Zobrazeno 1 - 10
of 566
pro vyhledávání: '"Giordano, Paolo"'
In this paper, we establish a suitable version of the Hahn-Banach theorem within the framework of Colombeau spaces, a class of spaces used to model generalized functions. Our approach addresses the case where maps are defined $\varepsilon$-wise, whic
Externí odkaz:
http://arxiv.org/abs/2410.08679
Autor:
Giordano, Paolo
We define a mathematical notion of complex adaptive system by following the original intuition of G.K. Zipf about the principle of least effort, an intuitive idea which is nowadays informally widespread in complex systems modeling. We call generalize
Externí odkaz:
http://arxiv.org/abs/2407.02181
Autor:
Giordano, Paolo
We present the first steps of interaction spaces theory, a universal mathematical theory of complex systems which is able to embed cellular automata, agent based models, master equation based models, stochastic or deterministic, continuous or discret
Externí odkaz:
http://arxiv.org/abs/2407.02175
Publikováno v:
IEEE Robotics and Automation Letters, 2024
This paper introduces an end-to-end trajectory planning algorithm tailored for multi-UAV systems that generates collision-free trajectories in environments populated with both static and dynamic obstacles, leveraging point cloud data. Our approach co
Externí odkaz:
http://arxiv.org/abs/2406.19742
By means of several examples, we motivate that universal properties are the simplest way to solve a given mathematical problem, explaining in this way why they appear everywhere in mathematics. In particular, we present the co-universal property of S
Externí odkaz:
http://arxiv.org/abs/2404.15730
Publikováno v:
Conference on decision and control 2024, IEEE, Dec 2024, Milan (Italie), Italy
In this paper, we propose the Liquid-Graph Time-constant (LGTC) network, a continuous graph neural network(GNN) model for control of multi-agent systems based on therecent Liquid Time Constant (LTC) network. We analyse itsstability leveraging contrac
Externí odkaz:
http://arxiv.org/abs/2404.13982
Autor:
Fernandez-Fernandez, Raul, Aggravi, Marco, Giordano, Paolo Robuffo, Victores, Juan G., Pacchierotti, Claudio
Neural Style Transfer (NST) refers to a class of algorithms able to manipulate an element, most often images, to adopt the appearance or style of another one. Each element is defined as a combination of Content and Style: the Content can be conceptua
Externí odkaz:
http://arxiv.org/abs/2402.00722