Seminari - Dipartimento Computer Science Seminari - Dipartimento Computer Science validi dal 20.06.2019 al 20.06.2020. Mean Field Games with state constraints: from mild to pointwise solutions of the PDE system. Relatore: Rossana Capuani; Provenienza: North Carolina State University; Data inizio: 2019-06-20; Ora inizio: 14.00; Referente interno: Antonio Marigonda; Riassunto: 20th June 2019 - h 14:00 - Room M Mean Field Games (MFG) with state constraints are differential games with infinitely many agents, each agent facing a constraint on his state. In this case, the existence and uniqueness of Nash equilibria cannot be deduced as for unrestricted state space because, for a large set of initial conditions, the uniqueness of solutions to the minimization problem which is solved by each agent is no longer guaranteed. Therefore, we attack the problem by interpreting equilibria as measures in a space of arcs and we introduce the definition of mild solution for MFG with state constraints. More precisely, we define a mild solution as a pair (u, m) isin; C([0, T ] times; Omega;) times; C([0, T ]; P(Omega;)), where m is given by m(t) = e_t#eta; for some constrained MFG equilibrium eta; and u(x,t) is the value function associated to the problem. For more details see [1]. The aim of this talk is to provide a meaning of the PDE system associated with these games, the so-called Mean Field Game system with state constraints. For this, we will analyze the regularity of mild solution and we will show that it satisfies the MFG system in suitable pointwise sense. These results have been obtained in collaboration with Piermarco Cannarsa (Rome Tor Vergata) and Pierre Cardaliaguet (Paris-Dauphine). References [1] Cannarsa, P., Capuani, R., Existence and uniqueness for Mean Field Games with state constraints,rdquo;PDE models for multi-agent phenomenardquo;, P. Cardaliaguet, A.Porretta, F. Salvarani editors, Springer INdAM Series, 2017. [2] Cannarsa, P., Capuani, R., and Cardaliaguet, P., C 1,1 ndash;smoothness of constrained solutions in the calculus of variations with application to mean field games, Mathematics in Engineering,1(1): 174-203,doi:10.3934/Mine.2018.1.174., 2018. [3] Cannarsa, P., Capuani, R., and Cardaliaguet, P., Mean Field Games with state constraints: from mild to pointwise solutions of the PDE system, submitted,, 2018. Thu, 20 Jun 2019 14:00:00 +0200 Modeling and Recognizing Network Scanners with Finite Mixture Models and Hidden Markov Models Relatore: Giulia De Santis; Provenienza: Inria Nancy; Data inizio: 2019-06-24; Ora inizio: 15.00; Note orario: Aula Verde; Riassunto: The talk will present how stochastic models of ZMap and Shodan, respectively (two Internet-wide scanners) have been built. More in detail, packets originated by each of the two considered scanners have been collected by the High Security Lab hosted in Inria Nancy - Grand Est, and have been used to learn Hidden Markov Models (HMMs). The first part of the talk models intensity of the two considered Network Scanners, in order to know if the intensity of ZMap varies with respect to the targeted service, and if the intensities of the two scanners are comparable. Results will be presented: the answer to the first question is positive, whereas the answer to the second question is negative. The talk follows with investigating spatial and temporal movements, respectively, of the same Network Scanners. Datasets containing logs of one single execution of ZMap and Shodan, respectively have been created. Then, differences of IP addresses consecutively targeted by the same scanner (i.e., in each sample), and of the corresponding timestamps have been computed. The former have been used to model spatial movements, whereas the latter temporal ones. Once the Hidden Markov Models are available, they have been applied to detect scanners from other sets of logs. In both cases, our models are not able to detect the targeted service, but they correctly detect the scanner that originates new logs, with an accuracy of 95% when exploiting spatial movements, and of 98% when using temporal movements. Contact person: Roberto Giacobazzi. Mon, 24 Jun 2019 15:00:00 +0200 Towards Collaborative 3D Design Relatore: Fabio Pellacini; Provenienza: Università Sapienza; Data inizio: 2019-06-25; Ora inizio: 16.30; Note orario: Aula Verde; Riassunto: Collaborative document editing, in real-time or offline, is ubiquitous when running projects in teams. When designing 3D objects and environments though, collaboration is hampered by the lack of reliable solutions. In this talk, I will summarise our recent work on bringing collaboration to 3D design tasks. In particular, I will show prototype systems for real-time collaborative shape modeling, like Google Docs, and for offline whole scene version control, like Git. Bio: Fabio Pellacini is a Full Professor of Computer Science at Sapienza University of Rome. His work focuses on algorithms and systems for designing and rendering complex 3D environments with an emphasis on the creative, fabrication and entertainment industries. Pellacini received his Laurea degree in Physics from the University of Parma (Italy), and his M.S. and Ph.D. in Computer Science from Cornell University. Pellacini received an NSF CAREER award and and an Alfred P. Sloan fellowship for his research contributions, the two major awards for young researchers in North America (roughly equivalent in prestige to an ERC Starting Grant). Before Sapienza he was a faculty member at Dartmouth, a US Ivy Legue university, and has worked for Pixar Animation Studios on lighting algorithms, where his work received credits on three Oscar-nominated movies. Contact Person: Umberto Castellani. Tue, 25 Jun 2019 16:30:00 +0200