Fractional non-homogeneous Poisson and Pólya-Aeppli processes of order kk and beyond

We introduce two classes of point processes: a fractional non-homogeneous Poisson process of order k and a fractional non-homogeneous Pólya-Aeppli process of order k. We characterize these processes by deriving their non-local governing equations. We further study the covariance structure of the processes and investigate the long-range dependence property

Fractional non-homogeneous Poisson and Pólya-Aeppli processes of order k and beyond

We introduce two classes of point processes: a fractional non-homogeneous Poisson process of order k and a fractional non-homogeneous Pólya-Aeppli process of order k: We characterize these processes by deriving their non-local governing equations. We further study the covariance structure of the processes and investigate the long-range dependence property

Approximations for sums of three-valued 1-dependent symmetric random variables

The sum of symmetric three-point 1-dependent nonidentically distributed random variables is approximated by a compound Poisson distribution. The accuracy of approximation is estimated in the local and total variation norms. For distributions uniformly bounded from zero,the accuracy of approximation is of the order O(n–1). In the general case of triangular arrays of identically distributed summands, the accuracy is at least of the order O(n–1/2). Nonuniform estimates are obtained for distribution functions and probabilities. The characteristic functionmethod is used. &nbsp

Testing a Random Number Generator: formal properties and automotive application

L'elaborato analizza un metodo di validazione dei generatori di numeri casuali (RNG), utilizzati per garantire la sicurezza dei moderni sistemi automotive. Il primo capitolo fornisce una panoramica della struttura di comunicazione dei moderni autoveicoli attraverso l'utilizzo di centraline (ECU): vengono riportati i principali punti di accesso ad un automobile, assieme a possibili tipologie di hacking; viene poi descritto l'utilizzo dei numeri casuali in crittografia, con particolare riferimento a quella utilizzata nei veicoli. Il secondo capitolo riporta le basi di probabilità necessarie all'approccio dei test statistici utilizzati per la validazione e riporta i principali approcci teorici al problema della casualità. Nei due capitoli centrali, viene proposta una descrizione dei metodi probabilistici ed entropici per l'analisi di dati reali utilizzati nei test. Vengono poi descritti e studiati i 15 test statistici proposti dal National Institute of Standards and Technology (NIST). Dopo i primi test, basati su proprietà molto semplici delle sequenze casuali, vengono proposti test più sofisticati, basati sull'uso della trasformata di Fourier (per testare eventuali comportamenti periodici), dell'entropia (strettamente connessi con la comprimibilità della sequenza), o sui random path. Due ulteriori test, permettono di valutare il buon funzionamento del generatore, e non solo delle singole sequenze generate. Infine, il quinto capitolo è dedicato all'implementazione dei test al fine di testare il TRNG delle centraline

Establish the expected number of induced motifs on unlabeled graphs through analytical models

AbstractComplex networks are usually characterized by the presence of small and recurrent patterns of interactions between nodes, called network motifs. These small modules can help to elucidate the structure and the functioning of complex systems. Assessing the statistical significance of a pattern as a motif in a network G is a time consuming task which entails the computation of the expected number of occurrences of the pattern in an ensemble of random graphs preserving some features of G, such as the degree distribution. Recently, few models have been devised to analytically compute expectations of the number of non-induced occurrences of a motif. Less attention has been payed to the harder analysis of induced motifs. Here, we illustrate an analytical model to derive the mean number of occurrences of an induced motif in an unlabeled network with respect to a random graph model. A comprehensive experimental analysis shows the effectiveness of our approach for the computation of the expected number of induced motifs up to 10 nodes. Finally, the proposed method is helpful when running subgraph counting algorithms to get the number of occurrences of a topology become unfeasible