Multi log-normal density structure in Cygnus-X molecular clouds: A fitting for N-PDF without power-law

We studied the H$_2$ column density probability distribution function (N-PDF) based on molecular emission lines using the Nobeyama 45-m Cygnus X CO survey data. Using the DENDROGRAM and SCIMES algorithms, we identified 124 molecular clouds in the $^{13}$CO data. From these identified molecular clouds, an N-PDF was constructed for 11 molecular clouds with an extent of more than 0.4 deg$^2$. From the fitting of the N-PDF, we found that the N-PDF could be well-fitted with one or two log-normal distributions. These fitting results provided an alternative density structure for molecular clouds from a conventional picture. We investigated the column density, dense molecular cloud cores, and radio continuum source distributions in each cloud and found that the N-PDF shape was less correlated with the star-forming activity over a whole cloud. Furthermore, we found that the log-normal N-PDF parameters obtained from the fitting showed two impressive features. First, the log-normal distribution at the low-density part had the same mean column density ($\sim$ 10$^{21.5}$ cm$^{-2}$) for almost all the molecular clouds. Second, the width of the log-normal distribution tended to decrease with an increasing mean density of the structures. These correlations suggest that the shape of the N-PDF reflects the relationship between the density and turbulent structure of the whole molecular cloud but is less affected by star-forming activities.Comment: 14 pages, 7 Figures, Accepted in MNRA

An Extensible Dialogue Script for a Robot Based on Unification of State-Transition Models

We propose extension-by-unification method to improve reusability of the dialogue components in the development of communication function of the robot. Compared to previous extension-by-connection method used in behavior-based communication robot developments, the extension-by-unification method has the ability to decompose the script into components. The decomposed components can be recomposed to build a new application easily. In this paper, first we, explain a reformulation we have applied to the conventional state-transition model. Second, we explain a set of algorithms to decompose, recompose, and detect the conflict of each component. Third, we explain a dialogue engine and a script management server we have developed. The script management server has a function to propose reusable components to the developer in real time by implementing the conflict detection algorithm. The dialogue engine SEAT (Speech Event-Action Translator) has flexible adapter mechanism to enable quick integration to robotic systems. We have confirmed that by the application of three robots, development efficiency has improved by 30%

Tracking Intermittently Speaking Multiple Speakers Using a Particle Filter

<p/> <p>The problem of tracking multiple intermittently speaking speakers is difficult as some distinct problems must be addressed. The number of active speakers must be estimated, these active speakers must be identified, and the locations of all speakers including inactive speakers must be tracked. In this paper we propose a method for tracking intermittently speaking multiple speakers using a particle filter. In the proposed algorithm the number of active speakers is firstly estimated based on the Exponential Fitting Test (EFT), a source number estimation technique which we have proposed. The locations of the speakers are then tracked using a particle filtering framework within which the decomposed likelihood is used in order to decouple the observed audio signal and associate each element of the decomposed signal with an active speaker. The tracking accuracy is then further improved by the inclusion of a silence region detection step and estimation of the noise-only covariance matrix. The method was evaluated using live recordings of 3 speakers and the results show that the method produces highly accurate tracking results.</p

Tracking Intermittently Speaking Multiple Speakers Using a Particle Filter

The problem of tracking multiple intermittently speaking speakers is difficult as some distinct problems must be addressed. The number of active speakers must be estimated, these active speakers must be identified, and the locations of all speakers including inactive speakers must be tracked. In this paper we propose a method for tracking intermittently speaking multiple speakers using a particle filter. In the proposed algorithm the number of active speakers is firstly estimated based on the Exponential Fitting Test (EFT), a source number estimation technique which we have proposed. The locations of the speakers are then tracked using a particle filtering framework within which the decomposed likelihood is used in order to decouple the observed audio signal and associate each element of the decomposed signal with an active speaker. The tracking accuracy is then further improved by the inclusion of a silence region detection step and estimation of the noise-only covariance matrix. The method was evaluated using live recordings of 3 speakers and the results show that the method produces highly accurate tracking results

Higher methylation subtype of malignant melanoma and its correlation with thicker progression and worse prognosis

Abstract Malignant melanoma (MM) is the most life‐threatening disease among all skin malignancies, and recent genome‐wide studies reported BRAF, RAS, and NF1 as the most frequently mutated driver genes. While epigenetic aberrations are known to contribute to the oncogenic activity seen in various cancers, their role in MM has not been fully investigated. To investigate the role of epigenetic aberrations in MM, we performed genome‐wide DNA methylation analysis of 51 clinical MM samples using Infinium 450k beadarray. Hierarchical clustering analysis stratified MM into two DNA methylation epigenotypes: high‐ and low‐methylation subgroups. Tumor thickness was significantly greater in case of high‐methylation tumors than low‐methylation tumors (8.3 ± 5.3 mm vs 4.5 ± 2.9 mm, P = .003). Moreover, prognosis was significantly worse in high‐methylation cases (P = .03). Twenty‐seven genes were found to undergo significant and frequent hypermethylation in high‐methylation subgroup, where TFPI2 was identified as the most frequently hypermethylated gene. MM cases with lower expression levels of TFPI2 showed significantly worse prognosis (P = .001). Knockdown of TFPI2 in two MM cell lines, CHL‐1 and G361, resulted in significant increases of cell proliferation and invasion. These indicate that MM can be stratified into at least two different epigenetic subgroups, that the MM subgroup with higher DNA methylation shows a more progressive phenotype, and that methylation of TFPI2 may contribute to the tumor progression of MM