3,842 research outputs found
Design of a neural network simulator on a transputer array
A brief summary of neural networks is presented which concentrates on the design constraints imposed. Major design issues are discussed together with analysis methods and the chosen solutions. Although the system will be capable of running on most transputer architectures, it currently is being implemented on a 40-transputer system connected to a toroidal architecture. Predictions show a performance level equivalent to that of a highly optimized simulator running on the SX-2 supercomputer
Learning the Irreducible Representations of Commutative Lie Groups
We present a new probabilistic model of compact commutative Lie groups that
produces invariant-equivariant and disentangled representations of data. To
define the notion of disentangling, we borrow a fundamental principle from
physics that is used to derive the elementary particles of a system from its
symmetries. Our model employs a newfound Bayesian conjugacy relation that
enables fully tractable probabilistic inference over compact commutative Lie
groups -- a class that includes the groups that describe the rotation and
cyclic translation of images. We train the model on pairs of transformed image
patches, and show that the learned invariant representation is highly effective
for classification
Nonlinear mean-field dynamo and prediction of solar activity
We apply a nonlinear mean-field dynamo model which includes a budget equation
for the dynamics of Wolf numbers to predict solar activity. This dynamo model
takes into account the algebraic and dynamic nonlinearities of the alpha
effect, where the equation for the dynamic nonlinearity is derived from the
conservation law for the magnetic helicity. The budget equation for the
evolution of the Wolf number is based on a formation mechanism of sunspots
related to the negative effective magnetic pressure instability. This
instability redistributes the magnetic flux produced by the mean-field dynamo.
To predict solar activity on the time scale of one month we use a method based
on a combination of the numerical solution of the nonlinear mean-field dynamo
equations and the artificial neural network. A comparison of the results of the
prediction of the solar activity with the observed Wolf numbers demonstrates a
good agreement between the forecast and observations.Comment: 15 pages, 6 figures, jpp.cls, final versio
Developement of real time diagnostics and feedback algorithms for JET in view of the next step
Real time control of many plasma parameters will be an essential aspect in
the development of reliable high performance operation of Next Step Tokamaks.
The main prerequisites for any feedback scheme are the precise real-time
determination of the quantities to be controlled, requiring top quality and
highly reliable diagnostics, and the availability of robust control algorithms.
A new set of real time diagnostics was recently implemented on JET to prove the
feasibility of determining, with high accuracy and time resolution, the most
important plasma quantities. With regard to feedback algorithms, new
model–based controllers were developed to allow a more robust control of
several plasma parameters. Both diagnostics and algorithms were successfully
used in several experiments, ranging from H-mode plasmas to configuration with
ITBs. Since elaboration of computationally heavy measurements is often
required, significant attention was devoted to non-algorithmic methods like
Digital or Cellular Neural/Nonlinear Networks. The real time hardware and
software adopted architectures are also described with particular attention to
their relevance to ITER.Comment: 12th International Congress on Plasma Physics, 25-29 October 2004,
Nice (France
Modeling Cultural Dynamics
EVOC (for EVOlution of Culture) is a computer model of culture that enables us to investigate how various factors such as barriers to cultural diffusion, the presence and choice of leaders, or changes in the ratio of innovation to imitation affect the diversity and effectiveness of ideas. It consists of neural network based agents that invent ideas for actions, and imitate neighbors’ actions. The model is based on a theory of culture according to which what evolves through culture is not memes or artifacts, but the internal models of the world that give rise to them, and they evolve not through a Darwinian process of competitive exclusion but a Lamarckian process involving exchange of innovation protocols. EVOC shows an increase in mean fitness of actions over time, and an increase and then decrease in the diversity of actions. Diversity of actions is positively correlated with population size and density, and with barriers between populations. Slowly eroding borders increase fitness without sacrificing diversity by fostering specialization followed by sharing of fit actions. Introducing a leader that broadcasts its actions throughout the population increases the fitness of actions but reduces diversity of actions. Increasing the number of leaders reduces this effect. Efforts are underway to simulate the conditions under which an agent immigrating from one culture to another contributes new ideas while still ‘fitting in’
Experimental Evaluation of Book Drawing Algorithms
A -page book drawing of a graph consists of a linear ordering of
its vertices along a spine and an assignment of each edge to one of the
pages, which are half-planes bounded by the spine. In a book drawing, two edges
cross if and only if they are assigned to the same page and their vertices
alternate along the spine. Crossing minimization in a -page book drawing is
NP-hard, yet book drawings have multiple applications in visualization and
beyond. Therefore several heuristic book drawing algorithms exist, but there is
no broader comparative study on their relative performance. In this paper, we
propose a comprehensive benchmark set of challenging graph classes for book
drawing algorithms and provide an extensive experimental study of the
performance of existing book drawing algorithms.Comment: Appears in the Proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
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