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Remote healthcare technology can help tackle societal issues by improving access to quality healthcare services and enhancing diagnoses through in-place monitoring. These services can be implemented through a combination of mobile devices, applications, wearable sensors, and other smart technology. It is paramount to handle sensitive data that is collected in ways that meet users' privacy expectations. We surveyed 384 people in Canada aged 20 to 93 years old to explore participants' comfort with data collection, sharing preferences, and potential privacy concerns related to remote healthcare technology. We explore these topics within the context of various healthcare scenarios including health emergencies and managing chronic health conditions.

We extend the formalism of Conjectural Variations games to Stackelberg games involving multiple leaders and a single follower. To solve these nonconvex games, a common assumption is that the leaders compute their strategies having perfect knowledge of the follower's best response. However, in practice, the leaders may have little to no knowledge about the other players' reactions. To deal with this lack of knowledge, we assume that each leader can form conjectures about the other players' best responses, and update its strategy relying on these conjectures. Our contributions are twofold: (i) On the theoretical side, we introduce the concept of Conjectural Stackelberg Equilibrium -- keeping our formalism conjecture agnostic -- with Stackelberg Equilibrium being a refinement of it. (ii) On the algorithmic side, we introduce a two-stage algorithm with guarantees of convergence, which allows the leaders to first learn conjectures on a training data set, and then update their strategies. Theoretical results are illustrated numerically.

In ultrathin ferromagnetic films sandwiched between two distinct heavy metal layers or between a heavy metal and an oxide layer, the Dzyaloshinskii-Moriya interaction (DMI) is recognized as being of interfacial origin. Its chirality and strength are determined by the properties of the adjacent heavy metals and the degree of oxidation at the interfaces. Here, we demonstrate that the chirality of the DMI can change solely with variations in the thickness of the ferromagnetic layer - an effect that has not been experimentally observed or explained until now. Our experimental observation in the trilayer system Ta/FeCoB/TaOx is supported by ab initio calculations: they reveal that variations in orbital filling and inter-atomic distances at the interface, driven by the number of ferromagnetic atomic layers, lead to an inversion of DMI chirality. This mechanism takes place for ferromagnetic layers with more than three atomic layers, for which the two interfaces start to be decoupled. We hence propose a new degree of freedom to tune DMI chirality and the associated chiral spin textures by tailoring crystal structure e.g. using strain or surface acoustic waves., Comment: 5 figures + supplementary materials

Dy$^{3+}$-doped silica glass whispering gallery mode microspheres are fabricated by fiber fusion splicing. They present an almost ideal spherical morphology with a radius ranging from 60 to 67 ${\mu}$m as determined by confocal laser microscopy. The host composition of the microsphere is close to that of the fiber core. The dopant Dy$^{3+}$ ions are uniformly distributed across the microsphere as evidenced by ${\mu}$-luminescence studies and present a luminescence lifetime. The Dy$^{3+}$-doped glass microspheres were excited via evanescent field coupling using a half-tapered fiber and a blue 450-nm GaN laser diode (direct pumping scheme). The yellow fluorescence of Dy$^{3+}$ ions is filtered by the whispering gallery modes (free spectral range: 0.5 nm for 67-${\mu}$m radius microsphere). The onset of stimulated emission is further observed highlighting the potential of such microresonators for narrow-linewidth light sources directly emitting visible light., Comment: 11 pages, 11 figures, journal article

This work introduces a novel methodology to assess the performance of Piezoelectric Energy Harvesters (PEHs) in order to study auxetic enhancement possibilities. For this purpose, a new approach for evaluating the intrinsic effective Electromechanical Coupling Coefficient (EMCC) of piezoelectric layers is presented. As the current assessment methods are questioned under resonance exposures, theoretical models are presented to suggest what characteristics the harvested power will depend on. A two axis graph is introduced to enable the comparison of different PEHs. The method is finally applied to PEHs with different types of substrates: filled, hollow and auxetic. First results show that, generally, auxetic structures might not increase the intrinsic EMCC but only improve the elastic energy ratio in the piezoelectric layers.

Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management. Speech analysis offers a non-intrusive and scalable screening method, particularly through narrative tasks in neuropsychological assessment tools. Traditional narrative analysis often focuses on local indicators in microstructure, such as word usage and syntax. While these features provide insights into language production abilities, they often fail to capture global narrative patterns, or microstructures. Macrostructures include coherence, thematic organization, and logical progressions, reflecting essential cognitive skills potentially critical for recognizing NCDs. Addressing this gap, we propose to investigate specific cognitive and linguistic challenges by analyzing topical shifts, temporal dynamics, and the coherence of narratives over time, aiming to reveal cognitive deficits by identifying narrative impairments, and exploring their impact on communication and cognition. The investigation is based on the CU-MARVEL Rabbit Story corpus, which comprises recordings of a story-telling task from 758 older adults. We developed two approaches: the Dynamic Topic Models (DTM)-based temporal analysis to examine the evolution of topics over time, and the Text-Image Temporal Alignment Network (TITAN) to evaluate the coherence between spoken narratives and visual stimuli. DTM-based approach validated the effectiveness of dynamic topic consistency as a macrostructural metric (F1=0.61, AUC=0.78). The TITAN approach achieved the highest performance (F1=0.72, AUC=0.81), surpassing established microstructural and macrostructural feature sets. Cross-comparison and regression tasks further demonstrated the effectiveness of proposed dynamic macrostructural modeling approaches for NCD detection., Comment: 12 pages, 8 figures

Autonomous high-speed navigation through large, complex environments requires real-time generation of agile trajectories that are dynamically feasible, collision-free, and satisfy state or actuator constraints. Most modern trajectory planning techniques rely on numerical optimization because high-quality, expressive trajectories that satisfy various constraints can be systematically computed. However, meeting computation time constraints and the potential for numerical instabilities can limit the use of optimization-based planners in safety-critical scenarios. This work presents an optimization-free planning framework that stitches short trajectory segments together with graph search to compute long range, expressive, and near-optimal trajectories in real-time. Our STITCHER algorithm is shown to outperform modern optimization-based planners through our innovative planning architecture and several algorithmic developments that make real-time planning possible. Extensive simulation testing is conducted to analyze the algorithmic components that make up STITCHER, and a thorough comparison with two state-of-the-art optimization planners is performed. It is shown STITCHER can generate trajectories through complex environments over long distances (tens of meters) with low computation times (milliseconds)., Comment: V1 Draft

In eXplainable Constraint Solving (XCS), it is common to extract a Minimal Unsatisfiable Subset (MUS) from a set of unsatisfiable constraints. This helps explain to a user why a constraint specification does not admit a solution. Finding MUSes can be computationally expensive for highly symmetric problems, as many combinations of constraints need to be considered. In the traditional context of solving satisfaction problems, symmetry has been well studied, and effective ways to detect and exploit symmetries during the search exist. However, in the setting of finding MUSes of unsatisfiable constraint programs, symmetries are understudied. In this paper, we take inspiration from existing symmetry-handling techniques and adapt well-known MUS-computation methods to exploit symmetries in the specification, speeding-up overall computation time. Our results display a significant reduction of runtime for our adapted algorithms compared to the baseline on symmetric problems., Comment: Accepted at AAAI25 conference

In industrial contexts, effective workforce allocation is crucial for operational efficiency. This paper presents an ongoing project focused on developing a decision-making tool designed for workforce allocation, emphasising the explainability to enhance its trustworthiness. Our objective is to create a system that not only optimises the allocation of teams to scheduled tasks but also provides clear, understandable explanations for its decisions, particularly in cases where the problem is infeasible. By incorporating human-in-the-loop mechanisms, the tool aims to enhance user trust and facilitate interactive conflict resolution. We implemented our approach on a prototype tool/digital demonstrator intended to be evaluated on a real industrial scenario both in terms of performance and user acceptability., Comment: Accepted for presentation at PTHG-24: The Seventh Workshop on Progress Towards the Holy Grail, part of the 30th International Conference on Principles and Practice of Constraint Programming. For more details, visit the workshop webpage: https://freuder.wordpress.com/progress-towards-the-holy-grail-workshops/pthg-24-the-seventh-workshop-on-progress-towards-the-holy-grail/

Two substantial technological advances have reshaped the public square in recent decades: first with the advent of the internet and second with the recent introduction of large language models (LLMs). LLMs offer opportunities for a paradigm shift towards more decentralized, participatory online spaces that can be used to facilitate deliberative dialogues at scale, but also create risks of exacerbating societal schisms. Here, we explore four applications of LLMs to improve digital public squares: collective dialogue systems, bridging systems, community moderation, and proof-of-humanity systems. Building on the input from over 70 civil society experts and technologists, we argue that LLMs both afford promising opportunities to shift the paradigm for conversations at scale and pose distinct risks for digital public squares. We lay out an agenda for future research and investments in AI that will strengthen digital public squares and safeguard against potential misuses of AI., Comment: 40 pages, 5 figures

Dynamic event prediction, using joint modeling of survival time and longitudinal variables, is extremely useful in personalized medicine. However, the estimation of joint models including many longitudinal markers is still a computational challenge because of the high number of random effects and parameters to be estimated. In this paper, we propose a model averaging strategy to combine predictions from several joint models for the event, including one longitudinal marker only or pairwise longitudinal markers. The prediction is computed as the weighted mean of the predictions from the one-marker or two-marker models, with the time-dependent weights estimated by minimizing the time-dependent Brier score. This method enables us to combine a large number of predictions issued from joint models to achieve a reliable and accurate individual prediction. Advantages and limits of the proposed methods are highlighted in a simulation study by comparison with the predictions from well-specified and misspecified all-marker joint models as well as the one-marker and two-marker joint models. Using the PBC2 data set, the method is used to predict the risk of death in patients with primary biliary cirrhosis. The method is also used to analyze a French cohort study called the 3C data. In our study, seventeen longitudinal markers are considered to predict the risk of death.

We propose an overview of the decentralized reconfiguration language Concerto-D through its Maude formalization. Concerto-D extends the already published Concerto language. Concerto-D improves on two different parameters compared with related work: the decentralized coordination of numerous local reconfiguration plans which avoid a single point of failure when considering unstable networks such as edge computing, or cyber-physical systems (CPS) for instance; and a mechanized formal semantics of the language with Maude which offers guarantees on the executability of the semantics. Throughout the paper, the Concerto-D language and its semantics are exemplified with a reconfiguration extracted from a real case study on a CPS. We rely on the Maude formal specification language, which is based on rewriting logic, and consequently perfectly suited for describing a concurrent model., Comment: In Proceedings ICE 2024, arXiv:2412.07570

Machine Learning (ML) models, such as deep neural networks, are widely applied in autonomous systems to perform complex perception tasks. New dependability challenges arise when ML predictions are used in safety-critical applications, like autonomous cars and surgical robots. Thus, the use of fault tolerance mechanisms, such as safety monitors, is essential to ensure the safe behavior of the system despite the occurrence of faults. This paper presents an extensive literature review on safety monitoring of perception functions using ML in a safety-critical context. In this review, we structure the existing literature to highlight key factors to consider when designing such monitors: threat identification, requirements elicitation, detection of failure, reaction, and evaluation. We also highlight the ongoing challenges associated with safety monitoring and suggest directions for future research., Comment: 25 pages, 2 figures

The creep behavior of an amorphous poly(etherimide) (PEI) polymer is investigated in the vicinity of its glass transition in a weakly non linear regime where the acceleration of the creep response is driven by local configurational rearrangements. From the time shifts of the creep compliance curves under stresses from 1 to 15~\si{\mega\pascal} and in the temperature range between $T_g -10K$ and $T_g$, where $T_g$ is the glass transition, we determine a macroscopic acceleration factor. The macroscopic acceleration is shown to vary as $e^{-(\Sigma/Y)^n} $ with $n=2 \pm 0.2$, where $\Sigma$ is the macroscopic stress and $Y$ is a decreasing function of compliance. Because at the beginning of creep, the stress is homogeneous, the macroscopic acceleration is thus similar to the local one, in agreement with the recent theory of Long \textit{et al.} (\textit{Phys. Rev. Mat.} (2018) \textbf{2}, 105601 ) which predicts $n=2$. For larger compliances, the decrease of the of $Y$ is interpreted as a signature of the development of stress disorder during creep., Comment: arXiv admin note: substantial text overlap with arXiv:2403.00355

Collecting multiple longitudinal measurements and time-to-event outcomes is a common practice in clinical and epidemiological studies, often focusing on exploring associations between them. Joint modeling is the standard analytical tool for such data, with several R packages available. However, as the number of longitudinal markers increases, the computational burden and convergence challenges make joint modeling increasingly impractical. This paper introduces a novel two-stage Bayesian approach to estimate joint models for multiple longitudinal measurements and time-to-event outcomes. The method builds on the standard two-stage framework but improves the initial stage by estimating a separate one-marker joint model for the event and each longitudinal marker, rather than relying on mixed models. These estimates are used to derive predictions of individual marker trajectories, avoiding biases from informative dropouts. In the second stage, a proportional hazards model is fitted, incorporating the predicted current values and slopes of the markers as time-dependent covariates. To address uncertainty in the first-stage predictions, a multiple imputation technique is employed when estimating the Cox model in the second stage. This two-stage method allows for the analysis of numerous longitudinal markers, which is often infeasible with traditional multi-marker joint modeling. The paper evaluates the approach through simulation studies and applies it to the PBC2 dataset and a real-world dementia dataset containing 17 longitudinal markers. An R package, TSJM, implementing the method is freely available on GitHub: https://github.com/tbaghfalaki/TSJM., Comment: arXiv admin note: text overlap with arXiv:2412.03797

Background: In clinical and epidemiological research, the integration of longitudinal measurements and time-to-event outcomes is vital for understanding relationships and improving risk prediction. However, as the number of longitudinal markers increases, joint model estimation becomes more complex, leading to long computation times and convergence issues. This study introduces a novel two-stage Bayesian approach for variable selection in joint models, illustrated through a practical application. Methods: Our approach conceptualizes the analysis in two stages. In the first stage, we estimate one-marker joint models for each longitudinal marker related to the event, allowing for bias reduction from informative dropouts through individual marker trajectory predictions. The second stage employs a proportional hazard model that incorporates expected current values of all markers as time-dependent covariates. We explore continuous and Dirac spike-and-slab priors for variable selection, utilizing Markov chain Monte Carlo (MCMC) techniques. Results: The proposed method addresses the challenges of parameter estimation and risk prediction with numerous longitudinal markers, demonstrating robust performance through simulation studies. We further validate our approach by predicting dementia risk using the Three-City (3C) dataset, a longitudinal cohort study from France. Conclusions: This two-stage Bayesian method offers an efficient process for variable selection in joint modeling, enhancing risk prediction capabilities in longitudinal studies. The accompanying R package VSJM, which is freely available at https://github.com/tbaghfalaki/VSJM, facilitates implementation, making this approach accessible for diverse clinical applications.

Motor imagery corresponds to the mental practice of simulating visual and kinesthetic aspects of a given motor task. This practice shares a similar neural substrate and correlated temporal scale with motor execution. Besides that, it can lead to performance improvements in the actual execution of the imagined task. Therefore it is important to understand functional differences and equivalences between motor imagery and motor execution. To tackle that we employed a finger-tapping serial reaction time task in two groups of participants, a Motor Execution (n=10) and a Motor imagery (n=10). The sequence of stimuli defining the task had 750 items composed of three distinct auditory stimuli. Also, this sequence had some intrinsic variability making some of the next items unpredictable. Each auditory stimulus was mapped to a single right hand finger in the Motor Imagery group. The Motor imagery group indicated the end of the imagination with a single response using the left hand. The results show improvement in performance of the Motor Imagery group throughout the task and that the duration of the motor imagery, indirectly measured by reaction times, are influenced by distinct factors than those of Motor Execution.

We propose risk models for a portfolio of risks, each following a compound Poisson distribution, with dependencies introduced through a family of tree-based Markov random fields with Poisson marginal distributions inspired in C\^ot\'e et al. (2024b, arXiv:2408.13649). The diversity of tree topologies allows for the construction of risk models under several dependence schemes. We study the distribution of the random vector of risks and of the aggregate claim amount of the portfolio. We perform two risk management tasks: the assessment of the global risk of the portfolio and its allocation to each component. Numerical examples illustrate the findings and the efficiency of the computation methods developed throughout. We also show that the discussed family of Markov random fields is a subfamily of the multivariate Poisson distribution constructed through common shocks., Comment: 21 pages, 8 figures

This work uniquely combines an affine linear decision rule known from adjustable robustness with min-max-regret robustness. By doing so, the advantages of both concepts can be obtained with an adjustable solution that is not over-conservative. This combination results in a bilevel optimization problem. For solving this problem, a three-stage algorithm which uses adaptive discretization of the uncertainty set via two criteria is presented and its convergence is proven. The algorithm is applicable for an example of optimizing a robust pump operation plan for a drinking water supply system facing uncertain demand. The algorithm shows a notable ability to scale, presenting an opportunity to solve larger instances that might challenge existing optimization approaches., Comment: 25 pages, 3 figures

Intermittency as it occurs in fast dynamos in the MHD framework is evaluated through the examination of relations between normalized moments at third order (skewness S) and fourth order (kurtosis K) for both the velocity and magnetic field, and for their local dissipations. As investigated by several authors in various physical contexts such as fusion plasmas (Krommes (2008)), climate evolution (Sura & Sardeshmukh (2008)), fluid turbulence or rotating stratified flows (Pouquet et al. (2023)), approximate parabolic K(S) ~ alpha laws emerge whose origin may be related to the applicability of intermittency models to their dynamics. The results analyzed herein are obtained through direct numerical simulations of MHD flows for both Taylor-Green and Beltrami ABC forcing at moderate Reynolds numbers, and for up to 314000 turn-over times. We observe for the dissipation 0.2 < alpha <3.0, an evaluation that varies with the field, the forcing, and when filtering for high-skewness intermittent structures. When using the She & Leveque (1994) intermittency model, one can compute alpha analytically; we then find alpha = 2.5, clearly differing from a (strict) parabolic scaling, a result consistent with the numerical data.

Fetal brain tissue segmentation in magnetic resonance imaging (MRI) is a crucial tool that supports the understanding of neurodevelopment, yet it faces challenges due to the heterogeneity of data coming from different scanners and settings, and due to data scarcity. Recent approaches based on domain randomization, like SynthSeg, have shown a great potential for single source domain generalization, by simulating images with randomized contrast and image resolution from the label maps. In this work, we investigate how to maximize the out-of-domain (OOD) generalization potential of SynthSeg-based methods in fetal brain MRI. Specifically, when studying data generation, we demonstrate that the simple Gaussian mixture models used in SynthSeg enable more robust OOD generalization than physics-informed generation methods. We also investigate how intensity clustering can help create more faithful synthetic images, and observe that it is key to achieving a non-trivial OOD generalization capability when few label classes are available. Finally, by combining for the first time SynthSeg with modern fine-tuning approaches based on weight averaging, we show that fine-tuning a model pre-trained on synthetic data on a few real image-segmentation pairs in a new domain can lead to improvements in the target domain, but also in other domains. We summarize our findings as five key recommendations that we believe can guide practitioners who would like to develop SynthSeg-based approaches in other organs or modalities.

A new approach using scalar metasurfaces for the design of linearly polarized antennas is presented. The proposed method is based on the construction of the surface impedance Zs using a technique called "phase-matching," which employs the sum of two circular polarizations in phase opposition. This process allows for the achievement of good performance of the synthesized antenna, such as the reduction of side lobe levels and the attainment of an almost symmetric main lobe regardless of the pointing direction. Numerical and measurement results are also presented., Comment: in French language, JNM, Scienceconf, Jun 2022, Limoges (Universit{\'e}), France

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

The relationships between workload and fatigue or sleepiness are investigated through the analysis of rosters and responses to questionnaires from Brazilian aircrews, taken from Fadig\^ometro database. The approach includes temporal markers - coinciding with Samn-Perelli (SP) and Karolinska Sleepiness Scale (KSS) responses - where SAFTE-FAST model outcomes are calculated. The model results follow the increase of fatigue and sleepiness perceptions during the dawn (0h00 to 05h59), but underestimate the self-rated scores during the evening (18h00 to 23h59). On the other hand, the KSS scores fit the relative risk of pilot errors, representing a reasonable proxy for risk assessment. Linear relationships obtained between workload metrics, computed within 168-hours prior to the responses, and self-rated SP and KSS scores provide a consistent method to estimate accumulated fatigue and sleepiness. Considering 7149 rosters of 2023, the duty time ($DT$), the number of flight sectors ($N_{CREW}$) and the sum of flight sectors with sit periods longer than one hour ($N_{CREW}+N_{SIT}$) are associated with 70.1%/60.6% of the highest predicted scores of SP/KSS. Applying the mitigations $DT\leq44h$, $N_{CREW}\leq15$ and $N_{CREW}+N_{SIT}\leq19$ for every 168-hour interval yields a significant decrease in the higher values of SP/KSS with minimal impact on aircrew productivity., Comment: 16 pages and 5 figures

ALMA surveys have suggested that protoplanetary disks are not massive enough to form the known exoplanet population, under the assumption that the millimeter continuum emission is optically thin. In this work, we investigate how the mass determination is influenced when the porosity of dust grains is considered in radiative transfer models. The results show that disks with porous dust opacities yield similar dust temperature, but systematically lower millimeter fluxes compared to disks incorporating compact dust grains. Moreover, we recalibrate the relation between dust temperature and stellar luminosity for a wide range of stellar parameters, and calculate the dust masses of a large sample of disks using the traditionally analytic approach. The median dust mass from our calculation is about 6 times higher than the literature result, and this is mostly driven by the different opacities of porous and compact grains. A comparison of the cumulative distribution function between disk dust masses and exoplanet masses show that the median exoplanet mass is about 2 times lower than the median dust mass, if grains are porous, and there are no exoplanetary systems with masses higher than the most massive disks. Our analysis suggests that adopting porous dust opacities may alleviate the mass budget problem for planet formation. As an example illustrating the combined effects of optical depth and porous dust opacities on the mass estimation, we conduct new IRAM/NIKA-2 observations toward the IRAS 04370+2559 disk and perform a detailed radiative transfer modeling of the spectral energy distribution. The best-fit dust mass is roughly 100 times higher than the value from the traditionally analytic calculation. Future spatially resolved observations at various wavelengths are required to better constrain the dust mass., Comment: 11 pages, 7 figures, Accepted for publication in A&A

Friction is a phenomenon that manifests across all spatial and temporal scales, from the molecular to the macroscopic scale. It describes the dissipation of energy from the motion of particles or abstract reaction coordinates and arises in the transition from a detailed molecular-level description to a simplified, coarse-grained model. It has long been understood that time-dependent (non-Markovian) friction effects are critical for describing the dynamics of many systems, but that they are notoriously difficult to evaluate for complex physical, chemical, and biological systems. In recent years, the development of advanced numerical friction extraction techniques and methods to simulate the generalized Langevin equation have enabled exploration of the role of time-dependent friction across all scales. We discuss recent applications of these friction extraction techniques and the growing understanding of the role of friction in complex equilibrium and non-equilibrium dynamic many-body systems., Comment: 26 pages, 6 figures

The topology of the tree underlying a tree-structured Markov random field (MRF) is central to the understanding of its stochastic dynamics: it is, after all, what synthesizes the rich dependence relations within the MRF. In this paper, we shed light on the influence of the tree's topology, through an extensive comparison-based analysis, on the aggregate distribution of the MRF. This is done within the framework of a recently introduced family of tree-structured MRFs with the uncommon property of having fixed Poisson marginal distributions unaffected by the dependence scheme. We establish convex orderings of sums of MRFs encrypted on trees having different topologies, leading to the devising of a new poset of trees. Hasse diagrams, cataloguing trees of dimension up to 9, and methods for the comparison of higher-dimension trees are provided to offer an exhaustive investigation of the new poset. We also briefly discuss its relation to other existing posets of trees and to invariants from spectral graph theory. Such an analysis requires, beforehand, to study the joint distribution of a MRF's component and its sum, a random vector we refer to as a synecdochic pair. To assess if a component is less or more contributing than another to the sum, we employ stochastic orders to compare synecdochic pairs within a MRF. The resulting orderings are reflected through allocation-related quantities, which thus act as centrality indices., Comment: 22 pages, 12 figures

Markov processes serve as a universal model for many real-world random processes. This paper presents a data-driven approach for learning these models through the spectral decomposition of the infinitesimal generator (IG) of the Markov semigroup. The unbounded nature of IGs complicates traditional methods such as vector-valued regression and Hilbert-Schmidt operator analysis. Existing techniques, including physics-informed kernel regression, are computationally expensive and limited in scope, with no recovery guarantees for transfer operator methods when the time-lag is small. We propose a novel method that leverages the IG's resolvent, characterized by the Laplace transform of transfer operators. This approach is robust to time-lag variations, ensuring accurate eigenvalue learning even for small time-lags. Our statistical analysis applies to a broader class of Markov processes than current methods while reducing computational complexity from quadratic to linear in the state dimension. Finally, we illustrate the behaviour of our method in two experiments., Comment: 35 pages

This report summarizes what has happened in the mini-workshops entitled ''My Earth in 180 minutes'' organized during the lunch break at the SF2A 2024 conference in Marseille. The project showcased an innovative serious game designed to raise awareness of greenhouse gas (GHG) emissions in astronomical research laboratories. Participants, organized into teams, simulate strategies to reduce their carbon footprints by 50\%, focusing on key astronomical activities such as space instrumentation, data analysis, and laboratory work. The sessions highlight the challenges of achieving significant emissions reductions without disrupting core research activities, such as telescope observations. While the serious game facilitates important discussions on sustainable practices, the results point to the need for broader engagement, adaptation to different cultural contexts, and institutional support. The project highlights the importance of integrating climate action into the academic environment and suggests potential future directions for expanding its impact., Comment: SF2A 2024, Jun 2024, Marseille, France

From the observation of a diffusion path $(X_t)_{t\in [0,T]}$ on a compact connected $d$-dimensional manifold $M$ without boundary, we consider the problem of estimating the stationary measure $\mu$ of the process. Wang and Zhu (2023) showed that for the Wasserstein metric $W_2$ and for $d\ge 5$, the convergence rate of $T^{-1/(d-2)}$ is attained by the occupation measure of the path $(X_t)_{t\in [0,T]}$ when $(X_t)_{t\in [0,T]}$ is a Langevin diffusion. We extend their result in several directions. First, we show that the rate of convergence holds for a large class of diffusion paths, whose generators are uniformly elliptic. Second, the regularity of the density $p$ of the stationary measure $\mu$ with respect to the volume measure of $M$ can be leveraged to obtain faster estimators: when $p$ belongs to a Sobolev space of order $\ell>0$, smoothing the occupation measure by convolution with a kernel yields an estimator whose rate of convergence is of order $T^{-(\ell+1)/(2\ell+d-2)}$. We further show that this rate is the minimax rate of estimation for this problem., Comment: 36 pages, 0 figures

This paper presents a review and perspective on quantum gases in bubble traps. We emphasize how the idea of realizing shell shaped condensates emerged and was enabled by the invention of the radiofrequency adiabatic potential technique. We review the many subsequent theoretical works that address the new physics emerging for a condensate trapped on a closed surface. We present the current status of the experiments, the challenges ahead and highlight how a different approach using an immiscible mixture of two condensates enabled the first observation of a shell-shaped degenerate gas. Finally we list a few open questions that we believe provide interesting research directions.

The Calar Alto Void Integral-field Treasury surveY (CAVITY) is a legacy project aimed at characterising the population of galaxies inhabiting voids, which are the most under-dense regions of the cosmic web, located in the Local Universe. This paper describes the first public data release (DR1) of CAVITY, comprising science-grade optical data cubes for the initial 100 out of a total of $\sim$300 galaxies in the Local Universe ($0.005 < z < 0.050$). These data were acquired using the integral-field spectrograph PMAS/PPak mounted on the 3.5m telescope at the Calar Alto observatory. The DR1 galaxy sample encompasses diverse characteristics in the color-magnitude space, morphological type, stellar mass, and gas ionisation conditions, providing a rich resource for addressing key questions in galaxy evolution through spatially resolved spectroscopy. The galaxies in this study were observed with the low-resolution V500 set-up, spanning the wavelength range 3745-7500 \AA, with a spectral resolution of 6.0 \AA\ (FWHM). Here, we describe the data reduction and characteristics and data structure of the CAVITY datasets essential for their scientific utilisation, highlighting such concerns as vignetting effects, as well as the identification of bad pixels and management of spatially correlated noise. We also provide instructions for accessing the CAVITY datasets and associated ancillary data through the project's dedicated database., Comment: 17 pages, 9 figures, 5 tables. Accepted for publication in A&A (replacement after minor language edits)

Carcinogenesis is a proteiform phenomenon, with tumors emerging in various locations and displaying complex, diverse shapes. At the crucial intersection of research and clinical practice, it demands precise and flexible assessment. However, current biomarkers, such as RECIST 1.1's long and short axis measurements, fall short of capturing this complexity, offering an approximate estimate of tumor burden and a simplistic representation of a more intricate process. Additionally, existing supervised AI models face challenges in addressing the variability in tumor presentations, limiting their clinical utility. These limitations arise from the scarcity of annotations and the models' focus on narrowly defined tasks. To address these challenges, we developed ONCOPILOT, an interactive radiological foundation model trained on approximately 7,500 CT scans covering the whole body, from both normal anatomy and a wide range of oncological cases. ONCOPILOT performs 3D tumor segmentation using visual prompts like point-click and bounding boxes, outperforming state-of-the-art models (e.g., nnUnet) and achieving radiologist-level accuracy in RECIST 1.1 measurements. The key advantage of this foundation model is its ability to surpass state-of-the-art performance while keeping the radiologist in the loop, a capability that previous models could not achieve. When radiologists interactively refine the segmentations, accuracy improves further. ONCOPILOT also accelerates measurement processes and reduces inter-reader variability, facilitating volumetric analysis and unlocking new biomarkers for deeper insights. This AI assistant is expected to enhance the precision of RECIST 1.1 measurements, unlock the potential of volumetric biomarkers, and improve patient stratification and clinical care, while seamlessly integrating into the radiological workflow.

We study theoretically Meissner effect in non-Hermitian systems of BCS type via superfluid stiffness. We show that depending on the values of the mean fields, chemical potential, and temperature, we obtain paramagnetic or diamagnetic Meissner effect. Notably, positive real part of the product of mean fields guarantees diamagnetic Meissner effect in an $s$-wave 3D non-Hermitian superconductor. Once the mean fields are close to being anti-Hermitian, 2D $s$-, $p_x$-, and $d$-wave superconductors exhibit interesting behaviour, including paramagnetic Meissner effect for certain parameters.

Density functional theory is used to study the effect of compressive and tensile biaxial strain on Mn$_{3}$GaN. Mn$_{3}$GaN is a non-collinear antiferromagnetic antiperovskite with a similar structure to that of an ideal cubic oxide perovskite, but with cations at the octahedral sites while the anion, nitrogen, is found at the B site. The present study explores the response of Mn$_{3}$GaN to (001) strain, considering biaxial strain levels ranging from -5% to 5%. It is found that the electron structure is insensitive to tensile strain. The study supports previous results in that a spin-canted antiferromagnetic order emerges due to tensile strain, inducing net magnetization. Compressive strain collapses the non-collinear antiferromagnetic spin structure and induces a ferrimagnetic order at -2% strain. Notably, in contrast with oxide perovskites, Mn$_{3}$GaN does not respond to strain by octahedral tilt, but rather by intraband redistributions of charge between Mn $d$ states. Despite the similar structure to oxide perovskites, the bonds between the B site anion and octahedral site cations in Mn$_{3}$GaN bonds are less rigid, such that strain is instead accommodated by a change in bond length rather than a change in bond angles., Comment: 9 pages, 9 figures

We present resolved $3.6-250~\mu$m dust spectral energy distribution (SED) fitting for $\sim 800$ nearby galaxies. We measure the distribution of radiation field intensities heating the dust, the dust mass surface density ($\Sigma_{\rm d}$), and the fraction of dust in the form of polycyclic aromatic hydrocarbons (PAHs; $q_{\rm PAH}$). We find that the average interstellar radiation field ($\overline{U}$) is correlated both with stellar mass surface density ($\Sigma_{\star}$) and star formation rate surface density ($\Sigma_{\rm SFR}$), while more intense radiation fields are only correlated with $\Sigma_{\rm SFR}$. We show that $q_{\rm PAH}$ is a steeply decreasing function of $\Sigma_{\rm SFR}$, likely reflecting PAH destruction in H II regions. Galaxy integrated $q_{\rm PAH}$ is strongly, negatively correlated with specific star formation rate (sSFR) and offset from the star-forming ``main sequence'' ($\Delta$MS), suggesting that both metallicity and star formation intensity play a role in setting the global $q_{\rm PAH}$. We also find a nearly constant M$_{\rm d}$/M$_\star$ ratio for galaxies on the main sequence, with a lower ratio for more quiescent galaxies, likely due to their lower gas fractions. From these results, we construct prescriptions to estimate the radiation field distribution in both integrated and resolved galaxies. We test these prescriptions by comparing our predicted $\overline{U}$ to results of SED fitting for stacked "main sequence" galaxies at $0

Natural language processing (NLP) in the medical domain can underperform in real-world applications involving small datasets in a non-English language with few labeled samples and imbalanced classes. There is yet no consensus on how to approach this problem. We evaluated a set of NLP models including BERT-like transformers, few-shot learning with sentence transformers (SetFit), and prompted large language models (LLM), using three datasets of radiology reports on magnetic resonance images of epilepsy patients in Danish, a low-resource language. Our results indicate that BERT-like models pretrained in the target domain of radiology reports currently offer the optimal performances for this scenario. Notably, the SetFit and LLM models underperformed compared to BERT-like models, with LLM performing the worst. Importantly, none of the models investigated was sufficiently accurate to allow for text classification without any supervision. However, they show potential for data filtering, which could reduce the amount of manual labeling required.

In this paper, we introduce and analyze a numerical scheme for solving the Cauchy-Dirichlet problem associated with fractional nonlinear diffusion equations. These equations generalize the porous medium equation and the fast diffusion equation by incorporating a fractional diffusion term. We provide a rigorous analysis showing that the discretization preserves main properties of the continuous equations, including algebraic decay in the fractional porous medium case and the extinction phenomenon in the fractional fast diffusion case. The study is supported by extensive numerical simulations. In addition, we propose a novel method for accurately computing the extinction time for the fractional fast diffusion equation and illustrate numerically the convergence of rescaled solutions towards asymptotic profiles near the extinction time.

For a fixed elliptic curve $E$ without complex multiplication, $a_p := p+1 - \#E(\mathbb{F}_p)$ is $O(\sqrt{p})$ and $a_p/2\sqrt{p}$ converges to a semicircular distribution. Michel proved that for a one-parameter family of elliptic curves $y^2 = x^3 + A(T)x + B(T)$ with $A(T), B(T) \in \mathbb{Z}[T]$ and non-constant $j$-invariant, the second moment of $a_p(t)$ is $p^2 + O(p^{{3}/{2}})$. The size and sign of the lower order terms has applications to the distribution of zeros near the central point of Hasse-Weil $L$-functions and the Birch and Swinnerton-Dyer conjecture. S. J. Miller conjectured that the highest order term of the lower order terms of the second moment that does not average to zero is on average negative. Previous work on the conjecture has been restricted to a small set of highly nongeneric families. We create a database and a framework to quickly and systematically investigate biases in the second moment of any one-parameter family. When looking at families which have so far been beyond current theory, we find several potential violations of the conjecture for $p \leq 250,000$ and discuss new conjectures motivated by the data., Comment: 16 pages, 18 figures

Some accreting binary systems containing a white dwarf (such as classical novae or persistent supersoft sources) are seen to emit low-energy X-rays with temperatures of ~10^6 K and luminosities exceeding 10^35 erg/s. These X-rays are thought to originate from nuclear burning on the white dwarf surface, either caused by a thermonuclear runaway (classical novae) or a high mass-accretion rate that sustains steady nuclear burning (persistent sources). The discovery of transient supersoft X-rays from ASASSN-16oh challenged these ideas, as no clear signatures of mass ejection indicative of a classical nova eruption were detected, and the origin of these X-rays remains controversial. It was unclear whether this star was one of a kind or representative of a larger, as yet undiscovered, group. Here, we present the discovery of 29 stars located in the direction of the Magellanic Clouds exhibiting long-duration, symmetrical optical outbursts similar to that seen in ASASSN-16oh. We observed one of these objects during an optical outburst and found it to be emitting transient supersoft X-rays, while no signatures of mass ejection (indicative of a classical nova eruption) were detected. We therefore propose that these objects form a homogeneous group of transient supersoft X-ray sources, which we dub ``millinovae'' because their optical luminosities are approximately a thousand times fainter than those of ordinary classical novae., Comment: accepted for publication in ApJ Letters

Purpose: Bone metastasis have a major impact on the quality of life of patients and they are diverse in terms of size and location, making their segmentation complex. Manual segmentation is time-consuming, and expert segmentations are subject to operator variability, which makes obtaining accurate and reproducible segmentations of bone metastasis on CT-scans a challenging yet important task to achieve. Materials and Methods: Deep learning methods tackle segmentation tasks efficiently but require large datasets along with expert manual segmentations to generalize on new images. We propose an automated data synthesis pipeline using 3D Denoising Diffusion Probabilistic Models (DDPM) to enchance the segmentation of femoral metastasis from CT-scan volumes of patients. We used 29 existing lesions along with 26 healthy femurs to create new realistic synthetic metastatic images, and trained a DDPM to improve the diversity and realism of the simulated volumes. We also investigated the operator variability on manual segmentation. Results: We created 5675 new volumes, then trained 3D U-Net segmentation models on real and synthetic data to compare segmentation performance, and we evaluated the performance of the models depending on the amount of synthetic data used in training. Conclusion: Our results showed that segmentation models trained with synthetic data outperformed those trained on real volumes only, and that those models perform especially well when considering operator variability., Comment: 14 pages, 5 figures 3 tables

When the memory parameter of the elephant random walk is above a critical threshold, the process becomes superdiffusive and, once suitably normalised, converges to a non-Gaussian random variable. In a recent paper by the three first authors, it was shown that this limit variable has a density and that the associated moments satisfy a nonlinear recurrence relation. In this work, we exploit this recurrence to derive an asymptotic expansion of the moments and the asymptotic behaviour of the density at infinity. In particular, we show that an asymmetry in the distribution of the first step of the random walk leads to an asymmetry of the tails of the limit variable. These results follow from a new, explicit expression of the Stieltjes transformation of the moments in terms of special functions such as hypergeometric series and incomplete beta integrals. We also obtain other results about the random variable, such as unimodality and, for certain values of the memory parameter, log-concavity., Comment: 25 pages

Advanced AI systems capable of generating humanlike text and multimodal content are now widely available. In this paper, we discuss the impacts that generative artificial intelligence may have on democratic processes. We consider the consequences of AI for citizens' ability to make informed choices about political representatives and issues (epistemic impacts). We ask how AI might be used to destabilise or support democratic mechanisms like elections (material impacts). Finally, we discuss whether AI will strengthen or weaken democratic principles (foundational impacts). It is widely acknowledged that new AI systems could pose significant challenges for democracy. However, it has also been argued that generative AI offers new opportunities to educate and learn from citizens, strengthen public discourse, help people find common ground, and to reimagine how democracies might work better., Comment: 25 pages

We formulate and prove a non-abelian analog of Deligne's Fixed Part theorem on Hodge classes, revisiting previous work of Jost--Zuo, Katzarkov--Pantev and Landesman--Litt. To this aim we study algebraically isomonodromic extensions of local systems and we relate them to variations of Hodge structures, for example we show that the Mumford-Tate group at a generic point stays constant in an algebraically isomonodromic extension of a variation of Hodge structure. v2: a few typos ironed and Thm 1.1 5) completed. v3: there was a Schlamassel leading to a mix-up of files. Apologies. Else identical version (one minor change)., Comment: 23 pages

A new family of tree-structured Markov random fields for a vector of discrete counting random variables is introduced. According to the characteristics of the family, the marginal distributions of the Markov random fields are all Poisson with the same mean, and are untied from the strength or structure of their built-in dependence. This key feature is uncommon for Markov random fields and most convenient for applications purposes. The specific properties of this new family confer a straightforward sampling procedure and analytic expressions for the joint probability mass function and the joint probability generating function of the vector of counting random variables, thus granting computational methods that scale well to vectors of high dimension. We study the distribution of the sum of random variables constituting a Markov random field from the proposed family, analyze a random variable's individual contribution to that sum through expected allocations, and establish stochastic orderings to assess a wide understanding of their behavior., Comment: 27 pages, 10 figures

Under the generalized Riemann Hypothesis (GRH), Baluyot, Chandee, and Li nearly doubled the range in which the density of low lying zeros predicted by Katz and Sarnak is known to hold for a large family of automorphic $L$-functions with orthogonal symmetry. We generalize their main techniques to the study of higher centered moments of the one-level density of this family, leading to better results on the behavior near the central point. Numerous technical obstructions emerge that are not present in the one-level density. Averaging over the level of the forms and assuming GRH, we prove the density predicted by Katz and Sarnak holds for the $n$-th centered moments for test functions whose Fourier transform is compactly supported in $(-\sigma, \sigma)$ for $\sigma~=~\min\left\{3/2(n-1), 4/(2n-\mathbf{1}_{2\nmid n})\right\}$. For $n=3$, our results improve the previously best known $\sigma=2/3$ to $\sigma=3/4$. We also prove the two-level density agrees with the Katz-Sarnak density conjecture for test functions whose Fourier transform is compactly supported in $\sigma_1 = 3/2$ and $\sigma_2 = 5/6$, respectively, extending the previous best known sum of supports $\sigma_1 + \sigma_2 = 2$. This work is the first evidence of an interesting new phenomenon: by taking different test functions, we are able to extend the range in which the Katz-Sarnak density predictions hold. The techniques we develop can be applied to understanding quantities related to this family containing sums over multiple primes., Comment: 36 pages

The human brain undergoes rapid development during the third trimester of pregnancy. In this work, we model the neonatal development of the infant brain in this age range. As a basis, we use MR images of preterm- and term-birth neonates from the developing human connectome project (dHCP). We propose a neural network, specifically an implicit neural representation (INR), to predict 2D- and 3D images of varying time points. In order to model a subject-specific development process, it is necessary to disentangle the age from the subjects' identity in the latent space of the INR. We propose two methods, Subject Specific Latent Vectors (SSL) and Stochastic Global Latent Augmentation (SGLA), enabling this disentanglement. We perform an analysis of the results and compare our proposed model to an age-conditioned denoising diffusion model as a baseline. We also show that our method can be applied in a memory-efficient way, which is especially important for 3D data., Comment: Preprint, Accepted for PRIME MICCAI 2024

We study a generalization of the Erd\H{o}s-Falconer distance problem over finite fields. For a graph $G$, two embeddings $p, p': V(G) \to \mathbb{F}_q^d$ of a graph $G$ are congruent if for all edges $(v_i, v_j)$ of $G$ we have that $||p(v_i) - p(v_j)|| = ||p'(v_i) - p'(v_j)||$. What is the infimum of $s$ such that for any subset $E\subset \mathbb{F}_q^d$ with $|E| \gtrsim q^s$, $E$ contains a positive proportion of congruence classes of $G$ in $\mathbb{F}_q^d$? Bennett et al. and McDonald used group action methods to prove results in the case of $k$-simplices. The work of Iosevich, Jardine, and McDonald as well as that of Bright et al. have proved results in the case of trees and trees of simplices, utilizing the inductive nature of these graphs. Recently, Aksoy, Iosevich, and McDonald combined these two approaches to obtain nontrivial bounds on the "bowtie" graph, two triangles joined at a vertex. Their proof relies on an application of the Hadamard three-lines theorem to pass to a different graph. We develop novel geometric techniques called branch shifting and simplex unbalancing to reduce our analysis of trees of simplices to a much smaller class of simplex structures. This allows us to establish a framework that handles a wide class of graphs exhibiting a combination of rigid and loose behavior. In $\mathbb{F}_q^2$, this approach gives new nontrivial bounds on chains and trees of simplices. In $\mathbb{F}_q^d$, we improve on the results of Bright et al. in many cases and generalize their work to a wider class of simplex trees. We discuss partial progress on how this framework can be extended to more general simplex structures, such as cycles of simplices and structures of simplices glued together along an edge or a face., Comment: 37 pages, 8 figures, references updated

Dementia currently affects about 50 million people worldwide, and this number is rising. Since there is still no cure, the primary focus remains on preventing modifiable risk factors such as cardiovascular factors. It is now recognized that high blood pressure is a risk factor for dementia. An increasing number of studies suggest that blood pressure variability may also be a risk factor for dementia. However, these studies have significant methodological weaknesses and fail to distinguish between long-term and short-term variability. The aim of this work was to propose a new joint model that combines a mixed-effects model, which handles the residual variance distinguishing inter-visit variability from intra-visit variability, and an illness-death model that allows for interval censoring and semi-competing risks. A subject-specific random effect is included in the model for both variances. Risks can simultaneously depend on the current value and slope of the marker, as well as on each of the two components of the residual variance. The model estimation is performed by maximizing the likelihood function using the Marquardt-Levenberg algorithm. A simulation study validates the estimation procedure, which is implemented in an R package. The model was estimated using data from the Three-City (3C) cohort to study the impact of intra- and inter-visits blood pressure variability on the risk of dementia and death., Comment: 16 pages, 2 figures, 4 tables

We obtain geometric lower bounds for the low Steklov eigenvalues of finite-volume hyperbolic surfaces with geodesic boundary. The bounds we obtain depend on the length of a shortest multi-geodesic disconnecting the surfaces into connected components each containing a boundary component and the rate of dependency on it is sharp. Our result also identifies situations when the bound is independent of the length of this multi-geodesic. The bounds also hold when the Gaussian curvature is bounded between two negative constants and can be viewed as a counterpart of the well-known Schoen-Wolpert-Yau inequality for Laplace eigenvalues. The proof is based on analysing the behaviour of the {corresponding Steklov} eigenfunction on an adapted version of thick-thin decomposition for hyperbolic surfaces with geodesic boundary. Our results extend and improve the previously known result in the compact case obtained by a different method., Comment: 19 pages