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Adversarial training is arguably the most popular way to provide empirical robustness against specific adversarial examples. While variants based on multi-step attacks incur significant computational overhead, single-step variants are vulnerable to a failure mode known as catastrophic overfitting, which hinders their practical utility for large perturbations. A parallel line of work, certified training, has focused on producing networks amenable to formal guarantees of robustness against any possible attack. However, the wide gap between the best-performing empirical and certified defenses has severely limited the applicability of the latter. Inspired by recent developments in certified training, which rely on a combination of adversarial attacks with network over-approximations, and by the connections between local linearity and catastrophic overfitting, we present experimental evidence on the practical utility and limitations of using certified training towards empirical robustness. We show that, when tuned for the purpose, a recent certified training algorithm can prevent catastrophic overfitting on single-step attacks, and that it can bridge the gap to multi-step baselines under appropriate experimental settings. Finally, we present a novel regularizer for network over-approximations that can achieve similar effects while markedly reducing runtime.

Generating realistic and diverse layouts of furnished indoor 3D scenes unlocks multiple interactive applications impacting a wide range of industries. The inherent complexity of object interactions, the limited amount of available data and the requirement to fulfill spatial constraints all make generative modeling for 3D scene synthesis and arrangement challenging. Current methods address these challenges autoregressively or by using off-the-shelf diffusion objectives by simultaneously predicting all attributes without 3D reasoning considerations. In this paper, we introduce DeBaRA, a score-based model specifically tailored for precise, controllable and flexible arrangement generation in a bounded environment. We argue that the most critical component of a scene synthesis system is to accurately establish the size and position of various objects within a restricted area. Based on this insight, we propose a lightweight conditional score-based model designed with 3D spatial awareness at its core. We demonstrate that by focusing on spatial attributes of objects, a single trained DeBaRA model can be leveraged at test time to perform several downstream applications such as scene synthesis, completion and re-arrangement. Further, we introduce a novel Self Score Evaluation procedure so it can be optimally employed alongside external LLM models. We evaluate our approach through extensive experiments and demonstrate significant improvement upon state-of-the-art approaches in a range of scenarios., Comment: Accepted at NeurIPS 2024. Preprint version

We point out a very simple method for calculating the mixed Coulomb-nuclear corrections to the $pp$ and $\bar pp$ scattering amplitudes that has been missed in the extensive past work on this problem. The method expresses the correction in terms of a rapidly convergent integral involving the inverse Fourier-Bessel transform of the nuclear amplitude and a known factor containing the Coulomb phase shift with form-factor corrections. The transform can be calculated analytically for the exponential-type model nuclear amplitudes commonly used in fits to the high-energy data at small momentum transfers, and gives very accurate results for the corrections. We examine the possible effects of the Martin zero in the real part of the nuclear amplitude, and the accuracy of the Bethe-West-Yennie phase approximation for the Coulomb-nuclear corrections. We then apply the method to a redetermination of the ratio $\rho$ of the real to the imaginary parts of the forward scattering amplitude in fits to high-energy ISR data previously analyzed using an approximate version of the correction. The only significant changes relative the accuracy of those fits are at 52.8 GeV. Our method is applicable more generally, and can be used also at lower energies and for proton-nucleus scattering., Comment: 14 Pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:2401.06746

We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning. The focus is on learning the analysis step of sequential DA, from state trajectories and their observations, using a simple residual convolutional neural network, while assuming the dynamics to be known. Experiments are performed with the Lorenz 96 dynamics, which display spatiotemporal chaos and for which solid benchmarks for DA performance exist. The accuracy of the states obtained from the learned analysis approaches that of the best possibly tuned ensemble Kalman filter, and is far better than that of variational DA alternatives. Critically, this can be achieved while propagating even just a single state in the forecast step. We investigate the reason for achieving ensemble filtering accuracy without an ensemble. We diagnose that the analysis scheme actually identifies key dynamical perturbations, mildly aligned with the unstable subspace, from the forecast state alone, without any ensemble-based covariances representation. This reveals that the analysis scheme has learned some multiplicative ergodic theorem associated to the DA process seen as a non-autonomous random dynamical system.

Nuclear Magnetic Resonance (NMR) spans diverse fields from biology to quantum science. Employing NMR on a floating object could unveil novel possibilities beyond conventional operational paradigms. Here, we observe Nuclear Magnetic Resonance (NMR) within a levitating micro-diamond using the nuclear spins of nitrogen-14 atoms. By tightly confining the angular degrees of freedom of the diamond in a Paul trap, we achieve efficient hyperfine interaction between optically polarized electronic spins of nitrogen-vacancy centers and the $^{14}$N nuclear spin, enabling nuclear spin polarization and quantum state read-out revealing coherence times up to hundreds of microseconds. This represents the longest recorded spin coherence time in a levitated system, surpassing previous records by three orders of magnitude. Our results offer promise for various applications, including cooling macroscopic particles to their motional ground state and exploring geometric phases for gyroscopy.

We study the fundamental properties of pointwise semi-Lipschitz functions between asymmetric spaces, which are the natural asymmetric counterpart of pointwise Lipschitz functions. We also study the influence that partial symmetries of a given space may have on the behavior of pointwise semi-Lipschitz functions defined on it. Furthermore, we are interested in characterizing the pointwise semi-Lipschitz structure of an asymmetric space in terms of real-valued pointwise semi-Lipschitz functions defined on it. By using two algebras of functions naturally associated to our spaces of pointwise real-valued semi-Lipschitz functions, we are able to provide two Banach-Stone type results in this context. In fact, these results are obtained as consequences of a general Banach-Stone type theorem of topological nature, stated for abstract functional spaces, which is quite flexible and can be applied to many spaces of continuous functions over metric and asymmetric spaces., Comment: No additional comments

Formulating a real-world problem under the Reinforcement Learning framework involves non-trivial design choices, such as selecting a discount factor for the learning objective (discounted cumulative rewards), which articulates the planning horizon of the agent. This work investigates the impact of the discount factor on the biasvariance trade-off given structural parameters of the underlying Markov Decision Process. Our results support the idea that a shorter planning horizon might be beneficial, especially under partial observability., Comment: Presented at deployable RL (RLC conference 2024)

In this letter we report the first scientific result based on antineutrinos emitted from the BR2 reactor at SCK CEN. The SoLid experiment uses a novel type of highly granular detector whose basic detection unit combines two scintillators, PVT and 6LiF:ZnS(Ag), to measure antineutrinos via their inverse-beta-decay products. An advantage of PVT is its highly linear response as a function of deposited particle energy. The full-scale detector comprises 12800 voxels and operates over a very short 6.3--8.9 m baseline from the reactor core. The detector segmentation and its 3D imaging capabilities facilitate the extraction of the positron energy from the rest of the visible energy, allowing the latter to be utilised for signal-background discrimination. We present a result based on 280 reactor-on days (55 MW mean power) and 172 reactor-off days, respectively, of live data-taking. A total of 29479 $\pm$ 603 (stat.) antineutrino candidates have been selected, corresponding to an average rate of 105 events per day and a signal-to-background ratio of 0.27. A search for disappearance of antineutrinos to a sterile state has been conducted using complementary model-dependent frequentist and Bayesian fits, providing constraints on the allowed region of the Reactor Antineutrino Anomaly., Comment: 8 pages, 6 figures

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

We make the first steps towards diffusion models for unconditional generation of multivariate and Arctic-wide sea-ice states. While targeting to reduce the computational costs by diffusion in latent space, latent diffusion models also offer the possibility to integrate physical knowledge into the generation process. We tailor latent diffusion models to sea-ice physics with a censored Gaussian distribution in data space to generate data that follows the physical bounds of the modelled variables. Our latent diffusion models reach similar scores as the diffusion model trained in data space, but they smooth the generated fields as caused by the latent mapping. While enforcing physical bounds cannot reduce the smoothing, it improves the representation of the marginal ice zone. Therefore, for large-scale Earth system modelling, latent diffusion models can have many advantages compared to diffusion in data space if the significant barrier of smoothing can be resolved., Comment: 21 pages, 5 Figures, Camera-ready version for the ICML 2024 Machine Learning for Earth System Modeling workshop

This article presents two novel algorithms for generating random increasing trees. The first algorithm efficiently generates strictly increasing binary trees using an ad hoc method. The second algorithm improves the recursive method for weighted strictly increasing unary-binary increasing trees, optimizing randomness usage., Comment: In Proceedings GASCom 2024, arXiv:2406.14588

We report the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on germanium, measured at the Spallation Neutron Source at Oak Ridge National Laboratory. The Ge-Mini detector of the COHERENT collaboration employs large-mass, low-noise, high-purity germanium spectrometers, enabling excellent energy resolution, and an analysis threshold of 1.5 keV electron-equivalent ionization energy. We observe a on-beam excess of 20.6$_{+7.1}^{-6.3}$ counts with a total exposure of 10.22 GWhkg and we reject the no-CEvNS hypothesis with 3.9 sigma significance. The result agrees with the predicted standard model of particle physics signal rate within 2 sigma., Comment: 7 pages, 5 figures

When a solid metal is struck, its free surface can eject fast and fine particles. Despite the many diagnostics that have been implemented to measure the mass, size, velocity or temperature of ejecta, these efforts provide only a partial picture of this phenomenon. Ejecta characterization, especially in constrained geometries, is an inherently ill-posed problem. In this context, Photon Doppler Velocimetry (PDV) has been a valuable diagnostic, measuring reliably particles and free surface velocities in the single scattering regime. Here we present ejecta experiments in gas and how, in this context, PDV allows one to retrieve additional information on the ejecta, i.e. information on the particles' size. We explain what governs ejecta transport in gas and how it can be simulated. To account for the multiple scattering of light in these ejecta, we use the Radiative Transfer Equation (RTE) that quantitatively describes PDV spectrograms, and their dependence on the velocity but also on the size distribution of the ejecta. We remind how spectrograms can be simulated by solving numerically this RTE and we show how to do so on hydrodynamic ejecta simulation results. Finally, we use this complex machinery in different ejecta transport scenarios to simulate the corresponding spectrograms. Comparing these to experimental results, we iteratively constrain the ejecta description at an unprecedented level. This work demonstrates our ability to recover particle size information from what is initially a velocity diagnostic, but more importantly it shows how, using existing simulation of ejecta, we capture through simulation the complexity of experimental spectrograms.

Prethermalization occurs as an important phase in the dynamics of many-body systems when strong coupling drives a quasi-equilibrium in a subspace separated from the thermodynamic equilibrium by the restriction of a gap in energy or other conserved quantity. Here, we report the signature of an enduring prethermal regime of arrested relaxation in the molecular ultracold plasma that forms following the avalanche of a state-selected Rydberg gas of nitric oxide. Electron collisions mix orbital angular momentum, scattering Rydberg molecules to states of very high-$\ell$. Spontaneous predissociation purifies this non-penetrating character, creating an extraordinary gap between the plasma states of $n \approx \ell$, with measured $n>200$ and penetrating states of $\ell = 0, ~1$ and 2. Evolution to a statistically equilibrated state of N and O atoms cannot occur without Rydberg electron penetration, and this gap blocks relaxation for a millisecond or more. Evolving through the critical phase, electrons that balance the NO$^+$ charge behave as though localized in the prethermal phase and play an ineffective role in bridging this gap. However, the application of a weak radiofrequency (RF) field promotes a dramatic degree of relaxation owing to electron collisions. On an entirely different scale, exciting a quantum-state transition in an exceedingly small fraction of the molecules in the prethermalized ensemble acts with even greater effect to drive the entire system toward equilibrium. We ascribe this to dissipative character added to a small fraction of the states in the prethermally localized ensemble. Using the Lindblad master equation, we illustrate qualitatively similar dynamics for a toy model of an open quantum system that consists of a localized set of spins on which dissipation acts locally at a single site.

Recent approaches have shown promises distilling diffusion models into efficient one-step generators. Among them, Distribution Matching Distillation (DMD) produces one-step generators that match their teacher in distribution, without enforcing a one-to-one correspondence with the sampling trajectories of their teachers. However, to ensure stable training, DMD requires an additional regression loss computed using a large set of noise-image pairs generated by the teacher with many steps of a deterministic sampler. This is costly for large-scale text-to-image synthesis and limits the student's quality, tying it too closely to the teacher's original sampling paths. We introduce DMD2, a set of techniques that lift this limitation and improve DMD training. First, we eliminate the regression loss and the need for expensive dataset construction. We show that the resulting instability is due to the fake critic not estimating the distribution of generated samples accurately and propose a two time-scale update rule as a remedy. Second, we integrate a GAN loss into the distillation procedure, discriminating between generated samples and real images. This lets us train the student model on real data, mitigating the imperfect real score estimation from the teacher model, and enhancing quality. Lastly, we modify the training procedure to enable multi-step sampling. We identify and address the training-inference input mismatch problem in this setting, by simulating inference-time generator samples during training time. Taken together, our improvements set new benchmarks in one-step image generation, with FID scores of 1.28 on ImageNet-64x64 and 8.35 on zero-shot COCO 2014, surpassing the original teacher despite a 500X reduction in inference cost. Further, we show our approach can generate megapixel images by distilling SDXL, demonstrating exceptional visual quality among few-step methods., Comment: Code, model, and dataset are available at https://tianweiy.github.io/dmd2

Topological insulators (TI) are promising materials for future spintronics applications and their epitaxial integration would allow the realization of new hybrid interfaces. As the first materials studied, Bismuth Antimony alloys (Bi1-xSbx) show great potential due to their tuneable electronic band structure and efficient charge-to-spin conversion. Here, we report the growth of Bi1-xSbx thin films on GaAs (111)A substrates following two different protocols. For the conventional epitaxy process, the grown films show excellent crystallinity and twin domains corresponding to an in-plane 180{\textdegree} rotation of the crystalline structure. Domain walls are found to be composition-dependent and have a lower density for Antimony-rich films. For the optimized process, depositing an Antimony bilayer prior to BiSb growth allows achieving single crystallinity of the TI films. The topologically protected surface states are evidenced by ex-situ ARPES measurements for domains-free and conventional films. To the best of our knowledge, this work presents the first large-scale epitaxial integration of single crystalline Bi1-xSbx thin films on industrial substrates., Comment: ACS Applied Electronic Materials, 2024

We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the costly acquisition of information (labelled observations) and the cost of prediction errors. We propose a novel foundation for OAL tasks based on partial monitoring, a theoretical framework specialized in online learning from partially informative actions. We show that previously studied binary and multi-class OAL tasks are instances of partial monitoring. We expand the real-world potential of OAL by introducing a new class of cost-sensitive OAL tasks. We propose NeuralCBP, the first PM strategy that accounts for predictive uncertainty with deep neural networks. Our extensive empirical evaluation on open source datasets shows that NeuralCBP has favorable performance against state-of-the-art baselines on multiple binary, multi-class and cost-sensitive OAL tasks.

This paper addresses the anytime sorting problem, aiming to develop algorithms providing tentative estimates of the sorted list at each execution step. Comparisons are treated as steps, and the Spearman's footrule metric evaluates estimation accuracy. We propose a general approach for making any sorting algorithm anytime and introduce two new algorithms: multizip sort and Corsort. Simulations showcase the superior performance of both algorithms compared to existing methods. Multizip sort keeps a low global complexity, while Corsort produces intermediate estimates surpassing previous algorithms., Comment: 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024), Aug 2024, Jeju City, Jeju Island, South Korea

Among the wealth of single fluorescent defects recently detected in silicon, the G center catches interest for its telecom single-photon emission that could be coupled to a metastable electron spin triplet. The G center is a unique defect where the standard Born-Oppenheimer approximation breaks down as one of its atoms can move between 6 lattice sites under optical excitation. The impact of this atomic reconfiguration on the photoluminescence properties of G centers is still largely unknown, especially in silicon-on-insulator (SOI) samples. Here, we investigate the displacement of the center-of-mass of the G center in silicon. We show that single G defects in SOI exhibit a multipolar emission and zero-phonon line fine structures with splittings up to $\sim1$ meV, both indicating a motion of the defect central atom over time. Combining polarization and spectral analysis at the single-photon level, we evidence that the reconfiguration dynamics are drastically different from the one of the unperturbed G center in bulk silicon. The SOI structure freezes the delocalization of the G defect center-of-mass and as a result, enables to isolate linearly polarized optical lines. Under above-bandgap optical excitation, the central atom of G centers in SOI behaves as if it were in a 6-slot roulette wheel, randomly alternating between localized crystal sites at each optical cycle. Comparative measurements in a bulk silicon sample and ab initio calculations highlight that strain is likely the dominant perturbation impacting the G center geometry. These results shed light on the importance of the atomic reconfiguration dynamics to understand and control the photoluminescence properties of the G center in silicon. More generally, these findings emphasize the impact of strain fluctuations inherent to SOI wafers for future quantum integrated photonics applications based on color centers in silicon.

The negatively-charged boron vacancy (V$_\text{B}^-$) center in hexagonal boron nitride (hBN) is currently garnering considerable attention for the design of two-dimensional (2D) quantum sensing units. Such developments require a precise understanding of the spin-dependent optical response of V$_\text{B}^-$ centers, which still remains poorly documented despite its key role for sensing applications. Here we investigate the spin-dependent photodynamics of V$_\text{B}^-$ centers in hBN by a series of time-resolved photoluminescence (PL) measurements. We first introduce a robust all-optical method to infer the spin-dependent lifetime of the excited states and the electron spin polarization of V$_\text{B}^-$ centers under optical pumping. Using these results, we then analyze PL time traces recorded at different optical excitation powers with a seven-level model of the V$_\text{B}^-$ center and we extract all the rates involved in the spin-dependent optical cycles, both under ambient conditions and at liquid helium temperature. These findings are finally used to study the impact of a vector magnetic field on the optical response. More precisely, we analyze PL quenching effects resulting from electron spin mixing induced by the magnetic field component perpendicular to the V$_\text{B}^-$ quantization axis. All experimental results are well reproduced by the seven-level model, illustrating its robustness to describe the spin-dependent photodymanics of V$_\text{B}^-$ centers. This work provides important insights into the properties of V$_\text{B}^-$ centers in hBN, which are valuable for future developments of 2D quantum sensing units., Comment: 11 pages, 9 figures

In Marandon (2023), the author introduces a procedure to detect true edges from a partially observed graph using a conformal prediction fashion: first computing scores from a trained function, deriving conformal p-values from them and finally applying a multiple testing procedure. In this paper, we prove that the resulting procedure indeed controls the FDR, and we also derive uniform FDP bounds, thanks to an exchangeability argument and the previous work of Marandon et al. (2022).

We study the collective schedules problem, which consists in computing a one machine schedule of a set of tasks, knowing that a set of individuals (also called voters) have preferences regarding the order of the execution of the tasks. Our aim is to return a consensus schedule. We consider the setting in which all tasks have the same length -- such a schedule can therefore also be viewed as a ranking. We study two rules, one based on a distance criterion, and another one based one a binary criterion, and we show that these rules extend classic scheduling criteria. We also consider time constraints and precedence constraints between the tasks, and focus on two cases: the preferences of the voters fulfill these constraints, or they do not fulfill these constraints (but the collective schedule should fulfill them). In each case, either we show that the problem is NP-hard, or we provide a polynomial time algorithm which solves it. We also provide an analysis of a heuristic, which appears to be a 2 approximation of the Spearman's rule.

The aim of this paper is to introduce models and algorithms for the Participatory Budgeting problem when projects can interact with each other. In this problem, the objective is to select a set of projects that fits in a given budget. Voters express their preferences over the projects and the goal is then to find a consensus set of projects that does not exceed the budget. Our goal is to detect such interactions thanks to the preferences expressed by the voters. Through the projects selected by the voters, we detect positive and negative interactions between the projects by identifying projects that are consistently chosen together. In presence of project interactions, it is preferable to select projects that interact positively rather than negatively, all other things being equal. We introduce desirable properties that utility functions should have in presence of project interactions and we build a utility function which fulfills the desirable properties introduced. We then give axiomatic properties of aggregation rules, and we study three classical aggregation rules: the maximization of the sum of the utilities, of the product of the utilities, or of the minimal utility. We show that in the three cases the problems solved by these rules are NP-hard, and we propose a branch and bound algorithm to solve them. We conclude the paper by experiments.

We combine Kirchheim's metric differentials with Cheeger charts in order to establish a non-embeddability principle for any collection $\mathcal C$ of Banach (or metric) spaces: if a metric measure space $X$ bi-Lipschitz embeds in some element in $\mathcal C$, and if every Lipschitz map $X\to Y\in \mathcal C$ is differentiable, then $X$ is rectifiable. This gives a simple proof of the rectifiability of Lipschitz differentiability spaces that are bi-Lipschitz embeddable in Euclidean space, due to Kell--Mondino. Our principle also implies a converse to Kirchheim's theorem: if all Lipschitz maps from a domain space to arbitrary targets are metrically differentiable, the domain is rectifiable. We moreover establish the compatibility of metric and w$^*$-differentials of maps from metric spaces in the spirit of Ambrosio--Kirchheim.

The collective schedules problem consists in computing a schedule of tasks shared between individuals. Tasks may have different duration, and individuals have preferences over the order of the shared tasks. This problem has numerous applications since tasks may model public infrastructure projects, events taking place in a shared room, or work done by co-workers. Our aim is, given the preferred schedules of individuals (voters), to return a consensus schedule. We propose an axiomatic study of the collective schedule problem, by using classic axioms in computational social choice and new axioms that take into account the duration of the tasks. We show that some axioms are incompatible, and we study the axioms fulfilled by three rules: one which has been studied in the seminal paper on collective schedules (Pascual et al. 2018), one which generalizes the Kemeny rule, and one which generalizes Spearman's footrule. From an algorithmic point of view, we show that these rules solve NP-hard problems, but that it is possible to solve optimally these problems for small but realistic size instances, and we give an efficient heuristic for large instances. We conclude this paper with experiments.

Detecting latent confounders from proxy variables is an essential problem in causal effect estimation. Previous approaches are limited to low-dimensional proxies, sorted proxies, and binary treatments. We remove these assumptions and present a novel Proxy Confounder Factorization (PCF) framework for continuous treatment effect estimation when latent confounders manifest through high-dimensional, mixed proxy variables. For specific sample sizes, our two-step PCF implementation, using Independent Component Analysis (ICA-PCF), and the end-to-end implementation, using Gradient Descent (GD-PCF), achieve high correlation with the latent confounder and low absolute error in causal effect estimation with synthetic datasets in the high sample size regime. Even when faced with climate data, ICA-PCF recovers four components that explain $75.9\%$ of the variance in the North Atlantic Oscillation, a known confounder of precipitation patterns in Europe. Code for our PCF implementations and experiments can be found here: https://github.com/IPL-UV/confound_it. The proposed methodology constitutes a stepping stone towards discovering latent confounders and can be applied to many problems in disciplines dealing with high-dimensional observed proxies, e.g., spatiotemporal fields.

We introduce a causal regularisation extension to anchor regression (AR) for improved out-of-distribution (OOD) generalisation. We present anchor-compatible losses, aligning with the anchor framework to ensure robustness against distribution shifts. Various multivariate analysis (MVA) algorithms, such as (Orthonormalized) PLS, RRR, and MLR, fall within the anchor framework. We observe that simple regularisation enhances robustness in OOD settings. Estimators for selected algorithms are provided, showcasing consistency and efficacy in synthetic and real-world climate science problems. The empirical validation highlights the versatility of anchor regularisation, emphasizing its compatibility with MVA approaches and its role in enhancing replicability while guarding against distribution shifts. The extended AR framework advances causal inference methodologies, addressing the need for reliable OOD generalisation., Comment: 21 pages, 15 figures

Large-size hybrid and pixelized GEM-Micromegas gaseous detectors (40x40 cm$^2$ active area) were developed and installed in 2014 and 2015 for the COMPASS2 physics program which started at the same time. That program involved in particular two full years of Drell-Yan studies using a high-intensity pion beam on a thick polarized target. Although the detectors were placed behind a thick absorber, they were exposed to an important flux of low energy neutrons and photons. The detectors were designed to drastically reduce the discharge rate, a major issue for non-resistive Micromegas in high hadron flux, by a factor of more than 100 compared to the former ones. A hybrid solution was chosen where a pre-amplifying GEM foil is placed 2 mm above the micromesh electrode. A pixelized readout was also added in the center of the detector, where the beam is going through, in order to track particles scattered at very low angles. The combination of the hybrid structure and the pixelized central readout allowed the detector to be operated in an environment with particle flux above 10 MHz/cm$^2$ with very good detection efficiencies and spatial resolution. The performance has remained stable since 2015 in terms of gain and resolution, showing the interest of hybrid structures associating a GEM foil to a Micromegas board to protect gaseous detectors against discharges and aging effects, Comment: 9 pages, 9 figures, proceedings to the 3rd International Conference on Detector Stability and Aging Phenomena in Gaseous Detectors, CERN, November 6-10 2023, will be published in the NIMA journal Proceedings Section

Patents and scientific papers provide an essential source for measuring science and technology output, to be used as a basis for the most varied scientometric analyzes. Authors' and inventors' names are the key identifiers to carry out these analyses, which however, run up against the issue of disambiguation. By extension identifying inventors who are also academic authors is a non-trivial challenge. We propose a method using the International Patent Classification (IPC) and the IPCCAT API to assess the degree of similarity of patents and papers abstracts of a given inventor, in order to match both types of documents. The method is developed and manually qualified based on three corpora of patents extracted from the international EPO database Espacenet. Among a set of 4679 patents and 7720 inventors, we obtain 2501 authors. The proposed algorithm solves the general problem of disambiguation with an error rate lower than 5%., Comment: in French language

The miniaturization of integrated circuits is facing an obstruction due to the escalating electrical resistivity of conventional copper interconnects. The underlying reason for this problem was unveiled by Fuchs and Sondheimer, who showed that thinner wires are more resistive because current-carrying electrons encounter the rough surfaces of the wire more frequently therein. Here, we present a generalization of the Fuchs-Sondheimer theory to Dirac and Weyl materials, which are candidates for next-generation interconnects. We predict a nonlinear longitudinal electric current originating from the combined action of the Berry curvature and non-specular surface-scattering., Comment: 5 pages + appendices

Optimal postural stability is required to perform in archery. Since the dynamic consequences of the string release may disturb the archer's postural equilibrium, they should have integrated them in their motor program to optimize postural stability. This study aimed to characterize the postural strategy archers use to limit the potentially detrimental impact of the bow release on their postural stability and identify characteristics that may explain a better performance. Six elite and seven sub-elite archers performed a series of 18 shots at 70 meters, standing on two force plates. Postural stability indicators were computed during the aiming and the shooting phase using the trajectory of the center of pressure. Two postural strategies were defined, as whether they were triggered before (early) or after (late) the string release time. Both groups used anticipated postural adjustments, but elite archers triggered them before the string release more often and sooner. Scores differed between the two groups, but no differences were found between early and late shots. Trained archers seem to have finely integrated the dynamic consequences of their bow motion, triggering anticipated postural adjustments prior to the string release. However, it remains unclear whether this anticipation can positively influence the performance outcome.

Among the wide variety of single fluorescent defects investigated in silicon, numerous studies have focused on color centers with a zero-phonon line around $1.28 \mu$m and identified to a common carbon-complex in silicon, namely the G center. However, inconsistent estimates regarding their quantum efficiency cast doubt on the correct identification of these individual emitters. Through a comparative analysis of their single-photon emission properties, we demonstrate that these single color centers are split in two distinct families of point defects. A first family consists of the genuine single G centers with a well-identified microscopic structure and whose photoluminescence has been investigated on ensemble measurements since the 60's. The remaining defects belong to a new color center, which we will refer to as G$^{\star}$ center, whose atomic configuration has yet to be determined. These results provide a safeguard against future defect misidentifications, which is crucial for further development of quantum technologies relying on G or G$^{\star}$ center quantum properties.

The partial monitoring (PM) framework provides a theoretical formulation of sequential learning problems with incomplete feedback. On each round, a learning agent plays an action while the environment simultaneously chooses an outcome. The agent then observes a feedback signal that is only partially informative about the (unobserved) outcome. The agent leverages the received feedback signals to select actions that minimize the (unobserved) cumulative loss. In contextual PM, the outcomes depend on some side information that is observable by the agent before selecting the action on each round. In this paper, we consider the contextual and non-contextual PM settings with stochastic outcomes. We introduce a new class of PM strategies based on the randomization of deterministic confidence bounds. We also extend regret guarantees to settings where existing stochastic strategies are not applicable. Our experiments show that the proposed RandCBP and RandCBPsidestar strategies have favorable performance against state-of-the-art baselines in multiple PM games. To advocate for the adoption of the PM framework, we design a use case on the real-world problem of monitoring the error rate of any deployed classification system.