Growing self-organizing map for online continuous clustering

The internet age has fuelled an enormous explosion in the amount of information generated by humanity. Much of this information is transient in nature, created to be immediately consumed and built upon (or discarded). The field of data mining is surprisingly scant with algorithms that are geared towards the unsupervised knowledge extraction of such dynamic data streams. This chapter describes a new neural network algorithm inspired by self-organising maps. The new algorithm is a hybrid algorithm from the growing self-organising map (GSOM) and the cellular probabilistic self-organising map (CPSOM). The result is an algorithm which generates a dynamically growing feature map for the purpose of clustering dynamic data streams and tracking clusters as they evolve in the data stream.

Hybrid hierarchical patterns of gold nanoparticles and poly(ethylene glycol) microstructures

Hybrid surface micro-patterns composed of topographic structures of polyethylene glycol (PEG)-hydrogels and hierarchical lines of gold nanoparticles (Au NPs) were fabricated on silicon wafers. Micro-sized lines of Au NPs were first obtained on the surface of a silicon wafer via “micro-contact deprinting”, a method recently developed by our group. Topographic micro-patterns of PEG, of both low and high aspect ratio (AR up to 6), were then aligned on the pre-patterned surface via a procedure adapted from the soft lithographic method MIMIC (Micro-Molding in Capillaries), which is denoted as “adhesive embossing”. The result is a complex surface pattern consisting of alternating flat Au NP lines and thick PEG bars. Such patterns provide novel model surfaces for elucidating the interplay between (bio)chemical and physical cues on cell behavior.