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Neural networks can synchronize by learning from each other. For that purpose they receive common inputs and exchange their outputs. Adjusting discrete weights according to a suitable learning rule then leads to full synchronization in a finite number of steps. It is also possible to train additional neural networks by using the inputs and outputs generated during this process as examples. Several algorithms for both tasks are presented and analyzed. In the case of Tree Parity Machines the dynamics of both processes is driven by attractive and repulsive stochastic forces. Thus it can be described well by models based on random walks, which represent either the weights themselves or order parameters of their distribution. However, synchronization is much faster than learning. This effect is caused by different frequencies of attractive and repulsive steps, as only neural networks interacting with each other are able to skip unsuitable inputs. Scaling laws for the number of steps needed for full synchronization and successful learning are derived using analytical models. They indicate that the difference between both processes can be controlled by changing the synaptic depth. In the case of bidirectional interaction the synchronization time increases proportional to the square of this parameter, but it grows exponentially, if information is transmitted in one direction only. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Here the partners benefit from mutual interaction, so that a passive attacker is usually unable to learn the generated key in time. The success probabilities of different attack methods are determined by numerical simulations and scaling laws are derived from the data. If the synaptic depth is increased, the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. Therefore the partners can reach any desired level of security by choosing suitable parameters. In addition, the entropy of the weight distribution is used to determine the effective number of keys, which are generated in different runs of the key-exchange protocol using the same sequence of input vectors. If the common random inputs are replaced with queries, synchronization is possible, too. However, the partners have more control over the difficulty of the key exchange and the attacks. Therefore they can improve the security without increasing the average synchronization time.
The astronomical exploration at energies between 30\,GeV and $\lesssim$\,350\,GeV was the main motivation for building the \MAGIC-telescope. With its 17\,m \diameter\ mirror it is the worldwide largest imaging air-Cherenkov telescope. It is located at the Roque de los Muchachos at the Canary island of San Miguel de La Palma at 28.8$^\circ$\,N, 17.8$^\circ$\,W, 2200\,m a.s.l. The telescope detects Cherenkov light produced by relativistic electrons and positrons in air showers initiated by cosmic gamma-rays. The imaging technique is used to powerfully reject the background due to hadronically induced air showers from cosmic rays. Their inverse power-law energy-distribution leads to an increase of the event rate with decreasing energy threshold. For \MAGIC this implies a trigger rate in the order of 250\,Hz, and a correspondingly large data stream to be recorded and analyzed. A robust analysis software package, including the general framework \MARS, was developed and commissioned to allow automation, necessary for data taken under variable observing conditions. Since many of the astronomical sources of high-energy radiation, in particular the enigmatic gamma-ray bursts, are of a transient nature, the telescope was designed to allow repositioning in several tens of seconds, keeping a tracking accuracy of $\lesssim\,$0.01$^\circ$. Employing a starguider, a tracking accuracy of $\lesssim\,$1.3\,minutes of arc was obtained. The main class of sources at very high gamma-ray energies, known from previous imaging air-Cherenkov telescopes, are Active Galactic Nuclei with relativistic jets, the so-called high-peaked Blazars. Their spectrum is entirely dominated by non-thermal emission, spanning more than 15 orders of magnitude in energy, from radio to gamma-ray energies. Predictions based on radiation models invoking a synchrotron self-Compton or hadronic origin of the gamma-rays suggest, that a fairly large number of them should be detectable by \MAGIC. Promising candidates have been chosen from existing compilations, requiring high (synchrotron) X-ray flux, assumed to be related to a high (possibly inverse-Compton) flux at GeV energies, and a low distance, in oder to avoid strong attenuation due to pair-production in interactions with low-energy photons from the extragalactic background radiation along the line of sight. Based on this selection the first \AGN, emitting gamma-rays at 100\,GeV, 1ES\,1218+304 at a redshift of $z=0.182$, was discovered, one of the two farthest known \AGN emitting in the TeV energy region. In this context, the automated analysis chain was successfully demonstrated. The source was observed in January 2005 during six moonless nights for 8.2\,h. At the same time the collaborating \KVA-telescope, located near the \MAGIC site, observed in the optical band. The lightcurve calculated showed no day-to-day variability and is compatible with a constant flux of $F($\,$>$\,$100\,\mbox{GeV})=(8.7\pm1.4) \cdot 10^{-7}\,\mbox{m}^{-2}\,\mbox{s}^{-1}$ within the statistical errors. A differential spectrum between 87\,GeV and 630\,GeV was calculated and is compatible with a power law of $F_E(E) = (8.1\pm 2.1) \cdot 10^{-7}(E/\mbox{250\,GeV})^{-3.0\pm0.4}\,\mbox{TeV}^{-1}\,\mbox{m}^{-2}\,\mbox{s}^{-1}$ within the statistical errors. The spectrum emitted by the source was obtained by taking into account the attenuation due to pair-production with photons of the extragalactic background at low photon energies. A homogeneous, one-zone synchrotron self-Compton model has been fitted to the collected multi-wavelength data. Using the simultaneous optical data, a best fit model could be obtained from which some physical properties of the emitting plasma could be inferred. The result was compared with the so-called {\em Blazar sequence}.