645,475 research outputs found
Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package
This paper describes the R package crqa to perform cross-recurrence
quantification analysis of two time series of either a categorical or
continuous nature. Streams of behavioral information, from eye movements to
linguistic elements, unfold over time. When two people interact, such as in
conversation, they often adapt to each other, leading these behavioral levels
to exhibit recurrent states. In dialogue, for example, interlocutors adapt to
each other by exchanging interactive cues: smiles, nods, gestures, choice of
words, and so on. In order for us to capture closely the goings-on of dynamic
interaction, and uncover the extent of coupling between two individuals, we
need to quantify how much recurrence is taking place at these levels. Methods
available in crqa would allow researchers in cognitive science to pose such
questions as how much are two people recurrent at some level of analysis, what
is the characteristic lag time for one person to maximally match another, or
whether one person is leading another. First, we set the theoretical ground to
understand the difference between 'correlation' and 'co-visitation' when
comparing two time series, using an aggregative or cross-recurrence approach.
Then, we describe more formally the principles of cross-recurrence, and show
with the current package how to carry out analyses applying them. We end the
paper by comparing computational efficiency, and results' consistency, of crqa
R package, with the benchmark MATLAB toolbox crptoolbox. We show perfect
comparability between the two libraries on both levels
High resolution in-vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm
MR-STAT is a recently proposed framework that allows the reconstruction of
multiple quantitative parameter maps from a single short scan by performing
spatial localisation and parameter estimation on the time domain data
simultaneously, without relying on the FFT. To do this at high-resolution,
specialized algorithms are required to solve the underlying large-scale
non-linear optimisation problem. We propose a matrix-free and parallelized
inexact Gauss-Newton based reconstruction algorithm for this purpose. The
proposed algorithm is implemented on a high performance computing cluster and
is demonstrated to be able to generate high-resolution (
in-plane resolution) quantitative parameter maps in simulation, phantom and
in-vivo brain experiments. Reconstructed and values for the gel
phantoms are in agreement with results from gold standard measurements and for
the in-vivo experiments the quantitative values show good agreement with
literature values. In all experiments short pulse sequences with robust
Cartesian sampling are used for which conventional MR Fingerprinting
reconstructions are shown to fail.Comment: Accepted by NMR in Biomedicine on 2019-12-0
A computational approach for cam size optimization of disc cam-follower mechanisms with translating roller followers
The main objective of this work is to present a computational approach for design optimization of disc cam mechanisms with eccentric translating roller followers. For this purpose, the objective function defined here takes into account the three major parameters that influence the final cam size, namely the base circle radius of the cam, the radius of the roller and the offset of the follower. Furthermore, geometric constraints related to the maximum pressure angle and minimum radius of curvature are included to ensure good working conditions of the system. Finally, an application example is presented and used to discuss the main assumptions and procedure adopted throughout this work.Fundação para a Ciência e a Tecnologia (FCT
Challenges of Feminism and Gender Equalization
The following article presents the views of feminist theorists and the issue of raising gender equality in society
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