We measured time-resolved differential transmission in InMnAs for different pump/probe schemes as a function of temperature, laser fluence, and external magnetic field. We observed tunability of the carrier relaxation time. In addition, we found that the sign of the differential transmission changed as a function of probe wavelength. The electronic structure for InMnAs was calculated for B = 0, using an eight-band k·p model, which includes conduction and valence band mixing as well as coupling of electrons and holes to the magnetic Mn impurities. This allows us to explain some of the carrier dynamics and the sign changes in the differential transmission.

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Various natural language processing tasks are structured prediction problems where outputs are constructed with multiple interdependent decisions. Past work has shown that domain knowledge, framed as constraints over the output space, can help improve predictive accuracy. However, designing good constraints often relies on domain expertise. In this paper, we study the problem of learning such constraints. We frame the problem as that of training a two-layer rectifier network to identify valid structures or substructures, and show a construction for converting a trained network into a system of linear constraints over the inference variables. Our experiments on several NLP tasks show that the learned constraints can improve the prediction accuracy, especially when the number of training examples is small.

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We have modified the model for optically-pumped NMR (OPNMR) to incorporate a revised expression for the expectation value of the z-projection of the electron spin, 〈S〉 and apply this model to both bulk GaAs and a new material, InP. This expression includes the photon energy dependence of the electron polarization when optically pumping direct-gap semiconductors in excess of the bandgap energy, E. Rather than using a fixed value arising from coefficients (the matrix elements) for the optical transitions at the k=0 bandedge, we define a new parameter, S(E). Incorporating this revised element into the expression for 〈S〉, we have simulated the photon energy dependence of the OPNMR signals from bulk semi-insulating GaAs and semi-insulating InP. In earlier work, we matched calculations of electron spin polarization (alone) to features in a plot of OPNMR signal intensity versus photon energy for optical pumping (Ramaswamy et al., 2010). By incorporating an electron spin polarization which varies with pump wavelength into the penetration depth model of OPNMR signal, we are able to model features in both III-V semiconductors. The agreement between the OPNMR data and the corresponding model demonstrates that fluctuations in the OPNMR intensity have particular sensitivity to light hole-to-conduction band transitions in bulk systems. We provide detailed plots of the theoretical predictions for optical pumping transition probabilities with circularly-polarized light for both helicities of light, broken down into illustrative plots of optical magnetoabsorption and spin polarization, shown separately for heavy-hole and light-hole transitions. These plots serve as an effective roadmap of transitions, which are helpful to other researchers investigating optical pumping effects.