32,931 research outputs found
Herding as a Learning System with Edge-of-Chaos Dynamics
Herding defines a deterministic dynamical system at the edge of chaos. It
generates a sequence of model states and parameters by alternating parameter
perturbations with state maximizations, where the sequence of states can be
interpreted as "samples" from an associated MRF model. Herding differs from
maximum likelihood estimation in that the sequence of parameters does not
converge to a fixed point and differs from an MCMC posterior sampling approach
in that the sequence of states is generated deterministically. Herding may be
interpreted as a"perturb and map" method where the parameter perturbations are
generated using a deterministic nonlinear dynamical system rather than randomly
from a Gumbel distribution. This chapter studies the distinct statistical
characteristics of the herding algorithm and shows that the fast convergence
rate of the controlled moments may be attributed to edge of chaos dynamics. The
herding algorithm can also be generalized to models with latent variables and
to a discriminative learning setting. The perceptron cycling theorem ensures
that the fast moment matching property is preserved in the more general
framework
Testing the SOC hypothesis for the magnetosphere
As noted by Chang, the hypothesis of Self-Organised Criticality provides a
theoretical framework in which the low dimensionality seen in magnetospheric
indices can be combined with the scaling seen in their power spectra and the
recently-observed plasma bursty bulk flows. As such, it has considerable
appeal, describing the aspects of the magnetospheric fuelling:storage:release
cycle which are generic to slowly-driven, interaction-dominated, thresholded
systems rather than unique to the magnetosphere. In consequence, several recent
numerical "sandpile" algorithms have been used with a view to comparison with
magnetospheric observables. However, demonstration of SOC in the magnetosphere
will require further work in the definition of a set of observable properties
which are the unique "fingerprint" of SOC. This is because, for example, a
scale-free power spectrum admits several possible explanations other than SOC.
A more subtle problem is important for both simulations and data analysis
when dealing with multiscale and hence broadband phenomena such as SOC. This is
that finite length systems such as the magnetosphere or magnetotail will by
definition give information over a small range of orders of magnitude, and so
scaling will tend to be narrowband. Here we develop a simple framework in which
previous descriptions of magnetospheric dynamics can be described and
contrasted. We then review existing observations which are indicative of SOC,
and ask if they are sufficient to demonstrate it unambiguously, and if not,
what new observations need to be made?Comment: 29 pages, 0 figures. Based on invited talk at Spring American
Geophysical Union Meeting, 1999. Journal of Atmospheric and Solar Terrestrial
Physics, in pres
Symptoms of complexity in a tourism system
Tourism destinations behave as dynamic evolving complex systems, encompassing
numerous factors and activities which are interdependent and whose
relationships might be highly nonlinear. Traditional research in this field has
looked after a linear approach: variables and relationships are monitored in
order to forecast future outcomes with simplified models and to derive
implications for management organisations. The limitations of this approach
have become apparent in many cases, and several authors claim for a new and
different attitude.
While complex systems ideas are amongst the most promising interdisciplinary
research themes emerged in the last few decades, very little has been done so
far in the field of tourism. This paper presents a brief overview of the
complexity framework as a means to understand structures, characteristics,
relationships, and explores the implications and contributions of the
complexity literature on tourism systems. The objective is to allow the reader
to gain a deeper appreciation of this point of view.Comment: 32 pages, 3 figures, 1 table; accepted in Tourism Analysi
Can biological quantum networks solve NP-hard problems?
There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.Comment: 38 page
- âŠ