Perfect simulation of autoregressive models with infinite memory

In this paper we consider the problem of determining the law of binary stochastic processes from transition kernels depending on the whole past. These kernels are linear in the past values of the process. They are allowed to assume values close to both 0 and 1, preventing the application of usual results on uniqueness. More precisely we give sufficient conditions for uniqueness and non-uniqueness. In the former case a perfect simulation algorithm is also given.Comment: 12 page

One-dimensional infinite memory imitation models with noise

In this paper we study stochastic process indexed by $\mathbb {Z}$ constructed from certain transition kernels depending on the whole past. These kernels prescribe that, at any time, the current state is selected by looking only at a previous random instant. We characterize uniqueness in terms of simple concepts concerning families of stochastic matrices, generalizing the results previously obtained in De Santis and Piccioni (J. Stat. Phys., 150(6):1017--1029, 2013).Comment: 22 pages, 3 figure

Complex Networks from Classical to Quantum

Recent progress in applying complex network theory to problems in quantum information has resulted in a beneficial crossover. Complex network methods have successfully been applied to transport and entanglement models while information physics is setting the stage for a theory of complex systems with quantum information-inspired methods. Novel quantum induced effects have been predicted in random graphs---where edges represent entangled links---and quantum computer algorithms have been proposed to offer enhancement for several network problems. Here we review the results at the cutting edge, pinpointing the similarities and the differences found at the intersection of these two fields.Comment: 12 pages, 4 figures, REVTeX 4-1, accepted versio

Equilibration and nonclassicality of a double-well potential

A double well loaded with bosonic atoms represents an ideal candidate to simulate some of the most interesting aspects in the phenomenology of thermalisation and equilibration. Here we report an exhaustive analysis of the dynamics and steady state properties of such a system locally in contact with different temperature reservoirs. We show that thermalisation only occurs 'accidentally'. We further examine the nonclassical features and energy fluxes implied by the dynamics of the double-well system, thus exploring its finite-time thermodynamics in relation to the settlement of nonclassical correlations between the wells.Comment: 10 pages, 7 figures, Close to published versio

Know Your Enemy: Stealth Configuration-Information Gathering in SDN

Software Defined Networking (SDN) is a network architecture that aims at providing high flexibility through the separation of the network logic from the forwarding functions. The industry has already widely adopted SDN and researchers thoroughly analyzed its vulnerabilities, proposing solutions to improve its security. However, we believe important security aspects of SDN are still left uninvestigated. In this paper, we raise the concern of the possibility for an attacker to obtain knowledge about an SDN network. In particular, we introduce a novel attack, named Know Your Enemy (KYE), by means of which an attacker can gather vital information about the configuration of the network. This information ranges from the configuration of security tools, such as attack detection thresholds for network scanning, to general network policies like QoS and network virtualization. Additionally, we show that an attacker can perform a KYE attack in a stealthy fashion, i.e., without the risk of being detected. We underline that the vulnerability exploited by the KYE attack is proper of SDN and is not present in legacy networks. To address the KYE attack, we also propose an active defense countermeasure based on network flows obfuscation, which considerably increases the complexity for a successful attack. Our solution offers provable security guarantees that can be tailored to the needs of the specific network under consideratio

Cosmic Star Formation: a simple model of the SFRD(z)

We investigate the evolution of the cosmic star formation rate density (SFRD) from redshift z=20 to z=0 and compare it with the observational one by Madau and Dickinson derived from recent compilations of UV and IR data. The theoretical SFRD(z) and its evolution are obtained using a simple model which folds together the star formation histories of prototype galaxies designed to represent real objects of different morphological type along the Hubble sequence and the hierarchical growing of structures under the action of gravity from small perturbations to large scale objects in \Lambda-CDM cosmogony, i.e. the number density of dark matter halos N(M,z). Although the overall model is very simple and easy to set up, it provides results that well mimic those obtained from large scale N-body simulations of great complexity. The simplicity of our approach allows us to test different assumptions for the star formation law in galaxies, the effects of energy feedback from stars to interstellar gas and the efficiency of galactic winds, and also the effect of N(M,z). The result of our analysis is that in the framework of the hierarchical assembly of galaxies the so-called time-delayed star formation under plain assumptions mainly for the energy feedback and galactic winds can reproduce the observational SFRD(z).Comment: ApJ (accepted for publication