We consider learning of fundamental properties of communities in large noisy networks, in the prototypical situation where the nodes or users are split into two classes, e.g., according to their opinions or preferences on a topic. We propose a nonparametric, unsupervised, and scalable graph scan procedure that is, in addition, robust against a class of powerful adversaries. In our setup, one of...

This paper presents vCorfu, a strongly consistent cloudscale object store built over a shared log. vCorfu augments the traditional replication scheme of a shared log to provide fast reads and leverages a new technique, composable state machine replication, to compose large state machines from smaller ones, enabling the use of state machine replication to be used to efficiently in huge data stor...

In a distributed computing environment, it is vital to maintain the states of the processes involved in order to cater to failures that are arbitrary in nature. To reach a consistent state among all the processes, checkpoints are taken locally by each process and are combined together based on uniformity criteria such as consistency, transitlessness, and strong consistency. In this article, fir...

NoSQL storage systems are used extensively by web applications and provide an attractive alternative to conventional databases when the need for scalability outweighs the need for transactions. Several of these systems provide quorum-based replication and present the application developer with a choice of multiple client-side “consistency levels” that determine the number of replicas accessed b...

The aim of this article is to provide a strong consistency Theorem for approximated M -estimators. It contains both Wald and Pfanzagl type results for maximum likelihood. The proof relies, in particular, on the existence of a sort of contraction of the parameter space which admits the true parameter as a fixed point. In a way, it can be seen as a simplification of ideas of Wang and Pfanzagl, ge...

Dependencies of extreme events (extremal dependencies) are attracting an increasing attention in modern risk management. In practice, the concept of tail dependence represents the current standard to describe the amount of extremal dependence. In theory, multivariate extreme-value theory (EVT) turns out to be the natural choice to model the latter dependencies. The present paper embeds tail dep...

When the unobservable Markov chain in a hidden Markov model is stationary the marginal distribution of the observations is a finite mixture with the number of terms equal to the number of the states of the Markov chain. This suggests estimating the number of states of the unobservable Markov chain by determining the number of mixture components in the marginal distribution. We therefore present...

In finite mixture of location-scale distributions maximum likelihood estimator does not exist because of the unboundedness of the likelihood function when the scale parameter of some mixture component approaches zero. In order to study the strong consistency of maximum likelihood estimator, we consider the case that the scale parameters of the component distributions are restricted from below b...

The aim of this paper is to study the asymptotic properties of a class of kernel conditional mode estimates whenever functional stationary ergodic data are considered. To be more precise on the matter, in the ergodic data setting, we consider a random element (X,Z) taking values in some semimetric abstract space E×F . For a real function φ defined on the space F and x ∈ E, we consider the condi...

Cluster number counts offer sensitive probes of the dark energy if and only if the evolution of the cluster mass versus observable relation(s) is well calibrated. We investigate the potential for internal calibration by demanding consistency in the counts as a function of the observable. In the context of a constant dark energy equation of state, known initial fluctuation amplitude expected fro...