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A description method for heterogeneous geo-analysis models is studied.
The application of this method for heterogeneous catalytic reactions, however, has not yet been exploited.
This paper presents a novel multi-directional blending method for heterogeneous object design.
Dai et al. [37] proposed a method for heterogeneous transfer learning called Translated Learning via Risk Minimization (TLRisk).
The effective stiffness is calculated using a FEM formulation of asymptotic homogenization method for heterogeneous periodic beam.
Furthermore, the analysis on time complexity and space complexity shows that our solution is much more efficient than the state-of-the-art method for heterogeneous feedbacks.
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Furthermore, the computed results by the mentioned methods for heterogeneous super molecule are given in Table 3.
Our main contribution is a comprehensive survey of 38 methods for heterogeneous transfer learning which operate under varied settings, requirements, and domains.
They show that it is competitive, in terms of makespan, compared to existing reference scheduling methods for heterogeneous processors (HEFT), while providing a better reliability.
We further demonstrate with our epidermal model that the integration of multiple complex modeling methods for heterogeneous multicellular biological processes is both feasible and computationally tractable using this new technology.
The paper by Zhou [144] proposes a solution called the hybrid heterogeneous transfer learning (HHTL) method for a heterogeneous environment with abundant labeled source data and abundant unlabeled target data.
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