The aim of this study was to critically examine the role and function of emotions in primary school children's meaningful learning. Emotions that are commonly experienced by primary school children were identified and an indication was given of how they relate to meaningful learning. Factors that affect both emotions and meaningful learning were also discussed. In an empirical investigation that was undertaken, it was found that emotions influence meaningful learning of primary school children either positively or negatively. The following emotions pointed to both positive and negative significant correlations with regard to meaningful learning: anger, aggression, anxiety, fear, love, joy and affection. Factors like family size, gender and the environment (life world of primary school children) also influence meaningful learning. / Psychology of Education / M.Ed.(Psychology of Education)

Esta tese tem por objetivo analisar as práticas de desaparecimento das ditaduras civil-militares de segurança nacional argentina e brasileira como componentes das estratégias de implantação do terror desses regimes, como a questão dos desaparecidos foi tratada durante os governos transicionais e administrações civis posteriores ao período ditatorial e como a temática foi trabalhada a partir dos governos de Luiz Inácio Lula da Silva e Néstor Kirchner. Pretende-se, desta forma, estabelecer uma relação entre a forma como foram realizados os processos de transição política – em se tratando dos Direitos Humanos, mais especificamente, a garantia do direito à justiça e à verdade – e a elaboração de políticas de memória e reparação ou desmemória e esquecimento no presente. A partir dessa compreensão, têm-se indícios de que as rupturas, caracterizadas por uma condenação moral em relação ao passado ditatorial, são fundamentais para o desenvolvimento dessas políticas. Para o cumprimento deste objetivo, este estudo está dividido em três capítulos. No primeiro, apresenta-se uma análise da prática do desaparecimento, salientado as semelhanças existentes entre o caso argentino e brasileiro, resguardadas as diferenças em extensividade e intensidade. O segundo estuda os processos de transição política e o tratamento conferido à questão dos desaparecidos pelos governos transicionais e pelas administrações civis até o término da década de 1990. O terceiro e último capítulo analisa as políticas de memória e reparação e desmemória e esquecimento elaboradas a partir dos anos 2000, quando se efetivou uma mudança no marco interpretativo sobre o passado recente da região e se tornou possível a consecução dessas políticas. / This thesis aims to analyze the practice of disappearance of Argentine and Brazilian national security civil-military dictatorship as components of strategies for implementing terror. It also aims to approach how the transitional governments and subsequent civil administrations dealt with the disappeared and how this issue had been discussed from the governments of Luiz Inácio Lula da Silva and Néstor Kirchner. This approach aims to establish a relation between the ways the political transitions took place in these countries – regarding the Human Rights, more specifically, in guaranteeing the rights to justice and truth – and the development of memory and repair policies or oblivion flaring and forgetting policies in the present. Based on this comprehension, there are evidences that the disruptions, characterized by a moral conviction against the dictatorial past, are fundamental to the development of these policies. For the fulfillment of these objectives, this study is divided into three chapters. The first one presents an analysis of the practice of disappearance, stressing the similarities between the Argentine and Brazilian case, safeguarding the differences in extensiveness and intensity. The second one deals with the processes of political transition and the treatment given to the issue of disappearance by the transitional governments and the civil administrations until the end of the 1990’s. The third and final chapter analyzes the memory and repair policies or oblivion flaring and forgetting policies prepared since the year 2000, when there was a change in the interpretative framework about the region recent past and it became possible to achieve these policies.

Esta tese tem por objetivo analisar as práticas de desaparecimento das ditaduras civil-militares de segurança nacional argentina e brasileira como componentes das estratégias de implantação do terror desses regimes, como a questão dos desaparecidos foi tratada durante os governos transicionais e administrações civis posteriores ao período ditatorial e como a temática foi trabalhada a partir dos governos de Luiz Inácio Lula da Silva e Néstor Kirchner. Pretende-se, desta forma, estabelecer uma relação entre a forma como foram realizados os processos de transição política – em se tratando dos Direitos Humanos, mais especificamente, a garantia do direito à justiça e à verdade – e a elaboração de políticas de memória e reparação ou desmemória e esquecimento no presente. A partir dessa compreensão, têm-se indícios de que as rupturas, caracterizadas por uma condenação moral em relação ao passado ditatorial, são fundamentais para o desenvolvimento dessas políticas. Para o cumprimento deste objetivo, este estudo está dividido em três capítulos. No primeiro, apresenta-se uma análise da prática do desaparecimento, salientado as semelhanças existentes entre o caso argentino e brasileiro, resguardadas as diferenças em extensividade e intensidade. O segundo estuda os processos de transição política e o tratamento conferido à questão dos desaparecidos pelos governos transicionais e pelas administrações civis até o término da década de 1990. O terceiro e último capítulo analisa as políticas de memória e reparação e desmemória e esquecimento elaboradas a partir dos anos 2000, quando se efetivou uma mudança no marco interpretativo sobre o passado recente da região e se tornou possível a consecução dessas políticas. / This thesis aims to analyze the practice of disappearance of Argentine and Brazilian national security civil-military dictatorship as components of strategies for implementing terror. It also aims to approach how the transitional governments and subsequent civil administrations dealt with the disappeared and how this issue had been discussed from the governments of Luiz Inácio Lula da Silva and Néstor Kirchner. This approach aims to establish a relation between the ways the political transitions took place in these countries – regarding the Human Rights, more specifically, in guaranteeing the rights to justice and truth – and the development of memory and repair policies or oblivion flaring and forgetting policies in the present. Based on this comprehension, there are evidences that the disruptions, characterized by a moral conviction against the dictatorial past, are fundamental to the development of these policies. For the fulfillment of these objectives, this study is divided into three chapters. The first one presents an analysis of the practice of disappearance, stressing the similarities between the Argentine and Brazilian case, safeguarding the differences in extensiveness and intensity. The second one deals with the processes of political transition and the treatment given to the issue of disappearance by the transitional governments and the civil administrations until the end of the 1990’s. The third and final chapter analyzes the memory and repair policies or oblivion flaring and forgetting policies prepared since the year 2000, when there was a change in the interpretative framework about the region recent past and it became possible to achieve these policies.

Initialement encodés dans l’hippocampe, les nouveaux souvenirs déclaratifs deviennent progressivement dépendants d’un réseau distribué de neurones corticaux au cours de leur maturation dans le temps. Cependant, les mécanismes cellulaires et moléculaires sous-­‐tendant la consolidation et le stockage à long terme de ces nouveaux souvenirs au sein des réseaux corticaux restent à élucider. Les récepteurs N-­‐méthyl-­‐D-­‐aspartate (RNMDA) jouent un rôle essentiel dans l’induction et la régulation des changements synaptiques sous-­‐tendant les processus mnésiques de type associatifs. Sur la base de leurs propriétés biophysiques respectives, nous avons formulé l’hypothèse que la redistribution synaptique des deux formes principales de sous-­‐unités GluN2 exprimées dans le néocortex adulte (GluN2A and GluN2B), pourrait constituer un mécanisme de régulation de la plasticité synaptique supportant l’intégration et la stabilisation progressive des souvenirs au niveau cortical au cours du processus de consolidation mnésique. En combinant, chez le rat adulte, une approche comportementale, biochimique, pharmacologique et des stratégies innovantes consistant à manipuler le trafic de sous-­‐unités des RNMDA à la surface synaptique, nos résultats mettent en évidence un changement cortical dans la composition synaptique en sous unités GluN2, lequel régule la stabilisation progressive de la mémoire à long terme au sein des réseaux corticaux. Nous avons d'abord établi que les RNMDA contenant la sous-­‐unité GluN2B, via leur interaction spécifique avec une protéine clé de la signalisation synaptique, la CaMKII, sont préférentiellement recrutés lors de la phase d’encodage pour permettre l’allocation des nouveaux souvenirs olfactifs associatifs dans un réseau de neurones corticaux spécifique. Au cours du processus de consolidation, nous avons révélé que la redistribution des RNMDA corticaux contenant les sous-­‐unités GluN2B vers l’extérieur ou l’intérieur de l’espace synaptique suite à l’apprentissage, contrôle respectivement la stabilisation de la mémoire à long terme et son oubli au cours du temps. Enfin, renforcer l’acquisition initiale conduit à une augmentation plus rapide du ratio post-­‐synaptique GluN2A/GluN2B et accélère la cinétique du dialogue hippocampo-­‐cortical, ce qui se traduit par une stabilisation accélérée des souvenirs au sein des réseaux corticaux. Pris dans leur ensemble, nos travaux montrent que le trafic des GluN2B-­‐RNMDA corticaux représente un mécanisme cellulaire majeur conditionnant le devenir des traces mnésiques (i.e. stabilisation versus oubli) et apporte un éclairage nouveau sur la façon dont le cerveau organise les souvenirs récents et anciens. / Initially encoded in the hippocampus, new declarative memories are thought to become progressively dependent on a broadly distributed cortical network as they mature and consolidate over time. Although we have a good understanding of the mechanisms underlying the formation of new memories in the hippocampus, little is known about the cellular and molecular mechanisms by which recently acquired information is transformed into remote memories at the cortical level. The N-­‐methyl-­‐D-­‐aspartate receptor (NMDAR) is widely known to be a key player in many aspects of long-­‐term experience-­‐dependent synaptic changes underlying associative memory processes. Based on their distinct biophysical properties, we postulated that the activity-­‐dependent surface dynamics of the two predominant GluN2 subunits (GluN2A and GluN2B) of NMDARs present in the adult neocortex could provide a metaplastic control of synaptic plasticity supporting the progressive embedding and stabilization of long-­‐lasting associative memories within cortical networks during memory consolidation. By combining, in adult rats, behavioral, biochemical, pharmacological and innovative strategies consisting in manipulating trafficking of NMDAR subunits at the cell membrane, our results identify a cortical switch in the synaptic GluN2-­‐containing NMDAR composition which drives the progressive embedding and stabilization of long-­‐lasting memories within cortical networks. We first established that cortical GluN2B-­‐containing NMDARs and their specific interactions with the synaptic signaling CaMKII protein are preferentially recruited upon encoding of associative olfactory memories to enable neuronal allocation, the process via which a new memory trace is thought to be allocated to a given neuronal network. As these memories are progressively processed and embedded into cortical networks, we observed a learning-­‐induced surface redistribution of cortical GluN2B-­‐containing NMDARs outwards or inwards synapses which respectively drives the progressive stabilization and subsequent forgetting of remote memories over time. Finally, increasing the strength, upon encoding, of the initial memory leads to a faster increase of the cortical GluN2A/GluN2B synaptic ratio and accelerates the kinetics of hippocampal-­‐cortical interactions, which translated into a faster stabilization of memories within cortical networks. Taken together, our results provide evidence that GluN2B-­‐NMDAR surface trafficking controls the fate of remote memories (i.e. stabilization versus forgetting), shedding light on a novel mechanism used by the brain to organize recent and remote memories.

The thesis deals with the topic of advertisement and advertising campaigns from the perspective of psychology. It firstly tries to explain the basic psychological concepts relating to advertisement. Then the thesis tries to describe the most common elements used in advertisement. These findings are applied in the practical part to a specific campaign with the purpose of creating hypotheses about impact of advertisement on customer. The aim of the practical part is using the results of the survey to assess validity of hypotheses.

Simultaneous evaluation of the whole set of the model parameters of different orders together with an ability to track unmodeled dynamics are desired features in the tasks of parameter estimation. A technique handling with the factors produced by an augmented covariance (ACM) or information (AIM) matrices is considered to be an appropriate tool for designing multiple model estimation. This is where the name augmented identification (AI) by using the least-squares method was taken. The method AI attains numerical stability of the calculation of the conventional least squares method while in the same time, fully extracts information contained in the observation. In order to track time varying parameters can be found that all the information pertinent to recursive identification and thus to data driven forgetting is concentrated in ACM as well as in AIM. In this thesis will be introduced how to selective forgetting to ACM should be applied in an effective way. It means forget only a portion of accumulated information which will be further modified by the newest data included in the regressor. In the estimation problems the knowledge of the inner states of the identified system is often required. Because the augmented identification belongs within the class so called prediction error method (PEM), some rational requirements can be deduced. As a result, state filter should constitute optimization procedure minimizing the predicted error of given state space model representation with respect to the vector of states. The proposed scheme will considerably extend the family of algorithms based on processing of ACM (AIM) about augmented filtering (AF). This all will establish a comprehensive concept of parametric estimation that compared with conventional approaches is characterized by versatility, low demands on a priori process information and by excellent numerical properties (robust against overparametrization, capable solving the multiple model problem).

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A high quality and large scale training data-set is an important guarantee to teach an ideal classifier for image classification. Manually constructing a training data- set  with  appropriate  labels  is  an  expensive  and  time  consuming  task.    Active learning techniques have been used to improved the existing models by reducing the  number  of  required  annotations.    The  present  work  aims  to  investigate the  way  to  build  a  model  for  identifying  and  utilizing  potential  informative and  representativeness  unlabeled  samples.    To  this  end,  two  approaches  for deep image classification using active learning are proposed, implemented and evaluated.  The two versions of active leaning for deep image classification differ in  the  input  space  exploration  so  as  to  investigate  how  classifier  performance varies  when  automatic  labelization  on  the  high  confidence  unlabeled  samples is  performed.    Active  learning  heuristics  based  on  uncertainty  measurements on low confidence predicted samples,  a pseudo-labelization technique to boost active  learning  by  reducing  the  number  of  human  interactions  and  knowledge transferring  form  pre-trained  models,  are  proposed  and  combined  into  our methodology.  The experimental results on two benchmark image classification data-sets  verify  the  effectiveness  of  the  proposed  methodology.    In  addition, a  new  pool-based  active  learning  query  strategy  is  proposed.     Dealing  with retraining-based algorithms we define a ”forgetting event” to have occurred when an  individual  training  example  transitions  the  maximum  predicted  probability class over the course of retraining. We integrated the new approach with the semi- supervised learning method in order to tackle the above challenges and observedgood performance against existing methods. / En  högkvalitativ  och  storskalig  träningsdataset  är  en  viktig  garanti  för  att  bli en  idealisk  klassificerare  för  bildklassificering.     Att  manuellt  konstruera  en träningsdatasats  med  lämpliga  etiketter  är  en  dyr  och  tidskrävande  uppgift. Aktiv  inlärningstekniker  har  använts  för  att  förbättra  de  befintliga  modellerna genom att minska antalet nödvändiga annoteringar. Det nuvarande arbetet syftar till  att  undersöka  sättet  att  bygga  en  modell  för  att  identifiera  och  använda potentiella informativa och representativa omärkta prover.   För detta ändamål föreslås, genomförs och genomförs två metoder för djup bildklassificering med aktivt  lärande  utvärderas.      De  två  versionerna  av  aktivt  lärande  för  djup bildklassificering  skiljer  sig  åt  i  undersökningen  av  ingångsutrymmet  för  att undersöka hur klassificeringsprestanda varierar när automatisk märkning på de omärkta  proverna  med  hög  konfidens  utförs.   Aktiv  lärande  heuristik  baserad på  osäkerhetsmätningar  på  förutsagda  prover  med  låg  konfidens,  en  pseudo- märkningsteknik för att öka aktivt lärande genom att minska antalet mänskliga interaktioner  och  kunskapsöverföring  av  förutbildade  modeller,  föreslås  och kombineras   i   vår   metod.      Experimentella   resultat   på   två   riktmärken   för bildklassificering datauppsättningar verifierar effektiviteten hos den föreslagna metodiken.   Dessutom föreslås en ny poolbaserad aktiv inlärningsfrågestrategi. När  vi  använder  omskolningsbaserade  algoritmer  definierar  vi  en  ”glömmer händelse” som skulle ha inträffat när ett individuellt träningsexempel överskrider den maximala förutsagda sannolikhetsklassen under omskolningsprocessen.  Vi integrerade den nya metoden med den semi-övervakad inlärning för att hanteraovanstående utmaningar och observeras bra prestanda mot befintliga metoder.

Dans un problème de prédiction à multiples pas discrets, la prédiction à chaque instant peut dépendre de l’entrée à n’importe quel moment dans un passé lointain. Modéliser une telle dépendance à long terme est un des problèmes fondamentaux en apprentissage automatique. En théorie, les Réseaux de Neurones Récurrents (RNN) peuvent modéliser toute dépendance à long terme. En pratique, puisque la magnitude des gradients peut croître ou décroître exponentiellement avec la durée de la séquence, les RNNs ne peuvent modéliser que les dépendances à court terme. Cette thèse explore ce problème dans les réseaux de neurones récurrents et propose de nouvelles solutions pour celui-ci. Le chapitre 3 explore l’idée d’utiliser une mémoire externe pour stocker les états cachés d’un réseau à Mémoire Long et Court Terme (LSTM). En rendant l’opération d’écriture et de lecture de la mémoire externe discrète, l’architecture proposée réduit le taux de décroissance des gradients dans un LSTM. Ces opérations discrètes permettent également au réseau de créer des connexions dynamiques sur de longs intervalles de temps. Le chapitre 4 tente de caractériser cette décroissance des gradients dans un réseau de neurones récurrent et propose une nouvelle architecture récurrente qui, grâce à sa conception, réduit ce problème. L’Unité Récurrente Non-saturante (NRUs) proposée n’a pas de fonction d’activation saturante et utilise la mise à jour additive de cellules au lieu de la mise à jour multiplicative. Le chapitre 5 discute des défis de l’utilisation de réseaux de neurones récurrents dans un contexte d’apprentissage continuel, où de nouvelles tâches apparaissent au fur et à mesure. Les dépendances dans l’apprentissage continuel ne sont pas seulement contenues dans une tâche, mais sont aussi présentes entre les tâches. Ce chapitre discute de deux problèmes fondamentaux dans l’apprentissage continuel: (i) l’oubli catastrophique d’anciennes tâches et (ii) la capacité de saturation du réseau. De plus, une solution est proposée pour régler ces deux problèmes lors de l’entraînement d’un réseau de neurones récurrent. / In a multi-step prediction problem, the prediction at each time step can depend on the input at any of the previous time steps far in the past. Modelling such long-term dependencies is one of the fundamental problems in machine learning. In theory, Recurrent Neural Networks (RNNs) can model any long-term dependency. In practice, they can only model short-term dependencies due to the problem of vanishing and exploding gradients. This thesis explores the problem of vanishing gradient in recurrent neural networks and proposes novel solutions for the same. Chapter 3 explores the idea of using external memory to store the hidden states of a Long Short Term Memory (LSTM) network. By making the read and write operations of the external memory discrete, the proposed architecture reduces the rate of gradients vanishing in an LSTM. These discrete operations also enable the network to create dynamic skip connections across time. Chapter 4 attempts to characterize all the sources of vanishing gradients in a recurrent neural network and proposes a new recurrent architecture which has significantly better gradient flow than state-of-the-art recurrent architectures. The proposed Non-saturating Recurrent Units (NRUs) have no saturating activation functions and use additive cell updates instead of multiplicative cell updates. Chapter 5 discusses the challenges of using recurrent neural networks in the context of lifelong learning. In the lifelong learning setting, the network is expected to learn a series of tasks over its lifetime. The dependencies in lifelong learning are not just within a task, but also across the tasks. This chapter discusses the two fundamental problems in lifelong learning: (i) catastrophic forgetting of old tasks, and (ii) network capacity saturation. Further, it proposes a solution to solve both these problems while training a recurrent neural network.