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Zone extrapolations in parametric timed automata
No full textInternational audienceTimed automata (TAs) are an efficient formalism to model and verify systems with hard timing constraints and concurrency. While TAs assume exact timing constants with infinite precision, parametric timed automata (PTAs) overcome this limitation and increase their expressiveness—at the cost of undecidability of most interesting problems. A practical explanation for the efficiency of nonparametric TAs is zone extrapolation, where clock valuations beyond a given constant are considered equivalent. This concept cannot be easily extended to PTAs, due to the fact that parameters can be unbounded, meaning that the constants compared to the clocks have no upper bound. In this work, we propose several definitions of extrapolation for PTAs, and we study their correctness. Our experiments show an overall decrease of the computation time and, most importantly, allow termination of some previously unsolvable benchmarks
Minimizing total completion time and makespan for a multi-scenario bi-criteria parallel machine scheduling problem
No full textInternational audienceMulti-criteria scheduling problems under uncertainty remain a relatively unexplored topic in theoretical computer science despite substantial practical interests. This work studies a bi-objective identical parallel machine scheduling problem under uncertainty, in which the first objective is to minimize the total completion time, and the second is to minimize the makespan. Especially a job's processing time is assumed to be represented by a polynomial function with respect to scenario u∈U, where U⊂R+ is an interval containing an infinite number of scenarios. In this work, we are looking for a compact and complete description of the set of possibly optimal solutions, along with their objective function values, over the set of scenarios or an approximation with a performance guarantee. First, to better understand the characteristics of the studied problem, we consider two single-objective problems: parallel machine scheduling problem under uncertainty with the total completion time and makespan criterion, respectively. For the problem with the total completion time criterion, we demonstrate that the set of possibly optimal schedules can be found in polynomial time. In contrast, for the problem with the makespan criterion, we provide a (1+ϵ)-approximation algorithm. For the bi-objective problem, we provide a 2-approximation algorithm for any number of parallel machines and a (1+ϵ)-approximation algorithm where a fixed number of parallel machines is considered
DH-PTAM: A Deep Hybrid Stereo Events-Frames Parallel Tracking And Mapping System
No full textInternational audienceThis paper presents a robust approach for a visual parallel tracking and mapping (PTAM) system that excels in challenging environments. Our proposed method combines the strengths of heterogeneous multi-modal visual sensors, including stereo event-based and frame-based sensors, in a unified reference frame through a novel spatio-temporal synchronization approach. We employ deep learning-based feature extraction and description for estimation to enhance robustness further. We also introduce an end-to-end parallel tracking and mapping optimization layer complemented by a simple loop-closure algorithm for efficient SLAM behavior. Through comprehensive experiments on both small-scale and large-scale real-world sequences of VECtor and TUM-VIE benchmarks, our proposed method (DH-PTAM) demonstrates superior performance in terms of robustness and accuracy in adverse conditions, especially in large-scale HDR scenarios. Our implementation's research-based Python API is publicly available on GitHub for further research and development: \url{https://github.com/AbanobSoliman/DH-PTAM}
User-Centric Challenges in Digital Identity Wallets: Insights from Industry Experimentation
No full textInternational audienceThis article explores the human constraints associated with the use of digital identity wallets, emphasizing the importance of a user-centered approach. We have examined the regulatory constraints, such as the eIDAS regulation, which impact the use cases of digital identity wallets. By developing an innovative solution that constructs documents from identity attributes stored in digital wallets, we aim to identify and address users’ privacy concerns and strengthen user control. Our findings provide valuable insights into the future development of digital identity technologies, highlighting the need for security, privacy, and user autonomy
De l’investissement aux engagements pour l'écologie. Le cas desporteurs de l’enjeu environnemental dans les organisations sportives
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Unraveling genetic load dynamics during biological invasion: insights from two invasive insect species
No full textInternational audienceMany invasive species undergo a significant reduction in genetic diversity, i.e. a genetic bottleneck, in the early stages of invasion. However, this reduction does not necessarily prevent them from achieving considerable ecological success and becoming highly efficient colonizers. Here we investigated the purge hypothesis, which suggests that demographic bottlenecks may facilitate conditions (e.g., increased homozygosity and inbreeding) under which natural selection can purge deleterious mutations, thereby reducing genetic load. We used a transcriptome-based exome capture protocol to identify thousands of SNPs in coding regions of native and invasive populations of two highly successful invasive insect species, the western corn rootworm (Chrysomelidae: Diabrotica virgifera virgifera) and the harlequin ladybird (Coccinelidae: Harmonia axyridis). We categorized and polarized SNPs to investigate changes in genetic load between invasive populations and their sources. Our results differed between species. In D. virgifera virgifera, although there was a general reduction in genetic diversity in invasive populations, including that associated with genetic load, we found no clear evidence for purging of genetic load, except marginally for highly deleterious mutations in one European population. Conversely, in H. axyridis, the reduction in genetic diversity was minimal, and we detected signs of genetic load fixation in invasive populations. These findings provide new insights into the evolution of genetic load during invasions, but do not offer a definitive answer to the purge hypothesis. Future research should include larger genomic datasets and a broader range of invasive species to further elucidate these dynamics
HKT: A Biologically Inspired Framework for Modular Hereditary Knowledge Transfer in Neural Networks
No full textA prevailing trend in neural network research suggests that model performance improves with increasing depth and capacity - often at the cost of integrability and efficiency. In this paper, we propose a strategy to optimize small, deployable models by enhancing their capabilities through structured knowledge inheritance. We introduce Hereditary Knowledge Transfer (HKT), a biologically inspired framework for modular and selective transfer of task-relevant features from a larger, pretrained parent network to a smaller child model. Unlike standard knowledge distillation, which enforces uniform imitation of teacher outputs, HKT draws inspiration from biological inheritance mechanisms - such as memory RNA transfer in planarians - to guide a multi-stage process of feature transfer. Neural network blocks are treated as functional carriers, and knowledge is transmitted through three biologically motivated components: Extraction, Transfer, and Mixture (ETM). A novel Genetic Attention (GA) mechanism governs the integration of inherited and native representations, ensuring both alignment and selectivity. We evaluate HKT across diverse vision tasks, including optical flow (Sintel, KITTI), image classification (CIFAR-10), and semantic segmentation (LiTS), demonstrating that it significantly improves child model performance while preserving its compactness. The results show that HKT consistently outperforms conventional distillation approaches, offering a general-purpose, interpretable, and scalable solution for deploying high-performance neural networks in resource-constrained environments
Additive manufacturing for improving supply chain resilience under the ripple effect
No full textInternational audienceAdditive manufacturing (AM) is a revolutionary technology gaining substantial interest from academia and industry. Facing supply chain (SC) disruption risks, AM helps SC partners restore capacities through in-house and on-demand production. Compared to common resilience strategies, e.g. using backup suppliers and outsourcing, AM can reduce structural SC redundancy and enhance responsiveness to market demands. However, AM's impacts on SCs under the ripple effect remain insufficiently explored. This work investigates a SC resilience improvement problem by combining a novel AM strategy with inventory redundancy. For the problem, a dynamic Bayesian network is applied to portray the ripple effect, and a problem-specific Markov decision process is proposed to quantify the impacts of resilience strategies. A new mixed-integer non-linear non-convex optimisation model is established to minimise the disruption risk, and a Q-learning-based genetic algorithm is designed for solving large-scale problems. Key managerial insights from the case study include: (i) the proposed approach assists SC managers in prioritising key partners and implementing differentiated strategies based on partners' positions within the SC under limited budgets; and (ii) the temporal factor is critical, necessitating AM machine rentals for immediate post-disruption response and early AM machine purchases to ensure long-term resilience
A prime editing strategy to rewrite the γ-globin promoters and reactivate fetal hemoglobin for sickle cell disease
No full textInternational audienceFetal hemoglobin reactivation is a promising therapy for β-hemoglobinopathies. We developed a prime editing strategy that introduces multiple mutations in the fetal γ-globin promoters that are expected to increase their activity. We tested multiple targets and optimized a variety of parameters to achieve ∼50% of precise edits in a hematopoietic cell line, with minimal off-target effects. This work improved our understanding of the complex DNA repair mechanisms involved in prime editing. We tested this strategy in patients’ hematopoietic stem/progenitor cells. Although editing efficiency was variable among donors, erythroid clones carrying multiple mutations expressed a significantly higher γ-globin level than cells carrying individual mutations, confirming the potential therapeutic benefit of our combined strategy for patients with β-hemoglobinopathies
A new dystrophin deficient rat model mirroring exon skipping in patients with DMD exon 45 deletions
No full textInternational audienceABSTRACT Mutations in the dystrophin (DMD) gene can cause a spectrum of muscle-wasting disorders ranging from the milder Becker muscular dystrophy (BMD) to the more severe Duchenne muscular dystrophy (DMD). Among these, exon 45 deletion is the most frequently reported single exon deletion in DMD patients worldwide. In this study, we generated a novel rat model with an exon 45 deletion using CRISPR/Cas9 technology. The Dmd Δ45 rat recapitulate key clinical and molecular features of DMD, including progressive skeletal muscle degeneration, cardiac dysfunction, cognitive deficits, elevated circulating muscle damage biomarkers, impaired muscle function, and overall reduced lifespan. Transcriptomics analyses confirmed the deletion of exon 45 and revealed gene expression patterns consistent with dystrophin deficiency. In the skeletal muscle, RNA-seq profiles demonstrated a transition from early stress responses and regenerative activity at 6 months to chronic inflammation, fibrosis, and metabolic dysfunction by 12 months. Similarly, the cardiac transcriptomic shifted from an early inflammatory and stress-responsive state to one characterized by fibrotic remodelling and metabolic impairment. Despite these pathological features, the Dmd Δ45 rats exhibited a milder phenotype than other DMD rat models. This attenuation may be attributed to spontaneous exon 44 skipping, which partially restores the reading frame and results in an age-dependent increase in revertant dystrophin-positive fibres. Further analysis indicated downregulation of spliceosome-related genes, suggesting a potential mechanism driving exon skipping in this model. In summary, the Dmd Δ45 rat represents a valuable model for investigating both the molecular determinants of phenotypic variability and the endogenous mechanisms of exon skipping. These findings offer important insights for the development of personalized exon-skipping therapies, particularly for DMD patients with exon 45 deletions