Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity

This paper presents a new approach for unsupervised Spoken Term Detection with spoken queries using multiple sets of acoustic patterns automatically discovered from the target corpus. The different pattern HMM configurations(number of states per model, number of distinct models, number of Gaussians per state)form a three-dimensional model granularity space. Different sets of acoustic patterns automatically discovered on different points properly distributed over this three-dimensional space are complementary to one another, thus can jointly capture the characteristics of the spoken terms. By representing the spoken content and spoken query as sequences of acoustic patterns, a series of approaches for matching the pattern index sequences while considering the signal variations are developed. In this way, not only the on-line computation load can be reduced, but the signal distributions caused by different speakers and acoustic conditions can be reasonably taken care of. The results indicate that this approach significantly outperformed the unsupervised feature-based DTW baseline by 16.16\% in mean average precision on the TIMIT corpus.Comment: Accepted by ICASSP 201

Influence of Stress Treatments on the Resistance of \u3cem\u3eLactococcus lactis\u3c/em\u3e to Freezing and Freeze-Drying

This study investigated the effect of cold, heat, or osmotic shock treatment on the resistance of L. lactis subsp. cremoris MM160 and MM310 and Lactococcus lactis subsp. lactis MM210 and FG2 cheese starter bacteria to freezing and freeze-drying. The ability to withstand freezing at -60°C for 24 h was variable among lactococci, but resistance to this treatment was significantly improved (P \u3c 0.05) in most strains by a 2-h cold shock at l0°C or a 25-min heat shock at 39°C (L. lactis subsp. cremoris) or 42°C (L. lactis subsp. lactis). Stress treatments that improved lactococcal freeze resistance were also found to significantly (P \u3c 0.05) enhance the resistance of most strains to lyophilization. Increased resistance to freezing or lyophilization was not detected when stress treatments were performed in broth that contained erythromycin, which indicated stress-inducible proteins were involved in cell protection. Membrane fatty acid analysis of stress-treated cells suggested that enhanced resistance to freezing and lyophilization may be related to heat or cold shock-induced changes in cell membrane composition. Heat-shocked cells had a higher 19:0 cyclopropane fatty acid content than did control cells, and cold-shocked cells contained a lower ratio of saturated to unsaturated fatty acids. Other factors must also be involved in cell protection, however, because similar changes in membrane composition were also detected in strains whose resistance to freezing and lyophilization was not improved by heat or cold shock

Projected Density Matrix Embedding Theory with Applications to the Two-Dimensional Hubbard Model

Density matrix embedding theory (DMET) is a quantum embedding theory for strongly correlated systems. From a computational perspective, one bottleneck in DMET is the optimization of the correlation potential to achieve self-consistency, especially for heterogeneous systems of large size. We propose a new method, called projected density matrix embedding theory (p-DMET), which achieves self-consistency without needing to optimize a correlation potential. We demonstrate the performance of p-DMET on the two-dimensional Hubbard model.Comment: 25 pages, 8 figure