Speaker Change Detection Based on the Pairwise Distance Matrix

The need for a comparative analysis of natural metagenomes stimulated the development of new methods for their taxonomic profiling. Alignment-free approaches based on the search for marker k-mers turned out to be capable of identifying not only species, but also strains of microorganisms with known genomes. Here, we evaluated the ability of genus-specific k-mers to distinguish eight phylogroups of Escherichia coli (A, B1, C, E, D, F, G, B2) and assessed the presence of their unique 22-mers in clinical samples from microbiomes of four healthy people and four patients with Crohn’s disease. We found that a phylogenetic tree inferred from the pairwise distance matrix for unique 18-mers and 22-mers of 124 genomes was fully consistent with the topology of the tree, obtained with concatenated aligned sequences of orthologous genes. Therefore, we propose strain-specific “barcodes” for rapid phylotyping. Using unique 22-mers for taxonomic analysis, we detected microbes of all groups in human microbiomes; however, their presence in the five samples was significantly different. Pointing to the intraspecies heterogeneity of E. coli in the natural microflora, this also indicates the feasibility of further studies of the role of this heterogeneity in maintaining population homeostasis.

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Cluster Analysis Stopping Rules in Stata

10.31235/osf.io/rjqe3 ◽

2016 ◽

Cited By ~ 5

Author(s):

Brendan Halpin

Keyword(s):

Cluster Analysis ◽

Distance Matrix ◽

Stopping Rules ◽

Pairwise Distance ◽

Optimum Number ◽

Pairwise Similarity ◽

Number Of Clusters ◽

Euclidean Distances ◽

New Applications ◽

Pairwise Distance Matrix

AbstractAnalysts doing cluster analysis sometimes want the data to tell them the optimum number of clusters. Common "stopping rules" use the Calinski-Harabasz pseudo-F statistic and Duda-Hart indices, which are based on squared Euclidean distances between cases. Cluster analysis operates on a pairwise matrix of distances between the objects clusters, which are usually created from the observed variables. However, approaches such as expert judgement or algorithmic pattern-recognition (as used for instance in sequence analysis) often output matrices of pairwise similarity or difference whose relationship to the observed variables is much less direct. Built-in Stata utilities allow calculation of the CH and DH indices when cluster analysis starts from variables, but not with cluster analysis that starts from a pairwise distance matrix (unless the distances are squared Euclidean distances defined on variables which are still available). In this note I present two small Stata utilities that will calculate the CH and DH statistics from the distance matrix, if the distances are squared Euclidean. If the distances have another metric, these utilities can be seen as calculating a pseudo-CH pseudo-F or pseudo-DH statistic, potentially extending their use to new applications.-- Brendan Halpin, Head, Department of Sociology, University of Limerick, IrelandTel: w +353-61-213147 f +353-61-202569 h +353-61-338562; Room F1-002 x 3147mailto:[email protected] ULSociology on Facebook: http://on.fb.me/fjIK9thttp://teaching.sociology.ul.ie/bhalpin/wordpress twitter:@ULSociology

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RAMP based fingerprinting and assessment of relationships among Australian narrow-leafed lupin (Lupinus angustifolius L.) cultivars

Australian Journal of Agricultural Research ◽

10.1071/ar05188 ◽

2005 ◽

Vol 56 (12) ◽

pp. 1339 ◽

Cited By ~ 7

Author(s):

H. Yuan ◽

G. Yan ◽

K. H. M. Siddique ◽

H. Yang

Keyword(s):

Cluster Analysis ◽

Molecular Identification ◽

Distance Matrix ◽

Farming System ◽

Pairwise Distance ◽

Grain Legume ◽

Dna Variation ◽

Legume Crop ◽

Commercial Cultivars ◽

Pairwise Distance Matrix

Narrow-leafed lupin is a major winter grain legume crop in the Australian farming system and a number of commercial cultivars are currently available to growers. A significant level of polymorphism was detected in narrow-leafed lupin cultivars by the randomly amplified microsatellite polymorphism (RAMP) approach, suggesting that cultivars harbour considerable DNA variation. Seventy-seven cultivar-specific markers were found among the 23 lupin cultivars examined and a dichotomous fingerprinting key was developed for the molecular identification of lupin cultivars. Cluster analysis of pairwise distance matrix computed from RAMP profiles grouped the 23 cultivars into 4–5 clusters, which generally agreed with their pedigree relationships.

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Exploring Evolution of Public Opinions on Tianya Club Using Dynamic Topic Models

Journal of Systems Science and Information ◽

10.21078/jssi-2020-309-16 ◽

2020 ◽

Vol 8 (4) ◽

pp. 309-324

Author(s):

Zhihua Yan ◽

Xijin Tang

Keyword(s):

Family Life ◽

Topic Model ◽

Distance Matrix ◽

Pairwise Distance ◽

Online Media ◽

Traditional Media ◽

Public Opinions ◽

Government Administration ◽

Media Data ◽

Pairwise Distance Matrix

AbstractOnline media have brought tremendous changes to civic life, public opinions, and government administration. Compared with traditional media, online media not only allow individuals to browse news and express their views more freely, but also accelerate the transmission of opinions and expand influence. As public opinions may arouse societal unrest, it is worth detecting the primary topics and uncovering the evolution trends of public opinions for societal administration. Various algorithms are developed to deal with the huge volume of unstructured online media data. In this study, dynamic topic model is employed to explore topic content evolution and prevalence evolution using the original posts published from 2013 to 2017 on the Tianya Zatan Board of Tianya Club, which is one of the most popular BBS in China. Based on semantic similarities, topics are grouped into three themes: Family life, societal affairs, and government administration. The evolution of topic prevalence and content are affected by emergent incidents. Topics on family life become popular, while themes “societal affairs” and “government administration” with bigger standard deviations are more likely to be influenced by emergent hot events. Content evolution represented by monthly pairwise distance matrix is very easy to find change points of topic content.

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Chatbot to improve learning punctuation in Spanish and to enhance open and flexible learning environments

International Journal of Educational Technology in Higher Education ◽

10.1186/s41239-021-00269-8 ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Esteban Vázquez-Cano ◽

Santiago Mengual-Andrés ◽

Eloy López-Meneses

Keyword(s):

Learning Process ◽

Latent Dirichlet Allocation ◽

Distance Matrix ◽

Ease Of Use ◽

Three Dimensions ◽

Pairwise Distance ◽

Control Group ◽

Quantitative Methodology ◽

Quasi Experimental ◽

Experimental Group

AbstractThe objective of this article is to analyze the didactic functionality of a chatbot to improve the results of the students of the National University of Distance Education (UNED / Spain) in accessing the university in the subject of Spanish Language. For this, a quasi-experimental experiment was designed, and a quantitative methodology was used through pretest and posttest in a control and experimental group in which the effectiveness of two teaching models was compared, one more traditional based on exercises written on paper and another based on interaction with a chatbot. Subsequently, the perception of the experimental group in an academic forum about the educational use of the chatbot was analyzed through text mining with tests of Latent Dirichlet Allocation (LDA), pairwise distance matrix and bigrams. The quantitative results showed that the students in the experimental group substantially improved the results compared to the students with a more traditional methodology (experimental group / mean: 32.1346 / control group / mean: 28.4706). Punctuation correctness has been improved mainly in the usage of comma, colon and periods in different syntactic patterns. Furthermore, the perception of the students in the experimental group showed that they positively value chatbots in their teaching–learning process in three dimensions: greater “support” and companionship in the learning process, as they perceive greater interactivity due to their conversational nature; greater “feedback” and interaction compared to the more traditional methodology and, lastly, they especially value the ease of use and the possibility of interacting and learning anywhere and anytime.

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