J
John W. Clark
Researcher at Harvard University
Publications - 715
Citations - 14878
John W. Clark is an academic researcher from Harvard University. The author has contributed to research in topics: Nuclear matter & Neutron. The author has an hindex of 60, co-authored 707 publications receiving 13999 citations. Previous affiliations of John W. Clark include Washington University in St. Louis & Los Alamos National Laboratory.
Papers
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Mathematical model of an adult human atrial cell: the role of K+ currents in repolarization.
TL;DR: A mathematical model of the human atria myocyte based on averaged voltage-clamp data recorded from isolated single myocytes and simulations suggest that the functional role of I(sus) can depend on the physiological/disease state of the cell.
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On the controllability of quantum‐mechanical systems
TL;DR: In this paper, the Schrodinger equation for a quantum object influenced by adjustable external fields provides a state-evolution equation which is linear in ψ and linear in the external controls (thus a bilinear control system).
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Detecting network communities by propagating labels under constraints.
Michael J. Barber,John W. Clark +1 more
TL;DR: This work reformulates the recently proposed label-propagation algorithm (LPA) as an equivalent optimization problem, giving an objective function whose maxima correspond to community solutions, and produces a variety of algorithms that propagate labels subject to constraints.
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A motion-incorporated reconstruction method for gated PET studies
TL;DR: A novel 4D model for the PET imaging system is presented, which can incorporate motion information to generate a motion-free image with all acquired data and reduced quantification error of the radioactivity concentration when compared to a corresponding static acquisition.
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Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images
TL;DR: A novel nonlinear multiscale wavelet diffusion method for ultrasound speckle suppression and edge enhancement designed to utilize the favorable denoising properties of two frequently used techniques: the sparsity and multiresolution properties of the wavelet and the iterative edge enhancement feature of nonlinear diffusion.