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School improvement is an iterative process through which districts and schools develop their capacity, implement and refine new policies and practices, and respond to new developments and needs over time. School improvement policy can also be considered an iterative process, with policy implementors learning from previous rounds of a policy. Michigan developed the Partnership Model for School and District Turnaround to provide support and accountability for its lowest-performing schools (i.e., Comprehensive Support and Improvement schools, or CSI schools). In November 2022, the Michigan Department of Education (MDE) identified the current round (Round 4) of Partnership schools and districts. By the spring of 2023, Partnership districts outlined new improvement goals and accountability measures, and by fall of 2023 they developed improvement plans to meet those goals. Throughout the 2023-2024 school year, they began to implement those improvement plans. This report describes the goals and planning activities for Round 4 of Partnership schools and districts and analyzes the targets that they set, the accountability measures they selected, and the specific activities they planned to implement as part of their improvement efforts.

District leaders seeking to close schools often frame decisions as "right-sizing" or "modernizing the footprint" of the district. Historical influences such as population and economic shifts and desegregation during the civil rights era, and more recent factors like deteriorating building conditions, school performance "accountability" measures, declining enrollment, and the expansion of school choice policies have contributed to school closure decisions. Whatever language used, or factors considered, these consequential decisions often spark fierce community debate. In Pennsylvania, which operates one of the most inequitable public education systems in the nation, several communities have controversial histories of school closures, including Philadelphia, Pittsburgh, and rural regions. Now, the prospect of future school closures again looms large as many districts have experienced several years of declining enrollment and face financial cliffs, challenges made worse in the wake of COVID-19. To inform community members and guide district leaders navigating these difficult decisions, this brief revisits Research for Action's 2013 Issue Brief on School Closings Policy. In this update, the authors focus on the inequitable impacts of school closures on students, staff, and surrounding communities, with particular attention to how systemic and historical inequities continue to influence the school closure process. The brief concludes with considerations from research on school closures for school district leaders and decision-makers contemplating closing schools that can better inform plans and address potential equity issues for students and surrounding communities.

In this article, we introduce and analyse some statistical properties of a class of models of random landscapes of the form ${\cal H}({\bf x})=\frac{\mu}{2}{\bf x}^2+\sum_{l=1}^M \phi_l({\bf k}_l\cdot {\bf x}), \, \, {\bf x}\in \mathbb{R}^N,\,\, \mu>0 $ where both the functions $\phi_l(z)$ and vectors ${\bf k}_l$ are random. An important example of such landscape describes superposition of $M$ plane waves with random amplitudes, directions of the wavevectors, and phases, further confined by a parabolic potential of curvature $\mu$. Our main efforts are directed towards analysing the landscape features in the limit $N\to \infty, M\to \infty$ keeping $\alpha=M/N$ finite. In such a limit we find (i) the rates of asymptotic exponential growth with $N$ of the mean number of all critical points and of local minima known as the annealed complexities and (ii) the expression for the mean value of the deepest landscape minimum (the ground-state energy). In particular, for the latter we derive the Parisi-like optimisation functional and analyse conditions for the optimiser to reflect various phases for different values of $\mu$ and $\alpha$: replica-symmetric, one-step and full replica symmetry broken, as well as criteria for continuous, Gardner and random first order transitions between different phases., Comment: 42 pages, 0 figure

Few people use the probability theory in order to achieve image segmentation with snake models. In this article, we are presenting an active contour algorithm based on a probability approach inspired by A. Blake work and P. R{\'e}fr{\'e}gier's team research in France. Our algorithm, both very fast and highly accurate as far as contour description is concerned, is easily adaptable to any specific application.

We demonstrate a quantum walk comb in synthetic frequency space formed by externally modulating a semiconductor optical amplifier operating in the telecommunication wavelength range in a unidirectional ring cavity. The ultrafast gain saturation dynamics of the gain medium and its operation at high current injections is responsible for the stabilization of the comb in a broad frequency modulated state. Our device produces a nearly flat broadband comb with a tunable repetition frequency reaching a bandwidth of 1.8THz at the fundamental repetition rate of 1GHz while remaining fully locked to the RF drive. Comb operation at harmonics of the repetition rate up to 14.1GHz is also demonstrated. This approach paves the way for next-generation optical frequency comb devices with potential applications in precision ranging and high-speed communications.

Hyperspectral imaging (HSI) holds significant potential for transforming the field of computational pathology. However, there is currently a shortage of pixel-wise annotated HSI data necessary for training deep learning (DL) models. Additionally, the number of HSI-based research studies remains limited, and in many cases, the advantages of HSI over traditional RGB imaging have not been conclusively demonstrated, particularly for specimens collected intraoperatively. To address these challenges we present a database consisted of 27 HSIs of hematoxylin-eosin stained frozen sections, collected from 14 patients with colon adenocarcinoma metastasized to the liver. It is aimed to validate pixel-wise classification for intraoperative tumor resection. The HSIs were acquired in the spectral range of 450 to 800 nm, with a resolution of 1 nm, resulting in images of 1384x1035 pixels. Pixel-wise annotations were performed by three pathologists. To overcome challenges such as experimental variability and the lack of annotated data, we combined label-propagation-based semi-supervised learning (SSL) with spectral-spatial features extracted by: the multiscale principle of relevant information (MPRI) method and tensor singular spectrum analysis method. Using only 1% of labeled pixels per class the SSL-MPRI method achieved a micro balanced accuracy (BACC) of 0.9313 and a micro F1-score of 0.9235 on the HSI dataset. The performance on corresponding RGB images was lower, with a micro BACC of 0.8809 and a micro F1-score of 0.8688. These improvements are statistically significant. The SSL-MPRI approach outperformed six DL architectures trained with 63% of labeled pixels. Data and code are available at: https://github.com/ikopriva/ColonCancerHSI., Comment: 12 pages, 5 figures, 5 tables

Digitalization and virtualization are extremely active and important approaches in a large scope of activities (marketing, selling, enterprise management, logistics). Tourism management is also highly concerned by this evolution. In this paper we try to present today's situation based on a 7-week trip showing appropriate and shame situations. After this case study, we give a list of appropriate practices and orientations and confirm the fundamental role of User Experience in validating the proposed assistive system and the User Interfaces needed for client/user satisfaction. We also outline the expected role of Metaverse in the future of the evolution of this domain.

Towards the role of humanoid robots as squad mates in urban operations and other domains, we identified doors as a major area lacking capability development. In this paper, we focus on the ability of humanoid robots to navigate and deal with doors. Human-sized doors are ubiquitous in many environment domains and the humanoid form factor is uniquely suited to operate and traverse them. We present an architecture which incorporates GPU accelerated perception and a tree based interactive behavior coordination system with a whole body motion and walking controller. Our system is capable of performing door traversals on a variety of door types. It supports rapid authoring of behaviors for unseen door types and techniques to achieve re-usability of those authored behaviors. The behaviors are modelled using trees and feature logical reactivity and action sequences that can be executed with layered concurrency to increase speed. Primitive actions are built on top of our existing whole body controller which supports manipulation while walking. We include a perception system using both neural networks and classical computer vision for door mechanism detection outside of the lab environment. We present operator-robot interdependence analysis charts to explore how human cognition is combined with artificial intelligence to produce complex robot behavior. Finally, we present and discuss real robot performances of fast door traversals on our Nadia humanoid robot. Videos online at https://www.youtube.com/playlist?list=PLXuyT8w3JVgMPaB5nWNRNHtqzRK8i68dy., Comment: 15 pages, 23 figure, for submission to Elsevier RAS

We investigate the momentum fluxes between a turbulent air boundary layer and a growing-breaking wave field by solving the air-water two-phase Navier-Stokes equations through direct numerical simulations (DNS). A fully-developed turbulent airflow drives the growth of a narrowbanded wave field, whose amplitude increases until reaching breaking conditions. The breaking events result in a loss of wave energy, transferred to the water column, followed by renewed growth under wind forcing. We revisit the momentum flux analysis in a high-wind speed regime, characterized by the ratio of the friction velocity to wave speed $u_\ast/c$ in the range $[0.3-0.9]$, through the lens of growing-breaking cycles. The total momentum flux across the interface is dominated by pressure, which increases with $u_\ast/c$ during growth and reduces sharply during breaking. Drag reduction during breaking is linked to airflow separation, a sudden acceleration of the flow, an upward shift of the mean streamwise velocity profile, and a reduction in Reynolds shear stress. We characterize the reduction of pressure stress and flow acceleration through an aerodynamic drag coefficient by splitting the analysis between growing and breaking stages, treating them as separate sub-processes. While drag increases with $u_\ast/c$ during growth, it drops during breaking. Averaging over both stages leads to a saturation of the drag coefficient at high $u_\ast/c$, comparable to what is observed at high wind speeds in laboratory and field conditions. Our analysis suggests this saturation is controlled by breaking dynamics.

The study of phase transitions in frustrated magnetic systems with $O(N)\times O(2)$ symmetry has been the subject of controversy for more than twenty years, with theoretical, numerical and experimental results in disagreement. Even theoretical studies lead to different results, with some predicting a first-order phase transition while others find it to be second-order. Recently, a series of results from both numerical simulations and theoretical analyses, in particular those based on the Conformal Bootstrap, have rekindled interest in this controversy, especially as they are still not in agreement with each other. Studies based on the functional renormalization group have played a major role in this controversy in the past, and we revisit these studies, taking them a step further by adding non-trivial second order derivative terms to the derivative expansion of the effective action. We confirm the first-order nature of the phase transition for physical values of $N$, i.e. for $N=2$ and $N=3$ in agreement with the latest results obtained with the Conformal Bootstrap. We also study an other phase of the $O(N)\times O(2)$ models, called the sinusoidal phase, qualitatively confirming earlier perturbative results., Comment: 21 pages, 16 figures, 1 supplementary material

The measurement of type Ia supernovae magnitudes provides cosmological distances, which can be used to constrain dark energy parameters. Large photometric surveys require a substantial improvement in the calibration precision of their photometry to reduce systematic uncertainties in cosmological constraints. The StarDICE experiment is designed to establish accurate broadband flux references for these surveys, aiming for sub-percent precision in magnitude measurements. This requires a precise measurement of the filter bandpasses of both the StarDICE and survey instruments with sub-nanometer accuracy. To that end, we have developed the Collimated Beam Projector (CBP), an optical device capable of calibrating the throughput of an astronomical telescope and of its filters. The CBP is built from a tunable laser source and a reversed telescope to emit a parallel monochromatic light beam that is continuously monitored in flux and wavelength. The CBP output light flux is measured using a large area photodiode, previously calibrated relative to a NIST photodiode. We derive the StarDICE telescope throughput and filter transmissions from the CBP measurements, anchoring it to the absolute calibration provided by the NIST. After analyzing the systematic uncertainties, we achieved sub-nanometer accuracy in determining filter central wavelengths, measured each filter transmission with a precision of 0.5% per 1nm bin, and detected out-of-band leakages at 0.01%. Furthermore, we have synthesized the equivalent transmission for full pupil illumination from four sample positions in the StarDICE telescope mirror, with an accuracy of approximately 0.2nm for central wavelengths and 7mmag for broadband fluxes. We demonstrated our ability to characterize a telescope throughput down to the mmag, and paved the way for future developments, such as a portable CBP version for in-situ transmission monitoring., Comment: 29 pages, 50 figures

In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering technique is conceptually interesting for integration in MLOps pipelines deploying trustworthy machine learning models in critical sectors like energy. Additionally, we open the first dataset showcasing localized critical peak rebate demand response in a northern climate. We demonstrate the capabilities of our method on synthetic datasets as well as standard AD datasets and use it in the making of a first benchmark for our open-source localized critical peak rebate dataset.

We report the discovery of a peculiar X-ray transient, EP240408a, by Einstein Probe (EP) and follow-up studies made with EP, Swift, NICER, GROND, ATCA and other ground-based multi-wavelength telescopes. The new transient was first detected with Wide-field X-ray Telescope (WXT) on board EP on April 8th, 2024, manifested in an intense yet brief X-ray flare lasting for 12 seconds. The flare reached a peak flux of 3.9x10^(-9) erg/cm2/s in 0.5-4 keV, about 300 times brighter than the underlying X-ray emission detected throughout the observation. Rapid and more precise follow-up observations by EP/FXT, Swift and NICER confirmed the finding of this new transient. Its X-ray spectrum is non-thermal in 0.5-10 keV, with a power-law photon index varying within 1.8-2.5. The X-ray light curve shows a plateau lasting for about 4 days, followed by a steep decay till becoming undetectable about 10 days after the initial detection. Based on its temporal property and constraints from previous EP observations, an unusual timescale in the range of 7-23 days is found for EP240408a, which is intermediate between the commonly found fast and long-term transients. No counterparts have been found in optical and near-infrared, with the earliest observation at 17 hours after the initial X-ray detection, suggestive of intrinsically weak emission in these bands. We demonstrate that the remarkable properties of EP240408a are inconsistent with any of the transient types known so far, by comparison with, in particular, jetted tidal disruption events, gamma-ray bursts, X-ray binaries and fast blue optical transients. The nature of EP240408a thus remains an enigma. We suggest that EP240408a may represent a new type of transients with intermediate timescales of the order of about 10 days. The detection and follow-ups of more of such objects are essential for revealing their origin., Comment: 25 pages, 11 figures

In this study, we provide a full experimental characterization of the parametric longitudinal coupling between a CMOS charge qubit and an off-chip RF resonator. Following Corrigan et al, Phys. Rev. Applied 20, 064005 (2023), we activate parametric longitudinal coupling by driving the charge qubit at the resonator frequency. Managing the crosstalk between the drive applied to the qubit and the resonator allows for the systematic study of the dependence of the longitudinal and dispersive charge-photon couplings on the qubit-resonator detuning and the applied drive. Our experimental estimations of the charge-photon couplings are perfectly reproduced by theoretical simple formulas, without relying on any fitting parameter. We go further by showing a parametric displacement of the resonator's steady state, conditional on the qubit state, and the insensitivity of the longitudinal coupling constant on the photon population of the resonator. Our results open to the exploration of the photon-mediated longitudinal readout and coupling of multiple and distant spins, with long coherent times, in hybrid CMOS cQED architectures., Comment: 11 pages, 12 figures

With the emergence of wireless sensor networks (WSNs), many traditional signal processing tasks are required to be computed in a distributed fashion, without transmissions of the raw data to a centralized processing unit, due to the limited energy and bandwidth resources available to the sensors. In this paper, we propose a distributed independent component analysis (ICA) algorithm, which aims at identifying the original signal sources based on observations of their mixtures measured at various sensor nodes. One of the most commonly used ICA algorithms is known as FastICA, which requires a spatial pre-whitening operation in the first step of the algorithm. Such a pre-whitening across all nodes of a WSN is impossible in a bandwidth-constrained distributed setting as it requires to correlate each channel with each other channel in the WSN. We show that an explicit network-wide pre-whitening step can be circumvented by leveraging the properties of the so-called Distributed Adaptive Signal Fusion (DASF) framework. Despite the lack of such a network-wide pre-whitening, we can still obtain the $Q$ least Gaussian independent components of the centralized ICA solution, where $Q$ scales linearly with the required communication load., Comment: 5 pages

Habitable Worlds Observatory (HWO) will search for biosignatures from Earth-size exoplanets in the habitable zones of nearby stars. The wavelength range for biosignatures used by the HabEx and LUVOIR mission concept studies was 200 nm to 2 microns and, as such, this is a candidate wavelength range for HWO. The visible wavelength range (500-1000 nm) provides for detection of water, oxygen, and Raleigh scattering; the near-ultraviolet is valuable for detection of ozone; and the near-infrared enables detection of carbon dioxide and methane for Earth-like atmospheres. Damiano et al. 2023 showed the significant improvement in spectral retrieval reliability when the NUV and NIR are both used with the visible. However, the challenge of the NUV, in addition to the technological and engineering challenges of starlight suppression in the NUV, is the drop in flux of host stars. In the NIR, the challenge is the geometric access to the habitable zone due to the wavelength dependency of the inner working angle limit of coronagraphs. For these reasons, exoplanet yields are lower in the NUV and NIR than in the visible (Morgan et al. 2023, Morgan et al. 2024) and some instrument parameters are more critical for improving NUV and NIR yields than others. In this paper we present a new capability for performing a large number of end-to-end yield modeling simulations to enable large, multivariate parameter sweeps. We utilize this capability to calculate the Visible, NIR, and NUV yield sensitivities to the instrument parameters: aperture diameter, coronagraph core throughput, contrast, and inner working angle (IWA). We find that parameter interactions are important in determining yield, the most important of which is the interaction between contrast and IWA, but that the strength of that interaction is different in each of the three wavebands., Comment: 20 pages, 11 figures, 5 tables

In this paper, we develop the notion of a Morse sequence, which provides an alternative approach to discrete Morse theory, and which is both simple and effective. A Morse sequence on a finite simplicial complex is a sequence composed solely of two elementary operations, that is, expansions (the inverse of a collapse), and fillings (the inverse of a perforation). In a dual manner, a Morse sequence may be obtained by considering only collapses and perforations. Such a sequence is another way to represent the gradient vector field of an arbitrary discrete Morse function. To each Morse sequence, we assign a reference map and an extension map. A reference map associates a set of critical simplexes to each simplex of a given complex, and an extension map associates a set of simplexes to each critical simplex. By considering the boundary of each critical simplex, we obtain a chain complex from these maps, which corresponds precisely to the Morse complex. We show that, when restricted to homology, an extension map is the inverse of a reference map. Also we show that these two maps allow us to recover directly the isomorphism theorem between the homology of an object and the homology of its Morse complex. At last, we introduce the notion of a flow complex, which is based solely on extension maps. We prove that this notion is equivalent to the classical one based on gradient flows.

In recent years, Full-Waveform Inversion (FWI) has been extensively used to derive high-resolution subsurface velocity models from seismic data. However, due to the nonlinearity and ill-posed nature of the problem, FWI requires a good starting model to avoid producing non-physical solutions. Moreover, conventional optimization methods fail to quantify the uncertainty associated with the recovered solution, which is critical for decision-making processes. Bayesian inference offers an alternative approach as it directly or indirectly evaluates the posterior probability density function. For example, Markov Chain Monte Carlo (MCMC) methods generate multiple sample chains to characterize the solution's uncertainty. Despite their ability to theoretically handle any form of distribution, MCMC methods require many sampling steps; this limits their usage in high-dimensional problems with computationally intensive forward modeling, as is the FWI case. Variational Inference (VI), on the other hand, provides an approximate solution to the posterior distribution in the form of a parametric or non-parametric proposal distribution. Among the various algorithms used in VI, Stein Variational Gradient Descent (SVGD) is recognized for its ability to iteratively refine a set of samples to approximate the target distribution. However, mode and variance-collapse issues affect SVGD in high-dimensional inverse problems. This study aims to improve the performance of SVGD within the context of FWI by utilizing, for the first time, an annealed variant of SVGD and combining it with a multi-scale strategy. Additionally, we demonstrate that Principal Component Analysis (PCA) can be used to evaluate the performance of the optimization process. Clustering techniques are also employed to provide more rigorous and meaningful statistical analysis of the particles in the presence of multi-modal distributions.

This contribution shows how a-posteriori error estimators based on equilibrated fluxes - H(div) functions fulfilling the underlying conservation law - can be implemented in FEniCSx. Therefore, dolfinx_eqlb is introduced, its algorithmic structure is described and classical benchmarks for adaptive solution procedures for the Poisson problem and linear elasticity are presented.

Materials imperfections in Nniobium based superconducting quantum circuits, in particular, two-level-system (TLS) defects, are a major source of decoherence, ultimately limiting the performance of quantum computation and sensing. Thus, identifying and understanding the microscopic origin of possible TLS defects in these devices and developing strategies to eliminate them is key to superconducting qubit performance improvement. In this paper, we demonstrate the reduction of two-level system losses in three-dimensional superconducting radio frequency (SRF) niobium resonators by a 10-hour high vacuum (HV) heat treatment at 650{\deg}C, even after exposure to air and high pressure rinsing (HPR). By probing the effect of this annealing on niobium samples using X-ray photoelectron spectroscopy (XPS) and high-resolution scanning transmission electron microscopy (STEM), we witness an alteration of the native oxide composition re-grown after air exposure and HPR and the creation of nano-scale crystalline oxide regions, which correlates with the measured tenfold quality factor enhancement at low fields of the 1.3 GHz niobium resonator.

IceCloudNet is a novel method based on machine learning able to predict high-quality vertically resolved cloud ice water contents (IWC) and ice crystal number concentrations (N$_\textrm{ice}$). The predictions come at the spatio-temporal coverage and resolution of geostationary satellite observations (SEVIRI) and the vertical resolution of active satellite retrievals (DARDAR). IceCloudNet consists of a ConvNeXt-based U-Net and a 3D PatchGAN discriminator model and is trained by predicting DARDAR profiles from co-located SEVIRI images. Despite the sparse availability of DARDAR data due to its narrow overpass, IceCloudNet is able to predict cloud occurrence, spatial structure, and microphysical properties with high precision. The model has been applied to ten years of SEVIRI data, producing a dataset of vertically resolved IWC and N$_\textrm{ice}$ of clouds containing ice with a 3 kmx3 kmx240 mx15 minute resolution in a spatial domain of 30{\deg}W to 30{\deg}E and 30{\deg}S to 30{\deg}N. The produced dataset increases the availability of vertical cloud profiles, for the period when DARDAR is available, by more than six orders of magnitude and moreover, IceCloudNet is able to produce vertical cloud profiles beyond the lifetime of the recently ended satellite missions underlying DARDAR., Comment: his paper was submitted to Artificial Intelligence for the Earth Systems

The effectiveness of Recommender Systems (RS) is closely tied to the quality and distinctiveness of user profiles, yet despite many advancements in raw performance, the sensitivity of RS to user profile quality remains under-researched. This paper introduces novel information-theoretic measures for understanding recommender systems: a "surprise" measure quantifying users' deviations from popular choices, and a "conditional surprise" measure capturing user interaction coherence. We evaluate 7 recommendation algorithms across 9 datasets, revealing the relationships between our measures and standard performance metrics. Using a rigorous statistical framework, our analysis quantifies how much user profile density and information measures impact algorithm performance across domains. By segmenting users based on these measures, we achieve improved performance with reduced data and show that simpler algorithms can match complex ones for low-coherence users. Additionally, we employ our measures to analyze how well different recommendation algorithms maintain the coherence and diversity of user preferences in their predictions, providing insights into algorithm behavior. This work advances the theoretical understanding of user behavior and practical heuristics for personalized recommendation systems, promoting more efficient and adaptive architectures.

Semiconductor quantum dot arrays are a promising platform to perform spin-based error-corrected quantum computation with large numbers of qubits. However, due to the diverging number of possible charge configurations combined with the limited sensitivity of large-footprint charge sensors, achieving single-spin occupancy in each dot in a growing quantum dot array is exceedingly complex. Therefore, to scale-up a spin-based architecture we must change how individual charges are readout and controlled. Here, we demonstrate single-spin occupancy of each dot in a foundry-fabricated array by combining two methods. 1/ Loading a finite number of electrons into the quantum dot array; simplifying electrostatic tuning by isolating the array from the reservoirs. 2/ Deploying multiplex gate-based reflectometry to dispersively probe charge tunneling and spin states without charge sensors or reservoirs. Our isolated arrays probed by embedded multiplex readout can be readily electrostatically tuned. They are thus a viable, scalable approach for spin-based quantum architectures., Comment: 15 pages 5 figures

We investigate the location of the critical and tricritical points of the three-dimensional Blume-Capel model by analyzing the behavior of the first Lee-Yang zero, the density of partition function zeros, and higher-order cumulants of the magnetization. Our analysis is conducted through Monte Carlo simulations, intentionally using only small system sizes. We demonstrate that this approach yields excellent results for studying the critical behavior of the model. Our findings indicate that at the tricritical point, where logarithmic corrections are anticipated, the numerical results align closely with the theoretical exponents describing these corrections. These expected values are then employed to accurately determine the coordinates of the tricritical point. At the model's critical point, the corrections correspond to those of the three-dimensional Ising model criticality, which we also use to precisely ascertain the critical temperature at zero crystal field. Additionally, we utilize more traditional thermodynamic quantities to validate the self-consistency of our analysis., Comment: 12 pages, 9 figures

We show how to use diagrammatic techniques to compute the weak-coupling perturbation series of the self-consistent solution to a Dynamical Mean Field Theory (DMFT) problem. This approach constitutes an alternative to using diagrammatic techniques directly as an impurity solver. It allows one to bypass the need of multiple perturbative series resummations within the DMFT self-consistency loop. It can be applied at or out of equilibrium, with any diagrammatic formalism, such as real times, imaginary times, or Matsubara frequencies formalisms. As a proof of principle, we illustrate our method with the half-filled Hubbard model on the Bethe lattice in the DMFT approximation, using Quantum Quasi-Monte Carlo (QQMC) to obtain the impurity perturbation series on the real time axis., Comment: 8 pages, 3 figures

In response to the growing need for dynamic software features in automobiles, Software Defined Vehicles (SDVs) have emerged as a promising solution. They integrate dynamic onboard service management to handle the large variety of user-requested services during vehicle operation. Allocating onboard resources efficiently in this setting is a challenging task, as it requires a balance between maximizing user experience and guaranteeing mixed-criticality Quality-of-Service (QoS) network requirements. Our previous research introduced a dynamic resource-based onboard service orchestration algorithm. This algorithm considers real-time invehicle and V2X network health, along with onboard resource constraints, to globally select degraded modes for onboard applications. It maximizes the overall user experience at all times while being embeddable onboard for on-the-fly decisionmaking. A key enabler of this approach is the introduction of the Automotive eXperience Integrity Level (AXIL), a metric expressing runtime priority for non-safety-critical applications. While initial simulation results demonstrated the algorithm's effectiveness, a comprehensive performance assessment would greatly contribute in validating its industrial feasibility. In this current work, we present experimental results obtained from a dedicated test bench. These results illustrate, validate, and assess the practicality of our proposed solution, providing a solid foundation for the continued advancement of dynamic onboard service orchestration in SDVs., Comment: IEEE ITSC 2024, IEEE ITSS, Sep 2024, Edmonton, Canada

We revisit the critical behavior of classical frustrated systems using the nonperturbative renormalization group (NPRG) equation. Our study is performed within the local potential approximation of this equation to which is added the flow of the field renormalization. Our flow equations are functional to avoid possible artifacts coming from the field expansion of the fixed point potential which consists in keeping only a limited number of coupling constants. We explain in detail our numerical implementation, its advantages and the difficulties encountered in the vicinity of $d=2$. For $N$-component spins, the function $N_c(d)$ separating the regions of first and second order transitions in the $(d,N)$ plane is computed for $d$ between 4 and 2.3. Our results confirm what was previously found with cruder approximations of the NPRG equation and contradict both the fixed dimension perturbative approach and some of the results obtained within the conformal bootstrap approach., Comment: 26 pages, 6 figures. With minor revision on Introduction. arXiv admin note: substantial text overlap with arXiv:1604.01916

We show that the $n$-point, genus-$g$ correlation functions of topological recursion on any regular spectral curve with simple ramifications grow at most like $(2g - 2 + n)!$ as $g \rightarrow \infty$, which is the expected growth rate. This provides, in particular, an upper bound for many curve counting problems in large genus and serves as a preliminary step for a resurgence analysis., Comment: 33 pages. Comments are welcome! v2: added references

Modern organizations necessitate continuous business processes improvement to maintain efficiency, adaptability, and competitiveness. In the last few years, the Internet of Things, via the deployment of sensors and actuators, has heavily been adopted in organizational and industrial settings to monitor and automatize physical processes influencing and enhancing how people and organizations work. Such advancements are now pushed forward by the rise of the Digital Twin paradigm applied to organizational processes. Advanced ways of managing and maintaining business processes come within reach as there is a Digital Twin of a business process - a virtual replica with real-time capabilities of a real process occurring in an organization. Combining business process models with real-time data and simulation capabilities promises to provide a new way to guide day-to-day organization activities. However, integrating Digital Twins and business processes is a non-trivial task, presenting numerous challenges and ambiguities. This manifesto paper aims to contribute to the current state of the art by clarifying the relationship between business processes and Digital Twins, identifying ongoing research and open challenges, thereby shedding light on and driving future exploration of this innovative interplay., Comment: 15 pages, 3 figures, 1 table

We propose the use of latent space generative world models to address the covariate shift problem in autonomous driving. A world model is a neural network capable of predicting an agent's next state given past states and actions. By leveraging a world model during training, the driving policy effectively mitigates covariate shift without requiring an excessive amount of training data. During end-to-end training, our policy learns how to recover from errors by aligning with states observed in human demonstrations, so that at runtime it can recover from perturbations outside the training distribution. Additionally, we introduce a novel transformer-based perception encoder that employs multi-view cross-attention and a learned scene query. We present qualitative and quantitative results, demonstrating significant improvements upon prior state of the art in closed-loop testing in the CARLA simulator, as well as showing the ability to handle perturbations in both CARLA and NVIDIA's DRIVE Sim., Comment: 7 pages, 6 figures, for ICRA 2025 conference, for associated video file, see https://youtu.be/fO7RZ57gVxk

The intensity noise of a laser source represents one of the key factors limiting the ultimate sensitivity in laser-based systems for sensing and telecommunication. For advanced applications based on interferometry, the availability of a shot-noise-limited local oscillator is even more important for the effective feasibility of high-precision measurements. This is particularly crucial in quantum optics applications based on homodyne detection schemes to measure non-classical light states, such as squeezed states. This work deeply investigates and analyzes the intensity noise features of the most widely used mid-infrared semiconductor heterostructured lasers: quantum cascade and interband cascade lasers. For this purpose, a comprehensive comparison of three different continuous-wave lasers operating at room temperature around 4.5 {\mu}m wavelength is presented. First, a thorough electro-optical characterization is given, highlighting the differences and the shared common characteristics of the tested devices. Then, a detailed intensity noise analysis is reported, identifying their different noise operations with a particular reference to shot-noise-limited operations. Finally, some perspectives towards advanced applications are discussed., Comment: 17 pages, 5 figures

Selective attention enables humans to efficiently process visual stimuli by enhancing important locations or objects and filtering out irrelevant information. Locating visual attention is a fundamental problem in neuroscience with potential applications in brain-computer interfaces. Conventional paradigms often use synthetic stimuli or static images, but visual stimuli in real life contain smooth and highly irregular dynamics. In this study, we show that these irregular dynamics in natural videos can be decoded from electroencephalography (EEG) signals to perform selective visual attention decoding. To this end, we propose an experimental paradigm in which participants attend to one of two superimposed videos, each showing a center-aligned person performing a stage act. We then train a stimulus-informed decoder to extract EEG signal components that are correlated with the motion patterns of the attended object, and show that this decoder can be used on unseen data to detect which of both objects is attended. Eye movements are also found to be correlated to the motion patterns in the attended video, despite the spatial overlap between the target and the distractor. We further show that these eye movements do not dominantly drive the EEG-based decoding and that complementary information exists in EEG and gaze data. Moreover, our results indicate that EEG also captures information about unattended objects. To our knowledge, this study is the first to explore EEG-based selective visual attention decoding on natural videos, opening new possibilities for experiment design in related fields.

Flocking is a prime example of how robust collective behavior can emerge from simple interaction rules. The flocking transition has been studied extensively since the inception of the original Vicsek model. Here, we introduce a novel self-propelled particle model with quenched disorder in the pairwise alignment interaction couplings akin to a spin glass model. We show that the presence of quenched disorder can promote (rather than destroy) the emergence of global polar order. In particular, we show that our model can display a flocking phase even when the majority of the interaction couplings are anti-aligning. Activity is the key ingredient to reduce frustration in the system as it allows local particle clustering combined with self-organization of the particles to favor neighborhoods with strong cooperative interactions., Comment: 7 pages (3 figures) of main text + 10 pages (13 figures) of supplementary information

The X-ray Integral Field Unit (X-IFU) is an instrument of ESA's future NewAthena space observatory, with the goal to provide high-energy resolution ($<$ 4 eV at X-ray energies up to 7 keV) and high-spatial resolution (9") spectroscopic imaging over the X-ray energy range from 200 eV to 12 keV, by means of an array of about 1500 transition-edge sensors (TES) read out via SQUID time-division multiplexing (TDM). A TDM-based laboratory test-bed has been assembled at SRON, hosting an array of $75\times 75\ \upmu$m$^2$ TESs that are read out via 2-column $\times$ 32-row TDM. A system component that is critical to high-performance operation is the wiring harness that connects the room-temperature electronics to the cryogenic readout componentry. We report here on our characterization of such a test-bed, whose harness has a length close to what envisioned for X-IFU, which allowed to achieve a co-added energy resolution at a level of 2.7~eV FWHM at 6~keV via 32-row readout. In addition, we provide an outlook on the integration of TDM readout into the X-IFU Focal-Plane Assembly Development Model., Comment: Submitted for publication to Journal of Astronomical Telescopes, Instrumentation and Systems. arXiv admin note: text overlap with arXiv:2403.02978

Teleoperation of humanoid robots has long been a challenging domain, necessitating advances in both hardware and software to achieve seamless and intuitive control. This paper presents an integrated solution based on several elements: calibration-free motion capture and retargeting, low-latency fast whole-body kinematics streaming toolbox and high-bandwidth cycloidal actuators. Our motion retargeting approach stands out for its simplicity, requiring only 7 IMUs to generate full-body references for the robot. The kinematics streaming toolbox, ensures real-time, responsive control of the robot's movements, significantly reducing latency and enhancing operational efficiency. Additionally, the use of cycloidal actuators makes it possible to withstand high speeds and impacts with the environment. Together, these approaches contribute to a teleoperation framework that offers unprecedented performance. Experimental results on the humanoid robot Nadia demonstrate the effectiveness of the integrated system.

This paper introduces a simplified and design-oriented version of the EPFL HEMT model [1], focusing on the normalized transconductance-to-current characteristic (Gm/ID ). Relying on these figures, insights into GaN HEMT modeling in relation to technology offers a comprehensive understanding of the device behavior. Validation is achieved through measured transfer characteristics of GaN HEMTs fabricated at IMEC on a broad range of biases. This simplified approach should enable a simple and effective circuit design methodology with AlGaN/GaN HEMT heterostructures.

We tune the onset of optical response in aluminium kinetic inductance detectors from a natural cutoff frequency of 90 GHz to 60 GHz by applying an external magnetic field. The change in spectral response is due to the decrease of the superconducting gap, from 90 GHz at zero magnetic field to 60 GHz at a magnetic field of around 3 mT. We characterize the variation of the superconducting gap, the detector frequency shift and the internal quality factor as a function of the applied field. In principle, the magnetic field tunable response could be used to make spectroscopic measurements. In practice, the internal quality factor behaves hysteretically with the magnetic field due to the presence of vortices in the thin superconducting film. We conclude by discussing possible solutions to achieve spectroscopy measurements using kinetic inductance detectors and magnetic field.

In this work, we consider a general time-periodic linear transport equation with integral source term. We prove the existence of a Floquet principal eigenvalue, namely a real number such that the equation rescaled by this number admits nonnegative periodic solutions. We also prove the exponential attractiveness of these solutions. The method relies on general spectral results about positive operators.

Triton and Pluto are believed to share a common origin, both forming initially in the Kuiper Belt but Triton being later captured by Neptune. Both objects display similar sizes, densities, and atmospheric and surface ice composition, with the presence of volatile ices N2, CH4 and CO. Yet their appearance, including their surface albedo and ice distribution strongly differ. What can explain these different appearances? A first disparity is that Triton is experiencing significant tidal heating due to its orbit around Neptune, with subsequent resurfacing and a relatively flat surface, while Pluto is not tidally activated and displays a pronounced topography. Here we present long-term volatile transport simulations of Pluto and Triton, using the same initial conditions and volatile inventory, but with the known orbit and rotation of each object. The model reproduces, to first order, the observed volatile ice surface distribution on Pluto and Triton. Our results unambiguously demonstrate that obliquity is the main driver of the differences in surface appearance and in climate properties on Pluto and Triton, and give further support to the hypothesis that both objects had a common origin followed by a different dynamical history., Comment: 16 pages, 3 figures

Rare events play a crucial role in understanding complex systems. Characterizing and analyzing them in scale-invariant situations is challenging due to strong correlations. In this work, we focus on characterizing the tails of probability distribution functions (PDFs) for these systems. Using a variety of methods, perturbation theory, functional renormalization group, hierarchical models, large $n$ limit, and Monte Carlo simulations, we investigate universal rare events of critical $O(n)$ systems. Additionally, we explore the crossover from universal to nonuniversal behavior in PDF tails, extending Cram\'er's series to strongly correlated variables. Our findings highlight the universal and nonuniversal aspects of rare event statistics and challenge existing assumptions about power-law corrections to the leading stretched exponential decay in these tails., Comment: 17 pages, 8 figures

Computational modeling is an integral part of catalysis research. With it, new methodologies are being developed and implemented to improve the accuracy of simulations while reducing the computational cost. In particular, specific machine-learning techniques have been applied to build interatomic potential from ab initio results. Here, We report an energy-free machine-learning calculator that combines three individually trained neural networks to predict the energy and atomic forces of particulate matter. Three structures were investigated: a monometallic nanoparticle, a bimetallic nanoalloy, and a supported metal crystallites. Atomic energies were predicted via a graph neural network, leading to a mean absolute error (MAE) within 0.004 eV from Density Functional Theory (DFT) calculations. The task of predicting atomic forces was split over two feedforward networks, one predicting the force's norm and another its direction. The force prediction resulted in a MAE within 0.080 eV/A against DFT results. The interpretability of the graph neural network predictions was demonstrated by underlying the physics of the monometallic particle in the form of cohesion energy.

Causal machine learning (ML) promises to provide powerful tools for estimating individual treatment effects. Although causal ML methods are now well established, they still face the significant challenge of interpretability, which is crucial for medical applications. In this work, we propose a new algorithm based on the Conditional Permutation Importance (CPI) method for statistically rigorous variable importance assessment in the context of Conditional Average Treatment Effect (CATE) estimation. Our method termed PermuCATE is agnostic to both the meta-learner and the ML model used. Through theoretical analysis and empirical studies, we show that this approach provides a reliable measure of variable importance and exhibits lower variance compared to the standard Leave-One-Covariate-Out (LOCO) method. We illustrate how this property leads to increased statistical power, which is crucial for the application of explainable ML in small sample sizes or high-dimensional settings. We empirically demonstrate the benefits of our approach in various simulation scenarios, including previously proposed benchmarks as well as more complex settings with high-dimensional and correlated variables that require advanced CATE estimators.

\texttt{ml\_edm} is a Python 3 library, designed for early decision making of any learning tasks involving temporal/sequential data. The package is also modular, providing researchers an easy way to implement their own triggering strategy for classification, regression or any machine learning task. As of now, many Early Classification of Time Series (ECTS) state-of-the-art algorithms, are efficiently implemented in the library leveraging parallel computation. The syntax follows the one introduce in \texttt{scikit-learn}, making estimators and pipelines compatible with \texttt{ml\_edm}. This software is distributed over the BSD-3-Clause license, source code can be found at \url{https://github.com/ML-EDM/ml_edm}.

Synthetic images disseminated online significantly differ from those used during the training and evaluation of the state-of-the-art detectors. In this work, we analyze the performance of synthetic image detectors as deceptive synthetic images evolve throughout their online lifespan. Our study reveals that, despite advancements in the field, current state-of-the-art detectors struggle to distinguish between synthetic and real images in the wild. Moreover, we show that the time elapsed since the initial online appearance of a synthetic image negatively affects the performance of most detectors. Ultimately, by employing a retrieval-assisted detection approach, we demonstrate the feasibility to maintain initial detection performance throughout the whole online lifespan of an image and enhance the average detection efficacy across several state-of-the-art detectors by 6.7% and 7.8% for balanced accuracy and AUC metrics, respectively.

YbOCl is a representative member of the van der Waals layered honeycomb rare-earth chalcohalide REChX (RE = rare earth, Ch = O, S, Se, and Te, and X = F, Cl, Br, and I) family reported recently. Its spin ground state remains to be explored experimentally. In this paper, we have grown high-quality single crystals of YbOCl and conducted comprehensive thermodynamic, elastic, and inelastic neutron scattering experiments down to 50 mK. The experiments reveal an antiferromagnetic phase below 1.3 K, which is identified as a spin ground state with an intralayer ferromagnetic and interlayer antiferromagnetic ordering. By applying sophisticated numerical techniques to a honeycomb (nearest-neighbor)-triangle (next-nearest-neighbor) model Hamiltonian which accurately describes the highly anisotropic spin system, we are able to well simulate the experiments and determine the diagonal and off-diagonal spin-exchange interactions. The simulations give an antiferromagnetic Kitaev term comparable to the Heisenberg one. The experiments under magnetic fields allow us to establish a magnetic field-temperature phase diagram around the spin ground state. Most interestingly, a relatively small magnetic field (~ 0.3 to 3 T) can significantly suppress the antiferromagnetic order, suggesting an intriguing interplay of the Kitaev interaction and magnetic fields in the spin system. The present study provides fundamental insights into the highly anisotropic spin systems and opens a new window to look into Kitaev spin physics in a rare-earth-based system., Comment: 9 pages, 4 figures

Chaos makes isolated systems of many interacting particles quickly thermalize and forget about their past. Here, we show that quantum mechanics hinders chaos in many-body systems: although the quantum eigenstates are thermal and strongly entangled, exponentially many of them are scarred, that is, have an enlarged weight along underlying classical unstable periodic orbits. Scarring makes the system more likely to be found on an orbit it was initialized on, retaining a memory of its past and thus weakly breaking ergodicity, even at long times and despite the system being fully thermal. We demonstrate the ubiquity of quantum scarring in many-body systems by considering a large family of spin models, including some of the most popular ones from condensed matter physics. Our findings, at hand for modern quantum simulators, prove structure in spite of chaos in many-body quantum systems., Comment: 5 pages, 3 figures

Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs), aimed at overcoming the computational and scalability challenges inherent in classical GNNs that are powerful tools for analyzing data with complex relational structures but suffer from limitations such as high computational complexity and over-smoothing in large-scale applications. Quantum computing, leveraging principles like superposition and entanglement, offers a pathway to enhanced computational capabilities. This paper critically reviews the state-of-the-art in QGNNs, exploring various architectures. We discuss their applications across diverse fields such as high-energy physics, molecular chemistry, finance and earth sciences, highlighting the potential for quantum advantage. Additionally, we address the significant challenges faced by QGNNs, including noise, decoherence, and scalability issues, proposing potential strategies to mitigate these problems. This comprehensive review aims to provide a foundational understanding of QGNNs, fostering further research and development in this promising interdisciplinary field., Comment: 21 pages, 9 figures, 2 tables. arXiv admin note: text overlap with arXiv:1909.12264 by other authors