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Phase diagram of the interacting partially directed self-avoiding walk attracted by a vertical wall
No full textIn the present paper, we consider the interacting partially-directed self-avoiding walk (IPDSAW) attracted by a vertical wall. The IPDSAW was introduced by Zwanzig and Lauritzen (J. Chem. Phys., 1968) as a manner of investigating the collapse transition of a homopolymer dipped in a repulsive solvent. We prove in particular that a surface transition occurs inside the collapsed phase between (i) a regime where the attractive vertical wall does not influence the geometry of the polymer and (ii) a regime where the polymer is partially attached at the wall on a length that is comparable to its horizontal extension, modifying its asymptotic Wulff shape. The latter rigorously confirms the conjecture exposed by physicists in (Physica A: Stat. Mech. \& App., 2002). We push the analysis even further by providing sharp asymptotics of the partition function inside the collapsed phase
What's next? (Un)learning Nothingness and Non-events in Management Education
No full textInternational audienceMost management and organization theories focus on the full existence and finitude of things. They deal with fullness and the full happening of things. Both organizing and managing mean fully producing something, doing something, or giving value to something. A good manager should follow what is happening and, even better, make things fully happen. But in everyday life, our managerial capitalism makes the world more and more impatient, problematic and incomplete, full of more and more holes, interruptions and voids that permeate experience. This is true both emotionally (as frustration), narratively (as cliffhangers and suspense) and materially (as creative destruction scars our earth). In this essay for ML's 55th anniversary, I argue for a process-oriented perspective on managerial emptiness and incompleteness based on three core interwoven negative processes - representation, narration and materialization. I explain how each of these processes contributes to a nexus of incompleting events at the heart of m
Greening aviation with sustainable aviation fuels : Insights from decarbonization scenarios
No full textInternational audienceRecent studies outline markedly different possible decarbonization pathways for civil aviation by 2050. This paper examines how the key assumptions retained in these scenarios (i.e., the posited deployment of sustainable aviation fuels [SAFs], the projected demand trajectory, and the availability of electric and hydrogen-fueled solutions) affect the sector's future emissions of greenhouse gas. Data for 67 recent scenarios from industry-related, academic, institutional, and think tanks/NGO sources are used to perform the analysis. The results shed light on the shared properties of these scenarios. First, we find a clear consensus on the negative impact of SAFs on residual GHG emissions by 2050, conditioning to a high level of SAF penetration. Second, these scenarios posit a smaller decarbonizing power of biomass-based SAF than that of e-fuel. Third, we find signs of authorship bias in some scenarios. This last finding, therefore, raises concerns about the direct use of these scenarios in policymaking
Piercing the holding veil to enter family capital: Financialization dynamics and structures of the Peugeot family's capital accumulation 1965-2020
No full textInternational audienceThrough a longitudinal case study spanning 1965 to 2020, this article scrutinizes the evolution of the influential French Peugeot family's ownership strategies. It elucidates how the family transitioned from industrial management to becoming a significant player in international financial investment. By delving into archival materials and conducting interviews with key executives and family members, this study illustrates the family's adeptness at maintaining control over its industrial empire by associating external financiers and in fine unlocking resources for other lucrative financial ventures. This transformation was facilitated by a sophisticated three-tiered holding structure, which served dual purposes: overseeing capital control and managing private wealth, often through private equity mechanisms. Given the widespread adoption of such financial structures among European corporations and wealthy families, we advocate the need to pierce this "holding veil" to understand capital accumulation transformations for large families in the long run
On the Fisher infinitesimal model without variability
No full textInternational audienceWe study the long-time behavior of solutions to a model of sexual populations structured in phenotypes. The model features a nonlinear integral reproduction operator derived from the Fisher infinitesimal operator and a trait-dependent selection term. The reproduction operator describes here the inheritance of the mean parental traits to the offspring without variability. We show that, under assumptions on the growth of the selection rate, Dirac masses are stable around phenotypes for which the difference between the selection rate and its minimum value is less than 1/2. Moreover, we prove the convergence in some Fourier-based distance of the centered and rescaled solution to a stationary profile under some conditions on the initial moments of the solution
What do my users want? Leveraging users insights to improve recommender systems in eWOM communities
No full textInternational audienceeWOM (electronic word-of-mouth) communities not only help their users to gain insights through the exchange of information about products, but also to make the right purchase decisions on the basis of other users' opinions. The vast number of reviews and ratings contain plenty of useful information and recommender systems are an effective tool for filtering them and providing users with the information they are looking for. However, traditional recommender systems use the rating as an input to recommend items, which leads to the cold-start problem and data sparsity. The aim of this paper is to reduce the undesirable outcomes caused by these problems and to optimize the predictive outcomes of the recommendations in the context of eWOM communities. To this end, we propose a hybrid recommender system that combines Social and eWOM variables as an input and uses the Kmeans algorithm for dimensionality reduction and the collaborative filtering SVD++ algorithm to optimize the accuracy of recommendations. Our results show that recommender systems based on users' behavioral data from eWOM communities improve recommendations compared to other recommender systems that use different variables as an input and PCA as a dimensionality reduction technique
Interpretability of Riemannian tools used in brain computer interfaces
No full textInternational audienceRiemannian methods have established themselves as stateof-the-art approaches in Brain-Computer Interfaces (BCI) in terms of performance. However, their adoption by experimenters is often hindered by a lack of interpretability. In this work, we propose a set of tools designed to enhance practitioners' understanding of the decisions made by Riemannian methods. Specifically, we develop techniques to quantify and visualize the influence of the different sensors on classification outcomes. Our approach includes a visualization tool for high-dimensional covariance matrices, a classifieragnostic tool that focuses on the classification process, as well as methods that leverage the data's topology to better characterize the role of each sensor. We demonstrate these tools on a specific dataset and provide Python code to facilitate their use by practitioners, thereby promoting the adoption of Riemannian methods in BCI
Les permutations accélèrent l'Approximate Bayesian Computation
No full textApproximate Bayesian Computation (ABC) methods have become essential tools for performing inference when likelihood functions are intractable or computationally prohibitive. However, their scalability remains a major challenge in hierarchical or high-dimensional models. In this paper, we introduce permABC, a new ABC framework designed for settings with both global and local parameters, where observations are grouped into exchangeable compartments. Building upon the Sequential Monte Carlo ABC (ABC-SMC) framework, permABC exploits the exchangeability of compartments through permutation-based matching, significantly improving computational efficiency. We then develop two further, complementary sequential strategies: Over Sampling, which facilitates early-stage acceptance by temporarily increasing the number of simulated compartments, and Under Matching, which relaxes the acceptance condition by matching only subsets of the data. These techniques allow for robust and scalable inference even in high-dimensional regimes. Through synthetic and real-world experiments -- including a hierarchical Susceptible-Infectious-Recover model of the early COVID-19 epidemic across 94 French departments -- we demonstrate the practical gains in accuracy and efficiency achieved by our approach.Les méthodes d’Approximate Bayesian Computation (ABC) sont devenues des outils essentiels pour réaliser de l’inférence lorsque les fonctions de vraisemblance sont inaccessibles ou trop coûteuses à calculer. Toutefois, leur passage à l’échelle demeure un défi majeur dans les modèles hiérarchiques ou de grande dimension.Dans cet article, nous introduisons permABC, un nouveau cadre ABC conçu pour les situations comportant à la fois des paramètres globaux et locaux, où les observations sont regroupées en compartiments échangeables. En s’appuyant sur le schéma Sequential Monte Carlo ABC (ABC-SMC), permABC exploite l’échangeabilité des compartiments via un appariement fondé sur des permutations, ce qui améliore significativement l’efficacité computationnelle.Nous développons ensuite deux stratégies séquentielles complémentaires : Over Sampling, qui facilite l’acceptation en début de procédure en augmentant temporairement le nombre de compartiments simulés, et Under Matching, qui assouplit la condition d’acceptation en n’appariement que des sous-ensembles de données. Ces techniques permettent une inférence robuste et scalable, même en régime de grande dimension.À travers des expériences synthétiques et empiriques — notamment un modèle hiérarchique Susceptible–Infectious–Recover (SIR) de la première vague de l’épidémie de COVID-19 dans 94 départements français — nous démontrons les gains pratiques en termes de précision et d’efficacité obtenus par notre approche
Survivable two-fault-tolerant ring star problem
No full textInternational audienceDue to the increasing demand for reliability in the telecommunication and transportation sectors, designing a network that can survive equipment failures is critical. This work addresses the challenge by proposing a 2-fault-tolerant network design for preserving the ring star topology. We study the survivable ring star problem, assuming that at most two hubs can be down simultaneously, and propose an integer linear programming formulation to describe this problem. Moreover, we perform a Branch-and-Benders-Cut (BBC) approach to solve the problem more efficiently. BBC is a branch-and-cut approach where we solve the master problem of Benders decomposition and add optimality cuts from the Benders subproblem on the fly, using callbacks. We also present how we transform subproblems into linear programs and some techniques to accelerate the solution process. Using two classes of instances, we compare the proposed Branch-and-Benders-Cut method with the integer linear programming formulation. The results indicate that the performance of the former method is better when the number of nodes is substantial, whereas the latter method is prone to memory-related issues. The comparison of the 2-fault-tolerant ring star problem with the 1-fault-tolerant version shows that the 2-fault-tolerant problem version is more difficult to solve