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In this paper, we introduce a novel method for safe navigation in agricultural robotics. As global environmental challenges intensify, robotics offers a powerful solution to reduce chemical usage while meeting the increasing demands for food production. However, significant challenges remain in ensuring the autonomy and resilience of robots operating in unstructured agricultural environments. Obstacles such as crops and tall grass, which are deformable, must be identified as safely traversable, compared to rigid obstacles. To address this, we propose a new traversability analysis method based on a 3D spectral map reconstructed using a LIDAR and a multispectral camera. This approach enables the robot to distinguish between safe and unsafe collisions with deformable obstacles. We perform a comprehensive evaluation of multispectral metrics for vegetation detection and incorporate these metrics into an augmented environmental map. Utilizing this map, we compute a physics-based traversability metric that accounts for the robot's weight and size, ensuring safe navigation over deformable obstacles., Comment: This work has been submitted to the IEEE for possible publication

This chapter explores anomaly localization in medical images using denoising diffusion models. After providing a brief methodological background of these models, including their application to image reconstruction and their conditioning using guidance mechanisms, we provide an overview of available datasets and evaluation metrics suitable for their application to anomaly localization in medical images. In this context, we discuss supervision schemes ranging from fully supervised segmentation to semi-supervised, weakly supervised, self-supervised, and unsupervised methods, and provide insights into the effectiveness and limitations of these approaches. Furthermore, we highlight open challenges in anomaly localization, including detection bias, domain shift, computational cost, and model interpretability. Our goal is to provide an overview of the current state of the art in the field, outline research gaps, and highlight the potential of diffusion models for robust anomaly localization in medical images.

This paper presents the development of machine learning (ML) models to predict hypoxemia severity during emergency triage, especially in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) events, using physiological data from medical-grade sensors. Gradient Boosting Models (XGBoost, LightGBM, CatBoost) and sequential models (LSTM, GRU) were trained on physiological and demographic data from the MIMIC-III and IV datasets. A robust preprocessing pipeline addressed missing data, class imbalances, and incorporated synthetic data flagged with masks. Gradient Boosting Models (GBMs) outperformed sequential models in terms of training speed, interpretability, and reliability, making them well-suited for real-time decision-making. While their performance was comparable to that of sequential models, the GBMs used score features from six physiological variables derived from the enhanced National Early Warning Score (NEWS) 2, which we termed NEWS2+. This approach significantly improved prediction accuracy. While sequential models handled temporal data well, their performance gains did not justify the higher computational cost. A 5-minute prediction window was chosen for timely intervention, with minute-level interpolations standardizing the data. Feature importance analysis highlighted the significant role of mask and score features in enhancing both transparency and performance. Temporal dependencies proved to be less critical, as Gradient Boosting Models were able to capture key patterns effectively without relying on them. This study highlights ML's potential to improve triage and reduce alarm fatigue. Future work will integrate data from multiple hospitals to enhance model generalizability across clinical settings., Comment: 12 figures, 12 tables and 39 pages

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).

We prove a conjecture of Arunachalam & Dutt ([AD24]) on the existence of a tolerant stabilizer testing algorithm, and achieve an exponential improvement in the parameters of the tester. Key to our argument is a generalized uncertainty relation for sets of Pauli operators, based on the Lovasz theta function., Comment: 9 pages, several really large powers. Comments welcome!

After over three decades of unsuccessful attempts, we report the first detection of molecular gas emission in Malin 1, the largest spiral galaxy observed to date, and one of the most iconic giant low surface brightness galaxies. Using ALMA, we detect significant $^{12}$CO(J=1-0) emission in the galaxy's central region and tentatively identify CO emission across three regions on the disc. These observations allow for a better estimate of the H$_2$ mass and molecular gas mass surface density, both of which are remarkably low given the galaxy's scale. By integrating data on its HI mass, we derive a very low molecular-to-atomic gas mass ratio. Overall, our results highlight the minimal presence of molecular gas in Malin 1, contrasting sharply with its extensive, homogeneous atomic gas reservoir. For the first time, we position Malin 1 on the Kennicutt-Schmidt (K-S) diagram, where it falls below the main sequence for normal spirals, consistent with previous upper limits but now with more accurate figures. These findings are crucial for constraining our understanding of star formation processes in environments characterized by extremely low molecular gas densities and for refining models of galaxy formation, thereby improving predictions concerning the formation, evolution, and distribution of these giant, elusive galaxies., Comment: Accepted for publication in The Astrophysical Journal Letters. In press. Final version after proof corrections should appear in the ApJ Letters web page shortly. The final version includes minor corrections, wording improvements, and matches new journal citation style

The classical nucleation theory (CNT) and its modified versions provide a convenient framework for describing the nucleation process under the capillary approximation. However, these models often predict nucleation rates that depart significantly from simulation results, even for a simple Lennard-Jones fluid. This large discrepancy is likely due to the inaccurate estimation of the driving force for nucleation, which most traditional models estimate within the ideal solution approximation. In this study, we address this issue by directly calculating the driving force for nucleation using equations of state (EOS) and integrating this approach into the calculation of nucleation rates within the framework of CNT and its modified model. We apply this method to examine the condensation of a Lennard-Jones fluid and compare the resulting nucleation rates with molecular dynamics (MD) simulation data. Our results demonstrate that at relatively low supersaturation, where the capillary approximation is reasonable, our thermodynamic models exhibit excellent agreement with MD results, significantly outperforming traditional models. At moderate and high supersaturation, our approach continues to show a reasonable agreement with MD results. Furthermore, when comparing the results obtained by using different EOS, we find that more precise EOS generally yield better agreement with MD data.

We present high precision measurements of the radiative heat transfer of a glass microsphere immersed in a thermal bath in vacuum facing three different planar substrates (SiO2, SiC and Au), which exhibit very different optical behaviors in the infrared region. Using a thermoresistive probe on a cantilever, we show the non-monotonic behavior of the radiative flux between the microsphere and its environment when the microsphere is brought closer to the substrate in the far-field to near-field transition regime. We demonstrate that this unexpected behavior is related to the singularities of dressed emission mechanisms in this three-body system sphere-substrate-bath with respect to the separation distance., Comment: 5 pages, 3 figures + Supplementary

When a magnetic field is applied in the perpendicular direction to a metallic disk under the action of a radial bias voltage, a tangential electric current superimposes to the radial current due to the presence of the Lorentz force which acts on electrons. Here we introduce a thermal analog of this Corbino effect in many-body systems made of nonreciprocal bodies which interact by exchanging photons in near-field regime. In systems out of thermal equilibrium with a radial temperature gradient, we demonstrate that the Poynting field in the Corbino geometry is bent in presence of an external magnetic field, giving rise to a tangential heat flux. This thermomagnetic effect could find applications in the field of thermal management and energy conversion at nanoscale., Comment: 5 pages, 4 figures

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

Quantifying the myelin sheath radius of myelinated axons in vivo is important for understanding, diagnosing, and monitoring various neurological disorders. Despite advancements in diffusion MRI (dMRI) microstructure techniques, models specifically designed to estimate myelin sheath radii remain unavailable. In this proof-of-concept theoretical study, we present two novel dMRI models that characterize the signal from water diffusion confined to cylindrical surfaces, approximating myelin water diffusion. We derive their spherical mean signals, which conveniently eliminate fiber orientation and dispersion effects. These models are further extended to account for multiple concentric cylinders, mimicking the layered structure of myelin. Additionally, we introduce a method to convert histological distributions of axonal inner radii from the literature into myelin sheath radius distributions and derive analytical expressions to estimate the effective myelin sheath radius expected from these distributions. Monte Carlo (MC) simulations conducted in cylindrical and spiral geometries validate the models, demonstrating agreement with analytical predictions across various diffusion regimes and significant correlations between the effective radii estimated from the histological distributions and the effective radius obtained by fitting the resulting dMRI signal to a single-cylinder model. These models may be integrated into existing multi-compartment dMRI techniques, opening the door to non-invasive, in vivo assessments of myelin sheath radii in MRI scanners equipped with strong diffusion gradients that enable measurements with short echo times. Further work is required to validate the technique with real dMRI data., Comment: 55 pages, 7 figures, 1 table, manuscript under review

Track finding can be considered as a complex optimization problem initially introduced in particle physics involving the reconstruction of particle trajectories. A track is typically composed of several consecutive segments (track segments) that resembles a smooth curve without bifurcations. In this paper various modeling approaches are explored in order to assess both their impact and their effectiveness in solving them using quantum and classical methods. We present implementations of three classical models using CPLEX, two quantum models running on actual D-Wave quantum computers, and one quantum model on a D-Wave simulator. To facilitate a fair comparative study and encourage future research in this area, we introduce a new set of benchmark instances, categorized into small, medium, and large scales. Our evaluation of these methods on the benchmark instances indicates that D-Wave methods offer an excellent balance between computation time and result quality, outperforming CPLEX in numerous cases.

Stacking van der Waals magnets holds promise for creating new hybrid materials with properties that do not exist in bulk materials. Here we investigate orthogonally twisted stacks of the van der Waals antiferromagnet CrSBr, aiming to exploit an extreme misalignment of magnetic anisotropy across the twisted interface.Using nitrogen-vacancy centre microscopy, we construct vector maps of the magnetisation, and track their evolution under an external field, in a range of twisted compensated and uncompensated configurations differing by the number of layers. We show that twisted stacking consistently modifies the local magnetic switching behaviour of constituent flakes, and that these modifications are spatially non-uniform. In the case of compensated component flakes (even number of layers), we demonstrate that the combination of dipolar coupling and stacking-induced strain can reduce the switching field by over an order of magnitude. Conversely, in uncompensated component flakes (odd number of layers), we observe indications of a non-zero interlayer exchange interaction between twisted flakes during magnetization reversal, which can persistently modify magnetic order. This work highlights the importance of spatial imaging in investigating stacking-induced magnetic effects, particularly in the case of twistronics where spatial variation is expected and can be conflated with structural imperfections., Comment: 6 main text pages, 4 figures plus 8 supplementary information pages, 9 figures

One of the outstanding goals of the planetary science community is to measure the present-day atmospheric composition of planets and link this back to formation. As giant planets are formed by accreting gas, ices, and rocks, constraining the relative amounts of these components is critical to understand their formation and evolution. For most known planets, including the Solar System giants, this is difficult as they reside in a temperature regime where only volatile elements (e.g., C, O) can be measured, while refractories (e.g., Fe, Ni) are condensed to deep layers of the atmosphere where they cannot be remotely probed. With temperatures allowing for even rock-forming elements to be in the gas phase, ultra-hot Jupiter atmospheres provide a unique opportunity to simultaneously probe the volatile and refractory content of giant planets. Here we directly measure and obtain bounded constraints on the abundances of volatile C and O as well as refractory Fe and Ni on the ultra-hot giant exoplanet WASP-121b. We find that ice-forming elements are comparatively enriched relative to rock-forming elements, potentially indicating that WASP-121b formed in a volatile-rich environment much farther away from the star than where it is currently located. The simultaneous constraint of ice and rock elements in the atmosphere of WASP-121b provides insights into the composition of giant planets otherwise unattainable from Solar System observations., Comment: 24 pages, 9 figures, 2 tables, Accepted for publication in The Astronomical Journal

Deep generative modeling has emerged as a powerful tool for synthesizing realistic medical images, driving advances in medical image analysis, disease diagnosis, and treatment planning. This chapter explores various deep generative models for 3D medical image synthesis, with a focus on Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Models (DDMs). We discuss the fundamental principles, recent advances, as well as strengths and weaknesses of these models and examine their applications in clinically relevant problems, including unconditional and conditional generation tasks like image-to-image translation and image reconstruction. We additionally review commonly used evaluation metrics for assessing image fidelity, diversity, utility, and privacy and provide an overview of current challenges in the field.

We report on spin atomistic calculations used to model static and dynamic magnetic properties of inhomogeneous ultrathin iron films. Active magnetic layers in next-generation spintronic devices are becoming so thin that they exhibit some variable degree of roughness at the low-scale making them magnetically inhomogeneous. We propose a multiscale approach to progressively shift from a rough atomic-scale system to an ensemble of macrospins. By studying nanoscale islands of atoms in contact with each other, we demonstrate that ultrathin rough layers can be described by a set of macrospins coupled by a Heisenberg-like exchange interaction driven by the existence and shape of nanoconstrictions linking the islands. We show that the magnetization dynamics at 0 K is strongly impacted by this surface morphology since the resonant frequency of a typical ultrathin iron layer can drop by up to an order of magnitude due to inhomogeneities. Additionally, we used Monte Carlo simulations to determine the ferromagnetic-paramagnetic and spin reorientation transition temperatures for various morphology parameters and we show how nanoconstrictions and shapes of the atomic clusters can modify these transition temperatures. Our results demonstrate the possibility to account for the morphology of ultrathin structures with significant roughness. We believe that our approach makes it possible to model complete devices as close as possible to experimental reality.

In this communication, we report on the results of the second phase of the Exoplanet Imaging Data Challenge started in 2019. This second phase focuses on the characterization of point sources (exoplanet signals) within multispectral high-contrast images from ground-based telescopes. We collected eight data sets from two high-contrast integral field spectrographs (namely Gemini-S/GPI and VLT/SPHERE-IFS) that we calibrated homogeneously, and in which we injected a handful of synthetic planetary signals (ground truth) to be characterized by the data challenge participants. The tasks of the participants consist of (1) extracting the precise astrometry of each injected planetary signals, and (2) extracting the precise spectro-photometry of each injected planetary signal. Additionally, the participants may provide the 1-sigma uncertainties on their estimation for further analyses. When available, the participants can also provide the posterior distribution used to estimate the position/spectrum and uncertainties. The data are permanently available on a Zenodo repository and the participants can submit their results through the EvalAI platform. The EvalAI submission platform opened on April 2022 and closed on the 31st of May 2024. In total, we received 4 valid submissions for the astrometry estimation and 4 valid submissions for the spectrophotometry (each submission, corresponding to one pipeline, has been submitted by a unique participant). In this communication, we present an analysis and interpretation of the results., Comment: Submitted to SPIE Astronomical Telescopes + Instrumentation 2024, Adaptive Optics Systems IX, Paper 13097-40

Magnetized beams beam with significant canonical angular momentum are critical to electron cooling of hadron beams such as contemplated in next-generation hadron and electron-ion colliders. The transport of magnetized electron beams over long distances in a locally non-axisymmetric external field is challenging. An alternative is to transform the beam into an uncoupled "flat beam", transport the produced "flat" beam over a long distance, and reintroduce the cross-plane coupling to "re-magnetize" the beam. In this paper, we demonstrate via numerical simulation and laboratory experiments such a cascaded-transformation approach., Comment: Submitted to Proceedings of AAC 2024 Workshop

We propose a unified view of the polarity of functions, that encompasses all specific definitions, generalizes several well-known properties and provides new results. We show that bipolar sets and bipolar functions are isomorphic lattices. Also, we explore three possible notions of polar subdifferential associated with a nonnegative function, and we make the connection with the notion of alignement of vectors.

We constrain the fundamental-mode ($f$-mode) oscillation frequencies of nonrotating neutron stars using a phenomenological Gaussian process model for the unknown dense-matter equation of state conditioned on a suite of gravitational-wave, radio and X-ray observations. We infer the quadrupolar $f$-mode frequency preferred by the astronomical data as a function of neutron star mass, with error estimates that quantify the impact of equation of state uncertainty, and compare it to the contact frequency for inspiralling neutron-star binaries, finding that resonance with the orbital frequency can be achieved for the coalescences with the most unequal mass ratio. For an optimally configured binary neutron star merger, we estimate the gravitational waveform's tidal phasing due to $f$-mode dynamical tides as $7^{+2}_{-3}$ rad at merger. We assess prospects for distinguishing $f$-mode dynamical tides with current and future-generation gravitational-wave observatories., Comment: 12 pages, 7 figures

Under some initial and boundary conditions, the rapid reaction-thermal diffusion process taking place during frontal polymerization (FP) destabilizes the planar mode of front propagation, leading to spatially varying, complex hierarchical patterns in thermoset polymeric materials. Although modern reaction-diffusion models can predict the patterns resulting from unstable FP, the inverse design of patterns, which aims to retrieve process conditions that produce a desired pattern, remains an open challenge due to the non-unique and non-intuitive mapping between process conditions and manufactured patterns. In this work, we propose a probabilistic generative model named univariate conditional variational autoencoder (UcVAE) for the inverse design of hierarchical patterns in FP-based manufacturing. Unlike the cVAE, which encodes both the design space and the design target, the UcVAE encodes only the design space. In the encoder of the UcVAE, the number of training parameters is significantly reduced compared to the cVAE, resulting in a shorter training time while maintaining comparable performance. Given desired pattern images, the trained UcVAE can generate multiple process condition solutions that produce high-fidelity hierarchical patterns.

The upcoming Electron-Ion Collider (EIC), with its high luminosity, will offer an unprecedented opportunity to explore the internal structure of atomic nucleus over an extended energy range from $\sqrt{s_{ep}} =$ 45 GeV to $\sqrt{s_{ep}} =$ 140 GeV. A particularly promising aspect of this collider is the study of the partonic structure with quasi-real photons which can also be studied in inclusive ultra-peripheral collisions at the Large Hadron Collider (LHC). In this work, we present our validation of resolved photoproduction at fixed order for (next-to-)leading order using \texttt{MadGraph5\_aMC@NLO}, a widely adopted framework at the LHC., Comment: 6 pages. 4 figures, proceedings for the "31st International Workshop on Deep Inelastic Scattering (DIS2024)", 8-12 April 2024, Grenoble, France

A transverse radiative heat flux induced by the gradient of spin angular momentum of photons in non-reciprocal systems is predicted. This thermal analog of the inverse spin Hall effect is analyzed in magneto-optical networks exhibiting C4 symmetry, under the action of spatially variable external magnetic fields. This finding opens new avenues for thermal management and energy conversion with non-reciprocal systems through a localized and dynamic control of the spin angular momentum of light.

In any scalar-tensor theory of gravity exhibiting a screening mechanism, the fifth force mediated by the scalar field is dynamically suppressed at sub-Solar system scales, allowing it to pass existing tests of gravity. As a result, a major research effort has been carried out over the past decades to 'outsmart' screened scalars in this game of hide-and-seek. While most of such tests rely on fifth force effects, one should keep in mind that the latter are by no means the only physical feature of scalar-tensor gravity. In particular, this article investigates the possibility of testing screened scalar-tensor models by means of gravitational redshift measurements performed with atomic clocks. Upon deriving the expression for the redshift in this framework, we propose a thought experiment for testing the chameleon model by clock comparisons, which guides us towards more realistic experimental setups, in the laboratory and in space. We find that the current level of precision exhibited by leading-edge technology is still too low to yield competitive constraints on the chameleon model., Comment: 22 pages, 8 figures

Evaluating retrieval-augmented generation (RAG) systems remains challenging, particularly for open-ended questions that lack definitive answers and require coverage of multiple sub-topics. In this paper, we introduce a novel evaluation framework based on sub-question coverage, which measures how well a RAG system addresses different facets of a question. We propose decomposing questions into sub-questions and classifying them into three types -- core, background, and follow-up -- to reflect their roles and importance. Using this categorization, we introduce a fine-grained evaluation protocol that provides insights into the retrieval and generation characteristics of RAG systems, including three commercial generative answer engines: You.com, Perplexity AI, and Bing Chat. Interestingly, we find that while all answer engines cover core sub-questions more often than background or follow-up ones, they still miss around 50% of core sub-questions, revealing clear opportunities for improvement. Further, sub-question coverage metrics prove effective for ranking responses, achieving 82% accuracy compared to human preference annotations. Lastly, we also demonstrate that leveraging core sub-questions enhances both retrieval and answer generation in a RAG system, resulting in a 74% win rate over the baseline that lacks sub-questions.

As language technologies become widespread, it is important to understand how variations in language affect reader perceptions -- formalized as the isolated causal effect of some focal language-encoded intervention on an external outcome. A core challenge of estimating isolated effects is the need to approximate all non-focal language outside of the intervention. In this paper, we introduce a formal estimation framework for isolated causal effects and explore how different approximations of non-focal language impact effect estimates. Drawing on the principle of omitted variable bias, we present metrics for evaluating the quality of isolated effect estimation and non-focal language approximation along the axes of fidelity and overlap. In experiments on semi-synthetic and real-world data, we validate the ability of our framework to recover ground truth isolated effects, and we demonstrate the utility of our proposed metrics as measures of quality for both isolated effect estimates and non-focal language approximations.

Neutron star properties depend on both nuclear physics and astrophysical processes, and thus observations of neutron stars offer constraints on both large-scale astrophysics and the behavior of cold, dense matter. In this study, we use astronomical data to jointly infer the universal equation of state of dense matter along with two distinct astrophysical populations: Galactic neutron stars observed electromagnetically and merging neutron stars in binaries observed with gravitational waves. We place constraints on neutron star properties and quantify the extent to which they are attributable to macrophysics or microphysics. We confirm previous results indicating that the Galactic and merging neutron stars have distinct mass distributions. The inferred maximum mass of both Galactic neutron stars, $M_{\rm pop, EM}=2.05^{+0.11}_{-0.06}\,M_{\odot}$ (median and 90\% symmetric credible interval), and merging neutron star binaries, $M_{\rm pop, GW}=1.85^{+0.39}_{-0.16}\,M_{\odot}$, are consistent with the maximum mass of nonrotating neutron stars set by nuclear physics, $M_{\rm TOV} =2.28^{+0.41}_{-0.21}\,M_\odot$. The radius of a $1.4\,M_{\odot}$ neutron star is $12.2^{+0.8}_{-0.9}\,$km, consistent with, though $\sim 20\%$ tighter than, previous results using an identical equation of state model. Even though observed Galactic and merging neutron stars originate from populations with distinct properties, there is currently no evidence that astrophysical processes cannot produce neutron stars up to the maximum value imposed by nuclear physics., Comment: 16 pages, 13 figures

Electricity forecasting has been a recurring research topic, as it is key to finding the right balance between production and consumption. While most papers are focused on the national or regional scale, few are interested in the household level. Desegregated forecast is a common topic in Machine Learning (ML) literature but lacks explainability that household energy forecasts require. This paper specifically targets the challenges of forecasting electricity use at the household level. This paper confronts common Machine Learning algorithms to electricity household forecasts, weighing the pros and cons, including accuracy and explainability with well-known key metrics. Furthermore, we also confront them in this paper with the business challenges specific to this sector such as explainability or outliers resistance. We introduce a custom decision tree, aiming at providing a fair estimate of the energy consumption, while being explainable and consistent with human intuition. We show that this novel method allows greater explainability without sacrificing much accuracy. The custom tree methodology can be used in various business use cases but is subject to limitations, such as a lack of resilience with outliers.

We present several generative and predictive algorithms based on the RKHS (reproducing kernel Hilbert spaces) methodology, which most importantly are scalable in the following sense. It is well recognized that the RKHS methodology leads one to efficient and robust algorithms for numerous tasks in data science, statistics, and scientific computations.However, the standard implementations remain difficult to scale to encompass large data sets. In this paper, we introduce a simple and robust, divide-and-conquer approach which applies to large scale data sets and relies on suitably formulated, kernel-based algorithms: we distinguish between extrapolation, interpolation, and optimal transport steps. We explain how to select the best algorithms in specific applications thanks to some feedback, mainly consisting of perfomance criteria. Our main focus for the applications and challenging problems arising in industrial applications, such as the generation of mesh for efficient numerical simulations, the design of generators of conditional distributions, the transition probability matrix for statistic or stochastic applications, as well as various tasks of interest to the artificial intelligence community. Indeed, the proposed algorithms are relevant for supervised or unsupervised learning, generative methods, and reinforcement learning., Comment: 15 pages

The advancement of concrete 3D printing (C3DP) technology has revolutionized the construction industry, offering unique opportunities for innovation and efficiency. At the heart of this process lies a comprehensive digital chain that integrates various stages, from initial design to post-processing. This article provides an overview of this digital chain, explaining each crucial step. The chain begins with design, utilizing Design for Additive Manufacturing (DFAM) concept and parametric modeling to create optimized structures. Path generation follows, determining the precise toolpath for extruding concrete layers. Simulations, both numerical and analytical, ensure the design's integrity and feasibility. Several articles have addressed parametric modeling, process and numerical simulation, and the post-processing phase. However, none has proposed an updated methodology for the workflow. This study aims to propose a robust digital chain for C3DP technology, using one platform (3Dexperience) and seamless data transfer between applications. These steps provide insights into the structural performance of printed components, enabling necessary adjustments and optimizations. In essence, the digital chain coordinates a seamless workflow that transforms digital designs into concrete structures, unlocking the full potential of C3DP and paving the way for innovative and efficient construction., Comment: Digital Concrete 2024 - Supplementary Proceedings, Sep 2024, Munich, France

Significant effects of electric currents on mass transport in liquid metals have been observed for long, but the origin of the corresponding driving forces remains unclear in the literature. Without current, two driving forces induce mass transport in liquid metals. (i) A chemical force, coming from concentration gradients. In that case, mass transport occurs by diffusion. (ii) A physical force, resulting from density gradients thermally and/or chemically induced. Here, mass transport occurs by thermal and/or solutal convection. Under electric currents, these driving forces are modified, either by electrostatic or magnetic forces, the corresponding mechanisms being referred to as electroconvection and magnetoconvection, respectively. However, these mechanisms cannot easily be distinguished from each other, leading to confusion in literature. Here, it has been shown that, in the liquid Sn-Zn system, the driving force induced by 500-1000 A/cm 2 electric current densities is magnetic rather than electrostatic, the mechanism being therefore magnetoconvection.

We develop a quasisymmetric analogue of the theory of Schubert cycles, building off of our previous work on a quasisymmetric analogue of Schubert polynomials and divided differences. Our constructions result in a natural geometric interpretation for the ring of quasisymmetric coinvariants., Comment: 44 pages + 3 appendices (12 pages)

We introduce a planner designed to guide robot manipulators in stably placing objects within intricate scenes. Our proposed method reverses the traditional approach to object placement: our planner selects contact points first and then determines a placement pose that solicits the selected points. This is instead of sampling poses, identifying contact points, and evaluating pose quality. Our algorithm facilitates stability-aware object placement planning, imposing no restrictions on object shape, convexity, or mass density homogeneity, while avoiding combinatorial computational complexity. Our proposed stability heuristic enables our planner to find a solution about 20 times faster when compared to the same algorithm not making use of the heuristic and eight times faster than a state-of-the-art method that uses the traditional sample-and-evaluate approach. Our proposed planner is also more successful in finding stable placements than the five other benchmarked algorithms. Derived from first principles and validated in ten real robot experiments, our planner offers a general and scalable method to tackle the problem of object placement planning with rigid objects., Comment: Submitted to IEEE Transactions on Robotics. Contains 14 pages, 11 figures, and 3 tables

Hexagonal boron nitride (hBN) presents valuable intrinsic properties and attracts considerable attention for the development of novel two-dimensional (2D) materials-based technologies. Even though huge efforts have been made to improve the bottom-up synthesis of integrated and high quality hBN, the devices presenting the best performances are still made using hBN exfoliated from bulk crystals. In this context, we explore the Polymer-Derived Ceramics (PDC) route coupled to a high temperature process that produces millimetric and high quality hBN crystals. By investigating the (micro)structure of several samples, we demonstrate that the crystal growth occurs by segregation from a Li3BN2-BN solution upon cooling and from hBN seeds. In particular, we show that crystallization can occur at a temperature as low as 1400{\textdegree}C. Overall, these results show that hBN crystal growth in the Li3BN2-BN system is compatible with conventional flux methods that may be the most promising platform for continuous seeded hBN crystal growth.

Exploiting symmetries in tensor network algorithms plays a key role for reducing the computational and memory costs. Here we explain how to incorporate the Hermitian symmetry in double-layer tensor networks, which naturally arise in methods based on projected entangled-pair states (PEPS). For real-valued tensors the Hermitian symmetry defines a $\mathbb{Z}_2$ symmetry on the combined bra and ket auxiliary level of the tensors. By implementing this symmetry, a speedup of the computation time by up to a factor 4 can be achieved, while expectation values of observables and reduced density matrices remain Hermitian by construction. Benchmark results based on the corner transfer matrix renormalization group (CTMRG) and higher-order tensor renormalization group (HOTRG) are presented. We also discuss how to implement the Hermitian symmetry in the complex case, where a similar speedup can be achieved., Comment: 6 pages, 4 figures

Constrained synthesizability is an unaddressed challenge in generative molecular design. In particular, designing molecules satisfying multi-parameter optimization objectives, while simultaneously being synthesizable and enforcing the presence of specific commercial building blocks in the synthesis. This is practically important for molecule re-purposing, sustainability, and efficiency. In this work, we propose a novel reward function called TANimoto Group Overlap (TANGO), which uses chemistry principles to transform a sparse reward function into a dense and learnable reward function -- crucial for reinforcement learning. TANGO can augment general-purpose molecular generative models to directly optimize for constrained synthesizability while simultaneously optimizing for other properties relevant to drug discovery using reinforcement learning. Our framework is general and addresses starting-material, intermediate, and divergent synthesis constraints. Contrary to most existing works in the field, we show that incentivizing a general-purpose (without any inductive biases) model is a productive approach to navigating challenging optimization scenarios. We demonstrate this by showing that the trained models explicitly learn a desirable distribution. Our framework is the first generative approach to tackle constrained synthesizability.

Large Language Model (LLM)-based applications are graduating from research prototypes to products serving millions of users, influencing how people write and consume information. A prominent example is the appearance of Answer Engines: LLM-based generative search engines supplanting traditional search engines. Answer engines not only retrieve relevant sources to a user query but synthesize answer summaries that cite the sources. To understand these systems' limitations, we first conducted a study with 21 participants, evaluating interactions with answer vs. traditional search engines and identifying 16 answer engine limitations. From these insights, we propose 16 answer engine design recommendations, linked to 8 metrics. An automated evaluation implementing our metrics on three popular engines (You.com, Perplexity.ai, BingChat) quantifies common limitations (e.g., frequent hallucination, inaccurate citation) and unique features (e.g., variation in answer confidence), with results mirroring user study insights. We release our Answer Engine Evaluation benchmark (AEE) to facilitate transparent evaluation of LLM-based applications.

The ability to coherently control and read out qubits with long coherence times in a scalable system is a crucial requirement for any quantum processor. Nuclear spins in the solid state have shown great promise as long-lived qubits. Control and readout of individual nuclear spin qubit registers has made major progress in the recent years using individual electron spin ancilla addressed either electrically or optically. Here, we present a new platform for quantum information processing, consisting of $^{183}$W nuclear spin qubits adjacent to an Er$^{3+}$ impurity in a CaWO$_4$ crystal, interfaced via a superconducting resonator and detected using a microwave photon counter at 10mK. We study two nuclear spin qubits with $T_2^*$ of $0.8(2)~$s and $1.2(3)~$s, $T_2$ of $3.4(4)~$s and $4.4(6)~$ s, respectively. We demonstrate single-shot quantum non-demolition readout of each nuclear spin qubit using the Er$^{3+}$ spin as an ancilla. We introduce a new scheme for all-microwave single- and two-qubit gates, based on stimulated Raman driving of the coupled electron-nuclear spin system. We realize single- and two-qubit gates on a timescale of a few milliseconds, and prepare a decoherence-protected Bell state with 88% fidelity and $T_2^*$ of $1.7(2)~$s. Our results are a proof-of-principle demonstrating the potential of solid-state nuclear spin qubits as a promising platform for quantum information processing. With the potential to scale to tens or hundreds of qubits, this platform has prospects for the development of scalable quantum processors with long-lived qubits., Comment: 14 pages, 4 main figures, 7 supplementary figures

In scalar-tensor theories it is the two-derivative sigma-model interactions that like to compete at low energies with the two-derivative interactions of General Relativity (GR) $\unicode{x2014}$ at least once the dangerous zero-derivative terms of the scalar potential are suppressed (such as by a shift symmetry). But nontrivial two-derivative interactions require at least two scalars to exist and so never arise in the single-scalar models most commonly explored. Axio-dilaton models provide a well-motivated minimal class of models for which these self-interactions can be explored. We review this class of models and investigate whether these minimal two fields can suffice to describe both Dark Matter and Dark Energy. We find that they can $\unicode{x2014}$ the axion is the Dark Matter and the dilaton is the Dark Energy $\unicode{x2014}$ and that they robustly predict several new phenomena for the CMB and structure formation that can be sought in observations. These include specific types of Dark Energy evolution and small space- and time-dependent changes to particle masses post-recombination that alter the Integrated Sachs-Wolfe effect, cause small changes to structure growth and more., Comment: 27 pages, 5 figures

Accurate classification of transients obtained from spectroscopic data are important to understand their nature and discover new classes of astronomical objects. For supernovae (SNe), SNID, NGSF (a Python version of SuperFit), and DASH are widely used in the community. Each tool provides its own metric to help determine classification, such as rlap of SNID, chi2/dof of NGSF, and Probability of DASH. However, we do not know how accurate these tools are, and they have not been tested with a large homogeneous dataset. Thus, in this work, we study the accuracy of these spectral classification tools using 4,646 SEDMachine spectra, which have accurate classifications obtained from the Zwicky Transient Facility Bright Transient Survey (BTS). Comparing our classifications with those from BTS, we have tested the classification accuracy in various ways. We find that NGSF has the best performance (overall Accuracy 87.6% when samples are split into SNe Ia and Non-Ia types), while SNID and DASH have similar performance with overall Accuracy of 79.3% and 76.2%, respectively. Specifically for SNe Ia, SNID can accurately classify them when rlap > 15 without contamination from other types, such as Ibc, II, SLSN, and other objects that are not SNe (Purity > 98%). For other types, determining their classification is often uncertain. We conclude that it is difficult to obtain an accurate classification from these tools alone. This results in additional human visual inspection effort being required in order to confirm the classification. To reduce this human visual inspection and to support the classification process for future large-scale surveys, this work provides supporting information, such as the accuracy of each tool as a function of its metric., Comment: 13 pages, 6 figures, and 6 tables; accepted for publication in PASP

The recently discovered Pa 30 nebula, the putative type Iax supernova remnant associated with the historical supernova of 1181 AD, shows puzzling characteristics that make it unique among known supernova remnants. In particular, Pa 30 exhibits a complex morphology, with a unique radial and filamentary structure, and it hosts a hot stellar remnant at its center, which displays oxygen-dominated, ultra-fast winds. Because of the surviving stellar remnant and the lack of hydrogen and helium in its filaments, it has been suggested that Pa 30 is the product of a failed thermonuclear explosion in a near- or super-Chandrasekhar white dwarf, which created a sub-luminous transient, a rare sub-type of the Ia class of supernovae called type Iax. We here present a detailed study of the 3D structure and velocities of a full radial section of the remnant. The Integral Field Unit (IFU) observations, obtained with the new red channel of the Keck Cosmic Web Imager spectrograph, reveal that the ejecta are consistent with being ballistic, with velocities close to the free-expansion velocity. Additionally, we detect a large cavity inside the supernova remnant and a sharp inner edge to the filamentary structure, which coincides with the outer edge of a bright ring detected in infrared images. Finally, we detect a strong asymmetry in the amount of ejecta along the line of sight, which might hint to an asymmetric explosion. Our analysis provides strong confirmation that the explosion originated from SN 1181., Comment: 13 pages, 8 figures. Accepted for publication in ApJL

Efficient multi-messenger observations of gravitational-wave candidates from compact binary coalescence candidate events rely on data products reported in low-latency by the International Gravitational-wave Network (IGWN). While data products such as $\texttt{HasNS}$, the probability of at least one neutron star, and $\texttt{HasRemnant}$, the probability of remnant matter forming after merger, exist, these are not direct observables for a potential kilonova. Here, we present new kilonova light curve and ejecta mass data products derived from merger quantities measured in low latency, by marginalizing over our uncertainty in our understanding of the neutron star equation of state and using measurements of the source properties of the merger, including masses and spins. Two additional types of data products are proposed. The first is the probability of a candidate event having mass ejecta ($m_{\mathrm{ej}}$) greater than $10^{-3} M_\odot$, which we denote as $\texttt{HasEjecta}$. The second are $m_{\mathrm{ej}}$ estimates and accompanying $\texttt{ugrizy}$ and $\texttt{HJK}$ kilonova light curves predictions produced from a surrogate model trained on a grid of kilonova light curves from $\texttt{POSSIS}$, a time-dependent, three-dimensional Monte Carlo radiative transfer code. We are developing these data products in the context of the IGWN low-latency alert infrastructure, and will be advocating for their use and release for future detections.

High anisotropy in volumetric medical images can lead to the inconsistent quantification of anatomical and pathological structures. Particularly in optical coherence tomography (OCT), slice spacing can substantially vary across and within datasets, studies, and clinical practices. We propose to standardize OCT volumes to less anisotropic volumes by conditioning 3D diffusion models with en face scanning laser ophthalmoscopy (SLO) imaging data, a 2D modality already commonly available in clinical practice. We trained and evaluated on data from the multicenter and multimodal MACUSTAR study. While upsampling the number of slices by a factor of 8, our method outperforms tricubic interpolation and diffusion models without en face conditioning in terms of perceptual similarity metrics. Qualitative results demonstrate improved coherence and structural similarity. Our approach allows for better informed generative decisions, potentially reducing hallucinations. We hope this work will provide the next step towards standardized high-quality volumetric imaging, enabling more consistent quantifications., Comment: Accepted at NeurIPS 2024 Workshop on GenAI for Health

The use of tunable pre-pulse is a common technique to enhance the high-order harmonic generation from surface plasma. The shape and dynamic of the electron density, the degree of ionization and its rate, and the plasma heating are influenced by the pre-pulse properties. Non-uniform pre-pulse could cause a spatially varying density map to the pre-plasma region, which serves as the spectrally up-conversion and reflection surface. The corresponding geometrical feature and plasma nature under laser field will affect the harmonic emission properties. In this study, the variation in harmonic beam pointing due to the electron density shape was investigated. Particle-in-cell simulations demonstrated that both plasma hydrodynamics and geometrical optical effect induce the deviation of harmonic beam from specular reflection. This research contributes to the understanding of the surface plasma dynamics during high harmonic generation process.

We discuss the geometry of three-dimensional warped Anti-de Sitter spaces and quotients thereof, paying special attention to the underlying group manifold and global causal structure., Comment: 54 pages, 11 figures, 3 tables

We present an analysis of adaptive optics (AO) images from the Keck-I telescope of the microlensing event MOA-2011-BLG-262. The original discovery paper by Bennett et al. 2014 reports two distinct possibilities for the lens system; a nearby gas giant lens with an exomoon companion or a very low mass star with a planetary companion in the galactic bulge. The $\sim$10 year baseline between the microlensing event and the Keck follow-up observations allows us to detect the faint candidate lens host (star) at $K = 22.3$ mag and confirm the distant lens system interpretation. The combination of the host star brightness and light curve parameters yields host star and planet masses of $M_{\rm host} = 0.19 \pm 0.03M_{\odot}$ and $m_p = 28.92 \pm 4.75M_{\oplus}$ at a distance of $D_L = 7.49 \pm 0.91\,$kpc. We perform a multi-epoch cross reference to \textit{Gaia} DR3 and measure a transverse velocity for the candidate lens system of $v_L = 541.31 \pm 65.75$ km s$^{-1}$. We conclude this event consists of the highest velocity exoplanet system detected to date, and also the lowest mass microlensing host star with a confirmed mass measurement. The high-velocity nature of the lens system can be definitively confirmed with an additional epoch of high-resolution imaging at any time now. The methods outlined in this work demonstrate that the \textit{Roman} Galactic Exoplanet Survey (RGES) will be able to securely measure low-mass host stars in the bulge., Comment: 21 pages, 6 figures, 4 tables, submitted to AJ

Context. Betelgeuse is a red supergiant (RSG) that is known to vary semi-regularly on both short and long timescales. The origin of the short period of Betelgeuse has often been associated to radial pulsations but could also be due to the convection motions present at the surface of RSGs. Aims. We investigate the link between surface activity and the variability of the star. Methods. Linear polarization in Betelgeuse is a proxy of convection which is unrelated to pulsations. Using 10 years of spectropolarimetric data of Betelgeuse, we seek for periodicities in the least-squares deconvolution profiles of Stokes I, Q, U and the total linear polarization using Lomb-Scargle periodograms. Results. We find similar periods in linear polarization signals than in photometric variability. The 400 d period is too close to a peak of the window function of our data. But the two periods of 330 d and 200 d are present in the periodogram of Stokes Q and U, showing that the variability of Betelgeuse can be interpreted as due to surface convection. Conclusions. Since linear polarization in the spectrum of Betelgeuse is not known to vary with pulsations, but is linked to surface convection, and since similar periods are found in time series of photometric measurements and spectropolarimetry, we conclude that the photometric variability is due to the surface convective structures, and not to any pulsation phenomenon., Comment: 6 pages, 7 figures, accepted for publication in A&A

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.

Carcinogenesis is a proteiform phenomenon, with tumors emerging in various locations and displaying complex, diverse shapes. At the crucial intersection of research and clinical practice, it demands precise and flexible assessment. However, current biomarkers, such as RECIST 1.1's long and short axis measurements, fall short of capturing this complexity, offering an approximate estimate of tumor burden and a simplistic representation of a more intricate process. Additionally, existing supervised AI models face challenges in addressing the variability in tumor presentations, limiting their clinical utility. These limitations arise from the scarcity of annotations and the models' focus on narrowly defined tasks. To address these challenges, we developed ONCOPILOT, an interactive radiological foundation model trained on approximately 7,500 CT scans covering the whole body, from both normal anatomy and a wide range of oncological cases. ONCOPILOT performs 3D tumor segmentation using visual prompts like point-click and bounding boxes, outperforming state-of-the-art models (e.g., nnUnet) and achieving radiologist-level accuracy in RECIST 1.1 measurements. The key advantage of this foundation model is its ability to surpass state-of-the-art performance while keeping the radiologist in the loop, a capability that previous models could not achieve. When radiologists interactively refine the segmentations, accuracy improves further. ONCOPILOT also accelerates measurement processes and reduces inter-reader variability, facilitating volumetric analysis and unlocking new biomarkers for deeper insights. This AI assistant is expected to enhance the precision of RECIST 1.1 measurements, unlock the potential of volumetric biomarkers, and improve patient stratification and clinical care, while seamlessly integrating into the radiological workflow.

The aviation industry is vital for global transportation but faces increasing pressure to reduce its environmental footprint, particularly CO2 emissions from ground operations such as taxiing. Single Engine Taxiing (SET) has emerged as a promising technique to enhance fuel efficiency and sustainability. However, evaluating SET's benefits is hindered by the limited availability of SET-specific data, typically accessible only to aircraft operators. In this paper, we present a novel deep learning approach to detect SET operations using ground trajectory data. Our method involves using proprietary Quick Access Recorder (QAR) data of A320 flights to label ground movements as SET or conventional taxiing during taxi-in operations, while using only trajectory features equivalent to those available in open-source surveillance systems such as Automatic Dependent Surveillance-Broadcast (ADS-B) or ground radar. This demonstrates that SET can be inferred from ground movement patterns, paving the way for future work with non-proprietary data sources. Our results highlight the potential of deep learning to improve SET detection and support more comprehensive environmental impact assessments.