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Astrometry is one of the oldest branches of astronomy which measures the position, the proper motion and parallax of celestial objects. Following the Hipparcos and Gaia missions that have measured several billions of them using global astrometry, we propose to increase astrometry precision on pointed objects using differential astrometry in a large field in order to unravel rocky planets in habitable zones of stars in the Sun vicinity and investigate the nature of dark matter in galactic environments as recommended by the ESA Senior Committee in the Voyager 2050 prospective. Substantial technology developments in a number of critical areas is needed in order to reach the highest required precision of sub-micro-arcsecond. One of them is CMOS image sensors using the stitching technique to merge the multiple design structures on the wafer and produce array with very large number of pixels. Another one is to calibrate the pixel positions using projecting modulating interferometric laser fringes on the array. Finally, the distortion of the optical system can be monitored and compensated using reference stars as metrology sources. The final precision depends on the diameter and the field of view of the telescope that is used as well as the time spent on each target. We present here the science goals that can be achieved with such missions either within the framework of an ESA Medium-class mission or even in the NASA most challenging Habitable Worlds Observatory, a large space telescope recommended by the American Astronomy and Astrophysics prospective for the 2020s and designed specifically to search for signs of life on planets orbiting other stars.

High precision differential astrometry assesses the positions, distances, and motions of celestial objects in relation to the stars. The focal plane of such space telescope must be calibrated with a precision down to the level of 1e-5 pixel in order to be able to detect Earth-like planets in the close vicinity of the Sun. The presented characterization bench is designed to improve the technology readiness level for the following key points: calibration of new detectors with a high number of pixels and correcting the field distortion using stars in the field of view. The first aim of the project concentrates on the characterization of a 46 megapixels sensor from PYXALIS, to assess its typical parameters using an integrating sphere. The next objective intends to map the intra and extra pixel quantum yield of the detector with a precision of 1e-5 pixels and investigate the evolution of the pixel geometry in response to environment fluctuations. To conduct these tests, an optical bench is designed with an LCD screen and a doublet, used as a source that allows directing light to specific groups of pixels. Interferometric calibration of the detector pixel centroid position will be achieved using fibers that illuminate the detector with Young's fringes. To characterize the distortion of the detector, a diaphragm will produce adjustable optical aberrations to be corrected and therefore change the source sensor positional relationship. The final step involves the simulation of a star's field, which will be imaged on the detector to assess optical quality., Comment: 5 pages, 4 figures, conference "ICSO 2024"

This paper introduces smoothed pseudo-population bootstrap methods for the purposes of variance estimation and the construction of confidence intervals for finite population quantiles. In an i.i.d. context, it has been shown that resampling from a smoothed estimate of the distribution function instead of the usual empirical distribution function can improve the convergence rate of the bootstrap variance estimator of a sample quantile. We extend the smoothed bootstrap to the survey sampling framework by implementing it in pseudo-population bootstrap methods for high entropy, single-stage survey designs, such as simple random sampling without replacement and Poisson sampling. Given a kernel function and a bandwidth, it consists of smoothing the pseudo-population from which bootstrap samples are drawn using the original sampling design. Given that the implementation of the proposed algorithms requires the specification of the bandwidth, we develop a plug-in selection method along with a grid search selection method based on a bootstrap estimate of the mean squared error. Simulation results suggest a gain in efficiency associated with the smoothed approach as compared to the standard pseudo-population bootstrap for estimating the variance of a quantile estimator together with mixed results regarding confidence interval coverage.

Nonnegative cross-curvature (NNCC) is a geometric property of a cost function defined on a product space that originates in optimal transportation and the Ma-Trudinger-Wang theory. Motivated by applications in optimization, gradient flows and mechanism design, we propose a variational formulation of nonnegative cross-curvature on c-convex domains applicable to infinite dimensions and nonsmooth settings. The resulting class of NNCC spaces is closed under Gromov-Hausdorff convergence and for this class, we extend many properties of classical nonnegative cross-curvature: stability under generalized Riemannian submersions, characterization in terms of the convexity of certain sets of c-concave functions, and in the metric case, it is a subclass of positively curved spaces in the sense of Alexandrov. One of our main results is that Wasserstein spaces of probability measures inherit the NNCC property from their base space. Additional examples of NNCC costs include the Bures-Wasserstein and Fisher-Rao squared distances, the Hellinger-Kantorovich squared distance (in some cases), the relative entropy on probability measures, and the 2-Gromov-Wasserstein squared distance on metric measure spaces.

With sub-microarcsecond angular accuracy, the \theia telescope will be capable of revealing the architectures of nearby exoplanetary systems down to the mass of Earth. This research addresses the challenges inherent in space astrometry missions, focusing on focal plane calibration and telescope optical distortion. We propose to assess the future feasibility of large-format detectors (50 to 200 megapixels) in a controlled laboratory environment. The aim is to improve the architecture of the focal plane while ensuring that specifications are met. The use of field stars as metrological sources for calibrating the optical distortion of the field may help to constrain telescope stability. The paper concludes with an attempt to confirm in the laboratory the performance predicted by simulations. We will also address the possibility of using such techniques with a dedicated instrument for the Habitable World Observatory., Comment: SPIE Conference "Space Telescopes and Instrumentation 2024: Optical, Infrared, and Millimeter Wave", Yokohama, Japan. arXiv admin note: text overlap with arXiv:2207.12540

In this paper we construct global strong dispersive solutions to the space inhomogeneous kinetic wave equation (KWE) which propagate $L^1_{xv}$ -- moments and conserve mass, momentum and energy. We prove that they scatter, and that the wave operators mapping the initial data to the scattering states are 1-1, onto and continuous in a suitable topology. Our proof is carried out entirely in physical space, and combines dispersive estimates for the free transport with new trilinear bounds for the gain and loss operators of the KWE on weighted Lebesgue spaces. A fundamental tool in obtaining these bounds is a novel collisional averaging estimate. Finally we show that the nonlinear evolution preserves positivity forward in time. For this, we use the Kaniel-Shinbrot iteration scheme \cite{KS}, properly initialized to ensure the successive approximations are dispersive., Comment: 41 pages

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

As large language models (LLMs) start interacting with each other and generating an increasing amount of text online, it becomes crucial to better understand how information is transformed as it passes from one LLM to the next. While significant research has examined individual LLM behaviors, existing studies have largely overlooked the collective behaviors and information distortions arising from iterated LLM interactions. Small biases, negligible at the single output level, risk being amplified in iterated interactions, potentially leading the content to evolve towards attractor states. In a series of telephone game experiments, we apply a transmission chain design borrowed from the human cultural evolution literature: LLM agents iteratively receive, produce, and transmit texts from the previous to the next agent in the chain. By tracking the evolution of text toxicity, positivity, difficulty, and length across transmission chains, we uncover the existence of biases and attractors, and study their dependence on the initial text, the instructions, language model, and model size. For instance, we find that more open-ended instructions lead to stronger attraction effects compared to more constrained tasks. We also find that different text properties display different sensitivity to attraction effects, with toxicity leading to stronger attractors than length. These findings highlight the importance of accounting for multi-step transmission dynamics and represent a first step towards a more comprehensive understanding of LLM cultural dynamics., Comment: Code available at https://github.com/jeremyperez2/TelephoneGameLLM. Companion website with a Data Explorer tool at https://sites.google.com/view/telephone-game-llm

Two-dimensional materials are a class of atomically thin materials with assorted electronic and quantum properties. Accurate identification of layer thickness, especially for a single monolayer, is crucial for their characterization. This characterization process, however, is often time-consuming, requiring highly skilled researchers and expensive equipment like atomic force microscopy. This project aims to streamline the identification process by using machine learning to analyze optical images and quickly determine layer thickness. In this paper, we evaluate the performance of three machine learning models -- SegNet, 1D U-Net, and 2D U-Net -- in accurately identifying monolayers in microscopic images. Additionally, we explore labeling and image processing techniques to determine the most effective method for identifying layer thickness in this class of materials., Comment: 21 pages, 6 figures

The unveiling of 2D van der Waals magnetism in 2017 ignited a surge of interest in low-dimensional magnetism. With dimensions reduced, research has delved into facile electric control of 2D magnetism, high-quality heterostructure design, and new device functionality. These atomically thin magnetic materials have spawned a burgeoning field known as 2D spintronics, holding immense promise for future quantum technologies. In this review, we comprehensively survey the current advancements in 2D magnet-based quantum devices, accentuating their role in manifesting exotic properties and enabling novel functionalities. Topological states, spin torques, voltage control of magnetic anisotropy, strain engineering, twistronics and designer interface will be discussed. Furthermore, we offer an outlook to guide their development in future CMOS and quantum hardware paradigms., Comment: 35 pages, 8 figures

We study spontaneous photon decay into multiple photons triggered by strong non-linearities in a superconducting quantum simulator of the boundary sine-Gordon impurity model. Previously, spectroscopic signatures of photon-conversion were reported and evidenced as resonances in the many-body spectrum of these systems. Here, we report on the observation of multi-mode fluorescence of a small Josephson junction embedded in a high impedance superconducting transmission line. Measurement of the down-converted photons is achieved using state-of-the-art broadband parametric amplifiers. Photon triplet emission is explicitly demonstrated at a given frequency as the counterpart of inelastic photon decay at three-times the emission frequency. These results open exciting prospects for the burgeoning field of many-body quantum optics and offer a direct signature of the ultra-strong light-matter coupling., Comment: 16 pages including supplementary material, 8 figures

Continuous quantum phase transitions are widely assumed and frequently observed in various systems of quantum particles or spins. Their characteristic trait involves scaling laws governing a second-order, gradual suppression of the order parameter as the quantum critical point is approached. The localization of Cooper pairs in disordered superconductors and the resulting breakdown of superconductivity have long stood as a prototypical example. Here, we show a departure from this paradigm, showcasing that amorphous superconducting films of indium oxide undergo a distinctive, discontinuous first-order quantum phase transition tuned by disorder. Through systematic measurements of the plasmon spectrum in superconducting microwave resonators, we provide evidence for a marked jump of both the zero-temperature superfluid stiffness and the transition temperature at the critical disorder. This discontinuous transition sheds light on the previously overlooked role of repulsive interactions between Cooper pairs and the subsequent competition between superconductivity and insulating Cooper-pair glass. Furthermore, our investigation shows that the critical temperature of the films no longer relates to the pairing amplitude but aligns with the superfluid stiffness, consistent with the pseudogap regime of preformed Cooper pairs. Our findings raise fundamental new questions into the role of disorder in quantum phase transitions and carry implications for superinductances in quantum circuits., Comment: Updated text

Space-time modulation systems have garnered significant attention due to their resemblance to moving-matter systems and promising applications. Unlike conventional moving-matter systems, modulation systems do not involve net motion of matter, and are therefore easier to implement and not restricted to subluminal velocities. However, canonical wave-medium interaction aspects, such as scattering and energy-momentum relations, have remained largely unexplored. In this paper, we address the aforementioned issues for three dynamic systems: moving-matter blocs, moving-perturbation interfaces and moving-perturbation periodic structures, and provide corresponding general formulations along with comparisons. Our investigation reveals the significant roles played by the "catch-up" effect between waves and interfaces. Even more interestingly, it reveals different energy and momentum exchanges between moving media and homogenized moving-perturbation structures as a result of conventional and reverse Fresnel-Fizeau drag effects., Comment: 11 pages, 9 figures

This article considers a semi-supervised classification setting on a Gaussian mixture model, where the data is not labeled strictly as usual, but instead with uncertain labels. Our main aim is to compute the Bayes risk for this model. We compare the behavior of the Bayes risk and the best known algorithm for this model. This comparison eventually gives new insights over the algorithm.

Research in cultural evolution aims at providing causal explanations for the change of culture over time. Over the past decades, this field has generated an important body of knowledge, using experimental, historical, and computational methods. While computational models have been very successful at generating testable hypotheses about the effects of several factors, such as population structure or transmission biases, some phenomena have so far been more complex to capture using agent-based and formal models. This is in particular the case for the effect of the transformations of social information induced by evolved cognitive mechanisms. We here propose that leveraging the capacity of Large Language Models (LLMs) to mimic human behavior may be fruitful to address this gap. On top of being an useful approximation of human cultural dynamics, multi-agents models featuring generative agents are also important to study for their own sake. Indeed, as artificial agents are bound to participate more and more to the evolution of culture, it is crucial to better understand the dynamics of machine-generated cultural evolution. We here present a framework for simulating cultural evolution in populations of LLMs, allowing the manipulation of variables known to be important in cultural evolution, such as network structure, personality, and the way social information is aggregated and transformed. The software we developed for conducting these simulations is open-source and features an intuitive user-interface, which we hope will help to build bridges between the fields of cultural evolution and generative artificial intelligence., Comment: 17 pages, 20 figures. Open-source code available at https://github.com/jeremyperez2/LLM-Culture

This study investigates the acceptability of different artificial intelligence (AI) applications in education from a multi-stakeholder perspective, including students, teachers, and parents. Acknowledging the transformative potential of AI in education, it addresses concerns related to data privacy, AI agency, transparency, explainability and the ethical deployment of AI. Through a vignette methodology, participants were presented with four scenarios where AI's agency, transparency, explainability, and privacy were manipulated. After each scenario, participants completed a survey that captured their perceptions of AI's global utility, individual usefulness, justice, confidence, risk, and intention to use each scenario's AI if available. The data collection comprising a final sample of 1198 multi-stakeholder participants was distributed through a partner institution and social media campaigns and focused on individual responses to four AI use cases. A mediation analysis of the data indicated that acceptance and trust in AI varies significantly across stakeholder groups. We found that the key mediators between high and low levels of AI's agency, transparency, and explainability, as well as the intention to use the different educational AI, included perceived global utility, justice, and confidence. The study highlights that the acceptance of AI in education is a nuanced and multifaceted issue that requires careful consideration of specific AI applications and their characteristics, in addition to the diverse stakeholders' perceptions., Comment: 28 pages, 2 appendices, 3 figures, 5 tables, original research

This article conducts a large dimensional study of a simple yet quite versatile classification model, encompassing at once multi-task and semi-supervised learning, and taking into account uncertain labeling. Using tools from random matrix theory, we characterize the asymptotics of some key functionals, which allows us on the one hand to predict the performances of the algorithm, and on the other hand to reveal some counter-intuitive guidance on how to use it efficiently. The model, powerful enough to provide good performance guarantees, is also straightforward enough to provide strong insights into its behavior., Comment: 13 pages, 9 figures

Encoding quantum information in quantum states with disjoint wave-function support and noise insensitive energies is the key behind the idea of qubit protection. While fully protected qubits are expected to offer exponential protection against both energy relaxation and pure dephasing, simpler circuits may grant partial protection with currently achievable parameters. Here, we study a fluxonium circuit in which the wave-functions are engineered to minimize their overlap while benefiting from a first-order-insensitive flux sweet spot. Taking advantage of a large superinductance ($L\sim 1~\mu \rm{H}$), our circuit incorporates a resonant tunneling mechanism at zero external flux that couples states with the same fluxon parity, thus enabling bifluxon tunneling. The states $|0\rangle$ and $|1\rangle$ are encoded in wave-functions with parities 0 and 1, respectively, ensuring a minimal form of protection against relaxation. Two-tone spectroscopy reveals the energy level structure of the circuit and the presence of $4 \pi$ quantum-phase slips between different potential wells corresponding to $m=\pm 1$ fluxons, which can be precisely described by a simple fluxonium Hamiltonian or by an effective bifluxon Hamiltonian. Despite suboptimal fabrication, the measured relaxation ($T_1 = 177\pm 3 ~\mu s$) and dephasing ($T_2^E = 75\pm 5~\mu \rm{s}$) times not only demonstrate the relevance of our approach but also opens an alternative direction towards quantum computing using partially-protected fluxonium qubits., Comment: 14 pages, 12 figures

We study the stability of the antiferromagnetic all-in--all-out state observed in dipolar-octupolar pyrochlores that have neodymium as the magnetic species. Different types of disorder are considered, either affecting the immediate environment of the Nd$^{3+}$ ion, or substituting it with a non-magnetic ion. Starting from the well studied Nd$_2$Zr$_2$O$_7$ compound, Ti substitution on the Zr site and dilution on the Nd magnetic site with La substitution are investigated. The recently discovered entropy stabilized compound NdMox, which exhibits a high degree of disorder on the non magnetic site is also studied. Using a range of experimental techniques, especially very low-temperature magnetization and neutron scattering, we show that the all-in--all-out state is very robust and withstands substitutional disorder up to large rates. From these measurements, we estimate the Hamiltonian parameters and discuss their evolution in the framework of the phase diagram of dipolar-octupolar pyrochlore magnets., Comment: 19 pages, 20 figures (main text: 12 pages, 13 figures)

Animals often demonstrate a remarkable ability to adapt to their environments during their lifetime. They do so partly due to the evolution of morphological and neural structures. These structures capture features of environments shared between generations to bias and speed up lifetime learning. In this work, we propose a computational model for studying a mechanism that can enable such a process. We adopt a computational framework based on meta reinforcement learning as a model of the interplay between evolution and development. At the evolutionary scale, we evolve reservoirs, a family of recurrent neural networks that differ from conventional networks in that one optimizes not the synaptic weights, but hyperparameters controlling macro-level properties of the resulting network architecture. At the developmental scale, we employ these evolved reservoirs to facilitate the learning of a behavioral policy through Reinforcement Learning (RL). Within an RL agent, a reservoir encodes the environment state before providing it to an action policy. We evaluate our approach on several 2D and 3D simulated environments. Our results show that the evolution of reservoirs can improve the learning of diverse challenging tasks. We study in particular three hypotheses: the use of an architecture combining reservoirs and reinforcement learning could enable (1) solving tasks with partial observability, (2) generating oscillatory dynamics that facilitate the learning of locomotion tasks, and (3) facilitating the generalization of learned behaviors to new tasks unknown during the evolution phase.

A key inequality which underpins the regularity theory of optimal transport for costs satisfying the Ma--Trudinger--Wang condition is the Pogorelov second derivative bound. This translates to an apriori interior $C^1$ estimate for smooth optimal maps. Here we give a new derivation of this estimate which relies in part on Kim, McCann and Warren's observation that the graph of an optimal map becomes a volume maximizing spacelike submanifold when the product of the source and target domains is endowed with a suitable pseudo-Riemannian geometry that combines both the marginal densities and the cost., Comment: final version; to appear in J. Reine Angew. Math

This paper continues the study of the homotopy theory of algebras over polynomial monads initiated by the first author and Clemens Berger. We introduce the notion of a quasi-tame polynomial monad (generalizing tame ones) and produce transferred model structures (left proper in many settings) on algebras over such a monad. Our motivating application is to produce model structures on Grothendieck categories, which are used in a companion paper to give a unified approach to the study of operads, their algebras, and their modules. We prove a general result regarding when a Grothendieck construction can be realized as a category of algebras over a polynomial monad, examples illustrating that quasi-tameness is necessary as well as sufficient for admissibility, and an extension of classifier methods to a non-polynomial situation, namely the case of commutative monoids., Comment: Comments welcome. This paper has a companion paper, "Model structures on operads and algebras from a global perspective"

This paper studies the homotopy theory of the Grothendieck construction using model categories and semi-model categories, provides a unifying framework for the homotopy theory of operads and their algebras and modules, and uses this framework to produce model structures, rectification results, and properness results in new settings. In contrast to previous authors, we begin with a global (semi-)model structure on the Grothendieck and induce (semi-)model structures on the base and fibers. In a companion paper, we show how to produce such global model structures in general settings. Applications include numerous flavors of operads encoded by polynomial monads and substitudes (symmetric, non-symmetric, cyclic, modular, higher operads, dioperads, properads, and PROPs), (commutative) monoids and their modules, and twisted modular operads. We also prove a general result for upgrading a semi-model structure to a full model structure., Comment: Comments welcome. This paper has a companion paper "Quasi-tame substitudes and the Grothendieck construction"

Context. Package repositories for a programming language are increasingly common. A repository can keep a register of the evolution of its packages. In the programming language Haskell, with its defining characteristic monads, we can find the Stackage repository, which is a curated repository for stable Haskell packages in the Hackage repository. Despite the widespread use of Stackage in its industrial target, we are not aware of much empirical research about how this repository has evolved, including the use of monads. Objective. This paper conducts empirical research about the evolution of Stackage considering monad packages through 22 Long-Term Support releases during the period 2014-2023. Focusing on five research questions, this evolution is analyzed in terms of packages with their dependencies and imports; including the most used monad packages. To the best of our knowledge, this is the first large-scale analysis of the evolution of the Stackage repository regarding packages used and monads. Method. We define six research questions regarding the repository's evolution, and analyze them on 51,716 packages (17.05 GB) spread over 22 releases. For each package, we parse its cabal file and source code to extract the data, which is analyzed in terms of dependencies and imports using Pandas scripts. Results. From the methodology we get different findings. For example, there are packages that depend on other packages whose versions are not available in a particular release of Stackage; opening a potential stability issue. The mtl and transformers are on the top 10 packages most used/imported across releases of the Stackage evolution. We discussed these findings with Stackage maintainers, which allowed us to refine the research questions., Comment: 22 pages, 12 figures

Graph neural networks (GNNs) are becoming increasingly popular in the medical domain for the tasks of disease classification and outcome prediction. Since patient data is not readily available as a graph, most existing methods either manually define a patient graph, or learn a latent graph based on pairwise similarities between the patients. There are also hypergraph neural network (HGNN)-based methods that were introduced recently to exploit potential higher order associations between the patients by representing them as a hypergraph. In this work, we propose a patient hypergraph network (PHGN), which has been investigated in an inductive learning setup for binary outcome prediction in oropharyngeal cancer (OPC) patients using computed tomography (CT)-based radiomic features for the first time. Additionally, the proposed model was extended to perform time-to-event analyses, and compared with GNN and baseline linear models.

Using techniques developed by Batanin and the first author, we extend the Turchin/Dwyer-Hess double delooping result to further iterations of the Baez-Dolan plus construction. For $0 \leq m \leq n$, we introduce a notion of $(m,n)$-bimodules which extends the notions of bimodules and infinitesimal bimodules over the terminal non-symmetric operad. We show that a double delooping always exists for these bimodules. For the triple iteration of the Baez-Dolan construction starting from the initial $1$-coloured operad, we provide a further reduceness condition to have a third delooping.

Only 21% of employees consider themselves engaged at work. Moreover, disengagement has been shown to be even more problematic when work is repetitive in nature. Lack of engagement has been linked to variety of negative outcomes for employees and companies (e.g., turnover, absenteeism, well-being, safety incidents, productivity). Gamification, i.e., integrating game elements into work systems, has been successfully used to increase engagement and motivation, even when work tasks were mundane and repetitive. In the current study, we focused on a commonly used game element, goal setting, through the lens of self-determination theory and goal-setting theory. We argue that goals given by an external source (e.g., company, experimenter) produce extrinsic motivation, which improves engagement and performance only in the short term. We posit that self-set goals lead to more autonomous motivation, and therefore long-term engagement and performance. One hundred two participants completed a repetitive material-handling task in one of three conditions (assigned goal, self-set goal, no goal). Results showed that perceived autonomy (autonomous motivation) and performance were best when goals were self-set. Engagement, however, was equal between self-set and assigned goals. The results indicate that self-set goals have the greatest potential to generate long-term positive outcomes both for employees and companies., Comment: in French language, CIGI QUALITA MOSIM 2023, Jun 2023, Trois Rivi{\`e}res, Canada

Metasurfaces, optics made from subwavelength-scale nanostructures, have been limited to millimeter-sizes by the scaling challenge of producing vast numbers of precisely engineered elements over a large area. In this study, we demonstrate an all-glass 100 mm diameter metasurface lens (metalens) comprising 18.7 billion nanostructures that operates in the visible spectrum with a fast f-number (f/1.5, NA=0.32) using deep-ultraviolet (DUV) projection lithography. Our work overcomes the exposure area constraints of lithography tools and demonstrates that large metasurfaces are commercially feasible. Additionally, we investigate the impact of various fabrication errors on the imaging quality of the metalens, several of which are unique to such large area metasurfaces. We demonstrate direct astronomical imaging of the Sun, the Moon, and emission nebulae at visible wavelengths and validate the robustness of such metasurfaces under extreme environmental thermal swings for space applications.

We discuss the foundation for a new global nCTEQ nuclear PDF analysis, combining a number of our previous analyses into one consistent framework with updates to the underlying theoretical treatment as well as the addition of new available data. In particular, the new global release will be the first nCTEQ release containing neutrino DIS scattering data in a consistent manner together with JLab high-x DIS data and new LHC p-Pb data. These additions will improve the data-driven description of nuclear PDFs in new regions, especially the strange quark and the gluon PDF at low-x., Comment: DIS2023

Two-dimensional (2D) semiconductors are promising candidates for optoelectronic application and quantum information processes due to their inherent out-of-plane 2D confinement. In addition, they offer the possibility of achieving low-dimensional in-plane exciton confinement, similar to zero-dimensional quantum dots, with intriguing optical and electronic properties via strain or composition engineering. However, realizing such laterally confined 2D monolayers and systematically controlling size-dependent optical properties remain significant challenges. Here, we report the observation of lateral confinement of excitons in epitaxially grown in-plane MoSe2 quantum dots (~15-60 nm wide) inside a continuous matrix of WSe2 monolayer film via a sequential epitaxial growth process. Various optical spectroscopy techniques reveal the size-dependent exciton confinement in the MoSe2 monolayer quantum dots with exciton blue shift (12-40 meV) at a low temperature as compared to continuous monolayer MoSe2. Finally, single-photon emission was also observed from the smallest dots at 1.6 K. Our study opens the door to compositionally engineered, tunable, in-plane quantum light sources in 2D semiconductors., Comment: Main Manuscript: 29 pages, 4 figures Supplementary Information: 14 pages, 12 figures

Space-time modulation-based metamaterials have recently spurred considerable interest, owing to the fundamental addition of the time dimension to the medium parameters, and resulting novel properties and potential applications. However, the implementation of most related structures -- e.g., involving step, slab or gradient discontinuities -- has been hindered by the impossible requirement of infinitely or prohibitively large device sizes. We provide here a solution to this issue, consisting in a space-time transposition of the conventional Fresnel prism, whereby a copy of the target modulation is periodically re-injected at the input of a Fresnel-reduced finite structure, so as to provide the same anharmonic and nonreciprocal frequency conversion as the target space-time interface in the case of a modulation step. This concept, which may readily extend to slab or gradient modulations, as well as accelerated profiles for space-time chirping operations, may pave the way for the practical development of a wide range of novel microwave and optical space-time systems.

Latent variable models are powerful tools for modeling complex phenomena involving in particular partially observed data, unobserved variables or underlying complex unknown structures. Inference is often difficult due to the latent structure of the model. To deal with parameter estimation in the presence of latent variables, well-known efficient methods exist, such as gradient-based and EM-type algorithms, but with practical and theoretical limitations. In this paper, we propose as an alternative for parameter estimation an efficient preconditioned stochastic gradient algorithm. Our method includes a preconditioning step based on a positive definite Fisher information matrix estimate. We prove convergence results for the proposed algorithm under mild assumptions for very general latent variables models. We illustrate through relevant simulations the performance of the proposed methodology in a nonlinear mixed effects model and in a stochastic block model.

In this paper, we prove $L^2 \to L^p$ estimates, where $p>2$, for spectral projectors on a wide class of hyperbolic surfaces. More precisely, we consider projections in small spectral windows $[\lambda-\eta,\lambda+\eta]$ on geometrically finite hyperbolic surfaces of infinite volume. In the convex cocompact case, we obtain optimal bounds with respect to $\lambda$ and $\eta$, up to subpolynomial losses. The proof combines the resolvent bound of Bourgain-Dyatlov and improved estimates for the Schr\"odinger group (Strichartz and smoothing estimates) on hyperbolic surfaces., Comment: 46 pages

We consider the problem of model building for rare events prediction in longitudinal follow-up studies. In this paper, we compare several resampling methods to improve standard regression models on a real life example. We evaluate the effect of the sampling rate on the predictive performances of the models. To evaluate the predictive performance of a longitudinal model, we consider a validation technique that takes into account time and corresponds to the actual use in real life., Comment: 15 pages

This paper presents a space-time-wise orthogonal analysis of space-time crystals. This analysis provides a solution consisting of a pair of explicit parametric equations that result from a separate application of the Bloch-Floquet theorem in the (orthogonal) directions of space and time. Compared to previous approaches, this solution offers the benefits of greater simplicity, clearer emphasis on space-time duality and deeper physical insight., Comment: letter (4 pages)

We present a generalized FDTD scheme to simulate moving electromagnetic structures with arbitrary space-time configurations. This scheme is a local adaptation and 2+1-dimensional extension of the uniform and 1+1-dimensional scheme recently reported in [1]. The local adaptation, which is allowed by the inherently matched nature of the generalized Yee cell to the conventional Yee cell, extends the range of applicability of the scheme in [1] to moving structures that involve multiple and arbitrary velocity profiles while being fully compatible with conventional absorbing boundary conditions and standard treatments of medium dispersion. We show that a direct application of the conventional FDTD scheme predicts qualitatively correct spectral transitions but quantitatively erroneous scattering amplitudes, we infer from this observation generalized, hybrid-physical and auxiliary (non-physical) - fields that automatically satisfy moving boundary conditions in the laboratory frame, and accordingly establish local update equations based on the related Maxwell's equations and constitutive relations. We subsequently provide a detailed stability analysis with a generalization of the Courant criterion to the dynamic regime. We finally validate and illustrate the proposed method by several representative examples. The proposed scheme fills an important gap in the open literature on computational electromagnetics and offers an unprecedented, direct solution for moving structures in commercial software platforms., Comment: 13 pages, 11 figures