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Denoising Diffusion Probabilistic Models (DDPM) are powerful state-of-the-art methods used to generate synthetic data from high-dimensional data distributions and are widely used for image, audio and video generation as well as many more applications in science and beyond. The manifold hypothesis states that high-dimensional data often lie on lower-dimensional manifolds within the ambient space, and is widely believed to hold in provided examples. While recent results has provided invaluable insight into how diffusion models adapt to the manifold hypothesis, they do not capture the great empirical success of these models, making this a very fruitful research direction. In this work, we study DDPMs under the manifold hypothesis and prove that they achieve rates independent of the ambient dimension in terms of learning the score. In terms of sampling, we obtain rates independent of the ambient dimension w.r.t. the Kullback-Leibler divergence, and $O(\sqrt{D})$ w.r.t. the Wasserstein distance. We do this by developing a new framework connecting diffusion models to the well-studied theory of extrema of Gaussian Processes.

Transit is essential for urban transportation and achieving net-zero targets. In urban areas like the Chicago Metropolitan Region, transit enhances mobility and connects people, fostering a dynamic economy. To quantify the mobility and selected economic impacts of transit, we use a novel agent-based simulation model POLARIS to compare baseline service against a scenario in which transit is completely removed. The transit-removal scenario assumes higher car ownership and results in higher traffic congestion, numerous activity cancellations, and economic decline. In this scenario, average travel times increase by 14.2% regionally and 34.7% within the City of Chicago. The resulting congestion causes significant activity cancellations despite increased car ownership: 11.8% of non-work and 2.8% of work/school activities regionally, totaling an 8.6% overall cancellation rate. In the city, non-work cancellations would reach 26.9%, and work/school cancellations 7.3%, leading to a 19.9% overall cancellation rate. The impact varies between groups. Women and lower-income individuals are more likely to cancel activities than men and higher-income groups. Women account for 53.7% of non-work and 53.0% of total cancellations. The lowest 40% income group experiences 50.2% of non-work and 48.0% of overall cancellations. Combined, activity cancellations, travel time losses, and increased car ownership cost the region $35.4 billion. With annual public transit funding at $2.7 billion, the ratio is 13 to 1, underscoring transit's critical role in mobility, equity, and economic health.

The formation, dissolution, and dynamics of multi-particle complexes is of fundamental interest in the study of stochastic chemical systems. In 1976, Masao Doi introduced a Fock space formalism for modeling classical particles. Doi's formalism, however, does not support the assembly of multiple particles into complexes. Starting in the 2000's, multiple groups developed rule-based methods for computationally simulating biochemical systems involving large macromolecular complexes. However, these methods are based on graph-rewriting rules and/or process algebras that are mathematically disconnected from the statistical physics methods generally used to analyze equilibrium and nonequilibrium systems. Here we bridge these two approaches by introducing an operator algebra for the rule-based modeling of multi-particle complexes. Our formalism is based on a Fock space that supports not only the creation and annihilation of classical particles, but also the assembly of multiple particles into complexes, as well as the disassembly of complexes into their components. Rules are specified by algebraic operators that act on particles through a manifestation of Wick's theorem. We further describe diagrammatic methods that facilitate rule specification and analytic calculations. We demonstrate our formalism on systems in and out of thermal equilibrium, and for nonequilibrium systems we present a stochastic simulation algorithm based on our formalism. The results provide a unified approach to the mathematical and computational study of stochastic chemical systems in which multi-particle complexes play an important role., Comment: 26 pages, 9 figures

Monitoring of conflict risk in the humanitarian sector is largely based on simple historic averages. To advance our understanding, we propose Hawkes processes, a self-exciting stochastic process used to describe phenomena whereby past events increase the probability of future events occurring. The overarching goal of this work is to assess the potential for using a more statistically rigorous approach to monitor the risk of political violence and conflict events in practice and characterise their temporal and spatial patterns. The region of South Asia was selected as an exemplar of how our model can be applied globally. We individually analyse the various types of conflict events for the countries in this region and compare the results. A Bayesian, spatiotemporal variant of the Hawkes process is fitted to data gathered by the Armed Conflict Location and Event Data (ACLED) project to obtain sub-national estimates of conflict risk over time and space. Our model can effectively estimate the risk level of these events within a statistically sound framework, with a more precise understanding of the uncertainty around these estimates than was previously possible. This work enables a better understanding of conflict events which can inform preventative measures. We demonstrate the advantages of the Bayesian framework by comparing our results to maximum likelihood estimation. While maximum likelihood gives reasonable point estimates, the Bayesian approach is preferred when possible. Practical examples are presented to demonstrate how the proposed model can be used to monitor conflict risk. Comparing to current practices that rely on historical averages, we also show that our model is more stable and robust to outliers. In this work we aim to support actors in the humanitarian sector in making data-informed decisions, such as the allocation of resources in conflict-prone regions.

We investigate the irregular eutectic growth dynamics of the Al-Al3Ni alloy, in which one of the solid phases (Al3Ni) grows faceted from the liquid. Leveraging in situ optical microscopy and synchrotron transmission x-ray microscopy, we address the question of the degree of coupling between Al and Al3Ni at the growth front and that of the shape of the microstructures left behind in the bulk solid during directional solidification. Real-time optical observations bring evidence for a morphological transition from a eutectic-grain dependent, irregular eutectic growth at low solidification velocity V (typically 1 ${\mu}ms^{-1}$), to a weakly anisotropic, regular growth at higher V (reaching 10 ${\mu}ms^{-1}$). Unprecedented x-ray nano-imaging of the solid-liquid interface, and 3D characterization of the growth patterns, were made possible by a new Directional Solidification (DS) setup at Brookhaven National Laboratory's NSLS-II. At low V, the leading tips of partly faceted Al3Ni crystals are observed to grow not far ahead of the Al growth front. Correlating in situ images and postmortem 3D tomographic reconstructions reveals that the presence of faceted and non-faceted regions of Al3Ni crystals in the solid is a direct consequence of coupling and decoupling during DS, respectively. Upon increasing V , the lead distance of Al3Ni vanishes, and the shape of Al3Ni ceases to be governed by faceted growth. These observations shed light on the basic mechanisms (faceted growth, diffusive coupling, and the dynamics of trijunctions) governing the transition from faceted to rod-like growth upon increasing V in the Al3Ni system, with broad implications for a large class of irregular eutectics., Comment: 56 pages, 20 figures, preprint accepted to Acta Materialia

Lagrangian decomposition (LD) is a relaxation method that provides a dual bound for constrained optimization problems by decomposing them into more manageable sub-problems. This bound can be used in branch-and-bound algorithms to prune the search space effectively. In brief, a vector of Lagrangian multipliers is associated with each sub-problem, and an iterative procedure (e.g., a sub-gradient optimization) adjusts these multipliers to find the tightest bound. Initially applied to integer programming, Lagrangian decomposition also had success in constraint programming due to its versatility and the fact that global constraints provide natural sub-problems. However, the non-linear and combinatorial nature of sub-problems in constraint programming makes it computationally intensive to optimize the Lagrangian multipliers with sub-gradient methods at each node of the tree search. This currently limits the practicality of LD as a general bounding mechanism for constraint programming. To address this challenge, we propose a self-supervised learning approach that leverages neural networks to generate multipliers directly, yielding tight bounds. This approach significantly reduces the number of sub-gradient optimization steps required, enhancing the pruning efficiency and reducing the execution time of constraint programming solvers. This contribution is one of the few that leverage learning to enhance bounding mechanisms on the dual side, a critical element in the design of combinatorial solvers. To our knowledge, this work presents the first generic method for learning valid dual bounds in constraint programming.

We introduce a system of Saint-Venant-type equations to model laboratory experiments of dam-break particle-laden flows. We explore homogeneous and non-cohesive liquid-solid suspensions of monodispersed glass beads that propagate as single-phase flows, forming a progressively growing deposit of particles at the bottom of a smooth channel and creating a thin layer of pure liquid at the surface. The novelty of this model is twofold. First, we fully characterize the first-order behavior of these flows (mean velocity, runout distances and deposits geometry) through the sole sedimentation process of the grains, thus avoiding the use of any artificial friction to stop the flow. The model remains very simple and turns out to be effective despite the complex nature of interactions involved in these phenomena. Secondly, the sedimentation dynamics of the grains is observed to not being mainly affected by the flow, but remains comparable to that measured in static suspensions. The mathematical model is validated by comparing the experimental kinematics and deposit profiles with the simulations. The results highlight that this simplified model is sufficient to describe the general features of these flows as well as their deposit morphology, provided that the settling rate is adjusted starting from a critical value of the Reynolds number where the flow agitation begins to significantly delay the mean sedimentation velocity.

Creation of new annotated public datasets is crucial in helping advances in 3D computer vision and machine learning meet their full potential for automatic interpretation of 3D plant models. In this paper, we introduce PLANesT-3D; a new annotated dataset of 3D color point clouds of plants. PLANesT-3D is composed of 34 point cloud models representing 34 real plants from three different plant species: \textit{Capsicum annuum}, \textit{Rosa kordana}, and \textit{Ribes rubrum}. Both semantic labels in terms of "leaf" and "stem", and organ instance labels were manually annotated for the full point clouds. As an additional contribution, SP-LSCnet, a novel semantic segmentation method that is a combination of unsupervised superpoint extraction and a 3D point-based deep learning approach is introduced and evaluated on the new dataset. Two existing deep neural network architectures, PointNet++ and RoseSegNet were also tested on the point clouds of PLANesT-3D for semantic segmentation.

Extracting social determinants of health (SDoH) from unstructured medical notes depends heavily on labor-intensive annotations, which are typically task-specific, hampering reusability and limiting sharing. In this study we introduced SDoH-GPT, a simple and effective few-shot Large Language Model (LLM) method leveraging contrastive examples and concise instructions to extract SDoH without relying on extensive medical annotations or costly human intervention. It achieved tenfold and twentyfold reductions in time and cost respectively, and superior consistency with human annotators measured by Cohen's kappa of up to 0.92. The innovative combination of SDoH-GPT and XGBoost leverages the strengths of both, ensuring high accuracy and computational efficiency while consistently maintaining 0.90+ AUROC scores. Testing across three distinct datasets has confirmed its robustness and accuracy. This study highlights the potential of leveraging LLMs to revolutionize medical note classification, demonstrating their capability to achieve highly accurate classifications with significantly reduced time and cost.

This work introduces an on-the-fly (i.e., online) linear unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After deriving a sequential counterpart of the conventional linear mixing model, the proposed approach recasts the linear unmixing problem into a linear state-space estimation framework. Under Gaussian noise and state models, the estimation of the pure spectra can be efficiently conducted by resorting to Kalman filtering. Interestingly, it is shown that this Kalman filter can operate in a lower-dimensional subspace while ensuring the nonnegativity constraint inherent to pure spectra. This dimensionality reduction allows significantly lightening the computational burden, while leveraging recent advances related to the representation of essential spectral information. The proposed method is evaluated through extensive numerical experiments conducted on synthetic and real Raman data sets. The results show that this Kalman filter-based method offers a convenient trade-off between unmixing accuracy and computational efficiency, which is crucial for operating in an on-the-fly setting. To the best of the authors' knowledge, this is the first operational method which is able to solve the spectral unmixing problem efficiently in a dynamic fashion. It also constitutes a valuable building block for benefiting from acquisition and processing frameworks recently proposed in the microscopy literature, which are motivated by practical issues such as reducing acquisition time and avoiding potential damages being inflicted to photosensitive samples.

The first generation of ELT instruments includes an optical-infrared high-resolution spectrograph, indicated as ELT-HIRES and recently christened ANDES (ArmazoNes high Dispersion Echelle Spectrograph). ANDES consists of three fibre-fed spectrographs ([U]BV, RIZ, YJH) providing a spectral resolution of $\sim$100,000 with a minimum simultaneous wavelength coverage of 0.4-1.8 $\mu$m with the goal of extending it to 0.35-2.4 $\mu$m with the addition of a U arm to the BV spectrograph and a separate K band spectrograph. It operates both in seeing- and diffraction-limited conditions and the fibre feeding allows several, interchangeable observing modes including a single conjugated adaptive optics module and a small diffraction-limited integral field unit in the NIR. Modularity and fibre-feeding allow ANDES to be placed partly on the ELT Nasmyth platform and partly in the Coud\'e room. ANDES has a wide range of groundbreaking science cases spanning nearly all areas of research in astrophysics and even fundamental physics. Among the top science cases, there are the detection of biosignatures from exoplanet atmospheres, finding the fingerprints of the first generation of stars, tests on the stability of Nature's fundamental couplings, and the direct detection of the cosmic acceleration. The ANDES project is carried forward by a large international consortium, composed of 35 Institutes from 13 countries, forming a team of almost 300 scientists and engineers which include the majority of the scientific and technical expertise in the field that can be found in ESO member states., Comment: SPIE astronomical telescope and instrumentation 2024, in press

We investigate the relation between degree sequences of trees and the majorization order using the Muirhead theorem. In this way, we prove a theorem that provides a necessary and sufficient condition for delta sequences of trees to be comparable in the majorization order. Although our investigation is largely theoretical, our study contributes to a better knowledge of trees as an important data structure. We point out that this study is among the few combining Lorenz curves and majorization on the one hand, and degree sequences of networks on the other., Comment: 17 pages

This paper surveys Campana's theory of C-pairs (or "geometric orbifolds") in the complex-analytic setting, to serve as a reference for future work. Written with a view towards applications in hyperbolicity, rational points, and entire curves, it introduces the fundamental definitions of C-pair-theory systematically. In particular, it establishes an appropriate notion of "morphism", which agrees with notions from the literature in the smooth case, but is better behaved in the singular setting and has functorial properties that relate it to minimal model theory.

NOTT (formerly Hi-5) is the L'-band (3.5-4.0~microns) nulling interferometer of Asgard, an instrument suite in preparation for the VLTI visitor focus. The primary scientific objectives of NOTT include characterizing (i) young planetary systems near the snow line, a critical region for giant planet formation, and (ii) nearby main-sequence stars close to the habitable zone, with a focus on detecting exozodiacal dust that could obscure Earth-like planets. In 2023-2024, the final warm optics have been procured and assembled in a new laboratory at KU Leuven. First fringes and null measurements were obtained using a Gallium Lanthanum Sulfide (GLS) photonic chip that was also tested at cryogenic temperatures. In this paper, we present an overall update of the NOTT project with a particular focus on the cold mechanical design, the first results in the laboratory with the final NOTT warm optics, and the ongoing Asgard integration activities. We also report on other ongoing activities such as the characterization of the photonic chip (GLS, LiNbO3, SiO), the development of the exoplanet science case, the design of the dispersion control module, and the progress with the self-calibration data reduction software., Comment: 11 pages (incl. 5 figures); Proc. SPIE Astronomical Telescopes + Instrumentation 2024 (Yokohama; Japan), Optical and Infrared Interferometry and Imaging VI

The paper studies the complex 1-dimensional polynomial vector fields with real coefficients under topological orbital equivalence preserving the separatrices of the pole at infinity. The number of generic strata is determined, and a complete parametrization of these strata is given in terms of a modulus formed by a combinatorial and an analytic part. The bifurcation diagram is described for the degree 4. A realization theorem is proved for any generic modulus., Comment: 24 pages, 9 figures

Nonnegative measures that are solutions to a transport equation with continuous coefficients have been widely studied. Because of the low regularity of the associated vector field, there is no natural flow since nonuniqueness of integral curves is the general rule. It has been known since the works by L. Ambrosio [2] and L. Ambrosio and G. Crippa [3, 4] that such measures can be described as a superposition of $\delta$-measures supported on integral curves. In this article, motivated by some observability questions for the wave equation, we are interested in such transport equations in the case of domains with boundary. Associated with a wave equation with C^1-coefficients are bicharacteristics that are integral curves of a continuous Hamiltonian vector field. We first study in details their behaviour in the presence of a boundary and define their natural generalisation that follows the laws of geometric optics. Then, we introduce a natural class of transport equations with a source term on the boundary, and we prove that any nonnegative measure satisfying such an equation has a union of maximal generalized bicharacteristics for support. This result is a weak form of the superposition principle in the presence of a boundary. With its companion article [7], this study completes the proof of wave observability generalizing the celebrated result of Bardos, Lebeau, and Rauch [5] in a low regularity framework where coefficients of the wave equation (and associated metric) are C^1 and the boundary and the manifold are C^2., Comment: 103 pages, 9 figures

The celebrated geometric control condition of Bardos, Lebeau, and Rauch is necessary and sufficient for wave observability [1,7] and exact controllability. It requires that any point in phase-space be transported by the generalized geodesic flow to the region of observation in some finite time. The initial smoothness (\Cinf) required on the coefficients of the metric to prove that exact control and geometric control are essentially equivalent was subsequently relaxed to \Con2-metrics/coefficients and \Con3-domains [2] which is close to the optimal smoothness required to preserve a generalized geodesic flow. In this article, we investigate a natural generalization of the geometric control condition that makes sense for \Con1-metrics and we prove that wave observability holds under this condition. Moreover, we establish that the observability property is stable under rougher (Lipschitz) perturbation of the metric. We also provide a geometric necessary condition for wave observability to hold. Transport equations that describe the propagation of semi-classical measures are at the heart of the arguments. They are natural extensions to geometries with boundaries of usual transport equations. This article is mainly dedicated to the proof of such propagation equations in this very rough context., Comment: 119 pages

We introduce three representative topics in semi-classical analysis. Starting from the correspondence between classical and quantum mechanics, basic semi-classical analysis tools and results are presented. The three topics are investigated in the light of the introduced techniques allowing one to emphasize different aspects of semi-classical analysis., Comment: This article presents some ideas and applications of semi-classical analysis

We present a spatially resolved study of the kinematical properties of known supernova remnants (SNRs) in the nearest galaxies of the SIGNALS survey, namely NGC 6822 (one object) and M33 (163 objects), based on data obtained with the SITELLE Imaging Fourier Transform Spectrometer (iFTS) at the Canada-France-Hawaii Telescope. The purpose of this paper is to provide a better scheme of identification for extragalactic SNRs and, in particular, to distinguish between HII regions and SNRs. For that we have used diagrams which involve both the [SII]/H$\alpha$ ratio and the velocity dispersion ($\sigma$). We also introduce a new parameter, $\xi = {[SII] \over H\alpha} \times \sigma$, which enhances still the contrast between SNRs and the rest of the ionised gas. More than 90\% of the SNRs in our entire sample show an integrated [SII]/H$\alpha$ ratio larger than the canonical value (0.4). 86\% of the SNRs present in our field show a significant velocity dispersion. The spectral resolution of our observations allows us to observe the complex velocity structure of some SNRs., Comment: Accepted for publication in MNRAS, 31 pages, 34 figures, and 6 tables

Grazing incidence fast atom diffraction (GIFAD or FAD) has become a technique to track the surface topology of crystal surface at the atomic scale. The paper retraces the events that led to the discovery of unexpected quantum behavior of keV atoms during the thesis of Patrick Rousseau in Orsay and Andreas Schueller in Berlin. In Orsay, it started by diffraction spots whereas in Berlin supernumerary rainbows were first identified at keV. Though the discovery was not anticipated, it did not take place by accident, everything was in place several years before, waiting only for an interest in neutral projectiles with a touch of curiosity., Comment: 9 pages, 9 figures, and 152 references, Submitted to NIM-B special issue dedicated to the IISC-24 conference held in Charleston SC 10-15 Sept. 2023

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

Early-type galaxies (ETGs) are known to harbour dense spheroids of stars but scarce star formation (SF). Approximately a quarter of these galaxies have rich molecular gas reservoirs yet do not form stars efficiently. We study here the ETG NGC~524, with strong shear suspected to result in a smooth molecular gas disc and low star-formation efficiency (SFE). We present new spatially-resolved observations of the \textsuperscript{12}CO(2-1)-emitting cold molecular gas from the Atacama Large Millimeter/sub-millimeter Array (ALMA) and of the warm ionised-gas emission lines from SITELLE at the Canada-France-Hawaii Telescope. Although constrained by the resolution of the ALMA observations ($\approx37$~pc), we identify only $52$ GMCs with radii ranging from $30$ to $140$~pc, a low mean molecular gas mass surface density $\langle\Sigma_{\rm gas}\rangle\approx125$~M$_\odot$~pc$^{-2}$ and a high mean virial parameter $\langle\alpha_{\rm obs,vir}\rangle\approx5.3$. We measure spatially-resolved molecular gas depletion times ($\tau_{\rm dep}\equiv1/{\rm SFE}$) with a spatial resolution of $\approx100$~pc within a galactocentric distance of $1.5$~kpc. The global depletion time is $\approx2.0$~Gyr but $\tau_{\rm dep}$ increases toward the galaxy centre, with a maximum $\tau_{\rm dep,max}\approx5.2$~Gyr. However, no pure \ion{H}{II} region is identified in NGC~524 using ionised-gas emission-line ratio diagnostics, so the $\tau_{\rm dep}$ inferred are in fact lower limits. Measuring the GMC properties and dynamical states, we conclude that shear is the dominant mechanism shaping the molecular gas properties and regulating SF in NGC~524. This is supported by analogous analyses of the GMCs in a simulated ETG similar to NGC~524., Comment: 17 pages, 10 figures. To be published in MNRAS, accepted on May 27

We consider the nonparametric regression problem when the covariates are located on an unknown smooth compact submanifold of a Euclidean space. Under defining a random geometric graph structure over the covariates we analyze the asymptotic frequentist behaviour of the posterior distribution arising from Bayesian priors designed through random basis expansion in the graph Laplacian eigenbasis. Under Holder smoothness assumption on the regression function and the density of the covariates over the submanifold, we prove that the posterior contraction rates of such methods are minimax optimal (up to logarithmic factors) for any positive smoothness index.

Defects in crystals can have a transformative effect on the properties and functionalities of solid-state systems. Dopants in semiconductors are core components in electronic and optoelectronic devices. The control of single color centers is at the basis of advanced applications for quantum technologies. Unintentional defects can also be detrimental to the crystalline structure and hinder the development of novel materials. Whatever the research perspective, the identification of defects is a key but complicated, and often long-standing issue. Here, we present a general methodology to identify point defects by combining isotope substitution and polytype control, with a systematic comparison between experiments and first-principles calculations. We apply this methodology to hexagonal boron nitride (hBN) and its ubiquitous color center emitting in the ultraviolet spectral range. From isotopic purification of the host hBN matrix, a local vibrational mode of the defect is uncovered, and isotope-selective carbon doping proves that this mode belongs to a carbon-based center. Then, by varying the stacking sequence of the host hBN matrix, we unveil different optical responses to hydrostatic pressure for the non-equivalent configurations of this ultraviolet color center. We conclude that this defect is a carbon dimer in the honeycomb lattice of hBN. Our results show that tuning the stacking sequence in different polytypes of a given crystal provides unique fingerprints contributing to the identification of defects in 2D materials.

Ground-based large-aperture telescopes, interferometers, and future Extremely Large Telescopes equipped with adaptive-optics systems provide angular resolution and high-contrast performance that are superior to space-based telescopes at thermal-infrared wavelengths. Their sensitivity, however, is critically limited by the high thermal background inherent to ground-based observations in this wavelength regime. We aim to improve the subtraction quality of the thermal-infrared background from ground-based observations, using Principal Component Analysis (PCA). We use data obtained with the Nulling-Optimized Mid-Infrared Camera on the Large Binocular Telescope Interferometer as a proxy for general high-sensitivity, AO-assisted ground-based data. We apply both a classical background subtraction -- using the mean of dedicated background observations -- and a new background subtraction based on a PCA of the background observations. We compare the performances of these two methods in both high-contrast imaging and aperture photometry. Compared to the classical background subtraction approach, PCA background subtraction delivers up to two times better contrasts down to the diffraction limit of the LBT's primary aperture (i.e., 350 mas in N band), that is, in the case of high-contrast imaging. Improvement factor between two and three are obtained over the mean background retrieval within the diffraction limit in the case of aperture photometry. PCA background subtraction significantly improves the sensitivity of ground-based thermal-infrared imaging observations. When applied to LBTI's nulling interferometry data, we expect the method to improve the sensitivity by a similar factor 2-3. This study paves the way to maximising the potential of future infrared ground-based instruments and facilities, such as the future 30m-class telescopes., Comment: 14 pages, 14 figures

This paper presents the specification, design, and development of the Visible Camera (VIS) on the ESA Euclid mission. VIS is a large optical-band imager with a field of view of 0.54 deg^2 sampled at 0.1" with an array of 609 Megapixels and spatial resolution of 0.18". It will be used to survey approximately 14,000 deg^2 of extragalactic sky to measure the distortion of galaxies in the redshift range z=0.1-1.5 resulting from weak gravitational lensing, one of the two principal cosmology probes of Euclid. With photometric redshifts, the distribution of dark matter can be mapped in three dimensions, and, from how this has changed with look-back time, the nature of dark energy and theories of gravity can be constrained. The entire VIS focal plane will be transmitted to provide the largest images of the Universe from space to date, reaching m_AB>24.5 with S/N >10 in a single broad I_E~(r+i+z) band over a six year survey. The particularly challenging aspects of the instrument are the control and calibration of observational biases, which lead to stringent performance requirements and calibration regimes. With its combination of spatial resolution, calibration knowledge, depth, and area covering most of the extra-Galactic sky, VIS will also provide a legacy data set for many other fields. This paper discusses the rationale behind the VIS concept and describes the instrument design and development before reporting the pre-launch performance derived from ground calibrations and brief results from the in-orbit commissioning. VIS should reach fainter than m_AB=25 with S/N>10 for galaxies of full-width half-maximum of 0.3" in a 1.3" diameter aperture over the Wide Survey, and m_AB>26.4 for a Deep Survey that will cover more than 50 deg^2. The paper also describes how VIS works with the other Euclid components of survey, telescope, and science data processing to extract the cosmological information., Comment: Paper submitted as part of the A&A special issue `Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations

Supercontinua are broadband spectra that are essential to optical spectroscopy, sensing, imaging, and metrology. They are generated from ultrashort laser pulses through nonlinear frequency conversion in fibers, bulk media, and chip-integrated waveguides. For any generating platform, balancing the competing criteria of strong nonlinearity, transparency, and absence of multiphoton absorption is a key challenge. Here, we explore supercontinuum generation in integrated gallium nitride (GaN) waveguides, which combine a high Kerr-nonlinearity, mid-infrared transparency, and a large bandgap that prevents two- and three-photon absorption in the technologically important telecom C-band, where compact erbium-based pump lasers exist. Using this type of laser, we demonstrate tunable dispersive waves and gap-free spectra extending to almost 4 micron in wavelength, relevant to functional group chemical sensing. Additionally, leveraging the material's second-order nonlinearity, we implement on-chip f-2f interferometry to detect the pump laser's carrier-envelope offset frequency, which enables precision metrology. These results demonstrate the versatility of GaN-on-sapphire as a new platform for broadband nonlinear photonics.

Boron nitride (BN) is today considered as one of the most promising materials for many novel applications including bright single photon emission, deep UV opto-electronics, small sized solid-state neutron detector, and high-performance two-dimensional materials, etc. Despite the recent successful fabrication of large-area BN single-crystals (typically <= 5 atomic layers), the scalable growth of thicker single-crystalline BN films still constitutes a great challenge. In this work, we demonstrate an approach to grow large-area multilayer single-crystal BN films by chemical vapor deposition on face-centered cubic Fe-Ni (111) single crystal alloy thin films with different stoichiometric phases. We show that the BN growth is greatly tunable and improved by increasing the Fe content in single-crystal Fe-Ni (111). The formation of pyramid-shaped multilayer BN domains with aligned orientation enables a continuous connection following a layer-by-layer, 'first-meet-first-connect', mosaic stitching mechanism. By means of selected area electron diffraction, micro-photoluminescence spectroscopy in the deep UV and high-resolution transmission electron microscopy, the layer-by-layer connection mechanism is unambiguously evidenced, and the stacking order has been verified to occur as unidirectional AB and ABC stackings, i.e., in the Bernal and rhombohedral BN phase.

Atom probe tomography data is composed of a list of coordinates of the reconstructed atoms in the probed volume. The elemental identity of each atom is derived from time-of-flight mass spectrometry, with no local energetic or chemical information readily available within the mass spectrum. Here, we used a new data processing technique referred to as field evaporation energy loss spectroscopy (FEELS), which analyses the tails of mass peaks. FEELS was used to extract critical energetic parameters that characterize the field evaporation process, which are related to the binding energy of atoms to the surface under intense electrostatic field and dependent of the path followed by the departing atoms during the field evaporation process. We focused our study on different pure face centered cubic metals (Al, Ni, Rh). We demonstrate that the energetic parameters extracted from mass spectra can be mapped in 2D with nanometric resolution. A dependence on the considered crystallographic planes is observed, with sets of planes of low Miller indices showing a lower sensitivity to the intensity of the electric field, which indicates a lower effective attachment energy. The temperature is also an important parameter in particular for Al, which we attribute to an energetic transition between two paths of field evaporation between 25K and 60K close to (002) pole at the specimen's surface. This paper shows that the complex information that can be retrieved from the measured energy loss of surface atoms is important both instrumentally and fundamentally.

In astronomy, spectroscopy consists of observing an astrophysical source and extracting its spectrum of electromagnetic radiation. Once extracted, a model is fit to the spectra to measure the observables, leading to an understanding of the underlying physics of the emission mechanism. One crucial, and often overlooked, aspect of this model is the background emission, which contains foreground and background astrophysical sources, intervening atmospheric emission, and artifacts related to the instrument such as noise. This paper proposes an algorithmic approach to constructing a background model for SITELLE observations using statistical tools and supervised machine learning algorithms. We apply a segmentation algorithm implemented in photutils to divide the data cube into background and source spaxels. After applying a principal component analysis (PCA) on the background spaxels, we train an artificial neural network to interpolate from the background to the source spaxels in the PCA coefficient space, which allows us to generate a local background model over the entire data cube. We highlight the performance of this methodology by applying it to SITELLE observations obtained of a SIGNALS galaxy, \NGC4449, and the Perseus galaxy cluster of galaxies, NGC 1275. We discuss the physical interpretation of the principal components and noise reduction in the resulting PCA-based reconstructions. Additionally, we compare the fit results using our new background modeling approach to standard methods used in the literature and find that our method better captures the emission from HII regions in NGC 4449 and the faint emission regions in NGC 1275. These methods also demonstrate that the background does change as a function of the position of the datacube., Comment: Accepted to RASTI

We have conducted an analysis of nebulae around Wolf-Rayet (WR) stars in M33 using data collected by the imaging Fourier transform spectrometer SITELLE at the Canada-France-Hawaii telescope as part of the SIGNALS Large Program. Of the 211 known Wolf-Rayet stars in M33, 178 are located in the fields observed in this study. We present the results of this analysis in the form of a comprehensive summary of all nebulae found around the observed WR stars. Based on three criteria we find to be the most effective for their detection, we detect a clear association with a circumstellar bubble around 33 of them (19\%). Our results show that the presence of bubbles does not correlate with the spectral type of the central star. The mean diameter of the WR nebulae we have found is 21 parsecs., Comment: 52 pages, 57 figures, accepted by MNRAS

This paper introduces a novel Functional Graph Convolutional Network (funGCN) framework that combines Functional Data Analysis and Graph Convolutional Networks to address the complexities of multi-task and multi-modal learning in digital health and longitudinal studies. With the growing importance of health solutions to improve health care and social support, ensure healthy lives, and promote well-being at all ages, funGCN offers a unified approach to handle multivariate longitudinal data for multiple entities and ensures interpretability even with small sample sizes. Key innovations include task-specific embedding components that manage different data types, the ability to perform classification, regression, and forecasting, and the creation of a knowledge graph for insightful data interpretation. The efficacy of funGCN is validated through simulation experiments and a real-data application.

Rapid technological progress and innovation in the areas of vehicle connectivity, automation and electrification, new modes of shared and alternative mobility, and advanced transportation system demand and supply management strategies, have motivated numerous questions and studies regarding the potential impact on key performance and equity metrics. Several of these areas of development may or may not have a synergistic outcome on the overall benefits such as reduction in congestion and travel times. In this study, the use of an end-to-end modeling workflow centered around an activity-based agent-based travel demand forecasting tool called POLARIS is explored to provide insights on the effects of several different technology deployments and operational policies in combination for the Chicago region. The objective of the research was to explore the direct impacts and observe any interactions between the various policy and technology scenarios to help better characterize and evaluate their potential future benefits. We analyze system outcome metrics on mobility, energy and emissions, equity and environmental justice and overall efficiency for a scenario design of experiments that looks at combinations of supply interventions (congestion pricing, transit expansion, tnc policy, off-hours freight policy, connected signal optimization) for different potential demand scenarios defined by e-commerce and on-demand delivery engagement, and market penetration of electric vehicles. We found different combinations of strategies that can reduce overall travel times up to 7% and increase system efficiency up to 53% depending on how various metrics are prioritized. The results demonstrate the importance of considering various interventions jointly.

In applications of Bayesian procedures, even when the prior law is carefully specified, it may be delicate to elicit the prior hyperparameters so that it is often tempting to fix them from the data, usually by their maximum likelihood estimates (MMLE), obtaining a so-called empirical Bayes posterior distribution. Although questionable, this is a common practice; but theoretical properties seem mostly only available on a case-by-case basis. In this paper we provide general properties for parametric models. First, we study the limit behavior of the MMLE and prove results in quite general settings, while also conceptualizing the frequentist context as an unexplored case of maximum likelihood estimation under model misspecification. We cover both identifiable models, illustrating applications to sparse regression, and non-identifiable models - specifically, overfitted mixture models. Finally, we prove higher order merging results. In regular cases, the empirical Bayes posterior is shown to be a fast approximation to the Bayesian posterior distribution of the researcher who, within the given class of priors, has the most information about the true model's parameters. This is a faster approximation than classic Bernstein-von Mises results. Given the class of priors, our work provides formal contents to common beliefs on this popular practice.

This paper introduces a novel compact mixed integer linear programming (MILP) formulation and a discretization discovery-based solution approach for the Vehicle Routing Problem with Time Windows (VRPTW). We aim to solve the optimization problem efficiently by constraining the linear programming (LP) solutions to use only flows corresponding to time and capacity-feasible routes that are locally elementary (prohibiting cycles of customers localized in space). We employ a discretization discovery algorithm to refine the LP relaxation iteratively. This iterative process alternates between two steps: (1) increasing time/capacity/elementarity enforcement to increase the LP objective, albeit at the expense of increased complexity (more variables and constraints), and (2) decreasing enforcement without decreasing the LP objective to reduce complexity. This iterative approach ensures we produce an LP relaxation that closely approximates the optimal MILP objective with minimal complexity, facilitating an efficient solution via an off-the-shelf MILP solver. The effectiveness of our method is demonstrated through empirical evaluations on classical VRPTW instances. We showcase the efficiency of solving the final MILP and multiple iterations of LP relaxations, highlighting the decreased integrality gap of the final LP relaxation. We believe that our approach holds promise for addressing a wide range of routing problems within and beyond the VRPTW domain.

We introduce and define three types of small worlds: small worlds based on the diameter of the network (SWD), those based on the average geodesic distance between nodes (SWA), and those based on the median geodesic distance (SWMd). These types of networks are defined as limiting properties of sequences of sets. We show the exact relation between these three types, namely that each SWD network is also an SWA network and that each SWA network is also an SWMd network. Yet, having the small-world property is rather evident, in the sense that most networks are small-world networks in one of the three ways. We introduce sequences of distance frequencies, so-called alpha-sequences, and prove a relation between the majorization property between alpha-sequences and small-world properties.

We have performed classical molecular dynamics simulations of 3 keV Ar + $(\mathrm{C}_{24}\mathrm{H}_{12})_n(\mathrm{C}_{60})_{m}$ collisions where $(n,m)=(3,2), (1,4), (9,4)$ and $(2,11)$. The simulated mass spectra of covalently bound reaction products reproduce the main features of the corresponding experimental results reported by Domaracka et al., PCCP, 2018, 20, 15052. The present results support their conclusion that molecular growth is mainly driven by knockout where individual atoms are promptly removed in Rutherford type scattering processes. The so formed highly reactive fragments may then bind with neighboring molecules in the clusters producing a rich variety of growth products extending up to sizes containing several hundreds of atoms, and here we show examples of such structures. In addition, knocked out atoms may be absorbed such that e.g. hydrogenated coronene and fullerene molecules are formed., Comment: 7 pages, 7 figures

Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4V's rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges - an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4V's high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows.