Consider the case of a TIDB with $\numTup$ tuples, with $\prob=\frac{1}{2}$ for given tuple $t$. Because TIDB has the property of set semantics, the vector $\genV$ can then be defined as a binary bit vector $\{0, 1\}^\numTup$, whose value represents a possible world, and, where each index represents a specific tuple $t$ id. Under these semantics, with $w_t$ representing the index mapped to a a tuple $t$'s identity, $\genV$ can alternatively be viewed as a function
where a value of $1$ indicates that the tuple is present in a given world, and $0$ denotes that the tuple is absent in the world represented by the binary bit string.
In this representation, a few properties of $\genV$ immediately stand out. First, the length of $\genV$ is the same as the number of tuples in the TIDB, $|\genV| =\numTup$. This combined with the assumption of $\prob=\frac{1}{2}$ implies that the L1 norm of $\genV$ is $\frac{\numTup}{2}$ and that the L2 norm of $\genV$ squared is also the same value,