A Randomized Algorithm for Large Scale Support Vector Learning

We propose a randomized algorithm for large scale SVM learning which solves the problem by iterating over random subsets of the data. Crucial to the algorithm for scalability is the size of the subsets chosen. In the context of text classification we show that, by using ideas from random projections, a sample size of O(log n) can be used to obtain a solution which is close to the optimal with a high probability. Experiments done on synthetic and real life data sets demonstrate that the algorithm scales up SVM learners, without loss in accuracy. 1

Focussed Crawling with large scale Ordinal Regression Solvers

In this paper we propose a novel, scalable, clustering based Ordinal Regression formulation, which is an instance of a Second Order Cone Program (SOCP) with one Second Order Cone (SOC) constraint. The main contribution of the paper is a fast algorithm, CB-OR, which solves the proposed formulation more eficiently than general purpose solvers. Another main contribution of the paper is to pose the problem of focused crawling as a large scale Ordinal Regression problem and solve using the proposed CB-OR. Focused crawling is an efficient mechanism for discovering resources of interest on the web. Posing the problem of focused crawling as an Ordinal Regression problem avoids the need for a negative class and topic hierarchy, which are the main drawbacks of the existing focused crawling methods. Experiments on large synthetic and benchmark datasets show the scalability of CB-OR. Experiments also show that the proposed focused crawler outperforms the state-of-the-art.

Explicit filtering LES for turbulent non-premixed combustion

Two models for large eddy simulation of turbulent reacting flow in homogeneous turbulence were studied. The sub-grid stress arising out of non-linearities of the Navier-Stokes equations were modeled using an explicit filtering approach. A filtered mass density function (FMDF) approach was used for closure of the sub-grid scalar fluctuations. A posteriori calculations, when compared with the results from the direct numerical simulation, indicate that the explicit filtering is adequate in representing the effect of sub-grid stress on the filtered velocity field in the absence of reaction. Discrepancies arise when reactions occur, but the FMDF approach suffices to account for sub-grid scale fluctuations of the reacting scalars, accurately.

Effect of density variation and non-covalent functionalization on the compressive behavior of carbon nanotube arrays

Arrays of aligned carbon nanotubes (CNTs) have been proposed for different applications, including electrochemical energy storage and shock-absorbing materials. Understanding their mechanical response, in relation to their structural characteristics, is important for tailoring the synthesis method to the different operational conditions of the material. In this paper, we grow vertically aligned CNT arrays using a thermal chemical vapor deposition system, and we study the effects of precursor flow on the structural and mechanical properties of the CNT arrays. We show that the CNT growth process is inhomogeneous along the direction of the precursor flow, resulting in varying bulk density at different points on the growth substrate. We also study the effects of non-covalent functionalization of the CNTs after growth, using surfactant and nanoparticles, to vary the effective bulk density and structural arrangement of the arrays. We find that the stiffness and peak stress of the materials increase approximately linearly with increasing bulk density.

The advection-condensation model and water-vapour probability density functions

The statistically steady humidity distribution resulting from an interaction of advection, modelled as an uncorrelated random walk of moist parcels on an isentropic surface, and a vapour sink, modelled as immediate condensation whenever the specific humidity exceeds a specified saturation humidity, is explored with theory and simulation. A source supplies moisture at the deep-tropical southern boundary of the domain and the saturation humidity is specified as a monotonically decreasing function of distance from the boundary. The boundary source balances the interior condensation sink, so that a stationary spatially inhomogeneous humidity distribution emerges. An exact solution of the Fokker-Planck equation delivers a simple expression for the resulting probability density function (PDF) of the wate-rvapour field and also the relative humidity. This solution agrees completely with a numerical simulation of the process, and the humidity PDF exhibits several features of interest, such as bimodality close to the source and unimodality further from the source. The PDFs of specific and relative humidity are broad and non-Gaussian. The domain-averaged relative humidity PDF is bimodal with distinct moist and dry peaks, a feature which we show agrees with middleworld isentropic PDFs derived from the ERA interim dataset. Copyright (C) 2011 Royal Meteorological Society

Ultra fine scale phase separated microstructure for Ag-Fe nanoparticle

We report the formation of Ag-Fe nanoparticles with an ultrafine scale phase separated microstructure consisting of Ag and Fe(3)O(4) phases. Ag-Fe particles were synthesised by the co-reduction of Ag and Fe salts in water medium. The co-existing Ag and Fe(3)O(4) phase volumes were around similar to 1 nm in one of the dimensions. (C) 2011 Elsevier B. V. All rights reserved.

Recent Advances in Low CTE and High Density System-on-a-Package (SOP) Substrate with Thin Film Component Integration

The Packaging Research Center has been developing next generation system-on-a-package (SOP) technology with digital, RF, optical, and sensor functions integrated in a single package/module. The goal of this effort is to develop a platform substrate technology providing very high wiring density and embedded thin film passive and active components using PWB compatible materials and processes. The latest SOP baseline process test vehicle has been fabricated on novel Si-matched CTE, high modulus C-SiC composite core substrates using 10mum thick BCB dielectric films with loss tangent of 0.0008 and dielectric constant of 2.65. A semi-additive plating process has been developed for multilayer microvia build-up using BCB without the use of any vacuum deposition or polishing/CMP processes. PWB and package substrate compatible processes such as plasma surface treatment/desmear and electroless/electrolytic pulse reverse plating was used. The smallest line width and space demonstrated in this paper is 6mum with microvia diameters in the 15-30mum range. This build-up process has also been developed on medium CTE organic laminates including MCL-E-679F from Hitachi Chemical and PTFE laminates with Cu-Invar-Cu core. Embedded decoupling capacitors with capacitance density of >500nF/cm2 have been integrated into the build-up layers using sol-gel synthesized BaTiO3 thin films (200-300nm film thickness) deposited on copper foils and integrated using vacuum lamination and subtractive etch processes. Thin metal alloy resistor films have been integrated into the SOP substrate using two methods: (a) NiCrAlSi thin films (25ohms per square) deposited on copper foils (Gould Electronics) laminated on the build-up layers and two step etch process for resistor definition, and (b) electroless plated Ni-W-P thin films (70 ohms to few Kohms per square) on the BCB dielectric by plasma surface treatment and activation. The electrical design and build-up layer structure along- - with key materials and processes used in the fabrication of the SOP4 test vehicle were presented in this paper. Initial results from the high density wiring and embedded thin film components were also presented. The focus of this paper is on integration of materials, processes and structures in a single package substrate for system-on-a-package (SOP) implementation

A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright (C) 2011 John Wiley & Sons, Ltd.

Biofuel production and implications for land use, food production and environment in India

There is a large interest in biofuels in India as a substitute to petroleum-based fuels, with a purpose of enhancing energy security and promoting rural development. India has announced an ambitious target of substituting 20% of fossil fuel consumption by biodiesel and bioethanol by 2017. India has announced a national biofuel policy and launched a large program to promote biofuel production, particularly on wastelands: its implications need to be studied intensively considering the fact that India is a large developing country with high population density and large rural population depending upon land for their livelihood. Another factor is that Indian economy is experiencing high growth rate, which may lead to enhanced demand for food, livestock products, timber, paper, etc., with implications for land use. Studies have shown that area under agriculture and forest has nearly stabilized over the past 2-3 decades. This paper presents an assessment of the implications of projected large-scale biofuel production on land available for food production, water, biodiversity, rural development and GHG emissions. The assessment will be largely focused on first generation biofuel crops, since the Indian program is currently dominated by these crops. Technological and policy options required for promoting sustainable biofuel production will be discussed. (C) 2010 Elsevier Ltd. All rights reserved.