Micro-dynamics of ice

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RankMerging: A supervised learning-to-rank framework to predict links in large social network

Uncovering unknown or missing links in social networks is a difficult task because of their sparsity and because links may represent different types of relationships, characterized by different structural patterns. In this paper, we define a simple yet efficient supervised learning-to-rank framework, called RankMerging, which aims at combining information provided by various unsupervised rankings. We illustrate our method on three different kinds of social networks and show that it substantially improves the performances of unsupervised metrics of ranking. We also compare it to other combination strategies based on standard methods. Finally, we explore various aspects of RankMerging, such as feature selection and parameter estimation and discuss its area of relevance: the prediction of an adjustable number of links on large networks.Comment: 43 pages, published in Machine Learning Journa

Benchmarking some Portuguese S&T system research units: 2nd Edition

The increasing use of productivity and impact metrics for evaluation and comparison, not only of individual researchers but also of institutions, universities and even countries, has prompted the development of bibliometrics. Currently, metrics are becoming widely accepted as an easy and balanced way to assist the peer review and evaluation of scientists and/or research units, provided they have adequate precision and recall. This paper presents a benchmarking study of a selected list of representative Portuguese research units, based on a fairly complete set of parameters: bibliometric parameters, number of competitive projects and number of PhDs produced. The study aimed at collecting productivity and impact data from the selected research units in comparable conditions i.e., using objective metrics based on public information, retrievable on-line and/or from official sources and thus verifiable and repeatable. The study has thus focused on the activity of the 2003-06 period, where such data was available from the latest official evaluation. The main advantage of our study was the application of automatic tools, achieving relevant results at a reduced cost. Moreover, the results over the selected units suggest that this kind of analyses will be very useful to benchmark scientific productivity and impact, and assist peer review.Comment: 26 pages, 20 figures F. Couto, D. Faria, B. Tavares, P. Gon\c{c}alves, and P. Verissimo, Benchmarking some portuguese S\&T system research units: 2nd edition, DI/FCUL TR 13-03, Department of Informatics, University of Lisbon, February 201

STRENGTHS AND LIMITATIONS OF QUALITATIVE AND QUANTITATIVE RESEARCH METHODS

Scientific research adopts qualitative and quantitative methodologies in the modeling and analysis of numerous phenomena. The qualitative methodology intends to understand a complex reality and the meaning of actions in a given context. On the other hand, the quantitative methodology seeks to obtain accurate and reliable measurements that allow a statistical analysis. Both methodologies offer a set of methods, potentialities and limitations that must be explored and known by researchers. This paper concisely maps a total of seven qualitative methods and five quantitative methods. A comparative analysis of the most relevant and adopted methods is done to understand the main strengths and limitations of them. Additionally, the work developed intends to be a fundamental reference for the accomplishment of a research study, in which the researcher intends to adopt a qualitative or quantitative methodology. Through the analysis of the advantages and disadvantages of each method, it becomes possible to formulate a more accurate, informed and complete choice.  Article visualizations

Synergistic Smart Morphing Aileron

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106472/1/AIAA2013-1512.pd

Patrones ecomorfológicos en insectos neotropicales: efectos de la fragmentacion en la morfologia de los organismos: variacion en los patrones de coloracion de abejas y avispas (insecta: hymenoptera) en un paisaje altamente fragmentado del Oeste de Paraná

Anais do VI Encontro de Iniciação Científica e II Encontro Anual de Iniciação ao Desenvolvimento Tecnológico e Inovação – EICTI 2017 - 04 a 06 de outubro de 2017 - temática Ciências BiológicasEl proceso de fragmentación forestal viene afectando negativamente las dinámicas de las poblaciones de insectos polinizadores, elementos críticos para la manutención de los servicios ecosistémicos en ambientes naturales y en paisajes agrícolas. La naturaleza adaptativa de los colores para la regulación de la temperatura del cuerpo de los insectos en diferentes condiciones ambientales es central en la biología de estos organismos, pero la explotación cuantitativa de la variación en los patrones de coloración de los insectos parece bastante aislada. Y considerando que en ambientes fragmentados hay, en muchos casos, una transición abrupta entre ambientes, es razonable suponer que las diferentes presiones encontradas en ambientes de interior de bosque, borde y matriz puedan llevar a patrones bastante distintos en la coloración de los organismos encontrados en áreas de bosque y de matrizUniversidade Federal da Integração Latino-Americana (Unila); Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); Fundação Araucária; Parque Tecnológico Itaipu (PTI) e Companhia de Saneamento do Paraná (SANEPAR

Automated knowledge extraction from protein sequence

Efficient and reliable prediction of protein functions based on their sequences is one of the standing problems in genetics and bioinformatics, as experimental methods to determine protein function are unable to keep up with the rate at which new sequences are published. The function of a protein is conditioned by its three-dimensional structure, which is deeply tied to the sequence, but we cannot yet model this information with sufficient reliability to make de novo protein function predictions. Thus, protein function predictions are necessarily comparative. The most common approaches to protein function prediction rely on sequence alignments and on the assumption that proteins of similar sequence have evolved from a common ancestor and thus should perform similar functions. However, cases of divergent evolution are relatively common, and can lead to prediction errors from these approaches. Machine learning approaches not involving sequence alignments methods have also been applied to protein function prediction. However, their application has been mostly restricted to predicting generic functional aspects of proteins. My thesis is that it is possible to extract suficient information from protein sequences to make reliable detailed function predictions without the use of sequence alignments, and therefore develop machine learning approaches that can compete in general with alignment-based approaches. To prove this thesis, I developed and evaluated multiple machine learning approaches in the context of detailed function prediction. Several of these approaches were able to compete with alignmentbased classiffiers in precision, and two outperformed them notably in small classiffication problems. The main contribution of my work was the discovery of the informativeness of tripeptide subsequences. The tripeptide composition of protein sequences not only led to the most precise classification of all approaches tested, but also was suficiently informative to measure similarity between proteins directly, and compete with sequence alignments.Fundação para a Ciência e Tecnologi