Clustering behaviors in networks of integrate-and-fire oscillators

Clustering behavior is studied in a model of integrate-and-fire oscillators with excitatory pulse coupling. When considering a population of identical oscillators, the main result is a proof of global convergence to a phase-locked clustered behavior. The robustness of this clustering behavior is then investigated in a population of nonidentical oscillators by studying the transition from total clustering to the absence of clustering as the group coherence decreases. A robust intermediate situation of partial clustering, characterized by few oscillators traveling among nearly phase-locked clusters, is of particular interest. The analysis complements earlier studies of synchronization in a closely related model. © 2008 American Institute of Physics.

Polarity driven formation of InAs/GaAs hierarchical nanowire heterostructures

The structural and morphological characteristics of InAs/GaAs radial nanowire heterostructures were investigated using transmission electron microscopy. It has been found that the radial growth of InAs was preferentially initiated on the { 112 } A sidewalls of GaAs nanowires. This preferential deposition leads to extraordinarily asymmetric InAs/GaAs radial nanowire heterostructures. Such formation of radial nanowire heterostructures provides an opportunity to engineer hierarchical nanostructures, which further widens the potential applications of semiconductor nanostructures. © 2008 American Institute of Physics.

Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering

Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input. Dirichlet process mixture models are appealing as they can infer the number of clusters from the data. However, these models do not deal with high dimensional data well and can encounter difficulties in inference. We present a novel nonparameteric Bayesian kernel based method to cluster data points without the need to prespecify the number of clusters or to model complicated densities from which data points are assumed to be generated from. The key insight is to use determinants of submatrices of a kernel matrix as a measure of how close together a set of points are. We explore some theoretical properties of the model and derive a natural Gibbs based algorithm with MCMC hyperparameter learning. The model is implemented on a variety of synthetic and real world data sets.

Bio-inspired hierarchical polymer fiber-carbon nanotube adhesives.

Hierarchical pillar arrays consisting of micrometer-sized polymer setae covered by carbon nanotubes are engineered to deliver the role of spatulae, mimicking the fibrillar adhesive surfaces of geckos. These biomimetic structures conform well and achieve better attachment to rough surfaces, providing a new platform for a variety of applications.

Bio-inspired hierarchical polymer fiber-carbon nanotube adhesives

Hierarchical pillar arrays consisting of micrometer-sized polymer setae covered by carbon nanotubes are engineered to deliver the role of spatulae, mimicking the fibrillar adhesive surfaces of geckos. These biomimetic structures conform well and achieve better attachment to rough surfaces, providing a new platform for a variety of applications. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.