Schottky nanocontacts on ZnO nanorod arrays

We report on fabrication and electrical characteristics of ZnO nanorod Schottky diode arrays. High quality ZnO nanorods were grown for the fabrication of the Schottky diodes using noncatalytic metalorganic vapor phase epitaxy and Au was evaporated on the tips of the vertically well-aligned ZnO nanorods. I-V characteristics of both bare ZnO and Au/ZnO heterostructure nanorod arrays were measured using current-sensing atomic force microscopy. Although both nanorods exhibited nonlinear and asymmetric I-V characteristic curves, Au/ZnO heterostructure nanorods demonstrated much improved electrical characteristics: the reverse-bias breakdown voltage was improved from -3 to -8 V by capping a Au layer on the nanorod tips. The origin of the enhanced electrical characteristics for the heterostructure nanorods is suggested. (C) 2003 American Institute of Physics.X11326sciescopu

Adversarial Sampling and Training for Semi-Supervised Information Retrieval

Ad-hoc retrieval models with implicit feedback often have problems, e.g., the imbalanced classes in the data set. Too few clicked documents may hurt generalization ability of the models, whereas too many non-clicked documents may harm effectiveness of the models and efficiency of training. In addition, recent neural network-based models are vulnerable to adversarial examples due to the linear nature in them. To solve the problems at the same time, we propose an adversarial sampling and training framework to learn ad-hoc retrieval models with implicit feedback. Our key idea is (i) to augment clicked examples by adversarial training for better generalization and (ii) to obtain very informational non-clicked examples by adversarial sampling and training. Experiments are performed on benchmark data sets for common ad-hoc retrieval tasks such as Web search, item recommendation, and question answering. Experimental results indicate that the proposed approaches significantly outperform strong baselines especially for high-ranked documents, and they outperform IRGAN in NDCG@5 using only 5% of labeled data for the Web search task.Comment: Published in WWW 201