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The extraction of blood vessels has recently experienced a widespread interest in medical image analysis. Automatic vessel segmentation is highly desirable to guide clinicians in computer-assisted diagnosis, therapy or surgical planning. Despite a good ability to extract large anatomical structures, the capacity of U-Net inspired architectures to automatically delineate vascular systems remains a major issue, especially given the scarcity of existing datasets. In this paper, we present a novel approach that integrates into deep segmentation shape priors from a Semi-Overcomplete Convolutional Auto-Encoder (S-OCAE) embedding. Compared to standard Convolutional Auto-Encoders (CAE), it exploits an over-complete branch that projects data onto higher dimensions to better characterize tiny structures. Experiments on retinal and liver vessel extraction, respectively performed on publicly-available DRIVE and 3D-IRCADb datasets, highlight the effectiveness of our method compared to U-Net trained without and with shape priors from a traditional CAE., Comment: 5 pages, 4 figures, conference

We start from the classical Kadomtsev-Petviashvili hierarchy posed on formal pseudo-differential operators, and we produce two hierarchies of non-linear equations posed on non-formal pseudo-differential operators lying in the Kontsevich and Vishik's odd class, one of them with values in formal pseudo-differential operators. We prove that the corresponding Zakharov-Shabat equations hold in this context, and we express one of our hierarchies as the minimization of a class of Yang-Mills action functionals on a space of pseudo-differential connections whose curvature takes values in the Dixmier ideal. We finish by comparing our Kadomtsev-Petviashvili hierarchies in terms of the solutions that they produce to the KP-II equation: existence, uniqueness and formality.

Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments. In this respect, an automated liver blood vessel extraction is widely summoned. Despite the significant growth in performance of semantic segmentation methodologies, preserving the complex multi-scale geometry of main vessels and ramifications remains a major challenge. This paper provides a new deep supervised approach for vessel segmentation, with a strong focus on representations arising from the different scales inherent to the vascular tree geometry. In particular, we propose a new clustering technique to decompose the tree into various scale levels, from tiny to large vessels. Then, we extend standard 3D UNet to multi-task learning by incorporating scale-specific auxiliary tasks and contrastive learning to encourage the discrimination between scales in the shared representation. Promising results, depicted in several evaluation metrics, are revealed on the public 3D-IRCADb dataset., Comment: 5 pages, 5 figures, conference

The Compton Spectrometer and Imager (COSI) is an upcoming NASA Small Explorer satellite mission scheduled for launch in 2027 and designed to conduct an all-sky survey in the energy range of 0.2-5 MeV. Its instrument consists of an array of germanium detectors surrounded on four sides and underneath by active shields that work as anticoincidence system (ACS) to reduce the contribution of background events in the detectors. These shields are composed of bismuth germanium oxide (BGO), a scintillator material, coupled with Silicon photomultipliers, aimed to collect optical photons produced from interaction of ionizing particles in the BGO and convert them into an electric signal. The reference simulation framework for COSI is MEGAlib, a set of software tools based on the Geant4 toolkit. The interaction point of the incoming radiation, the design of the ACS modules and the BGO surface treatment change the light collection and the overall shielding accuracy. The use of the Geant4 optical physics library, with the simulation of the scintillation process, is mandatory for a more realistic evaluation of the ACS performances. However, including the optical processes in MEGAlib would dramatically increase the computing time of the COSI simulations. We propose the use of a response function encoding the energy resolution and 3D light yield correction based on a separate Geant4 simulation of the ACS that includes the full optical interaction. We present the verification of the Geant4 optical physics library against analytical computations and available laboratory measurements obtained using PMTs as readout device, as a preparatory phase for the simulation of the COSI ACS response., Comment: Proceedings Volume 13093, Space Telescopes and Instrumentation 2024: Ultraviolet to Gamma Ray, Yokohama, Japan

We analyze both finite and infinite systems of Riccati equations derived from stochastic differential games on infinite networks. We discuss a connection to the Catalan numbers and the convergence of the Catalan functions by Fourier transforms., Comment: 9 pages

Intensity interferometry is a reemerging astronomical technique for performing high angular resolution studies at visible wavelengths, benefiting immensely from the recent improvements in (single) photon detection instrumentation. Contrary to direct imaging or amplitude interferometry, intensity interferometry correlates light intensities rather than light amplitudes, circumventing atmospheric seeing limitations at the cost of reduced sensitivity. In this paper we present measurements with the 1.04 m Omicron telescope of C2PU (Centre P\'edagogique Plan\`ete Univers) at the Calern Observatory in the south of France featuring hybrid single photon counting detectors (HPDs). We successfully measured photon bunching from temporal correlations of three different A-type stars - Vega, Altair and Deneb - in the blue at 405 nm. In all cases the observed coherence time fits well to both the pre-calculated expectations as well as the values measured in preceding laboratory tests. The best signal to noise ratio (SNR), with a value of 12, is obtained for Vega for an observation time of 12.1 h. The combination of HPDs and time to digital converter (TDC) results in a timing jitter of the detection system < 50 ps. Our setup demonstrates stable and efficient detection of the starlight owed to the large active area of the HPDs. Utilizing a new class of large area single photon detectors based on multichannel plate amplification, high resolution spatial intensity interferometry experiments are within reach at 1 m diameter class telescopes within one night of observation time for bright stars.

Long baseline optical interferometry and aperture synthesis using ground-based telescopes can enable unprecedented angular resolution astronomy in the optical domain. However, atmospheric turbulence leads to large, dynamic phase errors between participating apertures that limit fringe visibility using telescopes arrays or subaperture configurations in a single large telescope. Diffraction limited optics or adaptive optics can be used to ensure coherence at each aperture, but correlating the phase between apertures requires high speed, high stroke phase correction and recombination that is extremely challenging and costly. As a solution, we show an alternative phase correction and beam combination method using a centimeter-scale silicon astrophotonic chip optimized for H-band operation. The 4.7x10mm silicon photonic chip is fabricated using electron beam lithography with devices with 2 up to 32 independent channels. Light is coupled into the chip using single mode fiber ribbons. An array of microheaters is used to individually tune the effective index of each spiral delay waveguides. Narrowband spectral splitters at each spatial channel divert a modulated digital reference signal from an artificial guide star off-chip for phase measurement. Science light from other wavelengths is coherently combined using on-chip beam combiners and outputted to a single waveguide. We described the role, design, fabrication and characterization of the photonic chip. This photonic phase control scheme can be applied in astronomical interferometry or optical satellite communications., Comment: 12 pages, 13 figures, table 1, SPIE Astronomical Telescopes and Instrumentation

Transpiration cooling is an active thermal protection system of increasing interest in aerospace applications wherein a coolant is effused through a porous wall into a hot external flow. The present work focuses on the interaction between the high-temperature turbulent boundary layer and the pressure-driven coolant flow through the porous wall. Coupling functions were obtained from pore-network simulations to characterize the flow through the porous medium. These were then coupled to direct numerical simulations of a turbulent boundary layer over a massively-cooled flat plate. Two different types of coupling function were used: linear expressions, which do not account for flow interactions between neighbouring pores, and shallow convolutional neural networks (CNN) which incorporate spatial correlations. All coupled cases demonstrated a significant variation in blowing due to the streamwise variation in mean pressure associated with the onset of coolant injection. This trend was reflected in the cooling effectiveness, and was mitigated in the CNN-coupled cases due to the incorporation of lateral flow between neighbouring pores. The distribution of turbulent kinetic energy (TKE) in the coupled cases was also modified by the coupling due to the competing effects of near-wall turbulence attenuation and increased shear due to increasing blowing ratio. Finally, the coupling was shown to impact the power spectral density of the pressure fluctuations at the wall within the transpiration region, attenuating the largest scales of the turbulence whilst leaving the smaller scales relatively unaffected.

In this brief, we discuss the implementation of a third order semi-implicit differentiator as a complement of the recent work by the author that proposes an interconnected semi-implicit Euler double differentiators algorithm through Taylor expansion refinement. The proposed algorithm is dual to the interconnected approach since it offers alternative flexibility to be tuned and to be implemented in real-time processes. In particular, an application to a numerical integration scheme is presented as the Taylor refinement can be of interest to improve the global convergence. Numerical results are presented to support the rightness of the proposed method., Comment: 11 pages, 7 figures

Color centers in hexagonal boron nitride (hBN) advantageously combine excellent photophysical properties with a potential for integration in highly compact devices. Progress towards scalable integration necessitates a high quantum efficiency and an efficient photon collection. In this context, we compare the optical characteristics of individual hBN color centers generated by electron irradiation, in two different electromagnetic environments. We keep track of well-identified emitters that we characterize before and after dry transfer of exfoliated crystals. This comparison provides information about their quantum efficiency - which we find close to unity - as well as their vertical position in the crystal with nanometric precision, which we find away from the flake surfaces. Our work suggests hybrid dielectric-metal planar structures as an efficient tool for characterizing quantum emitters in addition to improving the count rate, and can be generalized to other emitters in 2D materials or in planar photonic structures.

Remote sensing data are increasingly available and frequently used to produce forest attributes maps. The sampling strategy of the calibration plots may directly affect predictions and map qualities. The aim of this manuscript is to evaluate models transferability at different spatial scales according to the sampling efforts and the calibration domain of these models. Forest inventory plots from locals and regionals networks were used to calibrate randomForest (RF) models for stand basal area predictions. Auxiliary data from ALS flights and a Sentinel-2 image were used. Model transferability was assessed by comparing models developed over a given area and applied elsewhere. Performances were measured in terms of precision (RMSE and bias), coefficient of determination (R2) and the proportion of extrapolated predictions. Regional networks were also thinned to evaluate the effect of sampling efforts on models' performances. Local models showed large bias and extrapolation issues when applied elsewhere. Local issues of regional models were also observed, raising transferability and extrapolation concerns. An increase in sampling efforts was shown to reduce extrapolation issues. The outcoming results of this study underline the importance of considering models' validity domain while producing forest attribute maps, since their transferability is of crucial importance from a forest management perspective.

We consider intersection graphs of disks of radius $r$ in the hyperbolic plane. Unlike the Euclidean setting, these graph classes are different for different values of $r$, where very small $r$ corresponds to an almost-Euclidean setting and $r \in \Omega(\log n)$ corresponds to a firmly hyperbolic setting. We observe that larger values of $r$ create simpler graph classes, at least in terms of separators and the computational complexity of the \textsc{Independent Set} problem. First, we show that intersection graphs of disks of radius $r$ in the hyperbolic plane can be separated with $\mathcal{O}((1+1/r)\log n)$ cliques in a balanced manner. Our second structural insight concerns Delaunay complexes in the hyperbolic plane and may be of independent interest. We show that for any set $S$ of $n$ points with pairwise distance at least $2r$ in the hyperbolic plane the corresponding Delaunay complex has outerplanarity $1+\mathcal{O}(\frac{\log n}{r})$, which implies a similar bound on the balanced separators and treewidth of such Delaunay complexes. Using this outerplanarity (and treewidth) bound we prove that \textsc{Independent Set} can be solved in $n^{\mathcal{O}(1+\frac{\log n}{r})}$ time. The algorithm is based on dynamic programming on some unknown sphere cut decomposition that is based on the solution. The resulting algorithm is a far-reaching generalization of a result of Kisfaludi-Bak (SODA 2020), and it is tight under the Exponential Time Hypothesis. In particular, \textsc{Independent Set} is polynomial-time solvable in the firmly hyperbolic setting of $r\in \Omega(\log n)$. Finally, in the case when the disks have ply (depth) at most $\ell$, we give a PTAS for \textsc{Maximum Independent Set} that has only quasi-polynomial dependence on $1/\varepsilon$ and $\ell$. Our PTAS is a further generalization of our exact algorithm., Comment: 31 pages, 11 figures

How can technical advice on operating experience feedback be managed efficiently in an organization that has never used knowledge engineering techniques and methods? This article explains how an industrial company in the nuclear and defense sectors adopted such an approach, adapted to its "TA KM" organizational context and falls within the ISO30401 framework, to build a complete system with a "SARBACANES" application to support its business processes and perpetuate its know-how and expertise in a knowledge base. Over and above the classic transfer of knowledge between experts and business specialists, SARBACANES also reveals the ability of this type of engineering to deliver multi-functional operation. Modeling was accelerated by the use of a tool adapted to this type of operation: the Ardans Knowledge Maker platform., Comment: in French language. 35es Journ{\'e}es francophones d'Ing{\'e}nierie des Connaissances (IC 2024) @ Plate-Forme Intelligence Artificielle (PFIA 2024), Association Fran\c{c}aise pour l'Intelligence Artificielle; Laboratoire L3i La Rochelle Universit{\'e}, Jul 2024, La Rochelle, France

Chemical staining methods are dependable but require extensive time, expensive chemicals, and raise environmental concerns. These challenges highlight the need for alternative solutions like virtual staining, which accelerates the diagnostic process and enhances stain application flexibility. Generative AI technologies are pivotal in addressing these issues. However, the high-stakes nature of healthcare decisions, especially in computational pathology, complicates the adoption of these tools due to their opaque processes. Our work introduces the use of generative AI for virtual staining, aiming to enhance performance, trustworthiness, scalability, and adaptability in computational pathology. The methodology centers on a singular H&E encoder supporting multiple stain decoders. This design focuses on critical regions in the latent space of H&E, enabling precise synthetic stain generation. Our method, tested to generate 8 different stains from a single H&E slide, offers scalability by loading only necessary model components during production. We integrate label-free knowledge in training, using loss functions and regularization to minimize artifacts, thus improving paired/unpaired virtual staining accuracy. To build trust, we use real-time self-inspection with discriminators for each stain type, providing pathologists with confidence heat-maps. Automatic quality checks on new H&E slides ensure conformity to the trained distribution, ensuring accurate synthetic stains. Recognizing pathologists' challenges with new technologies, we have developed an open-source, cloud-based system, that allows easy virtual staining of H&E slides through a browser, addressing hardware/software issues and facilitating real-time user feedback. We also curated a novel dataset of 8 paired H&E/stains related to pediatric Crohn's disease, comprising 480 WSIs to further stimulate computational pathology research.

Astronomical observatories have been identified as substantial contributors to the carbon footprint of astrophysical research. Being part of the collaboration that currently develops the Medium-Sized Telescope (MST) of the Cherenkov Telescope Array, a ground-based observatory for very-high-energy gamma rays that will comprise 64 telescopes deployed on two sites, we assessed the environmental impacts of one MST on the Northern site by means of a Life Cycle Assessment. We identified resource use and climate change as the most significant impacts, being driven by telescope manufacturing and energy consumption during operations. We estimate life cycle greenhouse gas emissions of 2,660 +/- 274 tCO2 equivalent for the telescope, 44% of which arise from construction, 1% from on-site assembly and commissioning, and 55% from operations over 30 years. Environmental impacts can be reduced by using renewable energies during construction and operations, use of less electronic components and metal casting, and use of recycled materials. We propose complementing project requirements with environmental budgets as an effective measure for impact management and reductions., Comment: 32 pages, 4 main figures, 3 extended data figures

A cylinder will roll down an inclined plane in a straight line. A cone will roll around a circle on that plane and then will stop rolling. We ask the inverse question: For which curves drawn on the inclined plane $\mathbb{R}^2$ can one carve a shape that will roll downhill following precisely this prescribed curve and its translationally repeated copies? This simple question has a solution essentially always, but it turns out that for most curves, the shape will return to its initial orientation only after crossing a few copies of the curve - most often two copies will suffice, but some curves require an arbitrarily large number of copies.

We present a new and powerful algebraic framework for graph rewriting, based on drags, a class of graphs enjoying a novel composition operator. Graphs are embellished with roots and sprouts, which can be wired together to form edges. Drags enjoy a rich algebraic structure with sums and products. Drag rewriting naturally extends graph rewriting, dag rewriting, and term rewriting models.

The Takagi-Taupin equations are solved in their simplest form (zero deformation) to obtain the Bragg-diffracted and transmitted complex amplitudes. The case of plane-parallel crystal plates is discussed using a matrix model. The equations are implemented in an open-source python library crystalpy adapted for numerical applications such as crystal reflectivity calculations and ray tracing., Comment: 11 pages, 6 figures

The design and benchtop operation of a wireless miniature epiretinal stimulator implant is reported. The implant is optically powered and controlled using safe illumination at near-infrared wavelengths. An application-specific integrated circuit (ASIC) hosting a digital control unit is used to control the implant's electrodes. The ASIC is powered using an advanced photovoltaic (PV) cell and programmed using a single photodiode. Diamond packaging technology is utilized to achieve high-density integration of the implant optoelectronic circuitry, as well as individual connections between a stimulator chip and 256 electrodes, within a 4.6 mm x 3.7 mm x 0.9 mm implant package. An ultrahigh efficiency PV cell with a monochromatic power conversion efficiency of 55% is used to power the implant. On-board photodetection circuity with a bandwidth of 3.7 MHz is used for forward data telemetry of stimulation parameters. In comparison to implants which utilize inductively coupled coils, laser power delivery enables a high degree of miniaturization and lower surgical complexity. The device presented combines the benefits of implant miniaturization and a flexible stimulation strategy provided by a dedicated stimulator chip. This development provides a route to fully wireless miniaturized minimally invasive implants with sophisticated functionalities.

Fluorescence-guided surgery has emerged as a vital tool for tumour resection procedures. As well as intraoperative tumour visualisation, 5-ALA-induced PpIX provides an avenue for quantitative tumour identification based on ratiometric fluorescence measurement. To this end, fluorescence imaging and fibre-based probes have enabled more precise demarcation between the cancerous and healthy tissues. These sensing approaches, which rely on collecting the fluorescence light from the tumour resection site and its remote spectral sensing, introduce challenges associated with optical losses. In this work, we demonstrate the viability of tumour detection at the resection site using a miniature fluorescence measurement system. Unlike the current bulky systems, which necessitate remote measurement, we have adopted a millimetre-sized spectral sensor chip for quantitative fluorescence measurements. A reliable measurement at the resection site requires a stable optical window between the tissue and the optoelectronic system. This is achieved using an antifouling diamond window, which provides stable optical transparency. The system achieved a sensitivity of 92.3% and specificity of 98.3% in detecting a surrogate tumour at a resolution of 1 x 1 mm2. As well as addressing losses associated with collecting and coupling fluorescence light in the current remote sensing approaches, the small size of the system introduced in this work paves the way for its direct integration with the tumour resection tools with the aim of more accurate interoperative tumour identification.

As a biomarker for liver disease, bilirubin has been utilized in prognostic scoring systems for cirrhosis. While laboratory-based methods are used to determine bilirubin levels in clinical settings, they do not readily lend themselves to applications outside of hospitals. Consequently, bilirubin monitoring for cirrhotic patients is often performed only intermittently; thus, episodes requiring clinical interventions could be missed. This work investigates the feasibility of measuring bilirubin concentration in whole porcine blood samples using dual-wavelength transmission measurement. A compact and low-cost dual-wavelength transmission measurement setup is developed and optimized to measure whole blood bilirubin concentrations. Using small volumes of whole porcine blood (72 {\mu}L), we measured the bilirubin concentration within a range corresponding to healthy individuals and cirrhotic patients (1.2-30 mg/dL). We demonstrate that bilirubin levels can be estimated with a positive correlation (R-square > 0.95) and an accuracy of +/- 1.7 mg/dL, with higher reliability in cirrhotic bilirubin concentrations (> 4 mg/dL), critical for high-risk patients. The optical and electronic components utilized are economical and can be readily integrated into a miniature, low-cost, and user-friendly system. This could provide a pathway for point-of-care monitoring of blood bilirubin outside of medical facilities.

Personalized medicine based on medical images, including predicting future individualized clinical disease progression and treatment response, would have an enormous impact on healthcare and drug development, particularly for diseases (e.g. multiple sclerosis (MS)) with long term, complex, heterogeneous evolutions and no cure. In this work, we present the first stochastic causal temporal framework to model the continuous temporal evolution of disease progression via Neural Stochastic Differential Equations (NSDE). The proposed causal inference model takes as input the patient's high dimensional images (MRI) and tabular data, and predicts both factual and counterfactual progression trajectories on different treatments in latent space. The NSDE permits the estimation of high-confidence personalized trajectories and treatment effects. Extensive experiments were performed on a large, multi-centre, proprietary dataset of patient 3D MRI and clinical data acquired during several randomized clinical trials for MS treatments. Our results present the first successful uncertainty-based causal Deep Learning (DL) model to: (a) accurately predict future patient MS disability evolution (e.g. EDSS) and treatment effects leveraging baseline MRI, and (b) permit the discovery of subgroups of patients for which the model has high confidence in their response to treatment even in clinical trials which did not reach their clinical endpoints.

The deployment of safe and trustworthy machine learning systems, and particularly complex black box neural networks, in real-world applications requires reliable and certified guarantees on their performance. The conformal prediction framework offers such formal guarantees by transforming any point into a set predictor with valid, finite-set, guarantees on the coverage of the true at a chosen level of confidence. Central to this methodology is the notion of the nonconformity score function that assigns to each example a measure of ''strangeness'' in comparison with the previously seen observations. While the coverage guarantees are maintained regardless of the nonconformity measure, the point predictor and the dataset, previous research has shown that the performance of a conformal model, as measured by its efficiency (the average size of the predicted sets) and its informativeness (the proportion of prediction sets that are singletons), is influenced by the choice of the nonconformity score function. The current work introduces the Penalized Inverse Probability (PIP) nonconformity score, and its regularized version RePIP, that allow the joint optimization of both efficiency and informativeness. Through toy examples and empirical results on the task of crop and weed image classification in agricultural robotics, the current work shows how PIP-based conformal classifiers exhibit precisely the desired behavior in comparison with other nonconformity measures and strike a good balance between informativeness and efficiency.

It is known that the momentum operator canonically conjugated to the position operator for a particle moving in some bounded interval of the line {(with Dirichlet boundary conditions) is not essentially self-adjoint}: it has a continuous set of self-adjoint extensions. We prove that essential self-adjointness can be recovered by symmetrically weighting the momentum operator with a positive bounded function approximating the indicator function of the considered interval. This weighted momentum operator is consistently obtained from a similarly weighted classical momentum through the so-called Weyl-Heisenberg covariant integral quantization of functions or distributions., Comment: in press in Journal of Mathematical Analysis and Applications

Coverage-guided fuzz testing has received significant attention from the research community, with a strong focus on binary applications, greatly disregarding other targets, such as web applications. The importance of the World Wide Web in everyone's life cannot be overstated, and to this day, many web applications are developed in PHP. In this work, we address the challenges of applying coverage-guided fuzzing to PHP web applications and introduce PHUZZ, a modular fuzzing framework for PHP web applications. PHUZZ uses novel approaches to detect more client-side and server-side vulnerability classes than state-of-the-art related work, including SQL injections, remote command injections, insecure deserialization, path traversal, external entity injection, cross-site scripting, and open redirection. We evaluate PHUZZ on a diverse set of artificial and real-world web applications with known and unknown vulnerabilities, and compare it against a variety of state-of-the-art fuzzers. In order to show PHUZZ' effectiveness, we fuzz over 1,000 API endpoints of the 115 most popular WordPress plugins, resulting in over 20 security issues and 2 new CVE-IDs. Finally, we make the framework publicly available to motivate and encourage further research on web application fuzz testing., Comment: Preprint; Final version to be published in ASIA CCS '24: Proceedings of the 2024 ACM Asia Conference on Computer and Communications Security

Schaefer (2024) has recently published observations of binary period derivatives $\dot P$ for 52 cataclysmic variables, and concluded that these strongly conflict with all proposed evolutionary pictures for these systems. We point out once again that using measurements of $\dot P$ is likely in practice to produce misleading evolutionary constraints in almost every case. The one identified exception to this is probably the recently-born X-ray binary SN 2022jli, because of its extremely high mass transfer rate., Comment: 2 pages, no figures

In data mining, density-based clustering, which entails classifying datapoints according to their distributions in some space, is an essential method to extract information from large datasets. With the advent of software-based radio, ionospheric radars are capable of producing unprecedentedly large datasets of plasma turbulence backscatter observations, and new automatic techniques are needed to sift through them. We present an algorithm to automatically identify and track clusters of radar echoes through time, using dbscan, a celebrated density-based clustering method for noisy point-clouds. We demonstrate its efficiency by tracking turbulent structures in the E-region ionosphere, the so-called radar aurora. Through conjugate auroral imagery, as well as in-situ satellite observations, we demonstrate that the observed turbulent structures generally track the motion of the aurora. We discuss instances when this prediction is brought to fruition and when it ostensibly is not. Through case studies, we highlight the important instances when the radar echo bulk motions vary considerably around discrete auroral arcs, an effect we argue is produced by strong electric field modulations caused by energetic particle precipitation., Comment: 11 pages, 6 figures

We investigate a real 3D stationary flow characterized by chaotic advection generated by a magnetic field created by permanent magnets acting on a weakly conductive fluid subjected to a weak constant current. The model under consideration involves the Stokes equations for viscous incompressible fluid at low Reynolds number in which the density forces correspond to the Lorentz force generated by the magnetic field of the magnets and the electric current through the fluid. An innovative numerical approach based on a mixed finite element method has been developed and implemented for computing the flow velocity fields with the electromagnetic force. This ensures highly accurate numerical results, allowing a detailed analysis of the chaotic behavior of fluid trajectories through the computations of associated Poincar\'e sections and Lyapunov exponents. Subsequently, an examination of mixing efficiency is conducted, employing computations of contamination and homogeneity rates, as well as mixing time. The obtained results underscore the relevance of the modeling and computational tools employed, as well as the design of the magnetohydrodynamic device used., Comment: Pre-submission version (preprint). Submitted to Physical Review Fluids

Mean Field Control Games (MFCG), introduced in [Angiuli et al., 2022a], represent competitive games between a large number of large collaborative groups of agents in the infinite limit of number and size of groups. In this paper, we prove the convergence of a three-timescale Reinforcement Q-Learning (RL) algorithm to solve MFCG in a model-free approach from the point of view of representative agents. Our analysis uses a Q-table for finite state and action spaces updated at each discrete time-step over an infinite horizon. In [Angiuli et al., 2023], we proved convergence of two-timescale algorithms for MFG and MFC separately highlighting the need to follow multiple population distributions in the MFC case. Here, we integrate this feature for MFCG as well as three rates of update decreasing to zero in the proper ratios. Our technique of proof uses a generalization to three timescales of the two-timescale analysis in [Borkar, 1997]. We give a simple example satisfying the various hypothesis made in the proof of convergence and illustrating the performance of the algorithm., Comment: arXiv admin note: text overlap with arXiv:2312.06659

A control based power tower function at order 2 is proposed in this paper. This leads to a new sliding mode control, which allows employing backstepping technique that combines both guaranteed and finite time convergence. The proposed control is applied to a double integrator subject to perturbation $d$. Both guaranteed and finite convergence are ensured by the controller when $d$ is considered constant and bounded, without knowing its upper bound. For the case, when $d$ is variable and bounded with its upper bound known, only a finite time convergence is obtained. Simulation results are given to show the well founded of the proposed novel control.

In recent years, quantum Fourier transform infrared (QFTIR) spectroscopy emerged as an alternative to conventional spectroscopy in the mid-infrared region of the spectrum. By harnessing induced coherence and spectral entanglement, QFTIR offers promising potential for the practical detection of organic gasses. However, little research was conducted to bring QFTIR spectrometers closer to domestic or in-field usage. In this work, we present the first use of a QFTIR spectrometer for open-path detection of multiple interfering organic gases in ambient air. The accurate identification of mixtures of acetone, methanol, and ethanol vapors is demonstrated with a QFTIR spectrometer. We achieved this breakthrough by building a nonlinear Michelson interferometer with 1.7m-long arms to increase the absorption length, coupled with analysis techniques from differential absorption spectroscopy. The evolution of different gasses' concentrations in ambient air was measured through time. These results constitute the first use-case of a QFTIR spectrometer as a detector of organic gasses, and thus represent an important milestone towards the development of such detectors in practical situations., Comment: 8 pages, 7 figures, 1 table. For associated gif file, see https://filesender.switch.ch/filesender2/?s=download&token=55a748d4-ab44-4621-9d4d-2266f4223270

In this work, we consider an initial-boundary value problem for a time-fractional biharmonic equation in a bounded polygonal domain with a Lipschitz continuous boundary in $\mathbb{R}^2$ with clamped boundary conditions. After establishing the well-posedness, we focus on some regularity results of the solution with respect to the regularity of the problem data. The spatially semidiscrete scheme covers several popular lowest-order piecewise-quadratic finite element schemes, namely, Morley, discontinuous Galerkin, and $C^0$ interior penalty methods, and includes both smooth and nonsmooth initial data. Optimal order error bounds with respect to the regularity assumptions on the data are proved for both homogeneous and nonhomogeneous problems. The numerical experiments validate the theoretical convergence rate results.

We introduce a lattice model to probe the effect of active self-disassembly on equilibrium self-assembly. Surprisingly, we find conditions under which active self-disassembly enhances the yield of a target structure above that achieved by self-assembly alone when the latter is already favoured thermodynamically. We discuss biological implications of our findings., Comment: 5 pages, 4 figures

High-precision ephemerides are not only useful in supporting space missions, but also in investigating the physical nature of celestial bodies. This paper reports an update to the orbit and rotation model of the Martian moon Phobos. In contrast to earlier numerical models, this paper details a dynamical model that fully considers the rotation of Phobos. Here, Phobos' rotation is first described by Euler's rotational equations and integrated simultaneously with the orbital motion equations. We discuss this dynamical model, along with the differences with respect to the model now in use. We present the variational equation for Phobos' rotation employing the symbolic \emph{Maple} computation software. The adjustment test simulations confirm the latitude libration of Phobos, suggesting gravity field coefficients obtained using a shape model and homogeneous density hypothesis should be re-examined in the future in the context of dynamics. Furthermore, the simulations with different $k_2$ values indicate that it is difficult to determine k_2 efficiently using the current data.

We introduce a novel technique to mitigate the adverse effects of atmospheric turbulence on astronomical imaging. Utilizing a video-to-image neural network trained on simulated data, our method processes a sliding sequence of short-exposure ($\sim$0.2s) stellar field images to reconstruct an image devoid of both turbulence and noise. We demonstrate the method with simulated and observed stellar fields, and show that the brief exposure sequence allows the network to accurately associate speckles to their originating stars and effectively disentangle light from adjacent sources across a range of seeing conditions, all while preserving flux to a lower signal-to-noise ratio than an average stack. This approach results in a marked improvement in angular resolution without compromising the astrometric stability of the final image., Comment: Accepted for publication in MNRAS (advance copy available at https://doi.org/10.1093/mnras/stae1018). Project website available at https://dancecam.info/ . 20 pages, 17 figures

We developed the X-ray, Gamma-ray and Relativistic Electron detector (XGRE) onboard the TARANIS satellite, to investigate high-energy phenomena associated with lightning discharges such as terrestrial gamma-ray flashes and terrestrial electron beams. XGRE consisted of three sensors. Each sensor has one layer of LaBr$_{3}$ crystals for X-ray/gamma-ray detections, and two layers of plastic scintillators for electron and charged-particle discrimination. Since 2018, the flight model of XGRE was developed, and validation and calibration tests, such as a thermal cycle test and a calibration test with the sensors onboard the satellite were performed before the launch of TARANIS on 17 November 2020. The energy range of the LaBr$_{3}$ crystals sensitive to X-rays and gamma rays was determined to be 0.04-11.6 MeV, 0.08-11.0 MeV, and 0.08-11.3 MeV for XGRE1, 2, and 3, respectively. The energy resolution at 0.662 MeV (full width at half maximum) was to be 20.5%, 25.9%, and 28.6%, respectively. Results from the calibration test were then used to validate a simulation model of XGRE and TARANIS. By performing Monte Carlo simulations with the verified model, we calculated effective areas of XGRE to X-rays, gamma rays, electrons, and detector responses to incident photons and electrons coming from various elevation and azimuth angles., Comment: 25 pages, 20 figures, 1 table, published in Journal of Astronomical Telescopes, Instruments, and Systems. Copyright 2024 Society of Photo-Optical Instrumentation Engineers (SPIE)

We describe basic diffeological structures related to splittings and Grassmannians for infinite dimensional vector spaces. We analyze and expand the notion of non-commutative cross-ratio and prove its smoothness. Then we illustrate this theory by examples, with some of them extracted from the existing literature related to infinite dimensional (Banach) Grassmannians, and others where the diffeological setting is a key primary step for rigorous definitions.

We here analyse the question of developing artificial consciousness from an evolutionary perspective, taking the evolution of the human brain and its relation with consciousness as a reference model. This kind of analysis reveals several structural and functional features of the human brain that appear to be key for reaching human-like complex conscious experience and that current research on Artificial Intelligence (AI) should take into account in its attempt to develop systems capable of conscious processing. We argue that, even if AI is limited in its ability to emulate human consciousness for both intrinsic (structural and architectural) and extrinsic (related to the current stage of scientific and technological knowledge) reasons, taking inspiration from those characteristics of the brain that make conscious processing possible and/or modulate it, is a potentially promising strategy towards developing conscious AI. Also, it is theoretically possible that AI research can develop partial or potentially alternative forms of consciousness that is qualitatively different from the human, and that may be either more or less sophisticated depending on the perspectives. Therefore, we recommend neuroscience-inspired caution in talking about artificial consciousness: since the use of the same word consciousness for humans and AI becomes ambiguous and potentially misleading, we propose to clearly specify what is common and what differs in AI conscious processing from full human conscious experience.

The growing database of gravitational-wave (GW) detections with the binary black holes (BHs) merging in the distant Universe contains subtle insights into their formation scenarios. One of the puzzling properties of detected GW sources is the possible (anti)correlation between mass ratio q of BH-BH binaries and their effective spin. We use rapid binary evolution models to demonstrate that the isolated binary evolution followed by efficient tidal spin-up of stripped helium core produces a similar pattern in Xeff vs q distributions of BH-BH mergers. In our models, the progenitors of unequal BH-BH systems in the stable mass transfer formation scenario are more likely to efficiently shrink their orbits during the second Roche-lobe overflow than the binaries that evolve into nearly equal-mass component systems. This makes it easier for unequal-mass progenitors to enter the tidal spin-up regime and later merge due to GW emission. Our results are, however, sensitive to some input assumptions, especially, the stability of mass transfer and the angular momentum loss during non-conservative mass transfer. We note that mass transfer prescriptions widely adopted in rapid codes favor the formation of BH-BH merger progenitors with unequal masses and moderate separations. We compare our results with detailed stellar model grids and find reasonable agreement after appropriate calibration of the physics models. We anticipate that future detections of unequal-mass BH-BH mergers could provide valuable constraints on the role of the stable mass transfer formation channel. A significant fraction of BH-BH detections with mass ratio q in range (0.4 - 0.7) would be consistent with the mass ratio reversal scenario during the first, relatively conservative mass transfer, and a non-enhanced angular momentum loss during the second, highly non-conservative mass transfer phase., Comment: Version accepted for publication in A&A

Quantization lowers memory usage, computational requirements, and latency by utilizing fewer bits to represent model weights and activations. In this work, we investigate the generalization properties of quantized neural networks, a characteristic that has received little attention despite its implications on model performance. In particular, first, we develop a theoretical model for quantization in neural networks and demonstrate how quantization functions as a form of regularization. Second, motivated by recent work connecting the sharpness of the loss landscape and generalization, we derive an approximate bound for the generalization of quantized models conditioned on the amount of quantization noise. We then validate our hypothesis by experimenting with over 2000 models trained on CIFAR-10, CIFAR-100, and ImageNet datasets on convolutional and transformer-based models.

The recent development of high average, high peak power lasers has revived the effort of using lasers as a potential tool to influence natural lightning. Although impressive, the current progress in laser lightning control technology may only be the beginning of a new area involving a positive feedback between powerful laser development and atmospheric research. In this review paper, we critically evaluate the past, present and future of Laser Lightning Control (LLC), considering both its technological and scientific significance in atmospheric research., Comment: Revised version after first round of peer-review. Restructuration, adding more physics contextuality and pushed some tables in a newly created Appendix

Superconducting RF cavities are used in particle accelerators to provide energy to the particle beam. Such cavities are mostly fabricated in niobium and often operated in superfluid helium. One of their limits of operation is the appearance of a local quench, initiated by a local field enhancement due to a defect, which leads to a normal conducting transition of the cavity. Localizing the quench area can be achieved with temperature mapping systems. Another method is the use of second sound wave propagation in superfluid helium. Measuring the time of propagation of these waves from quench location to special sensors, called Oscillating Superleak Transducers (OSTs), and using their well-known velocity should allow trilateration. However, most of experimental measurements on cavities show "premature signals", i.e. the second sound signals arrive earlier on the OSTs than expected. This paper presents several quench experiments on cavities equipped with OSTs and temperature mapping quench detection systems. Two hypotheses can explain the observed premature signals. The first one assesses faster propagation in helium. An experimental setup has been developed for testing this hypothesis, where second sound is created by a localized heater in a controlled environment up to 4.3 kW/cm2 and 2.8 J. Premature signals could not be verified in this setup. A second hypothesis based on a simple model including several processes in niobium and second sound propagation in helium is discussed. The model improves significantly the prediction of the times of arrival of the second sound waves. The overall study shows that the processes in niobium play a prominent role in the second sound detection for superconducting cavities., Comment: 12 pages, 22 images

The emergence of differentiable simulators enabling analytic gradient computation has motivated a new wave of learning algorithms that hold the potential to significantly increase sample efficiency over traditional Reinforcement Learning (RL) methods. While recent research has demonstrated performance gains in scenarios with comparatively smooth dynamics and, thus, smooth optimization landscapes, research on leveraging differentiable simulators for contact-rich scenarios, such as legged locomotion, is scarce. This may be attributed to the discontinuous nature of contact, which introduces several challenges to optimizing with analytic gradients. The purpose of this paper is to determine if analytic gradients can be beneficial even in the face of contact. Our investigation focuses on the effects of different soft and hard contact models on the learning process, examining optimization challenges through the lens of contact simulation. We demonstrate the viability of employing analytic gradients to learn physically plausible locomotion skills with a quadrupedal robot using Short-Horizon Actor-Critic (SHAC), a learning algorithm leveraging analytic gradients, and draw a comparison to a state-of-the-art RL algorithm, Proximal Policy Optimization (PPO), to understand the benefits of analytic gradients.

Fabrication of low-RF loss GaN-on-Si HEMT stacks is critical to enable competitive front-end-modules for 5G and 6G applications. The main contribution to RF losses is the interface between the III-N layer and the HR Si wafer, more specifically the AlN/Si interface. At this interface, a parasitic surface conduction layer exists in Si, which decreases the substrate effective resistivity sensed by overlying circuitry below the nominal Si resistivity. However, a clear understanding of this interface with control of the parasitic channel is lacking. In this letter, a detailed physical and electrical description of MOCVD-grown AlN/Si structures is presented. The presence of a $\text{SiC}_\text{x}\text{N}_\text{y}$ interfacial layer is revealed and its importance for RF losses is shown. Through C-V and I-V characterisation, an increase in the C concentration of this interfacial layer is linked to the formation of negative charge at the AlN/Si interface, which counteracts the positive charge present in the 0-predose limit. The variation of TMAl predose is shown to allow precise tuning of the C composition and, consequently, the resulting interface charge. Notably, a linear relationship between predose and net interface charge is observed and confirmed by the fabrication of an AlN/Si sample with close to zero net charge. In addition, a higher $D_{it}$ ($\sim 2\times 10^{12}$ cm$^\text{-2}$) for such compensated samples is observed and can contribute to low RF loss. An exceptionally high effective resistivity of above 8 k$\Omega\cdot$cm is achieved, corresponding to an RF loss below 0.3 dB/mm at 10 GHz., Comment: The following article has been accepted for publication in Applied Physics Letters. After it is published, it will be found at https://pubs.aip.org/aip/apl

We revisit the concept of `tachyonic' scalar fields, characterized by a real negative squared `mass', in de Sitter (dS) spacetime. Through a rigorous group-theoretical analysis, we argue that labeling these fields as `tachyonic' lacks physical relevance, often leading to misinterpretations in scholarly discourse, as their `mass' parameter holds no genuine physical significance., Comment: 5 pages, no figure

Non-thermal molecular plasmas play a crucial role in numerous industrial processes and hold significant potential for driving essential chemical transformations. Accurate information about the molecular composition of the plasmas and the distribution of populations among quantum states is essential for understanding and optimizing plasma processes. Here, we apply a mid-infrared frequency comb-based Fourier transform spectrometer to measure high-resolution spectra of plasmas containing hydrogen, nitrogen, and a carbon source in the 2800 - 3400 cm$^{-1}$ range. The spectrally broadband and high-resolution capabilities of this technique enable quantum-state-resolved spectroscopy of multiple plasma-generated species simultaneously, including CH$_4$, C$_2$H$_2$, C$_2$H$_6$, NH$_3$, and HCN, providing detailed information beyond the limitations of current methods. Using a line-by-line fitting approach, we analyzed 548 resolved transitions across five vibrational bands of plasma-generated HCN. The results indicate a significant non-thermal distribution of the populations among the quantum states, with distinct temperatures observed for lower and higher rotational quantum numbers, with a temperature difference of about 62 K. Broadband state-resolved-spectroscopy via comb-based methods provides unprecedented fundamental insights into the non-thermal nature of molecular plasmas - a detailed picture that has never been accomplished before for such complex non-thermal environment.

A $3$-fold and a $5$-fold quadratic Pfister forms are canonically associated to every symplectic involution on a central simple algebra of degree $8$ over a field of characteristic $2$. The same construction on central simple algebras of degree $4$ associates to every unitary involution a $2$-fold and a $4$-fold Pfister quadratic forms, and to every orthogonal involution a $1$-fold and a $3$-fold quasi-Pfister forms. These forms hold structural information on the algebra with involution., Comment: in French language

Connectivity is a main driver for the ongoing megatrend of automated mobility: future Cooperative Intelligent Transport Systems (C-ITS) will connect road vehicles, traffic signals, roadside infrastructure, and even vulnerable road users, sharing data and compute for safer, more efficient, and more comfortable mobility. In terms of communication technology for realizing such vehicle-to-everything (V2X) communication, the WLAN-based peer-to-peer approach (IEEE 802.11p, ITS-G5 in Europe) competes with C-V2X based on cellular technologies (4G and beyond). Irrespective of the underlying communication standard, common message interfaces are crucial for a common understanding between vehicles, especially from different manufacturers. Targeting this issue, the European Telecommunications Standards Institute (ETSI) has been standardizing V2X message formats such as the Cooperative Awareness Message (CAM). In this work, we present V2AIX, a multi-modal real-world dataset of ETSI ITS messages gathered in public road traffic, the first of its kind. Collected in measurement drives and with stationary infrastructure, we have recorded more than 285 000 V2X messages from more than 2380 vehicles and roadside units in public road traffic. Alongside a first analysis of the dataset, we present a way of integrating ETSI ITS V2X messages into the Robot Operating System (ROS). This enables researchers to not only thoroughly analyze real-world V2X data, but to also study and implement standardized V2X messages in ROS-based automated driving applications. The full dataset is publicly available for non-commercial use at v2aix.ika.rwth-aachen.de., Comment: 7 pages; Accepted to be published as part of the 27th International Conference on Intelligent Transportation Systems (ITSC), Edmonton, Canada, September 24-27, 2024