Abstract
RepeatAround is a Windows based software tool designed to find "direct repeats", "inverted repeats", "mirror repeats" and "complementary repeats", from 3 to 64 bp length, in circular genomes. It processes input files directly extracted from GenBank database, providing visualisation of the repeats location in the genomic structure, so that for instance, in most mtDNAs the user can check if the repeats are located in coding or non-coding region (and in the first case in which gene), and how far apart the repeat pair(s) are. Besides the visual tool, it provides other outputs in a spreadsheet containing information on the number and location of the repeats, facilitating graphic analyses. Several genomes can be inputed simultaneously, for phylogenetic comparison purposes. Other capabilities of the software are the generation of random circular genomes, for statistical evaluation of comparison between observed repeats distributions with their shuffled counterparts, as well as the search for specific motifs, allowing an easy confirmation of repeats flanking a newly detected rearrangement. As an example of the programme's applications we analysed the Direct Repeats distribution in a large human mtDNA database. Results showed that Direct Repeats, even the larger ones, are evenly distributed among the human mtDNA haplogroups, enabling us to state that, based only on the repetitive motifs, no haplogroup is particularly more or less prone to mtDNA macrodeletions.

Abstract

Abstract
Several techniques for implementing Prolog in a efficient manner have been devised since the original interpreter, many of them aimed at achieving more speed. There are two main approaches to efficient Prolog implementation: (1) compilers to bytecode and then interpreting it (emulators) or (2) compilers to native code. Emulators have smaller load/compilation time and are a good solution for their simplicity when speed is not a priority. Compilers are more complex than emulators, and the difference is much more acute if some form of code analysis is performed as part of the compilation, which impacts development time. Generation of low level code promises faster programs at the expense of using more resources during the compilation phase. In our work besides using an mixed execution mode, we design an optimizing compiler that using type feedback profiling, dynamic compilation and dynamic deoptimization for improving the performance of logic programming languages. © J.UCS.

Abstract
There has been significant recent progress in the integration of probabilistic reasoning with first order logic representations (SRL). So far, the learning algorithms developed for these models all learn from scratch, assuming an invariant background knowledge. As an alternative, theory revision techniques have been shown to perform well on a variety of machine learning problems. These techniques start from an approximate initial theory and apply modifications in places that performed badly in classification. In this work we describe the first revision system for SRL classification, PFORTE, which addresses two problems: all examples must be classified, and they must be classified well. PFORTE uses a two step-approach. The completeness component uses generalization operators to address failed proofs and the classification component addresses classification problems using generalization and specialization operators. Experimental results show significant benefits from using theory revision techniques compared to learning from scratch. © Springer-Verlag Berlin Heidelberg 2006.

Abstract
Modern Java Compilers, such as Sun's HotSpot compilers, implement a number of optimizations, ranging from high-level program transformations to low-level architecure dependent operations such as instruction scheduling. In a Just-in-Time (JIT) environment, the impact of each optimization must be weighed against its cost in terms of total runtime. Towards better understanding the usefulness of individual optimizations, we study the main optimizations available on Sun HotSpot compilers for a wide range of scientific and non-scientific benchmarks, weighing their cost and benefits in total runtime. We chose the HotSpot technology because it is state of the art and its source code is available. © J.UCS.

Abstract