Meta-heuristics self-parameterization in a multi-agent scheduling system using case-based reasoning

This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. Finally, some conclusions are reached and future work outlined.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. © 2014 Springer-Verlag Berlin Heidelberg.

Internal consistency of the self-reporting questionnaire-20 in occupational groups

ABSTRACT OBJECTIVE To assess the internal consistency of the measurements of the Self-Reporting Questionnaire (SRQ-20) in different occupational groups. METHODS A validation study was conducted with data from four surveys with groups of workers, using similar methods. A total of 9,959 workers were studied. In all surveys, the common mental disorders were assessed via SRQ-20. The internal consistency considered the items belonging to dimensions extracted by tetrachoric factor analysis for each study. Item homogeneity assessment compared estimates of Cronbach’s alpha (KD-20), the alpha applied to a tetrachoric correlation matrix and stratified Cronbach’s alpha. RESULTS The SRQ-20 dimensions showed adequate values, considering the reference parameters. The internal consistency of the instrument items, assessed by stratified Cronbach’s alpha, was high (> 0.80) in the four studies. CONCLUSIONS The SRQ-20 showed good internal consistency in the professional categories evaluated. However, there is still a need for studies using alternative methods and additional information able to refine the accuracy of latent variable measurement instruments, as in the case of common mental disorders.

Health self-assessment by hemodialysis patients in the Brazilian Unified Health System

ABSTRACT OBJECTIVE To examine whether the level of complexity of the services structure and sociodemographic and clinical characteristics of patients in hemodialysis are associated with the prevalence of poor health self-assessment. METHODS In this cross-sectional study, we evaluated 1,621 patients with chronic terminal kidney disease on hemodialysis accompanied in 81 dialysis services in the Brazilian Unified Health System in 2007. Sampling was performed by conglomerate in two stages and a structured questionnaire was applied to participants. Multilevel multiple logistic regression was used for data analysis. RESULTS The prevalence of poor health self-assessment was of 54.5%, and in multivariable analysis it was associated with the following variables: increasing age (OR = 1.02; 95%CI 1.01–1.02), separated or divorced marital status (OR = 0.62; 95%CI 0.34–0.88), having 12 years or more of study (OR = 0.51; 95%CI 0.37–0.71), spending more than 60 minutes in commuting between home and the dialysis service (OR = 1.80; 95%CI 1.29–2.51), having three or more self-referred diseases (OR = 2.20; 95%CI 1.33–3.62), and reporting some (OR = 2.17; 95%CI 1.66–2.84) or a lot of (OR = 2.74; 95%CI 2.04–3.68) trouble falling asleep. Individuals in treatment in dialysis services with the highest level of complexity in the structure presented less chance of performing a self-assessment of their health as bad (OR = 0.59; 95%CI 0.42–0.84). CONCLUSIONS We showed poor health self-assessment is associated with age, years of formal education, marital status, home commuting time to the dialysis service, number of self-referred diseases, report of trouble sleeping, and also with the level of complexity of the structure of health services. Acknowledging these factors can contribute to the development of strategies to improve the health of patients in hemodialysis in the Brazilian Unified Health System.

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

Optical nonlinearity in Tandem SI-C photodetectors

The behavior of tandem pin heterojunctions based on a-SiC: H alloys is investigated under different optical and electrical bias conditions. The devices are optimized to act as optically selective wavelength filters. Depending on the device configuration (optical gaps, thickness, sequence of cells in the stack structure) and on the applied voltage (positive or negative) and optical bias (wavelength, intensity, frequency) it is possible to combine the wavelength discrimination function with the self amplification of the signal. This wavelength nonlinearity allows the amplification or the rejection of a weak signal-impulse. The device works as an active tunable optical filter for wavelength selection and can be used as an add/drop multiplexer (ADM) which enables data to enter and leave an optical network bit stream without having to demultiplex the stream. Results show that, even under weak transient input signals, the background wavelength controls the output signal. This nonlinearity, due to the transient asymmetrical light penetration of the input channels across the device together with the modification on the electrical field profile due to the optical bias, allows tuning an input channel without demultiplexing the stream. This high optical nonlinearity makes the optimized devices attractive for the amplification of all optical signals. Transfer characteristics effects due to changes in steady state light, control d.c. voltage and applied light pulses are presented. Based on the experimental results and device configuration an optoelectronic model is developed. The transfer characteristics effects due to changes in steady state light, dc control voltage or applied light pulses are simulated and compared with the experimental data. A good agreement was achieved.

Digital-domain self-calibration technique for video-rate pipeline A/D converters using Gaussian white noise

Electronics Letters Vol.38, nº 19

Quantitative description of the self-assembly of patchy particles into chains and rings

We numerically study a simple fluid composed of particles having a hard-core repulsion complemented by two patchy attractive sites on the particle poles. An appropriate choice of the patch angular width allows for the formation of ring structures which, at low temperatures and low densities, compete with the growth of linear aggregates. The simplicity of the model makes it possible to compare simulation results and theoretical predictions based on the Wertheim perturbation theory, specialized to the case in which ring formation is allowed. Such a comparison offers a unique framework for establishing the quality of the analytic predictions. We find that the Wertheim theory describes remarkably well the simulation results.

Reply to "Comment on 'Effect of polydispersity on the ordering transition of adsorbed self- assembled rigid rods'"

We comment on the nature of the ordering transition of a model of equilibrium polydisperse rigid rods on the square lattice, which is reported by Lopez et al. to exhibit random percolation criticality in the canonical ensemble, in sharp contrast to (i) our results of Ising criticality for the same model in the grand canonical ensemble [Phys. Rev. E 82, 061117 (2010)] and (ii) the absence of exponent(s) renormalization for constrained systems with logarithmic specific-heat anomalies predicted on very general grounds by Fisher [Phys. Rev. 176, 257 (1968)].

The nature of the ordered phase of the confined self-assembled rigid Rod model

We investigate the nature of the ordered phase and the orientational correlations between adjacent layers of the confined three-dimensional self-assembled rigid rod model, on the cubic lattice. We find that the ordered phase at finite temperatures becomes uniaxial in the thermodynamic limit, by contrast to the ground state (partial) order where the orientation of the uncorrelated layers is perpendicular to one of the three lattice directions. The increase of the orientational correlation between layers as the number of layers increases suggests that the unconfined model may also exhibit uniaxial ordering at finite temperatures.

Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Aprendizagem autónoma (self-directed learning): relatório de um atelier pedagógico

Educação Médica. 1994, 5(3):178-181.

On-line self-healing of circuits implemented on reconfigurable FPGAs

To boost logic density and reduce per unit power consumption SRAM-based FPGAs manufacturers adopted nanometric technologies. However, this technology is highly vulnerable to radiation-induced faults, which affect values stored in memory cells, and to manufacturing imperfections. Fault tolerant implementations, based on Triple Modular Redundancy (TMR) infrastructures, help to keep the correct operation of the circuit. However, TMR is not sufficient to guarantee the safe operation of a circuit. Other issues like module placement, the effects of multi- bit upsets (MBU) or fault accumulation, have also to be addressed. In case of a fault occurrence the correct operation of the affected module must be restored and/or the current state of the circuit coherently re-established. A solution that enables the autonomous restoration of the functional definition of the affected module, avoiding fault accumulation, re-establishing the correct circuit state in real-time, while keeping the normal operation of the circuit, is presented in this paper.

A framework for self-healing radiation-tolerant implementations on reconfigurable FPGAs

To increase the amount of logic available in SRAM-based FPGAs manufacturers are using nanometric technologies to boost logic density and reduce prices. However, nanometric scales are highly vulnerable to radiation-induced faults that affect values stored in memory cells. Since the functional definition of FPGAs relies on memory cells, they become highly prone to this type of faults. Fault tolerant implementations, based on triple modular redundancy (TMR) infrastructures, help to keep the correct operation of the circuit. However, TMR is not sufficient to guarantee the safe operation of a circuit. Other issues like the effects of multi-bit upsets (MBU) or fault accumulation, have also to be addressed. Furthermore, in case of a fault occurrence the correct operation of the affected module must be restored and the current state of the circuit coherently re-established. A solution that enables the autonomous correct restoration of the functional definition of the affected module, avoiding fault accumulation, re-establishing the correct circuit state in realtime, while keeping the normal operation of the circuit, is presented in this paper.