X-ray absorption fine-structure ͑XAFS͒ measurements supported by ab initio computations within the density functional theory ͑DFT͒ are employed to systematically characterize Fe-doped as well as Fe-and Sicodoped films grown by metalorganic vapor-phase epitaxy. The analysis of extended-XAFS data shows that depending on the growth conditions, Fe atoms either occupy Ga substitutional sites in GaN or precipitate in the form of ⑀-Fe 3 N nanocrystals, which are ferromagnetic and metallic according to the DFT results. Precipitation can be hampered by reducing the Fe content, by increasing the growth rate, or by codoping with Si. The near-edge region of the XAFS spectra provides information on the Fe charge state and shows its partial reduction from Fe +3 to Fe +2 upon Si codoping, in agreement with the Fe electronic configurations expected within various implementations of DFT.

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Traditional convolutional neural network (CNN) based query classification uses linear feature mapping in its convolution operation. The recurrent neural network (RNN), differs from a CNN in representing word sequence with their ordering information kept explicitly. We propose using a deep long-short-term-memory (DLSTM) based feature mapping to learn feature representation for CNN. The DLSTM, which is a stack of LSTM units, has different order of feature representations at different depth of LSTM unit. The bottom LSTM unit equipped with input and output gates, extracts the first order feature representation from current word. To extract higher order nonlinear feature representation, the LSTM unit at higher position gets input from two parts. First part is the lower LSTM unit's memory cell from previous word. Second part is the lower LSTM unit's hidden output from current word. In this way, the DLSTM captures the nonlinear nonconsecutive interaction within n-grams. Using an architecture that combines a stack of the DLSTM layers with a tradition CNN layer, we have observed new state-of-the-art query classification accuracy on benchmark data sets for query classification.

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Maleic anhydride (MA) grafted polylactic acid (PLA) acting as reactive compatibilizer for PLA blends and composites has been reported. However, melt free-radical grafting of MA on PLA is often subject to steric and electron effects of the substituents in the monomer and low initiation efficiency, yielding low grafting efficiency (E g). In this work, five dicarboxylic anhydride monomers, including MA, itaconic anhydride (IA), cis-1,2,3,6-tetrahydrophthalic anhydride (TA), 4-allyltrimellitate anhydride (ATA), and 4-methacryloxyethyl trimellitate anhydride (META), were grafted onto PLA, and the effect of steric hindrance on E g was assessed. It is noted that E g values followed the order of ATA > META ≫ MA ≥ TA > IA. The introduction of styrene as a comonomer selectively increased the E g values of three electron-deficient monomers, MA, IA, and META, while Sn­(Oct)2 as a reducing agent increased E g for all monomers. Both styrene and Sn­(Oct)2 exhibited a synergistic effect when used in grafting MA, IA, and META.