1,729,290 research outputs found
Govind Krishnamoorthy on international student life and trauma-informed behaviour support
No full textData for "Estimates on the Frequency of Volcanic Eruptions on Venus" by Byrne and Krishnamoorthy
No full textThis repository contains two files related to the publication "Estimates on the Frequency of Volcanic Eruptions on Venus" by Byrne and Krishnamoorthy in Journal of Geophysical Research: Planets.1) GVP_1900_Jan21_2021.xlsx: This spreadsheet is collated from the Smithsonian Institution's Global Volcanism Program database (https://volcano.si.edu/search_eruption.cfm ) and contains details for subaerial and undersea eruptions between January 1, 1900 and January 22, 2021.2) GVP_Classified1980.csv: This CSV file contains classifications of all volcanoes that had eruptions which started between January 1980 and January 2021. Each volcano is classified into four categories: Class 1: Continental IntraplateClass 2: Rift ZoneClass 3: Oceanic IntraplateClass 4: Subduction Zone </div
On a Conjecture of Krishnamoorthy and Gupta
No full textAbstractWe consider the problem of estimating the precision matrix (Σ−1) under a fully invariant convex loss. Suppose that there exists a minimax constant risk estimatorΦ(say) for this problem. K. Krishnamoorthy and A. K. Gupta have proposed an operation which transforms this estimator into an orthogonally invariant estimatorΦ* (say) and they have a conjecture saying thatΦ* is minimax as well. This paper contains two parts. In the first part, we present counterexamples. In the second part, we elaborate a technique which can be used to prove that certain estimators are minimax. This technique is then applied successfully to some of the estimators proposed in the Krishnamoorthy and Gupta paper
On a Conjecture of Krishnamoorthy and Gupta, ,
No full textWe consider the problem of estimating the precision matrix ([Sigma]-1) under a fully invariant convex loss. Suppose that there exists a minimax constant risk estimator[Phi](say) for this problem. K. Krishnamoorthy and A. K. Gupta have proposed an operation which transforms this estimator into an orthogonally invariant estimator[Phi]* (say) and they have a conjecture saying that[Phi]* is minimax as well. This paper contains two parts. In the first part, we present counterexamples. In the second part, we elaborate a technique which can be used to prove that certain estimators are minimax. This technique is then applied successfully to some of the estimators proposed in the Krishnamoorthy and Gupta paper.covariance matrix precision matrix equivariant estimators unbiased estimate of the risk Wishart distribution Haar probability measure on the orthogonal group
ANALYZING CONNECTION PATTERNS IN A TRANSHIPMENT PORT
No full textMaster'sMASTER OF SCIENCE IN COMPUTATIONAL ENGINEERINGDissertation Supervisors: Professor Sun Jie, National University of Singapore Rajeeya Lochana Moorthy Krishnamoorthy, PSA Corporation Limite
Author response: Sensitivity to image recurrence across eye-movement-like image transitions through local serial inhibition in the retina
No full textStandard models of stimulus encoding in the retina postulate that image presentations activate neurons according to the increase of preferred contrast inside the receptive field. During natural vision, however, images do not arrive in isolation, but follow each other rapidly, separated by sudden gaze shifts. We here report that, contrary to standard models, specific ganglion cells in mouse retina are suppressed after a rapid image transition by changes in visual patterns across the transition, but respond with a distinct spike burst when the same pattern reappears. This sensitivity to image recurrence depends on opposing effects of glycinergic and GABAergic inhibition and can be explained by a circuit of local serial inhibition. Rapid image transitions thus trigger a mode of operation that differs from the processing of simpler stimuli and allows the retina to tag particular image parts or to detect transition types that lead to recurring stimulus patterns
Data for: ITC and SPR Analysis Using Dynamic Approach
No full textMatlab codes for ITC, SPR simulations and links to download global sensitivity analysis codes used in this wor
Evolutionary multi-objective optimization and Pareto-frontal uncertainty quantification of interatomic forcefields for thermal conductivity simulations
No full textPredictive Molecular Dynamics simulations of thermal transport require forcefields that can simultaneously reproduce several structural, thermodynamic and vibrational properties of materials like lattice constants, phonon density of states, and specific heat. This requires a multi-objective optimization approach for forcefield parameterization. Existing methodologies for forcefield parameterization use ad-hoc and empirical weighting schemes to convert this into a single-objective optimization problem. Here, we provide and describe software to perform multi-objective optimization of Stillinger–Weber forcefields (SWFF) for two-dimensional layered materials using the recently developed 3rd generation non-dominated sorting genetic algorithm (NSGA-III). NSGA-III converges to the set of optimal forcefields lying on the Pareto front in the multi-dimensional objective space. This set of forcefields is used for uncertainty quantification of computed thermal conductivity due to variability in the forcefield parameters. We demonstrate this new optimization scheme by constructing a SWFF for a representative two-dimensional material, 2H-MoSe_2 and quantifying the uncertainty in their computed thermal conductivity
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