711 research outputs found

    Essentializing the binary self: individualism and collectivism in cultural neuroscience

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    Within the emerging field of cultural neuroscience (CN) one branch of research focuses on the neural underpinnings of “individualistic/Western” vs. “collectivistic/Eastern” self-views. These studies uncritically adopt essentialist assumptions from classic cross-cultural research, mainly following the tradition of Markus and Kitayama (1991), into the domain of functional neuroimaging. In this perspective article we analyze recent publications and conference proceedings of the 18th Annual Meeting of the Organization for Human Brain Mapping (2012) and problematize the essentialist and simplistic understanding of “culture” in these studies. Further, we argue against the binary structure of the drawn “cultural” comparisons and their underlying Eurocentrism. Finally we scrutinize whether valuations within the constructed binarities bear the risk of constructing and reproducing a postcolonial, orientalist argumentation pattern

    A Sabbatical Experience: Nurturing a Partnership

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    The Client-Fraud Dilemma: a Need for Consensus

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    System of Accounts for Country Clubs

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    Catching Up with State of the Art Continuous Integration Pipelines in Palladio - An Experience Report

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    Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs

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    This paper studies the problem of forecasting general stochastic processes using an extension of the Neural Jump ODE (NJ-ODE) framework. While NJ-ODE was the first framework to establish convergence guarantees for the prediction of irregularly observed time series, these results were limited to data stemming from It\^o-diffusions with complete observations, in particular Markov processes where all coordinates are observed simultaneously. In this work, we generalise these results to generic, possibly non-Markovian or discontinuous, stochastic processes with incomplete observations, by utilising the reconstruction properties of the signature transform. These theoretical results are supported by empirical studies, where it is shown that the path-dependent NJ-ODE outperforms the original NJ-ODE framework in the case of non-Markovian data. Moreover, we show that PD-NJ-ODE can be applied successfully to limit order book (LOB) data
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