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We present the largest Galactic neutral hydrogen HI absorption survey to date, utilizing the Australian SKA Pathfinder Telescope at an unprecedented spatial resolution of 30''. This survey, GASKAP-HI, unbiasedly targets 2,714 continuum background sources over 250 square degrees in the direction of the Magellanic Clouds, a significant increase compared to a total of 373 sources observed by previous Galactic absorption surveys across the entire Milky Way. We aim to investigate the physical properties of cold (CNM) and warm (WNM) neutral atomic gas in the Milky Way foreground, characterized by two prominent filaments at high Galactic latitudes (between $-45^{\circ}$ and $-25^{\circ}$). We detected strong HI absorption along 462 lines of sight above the 3$\sigma$ threshold, achieving an absorption detection rate of 17%. GASKAP-HI's unprecedented angular resolution allows for simultaneous absorption and emission measurements to sample almost the same gas clouds along a line of sight. A joint Gaussian decomposition is then applied to absorption-emission spectra to provide direct estimates of HI optical depths, temperatures, and column densities for the CNM and WNM components. The thermal properties of CNM components are consistent with those previously observed along a wide range of Solar neighborhood environments, indicating that cold HI properties are widely prevalent throughout the local interstellar medium. Across our region of interest, CNM accounts for ~30% of the total HI gas, with the CNM fraction increasing with column density toward the two filaments. Our analysis reveals an anti-correlation between CNM temperature and its optical depth, which implies that CNM with lower optical depth leads to a higher temperature., Comment: Largest Galactic HI Absorption Survey To Date (GASKAP-HI): Cold Atomic Gas in the Magellanic Cloud foreground using Australian SKA Pathfinder. This paper has 19 pages, 17 figures. This paper has been accepted for publication in Monthly Notices of the Royal Astronomical Society Main Journal

Dense cold molecular cores/filaments are surrounded by an envelope of translucent gas. Some of the low-J emission lines of CO and HCO$^+$ isotopologues are more sensitive to the conditions either in the translucent environment or in the dense cold one. We propose a cloud model composed of three homogeneous slabs of gas along each line of sight (LoS), representing an envelope and a shielded inner layer. IRAM-30m data from the ORION-B large program toward the Horsehead nebula are used to demonstrate the method's capability. We use the non-LTE radiative transfer code RADEX to model the line profiles from the kinetic temperature $T_{kin}$, the volume density $n_{H_2}$, kinematics and chemical properties of the different layers. We then use a maximum likelihood estimator to simultaneously fit the lines of the CO and HCO$^+$ isotopologues. We constrain column density ratios to limit the variance on the estimates. This simple heterogeneous model provides good fits of the fitted lines over a large part of the cloud. The decomposition of the intensity into three layers allows to discuss the distribution of the estimated physical/chemical properties along the LoS. About 80$\%$ the CO integrated intensity comes from the envelope, while $\sim55\%$ of that of the (1-0) and (2-1) lines of C$^{18}$O comes from the inner layer. The $N(^{13}CO)/N(C^{18}O)$ in the envelope increases with decreasing $A_v$, and reaches $25$ in the pillar outskirts. The envelope $T_{kin}$ varies from 25 to 40 K, that of the inner layer drops to $\sim 15$ K in the western dense core. The inner layer $n_{H_2}$ is $\sim 3\times10^4\,\text{cm}^{-3}$ toward the filament and it increases by a factor $10$ toward dense cores. The proposed method correctly retrieves the physical/chemical properties of the Horsehead nebula and offers promising prospects for less supervised model fits of wider-field datasets.

The exponential growth of IoT networks necessitates a paradigm shift towards architectures that offer high flexibility and learning capabilities while maintaining low energy consumption, minimal communication overhead, and low latency. Traditional IoT systems, particularly when integrated with machine learning approaches, often suffer from high communication overhead and significant energy consumption. This work addresses these challenges by proposing a neuromorphic architecture inspired by biological systems. To illustrate the practical application of our proposed architecture, we present a case study focusing on water management in the Carinthian community of Neuhaus. Preliminary results regarding water consumption prediction and anomaly detection in this community are presented. We also introduce a novel neuromorphic IoT architecture that integrates biological principles into the design of IoT systems. This architecture is specifically tailored for edge computing scenarios, where low power and high efficiency are crucial. Our approach leverages the inherent advantages of neuromorphic computing, such as asynchronous processing and event-driven communication, to create an IoT framework that is both energy-efficient and responsive. This case study demonstrates how the neuromorphic IoT architecture can be deployed in a real-world scenario, highlighting its benefits in terms of energy savings, reduced communication overhead, and improved system responsiveness., Comment: Submitted to Edge AI conference, Cagliari, Italy

We construct a mean-field model that describes the nonlinear dynamics of a spin-polarized electron gas interacting with fixed, positively-charged ions possessing a magnetic moment that evolves in time. The mobile electrons are modeled by a four-component distribution function in the two-dimensional phase space $(x,v)$, obeying a Vlasov-Poisson set of equations. The ions are modeled by a Landau-Lifshitz equation for their spin density, which contains ion-ion and electron-ion magnetic exchange terms. We perform a linear response study of the coupled Vlasov-Poisson-Landau-Lifshitz (VPLL) equations for the case of a Maxwell-Boltzmann equilibrium, focussing in particular on the spin dispersion relation. Condition of stability or instability for the spin modes are identified, which depend essentially on the electron spin polarization rate $\eta$ and the electron-ion magnetic coupling constant $K$. We also develop an Eulerian grid-based computational code for the fully nonlinear VPLL equations, based on the geometric Hamiltonian method first developed in [N. Crouseilles et al. Journal of Plasma Physics, 89(2):905890215, 2023]. This technique allows us to achieve great accuracy for the conserved quantities, such as the modulus of the ion spin vector and the total energy. Numerical tests in the linear regime are in accordance with the estimations of the linear response theory. For two-stream equilibria, we study the interplay of instabilities occurring in both the charge and the spin sectors. The set of parameters used in the simulations, with densities close to those of solids ($\approx 10^{29} \rm m^{-3}$) and temperatures of the order of 10 eV, may be relevant to the warm dense matter regime appearing in some inertial fusion experiments.

Current progress in artificial intelligence is centered around so-called large language models that consist of neural networks processing long sequences of high-dimensional vectors called tokens. Statistical physics provides powerful tools to study the functioning of learning with neural networks and has played a recognized role in the development of modern machine learning. The statistical physics approach relies on simplified and analytically tractable models of data. However, simple tractable models for long sequences of high-dimensional tokens are largely underexplored. Inspired by the crucial role models such as the single-layer teacher-student perceptron (aka generalized linear regression) played in the theory of fully connected neural networks, in this paper, we introduce and study the bilinear sequence regression (BSR) as one of the most basic models for sequences of tokens. We note that modern architectures naturally subsume the BSR model due to the skip connections. Building on recent methodological progress, we compute the Bayes-optimal generalization error for the model in the limit of long sequences of high-dimensional tokens, and provide a message-passing algorithm that matches this performance. We quantify the improvement that optimal learning brings with respect to vectorizing the sequence of tokens and learning via simple linear regression. We also unveil surprising properties of the gradient descent algorithms in the BSR model.

We construct backreacted geometries dual to the supersymmetric mass deformation of the IKKT matrix model. They are Euclidean type IIB supergravity solutions given in terms of an electrostatic potential, having $SO(7)\times SO(3)$ isometry and 16 supersymmetries. Quantizing the fluxes, we find that the supergravity solutions are in one-to-one correspondence with fuzzy sphere vacua of the matrix model., Comment: 22 pages + appendices, 3 figures

The paper describes the algebraic structure of the graded algebra of differentially homogeneous polynomials of fixed finite order. We show that it is a finitely generated algebra, and we exhibit a minimal set of generators. Along the way, we provide a simpler proof of the so-called Schmidt--Kolchin conjecture (proved in a previous paper) . From the algebraic point of view, this provides natural compactifications of jet bundles of projective spaces. From the invariant theoretic point of view, this provides new examples, not covered (to our knowledge) by known conjectures in the subject, of unipotent sub-groups of general linear groups, whose algebras of invariants are finitely generated (and more precisely gives a First Fundamental Theorem for such groups, following the terminology in Invariant Theory)., Comment: 18 pages

Given a sequence of $d \times d$ symmetric matrices $\{\mathbf{W}_i\}_{i=1}^n$, and a margin $\Delta > 0$, we investigate whether it is possible to find signs $(\epsilon_1, \dots, \epsilon_n) \in \{\pm 1\}^n$ such that the operator norm of the signed sum satisfies $\|\sum_{i=1}^n \epsilon_i \mathbf{W}_i\|_\mathrm{op} \leq \Delta$. Kunisky and Zhang (2023) recently introduced a random version of this problem, where the matrices $\{\mathbf{W}_i\}_{i=1}^n$ are drawn from the Gaussian orthogonal ensemble. This model can be seen as a random variant of the celebrated Matrix Spencer conjecture and as a matrix-valued analog of the symmetric binary perceptron in statistical physics. In this work, we establish a satisfiability transition in this problem as $n, d \to \infty$ with $n / d^2 \to \tau > 0$. Our contributions are twofold. First, we prove that the expected number of solutions with margin $\Delta=\kappa \sqrt{n}$ has a sharp threshold at a critical $\tau_1(\kappa)$: for $\tau < \tau_1(\kappa)$ the problem is typically unsatisfiable, while for $\tau > \tau_1(\kappa)$ the average number of solutions becomes exponentially large. Second, combining a second-moment method with recent results from Altschuler (2023) on margin concentration in perceptron-type problems, we identify a second threshold $\tau_2(\kappa)$, such that for $\tau>\tau_2(\kappa)$ the problem admits solutions with high probability. In particular, we establish for the first time that a system of $n = \Theta(d^2)$ Gaussian random matrices can be balanced so that the spectrum of the resulting matrix macroscopically shrinks compared to the typical semicircle law. Our proofs rely on deriving concentration inequalities and large deviation properties for the law of correlated Gaussian matrices under spectral norm constraints, extending results in the literature and offering insights of independent interest., Comment: 29 pages, 2 figures

Variational Autoencoders (VAE) are popular generative models used to sample from complex data distributions. Despite their empirical success in various machine learning tasks, significant gaps remain in understanding their theoretical properties, particularly regarding convergence guarantees. This paper aims to bridge that gap by providing non-asymptotic convergence guarantees for VAE trained using both Stochastic Gradient Descent and Adam algorithms.We derive a convergence rate of $\mathcal{O}(\log n / \sqrt{n})$, where $n$ is the number of iterations of the optimization algorithm, with explicit dependencies on the batch size, the number of variational samples, and other key hyperparameters. Our theoretical analysis applies to both Linear VAE and Deep Gaussian VAE, as well as several VAE variants, including $\beta$-VAE and IWAE. Additionally, we empirically illustrate the impact of hyperparameters on convergence, offering new insights into the theoretical understanding of VAE training.

Creativity is the ability to produce novel, useful, and surprising ideas, and has been widely studied as a crucial aspect of human cognition. Machine creativity on the other hand has been a long-standing challenge. With the rise of advanced generative AI, there has been renewed interest and debate regarding AI's creative capabilities. Therefore, it is imperative to revisit the state of creativity in AI and identify key progresses and remaining challenges. In this work, we survey leading works studying the creative capabilities of AI systems, focusing on creative problem-solving, linguistic, artistic, and scientific creativity. Our review suggests that while the latest AI models are largely capable of producing linguistically and artistically creative outputs such as poems, images, and musical pieces, they struggle with tasks that require creative problem-solving, abstract thinking and compositionality and their generations suffer from a lack of diversity, originality, long-range incoherence and hallucinations. We also discuss key questions concerning copyright and authorship issues with generative models. Furthermore, we highlight the need for a comprehensive evaluation of creativity that is process-driven and considers several dimensions of creativity. Finally, we propose future research directions to improve the creativity of AI outputs, drawing inspiration from cognitive science and psychology., Comment: 44 pages

Hadronic contributions dominate the uncertainty of the Standard Model prediction for the anomalous magnetic moment of the muon. In this work, we present results on the hadronic light-by-light contribution obtained from the evaluation of the hadronic four-point function of electromagnetic currents using the position-space formalism developed by the Mainz group. The simulations are performed with staggered fermions directly at the physical point. Several physical volumes are used to estimate finite volume effects. This direct lattice study is supplemented by considering the contribution of the light pseudoscalar pole in both finite and infinite volumes, where we reuse the pseudoscalar transition form factors that have been evaluated in previous simulations on the same ensembles., Comment: 7 pages, 6 figures

Reinforcement Learning from Human Feedback (RLHF) has become a popular approach to align language models (LMs) with human preferences. This method involves collecting a large dataset of human pairwise preferences across various text generations and using it to infer (implicitly or explicitly) a reward model. Numerous methods have been proposed to learn the reward model and align a LM with it. However, the costly process of collecting human preferences has received little attention and could benefit from theoretical insights. This paper addresses this issue and aims to formalize the reward training model in RLHF. We frame the selection of an effective dataset as a simple regret minimization task, using a linear contextual dueling bandit method. Given the potentially large number of arms, this approach is more coherent than the best-arm identification setting. We then propose an offline framework for solving this problem. Under appropriate assumptions - linearity of the reward model in the embedding space, and boundedness of the reward parameter - we derive bounds on the simple regret. Finally, we provide a lower bound that matches our upper bound up to constant and logarithmic terms. To our knowledge, this is the first theoretical contribution in this area to provide an offline approach as well as worst-case guarantees.

In this paper, we study the ability of large language models to learn specific mathematical rules such as distributivity or simplifying equations. We present an empirical analysis of their ability to generalize these rules, as well as to reuse them in the context of word problems. For this purpose, we provide a rigorous methodology to build synthetic data incorporating such rules, and perform fine-tuning of large language models on such data. Our experiments show that our model can learn and generalize these rules to some extent, as well as suitably reuse them in the context of word problems., Comment: NeurIPS'24 MATH-AI, the 4th Workshop on Mathematical Reasoning and AI

A field $K$ is $d$-local if there exist fields $K=k_d,\dots,k_0$ with $k_{i+1}$ completely discretely valued with residue field $k_i$, and $k_0$ quasi-finite. We prove a duality theorem for the Galois cohomology of such $K$ with many coefficients, including finite coefficients of any order. Previously, such duality was only known in few cases : as a perfect pairing of finite groups for finite coefficients prime to $\mathrm{char} k_0$ in general, or for any finite coefficients when $k_1$ is $p$-adic ; or as a perfect pairing of locally compact Hausdorff groups for the $\mathrm{fppf}$ cohomology of finite group schemes when $K$ is local. With no obvious reasonable topology available, we abandon perfectness altogether and instead obtain nondegenerate pairings of abstract abelian groups. This is done with new diagram-chasing results for pairings of torsion groups, allowing an approach by d\'evissage which breaks our results down to the study of pairings $K^M_r(K)/p\times H^{d+1-r}_p(K)\to\mathbb Z/p$ using results of Kato., Comment: 48 pages, comments welcome !

We report on continuous high-harmonic generation at 1 kHz repetition rate from a liquid-sheet plasma mirror driven by relativistic-intensity near-single-cycle light transients. Through precise control of both the surface plasma density gradient and the driving light waveform, we can produce highly stable and reproducible extreme ultraviolet spectral quasi-continua, corresponding to the generation of stable kHz-trains of isolated attosecond pulses in the time domain. This confirms the exciting potential of liquid sheet targets as one of the building blocks of future high-power attosecond lasers.

Optical-based terahertz sources are important for many burgeoning scientific and technological applications. Among such applications is precision spectroscopy of molecules, which exhibit rotational transitions at terahertz frequencies. Stemming from precision spectroscopy is frequency discrimination and stabilization of terahertz sources. Because many molecular species exist in the gas phase at room temperature, their transitions are prime candidates for practical terahertz frequency references. We demonstrate the stabilization of a low phase-noise, dual-wavelength Brillouin laser (DWBL) terahertz oscillator to a rotational transition of carbonyl sulfide (\ce{OCS}). We achieve an instability of $1.2\times10^{-12}/\sqrt{\tau}$, where $\tau$ is the averaging time in seconds. The signal-to-noise ratio and intermodulation limitations of the experiment are also discussed. We thus demonstrate a highly stable and spectrally pure terahertz frequency source. Our presented architecture will likely benefit metrology, spectroscopy, precision terahertz studies, and beyond.

A unified algebraic interpretation of both finite families of orthogonal polynomials and biorthogonal rational functions of $q$-Hahn type is provided. The approach relies on the meta $q$-Hahn algebra and its finite-dimensional bidiagonal representations. The functions of $q$-Hahn type are identified as overlaps (up to global factors) between bases solving ordinary or generalized eigenvalue problems in the representation of the meta $q$-Hahn algebra. Moreover, (bi)orthogonality relations, recurrence relations, difference equations and some contiguity relations satisfied by these functions are recovered algebraically using the actions of the generators of the meta $q$-Hahn algebra on various bases., Comment: 27 pages

Transcranial ultrasound imaging is usually limited by skull-induced attenuation and high-order aberrations. By using contrast agents such as microbubbles in combination with ultrafast imaging, not only can the signal-to-noise ratio be improved, but super-resolution images down to the micrometer scale of the brain vessels can be obtained. However, ultrasound localization microscopy (ULM) remains impacted by wave-front distortions that limit the microbubble detection rate and hamper their localization. In this work, we show how matrix imaging, which relies on the prior recording of the reflection matrix, can provide a solution to those fundamental issues. As an experimental proof-of-concept, an in-vivo reconstruction of deep brain microvessels is performed on three anesthetized sheeps. The compensation of wave distortions is shown to drastically enhance the contrast and resolution of ULM. This experimental study thus opens up promising perspectives for a transcranial and non-ionizing observation of human cerebral microvascular pathologies, such as stroke., Comment: 43 pages, 11 figures, 3 tables

We propose a novel approach to quantify quantum coherence which, contrary to the previous ones, does not rely on resource theory but rather on ontological considerations. In this framework, coherence is understood as the ability for a quantum system's statistics to deviate from the total probability formula. After motivating the importance of the total probability formula in quantum foundations, we propose a new set of axioms that a measure of coherence should satisfy, and show that it defines a class of measures different from the main previous proposal. Finally, we prove a general result about the dependence of the l2-coherence norm on the basis of interest, and show that it is well approximated by the square root of the purity in most bases.

Large language models (LLMs) have demonstrated significant progress in various natural language generation and understanding tasks. However, their linguistic generalization capabilities remain questionable, raising doubts about whether these models learn language similarly to humans. While humans exhibit compositional generalization and linguistic creativity in language use, the extent to which LLMs replicate these abilities, particularly in morphology, is under-explored. In this work, we systematically investigate the morphological generalization abilities of LLMs through the lens of compositionality. We define morphemes as compositional primitives and design a novel suite of generative and discriminative tasks to assess morphological productivity and systematicity. Focusing on agglutinative languages such as Turkish and Finnish, we evaluate several state-of-the-art instruction-finetuned multilingual models, including GPT-4 and Gemini. Our analysis shows that LLMs struggle with morphological compositional generalization particularly when applied to novel word roots, with performance declining sharply as morphological complexity increases. While models can identify individual morphological combinations better than chance, their performance lacks systematicity, leading to significant accuracy gaps compared to humans., Comment: 33 pages

In Deep Reinforcement Learning models trained using gradient-based techniques, the choice of optimizer and its learning rate are crucial to achieving good performance: higher learning rates can prevent the model from learning effectively, while lower ones might slow convergence. Additionally, due to the non-stationarity of the objective function, the best-performing learning rate can change over the training steps. To adapt the learning rate, a standard technique consists of using decay schedulers. However, these schedulers assume that the model is progressively approaching convergence, which may not always be true, leading to delayed or premature adjustments. In this work, we propose dynamic Learning Rate for deep Reinforcement Learning (LRRL), a meta-learning approach that selects the learning rate based on the agent's performance during training. LRRL is based on a multi-armed bandit algorithm, where each arm represents a different learning rate, and the bandit feedback is provided by the cumulative returns of the RL policy to update the arms' probability distribution. Our empirical results demonstrate that LRRL can substantially improve the performance of deep RL algorithms.

We propose a simple method to estimate the parameters of a continuously measured quantum system, by fitting correlation functions of the measured signal. We demonstrate the approach in simulation, both on toy examples and on a recent superconducting circuits experiment which proved particularly difficult to characterise using conventional methods. The idea is applicable to any system whose evolution is described by a jump or diffusive stochastic master equation. It allows the simultaneous estimation of many parameters, is practical for everyday use, is suitable for large Hilbert space dimensions, and takes into account experimental constraints such as detector imperfections and signal filtering and digitisation. Unlike existing methods, it also provides a direct way to understand how each parameter is estimated from the measured signal. This makes the approach interpretable, facilitates debugging, and enables validating the adequacy of a model with the observed data., Comment: 26 pages, 15 figures

We theoretically and numerically study the evolution of soliton-like waves supported by stimulated Brillouin scattering. First, the emergence and unusual behaviour of resonant solitary waves are investigated for both backward and forward three wave interactions. We find that these waves can be characterized by the ratio between the optical and acoustic damping coefficients. We also examine a second class of non-resonant anti-symmetric soliton-like waves, which have a more complicated pulse shape than traditional solitons. These waves are superluminal, with pulse velocities that can be tuned by the input Stokes and pump fields. We discuss the excitation of these types of waves and the physical conditions required for their observation., Comment: 9 pages, 7 figures

The near-infrared (NIR) emission of the youngest protostars still needs to be characterized to better understand the evolution of their accretion and ejection activity. We analyze James Webb Space Telescope NIRSpec 1.7 -- 5.3 $\mu$m observations of two deeply embedded sources in the S68N protostellar core in Serpens. The North Central (NC) source exhibits a highly obscured spectrum (A_K ~ 4.8 mag) that is modeled with a pre-main-sequence photosphere and a hot disk component. The photospheric parameters are consistent with a young, low-mass photosphere, as suggested by the low surface gravity, log g of 1.94 $\pm$ 0.15 cm s$^{-2}$. The hot disk suggests that accretion onto the central protostellar embryo is ongoing, although prototypical accretion-tracing emission lines HI are not detected. The South Central (SC) source, which is even more embedded (A_K ~ 8 mag; no continuum is detected shortward of 3.6 $\mu$m) appears to be driving the large-scale S68N protostellar outflow, and launches a collimated hot molecular jet detected in \Ht and CO ro-vibrational lines. Shock modeling of the \Ht (ro)vibrational lines establishes that fast $C$-type shocks ($\geq$ 30 km s$^{-1}$), with high pre-shock density ($\geq$ $10^7$ cm$^{-3}$), and strong magnetic field (b ~ 3--10, where $B = b\,\times\,\sqrt{\textrm{n}_{\textrm{H}} (\textrm{cm}^{-3})}\,\mu\textrm{G}$) best match the data. The bright CO fundamental line forest suggests energetic excitation, with the contribution of non-LTE effects, ie irradiation pumping. Detected OH and CH$^{+}$ ro-vibrational lines support this hypothesis. These two Class 0 protostars seem to be in very young evolutionary stages and still have to acquire the bulk of their final stellar masses. These results demonstrate that JWST enables unprecedented diagnostics of these first stages of the protostellar evolutionary phase.

We observed the ultra hot Jupiter WASP-33b with the Spectro-Polarim\`etre Infra-Rouge on the Canada Fance Hawaii Telescope. Previous observations of the dayside of WASP-33b show evidence of CO and Fe emission indicative of a thermal inversion. We observed its nightside over five Earth-nights to search for spectral signatures of CO in the planet's thermal emission. Our three pre-transit observations and two post-transit observations are sensitive to regions near the morning or evening terminators, respectively. From spectral retrievals, we detect CO molecular absorption in the planet's emission spectrum after transit at $\sim$6.6$\sigma$. This is the strongest ground-based detection of nightside thermal emission from an exoplanet, and only the third ever. CO appearing in absorption suggests that the nightside near the evening terminator does not have a temperature inversion; this makes sense if the dayside inversion is driven by absorption of stellar radiation. On the contrary, we do not detect CO from the morning terminator. This may be consistent with heat advection by an eastward jet. Phase-resolved high-resolution spectroscopy offers an economical alternative to space-based full-orbit spectroscopic phase curves for studying the vertical and horizontal atmospheric temperature profiles of short-period exoplanets., Comment: 13 pages, 9 figures; Accepted for publication in ApJ Letters

PAC-Bayes learning is a comprehensive setting for (i) studying the generalisation ability of learning algorithms and (ii) deriving new learning algorithms by optimising a generalisation bound. However, optimising generalisation bounds might not always be viable for tractable or computational reasons, or both. For example, iteratively querying the empirical risk might prove computationally expensive. In response, we introduce a novel principled strategy for building an iterative learning algorithm via the optimisation of a sequence of surrogate training objectives, inherited from PAC-Bayes generalisation bounds. The key argument is to replace the empirical risk (seen as a function of hypotheses) in the generalisation bound by its projection onto a constructible low dimensional functional space: these projections can be queried much more efficiently than the initial risk. On top of providing that generic recipe for learning via surrogate PAC-Bayes bounds, we (i) contribute theoretical results establishing that iteratively optimising our surrogates implies the optimisation of the original generalisation bounds, (ii) instantiate this strategy to the framework of meta-learning, introducing a meta-objective offering a closed form expression for meta-gradient, (iii) illustrate our approach with numerical experiments inspired by an industrial biochemical problem.

We demonstrate efficient in-plane optical fiber collection of single photon emission from quantum dots embedded in photonic crystal cavities. This was achieved via adiabatic coupling between a tapered optical fiber and a tapered on-chip photonic waveguide coupled to the photonic crystal cavity. The collection efficiency of a dot in a photonic crystal cavity was measured to be 5 times greater via the tapered optical fiber compared to collection by a microscope objective lens above the cavity. The single photon source was also characterized by second order photon correlations measurements giving g(2)(0)=0.17 under non-resonant excitation. Numerical calculations demonstrate that the collection efficiency could be further increased by improving the dot-cavity coupling and by increasing the overlap length of the tapered fiber with the on-chip waveguide. An adiabatic coupling of near unity is predicted for an overlap length of 5 microns., Comment: 22 pages, 12 figures

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.

The measurement of the electronic structure of metal alloys is hampered by the in-applicability of conventional quantum oscillation measurements to high-temperatures and alloy scattering. Recent advancements in the study of the electronic contribution to the entropy suggest a path to ground transport properties in alloy equilibrium thermodynamics. Fe-Ni represents an interesting test-case because it exhibits an intricate electronic structure, order-disorder transformations, and a large solid-solution region where equilibrium data can be obtained. Using cluster modeling, the electronic contribution to the entropy can be inferred from high-temperature thermodynamic data. Electronic transport property measurements can be used to independently evaluate the electronic contribution to the entropy. Reconciling these two approaches at high temperature confirms a new path to study electronic structure for metal alloys.

In continuous-variable quantum computation, identifying key elements that enable a quantum computational advantage is a long-standing issue. Starting from the standard results on the necessity of Wigner negativity, we develop a comprehensive and versatile framework that not only enables the identification of a potential quantum computational advantage, but also allows to pinpoint the contribution of each quantum gate in achieving this objective. As such, it can be straightforwardly applied to current continuous-variables quantum circuits, while also constraining the tolerable amount of losses above which any potential quantum advantage can be ruled out. We use $(s)$-ordered quasiprobability distributions on phase-space to capture the non-classical features in the protocol, and focus our model entirely on the ordering parameter $s$. This allows us to highlight the resourcefulness and robustness to loss of a universal set of unitary gates comprising three distinct Gaussian gates, and a fourth one, the cubic gate, providing important insight on the role of non-Gaussianity., Comment: 16 pages, 5 figures

We investigate the evolution of the second-order temporal coherence during the emission of a superradiant burst by an elongated cloud of cold Rb atoms in free space. To do so, we measure the two-times intensity correlation function $g_N^{(2)}(t_1, t_2)$ following the pulsed excitation of the cloud. By monitoring $g_N^{(2)}(t, t)$ during the burst, we observe the establishment of second-order coherence, and contrast it with the situation where the cloud is initially prepared in a steady state. We compare our findings to the predictions of the Dicke model, using an effective atom number to account for finite size effects, finding that the model reproduces the observed trend at early time. For longer times, we observe a subradiant decay, a feature that goes beyond Dicke's model. Finally, we measure the $g_N^{(2)}(t_1, t_2)$ at different times and observe the appearance of anti-correlations during the burst, that are not present when starting from a steady state.

Initially designed to detect and characterize exoplanets, extreme adaptive optics systems (AO) open a new window on the solar system by resolving its small bodies. Nonetheless, despite the always increasing performances of AO systems, the correction is not perfect, degrading their image and producing a bright halo that can hide faint and close moons. Using a reference point spread function (PSF) is not always sufficient due to the random nature of the turbulence. In this work, we present our method to overcome this limitation. It blindly reconstructs the AO-PSF directly in the data of interest, without any prior on the instrument nor the asteroid's shape. This is done by first estimating the PSF core parameters under the assumption of a sharp-edge and flat object, allowing the image of the main body to be deconvolved. Then, the PSF faint extensions are reconstructed with a robust penalization optimization, discarding outliers on-the-fly such as cosmic rays, defective pixels and moons. This allows to properly model and remove the asteroid's halo. Finally, moons can be detected in the residuals, using the reconstructed PSF and the knowledge of the outliers learned with the robust method. We show that our method can be easily applied to different instruments (VLT/SPHERE, Keck/NIRC2), efficiently retrieving the features of AO-PSFs. Compared with state-of-the-art moon enhancement algorithms, moon signal is greatly improved and our robust detection method manages to discriminate faint moons from outliers., Comment: arXiv admin note: text overlap with arXiv:2407.21548

In this paper, we consider a differential stochastic zero-sum game in which two players intervene by adopting impulse controls in a finite time horizon. We provide a numerical solution as an approximation of the value function, which turns out to be the same for both players. While one seeks to maximize the value function, the other seeks to minimize it. Thus we find a single numerical solution for the Nash equilibrium as well as the optimal impulse controls strategy pair for both player based on the classical Policy Iteration (PI) algorithm. Then, we perform a rigorous convergence analysis on the approximation scheme where we prove that it converges to its corresponding viscosity solution as the discretization step approaches zero, and under certain conditions. We showcase our algorithm by implementing a two-player almost analytically solvable game in which the players act through impulse control and compete over the exchange rate.

One of the primary sources of gravitational waves (GWs) anticipated to be detected by the Laser Interferometer Space Antenna (LISA) are Galactic double white dwarf binaries (DWDs). However, most of these binaries will be unresolved, and their GWs will overlap incoherently, creating a stochastic noise known as the Galactic foreground. Similarly, the population of unresolved systems in the Milky Way's (MW) satellites is expected to contribute to a stochastic gravitational wave background (SGWB). Due to their anisotropy and the annual motion of the LISA constellation, both the Galactic foreground and the satellite SGWB fall into the category of cyclostationary processes. Leveraging this property, we develop a purely frequency-based method to study LISA's capability to detect the MW foreground and SGWBs from the most promising MW satellites. We analyze both mock data generated by an astrophysically motivated SGWB spectrum, and realistic ones from a DWD population generated via binary population synthesis. We are able to recover or put constrains on the candidate foregrounds, reconstructing -- in the presence of noise uncertainties -- their sky distribution and spectrum. Our findings highlight the significance of the interplay between the astrophysical spectrum and LISA's sensitivity to detect the satellites' SGWB. Considering an astrophysically motivated prior on the satellite positions improves their detectability, which becomes otherwise challenging in the presence of the Galactic foreground. Furthermore, we explore the potential to observe a hypothetical satellite located behind the Galactic disk. Our results suggest that a Large Magellanic Cloud-like satellite could indeed be observable by LISA., Comment: 19 pages, 13 figures, 2 tables

Upcoming space-based gravitational-wave detectors will be sensitive to millions and resolve tens of thousands of stellar-mass binary systems at mHz frequencies. The vast majority of these will be double white dwarfs in our Galaxy. The greatest part will remain unresolved, forming an incoherent stochastic foreground signal. Using state-of-the-art Galactic models for the formation and evolution of binary white dwarfs and accurate LISA simulated signals, we introduce a test for foreground Gaussianity and stationarity. We explain the former with a new analytical modulation induced by the LISA constellation motion and the intrinsic anisotropy of the source distribution. By demodulating the foreground signal, we reveal a deviation from Gaussianity in the 2-10 mHz frequency band. Our finding is crucial to design faithful data models, i.e. unbiased likelihoods for both individual sources and astrophysical foregrounds parameter estimation, and ultimately for an accurate interpretation of the LISA data., Comment: 17 pages, 8 figures

We consider the problem of recovering elements of a low-dimensional model from linear measurements. From signal and image processing to inverse problems in data science, this question has been at the center of many applications. Lately, with the success of models and methods relying on deep neural networks leading to non-convex formulations, traditional convex variational approaches have shown their limits. Furthermore, the multiplication of algorithms and recovery results makes identifying the best methods a complex task. In this article, we study recovery with a class of widely used algorithms without considering any underlying functional. This result leads to a class of projected gradient descent algorithms that recover a given low-dimensional with linear rates. The obtained rates decouple the impact of the quality of the measurements with respect to the model from its intrinsic complexity. As a consequence, we can directly measure the performance of this class of projected gradient descents through a restricted Lipschitz constant of the projection. By optimizing this constant, we define optimal algorithms. Our general approach provides an optimality result in the case of sparse recovery. Moreover, we uncover underlying linear rates of convergence for some ''plug and play'' imaging methods relying on deep priors by interpreting our results in this context, thus linking low-dimensional recovery and recovery with deep priors under a unified theory, validated by experiments on synthetic and real data.

We propose a method to compute expectation values of observables in the stationary state of a (Markovian) bosonic open quantum system. Using a hierarchy of semi-definite relaxations, we obtain finer and finer upper and lower bounds to any expectation value of interest. The bounds are rigorous, robust to stationary state degeneracies, and numerically improve as the occupation number increases on the examples we considered. This makes it adapted to the simulation of stationary states of bosonic qubits and in particular dissipatively stabilized cat qubits.

Abstraction-based control design is a promising approach for ensuring safety-critical control of complex cyber-physical systems. A key aspect of this methodology is the relation between the original and abstract systems, which ensures that the abstract controller can be transformed into a valid controller for the original system through a concretization procedure. In this paper, we provide a comprehensive and systematic framework that characterizes various simulation relations, through their associated concretization procedures. We introduce the concept of augmented system, which universally enables a feedback refinement relation with the abstract system. This augmented system encapsulates the specific characteristics of each simulation relation within an interface, enabling a plug-and-play control architecture. Our results demonstrate that the existence of a particular simulation relation between the concrete and abstract systems is equivalent to the implementability of a specific control architecture, which depends on the considered simulation relation. This allows us to introduce new types of relations, and to establish the advantages and drawbacks of different relations, which we exhibit through detailed examples., Comment: 14 pages, 6 figures

A Physics-Augmented Neural network is trained to model a hyperelastic behavior. The dataset used for the training, validation, and test are displacement-force couples obtained from two experiments on a rubber-like material. One experiment was dedicated for the test, to assess the capacity of the model to generalize on unseen loadings and geometries. The trained AI model outperforms a standard Neo Hookean model identified on the same data. Particular attention is paid to the mechanical data information contained in the different datasets., Comment: Photomechanics - IDICs, Oct 2024, Clermont - Ferrand, France

We propose Diffusion Model Predictive Control (D-MPC), a novel MPC approach that learns a multi-step action proposal and a multi-step dynamics model, both using diffusion models, and combines them for use in online MPC. On the popular D4RL benchmark, we show performance that is significantly better than existing model-based offline planning methods using MPC and competitive with state-of-the-art (SOTA) model-based and model-free reinforcement learning methods. We additionally illustrate D-MPC's ability to optimize novel reward functions at run time and adapt to novel dynamics, and highlight its advantages compared to existing diffusion-based planning baselines., Comment: Preprint

Large Language Models (LLMs) can learn new tasks through in-context supervised learning (i.e., ICL). This work studies if this ability extends to in-context reinforcement learning (ICRL), where models are not given gold labels in context, but only their past predictions and rewards. We show that a naive application of ICRL fails miserably, and identify the root cause as a fundamental deficiency at exploration, which leads to quick model degeneration. We propose an algorithm to address this deficiency by increasing test-time compute, as well as a compute-bound approximation. We use several challenging classification tasks to empirically show that our ICRL algorithms lead to effective learning from rewards alone, and analyze the characteristics of this ability and our methods. Overall, our results reveal remarkable ICRL abilities in LLMs.

Whole-brain proton magnetic resonance spectroscopic imaging (1H-MRSI) is a non-invasive technique for assessing neurochemical distribution in the brain, offering valuable insights into brain functions and neural diseases. It greatly benefits from the improved SNR at ultrahigh field strengths ($\geq$7T). However, 1H-MRSI still faces several challenges, such as long acquisition time and severe signal contaminations from water and lipids. In this study, 2D and 3D short TR/TE 1H-FID-MRSI sequences using rosette trajectories were developed with spatial resolutions of 4.48$\times$4.48 mm$^2$ and 4.48$\times$4.48$\times$4.50 mm$^3$, respectively. Water signals were suppressed using an optimized Five-variable-Angle-gaussian-pulses-with-ShorT-total-duration of 76 ms (FAST) water suppression scheme, and lipid signals were removed using the L2 regularization method. Metabolic maps of major 1H metabolites were obtained within 5:40 min with 16 averages and 1 average for the 2D and 3D acquisitions, respectively. Excellent inter-session reproducibility was shown, with the coefficients of variance (CV) being lower than 6% for N-Acetyle-L-aspartic acid (NAA), Glutamate (Glu), Choline Chloride and glycerophosphocholine (tCho), Creatine and Phosphocreatine (tCr), and Glycine and Myo-inositol (Gly+Ins). To explore the potential of further accelerating the acquisition, compressed sensing was applied retrospectively to the 3D datasets. The structural similarity index (SSIM) remained above 0.85 and 0.8 until $R = 2$ and $R = 3$ for the metabolite maps of Glu, NAA, tCr, and tCho, indicating the possibility for further reduction of acquisition time to around 2min., Comment: 25 pages, 9 figures

Using the EXES instrument on SOFIA, we have obtained velocity-resolved spectra of several pure rotational lines of H2 toward shocked molecular gas within three Galactic sources: the supernova remnant (SNR) IC443 (Clump C), a protostellar outflow in the intermediate-mass star-forming region NGC 2071, and the SNR 3C391. These observations had the goal of searching for expected velocity shifts between ortho- and para-H2 transitions emitted by C-type shocks. In contrast in our previous similar study of HH7, the result of our search was negative: no velocity shifts were reliably detected. Several possible explanations for the absence of such shifts are discussed: these include a preshock ortho-to-para ratio that is already close to the high-temperature equilibrium value of 3 (in the case of IC443C), the more complex shock structures evident in all these sources, and the larger projected aperture sizes relative to those in the observations of HH7., Comment: 18 pages, including 9 figures. Accepted for publication in the Astrophysical Journal

Context: A new generation of instruments (e.g., JWST, ELTs, PLATO, Ariel) is providing atmospheric spectra and mass/radius measurements for large exoplanet populations, challenging planetary models used to interpret these findings. Aims: We develop a new model, the Heat Atmosphere Density Evolution Solver (HADES), by coupling an atmosphere and interior model self-consistently and comparing its results to observed data. Methods: Atmospheric calculations are performed under radiative-convective equilibrium, while the interior relies on recent ab initio equations of state. We ensure continuity in the thermal, gravity, and molecular mass profiles between models. Results: The model is applied to the known exoplanet database to characterize intrinsic thermal properties. We find that intrinsic temperatures (T$_{int}$) of 200-400 K, increasing with equilibrium temperature, are needed to explain radius inflation in hot Jupiters. Additionally, we perform atmosphere-interior retrievals using observed spectra and measured parameters for WASP-39 b and 51 Eridani b. For WASP-39 b, spectroscopic data breaks degeneracies in metallicity and Tint, deriving high values: Z = 14.79$^{+1.80}_{-1.91}$ x Solar and T$_{int} = 297.39^{+8.95}_{-16.9}$ K. For 51 Eridani b, we show the importance of using self-consistent models with radius as a constrained parameter, deriving a planet mass M$_{p} = 3.13^{+0.05}_{-0.04}$ M$_{J}$ and a core mass M$_{core} = 31.86^{+0.32}_{-0.18}$ M$_{E}$, suggesting formation via core accretion with a "hot start." Conclusions: Self-consistent atmosphere-interior models can efficiently break degeneracies in the structure of transiting and directly imaged exoplanets, offering new insights into exoplanet formation and evolution., Comment: 20 pages, 10 Figures + 2 Figures in appendix, Accepted for publication in Astronomy & Astrophysics

We propose a convex controller synthesis framework for a large class of constrained linear systems, including those described by (deterministic and stochastic) partial differential equations and integral equations, commonly used in fluid dynamics, thermo-mechanical systems, quantum control, or transportation networks. Most existing control techniques rely on a (finite-dimensional) discrete description of the system, via ordinary differential equations. Here, we work instead with more general (infinite-dimensional) Hilbert spaces. This enables the discretization to be applied after the optimization (optimize-then-discretize). Using output-feedback SLS, we formulate the controller synthesis as a convex optimization problem. Structural constraints like sensor and communication delays, and locality constraints, are incorporated while preserving convexity, allowing parallel implementation and extending key SLS properties to infinite dimensions. The proposed approach and its benefits are demonstrated on a linear Boltzmann equation.

Cellular automata have become a cornerstone for investigating emergence and self-organization across diverse scientific disciplines, spanning neuroscience, artificial life, and theoretical physics. However, the absence of a hardware-accelerated cellular automata library limits the exploration of new research directions, hinders collaboration, and impedes reproducibility. In this work, we introduce CAX (Cellular Automata Accelerated in JAX), a high-performance and flexible open-source library designed to accelerate cellular automata research. CAX offers cutting-edge performance and a modular design through a user-friendly interface, and can support both discrete and continuous cellular automata with any number of dimensions. We demonstrate CAX's performance and flexibility through a wide range of benchmarks and applications. From classic models like elementary cellular automata and Conway's Game of Life to advanced applications such as growing neural cellular automata and self-classifying MNIST digits, CAX speeds up simulations up to 2,000 times faster. Furthermore, we demonstrate CAX's potential to accelerate research by presenting a collection of three novel cellular automata experiments, each implemented in just a few lines of code thanks to the library's modular architecture. Notably, we show that a simple one-dimensional cellular automaton can outperform GPT-4 on the 1D-ARC challenge.

We present the results of an asteroseismic study of HD 118660 (TIC 171729860), being a chemically peculiar (mild Am) star exhibiting $\delta$ Scuti ($\delta$ Sct) pulsations. It is based on the analysis of two sectors of time-series photometry from the space mission TESS and seismic modelling. It yielded the detection of 15 and 16 frequencies for TESS sectors 23 and 50, respectively. The identified pulsation modes include four radial ($\ell=0$) and five dipolar ($\ell=1$) ones. The radial modes are overtones with order $n$ ranging from $3$ and $6$. Such high values of $n$ are theoretically not expected for stars with the effective temperature of HD 118660 ($\rm T_{\rm eff}\approx 7550 \rm K$ ) located near the red edge of the $\delta$ Sct instability strip. To estimate the asteroseismic parameters, we have generated a grid of stellar models assuming a solar metallicity ($Z=0.014$) and different values for the convective overshooting parameter ($0.1\leq \alpha_{\rm ov}\leq 0.3$). We conclude that the analysis of the radial modes is insufficient to constrain $\alpha_{\rm ov}$ and $Z$ for $\delta$ Sct stars. The value for the equatorial velocity of HD 118660 derived from the seismic radius and the rotational frequency is consistent with values found in the literature., Comment: 10 pages, 7 figures, Accepted for publication in MNRAS

We present one of the first comprehensive field datasets capturing dense pedestrian dynamics across multiple scales, ranging from macroscopic crowd flows over distances of several hundred meters to microscopic individual trajectories, including approximately 7,000 recorded trajectories. The dataset also includes a sample of GPS traces, statistics on contact and push interactions, as well as a catalog of non-standard crowd phenomena observed in video recordings. Data were collected during the 2022 Festival of Lights in Lyon, France, within the framework of the French-German MADRAS project, covering pedestrian densities up to 4 individuals per square meter.

Wavefunction forms based on products of electron pairs are usually constructed as closed-shell singlets, which is insufficient when the molecular state has a nonzero spin or when the chemistry is determined by $d$- or $f-$electrons. A set of two-electron forms are considered as explicit couplings of second-quantized operators to open-shell singlets. Geminal wavefunctions are constructed and their structure is elaborated. Numerical results for small model systems clearly demonstrate improvement over closed-shell singlet pairs.

M-type stars are the most common stars in the universe. They are ideal hosts for the search of exoplanets in the habitable zone (HZ), as their small size and low temperature make the HZ much closer in than their solar twins. Harboring very deep convective layers, they also usually exhibit very intense magnetic fields. Understanding their environment, in particular their coronal and wind properties, is thus very important, as they might be very different from what is observed in the solar system. The mass loss rate of M-type stars is poorly known observationally, and recent attempts to estimate it for some of them (TRAPPIST-1, Proxima Cen) can vary by an order of magnitude. In this work, we revisit the stellar wind properties of M-dwarfs in the light of the latest estimates of $\dot{M}$ through Lyman-$\alpha$ absorption at the astropause and slingshot prominences. We outline a modeling strategy to estimate the mass loss rate, radiative loss and wind speed, with uncertainties, based on an Alfv\'en wave driven stellar wind model. We find that it is very likely that several TRAPPIST-1 planets lie within the Alfv\'en surface, which imply that these planets experience star-planet magnetic interactions (SPMI). We also find that SPMI between Proxima Cen b and its host star could be the reason of recently observed radio emissions., Comment: 15 pages, 9 figures, accepted for publication in ApJ