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.

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.

Evolving corporate entrepreneurship strategy: Technology incubation at Philips

Established firms tend to pursue incremental innovation by modifying and refining their existing products and processes rather than developing radical innovations. In the face of resistance to change and incumbent inertia, which prevent the generation of novelty, established firms have turned towards corporate entrepreneurship as a means of exploiting knowledge accumulated within its own boundaries and exploring external markets. This paper focuses on one mode of corporate entrepreneurship, corporate incubation, informed by a study of a Technology Incubator at Philips. An account of the history of the incubator traces its progress from its inception in 2002-2006 when further incubators were formed, building on this experience and focusing on lifestyle and healthcare technologies. We identify ways in which the Philips incubator represents an alternative selection environment that effectively simulated the venture capitalist model of entrepreneurial innovation. © 2009 Blackwell Publishing Ltd.