194 research outputs found
Decision region approximation by polynomials or neural networks
We give degree of approximation results for decision regions which are defined by polynomial and neural network
parametrizations. The volume of the misclassified region is used to measure the approximation error, and results for the degree
of L1 approximation of functions are used. For polynomial parametrizations, we show that the degree of approximation is at
least 1, whereas for neural network parametrizations we prove the slightly weaker result that the degree of approximation is at least r, where r can be any number in the open interval (0, 1)
Autoregressive models for biomedical signal processing
Autoregressive models are ubiquitous tools for the analysis of time series in
many domains such as computational neuroscience and biomedical engineering. In
these domains, data is, for example, collected from measurements of brain
activity. Crucially, this data is subject to measurement errors as well as
uncertainties in the underlying system model. As a result, standard signal
processing using autoregressive model estimators may be biased. We present a
framework for autoregressive modelling that incorporates these uncertainties
explicitly via an overparameterised loss function. To optimise this loss, we
derive an algorithm that alternates between state and parameter estimation. Our
work shows that the procedure is able to successfully denoise time series and
successfully reconstruct system parameters. This new paradigm can be used in a
multitude of applications in neuroscience such as brain-computer interface data
analysis and better understanding of brain dynamics in diseases such as
epilepsy
Path Signatures for Seizure Forecasting
Forecasting the state of a system from an observed time series is the subject
of research in many domains, such as computational neuroscience. Here, the
prediction of epileptic seizures from brain measurements is an unresolved
problem. There are neither complete models describing underlying brain
dynamics, nor do individual patients exhibit a single seizure onset pattern,
which complicates the development of a `one-size-fits-all' solution. Based on a
longitudinal patient data set, we address the automated discovery and
quantification of statistical features (biomarkers) that can be used to
forecast seizures in a patient-specific way. We use existing and novel feature
extraction algorithms, in particular the path signature, a recent development
in time series analysis. Of particular interest is how this set of complex,
nonlinear features performs compared to simpler, linear features on this task.
Our inference is based on statistical classification algorithms with in-built
subset selection to discern time series with and without an impending seizure
while selecting only a small number of relevant features. This study may be
seen as a step towards a generalisable pattern recognition pipeline for time
series in a broader context
Transforming teacher education, an activity theory analysis
This paper explores the work of teacher education in England and Scotland. It seeks to locate this work within conflicting socio-cultural views of professional practice and academic work. Drawing on an activity theory framework that integrates the analysis of these contradictory discourses with a study of teacher educators’ practical activities, including the material artefacts that mediate the work, the paper offers a critical perspective on the social organisation of university-based teacher education. Informed by Engeström’s activity theory concept of transformation, the paper extends the discussion of contradictions in teacher education to consider the wider socio-cultural relations of the work. The findings raise important questions about the way in which teacher education work within universities is organised and the division of labour between schools and universities
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