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3. Atomic-level study of twin–twin interactions in hexagonal metals

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   Gong, Mingyu; Wu, Wenqian (July 2020, Journal of Materials Research)

   null (Ed.)

   Twin–twin interactions (TTIs) take place when multiple twinning modes and/or twin variants are activated and interact with each other. Twin–twin junctions (TTJs) form and affect subsequent twinning/detwinning and dislocation slip, which is particularly important in determining mechanical behavior of hexagonal metals because twinning is one major deformation mode. Atomic-level study, including crystallographic analysis, transmission electronic microscopy (TEM), and molecular dynamics (MD) simulations, can provide insights into understanding the process of TTIs and structural characters associated with TTJs. Crystallographic analysis enables the classification of TTIs and the prediction of possible interfaces of twin–twin boundaries (TTBs), characters of boundary dislocations, and possible reactions of twinning dislocations and lattice dislocations at TTBs. MD simulations can explore the process of TTIs, microstructures of TTJs, atomic structures of TTBs, and stress fields associated with TTJs. The predictions based on crystallographic analysis and the findings from MD can be partially verified by TEM. More importantly, these results provide explanation for microstructural characters of TTJs and guidance for further TEM characterizations. 

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5. Twin Auxiliary Classifiers GAN

   Mingming Gong, Yanwu Xu (January 2019, Advances in neural information processing systems)

   null (Ed.)

   Conditional generative models enjoy remarkable progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier. However, the diversity of the generated samples by AC-GAN tends to decrease as the number of classes increases, hence limiting its power on large-scale data. In this paper, we identify the source of the low diversity issue theoretically and propose a practical solution to solve the problem. We show that the auxiliary classifier in AC-GAN imposes perfect separability, which is disadvantageous when the supports of the class distributions have significant overlap. To address the issue, we propose Twin Auxiliary Classifiers Generative Adversarial Net (TAC-GAN) that further benefits from a new player that interacts with other players (the generator and the discriminator) in GAN. Theoretically, we demonstrate that TAC-GAN can effectively minimize the divergence between the generated and real-data distributions. Extensive experimental results show that our TAC-GAN can successfully replicate the true data distributions on simulated data, and significantly improves the diversity of class-conditional image generation on real datasets. 

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