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pro vyhledávání: '"Mattsson, A"'
In data-driven control design, an important problem is to deal with uncertainty due to limited and noisy data. One way to do this is to use a min-max approach, which aims to minimize some design criteria for the worst-case scenario. However, a strate
Externí odkaz:
http://arxiv.org/abs/2409.16041
Autor:
Pérez, Elsa López, Selander, Inria Göran, Mattsson, John Preuß, Watteyne, Thomas, Vučinić, Mališa
Publikováno v:
IEEE Computer Society, 2024
The paper wraps up the call for formal analysis of the new security handshake protocol EDHOC by providing an overview of the protocol as it was standardized, a summary of the formal security analyses conducted by the community, and a discussion on op
Externí odkaz:
http://arxiv.org/abs/2407.07444
Autor:
Mattsson, Carolina ES
Growing network models can potentially be a useful tool in the development of economic theory. This work introduces an "opportunistic attachment" mechanism where incoming nodes, in deciding where to join a network, consider features of the entry poin
Externí odkaz:
http://arxiv.org/abs/2406.11405
Previous research has raised concerns about energy droughts in renewables-based energy systems. This study explores the ability of reservoir hydropower to sustain a high output and, thereby, mitigate such energy droughts. Using detailed modelling, we
Externí odkaz:
http://arxiv.org/abs/2405.13530
We show how dynamic heterogeneities (DH), a hallmark of glass-forming materials, depend on chain flexibility and chain length in polymers. For highly flexible polymers, a relatively large number of monomers ($N_c\sim500$) undergo correlated motion at
Externí odkaz:
http://arxiv.org/abs/2405.02733
Recently, several direct Data-Driven Predictive Control (DDPC) methods have been proposed, advocating the possibility of designing predictive controllers from historical input-output trajectories without the need to identify a model. In this work, we
Externí odkaz:
http://arxiv.org/abs/2403.05860
Publikováno v:
Nat Commun 15, 1841 (2024)
Dust in the interstellar medium (ISM) is critical to the absorption and intensity of emission profiles used widely in astronomical observations, and necessary for star and planet formation. Supernovae (SNe) both produce and destroy ISM dust. In parti
Externí odkaz:
http://arxiv.org/abs/2402.06543
This paper presents advanced techniques of training diffusion policies for offline reinforcement learning (RL). At the core is a mean-reverting stochastic differential equation (SDE) that transfers a complex action distribution into a standard Gaussi
Externí odkaz:
http://arxiv.org/abs/2402.04080
In an effort to inform the discussion surrounding existential risks from AI, we formulate Extinction-level Goodhart's Law as "Virtually any goal specification, pursued to the extreme, will result in the extinction of humanity", and we aim to understa
Externí odkaz:
http://arxiv.org/abs/2403.05540
The goal of this paper is to provide a system identification-friendly introduction to the Structured State-space Models (SSMs). These models have become recently popular in the machine learning community since, owing to their parallelizability, they
Externí odkaz:
http://arxiv.org/abs/2312.06211