VAT tax gap prediction: a 2-steps Gradient Boosting approach

Tax evasion is the illegal evasion of taxes by individuals, corporations, and trusts. The revenue loss from tax avoidance can undermine the effectiveness and equity of the government policies. A standard measure of tax evasion is the tax gap, that can be estimated as the difference between the total amounts of tax theoretically collectable and the total amounts of tax actually collected in a given period. This paper presents an original contribution to bottom-up approach, based on results from fiscal audits, through the use of Machine Learning. The major disadvantage of bottom-up approaches is represented by selection bias when audited taxpayers are not randomly selected, as in the case of audits performed by the Italian Revenue Agency. Our proposal, based on a 2-steps Gradient Boosting model, produces a robust tax gap estimate and, embeds a solution to correct for the selection bias which do not require any assumptions on the underlying data distribution. The 2-steps Gradient Boosting approach is used to estimate the Italian Value-added tax (VAT) gap on individual firms on the basis of fiscal and administrative data income tax returns gathered from Tax Administration Data Base, for the fiscal year 2011. The proposed method significantly boost the performance in predicting with respect to the classical parametric approaches.Comment: 27 pages, 4 figures, 8 tables Presented at NTTS 2019 conference Under review at another peer-reviewed journa

Steps towards NNLO QCD calculations: collinear factorization at O(\alpha_S^2)

I consider the singular behaviour of tree-level QCD amplitudes when the momenta of three partons become simultaneously parallel and I discuss the universal factorization formula that controls the singularities of the multiparton matrix elements in this collinear limit.Comment: 3 pages, 1 eps figure, talk given at the DIS99 Workshop, Zeuthen, Germany, April 19-23, 199

Fetching marked items from an unsorted database in NMR ensemble computing

Searching a marked item or several marked items from an unsorted database is a very difficult mathematical problem. Using classical computer, it requires $O(N=2^n)$ steps to find the target. Using a quantum computer, Grover's algorithm uses $O(\sqrt{N=2^n})$ steps. In NMR ensemble computing, Brushweiler's algorithm uses $\log N$ steps. In this Letter, we propose an algorithm that fetches marked items in an unsorted database directly. It requires only a single query. It can find a single marked item or multiple number of items.Comment: 4 pages and 1 figur

Adsorption induced reconstruction of the Cu(110) surface

The formation of the O/Cu(110)-(2 × 1) and H/Cu(110)-(1 × 2) superstructures has been investigated by a LEED beam profile analysis. The oxygen induced reconstruction proceeds at later stages by creation of holes on flat terraces. This could not be observed at the hydrogen induced missing row reconstruction. The formation of the missing row structure proceeds most probably via nucleation at steps and subsequent growth of (1 × 2) islands. The influence of different distributions of steps and islands on beam profiles is discussed