711 research outputs found
Essentializing the binary self: individualism and collectivism in cultural neuroscience
Within the emerging field of cultural neuroscience (CN) one branch of research focuses on the neural underpinnings of âindividualistic/Westernâ vs. âcollectivistic/Easternâ 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 âcultureâ in these studies. Further, we argue against the binary structure of the drawn âculturalâ 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
Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
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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