Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Quadratic M-Estimators for ARCH-Type Processes

This paper addresses the issue of estimating semiparametric time series models specified by their conditional mean and conditional variance. We stress the importance of using joint restrictions on the mean and variance. This leads us to take into account the covariance between the mean and the variance and the variance of the variance, that is, the skewness and kurtosis. We establish the direct links between the usual parametric estimation methods, namely, the QMLE, the GMM and the M-estimation. The ususal univariate QMLE is, under non-normality, less efficient than the optimal GMM estimator. However, the bivariate QMLE based on the dependent variable and its square is as efficient as the optimal GMM one. A Monte Carlo analysis confirms the relevance of our approach, in particular, the importance of skewness.

Evaluation of mitochondrial function in a model of developmental programming of hypertension associated with transient neonatal oxygen exposure

UNE EXPOSITION NÉONATALE À L’OXYGÈNE MÈNE À DES MODIFICATIONS DE LA FONCTION MITOCHONDRIALE CHEZ LE RAT ADULTE Introduction: L’exposition à l’oxygène (O2) des ratons nouveau-nés a des conséquences à l’âge adulte dont une hypertension artérielle (HTA), une dysfonction vasculaire, une néphropénie et des indices de stress oxydant. En considérant que les reins sont encore en développement actif lors des premiers jours après la naissance chez les rats, jouent un rôle clé dans le développement de l’hypertension et qu’une dysfonction mitochondriale est associé à une augmentation du stress oxydant, nous postulons que les conditions délétères néonatales peuvent avoir un impact significatif au niveau rénal sur la modulation de l’expression de protéines clés du fonctionnement mitochondrial et une production mitochondriale excessive d’espèces réactives de l’ O2. Méthodes: Des ratons Sprague-Dawley sont exposés à 80% d’O2 (H) ou 21% O2 (Ctrl) du 3e au 10e jr de vie. En considérant que plusieurs organes des rats sont encore en développement actif à la naissance, ces rongeurs sont un modèle reconnu pour étudier les complications d’une hyperoxie néonatale, comme celles liées à une naissance prématurée chez l’homme. À 4 et à 16 semaines, les reins sont prélevés et les mitochondries sont extraites suivant une méthode d’extraction standard, avec un tampon contenant du sucrose 0.32 M et différentes centrifugations. L’expression des protéines mitochondriales a été mesurée par Western blot, tandis que la production d’ H202 et les activités des enzymes clés du cycle de Krebs ont été évaluées par spectrophotométrie. Les résultats sont exprimés par la moyenne ± SD. Résultats: Les rats mâles H de 16 semaines (n=6) présentent une activité de citrate synthase (considéré standard interne de l’expression protéique et de l’abondance mitochondriales) augmentée (12.4 ± 8.4 vs 4.1 ± 0.5 μmole/mL/min), une diminution de l’activité d’aconitase (enzyme sensible au redox mitochondrial) (0.11 ± 0.05 vs 0.20 ± 0.04 μmoles/min/mg mitochondrie), ainsi qu’une augmentation dans la production de H202 (7.0 ± 1.3 vs 5.4 ± 0.8 ρmoles/mg protéines mitochondriales) comparativement au groupe Ctrl (n=6 mâles et 4 femelles). Le groupe H (vs Ctrl) présente également une diminution dans l’expression de peroxiredoxin-3 (Prx3) (H 0.61±0.06 vs. Ctrl 0.78±0.02 unité relative, -23%; p<0.05), une protéine impliquée dans l’élimination d’ H202, de l’expression du cytochrome C oxidase (Complexe IV) (H 1.02±0.04 vs. Ctrl 1.20±0.02 unité relative, -15%; p<0.05), une protéine de la chaine de respiration mitochondriale, tandis que l’expression de la protéine de découplage (uncoupling protein)-2 (UCP2), impliquée dans la dispersion du gradient proton, est significativement augmentée (H 1.05±0.02 vs. Ctrl 0.90±0.03 unité relative, +17%; p<0.05). Les femelles H (n=6) (vs Ctrl, n=6) de 16 semaines démontrent une augmentation significative de l’activité de l’aconitase (0.33±0.03 vs 0.17±0.02 μmoles/min/mg mitochondrie), de l’expression de l’ATP synthase sous unité β (H 0.73±0.02 vs. Ctrl 0.59±0.02 unité relative, +25%; p<0.05) et de l’expression de MnSOD (H 0.89±0.02 vs. Ctrl 0.74±0.03 unité relative, +20%; p<0.05) (superoxide dismutase mitochondriale, important antioxidant), tandis que l’expression de Prx3 est significativement réduite (H 1.1±0.07 vs. Ctrl 0.85±0.01 unité relative, -24%; p<0.05). À 4 semaines, les mâles H (vs Ctrl) présentent une augmentation significative de l’expression de Prx3 (H 0.72±0.03 vs. Ctrl 0.56±0.04 unité relative, +31%; p<0.05) et les femelles présentent une augmentation significative de l’expression d’UCP2 (H 1.22±0.05 vs. Ctrl 1.03±0.04 unité relative, +18%; p<0.05) et de l’expression de MnSOD (H 1.36±0.01 vs. 1.19±0.06 unité relative, +14%; p<0.05). Conclusions: Une exposition néonatale à l’O2 chez le rat adulte mène à des indices de dysfonction mitochondriale dans les reins adultes, associée à une augmentation dans la production d’espèces réactives de l’oxygène, suggérant que ces modifications mitochondriales pourraient jouer un rôle dans l’hypertension artérielle et d’un stress oxydant, et par conséquent, être un facteur possible dans la progression vers des maladies cardiovasculaires. Mots-clés: Mitochondries, Reins, Hypertension, Oxygène, Stress Oxydant, Programmation

Lagrangian-informed mixed integer programming reformulations

La programmation linéaire en nombres entiers est une approche robuste qui permet de résoudre rapidement de grandes instances de problèmes d'optimisation discrète. Toutefois, les problèmes gagnent constamment en complexité et imposent parfois de fortes limites sur le temps de calcul. Il devient alors nécessaire de développer des méthodes spécialisées afin de résoudre approximativement ces problèmes, tout en calculant des bornes sur leurs valeurs optimales afin de prouver la qualité des solutions obtenues. Nous proposons d'explorer une approche de reformulation en nombres entiers guidée par la relaxation lagrangienne. Après l'identification d'une forte relaxation lagrangienne, un processus systématique permet d'obtenir une seconde formulation en nombres entiers. Cette reformulation, plus compacte que celle de Dantzig et Wolfe, comporte exactement les mêmes solutions entières que la formulation initiale, mais en améliore la borne linéaire: elle devient égale à la borne lagrangienne. L'approche de reformulation permet d'unifier et de généraliser des formulations et des méthodes de borne connues. De plus, elle offre une manière simple d'obtenir des reformulations de moins grandes tailles en contrepartie de bornes plus faibles. Ces reformulations demeurent de grandes tailles. C'est pourquoi nous décrivons aussi des méthodes spécialisées pour en résoudre les relaxations linéaires. Finalement, nous appliquons l'approche de reformulation à deux problèmes de localisation. Cela nous mène à de nouvelles formulations pour ces problèmes; certaines sont de très grandes tailles, mais nos méthodes de résolution spécialisées les rendent pratiques.

Invariant density for a class of initial distributions under quadratic mapping

For the discrete-time quadratic map xt+1=4xt(1-xt) the evolution equation for a class of non-uniform initial densities is obtained. It is shown that in the t to infinity limit all of them approach the invariant density for the map.

Quadratic tranverse anisotropy term due to dislocations in Mn12 acetate crystals directly observed by EPR spectroscopy

High-sensitivity electron paramagnetic resonance experiments have been carried out in fresh and stressed Mn12 acetate single crystals for frequencies ranging from 40 GHz up to 110 GHz. The high number of crystal dislocations formed in the stressing process introduces a E(Sx2-Sy2) transverse anisotropy term in the spin Hamiltonian. From the behavior of the resonant absorptions on the applied transverse magnetic field we have obtained an average value for E=22 mK, corresponding to a concentration of dislocations per unit cell of c=10-3.

Enhancement of Calcifications in Mammograms using Volterra Series based Quadratic Filter

The paper summarizes the design and implementation of a quadratic edge detection filter, based on Volterra series, for enhancing calcifications in mammograms. The proposed filter can account for much of the polynomial nonlinearities inherent in the input mammogram image and can replace the conventional edge detectors like Laplacian, gaussian etc. The filter gives rise to improved visualization and early detection of microcalcifications, which if left undetected, can lead to breast cancer. The performance of the filter is analyzed and found superior to conventional spatial edge detectors

Quadratic Predictor based Differential Encoding and Decoding of Speech Signals

Modeling nonlinear systems using Volterra series is a century old method but practical realizations were hampered by inadequate hardware to handle the increased computational complexity stemming from its use. But interest is renewed recently, in designing and implementing filters which can model much of the polynomial nonlinearities inherent in practical systems. The key advantage in resorting to Volterra power series for this purpose is that nonlinear filters so designed can be made to work in parallel with the existing LTI systems, yielding improved performance. This paper describes the inclusion of a quadratic predictor (with nonlinearity order 2) with a linear predictor in an analog source coding system. Analog coding schemes generally ignore the source generation mechanisms but focuses on high fidelity reconstruction at the receiver. The widely used method of differential pnlse code modulation (DPCM) for speech transmission uses a linear predictor to estimate the next possible value of the input speech signal. But this linear system do not account for the inherent nonlinearities in speech signals arising out of multiple reflections in the vocal tract. So a quadratic predictor is designed and implemented in parallel with the linear predictor to yield improved mean square error performance. The augmented speech coder is tested on speech signals transmitted over an additive white gaussian noise (AWGN) channel.

Design, Development and Realization of Quadratic Volterra Filters for Selected Applications

The basic concepts of digital signal processing are taught to the students in engineering and science. The focus of the course is on linear, time invariant systems. The question as to what happens when the system is governed by a quadratic or cubic equation remains unanswered in the vast majority of literature on signal processing. Light has been shed on this problem when John V Mathews and Giovanni L Sicuranza published the book Polynomial Signal Processing. This book opened up an unseen vista of polynomial systems for signal and image processing. The book presented the theory and implementations of both adaptive and non-adaptive FIR and IIR quadratic systems which offer improved performance than conventional linear systems. The theory of quadratic systems presents a pristine and virgin area of research that offers computationally intensive work. Once the area of research is selected, the next issue is the choice of the software tool to carry out the work. Conventional languages like C and C++ are easily eliminated as they are not interpreted and lack good quality plotting libraries. MATLAB is proved to be very slow and so do SCILAB and Octave. The search for a language for scientific computing that was as fast as C, but with a good quality plotting library, ended up in Python, a distant relative of LISP. It proved to be ideal for scientific computing. An account of the use of Python, its scientific computing package scipy and the plotting library pylab is given in the appendix Initially, work is focused on designing predictors that exploit the polynomial nonlinearities inherent in speech generation mechanisms. Soon, the work got diverted into medical image processing which offered more potential to exploit by the use of quadratic methods. The major focus in this area is on quadratic edge detection methods for retinal images and fingerprints as well as de-noising raw MRI signals

Design and Programming of Cathodic Protection for SHIPS

In order to minimize the risk of failures or major renewals of hull structures during the ship's expected life span, it is imperative that the precaution must be taken with regard to an adequate margin of safety against any one or combination of failure modes including excessive yielding, buckling, brittle fracture, fatigue and corrosion. The most efficient system for combating underwater corrosion is 'cathodic protection'. The basic principle of this method is that the ship's structure is made cathodic, i.e. the anodic (corrosion) reactions are suppressed by the application of an opposing current and the ship is there by protected. This paper deals with state of art in cathodic protection and its programming in ship structure

On the equivariant Tamagawa number conjecture for abelian extensions of a quadratic imaginary field

Let k be a quadratic imaginary field, p a prime which splits in k/Q and does not divide the class number hk of k. Let L denote a finite abelian extention of k and let K be a subextention of L/k. In this article we prove the p-part of the Equivariant Tamagawa Number Conjecture for the pair (h0(Spec(L)),Z[Gal(L/K)]).

Evaluating the state of the art of parallel programming systems

This paper describes our plans to evaluate the present state of affairs concerning parallel programming and its systems. Three subprojects are proposed: a survey among programmers and scientists, a comparison of parallel programming systems using a standard set of test programs, and a wiki resource for the parallel programming community - the Parawiki. We would like to invite you to participate and turn these subprojects into true community efforts.

Bayes quadratic unbiased estimator of spatial covariance parameters

This work presents Bayes invariant quadratic unbiased estimator, for short BAIQUE. Bayesian approach is used here to estimate the covariance functions of the regionalized variables which appear in the spatial covariance structure in mixed linear model. Firstly a brief review of spatial process, variance covariance components structure and Bayesian inference is given, since this project deals with these concepts. Then the linear equations model corresponding to BAIQUE in the general case is formulated. That Bayes estimator of variance components with too many unknown parameters is complicated to be solved analytically. Hence, in order to facilitate the handling with this system, BAIQUE of spatial covariance model with two parameters is considered. Bayesian estimation arises as a solution of a linear equations system which requires the linearity of the covariance functions in the parameters. Here the availability of prior information on the parameters is assumed. This information includes apriori distribution functions which enable to find the first and the second moments matrix. The Bayesian estimation suggested here depends only on the second moment of the prior distribution. The estimation appears as a quadratic form y'Ay , where y is the vector of filtered data observations. This quadratic estimator is used to estimate the linear function of unknown variance components. The matrix A of BAIQUE plays an important role. If such a symmetrical matrix exists, then Bayes risk becomes minimal and the unbiasedness conditions are fulfilled. Therefore, the symmetry of this matrix is elaborated in this work. Through dealing with the infinite series of matrices, a representation of the matrix A is obtained which shows the symmetry of A. In this context, the largest singular value of the decomposed matrix of the infinite series is considered to deal with the convergence condition and also it is connected with Gerschgorin Discs and Poincare theorem. Then the BAIQUE model for some experimental designs is computed and compared. The comparison deals with different aspects, such as the influence of the position of the design points in a fixed interval. The designs that are considered are those with their points distributed in the interval [0, 1]. These experimental structures are compared with respect to the Bayes risk and norms of the matrices corresponding to distances, covariance structures and matrices which have to satisfy the convergence condition. Also different types of the regression functions and distance measurements are handled. The influence of scaling on the design points is studied, moreover, the influence of the covariance structure on the best design is investigated and different covariance structures are considered. Finally, BAIQUE is applied for real data. The corresponding outcomes are compared with the results of other methods for the same data. Thereby, the special BAIQUE, which estimates the general variance of the data, achieves a very close result to the classical empirical variance.