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The motivation for this paper is to present a way to verify if an utterance within a corpus is pronounced at a fast or slow pace. An alternative method to the well-known Word-Per-Minute (wpm) method for cases where this approach is not applicable. For long segmentations, such as the full introduction section of a speech or presentation, the measurement of wpm is a viable option. For short comparisons of the same single word or multiple syllables, Syllables-Per-Second (sps) is also a viable option. However, when there are multiple short utterances that are frequent in task oriented dialogues or natural free flowing conversation, such as those of the direct Human-to-Human dialogues of the HCRC Map Task corpus or the computer mediated inter-lingual dialogues of the ILMT-s2s corpus, it becomes difficult to obtain a meaningful value for the utterance speech rate. In this paper we explain the method used to provide a alternative speech rate value to the utterance of the ILMT-s2s corpus and the HCRC Map Task corpus.

Understanding how cells use complex transcriptional programs to alter their fate in response to specific stimuli is an important question in biology. For the MCF-7 human breast cancer cell line, we applied gene expression trajectory models to identify the genes involved in driving cell fate transitions. We modified trajectory models to account for the scenario where cells were exposed to different stimuli, in this case epidermal growth factor and heregulin, to arrive at different cell fates, i.e. proliferation and differentiation respectively. Using genome-wide CAGE time series data collected from the FANTOM5 consortium, we identified the sets of promoters that were involved in the transition of MCF-7 cells to their specific fates versus those with expression changes that were generic to both stimuli. Of the 1,552 promoters identified, 1,091 had stimulus-specific expression while 461 promoters had generic expression profiles over the time course surveyed. Many of these stimulus-specific promoters mapped to key regulators of the ERK (extracellular signal-regulated kinases) signaling pathway such as FHL2 (four and a half LIM domains 2). We observed that in general, generic promoters peaked in their expression early on in the time course, while stimulus-specific promoters tended to show activation of their expression at a later stage. The genes that mapped to stimulus-specific promoters were enriched for pathways that control focal adhesion, p53 signaling and MAPK signaling while generic promoters were enriched for cell death, transcription and the cell cycle. We identified 162 genes that were controlled by an alternative promoter during the time course where a subset of 37 genes had separate promoters that were classified as stimulus-specific and generic. The results of our study highlighted the degree of complexity involved in regulating a cell fate transition where multiple promoters mapping to the same gene can demonstrate quite divergent expression profiles.

Multi-view clustering, which explores complementary \ud information between multiple distinct feature sets, has received considerable attention. For accurate clustering, all data with the same label should be clustered together regardless of their multiple views. However, this is not guaranteed in existing approaches. To address this issue, we propose Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization (AMVNMF), which uses label information as hard constraints to ensure data with same label are clustered together, so that the discriminating power of new representations are enhanced. Besides, AMVNMF provides a viable solution to learn the weight of each view adaptively\ud with only a single parameter. Using L2,1-norm, AMVNMF is also robust to noises and outliers. We further develop an efficient iterative algorithm for solving the optimization problem. Experiments carried out on five well-known datasets have demonstrated the effectiveness of AMVNMF\ud in comparison to other existing state-of-the-art approaches in terms of accuracy and normalized mutual information.