Age-Related Changes in Visual Spatial Working Memory Cognits: Frontal-Parietal EEG Coherence During Delay

This study explored changes in scalp electrophysiology across two Working Memory (WM) tasks and two age groups. Continuous electroencephalography (EEG) was recorded from 18 healthy adults (18-34 years) and 12 healthy adolescents (14-17) during the performance of two Oculomotor Delayed Response (ODR) WM tasks; (i.e. eye movements were the metric of motor response). Delay-period, EEG data in the alpha frequency was sampled from anterior and parietal scalp sites to achieve a general measure of frontal and parietal activity, respectively. Frontal-parietal, alpha coherence was calculated for each participant for each ODR-WM task. Coherence significantly decreased in adults moving across the two ODR tasks, whereas, coherence significantly increased in adolescents moving across the two ODR tasks. The effects of task in the adolescent and adult groups were large and medium, respectively. Within the limits of this study, the results provide empirical support that WM development during adolescence include complex, qualitative, change.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Inverse Illumination Design with Genetic Programming

Interior illumination is a complex problem involving numerous interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. Illumination requirements include local illumination intensity from natural and artificial sources, colour, and uniformity. Evolved solutions incorporate design elements such as artificial lights, room materials, windows, and glass properties. A number of case studies are examined, including many-objective problems involving up to 7 illumination requirements, the design of a decorative wall of lights, and the creation of a stained-glass window for a large public space. Our results show the technical and creative possibilities of applying genetic programming to illumination design.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.

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.

La percepción visual del obrero industrial de Bogotá.

Tesis (Maestría en Formacion y Capacitacion de Recursos Humanos) UANL

Estrategias integradoras de la percepción visual y auditiva en la clase de educación artística en el primer grado de nivel primario

[Tesis] ( Maestría en Artes con Especialidad en Educación por el Arte) U.A.N.L.

La poesía visual de Alfredo Espinosa, en el corazón sin sentido, a través dela imagen visual.

Tesis (Maestría en Artes con Acentuación en Artes Visuales) UANL, 2013.

La instauración de un self falso como efecto de una discapacidad visual.

Tesis (Maestría en Área Especifica Clínica Psicoanalítica) UANL, 2009.

Confort visual: estrategias para el diseño de iluminación natural en aulas del sistema de educación básica primaria en el AMM Nuevo León.

Tesis (Maestría en Ciencias con orientación en Arquitectura) UANL, 2014.

II bienal internacional de poesía visual y alternativa, 1987-1988.

UANL

Evaluación de la clasificación visual para el vigor de la plántula en el sorgo [por] R.K. Maiti, P.S. Raju [y] F.R. Bidinger

UANL

Affective word priming in the left and right visual fields in young and older individuals

Alors que les hypothèses de valence et de dominance hémisphérique droite ont longtemps été utilisées afin d’expliquer les résultats de recherches portant sur le traitement émotionnel de stimuli verbaux et non-verbaux, la littérature sur le traitement de mots émotionnels est généralement en désaccord avec ces deux hypothèses et semble converger vers celle du décours temporel. Cette dernière hypothèse stipule que le décours temporal lors du traitement de certains aspects du système sémantique est plus lent pour l’hémisphère droit que pour l’hémisphère gauche. L’objectif de cette thèse est d’examiner la façon dont les mots émotionnels sont traités par les hémisphères cérébraux chez des individus jeunes et âgés. À cet effet, la première étude a pour objectif d’évaluer l’hypothèse du décours temporel en examinant les patrons d’activations relatif au traitement de mots émotionnels par les hémisphères gauche et droit en utilisant un paradigme d’amorçage sémantique et une tâche d’évaluation. En accord avec l’hypothèse du décours temporel, les résultats obtenus pour les hommes montrent que l’amorçage débute plus tôt dans l’hémisphère gauche et plus tard dans l’hémisphère droit. Par contre, les résultats obtenus pour les femmes sont plutôt en accord avec l’hypothèse de valence, car les mots à valence positive sont principalement amorcés dans l’hémisphère gauche, alors que les mots à valence négative sont principalement amorcés dans l’hémisphère droit. Puisque les femmes sont considérées plus « émotives » que les hommes, les résultats ainsi obtenus peuvent être la conséquence des effets de la tâche, qui exige une décision explicite au sujet de la cible. La deuxième étude a pour objectif d’examiner la possibilité que la préservation avec l’âge de l’habileté à traiter des mots émotionnels s’exprime par un phénomène compensatoire d’activations bilatérales fréquemment observées chez des individus âgés et maintenant un haut niveau de performance, ce qui est également connu sous le terme de phénomène HAROLD (Hemispheric Asymmetry Reduction in OLDer adults). En comparant les patrons d’amorçages de mots émotionnels auprès de jeunes adultes et d’adultes âgés performants à des niveaux élevés sur le plan comportemental, les résultats révèlent que l’amorçage se manifeste unilatéralement chez les jeunes participants et bilatéralement chez les participants âgés. Par ailleurs, l’amorçage se produit chez les participants âgés avec un léger délai, ce qui peut résulter d’une augmentation des seuils sensoriels chez les participants âgés, qui nécessiteraient alors davantage de temps pour encoder les stimuli et entamer l’activation à travers le réseau sémantique. Ainsi, la performance équivalente au niveau de la précision retrouvée chez les deux groupes de participants et l’amorçage bilatéral observé chez les participants âgés sont en accord avec l’hypothèse de compensation du phénomène HAROLD.