Sequence to Sequence Mixture Model for Diverse Machine Translation

Sequence to sequence (SEQ2SEQ) models often lack diversity in their generated translations. This can be attributed to the limitation of SEQ2SEQ models in capturing lexical and syntactic variations in a parallel corpus resulting from different styles, genres, topics, or ambiguity of the translation process. In this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that improves both translation diversity and quality by adopting a committee of specialized translation models rather than a single translation model. Each mixture component selects its own training dataset via optimization of the marginal loglikelihood, which leads to a soft clustering of the parallel corpus. Experiments on four language pairs demonstrate the superiority of our mixture model compared to a SEQ2SEQ baseline with standard or diversity-boosted beam search. Our mixture model uses negligible additional parameters and incurs no extra computation cost during decoding.Comment: 11 pages, 5 figures, accepted to CoNLL201

Fast Exact Search in Hamming Space with Multi-Index Hashing

There is growing interest in representing image data and feature descriptors using compact binary codes for fast near neighbor search. Although binary codes are motivated by their use as direct indices (addresses) into a hash table, codes longer than 32 bits are not being used as such, as it was thought to be ineffective. We introduce a rigorous way to build multiple hash tables on binary code substrings that enables exact k-nearest neighbor search in Hamming space. The approach is storage efficient and straightforward to implement. Theoretical analysis shows that the algorithm exhibits sub-linear run-time behavior for uniformly distributed codes. Empirical results show dramatic speedups over a linear scan baseline for datasets of up to one billion codes of 64, 128, or 256 bits

Controlled Synthesis and Characterization of Metal Oxide Nanowires by Chemical Vapor Deposition on Silicon and Carbon Substrates

Nanotechnology and nanomaterials have attracted considerable interest and are predicted to revolutionize many materials and technologies that we use in everyday life. In the past few years, significant research has focused on one dimensional metal oxide nanostructures due to their unique properties and potential applications in various fields from nanoelectronics to energy. However, controlled synthesis of these nanostructures is still a challenge. The objective of this thesis is to synthesize metal oxide nanowires by chemical vapour deposition directly on various substrates. The nanostructures include (i) silicon oxide nanostructures on silicon substrate, (ii) manganese oxide nanostructures on silicon substrate, and (iii) manganese oxide nanostructures on carbon paper substrate. Firstly, silicon oxide nanowires were synthesized on silicon substrate by a VO2 assisted chemical vapor deposition. Networked features of silicon oxide nanowires were found. Systematic study on the nanowire growth has indicated that morphology and composition of the final products are considerably sensitive to the catalyst components, reaction atmosphere and temperature. These results will help in better understanding the growth process of silicon oxide nanowires. Secondly, manganese oxide nanostructures were synthesized on silicon substrate by chemical vapor deposition method. It was found that MnO nanowires are high density and single crystalline with average diameter of 150 nm. These nanowires were characterized using FESEM, EDX, TEM and XRD. The synthesis process and effects of growth parameters such as temperature, heating rate and source/substrate distance on the morphology, composition and structure of the products were systematically studied. Finally, manganese oxide nanostructures were synthesized on carbon paper substrate by chemical vapor deposition method. It was revealed that manganese oxide nanowires and nanobelts can be selectively grown on carbon paper substrate by using a catalyst (gold) assisted or catalyst free thermal evaporation of manganese powder under an argon gas atmosphere. Various effects of growth parameters such as temperature, catalyst and buffered substrate on the growth product were also systematically investigated by using SEM, TEM and XPS