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The first type does not fix the random selection for each different online set, the second one does.
In order to achieve this, we are going to fix the random choice of T r, given the same online user set U r. To illustrate its effect, we take the same example, where the online user set U is either {1,2,4,5} or {1,2,3,4,5}.
However, in order to obtain "deterministic" results since the goal of this exercise is to ascertain whether the original network can be recovered, we fix the random number seed so that given a particular representation of the proteins and RNAP-cofactor complexes, we will obtain the same, reproducible results.
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The doping technique consists in fixing the random matrix such that, under BP, all the variable nodes can be recovered after a certain number of iterations.
The experiments show that fixing the random user selection sufficiently hides input data because the adversary is forced to collect data samples from different online user sets.
Therefore, fixing the random user selection will be a proper solution for most group services in practice, especially when the same online user set is likely to return.
In Section 2.2, a new security measure was introduced, which consisted of fixing the random subset, given the same online user set.
We have Privacyu′=H(x u ∣y1′,…,y k′,U 1,…U k ), where the outputs (y^{prime }_{r}) are the result of the modified output computation, which includes fixing the random choice of T r.
For each available nutrient, we fix the uptake rate at a random value between 0 and 20.
Together, the fixed and the random factors explain 78% of the variance, whereas the fixed factors alone explain 10%.
The coefficients can be obtained both from the fixed and the random effect models; we use the random effect coefficients.
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CEO of Professional Science Editing for Scientists @ prosciediting.com