Search

We analyze the discrete-time dynamics of a model of non-interacting fermions on a finite graph coupled to an infinite reservoir formed by a bosonic quantum walk on Z. This dynamics consists of consecutive applications of free evolutions of the fermions and bosons followed by a local coupling between them. The unitary operator implementing this coupling generates fermionic transitions between two fixed vertices induced by absorption and emission of bosons at a single lattice site. The free fermion evolution is given by a second-quantized single-particle unitary operator satisfying some genericity assumptions, in particular it produces hopping of fermions between all vertices. The free boson evolution is given by the second-quantized shift operator on Z. We derive explicitly the Heisenberg dynamics of fermionic observables and obtain a systematic expansion in the large-coupling regime, which we control by using spectral methods. We also prove that the reduced state of the fermions converges in the large-time limit to an infinite-temperature Gibbs

Driving quantum materials out-of-equilibrium makes it possible to generate states of matter inaccessible through standard equilibrium tuning methods. Upon time-periodic coherent driving of electrons using electromagnetic fields, the emergence of Floquet-Bloch states enables the creation and control of exotic quantum phases. In transition metal dichalcogenides, broken inversion symmetry within each monolayer results in a non-zero Berry curvature at the K and K$^{\prime}$ valley extrema, giving rise to chiroptical selection rules that are fundamental to valleytronics. Here, we bridge the gap between these two concepts and introduce Floquet-Bloch valleytronics. Using time- and polarization-resolved extreme ultraviolet momentum microscopy combined with state-of-the-art ab initio theory, we demonstrate the formation of valley-polarized Floquet-Bloch states in 2H-WSe$_2$ upon below-bandgap coherent electron driving with chiral light pulses. We investigate quantum path interference between Floquet-Bloch and Volkov states, showing that this interferometric process depends on the valley pseudospin and light polarization-state. Conducting extreme ultraviolet photoemission circular dichroism in these nonequilibrium settings reveals the potential for controlling the orbital character of Floquet-engineered states. These findings link Floquet engineering and quantum geometric light-matter coupling in two-dimensional materials. They can serve as a guideline for reaching novel out-of-equilibrium phases of matter by dynamically breaking symmetries through coherent dressing of winding Bloch electrons with tailored light pulses.

We study the long-term behavior of the iteration of a random map consisting of Lipschitz transformations on a compact metric space, independently and randomly selected according to a fixed probability measure. Such a random map is said to be \emph{mostly contracting} if all Lyapunov exponents associated with stationary measures are negative. This requires introducing the notion of (maximal) Lyapunov exponent in this general context of Lipschitz transformations on compact metric spaces. We show that this class is open with respect to the appropriate topology and satisfies the strong law of large numbers for non-uniquely ergodic systems, the limit theorem for the law of random iterations, the global Palis' conjecture, and that the associated annealed Koopman operator is quasi-compact. This implies many statistical properties such as central limit theorems, large deviations, statistical stability, and the continuity and H\"older continuity of Lyapunov exponents. Examples from this class of random maps include random products of circle $C^1$ diffeomorphisms, interval $C^1$ diffeomorphisms onto their images, and $C^1$ diffeomorphisms of a Cantor set on a line, all considered under the assumption of no common invariant measure. This class also includes projective actions of locally constant linear cocycles under the assumptions of simplicity of the first Lyapunov exponent and some kind of irreducibility. One of the main tools to prove the above results is the generalization of Kingman's subadditive ergodic theorem and the uniform Kingman's subadditive ergodic theorem for general Markov operators. These results are of independent interest, as they may have broad applications in other contexts.

Modern time-series forecasting models often fail to make full use of rich unstructured information about the time series themselves. This lack of proper conditioning can lead to obvious model failures; for example, models may be unaware of the details of a particular product, and hence fail to anticipate seasonal surges in customer demand in the lead up to major exogenous events like holidays for clearly relevant products. To address this shortcoming, this paper introduces a novel forecast post-processor -- which we call LLMForecaster -- that fine-tunes large language models (LLMs) to incorporate unstructured semantic and contextual information and historical data to improve the forecasts from an existing demand forecasting pipeline. In an industry-scale retail application, we demonstrate that our technique yields statistically significantly forecast improvements across several sets of products subject to holiday-driven demand surges., Comment: Presented at NeurIPS Time Series in the Age of Large Models (2024)

Machine Learning Interatomic Potentials (MLIPs) are a highly promising alternative to force-fields for molecular dynamics (MD) simulations, offering precise and rapid energy and force calculations. However, Quantum-Mechanical (QM) datasets, crucial for MLIPs, are fragmented across various repositories, hindering accessibility and model development. We introduce the openQDC package, consolidating 37 QM datasets from over 250 quantum methods and 400 million geometries into a single, accessible resource. These datasets are meticulously preprocessed, and standardized for MLIP training, covering a wide range of chemical elements and interactions relevant in organic chemistry. OpenQDC includes tools for normalization and integration, easily accessible via Python. Experiments with well-known architectures like SchNet, TorchMD-Net, and DimeNet reveal challenges for those architectures and constitute a leaderboard to accelerate benchmarking and guide novel algorithms development. Continuously adding datasets to OpenQDC will democratize QM dataset access, foster more collaboration and innovation, enhance MLIP development, and support their adoption in the MD field.

En plus de soixante ans, huit pr\'esidents se sont succ\'ed\'e \`a la t\^ete de la Ve R\'epublique fran\c{c}aise (de Gaulle, Pompidou, Giscard d'Estaing, Mitterrand, Chirac, Sarkozy, Hollande, Macron). Apr\`es avoir pr\'esent\'e le corpus de leurs discours -- soit 9202 textes et plus de 20 millions de mots \'etiquet\'es -- le style de chacun des pr\'esidents sera caract\'eris\'e \`a l'aide de leurs vocabulaire (vocables et cat\'egories grammaticales). Une analyse plus approfondie r\'ev\`ele les s\'equences typiques de chaque locataire de l'\'Elys\'ee. Bas\'ee sur les distances entre l'ensemble des allocutions, une figure illustre les similitudes et diff\'erences entre les diff\'erents pr\'esidents. Over the past sixty-six years, eight presidents successively headed the Fifth French Republic (de Gaulle, Pompidou, Giscard d'Estaing, Mitterrand, Chirac, Sarkozy, Holland, Macron). After presenting the corpus of their speeches -- 9,202 texts and more than 20 million labelled words -- the style of each of them will be characterized by their vocabulary (lemmas and part-of-speech). A deeper analysis reveals the typical sequences of each tenant of the Elys\'ee. Based on an intertextual distance between all presidential speeches, a synthesis can be drawn reflecting the similarities and differences between presidents., Comment: in French language

Generative AI proposes several large language models (LLMs) to automatically generate a message in response to users' requests. Such scientific breakthroughs promote new writing assistants but with some fears. The main focus of this study is to analyze the written style of one LLM called ChatGPT by comparing its generated messages with those of the recent French presidents. To achieve this, we compare end-of-the-year addresses written by Chirac, Sarkozy, Hollande, and Macron with those automatically produced by ChatGPT. We found that ChatGPT tends to overuse nouns, possessive determiners, and numbers. On the other hand, the generated speeches employ less verbs, pronouns, and adverbs and include, in mean, too standardized sentences. Considering some words, one can observe that ChatGPT tends to overuse "to must" (devoir), "to continue" or the lemma "we" (nous). Moreover, GPT underuses the auxiliary verb "to be" (^etre), or the modal verbs "to will" (vouloir) or "to have to" (falloir). In addition, when a short text is provided as example to ChatGPT, the machine can generate a short message with a style closed to the original wording. Finally, we reveal that ChatGPT style exposes distinct features compared to real presidential speeches.

This paper proves sharp small cap decoupling estimates for the moment curve $\mathcal{M}^n=\{(t,t^2,\ldots,t^n):0\leq t\leq 1\}$ in the remaining small cap parameter ranges for $\mathbb{R}^2$ and $\mathbb{R}^3$., Comment: 20 pages

Multivariate (average) equivalence testing is widely used to assess whether the means of two conditions of interest are `equivalent' for different outcomes simultaneously. The multivariate Two One-Sided Tests (TOST) procedure is typically used in this context by checking if, outcome by outcome, the marginal $100(1-2\alpha$)\% confidence intervals for the difference in means between the two conditions of interest lie within pre-defined lower and upper equivalence limits. This procedure, known to be conservative in the univariate case, leads to a rapid power loss when the number of outcomes increases, especially when one or more outcome variances are relatively large. In this work, we propose a finite-sample adjustment for this procedure, the multivariate $\alpha$-TOST, that consists in a correction of $\alpha$, the significance level, taking the (arbitrary) dependence between the outcomes of interest into account and making it uniformly more powerful than the conventional multivariate TOST. We present an iterative algorithm allowing to efficiently define $\alpha^{\star}$, the corrected significance level, a task that proves challenging in the multivariate setting due to the inter-relationship between $\alpha^{\star}$ and the sets of values belonging to the null hypothesis space and defining the test size. We study the operating characteristics of the multivariate $\alpha$-TOST both theoretically and via an extensive simulation study considering cases relevant for real-world analyses -- i.e.,~relatively small sample sizes, unknown and heterogeneous variances, and different correlation structures -- and show the superior finite-sample properties of the multivariate $\alpha$-TOST compared to its conventional counterpart. We finally re-visit a case study on ticlopidine hydrochloride and compare both methods when simultaneously assessing bioequivalence for multiple pharmacokinetic parameters., Comment: 34 pages, 13 figures. To be published in: Statistics in Medicine

Underground hydrogen storage in saline aquifers is a potential solution for seasonal renewable energy storage. Among potential storage sites, facilities used for underground natural gas storage have advantages, including well-characterized cyclical injection-withdrawal behavior and partially reusable infrastructure. However, the differences between hydrogen-brine and natural gas-brine flow, particularly through fractures in the reservoir and the sealing caprock, remain unclear due to the complexity of two-phase flow. Therefore, we investigate fracture relative permeability for hydrogen versus methane (natural gas) and nitrogen (commonly used in laboratories). Steady-state relative permeability experiments were conducted at 10 MPa on fractured carbonate rock from the Loenhout natural gas storage in Belgium, where gas flows through {\textmu}m-to-mm scale fractures. Our results reveal that the hydrogen exhibits similar relative permeability curves to methane, but both are significantly lower than those measured for nitrogen. This implies that nitrogen cannot reliably serve as a proxy for hydrogen at typical reservoir pressures. The low relative permeabilities for hydrogen and methane indicate strong fluid phase interference, which traditional relative permeability models fail to capture. This is supported by our observation of periodic pressure fluctuations associated with intermittent fluid connectivity for hydrogen and methane. In conclusion, our findings suggest that the fundamental flow properties of fractured rocks are complex but relatively similar for hydrogen and natural gas. This is an important insight for predictive modeling of the conversion of Loenhout and similar natural gas storage facilities, which is crucial to evaluate their hydrogen storage efficiency and integrity., Comment: 58 pages, 24 figures

Heatwaves, prolonged periods of extreme heat, have intensified in frequency and severity due to climate change, posing substantial risks to public health, ecosystems, and infrastructure. Despite advancements in Machine Learning (ML) modeling, accurate heatwave forecasting at weather scales (1--15 days) remains challenging due to the non-linear interactions between atmospheric drivers and the rarity of these extreme events. Traditional models relying on heuristic feature engineering often fail to generalize across diverse climates and capture the complexities of heatwave dynamics. This study introduces the Distribution-Informed Graph Neural Network (DI-GNN), a novel framework that integrates principles from Extreme Value Theory (EVT) into the graph neural network architecture. DI-GNN incorporates Generalized Pareto Distribution (GPD)-derived descriptors into the feature space, adjacency matrix, and loss function to enhance its sensitivity to rare heatwave occurrences. By prioritizing the tails of climatic distributions, DI-GNN addresses the limitations of existing methods, particularly in imbalanced datasets where traditional metrics like accuracy are misleading. Empirical evaluations using weather station data from British Columbia, Canada, demonstrate the superior performance of DI-GNN compared to baseline models. DI-GNN achieved significant improvements in balanced accuracy, recall, and precision, with high AUC and average precision scores, reflecting its robustness in distinguishing heatwave events., Comment: 23 pages, 13 figures, pdf format

This study presents an analytical solution for the vehicle state evolution of Adaptive Cruise Control (ACC) systems under cut-in scenarios, incorporating sensing delays and anticipation using the Lambert W function. The theoretical analysis demonstrates that the vehicle state evolution and the corresponding safety of ACC in cut-in situations are influenced by multiple factors, including the original leading vehicle's state, the initial conditions of the cut-in vehicle, subsequent cut-in maneuvers, sensing delays, and the ACC's anticipation capabilities. To quantitatively assess these influences, a series of numerical experiments were conducted to perform a stochastic safety analysis of ACC systems, accounting for embedded sensing delays and anticipation, using empirically calibrated control parameters from real-world data. The experiments revealed that the impact of sensing delays on ACC is multifaceted. Specifically, sensing delays negatively affect ACC stability, with the severity increasing as the delay lengthens. Furthermore, collision risk in cut-in scenarios becomes more significant with sensing delays, particularly when the cut-in vehicle is slower than the following vehicle and when cut-ins are aggressive. However, anticipation plays a crucial role in mitigating these risks. Even with a 0.6-second anticipation, collision risk can be reduced by 91% in highly adverse scenarios. Finally, both sensing delays and anticipation have effects that intensify with their duration. An anticipation period of 2 seconds effectively ensures safety in aggressive cut-in conditions, even in the presence of sensing delays.

The Laboratory of Mechanics and Acoustics in Marseilles (France) was created in 1941, under the name of Centre de Recherches Scientifiques, Industrielles et Maritimes (CRSIM). But it was actually issued from the French Naval Research Center created in Toulon by the French Navy to work on submarine detection during World War I. LMA is therefore the result of a long and quite amazing story with several moves and even more name changes. It benefited from all these events and is today established in a new campus with large facilities specially designed for its latest research activities. This article presents the story in some details, summarize the evolution of the research domains through all these years and finally gives a description of the LMA today.

We present the architecture of a fully autonomous, bio-inspired cognitive agent built around a spiking neural network (SNN) implementing the agent's semantic memory. The agent explores its universe and learns concepts of objects/situations and of its own actions in a one-shot manner. While object/situation concepts are unary, action concepts are triples made up of an initial situation, a motor activity, and an outcome. They embody the agent's knowledge of its universe's actions laws. Both kinds of concepts have different degrees of generality. To make decisions the agent queries its semantic memory for the expected outcomes of envisaged actions and chooses the action to take on the basis of these predictions. Our experiments show that the agent handles new situations by appealing to previously learned general concepts and rapidly modifies its concepts to adapt to environment changes.

Symmetry-breaking is pivotal for controlling ferroelectric, ferroelastic and/or ferromagnetic functions of materials, which enables applications in sensors, memories, transducers or actuators. Commonly, ferroic phases emerge from descending symmetry-breaking, as the laws of thermodynamics dictate that the ordered low entropy phases form at low temperature, which limits practical applications of many materials at room temperature. Rare examples of ascending symmetry-breakings have been observed, but the driving force remains often unclear. Here, we report on a ferroelastic symmetry-breaking occurring at high temperature in a spin-crossover material, studied by magnetic, DSC and X-ray diffraction measurements. Our DFT calculations and our model, based on the Landau theory of phase transitions, explain how the cooperative thermal switching of molecular spin state drives a ferroelastic symmetry breaking at high temperature, through a coupled Jahn-Teller distortion. Ferroelastic materials have rich properties, with important applications in memory, multifunctional and novel controllable devices. The electronic bistability in soft functional materials represents an important source of entropy gain, capable of overcoming the cost of symmetry-breaking entropy, which opens up new perspectives for stabilizing high-temperature and low-symmetry ferroic functions of advanced materials

Given a finite set of points $P$ sampling an unknown smooth surface $\mathcal{M} \subseteq \mathbb{R}^3$, our goal is to triangulate $\mathcal{M}$ based solely on $P$. Assuming $\mathcal{M}$ is a smooth orientable submanifold of codimension 1 in $\mathbb{R}^d$, we introduce a simple algorithm, Naive Squash, which simplifies the $\alpha$-complex of $P$ by repeatedly applying a new type of collapse called vertical relative to $\mathcal{M}$. Naive Squash also has a practical version that does not require knowledge of $\mathcal{M}$. We establish conditions under which both the naive and practical Squash algorithms output a triangulation of $\mathcal{M}$. We provide a bound on the angle formed by triangles in the $\alpha$-complex with $\mathcal{M}$, yielding sampling conditions on $P$ that are competitive with existing literature for smooth surfaces embedded in $\mathbb{R}^3$, while offering a more compartmentalized proof. As a by-product, we obtain that the restricted Delaunay complex of $P$ triangulates $\mathcal{M}$ when $\mathcal{M}$ is a smooth surface in $\mathbb{R}^3$ under weaker conditions than existing ones., Comment: 55 pages, 24 figures

Aims: This paper aims to demonstrate the importance of short-exposure extreme ultraviolet (EUV) observations of solar flares in the study of particle acceleration, heating and energy partition in flares. This work highlights the observations now available from the Extreme Ultraviolet Imager (EUI) instrument suite on board Solar Orbiter while operating in short-exposure mode. Methods: A selection of noteworthy flares observed simultaneously by the Spectrometer Telescope for Imaging X-rays (STIX) and the Full Sun Imager of EUI (EUI/FSI) are detailed. New insights are highlighted and potential avenues of investigation are demonstrated, including forward-modelling the atmospheric response to a non-thermal beam of electrons using the RADYN 1D hydrodynamic code, in order to compare the predicted and observed EUV emission. Results: The examples given in this work demonstrate that short-exposure EUI/FSI observations are providing important diagnostics during flares. A dataset of more than 9000 flares observed by STIX (from November 2022 until December 2023) with at least one short-exposure EUI/FSI 174 \r{A} image is currently available. The observations reveal that the brightest parts of short-exposure observations consist of substructure in flaring ribbons that spatially overlap with the hard X-ray emission observed by STIX in the majority of cases. We show that these observations provide an opportunity to further constrain the electron energy flux required for flare modelling, among other potential applications., Comment: 9 pages, 5 figures, accepted by A&A. Version 2 includes formatting corrections from the editor

We study a class of non-reversible, continuous-time random walks in random environments on $\mathbb{Z}^d$ that admit a cycle representation with finite cycle length. The law of the transition rates, taking values in $[0, \infty)$, is assumed to be stationary and ergodic with respect to space shifts. Moreover, the transition rate from $x$ to $y$, denoted by $c^\omega(x,y)$, is a superposition of non-negative random weights on oriented cycles that contain the edge $(x,y)$. We prove a quenched invariance principle under moment conditions that are comparable to the well-known p-q moment condition of Andres, Deuschel, and Slowik [2] for the random conductance model. A key ingredient in proving the sublinearity is an energy estimate for the non-symmetric generator. Our result extends that of Deuschel and K\"osters [12] beyond strong ellipticity and bounded cycle lengths.

Sequential sampling models (SSMs) are a widely used framework describing decision-making as a stochastic, dynamic process of evidence accumulation. SSMs popularity across cognitive science has driven the development of various software packages that lower the barrier for simulating, estimating, and comparing existing SSMs. Here, we present a software tool, SequentialSamplingModels.jl (SSM.jl), designed to make SSM simulations more accessible to Julia users, and to integrate with the Julia ecosystem. We demonstrate the basic use of SSM.jl for simulation, plotting, and Bayesian inference.

This article presents the latest results of our ALMA program to study circumstellar disk characteristics as a function of orbital and stellar properties in a sample of young binary star systems known to host at least one disk. Optical and infrared observations of the eccentric, ~48-year period binary DF Tau indicated the presence of only one disk around the brighter component. However, our 1.3 mm ALMA thermal continuum maps show two nearly-equal brightness components in this system. We present these observations within the context of updated stellar and orbital properties which indicate that the inner disk of the secondary is absent. Because the two stars likely formed together, with the same composition, in the same environment, and at the same time, we expect their disks to be co-eval. However the absence of an inner disk around the secondary suggests uneven dissipation. We consider several processes which have the potential to accelerate inner disk evolution. Rapid inner disk dissipation has important implications for planet formation, particularly in the terrestrial-planet-forming region., Comment: 18 pages, 8 figures, accepted to AJ

Dynamic Range Compression (DRC) is a popular audio effect used to control the dynamic range of a signal. Inverting DRC can also help to restore the original dynamics to produce new mixes and/or to improve the overall quality of the audio signal. Since, state-of-the-art DRC inversion techniques either ignore parameters or require precise parameters that are difficult to estimate, we fill the gap by combining a model-based approach with neural networks for DRC inversion. To this end, depending on the scenario, we use different neural networks to estimate DRC parameters. Then, a model-based inversion is completed to restore the original audio signal. Our experimental results show the effectiveness and robustness of the proposed method in comparison to several state-of-the-art methods, when applied on two music datasets.

We report the redox switching of ferrocene moieties embedded in a double tunnel barrier plasmonic cavity fabricated from a click-chemistry self-assembled monolayers of ferrocenyl-alkylthiol on ultra flat gold surfaces, connected to a single poly(vinylpyrrolidone) capped silver nanocube, AgNC, which is contacted by the tip of a conductive-AFM to study the electron transport properties in the dark and under light irradiation at the plasmonic resonance wavelengths. We observe a dual behavior in the current-voltage (I-V) characteristics in the dark: a large hysteresis loop at positive voltages and an hysteretic negative differential conductance (NDC) at negative voltages, due to the redox switching of ferrocene between its oxidized (Fc+) and neutral (Fc0) states. The I-V curves are analyzed by a generalized combined Marcus-Landauer model. We determine the highest occupied molecular orbital of the Fc+ and Fc0 states at 0.54 and 0.42 eV below the Fermi energy, respectively, with a weak reorganization energy < 0.1 eV upon switching. Under plasmonic excitation, the hysteresis and NDC behaviors are no longer observed and the I-V characteristics of the Au-ferrocenyl-alkylthiol/AgNC junctions become similar to Au-ferrocenyl-alkylthiol SAMs. A virtual molecular orbital due to the plasmon-induced coupling (fast electron transfer) between the two redox states of the Fc is determined at 0.46 eV. This dynamic behavior opens perspectives in artificial synaptic devices for neuromorphic computing with the additional function to turn on/off this synaptic behavior on-demand by light., Comment: Full manuscript with the supporting information

We introduce and study the analytic relative Jacobian sheaf for a Lagrangian fibration of a hyperk\"ahler manifold. When the fibration has irreducible fibers in codimension 1 and a relative principal polarization, we show that it is isomorphic to the relative Albanese sheaf which acts on the Lagrangian fibration. We apply this to OG10 Lagrangian fibrations associated to a smooth cubic fourfold constructed by Laza-Sacc\`a-Voisin and Sacc\`a. For sufficiently general cubic fourfolds, we compute the Tate-Shafarevich group parametrizing twists of the original Lagrangian fibration in terms of certain degree twists and Brauer twists. Among the main tools we use are perverse sheaves, decomposition theorem, Hodge modules and Deligne cohomology., Comment: Comments are welcome

Dispersion measures (DM) of fast radio bursts (FRBs) probe the density of electrons in the intergalactic medium (IGM) along their lines-of-sight, including the average density versus distance to the source and its variations in direction. While previous study focused on low-redshift, FRBs are potentially detectable out to high redshift, where their DMs can, in principle, probe the epoch of reionization (EOR) and its patchiness. We present the first predictions from large-scale, radiation-hydrodynamical simulation of fully-coupled galaxy formation and reionization, using Cosmic Dawn (``CoDa")~II to model the density and ionization fields of the universe down to redshifts through the end of the EOR at $z_{re}\approx6.1$. Combining this with an N-body simulation CoDa~II--Dark Matter of the fully-ionized epoch from the EOR to the present, we calculate the mean and standard deviation of FRB DMs as functions of their source redshift. The mean and standard deviation of DM increase with redshift, reaching a plateau by $z(x_{HII}\lesssim0.25)\gtrsim8$, i.e. well above $z_{re}$. The mean-DM asymptote $\mathcal{DM}_{max} \approx 5900~\mathrm{pc\, cm^{-3}}$ reflects the end of the EOR and its duration. The standard deviation there is $\sigma_{DM, max}\approx497 ~\mathrm{pc\, cm^{-3}}$, reflecting inhomogeneities of both patchy reionization and density. Inhomogeneities in ionization during the EOR contribute $\mathcal{O}(1$ per cent) of this value of $\sigma_{DM,max}$ from FRBs at redshifts $z\gtrsim 8$. Current estimates of FRB rates suggest this may be detectable within a few years of observation., Comment: 14 pages, 10 figures, 2 tables, 2 appendices

Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery. Existing state-of-the-art approaches either resort to large scale transformer-based models that diffuse over conformer fields, or use computationally expensive methods to generate initial structures and diffuse over torsion angles. In this work, we introduce Equivariant Transformer Flow (ET-Flow). We showcase that a well-designed flow matching approach with equivariance and harmonic prior alleviates the need for complex internal geometry calculations and large architectures, contrary to the prevailing methods in the field. Our approach results in a straightforward and scalable method that directly operates on all-atom coordinates with minimal assumptions. With the advantages of equivariance and flow matching, ET-Flow significantly increases the precision and physical validity of the generated conformers, while being a lighter model and faster at inference. Code is available https://github.com/shenoynikhil/ETFlow., Comment: NeurIPS 2024

Pedestrian activity near bus stops is typically high, especially in urban areas with bus transit corridors. These locations are critical nodes in the urban network, where frequent interactions between pedestrians and vehicles occur. Consequently, pedestrian safety near bus stops has become a significant concern due to increasing crashes. However, research often needs more comprehensive analysis, specifically examining exposure (e.g., vehicle, pedestrian volumes) characteristics, roadway environment, and bus stop design features contributing to pedestrian-vehicle crashes. This study contributes by exploring influencing factors related to these crashes by the development of statistical models. Key factors identified include bus stop design, traffic conditions, passenger activity, and the roadway environment near bus stops in Fort Worth, Texas. The analysis incorporates data from 596 bus stop locations with non and crash history and integrates crash records from 2018 to 2022. The result demonstrates that the Random Parameters Negative Binomial-Lindley (RPNB-L) model provides a detailed understanding of factors influencing crash frequency. Significant factors associated with increased crash frequency include average annual daily traffic (AADT), bus passenger boarding activity, and the absence of safety features such as medians, crosswalks, and lighting. Additional factors, including school numbers and lower speed limits (e.g., 35 mph or less), were also linked to positively associated higher crash rates. Conversely, signalized intersections, far-side bus stops, locations in mixed-use areas, and sidewalk availability were found to be negatively associated with crash frequency. The study further applies the full Bayes (FB) based Potential for Safety Improvement (PSI) metric to identify high-risk stops and hazardous corridors, helping prioritize safety interventions., Comment: 26 pages, 8 figures

Obtaining well-calibrated photometric redshift probability densities for galaxies without a spectroscopic measurement remains a challenge. Deep learning discriminative models, typically fed with multi-band galaxy images, can produce outputs that mimic probability densities and achieve state-of-the-art accuracy. However, such models may be affected by miscalibration that would result in discrepancies between the model outputs and the actual distributions of true redshifts. Our work develops a novel method called the Contrastive Learning and Adaptive KNN for Photometric Redshift (CLAP) that resolves this issue. It leverages supervised contrastive learning (SCL) and k-nearest neighbours (KNN) to construct and calibrate raw probability density estimates, and implements a refitting procedure to resume end-to-end discriminative models ready to produce final estimates for large-scale imaging data. The harmonic mean is adopted to combine an ensemble of estimates from multiple realisations for improving accuracy. Our experiments demonstrate that CLAP takes advantage of both deep learning and KNN, outperforming benchmark methods on the calibration of probability density estimates and retaining high accuracy and computational efficiency. With reference to CLAP, we point out that miscalibration is particularly sensitive to the method-induced excessive correlations among data instances in addition to the unaccounted-for epistemic uncertainties. Reducing the uncertainties may not guarantee the removal of miscalibration due to the presence of such excessive correlations, yet this is a problem for conventional deep learning methods rather than CLAP. These discussions underscore the robustness of CLAP for obtaining photometric redshift probability densities required by astrophysical and cosmological applications. This is the first paper in our series on CLAP., Comment: 22 + 6 pages, 9 + 5 figures

The Cluster HEritage project with XMM-Newton - Mass Assembly and Thermodynamics at the Endpoint of structure formation (CHEX-MATE) is a programme to study a minimally biased sample of 118 galaxy clusters detected by Planck through the Sunyaev-Zeldovich effect. Accurate and precise mass measurements are required to exploit CHEX-MATE as an astrophysical laboratory and a calibration sample for cosmological probes in the era of large surveys. We measured masses based on the galaxy dynamics, which are highly complementary to weak-lensing or X-ray estimates. We analysed the sample with a uniform pipeline that is stable both for poorly sampled or rich clusters - using spectroscopic redshifts from public (NED, SDSS, and DESI) or private archives - and dedicated observational programmes. We modelled the halo mass density and the anisotropy profile. Membership is confirmed with a cleaning procedure in phase space. We derived masses from measured velocity dispersions under the assumed model. We measured dynamical masses for 101 CHEX-MATE clusters with at least ten confirmed members within the virial radius r_200c. Estimated redshifts and velocity dispersions agree with literature values when available. Validation with weak-lensing masses shows agreement within 8+-16(stat.)+-5(sys.)%, and confirms dynamical masses as an unbiased proxy. Comparison with {\it Planck} masses shows them to be biased low by 34+-3(stat.)+-5(sys.)%. A follow-up spectroscopic campaign is underway to cover the full CHEX-MATE sample., Comment: 18 pages, 12 figures. In press on A&A

One-dimensional Coulomb drag has been an essential tool to probe the physics of interacting Tomonaga-Luttinger liquids. To date, most experimental work has focused on the linear regime while the predictions for Luttinger liquids beyond the linear response theory remain largely untested. In this letter, we report measurements of momentum transfer induced Coulomb drag between vertically-coupled quasi-one-dimensional quantum wires in the nonlinear regime. Measurements were performed at ultra-low temperatures between wires only 15 nm apart. Our results reveal a nonlinear dependence of the drag voltage as a function of the drive current superimposed with an oscillatory contribution, in agreement with theoretical predictions for Coulomb drag between Tomonaga-Luttinger liquids. Additionally, the observed current-voltage ($I$-$V$) characteristics exhibit a nonmonotonic temperature dependence, further corroborating the presence of non-Fermi-liquid behavior in our system. These findings are observed both in the single and in the multiple subband regimes and in the presence of disorder, extending the onset of this behavior beyond the clean single channel Tomonaga-Luttinger regime where the predictions were originally formulated., Comment: 5 pages, 4 figures

This paper reports the compatibility of heterostructure-based spin qubit devices with industrial CMOS technology. It features Si/Si-Ge quantum dot devices fabricated using Infineon's 200 mm production line within a restricted thermal budget. The devices exhibit state-of-the-art charge sensing, charge noise and valley splitting characteristics, showing that industrial fabrication is not harming the heterostructure quality. These measured parameters are all correlated to spin qubit coherence and qubit gate fidelity. We describe the single electron device layout, design and its fabrication process using electron beam lithography. The incorporated standard 90 nm back-end of line flow for gate-layer independent contacting and wiring can be scaled up to multiple wiring layers for scalable quantum computing architectures. In addition, we present millikelvin characterization results. Our work exemplifies the potential of industrial fabrication methods to harness the inherent CMOS-compatibility of the Si/Si-Ge material system, despite being restricted to a reduced thermal budget. It paves the way for advanced quantum processor architectures with high yield and device quality., Comment: This work has been submitted to the IEEE for possible publication. 4 pages, 3 figures

MgII or H$\alpha$ line profile distortions observed in five gravitationally lensed quasars have been compared with simulated ones. The simulations are based on three BLR models, a Keplerian disk (KD), an equatorial wind (EW), and a polar wind (PW). We find that the wide variety of observed line profile distortions can be reproduced with microlensing-induced distortions of line profiles generated by our BLR models. For three quasars, the most likely model is either KD or EW, depending on the orientation of the magnification map with respect to the BLR axis. This shows that the line profile distortions depend on the position and orientation of the BLR with respect to the caustic network, and not only on their different effective sizes. In the other quasars, the EW model is preferred. For all objects, the PW model has a lower probability. We conclude that disk geometries with kinematics dominated by either Keplerian rotation or equatorial outflow best reproduce the microlensing effects on the MgII and H$\alpha$ emission lines. The half-light radii of the MgII and H$\alpha$ BLRs are measured in the range of 3 to 25 light-days. The size of the region emitting the low-ionization lines is larger than the region emitting the high-ionization lines, with a factor of four measured between the sizes of the MgII and CIV emitting regions. The microlensing radii of the BLRs are found to be systematically below the radius-luminosity ($R -L$) relations derived from reverberation mapping, confirming that the intrinsic dispersion of the BLR radii with respect to the $R-L$ relations is large, but also revealing a selection bias that affects microlensing-based BLR size measurements., Comment: Accepted for publication in A\&A. arXiv admin note: text overlap with arXiv:2405.09476

We present a proposal for a future light dark matter search experiment at the Electron Stretcher Accelerator ELSA in Bonn: Lohengrin. It employs the fixed-target missing momentum based technique for searching for dark-sector particles. The Lohengrin experiment uses a high intensity electron beam that is shot onto a thin target to produce mainly SM bremsstrahlung and - in rare occasions - possibly new particles coupling feebly to the electron. A well motivated candidate for such a new particle is the dark photon, a new massive gauge boson arising from a new gauge interaction in a dark sector and mixing kinetically with the standard model photon. The Lohengrin experiment is estimated to reach sensitivity to couplings small enough to explain the relic abundance of dark matter in various models for dark photon masses between approximately 1 MeV and approximately 100 MeV.

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

In this paper, we report the long-range and wireless interrogation of multiple chipless sensors from the isoline processing of three-dimensional polarimetric radar images. A Frequency-Modulated Continuous-Wave Radar operating at 24 GHz is used for the indoor interrogation of four sensors in the basement of a Laboratory. In such cluttered environment, the proposed radar image processing based on isolines computation allows the wireless measurement range of sensors up to 5.8m.

DJ mix transcription is a crucial step towards DJ mix reverse engineering, which estimates the set of parameters and audio effects applied to a set of existing tracks to produce a performative DJ mix. We introduce a new approach based on a multi-pass NMF algorithm where the dictionary matrix corresponds to a set of spectrogram slices of the source tracks present in the mix. The multi-pass strategy is motivated by the high computational cost resulting from the use of a large NMF dictionary. The proposed method uses inter-pass filtering to favor temporal continuity and sparseness and is evaluated on a publicly available dataset. Our comparative results considering a baseline method based on dynamic time warping (DTW) are promising and pave the way of future NMF-based applications., Comment: Submitted to ICASSP 2025

We explore a scaled spectral preconditioner for the efficient solution of sequences of symmetric and positive-definite linear systems. We design the scaled preconditioner not only as an approximation of the inverse of the linear system but also with consideration of its use within the conjugate gradient (CG) method. We propose three different strategies for selecting a scaling parameter, which aims to position the eigenvalues of the preconditioned matrix in a way that reduces the energy norm of the error, the quantity that CG monotonically decreases at each iteration. Our focus is on accelerating convergence especially in the early iterations, which is particularly important when CG is truncated due to computational cost constraints. Numerical experiments provide in data assimilation confirm that the scaled spectral preconditioner can significantly improve early CG convergence with negligible computational cost.

Penalty methods are a well known class of algorithms for constrained optimization. They transform a constrained problem into a sequence of unconstrained penalized problems in the hope that approximate solutions of the latter converge to a solution of the former. If Lagrange multipliers exist, exact penalty methods ensure that the penalty parameter only need increase a finite number of times, but are typically scorned in smooth optimization for the penalized problems are not smooth. This led researchers to consider the implementation of exact penalty methods inconvenient. Recently, advances in proximal methods have led to increasingly efficient solvers for nonsmooth optimization. We show that the exact $\ell_2$-penalty method for equality-constrained optimization can in fact be implemented efficiently by solving the penalized problem with a proximal-type algorithm. We study the convergence of our algorithm and establish a worst-case complexity bound of $O(\epsilon^{-2})$ to bring a stationarity measure below $\epsilon > 0$ under the Mangarasian-Fromowitz constraint qualification and Lipschitz continuity of the objective gradient and constraint Jacobian. In a degenerate scenario where the penalty parameter grows unbounded, the complexity becomes $O(\epsilon^{-8})$, which is worse than another bound found in the literature. We justify the difference by arguing that our feasibility measure is properly scaled. Finally, we report numerical experience on small-scale problems from a standard collection and compare our solver with an augmented-Lagrangian and an SQP method. Our preliminary implementation is on par with the augmented Lagrangian in terms of robustness and efficiency. It is on par with the SQP method in terms of robustness, though the former remains ahead in terms of number of problem function evaluations.

The use of Bayesian adaptive designs for randomised controlled trials has been hindered by the lack of software readily available to statisticians. We have developed a new software package (Bayesian Adaptive Trials Simulator Software - BATSS for the statistical software R, which provides a flexible structure for the fast simulation of Bayesian adaptive designs for clinical trials. We illustrate how the BATSS package can be used to define and evaluate the operating characteristics of Bayesian adaptive designs for various different types of primary outcomes (e.g., those that follow a normal, binary, Poisson or negative binomial distribution) and can incorporate the most common types of adaptations: stopping treatments (or the entire trial) for efficacy or futility, and Bayesian response adaptive randomisation - based on user-defined adaptation rules. Other important features of this highly modular package include: the use of (Integrated Nested) Laplace approximations to compute posterior distributions, parallel processing on a computer or a cluster, customisability, adjustment for covariates and a wide range of available conditional distributions for the response.

Protecting qubits from environmental noise while maintaining strong coupling for fast high-fidelity control is a central challenge for quantum information processing. Here, we demonstrate a novel control scheme for superconducting fluxonium qubits that eliminates qubit decay through the control channel by reducing the environmental density of states at the transition frequency. Adding a low-pass filter on the flux line allows for flux-biasing and at the same time coherently controlling the fluxonium qubit by parametrically driving it at integer fractions of its transition frequency. We compare the filtered to the unfiltered configuration and find a five times longer $T_1$, and ten times improved $T_2$-echo time in the protected case. We demonstrate coherent control with up to 11-photon sub-harmonic drives, highlighting the strong non-linearity of the fluxonium potential. We experimentally determine Rabi frequencies and drive-induced frequency shifts in excellent agreement with numerical and analytical calculations. Furthermore, we show the equivalence of a 3-photon sub-harmonic drive to an on-resonance drive by benchmarking sub-harmonic gate fidelities above 99.94 %. These results open up a scalable path for full qubit control via a single protected channel, strongly suppressing qubit decoherence caused by control lines., Comment: 18 pages, 9 figures

Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a convolutional neural network integrated into a closed-loop calibration system. The autotuning algorithm explores the gates' voltage space to localize charge transition lines, thereby isolating the one-electron regime without human intervention. In 20 experimental runs on a device cooled to 25mK, the method achieved a success rate of 95% in locating the target electron regime, highlighting the robustness of this method against noise and distribution shifts from the offline training set. Each tuning run lasted an average of 2 hours and 9 minutes, primarily due to the limited speed of the current measurement. This work validates the feasibility of machine learning-driven real-time charge autotuning for quantum dot devices, advancing the development toward the control of large qubit arrays., Comment: 6 pages (main) + 5 pages (supplementary)

We develop R2N, a modified quasi-Newton method for minimizing the sum of a $\mathcal{C}^1$ function $f$ and a lower semi-continuous prox-bounded $h$. Both $f$ and $h$ may be nonconvex. At each iteration, our method computes a step by minimizing the sum of a quadratic model of $f$, a model of $h$, and an adaptive quadratic regularization term. A step may be computed by a variant of the proximal-gradient method. An advantage of R2N over trust-region (TR) methods is that proximal operators do not involve an extra TR indicator. We also develop the variant R2DH, in which the model Hessian is diagonal, which allows us to compute a step without relying on a subproblem solver when $h$ is separable. R2DH can be used as standalone solver, but also as subproblem solver inside R2N. We describe non-monotone variants of both R2N and R2DH. Global convergence of a first-order stationarity measure to zero holds without relying on local Lipschitz continuity of $\nabla f$, while allowing model Hessians to grow unbounded, an assumption particularly relevant to quasi-Newton models. Under Lipschitz-continuity of $\nabla f$, we establish a tight worst-case complexity bound of $O(1 / \epsilon^{2/(1 - p)})$ to bring said measure below $\epsilon > 0$, where $0 \leq p < 1$ controls the growth of model Hessians. The latter must not diverge faster than $|\mathcal{S}_k|^p$, where $\mathcal{S}_k$ is the set of successful iterations up to iteration $k$. When $p = 1$, we establish the tight exponential complexity bound $O(\exp(c \epsilon^{-2}))$ where $c > 0$ is a constant. We describe our Julia implementation and report numerical experience on a basis-pursuit problem, image denoising, minimum-rank matrix completion, and a nonlinear support vector machine. In particular, the minimum-rank problem cannot be solved directly at this time by a TR approach as corresponding proximal operators are not known analytically.

Autonomous racing has rapidly gained research attention. Traditionally, racing cars rely on 2D LiDAR as their primary visual system. In this work, we explore the integration of an event camera with the existing system to provide enhanced temporal information. Our goal is to fuse the 2D LiDAR data with event data in an end-to-end learning framework for steering prediction, which is crucial for autonomous racing. To the best of our knowledge, this is the first study addressing this challenging research topic. We start by creating a multisensor dataset specifically for steering prediction. Using this dataset, we establish a benchmark by evaluating various SOTA fusion methods. Our observations reveal that existing methods often incur substantial computational costs. To address this, we apply low-rank techniques to propose a novel, efficient, and effective fusion design. We introduce a new fusion learning policy to guide the fusion process, enhancing robustness against misalignment. Our fusion architecture provides better steering prediction than LiDAR alone, significantly reducing the RMSE from 7.72 to 1.28. Compared to the second-best fusion method, our work represents only 11% of the learnable parameters while achieving better accuracy. The source code, dataset, and benchmark will be released to promote future research.

We performed time- and polarization-resolved extreme ultraviolet momentum microscopy on topological Dirac semimetal candidate 1T-ZrTe$_2$. Excited states band mapping uncovers the previously inaccessible linear dispersion of the Dirac cone above the Fermi level. We study the orbital texture of bands using linear dichroism in photoelectron angular distributions. These observations provide hints on the topological character of 1T-ZrTe$_2$. Time-, energy- and momentum-resolved nonequilibrium carrier dynamics reveal that intra- and inter-band scattering processes play a capital role in the relaxation mechanism, leading to multivalley electron-hole accumulation near the Fermi level. We also show that electrons' inverse lifetime has a linear dependence on their binding energy. Our time- and polarization-resolved XUV photoemission results shed light on the excited state electronic structure of 1T-ZrTe$_2$ and provide valuable insights into the relatively unexplored field of quantum-state-resolved ultrafast dynamics in 3D topological Dirac semimetals.

Human speech conveys prosody, linguistic content, and speaker identity. This article investigates a novel speaker anonymization approach using an end-to-end network based on a Vector-Quantized Variational Auto-Encoder (VQ-VAE) to deal with these speech components. This approach is designed to disentangle these components to specifically target and modify the speaker identity while preserving the linguistic and emotionalcontent. To do so, three separate branches compute embeddings for content, prosody, and speaker identity respectively. During synthesis, taking these embeddings, the decoder of the proposed architecture is conditioned on both speaker and prosody information, allowing for capturing more nuanced emotional states and precise adjustments to speaker identification. Findings indicate that this method outperforms most baseline techniques in preserving emotional information. However, it exhibits more limited performance on other voice privacy tasks, emphasizing the need for further improvements.

We compute the cobordism group $\Omega^{\operatorname{lag}}(M)$ of Lagrangian immersions into a symplectic manifold $(M, \omega)$ in terms of a stable homotopy group of a Thom spectrum constructed from $M$. This generalizes a result of Eliashberg in the case of exact symplectic manifolds. As applications, we compute $\Omega^{\operatorname{lag}}(M)$ when $M$ is a closed surface and give a partial description of $\Omega^{\operatorname{lag}}(M)$ when $M$ is a monotone manifold., Comment: 28 pages

Jupiter's icy moon Ganymede has a tenuous exosphere produced by sputtering and possibly sublimation of water ice. To date, only atomic hydrogen and oxygen have been directly detected in this exosphere. Here, we present observations of Ganymede's CO$_2$ exosphere obtained with the James Webb Space Telescope. CO$_2$ gas is observed over different terrain types, mainly over those exposed to intense Jovian plasma irradiation, as well as over some bright or dark terrains. Despite warm surface temperatures, the CO$_2$ abundance over equatorial subsolar regions is low. CO$_2$ vapor has the highest abundance over the north polar cap of the leading hemisphere, reaching a surface pressure of 1 pbar. From modeling we show that the local enhancement observed near 12 h local time in this region can be explained by the presence of cold traps enabling CO$_2$ adsorption. However, whether the release mechanism in this high-latitude region is sputtering or sublimation remains unclear. The north polar cap of the leading hemisphere also has unique surface-ice properties, probably linked to the presence of the large atmospheric CO2 excess over this region. These CO2 molecules might have been initially released in the atmosphere after the radiolysis of CO$_2$ precursors, or from the sputtering of CO$_2$ embedded in the H$_2$O ice bedrock. Dark terrains (regiones), more widespread on the north versus south polar regions, possibly harbor CO$_2$ precursors. CO$_2$ molecules would then be redistributed via cold trapping on ice-rich terrains of the polar cap and be diurnally released and redeposited on these terrains. Ganymede's CO$_2$ exosphere highlights the complexity of surface-atmosphere interactions on Jupiter's icy Galilean moons., Comment: 21 pages, 21 figures, Accepted as a Letter in Astronomy and Astrophysics

We report a combined experimental (C-AFM and SThM) and theoretical (DFT) study of the thermoelectric properties of molecular junctions made of self-assembled monolayers on Au of thiolated benzothieno-benzothiophene (BTBT) and alkylated BTBT derivatives (C8-BTBT-C8). We measure the thermal conductance per molecule at 15 pW/K and 8.8 pW/K, respectively, among the lowest values for molecular junctions so far reported (10-50 pW/K). The lower thermal conductance for C8-BTBT-C8 is consistent with two interfacial thermal resistances introduced by the alkyl chains, which reduce the phononic thermal transport in the molecular junction. The Seebeck coefficients are 36 {\mu}V/K and 245 {\mu}V/K, respectively, the latter due to the weak coupling of the core BTBT with the electrodes. We deduce a thermoelectric figure of merit ZT up to ca. 1E-4 for the BTBT molecular junctions at 300K, on a par with the values reported for archetype molecular junctions (olygo(phenylene ethynylene) derivatives)., Comment: Full paper and supporting information