Author

# P. N. Raychowdhury

Bio: P. N. Raychowdhury is an academic researcher from Virginia Commonwealth University. The author has contributed to research in topics: Log-polar coordinates & Trilinear coordinates. The author has an hindex of 5, co-authored 38 publications receiving 99 citations.

##### Papers

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TL;DR: The class I orthogonal polynomials in the complex variables z and are reviewed in this paper, where they are shown to be eigenfunctions of a certain partial differential operator.

Abstract: The Class I orthogonal polynomials in the complex variables z and are reviewed [7]. These polynomials are two dimensional analogs of the Jacobi polynomials and are orthogonal on the unit disk with respect to the weight function The polynomials are then shown to be eigenfunctions of a certain partial differential operator. Using these polynomials class of zonal, harmonic, homogeneous polynomials is defined in . In the case that n-2, applications to electrostatic potentials are given.

13 citations

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TL;DR: In this article, the physical meaning of Coriolis acceleration in the general motion of a particle with respect to an acelerated coordinate system was investigated, and the authors considered the physical properties of the acceleration in a particle.

Abstract: The authors consider the physical meaning of Coriolis acceleration in the general motion of a particle with respect to an acelerated coordinate system. (AIP)

9 citations

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TL;DR: In this paper, the authors apply projection operator techniques to the computation of the natural frequencies of oscillation for three symmetrically coupled mechanical systems, where the rotation subgroup of the full symmetry group is used to determine the projection operators with the result that the Lagrangian must be expressed in terms of complex-valued coordinates.

Abstract: We apply projection operator techniques to the computation of the natural frequencies of oscillation for three symmetrically coupled mechanical systems. In each case, the rotation subgroup of the full symmetry group is used to determine the projection operators with the result that the Lagrangian must be expressed in terms of complex-valued coordinates. In the coordinate system obtained from the action of the projection operators upon the original coordinates, the Lagrangian yields equations of motion which are separated to the maximum extent made possible by symmetry considerations.

5 citations

##### Cited by

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01 Aug 2019

TL;DR: The authors and publisher have taken care in the preparation of this book, but make no expressed or implied warranty of any kind and assume no responsibility for errors or omissions as discussed by the authors.

Abstract: Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed with initial capital letters or in all capitals. The authors and publisher have taken care in the preparation of this book, but make no expressed or implied warranty of any kind and assume no responsibility for errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of the use of the information or programs contained herein. The publisher offers excellent discounts on this book when ordered in quantity for bulk purchases or special sales, which may include electronic versions and/or custom covers and content particular to your business, training goals, marketing focus, and branding interests. Jersey 07458, or you may fax your request to (201) 236-3290.

94 citations

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TL;DR: A novel subgraph routing mechanism that propagates neural messages between the subgraph's components and randomly sampled anchor patches from the underlying graph, yielding highly accurate subgraph representations, as well as designing a series of new synthetic and real-world subgraph datasets.

Abstract: Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks However, despite the proliferation of the methods and their success, prevailing Graph Neural Networks (GNNs) neglect subgraphs, rendering subgraph prediction tasks challenging to tackle in many impactful applications Further, subgraph prediction tasks present several unique challenges: subgraphs can have non-trivial internal topology, but also carry a notion of position and external connectivity information relative to the underlying graph in which they exist Here, we introduce SubGNN, a subgraph neural network to learn disentangled subgraph representations We propose a novel subgraph routing mechanism that propagates neural messages between the subgraph's components and randomly sampled anchor patches from the underlying graph, yielding highly accurate subgraph representations SubGNN specifies three channels, each designed to capture a distinct aspect of subgraph topology, and we provide empirical evidence that the channels encode their intended properties We design a series of new synthetic and real-world subgraph datasets Empirical results for subgraph classification on eight datasets show that SubGNN achieves considerable performance gains, outperforming strong baseline methods, including node-level and graph-level GNNs, by 198% over the strongest baseline SubGNN performs exceptionally well on challenging biomedical datasets where subgraphs have complex topology and even comprise multiple disconnected components

47 citations

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TL;DR: In this paper, the authors present the current results in the study of weighted composition operators on the Bloch space of bounded homogeneous domains in ℂ n with particular emphasis on the issues of boundedness and compactness.

Abstract: In this paper, we present the current results in the study of weighted composition operators on the Bloch space of bounded homogeneous domains in ℂ n with particular emphasis on the issues of boundedness and compactness. We also discuss the bounded and the compact weighted composition operators from the Bloch space to the Hardy space H∞.

42 citations