Showing papers by "Alessandro Rizzo published in 2016"

Innovation diffusion on time-varying activity driven networks

Abstract: Since its introduction in the 1960s, the theory of innovation diffusion has contributed to the advancement of several research fields, such as marketing management and consumer behavior. The 1969 seminal paper by Bass [F.M. Bass, Manag. Sci. 15, 215 (1969)] introduced a model of product growth for consumer durables, which has been extensively used to predict innovation diffusion across a range of applications. Here, we propose a novel approach to study innovation diffusion, where interactions among individuals are mediated by the dynamics of a time-varying network. Our approach is based on the Bass’ model, and overcomes key limitations of previous studies, which assumed timescale separation between the individual dynamics and the evolution of the connectivity patterns. Thus, we do not hypothesize homogeneous mixing among individuals or the existence of a fixed interaction network. We formulate our approach in the framework of activity driven networks to enable the analysis of the concurrent evolution of the interaction and individual dynamics. Numerical simulations offer a systematic analysis of the model behavior and highlight the role of individual activity on market penetration when targeted advertisement campaigns are designed, or a competition between two different products takes place.

Decentralized motion control for cooperative manipulation with a team of networked mobile manipulators

Abstract: In this paper we consider the cooperative control of the manipulation of a load on a plane by a team of mobile robots. We propose two different novel solutions. The first is a controller which ensures exact tracking of the load twist. This controller is partially decentralized since, locally, it does not rely on the state of all the robots but needs only to know the system parameters and load twist. Then we propose a fully decentralized controller that differs from the first one for the use of i) a decentralized estimation of the parameters and twist of the load based only on local measurements of the velocity of the contact points and ii) a discontinuous robustification term in the control law. The second controller ensures a practical stabilization of the twist in presence of estimation errors. The theoretical results are finally corroborated with a simulation campaign evaluating different manipulation settings.

Dark matter search in a Beam-Dump eXperiment (BDX) at Jefferson Lab

Abstract: MeV-GeV dark matter (DM) is theoretically well motivated but remarkably unexplored. This proposal presents the MeV-GeV DM discovery potential for a $\sim$1 m$^3$ segmented CsI(Tl) scintillator detector placed downstream of the Hall A beam-dump at Jefferson Lab, receiving up to 10$^{22}$ electrons-on-target (EOT) in 285 days. This experiment (Beam-Dump eXperiment or BDX) would be sensitive to elastic DM-electron and to inelastic DM scattering at the level of 10 counts per year, reaching the limit of the neutrino irreducible background. The distinct signature of a DM interaction will be an electromagnetic shower of few hundreds of MeV, together with a reduced activity in the surrounding active veto counters. A detailed description of the DM particle $\chi$ production in the dump and subsequent interaction in the detector has been performed by means of Monte Carlo simulations. Different approaches have been used to evaluate the expected backgrounds: the cosmogenic background has been extrapolated from the results obtained with a prototype detector running at INFN-LNS (Italy), while the beam-related background has been evaluated by GEANT4 Monte Carlo simulations. The proposed experiment will be sensitive to large regions of DM parameter space, exceeding the discovery potential of existing and planned experiments in the MeV-GeV DM mass range by up to two orders of magnitude.

Continuous-Time Discrete-Distribution Theory for Activity-Driven Networks.

Abstract: Activity-driven networks are a powerful paradigm to study epidemic spreading over time-varying networks. Despite significant advances, most of the current understanding relies on discrete-time computer simulations, in which each node is assigned an activity potential from a continuous distribution. Here, we establish a continuous-time discrete-distribution framework toward an analytical treatment of the epidemic spreading, from its onset to the endemic equilibrium. In the thermodynamic limit, we derive a nonlinear dynamical system to accurately model the epidemic spreading and leverage techniques from the fields of differential inclusions and adaptive estimation to inform short- and long-term predictions. We demonstrate our framework through the analysis of two real-world case studies, exemplifying different physical phenomena and time scales.

Asynchronous Max-Consensus Protocol With Time Delays: Convergence Results and Applications

Abstract: This paper deals with the analysis of the convergence properties of the max-consensus protocol in presence of asynchronous updates and bounded time delays on directed static networks. The work is motivated by real-world applications in distributed decision-making systems, for which max-consensus is an effective paradigm. The main result of this paper is that the strongly connectedness of the directed communication network is a sufficient condition for the asynchronous max-consensus protocol to let a distributed system converge in finite time. Implementation issues are also taken into account, by complementing the theoretical analysis with the definition of a mechanism to detect convergence in a distributed fashion. Finally, a numerical example is given, highlighting both the issues related to the failure of synchronous protocols applied to asynchronous settings and the effectiveness of the proposed asynchronous framework.

Target and beam-target spin asymmetries in exclusive π+ and π- Electroproduction with 1.6- to 5.7-GeV electrons

Abstract: Beam-target double spin asymmetries and target single-spin asymmetries in exclusive $\pi^+$ and $\pi^-$ electroproduction were obtained from scattering of 1.6 to 5.7 GeV longitudinally polarized electrons from longitudinally polarized protons (for $\pi^+$) and deuterons (for $\pi^-$) using the CEBAF Large Acceptance Spectrometer (CLAS) at Jefferson Lab. The kinematic range covered is $1.1 1.5$ GeV. Very large target-spin asymmetries are observed for $W>1.6$ GeV. When combined with cross section measurements, the present results will provide powerful constraints on nucleon resonance amplitudes at moderate and large values of $Q^2$, for resonances with masses as high as 2.3 GeV.

Distributed Estimation of State and Parameters in Multi-Agent Cooperative Load Manipulation

Abstract: We present two distributed methods for the estimation of the kinematic parameters, the dynamic parameters, and the kinematic state of an unknown planar body manipulated by a decentralized multi-agent system. The proposed approaches rely on the rigid body kinematics and dynamics, on nonlinear observation theory, and on consensus algorithms. The only three requirements are that each agent can exert a 2D wrench on the load, it can measure the velocity of its contact point, and that the communication graph is connected. Both theoretical nonlinear observability analysis and convergence proofs are provided. The first method assumes constant parameters while the second one can deal with time-varying parameters and can be applied in parallel to any task-oriented control law. For the cases in which a control law is not provided, we propose a distributed and safe control strategy satisfying the observability condition. The effectiveness and robustness of the estimation strategy is showcased by means of realistic MonteCarlo simulations.

Distributed Estimation for Cooperative Mobile Manipulation

Abstract: We present a distributed method for the estimation of the kinematic parameters, the dynamic parameters, and the kinematic state of an unknown body manipulated by a decentralized team of mobile ground (planar) robots. The proposed approach relies on the geometry of the rigid body kinematics, the rigid body dynamics, on nonlinear observation, and on consensus algorithms. The only three requirements are that each robot is able to control the 2D wrench exerted locally on the load, it can measure the velocity of its contact point, and the communication graph is connected. The finite time convergence of the strategy is proven and all the robots agree on the same estimated quantities at the end of the procedure. We present also two basic distributed control strategies that are proven to satisfy nonlinear observability conditions needed for the estimation accomplishment. Finally, a numerical test that demonstrates the evolution of the estimation algorithm is given.