clarify caption

paper.md

@@ -39,7 +39,7 @@ One of the most significant complications here is to parallelize this algorithm,
 ![Visualization of a 1-D point choosing algorithm for a black box function (grey).
 We start by calculating the two boundary points.
 Two consecutive existing data points (black) $\{x_i, y_i\}$ define an interval.
-Each interval has a loss associated with it that can be calculated from the points inside the interval $L_{i,i+1}(x_i, x_{i+1}, y_i, y_{i+1})$.
+Each interval has a loss $L_{i,i+1}$ associated with it that can be calculated from the points inside the interval $L_{i,i+1}(x_i, x_{i+1}, y_i, y_{i+1})$ and optionally of $N$ next nearest neighbouring intervals.
 At each iteration the interval with the largest loss is indicated (red), with its corresponding candidate point (green) picked in the middle of the interval.
 The loss function in this example is the curvature loss.
 ](figures/algo.pdf){#fig:algo}