Existence and stability of standing waves for nonlinear fractional Schrödinger equations J. Math. Phys. 53, 083702 (2012) N-fold Darboux transformations and soliton solutions of three nonlinear equations J. Math. Phys. 53, 083502 (2012) Some algebro-geometric solutions for the coupled modified Kadomtsev-Petviashvili equations arising from the Neumann type systems
The paper is concerned with the uniqueness of the Maximum a Posteriori estimate for restoration problems of data corrupted by Poisson noise, when we have to minimize a combination of the generalized Kullback-Leibler divergence and a regularization penalty function. The aim of this paper is to prove the uniqueness result for 2D and 3D problems for several penalty functions, such as an edge preserving functional, a simple case of the class of Markov Random Field (MRF) regularization functionals and the classical Tikhonov regularization
We review the class of partial-propensity exact stochastic simulation algorithms (SSA) for chemical reaction networks. We show which modules partial-propensity SSAs are composed of and how partial-propensity variants of known SSAs can be constructed by adjusting the sampling strategy used. We demonstrate this on the example of two instances, namely the partial-propensity variant of Gillespie's original direct method and that of the SSA with composition-rejection sampling (SSA-CR). Partialpropensity methods may outperform the corresponding classical SSA, particularly on strongly coupled reaction networks. Changing the different modules of partial-propensity SSAs provides flexibility in tuning them to perform particularly well on certain classes of reaction networks. The framework presented here defines the design space of such adaptations. Abstract. We review the class of partial-propensity exact stochastic simulation algorithms (SSA) for chemical reaction networks. We show which modules partial-propensity SSAs are composed of and how partial-propensity variants of known SSAs can be constructed by adjusting the sampling strategy used. We demonstrate this on the example of two instances, namely the partial-propensity variant of Gillespie's original direct method and that of the SSA with composition-rejection sampling (SSA-CR). Partial-propensity methods may outperform the corresponding classical SSA, particularly on strongly coupled reaction networks. Changing the different modules of partial-propensity SSAs provides flexibility in tuning them to perform particularly well on certain classes of reaction networks. The framework presented here defines the design space of such adaptations.
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