diff --git a/DSD/qrs/images/preds.pdf b/DSD/qrs/images/preds.pdf
index d929b97..f3d8865 100644
Binary files a/DSD/qrs/images/preds.pdf and b/DSD/qrs/images/preds.pdf differ
diff --git a/DSD/qrs/main.tex b/DSD/qrs/main.tex
index 058d56d..5f62b0a 100644
--- a/DSD/qrs/main.tex
+++ b/DSD/qrs/main.tex
@@ -535,14 +535,10 @@ The activity in this second time series was very sparse with long periods withou
 The no consumption sections: are not a challenging --- i.e., all detectors perform well on this type of pattern ---, make the manual labeling more difficult, and level all results up.
 For this reason we removed large sections of inactivity between active segments to make the time series more challenging without tempering with the order of detector performances.
 
-%\input{refit_table}
-\agd{include table about refit dataset}
-
 \subsection{Alternative Methods}
-\agd{Explain better why the alternative methods are chosen.}
 \agd{explain how the svm and mlp are trained.}
 We implemented three alternative methods to compare with the performance of \gls{mad}.
-These methods are commonly deployed to detect patterns in a time series.
+The alternative methodes are chosen to be well-established and of comparable complexity.
 The methods are: a \gls{1nn} detector, an \gls{svm} classifier, and an \gls{mlp} classifier.
 More complex solutions like \gls{rnn} or \gls{cnn} show good performances on time series analysis but require too much data to be applicable to one-shot classification.
 All alternative methods rely on a sliding window to extract substring to classify.