Neuroevolutionary learning in nonstationary environments

This work presents a new neuro-evolutionary model, called NEVE (Neuroevolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in nonstationary environments. NEVE makes use of quantum-inspired evolutionary models to automatically configure the ensemble members and combine their output. The quantum-inspired evolutionary models identify the most appropriate topology for each MLP network, select the most relevant input variables, determine the neural network weights and calculate the voting weight of each ensemble member. Four different approaches of NEVE are developed, varying the mechanism for detecting and treating concepts drifts, including proactive drift detection approaches. The proposed models were evaluated in real and artificial datasets, comparing the results obtained with other consolidated models in the literature. The results show that the accuracy of NEVE is higher in most cases and the best configurations are obtained using some mechanism for drift detection. These results reinforce that the neuroevolutionary ensemble approach is a robust choice for situations in which the datasets are subject to sudden changes in behaviour

DetectA: abrupt concept drift detection in non-stationary environments

Almost all drift detection mechanisms designed for classification problems work reactively: after receiving the complete data set (input patterns and class labels) they apply a sequence of procedures to identify some change in the class-conditional distribution – a concept drift. However, detecting changes after its occurrence can be in some situations harmful to the process under analysis. This paper proposes a proactive approach for abrupt drift detection, called DetectA (Detect Abrupt Drift). Briefly, this method is composed of three steps: (i) label the patterns from the test set (an unlabelled data block), using an unsupervised method; (ii) compute some statistics from the train and test sets, conditioned to the given class labels for train set; and (iii) compare the training and testing statistics using a multivariate hypothesis test. Based on the results of the hypothesis tests, we attempt to detect the drift on the test set, before the real labels are obtained. A procedure for creating datasets with abrupt drift has been proposed to perform a sensitivity analysis of the DetectA model. The result of the sensitivity analysis suggests that the detector is efficient and suitable for datasets of high-dimensionality, blocks with any proportion of drifts, and datasets with class imbalance. The performance of the DetectA method, with different configurations, was also evaluated on real and artificial datasets, using an MLP as a classifier. The best results were obtained using one of the detection methods, being the proactive manner a top contender regarding improving the underlying base classifier accuracy

GRAPE: Genetic Routine for Astronomical Period Estimation

Period estimation is an important task in the classification of many variable astrophysical objects. Here we present GRAPE: A Genetic Routine for Astronomical Period Estimation, a genetic algorithm optimised for the processing of survey data with spurious and aliased artefacts. It uses a Bayesian Generalised Lomb-Scargle (BGLS) fitness function designed for use with the Skycam survey conducted at the Liverpool Telescope. We construct a set of simulated light curves using both regular survey cadence and the unique Skycam variable cadence with four types of signal: sinusoidal, sawtooth, symmetric eclipsing binary and eccentric eclipsing binary. We apply GRAPE and a frequency spectrum BGLS periodogram to the light curves and show that the performance of GRAPE is superior to the frequency spectrum for any signal well modelled by the fitness function. This is due to treating the parameter space as a continuous variable.We also show that the Skycam sampling is sufficient to correctly estimate the period of over 90% of the sinusoidal shape light curves relative to the more standard regular cadence.We note that GRAPE has a computational overhead which makes it slower on light curves with low numbers of observations and faster with higher numbers of observations and discuss the potential optimisations used to speedup the runtime. Finally, we analyse the period dependence and baseline importance of the performance of both methods and propose improvements which will extend this method to the detection of quasi-periodic signals

An Application of Combined Neural Networks to Remotely Sensed Images

Studies in the area of pattern recognition have indicated that in most cases a classifier performs differently from one pattern class to another. This observation gave birth to the idea of combining the individual results from different classifiers to derive a consensus decision. This work investigates the potential of combining neural networks to remotely sensed images. A classifier system is built by integrating the results of a plurarity of feed-forward neural networks, each of them designed to have the best performance for one class. Fuzzy Integrals are used as the combining strategy. Experiments carried out to evaluate the system, using a satellite image of an area undergoing a rapid degradation process, have shown that the combination may yield a better performance than that of a single neural network

Classifying Periodic Astrophysical Phenomena from non-survey optimized variable-cadence observational data

Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. Therefore, the production of methods and systems for the automated classification of time-domain astronomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure, the Small Telescopes Installed at the Liverpool Telescope. These instruments have been in operation since March 2009 gathering data of large areas of sky around the current field of view of the main telescope generating a large dataset containing millions of light sources. The instruments are inexpensive to run as they do not require a separate telescope to operate but this style of surveying the sky introduces structured artifacts into our data due to the variable cadence at which sky fields are resampled. These artifacts can make light sources appear variable and must be addressed in any processing method. The data from large sky surveys can lead to the discovery of interesting new variable objects. Efficient software and analysis tools are required to rapidly determine which potentially variable objects are worthy of further telescope time. Machine learning offers a solution to the quick detection of variability by characterising the detected signals relative to previously seen exemplars. In this paper, we introduce a processing system designed for use with the Liverpool Telescope identifying potentially interesting objects through the application of a novel representation learning approach to data collected automatically from the wide-field instruments. Our method automatically produces a set of classification features by applying Principal Component Analysis on set of variable light curves using a piecewise polynomial fitted via a genetic algorithm applied to the epoch-folded data. The epoch-folding requires the selection of a candidate period for variable light curves identified using a genetic algorithm period estimation method specifically developed for this dataset. A Random Forest classifier is then used to classify the learned features to determine if a light curve is generated by an object of interest. This system allows for the telescope to automatically identify new targets through passive observations which do not affect day-to-day operations as the unique artifacts resulting from such a survey method are incorporated into the methods. We demonstrate the power of this feature extraction method compared to feature engineering performed by previous studies by training classification models on 859 light curves of 12 known variable star classes from our dataset. We show that our new features produce a model with a superior mean cross-validation F1 score of 0.4729 with a standard deviation of 0.0931 compared with the engineered features at 0.3902 with a standard deviation of 0.0619. We show that the features extracted from the representation learning are given relatively high importance in the final classification model. Additionally, we compare engineered features computed on the interpolated polynomial fits and show that they produce more reliable distributions than those fit to the raw light curve when the period estimation is correct

Efeito da implantação de uma floresta mista sobre a população de microrganismos celulolíticos em solos do semi-árido mineiro.

Na recuperacao e manutencao da fertilidade dos solos degradados, a decomposicao apresenta-se particularmente importante sendo a razao celulose/N um dos principais fatores que influenciam a efetividade desses processo. Neste trabalho avaliou-se a ocorrencia e a dinamica da populacao de microorganismos celuloliticos nos solos de diferentes modelos de reflorestamento de uma area degradada do Projeto Jaiba/MG. Esses modelos incluiram especies arboreas de eucalipto, leguminosas e nao leguminosas nativas. Observaram-se diferencas significativas no numero de celuloliticos entre as estacoes e os locais estudados. Nas areas impactadas ocorreu elevacao menos intensa dos microrganismos evidenciando o efeito limitante do impacto sobre essa populacao microbiana. Nos modelos de plantio avaliados o numero de celuloliticos diferiu significativamente, sendo o mais elevado no modelo representado pelo plantio do maior numero de especies. em todos os experimentos, a maior populacao de celuloliticos ocorreu nos consorcios entre leguminosas e outras especies vegetais nativas e a menor nas areas de plantio de eucalipto, sugerindo uma acao limitante dessa planta sobre o crescimento desses microrganismos. Esses resultados evidenciaram o papel dos celuloliticos como bioindicadores da qualidade do solo e da resposta as diferentes praticas de manejo

tert-Butyl N-[3-hy­droxy-1-phenyl-4-(pyrimidin-2-ylsulfan­yl)butan-2-yl]carbamate monohydrate

In the title hydrate, C19H25N3O3S·H2O, the configuration at each chiral centre in the organic mol­ecule is S, with the hy­droxy and carbamate substituents being anti [O—C—C—N torsion angle = −179.3 (3)°]. The thio­pyrimidyl and carbamate residues lie to one side of the pseudo-mirror plane defined by the C5S backbone of the mol­ecule; this plane approximately bis­ects the benzene ring at the 1- and 4-C atoms. The dihedral angle formed between the terminal rings is 5.06 (18)°. In the crystal, supra­molecular tubes aligned along the b axis are found: these are sustained by a combination of O—H⋯O, O—H⋯N and N—H⋯O hydrogen bonds

INFLUENCE OF SPLICES ON THE STABILITY BEHAVIOUR OF COLUMNS AND FRAMES

Abstract. The paper presents a study on the influence of splice connections on the stability behaviour of compressed steel columns. The column is modelled as two independent prismatic parts connected by a rotational spring at the splice location and rotational and extensional springs at the column ends to represent the effect of the adjacent structure. The general behaviour is characterized using a polynomial Rayleigh-Ritz approximation substituted into the potential energy function, in combination with the Lagrange's method of undetermined multipliers, and based on this model the critical load is found. The load-carrying capacity is analysed with respect to the following variables: (i) location and rotational stiffness of the splice, (ii) change in the column section serial size and (iii) column end-restraints stiffness coefficients. A nonlinear regression model is developed to predict simple relationships between the critical load and the relevant column characteristics