Deployable membranes designed from folding tree leaves.

A simple model of deploying tree leaves is assembled in different arrangements to produce polygonal foldable membranes for use as deployable structures. One family of folding patterns exhibits a small strain mechanism, which is investigated. Variations on the basic arrangements can be used to fold membranes with a discretized curvature.

Tree-Structured Stick Breaking Processes for Hierarchical Data

Many data are naturally modeled by an unobserved hierarchical structure. In this paper we propose a flexible nonparametric prior over unknown data hierarchies. The approach uses nested stick-breaking processes to allow for trees of unbounded width and depth, where data can live at any node and are infinitely exchangeable. One can view our model as providing infinite mixtures where the components have a dependency structure corresponding to an evolutionary diffusion down a tree. By using a stick-breaking approach, we can apply Markov chain Monte Carlo methods based on slice sampling to perform Bayesian inference and simulate from the posterior distribution on trees. We apply our method to hierarchical clustering of images and topic modeling of text data.

Red-Green-Blue 2D tuneable liquid crystal laser devices

In this paper, we review our recent experimental work on coherent and blue phase liquid crystal lasers.We will present results on thin-film photonic band edge lasing devices using dye-doped low molar mass liquid crystals in self-organised chiral nematic and blue phases. We show that high Q-factor lasers can be achieved in these materials and demonstrate that a single mode output with a very narrow line width can be readily achievable in well-aligned mono-domain samples. Further, we have found that the performance of the laser, i.e. the slope efficiency and the excitation threshold, are dependent upon the physical parameters of the low molar mass chiral nematic liquid crystals. Specifically, slope efficiencies greater than 60% could be achieved depending upon the materials used and the device geometry employed. We will discuss the important parameters of the liquid crystal host/dye guest materials and device configuration that are needed to achieve such high slope efficiencies. Further we demonstrate how the wavelength of the laser can be tuned using an in-plane electric field in a direction perpendicular to the helix axis via a flexoelectric mechanism as well as thermally using thermochromic effects. We will then briefly outline data on room temperature blue phase lasers and further show how liquid crystal/lenslet arrays have been used to demonstrate 2D laser emission of any desired wavelength. Finally, we present preliminary data on LED/incoherent pumping of RG liquid crystal lasers leading to a continuous wave output. © 2009 SPIE.

Decision tree-based context clustering based on cross validation and hierarchical priors

The standard, ad-hoc stopping criteria used in decision tree-based context clustering are known to be sub-optimal and require parameters to be tuned. This paper proposes a new approach for decision tree-based context clustering based on cross validation and hierarchical priors. Combination of cross validation and hierarchical priors within decision tree-based context clustering offers better model selection and more robust parameter estimation than conventional approaches, with no tuning parameters. Experimental results on HMM-based speech synthesis show that the proposed approach achieved significant improvements in naturalness of synthesized speech over the conventional approaches. © 2011 IEEE.

Semi-supervised video segmentation using tree structured graphical models

We present a novel, implementation friendly and occlusion aware semi-supervised video segmentation algorithm using tree structured graphical models, which delivers pixel labels alongwith their uncertainty estimates. Our motivation to employ supervision is to tackle a task-specific segmentation problem where the semantic objects are pre-defined by the user. The video model we propose for this problem is based on a tree structured approximation of a patch based undirected mixture model, which includes a novel time-series and a soft label Random Forest classifier participating in a feedback mechanism. We demonstrate the efficacy of our model in cutting out foreground objects and multi-class segmentation problems in lengthy and complex road scene sequences. Our results have wide applicability, including harvesting labelled video data for training discriminative models, shape/pose/articulation learning and large scale statistical analysis to develop priors for video segmentation. © 2011 IEEE.