Fabrication, modeling, and characterization of form-birefringent nanostructures

A 490-nm-deep nanostructure with a period of 200 nm was fabricated in a GaAs substrate by use of electron-beam lithography and dry-etching techniques. The form birefringence of this microstructure was studied numerically with rigorous coupled-wave analysis and compared with experimental measurements at a wavelength of 920 nm. The numerically predicted phase retardation of 163.3 degrees was found to be in close agreement with the experimentally measured result of 162.5 degrees, thereby verifying the validity of our numerical modeling. The fabricated microstructures show extremely large artificial anisotropy compared with that available in naturally birefringent materials and are useful for numerous polarization optics applications

Privacy preserving path recommendation for moving user on location based service

With the increasing adoption of location based services, privacy is becoming a major concern. To hide the identity and location of a request on location based service, most methods consider a set of users in a reasonable region so as to confuse their requests. When there are not enough users, the cloaking region needs expanding to a larger area or the response needs delay. Either way degrades the quality-of-service. In this paper, we tackle the privacy problem in a predication way by recommending a privacy-preserving path for a requester. We consider the popular navigation application, where users may continuously query different location based servers during their movements. Based on a set of metrics on privacy, distance and the quality of services that a LBS requester often desires, a secure path is computed for each request according to user's preference, and can be dynamically adjusted when the situation is changed. A set of experiments are performed to verify our method and the relationship between parameters are discussed in details. We also discuss how to apply our method into practical applications. © 2013 IEEE.published_or_final_versio

Semi-Supervised Learning for Neural Machine Translation

While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low-resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semi-supervised approach for training NMT models on the concatenation of labeled (parallel corpora) and unlabeled (monolingual corpora) data. The central idea is to reconstruct the monolingual corpora using an autoencoder, in which the source-to-target and target-to-source translation models serve as the encoder and decoder, respectively. Our approach can not only exploit the monolingual corpora of the target language, but also of the source language. Experiments on the Chinese-English dataset show that our approach achieves significant improvements over state-of-the-art SMT and NMT systems.Comment: Corrected a typ