Download Dark Cosmos: In Search of Our Universe's Missing Mass and by Dan Hooper PDF

By Dan Hooper

The 20th century used to be astounding in all regards, shaking the rules of essentially each point of human lifestyles and proposal, physics now not least of all. starting with the e-book of Albert Einstein's idea of relativity, in the course of the wild revolution of quantum mechanics, and up till the physics of the trendy day (including the excellent revelation, in 1998, that the Universe isn't just increasing, yet doing so at an ever-quickening pace), a lot of what physicists have obvious in our Universe means that a lot of our Universe is unseen-that we are living in a depressing cosmos.

Everyone understands that there are issues nobody can see-the air you're respiring, for instance, or, to be extra unique, a black gap. yet what everybody doesn't understand is that what we will be able to see-a booklet, a cat, or our planet-makes up purely five percentage of the Universe. The rest-fully ninety five percent-is completely invisible to us; its presence discernible purely via the vulnerable results it has on seen topic round it.

This invisible stuff is available in varieties-dark topic and darkish strength. One holds the Universe jointly, whereas the opposite tears it aside. What those forces relatively are has been a secret for so long as someone has suspected they have been there, however the newest discoveries of experimental physics have introduced us toward that wisdom. Particle physicist Dan Hooper takes his readers, with wit, grace, and a willing knack for explaining the hardest rules technology has to provide, on a quest few may have ever anticipated: to find what makes up our darkish cosmos.

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A Threshold Model for the French Franc/Deutschmark Exchange Rate. Journal of Forecasting 15(3), 155–164 (1995) 3. : Neural Networks for Financial Market Prediction. In: IEEE International Conference on Neural Networks, vol. 7, pp. 1199–1202 (1994) 4. Deboeck, G. ): Trading of the Edge: Neural, Genetic and Fuzzy Systems for Chaotic Financial Markets. , Chichester (1994) 5. : Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization. In: Proceedings of the Congress on Evolutionary Computing, San Diego, USA, pp.

From this set of pre-validated models the system must be able to select, while the system is running, the model that best matches the data. For the initial movement model experiment, we examine the effectiveness of the various statistical tests and measures in their ability to rank simulations in their closeness to the baseline simulation. The baseline simulation models a crisis scenario where people are fleeing a disaster. All simulations, including the baseline, are started with 900 agents distributed among 20 Voronoi cells.

Combining both algorithms with GMDH provides better accuracy and speed. By starting with a low accuracy GA (fast calculations) for finding the rough global minima, then switching to PSO for finding a more accurate minima in the global GA minima area. The GA was set to 8 bit settings for each variable, and then preceded by PSO with the same previous settings. Figs. 75 actual (a) Training data (b) Testing data Fig. 3. 73 actual (a) Training data (b) Testing data Fig. 4. 73 actual (b) Testing data Fig.

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