Agenda
janvier
-
12:4513:45
Résumé : Le défi du changement climatique et de l’impact de l’activité humaine sur l’environnement est sans aucun doute le plus urgent de notre époque.
Depuis des années, des efforts sont déployés pour réduire l’impact direct des systèmes, notamment en ce qui concerne la consommation d’énergie.
Néanmoins, ces tentatives se sont révélées jusqu'à présent insuffisantes. De plus en plus de chercheurs reconnaissent la complexité inhérente à la soutenabilité forte et le rôle des effets rebond dans les échecs. Ces travaux de cette thèse visaient à développer des méthodes et des outils pratiques et systémiques pour aider les designers et les décideurs à comprendre et à combattre les effets rebond. Ils s'appuient sur des méthodes et des outils issus d’approches systémiques, notamment la pensée systémique, la dynamique des systèmes et le design systémique.
En particulier, cette thèse a permis de développer Rebound Archetypes, un outil sous forme de cartes conçu pour aider les designers et les décideurs à identifier les futurs effets rebond potentiels qui pourraient résulter d’une intervention ou d’une décision de design.
De plus, cette thèse propose une méthodologie de modélisation systémique collective, qui vise à faciliter la représentation et la compréhension des effets rebond existants grâce à la modélisation, ainsi que le développement et la comparaison de stratégies de design grâce à la simulation.
Magnitude, un prototype d’outil de simulation a été conçu et développé à cette fin, permettant de mieux comprendre les exigences de la modélisation des effets indirects au sein d’un système sociotechnique. La méthodologie et les outils proposés ont été mis en œuvre dans une série d’études de cas afin d’évaluer leur utilité et leur facilité d’utilisation, d’affiner leur conception, de contribuer à la connaissance sur la mitigation des effets rebond par le design, de développer des bonnes pratiques concernant la collecte de données et la modélisation, et d’identifier des pistes de recherche pour l’avenir.
Salle 178 (LaBRI) -
11:0012:00
Ralf Klasing (LaBRI)
Title: Greediness is not always a vice: Efficient Discovery Algorithms for Assignment Problems
Abstract:
Finding a maximum-weight matching is a classical and well-studied problem in computer science, solvable in cubic time in general graphs. We introduce and consider in this work the ‘‘discovery’’ variant of the bipartite matching problem (or assignment problem) where edge weights are not provided as input but must be queried, requiring additional and costly computations. Hence, discovery algorithms are developed aiming to minimize the number of queried weights while providing guarantees on the computed solution. We show in this work the hardness of the underlying problem in general while providing several efficient algorithms that can make use of natural assumptions about the order in which the nodes are processed by the greedy algorithms. Our motivations for exploring this problem stem from finding practical solutions to maximum-weight matching in hypergraphs, a problem recently emerging in the formation of peer-to-peer energy sharing communities.
LaBRI salle 178 - lien visio https://webconf.u-bordeaux.fr/b/arn-4tr-7gp
février
-
09:00
Ces journées, organisées par l'équipe de Combinatoire et interactions du LaBRI (https://ci.labri.fr/), rassemblent des combinatoristes répartis sur tout le territoire, géographique comme thématique : entre 12 et 15 exposés de recherche couvrant un large spectre des objets et méthodes de la combinatoire seront proposés aux participants.
Liens utiles :
https://jcb.labri.fr/2025
https://jcb.labri.fr/2025/inscription (avant le 6 janvier)La liste des orateurs et oratrices de cette édition est :
Alice Contat (LAGA, Université Sorbonne Paris Nord)
Alice Pellet-Mary (IMB, CNRS, Université de Bordeaux)
Alicia Castro (LaBRI, Université de Bordeaux)
Andrea Sportiello (CNRS, LIPN, Université Paris Nord)
Herman Goulet-Ouellet (Université Technique de Prague)
Jean-René Chazottes (CNRS, Centre de Physique Théorique de l'École polytechnique)
Maria Bras-Amorós (Universitat Rovira i Virgili)
Martin Rubey (Institut für Diskrete Mathematik und Geometrie, TU Wien)
Thomas Gerber (Institut Camille Jordan - Université Lyon 1)
Victor Dubach (Institut Élie Cartan de Lorraine, Université de Lorraine)
Zoé Varin (LaBRI, Université de Bordeaux)
Amphi LaBRI -
13:0014:00
In order to understand and verify complex systems, we need accurate models that are either understandable for humans or can be analyzed fully automatically. Such models, however, are typically not available for legacy software. Active automata learning is a black-box technique for constructing state machine models of software and hardware components from information obtained through testing (i.e., providing inputs and observing the resulting outputs) and may thus fill this gap. In many applications, timing plays a crucial role, which in turn makes extending automata learning to a setting that incorporates quantitative timing information challenging. In this talk, we present an active learning algorithm for a general class of Mealy machines with timers (MMTs) in a black-box context. A Mealy machine is a finite state machine that outputs a sequence of symbols for every processed input word. We then augment it with timers that force certain transitions to occur after a certain amount of time has elapsed. Our algorithm is an extension of the L# algorithm of Vaandrager et al. [1] to a timed setting. Like the algorithm for learning timed automata proposed by Waga [2], our algorithm is inspired by ideas of Maler & Pnueli [3]. Based on the elementary languages of [3], both Waga's and our algorithm use symbolic queries, which are then implemented using finitely many concrete queries. However, whereas Waga needs exponentially many concrete queries to implement a single symbolic query, we only need a polynomial number. This is because in order to learn a timed automaton, a learner needs to determine the exact guard and reset for each transition (out of exponentially many possibilities), whereas for learning an MMT a learner only needs to figure out which of the preceding transitions caused a timeout. As shown in a previous work [4], this can be done efficiently for a subclass of MMTs that are “race-avoiding”: if a timeout is caused by a preceding input then a slight change in the timing of this input will induce a corresponding change in the timing of the timeout.
[1]: Frits W. Vaandrager, Bharat Garhewal, Jurriaan Rot, and Thorsten Wißmann. A new approach for active automata learning based on apartness. TACAS 2022.
[2]: Masaki Waga. Active learning of deterministic timed automata with myhill-nerode style characterization. CAV 2023.
[3]: Oded Maler and Amir Pnueli. On recognizable timed languages. FOSSACS 2004.
[4]: Véronique Bruyère, Guillermo A. Pérez, Gaëtan Staquet, and Frits W. Vaandrager. Automata with timers. FORMATS 2023.076 -
10:4511:45
Quantum physics and chemistry have provided well-recognized theoretical tools to predict the behavior of molecules and materials described by the Schrödinger equation. However, many problems with high industrial and societal impact remain intractable for classical computers, urging us to reconsider our preconceptions and shift gears. The world of the infinitely small obeys the laws of quantum mechanics, suggesting the need for a machine governed by the same physics: this marks the birth of quantum computers, a new technological revolution which promises a quantum advantage (speed-up) over classical computers. In this talk, I will present the state-averaged orbital-optimized variational quantum eigensolver (SA-OO-VQE), designed to address the electronic structure problem for excited states [1-3], essential to unravel ubiquitous ultrafast (subpicosecond) photochemical and photophysical ‘energy/charge/matter/ information’-transfer processes induced upon the absorption of light by molecules within the UV-visible domain. I will show that SA-OO-VQE exhibits a propensity to produce an ab initio quasidiabatic representation “for free” if considered as a least-transformed block-diagonalization procedure [4]. These recent findings underscore the practical utility and potential of SA-OO-VQE for addressing systems with complex nonadiabatic phenomena.
[1] Nakanishi K. M., Mitarai K., & Fujii K. (2019). Subspace-search variational quantum eigensolver for excited states. Physical Review Research, 1(3), 033062.
[2] Yalouz S., Senjean B., Günther J., Buda F., O’Brien T. E., & Visscher L. (2021). A state-averaged orbital-optimized hybrid quantum–classical algorithm for a democratic description of ground and excited states. Quantum Science and Technology, 6(2), 024004.
[3] Yalouz S., Koridon E., Senjean B., Lasorne B., Buda F., & Visscher L. (2022). Analytical nonadiabatic couplings and gradients within the state-averaged orbital-optimized variational quantum eigensolver. Journal of chemical theory and computation, 18(2), 776-794.
[4] Illesova S., Beseda M., Yalouz S., Lasorne B., & Senjean B., to be submitted.Room 178