Abstract
Yet Another Prolog (YAP) is a Prolog system originally developed in the mid-eighties and that has been under almost constant development since then. This paper presents the general structure and design of the YAP system, focusing on three important contributions to the Logic Programming community. First, it describes the main techniques used in YAP to achieve an efficient Prolog engine. Second, most Logic Programming systems have a rather limited indexing algorithm. YAP contributes to this area by providing a dynamic indexing mechanism, or just-in-time indexer. Third, a important contribution of the YAP system has been the integration of both or-parallelism and tabling in a single Logic Programming system.

Abstract
Despite the availability of both multithreading and tabling in some Prolog systems, the implementation of these two features, such that they work together, implies complex ties to one another and to the underlying engine. In recent work, we have proposed an approach to combine multithreading with tabling, implemented on top of the Yap Prolog system, whose primary goal was to reduce memory usage for the table space. Regarding the execution times, we observed some problems related to Yap's memory allocator, which is based on the operating system's default memory allocator, when running programs that allocate a higher number of data structures in the table space. In this paper, we propose a more efficient and scalable memory allocator for multithreaded tabled evaluation of logic programs. Our goal is to minimize the performance degradation that the system suffers when it is exposed to simultaneous memory requests made by multiple threads. For that, we propose a memory allocator based on local and global pages, to split memory among specific data structures and different threads, together with a strategy where data structures of the same type are pre-allocated within a page. Experimental results show that our new memory allocator can effectively reduce the execution time and scale better, when increasing the number of threads, than the original allocator.

Abstract

Abstract
The functions of proteins in living organisms are related to their 3-D structure, which is known to be ultimately determined by their linear sequence of amino acids that together form these macromolecules. It is, therefore, of great importance to be able to understand and predict how the protein 3D-structure arises from a particular linear sequence of amino acids. In this paper we report the application of Machine Learning methods to predict, with high values of accuracy, the secondary structure of proteins, namely alpha-helices and beta-sheets, which are intermediate levels of the local structure.

Abstract

Abstract
Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used analogously to ROC curves and the area under ROC curves. It is known that PR curves vary as class skew changes. What was not recognized before this paper is that there is a region of PR space that is completely unachievable, and the size of this region depends only on the skew. This paper precisely characterizes the size of that region and discusses its implications for empirical evaluation methodology in machine learning. Copyright 2012 by the author(s)/owner(s).