Community-led resettlement: From a flood-affected slum to a new society in Pune, India

This paper describes the resettlement process of a community devastated by annual floods, to newly constructed housing in Pune, India. The relocation from Kamgar Putala slum to a housing society at Hadapsar was organized by a community-led NGO partnership in 2004. The housing development was coordinated by the local NGO Shelter Associates with significant community participation. The housing has been revisited in 2010 to evaluate the sustainability of the resettlement project's delivery model via stakeholder perception. The process of organizing for resettlement after natural disaster is described along with the implementation and evaluation of the new housing nearly six years after initial occupation. The strong partnership approach overcame a series of political and financial hurdles at various stages of the relocation project. The story of resettling Kamgar Putala is detailed alongside an outline of the current political climate for an alternative slum-upgrading policy in India and Pune. The advantages of an empowered community supported by an influential local NGO demonstrate a commendable team effort which has tackled the threat of floods. The paper highlights the merits of a community-led partnership approach to housing development for achieving sustainable urban development as well as the alleviation of poverty in a developing context. © 2011 Taylor & Francis.

An organizing center in a planar model of neuronal excitability

This paper studies the excitability properties of a generalized FitzHugh-Nagumo model. The model differs from the classical FitzHugh-Nagumo model in that it accounts for the effect of cooperative gating variables such as activation of calcium currents. Excitability is explored by unfolding a pitchfork bifurcation that is shown to organize five different types of excitability. In addition to the three classical types of neuronal excitability, two novel types are described and distinctly associated to the presence of cooperative variables. © 2012 Society for Industrial and Applied Mathematics.

Polychromatic single-shot spectroscopy on SOI-FET trapped electrons

The behavior of trapped electrons, in a dielectric close to the channel of a silicon SOI-FET, is studied by cryogenic microwave spectroscopy. On-resonance microwave excitation causes one of these trapped electrons to undergo spatial Rabi oscillations between widely separated trap sites. This charge displacement causes a change in the drain current of the transistor, resulting in high quality factor resonances in continuous wave spectroscopy. The potential of this effect for non-classical information processing is investigated through polychromatic single-shot spectroscopy, using on-resonance and difference frequencies. Interaction between different trapped electrons is seen in the post excitation behavior and the possibilities of quantum gate operations are discussed. © The Electrochemical Society.

Award Winner in the Young Investigator Category, 2014 Society for Biomaterials Annual Meeting and Exposition, Denver, Colorado, April 16-19, 2014: Periodically perforated core-shell collagen biomaterials balance cell infiltration, bioactivity, and mechanical properties.

Orthopedic tissue engineering requires biomaterials with robust mechanics as well as adequate porosity and permeability to support cell motility, proliferation, and new extracellular matrix (ECM) synthesis. While collagen-glycosaminoglycan (CG) scaffolds have been developed for a range of tissue engineering applications, they exhibit poor mechanical properties. Building on previous work in our lab that described composite CG biomaterials containing a porous scaffold core and nonporous CG membrane shell inspired by mechanically efficient core-shell composites in nature, this study explores an approach to improve cellular infiltration and metabolic health within these core-shell composites. We use indentation analyses to demonstrate that CG membranes, while less permeable than porous CG scaffolds, show similar permeability to dense materials such as small intestine submucosa (SIS). We also describe a simple method to fabricate CG membranes with organized arrays of microscale perforations. We demonstrate that perforated membranes support improved tenocyte migration into CG scaffolds, and that migration is enhanced by platelet-derived growth factor BB-mediated chemotaxis. CG core-shell composites fabricated with perforated membranes display scaffold-membrane integration with significantly improved tensile properties compared to scaffolds without membrane shells. Finally, we show that perforated membrane-scaffold composites support sustained tenocyte metabolic activity as well as improved cell infiltration and reduced expression of hypoxia-inducible factor 1α compared to composites with nonperforated membranes. These results will guide the design of improved biomaterials for tendon repair that are mechanically competent while also supporting infiltration of exogenous cells and other extrinsic mediators of wound healing.

Multiplexed classical and quantum transmission for high bitrate quantum key distribution systems

We report the operation of a gigahertz clocked quantum key distribution system, with two classical data communication channels using coarse wavelength division multiplexing over a record fibre distance of 80km. © OSA 2012.

Randomized nonlinear component analysis

Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to reveal linear re-lationships in data. Although nonlinear variants of PCA and CCA have been proposed, these are computationally prohibitive in the large scale. In a separate strand of recent research, randomized methods have been proposed to construct features that help reveal nonlinear patterns in data. For basic tasks such as regression or classification, random features exhibit little or no loss in performance, while achieving drastic savings in computational requirements. In this paper we leverage randomness to design scalable new variants of nonlinear PCA and CCA; our ideas extend to key multivariate analysis tools such as spectral clustering or LDA. We demonstrate our algorithms through experiments on real- world data, on which we compare against the state-of-the-art. A simple R implementation of the presented algorithms is provided.