In this paper we study sparse high dimensional additive partial linear models with nonparametric additive components of heterogeneous smoothness.

The large amount of work on community detection and its applications leaves unaddressed one important question: the statistical validation of the results. A methodology is presented that is able to clearly detect if the community structure found by some algorithms is statistically significant or is a result of chance, merely due to edge positions in the network. Given a community detection method and a network of interest, the proposal examines the stability of the partition recovered against random perturbations of the original graph structure.

In this paper we analyze least recently used (LRU) caches operating under the shot noise requests model (SNM). The SNM was recently proposed in [S. Traverso et al., ACM Comput. Comm. Rev., 43 (2013), pp. 5-12] to better capture the main characteristics of today's video on demand traffic. We investigate the validity of Che's approximation [H. Che, Y. Tung, and Z. Wang, IEEE J. Selected Areas Commun., 20 (2002), pp. 1305-1314] through an asymptotic analysis of the cache eviction time.

We provide a Clark-Ocone formula for square-integrable functionals of a general temporal point process satisfying only a mild moment condition, generalizing known results on the Poisson space. Some classical applications are given, namely a deviation bound and the construction of a hedging portfolio in a pure-jump market model. As a more modern application, we provide a bound on the total variation distance between two temporal point processes, improving in some sense a recent result in this direction.

The paper proposes a decision support system (DSS) for the supply chain of packaged fresh and highly perishable products. The DSS combines a unique tool for sales forecasting with order planning which includes an individual model selection system equipped with ARIMA, ARIMAX and transfer function forecasting model families, the latter two accounting for the impact of prices. Forecasting model parameters are chosen via two alternative tuning algorithms: a two-step statistical analysis, and a sequential parameter optimisation framework for automatic parameter tuning.

Short-Term Load Forecasting (STLF) is a fundamental instrument in the efficient operational management and planning of electric utilities. Emerging smart grid technologies pose new challenges and opportunities. Although load forecasting at the aggregate level has been extensively studied, electrical load forecasting at fine-grained geographical scales of households is more challenging. Among existing approaches, semi-parametric generalized additive models (GAM) have been increasingly popular due to their accuracy, flexibility, and interpretability.

The authors propose a Multi Attributes approach to meet the demand of personalized tourist tours into cultural cities. Respecting to others works present into the literature, in this paper the decisional process includes two phases and a high number of variables that don't increase the complexity of the problem. A real application in an Italian city, Florence, is presented to demonstrate the great potential of this system into real context.

We investigate the scattering of a spinning test particle by a Kerr black hole within the Mathisson-Papapetrou-Dixon model to linear order in spin. The particle's spin and orbital angular momentum are taken to be aligned with the black hole's spin. Both the particle's mass and spin length are assumed to be small in comparison with the characteristic length scale of the background curvature, in order to avoid backreaction effects.