Distinguishing Majorana and Dirac Gluinos and Neutralinos

While gluinos and neutralinos are Majorana fermions in the MSSM, they can be Dirac fermion fields in extended supersymmetry models. The difference between the two cases manifests itself in production and decay processes at colliders. In this contribution, results are presented for how the Majorana or Dirac nature of gluinos and neutralinos can be extracted from di-lepton signals at the LHC.Comment: 4 pages; to appear in the proceedings of the 17th International Conference on Supersymmetry and the Unification of Fundamental Interactions (SUSY09), Boston, USA, 5-10 Jun 200

Two-loop fermionic electroweak corrections to the Z-boson width and production rate

Improved predictions for the Z-boson decay width and the hadronic Z-peak cross-section within the Standard Model are presented, based on a complete calculation of electroweak two-loop corrections with closed fermion loops. Compared to previous partial results, the predictions for the Z width and hadronic cross-section shift by about 0.6 MeV and 0.004 nb, respectively. Compact parametrization formulae are provided, which approximate the full results to better than 4 ppm.Comment: 7 pages; v2: few typos fixed and minor corrections of numbers in table

A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction

This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization

Tuberculous Pericarditis

info:eu-repo/semantics/publishedVersio

On the Suitability of Genetic-Based Algorithms for Data Mining

Data mining has as goal to extract knowledge from large databases. A database may be considered as a search space consisting of an enormous number of elements, and a mining algorithm as a search strategy. In general, an exhaustive search of the space is infeasible. Therefore, efficient search strategies are of vital importance. Search strategies on genetic-based algorithms have been applied successfully in a wide range of applications. We focus on the suitability of genetic-based algorithms for data mining. We discuss the design and implementation of a genetic-based algorithm for data mining and illustrate its potentials

A Survey of Parallel Data Mining

With the fast, continuous increase in the number and size of databases, parallel data mining is a natural and cost-effective approach to tackle the problem of scalability in data mining. Recently there has been a considerable research on parallel data mining. However, most projects focus on the parallelization of a single kind of data mining algorithm/paradigm. This paper surveys parallel data mining with a broader perspective. More precisely, we discuss the parallelization of data mining algorithms of four knowledge discovery paradigms, namely rule induction, instance-based learning, genetic algorithms and neural networks. Using the lessons learned from this discussion, we also derive a set of heuristic principles for designing efficient parallel data mining algorithms

A lexicographic multi-objective genetic algorithm for multi-label correlation-based feature selection

This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Multi-Label Correlation-based Feature Selection (LexGA-ML-CFS), which is an extension of the previous single-objective Genetic Algorithm for Multi-label Correlation-based Feature Selection (GA-ML-CFS). This extension uses a LexGA as a global search method for generating candidate feature subsets. In our experiments, we compare the results obtained by LexGA-ML-CFS with the results obtained by the original hill climbing-based ML-CFS, the single-objective GA-ML-CFS and a baseline Binary Relevance method, using ML-kNN as the multi-label classifier. The results from our experiments show that LexGA-ML-CFS improved predictive accuracy, by comparison with other methods, in some cases, but in general there was no statistically significant different between the results of LexGA-ML-CFS and other methods