Software tools for stochastic programming: A Stochastic Programming Integrated Environment (SPInE)

SP models combine the paradigm of dynamic linear programming with modelling of random parameters, providing optimal decisions which hedge against future uncertainties. Advances in hardware as well as software techniques and solution methods have made SP a viable optimisation tool. We identify a growing need for modelling systems which support the creation and investigation of SP problems. Our SPInE system integrates a number of components which include a flexible modelling tool (based on stochastic extensions of the algebraic modelling languages AMPL and MPL), stochastic solvers, as well as special purpose scenario generators and database tools. We introduce an asset/liability management model and illustrate how SPInE can be used to create and process this model as a multistage SP application

Optimization of Nanoparticle-Based SERS Substrates through Large-Scale Realistic Simulations

Surface-enhanced Raman scattering (SERS) has become a widely used spectroscopic technique for chemical identification, providing unbeaten sensitivity down to the singlemolecule level. The amplification of the optical near field produced by collective electron excitations plasmons in nanostructured metal surfaces gives rise to a dramatic increase by many orders of magnitude in the Raman scattering intensities from neighboring molecules. This effect strongly depends on the detailed geometry and composition of the plasmonsupporting metallic structures. However, the search for optimized SERS substrates has largely relied on empirical data, due in part to the complexity of the structures, whose simulation becomes prohibitively demanding. In this work, we use state-of-the-art electromagnetic computation techniques to produce predictive simulations for a wide range of nanoparticle-based SERS substrates, including realistic configurations consisting of random arrangements of hundreds of nanoparticles with various morphologies. This allows us to derive rules of thumb for the influence of particle anisotropy and substrate coverage on the obtained SERS enhancement and optimum spectral ranges of operation. Our results provide a solid background to understand and design optimized SERS substrates.Peer ReviewedPostprint (published version

Scalable Rejection Sampling for Bayesian Hierarchical Models

Bayesian hierarchical modeling is a popular approach to capturing unobserved heterogeneity across individual units. However, standard estimation methods such as Markov chain Monte Carlo (MCMC) can be impracticable for modeling outcomes from a large number of units. We develop a new method to sample from posterior distributions of Bayesian models, without using MCMC. Samples are independent, so they can be collected in parallel, and we do not need to be concerned with issues like chain convergence and autocorrelation. The algorithm is scalable under the weak assumption that individual units are conditionally independent, making it applicable for large datasets. It can also be used to compute marginal likelihoods

Iterative Residual Rescaling: An Analysis and Generalization of LSI

We consider the problem of creating document representations in which inter-document similarity measurements correspond to semantic similarity. We first present a novel subspace-based framework for formalizing this task. Using this framework, we derive a new analysis of Latent Semantic Indexing (LSI), showing a precise relationship between its performance and the uniformity of the underlying distribution of documents over topics. This analysis helps explain the improvements gained by Ando's (2000) Iterative Residual Rescaling (IRR) algorithm: IRR can compensate for distributional non-uniformity. A further benefit of our framework is that it provides a well-motivated, effective method for automatically determining the rescaling factor IRR depends on, leading to further improvements. A series of experiments over various settings and with several evaluation metrics validates our claims.Comment: To appear in the proceedings of SIGIR 2001. 11 page

Stable hydrosols for TiO2 coatings

The optimum processing parameters required to synthesize, by hydrolysis of titanium isopropoxide (TIP), highly stable hydrosols composed of nanoparticles of the smallest possible size, are deduced both from data available in literature and from our own experiments. The colloids prepared in these conditions are composed of aggregates of anatase (*90%) and brookite crystallites (5–6 nm). They are suitable for coatings and have longterm stability (more than one year) in terms of polymorphic composition, crystallite and agglomerate size. Stable sols composed solely of anatase crystallites (4 nm) can be prepared by partially complexing the TIP by acetylacetone before hydrolysis. It is not possible to produce porous films with these colloids because they are stabilized by electrostatic repulsion which causes the particles to organize themselves, during the drying step, to form materials with a close packed structure. However, coatings with controlled porosity can be prepared from these stable sols through the post addition of polymers, like PEG or block copolymers