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In a typical JSM, we represent each signal x j ∈ ℝ N in terms of a decomposition x j = z C + z j, where z C ∈ ℝ N is a "common component" that is assumed to be present in all {x j }, and z j ∈ ℝ N is an "innovation component" that differs for each signal.
In this study, startle potentiation, a response component that is assumed to be mediated by the colliculo-amygdalar pathway, did not differ between the SVF and BVF.
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Similarly, in our structural components, we observed characteristics that were assumed to be unique to the functional networks.
In addition, each entry in the data vector corresponds to either the in-phase component or the quadrature component of a QAM symbol that is assumed to have unit energy.
That is assumed.
That's assuming new L.S.U.
If you identify a deal component that's not working as you assumed it would, you can try to come up with fixes.
For the core-collapse component, it is assumed that 1% of the original star's mass was converted into iron.
This approach models the system as a combination of SEA subsystems and FE components; it is assumed that the FE components have fully deterministic properties, while the SEA subsystems have a high degree of randomness.
In most of studies in terms of allocation of redundant components, it is assumed that the components are either non-repairable or repairable.
Besides osteolysis due to wear debris (especially of polyethylene components) it is assumed that a lack of initial bony incorporation of the implant favors aseptic loosening.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com