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We present several generative and predictive algorithms based on the RKHS (reproducing kernel Hilbert spaces) methodology, which most importantly are scalable in the following sense. It is well recognized that the RKHS methodology leads one to efficient and robust algorithms for numerous tasks in data science, statistics, and scientific computations.However, the standard implementations remain difficult to scale to encompass large data sets. In this paper, we introduce a simple and robust, divide-and-conquer approach which applies to large scale data sets and relies on suitably formulated, kernel-based algorithms: we distinguish between extrapolation, interpolation, and optimal transport steps. We explain how to select the best algorithms in specific applications thanks to some feedback, mainly consisting of perfomance criteria. Our main focus for the applications and challenging problems arising in industrial applications, such as the generation of mesh for efficient numerical simulations, the design of generators of conditional distributions, the transition probability matrix for statistic or stochastic applications, as well as various tasks of interest to the artificial intelligence community. Indeed, the proposed algorithms are relevant for supervised or unsupervised learning, generative methods, and reinforcement learning., Comment: 15 pages

We study the stellar mass function (SMF) and the co-evolution with dark matter halos via abundance matching in the largest redshift range to date $0.25$, we find increased abundances of massive (log$\, M_{\star}/M_{\odot}>10.5$) implying integrated star formation efficiencies (SFE) $\epsilon_{\star}\equiv M_{\star}\, f_{\rm b}^{-1} M_{\rm halo}^{-1} \gtrsim 0.5$. We find a flattening of the SMF at the high-mass end that is better described by a double power law at $z>5.5$. At $z \lesssim 5.5$ it transitions to a Schechter law which coincides with the emergence of the first massive quiescent galaxies in the Universe. We trace the cosmic stellar mass density (SMD) and infer the star formation rate density (SFRD), which at $z>7.5$ agrees remarkably with recent \JWST{} UV luminosity function-derived estimates. However, at $z \lesssim 3.5$, we find significant tension ($\sim 0.3$ dex) with the cosmic star formation (SF) history from instantaneous SF measures, the causes of which remain poorly understood. We infer the stellar-to-halo mass relation (SHMR) and the SFE from abundance matching out to $z=12$, finding a non-monotonic evolution. The SFE has the characteristic strong dependence with mass in the range of $0.02 - 0.2$, and mildly decreases at the low mass end out to $z\sim3.5$. At $z\sim3.5$ the SFE increases sharply from $\sim 0.1$ to approach high SFE of $0.8-1$ by $z\sim 10$ for log$(M_{\rm h}/M_{\odot})\approx11.5$, albeit with large uncertainties. Finally, we use the SHMR to track the SFE and stellar mass growth throughout the halo history and find that they do not grow at the same rate -- from the earliest times up until $z\sim3.5$ the halo growth rate outpaces galaxy assembly, but at $z>3.5$ halo growth stagnates and accumulated gas reservoirs keep the SF going and galaxies outpace halos.

Some metrological means, such as Shack-Hartmann, deflectometry sensors or fringe projection profilometry, measure the shape of an optical surface indirectly from slope measurements. Zonal shape reconstruction, a method to reconstruct shape with a high number of degrees of freedom, is used for all of these applications. It has risen in interest with the use of deflectometers for the acquisition of high resolution slope data for optical manufacturing, especially because shape reconstruction is limiting in terms of shape estimation error. Zonal reconstruction methods all rely on the choice of a data formation model, a basis on which the shape will be decomposed, and an estimator. In this paper, we first study the canonical Fried and Southwell models of the literature and analyze their limitations. We show that modeling the slope measurement by a point-wise derivative as they both do can induce a bias on the shape estimation, and that the bases on which the shape is decomposed are imposed because of this assumption. In the second part of this paper, we propose to build an unbiased model of the data formation, without constraints on the choice of the decomposition basis. We then compare these models to the canonical models of Fried and Southwell. Lastly, we perform a regularized MAP reconstruction, and compare the performance in terms of total shape error of this method to the state of the art for the Southwell and Fried models, first by simulation, then on experimental data. We demonstrate that the suggested method outperforms the canonical models in terms of total shape reconstruction error on a deflectometry measurement of the high-frequency content of a freeform mirror.

In the study of reading, eye-tracking technology offers unique insights into the time-course of how individuals extract information from text. A significant hurdle in using multi-line paragraph stimuli is the need to align eye gaze position with the correct line. This is made more difficult by positional noise in the eye-tracking data, primarily due to vertical drift, and often necessitates manual intervention. Such manual correction is labor-intensive, subjective, and limits the scalability of research efforts. As a result, automated solutions are desirable, especially those that do not require extensive technical skills and still allow close control over the outcome. To address this, we introduce GazeGenie: a comprehensive software solution designed specifically for researchers in eye-tracking studies on multi-line reading. Accessible via an intuitive web browser-based user interface and easily installed using Docker, GazeGenie streamlines the entire data processing pipeline from parsing fixations from raw data to calculation of word and sentence-based measures based on cleaned and drift-corrected fixations. The software's core features include the recently introduced Dual Input Stream Transformer (DIST) model and various classical algorithms all of which can be combined within a Wisdom of the Crowds (WOC) approach to enhance accuracy in fixation line-assignment. By providing an all-in-one solution for researchers, we hope to make automated fixation alignment more accessible, reducing researchers' reliance on manual intervention in vertical fixation alignment. This should lead to more accurate, efficient, and reproducible analyses of multi-line eye-movement data and pave the way to enabling larger scale studies to be carried out., Comment: This work has been submitted to the Springer Behavior Research Methods for possible publication

The stellar mass-star formation rate (M$_\star$ - SFR) plane is essential for distinguishing galaxy populations, but how galaxies move within this plane over cosmic time remains unclear. This study aims to describe galaxy migrations in the M$_\star$ - SFR plane by reconstructing star formation histories (SFHs) for a sample of galaxies out to redshift $z < 4$. This provides insights into the physical processes driving star formation. We use data from the COSMOS field, selecting 299131 galaxies at $z < 4$ with COSMOS-Web NIRCam data (m$_\mathrm{F444W} < 27$) over 0.54 deg$^2$. Using the SED modeling code CIGALE with non-parametric SFHs, we derive physical properties and migration vectors for these galaxies. These vectors describe the direction and velocity of evolutionary paths across the M$_\star$ - SFR plane. To assess the accuracy of these vectors, we compare them to results from the Horizon-AGN simulation. Galaxies within the main sequence show low migration amplitudes and dispersed movement directions, indicating oscillation within the main sequence. Most progenitors were already on the main sequence a billion years earlier. Starburst galaxies assembled half their mass in the last 350 Myr and originated from the main sequence. Passive galaxies show uniformly declining SFHs and include massive galaxies already in the passive region at $z = 3.5-4$, with increasing density over time. Using reconstructed SFHs up to $z < 4$, we propose a coherent picture of how galaxies migrate over cosmic time in the M$_\star$ - SFR plane, highlighting the connection between major phases in the star-formation history.

This article presents a new approach based on MiniRocket, called SelF-Rocket, for fast time series classification (TSC). Unlike existing approaches based on random convolution kernels, it dynamically selects the best couple of input representations and pooling operator during the training process. SelF-Rocket achieves state-of-the-art accuracy on the University of California Riverside (UCR) TSC benchmark datasets.

Potential patches are responsible for a force between closely-spaced objects that forms a parasitic contribution to sensitive force measurements. Existing analytical models cannot account for the patch force in the 3D geometries of real experiments. Here, we present a finite-element method model to evaluate the impact of patches in arbitrary geometries. First, we test our model against the plate-plate and sphere-plate geometries, for which the exact solutions are known. Then, we apply it to the analytically unwieldy case of plate-concave plate that is relevant for Casimir force measurements. This work provides a reliable estimation of the parasitic contribution from random potential patches., Comment: 4 pages, 3 figures

This study presents a two-level Deep Domain Decomposition Method (Deep-DDM) augmented with a coarse-level network for solving boundary value problems using physics-informed neural networks (PINNs). The addition of the coarse level network improves scalability and convergence rates compared to the single level method. Tested on a Poisson equation with Dirichlet boundary conditions, the two-level deep DDM demonstrates superior performance, maintaining efficient convergence regardless of the number of subdomains. This advance provides a more scalable and effective approach to solving complex partial differential equations with machine learning., Comment: Preprint proceeding format

High-resolution spectrographs open a detailed window onto the atmospheres of stars and planets. As the number of systems observed with different instruments grows, it is crucial to develop a standard in analyzing spectral time series of exoplanet transits and occultations, for the benefit of reproducibility. Here, we introduce the ANTARESS workflow, a set of methods aimed at processing high-resolution spectroscopy datasets in a robust way and extracting accurate exoplanetary and stellar spectra. While a fast preliminary analysis can be run on order-merged 1D spectra and cross-correlation functions (CCFs), the workflow was optimally designed for extracted 2D echelle spectra to remain close to the original detector counts, limit the spectral resampling, and propagate the correlated noise. Input data from multiple instruments and epochs were corrected for relevant environmental and instrumental effects, processed homogeneously, and analyzed independently or jointly. In this first paper, we show how planet-occulted stellar spectra extracted along the transit chord and cleaned from planetary contamination provide a direct comparison with theoretical stellar models and enable a spectral and spatial mapping of the photosphere. We illustrate this application of the workflow to archival ESPRESSO data, using the Rossiter-McLaughlin effect Revolutions (RMR) technique to confirm the spin-orbit alignment of HD\,209458b and unveil biases in WASP-76b's published orbital architecture. Because the workflow is modular and its concepts are general, it can support new methods and be extended to additional spectrographs to find a range of applications beyond the proposed scope. In a companion paper, we will present how planet-occulted spectra can be processed further to extract and analyze planetary spectra decontaminated from the star, providing clean and direct measurements of atmospheric properties., Comment: 30 pages and 30 figures (plus Appendix). Accepted for publication in A&A

Li-containing argyrodites represent a promising family of Li-ion conductors with several derived compounds exhibiting room-temperature ionic conductivity > 1 mS/cm and making them attractive as potential candidates as electrolytes in solid-state Li-ion batteries. Starting from the parent phase Li7PS6, several cation and anion substitution strategies have been attempted to increase the conductivity of Li ions. Nonetheless, a detailed understanding of the thermodynamics of native defects and doping of Li argyrodite and their effect on the ionic conductivity of Li is missing. Here, we report a comprehensive computational study of defect chemistry of the parent phase Li7PS6 in both intrinsic and extrinsic regimes, using a newly developed workflow to automate the computations of several defect formation energies in a thermodynamically consistent framework. Our findings agree with known experimental findings, rule out several unfavorable aliovalent dopants, narrowing down the potential promising candidates that can be tested experimentally. We also find that cation-anion co-doping can provide a powerful strategy to further optimize the composition of argyrodite. In particular, Si-F co-doping is predicted to be thermodynamically favorable; this could lead to the synthesis of the first F-doped Li-containing argyrodite. Finally, using DeePMD neural networks, we have mapped the ionic conductivity landscape as function of the concentration of the most promising cation and anion dopants identified from the defect calculations, and identified the most promising region in the compositional space with high Li conductivity that can be explored experimentally.

Joint models have proven to be an effective approach for uncovering potentially hidden connections between various types of outcomes, mainly continuous, time-to-event, and binary. Typically, longitudinal continuous outcomes are characterized by linear mixed-effects models, survival outcomes are described by proportional hazards models, and the link between outcomes are captured by shared random effects. Other modeling variations include generalized linear mixed-effects models for longitudinal data and logistic regression when a binary outcome is present, rather than time until an event of interest. However, in a clinical research setting, one might be interested in modeling the physician's chosen treatment based on the patient's medical history in order to identify prognostic factors. In this situation, there are often multiple treatment options, requiring the use of a multiclass classification approach. Inspired by this context, we develop a Bayesian joint model for longitudinal and categorical data. In particular, our motivation comes from a multiple myeloma study, in which biomarkers display nonlinear trajectories that are well captured through bi-exponential submodels, where patient-level information is shared with the categorical submodel. We also present a variable importance strategy for ranking prognostic factors. We apply our proposal and a competing model to the multiple myeloma data, compare the variable importance and inferential results for both models, and illustrate patient-level interpretations using our joint model., Comment: 26 pages, 5 figures

Tasks at the intersection of vision and language have had a profound impact in advancing the capabilities of vision-language models such as dialog-based assistants. However, models trained on existing tasks are largely limited to turn-based interactions, where each turn must be stepped (i.e., prompted) by the user. Open-ended, asynchronous interactions where an AI model may proactively deliver timely responses or feedback based on the unfolding situation in real-time are an open challenge. In this work, we present the QEVD benchmark and dataset which explores human-AI interaction in the challenging, yet controlled, real-world domain of fitness coaching - a task which intrinsically requires monitoring live user activity and providing timely feedback. It is the first benchmark that requires assistive vision-language models to recognize complex human actions, identify mistakes grounded in those actions, and provide appropriate feedback. Our experiments reveal the limitations of existing state of the art vision-language models for such asynchronous situated interactions. Motivated by this, we propose a simple end-to-end streaming baseline that can respond asynchronously to human actions with appropriate feedbacks at the appropriate time., Comment: The benchmark and dataset is available here: https://developer.qualcomm.com/software/ai-datasets/qevd

The implementation of road user models that realistically reproduce a credible behavior in a multi-agentsimulation is still an open problem. A data-driven approach consists on to deduce behaviors that may exist in real situation to obtain different types of trajectories from a large set of observations. The data, and its classification, could then be used to train models capable to extrapolate such behavior. Cars and two different types of Vulnerable Road Users (VRU) will be considered by the trajectory clustering methods proposed: pedestrians and cyclists. The results reported here evaluate methods to extract well-defined trajectory classes from raw data without the use of map information while also separating ''eccentric'' or incomplete trajectories from the ones that are complete and representative in any scenario. Two environments will serve as test for the methods develop, three different intersections and one roundabout. The resulting clusters of trajectories can then be used for prediction or learning tasks or discarded if it is composed by outliers.

We report a study of a cavity optomechanical system driven by narrow-band electromagnetic fields, which are applied either in the form of uncorrelated noise, or as a more structured spectrum. The bandwidth of the driving spectra is smaller than the mechanical resonant frequency, and thus we can describe the resulting physics using concepts familiar from regular cavity optomechanics in the resolved-sideband limit. With a blue-detuned noise driving, the noise-induced interaction leads to anti-damping of the mechanical oscillator, and a self-oscillation threshold at an average noise power that is comparable to that of a coherent driving tone. This process can be seen as noise-induced dynamical amplification of mechanical motion. However, when the noise bandwidth is reduced down to the order of the mechanical damping, we discover a large shift of the power threshold of self-oscillation. This is due to the oscillator adiabatically following the instantaneous noise profile. In addition to blue-detuned noise driving, we investigate narrow-band driving consisting of two coherent drive tones nearby in frequency. Also in these cases, we observe deviations from a naive optomechanical description relying only on the tones' frequencies and powers.

Predicting cancer-associated clinical events is challenging in oncology. In Multiple Myeloma (MM), a cancer of plasma cells, disease progression is determined by changes in biomarkers, such as serum concentration of the paraprotein secreted by plasma cells (M-protein). Therefore, the time-dependent behaviour of M-protein and the transition across lines of therapy (LoT) that may be a consequence of disease progression should be accounted for in statistical models to predict relevant clinical outcomes. Furthermore, it is important to understand the contribution of the patterns of longitudinal biomarkers, upon each LoT initiation, to time-to-death or time-to-next-LoT. Motivated by these challenges, we propose a Bayesian joint model for trajectories of multiple longitudinal biomarkers, such as M-protein, and the competing risks of death and transition to next LoT. Additionally, we explore two estimation approaches for our joint model: simultaneous estimation of all parameters (joint estimation) and sequential estimation of parameters using a corrected two-stage strategy aiming to reduce computational time. Our proposed model and estimation methods are applied to a retrospective cohort study from a real-world database of patients diagnosed with MM in the US from January 2015 to February 2022. We split the data into training and test sets in order to validate the joint model using both estimation approaches and make dynamic predictions of times until clinical events of interest, informed by longitudinally measured biomarkers and baseline variables available up to the time of prediction., Comment: 38 pages, 13 figures

For three applications of central interest in finance, we demonstrate the relevance of numerical algorithms based on reproducing kernel Hilbert space (RKHS) techniques. Three use cases are investigated. First, we show that extrapolating from few pricer examples leads to sufficiently accurate and computationally efficient results so that our algorithm can serve as a pricing framework. The second use case concerns reverse stress testing, which is formulated as an inversion function problem and is treated here via an optimal transport technique in combination with the notions of kernel-based encoders, decoders, and generators. Third, we show that standard techniques for time series analysis can be enhanced by using the proposed generative algorithms. Namely, we use our algorithm in order to extend the validity of any given quantitative model. Our approach allows for conditional analysis as well as for escaping the `Gaussian world'. This latter property is illustrated here with a portfolio investment strategy., Comment: 9 pages

We quantify the stellar rotation of galaxies by computing the $\lambda_{R}$ parameter, a proxy for the stellar angular momentum in a sample of 106 galaxies with redshift 0.1 $<$ z $<$ 0.8 and stellar masses from $\sim$10$^{7.5}$ to 10$^{11.8}$ M$_{\odot}$. The sample is located in the CANDELS/GOODS-S and COSMOS fields, and it was observed by various MUSE surveys. We create stellar velocity and velocity dispersion maps using a full-spectrum fitting technique, covering spatially $\sim$2$R_{e}$ for the galaxies. We study the impact of the atmospheric seeing on the spin parameter and apply corrections when pertinent. Through the analysis of the $\lambda_{R}-\epsilon$ diagram, we notice that the fraction of round and massive galaxies increases with redshift. We lack galaxies with $\lambda_{R}$ < 0.1 in the sample and we find only one potential, but uncertain, low-mass slow rotator at z $\sim0.3$. Moreover, we do not see an evident evolution or trend in the stellar angular momentum with redshift. We characterize the sample environment using two indicators: a local estimator based on the Voronoi tesselation method, and a global estimator derived by the use of the Friends-of-Friends algorithm. We find no correlation between the environment and $\lambda_{R}$ given that we are not probing dense regions or massive galaxy structures. We also analyze the kinematic maps of the sample finding that about 40$\%$ of galaxies are consistent with being regular rotators, having rotating stellar discs with flat velocity dispersion maps, while $\sim20\%$ of galaxies have complex velocity maps and can be identified as non-regular rotators in spite of their $\lambda_{R}$ values. For the remaining galaxies the classification is uncertain. As we lack galaxies with $\lambda_{R}$< 0.1, we are not able to identify when galaxies become slow rotators within the surveyed environments, area and redshift range., Comment: 26 pages, 18 figures, full abstract on the paper

Star formation in the early Universe has left its imprint on the chemistry of observable stars in galaxies. We derive elemental abundances and the slope of the low-mass end of the initial mass function (IMF) for a sample of 25 very massive galaxies, separated into brightest cluster galaxies (BCGs) and their massive satellites. The elemental abundances of BGCs and their satellites are similar, but for some elements, satellite galaxies show a correlation with the global velocity dispersion. Using a subset of derived elemental abundances, we model the star formation histories of these galaxies with chemical evolution models, and predict the high-mass end slope of the IMF and star formation timescales. The high-mass end IMF slope of the satellite galaxies correlates with the global velocity dispersion. The low- and the high-mass end IMF slopes are weakly correlated in a general sense that top heavy IMFs are paired with bottom heavy IMFs. Our results do not necessarily imply that the IMF was simultaneously bottom and top heavy. Instead, our findings can be considered consistent with a temporal variation in the IMF, where, for massive galaxies, the high-mass end IMF slope is representative of the very early age and the low-mass end slope of the later star formation. The small but noticeable differences between the BCGs and the satellites in terms of their elemental abundances and IMF slopes, together with their stellar kinematical properties, suggest somewhat different formation pathways, where BCGs experience more major, gas-free mergers., Comment: Accepted for publication in MNRAS, 27 pages, 18 figures, 5 tables (including Appendices and supplementary material)

Image sensors hold a pivotal role in society due to their ability to capture vast amounts of information. Traditionally, image sensors are opaque due to light absorption in both the pixels and the read-out electronics that are stacked on top of each other. Making image sensors visibly transparent would have a far-reaching impact in numerous areas such as human-computer interfaces, smart displays, and both augmented and virtual reality. In this paper, we present the development and analysis of the first semi-transparent image sensor and its applicability as an eye-tracking device. The device consists of an 8x8 array of semi-transparent photodetectors and electrodes disposed on a fully transparent substrate. Each pixel of the array has a size of 60 x 140 {\mu}m and an optical transparency of 85-95%. Pixels have a high sensitivity, with more than 90% of them showing a noise equivalent irradiance < 10-4 W/m2 for wavelengths of 637 nm. As the semi-transparent photodetectors have a large amount of built-in gain, the opaque read-out electronics can be placed far away from the detector array to ensure maximum transparency and fill factor. Indeed, the operation and appearance of transparent image sensors present a fundamental shift in how we think about cameras and imaging, as these devices can be concealed in plain sight.

This monograph offers an introduction to a collection of numerical algorithms implemented in the library CodPy (an acronym that stands for the Curse Of Dimensionality in PYthon), which has found widespread applications across various areas, including machine learning, statistics, and computational physics. We develop here a strategy based on the theory of reproducing kernel Hilbert spaces (RKHS) and the theory of optimal transport. Initially designed for mathematical finance, this library has since been enhanced and broadened to be applicable to problems arising in engineering and industry. In order to present the general principles and techniques employed in CodPy and its applications, we have structured this monograph into two main parts. First of all, we focus on the fundamental principles of kernel-based representations of data and solutions, also that the presentation therein is supplemented with illustrative examples only. Next, we discuss the application of these principles to many classes of concrete problems, spanning from the numerical approximation of partial differential equations to (supervised, unsupervised) machine learning, extending to generative methods with a focus on stochastic aspects., Comment: 135 pages

Resolved stellar morphology of z>1 galaxies was inaccessible before JWST. This limitation, due to the impact of dust on rest-frame UV light, had withheld major observational conclusions required to understand the importance of clumps in galaxy evolution. Essentially independent of this issue, we use the rest-frame near-IR for a stellar-mass dependent clump detection method and determine reliable estimations of selection effects. We exploit publicly available JWST/NIRCam and HST/ACS imaging data from CEERS, to create a stellar-mass based picture of clumps in a mass-complete sample of 418 galaxies within a wide wavelength coverage of 0.5-4.6${\mu}$m and a redshift window of 1

In 2022-23, SRI Education conducted an evaluation of a pilot implementation of the Maine Early Childhood Consultation Partnership® (ECCP®) for Maine's Department of Health and Human Services, Office of Child and Family Services. Created and developed by Advanced Behavioral Health, Inc., ECCP® is an evidence-based, manualized model of infant and early childhood mental health consultation (IECMHC) that builds capacity of the early care and education workforce to address young children's social-emotional and behavioral needs through intensive, time-limited consultation with a trained mental health consultant. SRI Education collected and analyzed data from state leaders, consultants, trainers, supervisors, teachers and parents/guardians in five counties to assess the pilot implementation and provide recommendations for statewide scale up and sustainability of the model. [This report was prepared for Office of Child & Family Services, Maine Department of Health and Human Services.]

Battery-constrained power consumption, compute limitations, and high frame rate requirements in head-mounted displays present unique challenges in the drive to present increasingly immersive and comfortable imagery in virtual reality. However, humans are not equally sensitive to all regions of the visual field, and perceptually-optimized rendering techniques are increasingly utilized to address these bottlenecks. Many of these techniques are gaze-contingent and often render reduced detail away from a user's fixation. Such techniques are dependent on spatio-temporally-accurate gaze tracking and can result in obvious visual artifacts when eye tracking is inaccurate. In this work we present a gaze-contingent rendering technique which only requires saccade detection, bypassing the need for highly-accurate eye tracking. In our first experiment, we show that visual acuity is reduced for several hundred milliseconds after a saccade. In our second experiment, we use these results to reduce the rendered image resolution after saccades in a controlled psychophysical setup, and find that observers cannot discriminate between saccade-contingent reduced-resolution rendering and full-resolution rendering. Finally, in our third experiment, we introduce a 90 pixels per degree headset and validate our saccade-contingent rendering method under typical VR viewing conditions., Comment: main paper and supplementary materials

This paper introduces a conversion matrix method for linear periodically time-variant (LPTV) digital phase-locked loop (DPLL) phase noise modeling that offers precise and computationally efficient results to enable rapid design iteration and optimization. Unlike many previous studies, which either assume linear time-invariance (LTI) and therefore overlook phase noise aliasing effects, or solve LPTV systems with noise folding and multiple sampling rate conversions that heightens modeling and computational complexity, the proposed conversion matrix method allows the designer to represent LPTV systems using intuitive LTI-like transfer functions with excellent accuracy. Additionally, the uncorrelated upsampling method addresses the cross-correlated spectrum of cyclostationary noise sources by a simple matrix multiplication. This eliminates the need to consider the beat frequency of the upsampled noise source and the system with different sampling rates, thus improving computational efficiency. The proposed algorithm is applied to modeling a integer-N DPLL with time-varying proportional loop gain, and the modeling accuracy is validated with Simulink transient simulations., Comment: 13 pages, 24 figures

This paper compares two imperfect repair models for a degrading system, with deterioration level modeled by a non homogeneous gamma process. Both models consider instantaneous and periodic repairs. The first model assumes that a repair reduces the degradation of the system accumulated from the last maintenance action. The second model considers a virtual age model and assumes that a repair reduces the age accumulated by the system since the last maintenance action. Stochastic comparison results between the two resulting processes are obtained. Furthermore, a specific case is analyzed, where the two repair models provide identical expected deterioration levels at maintenance times. Finally, two optimal maintenance strategies are explored, considering the two models of repair.

Uplift modeling, also known as individual treatment effect (ITE) estimation, is an important approach for data-driven decision making that aims to identify the causal impact of an intervention on individuals. This paper introduces a new benchmark dataset for uplift modeling focused on churn prediction, coming from a telecom company in Belgium, Orange Belgium. Churn, in this context, refers to customers terminating their subscription to the telecom service. This is the first publicly available dataset offering the possibility to evaluate the efficiency of uplift modeling on the churn prediction problem. Moreover, its unique characteristics make it more challenging than the few other public uplift datasets., Comment: 8 pages, 2 figures, 5 tables, post-proceedings of the ECML PKDD 2023 Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making

We study the validity of the dissipative Aw-Rascle system as a macroscopic model for pedestrian dynamics. The model uses a congestion term (a singular diffusion term) to enforce capacity constraints in the crowd density while inducing a steering behaviour. Furthermore, we introduce a semi-implicit, structure-preserving, and asymptotic-preserving numerical scheme which can handle the numerical solution of the model efficiently. We perform the first numerical simulations of the dissipative Aw-Rascle system in one and two dimensions. We demonstrate the efficiency of the scheme in solving an array of numerical experiments, and we validate the model, ultimately showing that it correctly captures the fundamental diagram of pedestrian flow.

The purpose of the present study is to extend the simple concept of apparent coefficient of restitution, widely approached in the literature for the case of a single-contact-point between a sphere and a wall, to the case of bouncing whose complexity is increased due to the shape of the contacting object. Experiments are carried out with a finite-length cylinder, freely falling in a liquid at rest. Complex 3D motions of the cylinder with respect to the wall occur during bouncing, including multi-contact-points between the cylinder and the bottom as well as cavitation. We investigate numerical modelling of an idealized situation (2D infinite cylinder falling parallel to the wall) with 2D simulations where the fluid equations of motion were coupled to the particle equation of motion through an Immersed Boundary Method. The particle equation of motion is coupled to an elastic force to model bouncing, requiring a parameterization of the cut-off length (interpreted as a roughness) and the contact time (associated with the contact elasticity) used here to capture the experimental observations. The simulations confirmed that i) the departure of the coefficient of restitution from 0 is strictly dependent on the apparent roughness and ii) the coefficient of restitution depends on the contact time. Finally, we model the coefficient of restitution as the product of two contributions to the mechanical loss of energy: the collision-to-terminal velocity ratio ($V_c/V_t$) of the approach-phase and the rebound-to-collision velocity ratio ($-V_r/V_c$) of the contact-phase. This leads to a reasonably good prediction of the coefficient of restitution in the intermediate regime in $St$. This suggests the relevance of lumping the complex details of physical phenomena involved during contact into a simple concept based on the contact apparent roughness and elasticity., Comment: 22 pages, 12 figures, submitted to Physical Review Fluids

We present a study of the cool gas ($\approx 10^4$ K) traced by MgII absorptions around groups of galaxies in the MEGAFLOW survey. Using a combination of two algorithms we blindly identify 32 groups of more than 5 galaxies at $0.3 < z < 1.5$ with $10.7 < \log_{10}(M/\rm M_{\odot}) < 13.7$. Among them 26 can be used to study potential counterpart MgII absorptions. We report that 21 out of the total 120 MgII absorption systems present in MEGAFLOW are associated with groups. We observe that the MgII rest-frame equivalent width ($W^{2796}_r$) drops at an impact parameter of $\approx 150$ projected kpc from the closest galaxy and $\approx$ one virial radius from the identified group center indicating that MgII halos scale with the mass of the groups.The impact parameter where the covering fraction exceeds $50\%$ is $\log_{10}(b/\rm kpc) = 2.17 \pm 0.47$ $(2 \sigma)$ and $(b/R_{\rm vir}) = 1.67 \pm 0.98$, which is $\approx 3$ times larger than for field galaxies ($\log_{10}(b/\rm kpc)=1.67\pm0.15$). Finally, we estimate the cool gas column density profile in groups (from the $W^{2796}_r$) and show that its shape follows closely the typical dark matter column density profile for halos at similar redshift and masses.

We introduce the MUSE gAlaxy Groups in COSMOS (MAGIC) survey, which was built to study the impact of environment on galaxy evolution over the last 8 Gyr. It consists of 17 MUSE fields targeting 14 massive structures at intermediate redshift ($0.321.5$. The spectroscopic redshift completeness is high: in the redshift range of [OII] emitters ($0.25 \le z < 1.5$), where most of the groups are found, it globally reaches a maximum of 80% down to $z_{app}^{++}=25.9$, and locally decreases from $\sim 100$% to $\sim50$% in magnitude bins from $z_{app}^{++}=23-24$ to $z_{app}^{++}=25.5$. We find that the fraction of quiescent galaxies increases with local density and with the time spent in groups. A morphological dichotomy is also found between bulge-dominated quiescent and disk-dominated star-forming galaxies. As environment gets denser, the peak of the stellar mass distribution shifts towards $M_*>10^{10}~M_\odot$, and the fraction of galaxies with $M_*<10^9~M_\odot$ decreases significantly, even for star-forming galaxies. We also highlight peculiar features such as close groups, extended nebulae, and a gravitational arc. Our results suggest that galaxies are preprocessed in groups of increasing mass before entering rich groups and clusters. We publicly release two catalogs containing the properties of galaxies and groups, respectively., Comment: 27 pages, 22 figures, accepted for publication in A&A

We introduce a novel Dual Input Stream Transformer (DIST) for the challenging problem of assigning fixation points from eye-tracking data collected during passage reading to the line of text that the reader was actually focused on. This post-processing step is crucial for analysis of the reading data due to the presence of noise in the form of vertical drift. We evaluate DIST against eleven classical approaches on a comprehensive suite of nine diverse datasets. We demonstrate that combining multiple instances of the DIST model in an ensemble achieves high accuracy across all datasets. Further combining the DIST ensemble with the best classical approach yields an average accuracy of 98.17 %. Our approach presents a significant step towards addressing the bottleneck of manual line assignment in reading research. Through extensive analysis and ablation studies, we identify key factors that contribute to DIST's success, including the incorporation of line overlap features and the use of a second input stream. Via rigorous evaluation, we demonstrate that DIST is robust to various experimental setups, making it a safe first choice for practitioners in the field., Comment: This work has been submitted to the IEEE Transactions on pattern analysis and machine intelligence for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible