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Alfred Stein, em memória de quem esse artigo é dedicado, mantinha, enquanto filósofo da história, a crença em valores absolutos como obsoletos bem como, enquanto filósofo da ética, o convencimento sobre a aleatoriedade relativista-cultural na valoração moral da ação humana. De uma tal valoração aparece indicado reconstruir a ação adequadamente, ou seja, compreendê-la intencionalmente e explicá-la por meio da causalidade. No decorrer desse compreender e desse explicar, se deve fazer uma referência a isso que Stern com, entre outros, Blaise Pascal, (...) chama de “a condição humana” (la condition humaine): o fato de que o humano é um ser consciente de si mesmo, de que ele é determinado por uma vontade de autopreservação, de que ele ama e odeia, de que ele sofre, busca escapar do sofrimento, de que ele sabe de sua mortalidade e, por fim, morre. De maneira semelhante à anthropéia physis de Thukydides, essas condições variantes resultam decorrer de ação bem diferentes e padrões de comportamento, de acordo com as especificidades da situação histórica – parecido com como um balão sobe, sob o efeito da mesma lei da gravidade, uma pedra, entretanto, cai ao chão. Seguindo essa construção compreensivo-explicativa da ação, vêm a sua valoração moral. Esta é, com Stein, guiada pelo princípio da liberdade como de um ser-livre de pressão e pelo princípio da maior minimização possível do sofrimento humano. (shrink)

This volume brings together those papers of mine which may be of interest not only to various specialists but also to philosophers. Many of my writings in mathematics were motivated by epistemological considerations; some papers originated in the critique of certain views that at one time dominated the discussions of the Vienna Cirele; others grew out of problems in teaching fundamental ideas of mathematics; sti II others were occasioned by personal relations with economists. Hence a wide range of subjects will (...) be discussed: epistemology, logic, basic concepts of pure and applied mathematics, philosophical ideas resulting from geometric studies, mathematical didactics and, finally, economics. The papers also span a period of more than fifty years. What unifies the various parts of the book is the spirit of searching for the elarification of basic concepts and methods and of articulating hidden ideas and tacit procedures. Part 1 ineludes papers published about 1930 which expound an idea that Carnap, after a short period of opposition in the Cirele, fully adopted ; and, under the name "Princip/e of To/erance", he eloquently formulated it in great generality in his book, Logica/ Syntax of Language, through which it was widely disseminated. "The New Logic" in Chapter 1 furthermore ineludes the first report to a larger public of Godel's epochal discovery presented among the great logic results of ali time. Chapter 2 is a translation of an often quoted 1930 paper presenting a detailed exposition and critique of intuitionism. (shrink)

1. Bd. Johannes Duns Scotus.--2. Bd. Die nachscotistische Scholastik.--3. Bd. Der Augustinismus in der Scholastik des späteren Mittelalters.--4. Bd. Der Endausgang der mittelalterlichen Scholastik. Der Übergang der Scholastik in ihr nachtridentinisches Entwickelungsstadium. 2 v.

In recent years, multivariate pattern analysis has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging, electroencephalography, and other neuroimaging methodologies. In a similar time frame, “deep learning” has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on (...) linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox intended to facilitate dMVPA for neuroscientists everywhere. (shrink)