Isospin dependence of Nucleon-Nucleon Short-range Correlations in Inclusive Scattering with Tritium and Helium-3

The nucleon-nucleon (NN) potential has a strong repulsive core. When a two-nucleon (sub)system falls into this range, they will interact strongly at short distance, and move away from each other with momenta above the Fermi level. This is called the NN Short-range Correlations (SRCs). Previous experiments reported a neutron-proton pair (isosinglet) dominance in high-momentum nucleons. In inclusive electron scattering, this np dominance will cause a scaling behavior of cross sections at 1.

Effect of a titanium nitride interlayer on the densification, properties and microstructure of cermets based on alumina and nickel. Part 2: Microstructures

SEM microstructural analyses in conjunction with EDX and TEM microstructural analyses have been conducted with cermets based on nickel and alumina, the latter as such and with a chemical-vapour-deposited titanium nitride layer. It has been proved that there is excellent bonding at both the Al2O3/TiN and the TiN/Ni interface, whereas Al2O3 and Ni do not adhere to each other. This is the reason for the observation that the mechanical properties as well as the densification of cermets consisting of Al2O3 and Ni are enhanced by applying a TiN interlayer between the ceramic phase and the metallic phase

Joint Training for Neural Machine Translation Models with Monolingual Data

Monolingual data have been demonstrated to be helpful in improving translation quality of both statistical machine translation (SMT) systems and neural machine translation (NMT) systems, especially in resource-poor or domain adaptation tasks where parallel data are not rich enough. In this paper, we propose a novel approach to better leveraging monolingual data for neural machine translation by jointly learning source-to-target and target-to-source NMT models for a language pair with a joint EM optimization method. The training process starts with two initial NMT models pre-trained on parallel data for each direction, and these two models are iteratively updated by incrementally decreasing translation losses on training data. In each iteration step, both NMT models are first used to translate monolingual data from one language to the other, forming pseudo-training data of the other NMT model. Then two new NMT models are learnt from parallel data together with the pseudo training data. Both NMT models are expected to be improved and better pseudo-training data can be generated in next step. Experiment results on Chinese-English and English-German translation tasks show that our approach can simultaneously improve translation quality of source-to-target and target-to-source models, significantly outperforming strong baseline systems which are enhanced with monolingual data for model training including back-translation.Comment: Accepted by AAAI 201