split in lines

paper.md

@@ -47,7 +47,11 @@ Here we associate a *local loss* to each of the *candidate points* within an int
 In the case of the integration algorithm the loss could just be an error estimate.
 The most significant advantage of these *local* algorithms is that they allow for easy parallelization and have a low computational overhead.
 
-![Visualization of point choosing algorithm for a blackbox function (grey). The existing data points (green) $\{x_i, y_i\}_{i \in 1...4}$ and corresponding candidate points (red) in the middle of each interval. Each candidate point has a loss $L$ indicated by the size of the red dots. The candidate point with the largest loss will be chosen which in this case is the one with $L_{1,2}$.](figures/loss_1D.pdf){#fig:loss_1D}
+![Visualization of the point choosing algorithm for a blackbox function (grey).
+The existing data points (green) $\{x_i, y_i\}_{i \in 1...4}$ and corresponding candidate points (red) in the middle of each interval.
+Each candidate point has a loss $L$ indicated by the size of the red dots.
+The candidate point with the largest loss will be chosen which in this case is the one with $L_{1,2}$.
+](figures/loss_1D.pdf){#fig:loss_1D}
 
 #### We provide a reference implementation, the Adaptive package, and demonstrate its performance.
 We provide a reference implementation, the open-source Python package called Adaptive[@Nijholt2019a], which has previously been used in several scientific publications[@vuik2018reproducing; @laeven2019enhanced; @bommer2019spin; @melo2019supercurrent].