High Curie temperatures in (Ga,Mn)N from Mn clustering

The effect of microscopic Mn cluster distribution on the Curie temperature (Tc) is studied using density-functional calculations. We find that the calculated Tc depends crucially on the microscopic cluster distribution, which can explain the abnormally large variations in experimental Tc values from a few K to well above room temperature. The partially dimerized Mn_2-Mn_1 distribution is found to give the highest Tc > 500 K, and in general, the presence of the Mn_2 dimer has a tendency to enhance Tc. The lowest Tc values close to zero are obtained for the Mn_4-Mn_1 and Mn_4-Mn_3 distributions.Comment: To appear in Applied Phyiscs Letter

Higher-order contributions and non-perturbative effects in the non-degenerate nonlinear optical absorption of direct-gap semiconductors

The semiconductor Bloch equations for a two-band model including inter- and intraband excitation are used to study the nonlinear absorption of single and multiple light pulses by direct-gap semiconductors. For a consistent analysis the contributions to the absorption originating from both the interband polarization and the intraband current need to be included. In the Bloch equation approach theses contributions as well as different excitation pathways in terms of sequences of inter- and intraband excitations can be evaluated separately which allows for a transparent analysis, the identification of the dominant terms, and analyzing their dependence on the excitation conditions. In the perturbative regime, we obtain analytical expressions for the multi-photon absorption coefficients for continuous-wave excitation. These results are shown to agree well with numerical results for short pulses and/or finite dephasing and relaxation times and we confirm the previously predicted strong enhancement of two-photon absorption for non-degenerate conditions for pulsed excitation. We discuss the dependencies on the light frequencies, initial band populations, and the time delay between the pulses. The frequency dependence of the two-photon absorption coefficient for non-degenerate excitation is evaluated perturbatively in third-order. The higher-order contributions to the optical absorption include three- and four-photon absorption and show a rich frequency dependence including negative regions and dispersive lineshapes. Non-perturbative solutions of the Bloch equations demonstrate a strongly non-monotonous behavior of the intensity-dependent optical absorption for a single incident pulse and in a pump-probe set-up

Best of Both Worlds – Relational Databases and Statistics

Statistics software packages and relational database systems possess considerable overlap in the area of data loading, handling, and transformation. However, only databases are mainly optimized towards high performance in this area. In this paper, we present our approach on bringing the best of these two worlds together. We integrate the analytics-optimized database MonetDB and the R environment for statistical computing in a non-obtrusive, transparent and compatible way

Measuring information-transfer delays

In complex networks such as gene networks, traffic systems or brain circuits it is important to understand how long it takes for the different parts of the network to effectively influence one another. In the brain, for example, axonal delays between brain areas can amount to several tens of milliseconds, adding an intrinsic component to any timing-based processing of information. Inferring neural interaction delays is thus needed to interpret the information transfer revealed by any analysis of directed interactions across brain structures. However, a robust estimation of interaction delays from neural activity faces several challenges if modeling assumptions on interaction mechanisms are wrong or cannot be made. Here, we propose a robust estimator for neuronal interaction delays rooted in an information-theoretic framework, which allows a model-free exploration of interactions. In particular, we extend transfer entropy to account for delayed source-target interactions, while crucially retaining the conditioning on the embedded target state at the immediately previous time step. We prove that this particular extension is indeed guaranteed to identify interaction delays between two coupled systems and is the only relevant option in keeping with Wiener’s principle of causality. We demonstrate the performance of our approach in detecting interaction delays on finite data by numerical simulations of stochastic and deterministic processes, as well as on local field potential recordings. We also show the ability of the extended transfer entropy to detect the presence of multiple delays, as well as feedback loops. While evaluated on neuroscience data, we expect the estimator to be useful in other fields dealing with network dynamics