Determinacy and Decidability of Reachability Games with Partial Observation on Both Sides

We prove two determinacy and decidability results about two-players stochastic reachability games with partial observation on both sides and finitely many states, signals and actions

Controlling a Population

We introduce a new setting where a population of agents, each modelled by a finite-state system, are controlled uniformly: the controller applies the same action to every agent. The framework is largely inspired by the control of a biological system, namely a population of yeasts, where the controller may only change the environment common to all cells. We study a synchronisation problem for such populations: no matter how individual agents react to the actions of the controller, the controller aims at driving all agents synchronously to a target state. The agents are naturally represented by a non-deterministic finite state automaton (NFA), the same for every agent, and the whole system is encoded as a 2-player game. The first player chooses actions, and the second player resolves non-determinism for each agent. The game with m agents is called the m-population game. This gives rise to a parameterized control problem (where control refers to 2 player games), namely the population control problem: can playerone control the m-population game for all m in N whatever playertwo does? In this paper, we prove that the population control problem is decidable, and it is a EXPTIME-complete problem. As far as we know, this is one of the first results on parameterized control. Our algorithm, not based on cut-off techniques, produces winning strategies which are symbolic, that i they do not need to count precisely how the population is spread between states. We also show that if the is no winning strategy, then there is a population size cutoff such that playerone wins the m-population game if and only if m< cutoff. Surprisingly, cutoff can be doubly exponential in the number of states of the NFA, with tight upper and lower bounds

Indépendance entre la croissance maximale du bulbe et la longévité foliaire chez l'éphémère printanière, Erythronium americanum (Ker-Gawl.)

L'Erythronium americanum (Ker-Gawl.) est une plante éphémère printanière dont la croissance est limitée par la force des puits (bulbe) plutôt que par la force des sources (feuille). Même si on augmente le taux de photosynthèse par un enrichissement en CO₂ ou une irradiance élevée, la croissance du bulbe demeure inchangée. Cependant, la température de croissance influence la biomasse que les bulbes peuvent atteindre: une température chaude (18 °C) réduit la croissance du bulbe par rapport à ce qui est observé au froid (8 à 12 °C). Nous ignorons toutefois si cet effet est dû à la réduction concomitante de la durée de vie des feuilles ou à un autre mécanisme. Ainsi, ce projet vise à moduler la durée de vie des feuilles de l'E. americanum grâce à l'application de Promalin ou de thiosulfate d'argent à une température qui entraîne normalement une sénescence hâtive et une faible croissance. L'augmentation de la longévité foliaire par le Promalin permet une plus grande assimilation de carbone pendant la saison, sans toutefois se traduire par une plus grande croissance du bulbe. La croissance du bulbe diminue au fil de la saison entraînant ainsi une baisse importante des taux photosynthétiques et une augmentation de la respiration du bulbe. L'application foliaire de thiosulfate d'argent a toutefois réduit la durée de vie de la feuille et la croissance du bulbe, suggérant qu'une sénescence précoce ne permet pas d'exploiter le plein potentiel de croissance du bulbe. Néanmoins, la surface de la feuille demeure fortement corrélée avec la biomasse du nouveau bulbe. Cette relation suggère que le potentiel de croissance chez cette espèce est déterminé tôt au début de la saison de croissance, et qu'il peut difficilement être augmenté par une modulation de l'activité photosynthétique ou de la durée de vie du feuillage.Growth of the spring ephemeral Erythronium americanum Ker-Gawl. is limited by sink strength (its bulb) rather than by source strength (its leaf). Increasing its photosynthetic rates through CO₂ enrichment or higher irradiance does not improve bulb growth. However, growth temperature influences bulb mass: warm growth temperature (18 °C) reduces bulb mass compared to what is observed at cooler temperatures (8 to 12 °C). We do not know if this reduced growth is due to the concomitant reduction in the leaf life span or to another mechanism. Therefore, this project aims at modulating the leaf longevity of E. americanum plants by applying either Promalin or silver thiosulphate at a growth temperature that normally hastens senescence and causes a reduced growth. Increasing leaf life span with Promalin enhances the total amount of carbon assimilated during the season, without, however, increasing bulb mass. Bulb growth decreases through the season inducing a strong decrease in photosynthetic rates and a concurrent increase in bulb respiration. Reducing leaf longevity with silver thiosulphate did reduce bulb growth, suggesting that an early senescence prevents the plant from being able to fully exploit its growth potential. Nevertheless, leaf area remains strongly correlated with final bulb mass. This relation suggests that plant's potential growth is determined early during the growing season in this species and cannot easily be increased by modulating photosynthetic rates or leaf life duration

FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning

Exemplar-free class-incremental learning is very challenging due to the negative effect of catastrophic forgetting. A balance between stability and plasticity of the incremental process is needed in order to obtain good accuracy for past as well as new classes. Existing exemplar-free class-incremental methods focus either on successive fine tuning of the model, thus favoring plasticity, or on using a feature extractor fixed after the initial incremental state, thus favoring stability. We introduce a method which combines a fixed feature extractor and a pseudo-features generator to improve the stability-plasticity balance. The generator uses a simple yet effective geometric translation of new class features to create representations of past classes, made of pseudo-features. The translation of features only requires the storage of the centroid representations of past classes to produce their pseudo-features. Actual features of new classes and pseudo-features of past classes are fed into a linear classifier which is trained incrementally to discriminate between all classes. The incremental process is much faster with the proposed method compared to mainstream ones which update the entire deep model. Experiments are performed with three challenging datasets, and different incremental settings. A comparison with ten existing methods shows that our method outperforms the others in most cases

Shared Independent Component Analysis for Multi-Subject Neuroimaging

Peer reviewe

SIG et évaluation des risques naturels: application aux risques sismiques de Quito

L'article retrace rapidement les principales étapes de la réalisation d'un scénario sismique sur la ville de Quito. Les croisements nécessaires entre les données provenant de domaines variés (sciences de la Terre, ingénierie civile, et sociodémographie) ont pu être effectués rapidement grâce à l'utilisation du SIG SAVANE. Le SIG a permis l'édition de documents graphiques décrivant de façon concrète la vulnérabilité sismique de la ville, facilitant ainsi la prise de conscience des responsables politiques et économiques

Rethinking Weight Decay For Efficient Neural Network Pruning

Introduced in the late 80's for generalization purposes, pruning has now become a staple to compress deep neural networks. Despite many innovations brought in the last decades, pruning approaches still face core issues that hinder their performance or scalability. Drawing inspiration from early work in the field, and especially the use of weight-decay to achieve sparsity, we introduce Selective Weight Decay (SWD), which realizes efficient continuous pruning throughout training. Our approach, theoretically-grounded on Lagrangian Smoothing, is versatile and can be applied to multiple tasks, networks and pruning structures. We show that SWD compares favorably to state-of-the-art approaches in terms of performance/parameters ratio on the CIFAR-10, Cora and ImageNet ILSVRC2012 datasets.Comment: 16 pages, 14 figures, submitted at ICML 2021, update : added new results, rewrit