The random user selection approach consists of choosing for each execution r a random subset T r of U r, containing M user indices, and having the server compute y r =F({x u ∣u∈T r }), which we also denote as y r =F(T r ).
After that, we generate c check-ins randomly choosing for each of them a gender (female or male), a location in L, and a user in U. Any element (gender, location, or user) is randomly sampled with replacement and thus can be chosen more than once.
Writing (U: = Lambda _2 otimes _{{mathbb {Z}}} {mathbb {C}}= oplus U_{lambda }), the desired decomposition is obtained by choosing, for each eigenvalue (lambda ), a decomposition ( U_{lambda } = U_{lambda }^{1,0} oplus U_{lambda }^{0,1} ) such that ( overline{U_{lambda }^{1,0}} = U^{0,1}_{bar{lambda }}).
By choosing for each scheme the value of L T which provides the best rate, the performance gain achieved by DC over SCP decreases with respect to the perfect CSI case to about 16%. Figure 11 Fifth percentile of the UE rate versus L T / L E with N = 4, l (MAX) = 1, and for the ETU channel.
Several solutions have been posed to contend with this issue: (i) only choosing probe sets with a one-to-one correspondence with genes[40], (ii) choosing for each gene the probe set with the maximum expression [41] and (iii) choosing for each gene a probe set at random[42].
We extracted from our miRNA target predictions [58] the top 1000 target sites with the highest probability of being under evolutionary selection, and an equal number of target sites with the lowest probability of being under evolutionary selection, by choosing for each miRNA having at least one high-probability target site, an equal number of low-probability sites.
