1,721,103 research outputs found
Symmetries of differential systems
No full textFagnani, F.; Willems, J.C.. (1992). Symmetries of differential systems. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/2305
Parametrized linear systems in the behavioral approach
No full textIn the behavioral approach a dynamical system is essentially determined by a set of trajectories B, which is called behavior. There exist various ways for representing behaviors that are linear and shift-invariant: kernel representations, image representations and latent variable representations. In this paper we deal with families of parametrized linear shift-invariant behaviors and with the problem of representing such families in an efficient way. The representation of parametrized families of behaviors we propose is based on the algebraic properties of a class of rings that are called Jacobson rings. Also in this case parametrized kernel representations, parametrized image representations, and parametrized latent variable representations play an essential role. Finally, algorithms for passing from one representation to another are proposed. This also solves the parametrized latent variable elimination problem. Key words: parametrized systems, behavioral approach, parametrized kern..
Levy E., Bungener M., Fagnani F. : La croissance des dépenses de santé Levy E.: La santé fait ses comptes - Une perspective internationale
No full textThoenig Jean-Claude. Levy E., Bungener M., Fagnani F. : La croissance des dépenses de santé Levy E.: La santé fait ses comptes - Une perspective internationale. In: Politiques et management public, vol. 1, n° 1, 1983. pp. 214-215
Relationship of electron transfer process in the erythrocyte membrane with metabolic energy efficiency and body-mass index.
No full textTargeting interventions for displacement minimization in opinion dynamics
No full textSocial influence is largely recognized as a key factor in opinion formation processes. Recently, the role of external forces in inducing opinion displacement and polarization in social networks has attracted significant attention. This is in particular motivated by the necessity to understand and possibly prevent interference phenomena during political campaigns and elections. In this paper, we formulate and solve a targeted intervention problem for opinion displacement minimization on a social network. Specifically, we consider a min-max problem whereby a social planner (the defender) aims at selecting the optimal network intervention within her given budget constraint in order to minimize the opinion displacement in the system that an adversary (the attacker) is instead trying to maximize. Our results show that the optimal intervention of the defender has two regimes. For large enough budget, the optimal intervention of the social planner acts on all nodes proportionally to a new notion of network centrality. For lower budget values, such optimal intervention has a more delicate structure and is rather concentrated on a few target individuals
Through the lens of sequence submodularity
Full text linkSeveral real-world problems in engineering and applied science require the selection of sequences that maximize a given reward function. Optimizing over sequences as opposed to sets requires exploring an exponentially larger search space and can become prohibitive in most cases of practical interest. However, if the objective function is submodular (intuitively, it exhibits a diminishing return property), the optimization problem becomes more manageable. Recently, there has been increasing interest in sequence submodularity in connection with applications such as recommender systems and online ad allocation. However, mostly ad hoc models and solutions have emerged within these applicative contexts. In consequence, the field appears fragmented and lacks coherence. In this paper, we offer a unified view of sequence submodularity and provide a generalized greedy algorithm that enjoys strong theoretical guarantees. We show how our approach naturally captures several application domains, and our algorithm encompasses existing methods, improving over them
Through the lens of sequence submodularity
Full text linkSeveral real-world problems in engineering and applied science require the selection of sequences that maximize a given reward function. Optimizing over sequences as opposed to sets requires exploring an exponentially larger search space and can become prohibitive in most cases of practical interest. However, if the objective function is submodular (intuitively, it exhibits a diminishing return property), the optimization problem becomes more manageable. Recently, there has been increasing interest in sequence submodularity in connection with applications such as recommender systems and online ad allocation. However, mostly ad hoc models and solutions have emerged within these applicative contexts. In consequence, the field appears fragmented and lacks coherence. In this paper, we offer a unified view of sequence submodularity and provide a generalized greedy algorithm that enjoys strong theoretical guarantees. We show how our approach naturally captures several application domains, and our algorithm encompasses existing methods, improving over them
A unifying look at sequence submodularity
No full textSeveral real-world problems in engineering and applied science require the selection of sequences that maximize a given reward function. Optimizing over sequences as opposed to sets requires exploring an exponentially larger search space and can become prohibitive in most cases of practical interest. However, if the objective function is submodular (intuitively, it exhibits a diminishing return property), the optimization problem becomes more manageable. Recently, there has been increasing interest in sequence submodularity in connection with applications such as recommender systems and online ad allocation. However, mostly ad hoc models and solutions have emerged within these applicative contexts. In consequence, the field appears fragmented and lacks coherence. In this paper, we offer a unified view of sequence submodularity and provide a generalized greedy algorithm that enjoys strong theoretical guarantees. We show how our approach naturally captures several application domains, and our algorithm encompasses existing methods, improving over them
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