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This paper proposes a novel approach to spectral computed tomography (CT) material decomposition that uses the recent advances in generative diffusion models (DMs) for inverse problems. Spectral CT and more particularly photon-counting CT (PCCT) can perform transmission measurements at different energy levels which can be used for material decomposition. It is an ill-posed inverse problem and therefore requires regularization. DMs are a class of generative model that can be used to solve inverse problems via diffusion posterior sampling (DPS). In this paper we adapt DPS for material decomposition in a PCCT setting. We propose two approaches, namely Two-step Diffusion Posterior Sampling (TDPS) and One-step Diffusion Posterior Sampling (ODPS). Early results from an experiment with simulated low-dose PCCT suggest that DPSs have the potential to outperform state-of-the-art model-based iterative reconstruction (MBIR). Moreover, our results indicate that TDPS produces material images with better peak signal-to-noise ratio (PSNR) than images produced with ODPS with similar structural similarity (SSIM)., Comment: 5 pages, 3 figures, submitted to EUSIPCO 2024

Silicon photonics arises as a viable solution to address the stringent resource demands of emergent technologies, such as neural networks. Within this framework, photonic memories are fundamental building blocks of photonic integrated circuits that have not yet found a standardized solution due to several trade-off among different metrics such as energy consumption, speed, footprint, or fabrication complexity, to name a few. In particular, a photonic memory exhibiting ultra-high endurance performance (> 10^6 cycles) has been elusive to date. Here, we report an ultra-high endurance silicon photonic memory using vanadium dioxide (VO_2) exhibiting a record cyclability of up to 10^7 cycles without degradation. Moreover, our memory features an ultra-compact footprint below 5 {\mu}m with potential for nanosecond and picojoule programming performance. Our silicon photonic memory could find application in emerging photonic applications demanding high number of memory updates such as photonic neural networks with in-situ training.

Using recent advances in generative artificial intelligence (AI) brought by diffusion models, this paper introduces a new synergistic method for spectral computed tomography (CT) reconstruction. Diffusion models define a neural network to approximate the gradient of the log-density of the training data, which is then used to generate new images similar to the training ones. Following the inverse problem paradigm, we propose to adapt this generative process to synergistically reconstruct multiple images at different energy bins from multiple measurements. The experiments suggest that using multiple energy bins simultaneously improves the reconstruction by inverse diffusion and outperforms state-of-the-art synergistic reconstruction techniques., Comment: 5 pages, 2 figures, IEEE ISBI 2024

In the solid state, a large variety of single-photon emitters present high quality photophysical properties together with a potential for integration. However, in many cases, the host matrix induces fluctuations of the emission wavelength in time, limiting the potential applications based on indistinguishable photons. A deep understanding of the underlying spectral diffusion processes is therefore of high importance for improving the stability of the light emission. Here, we theoretically investigate the photon statistics of an emitter driven by a resonant laser, and subject to either of two qualitatively different stationary spectral diffusion processes - a continuous diffusion process and a process based on discrete spectral jumps, both of which being known to model the spectral diffusion of various solid-state emitters. We show that the statistics of light emission carries several experimentally accessible signatures that allow to discriminate between the two classes of models, both at short times in the intensity correlation function, and at long times in the fluctuations of the integrated intensity. These results establish that resonant excitation combined with photon statistics offers a rich access to the spectral diffusion processes, yielding information that goes beyond the bare characterization of the inhomogeneous shape and noise correlation time. Incidentally, our findings shed a new light on recent experimental results of spectral diffusion of B centers in hexagonal boron nitride, providing more insight in their spectral diffusion mechanisms.

We implement an all electrical controller for 3D feedback cooling of an optically levitated nanoparticle capable of reaching sub-Kelvin temperatures for the center of mass motion. The controller is based on an optimal policy where state estimation is made by delayed position measurements. The method offers a simplified path for pre-cooling and decoupling the transverse degrees of freedom of the nanoparticle. Numerical simulations show that in an improved setup with quantum limited detection, 3D ground state cooling and all electrical quantum control can be achieved.

In this work, we focus on the challenge of transferring an autonomous driving controller from simulation to the real world (i.e. Sim2Real). We propose a data-efficient method for online and on-the-fly adaptation of parametrizable control architectures such that the target closed-loop performance is optimized while accounting for uncertainties as model mismatches, changes in the environment, and task variations. The novelty of the approach resides in leveraging black-box optimization enabled by Executable Digital Twins (xDTs) for data-driven parameter calibration through derivative-free methods to directly adapt the controller in real-time. The xDTs are augmented with Domain Randomization for robustness and allow for safe parameter exploration. The proposed method requires a minimal amount of interaction with the real-world as it pushes the exploration towards the xDTs. We validate our approach through real-world experiments, demonstrating its effectiveness in transferring and fine-tuning a NMPC with 9 parameters, in under 10 minutes. This eliminates the need for hours-long manual tuning and lengthy machine learning training and data collection phases. Our results show that the online adapted NMPC directly compensates for the Sim2Real gap and avoids overtuning in simulation. Importantly, a 75% improvement in tracking performance is achieved and the Sim2Real gap over the target performance is reduced from a factor of 876 to 1.033., Comment: This work has been accepted by IEEE for possible publication

This paper discusses the importance of reflective and socially conscious education in engineering schools, particularly within the EE/CS sector. While most engineering disciplines have historically aligned themselves with the demands of the technology industry, the lack of critical examination of industry practices and their impact on justice, equality, and sustainability is self-evident. Today, the for-profit engineering/technology companies, some of which are among the largest in the world, also shape the narrative of engineering education and research in universities. As engineering graduates form the largest cohorts within STEM disciplines in Western countries, they become future professionals who will work, lead, or even establish companies in this industry. Unfortunately, the curriculum within engineering education often lacks a deep understanding of social realities, an essential component of a comprehensive university education. Here we establish this unusual connection with the industry that has driven engineering higher education for several decades and its obvious negative impacts to society. We analyse this nexus and highlight the need for engineering schools to hold a more critical viewpoint. Given the wealth and power of modern technology companies, particularly in the ICT domain, questioning their techno-solutionism narrative is essential within the institutes of higher education.

We tackle the problem of bias mitigation of algorithmic decisions in a setting where both the output of the algorithm and the sensitive variable are continuous. Most of prior work deals with discrete sensitive variables, meaning that the biases are measured for subgroups of persons defined by a label, leaving out important algorithmic bias cases, where the sensitive variable is continuous. Typical examples are unfair decisions made with respect to the age or the financial status. In our work, we then propose a bias mitigation strategy for continuous sensitive variables, based on the notion of endogeneity which comes from the field of econometrics. In addition to solve this new problem, our bias mitigation strategy is a weakly supervised learning method which requires that a small portion of the data can be measured in a fair manner. It is model agnostic, in the sense that it does not make any hypothesis on the prediction model. It also makes use of a reasonably large amount of input observations and their corresponding predictions. Only a small fraction of the true output predictions should be known. This therefore limits the need for expert interventions. Results obtained on synthetic data show the effectiveness of our approach for examples as close as possible to real-life applications in econometrics., Comment: 30 pages, 25 figures

Electronic quantum coherence is a prerequisite for charge migration in molecules and emerging molecular quantum technologies. It also underlies new forms of spectroscopy and can enhance molecular function. Whether conical intersections can create electronic coherence and for how long electronic coherence can survive in aqueous environments has remained elusive and controversial, respectively. Here, we use X-ray spectroscopy to realize a breakthrough on both of these topics. We find that electronic relaxation from the $^1$B$_{2\rm{u}}$($\pi\pi^*$) state of pyrazine through conical intersections (CIs) induces previously unobserved electronic and vibrational quantum coherences between the $^1$B$_{3\rm{u}}$(n$\pi^*$) and $^1$A$_{\rm{u}}$(n$\pi^*$) states in the gas phase, which correspond to an electronic ring current. These coherences are entirely suppressed when pyrazine is dissolved in water. These observations, supported by the latest advances in multiconfigurational electronic-structure calculations and non-adiabatic dynamics simulations, confirm that CIs can create electronic coherences and that aqueous solvation can decohere them in less than 40 fs. This study opens the door to the investigation of the broad class of electronic coherences created during light-induced molecular dynamics and to quantify their susceptibility to aqueous solvation., Comment: 54 pages, 28 figures

Observations show that the 12.4 d binary system descending from the recent supernova SN 2022jli closely fits hypotheses of how low-mass X-ray binaries form, but requires an apparently super-Eddington accretion luminosity from the accreting component. We show that this agrees very well with the type of accretion-induced beaming found in ultraluminous X-ray sources, as recently strongly confirmed by X-ray polarimetry of the X-ray binary Cyg X-3. Beaming in the SN2022jli binary system occurs because of the very high mass-transfer rate induced by the violent effect of the supernova on the binary geometry. This explains the very soft nature of the accretion luminosity, its distinctive periodic light curve, and its luminosity decay on a ~250 day timescale. A test of this picture is that the system's orbital period should increase on a $10^5$ year timescale., Comment: Astronomy and Astrophysics Letters, in press

Background: Occupational safety and health institutions report that caregivers areparticularly at risk of developing work-related musculoskeletal disorders (WRMSDs) andpatient handling is often pointed out as one of the main causes. While lots of studiesaddressed the use of assistive devices in order to protect caregivers, it seems that motorlessstand-up lifts have not been studied yet.Objectives: The aim of this work is to provide quantitative data about the loads in thelow back area, as well as qualitative data about subjects perceptions, resulting from the useof a motorless stand-up lift and to compare them to those resulting from manual patienthandling.Methods: Nine caregivers participated to motion capture and ground reaction forcesmeasurement sessions. These recordings were performed in three cases of handling:manual handling with one caregiver, manual handling with two caregivers, motorless deviceassisted handling. Forces and torques at the L5/S1 joint were then estimated throughInverse Dynamics process. A questionnaire about manual and motorless device assistedhandling was also submitted.Results: Motorless device assisted handling involved the smallest loads whereasmanual handling with one caregiver involved the biggest loads.Conclusions: Our findings suggest that, if the situation allows it, caregivers should behelped by another caregiver or use a motorless stand-up lift when handling a patient fromsitting to standing position or from standing to sitting position considering the reduced loadsthese aids involve.

Domain decay is at the heart of the so-called evaporation-condensation Ostwald-ripening regime of phase ordering kinetics, where the growth of large domains occurs at the expense of smaller ones, which are expected to `evaporate'. We experimentally investigate such decay dynamics at the level of a single spherical domain picked from one phase in coexistence and brought into the other phase by an opto-mechanical approach, in a near-critical phase-separated binary liquid mixture. We observe that the decay dynamics is generally not compatible with the theoretically expected surface-tension decay laws for conserved order parameters. Using a mean-field description, we quantitatively explain this apparent disagreement by the gradient of solute concentrations induced by gravity close to a critical point. Finally, we determine the conditions for which buoyancy becomes negligible compared to capillarity and perform dedicated experiments that retrieve the predicted surface-tension induced decay exponent. The surface-tension driven decay dynamics of conserved order parameter systems in the presence and the absence of gravity, is thus established at the level of a single domain., Comment: 4 pages (main text) + 8 pages (SM); Accepted in PRL

Lagrangian stochastic methods are widely used to model turbulent flows. Scarce consideration has, however, been devoted to the treatment of the near-wall region and to the formulation of a proper wall-boundary condition. With respect to this issue, the main purpose of this paper is to present an in-depth analysis of such flows when relying on particle/mesh formulations of the probability density function (PDF) model. This is translated into three objectives. The first objective is to assess the existing an-elastic wall-boundary condition and present new validation results. The second objective is to analyse the impact of the interpolation of the mean fields at particle positions on their dynamics. The third objective is to investigate the spatial error affecting covariance estimators when they are extracted on coarse volumes. All these developments allow to ascertain that the key dynamical statistics of wall-bounded flows are properly captured even for coarse spatial resolutions.

In MRI studies, the aggregation of imaging data from multiple acquisition sites enhances sample size but may introduce site-related variabilities that hinder consistency in subsequent analyses. Deep learning methods for image translation have emerged as a solution for harmonizing MR images across sites. In this study, we introduce IGUANe (Image Generation with Unified Adversarial Networks), an original 3D model that leverages the strengths of domain translation and straightforward application of style transfer methods for multicenter brain MR image harmonization. IGUANe extends CycleGAN architecture by integrating an arbitrary number of domains for training through a many-to-one strategy. During inference, the model can be applied to any image, even from an unknown acquisition site, making it a universal generator for harmonization. Trained on a dataset comprising T1-weighted images from 11 different scanners, IGUANe was evaluated on data from unseen sites. The assessments included the transformation of MR images with traveling subjects, the preservation of pairwise distances between MR images within domains, the evolution of volumetric patterns related to age and Alzheimer$^\prime$s disease (AD), and the performance in age regression and patient classification tasks. Comparisons with other harmonization and normalization methods suggest that IGUANe better preserves individual information in MR images and is more suitable for maintaining and reinforcing variabilities related to age and AD. Future studies may further assess IGUANe in other multicenter contexts, either using the same model or retraining it for applications to different image modalities., Comment: 23 pages, 8 figures; typos corrected

Resistive switching is the fundamental process that triggers the sudden change of the electrical properties in solid-state devices under the action of intense electric fields. Despite its relevance for information processing, ultrafast electronics, neuromorphic devices, resistive memories and brain-inspired computation, the nature of the local stochastic fluctuations that drive the formation of metallic nuclei out of the insulating state has remained hidden. Here, using operando X-ray nano-imaging, we have captured the early-stages of resistive switching in a V2O3-based device under working conditions. V2O3 is a paradigmatic Mott material, which undergoes a first-order metal-to-insulator transition coupled to a lattice transformation that breaks the threefold rotational symmetry of the rhombohedral metal phase. We reveal a new class of volatile electronic switching triggered by nanoscale topological defects of the lattice order parameter of the insulating phase. Our results pave the way to the use of strain engineering approaches to manipulate topological defects and achieve the full control of the electronic Mott switching. The concept of topology-driven reversible electronic transition is of interest for a broad class of quantum materials, comprising transition metal oxides, chalcogenides and kagome metals, that exhibit first-order electronic transitions coupled to a symmetry-breaking order.

We determine the cohomological invariants and the Witt invariants of the alternating group $A_n$., Comment: in French

The autonomous driving industry is continuously dealing with safety-critical scenarios, and nonlinear model predictive control (NMPC) is a powerful control strategy for handling such situations. However, standard safety constraints are not scalable and require a long NMPC horizon. Moreover, the adoption of NMPC in the automotive industry is limited by the heavy computation of numerical optimization routines. To address those issues, this paper presents a real-time capable NMPC for automated driving in urban environments, using control barrier functions (CBFs). Furthermore, the designed NMPC is based on a novel collocation transcription approach, named RESAFE/COL, that allows to reduce the number of optimization variables while still guaranteeing the continuous time (nonlinear) inequality constraints satisfaction, through regional convex hull approximation. RESAFE/COL is proven to be 5 times faster than multiple shooting and more tractable for embedded hardware without a decrease in the performance, nor accuracy and safety of the numerical solution. We validate our NMPC-CBF with RESAFE/COL on digital twins of the vehicle and the urban environment and show the safe controller's ability to improve crash avoidance by 91\%. Supplementary visual material can be found at https://youtu.be/_EnbfYwljp4., Comment: 2024 the authors. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND

Let G be a compact real Lie group, and let f be an irreducible complex character of G, of degree > 1. We show that there exists an element g of G, of finite order, such that f(g)=0., Comment: 15 pages, in French

We present three-dimensional (3D) maps of Jupiter's atmospheric circulation at cloud-top level from Doppler-imaging data obtained in the visible domain with JIVE, the second node of the JOVIAL network, which is mounted on the Dunn Solar Telescope at Sunspot, New Mexico. We report on 12 nights of observations between May 4 and May 30, 2018, representing a total of about 80 hours. Firstly, the average zonal wind profile derived from our data is compatible with that derived from cloud-tracking measurements performed on Hubble Space Telescope images obtained in April 2018 from the Outer Planet Atmospheres Legacy (OPAL) program. Secondly, we present the first ever two-dimensional maps of Jupiter's atmospheric circulation from Doppler measurements. The zonal velocity map highlights well-known atmospheric features, such as the equatorial hot spots and the Great Red Spot (GRS). In addition to zonal winds, we derive meridional and vertical velocity fields from the Doppler data. The motions attributed to vertical flows are mainly located at the boundary between the equatorial belts and tropical zones, which could indicate active motion in theses regions. Qualitatively, these results compare well to recent Juno data that have unveiled the three-dimensional structure of Jupiter's wind field. To the contrary, the motions attributed to meridional circulation are very different from what is obtained by cloud tracking, except at the GRS. Because of limitations with data resolution and processing techniques, we acknowledge that our measurement of vertical or meridional flows of Jupiter are still to be confirmed.

We establish the convergence of the unified two-timescale Reinforcement Learning (RL) algorithm presented in a previous work by Angiuli et al. This algorithm provides solutions to Mean Field Game (MFG) or Mean Field Control (MFC) problems depending on the ratio of two learning rates, one for the value function and the other for the mean field term. Our proof of convergence highlights the fact that in the case of MFC several mean field distributions need to be updated and for this reason we present two separate algorithms, one for MFG and one for MFC. We focus on a setting with finite state and action spaces, discrete time and infinite horizon. The proofs of convergence rely on a generalization of the two-timescale approach of Borkar. The accuracy of approximation to the true solutions depends on the smoothing of the policies. We provide a numerical example illustrating the convergence.

Plasma-activated chemical transformations promise the efficient synthesis of salient chemical products. However, the reaction pathways that lead to desirable products are often unknown, and key quantum-state-resolved information regarding the involved molecular species is lacking. Here we use quantum cascade laser dual-comb spectroscopy (QCL-DCS) to probe plasma-activated NH$_3$ generation with rotational and vibrational state resolution, quantifying state-specific number densities via broadband spectral analysis. The measurements reveal unique translational, rotational and vibrational temperatures for NH$_3$ products, indicative of a highly reactive, non-thermal environment. Ultimately, we postulate on the energy transfer mechanisms that explain trends in temperatures and number densities observed for NH$_3$ generated in low-pressure nitrogen-hydrogen (N$_2$-H$_2$) plasmas.

Dimensional confinement has shown to be an effective strategy to tune competing degrees of freedom in complex oxides. Here, we achieved atomic layered growth of trigonal vanadium sesquioxide (V2O3) by means of oxygen-assisted molecular beam epitaxy. This led to a series of high-quality epitaxial ultrathin V2O3 films down to unit cell thickness, enabling the study of the intrinsic electron correlations upon confinement. By electrical and optical measurements, we demonstrate a dimensional confinement-induced metal-insulator transition in these ultrathin films. We shed light on the Mott-Hubbard nature of this transition, revealing an abrupt vanishing of the quasiparticle weight as demonstrated by photoemission spectroscopy. Furthermore, we prove that dimensional confinement acts as an effective out-of-plane stress. This highlights the structural component of correlated oxides in a confined architecture, while opening an avenue to control both in-plane and out-of-plane lattice components by epitaxial strain and confinement, respectively.

We show that here standard decoding algorithms for generic linear codes over a finite field can speeded up by a factor which is essentially the size of the finite field by reducing it to a low weight codeword problem and working in the relevant projective space. We apply this technique to SDitH and show that the parameters of both the original submission and the updated version fall short of meeting the security requirements asked by the NIST.

The security of code-based cryptography relies primarily on the hardness of decoding generic linear codes. Until very recently, all the best algorithms for solving the decoding problem were information set decoders (ISD). However, recently a new algorithm called RLPN-decoding which relies on a completely different approach was introduced and it has been shown that RLPN outperforms significantly ISD decoders for a rather large range of rates. This RLPN decoder relies on two ingredients, first reducing decoding to some underlying LPN problem, and then computing efficiently many parity-checks of small weight when restricted to some positions. We revisit RLPN-decoding by noticing that, in this algorithm, decoding is in fact reduced to a sparse-LPN problem, namely with a secret whose Hamming weight is small. Our new approach consists this time in making an additional reduction from sparse-LPN to plain-LPN with a coding approach inspired by coded-BKW. It outperforms significantly the ISD's and RLPN for code rates smaller than 0.42. This algorithm can be viewed as the code-based cryptography cousin of recent dual attacks in lattice-based cryptography. We depart completely from the traditional analysis of this kind of algorithm which uses a certain number of independence assumptions that have been strongly questioned recently in the latter domain. We give instead a formula for the LPNs noise relying on duality which allows to analyze the behavior of the algorithm by relying only on the analysis of a certain weight distribution. By using only a minimal assumption whose validity has been verified experimentally we are able to justify the correctness of our algorithm. This key tool, namely the duality formula, can be readily adapted to the lattice setting and is shown to give a simple explanation for some phenomena observed on dual attacks in lattices in [DP23].

We report on the observation and correction of an imaging artifact attributed to the Talbot effect in the context of acousto-optic imaging using structured acoustic waves. When ultrasound waves are emitted with a periodic structure, the Talbot effect produces $\pi$ -phase shifts of that periodic structure at every half of the Talbot distance in propagation. This unwanted artifact is detrimental to the image reconstruction, which assumes near-field diffraction is negligible. Here, we demonstrate both theoretically and experimentally how imposing an additional phase modulation on the acoustic periodic structure induces a symmetry constraint leading to the annihilation of the Talbot effect. This will significantly improve the acousto-optic image reconstruction quality and allows for an improvement of the reachable spatial resolution of the image.

Although during the last decade new observations and new theoretical results have brought better understanding of the physics of accretion onto compact objects, many old and several new questions and problems await answers and solutions. I show how the disc thermal-viscous instability model applied to both cataclysmic variable stars and X-ray binary transients compels us to conclude that assuming the existence in these systems of a flat accretion disc extending down to the accretor's surface or to the last stable orbit and fed with matter at its outer edge is too simple and inadequate a description of these objects. It is also clear that, in most cases, these discs cannot driven by (anomalous) viscosity only. The origin of the superhumps observed in cataclysmic variables and X-ray binaries is, contrary to the common opinion, still unknown. In accreting magnetic white dwarf systems outbursts not of the dwarf-nova type can be due to the magnetic gating instability and/or thermonuclear micronova explosions. Although the "typical" lightcvurves of X-ray transients can be described by analytical formulae (but their decay phase is not exponential), observations show that in many cases the light variations in these systems are much more complex. An elementary argument shows the impossibility of magnetars in pulsing ultraluminous X-ray systems, but we still do not have a complete, self-consistent description of supercritical accretion onto magnetized neutron stars and the resulting (necessarily beamed) emission. Although it is (almost) universally believed that active galactic nuclei contain accretion discs of the same type as those observed in binary systems, the evidence supporting this alleged truth is slim and the structure of accretion flows onto supermassive black holes is still to be determined., Comment: Invited book chapter in "Highlights of the Compact Objects Sciences in the Last Decade", edited by Solen Balman, IU Press (Istanbul University Press, Turkey) Errors corrected, text added, Fig. 1.6 replaced/corrected

This article is an invitation. It is, first, an invitation to consider as a subject worthy of attention the wide range of situations where small discrete elements, either bubbles, droplets or solid particles, are embedded in turbulent flows. Occurring often at a human scale and in our daily environments, these turbulent dispersed two-phase flows display complex behavior due to the interplay of two fundamental interactions, the fluid-particle and particle-particle interactions, compounded by the turbulence of the carrier flow. This is not a domain where the basic laws are unknown but where the huge number of degrees of freedom involved call for reduced, or coarse-grained, statistical descriptions to be developed. Since we are considering transport and collision phenomena or relaxation processes, it would seem that they can be handled by kinetic theory. In the general case of non-fully resolved turbulent flows, we are however dealing with particles influenced by random media with non-zero time and space correlations. The second invitation is therefore to recognize the limitations of kinetic-based descriptions and to address the challenges driving us to extend the classical framework, for fluid-particle as well as particle-particle interactions. Taking the standpoint provided by the modern formulation of stochastic processes and focusing on the description of the particle phase, this review proposes a step-by-step pedagogical presentation of current models while pointing out new directions and remaining uncharted territories. This is done to provide answers to the question `why?' as much as `how?' and to try to kindle interest into these open and fascinating issues.

Spectral computed tomography (CT) offers the possibility to reconstruct attenuation images at different energy levels, which can be then used for material decomposition. However, traditional methods reconstruct each energy bin individually and are vulnerable to noise. In this paper, we propose a novel synergistic method for spectral CT reconstruction, namely Uconnect. It utilizes trained convolutional neural networks (CNNs) to connect the energy bins to a latent image so that the full binned data is used synergistically. We experiment on two types of low-dose data: simulated and real patient data. Qualitative and quantitative analysis show that our proposed Uconnect outperforms state-of-art model-based iterative reconstruction (MBIR) techniques as well as CNN-based denoising.