Email spam is one of the major problems of the today's Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is an important and popular one. In this paper we give an overview of the state of the art of machine learning applications for spam filtering, and of the ways of evaluation and comparison of different filtering methods. We also provide a brief description of other branches of anti-spam protection and discuss the use of various approaches in commercial and non-commercial anti-spam software solutions.

BackgroundThe classical view on eukaryotic gene expression proposes the scheme of a forward flow for which fluctuations in mRNA levels upon a stimulus contribute to determine variations in mRNA availability for translation. Here we address this issue by simultaneously profiling with microarrays the total mRNAs (the transcriptome) and the polysome-associated mRNAs (the translatome) after EGF treatment of human cells, and extending the analysis to other 19 different transcriptome/translatome comparisons in mammalian cells following different stimuli or undergoing cell programs.ResultsTriggering of the EGF pathway results in an early induction of transcriptome and translatome changes, but 90% of the significant variation is limited to the translatome and the degree of concordant changes is less than 5%. The survey of other 19 different transcriptome/translatome comparisons shows that extensive uncoupling is a general rule, in terms of both RNA movements and inferred cell activities, with a strong tendency of translation-related genes to be controlled purely at the translational level. By different statistical approaches, we finally provide evidence of the lack of dependence between changes at the transcriptome and translatome levels.ConclusionsWe propose a model of diffused independency between variation in transcript abundances and variation in their engagement on polysomes, which implies the existence of specific mechanisms to couple these two ways of regulating gene expression.

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The complexity of the selection procedure of a genetic algorithm that requires reordering, if we restrict the class of the possible fitness functions to varying fitness functions, is O (N log N ) where N is the size of the population.The Quantum Genetic Optimization Algorithm (QGOA) exploits the power of quantum computation in order to speed up genetic procedures. While the quantum and classical genetic algorithms use the same number of generations, the QGOA outperforms the classical one in identifying the high-fitness subpopulation at each generation. In QGOA the classical fitness evaluation and selection procedures are replaced by a single quantum procedure. We show that the complexity of our QGOA is O (1) in terms of number of oracle calls in the selection procedure. Such theoretical results are confirmed by the simulations of the algorithm. Index TermsEvolutionary computing and genetic algorithms, quantum computation.

This paper investigates the effects of September 11, 2001 terrorists' attack on decision making. It was hypothesized that after terrorists' attacks people would make more conservative and less risky decisions, as a way of compensating for the feelings of insecurity caused by the disaster. Prospect Theory is used as theoretical framework. This theory has successfully accounted for decision making under normal circumstances. To verify whether and how the terrorists' attack against the USA influenced individual decision making processes, two samples of Italian university students were tested, one month and six months after the disaster. The results show the emergence of two tendencies, which are absent during 'normal' historical periods: a strong, long-term lasting search for security when the outcome of a decision is perceived as a gain, and a medium-term risk avoiding behavior in the loss domain.