We compute the channel capacity of non-binary fingerprinting under the Marking Assumption, in the limit of large coalition size c. The solution for the binary case was found by Huang and Moulin. They showed that asymptotically, the capacity is 1/(c 2 2 ln 2), the interleaving attack is optimal and the arcsine distribution is the optimal bias distribution. In this paper we prove that the asymptotic capacity for general alphabet size q is (q − 1)/(c 2 2 ln q). Our proof technique does not reveal the optimal attack or bias distribution. The fact that the capacity is an increasing function of q shows that there is a real gain in going to non-binary alphabets. Preliminaries

We study the channel capacity of q-ary fingerprinting in the limit of large attacker coalitions. We extend known results by considering the Combined Digit Model, an attacker model that captures signal processing attacks such as averaging and noise addition. For q = 2 we give results for various attack parameter settings. For q ≥ 3 we present the relevant equations without providing a solution. We show how the channel capacity in the Restricted Digit Model is obtained as a limiting case of the Combined Digit Model.

A novel model for ultrafast laser-induced magnetization dynamics is analyzed. Equilibration of the magnetic system is described by including electron-phonon scattering events with a finite spin flip probability. Recently, we demonstrated that such a model predicts a direct relation between the demagnetization time and the Gilbert damping. Here we present numerical simulations based on the same Hamiltonian, but including the presence of an external applied field. Thereby, reversal of the magnetization after heating above the Curie temperature (Tc) can be modeled. We demonstrate that magnetization reversal can be achieved even if the lattice temperature stays below Tc.

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