Semi-supervised clustering with deep metric learning and graph embedding

As a common technology in social network, clustering has attracted lots of research interest due to its high performance, and many clustering methods have been presented. The most of existing clustering methods are based on unsupervised learning. In fact, we usually can obtain some/few labeled samples in real applications. Recently, several semi-supervised clustering methods have been proposed, while there is still much space for improvement. In this paper, we aim to tackle two research questions in the process of semi-supervised clustering: (i) How to learn more discriminative feature representations to boost the process of the clustering; (ii) How to effectively make use of both the labeled and unlabeled data to enhance the performance of clustering. To address these two issues, we propose a novel semi-supervised clustering approach based on deep metric learning (SCDML) which leverages deep metric learning and semi-supervised learning effectively in a novel way. To make the extracted features of the contribution of data more representative and the label propagation network more suitable for real applications, we further improve our approach by adopting triplet loss in deep metric learning network and combining bedding with label propagation strategy to dynamically update the unlabeled to labeled data, which is named as semi-supervised clustering with deep metric learning and graph embedding (SCDMLGE). SCDMLGE enhances the robustness of metric learning network and promotes the accuracy of clustering. Substantial experimental results on Mnist, CIFAR-10, YaleB, and 20-Newsgroups benchmarks demonstrate the high effectiveness of our proposed approaches

Bis(pyridine-κN)bis[4,4,4-trifluoro-1-(4-fluorophenyl)butane-1,3-dionato-κ2O,O′]cobalt(II)

In the structure of the title compound, [Co(C10H5F4O2)2(C5H5N)2], cobalt(II) forms a complex with two 4,4,4-trifluoro-1-(4-fluorophenyl)butane-1,3-dionate anions and two pyridine molecules in an octahedral coordination environment, where the two dionate ligands are in equatorial positions and the two pyridine molecules in axial positions. The complex is located on a crystallographic inversion centre

Semi-supervised Clustering with Deep Metric Learning

Semi-supervised clustering has attracted lots of reserach interest due to its broad applications, and many methods have been presented. However there is still much space for improvement, (1) How to learn more discriminative feature representations to assist the traditional clustering methods; (2) How to make use of both the labeled and unlabelled data simultaneously and effectively during the process of clustering. To address these issues, we propose a novel semi-supervised clustering based on deep metric learning, namely SSCDML. By leveraging deep metric learning and semi-supervised learning effectively in a novel way, SSCDML dynamically update the unlabelled to labeled data through the limited labeled samples and obtain more meaningful data features, which make the classifier model more robust and the clustering results more accurate. Experimental results on Mnist, YaleB, and 20 Newsgroups databases demonstrate the high effectiveness of our proposed approach

The Molecular Epidemiological Characteristics and Genetic Diversity of <i>Salmonella</i> Typhimurium in Guangdong, China, 2007–2011

<div><p>Background</p><p><i>Salmonella enterica</i> serovar Typhimurium is the most important serovar associated with human salmonellosis worldwide. Here we aimed to explore the molecular epidemiology and genetic characteristics of this serovar in Guangdong, China.</p><p>Methodology</p><p>We evaluated the molecular epidemiology and genetic characteristics of 294 endemic <i>Salmonella</i> Typhimurium clinical isolates which were collected from 1977 to 2011 in Guangdong, China, and compared them with a global set of isolates of this serovar using epidemiological data and Multilocus Sequence Typing (MLST) analysis.</p><p>Principal Finding</p><p>The 294 isolates were assigned to 13 Sequencing types (STs) by MLST, of which ST34 and ST19 were the most common in Guangdong. All the STs were further assigned to two eBurst Groups, eBG1 and eBG138. The eBG1 was the major group endemic in Guangdong. Nucleotide and amino acid variability were comparable for all seven MLST loci. Tajima’s D test suggested positive selection in <i>hisD</i> and <i>thrA</i> genes (p<0.01), but positive selection was rejected for the five other genes (p>0.05). In addition, The Tajima’s D test within each eBG using the global set of isolates showed positive selection in eBG1 and eBG138 (p<0.05), but was rejected in eBG243 (p>0.05). We also analyzed the phylogenetic structure of <i>Salmonella</i> Typhimurium from worldwide sources and found that certain STs are geographically restricted. ACSSuT was the predominant multidrug resistance pattern for this serovar. The resistant profiles ACSSuTTmNaG, ACSSuTTmNa and ACSuTTmNaG seem to be specific for ST34, and ASSuTNa for ST19.</p><p>Conclusion</p><p>Here we presented a genotypic characterization of <i>Salmonella</i> Typhimurium isolates using MLST and found two major STs are endemic in Guangdong. Our analyses indicate that genetic selection may have shaped the <i>Salmonella</i> Typhimurium populations. However, further evaluation with additional isolates from various sources will be essential to reveal the scope of the epidemiological characteristics of <i>Salmonella</i> Typhimurium in Guangdong, China.</p></div

Application of geo-statistics in calculation of a uranium deposit

Based on the 3DMine three-dimensional mining software, a geological database model of the Changpai area is established, and a three-dimensional model of the ore body is established based on the principle and geological understanding of the circle. We carried out sample combination by a geological database model to process ore bodies of erratic high grades. We also stablished a block model, and used single assignment and ordinary Kriging method to assign value to the block model, used attribute visualization to intuitively reflect the distribution law of the ore grade in a three-dimensional space, and the block report can quickly estimate the grade, volume, ore quantity, etc., of which the results are more accurate. The common Kriging method has obvious advantages in valuation, and it is worthy for further promotion in the estimation of uranium reserves

Polymorphism summary and tests for neutral evolution in each locus of the Guangdong isolates and the global set of strains.

<p>a/b: the Guangdong isolates/the Global set of isolates.</p><p>Polymorphism summary and tests for neutral evolution in each locus of the Guangdong isolates and the global set of strains.</p

minimal spanning tree (MSTree) of the MLST data of <i>S. enterica</i> serovar Typhimurium.

<p>Each circle corresponds to each STs with the size proportional to the number of isolates. The topological arrangement within the MSTree is dictated by its graphic algorithm, which uses an iterative network approach to identify sequential links of increasing distance beginning with central STs that contain the largest numbers of isolates. As a result, singleton STs are scattered throughout the MSTree proximal to the first node that was encountered with shared alleles, even if equal levels of identity to other nodes that are distant within the MSTree exist. The figure only shows the links of six identical gene fragments (SLVs, thick black line) and five identical gene fragments (DLVs, thin black line) because these correlate with eBGs, which are indicated by grey shading. The key shows the isolates from Guangdong or Asia.</p

A consensus ClonalFrame tree constructed using ClonalFrame 1.1 in this study (ST = 47).

<p>The groups were marked using assigned clusters from the neighbor joining and minimal spanning trees.</p