Exercise 2.27 in Elements of Information Theory (2nd ed.) reads:
Let $\mathbf{p} = (p_{1}, p_{2}, \ldots, p_{m})$ be a probability distribution on $m$ elements (i.e., $p_{i} \geq 0$ and $\sum_{i=1}^{m} p_{i} = 1$). Define a new distribution $\mathbf{q}$ on $m - 1$ elements as $q_{1} = p_{1}, q_{2} = p_{2}, \ldots, q_{m-2} = p_{m-2}$, and $q_{m-1} = p_{m-1} + p_{m}$ [i.e., the distribution $\mathbf{q}$ is the same as $\mathbf{p}$ on $\{1, 2, \ldots, m - 2\}$, and the probability of the last element in $\mathbf{q}$ is the sum of the last two probabilities of $\mathbf{p}$]. Show that $ H(\mathbf{p}) = H(\mathbf{q}) + (p_{m-1} + p_{m})H\left( \frac{p_{m-1}}{p_{m-1} + p_{m}}, \frac{p_{m}}{p_{m-1} + p_{m}} \right). $
Question 1. What does $H\left( \frac{p_{m-1}}{p_{m-1} + p_{m}}, \frac{p_{m}}{p_{m-1} + p_{m}} \right)$ mean in this context?
Question 2. Where is this notation introduced in the text?
Please do not provide a solution to the exercise.