Zero-shot learning (ZSL) aims to recognize unseen objects using disjoint seen via sharing attributes. The generalization performance of ZSL is governed by the attributes, which transfer semantic information from classes classes. To take full advantage knowledge transferred in this paper, we introduce notion complementary attributes (CAs), as a supplement original enhance representation ability....

Many clustering algorithms have been proposed to partition a set of static data points into groups. In this paper, we consider an evolutionary clustering problem where the input data points may move, disappeare, and emerge. Generally, these changes should result in a smooth evolution of the clusters. Mining this naturally smooth evolution is valuable for providing an aggregated view of the nume...

In dense clusters of neurons in nuclei, cells may interconnect via soma-to-soma interactions, in addition to conventional synaptic connections. We illustrate this idea with a multi-layer architecture (MLA) composed of multiple clusters of recurrent sub-networks of spiking Random Neural Networks (RNN) with dense soma-to-soma interactions. We use this RNN-MLA architecture for deep learning. The i...

This paper reviews an almost new method for the design of optimal decision making controllers named as Human-Behavior learning. paradigm is inspired by complementary learning that different areas human brain have to improve and experience transference. It shown independent well identified sources knowledge can enhance facilitate controller. interaction modelled a Markov Decision Process defined...

This paper examines the emergence of retail clusters on a supply chain network comprised of suppliers, retailers, and consumers. An agent-based model is proposed to investigate retail location distribution in a market of two complementary goods. The methodology controls for supplier locales and unit sales prices of retailers and suppliers; a consumer’s willingness to patronize a retailer depend...

Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns...

Fifteen small-subunit rRNAs from methylotrophic bacteria have been sequenced. Comparisons of these sequences with 22 previously published sequences further defined the phylogenetic relationships among these bacteria and illustrated the agreement between phylogeny and physiological characteristics of the bacteria. Phylogenetic trees were constructed with 16S rRNA sequences from methylotrophic ba...

We present the first high redshift (0.3 < z < 1.1) galaxy clusters found by systematically identifying optical low surface brightness fluctuations in the background sky. Using spectra obtained with the Keck telescope and I-band images from the Palomar 1.5m telescope, we conclude that at least eight of the ten candidates examined are high redshift galaxy clusters. The identification of such clus...

Automated short answer scoring is increasingly used to give students timely feedback about their learning progress. Building scoring models comes with high costs, as stateof-the-art methods using supervised learning require large amounts of hand-annotated data. We analyze the potential of recently proposed methods for semi-supervised learning based on clustering. We find that all examined metho...