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This paper investigates reverse auctions that involve continuous values of different types of goods, general nonconvex constraints, and second stage costs. We seek to design the payment rules and conditions under which coalitions of participants cannot inf ...
The COVID-19 pandemic created a noticeable challenge to the cryptographic community with the development of contact tracing applications. The media reported a dispute between designers proposing a centralized or a decentralized solution (namely, the PEPP-P ...
Neural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates. In many applications physical constraints, such as mass or energy conservation, must be satisfied to obtai ...
Phasor data concentrators (PDCs) are essential functions in synchrophasor networks that collect and aggregate phasor measurement unit (PMU) data from across the electric grid. PDC buffers synchrophasor datasets during the “wait time” period, then communica ...
We propose two novel conditional gradient-based methods for solving structured stochastic convex optimization problems with a large number of linear constraints. Instances of this template naturally arise from SDP-relaxations of combinatorial problems, whi ...
In learning from demonstrations, many generative models of trajectories make simplifying assumptions of independence. Correctness is sacrificed in the name of tractability and speed of the learning phase. The ignored dependencies, which are often the kinem ...
The next technological revolution will be interwoven to the proliferation of intelligent systems. As we bridge the gap between physical and cyber worlds, we will give rise to large-scale, multi-agent based technologies. A key challenge that cities of the f ...
This chapter discusses some of the opportunities offered by Big Data to understand mobility practices. However, beyond its promises, it has to be considered as human and social constructions. Therefore, the author underlines the importance of describing al ...
Neural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates. In many applications physical constraints, such as mass or energy conservation, must be satised to obtain ...
We consider multiagent decision making where each agent optimizes its convex cost function subject to individual and coupling constraints. The constraint sets are compact convex subsets of a Euclidean space. To learn Nash equilibria, we propose a novel dis ...