Federal University of São Carlos

About: Federal University of São Carlos is a education organization based out in São Carlos, Brazil. It is known for research contribution in the topics: Population & Microstructure. The organization has 16471 authors who have published 34057 publications receiving 456654 citations. The organization is also known as: UFSCar & Federal University of São Carlos.

Comparative Genomics of Two Leptospira interrogans Serovars Reveals Novel Insights into Physiology and Pathogenesis

Abstract: Leptospira species colonize a significant proportion of rodent populations worldwide and produce life-threatening infections in accidental hosts, including humans. Complete genome sequencing of Leptospira interrogans serovar Copenhageni and comparative analysis with the available Leptospira interrogans serovar Lai genome reveal that despite overall genetic similarity there are significant structural differences, including a large chromosomal inversion and extensive variation in the number and distribution of insertion sequence elements. Genome sequence analysis elucidates many of the novel aspects of leptospiral physiology relating to energy metabolism, oxygen tolerance, two-component signal transduction systems, and mechanisms of pathogenesis. A broad array of transcriptional regulation proteins and two new families of afimbrial adhesins which contribute to host tissue colonization in the early steps of infection were identified. Differences in genes involved in the biosynthesis of lipopolysaccharide O side chains between the Copenhageni and Lai serovars were identified, offering an important starting point for the elucidation of the organism's complex polysaccharide surface antigens. Differences in adhesins and in lipopolysaccharide might be associated with the adaptation of serovars Copenhageni and Lai to different animal hosts. Hundreds of genes encoding surface-exposed lipoproteins and transmembrane outer membrane proteins were identified as candidates for development of vaccines for the prevention of leptospirosis.

Never-ending learning

Abstract: Whereas people learn many different types of knowledge from diverse experiences over many years, and become better learners over time, most current machine learning systems are much more narrow, learning just a single function or data model based on statistical analysis of a single data set We suggest that people learn better than computers precisely because of this difference, and we suggest a key direction for machine learning research is to develop software architectures that enable intelligent agents to also learn many types of knowledge, continuously over many years, and to become better learners over time In this paper we define more precisely this never-ending learning paradigm for machine learning, and we present one case study: the Never-Ending Language Learner (NELL), which achieves a number of the desired properties of a never-ending learner NELL has been learning to read the Web 24hrs/day since January 2010, and so far has acquired a knowledge base with 120mn diverse, confidence-weighted beliefs (eg, servedWith(tea,biscuits)), while learning thousands of interrelated functions that continually improve its reading competence over time NELL has also learned to reason over its knowledge base to infer new beliefs it has not yet read from those it has, and NELL is inventing new relational predicates to extend the ontology it uses to represent beliefs We describe the design of NELL, experimental results illustrating its behavior, and discuss both its successes and shortcomings as a case study in never-ending learning NELL can be tracked online at http://rtwmlcmuedu, and followed on Twitter at @CMUNELL

Tweet sentiment analysis with classifier ensembles

Abstract: Twitter is a microblogging site in which users can post updates (tweets) to friends (followers). It has become an immense dataset of the so-called sentiments. In this paper, we introduce an approach that automatically classifies the sentiment of tweets by using classifier ensembles and lexicons. Tweets are classified as either positive or negative concerning a query term. This approach is useful for consumers who can use sentiment analysis to search for products, for companies that aim at monitoring the public sentiment of their brands, and for many other applications. Indeed, sentiment classification in microblogging services (e.g., Twitter) through classifier ensembles and lexicons has not been well explored in the literature. Our experiments on a variety of public tweet sentiment datasets show that classifier ensembles formed by Multinomial Naive Bayes, SVM, Random Forest, and Logistic Regression can improve classification accuracy. We show that classifier ensembles are promising for tweet sentiment analysis.We compare bag-of-words and feature hashing for the representation of tweets.Classifier ensembles obtained from bag-of-words and feature hashing are discussed.

Orthotropic closure approximations for flow-induced fiber orientation

Abstract: A new family of closure approximations, called orthotropic closures, is developed for modeling of flow‐induced fiber orientation. These closures approximate the fourth‐order moment tensor for fiber orientation in terms of the second‐order moment tensor. Key theoretical concepts are that any approximate fourth‐order tensor must be orthotropic, that its principal axes must match those of the second‐order tensor, and that each principal fourth‐order component is a function of just two principal values of the second‐order tensor. Examples of orthotropic closures are presented, including a simple form based on linear interpolation and a formula that is fitted to numerical solutions for the probability density function. These closures are tested against distribution function solutions in a variety of flow fields, both steady and unsteady, by integrating the orientation evolution equation. A scalar measure of the difference between the exact and approximate second‐order tensors quantifies the errors of various closures. The orthotropic fitted closure is shown to be far more accurate than any earlier closure approximation, and slightly more accurate than Verleye and Dupret’s natural closure. Approaches for further increasing the accuracy of orthotropic closures and ultimate limits to the accuracy of any closure approximation are discussed.