Effect of size, morphology and crystallinity of seed crystal on the nucleation and growth of single grain Y-Ba-Cu-O

The effect of size, morphology and crystallinity of seed crystals on the nucleation and growth of large grain Y-Ba-Cu-O (YBCO) bulk superconductors fabricated by top seeded melt growth (TSMG) has been investigated. Seeding bulk samples with small, square shaped seed crystals leads to point nucleation and growth of the superconducting YBa2Cu3O7-y (Y-123) phase that exhibits the usual square habitual growth symmetry. The use of triangular and circular shaped seed crystals, however, modifies significantly the growth habit geometry of the grain. The use of large area seeds both increases the rate of epitaxial nucleation of the Y-123 phase and produces relatively large crystals in the incongruent melt, which decreases significantly the processing times of large grain samples. The present study is relevant to decrease processing times of samples with both preferred or no growth sectors and for multiple seeding of large grain samples which contain clean grain boundaries. © 2005 Published by Elsevier Ltd.

Natural versus artificial scene classification by ordering discrete fourier power spectra

Holistic representations of natural scenes is an effective and powerful source of information for semantic classification and analysis of arbitrary images. Recently, the frequency domain has been successfully exploited to holistically encode the content of natural scenes in order to obtain a robust representation for scene classification. In this paper, we present a new approach to naturalness classification of scenes using frequency domain. The proposed method is based on the ordering of the Discrete Fourier Power Spectra. Features extracted from this ordering are shown sufficient to build a robust holistic representation for Natural vs. Artificial scene classification. Experiments show that the proposed frequency domain method matches the accuracy of other state-of-the-art solutions. © 2008 Springer Berlin Heidelberg.

Cross-Lingual spoken language understanding from unaligned data using discriminative classification models and machine translation

This paper investigates several approaches to bootstrapping a new spoken language understanding (SLU) component in a target language given a large dataset of semantically-annotated utterances in some other source language. The aim is to reduce the cost associated with porting a spoken dialogue system from one language to another by minimising the amount of data required in the target language. Since word-level semantic annotations are costly, Semantic Tuple Classifiers (STCs) are used in conjunction with statistical machine translation models both of which are trained from unaligned data to further reduce development time. The paper presents experiments in which a French SLU component in the tourist information domain is bootstrapped from English data. Results show that training STCs on automatically translated data produced the best performance for predicting the utterance's dialogue act type, however individual slot/value pairs are best predicted by training STCs on the source language and using them to decode translated utterances. © 2010 ISCA.