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Chaos is a action thriller film starring Jason Statham, Ryan Phillippe and Wesley Snipes, and written and directed by Tony Giglio. The film premiered in the Starring: Jason Statham; Ryan Phillippe; Wesley.
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In a series of results reported in the journals Physical Review Letters and Chaos , scientists have used machine learning — the same computational technique behind recent successes in artificial intelligence — to predict the future evolution of chaotic systems out to stunningly distant horizons. The approach is being lauded by outside experts as groundbreaking and likely to find wide application.
The findings come from veteran chaos theorist Edward Ott and four collaborators at the University of Maryland. The evolving solution to this equation behaves like a flame front, flickering as it advances through a combustible medium. The Lyapunov time represents how long it takes for two almost-identical states of a chaotic system to exponentially diverge. As such, it typically sets the horizon of predictability.
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The algorithm knows nothing about the Kuramoto-Sivashinsky equation itself; it only sees data recorded about the evolving solution to the equation. Besides weather forecasting, experts say the machine-learning technique could help with monitoring cardiac arrhythmias for signs of impending heart attacks and monitoring neuronal firing patterns in the brain for signs of neuron spikes.
More speculatively, it might also help with predicting rogue waves, which endanger ships, and possibly even earthquakes. That magnetic outburst created aurora borealis visible all around the Earth and blew out some telegraph systems, while generating enough voltage to allow other lines to operate with their power switched off. He, Pathak and their colleagues Brian Hunt , Michelle Girvan and Zhixin Lu who is now at the University of Pennsylvania achieved their results by synthesizing existing tools.
They learned of a handful of promising results predating the deep-learning revolution. After training on the three series of numbers, the network could predict the future values of the three variables out to an impressively distant horizon. However, when there were more than a few interacting variables, the computations became impossibly unwieldy.
Ott and his colleagues needed a more efficient scheme to make reservoir computing relevant for large chaotic systems, which have huge numbers of interrelated variables.
Every position along the front of an advancing flame, for example, has velocity components in three spatial directions to keep track of. It took years to strike upon the straightforward solution. Locality means variables in one place are influenced by variables at nearby places but not by places far away.
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Parallelization allows the reservoir computing approach to handle chaotic systems of almost any size, as long as proportionate computer resources are dedicated to the task. Ott explained reservoir computing as a three-step procedure. Say you want to use it to predict the evolution of a spreading fire. First, you measure the height of the flame at five different points along the flame front, continuing to measure the height at these points on the front as the flickering flame advances over a period of time. You feed these data-streams in to randomly chosen artificial neurons in the reservoir.
The input data triggers the neurons to fire, triggering connected neurons in turn and sending a cascade of signals throughout the network. The second step is to make the neural network learn the dynamics of the evolving flame front from the input data. To do this, as you feed data in, you also monitor the signal strengths of several randomly chosen neurons in the reservoir. Weighting and combining these signals in five different ways produces five numbers as outputs.
The goal is to adjust the weights of the various signals that go into calculating the outputs until those outputs consistently match the next set of inputs — the five new heights measured a moment later along the flame front.
To learn the correct weights, the algorithm simply compares each set of outputs, or predicted flame heights at each of the five points, to the next set of inputs, or actual flame heights, increasing or decreasing the weights of the various signals each time in whichever way would have made their combinations give the correct values for the five outputs. Meanwhile, their Missouri hometown appears to be on the brink of chaos. Sectarian strife now empowers the civil wars in Syria, Iraq and Yemen and Al Qaedaism flourishes in the chaos.
Afar, is the reign of philosophy; close up is the chaos of the Carlovingian era. Their love was their creation: beyond all was night— chaos —nothing!
Machine Learning’s ‘Amazing’ Ability to Predict Chaos
Through all the chaos Stanief moved with a dignity never so great, carrying his head proudly above the conflict. As I stood there all immediate things were apparitions seen vague and distorted through a chaos of wild emotion. A new branch of science that deals with systems whose evolution depends very sensitively upon the initial conditions. Turbulent flows of fluids such as white water in a river and the prediction of the weather are two areas where chaos theory has been applied with some success.
Words related to chaos turmoil , discord , tumult , disarray , pandemonium , lawlessness , disorder , anarchy , misrule , bedlam , muddle , unruliness , snarl , entropy , free-for-all , clutter , mix-up , disorganization , mobocracy , ataxia.
Chaos | The Great Courses
Words nearby chaos chao'an , chaoan , chaochow , chaology , chaordic , chaos , chaos theory , chaotic , chaozhou , chap , chap hop. Origin of chaos —50; late Middle English Latin Greek; akin to chasm , yawn , gape. Examples from the Web for chaos He needed his art because, offstage, the chaos was sometimes too much.
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