arXiv subjects
Publications and source records attributed to Alberto Costa.
Two approximations of the integral of a class of sinusoidal composite functions, for which an explicit form does not exist, are derived. Numerical experiments show that the proposed approximations yield an error that does not depend on the width of the integration interval. Using such approximations, definite integrals can be computed in almost real-time.
A "quantitative function" for community detection called modularity density has been proposed by Li, Zhang, Wang, Zhang, and Chen in $[$Phys. Rev. E 77, 036109 (2008)$]$. We study the modularity density maximization problem and we discuss some features of the optimal solution. More precisely, we show that in the optimal solution there can be communities having negative modularity density, and we propose a modification of the original formulation to overcome this issue. Moreover, we show that a clique can be divided into two or more parts when maximizing the modularity density. We also compare the solution found by maximizing the modularity density with that obtained by maximizing the modularity on the Zachary karate club network.
In this paper, we describe the solution for a problem dealing with definite properties of binary sequences. This problem, proposed by Xavier Grandsart in the form of a mathematical contest, has been solved also by Maher Younan, Ph.D. student of Theoretical Physics at the University of Geneva, and Pierre Deligne, professor at Princeton and Field Medals, using different approaches with respect to the one presented in this work.
In this paper we present a method for reformulating the Recommender Systems problem in an Information Retrieval one. In our tests we have a dataset of users who give ratings for some movies; we hide some values from the dataset, and we try to predict them again using its remaining portion (the so-called "leave-n-out approach"). In order to use an Information Retrieval algorithm, we reformulate this Recommender Systems problem in this way: a user corresponds to a document, a movie corresponds to a term, the active user (whose rating we want to predict) plays the role of the query, and the ratings are used as weigths, in place of the weighting schema of the original IR algorithm. The output is the ranking list of the documents ("users") relevant for the query ("active user"). We use the ratings of these users, weighted according to the rank, to predict the rating of the active user. We carry out the comparison by means of a typical metric, namely the accuracy of the predictions returned by the algorithm, and we compare this to the real ratings from users. In our first tests, we use two different Information Retrieval algorithms: LSPR, a recently proposed model based on Discrete Fourier Transform, and a simple vector space model.