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Analyse Structurelle des changements de mode dans les DAE multimodes
No full textHybrid systems are an important concept in Cyber-Physical Systems modeling, for which multiphysics modeling from first principles and the reuse of models from libraries are key. To achieve this, DAEs must be used to specify the dynamics in each discrete state (or mode in our context). This led to the development of DAE-based equational languages supporting multiple modes, of which Modelica is a popular standard. Mode switching can be time-or state-based. Impulsive behaviors can occur at mode changes. While mode changes are well understood in particular physics (e.g., contact mechanics), this is not the case in physics-agnostic paradigms such as Modelica. This situation causes difficulties for the compilation of programs, often requiring users to manually "smooth out" mode changes. In this paper, we propose a novel approach for the hot restart at mode changes in such paradigms. We propose a mathematical meaning for hot restarts (such a mathematical meaning does not exist in general), as well as a combined structural-and-impulse analysis for mode changes, generating the hot restart even in the presence of impulses. Our algorithm detects at compile time if the mode change is insufficiently specified, in which case it returns diagnostics information to the user.La modélisation des systèmes cyber-physiques repose sur une modélisation à partir des principes de la physique, et en réutilisant au maximum des modèles prédéfinis issus d’unebibliothèque. Cela exige le recours aux Equations Différentielles Algébriques (DAE) admettant plusieurs modes (une DAE commutée, ou une DAE hybride). Le standard de modélisation est le langage Modelica. Les changements de mode peuvent ˆetre déclenchés de manière externe, ou par des conditions portant sur les états. Ces changements de mode sont connus et traités à l’intérieur de physiques particulières (mécanique avec contacts). Il en va autrement dans un cadre multi-physique général, qui est, pourtant, celui de Modelica et d’autres langages de modélisation multi-physique. Dans ce papier, nous proposons une approche nouvelle pour le redémarrage à chaud suite à un changement de mode. Noter qu’il n’existe pas de définition mathématique de ce qu’est une solution dans notre cadre général. Notre méthode utilise une analyse structurelle doublée d’un calcul symbolique des comportements impulsifs. Notre méthode s’applique lors de la phase de compilation et permet de détecter, avant toute simulation, si le modèle soumis est éventuellement insuffisamment spécifié
State and Unknown Input Estimation using a Left-Invertibility Constrained NeuralEstimator in Delayed Autonomic Cardiac Dynamics
No full textInternational audienceUnderstanding brain–heart interaction (BHI) requires models that capture how thecentral nervous and cardiovascular systems co-regulate under stressors to preserve homeostasis and generate macroscopic states such as sleep, arousal, or vigilance. At the core of this loop are interoceptive variables, latent autonomic control signals that drive cardiac adjustments; however, these variables are not directly measurable. Recovering these hidden drives from peripheral cardiac recordings is confounded by nonlinear dynamics, physiological delays, and limited measurement data. This work proposes a physics-informed neural estimator for joint state estimation and unknown autonomic input reconstruction in a delayed nonlinear model of autonomic cardiac regulation. The framework derives left-invertibility conditions from the delay-free system and determines a system-intrinsic bound on physiological delay driven only by heart-rate dynamics, preserving constraint validity for the delayed system within that bound.Validation on stress-evoked cardiac recordings shows accurate recovery of heart rate, blood pressure state estimation, and input reconstruction (the blood-pressure setpoint), enabling identifiable, physiology-consistent inference of interoceptive autonomic control dynamics
The Zermelo Navigation Problem on the 2-Sphere of Revolution: An Optimal Control Perspective with Applications to Micromagnetism
No full textInternational audienceThis article presents geometric optimal control techniques for analyzing geodesics in time-optimal Zermelo navigation problems on 2-spheres of revolution. We classify the problem by analyzing the pair , which represents the current (or wind) and the Riemannian metric. Using the maximum principle, the dynamics of geodesics are described by a Hamiltonian vector field on the cotangent bundle . Our primary motivation is the application to micromagnetism, specifically spin magnetization reversal in ferromagnetic ellipsoidal samples. This model depends on four parameters and the amplitude of the applied magnetic field. The problem is formulated as a Zermelo navigation on the 2-sphere, where geodesics are classified as elliptic, hyperbolic, or abnormal. We demonstrate that the transition set , which separates weak and strong current domains, is critical for understanding optimality. A key result shows that abnormal geodesics intersect this set with semi-cubical cusp singularities, a phenomenon we term the Landau–Lifshitz billiard. The analysis of the transition set's connected components is complex and complemented by algebraic geometry and symbolic computations. We further reveal that hyperbolic geodesics lose optimality at their second intersection with the abnormal arc. Our numerical simulations complement this analysis by computing conjugate and cut loci, wavefronts, and accessibility sets, providing new insights into optimal magnetization switching under bounded control
Radio-PPG: photoplethysmogram digital twin synthesis using deep neural representation of 6G/WiFi ISAC signals
No full textDigital twins for 1D bio-signals enable real-time monitoring of physiological processes of a person, which enables early disease diagnosis and personalized treatment. This work introduces a novel non-contact method for digital twin (DT) photoplethysmogram (PPG) signal synthesis under the umbrella of 6G/WiFi integrated sensing and communication (ISAC) systems. We employ a software-defined radio (SDR) operating at 5.23 GHz that illuminates the chest of a nearby person with a wideband 6G/WiFi signal and collects the reflected signals. This allows us to acquire Radio-PPG dataset that consists of 300 minutes worth of near synchronous 64-channel radio data, PPG data, along with the labels (three body vitals) of 30 healthy subjects. With this, we test two artificial intelligence (AI) models for DT-PPG signal synthesis: i) discrete cosine transform followed by a multi-layer perceptron, ii) two U-NET models (Approximation network, Refinement network) in cascade, along with a custom loss function. Experimental results indicate that U-NET model achieves an impressive relative mean absolute error of 0.194 with a small ISAC sensing overhead of 15.62%, for DT-PPG synthesis. Furthermore, we performed quality assessment of the synthetic DT-PPG by computing the accuracy of DT-PPG-based vitals estimation and feature extraction, which turned out to be at par with that of reference PPG-based vitals estimation and feature extraction. This work highlights the potential of generative AI and 6G/WiFi ISAC technologies and serves as a foundational step towards the development of non-contact screening tools for covid-19, cardiovascular diseases and well-being assessment of people with special needs
Modeling high dimensional point clouds with the spherical cluster model
No full textA parametric cluster model is a statistical model providing geometric insights onto the points defining a cluster. The spherical cluster model (SC) approximates a finite point set P ⊂ R d by a sphere S(c, r) as follows. Taking r as a fraction η ∈ (0, 1) (hyper-parameter) of the std deviation of distances between the center c and the data points, the cost of the SC model is the sum over all data points lying outside the sphere S of their power distance with respect to S. The center c of the SC model is the point minimizing this cost. Note that η = 0 yields the celebrated center of mass used in KMeans clustering. We make three contributions.First, we show fitting a spherical cluster yields a strictly convex but not smooth combinatorial optimization problem. Second, we present an exact solver using the Clarke gradient on a suitable stratified cell complex defined from an arrangement of hyper-spheres. Finally, we present experiments on a variety of datasets ranging in dimension from d = 9 to d = 10, 000, with two main observations. First, the exact algorithm is orders of magnitude faster than BFGS based heuristics for datasets of small/intermediate dimension and small values of η, and for high dimensional datasets (say d > 100) whatever the value of η. Second, the center of the SC model behave as a parameterized high-dimensional median.The SC model is of direct interest for high dimensional multivariate data analysis, and the application to the design of mixtures of SC will be reported in a companion paper
Importance of analyzing spasticity and co-activation as complementary biomarkers of gait in children with cerebral palsy
No full textInternational audienceBackground: Cerebral palsy (CP) is a neurological disorder characterized by motor impairments, including muscle spasticity, weakness, and abnormal co-activation leading to gait abnormalities. Understanding the relationship between these factors is essential for optimizing rehabilitation strategies but remains unclear, particularly in terms of phase-specific neuromuscular adaptations during gait. This study investigated the correlations between muscle spasticity, strength, coactivation, and gait variable scores (GVS) in children with CP during clinical gait analysis. Two muscle pairs were analyzed: Gastrocnemius Medialis-Tibialis Anterior (GM-TA) and Rectus Femoris-Semitendinosus (RF-ST). Methods:We retrospectively analyzed 55 children with CP using surface electromyography and clinical scales (Modified Ashworth Scale for spasticity, Medical Research Council scale for strength). Co-activation was computed for stance and swing phases and compared to reference values from literature data about typically developing children. Correlations between variables were assessed using Spearman's coefficient and Chi-square tests evaluated categorical relationships between spasticity and abnormal co-activation.Findings: No clear correlations between spasticity and co-activation were demonstrated, except for RF during swing (moderate correlation). GVS for ankle and hip flexion was moderately correlated with co-activation. Muscle strength negatively correlated with co-activation and deviations of joint angles relative to healthy gait.Interpretation: These findings highlight partial correlations between clinical examination (i.e., spasticity and strength) and gait data (i.e., muscle co-activation and kinematic alterations), reinforcing the importance of assessing multiple biomarkers to better characterize gait abnormalities. Future rehabilitation protocols should comprehensively evaluate spasticity, muscle strength, co-activation, and GVS to better adapt interventions and optimize motor function in children with CP.</div
Classical notions of computation and the Hasegawa-Thielecke theorem
No full textExtended version with more illustrations and proofsInternational audienceIn the spirit of the Curry-Howard correspondence between proofs and programs, we define and study a syntax and semantics for classical logic equipped with a computationally involutive negation, using a polarised effect calculus, the linear classical L-calculus. A main challenge in designing a denotational semantics for the calculus is to accommodate both call-by-value and call-by-name evaluation strategies, which leads to a failure of associativity of composition. In order to tackle this issue, we define a notion of adjunction between graph morphisms on non-associative categories, which we use to formulate polarized and non-associative notions of symmetric monoidal closed duploid and of dialogue duploid. We show that they provide a direct style counterpart to adjunction models: linear effect adjunctions for the (linear) call-by-push-value calculus and dialogue chiralities for linear continuations, respectively. In particular, we show that the syntax of the linear classical L-calculus can be interpreted in any dialogue duploid, and that it defines in fact a syntactic dialogue duploid. As an application, we establish, by semantic as well as syntactic means, the Hasegawa-Thielecke theorem, which states that the notions of central map and of thunkable map coincide in any dialogue duploid (in particular, for any double negation monad on a symmetric monoidal category)
Energy Consumption of Web Applications: Measurement Challenges in Practice
No full textInternational audienceSoftware systems consume about 6% of global energy, and new regulations promote energy efficiency. However, developers face challenges like standardized measurement tools, integrating energy monitoring, and interpreting data meaningfully, which hinders informed decisions balancing energy efficiency and performance metrics. While most studies focus on small-scale projects, we addressed this gap with an industrial case study. We developed a methodology to assess energy impact using Dynatrace Carbon Impact and NeoLoad, enabling the evaluation of technology-driven design decisions under production-like conditions. This enabled the evaluation of technology-driven design decisions, such as frontend frameworks (Java Swing vs. Angular), backend stacks (legacy Java vs. Spring Boot), and serialization formats (JSON vs. Protobuf) under production-like conditions. Our insights highlight several design recommendations for energy-efficient software in industrial contexts.</div
Reframing Pattern: A Comprehensive Approach to a Composite Visual Variable
No full textInternational audienceWe present a new comprehensive theory for explaining, exploring, and using pattern as a visual variable in visualization. Although patterns have long been used for data encoding and continue to be valuable today, their conceptual foundations are precarious: the concepts and terminology used across the research literature and in practice are inconsistent, making it challenging to use patterns effectively and to conduct research to inform their use. To address this problem, we conduct a comprehensive cross-disciplinary literature review that clarifies ambiguities around the use of "pattern" and "texture". As a result, we offer a new consistent treatment of pattern as a composite visual variable composed of structured groups of graphic primitives that can serve as marks for encoding data individually and collectively. This new and widely applicable formulation opens a sizable design space for the visual variable pattern, which we formalize as a new system comprising three sets of variables: the spatial arrangement of primitives, the appearance relationships among primitives, and the retinal visual variables that characterize individual primitives. We show how our pattern system relates to existing visualization theory and highlight opportunities for visualization design. We further explore patterns based on complex spatial arrangements, demonstrating explanatory power and connecting our conceptualization to broader theory on maps and cartography
Automatic Extraction of Timing Models for WCET Estimation From a High-Level Synthesis Flow
No full textInternational audienceReal-time, domain-specific processors require faithful timing models for WCET analysis. However, existing models are typically hand-crafted from sparse documentation, making them error-prone and difficult to maintain. This work aims to automatically extract WCET timing models from single-issue in-order processor pipelines generated by High-Level Synthesis (HLS). By deriving timing models directly from the SpecHLS intermediate representation, the models are faithful by construction. Experimental results show that our timing-model extraction process generalizes across diverse RISC-V core variants and yields WCET estimates within 0.48% on average of those from a handcrafted model, on the Mälardalen WCET benchmarks