Prototype learning methods for online handwriting recognition

In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise- distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios.

Five-stage Milstein methods for SDEs

In this paper, we consider the problem of computing numerical solutions for Ito stochastic differential equations (SDEs). The five-stage Milstein (FSM) methods are constructed for solving SDEs driven by an m-dimensional Wiener process. The FSM methods are fully explicit methods. It is proved that the FSM methods are convergent with strong order 1 for SDEs driven by an m-dimensional Wiener process. The analysis of stability (with multidimensional Wiener process) shows that the mean-square stable regions of the FSM methods are unbounded. The analysis of stability shows that the mean-square stable regions of the methods proposed in this paper are larger than the Milstein method and three-stage Milstein methods.

Comparison of the degree of creativity in the design outcomes using different design methods

This work analyses the influence of several design methods on the degree of creativity of the design outcome. A design experiment has been carried out in which the participants were divided into four teams of three members, and each team was asked to work applying different design methods. The selected methods were Brainstorming, Functional Analysis, and SCAMPER method. The `degree of creativity' of each design outcome is assessed by means of a questionnaire offered to a number of experts and by means of three different metrics: the metric of Moss, the metric of Sarkar and Chakrabarti, and the evaluation of innovative potential. The three metrics share the property of measuring the creativity as a combination of the degree of novelty and the degree of usefulness. The results show that Brainstorming provides more creative outcomes than when no method is applied, while this is not proved for SCAMPER and Functional Analysis.

Temperature gradient induced phase transitions and morphological changes in diamond thin film

The diamond films were deposited onto a wurtzite gallium nitride (GaN) thin film substrate using hot-filament chemical vapor deposition (HFCVD). During the film deposition a lateral temperature gradient was imposed across the substrate by inclining the substrate. As grown films predominantly showed the hexagonal phase, when no inclination was applied to the substrate. Tilting the substrate with respect to the heating filament by 6 degrees imposed a lateral temperature gradient across the substrate, which induced the formation of a cubic diamond phase. Diamond grains were predominantly oriented in the (100) direction. However, a further increase in the substrate tilt angle to 12 degrees, resulted in grains oriented in the (111) direction. The growth rate and hence the morphology of diamond grains varied along the inclined substrate. The present study focuses on the measurements of dominant phase formation and crystal orientation with varying substrate inclination using orientation-imaging microscopy (OIM). This technique enables direct examination of individual diamond grains and their crystallographic orientation. (C) 2012 Elsevier B.V. All rights reserved.

Anticancer property of Bryophyllum pinnata (Lam.) Oken. leaf on human cervical cancer cells

Background: Bryophyllum pinnata (B. pinnata) is a common medicinal plant used in traditional medicine of India and of other countries for curing various infections, bowel diseases, healing wounds and other ailments. However, its anticancer properties are poorly defined. In view of broad spectrum therapeutic potential of B. pinnata we designed a study to examine anti-cancer and anti-Human Papillomavirus (HPV) activities in its leaf extracts and tried to isolate its active principle. Methods: A chloroform extract derived from a bulk of botanically well-characterized pulverized B. pinnata leaves was separated using column chromatography with step-gradient of petroleum ether and ethyl acetate. Fractions were characterized for phyto-chemical compounds by TLC, HPTLC and NMR and Biological activity of the fractions were examined by MTT-based cell viability assay, Electrophoretic Mobility Shift Assay, Northern blotting and assay of apoptosis related proteins by immunoblotting in human cervical cancer cells. Results: Results showed presence of growth inhibitory activity in the crude leaf extracts with IC50 at 552 mu g/ml which resolved to fraction F4 (Petroleum Ether: Ethyl Acetate:: 50: 50) and showed IC50 at 91 mu g/ml. Investigations of anti-viral activity of the extract and its fraction revealed a specific anti-HPV activity on cervical cancer cells as evidenced by downregulation of constitutively active AP1 specific DNA binding activity and suppression of oncogenic c-Fos and c-Jun expression which was accompanied by inhibition of HPV18 transcription. In addition to inhibiting growth, fraction F4 strongly induced apoptosis as evidenced by an increased expression of the pro-apoptotic protein Bax, suppression of the anti-apoptotic molecules Bcl-2, and activation of caspase-3 and cleavage of PARP-1. Phytochemical analysis of fraction F4 by HPTLC and NMR indicated presence of activity that resembled Bryophyllin A. Conclusions: Our study therefore demonstrates presence of anticancer and anti-HPV an activity in B. pinnata leaves that can be further exploited as a potential anticancer, anti-HPV therapeutic for treatment of HPV infection and cervical cancer.

On maximum entropy and minimum KL-divergence optimization by Grobner basis methods

In this paper we study constrained maximum entropy and minimum divergence optimization problems, in the cases where integer valued sufficient statistics exists, using tools from computational commutative algebra. We show that the estimation of parametric statistical models in this case can be transformed to solving a system of polynomial equations. We give an implicit description of maximum entropy models by embedding them in algebraic varieties for which we give a Grobner basis method to compute it. In the cases of minimum KL-divergence models we show that implicitization preserves specialization of prior distribution. This result leads us to a Grobner basis method to embed minimum KL-divergence models in algebraic varieties. (C) 2012 Elsevier Inc. All rights reserved.

Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called `early warning signals', and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.

Simple and efficient methods for discrimination of chiral diacids and chiral alpha-methyl amines

The three-component chiral derivatization protocols have been developed for H-1, C-13 and F-19 NMR spectroscopic discrimination of chiral diacids by their coordination and self-assembly with optically active (R)-alpha-methylbenzylamine and 2-formylphenylboronic acid or 3-fluoro-2-formylmethylboronic acid. These protocols yield a mixture of diastereomeric imino-boronate esters which are identified by the well-resolved diastereotopic peaks with significant chemical shift differences ranging up to 0.6 and 2.1 ppm in their corresponding H-1 and F-19 NMR spectra, without any racemization or kinetic resolution, thereby enabling the determination of enantiopurity. A protocol has also been developed for discrimination of chiral alpha-methyl amines, using optically pure trans-1,2-cyclohexanedicarboxylic acid in combination with 2-formylphenylboronic acid or 3-fluoro-2-fluoromethylboronic acid. The proposed strategies have been demonstrated on large number of chiral diacids and chiral alpha-methyl amines.

Correlation of Calcification on Excised Aortic Valves by Micro-Computed Tomography with Severity of Aortic Stenosis

Background and aim of the study: The quantification of incidentally found aortic valve calcification on computed tomography (CT) is not performed routinely, as data relating to the accuracy of aortic valve calcium for estimating the severity of aortic stenosis (AS) is neither consistent nor validated. As aortic valve calcium quantification by CT is confounded by wall and coronary ostial calcification, as well as motion artifact, the ex-vivo micro-computed tomography (micro-CT) of stenotic aortic valves allows a precise measurement of the amounts of calcium present. The study aim, using excised aortic valves from patients with confirmed AS, was to determine if the amount of calcium on micro-CT correlated with the severity of AS. Methods: Each of 35 aortic valves that had been excised from patients during surgical valve replacement were examined using micro-CT imaging. The amount of calcium present was determined by absolute and proportional values of calcium volume in the specimen. Subsequently, the correlation between calcium volume and preoperative mean aortic valve gradient (MAVG), peak transaortic velocity (V-max), and aortic valve area (AVA) on echocardiography, was evaluated. Results: The mean calcium volume across all valves was 603.2 +/- 398.5 mm(3), and the mean ratio of calcium volume to total valve volume was 0.36 +/- 0.16. The mean aortic valve gradient correlated positively with both calcium volume and ratio (r = 0.72, p <0.001). V-max also correlated positively with the calcium volume and ratio (r = 0.69 and 0.76 respectively; p <0.001). A logarithmic curvilinear model proved to be the best fit to the correlation. A calcium volume of 480 mm(3) showed sensitivity and specificity of 0.76 and 0.83, respectively, for a diagnosis of severe AS, while a calcium ratio of 0.37 yielded sensitivity and specificity of 0.82 and 0.94, respectively. Conclusion: A radiological estimation of calcium amount by volume, and its proportion to the total valve volume, were shown to serve as good predictive parameters for severe AS. An estimation of the calcium volume may serve as a complementary measure for determining the severity of AS when aortic valve calcification is identified on CT imaging. The Journal of Heart Valve Disease 2012;21:320-327

Directional Diffusion Regulator (DDR) for some numerical solvers of hyperbolic conservation laws

A computational tool called ``Directional Diffusion Regulator (DDR)'' is proposed to bring forth real multidimensional physics into the upwind discretization in some numerical schemes of hyperbolic conservation laws. The direction based regulator when used with dimension splitting solvers, is set to moderate the excess multidimensional diffusion and hence cause genuine multidimensional upwinding like effect. The basic idea of this regulator driven method is to retain a full upwind scheme across local discontinuities, with the upwind bias decreasing smoothly to a minimum in the farthest direction. The discontinuous solutions are quantified as gradients and the regulator parameter across a typical finite volume interface or a finite difference interpolation point is formulated based on fractional local maximum gradient in any of the weak solution flow variables (say density, pressure, temperature, Mach number or even wave velocity etc.). DDR is applied to both the non-convective as well as whole unsplit dissipative flux terms of some numerical schemes, mainly of Local Lax-Friedrichs, to solve some benchmark problems describing inviscid compressible flow, shallow water dynamics and magneto-hydrodynamics. The first order solutions consistently improved depending on the extent of grid non-alignment to discontinuities, with the major influence due to regulation of non-convective diffusion. The application is also experimented on schemes such as Roe, Jameson-Schmidt-Turkel and some second order accurate methods. The consistent improvement in accuracy either at moderate or marked levels, for a variety of problems and with increasing grid size, reasonably indicate a scope for DDR as a regular tool to impart genuine multidimensional upwinding effect in a simpler framework. (C) 2012 Elsevier Inc. All rights reserved.

Statistical Methods for Accelerated Life Testing with Particular Reference to Resin Impregnated Paper Bushings

Electrical failure of insulation is known to be an extremal random process wherein nominally identical pro-rated specimens of equipment insulation, at constant stress fail at inordinately different times even under laboratory test conditions. In order to be able to estimate the life of power equipment, it is necessary to run long duration ageing experiments under accelerated stresses, to acquire and analyze insulation specific failure data. In the present work, Resin Impregnated Paper (RIP) a relatively new insulation system of choice used in transformer bushings, is taken as an example. The failure data has been processed using proven statistical methods, both graphical and analytical. The physical model governing insulation failure at constant accelerated stress has been assumed to be based on temperature dependent inverse power law model.

Modified Newton, rank-1 Broyden update and rank-2 BFGS update methods in helicopter trim: A comparative study

Nonlinear equations in mathematical physics and engineering are solved by linearizing the equations and forming various iterative procedures, then executing the numerical simulation. For strongly nonlinear problems, the solution obtained in the iterative process can diverge due to numerical instability. As a result, the application of numerical simulation for strongly nonlinear problems is limited. Helicopter aeroelasticity involves the solution of systems of nonlinear equations in a computationally expensive environment. Reliable solution methods which do not need Jacobian calculation at each iteration are needed for this problem. In this paper, a comparative study is done by incorporating different methods for solving the nonlinear equations in helicopter trim. Three different methods based on calculating the Jacobian at the initial guess are investigated. (C) 2011 Elsevier Masson SAS. All rights reserved.

Review of trend detection methods and their application to detect temperature changes in India

Present study performs the spatial and temporal trend analysis of annual, monthly and seasonal maximum and minimum temperatures (t(max), t(min)) in India. Recent trends in annual, monthly, winter, pre-monsoon, monsoon and post-monsoon extreme temperatures (t(max), t(min)) have been analyzed for three time slots viz. 1901-2003,1948-2003 and 1970-2003. For this purpose, time series of extreme temperatures of India as a whole and seven homogeneous regions, viz. Western Himalaya (WH), Northwest (NW), Northeast (NE), North Central (NC), East coast (EC), West coast (WC) and Interior Peninsula (IP) are considered. Rigorous trend detection analysis has been exercised using variety of non-parametric methods which consider the effect of serial correlation during analysis. During the last three decades minimum temperature trend is present in All India as well as in all temperature homogeneous regions of India either at annual or at any seasonal level (winter, pre-monsoon, monsoon, post-monsoon). Results agree with the earlier observation that the trend in minimum temperature is significant in the last three decades over India (Kothawale et al., 2010). Sequential MK test reveals that most of the trend both in maximum and minimum temperature began after 1970 either in annual or seasonal levels. (C) 2012 Elsevier B.V. All rights reserved.

A sequential dual method for structural SVMs

In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to compu- tational biology or computer vision and have been tackled using algorithms, referred to as structured output learning algorithms. We consider the problem of structured classifi- cation. In the last few years, large margin classifiers like sup-port vector machines (SVMs) have shown much promise for structured output learning. The related optimization prob -lem is a convex quadratic program (QP) with a large num-ber of constraints, which makes the problem intractable for large data sets. This paper proposes a fast sequential dual method (SDM) for structural SVMs. The method makes re-peated passes over the training set and optimizes the dual variables associated with one example at a time. The use of additional heuristics makes the proposed method more efficient. We present an extensive empirical evaluation of the proposed method on several sequence learning problems.Our experiments on large data sets demonstrate that the proposed method is an order of magnitude faster than state of the art methods like cutting-plane method and stochastic gradient descent method (SGD). Further, SDM reaches steady state generalization performance faster than the SGD method. The proposed SDM is thus a useful alternative for large scale structured output learning.