Enhancing Multimodal Silent Speech Interfaces With Feature Selection

In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known "curse of dimensionality". As a consequence, in order to extract useful information from this data, one has to resort to feature selection (FS) techniques to lower the dimensionality of the learning space. In this paper, we assess the impact of FS techniques for silent speech data, in a dataset with 4 non-invasive and promising modalities, namely: video, depth, ultrasonic Doppler sensing, and surface electromyography. We consider two supervised (mutual information and Fisher's ratio) and two unsupervised (meanmedian and arithmetic mean geometric mean) FS filters. The evaluation was made by assessing the classification accuracy (word recognition error) of three well-known classifiers (knearest neighbors, support vector machines, and dynamic time warping). The key results of this study show that both unsupervised and supervised FS techniques improve on the classification accuracy on both individual and combined modalities. For instance, on the video component, we attain relative performance gains of 36.2% in error rates. FS is also useful as pre-processing for feature fusion. Copyright © 2014 ISCA.

Recent history of fractional calculus

This survey intends to report some of the major documents and events in the area of fractional calculus that took place since 1974 up to the present date.

Analysis of the domain of applicability of an algorithm for a resources system selection problem for Distributed/Agile/Virtual Enterprises integration

The process of resources systems selection takes an important part in Distributed/Agile/Virtual Enterprises (D/A/V Es) integration. However, the resources systems selection is still a difficult matter to solve in a D/A/VE, as it is pointed out in this paper. Globally, we can say that the selection problem has been equated from different aspects, originating different kinds of models/algorithms to solve it. In order to assist the development of a web prototype tool (broker tool), intelligent and flexible, that integrates all the selection model activities and tools, and with the capacity to adequate to each D/A/V E project or instance (this is the major goal of our final project), we intend in this paper to show: a formulation of a kind of resources selection problem and the limitations of the algorithms proposed to solve it. We formulate a particular case of the problem as an integer programming, which is solved using simplex and branch and bound algorithms, and identify their performance limitations (in terms of processing time) based on simulation results. These limitations depend on the number of processing tasks and on the number of pre-selected resources per processing tasks, defining the domain of applicability of the algorithms for the problem studied. The limitations detected open the necessity of the application of other kind of algorithms (approximate solution algorithms) outside the domain of applicability founded for the algorithms simulated. However, for a broker tool it is very important the knowledge of algorithms limitations, in order to, based on problem features, develop and select the most suitable algorithm that guarantees a good performance.

Cervical cancer screening coverage in a high-incidence region

OBJECTIVE To analyze the coverage of a cervical cancer screening program in a city with a high incidence of the disease in addition to the factors associated with non-adherence to the current preventive program.METHODS A cross-sectional study based on household surveys was conducted. The sample was composed of women between 25 and 59 years of age of the city of Boa Vista, RR, Northern Brazil who were covered by the cervical cancer screening program. The cluster sampling method was used. The dependent variable was participation in a women’s health program, defined as undergoing at least one Pap smear in the 36 months prior to the interview; the explanatory variables were extracted from individual data. A generalized linear model was used.RESULTS 603 women were analyzed, with an mean age of 38.2 years (SD = 10.2). Five hundred and seventeen women underwent the screening test, and the prevalence of adherence in the last three years was up to 85.7% (95%CI 82.5;88.5). A high per capita household income and recent medical consultation were associated with the lower rate of not being tested in multivariate analysis. Disease ignorance, causes, and prevention methods were correlated with chances of non-adherence to the screening system; 20.0% of the women were reported to have undergone opportunistic and non-routine screening.CONCLUSIONS The informed level of coverage is high, exceeding the level recommended for the control of cervical cancer. The preventive program appears to be opportunistic in nature, particularly for the most vulnerable women (with low income and little information on the disease). Studies on the diagnostic quality of cervicovaginal cytology and therapeutic schedules for positive cases are necessary for understanding the barriers to the control of cervical cancer.

Recent advances in vacum sciences and applications

Recent advances in vacuum sciences and applications are reviewed. Novel optical interferometer cavity devices enable pressure measurements with ppm accuracy. The innovative dynamic vacuum standard allows for pressure measurements with temporal resolution of 2 ms. Vacuum issues in the construction of huge ultra-high vacuum devices worldwide are reviewed. Recent advances in surface science and thin films include new phenomena observed in electron transport near solid surfaces as well as novel results on the properties of carbon nanomaterials. Precise techniques for surface and thin-film characterization have been applied in the conservation technology of cultural heritage objects and recent advances in the characterization of biointerfaces are presented. The combination of various vacuum and atmospheric-pressure techniques enables an insight into the complex phenomena of protein and other biomolecule conformations on solid surfaces. Studying these phenomena at solid-liquid interfaces is regarded as the main issue in the development of alternative techniques for drug delivery, tissue engineering and thus the development of innovative techniques for curing cancer and cardiovascular diseases. A review on recent advances in plasma medicine is presented as well as novel hypotheses on cell apoptosis upon treatment with gaseous plasma. Finally, recent advances in plasma nanoscience are illustrated with several examples and a roadmap for future activities is presented.

A one-dimensional prescribed curvature equation modeling the corneal shape.

We prove existence, uniqueness, and stability of solutions of the prescribed curvature problem (u'/root 1 + u'(2))' = au - b/root 1 + u'(2) in [0, 1], u'(0) = u(1) = 0, for any given a > 0 and b > 0. We also develop a linear monotone iterative scheme for approximating the solution. This equation has been proposed as a model of the corneal shape in the recent paper (Okrasinski and Plociniczak in Nonlinear Anal., Real World Appl. 13:1498-1505, 2012), where a simplified version obtained by partial linearization has been investigated.

Complex variable positive definite functions

In this paper we develop an appropriate theory of positive definite functions on the complex plane from first principles and show some consequences of positive definiteness for meromorphic functions.

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.

Positive radial solutions of the Dirichlet problem for the Minkowski-Curvature equation in a ball

We study the existence and multiplicity of positive radial solutions of the Dirichlet problem for the Minkowski-curvature equation { -div(del upsilon/root 1-vertical bar del upsilon vertical bar(2)) in B-R, upsilon=0 on partial derivative B-R,B- where B-R is a ball in R-N (N >= 2). According to the behaviour off = f (r, s) near s = 0, we prove the existence of either one, two or three positive solutions. All results are obtained by reduction to an equivalent non-singular one-dimensional problem, to which variational methods can be applied in a standard way.

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.

Student performance analysis under different moodle course designs

This work extends a recent comparative study covering four different courses lectured at the Polytechnic of Porto - School of Engineering, in respect to the usage of a particular Learning Management System, i.e. Moodle, and its impact on students' results. A fifth course, which includes a number of resources especially supporting laboratory classes, is now added to the analysis. This particular course includes a number of remote experiments, made available through VISIR (Virtual Instrument Systems in Reality) and directly accessible through links included in the Moodle course page. We have analyzed the students' behavior in following these links and in effectively running experiments in VISIR (and also using other lab related resources, in Moodle). This data have been correlated with students' classifications in the lab component and in the exam, each one weighting 50% of their final marks. We aimed to compare students' performance in a richly Moodle-supported environment (with lab component) and in a poorly Moodle-supported environment (with only theoretical component). This question followed from conclusions drawn in the above referred comparative study, where it was shown that even though a positive correlation factor existed between the number of Moodle accesses and the final exam grade obtained by each student, its explanation behind was not straightforward, as the quality of the resources was preponderant over its quantity.

Clustering de dados biomédicos com algoritmos baseados em critérios entrópicos

A procura de padrões nos dados de modo a formar grupos é conhecida como aglomeração de dados ou clustering, sendo uma das tarefas mais realizadas em mineração de dados e reconhecimento de padrões. Nesta dissertação é abordado o conceito de entropia e são usados algoritmos com critérios entrópicos para fazer clustering em dados biomédicos. O uso da entropia para efetuar clustering é relativamente recente e surge numa tentativa da utilização da capacidade que a entropia possui de extrair da distribuição dos dados informação de ordem superior, para usá-la como o critério na formação de grupos (clusters) ou então para complementar/melhorar algoritmos existentes, numa busca de obtenção de melhores resultados. Alguns trabalhos envolvendo o uso de algoritmos baseados em critérios entrópicos demonstraram resultados positivos na análise de dados reais. Neste trabalho, exploraram-se alguns algoritmos baseados em critérios entrópicos e a sua aplicabilidade a dados biomédicos, numa tentativa de avaliar a adequação destes algoritmos a este tipo de dados. Os resultados dos algoritmos testados são comparados com os obtidos por outros algoritmos mais “convencionais" como o k-médias, os algoritmos de spectral clustering e um algoritmo baseado em densidade.

Acute Hepatitis B in a patient previously positive for antibody to surface antigen (anti-HBs) determined by radioimmunoassay: case report and review of the literature

The determination of anti-HBs as a screening test before vaccination has been advisable in order to encounter immune individuals that don't need to receive vaccine protection. A case-report is presented and three other cases are reviewed from the literature. Anti-HBs was positive in these health-care personnels that developped typical acute B hepatitis. Different subtyping involving the d/y determinants were found in the first case, but false-positive anti-HBs even with high titres, determined by RIA, were found in the other cases. Concomitant determination of anti-HBc or absence of screening tests seem to be more reasonable policies until a low-cost and risk-free vaccine is produced.