where {a n } is a fix sequence in [0, ∞) with a n → 0 as n → ∞. The infimum of constants k n in (2.1) is called nearly Lipschitz constant, which is denoted by η(T n ). Notice that η ( T n ) = sup | | T n x - T n y | | | | x - y | | + a n : x, y ∈ H, x ≠ y.
where {a n } is a fix sequence in [0, ∞) with a n → 0 as n → ∞. For an arbitrary, but fixed n ∈ ℕ, the infimum of constants k n in (2.1) is called nearly Lipschitz constant, which is denoted by η(T n ). Notice that η ( T n ) = sup | | T n x - T n y | | | | x - y | | + a n : x, y ∈ H, x ≠ y.
where { a n } is a fix sequence in [ 0, ∞ ) with a n → 0, as n → ∞. For an arbitrary, but fixed n ∈ N, the infimum of constants k n in (4.1) is called nearly Lipschitz constant and is denoted by η ( T n ). Notice that η ( T n ) = sup { ∥ T n x − T n y ∥ ∥ x − y ∥ + a n : x, y ∈ H, x ≠ y }.
where { a n } is a fix sequence in [ 0, ∞ ) with a n → 0, as n → ∞. For an arbitrary, but fixed n ∈ N, the infimum of constants k n in (2.3) is called nearly Lipschitz constant and is denoted by η ( T n ). Notice that η ( T n ) = sup { ∥ T n x − T n y ∥ ∥ x − y ∥ + a n : x, y ∈ H, x ≠ y }. Definition 2.5 [2.5.
The short linker peptide, derived from an endogenous FIX sequence involved in FIX activation, enables in vivo cleavage of activated FIX from the albumin carrier moiety when required for coagulation 10, 12, 13.
The construct pTTRhFIXopt, which directs the expression of a codon-optimized wild-type human factor IX (FIX) sequence from a liver-specific transthyretin (TTR) promoter, has been described previously.
The present experiment determined whether there are differences in the degree of discounting for a hypothetical $100 produced by a procedure that titrates the immediate amount (titrating sequence procedure) versus a procedure that presents a fixed sequence of immediate amounts (fixed sequence procedure) using a within-subject design.
There appears to be a reasonably fixed sequence of development.
Let us run M independent PFs with the same (fixed) sequence of observations Y1 T=y1 T, T<∞, and N particles each.
In order to analyze the impact of active expectancies on sequential effects, we used a fixed sequence learning task.
Their results revealed marginal learning with the 8-element fixed sequence task and significant learning with the 4-element task.
