diff --git a/g.tex b/g.tex
index ace438b..d42002c 100644
--- a/g.tex
+++ b/g.tex
@@ -480,7 +480,7 @@ This series can be rewritten
 where $\ket{I_0}=\ket{i_0}$ is the initial state, $n_0$ is the number of times the walker remains within the domain of $\ket{i_0}$ (with $1 \le n_0 \le N+1$), $\ket{I_1}$ is the first exit state that does not belong to $\cD_{i_0}$, $n_1$ is the number of times the walker remains in $\cD_{i_1}$ (with $1 \le n_1 \le N+1-n_0$), $\ket{I_2}$ is the second exit state, and so on.
 Here, the integer $p$ goes from 0 to $N$ and indicates the number of exit events occurring along the path. 
 The two extreme cases, $p=0$ and $p=N$, correspond to the cases where the walker remains in the initial domain during the entire path, and to the case where the walker exits a domain at each step, respectively. 
-\titou{In what follows, we shall systematically write the integers representing the exit states in capital letter.}
+\titou{In what follows, we shall systematically write the integers representing the exit states in capital letters.}
 
 %Generalizing what has been done for domains consisting of only one single state, the general idea here is to integrate out exactly the stochastic dynamics over the 
 %set of all paths having the same representation, Eq.(\ref{eff_series}). As a consequence, an effective Monte Carlo dynamics including only exit states