Simple Monte Carlo model for crowd dynamics

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Publications associées (29)

Fast Bayesian estimation of spatial count data models

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Spatial count data models are used to explain and predict the frequency of phenomena such as traffic accidents in geographically distinct entities such as census tracts or road segments. These models are typically estimated using Bayesian Markov chain Mont ...

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Steady-state neutronic measurements and comprehensive numerical analysis for the BME training reactor

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This paper describes steady-state reactor physics measurements and calculations that were performed for the Training Reactor of Budapest University of Technology and Economics (BME TR) with the purpose of benchmarking. Based on the available geometry speci ...

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On Consensusability of Linear Interconnected Multi-Agent Systems and Simultaneous Stabilization

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Consensusability of multi-agent systems (MASs) certifies the existence of a distributed controller capable of driving the states of each subsystem to a consensus value. We study the consensusability of linear interconnected MASs (LIMASs) where, as in sever ...

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Tenants’ residential mobility in Switzerland: the role of housing functions

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The interaction between residential preferences and dwellings is a complex system whose function thus far remains insufficiently explored. In this paper, we investigate housing functions as orchestrators of households’ residential mobility in the context o ...

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Vision-based Drone Flocking in Outdoor Environments

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Decentralized deployment of drone swarms usually relies on inter-agent communication or visual markers that are mounted on the vehicles to simplify their mutual detection. This letter proposes a vision-based detection and tracking algorithm that enables gr ...

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Flocking-enhanced social contagion

Ignacio Pagonabarraga Mora

Populations of mobile agents-animal groups, robot swarms, or crowds of people-self-organize into a large diversity of states as a result of information exchanges with their surroundings. While in many situations of interest the motion of the agents is driv ...

AMER PHYSICAL SOC2020

Adaptation And Learning In Multi-Task Decision Systems

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Adaptation and learning over multi-agent networks is a topic of great relevance with important implications. Elaborating on previous works on single-task networks engaged in decision problems, here we consider the multi-task version in the challenging scen ...

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Dynamic Federated Learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in an online manner, and are hence adaptive by nature, most performance analyse ...

IEEE2020

Distributed Learning over Networks under Subspace Constraints

Ali H. Sayed, Roula Nassif, Stefan Vlaski

This work presents and studies a distributed algorithm for solving optimization problems over networks where agents have individual costs to minimize subject to subspace constraints that require the minimizers across the network to lie in a low-dimensional ...

IEEE2019

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