Contours 2016-17

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Thermostat methods
Say we want to find out the distribution
of certain particles in a system.
The movement of these particles can
be simply modelled using Newton’s
equations. In reality, this model is
fairly poor, because it doesn’t take
into account friction, and other
forces, amongst the particles. Taking
these forces into account, we obtain
a modelling equation with two extra
terms.
But when we simulate the system using
the equations, we find that the
temperature of the simulated system
changes over time, when this
doesn’t actually happen in real life.
To solve the problem, mathematicians
like Ben and his PhD student
Xiaocheng Shang introduce what’s
called a ‘thermostat’ into the system.
Like a thermostat that keeps a
house a constant temperature, a
thermostat in this context is an extra
term in the modelling equation that
regulates the temperature of the
model. Similarly, when studying
large sets of data, introducing a
thermostat is essential to remove
the ‘noise’ that appears when
sampling.
Various thermostat methods exist,
but the traditional ones are not appropriate
when looking at such large
sets as those studied in Big Data research,
being either too slow or too
inaccurate. The method produced by
Ben and Xiaocheng has performed
very well in tests, converging much
faster than former methods, and
having a high accuracy. Similar
methods are also used by Google, in
machine learning – the study of pattern
recognition. It’s this that allows
Google to optimize its search results
so that you can find what you’re
looking for faster.
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Contours
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