Core excitation in Coulomb breakup reactions

Within the pure Coulomb breakup mechanism, we investigate the one-neutron removal reaction of the type A(a,b$\gamma$)X with $^{11}$Be and $^{19}$C projectiles on a heavy target nucleus $^{208}$Pb at the beam energy of 60 MeV/nucleon. Our intention is to examine the prospective of using these reactions to study the structure of neutron rich nuclei. Integrated partial cross sections and momentum distributions for the ground as well as excited bound states of core nuclei are calculated within the finite range distorted wave Born approximation as well as within the adiabatic model of the Coulomb breakup. Our results are compared with those obtained in the studies of the reactions on a light target where the breakup proceeds via the pure nuclear mechanism. We find that the transitions to excited states of the core are quite weak in the Coulomb dominated process as compared to the pure nuclear breakup.Comment: Revtex format, five postscript figures included, to appear in Phys. Rev.

Generic Graph Algorithms for Sparse Matrix Ordering

Fill-reducing sparse matrix orderings have been a topic of active research for many years. Although most such algorithms are developed and analyzed within a graph-theoretical framework, for reasons of performance, the corresponding implementations are typically realized with programming languages devoid of language features necessary to explicitly represent graph abstractions. Recently, generic programming has emerged as a programming paradigm capable of providing high levels of performance in the presence of programming abstractions. In this paper we present an implementation of the Minimum Degree ordering algorithm using the newly-developed Generic Graph Component Library. Experimental comparisons show that, despite our heavy use of abstractions, our implementation has performance indistinguishable from that of the Fortran implementation.

Towards generic pattern mining

Abstract. Frequent Pattern Mining (FPM) is a very powerful paradigm for mining informative and useful patterns in massive, complex datasets. In this paper we propose the Data Mining Template Library, a collection of generic containers and algorithms for FPM, as well as persistency and database management classes. DMTL provides a systematic solution to a whole class of common FPM tasks like itemset, sequence, tree and graph mining. DMTL is extensible, scalable, and high-performance for rapid response on massive datasets. Our experiments show that DMTL is competitive with special purpose algorithms designed for a particular pattern type, especially as database sizes increase.