Edge Potential Functions (EPF) and Genetic Algorithms (GA) for Edge-Based Matching of Visual Objects

Edges are known to be a semantically rich representation of the contents of a digital image. Nevertheless, their use in practical applications is sometimes limited by computation and complexity constraints. In this paper, a new approach is presented that addresses the problem of matching visual objects in digital images by combining the concept of Edge Potential Functions (EPF) with a powerful matching tool based on Genetic Algorithms (GA). EPFs can be easily calculated starting from an edge map and provide a kind of attractive pattern for a matching contour, which is conveniently exploited by GAs. Several tests were performed in the framework of different image matching applications. The results achieved clearly outline the potential of the proposed method as compared to state of the art methodologies. (c) 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Genetic Algorithms and Their Application to the Protein Folding Problem

The protein folding problem involves the prediction of the secondary and tertiary structure of a molecule given the primary structure. The primary structure defines sequence of amino-acid residues, while the secondary structure describes the local 3-dimensional arrangement of amino-acid residues within the molecule. The relative orientation of the secondary structural motifs, namely the tertiary structure, defines the shape of the entire biomolecule. The exact, mechanism by which a sequence of amino acids protein folds into its 3- dimensional conformation is unknown Current approaches to the protein folding problem include calculus-based methods, systematic search, model building and symbolic methods, random methods such as Monte Carlo simulation and simulated annealing, distance geometry, and molecular dynamics. Many of these current approaches search for conformations which minimize the internal energy of the molecule. A genetic algorithm GA, a stochastic search technique modeled after natural adaptive systems, potentially offers significant speedup over other search algorithms because of its inherent parallelizability. The results of applying a parallel GA to the protein folding problem show significant improvement in execution time when compared to serial implementations of the GA. In addition, the parallel GA demonstrates good scalability characteristics since the communications strategy used to manage the population can be tailored to the parallel architecture

Text categorization methods for automatic estimation of verbal intelligence

In this paper we investigate whether conventional text categorization methods may suffice to infer different verbal intelligence levels. This research goal relies on the hypothesis that the vocabulary that speakers make use of reflects their verbal intelligence levels. Automatic verbal intelligence estimation of users in a spoken language dialog system may be useful when defining an optimal dialog strategy by improving its adaptation capabilities. The work is based on a corpus containing descriptions (i.e. monologs) of a short film by test persons yielding different educational backgrounds and the verbal intelligence scores of the speakers. First, a one-way analysis of variance was performed to compare the monologs with the film transcription and to demonstrate that there are differences in the vocabulary used by the test persons yielding different verbal intelligence levels. Then, for the classification task, the monologs were represented as feature vectors using the classical TF–IDF weighting scheme. The Naive Bayes, k-nearest neighbors and Rocchio classifiers were tested. In this paper we describe and compare these classification approaches, define the optimal classification parameters and discuss the classification results obtained

Causality and Similarity in Autobiographical Event Structure: An Investigation Using Event Cueing and Latent Semantic Analysis

The influence of similarity- and causally-based relations on the organization of autobiographical events was investigated using extended strings of related event memories. These strings were elicited using an event cueing paradigm in which participants generated descriptions of memories from their life, which were then presented as cues to subsequent event memories. In Experiment 1, similarity between generated events was investigated using participants’ similarity ratings, Latent Semantic Analysis, and experimenter judgements of shared event properties. For events close together in a string, event owners’ similarity ratings were higher than non-owners’, and non-owners’ ratings were comparable to similarity calculated using LSA. In Experiment 2, the influence of causal connectivity of events on perceived event similarity was investigated using causality ratings by event owners and non-owners. Results indicated that many events cued other events based on causal relations, owners’ causal ratings were highly consistent, and the ownership advantage in Experiment 1 could in part be explained by causal connections among events. It is concluded that both event similarity and causality are important aspects of the organization of autobiographical memory

Deep R Programming

Deep R Programming is a comprehensive course on one of the most popular languages in data science (statistical computing, graphics, machine learning, data wrangling and analytics). It introduces the base language in-depth and is aimed at ambitious students, practitioners, and researchers who would like to become independent users of this powerful environment. This textbook is a non-profit project. Its online and PDF versions are freely available at . This early draft is distributed in the hope that it will be useful.Comment: Draft: v0.2.1 (2023-04-27

An interactive interface for NCAR Graphics

The NCAR Graphics package has been a valuable research tool for over 20 years. As a low level Fortran library, however, it was difficult to use for nonprogramming researchers. With this grant and NSF support, an interactive interface has been created which greatly facilitates use of the package by researchers of diverse computer skill levels