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We have produced a draft sequence of the rice genome for the most widely cultivated subspecies in China, Oryza sativa L. ssp. indica, by whole-genome shotgun sequencing. The genome was 466 megabases in size, with an estimated 46,022 to 55,615 genes. Functional coverage in the assembled sequences was 92.0%. About 42.2% of the genome was in exact(More)
Age Specific Human-Computer Interaction (ASHCI) has vast potential applications in daily life. However, automatic age estimation technique is still underdeveloped. One of the main reasons is that the aging effects on human faces present several unique characteristics which make age estimation a challenging task that requires non-standard classification(More)
Eighty-four post-1990 empirical studies of international tourism demand modeling and forecasting using econometric approaches are reviewed. New developments are identified and it is shown that applications of advanced econometric methods improve the understanding of international tourism demand. An examination of the 22 studies which compare forecasting(More)
We apply the recently proposed Context-Dependent Deep-Neural-Network HMMs, or CD-DNN-HMMs, to speech-to-text transcription. For single-pass speaker-independent recognition on the RT03S Fisher portion of phone-call transcription benchmark (Switchboard), the word-error rate is reduced from 27.4%, obtained by discriminatively trained Gaussian-mixture HMMs, to(More)
We propose a novel regularized adaptation technique for context dependent deep neural network hidden Markov models (CD-DNN-HMMs). The CD-DNN-HMM has a large output layer and many large hidden layers, each with thousands of neurons. The huge number of parameters in the CD-DNN-HMM makes adaptation a challenging task, esp. when the adaptation set is small. The(More)
Emotional expression and understanding are normal instincts of human beings, but auto-matical emotion recognition from speech without referring any language or linguistic information remains an unclosed problem. The limited size of existing emotional data samples , and the relative higher dimensionality have outstripped many dimensionality reduction and(More)
as much as one third—from 27.4%, obtained by discrimina-tively trained Gaussian-mixture HMMs with HLDA features, to 18.5%—using 300+ hours of training data (Switchboard), 9000+ tied triphone states, and up to 9 hidden network layers. In this paper, we evaluate the effectiveness of feature transforms developed for GMM-HMMs—HLDA, VTLN, and fMLLR—applied to(More)
BACKGROUND Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching(More)
We investigate back-propagation based sequence training of Context-Dependent Deep-Neural-Network HMMs, or CD-DNN-HMMs, for conversational speech transcription. Theoretically, sequence training integrates with back-propagation in a straightforward manner. However, we find that to get reasonable results, heuristics are needed that point to a problem with(More)