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Determining the Optimal Number of Signal Clusters Using Iterative HMM Classification
Duker Ernest Junior,Yoon Joong Kim 한국인터넷방송통신학회 2018 Journal of Advanced Smart Convergence Vol.7 No.2
In this study, we propose an iterative clustering algorithm that automatically clusters a set of voice signal data without a label into an optimal number of clusters and generates hmm model for each cluster. In the clustering process, the likelihood calculations of the clusters are performed using iterative hmm learning and testing while varying the number of clusters for given data, and the maximum likelihood estimation method is used to determine the optimal number of clusters. We tested the effectiveness of this clustering algorithm on a small-vocabulary digit clustering task by mapping the unsupervised decoded output of the optimal cluster to the ground-truth transcription, we found out that they were highly correlated.
Determining the Optimal Number of Signal Clusters Using Iterative HMM Classification
Ernest, Duker Junior,Kim, Yoon Joong The Institute of Internet 2018 International journal of advanced smart convergenc Vol.7 No.2
In this study, we propose an iterative clustering algorithm that automatically clusters a set of voice signal data without a label into an optimal number of clusters and generates hmm model for each cluster. In the clustering process, the likelihood calculations of the clusters are performed using iterative hmm learning and testing while varying the number of clusters for given data, and the maximum likelihood estimation method is used to determine the optimal number of clusters. We tested the effectiveness of this clustering algorithm on a small-vocabulary digit clustering task by mapping the unsupervised decoded output of the optimal cluster to the ground-truth transcription, we found out that they were highly correlated.
Colin M. Dinney,Lu-Dong Zhao,Charles D. Conrad,Jay M. Duker,Richard O. Karas,Zhibin Hu,Michele A. Hamilton,Thomas R. Gillis,Thomas M. Parker,Bing Fan,Andrew H. Advani,Fred B. Poordad,Paulette L. Fauce 한국미생물학회 2015 The journal of microbiology Vol.53 No.10
Chronic HBV infection is the leading cause of liver cirrhosis and hepatic cancer, but the individual responses toward HBV infection are highly variable, ranging from asymptomatic to chronic active hepatitis B inflammation. In this study, we hypothesized that the different individual responses to HBV infection was associated with differences in HBV-specific CD8+ T cell-mediated inflammation and cytotoxicity. Blood samples were collected from subjects with asymptomatic HBV-infection, subjects undergoing active chronic HBV flares (active CHB), and subjects with HBV-infected hepatocellular carcinoma (HBV-HCC). By tetramer staining, we found that all three groups had similar frequencies of HBVspecific CD8+ T cells. However, after HBV peptide stimulation, the HBV-specific CD8+ T cells in asymptomatic subjects had significantly stronger interferon gamma (IFN-γ), tumor necrosis factor alpha (TNF-α), and CD107a expression than those in active CHB and HBV-HCC patients. Examination of surface marker expression revealed that the PD-1-Tim-3- double-negative cell population was the main contributor to HBV-specific inflammation. In active CHB patients and HBV-HCC patients, however, the frequencies of activated PD-1-Tim-3- cells were significantly reduced. Moreover, the serum HBV DNA titer was not correlated with the frequencies of HBV-specific CD8+ T cells but was inversely correlated with the frequencies of IFN-g-expressing and CD107a-express cells in response to HBV stimulation. Together, our data demonstrated that the status of HBVspecific CD8+ T cell exhaustion was associated with different clinical outcomes of chronic HBV infection.