Esta dissertação tem como objetivo apresentar a Modelagem Causal em História da Ciência (MCHC), e aplicá-la ao período da história da astronomia que vai dos primórdios ao século III AEC. Na primeira parte, exponho o método e discorro sobre algumas de suas implicações filosóficas especialmente aquelas relacionadas com a noção de avanço e historiográficas, ao mesmo tempo em que procuro inseri-lo no panorama da filosofia da ciência. Na segunda parte, que já é uma aplicação da MCHC, apresento uma pequena história da astronomia antiga, mostrando os principais avanços dos egípcios, babilônios e gregos, até Aristarco de Samos. Na última parte, utilizo os conceitos mobilizados na primeira parte e os avanços da segunda para apresentar o modelo causal da astronomia antiga e as conclusões dele derivadas. / This thesis aims at presenting the Causal Modeling of the History of Science (MCHC), and applying it to the period of the history of astronomy comprising its beginning until the 3rd century BCE. In the first part, the method is discussed and some of its philosophical and historiographical implications are analyzed especially those related to the notion of advance. In the second part, an application of MCHC, a short history of ancient, is presented, showing the main advances of the Egyptians, Babylonians, and Greeks, up to Aristarchus of Samos. In the final part, I use the concepts deployed in the first part and the advances discussed in the second part to present a causal model of ancient astronomy and the conclusions therefrom derived.

<p>Diss. Umeå : Universitet, 1985</p> / digitalisering@umu

Esta dissertação tem como objetivo apresentar a Modelagem Causal em História da Ciência (MCHC), e aplicá-la ao período da história da astronomia que vai dos primórdios ao século III AEC. Na primeira parte, exponho o método e discorro sobre algumas de suas implicações filosóficas especialmente aquelas relacionadas com a noção de avanço e historiográficas, ao mesmo tempo em que procuro inseri-lo no panorama da filosofia da ciência. Na segunda parte, que já é uma aplicação da MCHC, apresento uma pequena história da astronomia antiga, mostrando os principais avanços dos egípcios, babilônios e gregos, até Aristarco de Samos. Na última parte, utilizo os conceitos mobilizados na primeira parte e os avanços da segunda para apresentar o modelo causal da astronomia antiga e as conclusões dele derivadas. / This thesis aims at presenting the Causal Modeling of the History of Science (MCHC), and applying it to the period of the history of astronomy comprising its beginning until the 3rd century BCE. In the first part, the method is discussed and some of its philosophical and historiographical implications are analyzed especially those related to the notion of advance. In the second part, an application of MCHC, a short history of ancient, is presented, showing the main advances of the Egyptians, Babylonians, and Greeks, up to Aristarchus of Samos. In the final part, I use the concepts deployed in the first part and the advances discussed in the second part to present a causal model of ancient astronomy and the conclusions therefrom derived.

L’importance des facteurs de risque professionnels dans l’asthme est bien établie, mais le rôle de certains agents doit être clarifié. Les objectifs de la thèse sont d’évaluer les liens entre les expositions aux produits de nettoyage et l’asthme, et d’étudier l’impact et la prise en compte du biais du travailleur sain, dans l’étude Epidémiologique des facteurs Génétiques et Environnementaux de l’Asthme (EGEA, 2047 sujets dont 1477 adultes avec des données professionnelles). L’exposition aux produits de nettoyage a été estimée par une expertise et une matrice emploi-exposition. Chez les femmes, l’asthme actuel était associé à l’exposition aux détartrants (OR=2.4 (1.1-5.3)), et aux sprays (2.9 (1.0-8.1)) et à l’ammoniac (3.1 (1.2-7.8)) chez les aides-soignantes. Les détartrants et l’ammoniac sont des irritants. L’exposition aux produits de nettoyage était associée à l’asthme sévère et sans sensibilisation allergique. Deux analyses ont souligné l’importance du biais du travailleur sain dans l’asthme. Un biais de sélection à l’embauche a été observé chez des sujets avec un asthme sévère dans l’enfance. Un modèle marginal structural a permis de prendre en compte le biais du travailleur sain dans l’étude de l’effet des expositions professionnelles sur l’expression clinique de l’asthme au cours de la vie. En plus du rôle d’asthmogènes connus, le rôle d’agents moins bien établis, comprenant des irritants (1.6 (1.0-2.4)) était suggéré. Les résultats sont cohérents avec un rôle des irritants dans l’asthme lié au travail. Ils soutiennent une utilisation plus large d’approches d’analyse causale pour contrôler le biais du travailleur sain dans les études des risques professionnels. / It is well-recognized that workplace exposures importantly contribute to the burden of asthma, but the role of some agents needs to be clarified. The aims of the thesis are to evaluate the relationships between occupational exposure to cleaning products and asthma, and to study the impact and the control of the healthy worker effect bias, in the Epidemiological study on the Genetics and Environment of Asthma (EGEA, 2047 subjects including 1477 adults with data regarding occupations).Exposure to cleaning products was estimated by an expert assessment and a job-exposure matrix. In women, current asthma was associated with exposure to decalcifiers (OR=2.4 (1.1-5.3)), and to sprays (2.9 (1.0-8.1)) and ammonia (3.1 (1.2-7.8)) in personal care workers. Decalcifiers and ammonia are irritants. Exposure to cleaning products was associated with severe asthma, and asthma without allergic sensitization. Two analyses underlined the important impact of the healthy worker effect in asthma. A healthy worker hire effect was observed in subjects with severe asthma in childhood. Using a marginal structural model, we studied the effect of occupational exposure on asthma clinical expression over a lifetime, while controlling for the healthy worker effect bias. Elevated risks of asthma were observed, not only for known asthmagens, but also for other agents which role in asthma is less established, including irritants (1.6 (1.0-2.4)). The results are consistent with a role of irritants in work-related asthma. They support a broader use of causal inference approaches, to control the healthy worker effect bias in studies of occupational risk factors.

[pt] Uma das principais preocupações mundiais atualmente está relacionada às questões ambientais. Essa preocupação é considerada na seleção de projetos de energia e, como resultado, a geração de energia elétrica a partir de fontes renováveis tem experimentado um forte crescimento em todo o mundo, incluindo o Brasil. Em relação às fontes de energia hidrelétrica, as Pequenas Centrais Hidrelétricas (PCHs) são uma alternativa para reduzir o impacto ambiental. Esses projetos produzem entre 5 e 30 megawatts (MW) e sua instalação tem um baixo custo e respeito ao meio ambiente, principalmente por não existir necessidade de reservatórios de regulação, o que não é o caso de grandes usinas hidrelétricas. Nos últimos anos, o número de PCHs tem aumentado bastante, como consequência dos incentivos para geração de eletricidade a partir de fontes renováveis. Como a geração de energia hidrelétrica é fortemente influenciada por regimes hidrológicos, especialmente no caso de usinas a fio d água como as PCHs, melhorar a assertividade das previsões de geração de energia elétrica de maneira estocástica torna-se altamente importante para as distribuidoras. Esta dissertação tem como principal objetivo apresentar o desempenho de um grupo de modelos de previsão aplicados para PCHs de uma distribuidora real de energia elétrica. Para isso foram utilizadas diferentes abordagens, incluindo dados de vazão de usinas hidrelétricas vizinhas como variável explicativa em modelos causais, assim como também modelos univariados. / [en] One of the main world concerns nowadays is related to the environment issues. Such concern is considered in the selection of energy projects and, as a result of that, the generation of electricity from renewable sources has experienced a sharp growth all over the world, Brazil included. Concerning hydropower sources, Small Hydropower Plants (SHPs) are an alternative to reduce environmental impact. These projects produce between 5 and 30 megawatts (MW) and its installation has a low cost and respect to the environment, mainly because there is no need of regulation reservoirs, which is not the case in bigger hydroelectric plants. In recent years the number of SHPs is increasing in a great deal, as a consequence of the incentives to generate electricity from renewable sources. Since hydro power generation is heavily influenced by hydrological regimes, especially in the case of run-of-river plants, as SHPs, improving the assertiveness of electric power generation forecasts in a stochastic way becomes highly important for distributing utilities. This master dissertation has as main objective to present the performance of an arrange of forecasting models applied to SHPs of a real distributing utility. It was used different approaches, including inflow data from neighboring hydro plants as exogenous variable, in causal models and also univariate models.

Motivation and background The enormous amount of capabilities that every human learns throughout his life, is probably among the most remarkable and fascinating aspects of life. Learning has therefore drawn lots of interest from scientists working in very different fields like philosophy, biology, sociology, educational sciences, computer sciences and mathematics. This thesis focuses on the information theoretical and mathematical aspects of learning. We are interested in the learning process of an agent (which can be for example a human, an animal, a robot, an economical institution or a state) that interacts with its environment. Common models for this interaction are Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). Learning is then considered to be the maximization of the expectation of a predefined reward function. In order to formulate general principles (like a formal definition of curiosity-driven learning or avoidance of unpleasant situation) in a rigorous way, it might be desirable to have a theoretical framework for the optimization of more complex functionals of the underlying process law. This might include the entropy of certain sensor values or their mutual information. An optimization of the latter quantity (also known as predictive information) has been investigated intensively both theoretically and experimentally using computer simulations by N. Ay, R. Der, K Zahedi and G. Martius. In this thesis, we develop a mathematical theory for learning in the sensorimotor loop beyond expected reward maximization. Approaches and results This thesis covers four different topics related to the theory of learning in the sensorimotor loop. First of all, we need to specify the model of an agent interacting with the environment, either with learning or without learning. This interaction naturally results in complex causal dependencies. Since we are interested in asymptotic properties of learning algorithms, it is necessary to consider infinite time horizons. It turns out that the well-understood theory of causal networks known from the machine learning literature is not powerful enough for our purpose. Therefore we extend important theorems on causal networks to infinite graphs and general state spaces using analytical methods from measure theoretic probability theory and the theory of discrete time stochastic processes. Furthermore, we prove a generalization of the strong Markov property from Markov processes to infinite causal networks. Secondly, we develop a new idea for a projected stochastic constraint optimization algorithm. Generally a discrete gradient ascent algorithm can be used to generate an iterative sequence that converges to the stationary points of a given optimization problem. Whenever the optimization takes place over a compact subset of a vector space, it is possible that the iterative sequence leaves the constraint set. One possibility to cope with this problem is to project all points to the constraint set using Euclidean best-approximation. The latter is sometimes difficult to calculate. A concrete example is an optimization over the unit ball in a matrix space equipped with operator norm. Our idea consists of a back-projection using quasi-projectors different from the Euclidean best-approximation. In the matrix example, there is another canonical way to force the iterative sequence to stay in the constraint set: Whenever a point leaves the unit ball, it is divided by its norm. For a given target function, this procedure might introduce spurious stationary points on the boundary. We show that this problem can be circumvented by using a gradient that is tailored to the quasi-projector used for back-projection. We state a general technical compatibility condition between a quasi-projector and a metric used for gradient ascent, prove convergence of stochastic iterative sequences and provide an appropriate metric for the unit-ball example. Thirdly, a class of learning problems in the sensorimotor loop is defined and motivated. This class of problems is more general than the usual expected reward maximization and is illustrated by numerous examples (like expected reward maximization, maximization of the predictive information, maximization of the entropy and minimization of the variance of a given reward function). We also provide stationarity conditions together with appropriate gradient formulas. Last but not least, we prove convergence of a stochastic optimization algorithm (as considered in the second topic) applied to a general learning problem (as considered in the third topic). It is shown that the learning algorithm converges to the set of stationary points. Among others, the proof covers the convergence of an improved version of an algorithm for the maximization of the predictive information as proposed by N. Ay, R. Der and K. Zahedi. We also investigate an application to a linear Gaussian dynamic, where the policies are encoded by the unit-ball in a space of matrices equipped with operator norm.

Motivation and background The enormous amount of capabilities that every human learns throughout his life, is probably among the most remarkable and fascinating aspects of life. Learning has therefore drawn lots of interest from scientists working in very different fields like philosophy, biology, sociology, educational sciences, computer sciences and mathematics. This thesis focuses on the information theoretical and mathematical aspects of learning. We are interested in the learning process of an agent (which can be for example a human, an animal, a robot, an economical institution or a state) that interacts with its environment. Common models for this interaction are Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). Learning is then considered to be the maximization of the expectation of a predefined reward function. In order to formulate general principles (like a formal definition of curiosity-driven learning or avoidance of unpleasant situation) in a rigorous way, it might be desirable to have a theoretical framework for the optimization of more complex functionals of the underlying process law. This might include the entropy of certain sensor values or their mutual information. An optimization of the latter quantity (also known as predictive information) has been investigated intensively both theoretically and experimentally using computer simulations by N. Ay, R. Der, K Zahedi and G. Martius. In this thesis, we develop a mathematical theory for learning in the sensorimotor loop beyond expected reward maximization. Approaches and results This thesis covers four different topics related to the theory of learning in the sensorimotor loop. First of all, we need to specify the model of an agent interacting with the environment, either with learning or without learning. This interaction naturally results in complex causal dependencies. Since we are interested in asymptotic properties of learning algorithms, it is necessary to consider infinite time horizons. It turns out that the well-understood theory of causal networks known from the machine learning literature is not powerful enough for our purpose. Therefore we extend important theorems on causal networks to infinite graphs and general state spaces using analytical methods from measure theoretic probability theory and the theory of discrete time stochastic processes. Furthermore, we prove a generalization of the strong Markov property from Markov processes to infinite causal networks. Secondly, we develop a new idea for a projected stochastic constraint optimization algorithm. Generally a discrete gradient ascent algorithm can be used to generate an iterative sequence that converges to the stationary points of a given optimization problem. Whenever the optimization takes place over a compact subset of a vector space, it is possible that the iterative sequence leaves the constraint set. One possibility to cope with this problem is to project all points to the constraint set using Euclidean best-approximation. The latter is sometimes difficult to calculate. A concrete example is an optimization over the unit ball in a matrix space equipped with operator norm. Our idea consists of a back-projection using quasi-projectors different from the Euclidean best-approximation. In the matrix example, there is another canonical way to force the iterative sequence to stay in the constraint set: Whenever a point leaves the unit ball, it is divided by its norm. For a given target function, this procedure might introduce spurious stationary points on the boundary. We show that this problem can be circumvented by using a gradient that is tailored to the quasi-projector used for back-projection. We state a general technical compatibility condition between a quasi-projector and a metric used for gradient ascent, prove convergence of stochastic iterative sequences and provide an appropriate metric for the unit-ball example. Thirdly, a class of learning problems in the sensorimotor loop is defined and motivated. This class of problems is more general than the usual expected reward maximization and is illustrated by numerous examples (like expected reward maximization, maximization of the predictive information, maximization of the entropy and minimization of the variance of a given reward function). We also provide stationarity conditions together with appropriate gradient formulas. Last but not least, we prove convergence of a stochastic optimization algorithm (as considered in the second topic) applied to a general learning problem (as considered in the third topic). It is shown that the learning algorithm converges to the set of stationary points. Among others, the proof covers the convergence of an improved version of an algorithm for the maximization of the predictive information as proposed by N. Ay, R. Der and K. Zahedi. We also investigate an application to a linear Gaussian dynamic, where the policies are encoded by the unit-ball in a space of matrices equipped with operator norm.

No description available.