Il est généralement facile pour les humains de distinguer rapidement différents lieux en se basant uniquement sur leur aspect visuel. . Ces catégories sémantiques peuvent être utilisées comme information contextuelle favorisant la détection et la reconnaissance d'objets. Des travaux récents en reconnaissance des lieux visent à doter les robots de capacités similaires. Contrairement aux travaux classiques, portant sur la localisation et la cartographie, cette tâche est généralement traitée comme un problème d'apprentissage supervisé.La reconnaissance de lieux sémantiques - la capacité à reconnaître la catégorie sémantique à laquelle une scène appartient – peut être considérée comme une condition essentielle en robotique autonome. Un robot autonome doit en effet pouvoir apprendre facilement l'organisation sémantique de son environnement pour pouvoir fonctionner et interagir avec succès. Pour atteindre cet objectif, différentes méthodes ont déjà été proposées. Certaines sont basées sur l'identification des objets comme une condition préalable à la reconnaissance des scènes, et d'autres fondées sur une description directe des caractéristiques de la scène. Si nous faisons l'hypothèse que les objets sont plus faciles à reconnaître quand la scène dans laquelle ils apparaissent est bien identifiée, la deuxième approche semble plus appropriée. Elle est cependant fortement dépendante de la nature des descripteurs d'images utilisées qui sont généralement dérivés empiriquement a partir des observations générales sur le codage d'images.En opposition avec ces propositions, une autre approche de codage des images, basée sur un point de vue plus théorique, a émergé ces dernières années. Les modèles d'extraction de caractéristiques fondés sur le principe de la minimisation d'une fonction d'énergie en relation avec un modèle statistique génératif expliquant au mieux les données, ont abouti à l'apparition des Machines de Boltzmann Restreintes (Rectricted Boltzmann Machines : RBMs) capables de coder une image comme la superposition d'un nombre limité de caractéristiques extraites à partir d'un plus grand alphabet. Il a été montré que ce processus peut être répété dans une architecture plus profonde, conduisant à une représentation parcimonieuse et efficace des données initiales dans l'espace des caractéristiques. Le problème complexe de la classification dans l'espace de début est ainsi remplacé par un problème plus simple dans l'espace des caractéristiques.Dans ce travail, nous montrons que la reconnaissance sémantiques des lieux peut être réalisée en considérant des mini-images au lieu d'approches plus classiques de type ''sacs-de-mots'' et par l'utilisation de réseaux profonds pour le codage des images. Après avoir realisé un codage approprié, une régression softmax dans l'espace de projection est suffisante pour obtenir des résultats de classification prometteurs. A notre connaissance, cette approche n'a pas encore été proposée pour la reconnaissance de scène en robotique autonome.Nous avons comparé nos méthodes avec les algorithmes de l'état-de-l'art en utilisant une base de données standard de localisation de robot. Nous avons étudié l'influence des paramètres du système et comparé les différentes conditions sur la même base de données. Les expériences réalisées montrent que le modèle que nous proposons, tout en étant très simple, conduit à des résultats comparables à l'état-de-l'art sur une tâche de reconnaissance de lieux sémantiques. / Usually, human beings are able to quickly distinguish between different places, solely from their visual appearance. This is due to the fact that they can organize their space as composed of discrete units. These units, called ``semantic places'', are characterized by their spatial extend and their functional unity. Such a semantic category can thus be used as contextual information which fosters object detection and recognition. Recent works in semantic place recognition seek to endow the robot with similar capabilities. Contrary to classical localization and mapping works, this problem is usually addressed as a supervised learning problem. The question of semantic places recognition in robotics - the ability to recognize the semantic category of a place to which scene belongs to - is therefore a major requirement for the future of autonomous robotics. It is indeed required for an autonomous service robot to be able to recognize the environment in which it lives and to easily learn the organization of this environment in order to operate and interact successfully. To achieve that goal, different methods have been already proposed, some based on the identification of objects as a prerequisite to the recognition of the scenes, and some based on a direct description of the scene characteristics. If we make the hypothesis that objects are more easily recognized when the scene in which they appear is identified, the second approach seems more suitable. It is however strongly dependent on the nature of the image descriptors used, usually empirically derived from general considerations on image coding.Compared to these many proposals, another approach of image coding, based on a more theoretical point of view, has emerged the last few years. Energy-based models of feature extraction based on the principle of minimizing the energy of some function according to the quality of the reconstruction of the image has lead to the Restricted Boltzmann Machines (RBMs) able to code an image as the superposition of a limited number of features taken from a larger alphabet. It has also been shown that this process can be repeated in a deep architecture, leading to a sparse and efficient representation of the initial data in the feature space. A complex problem of classification in the input space is thus transformed into an easier one in the feature space. This approach has been successfully applied to the identification of tiny images from the 80 millions image database of the MIT. In the present work, we demonstrate that semantic place recognition can be achieved on the basis of tiny images instead of conventional Bag-of-Word (BoW) methods and on the use of Deep Belief Networks (DBNs) for image coding. We show that after appropriate coding a softmax regression in the projection space is sufficient to achieve promising classification results. To our knowledge, this approach has not yet been investigated for scene recognition in autonomous robotics. We compare our methods with the state-of-the-art algorithms using a standard database of robot localization. We study the influence of system parameters and compare different conditions on the same dataset. These experiments show that our proposed model, while being very simple, leads to state-of-the-art results on a semantic place recognition task.

Memory and nostalgia work in complex, paradoxical ways in Cormac McCarthy's No Country for Old Men and The Road, both haunting the main protagonists, Sheriff Ed Tom Bell and the father, as well as bringing them to crucial realizations. These men give up the traditional hero role for the more meaningful and generative image of “carrying the fire,” which unites these two novels. Carrying the fire represents a memorial and nostalgic longing for home and family. Bell and the father attain this vision because of their obsession with the past, and because of their struggle with memory and nostalgia. Memory, for these characters, has both personal and collective dimensions. Nostalgia, likewise, has a dual function, following Svetlana Boym's definition of nostalgics as being capable of restorative and reflective longing for the past. Family, or Paul Ricœur’s theory of close relations, bridges the gap between the conflicts of memory and nostalgia, acting as the means by which they understand this vision of carrying the fire while also embodying it. Additionally, the duality of both memory and nostalgia drive Bell and the father to seek for a prophetic vision, for stability in the past to deal with the threats in the present, which appears in the narrative structures of each novel.

Made available in DSpace on 2016-04-28T20:57:55Z (GMT). No. of bitstreams: 1 Damares Souza Silva.pdf: 1747345 bytes, checksum: da6b797ec77a1265a8f50872e9e01e83 (MD5) Previous issue date: 2009-05-06 / This study has as objective to evaluate the repertoire of reading of 40 students of 3rd grade of elementary public school, identifying the types of difficulties occurred. Additionally, two repertoires on writing were evaluated: the copy and the construction of dictated words. Data were collected with the application of the Instrumento de Avaliação de Leitura Repertório Inicial (IAL-I), with the assistant of Mestre® software. Based on stimulus equivalences paradigm, the IAL-I evaluates the performance of students focusing the reading behavior as relations between different types of stimuli: Sound (A), picture / image (B), Text (C). In addition, the IAL-I assess two writing repertories: the copy and the construction of dictated words. Data showed that the students named correctly the letters of the alphabet and have good performance in (CC) (printed word-printed word) relation. A low level of performance was identified in (BC) (picture-printed word), (CB) (printed word - picture), (AC) (dictated word - printed word), specially when words were composed by complex syllables. The lower level of performance occurred in (CD) relation (expressive reading), in which only one participant has achieved satisfactory performance and only in words which were composed by simple syllables. The students achieved satisfactory performance in (CE) relation (copy), both in words with simple syllables and in words with complex syllables, but in (AE) relation (construction of dictated words) no participant has reached a satisfactory performance in words with simple syllables or complex syllables. Among other results, it was found that students have not discriminated the letters b, p , d and the letters m , n; they didn t know when to use s , z and /s/, /ss/, /c/ in words with similar sound, and when to write /r/ or /rr/ . A detailed assessment of students' repertoire of reading made by IAL-I and the analysis of errors emitted by the students in reading and writing can serve as a subsidy for the teacher to plan activities aimed to overcoming the student s difficulties in these repertories / O presente estudo teve como objetivo avaliar o repertório de leitura de 40 alunos que freqüentam a 3ª série do Ensino Fundamental de uma escola pública estadual e que, segundo seus professores, apresentam desempenho insatisfatório, identificando-se os tipos de dificuldades existentes. Adicionalmente, avaliaram-se dois repertórios relativos à escrita: o de cópia e o de construção de palavras ditadas. Os dados foram coletados com a aplicação do Instrumento de Avaliação de Leitura - Repertório Inicial (IAL-I), com o auxílio do Software Mestre®. Apoiando-se no paradigma de equivalência de estímulos, o IAL-I avalia o desempenho dos alunos, a partir de relações entre três modalidades de estímulos: Som (A), imagem/figura(B), Texto (C). Além de avaliar a leitura, o IAL-I avalia dois repertórios relacionados à escrita: a cópia e a construção de palavras ditadas. Os dados coletados pelo referido instrumento mostraram que os participantes identificaram, de modo satisfatório, as letras do alfabeto e os itens da relação (CC) (palavra escrita- palavra escrita). Um nível de baixo desempenho foi apresentado quando os alunos foram submetidos às tarefas da relação (BC) (figura- palavra escrita), (CB) (palavra escrita - figura), (AC) (palavra ditada - palavra escrita); foi constatado um índice de maior dificuldade quando os itens de tais relações apresentavam palavras com sílabas complexas. O pior desempenho ocorreu na relação (CD) (palavra escrita palavra falada), correspondente à leitura expressiva; apenas um participante alcançou desempenho satisfatório e somente quando as palavras eram compostas por sílabas simples. Na relação de avaliação de escrita (CE) (Texto-Letras), correspondente à cópia, os participantes alcançaram desempenho satisfatório tanto nas palavras com silabas simples quanto nas palavras com sílabas complexas. O mesmo resultado não ocorreu quando os alunos fizeram a relação (AE) (Som-Letras), correspondente à construção de palavras ditadas, na qual nenhum participante alcançou desempenho satisfatório seja em palavras compostas por sílabas simples, seja nas palavras que continham sílabas complexas. Quanto aos erros observados, verificou-se que alguns participantes confundiam as letras b, p, e d e também as letras m e n. Os participantes invertem o uso do s e do z, em palavras com sonorização semelhante; há também troca de letras nas palavras compostas por /s/, /ss/, /ç/ e por /r/, /rr/. Foi constatado que há interferência do aspecto fonológico das palavras e que há o desconhecimento dos alunos sobre as regras ortográficas que envolvem o uso das referidas letras, na leitura e na escrita. A avaliação detalhada do repertório de leitura dos alunos possibilitada pelo IAL-I e a análise dos erros de leitura e de escrita decorrentes das complexidades da língua portuguesa podem servir como subsídio para que o professor planeje atividades de ensino visando à superação das dificuldades existentes

The purpose of this study was to assess the effects of participation in a family coaching program on perceptions of parental self-efficacy, families' use of encouragement, and reinforcement behaviors. A second purpose of the study was to ascertain the lasting effect on families participating in a family involvement intervention that included coaching. Families with prekindergarten and/or kindergarten children attending school in a low-income neighborhood and neighboring child care centers were recruited for the study. The Family Coaching Institute, the family involvement intervention for this study, consisted of three 5-week, 2-hour biweekly sessions. Attendance ranged from 3 to 15 sessions. Child care, dinner, learning activities, materials, books and supplies were provided. Participants were encouraged to use the activities at home with their children between sessions. Pre-intervention and post-intervention interviews were conducted with the participants using scales designed to measure parental self-efficacy, encouragement, and reinforcement behaviors. Family members also participated in a focus group and completed the Family Involvement Learning Survey 6 months after the intervention. Results of the study indicated there were no statistically significant differences in responses from the beginning to the end of the intervention on the scales designed to measure parental self-efficacy, encouragement, and reinforcement behaviors. These findings are discussed in the context of a response shift bias. In contrast, ratings on the Family Involvement Learning Survey indicated participation in the intervention had a strong impact on family behaviors.

Artificial intelligence tools, which are fast, robust and adaptive can overcome the drawbacks of traditional solutions for several power systems problems. In this work, applications of AI techniques have been studied for solving two important problems in power systems. The first problem is static security evaluation (SSE). The objective of SSE is to identify the contingencies in planning and operations of power systems. Numerical conventional solutions are time-consuming, computationally expensive, and are not suitable for online applications. SSE may be considered as a binary-classification, multi-classification or regression problem. In this work, multi-support vector machine is combined with several evolutionary computation algorithms, including particle swarm optimization (PSO), differential evolution, Ant colony optimization for the continuous domain, and harmony search techniques to solve the SSE. Moreover, support vector regression is combined with modified PSO with a proposed modification on the inertia weight in order to solve the SSE. Also, the correct accuracy of classification, the speed of training, and the final cost of using power equipment heavily depend on the selected input features. In this dissertation, multi-object PSO has been used to solve this problem. Furthermore, a multi-classifier voting scheme is proposed to get the final test output. The classifiers participating in the voting scheme include multi-SVM with different types of kernels and random forests with an adaptive number of trees. In short, the development and performance of different machine learning tools combined with evolutionary computation techniques have been studied to solve the online SSE. The performance of the proposed techniques is tested on several benchmark systems, namely the IEEE 9-bus, 14-bus, 39-bus, 57-bus, 118-bus, and 300-bus power systems. The second problem is the non-convex, nonlinear, and non-differentiable economic dispatch (ED) problem. The purpose of solving the ED is to improve the cost-effectiveness of power generation. To solve ED with multi-fuel options, prohibited operating zones, valve point effect, and transmission line losses, genetic algorithm (GA) variant-based methods, such as breeder GA, fast navigating GA, twin removal GA, kite GA, and United GA are used. The IEEE systems with 6-units, 10-units, and 15-units are used to study the efficiency of the algorithms.

<p>Population pharmacokinetic-pharmacodynamic (PK-PD) models can be fitted using nonlinear mixed effects modelling (NONMEM). This is an efficient way of learning about drugs and diseases from data collected in clinical trials. Identifying covariates which explain differences between patients is important to discover patient subpopulations at risk of sub-therapeutic or toxic effects and for treatment individualization. Stepwise covariate modelling (SCM) is commonly used to this end. The aim of the current thesis work was to evaluate SCM and to develop alternative approaches. A further aim was to develop a mechanistic PK-PD model describing fasting plasma glucose, fasting insulin, insulin sensitivity and beta-cell mass.</p><p>The lasso is a penalized estimation method performing covariate selection simultaneously to shrinkage estimation. The lasso was implemented within NONMEM as an alternative to SCM and is discussed in comparison with that method. Further, various ways of incorporating information and propagating knowledge from previous studies into an analysis were investigated. In order to compare the different approaches, investigations were made under varying, replicated conditions. In the course of the investigations, more than one million NONMEM analyses were performed on simulated data. Due to selection bias the use of SCM performed poorly when analysing small datasets or rare subgroups. In these situations, the lasso method in NONMEM performed better, was faster, and additionally validated the covariate model. Alternatively, the performance of SCM can be improved by propagating knowledge or incorporating information from previously analysed studies and by population optimal design.</p><p>A model was also developed on a physiological/mechanistic basis to fit data from three phase II/III studies on the investigational drug, tesaglitazar. This model described fasting glucose and insulin levels well, despite heterogeneous patient groups ranging from non-diabetic insulin resistant subjects to patients with advanced diabetes. The model predictions of beta-cell mass and insulin sensitivity were well in agreement with values in the literature.</p>

Population pharmacokinetic-pharmacodynamic (PK-PD) models can be fitted using nonlinear mixed effects modelling (NONMEM). This is an efficient way of learning about drugs and diseases from data collected in clinical trials. Identifying covariates which explain differences between patients is important to discover patient subpopulations at risk of sub-therapeutic or toxic effects and for treatment individualization. Stepwise covariate modelling (SCM) is commonly used to this end. The aim of the current thesis work was to evaluate SCM and to develop alternative approaches. A further aim was to develop a mechanistic PK-PD model describing fasting plasma glucose, fasting insulin, insulin sensitivity and beta-cell mass. The lasso is a penalized estimation method performing covariate selection simultaneously to shrinkage estimation. The lasso was implemented within NONMEM as an alternative to SCM and is discussed in comparison with that method. Further, various ways of incorporating information and propagating knowledge from previous studies into an analysis were investigated. In order to compare the different approaches, investigations were made under varying, replicated conditions. In the course of the investigations, more than one million NONMEM analyses were performed on simulated data. Due to selection bias the use of SCM performed poorly when analysing small datasets or rare subgroups. In these situations, the lasso method in NONMEM performed better, was faster, and additionally validated the covariate model. Alternatively, the performance of SCM can be improved by propagating knowledge or incorporating information from previously analysed studies and by population optimal design. A model was also developed on a physiological/mechanistic basis to fit data from three phase II/III studies on the investigational drug, tesaglitazar. This model described fasting glucose and insulin levels well, despite heterogeneous patient groups ranging from non-diabetic insulin resistant subjects to patients with advanced diabetes. The model predictions of beta-cell mass and insulin sensitivity were well in agreement with values in the literature.

La prima parte della Tesi si occupa dello studio delle strategie di investimento ottime nel settore energetico e in ricerca e sviluppo, nell'ambito di politiche di stabilizzazione dei gas serra nell'atmosfera. La seconda parte tratta invece metodi per la quantificazione degli impatti economici dei cambiamenti climatici sul settore agricolo, considerando tutte le possibilità di adattamento. / The first part of the Thesis discusses optimal investment strategies in the energy sector and in R&D for knowledge advancements to stabilize atmospheric concentrations of GHG. The second part deals instead with the measurement of impacts of climate change on agriculture considering all possible adaptation options.

In questo studio, attraverso la realizzazione di un’indagine longitudinale basata su dati amministrativi, sono stati ricostruiti i redditi delle famiglie residenti nel comune di Brescia per gli anni 2005-2008, in una prospettiva trasversale e longitudinale, individuando situazioni di disuguaglianza, di disagio e povertà economica di alcune fasce della popolazione. L’indagine longitudinale è stata realizzata mediante il record-linkage tra i dati amministrativi provenienti dall’Anagrafe comunale e quelli fiscali del Sistema Interscambio Anagrafe Tributaria Enti Locali dell’Agenzia delle Entrate (SIATEL) per il comune di Brescia. La peculiarità e l’importanza di questo lavoro risiedono sia nell’utilizzare dati di fonte amministrativa fiscale e anagrafica nella stima sui redditi e povertà delle famiglie, sia nella possibilità di ottenere stime a livello comunale o small-area, ampliando il campo della ricerca sociale sul tema oggetto di studio. L’approccio teorico di riferimento adottato è quello unidimensionale, basato sul reddito, e relativo, in cui la povertà è definita in relazione allo standard di benessere raggiunto dalla popolazione di riferimento nel suo complesso. Pertanto, anziché la soglia di povertà nazionale, che tende a sottostimare la povertà nel nord d’Italia, è stata adottata una soglia di povertà locale, pari al 60% del reddito mediano equivalente delle famiglie residenti. / In this study the incomes of the families residing in the town of Brescia for the years 2005-2008 have been simulated in a cross-section and longitudinal perspective, through the creation of a Panel based on administrative data. The research aimed at identifying situations of inequality, dislocation and economic poverty of certain population groups. The Panel was carried out on the basis of the record-linkage administrative data from Registry Office and ” Informatics’ System of Local Italian Revenue Agency” (SIATEL), for the town of Brescia. The uniqueness and importance of this work is the use of live data in estimating income and family poverty using fiscal and personal data from registry office sources, as well as in the availability of estimates at the municipal level or small-area, thus expanding the social research on the subject of study. The theoretical approach adopted by reference is one-dimensional (based on income) and relative, in which poverty is defined in relation to the standards being achieved by the reference population as a whole. Therefore, instead of the national poverty line, which tends to underestimate poverty in northern Italy, a local poverty threshold was adopted, equal to 60% of equivalised median income of the families residing in the town of Brescia.

Attualmente i prezzi agricoli sono evidenziate in combinazione con i presunti effetti collaterali, come la fame e la malnutrizione nell’Africa sub-sahariana (SSA), tuttavia, oggi, SSA ha circa 47,5 per cento della popolazione rurale in condizioni di povertà e tra il 1990 e il 2005 quando i prezzi dei prodotti alimentari erano stabile e con prezzi bassi l'estrema povertà rurale in SSA è stato circa 64,6 per cento. Abbiamo ipotizzato che la malnutrizione o fame continuano in SSA perché li, la miseria persistono. La riduzione della povertà è l'unico modo per porre fine alla fame in Africa. Altresì per un paese agricolo in SSA – senza significative riserve di risorse minerarie – il modo migliore per risolvere la povertà è attraverso lo sviluppo agricolo. Nel nostro campione, sono nove paesi in SSA - Burundi, Ghana, Malawi, Mozambico, Ruanda, Uganda, Tanzania, Zambia e Zimbabwe – il cosiddetto SSA – 9. Abbiamo utilizzato un modello ricorsivo nel periodo tra 1990 e il 2005. Come risultato si è visto che gli strumenti principali che hanno avuto forte relazione con la riduzione della povertà in SSA - 9 sono alcune implicazioni politiche come; il diritto di proprietà, l'accesso al sistema di crediti, il capitale umano e le infrastrutture. / Nowadays, agricultural prices are highlighted combined with, as alleged collateral effects, hunger and malnutrition in Sub-Saharan Africa (SSA). However, today, SSA has around 47,5 percent of rural population in extreme poverty and between 1990 and 2005 when the food prices was stable and with low prices, extreme poverty in SSA involved around 64.6 percent. We assumed that the undernourishment or starvation continued in SSA because there the misery persisted. Poverty reduction is the only way to the end the hunger in Africa. Also, for an agricultural country in SSA – without significant mineral resources – the best way to solve the problem of poverty is through agricultural development. Our sample are nine countries in SSA – Burundi, Ghana, Malawi, Mozambique, Rwanda, Uganda, United Republic of Tanzania, Zambia and Zimbabwe – the so called SSA – 9. Thus, we built up a recursive model that answered how the agricultural gears in SSA – 9 were moving between 1990 and 2005, as well as assessed how the agriculture could reduce rural poverty. As a result we saw that the main tools that had a strong relation with poverty reduction in SSA – 9 are some policies implications as; property rights, access to the credit system, human capital and infrastructure.