Bootstrap performance profiles in stochastic algorithms assessment

Optimization with stochastic algorithms has become a relevant research field. Due to its stochastic nature, its assessment is not straightforward and involves integrating accuracy and precision. Performance profiles for the mean do not show the trade-off between accuracy and precision, and parametric stochastic profiles require strong distributional assumptions and are limited to the mean performance for a large number of runs. In this work, bootstrap performance profiles are used to compare stochastic algorithms for different statistics. This technique allows the estimation of the sampling distribution of almost any statistic even with small samples. Multiple comparison profiles are presented for more than two algorithms. The advantages and drawbacks of each assessment methodology are discussed.

Forecasting time series combining Holt-Winters and bootstrap approaches

Publicado em "AIP Conference Proceedings", Vol. 1648

A metodologia Bootstrap associada ao método de Holt-Winters na previsão de séries temporais

Os métodos de alisamento exponencial são muito utilizados na modelação e previsão de séries temporais, devido à sua versatilidade e opção de modelos que integram. Na estatística computacional, a metodologia Bootstrap é muito aplicada em inferência estatística no âmbito de séries temporais. Este estudo teve como principal objectivo analisar o desempenho do método de Holt-Winters associado à metodologia Bootstrap, como um processo alternativo na modelação e previsão de séries temporais.

Mechanical vs. electrical hysteresis of carbon nanotube/styrene-butadiene-styrene composites and their influence in the electromechanical response

The interesting properties of thermoplastics elastomers can be combined with carbon nanotubes (CNT) for the development of large strain piezoresistive composites for sensor applications. Piezoresistive properties of the composites depend on CNT content, with the gauge factor increasing for concentrations around the percolation threshold, mechanical and electrical hysteresis. The SBS copolymer composition (butadiene/styrene ratio) influences the mechanical and electrical hysteresis of composites and, therefore, the piezoresistive response. This work reports on the electrical and mechanical response of CNT/SBS composites with 4%wt nanofiller content, due to the larger electromechanical response. C401 and C540 SBS copolymers with 80% and 60% butadiene content, respectively have been selected. The copolymer with larger amount of soft phase (C401) shows a rubber-like mechanical behavior, with mechanical hysteresis increasing linearly with strain until 100% strain. The copolymer with the larger amount of hard phase (C540) just shows rubber-like behavior for low strains. The piezoresistive sensibility is similar for both composites for low strains, with a GF≈ 5 for 5% strain. The electrical hysteresis shows opposite behavior than the mechanical hysteresis, increasing with strain for both composites, but with higher increase for softer copolymer, C401. The GF increases with increasing strain, but this increase is larger for composites with lower amounts of soft phase due to the distinct initial modulus and deformation of the soft and hard phases of the copolymer. The soft phase shows larger strain under a given stress than the harder phase and the conductive pathway rearrangements in the composites are different for both phases, the harder copolymer (C540) showing higher piezoresistive sensibility, GF≈ 18, for 20% strain.

Predicting SO2 pollution incidents by means of additive models with optimum variable selection

The aim of this paper is to predict time series of SO2 concentrations emitted by coal-fired power stations in order to estimate in advance emission episodes and analyze the influence of some meteorological variables in the prediction. An emission episode is said to occur when the series of bi-hourly means of SO2 is greater than a specific level. For coal-fired power stations it is essential to predict emission epi- sodes sufficiently in advance so appropriate preventive measures can be taken. We proposed a meth- odology to predict SO2 emission episodes based on using an additive model and an algorithm for variable selection. The methodology was applied to the estimation of SO2 emissions registered in sampling lo- cations near a coal-fired power station located in Northern Spain. The results obtained indicate a good performance of the model considering only two terms of the time series and that the inclusion of the meteorological variables in the model is not significant.

BIOMedical search engine framework: lightweight and customized implementation of domain-specific biomedical search engines

The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsef