Cognition and Student Learning Through the Arts

An increasing number of recent research studies suggest connections between cognition, social and emotional development, and the arts. Some studies indicate that students in schools where the arts are an integral part of the academic program tend to do better in school than those students where that is not the case. This study examines home/school factors that contribute most to variance in student learning and achievement and the arts from over 8,000 students in grade 5. The findings suggest in-school arts programs may have less of an impact on student achievement than proposed by previous research.

UV laser-induced high resolution cleaving of Si wafers for micro-nano devices and polymeric waveguide characterization

In this work we propose a method for cleaving silicon-based photonic chips by using a laser based micromachining system, consisting of a ND:YVO4laser emitting at 355 nm in nanosecond pulse regime and a micropositioning system. The laser makes grooved marks placed at the desired locations and directions where cleaves have to be initiated, and after several processing steps, a crack appears and propagate along the crystallographic planes of the silicon wafer. This allows cleavage of the chips automatically and with high positioning accuracy, and provides polished vertical facets with better quality than the obtained with other cleaving process, which eases the optical characterization of photonic devices. This method has been found to be particularly useful when cleaving small-sized chips, where manual cleaving is hard to perform; and also for polymeric waveguides, whose facets get damaged or even destroyed with polishing or manual cleaving processing. Influence of length of the grooved line and speed of processing is studied for a variety of silicon chips. An application for cleaving and characterizing sol–gel waveguides is presented. The total amount of light coupled is higher than when using any other procedure.

Regularization for sparsity in statistical analysis and machine learning

Pragmatism is the leading motivation of regularization. We can understand regularization as a modification of the maximum-likelihood estimator so that a reasonable answer could be given in an unstable or ill-posed situation. To mention some typical examples, this happens when fitting parametric or non-parametric models with more parameters than data or when estimating large covariance matrices. Regularization is usually used, in addition, to improve the bias-variance tradeoff of an estimation. Then, the definition of regularization is quite general, and, although the introduction of a penalty is probably the most popular type, it is just one out of multiple forms of regularization. In this dissertation, we focus on the applications of regularization for obtaining sparse or parsimonious representations, where only a subset of the inputs is used. A particular form of regularization, L1-regularization, plays a key role for reaching sparsity. Most of the contributions presented here revolve around L1-regularization, although other forms of regularization are explored (also pursuing sparsity in some sense). In addition to present a compact review of L1-regularization and its applications in statistical and machine learning, we devise methodology for regression, supervised classification and structure induction of graphical models. Within the regression paradigm, we focus on kernel smoothing learning, proposing techniques for kernel design that are suitable for high dimensional settings and sparse regression functions. We also present an application of regularized regression techniques for modeling the response of biological neurons. Supervised classification advances deal, on the one hand, with the application of regularization for obtaining a na¨ıve Bayes classifier and, on the other hand, with a novel algorithm for brain-computer interface design that uses group regularization in an efficient manner. Finally, we present a heuristic for inducing structures of Gaussian Bayesian networks using L1-regularization as a filter. El pragmatismo es la principal motivación de la regularización. Podemos entender la regularización como una modificación del estimador de máxima verosimilitud, de tal manera que se pueda dar una respuesta cuando la configuración del problema es inestable. A modo de ejemplo, podemos mencionar el ajuste de modelos paramétricos o no paramétricos cuando hay más parámetros que casos en el conjunto de datos, o la estimación de grandes matrices de covarianzas. Se suele recurrir a la regularización, además, para mejorar el compromiso sesgo-varianza en una estimación. Por tanto, la definición de regularización es muy general y, aunque la introducción de una función de penalización es probablemente el método más popular, éste es sólo uno de entre varias posibilidades. En esta tesis se ha trabajado en aplicaciones de regularización para obtener representaciones dispersas, donde sólo se usa un subconjunto de las entradas. En particular, la regularización L1 juega un papel clave en la búsqueda de dicha dispersión. La mayor parte de las contribuciones presentadas en la tesis giran alrededor de la regularización L1, aunque también se exploran otras formas de regularización (que igualmente persiguen un modelo disperso). Además de presentar una revisión de la regularización L1 y sus aplicaciones en estadística y aprendizaje de máquina, se ha desarrollado metodología para regresión, clasificación supervisada y aprendizaje de estructura en modelos gráficos. Dentro de la regresión, se ha trabajado principalmente en métodos de regresión local, proponiendo técnicas de diseño del kernel que sean adecuadas a configuraciones de alta dimensionalidad y funciones de regresión dispersas. También se presenta una aplicación de las técnicas de regresión regularizada para modelar la respuesta de neuronas reales. Los avances en clasificación supervisada tratan, por una parte, con el uso de regularización para obtener un clasificador naive Bayes y, por otra parte, con el desarrollo de un algoritmo que usa regularización por grupos de una manera eficiente y que se ha aplicado al diseño de interfaces cerebromáquina. Finalmente, se presenta una heurística para inducir la estructura de redes Bayesianas Gaussianas usando regularización L1 a modo de filtro.

Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players

The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains.

Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-selection of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested in a decentralized solution where the robots themselves autonomously and in an individual manner, are responsible for selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-task distribution problem and we propose a solution using two different approaches by applying Response Threshold Models as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

On the Absence of Carrier Drift in Two-Terminal Devices and the Origin of Their Lowest Resistance per Carrier RK= h/q^2

After a criticism on today’s model for electrical noise in resistors, we pass to use a Quantum-compliant model based on the discreteness of electrical charge in a complex Admittance. From this new model we show that carrier drift viewed as charged particle motion in response to an electric field is unlike to occur in bulk regions of Solid-State devices where carriers react as dipoles against this field. The absence of the shot noise that charges drifting in resistors should produce and the evolution of the Phase Noise with the active power existing in the resonators of L-C oscillators, are two effects added in proof for this conduction model without carrier drift where the resistance of any two-terminal device becomes discrete and has a minimum value per carrier that is the Quantum resistance RK/(2pi)

Glutamate infused posttraining into the hippocampus or caudate-putamen differentially strengthens place and response learning

A cross-maze task that can be acquired through either place or response learning was used to examine the hypothesis that posttraining neurochemical manipulation of the hippocampus or caudate-putamen can bias an animal toward the use of a specific memory system. Male Long-Evans rats received four trials per day for 7 days, a probe trial on day 8, further training on days 9–15, and an additional probe trial on day 16. Training occurred in a cross-maze task in which rats started from a consistent start-box (south), and obtained food from a consistent goal-arm (west). On days 4–6 of training, rats received posttraining intrahippocampal (1 μg/0.5 μl) or intracaudate (2 μg/0.5 μl) injections of either glutamate or saline (0.5 μl). On days 8 and 16, a probe trial was given in which rats were placed in a novel start-box (north). Rats selecting the west goal-arm were designated “place” learners, and those selecting the east goal-arm were designated “response” learners. Saline-treated rats predominantly displayed place learning on day 8 and response learning on day 16, indicating a shift in control of learned behavior with extended training. Rats receiving intrahippocampal injections of glutamate predominantly displayed place learning on days 8 and 16, indicating that manipulation of the hippocampus produced a blockade of the shift to response learning. Rats receiving intracaudate injections of glutamate displayed response learning on days 8 and 16, indicating an accelerated shift to response learning. The findings suggest that posttraining intracerebral glutamate infusions can (i) modulate the distinct memory processes mediated by the hippocampus and caudate-putamen and (ii) bias the brain toward the use of a specific memory system to control learned behavior and thereby influence the timing of the switch from the use of cognitive memory to habit learning to guide behavior.

Effect of reduced myristoylated alanine-rich C kinase substrate expression on hippocampal mossy fiber development and spatial learning in mutant mice: Transgenic rescue and interactions with gene background

The myristoylated alanine-rich C kinase substrate (MARCKS) is a prominent protein kinase C (PKC) substrate in brain that is expressed highly in hippocampal granule cells and their axons, the mossy fibers. Here, we examined hippocampal infrapyramidal mossy fiber (IP-MF) limb length and spatial learning in heterozygous Macs mutant mice that exhibit an ≈50% reduction in MARCKS expression relative to wild-type controls. On a 129B6(N3) background, the Macs mutation produced IP-MF hyperplasia, a significant increase in hippocampal PKCɛ expression, and proficient spatial learning relative to wild-type controls. However, wild-type 129B6(N3) mice exhibited phenotypic characteristics resembling inbred 129Sv mice, including IP-MF hypoplasia relative to inbred C57BL/6J mice and impaired spatial-reversal learning, suggesting a significant contribution of 129Sv background genes to wild-type and possibly mutant phenotypes. Indeed, when these mice were backcrossed with inbred C57BL/6J mice for nine generations to reduce 129Sv background genes, the Macs mutation did not effect IP-MF length or hippocampal PKCɛ expression and impaired spatial learning relative to wild-type controls, which now showed proficient spatial learning. Moreover, in a different strain (B6SJL(N1), the Macs mutation also produced a significant impairment in spatial learning that was reversed by transgenic expression of MARCKS. Collectively, these data indicate that the heterozygous Macs mutation modifies the expression of linked 129Sv gene(s), affecting hippocampal mossy fiber development and spatial learning performance, and that MARCKS plays a significant role in spatial learning processes.

Innovative analytical strategies for the development of sensor devices and mass spectrometry methods

Il lavoro presentato in questa tesi di Dottorato è incentrato sullo sviluppo di strategie analitiche innovative basate sulla sensoristica e su tecniche di spettrometria di massa in ambito biologico e della sicurezza alimentare. Il primo capitolo tratta lo studio di aspetti metodologici ed applicativi di procedure sensoristiche per l’identificazione e la determinazione di biomarkers associati alla malattia celiaca. In tale ambito, sono stati sviluppati due immunosensori, uno a trasduzione piezoelettrica e uno a trasduzione amperometrica, per la rivelazione di anticorpi anti-transglutaminasi tissutale associati a questa malattia. L’innovazione di questi dispositivi riguarda l’immobilizzazione dell’enzima tTG nella conformazione aperta (Open-tTG), che è stato dimostrato essere quella principalmente coinvolta nella patogenesi. Sulla base dei risultati ottenuti, entrambi i sistemi sviluppati si sono dimostrati una valida alternativa ai test di screening attualmente in uso per la diagnosi della celiachia. Rimanendo sempre nel contesto della malattia celiaca, ulteriore ricerca oggetto di questa tesi di Dottorato, ha riguardato lo sviluppo di metodi affidabili per il controllo di prodotti “gluten-free”. Il secondo capitolo tratta lo sviluppo di un metodo di spettrometria di massa e di un immunosensore competitivo per la rivelazione di prolammine in alimenti “gluten-free”. E’ stato sviluppato un metodo LC-ESI-MS/MS basato su un’analisi target con modalità di acquisizione del segnale selected reaction monitoring per l’identificazione di glutine in diversi cereali potenzialmente tossici per i celiaci. Inoltre ci si è focalizzati su un immunosensore competitivo per la rivelazione di gliadina, come metodo di screening rapido di farine. Entrambi i sistemi sono stati ottimizzati impiegando miscele di farina di riso addizionata di gliadina, avenine, ordeine e secaline nel caso del sistema LC-MS/MS e con sola gliadina nel caso del sensore. Infine i sistemi analitici sono stati validati analizzando sia materie prime (farine) che alimenti (biscotti, pasta, pane, etc.). L’approccio sviluppato in spettrometria di massa apre la strada alla possibilità di sviluppare un test di screening multiplo per la valutazione della sicurezza di prodotti dichiarati “gluten-free”, mentre ulteriori studi dovranno essere svolti per ricercare condizioni di estrazione compatibili con l’immunosaggio competitivo, per ora applicabile solo all’analisi di farine estratte con etanolo. Terzo capitolo di questa tesi riguarda lo sviluppo di nuovi metodi per la rivelazione di HPV, Chlamydia e Gonorrhoeae in fluidi biologici. Si è scelto un substrato costituito da strips di carta in quanto possono costituire una valida piattaforma di rivelazione, offrendo vantaggi grazie al basso costo, alla possibilità di generare dispositivi portatili e di poter visualizzare il risultato visivamente senza la necessità di strumentazioni. La metodologia sviluppata è molto semplice, non prevede l’uso di strumentazione complessa e si basa sull’uso della isothermal rolling-circle amplification per l’amplificazione del target. Inoltre, di fondamentale importanza, è l’utilizzo di nanoparticelle colorate che, essendo state funzionalizzate con una sequenza di DNA complementare al target amplificato derivante dalla RCA, ne permettono la rivelazione a occhio nudo mediante l’uso di filtri di carta. Queste strips sono state testate su campioni reali permettendo una discriminazione tra campioni positivi e negativi in tempi rapidi (10-15 minuti), aprendo una nuova via verso nuovi test altamente competitivi con quelli attualmente sul mercato.

Calix[6]arene Derivatives as Pivotal Components of Working Devices and Molecular Machine Prototypes

This Ph.D. thesis describes the synthesis, characterization and study of calix[6]arene derivatives as pivotal components for the construction of molecular machine prototypes. Initially, the ability of a calix[6]arene wheel to supramolecularly assist and increase the rate of a nucleophilic substitution reaction was exploited for the synthesis of two constitutionally isomeric oriented rotaxanes. Then, the synthesis and characterization of several hetero-functionalised calix[6]arene derivatives and the possibility to obtain molecular muscle prototypes was reported. The ability of calix[6]arenes to form oriented pseudorotaxane towards dialkyl viologen axles was then exploited for the synthesis of two calixarene-based [2]catenanes. As last part of this thesis, studies on the electrochemical response of the threading-dethreading process of calix[6]arene-based pseudorotaxanes and rotaxanes supported on glassy carbon electrodes are reported.

Knowledge spirals, situational leadership and informal learning applied on b-learning methodology

The methodology “b-learning” is a new teaching scenario and it requires the creation, adaptation and application of new learning tools searching the assimilation of new collaborative competences. In this context, it is well known the knowledge spirals, the situational leadership and the informal learning. The knowledge spirals is a basic concept of the knowledge procedure and they are based on that the knowledge increases when a cycle of 4 phases is repeated successively.1) The knowledge is created (for instance, to have an idea); 2) The knowledge is decoded into a format to be easily transmitted; 3) The knowledge is modified to be easily comprehensive and it is used; 4) New knowledge is created. This new knowledge improves the previous one (step 1). Each cycle shows a step of a spiral staircase: by going up the staircase, more knowledge is created. On the other hand, the situational leadership is based on that each person has a maturity degree to develop a specific task and this maturity increases with the experience. Therefore, the teacher (leader) has to adapt the teaching style to the student (subordinate) requirements and in this way, the professional and personal development of the student will increase quickly by improving the results and satisfaction. This educational strategy, finally combined with the informal learning, and in particular the zone of proximal development, and using a learning content management system own in our University, gets a successful and well-evaluated learning activity in Master subjects focused on the collaborative activity of preparation and oral exhibition of short and specific topics affine to these subjects. Therefore, the teacher has a relevant and consultant role of the selected topic and his function is to guide and supervise the work, incorporating many times the previous works done in other courses, as a research tutor or more experienced student. Then, in this work, we show the academic results, grade of interactivity developed in these collaborative tasks, statistics and the satisfaction grade shown by our post-graduate students.

MOOCs, Open educational resources and social networking: bridging the gap between informal and formal learning

MOOCs and open educational resources (OER) provide a wealth of learning opportunities for people around the globe, many of whom have no access to formal higher education. OER are often difficult to locate and are accessed on their own without support from or dialogue with subject experts and peers. This paper looks at whether it is possible to develop effective learning communities around OER and whether these communities can emerge spontaneously and in a self-organised way without moderation. It examines the complex interplay between formal and informal learning, and examines whether MOOCs are the answer to providing effective interaction and dialogue for those wishing to study at university level for free on the Internet.

Work and family : learning to do the job.

Mode of access: Internet.