Givens rotation based fast backward elimination algorithm for RBF neural network pruning

A fast backward elimination algorithm is introduced based on a QR decomposition and Givens transformations to prune radial-basis-function networks. Nodes are sequentially removed using an increment of error variance criterion. The procedure is terminated by using a prediction risk criterion so as to obtain a model structure with good generalisation properties. The algorithm can be used to postprocess radial basis centres selected using a k-means routine and, in this mode, it provides a hybrid supervised centre selection approach.

Neural network basis function center selection using cluster analysis

This paper deals with the selection of centres for radial basis function (RBF) networks. A novel mean-tracking clustering algorithm is described as a way in which centers can be chosen based on a batch of collected data. A direct comparison is made between the mean-tracking algorithm and k-means clustering and it is shown how mean-tracking clustering is significantly better in terms of achieving an RBF network which performs accurate function modelling.

An elastic net orthogonal forward regression algorithm

In this paper we propose an efficient two-level model identification method for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularization parameters in the elastic net are optimized using a particle swarm optimization (PSO) algorithm at the upper level by minimizing the leave one out (LOO) mean square error (LOOMSE). Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Femtosecond pulse shaping using differential evolutionary algorithm and wavelet operators

Evolutionary meta-algorithms for pulse shaping of broadband femtosecond duration laser pulses are proposed. The genetic algorithm searching the evolutionary landscape for desired pulse shapes consists of a population of waveforms (genes), each made from two concatenated vectors, specifying phases and magnitudes, respectively, over a range of frequencies. Frequency domain operators such as mutation, two-point crossover average crossover, polynomial phase mutation, creep and three-point smoothing as well as a time-domain crossover are combined to produce fitter offsprings at each iteration step. The algorithm applies roulette wheel selection; elitists and linear fitness scaling to the gene population. A differential evolution (DE) operator that provides a source of directed mutation and new wavelet operators are proposed. Using properly tuned parameters for DE, the meta-algorithm is used to solve a waveform matching problem. Tuning allows either a greedy directed search near the best known solution or a robust search across the entire parameter space.

A fast algorithm for sparse probability density function construction

A new sparse kernel density estimator is introduced. Our main contribution is to develop a recursive algorithm for the selection of significant kernels one at time using the minimum integrated square error (MISE) criterion for both kernel selection. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

Advanced feature selection methods in multinominal dementia classification from structural MRI data

Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification.

Systematic selection of analogue redesign method for forward-type digital power converters

This article proposes a systematic approach to determine the most suitable analogue redesign method to be used for forward-type converters under digital voltage mode control. The focus of the method is to achieve the highest phase margin at the particular switching and crossover frequencies chosen by the designer. It is shown that at high crossover frequencies with respect to switching frequency, controllers designed using backward integration have the largest phase margin; whereas at low crossover frequencies with respect to switching frequency, controllers designed using bilinear integration with pre-warping have the largest phase margins. An algorithm has been developed to determine the frequency of the crossing point where the recommended discretisation method changes. An accurate model of the power stage is used for simulation and experimental results from a Buck converter are collected. The performance of the digital controllers is compared to that of the equivalent analogue controller both in simulation and experiment. Excellent closeness between the simulation and experimental results is presented. This work provides a concrete example to allow academics and engineers to systematically choose a discretisation method.

Plastic and evolutionary responses to heat stress in a temperate dung fly: negative correlation between basal and induced heat tolerance?

Extreme weather events such as heat waves are becoming more frequent and intense. Populations can cope with elevated heat stress by evolving higher basal heat tolerance (evolutionary response) and/or stronger induced heat tolerance (plastic response). However, there is ongoing debate about whether basal and induced heat tolerance are negatively correlated and whether adaptive potential in heat tolerance is sufficient under ongoing climate warming. To evaluate the evolutionary potential of basal and induced heat tolerance, we performed experimental evolution on a temperate source 4 population of the dung fly Sepsis punctum. Offspring of flies adapted to three thermal selection regimes (Hot, Cold and Reference) were subjected to acute heat stress after having been exposed to either a hot-acclimation or non-acclimation pretreatment. As different traits may respond differently to temperature stress, several physiological and life history traits were assessed. Condition dependence of the response was evaluated by exposing juveniles to different levels of developmental (food restriction/rearing density) stress. Heat knockdown times were highest, whereas acclimation effects were lowest in the Hot selection regime, indicating a negative association between basal and induced heat tolerance. However, survival, adult longevity, fecundity and fertility did not show such a pattern. Acclimation had positive effects in heat-shocked flies, but in the absence of heat stress hot-acclimated flies had reduced life spans relative to nonacclimated ones, thereby revealing a potential cost of acclimation. Moreover, body size positively affected heat tolerance and unstressed individuals were less prone to heat stress than stressed flies, offering support for energetic costs associated with heat tolerance. Overall, our results indicate that heat tolerance of temperate insects can evolve under rising temperatures, but this response could be limited by a negative relationship between basal and induced thermotolerance, and may involve some but not other fitness-related traits.

Apical negative pressure irrigation versus conventional irrigation plus triantibiotic intracanal dressing on root canal disinfection in dog teeth

Objective. The aim of this study was to compare in vivo the efficacy of 2 root canal disinfection techniques (apical negative pressure irrigation versus apical positive pressure irrigation plus triantibiotic intracanal dressing) in immature dog teeth with apical periodontitis. Study design. Two groups of root canals with pulp necrosis and apical periodontitis were evaluated according to the disinfection technique: group 1: apical negative pressure irrigation (EndoVac system); and group 2: apical positive pressure irrigation (conventional irrigation) plus triantibiotic intracanal dressing. The first sample (S1) was collected after lesions were radiographically visible, and the second sample (S2) was collected after apical negative pressure irrigation (group 1) or conventional irrigation/triantibiotic dressing (group 2). All samples were seeded in a culture medium for anaerobic bacteria. Colony-forming unit counts were analyzed statistically by the Mann-Whitney test (alpha = .05). Results. Microorganisms were present in 100% of canals of both groups in S1. In S2, microorganisms were absent in 88.6% of group 1`s canals and 78.28% of group 2`s canals. There was no significant difference between the groups in either S1 (P = .0963) or S2 (P = .0566). There was significant (P < .05) bacterial reduction from S1 to S2 in both groups. Conclusion. In immature teeth with apical periodontitis, use of the EndoVac system can be considered to be a promising disinfection protocol, because it provided similar bacterial reduction to that of apical positive pressure irrigation (conventional irrigation) plus intracanal dressing with the triantibiotic paste, and the use of intracanal antibiotics might not be necessary. (Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2010;109:e42-e46)

Towards improving cluster-based feature selection with a simplified silhouette filter

This paper proposes a filter-based algorithm for feature selection. The filter is based on the partitioning of the set of features into clusters. The number of clusters, and consequently the cardinality of the subset of selected features, is automatically estimated from data. The computational complexity of the proposed algorithm is also investigated. A variant of this filter that considers feature-class correlations is also proposed for classification problems. Empirical results involving ten datasets illustrate the performance of the developed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to state of the art algorithms that find clusters of features. We show that, if computational efficiency is an important issue, then the proposed filter May be preferred over their counterparts, thus becoming eligible to join a pool of feature selection algorithms to be used in practice. As an additional contribution of this work, a theoretical framework is used to formally analyze some properties of feature selection methods that rely on finding clusters of features. (C) 2011 Elsevier Inc. All rights reserved.

Second-order negative-curvature methods for box-constrained and general constrained optimization

A Nonlinear Programming algorithm that converges to second-order stationary points is introduced in this paper. The main tool is a second-order negative-curvature method for box-constrained minimization of a certain class of functions that do not possess continuous second derivatives. This method is used to define an Augmented Lagrangian algorithm of PHR (Powell-Hestenes-Rockafellar) type. Convergence proofs under weak constraint qualifications are given. Numerical examples showing that the new method converges to second-order stationary points in situations in which first-order methods fail are exhibited.

Likelihood estimate of treatment effects under selection bias

We consider methods for estimating causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but it is also less sensitive to model misspecifications, which we consider, than the existing methods.

Optimization Of Groundwater Remediation With New Efficient Parallel Algorithm For Global Optimization

Application of optimization algorithm to PDE modeling groundwater remediation can greatly reduce remediation cost. However, groundwater remediation analysis requires a computational expensive simulation, therefore, effective parallel optimization could potentially greatly reduce computational expense. The optimization algorithm used in this research is Parallel Stochastic radial basis function. This is designed for global optimization of computationally expensive functions with multiple local optima and it does not require derivatives. In each iteration of the algorithm, an RBF is updated based on all the evaluated points in order to approximate expensive function. Then the new RBF surface is used to generate the next set of points, which will be distributed to multiple processors for evaluation. The criteria of selection of next function evaluation points are estimated function value and distance from all the points known. Algorithms created for serial computing are not necessarily efficient in parallel so Parallel Stochastic RBF is different algorithm from its serial ancestor. The application for two Groundwater Superfund Remediation sites, Umatilla Chemical Depot, and Former Blaine Naval Ammunition Depot. In the study, the formulation adopted treats pumping rates as decision variables in order to remove plume of contaminated groundwater. Groundwater flow and contamination transport is simulated with MODFLOW-MT3DMS. For both problems, computation takes a large amount of CPU time, especially for Blaine problem, which requires nearly fifty minutes for a simulation for a single set of decision variables. Thus, efficient algorithm and powerful computing resource are essential in both cases. The results are discussed in terms of parallel computing metrics i.e. speedup and efficiency. We find that with use of up to 24 parallel processors, the results of the parallel Stochastic RBF algorithm are excellent with speed up efficiencies close to or exceeding 100%.

Is what you say what you do? Analyzing and comparing the effect of buyer-supplier relationship in the Brazilian and Chinese supplier selection criteria

Choosing properly and efficiently a supplier has been challenging practitioners and academics since 1960’s. Since then, countless studies had been performed and relevant changes in the business scenario were considered such as global sourcing, quality-orientation, just-in-time practices. It is almost consensus that quality should be the selection driver, however, some polemical findings questioned this general agreement. Therefore, one of the objectives of the study was to identify the supplier selection criteria and bring this discussion back again. Moreover, Dickson (1966) suggested existing business relationship as selection criterion, then it was reviewed the importance of business relationship for the company and noted a set of potential negative effects that could rise from it. By considering these side effects of relationship, this research aimed to investigate how the relationship could influence the supplier selection and how its harmful effects could affect the selection process. The impact of this phenomenon was investigated cross-nationally. The research strategy adopted was a controlled experiment via vignette combined with discrete choice analysis. The data collections were performed in China and Brazil. By examining the results, it could be drawn five major findings. First, when purchasers were asked to declare their supplier selection priorities, quality was stated as the most important independently of country and relationship. This result was consistent with diverse studies since 60’s. However, when purchasers were exposed to a multi-criteria trade-off situation, their actual selection priorities deviate from what they had declared. In the actual decision-making without influence of buyer-supplier relationship, Brazilian purchasers focused on price and Chinese buyers prioritized delivery then price. This observation reinforced some controversial prior studies of Verma & Pullman (1998) and Hirakubo & Kublin (1998). Second, through the introduction of the buyer-supplier relationship (operationalized via relational capital) in the supplier selection process, this research extended the existing studies and found that Brazilian buyers still focused on price. The relationship became just another criterion for supplier selection such as quality and delivery. However, from the Chinese sample, the results suggested that quality was totally discarded and the decision was majorly made through price and relationship. The third finding suggested that relational capital could legitimate the quality and sustainability of the supplier and replaces these selection criteria and made the decisional task less complex. Additionally, with the relational capital, the decision-makings were associated to few biases such as availability cognition, commitment, confirmatory and perceived biases. By analyzing the purchasers’ behavior, relational capital inducted buyers of both countries to relax in their purchasing requirements (quality, delivery and sustainability) leading to potential negative effects. In the Brazilian sample, the phenomenon of willing to pay a higher price for a lower quality offer demonstrated to be a potential counterproductive and suboptimal decision. Finally, the last finding was associated to the cultural effect on the buyers’ decisions. From the outcome, it is possible to observe that if a purchaser’s cultural background is more relation-oriented, the more he will tend to use relational capital as a decision heuristic, thus, the purchaser will be more susceptible to the potential relationship’s side effects

Breed and selection line differences in the temperament of beef cattle

O temperamento de quatro raças bovinas foi avaliado utilizando-se o teste de velocidade de fuga (FT) e o escore de comportamento (BST). FT foi definida como o tempo necessário para animais percorrerem uma distância de 2 m após a pesagem. BST foi baseada no comportamento dos animais na balança, amostrando-se quatro categorias de comportamento: movimentos, intensidade de respiração, vocalizações e coices. Os coeficientes de herdabilidade de FT e BST foram estimados com uso de um modelo de máxima verossimilhança restrita, considerando meio irmãos paternos. Caracu apresentou menores médias para BST do que as demais raças. Nelore apresentou resultados intermediários, seguida por Guzerat e Gyr com médias mais elevadas (p < 0,05). Resultados similares foram observados para FT, mas as médias de Caracu e Nelore não diferiram entre si. Observou-se baixa associação entre FT e BST (r p= -0,36; p < 0,01). A correlação entre rank de touros ordenados pelos seus valores preditos (p) para FT e BST foi moderada e negativa (r s = -0,63; p < 0,001). A herdabilidade de FT e BST foi de 0,35 e 0,34, respectivamente. A comparação de rebanhos Nelore com diferentes critérios de seleção para peso corporal mostrou que linhas de seleção podem modular positivamente o temperamento de Bos indicus.