Tensor factorization has become a popular method for learning from multirelational data. In this context, the rank of the factorization is an important parameter that determines runtime as well as generalization ability. To identify conditions under which factorization is an efficient approach for learning from relational data, we derive upper and lower bounds on the rank required to recover ad...

A multivariate Gaussian input density achieves capacity for multiple-input multiple-output flat fading channels with additive white Gaussian noise and perfect receiver channel state information. Capacity computation for these channels reduces to the problem of finding the best input covariance matrix, and is in general a convex semi-definite program. This paper presents Kuhn-Tucker optimality c...

An algorithm is described that constructs, in a polynomial time, a Nash equilibrium in a finite game with additive aggregation and decreasing best responses. MSC2010 Classifications: 91A10; 91A04.

The modularity is a quality function in community detection, which was introduced by Newman and Girvan (2004). Community detection in graphs is now often conducted through modularity maximization: given an undirected graph G = (V,E), we are asked to find a partition C of V that maximizes the modularity. Although numerous algorithms have been developed to date, most of them have no theoretical a...

Generalized additive models belong to modern techniques from statistical learning, and are applicable in many areas of prediction, e.g., in financial mathematics, computational biology, medicine, chemistry and environmental protection. These models have the form ( ) ( ) 0 1 m j j j G( ( X ) ) ψ X β f X μ = = = +∑ , where ψ is a function of the predictors. These models are fitted through local s...

Spectral reconstruction or spectral recovery refers to the method by which the spectral reflectance of the object is estimated using the output responses of the acquisition channels. Most of the algorithms are based on a linear optical model and additive noise. The acquisition responses are the integration of all of the light energy that reaches the sensor, weighted by the sensitivity of each c...

Our intention in this paper is to give a uniied investigation on a class of non-overlapping domain decomposition methods for solving second order elliptic problems in two and three dimensions. The methods under scrutiny fall into two major categories: the substructuring type methods and the Neumann-Neumann type methods. The basic framework used for analysis is the parallel subspace correction m...

In this paper we consider additive circulant graph codes over GF(4) and an algorithm for their construction. Also, we present some new results obtained by