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Integrating Model Checking into a Live Modeling Environment
No full textInternational audienceLive modeling is the ability to change an executable model at runtime, and without having to restart its execution. Sometimes this 'breaks' the ongoing execution, but in many cases, it does not have to. In earlier work, we reduced this problem to detecting fine-grained read/write conflicts between the recorded history of edit operations (created by the user) and execution steps (created by the interpreter). In this paper, we extend our approach by adding the ability to perform model checking during live modeling sessions. We motivate that this further enhances the live modeling experience, producing counter-examples or 'witness traces' relative to the current execution state, as a possible 'future', integrated with the execution and edit history, minimizing the mental gap. The model checker itself is generic, and uses (via an adapter) the language's existing interpreter. This way, we could implement this paper's running example in a working prototype with relatively little effort
Identifying Key Requirements for Maritime Bridge Simulators in Wind-Assisted Ship Propulsion Training
No full textInternational audienceMaritime transport is responsible for 3 percent of global greenhouse gas emissions. To meet environmental regulations, technologies such as Wind-Assisted Ship Propulsion (WASP) are being considered. However, maritime officers' training needs to be adapted, as sailing is not sufficiently covered in current curricula. The SOMOS project aims to develop a navigation simulator for merchant vessels equipped with WASP, incorporating realistic training scenarios such as failure management. This simulator will enable sailors to prepare for the use of these technologies and contribute to the reduction of emissions in maritime transport. In this paper, we present the results of a student work with the goal of identifying minimal requirements for bridge maritime simulators including WASP technologies
Factors influencing emotional driving: examining the impact of arousal on the interplay between age, personality, and driving behaviors
No full textInternational audienceIntroduction Drivers' emotions have been widely investigated in transportation due to their significant effects on driving behaviors and traffic accidents. Appraisal theory posits that emotional reactions are influenced by individuals' attitudes toward current circumstances and events, thereby shaping their driving attitudes and styles. However, In the study of emotional driving, research often focuses on the impact of single factors such as age, gender, and personality, while the interplay between these multiple factors is a challenge. This study aims to explore the impact of age, personality, and driving experience on driving behaviors, and to investigate the interaction effect between these factors, particularly the role of emotional arousal. Method Using moderated moderation and mediated moderation analyses, we examined how these individual factors interact and influence driving behaviors, including acceleration, speed stability, and steering performance. Data were collected from a driving simulation experiment involving 40 Chinese participants in various emotional states. Results Our findings revealed that higher-age drivers and experienced drivers displayed lower maximum acceleration and better speed stability. Extraversion significantly mediated the relationship between age and driving behaviors, with this relationship being moderated by arousal states. Additionally, Neuroticism moderated the relationship between driving experience and driving behaviors. Conclusion This study highlights how individual factors influence the trajectory of personality development in relation to driving behaviors. These findings have practical implications for improving traffic safety and driver education programs by incorporating emotional and personality-based interventions. Further long-term and individualized studies are needed to better understand these interactions and develop targeted interventions
Open Review of "Normal form analysis of nonlinear oscillator equations with automated arbitrary order expansions"
No full textThis is the Open Review of article with DOI https://doi.org/10.46298/jtcam.13234 published in JTCA
Withdrawal of: Solution of the Ovals problem
No full textIn the previous version of the preprint, we made a mistake in our proposed solution to the Ovals problem (formulated in [3, 24]). The erroneous claim is that the operator A_T, used in the proof of Lemma 2.4.1, is selfadjoint. But this fact is wrong, as kindly pointed out to us by Matthias Baur, Rupert L. Frank, Larry Read and Timo Weidl, whom we warmly thank. At the moment, unfortunately, this mistake seems fatal to us, so the Ovals conjecture remains open. Nevertheless, since our work contains arguments that may be useful to address the conjecture, we let it available as a preprint, with a warning on Section 2.4
A systematic review of immersive technologies for education: effects of cognitive load and curiosity state on learning performance
No full textInternational audienceImmersive technologies are assumed to have many benefits for learning due to their potential positive impact on optimizing learners' cognitive load and fostering intrinsic motivation. However, despite promising results, the findings regarding the actual impact on learning remain inconclusive, raising questions about the determinants of efficacy. To address these gaps, we conducted a PRISMA systematic review to investigate the contributions and limitations of Virtual Reality (VR) and Augmented Reality (AR) in learning, specifically by examining their effects on cognitive load and intrinsic motivations. Through the application of an analytical grid, we systematically classified the impact of VR/AR on the causal relationship between learning performance (i.e., objective learning improvement) and cognitive load or motivation, while respecting the fundamental assumptions of the main theories related to these factors.Analyzing 36 studies, the findings reveal that VR, often causing extraneous load, hinders learning, particularly among novices. In contrast, AR optimizes cognitive load, proving beneficial for novice learners but demonstrating less effectiveness for intermediate learners. The effects on intrinsic motivation remain inconclusive, likely due to variations in measurement methods. The review underscores the need for detailed, sophisticated evaluations and comprehensive frameworks that consider both cognitive load and intrinsic motivation to improve understanding of the impact of immersive technologies on learning
Continuous relativistic high-harmonic generation from a kHz liquid-sheet plasma mirror
No full textInternational audienceWe report on continuous high-harmonic generation (HHG) at 1 kHz repetition rate from a liquid-sheet plasma mirror driven by relativistic-intensity near-single-cycle light transients. Through precise control of both the surface plasma density gradient and the driving light waveform, we can produce highly stable and reproducible extreme ultraviolet spectral quasi-continua, expected to correspond to the generation of stable kHz-trains of isolated attosecond pulses in the time domain. This confirms the exciting potential of liquid-sheet targets as one of the building blocks of future high-power attosecond lasers
Silent sources on a surface for the Helmholtz equation and decomposition of L² vector fields
No full textInternational audienceWe study an inverse source problem with right hand side in divergence form for the Helmholtz equation, whose underlying model can be related to weak scattering from thin interfaces. This inverse problem is not uniquely solvable, as the forward operator has infinite-dimensional kernel. We present a decomposition of (not necessarily tangent) vector fields of L 2-class on a closed Lipschitz surface in R 3 , which allows one to discuss an ansatz for the solution and constraints that restore uniqueness. This work can be seen as a generalization of references [4, 6] dealing with the Laplace equation, but in the Helmholtz case new ties arise between the observations from each side of the surface. Our proof is based on properties of the Calderón projector on the boundary of Lipschitz domains, that we establish in a H-1 × L 2 setting.[4] L. Baratchart, C. Gerhards, and A. Kegeles. Decomposition of l 2-vector fields on lipschitz surfaces: characterization vianull-spaces of the scalar potential. SIAM Journal on Mathematical Analysis, 2021.[6] L. Baratchart, C. Villalobos Guillén, D. P. Hardin, M. C. Northington, and E. B. Saff. Inverse potential problems fordivergence of measures with total variation regularization. Foundations of Computational Mathematics, Nov 2019
BevGAN: Generative Fisheye Cross-View Transformers
No full textInternational audienceCurrent parking assistance and monitoring systems synthesize Bird Eye View (BEV) images to improve drivers visibility. These BEV images are created using a popular perspective transform called Inverse Perspective Mapping (IPM), which projects pixels of surround-view images captured by onboard fisheye cameras onto a flat plane. However, IPM faces challenges in accurately representing objects with varying heights and seamlessly stitching together the projected surround-views due to its reliance on rigid geometric transformations. To address these limitations, we present BevGAN, a novel geometry-guided Conditional Generative Adversarial Networks (cGANs) model that combines multi-scale discriminators along with a transformers-based generator that leverages fisheye cameras calibration and attention-mechanisms to implicitly model geometrical transformations between the views. Experimental results demonstrate that BevGAN outperforms IPM and state-of-the-art cross-view image generation methods in terms of image fidelity and quality. Compared to IPM, we report an improvement of +6.2db on PSNR and +170% on MS-SSIM when evaluated on a synthetic dataset depicting both parking and driving scenarios. Furthermore, the generalization ability of BevGAN on real-world fisheye images is also demonstrated through zero-shot inference.</div
Skeleton-Based Transformer for Classification of Errors and Better Feedback in Low Back Pain Physical Rehabilitation Exercises
No full textInternational audiencePhysical rehabilitation exercises suggested by healthcare professionals can help recovery from various musculoskeletal disorders and prevent re-injury. However, patients’ engagement tends to decrease over time without direct supervision, which is why there is a need for an automated monitoring system. In recent years, there has been great progress in quality assessment of physical rehabilitation exercises. Most of them only provide a binary classification if the performance is correct or incorrect, and a few provide a continuous score. This information is not sufficient for patients to improve their performance. In this work, we propose an algorithm for error classification of rehabilitation exercises, thus making the first step toward more detailed feedback to patients. We focus on skeleton-based exercise assessment, which utilizes human pose estimation to evaluate motion. Inspired by recent algorithms for quality assessment during rehabilitation exercises, we propose a Transformer-based model for the described classification. Our model is inspired by the HyperFormer method for human action recognition, and adapted to our problem and dataset. The evaluation is done on the KERAAL dataset, as it is the only medical dataset with clear error labels for the exercises, and our model significantly surpasses state-of-the-art methods. Furthermore, we bridge the gap towards better feedback to the patients by presenting a way to calculate the importance of joints for each exercise