Electrocortical indices of cognitive control in working memory : exploring the effects of proactive interference, cognitive load, and aging

Cognitive control involves the ability to flexibly adjust cognitive processing in order to resist interference and promote goal-directed behaviour. Although frontal cortex is considered to be broadly involved in cognitive control, the mechanisms by which frontal brain areas implement control functions are unclear. Furthermore, aging is associated with reductions in the ability to implement control functions and questions remain as to whether unique cortical responses serve a compensatory role in maintaining maximal performance in later years. Described here are three studies in which electrophysiological data were recorded while participants performed modified versions of the standard Sternberg task. The goal was to determine how top-down control is implemented in younger adults and altered in aging. In study I, the effects of frequent stimulus repetition on the interference-related N450 were investigated in a Sternberg task with a small stimulus set (requiring extensive stimulus resampling) and a task with a large stimulus set (requiring no stimulus resampling).The data indicated that constant stimulus res amp ling required by employing small stimulus sets can undercut the effect of proactive interference on the N450. In study 2, younger and older adults were tested in a standard version of the Sternberg task to determine whether the unique frontal positivity, previously shown to predict memory impairment in older adults during a proactive interference task, would be associated with the improved performance when memory recognition could be aided by unambiguous stimulus familiarity. Here, results indicated that the frontal positivity was associated with poorer memory performance, replicating the effect observed in a more cognitively demanding task, and showing that stimulus familiarity does not mediate compensatory cortical activations in older adults. Although the frontal positivity could be interpreted to reflect maladaptive cortical activation, it may also reflect attempts at compensation that fail to fully ameliorate agerelated decline. Furthermore, the frontal positivity may be the result of older adults' reliance on late occurring, controlled processing in contrast to younger adults' ability to identify stimuli at very early stages of processing. In the final study, working memory load was manipulated in the proactive interference Sternberg task in order to investigate whether the N450 reflects simple interference detection, with little need for cognitive resources, or an active conflict resolution mechanism that requires executive resources to implement. Independent component analysis was used to isolate the effect of interference revealing that the canonical N450 was based on two dissociable cognitive control mechanisms: a left frontal negativity that reflects active interference resolution, , but requires executive resources to implement, and a right frontal negativity that reflects global response inhibition that can be relied on when executive resources are minimal but at the cost of a slowed response. Collectively, these studies advance understanding of the factors that influence younger and older adults' ability to satisfy goal-directed behavioural requirements in the face of interference and the effects of age-related cognitive decline.

A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery

Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes and probabilistic models for the background sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.

New Contig Creation Algorithm for the de novo DNA Assembly Problem

DNA assembly is among the most fundamental and difficult problems in bioinformatics. Near optimal assembly solutions are available for bacterial and small genomes, however assembling large and complex genomes especially the human genome using Next-Generation-Sequencing (NGS) technologies is shown to be very difficult because of the highly repetitive and complex nature of the human genome, short read lengths, uneven data coverage and tools that are not specifically built for human genomes. Moreover, many algorithms are not even scalable to human genome datasets containing hundreds of millions of short reads. The DNA assembly problem is usually divided into several subproblems including DNA data error detection and correction, contig creation, scaffolding and contigs orientation; each can be seen as a distinct research area. This thesis specifically focuses on creating contigs from the short reads and combining them with outputs from other tools in order to obtain better results. Three different assemblers including SOAPdenovo [Li09], Velvet [ZB08] and Meraculous [CHS+11] are selected for comparative purposes in this thesis. Obtained results show that this thesis’ work produces comparable results to other assemblers and combining our contigs to outputs from other tools, produces the best results outperforming all other investigated assemblers.

A Study of Ordered Gene Problems Featuring DNA Error Correction and DNA Fragment Assembly with a Variety of Heuristics, Genetic Algorithm Variations, and Dynamic Representations

Ordered gene problems are a very common classification of optimization problems. Because of their popularity countless algorithms have been developed in an attempt to find high quality solutions to the problems. It is also common to see many different types of problems reduced to ordered gene style problems as there are many popular heuristics and metaheuristics for them due to their popularity. Multiple ordered gene problems are studied, namely, the travelling salesman problem, bin packing problem, and graph colouring problem. In addition, two bioinformatics problems not traditionally seen as ordered gene problems are studied: DNA error correction and DNA fragment assembly. These problems are studied with multiple variations and combinations of heuristics and metaheuristics with two distinct types or representations. The majority of the algorithms are built around the Recentering- Restarting Genetic Algorithm. The algorithm variations were successful on all problems studied, and particularly for the two bioinformatics problems. For DNA Error Correction multiple cases were found with 100% of the codes being corrected. The algorithm variations were also able to beat all other state-of-the-art DNA Fragment Assemblers on 13 out of 16 benchmark problem instances.

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

Using PageRank Algorithm in Analyzing Dictionary Graphs and PageRank in Dynamic Graphs

In this thesis we are going to analyze the dictionary graphs and some other kinds of graphs using the PagerRank algorithm. We calculated the correlation between the degree and PageRank of all nodes for a graph obtained from Merriam-Webster dictionary, a French dictionary and WordNet hypernym and synonym dictionaries. Our conclusion was that PageRank can be a good tool to compare the quality of dictionaries. We studied some artificial social and random graphs. We found that when we omitted some random nodes from each of the graphs, we have not noticed any significant changes in the ranking of the nodes according to their PageRank. We also discovered that some social graphs selected for our study were less resistant to the changes of PageRank.

Determination of viral load and integration status of HPV 16 in normal and LSIL exfoliated cervical cells

L’intégration du génome du virus papilloma humain (VPH) a été reconnu jusqu’`a récemment comme étant un événnement fréquent mais pourtant tardif dans la progression de la maladie du col de l’utérus. La perspective temporelle vient, pourtant, d’être mise au défi par la détection de formes intégrées de VPH dans les tissus normaux et dans les lésions prénéoplasiques. Notre objectif était de déterminer la charge virale de VPH-16 et son état physique dans une série de 220 échantillons provenant de cols uterins normaux et avec des lésions de bas-grade. La technique quantitative de PCR en temps réel, méthode Taqman, nous a permis de quantifier le nombre de copies des gènes E6, E2, et de la B-globine, permettant ainsi l’évaluation de la charge virale et le ratio de E6/E2 pour chaque spécimen. Le ratio E6/E2 de 1.2 ou plus était suggestif d’intégration. Par la suite, le site d’intégration du VPH dans le génome humain a été déterminé par la téchnique de RS-PCR. La charge virale moyenne était de 57.5±324.6 copies d'ADN par cellule et le ratio E6/E2 a évalué neuf échantillons avec des formes d’HPV intégrées. Ces intégrants ont été amplifiés par RS-PCR, suivi de séquençage, et l’homologie des amplicons a été déterminée par le programme BLAST de NCBI afin d’identifier les jonctions virales-humaines. On a réussi `a identifier les jonctions humaines-virales pour le contrôle positif, c'est-à-dire les cellules SiHa, pourtant nous n’avons pas detecté d’intégration par la technique de RS-PCR dans les échantillons de cellules cervicales exfoliées provenant de tissus normaux et de lésions de bas-grade. Le VPH-16 est rarement intégré dans les spécimens de jeunes patientes.

Routage adaptatif et stabilité dans les réseaux maillés sans fil

Grâce à leur flexibilité et à leur facilité d’installation, les réseaux maillés sans fil (WMNs) permettent un déploiement d’une infrastructure à faible coût. Ces réseaux étendent la couverture des réseaux filaires permettant, ainsi, une connexion n’importe quand et n’importe où. Toutefois, leur performance est dégradée par les interférences et la congestion. Ces derniers causent des pertes de paquets et une augmentation du délai de transmission d’une façon drastique. Dans cette thèse, nous nous intéressons au routage adaptatif et à la stabilité dans ce type de réseaux. Dans une première partie de la thèse, nous nous intéressons à la conception d’une métrique de routage et à la sélection des passerelles permettant d’améliorer la performance des WMNs. Dans ce contexte nous proposons un protocole de routage à la source basé sur une nouvelle métrique. Cette métrique permet non seulement de capturer certaines caractéristiques des liens tels que les interférences inter-flux et intra-flux, le taux de perte des paquets mais également la surcharge des passerelles. Les résultats numériques montrent que la performance de cette métrique est meilleure que celle des solutions proposées dans la littérature. Dans une deuxième partie de la thèse, nous nous intéressons à certaines zones critiques dans les WMNs. Ces zones se trouvent autour des passerelles qui connaissent une concentration plus élevé du trafic ; elles risquent de provoquer des interférences et des congestions. À cet égard, nous proposons un protocole de routage proactif et adaptatif basé sur l’apprentissage par renforcement et qui pénalise les liens de mauvaise qualité lorsqu’on s’approche des passerelles. Un chemin dont la qualité des liens autour d’une passerelle est meilleure sera plus favorisé que les autres chemins de moindre qualité. Nous utilisons l’algorithme de Q-learning pour mettre à jour dynamiquement les coûts des chemins, sélectionner les prochains nœuds pour faire suivre les paquets vers les passerelles choisies et explorer d’autres nœuds voisins. Les résultats numériques montrent que notre protocole distribué, présente de meilleurs résultats comparativement aux protocoles présentés dans la littérature. Dans une troisième partie de cette thèse, nous nous intéressons aux problèmes d’instabilité des réseaux maillés sans fil. En effet, l’instabilité se produit à cause des changements fréquents des routes qui sont causés par les variations instantanées des qualités des liens dues à la présence des interférences et de la congestion. Ainsi, après une analyse de l’instabilité, nous proposons d’utiliser le nombre de variations des chemins dans une table de routage comme indicateur de perturbation des réseaux et nous utilisons la fonction d’entropie, connue dans les mesures de l’incertitude et du désordre des systèmes, pour sélectionner les routes stables. Les résultats numériques montrent de meilleures performances de notre protocole en comparaison avec d’autres protocoles dans la littérature en termes de débit, délai, taux de perte des paquets et l’indice de Gini.

An algorithm for identifying agent-k-linked allocations in economies with indivisibilities

We consider envy-free (and budget-balanced) rules that are least manipulable with respect to agents counting or with respect to utility gains. Recently it has been shown that for any profile of quasi-linear preferences, the outcome of any such least manipulable envy-free rule can be obtained via agent-k-linked allocations. This note provides an algorithm for identifying agent-k-linked allocations.

A robust algorithm for segmenting fluorescence images and its application to single-molecule counting

La microscopie par fluorescence de cellules vivantes produit de grandes quantités de données. Ces données sont composées d’une grande diversité au niveau de la forme des objets d’intérêts et possèdent un ratio signaux/bruit très bas. Pour concevoir un pipeline d’algorithmes efficaces en traitement d’image de microscopie par fluorescence, il est important d’avoir une segmentation robuste et fiable étant donné que celle-ci constitue l’étape initiale du traitement d’image. Dans ce mémoire, je présente MinSeg, un algorithme de segmentation d’image de microscopie par fluorescence qui fait peu d’assomptions sur l’image et utilise des propriétés statistiques pour distinguer le signal par rapport au bruit. MinSeg ne fait pas d’assomption sur la taille ou la forme des objets contenus dans l’image. Par ce fait, il est donc applicable sur une grande variété d’images. Je présente aussi une suite d’algorithmes pour la quantification de petits complexes dans des expériences de microscopie par fluorescence de molécules simples utilisant l’algorithme de segmentation MinSeg. Cette suite d’algorithmes a été utilisée pour la quantification d’une protéine nommée CENP-A qui est une variante de l’histone H3. Par cette technique, nous avons trouvé que CENP-A est principalement présente sous forme de dimère.

A heuristic algorithm for the Capacitated Vehicle Routing Problem with Synchronized Pick-ups and Drop-offs : a case study for medications delivery and supervision in DR Congo

Dans des contextes de post-urgence tels que le vit la partie occidentale de la République Démocratique du Congo (RDC), l’un des défis cruciaux auxquels font face les hôpitaux ruraux est de maintenir un niveau de médicaments essentiels dans la pharmacie. Sans ces médicaments pour traiter les maladies graves, l’impact sur la santé de la population est significatif. Les hôpitaux encourent également des pertes financières dues à la péremption lorsque trop de médicaments sont commandés. De plus, les coûts du transport des médicaments ainsi que du superviseur sont très élevés pour les hôpitaux isolés ; les coûts du transport peuvent à eux seuls dépasser ceux des médicaments. En utilisant la province du Bandundu, RDC pour une étude de cas, notre recherche tente de déterminer la faisabilité (en termes et de la complexité du problème et des économies potentielles) d’un problème de routage synchronisé pour la livraison de médicaments et pour les visites de supervision. Nous proposons une formulation du problème de tournées de véhicules avec capacité limitée qui gère plusieurs exigences nouvelles, soit la synchronisation des activités, la préséance et deux fréquences d’activités. Nous mettons en œuvre une heuristique « cluster first, route second » avec une base de données géospatiales qui permet de résoudre le problème. Nous présentons également un outil Internet qui permet de visualiser les solutions sur des cartes. Les résultats préliminaires de notre étude suggèrent qu’une solution synchronisée pourrait offrir la possibilité aux hôpitaux ruraux d’augmenter l’accessibilité des services médicaux aux populations rurales avec une augmentation modique du coût de transport actuel.

Array pattern nulling by element position perturbations using a genetic algorithm

A genetic algorithm has been used for null steering in phased and adaptive arrays . It has been shown that it is possible to steer the array null s precisely to the required interference directions and to achieve any prescribed null depths . A comparison with the results obtained from the analytic solution shows the advantages of using the genetic algorithm for null steering in linear array patterns

A new method for load matching in multimode microwave feating applicators based on the use of dielectric layer superposition

Usage of a dielectric multilayer around a dielectric Sample is studied as a means for improving the efficiency in multimode microwave- heating cavities. The results show that by using additional dielectric constant layers the appearance of undesired reflections at the sample-air interface is avoided and higher power -absorption rates within the sample and high -efficiency designs are obtained

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Multi-objective assembly job shop scheduling using genetic algorithm and tabu search

Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.