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Start with a given affluent population of any size.
It's obvious: Start with a given stock of provisions in the cupboard, subtract consumption and eventually the cupboard will be bare.
To search for an optimal network, we start with a given ordering of genes.
This stands in contrast to previous studies which either start with a given list of types or a specific model of preferences.
The idea of the scenario analysis is that we start with a given life expectancy in 2050, and then consider different health scenarios to get there.
To more compactly encode this situation, we check whether the set of reads that start with a given kmer m consists entirely of d duplicates of the same sequence.
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Instead of starting with a given policy for controlling the network and then study the resulting dynamics, we start from a desired steady-state behavior and derive a policy which achieves this behavior.
The search algorithm is developed on PSO to iteratively search optimum point, starting with a given reference search space and improving it based on the location of best searched point in each iteration.
Starting with a given point, one can watch the trajectory of the point under repeated applications of the rule; one can think of the rule as moving the starting point around over time.
In contrast to the common approach which starts with a given efficiency function and derives a differential equation for the supporting polynomial of the D-optimal design, we derive a differential equation for the efficiency function, such that an explicit solution of the D-optimal design problem is possible.
Suppose action a 1 is consecutively taken n times starting with a given b t).
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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