Jesús P. Mena-Chalco

Identification of Protein Coding Regions Using the Modified Gabor-Wavelet Transform

An important topic in genomic sequence analysis is the identification of protein coding regions. In this context, several coding DNA model-independent methods based on the occurrence of specific patterns of nucleotides at coding regions have been proposed. Nonetheless, these methods have not been completely suitable due to their dependence on an empirically predefined window length required for a local analysis of a DNA region. We introduce a method based on a modified Gabor-wavelet transform (MGWT) for the identification of protein coding regions. This novel transform is tuned to analyze periodic signal components and presents the advantage of being independent of the window length. We compared the performance of the MGWT with other methods by using eukaryote data sets. The results show that MGWT outperforms all assessed model-independent methods with respect to identification accuracy. These results indicate that the source of at least part of the identification errors produced by the previous methods is the fixed working scale. The new method not only avoids this source of errors but also makes a tool available for detailed exploration of the nucleotide occurrence.

ScriptLattes: an open-source knowledge extraction system from the Lattes platform

The Lattes platform is the major scientific information system maintained by the National Council for Scientific and Technological Development (CNPq). This platform allows to manage the curricular information of researchers and institutions working in Brazil based on the so called Lattes Curriculum. However, the public information is individually available for each researcher, not providing the automatic creation of reports of several scientific productions for research groups. It is thus difficult to extract and to summarize useful knowledge for medium to large size groups of researchers. This paper describes the design, implementation and experiences with scriptLattes: an open-source system to create academic reports of groups based on curricula of the Lattes Database. The scriptLattes system is composed by the following modules: (a) data selection, (b) data preprocessing, (c) redundancy treatment, (d) collaboration graph generation among group members, (e) research map generation based on geographical information, and (f) automatic report creation of bibliographical, technical and artistic production, and academic supervisions. The system has been extensively tested for a large variety of research groups of Brazilian institutions, and the generated reports have shown an alternative to easily extract knowledge from data in the context of Lattes platform. The source code, usage instructions and examples are available at http://scriptlattes.sourceforge.net/.

Brazilian bibliometric coauthorship networks

The Brazilian Lattes Platform is an important academic/résumé data set that registers all academic activities of researchers associated with different major knowledge areas. The academic information collected in this data set is used to evaluate, analyze, and document the scientific production of research groups. Information about the interactions between Brazilian researchers in the form of coauthorships, however, has not been analyzed. In this article, we identified and characterized Brazilian academic coauthorship networks of researchers registered in the Lattes Platform using topological properties of graphs. For this purpose, we explored (a) strategies to develop a large Lattes curricula vitae data set, (b) an algorithm for identifying automatic coauthorships based on bibliographic information, and (c) topological metrics to investigate interactions among researchers. This study characterized coauthorship networks to gain an in‐depth understanding of the network structures and dynamics (social behavior) among researchers in all available major Brazilian knowledge areas. In this study, we evaluated information from a total of 1,131,912 researchers associated with the eight major Brazilian knowledge areas: agricultural sciences; biological sciences; exact and earth sciences; humanities; applied social sciences; health sciences; engineering; and linguistics, letters, and arts.

3D facial expression analysis by using 2D AND 3D wavelet transforms

This work presents a new approach for the 3D human facial expressions analysis. Our methodology is based on 2D and 3D wavelet transforms, which are used to estimate multi-scale features from real a face acquired by a 3D scanner. The proposed methodology starts by considering a dataset composed by faces displaying seven different facial expressions. An automatic pre-processing method, adopting an ellipsoidal cropping, is applied to the dataset. Thereafter, the 2D and 3D descriptors are extracted from different scales of wavelet transforms for the purpose of obtaining the facial expression features. The multi-scale features are represented in a multi-variate feature space, which is analysed by the Sequential Forward Floating Selection algorithm using an entropy criterion function to select the subset of features that best represents each facial expression model. The obtained results corroborate the potential of multi-scale feature extraction for analysis of 3D facial expression.

Towards Automatic Discovery of co-authorship Networks in the Brazilian Academic Areas

In Brazil, individual curricula vitae of academic researchers, that are mainly composed of professional information and scientific productions, are managed into a single software platform called Lattes. Currently, the information gathered from this platform is typically used to evaluate, analyze and document the scientific productions of Brazilian research groups. Despite the fact that the Lattes curricula has semi-structured information, the analysis procedure for medium and large groups becomes a time consuming and highly error-prone task. In this paper, we describe an extension of the script Lattés (an open-source knowledge extraction system from the Lattes platform), for analysing individuals Lattes curricula and automatically discover large-scale co-authorship networks for any academic area. Given some knowledge domain (academic area), the system automatically allows to identify researchers associated with the academic area, extract every list of scientific productions of the researchers, discretized by type and publication year, and for each paper, identify the co-authors registered in the Lattes Platform. The system also allows the generation of different types of networks which may be used to study the characteristics of academic areas at large scale. In particular, we explored the nodes degree and Author Rank measures for each identified researcher. Finally, we confirm through experiments that the system facilitates a simple way to generate different co-authorship networks. To the best of our knowledge, this is the first study to examine large-scale co-authorship networks for any Brazilian academic area.

Robust Range Finder Through a Laser Pointer and a Webcam

Currently, there is a great interest in the study of three-dimensional reconstruction from digital images. The applications of photogrammetric algorithms has allowed us significant improvements in procedures of camera calibration, movement of objects in scenes, shape from shading and range images. The procedure of the distance estimation from simple images is important to find the depth measurements in any procedure of three-dimensional reconstruction of scenes. In this work, we describe an adaptation of a scanner prototype based on a laser pointer and a webcam. It was applied to the robust estimation of absolute distance on images obtained from real time video sequences. Experimental tests were performed in order to demonstrate the effectiveness of the distance calculation in real time through a geometric model and a simple system of linear regression. From a wide data set of tests with different scanning parameter, good results on range finding were obtained.

PCA-Based 3D Face Photography

This paper presents a 3D face photography system based on a small set of training facial range images. The training set is composed by 2D texture and 3D range images (i.e. geometry) of a single subject with different facial expressions. The basic idea behind the method is to create texture and geometry spaces based on the training set and transformations to go from one space to the other. The main goal of the proposed approach is to obtain a geometry representation of a given face provided as a texture image, which undergoes a series of transformations through the texture and geometry spaces. Facial feature points are obtained by an active shape model (ASM) extracted from the 2D gray-level images. PCA then is used to represent the face dataset, thus defining an orthonormal basis of texture and range data. An input face is given by a gray-level face image to which the ASM is matched. The extracted ASM is fed to the PCA basis representation and a 3D version of the 2D input image is built. The experimental results on static images and video sequences using seven samples as training dataset show rapid reconstructed 3D faces which maintain spatial coherence similar to the human perception, thus corroborating the efficiency of our approach.

3D Linear Facial Animation Based on Real Data

In this paper we introduce a Facial Animation system using real three-dimensional models of people, acquired by a 3D scanner. We consider a dataset composed by models displaying different facial expressions and a linear interpolation technique is used to produce a smooth transition between them. One-to-one correspondences between the meshes of each facial expression are required in order to apply the interpolation process. Instead of focusing in the computation of dense correspondence, some points are selected and a triangulation is defined, being refined by consecutive subdivisions, that compute the matchings of intermediate points. We are able to animate any model of the dataset, given its texture information for the neutral face and the geometry information for all the expressions along with the neutral face. This is made by computing matrices with the variations of every vertex when changing from the neutral face to the other expressions. The knowledge of the matrices obtained in this process makes it possible to animate other models given only the texture and geometry information of the neutral face. Furthermore, the system uses 3D reconstructed models, being capable of generating a three-dimensional facial animation from a single 2D image of a person. Also, as an extension of the system, we use artificial models that contain expressions of visemes, that are not part of the expressions of the dataset, and their displacements are applied to the real models. This allows these models to be given as input to a speech synthesis application in which the face is able to speak phrases typed by the user. Finally, we generate an average face and increase the displacements between a subject from the dataset and the average face, creating, automatically, a caricature of the subject.

3D face computational photography using PCA spaces

In this paper, we present a 3D face photography system based on a facial expression training dataset, composed of both facial range images (3D geometry) and facial texture (2D photography). The proposed system allows one to obtain a 3D geometry representation of a given face provided as a 2D photography, which undergoes a series of transformations through the texture and geometry spaces estimated. In the training phase of the system, the facial landmarks are obtained by an active shape model (ASM) extracted from the 2D gray-level photography. Principal components analysis (PCA) is then used to represent the face dataset, thus defining an orthonormal basis of texture and another of geometry. In the reconstruction phase, an input is given by a face image to which the ASM is matched. The extracted facial landmarks and the face image are fed to the PCA basis transform, and a 3D version of the 2D input image is built. Experimental tests using a new dataset of 70 facial expressions belonging to ten subjects as training set show rapid reconstructed 3D faces which maintain spatial coherence similar to the human perception, thus corroborating the efficiency and the applicability of the proposed system.

Scholarly publication and collaboration in Brazil: The role of geography

Brazilian scholarly output has rapidly increased, accompanied by the expansion of domestic collaborations. In this paper, we identify spatial patterns of collaboration in Brazil and measure the role of geographic proximity in determining the interaction among researchers. Using a database comprising more than one million researchers and seven million publications, we consolidated information on interregional research collaboration in terms of scientific coauthorship networks among 4,615 municipalities during the period between 1992 and 2009, which allowed us to analyze a range of data unprecedented in the literature. The effects of geographic distance on collaboration were measured for different areas by estimating spatial interaction models. The main results provide strong evidence of geographic deconcentration of collaboration in recent years, with increased participation of authors in scientifically less traditional regions, such as south and northeast Brazil. Distance remains a significant factor in determining the intensity of knowledge flow in collaboration networks in Brazil, as an increase of 100 km between two researchers reduces the probability of collaboration by an average of 16%, and there is no evidence that the effect of distance has diminished over time, although the magnitude of such effects varies among networks of different areas.