How can there be more true positive than positive?
Of course it is impossible to have a higher than $100\%$ true positive rate in your results.

The author does explain how he arrives at his results, but I must admit it is not at all obvious. In the subsection Performance Metrics of section 2 Experiment Protocol the author explains that the metrics are calculated as


  The TP10 metric is the number of true positives found in the 10 test
  cases that are predicted most strongly by the classifier to be
  positive.


This mean that the TP10 is only calculated using the testing set. The author also explains in the subsection Experiment Procedure of section 2 Experiment Protocol states


  The condition on the second loop ensures that there are 40 positives
  available for training plus at least 10 others in the test set
  (likewise, 200 negatives for training and at least 50 for testing)


So we see that there is actually a minimum of 10 testing instances with which the TP10 is computed.