures

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 provide a new gravity field of Comet 67P-C/G up to degree 4. We detect mass heterogeneity in the comet nucleus. The loss of mass is restimated at 0.28\% of the comet's total mass (3 times larger than previous estimate). Comparison of the gravity field between pre- and post-perihelion allowed us to measure a shift in the comet's center of gravity of 35 m northward, attributed to ice sublimation process.

We present three separate void catalogs created using a volume-limited sample of the DESI Year 1 Bright Galaxy Survey. We use the algorithms VoidFinder and V2 to construct void catalogs out to a redshift of z=0.24. We obtain 1,461 interior voids with VoidFinder, 420 with V2 using REVOLVER pruning, and 295 with V2 using VIDE pruning. Comparing our catalog with an overlapping SDSS void catalog, we find generally consistent void properties but significant differences in the void volume overlap, which we attribute to differences in the galaxy selection and survey masks. These catalogs are suitable for studying the variation in galaxy properties with cosmic environment and for cosmological studies., Comment: 17 pages, 6 figures

The measurement of type Ia supernovae magnitudes provides cosmological distances, which can be used to constrain dark energy parameters. Large photometric surveys require a substantial improvement in the calibration precision of their photometry to reduce systematic uncertainties in cosmological constraints. The StarDICE experiment is designed to establish accurate broadband flux references for these surveys, aiming for sub-percent precision in magnitude measurements. This requires a precise measurement of the filter bandpasses of both the StarDICE and survey instruments with sub-nanometer accuracy. To that end, we have developed the Collimated Beam Projector (CBP), an optical device capable of calibrating the throughput of an astronomical telescope and of its filters. The CBP is built from a tunable laser source and a reversed telescope to emit a parallel monochromatic light beam that is continuously monitored in flux and wavelength. The CBP output light flux is measured using a large area photodiode, previously calibrated relative to a NIST photodiode. We derive the StarDICE telescope throughput and filter transmissions from the CBP measurements, anchoring it to the absolute calibration provided by the NIST. After analyzing the systematic uncertainties, we achieved sub-nanometer accuracy in determining filter central wavelengths, measured each filter transmission with a precision of 0.5% per 1nm bin, and detected out-of-band leakages at 0.01%. Furthermore, we have synthesized the equivalent transmission for full pupil illumination from four sample positions in the StarDICE telescope mirror, with an accuracy of approximately 0.2nm for central wavelengths and 7mmag for broadband fluxes. We demonstrated our ability to characterize a telescope throughput down to the mmag, and paved the way for future developments, such as a portable CBP version for in-situ transmission monitoring., Comment: 29 pages, 50 figures

Radio propagation modeling is essential in telecommunication research, as radio channels result from complex interactions with environmental objects. Recently, Machine Learning has been attracting attention as a potential alternative to computationally demanding tools, like Ray Tracing, which can model these interactions in detail. However, existing Machine Learning approaches often attempt to learn directly specific channel characteristics, such as the coverage map, making them highly specific to the frequency and material properties and unable to fully capture the underlying propagation mechanisms. Hence, Ray Tracing, particularly the Point-to-Point variant, remains popular to accurately identify all possible paths between transmitter and receiver nodes. Still, path identification is computationally intensive because the number of paths to be tested grows exponentially while only a small fraction is valid. In this paper, we propose a Machine Learning-aided Ray Tracing approach to efficiently sample potential ray paths, significantly reducing the computational load while maintaining high accuracy. Our model dynamically learns to prioritize potentially valid paths among all possible paths and scales linearly with scene complexity. Unlike recent alternatives, our approach is invariant with translation, scaling, or rotation of the geometry, and avoids dependency on specific environment characteristics., Comment: 6 pages, 6 figures, submitted to IEEE ICMLCN 2025

While the field of inverse graphics has been witnessing continuous growth, techniques devised thus far predominantly focus on learning individual scene representations. In contrast, learning large sets of scenes has been a considerable bottleneck in NeRF developments, as repeatedly applying inverse graphics on a sequence of scenes, though essential for various applications, remains largely prohibitive in terms of resource costs. We introduce a framework termed "scaled inverse graphics", aimed at efficiently learning large sets of scene representations, and propose a novel method to this end. It operates in two stages: (i) training a compression model on a subset of scenes, then (ii) training NeRF models on the resulting smaller representations, thereby reducing the optimization space per new scene. In practice, we compact the representation of scenes by learning NeRFs in a latent space to reduce the image resolution, and sharing information across scenes to reduce NeRF representation complexity. We experimentally show that our method presents both the lowest training time and memory footprint in scaled inverse graphics compared to other methods applied independently on each scene. Our codebase is publicly available as open-source. Our project page can be found at https://scaled-ig.github.io .

A well structured collection of the various Quantum Cascade Laser (QCL) design and working properties data provides a platform to analyze and understand the relationships between these properties. By analyzing these relationships, we can gain insights into how different design features impact laser performance properties such as the working temperature. Most of these QCL properties are captured in scientific text. There is therefore need for efficient methodologies that can be utilized to extract QCL properties from text and generate a semantically enriched and interlinked platform where the properties can be analyzed to uncover hidden relations. There is also the need to maintain provenance and reference information on which these properties are based. Semantic Web technologies such as Ontologies and Knowledge Graphs have proven capability in providing interlinked data platforms for knowledge representation in various domains. In this paper, we propose an approach for generating a QCL properties Knowledge Graph (KG) from text for semantic enrichment of the properties. The approach is based on the QCL ontology and a Retrieval Augmented Generation (RAG) enabled information extraction pipeline based on GPT 4-Turbo language model. The properties of interest include: working temperature, laser design type, lasing frequency, laser optical power and the heterostructure. The experimental results demonstrate the feasibility and effectiveness of this approach for efficiently extracting QCL properties from unstructured text and generating a QCL properties Knowledge Graph, which has potential applications in semantic enrichment and analysis of QCL data.

While pre-trained image autoencoders are increasingly utilized in computer vision, the application of inverse graphics in 2D latent spaces has been under-explored. Yet, besides reducing the training and rendering complexity, applying inverse graphics in the latent space enables a valuable interoperability with other latent-based 2D methods. The major challenge is that inverse graphics cannot be directly applied to such image latent spaces because they lack an underlying 3D geometry. In this paper, we propose an Inverse Graphics Autoencoder (IG-AE) that specifically addresses this issue. To this end, we regularize an image autoencoder with 3D-geometry by aligning its latent space with jointly trained latent 3D scenes. We utilize the trained IG-AE to bring NeRFs to the latent space with a latent NeRF training pipeline, which we implement in an open-source extension of the Nerfstudio framework, thereby unlocking latent scene learning for its supported methods. We experimentally confirm that Latent NeRFs trained with IG-AE present an improved quality compared to a standard autoencoder, all while exhibiting training and rendering accelerations with respect to NeRFs trained in the image space. Our project page can be found at https://ig-ae.github.io .

Skin dose in radiotherapy is a key issue for reducing patient side effects, but dose calculations in this high-gradient region remains a challenge. To support radiation therapists and medical physicist in their decisions, a computational tool has been developed to systematically recalculate the skin dose for each treatment session, taking into account changes in breast geometry. This tool is based on Monte Carlo skin dose calculations from deformed vector fields (DVF)-driven CT images, using 3D-scanner data. The application of deformation fields to the initial patient CT, as well as the validation of Monte Carlo skin dose calculations and comparison with TPS algorithm, are presented. The tool is currently being evaluated using data from a clinical study involving 60 patients (MorphoBreast3D).

Pattern formation in spin systems with continuous-rotational symmetry (CRS) provides a powerful platform to study emergent complex magnetic phases and topological defects in condensed-matter physics. However, its understanding and correlation with unconventional magnetic order along with high-resolution nanoscale imaging is challenging. Here, we employ scanning NV magnetometry to unveil the morphogenesis of spin cycloids at both the local and global scales within a single ferroelectric domain of (111)-oriented BiFeO$_3$ (which is a non-collinear antiferromagnet), resulting in formation of a glassy labyrinthine pattern. We find that the domains of locally oriented cycloids are interconnected by an array of topological defects and exhibit isotropic energy landscape predicted by first-principles calculations. We propose that the CRS of spin-cycloid propagation directions within the (111) drives the formation of the labyrinthine pattern and the associated topological defects such as antiferromagnetic skyrmions. Unexpectedly, reversing the as-grown ferroelectric polarization from [$\bar{1}$$\bar{1}$$\bar{1}$] to [111] induces a magnetic phase transition, destroying the labyrinthine pattern and producing a deterministic non-volatile non cycloidal, uniformly magnetized state. These findings highlight that (111)-oriented BiFeO$_3$ is not only important for studying the fascinating subject of pattern formation but could also be utilized as an ideal platform for integrating novel topological defects in the field of antiferromagnetic spintronics., Comment: 20 pages, 15 figures

In 2023, the 12th edition of Global Trajectory Competition was organised around the problem referred to as "Sustainable Asteroid Mining". This paper reports the developments that led to the solution proposed by ESA's Advanced Concepts Team. Beyond the fact that the proposed approach failed to rank higher than fourth in the final competition leader-board, several innovative fundamental methodologies were developed which have a broader application. In particular, new methods based on machine learning as well as on manipulating the fundamental laws of astrodynamics were developed and able to fill with remarkable accuracy the gap between full low-thrust trajectories and their representation as impulsive Lambert transfers. A novel technique was devised to formulate the challenge of optimal subset selection from a repository of pre-existing optimal mining trajectories as an integer linear programming problem. Finally, the fundamental problem of searching for single optimal mining trajectories (mining and collecting all resources), albeit ignoring the possibility of having intra-ship collaboration and thus sub-optimal in the case of the GTOC12 problem, was efficiently solved by means of a novel search based on a look-ahead score and thus making sure to select asteroids that had chances to be re-visited later on.

A twist between two systems offers the possibility to drastically change the underlying physical properties. To that end, we study the bandstructure of twisted moir\'e potentials in detail. At sets of commensurate twisting angles, the low energy single-particle spectrum of a twisted moir\'e potential will form into distinct bands and gaps. To a first approximation, energy bands can be qualitatively modelled by harmonic states, localised in different potential minima. The bands are intrinsically linked to the number of distinct minima and size of the moir\'e unit cell, with smaller cells producing larger gaps and vice versa. For shallower potential depths, degeneracies between harmonic states are lifted by virtue of anharmonic confinement and coupling between states. Depending on the exact geometry of potential minima, bands can then be classified by $4$ unique forms of tight-binding models. We find excellent agreement between the continuous spectrum and fitting to our tight-binding models, allowing for accurate tunnelling rates and onsite energies to be extracted. Our results are directly relevant to the bosonic, many-body problem, and thus provide further understanding on the relative stability of quantum phases both in theory and experiments. In particular, the prominence of gaps can be mapped to strongly correlated insulating phases. Furthermore, tunnelling rates of different bands serve as thresholds on temperature in which a phase can be either a normal fluid or superfluid., Comment: 19 pages, 14 figures, comments welcome

Traditional recommender systems rely on high-dimensional (latent) embeddings for modeling user-item interactions, often resulting in opaque representations that lack interpretability. Moreover, these systems offer limited control to users over their recommendations. Inspired by recent work, we introduce TExtuAl Representations for Scrutable recommendations (TEARS) to address these challenges. Instead of representing a user's interests through a latent embedding, TEARS encodes them in natural text, providing transparency and allowing users to edit them. To do so, TEARS uses a modern LLM to generate user summaries based on user preferences. We find the summaries capture user preferences uniquely. Using these summaries, we take a hybrid approach where we use an optimal transport procedure to align the summaries' representation with the learned representation of a standard VAE for collaborative filtering. We find this approach can surpass the performance of three popular VAE models while providing user-controllable recommendations. We also analyze the controllability of TEARS through three simulated user tasks to evaluate the effectiveness of a user editing its summary.

In numerous settings, it is increasingly common to deal with longitudinal data organized as high-dimensional multi-dimensional arrays, also known as tensors. Within this framework, the time-continuous property of longitudinal data often implies a smooth functional structure on one of the tensor modes. To help researchers investigate such data, we introduce a new tensor decomposition approach based on the CANDECOMP/PARAFAC decomposition. Our approach allows for representing a high-dimensional functional tensor as a low-dimensional set of functions and feature matrices. Furthermore, to capture the underlying randomness of the statistical setting more efficiently, we introduce a probabilistic latent model in the decomposition. A covariance-based block-relaxation algorithm is derived to obtain estimates of model parameters. Thanks to the covariance formulation of the solving procedure and thanks to the probabilistic modeling, the method can be used in sparse and irregular sampling schemes, making it applicable in numerous settings. We apply our approach to help characterize multiple neurocognitive scores observed over time in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Finally, intensive simulations show a notable advantage of our method in reconstructing tensors.

Morphing attacks have diversified significantly over the past years, with new methods based on generative adversarial networks (GANs) and diffusion models posing substantial threats to face recognition systems. Recent research has demonstrated the effectiveness of features extracted from large vision models pretrained on bonafide data only (attack-agnostic features) for detecting deep generative images. Building on this, we investigate the potential of these image representations for morphing attack detection (MAD). We develop supervised detectors by training a simple binary linear SVM on the extracted features and one-class detectors by modeling the distribution of bonafide features with a Gaussian Mixture Model (GMM). Our method is evaluated across a comprehensive set of attacks and various scenarios, including generalization to unseen attacks, different source datasets, and print-scan data. Our results indicate that attack-agnostic features can effectively detect morphing attacks, outperforming traditional supervised and one-class detectors from the literature in most scenarios. Additionally, we provide insights into the strengths and limitations of each considered representation and discuss potential future research directions to further enhance the robustness and generalizability of our approach., Comment: Published in the 2024 IEEE International Joint Conference on Biometrics (IJCB)

The heterogeneous distribution of water-repellent materials at the soil surface causes a phenomenon known as fractional wettability. This condition frequently triggers destabilization of the wetting front during water infiltration, resulting in the formation of fingered bypass flow. However, few analytical tools exist to understand and model this behavior. Moreover, existing infiltration models fail to fit certain infiltration curves that exist in experimental data. For these reasons, we introduce a novel infiltration model to simulate water infiltration under fractional wettable conditions. We conceptualize the soil surface as a composite of two distinct portions: a water-repellent fraction, where hydrophobic effects impede water infiltration, and a wettable fraction, where capillarity and gravity are the dominant forces controlling the process. The new model was validated using a dataset comprising infiltration data from 60 field measurements. Additionally, validation was performed using 660 analytically generated infiltration curves from six synthetic soils with varying textures. This innovative approach enabled us to account for the combined influence of these two fractions and to enhance the interpretation of infiltration curves with mixed shapes, which other common methods are unable to reproduce.

The increasing complexity of AI workloads, especially distributed Large Language Model (LLM) training, places significant strain on the networking infrastructure of parallel data centers and supercomputing systems. While Equal-Cost Multi- Path (ECMP) routing distributes traffic over parallel paths, hash collisions often lead to imbalanced network resource utilization and performance bottlenecks. This paper presents FlowTracer, a tool designed to analyze network path utilization and evaluate different routing strategies. FlowTracer aids in debugging network inefficiencies by providing detailed visibility into traffic distribution and helping to identify the root causes of performance degradation, such as issues caused by hash collisions. By offering flow-level insights, FlowTracer enables system operators to optimize routing, reduce congestion, and improve the performance of distributed AI workloads. We use a RoCEv2-enabled cluster with a leaf-spine network and 16 400-Gbps nodes to demonstrate how FlowTracer can be used to compare the flow imbalances of ECMP routing against a statically configured network. The example showcases a 30% reduction in imbalance, as measured by a new metric we introduce., Comment: Submitted for peer reviewing in IEEE ICC 2025

In this paper we construct a non-perturbative action of the higher spin symmetry algebra on the gravitational phase space. We introduce a symmetry algebroid $\mathcal{T}$ which allows us to include radiation in an algebraic framework. We show that $\mathcal{T}$ admits a non-linear realization on the asymptotic phase space generated by a Noether charge defined non-perturbatively for all spins. Besides, this Noether charge is conserved in the absence of radiation. Moreover, at non radiative cuts, the algebroid can be restricted to the wedge symmetry algebra studied in ArXiv:2409.12178. The key ingredient for our construction is to consider field and time dependent symmetry parameters constrained to evolve according to equations of motion dual to (a truncation of) the asymptotic Einstein's equations. This result then guarantees that the underlying symmetry algebra is also represented canonically., Comment: 63 pages

Accurate time-series forecasting is essential across a multitude of scientific and industrial domains, yet deep learning models often struggle with challenges such as capturing long-term dependencies and adapting to drift in data distributions over time. We introduce Future-Guided Learning, an approach that enhances time-series event forecasting through a dynamic feedback mechanism inspired by predictive coding. Our approach involves two models: a detection model that analyzes future data to identify critical events and a forecasting model that predicts these events based on present data. When discrepancies arise between the forecasting and detection models, the forecasting model undergoes more substantial updates, effectively minimizing surprise and adapting to shifts in the data distribution by aligning its predictions with actual future outcomes. This feedback loop, drawing upon principles of predictive coding, enables the forecasting model to dynamically adjust its parameters, improving accuracy by focusing on features that remain relevant despite changes in the underlying data. We validate our method on a variety of tasks such as seizure prediction in biomedical signal analysis and forecasting in dynamical systems, achieving a 40\% increase in the area under the receiver operating characteristic curve (AUC-ROC) and a 10\% reduction in mean absolute error (MAE), respectively. By incorporating a predictive feedback mechanism that adapts to data distribution drift, Future-Guided Learning offers a promising avenue for advancing time-series forecasting with deep learning.

This paper presents software implementations of batch computations, dealing with multi-precision integer operations. In this work, we use the Single Instruction Multiple Data (SIMD) AVX512 instruction set of the x86-64 processors, in particular the vectorized fused multiplier-adder VPMADD52. We focus on batch multiplications, squarings, modular multiplications, modular squarings and constant time modular exponentiations of 8 values using a word-slicing storage. We explore the use of Schoolbook and Karatsuba approaches with operands up to 4108 and 4154 bits respectively. We also introduce a truncated multiplication that speeds up the computation of the Montgomery modular reduction in the context of software implementation. Our Truncated Montgomery modular multiplication improvement offers speed gains of almost 20 % over the conventional non-truncated versions. Compared to the state-of-the-art GMP and OpenSSL libraries, our speedup modular operations are more than 4 times faster. Compared to OpenSSL BN_mod_exp_mont_consttimex2 using AVX512 and madd52* (madd52hi or madd52lo) in 256-bit registers, in fixed-window exponentiations of sizes 1024 and 2048 , our 512-bit implementation provides speedups of respectively 1.75 and 1.38, while the 256-bit version speedups are 1.51 and 1.05 for 1024 and 2048 -bit sizes (batch of 4 values in this case).

With the increasing presence of dynamic scenarios, such as Vehicle-to-Vehicle communications, radio propagation modeling tools must adapt to the rapidly changing nature of the radio channel. Recently, both Differentiable and Dynamic Ray Tracing frameworks have emerged to address these challenges. However, there is often confusion about how these approaches differ and which one should be used in specific contexts. In this paper, we provide an overview of these two techniques and a comparative analysis against two state-of-the-art tools: 3DSCAT from UniBo and Sionna from NVIDIA. To provide a more precise characterization of the scope of these methods, we introduce a novel simulation-based metric, the Multipath Lifetime Map, which enables the evaluation of spatial and temporal coherence in radio channels only based on the geometrical description of the environment. Finally, our metrics are evaluated on a classic urban street canyon scenario, yielding similar results to those obtained from measurement campaigns., Comment: 5 pages, 5 figures, 1 table, accepted at EuCAP 2025

Annotating 3D medical images demands expert knowledge and is time-consuming. As a result, semi-supervised learning (SSL) approaches have gained significant interest in 3D medical image segmentation. The significant size differences among various organs in the human body lead to imbalanced class distribution, which is a major challenge in the real-world application of these SSL approaches. To address this issue, we develop a novel Shape Transformation driven by Active Contour (STAC), that enlarges smaller organs to alleviate imbalanced class distribution across different organs. Inspired by curve evolution theory in active contour methods, STAC employs a signed distance function (SDF) as the level set function, to implicitly represent the shape of organs, and deforms voxels in the direction of the steepest descent of SDF (i.e., the normal vector). To ensure that the voxels far from expansion organs remain unchanged, we design an SDF-based weight function to control the degree of deformation for each voxel. We then use STAC as a data-augmentation process during the training stage. Experimental results on two benchmark datasets demonstrate that the proposed method significantly outperforms some state-of-the-art methods. Source code is publicly available at https://github.com/GuGuLL123/STAC.

This paper presents the design and implementation of the Scintillation Filter by Kalman-colored algorithm (SciFi), which is used to remove tropospheric scintillation from Q/V bands total attenuation data series. In contrast to the classical methods using low-pass filters, the SciFi algorithm allows to estimate both the attenuation, its slope and a confidence interval. Moreover, the linear observer structure of the Kalman filter allows it to operate in real time. Therefore, the states and uncertainties estimated by SciFi can be used as input for Fade Mitigation Techniques (FMT) such as Adaptive Coding and Modulation (ACM) or Site Diversity (SD). In this article, we propose a method to tune the noise level based on recommendations approved by the ITU-R. Finally, some results of filtering on Alphasat experimental data are discussed., Comment: Ka and Broadband Communications Conference, Seattle, WA, USA, Sept 2024

Integrated sensing and communication (ISAC) is a novel capability expected for sixth generation (6G) cellular networks. To that end, several challenges must be addressed to enable both mono- and bistatic sensing in existing deployments. A common impairment in both architectures is oscillator phase noise (PN), which not only degrades communication performance, but also severely impairs radar sensing. To enable a broader understanding of orthogonal-frequency division multiplexing (OFDM)-based sensing impaired by PN, this article presents an analysis of sensing peformance in OFDM-based ISAC for different waveform parameter choices and settings in both mono- and bistatic architectures. In this context, the distortion of the adopted digital constellation modulation is analyzed and the resulting PN-induced effects in range-Doppler radar images are investigated both without and with PN compensation. These effects include peak power loss of target reflections and higher sidelobe levels, especially in the Doppler shift direction. In the conducted analysis, these effects are measured by the peak power loss ratio, peak-to-sidelobe level ratio, and integrated sidelobe level ratio parameters, the two latter being evaluated in both range and Doppler shift directions. In addition, the signal-to-interference ratio is analyzed to allow not only quantifying the distortion of a target reflection, but also measuring the interference floor level in a radar image. The achieved results allow to quantify not only the PN-induced impairments to a single target, but also how the induced degradation may impair the sensing performance of OFDM-based ISAC systems in multi-target scenarios., Comment: This work has been submitted to the IEEE for possible publication

Efforts to combine the advantages of multiple systems to enhance functionlities through solid solution design present a great challenge due to the constraint imposed by the classical Vegard law. Here, we successfully navigate this trade off by leveraging the synergistic effect of chemical doping and strain engineering in solid solution system of BiFeO3 BaTiO3. Unlike bulks, a significant deviation from the Vegard law accompanying with enhanced multiferroism is observed in the strained solid solution epitaxial films, where we achieve a pronounced tetragonality, enhanced saturated magnetization, substantial polarization, high ferroelectric Curie temperature, all while maintaining impressively low leakage current. These characteristics surpass the properties of their parent BiFeO3 and BaTiO3 films. Moreover, the superior ferroelectricity has never been reported in corresponding bulks. These findings underscore the potential of strained BiFeO3 BaTiO3 films as lead-free, room-temperature multiferroics., Comment: 19pages, 5 figures

This report summarizes what has happened in the mini-workshops entitled ''My Earth in 180 minutes'' organized during the lunch break at the SF2A 2024 conference in Marseille. The project showcased an innovative serious game designed to raise awareness of greenhouse gas (GHG) emissions in astronomical research laboratories. Participants, organized into teams, simulate strategies to reduce their carbon footprints by 50\%, focusing on key astronomical activities such as space instrumentation, data analysis, and laboratory work. The sessions highlight the challenges of achieving significant emissions reductions without disrupting core research activities, such as telescope observations. While the serious game facilitates important discussions on sustainable practices, the results point to the need for broader engagement, adaptation to different cultural contexts, and institutional support. The project highlights the importance of integrating climate action into the academic environment and suggests potential future directions for expanding its impact., Comment: SF2A 2024, Jun 2024, Marseille, France

AfterLearnER (After Learning Evolutionary Retrofitting) consists in applying non-differentiable optimization, including evolutionary methods, to refine fully-trained machine learning models by optimizing a set of carefully chosen parameters or hyperparameters of the model, with respect to some actual, exact, and hence possibly non-differentiable error signal, performed on a subset of the standard validation set. The efficiency of AfterLearnER is demonstrated by tackling non-differentiable signals such as threshold-based criteria in depth sensing, the word error rate in speech re-synthesis, image quality in 3D generative adversarial networks (GANs), image generation via Latent Diffusion Models (LDM), the number of kills per life at Doom, computational accuracy or BLEU in code translation, and human appreciations in image synthesis. In some cases, this retrofitting is performed dynamically at inference time by taking into account user inputs. The advantages of AfterLearnER are its versatility (no gradient is needed), the possibility to use non-differentiable feedback including human evaluations, the limited overfitting, supported by a theoretical study and its anytime behavior. Last but not least, AfterLearnER requires only a minimal amount of feedback, i.e., a few dozens to a few hundreds of scalars, rather than the tens of thousands needed in most related published works. Compared to fine-tuning (typically using the same loss, and gradient-based optimization on a smaller but still big dataset at a fine grain), AfterLearnER uses a minimum amount of data on the real objective function without requiring differentiability.

Minimally invasive surgery (MIS) offers significant benefits such as reduced recovery time and minimised patient trauma, but poses challenges in visibility and access, making accurate 3D reconstruction a significant tool in surgical planning and navigation. This work introduces a robotic arm platform for efficient multi-view image acquisition and precise 3D reconstruction in MIS settings. We adapted a laparoscope to a robotic arm and captured ex-vivo images of several ovine organs across varying lighting conditions (operating room and laparoscopic) and trajectories (spherical and laparoscopic). We employed recently released learning-based feature matchers combined with COLMAP to produce our reconstructions. The reconstructions were evaluated against high-precision laser scans for quantitative evaluation. Our results show that whilst reconstructions suffer most under realistic MIS lighting and trajectory, many versions of our pipeline achieve close to sub-millimetre accuracy with an average of 1.05 mm Root Mean Squared Error and 0.82 mm Chamfer distance. Our best reconstruction results occur with operating room lighting and spherical trajectories. Our robotic platform provides a tool for controlled, repeatable multi-view data acquisition for 3D generation in MIS environments which we hope leads to new datasets for training learning-based models., Comment: 8 pages, 5 figures, 3 tables. This work has been submitted to the IEEE for possible publication

Spiking neural networks (SNNs) are neural networks that enable energy-efficient signal processing due to their event-based nature. This paper proposes a novel decoding algorithm for low-density parity-check (LDPC) codes that integrates SNNs into belief propagation (BP) decoding by approximating the check node update equations using SNNs. For the (273,191) and (1023,781) finite-geometry LDPC code, the proposed decoder outperforms sum-product decoder at high signal-to-noise ratios (SNRs). The decoder achieves a similar bit error rate to normalized sum-product decoding with successive relaxation. Furthermore, the novel decoding operates without requiring knowledge of the SNR, making it robust to SNR mismatch., Comment: Submitted to Communication Letters

We measure the growth of cosmic density fluctuations on large scales and across the redshift range $0.3

We consider the Landau-Coulomb equation for initial data with bounded mass, finite numbers of moments, and entropy. We show the existence of a global weak solution that has bounded Fisher information for positive times. This solution is therefore a global strong solution away from the initial time. We propose an alternative approach, based on already existing estimates, to the study of the appearance of Fisher information recently performed by Ji in [12].

Interband Cascade Lasers (ICLs) are semiconductor lasers emitting in the mid-wave infrared (MWIR 3-6 {\mu}m) and can operate as frequency combs (FCs). These demonstrations are based on double section cavities that can reduce dispersion and/or are adapted for radio-frequency operation. Here we show that ICLs FCs at long wavelengths, where the refractive index dispersion reduces, can be realized in a single long section cavity. We show FC generation for ICLs operating at {\lambda} ~ 4.2 {\mu}m, demonstrating narrow electrical beatnotes over a large current range. We also reconstruct the ultrafast temporal response through a modified SWIFT spectroscopy setup with two fast MWIR detectors, which shows a frequency modulated response in free-running operation. Further, we show that, through active modelocking, the ICL can be forced to generate short pulses on the order of 3 ps. This temporal response is in agreement with Maxwell Bloch simulations, highlighting that these devices possess long dynamics (~100ps) and potentially makes them appropriate for the generation of large peak powers in the MWIR., Comment: 11 pages, 6 figures

The Large Magellanic Cloud (LMC) is a Milky Way (MW) satellite that is massive enough to gravitationally attract the MW disc and inner halo, causing significant motion of the inner MW with respect to the outer halo. In this work, we probe this interaction by constructing a sample of 9,866 blue horizontal branch (BHB) stars with radial velocities from the DESI spectroscopic survey out to 120 kpc from the Galactic centre. This is the largest spectroscopic set of BHB stars in the literature to date, and it contains four times more stars with Galactocentric distances beyond 50 kpc than previous BHB catalogues. Using the DESI BHB sample combined with SDSS BHBs, we measure the bulk radial velocity of stars in the outer halo and observe that the velocity in the Southern Galactic hemisphere is different by 3.7$\sigma$ from the North. Modelling the projected velocity field shows that its dipole component is directed at a point 22 degrees away from the LMC along its orbit, which we interpret as the travel direction of the inner MW. The velocity field includes a monopole term that is -24 km/s, which we refer to as compression velocity. This velocity is significantly larger than predicted by the current models of the MW and LMC interaction. This work uses DESI data from its first two years of observations, but we expect that with upcoming DESI data releases, the sample of BHB stars will increase and our ability to measure the MW-LMC interaction will improve significantly., Comment: 22 pages, 19 figures. Submitted to MNRAS

In the context of the GRAVITY+ upgrade, the adaptive optics (AO) systems of the GRAVITY interferometer are undergoing a major lifting. The current CILAS deformable mirrors (DM, 90 actuators) will be replaced by ALPAO kilo-DMs (43x43, 1432 actuators). On top of the already existing 9x9 Shack-Hartmann wavefront sensors (SH-WFS) for infrared (IR) natural guide star (NGS), new 40x40 SH-WFSs for visible (VIS) NGS will be deployed. Lasers will also be installed on the four units of the Very Large Telescope to provide a laser guide star (LGS) option with 30x30 SH-WFSs and with the choice to either use the 9x9 IR-WFSs or 2x2 VIS-WFSs for low order sensing. Thus, four modes will be available for the GRAVITY+ AO system (GPAO): IR-NGS, IR-LGS, VIS-NGS and VIS-LGS. To prepare the instrument commissioning and help the observers to plan their observations, a tool is needed to predict the performances of the different modes and for different observing conditions (NGS magnitude, science object magnitude, turbulence conditions...) We developed models based on a Mar{\'e}chal approximation to predict the Strehl ratio of the four GPAO modes in order to feed the already existing tool that simulates the GRAVITY performances. Waiting for commissioning data, our model was validated and calibrated using the TIPTOP toolbox, a Point Spread Function simulator based on the computation of Power Spectrum Densities. In this work, we present our models of the NGS modes of GPAO and their calibration with TIPTOP.

As LLM-based agents exhibit exceptional capabilities in addressing complex problems, there is a growing focus on developing coding agents to tackle increasingly sophisticated tasks. Despite their promising performance, these coding agents often produce programs or modifications that contain runtime errors, which can cause code failures and are difficult for static analysis tools to detect. Enhancing the ability of coding agents to statically identify such errors could significantly improve their overall performance. In this work, we introduce Execution-free Runtime Error Detection for COding Agents (REDO), a method that integrates LLMs with static analysis tools to detect runtime errors for coding agents, without code execution. Additionally, we propose a benchmark task, SWE-Bench-Error-Detection (SWEDE), based on SWE-Bench (lite), to evaluate error detection in repository-level problems with complex external dependencies. Finally, through both quantitative and qualitative analyses across various error detection tasks, we demonstrate that REDO outperforms current state-of-the-art methods by achieving a 11.0% higher accuracy and 9.1% higher weighted F1 score; and provide insights into the advantages of incorporating LLMs for error detection., Comment: 27 pages, 13 figures, 6 tables

Some metrological means, such as Shack-Hartmann, deflectometry sensors or fringe projection profilometry, measure the shape of an optical surface indirectly from slope measurements. Zonal shape reconstruction, a method to reconstruct shape with a high number of degrees of freedom, is used for all of these applications. It has risen in interest with the use of deflectometers for the acquisition of high resolution slope data for optical manufacturing, especially because shape reconstruction is limiting in terms of shape estimation error. Zonal reconstruction methods all rely on the choice of a data formation model, a basis on which the shape will be decomposed, and an estimator. In this paper, we first study the canonical Fried and Southwell models of the literature and analyze their limitations. We show that modeling the slope measurement by a point-wise derivative as they both do can induce a bias on the shape estimation, and that the bases on which the shape is decomposed are imposed because of this assumption. In the second part of this paper, we propose to build an unbiased model of the data formation, without constraints on the choice of the decomposition basis. We then compare these models to the canonical models of Fried and Southwell. Lastly, we perform a regularized MAP reconstruction, and compare the performance in terms of total shape error of this method to the state of the art for the Southwell and Fried models, first by simulation, then on experimental data. We demonstrate that the suggested method outperforms the canonical models in terms of total shape reconstruction error on a deflectometry measurement of the high-frequency content of a freeform mirror.

We present the first emission spectrum of the hot Jupiter WASP-17 b using one eclipse observation from the JWST Near Infrared Imager and Slitless Spectrograph (NIRISS) Single Object Slitless Spectroscopy (SOSS) mode. Covering a wavelength range of 0.6 to 2.8 microns, our retrieval analysis reveals a strong detection of H2O in WASP-17b dayside atmosphere (6.4sigma). Our retrievals consistently favor a super-solar dayside H2O abundance and a non-inverted temperature-pressure profile over a large pressure range. Additionally, our examination of the brightness temperature reveals excess emission below 1 microns, suggesting the possibility of a high internal temperature (600 to 700 K) and/or contributions from reflected light. We highlight that JWST emission spectroscopy retrieval results can be sensitive to whether negative eclipse depths are allowed at optical wavelengths during light curve fitting. Our findings deepen our understanding of WASP-17b atmospheric composition while also highlighting the sensitivity of our results to pressure-temperature profile parameterizations. This work is part of a series of studies by our JWST Telescope Scientist Team (JWST-TST), in which we will use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy (DREAMS)., Comment: Accepted for Publication in the Astronomical Journal (AJ) October 4, 2024

Single metallocene molecules act as sensitive spin detectors when decorating the probe of a scanning tunneling microscope (STM). However, the impact of the atomic-scale electrode details on the molecular spin state has remained elusive to date. Here, a nickelocene (Nc) STM junction is manipulated in an atom-wise manner showing clearly the dependence of the spin excitation spectrum on the anchoring of Nc to Cu(111), a Cu monomer and trimer. Moreover, while the spin state of the same Nc tip is a triplet with tunable spin excitation energies upon contacting the surface, it transitions to a Kondo-screened doublet on a Cu atom. Notably, the non-trivial magnetic exchange interaction of the molecular spin with the electron continuum of the substrate determines the spectral line shape of the spin excitations.

The chemical vapor deposition (CVD) growth of boron carbide (B x C) layers on 4H-SiC, 4{\textdegree}off substrates was studied. Depending on the polarity of the substrate, different results were obtained. On Si face, the direct CVD growth at 1600{\textdegree}C under a mixture of BCl 3 +C 3 H 8 systematically led to polycrystalline B x C films, whatever the C/B ratio in the gas phase. On the C face, heteroepitaxial growth was obtained for C/B ratios = 12 or higher with a step bunched morphology. If a boridation step (10 min at 1200{\textdegree}C under BCl 3 flow) was used before the CVD growth, then heteroepitaxy was successful on both substrate polarities. To explain these results, a mechanism is proposed which involves the nature of the chemical bonds at the early stage of nucleation. It is suggested that a full B coverage of the SiC surface should favor the nucleation of the B-rich (0001) plane of B x C, promoting thus the heteroepitaxial growth along this direction.

We consider a system of three reaction-diffusion equations modeling the interaction between a prey species and two predators species including functional responses of Holly type-II and Leslie-Gower type. We propose a reaction-diffusion model with five equations with simpler functional responses which, in the fast reaction limit, allows to recover the zero-th order terms of the initially considered system. The diffusive part of the initial equations is however modified and cross diffusion terms pop up. We first study the equilibria of this new system and show that no Turing instability appears. We then rigorously prove a partial result of convergence for the fast reaction limit (in 1D and 2D)