Quantitative Study of the Geographical Distribution of the Authorship of High-Energy Physics Journals

The recent debate on Open Access publishing in High-Energy Physics has exposed the problem of assessing the scientific production of every country where scholars are active in this discipline. This assessment is complicated by the highly-collaborative cross-border tradition of High-Energy Physics research. We present the results of a quantitative study of the geographical distribution of authors of High-Energy Physics articles, which takes into account cross-border co-authorship by attributing articles to countries on a pro-rata basis. Aggregated data on the share of scientific results published by each country are presented together with a breakdown for the most popular journals in the field, and a separation for articles by small groups or large collaborations. Collaborative patterns across large geographic areas are also investigated. Finally, the High-Energy Physics production of each country is compared with some economic indicators

New Perturbation Results For Regularized Tikhonov Inverses And Pseudo-Inverses.

. Consider the Tikhonov regularized linear least squares problem minx kJx \Gamma bk 2 2 + ¯ 2 kL(x \Gamma c)k 2 2 , where J 2 ! m\Thetan ; b 2 ! m and L 2 ! p\Thetan . The interesting part of the solution to this problem (attained by putting c = 0) is J # L b; J # L = (J T J + ¯ 2 L T L) \Gamma1 J T . As ¯ ! 0 the solution of the regularized problem tends to the solution, J + L b, of minx kL(x \Gamma c)k2 subject to the constraint that kJx \Gamma bk2 is minimized. The main result of this paper is perturbation identities for J + L . However, in order to attain this result perturbation identities for J # L are derived first and then the fact that J # L ! J + L b is used. The perturbation identities for J + L and J # L are useful for ill-posed, ill-conditioned and rank-deficient problems. Key words. Tikhonov regularization, GSVD, perturbation theory, rank deficiency, pseudoinverses, filter factors, numerical rank AMS subject classifications. 65 K ..

Algorithms For Constrained And Weighted Nonlinear Least Squares

. A hybrid algorithm consisting of a Gauss-Newton method and a second order method for solving constrained and weighted nonlinear least squares problems is developed, analyzed and tested. One of the advantages of the algorithm is that arbitrary large weights can be handled and that the weights in the merit function do not get unnecessary large when the iterates diverge from a saddle point. The local convergence properties for the Gauss-Newton method is thoroughly analyzed and simple ways of estimating and calculating the local convergence rate for the Gauss-Newton method are given. Under the assumption that the constrained and weighted linear least squares subproblems attained in the Gauss-Newton method are not too ill-conditioned, global convergence towards a first order KKT point is proved. Key words. nonlinear least squares, optimization, parameter estimation, weights AMS subject classifications. 65K, 49D 1. Introduction. Assume that f : R n ! R m is a twice continuously diffe..