The Experts below are selected from a list of 15 Experts worldwide ranked by ideXlab platform
Keshab K Parhi - One of the best experts on this subject based on the ideXlab platform.
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computing radial basis function support vector machine using dna via fractional coding
2019Co-Authors: Xingyi Liu, Keshab K ParhiAbstract:This paper describes a novel approach to synthesize molecular reactions to compute a radial basis function (RBF) support vector machine (SVM) kernel. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key to translating known stochastic logic circuits to molecular computing. Although inspired by prior stochastic logic implementation of the RBF-SVM kernel, the proposed molecular reactions require non-obvious modifications. This paper introduces a new explicit bipolar-to-unipolar molecular converter for intermediate format Conversion. Two designs are presented; one is based on the explicit and the other is based on Implicit Conversion from prior stochastic logic. When 5 support vectors are used, it is shown that the DNA RBF-SVM realized using the explicit format Conversion has orders of magnitude less regression error than that based on Implicit Conversion.
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computing radial basis function support vector machine using dna via fractional coding
2019Co-Authors: Xingyi Liu, Keshab K ParhiAbstract:This paper describes a novel approach to synthesize molecular reactions to compute a radial basis function (RBF) support vector machine (SVM) kernel. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key to translating known stochastic logic circuits to molecular computing. Although inspired by prior stochastic logic implementation of the RBF-SVM kernel, the proposed molecular reactions require non-obvious modifications. This paper introduces a new explicit bipolar-to-unipolar molecular converter for intermediate format Conversion. Two designs are presented; one is based on the explicit and the other is based on Implicit Conversion from prior stochastic logic. When 5 support vectors are used, it is shown that the DNA RBF-SVM realized using the explicit format Conversion has orders of magnitude less regression error than that based on Implicit Conversion. CCS CONCEPTS • Applied computing → Molecular structural biology; •Hardware → Biology-related information processing;
Xingyi Liu - One of the best experts on this subject based on the ideXlab platform.
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computing radial basis function support vector machine using dna via fractional coding
2019Co-Authors: Xingyi Liu, Keshab K ParhiAbstract:This paper describes a novel approach to synthesize molecular reactions to compute a radial basis function (RBF) support vector machine (SVM) kernel. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key to translating known stochastic logic circuits to molecular computing. Although inspired by prior stochastic logic implementation of the RBF-SVM kernel, the proposed molecular reactions require non-obvious modifications. This paper introduces a new explicit bipolar-to-unipolar molecular converter for intermediate format Conversion. Two designs are presented; one is based on the explicit and the other is based on Implicit Conversion from prior stochastic logic. When 5 support vectors are used, it is shown that the DNA RBF-SVM realized using the explicit format Conversion has orders of magnitude less regression error than that based on Implicit Conversion.
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computing radial basis function support vector machine using dna via fractional coding
2019Co-Authors: Xingyi Liu, Keshab K ParhiAbstract:This paper describes a novel approach to synthesize molecular reactions to compute a radial basis function (RBF) support vector machine (SVM) kernel. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key to translating known stochastic logic circuits to molecular computing. Although inspired by prior stochastic logic implementation of the RBF-SVM kernel, the proposed molecular reactions require non-obvious modifications. This paper introduces a new explicit bipolar-to-unipolar molecular converter for intermediate format Conversion. Two designs are presented; one is based on the explicit and the other is based on Implicit Conversion from prior stochastic logic. When 5 support vectors are used, it is shown that the DNA RBF-SVM realized using the explicit format Conversion has orders of magnitude less regression error than that based on Implicit Conversion. CCS CONCEPTS • Applied computing → Molecular structural biology; •Hardware → Biology-related information processing;
Sylviane Valdois - One of the best experts on this subject based on the ideXlab platform.
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A connectionist multiple-trace memory model for polysyllabic word reading.
1998Co-Authors: Bernard Ans, Serge Carbonnel, Sylviane ValdoisAbstract:A connectionist feedforward network implementing a mapping from orthography to phonology is described. The model develops a view of the reading system that accounts for both irregular word and pseudoword reading without relying on any system of explicit or Implicit Conversion rules. The model assumes, however, that reading is supported by 2 procedures that work successively: a global procedure using knowledge about entire words and an analytic procedure based on the activation of word syllabic segments. The model provides an account of the basic effects that characterize human skilled reading performance including a frequency by consistency interaction and a position-of-irregularity effect. Furthermore, early in training, the network shows a performance similar to that of less skilled readers. It also offers a plausible account of the patterns of acquired phonological and surface dyslexia when lesioned in different ways.
Henrik Wann Jensen - One of the best experts on this subject based on the ideXlab platform.
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Importance Sampling Spherical Harmonics
2009Co-Authors: Wojciech Jarosz, Nathan A. Carr, Henrik Wann JensenAbstract:In this paper we present the first practical method for importance sampling functions represented as spherical harmonics (SH). Given a spherical probability density function (PDF) represented as a vector of SH coefficients, our method warps an input point set to match the target PDF using hierarchical sample warping. Our approach is efficient and produces high quality sample distributions. As a by-product of the sampling procedure we produce a multi-resolution representation of the density function as either a spherical mip-map or Haar wavelet. By exploiting this Implicit Conversion we can extend the method to distribute samples according to the product of an SH function with a spherical mip-map or Haar wavelet. This generalization has immediate applicability in rendering, e.g., importance sampling the product of a BRDF and an environment map where the lighting is stored as a single high-resolution wavelet and the BRDF is represented in spherical harmonics. Since spherical harmonics can be efficiently rotated, this product can be computed on-the-fly even if the BRDF is stored in local-space. Our sampling approach generates over 6 million samples per second while significantly reducing precomputation time and storage requirements compared to previous techniques.
Bernard Ans - One of the best experts on this subject based on the ideXlab platform.
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A connectionist multiple-trace memory model for polysyllabic word reading.
1998Co-Authors: Bernard Ans, Serge Carbonnel, Sylviane ValdoisAbstract:A connectionist feedforward network implementing a mapping from orthography to phonology is described. The model develops a view of the reading system that accounts for both irregular word and pseudoword reading without relying on any system of explicit or Implicit Conversion rules. The model assumes, however, that reading is supported by 2 procedures that work successively: a global procedure using knowledge about entire words and an analytic procedure based on the activation of word syllabic segments. The model provides an account of the basic effects that characterize human skilled reading performance including a frequency by consistency interaction and a position-of-irregularity effect. Furthermore, early in training, the network shows a performance similar to that of less skilled readers. It also offers a plausible account of the patterns of acquired phonological and surface dyslexia when lesioned in different ways.
