pch : modif ds l'intro un d'une ref dans reponse

diff --git a/prng_gpu.tex b/prng_gpu.tex
index f357476af968f72c49b9e4a4b7248b278e32bde7..bc0679701ad8ef020723f13e37e4a6a988d5d70d 100644 (file)
--- a/prng_gpu.tex
+++ b/prng_gpu.tex
@@ -180,8 +180,8 @@ Pseudorandom numbers are generated at a rate of 20GSamples/s, which is faster
 than in~\cite{conf/fpga/ThomasHL09,Marsaglia2003} (and with a better
 statistical behavior). Experiments are also provided using BBS as the initial
 random generator. The generation speed is significantly weaker.
-Note also that an original qualitative comparison between topological chaotic
-properties and statistical test is also proposed.
+%Note also that an original qualitative comparison between topological chaotic
+%properties and statistical test is also proposed.
 
 
 

diff --git a/reponse.tex b/reponse.tex
index dbe927ad7a6689b8467ea72fa6f093e775d3dbbb..a0d3da6f423d0318f522e8e61673e692c0f2f10b 100644 (file)
--- a/reponse.tex
+++ b/reponse.tex
@@ -47,7 +47,7 @@ Done.
 \bigskip
 \textit{There seems to have been no effort in showing how the new PRNG improves on a single (say) xorshift generator, considering the slowdown of calling 3 of them per iteration (cf. Listing 1). This could be done, if not with the mathematical rigor of chaos theory, then with simpler bit diffusion metrics, often used in cryptography to evaluate building blocks of ciphers.}
 
-A large section (Section 5) has been added, using and extending some previous works. It explains with more details why topological chaos 
+A large section (Section 2 of the Annex document) has been provided, using and extending some previous works. It explains with more details why topological chaos 
 is useful to pass statistical tests. This new section contains both qualitative explanations and quantitative (experimental) evaluations.
  Using several examples, this section illustrates  that defective PRNGs are always improved, according
 to the NIST, DieHARD, and TestU01 batteries.