The Experts below are selected from a list of 21447 Experts worldwide ranked by ideXlab platform
Brian R Pauw - One of the best experts on this subject based on the ideXlab platform.
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improvements and considerations for size distribution retrieval from small angle scattering data by monte carlo methods
Journal of Applied Crystallography, 2013Co-Authors: Brian R Pauw, Janskov Pedersen, Samuel Tardif, Masaki Takata, B B IversenAbstract:Monte Carlo (MC) methods, based on random updates and the trial-and-error principle, are well suited to retrieve form-free particle size distributions from small-angle scattering patterns of non-interacting low-concentration scatterers such as particles in solution or precipitates in metals. Improvements are presented to existing MC methods, such as a non-ambiguous Convergence Criterion, nonlinear scaling of contributions to match their observability in a scattering measurement, and a method for estimating the minimum visibility threshold and uncertainties on the resulting size distributions.
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improvements and considerations for size distribution retrieval from small angle scattering data by monte carlo methods
arXiv: Data Analysis Statistics and Probability, 2012Co-Authors: Brian R Pauw, Janskov Pedersen, Samuel Tardif, Masaki Takata, B B IversenAbstract:Monte-Carlo (MC) methods, based on random updates and the trial-and-error principle, are well suited to retrieve particle size distributions from small-angle scattering patterns of dilute solutions of scatterers. The size sensitivity of size determination methods in relation to the range of scattering vectors covered by the data is discussed. Improvements are presented to existing MC methods in which the particle shape is assumed to be known. A discussion of the problems with the ambiguous Convergence criteria of the MC methods are given and a Convergence Criterion is proposed, which also allows the determination of uncertainties on the determined size distributions.
B B Iversen - One of the best experts on this subject based on the ideXlab platform.
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improvements and considerations for size distribution retrieval from small angle scattering data by monte carlo methods
Journal of Applied Crystallography, 2013Co-Authors: Brian R Pauw, Janskov Pedersen, Samuel Tardif, Masaki Takata, B B IversenAbstract:Monte Carlo (MC) methods, based on random updates and the trial-and-error principle, are well suited to retrieve form-free particle size distributions from small-angle scattering patterns of non-interacting low-concentration scatterers such as particles in solution or precipitates in metals. Improvements are presented to existing MC methods, such as a non-ambiguous Convergence Criterion, nonlinear scaling of contributions to match their observability in a scattering measurement, and a method for estimating the minimum visibility threshold and uncertainties on the resulting size distributions.
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improvements and considerations for size distribution retrieval from small angle scattering data by monte carlo methods
arXiv: Data Analysis Statistics and Probability, 2012Co-Authors: Brian R Pauw, Janskov Pedersen, Samuel Tardif, Masaki Takata, B B IversenAbstract:Monte-Carlo (MC) methods, based on random updates and the trial-and-error principle, are well suited to retrieve particle size distributions from small-angle scattering patterns of dilute solutions of scatterers. The size sensitivity of size determination methods in relation to the range of scattering vectors covered by the data is discussed. Improvements are presented to existing MC methods in which the particle shape is assumed to be known. A discussion of the problems with the ambiguous Convergence criteria of the MC methods are given and a Convergence Criterion is proposed, which also allows the determination of uncertainties on the determined size distributions.
Jenchih Yao - One of the best experts on this subject based on the ideXlab platform.
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Convergence criteria of newton s method on lie groups
Fixed Point Theory and Applications, 2013Co-Authors: Jinhua Wang, Jenchih YaoAbstract:In the present paper, we study Newton’s method on Lie groups (independent of affine connections) for finding zeros of a mapping f from a Lie group to its Lie algebra. Under a generalized L-average Lipschitz condition of the differential of f, we establish a unified Convergence Criterion of Newton’s method. As applications, we get the Convergence criteria under the Kantorovich’s condition and the γ-condition, respectively. Moreover, applications to optimization problems are also provided.
Feng Huang - One of the best experts on this subject based on the ideXlab platform.
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a guaranteed Convergence analysis for the projected fast iterative soft thresholding algorithm in parallel mri
Medical Image Analysis, 2021Co-Authors: Xinlin Zhang, Di Guo, Lijun Bao, Feng HuangAbstract:Abstract Sparse sampling and parallel imaging techniques are two effective approaches to alleviate the lengthy magnetic resonance imaging (MRI) data acquisition problem. Promising data recoveries can be obtained from a few MRI samples with the help of sparse reconstruction models. To solve the optimization models, proper algorithms are indispensable. The pFISTA, a simple and efficient algorithm, has been successfully extended to parallel imaging. However, its Convergence Criterion is still an open question. Besides, the existing Convergence Criterion of single-coil pFISTA cannot be applied to the parallel imaging pFISTA, which, therefore, imposes confusions and difficulties on users about determining the only parameter - step size. In this work, we provide the guaranteed Convergence analysis of the parallel imaging version pFISTA to solve the two well-known parallel imaging reconstruction models, SENSE and SPIRiT. Along with the Convergence analysis, we provide recommended step size values for SENSE and SPIRiT reconstructions to obtain fast and promising reconstructions. Experiments on in vivo brain images demonstrate the validity of the Convergence Criterion.
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a guaranteed Convergence analysis for the projected fast iterative soft thresholding algorithm in parallel mri
arXiv: Image and Video Processing, 2019Co-Authors: Xinlin Zhang, Di Guo, Lijun Bao, Feng HuangAbstract:The boom of non-uniform sampling and compressed sensing techniques dramatically alleviates the lengthy data acquisition problem of magnetic resonance imaging. Sparse reconstruction, thanks to its fast computation and promising performance, has attracted researchers to put numerous efforts on it and has been adopted in commercial scanners. To perform sparse reconstruction, choosing a proper algorithm is essential in providing satisfying results and saving time in tuning parameters. The pFISTA, a simple and efficient algorithm for sparse reconstruction, has been successfully extended to parallel imaging. However, its Convergence Criterion is still an open question. And the existing Convergence Criterion of single-coil pFISTA cannot be applied to the parallel imaging pFISTA, which, therefore, imposes confusions and difficulties on users about determining the only parameter - step size. In this work, we provide the guaranteed Convergence analysis of the parallel imaging version pFISTA to solve the two well-known parallel imaging reconstruction models, SENSE and SPIRiT. Along with the Convergence analysis, we provide recommended step size values for SENSE and SPIRiT reconstructions to obtain fast and promising reconstructions. Experiments on in vivo brain images demonstrate the validity of the Convergence Criterion. Besides, experimental results show that compared to using backtracking and power iteration to determine the step size, our recommended step size achieves more than five times acceleration in reconstruction time in most tested cases.
John Z H Zhang - One of the best experts on this subject based on the ideXlab platform.
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optimization of Convergence criteria for fragmentation methods
Chemical Physics Letters, 2017Co-Authors: Zhaoxi Sun, Tong Zhu, Xiaohui Wang, Ye Mei, John Z H ZhangAbstract:Abstract Fragmentation methods reproduce the result of the exponential scaling full system calculation within an error about 1 kcal/mol. They serve as a robust linear scaling regime of quantum treatment for large molecular systems. As the response of fragmentation methods to Convergence criteria in SCF iterations is different from that of full system calculation and the propagated Convergence error is much smaller than the fragmentation error, the speed of fragmentation ab initio calculation can be further enhanced via loosening the Convergence Criterion in the calculation of each fragment without sacrificing the accuracy of the overall energy. Tests on single configuration calculations, geometry optimization and molecular dynamics simulation are performed.