Deep Clustering with Self-supervision using Pairwise Data Similarities

<div>Deep clustering incorporates embedding into clustering to find a lower-dimensional space appropriate for clustering. In this paper we propose a novel deep clustering framework with self-supervision using pairwise data similarities (DCSS). The proposed method consists of two successive phases. In the first phase we propose to form hypersphere-like groups of similar data points, i.e. one hypersphere per cluster, employing an autoencoder which is trained using cluster-specific losses. The hyper-spheres are formed in the autoencoder’s latent space. In the second phase, we propose to employ pairwise data similarities to create a K-dimensional space that is capable of accommodating more complex cluster distributions; hence, providing more accurate clustering performance. K is the number of clusters. The autoencoder’s latent space obtained in the first phase is used as the input of the second phase. Effectiveness of both phases are demonstrated on seven benchmark datasets through conducting a rigorous set of experiments.</div>

Jensen–Shannon Divergence-Based k-Medoids Clustering

Several conventional clustering methods use the squared L2-norm as the dissimilarity. The squared L2-norm is calculated from only the object coordinates and obtains a linear cluster boundary. To extract meaningful cluster partitions from a set of massive objects, it is necessary to obtain cluster partitions that consisting of complex cluster boundaries. In this study, a JS-divergence-based k-medoids (JSKMdd) is proposed. In the proposed method, JS-divergence, which is calculated from the object distribution, is considered as the dissimilarity. The object distribution is estimated from kernel density estimation to calculate the dissimilarity based on both the object coordinates and their neighbors. Numerical experiments were conducted using five artificial datasets to verify the effectiveness of the proposed method. In the numerical experiments, the proposed method was compared with the k-means clustering, k-medoids clustering, and spectral clustering. The results show that the proposed method yields better results in terms of clustering performance than other conventional methods.

Antibacterial activity and reinforcing effect of SiO2-ZnO complex cluster fillers for dental resin composites

The accumulation of bacteria at the margin of dental resin composites is the main reason of secondary caries, which may further cause failure of prosthodontics. Therefore, antibacterial activity is highly...

Introduction: Tools for Unravelling Heritage

Collins’s theory of symbolic interactionism is here introduced to the study of landscape heritage. His method for unravelling symbols in society can be profitably used to identify a signature of prestige indicating centres of attraction or civilizational poles charged with strong magnetism. The activation of that signature occurs through three levels of social circulation that culminate in the inclusion of symbols in the internal conversation of individuals. In the case of the Dolomites, the complex cluster of symbolic ingredients emerging in their heritage formation oscillates between competing zones of civilizational prestige – Venice and its Romantic aura, Switzerland and its Alpine sensationalism, Austria and its Germanic folklore, London and its cosmopolitan modernity – coexisting today in a multi-layered heritage, re-enacted, at various levels, through the interplay between different imaginative and contested geographies.

The Commenda

This chapter discusses the characteristics of the commenda, its origins, and its migration. The commenda was much more than a simple profit-sharing contract. It was a complex cluster of contracts that was not likely to have been invented casually and endemically. The chapter provides a survey of the migration of the commenda, which creates the discrete bodies of literature that discuss the appearances of the commenda in various regions of Eurasia. It talks about how the commenda is not an employment or a loan contract as the payoffs are not determined ex ante but rather ex post. It is an equity investment. The traveler does not receive a predetermined wage or interest but rather a share in the profits.

Cluster characteristics of waters in terms of their total antioxidant activity

The article presents the results of a biochemical study of aqueous media in terms of antioxidant activity, which was studied by coulometric analysis using electrogenerated bromine, the samples were analyzed on the coulometer "Expert-006" (LLC "Econix-Expert", Russia) by a certified method. Modern ideas about the properties of water are based on its cluster structure, which is devoted to a significant number of theoretical and experimental works. They indicate that the structure of water is formed by clusters that are constructed from water molecules held by hydrogen bonds. The 9 clusters of water differing in a set of characteristics are allocated. It is shown that oligotrophic waters with low organic matter content and aligned and simplified community structure of aquatic organisms characterized by a complex cluster structure of water and the lowest values of antioxidant activity and water production characteristics differ by a stable, simplified cluster structure with high values of antioxidant activity of the water. Following the research it is revealed that, water has various cluster structure. The most complex in terms of cluster composition is spring water (5 clusters) and artesian water (4 clusters). The most simple composition was the water of the seas and fisheries ponds-1-2 clusters. It was discovered, that oligotrophic waters with low organic matter content and aligned or simplified community structure of aquatic organisms characterized by a complex cluster structure of water and the lowest values of antioxidant activity and water production characteristics differ by a stable, simplified cluster structure with high values of antioxidant activity of the water.

Examining the efficacy of complex clusters in real words and nonwords during the treatment of speech sound disorders

This study compared the treatment efficacy of real words (RW) and non-words (NW) containing complex consonant clusters. Using a single-subject multiple baseline design, six children with speech sound disorders (SSD) (5;3 to 6;2) were split into two treatment groups (RW and NW). Children taught clusters in RWs had more accurate productions and greater generalization of their treatment target, while children in the NW treatment resulted in a greater decrease in error variants. Both groups demonstrated similar changes in their overall percent consonants correct and untreated cluster production accuracy. Thus, complex cluster targets in both RWs and NWs should be considered to be efficient and efficacious options when providing SSD treatment. Since children demonstrated widespread phonological change despite often lacking all cluster segments in their phonemic inventories, previous suggestions of complex cluster target selection may need to be revisited.