diff --git a/slides/sections/4.md b/slides/sections/4.md
index 35c3120..a07164d 100644
--- a/slides/sections/4.md
+++ b/slides/sections/4.md
@@ -81,8 +81,8 @@ How to estimate sample median, mode and FWHM?
 
 . . .
 
+\centering
 \setbeamercovered{}
-\begin{center}
 \begin{tikzpicture}[remember picture, >=Stealth]
   % line
   \draw [line width=3, ->, cyclamen] (-5,0) -- (5,0);
@@ -107,43 +107,34 @@ How to estimate sample median, mode and FWHM?
   \node at (2.45,-0.7) (f1) {};
   \node at (2.45,0.7)  (f2) {};
 \end{tikzpicture}
-\end{center}
 
 . . .
 
-\begin{center}
 \begin{tikzpicture}[remember picture, overlay]
   % region
-  \draw [orange, fill=orange, opacity=0.5] (1a) rectangle (1b);
+  \draw [gray, fill=gray, opacity=0.5] (1a) rectangle (1b);
 \end{tikzpicture}
-\end{center}
 
 . . .
 
-\begin{center}
 \begin{tikzpicture}[remember picture, overlay]
   % region
-  \draw [orange, fill=orange, opacity=0.5] (2a) rectangle (1b);
+  \draw [gray, fill=gray, opacity=0.6] (2a) rectangle (1b);
 \end{tikzpicture}
-\end{center}
 
 . . .
 
-\begin{center}
 \begin{tikzpicture}[remember picture, overlay]
   % region
-  \draw [orange, fill=orange, opacity=0.5] (3a) rectangle (1b);
+  \draw [gray, fill=gray, opacity=0.7] (3a) rectangle (1b);
 \end{tikzpicture}
-\end{center}
 
 . . .
 
-\begin{center}
 \begin{tikzpicture}[remember picture, overlay]
   % region
   \draw [ultra thick] (f1) -- (f2);
 \end{tikzpicture}
-\end{center}
 
 
 ## Sample FWHM
@@ -166,7 +157,7 @@ $$
     G \left( \frac{x-x_i}{\varepsilon} \right)
   $$
 
-  The parameter $\varepsilon$ controls the strength of the smoothing
+  - The parameter $\varepsilon$ controls the strength of the smoothing
 :::
 
 :::  {.column width=50%}
@@ -210,22 +201,21 @@ $$
 
 ## Sample FWHM
 
-Silverman's rule of thumb [@silver86]:
+**Silverman's rule of thumb** [@silver86]:
 
 $$
   \varepsilon = 0.88 \, S_N
   \left( \frac{d + 2}{4}N \right)^{-1/(d + 4)}
 $$
-
-with:
+where:
 
 - $S_N$ is the sample standard deviation
 - $d$ is number of dimensions ($d = 1$)
 
 . . .
 
-Numerical minimization (Brent) for $\quad f_{\varepsilon_{\text{max}}}$  
-Numerical root finding (Brent) for $\quad f_{\varepsilon}(x_{\pm}) =
+Minimization (Brent) for $\quad f_{\varepsilon_{\text{max}}}$  
+Root finding (Brent-Dekker) for $\quad f_{\varepsilon}(x_{\pm}) =
 \frac{f_{\varepsilon_{\text{max}}}}{2}$