Project

Containment, Avalanches and Optimisation in Spreading-processes

Goal: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 835913. It aims to develop theoretical methods to better understand spreading processes, contribute to the combat of COVID-19 and apply the developed techniques to neighbouring disciplines in complex systems.

Date: 1 August 2019

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Bo Li
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Bo Li
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Optimizing embedded systems, where the optimization of one depends on the state of another, is a formidable computational and algorithmic challenge, that is ubiquitous in real-world systems. We study flow networks, where bilevel optimization is relevant to traffic planning, network control and design, and where flows are governed by an optimization requirement subject to the network parameters. We employ message-passing algorithms in flow networks with sparsely coupled structures to adapt network parameters that govern the network flows, in order to optimize a global objective. We demonstrate the effectiveness and efficiency of the approach on randomly generated graphs.
We study the space of functions computed by random-layered machines, including deep neural networks and Boolean circuits. Investigating the distribution of Boolean functions computed on the recurrent and layer-dependent architectures, we find that it is the same in both models. Depending on the initial conditions and computing elements used, we characterize the space of functions computed at the large depth limit and show that the macroscopic entropy of Boolean functions is either monotonically increasing or decreasing with the growing depth.
Balancing traffic flow by influencing drivers' route choices to alleviate congestion is becoming increasingly more appealing in urban traffic planning. Here, we introduce a discrete dynamical model comprising users who make their own routing choices on the basis of local information and those who consider routing advice based on localized inducement. We identify the formation of traffic patterns, develop a scalable optimization method for identifying control values used for user guidance, and test the effectiveness of these measures on synthetic and real-world road networks.
Bo Li
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 835913. It aims to develop theoretical methods to better understand spreading processes, contribute to the combat of COVID-19 and apply the developed techniques to neighbouring disciplines in complex systems.