\usepackage{ctable}
\usepackage{tabularx}
\usepackage{multirow}
-
% Pour mathds : les ensembles IR, IN, etc.
\usepackage{dsfont}
\usepackage{graphicx}
% Pour faire des sous-figures dans les figures
\usepackage{subfigure}
+\usepackage{xr-hyper}
+\usepackage{hyperref}
+\externaldocument[A-]{supplementary}
+
\newtheorem{notation}{Notation}
use of more general chaotic iterations to generate pseudorandom numbers
faster, does not deflate their topological chaos properties.
-\subsection{Proofs of Chaos of the General Formulation of the Chaotic Iterations}
-\label{deuxième def}
-Let us consider the discrete dynamical systems in chaotic iterations having
-the general form: $\forall n\in \mathds{N}^{\ast }$, $ \forall i\in
-\llbracket1;\mathsf{N}\rrbracket $,
-\begin{equation}
- x_i^n=\left\{
-\begin{array}{ll}
- x_i^{n-1} & \text{ if } i \notin \mathcal{S}^n \\
- \left(f(x^{n-1})\right)_{S^n} & \text{ if }i \in \mathcal{S}^n.
-\end{array}\right.
-\label{general CIs}
-\end{equation}
+%%RAF proof en supplementary, j'ai mis le theorem.
+% A vérifier
-In other words, at the $n^{th}$ iteration, only the cells whose id is
-contained into the set $S^{n}$ are iterated.
+ \subsection{Proofs of Chaos of the General Formulation of the Chaotic Iterations}
+\label{deuxième def}
+The proof is given in Section~\ref{A-deuxième def} of the annex document.
+%% \label{deuxième def}
+%% Let us consider the discrete dynamical systems in chaotic iterations having
+%% the general form: $\forall n\in \mathds{N}^{\ast }$, $ \forall i\in
+%% \llbracket1;\mathsf{N}\rrbracket $,
-Let us now rewrite these general chaotic iterations as usual discrete dynamical
-system of the form $X^{n+1}=f(X^n)$ on an ad hoc metric space. Such a formulation
-is required in order to study the topological behavior of the system.
+%% \begin{equation}
+%% x_i^n=\left\{
+%% \begin{array}{ll}
+%% x_i^{n-1} & \text{ if } i \notin \mathcal{S}^n \\
+%% \left(f(x^{n-1})\right)_{S^n} & \text{ if }i \in \mathcal{S}^n.
+%% \end{array}\right.
+%% \label{general CIs}
+%% \end{equation}
-Let us introduce the following function:
-\begin{equation}
-\begin{array}{cccc}
- \chi: & \llbracket 1; \mathsf{N} \rrbracket \times \mathcal{P}\left(\llbracket 1; \mathsf{N} \rrbracket\right) & \longrightarrow & \mathds{B}\\
- & (i,X) & \longmapsto & \left\{ \begin{array}{ll} 0 & \textrm{if }i \notin X, \\ 1 & \textrm{if }i \in X, \end{array}\right.
-\end{array}
-\end{equation}
-where $\mathcal{P}\left(X\right)$ is for the powerset of the set $X$, that is, $Y \in \mathcal{P}\left(X\right) \Longleftrightarrow Y \subset X$.
+%% In other words, at the $n^{th}$ iteration, only the cells whose id is
+%% contained into the set $S^{n}$ are iterated.
-Given a function $f:\mathds{B}^\mathsf{N} \longrightarrow \mathds{B}^\mathsf{N} $, define the function:
-$F_{f}: \mathcal{P}\left(\llbracket1;\mathsf{N}\rrbracket \right) \times \mathds{B}^{\mathsf{N}}
-\longrightarrow \mathds{B}^{\mathsf{N}}$
-\begin{equation*}
-\begin{array}{rll}
- (P,E) & \longmapsto & \left( E_{j}.\chi (j,P)+f(E)_{j}.\overline{\chi(j,P)}\right) _{j\in \llbracket1;\mathsf{N}\rrbracket}%
-\end{array}%
-\end{equation*}%
-where + and . are the Boolean addition and product operations, and $\overline{x}$
-is the negation of the Boolean $x$.
-Consider the phase space:
-\begin{equation}
-\mathcal{X} = \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)^\mathds{N} \times
-\mathds{B}^\mathsf{N},
-\end{equation}
-\noindent and the map defined on $\mathcal{X}$:
-\begin{equation}
-G_f\left(S,E\right) = \left(\sigma(S), F_f(i(S),E)\right), %\label{Gf} %%RAPH, j'ai viré ce label qui existe déjà avant...
-\end{equation}
-\noindent where $\sigma$ is the \emph{shift} function defined by $\sigma
-(S^{n})_{n\in \mathds{N}}\in \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)^\mathds{N}\longrightarrow (S^{n+1})_{n\in
-\mathds{N}}\in \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)^\mathds{N}$ and $i$ is the \emph{initial function}
-$i:(S^{n})_{n\in \mathds{N}} \in \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)^\mathds{N}\longrightarrow S^{0}\in \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)$.
-Then the general chaotic iterations defined in Equation \ref{general CIs} can
-be described by the following discrete dynamical system:
-\begin{equation}
-\left\{
-\begin{array}{l}
-X^0 \in \mathcal{X} \\
-X^{k+1}=G_{f}(X^k).%
-\end{array}%
-\right.
-\end{equation}%
+%% Let us now rewrite these general chaotic iterations as usual discrete dynamical
+%% system of the form $X^{n+1}=f(X^n)$ on an ad hoc metric space. Such a formulation
+%% is required in order to study the topological behavior of the system.
-Once more, a shift function appears as a component of these general chaotic
-iterations.
-
-To study the Devaney's chaos property, a distance between two points
-$X = (S,E), Y = (\check{S},\check{E})$ of $\mathcal{X}$ must be defined.
-Let us introduce:
-\begin{equation}
-d(X,Y)=d_{e}(E,\check{E})+d_{s}(S,\check{S}),
-\label{nouveau d}
-\end{equation}
-\noindent where $ \displaystyle{d_{e}(E,\check{E})} = \displaystyle{\sum_{k=1}^{\mathsf{N}%
- }\delta (E_{k},\check{E}_{k})}$ is once more the Hamming distance, and
-$ \displaystyle{d_{s}(S,\check{S})} = \displaystyle{\dfrac{9}{\mathsf{N}}%
- \sum_{k=1}^{\infty }\dfrac{|S^k\Delta {S}^k|}{10^{k}}}$,
-%%RAPH : ici, j'ai supprimé tous les sauts à la ligne
+%% Let us introduce the following function:
+%% \begin{equation}
+%% \begin{array}{cccc}
+%% \chi: & \llbracket 1; \mathsf{N} \rrbracket \times \mathcal{P}\left(\llbracket 1; \mathsf{N} \rrbracket\right) & \longrightarrow & \mathds{B}\\
+%% & (i,X) & \longmapsto & \left\{ \begin{array}{ll} 0 & \textrm{if }i \notin X, \\ 1 & \textrm{if }i \in X, \end{array}\right.
+%% \end{array}
+%% \end{equation}
+%% where $\mathcal{P}\left(X\right)$ is for the powerset of the set $X$, that is, $Y \in \mathcal{P}\left(X\right) \Longleftrightarrow Y \subset X$.
+
+%% Given a function $f:\mathds{B}^\mathsf{N} \longrightarrow \mathds{B}^\mathsf{N} $, define the function:
+%% $F_{f}: \mathcal{P}\left(\llbracket1;\mathsf{N}\rrbracket \right) \times \mathds{B}^{\mathsf{N}}
+%% \longrightarrow \mathds{B}^{\mathsf{N}}$
+%% \begin{equation*}
+%% \begin{array}{rll}
+%% (P,E) & \longmapsto & \left( E_{j}.\chi (j,P)+f(E)_{j}.\overline{\chi(j,P)}\right) _{j\in \llbracket1;\mathsf{N}\rrbracket}%
+%% \end{array}%
+%% \end{equation*}%
+%% where + and . are the Boolean addition and product operations, and $\overline{x}$
+%% is the negation of the Boolean $x$.
+%% Consider the phase space:
+%% \begin{equation}
+%% \mathcal{X} = \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)^\mathds{N} \times
+%% \mathds{B}^\mathsf{N},
+%% \end{equation}
+%% \noindent and the map defined on $\mathcal{X}$:
+%% \begin{equation}
+%% G_f\left(S,E\right) = \left(\sigma(S), F_f(i(S),E)\right), %\label{Gf} %%RAPH, j'ai viré ce label qui existe déjà avant...
+%% \end{equation}
+%% \noindent where $\sigma$ is the \emph{shift} function defined by $\sigma
+%% (S^{n})_{n\in \mathds{N}}\in \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)^\mathds{N}\longrightarrow (S^{n+1})_{n\in
+%% \mathds{N}}\in \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)^\mathds{N}$ and $i$ is the \emph{initial function}
+%% $i:(S^{n})_{n\in \mathds{N}} \in \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)^\mathds{N}\longrightarrow S^{0}\in \mathcal{P}\left(\llbracket 1 ; \mathsf{N} \rrbracket\right)$.
+%% Then the general chaotic iterations defined in Equation \ref{general CIs} can
+%% be described by the following discrete dynamical system:
%% \begin{equation}
%% \left\{
-%% \begin{array}{lll}
-%% \displaystyle{d_{e}(E,\check{E})} & = & \displaystyle{\sum_{k=1}^{\mathsf{N}%
-%% }\delta (E_{k},\check{E}_{k})} \textrm{ is once more the Hamming distance}, \\
-%% \displaystyle{d_{s}(S,\check{S})} & = & \displaystyle{\dfrac{9}{\mathsf{N}}%
-%% \sum_{k=1}^{\infty }\dfrac{|S^k\Delta {S}^k|}{10^{k}}}.%
+%% \begin{array}{l}
+%% X^0 \in \mathcal{X} \\
+%% X^{k+1}=G_{f}(X^k).%
%% \end{array}%
%% \right.
-%% \end{equation}
-where $|X|$ is the cardinality of a set $X$ and $A\Delta B$ is for the symmetric difference, defined for sets A, B as
-$A\,\Delta\,B = (A \setminus B) \cup (B \setminus A)$.
-
-
-\begin{proposition}
-The function $d$ defined in Eq.~\ref{nouveau d} is a metric on $\mathcal{X}$.
-\end{proposition}
-
-\begin{proof}
- $d_e$ is the Hamming distance. We will prove that $d_s$ is a distance
-too, thus $d$, as being the sum of two distances, will also be a distance.
- \begin{itemize}
-\item Obviously, $d_s(S,\check{S})\geqslant 0$, and if $S=\check{S}$, then
-$d_s(S,\check{S})=0$. Conversely, if $d_s(S,\check{S})=0$, then
-$\forall k \in \mathds{N}, |S^k\Delta {S}^k|=0$, and so $\forall k, S^k=\check{S}^k$.
- \item $d_s$ is symmetric
-($d_s(S,\check{S})=d_s(\check{S},S)$) due to the commutative property
-of the symmetric difference.
-\item Finally, $|S \Delta S''| = |(S \Delta \varnothing) \Delta S''|= |S \Delta (S'\Delta S') \Delta S''|= |(S \Delta S') \Delta (S' \Delta S'')|\leqslant |S \Delta S'| + |S' \Delta S''|$,
-and so for all subsets $S,S',$ and $S''$ of $\llbracket 1, \mathsf{N} \rrbracket$,
-we have $d_s(S,S'') \leqslant d_e(S,S')+d_s(S',S'')$, and the triangle
-inequality is obtained.
- \end{itemize}
-\end{proof}
-
-
-Before being able to study the topological behavior of the general
-chaotic iterations, we must first establish that:
-
-\begin{proposition}
- For all $f:\mathds{B}^\mathsf{N} \longrightarrow \mathds{B}^\mathsf{N} $, the function $G_f$ is continuous on
-$\left( \mathcal{X},d\right)$.
-\end{proposition}
-
-
-\begin{proof}
-We use the sequential continuity.
-Let $(S^n,E^n)_{n\in \mathds{N}}$ be a sequence of the phase space $%
-\mathcal{X}$, which converges to $(S,E)$. We will prove that $\left(
-G_{f}(S^n,E^n)\right) _{n\in \mathds{N}}$ converges to $\left(
-G_{f}(S,E)\right) $. Let us remark that for all $n$, $S^n$ is a strategy,
-thus, we consider a sequence of strategies (\emph{i.e.}, a sequence of
-sequences).\newline
-As $d((S^n,E^n);(S,E))$ converges to 0, each distance $d_{e}(E^n,E)$ and $d_{s}(S^n,S)$ converges
-to 0. But $d_{e}(E^n,E)$ is an integer, so $\exists n_{0}\in \mathds{N},$ $%
-d_{e}(E^n,E)=0$ for any $n\geqslant n_{0}$.\newline
-In other words, there exists a threshold $n_{0}\in \mathds{N}$ after which no
-cell will change its state:
-$\exists n_{0}\in \mathds{N},n\geqslant n_{0}\Rightarrow E^n = E.$
-
-In addition, $d_{s}(S^n,S)\longrightarrow 0,$ so $\exists n_{1}\in %
-\mathds{N},d_{s}(S^n,S)<10^{-1}$ for all indexes greater than or equal to $%
-n_{1}$. This means that for $n\geqslant n_{1}$, all the $S^n$ have the same
-first term, which is $S^0$: $\forall n\geqslant n_{1},S_0^n=S_0.$
-
-Thus, after the $max(n_{0},n_{1})^{th}$ term, states of $E^n$ and $E$ are
-identical and strategies $S^n$ and $S$ start with the same first term.\newline
-Consequently, states of $G_{f}(S^n,E^n)$ and $G_{f}(S,E)$ are equal,
-so, after the $max(n_0, n_1)^{th}$ term, the distance $d$ between these two points is strictly less than 1.\newline
-\noindent We now prove that the distance between $\left(
-G_{f}(S^n,E^n)\right) $ and $\left( G_{f}(S,E)\right) $ is convergent to
-0. Let $\varepsilon >0$. \medskip
-\begin{itemize}
-\item If $\varepsilon \geqslant 1$, we see that the distance
-between $\left( G_{f}(S^n,E^n)\right) $ and $\left( G_{f}(S,E)\right) $ is
-strictly less than 1 after the $max(n_{0},n_{1})^{th}$ term (same state).
-\medskip
-\item If $\varepsilon <1$, then $\exists k\in \mathds{N},10^{-k}\geqslant
-\varepsilon > 10^{-(k+1)}$. But $d_{s}(S^n,S)$ converges to 0, so
-\begin{equation*}
-\exists n_{2}\in \mathds{N},\forall n\geqslant
-n_{2},d_{s}(S^n,S)<10^{-(k+2)},
-\end{equation*}%
-thus after $n_{2}$, the $k+2$ first terms of $S^n$ and $S$ are equal.
-\end{itemize}
-\noindent As a consequence, the $k+1$ first entries of the strategies of $%
-G_{f}(S^n,E^n)$ and $G_{f}(S,E)$ are the same ($G_{f}$ is a shift of strategies) and due to the definition of $d_{s}$, the floating part of
-the distance between $(S^n,E^n)$ and $(S,E)$ is strictly less than $%
-10^{-(k+1)}\leqslant \varepsilon $.
-
-In conclusion,
-%%RAPH : ici j'ai rajouté une ligne
-%%TOF : ici j'ai rajouté un commentaire
-%%TOF : ici aussi
-$
-\forall \varepsilon >0,$ $\exists N_{0}=max(n_{0},n_{1},n_{2})\in \mathds{N}
-,$ $\forall n\geqslant N_{0},$
-$ d\left( G_{f}(S^n,E^n);G_{f}(S,E)\right)
-\leqslant \varepsilon .
-$
-$G_{f}$ is consequently continuous.
-\end{proof}
-
-
-It is now possible to study the topological behavior of the general chaotic
-iterations. We will prove that,
-
-\begin{theorem}
-\label{t:chaos des general}
- The general chaotic iterations defined on Equation~\ref{general CIs} satisfy
-the Devaney's property of chaos.
-\end{theorem}
-
-Let us firstly prove the following lemma.
+%% \end{equation}%
-\begin{lemma}[Strong transitivity]
-\label{strongTrans}
- For all couples $X,Y \in \mathcal{X}$ and any neighborhood $V$ of $X$, we can
-find $n \in \mathds{N}^*$ and $X' \in V$ such that $G^n(X')=Y$.
-\end{lemma}
+%% Once more, a shift function appears as a component of these general chaotic
+%% iterations.
-\begin{proof}
- Let $X=(S,E)$, $\varepsilon>0$, and $k_0 = \lfloor log_{10}(\varepsilon)+1 \rfloor$.
-Any point $X'=(S',E')$ such that $E'=E$ and $\forall k \leqslant k_0, S'^k=S^k$,
-are in the open ball $\mathcal{B}\left(X,\varepsilon\right)$. Let us define
-$\check{X} = \left(\check{S},\check{E}\right)$, where $\check{X}= G^{k_0}(X)$.
-We denote by $s\subset \llbracket 1; \mathsf{N} \rrbracket$ the set of coordinates
-that are different between $\check{E}$ and the state of $Y$. Thus each point $X'$ of
-the form $(S',E')$ where $E'=E$ and $S'$ starts with
-$(S^0, S^1, \hdots, S^{k_0},s,\hdots)$, verifies the following properties:
-\begin{itemize}
- \item $X'$ is in $\mathcal{B}\left(X,\varepsilon\right)$,
- \item the state of $G_f^{k_0+1}(X')$ is the state of $Y$.
-\end{itemize}
-Finally the point $\left(\left(S^0, S^1, \hdots, S^{k_0},s,s^0, s^1, \hdots\right); E\right)$,
-where $(s^0,s^1, \hdots)$ is the strategy of $Y$, satisfies the properties
-claimed in the lemma.
-\end{proof}
-
-We can now prove the Theorem~\ref{t:chaos des general}.
-
-\begin{proof}[Theorem~\ref{t:chaos des general}]
-Firstly, strong transitivity implies transitivity.
-
-Let $(S,E) \in\mathcal{X}$ and $\varepsilon >0$. To
-prove that $G_f$ is regular, it is sufficient to prove that
-there exists a strategy $\tilde S$ such that the distance between
-$(\tilde S,E)$ and $(S,E)$ is less than $\varepsilon$, and such that
-$(\tilde S,E)$ is a periodic point.
-
-Let $t_1=\lfloor-\log_{10}(\varepsilon)\rfloor$, and let $E'$ be the
-configuration that we obtain from $(S,E)$ after $t_1$ iterations of
-$G_f$. As $G_f$ is strongly transitive, there exists a strategy $S'$
-and $t_2\in\mathds{N}$ such
-that $E$ is reached from $(S',E')$ after $t_2$ iterations of $G_f$.
-
-Consider the strategy $\tilde S$ that alternates the first $t_1$ terms
-of $S$ and the first $t_2$ terms of $S'$:
-%%RAPH : j'ai coupé la ligne en 2
-$$\tilde
-S=(S_0,\dots,S_{t_1-1},S'_0,\dots,S'_{t_2-1},S_0,$$$$\dots,S_{t_1-1},S'_0,\dots,S'_{t_2-1},S_0,\dots).$$ It
-is clear that $(\tilde S,E)$ is obtained from $(\tilde S,E)$ after
-$t_1+t_2$ iterations of $G_f$. So $(\tilde S,E)$ is a periodic
-point. Since $\tilde S_t=S_t$ for $t<t_1$, by the choice of $t_1$, we
-have $d((S,E),(\tilde S,E))<\epsilon$.
-\end{proof}
+%% To study the Devaney's chaos property, a distance between two points
+%% $X = (S,E), Y = (\check{S},\check{E})$ of $\mathcal{X}$ must be defined.
+%% Let us introduce:
+%% \begin{equation}
+%% d(X,Y)=d_{e}(E,\check{E})+d_{s}(S,\check{S}),
+%% \label{nouveau d}
+%% \end{equation}
+%% \noindent where $ \displaystyle{d_{e}(E,\check{E})} = \displaystyle{\sum_{k=1}^{\mathsf{N}%
+%% }\delta (E_{k},\check{E}_{k})}$ is once more the Hamming distance, and
+%% $ \displaystyle{d_{s}(S,\check{S})} = \displaystyle{\dfrac{9}{\mathsf{N}}%
+%% \sum_{k=1}^{\infty }\dfrac{|S^k\Delta {S}^k|}{10^{k}}}$,
+%% %%RAPH : ici, j'ai supprimé tous les sauts à la ligne
+%% %% \begin{equation}
+%% %% \left\{
+%% %% \begin{array}{lll}
+%% %% \displaystyle{d_{e}(E,\check{E})} & = & \displaystyle{\sum_{k=1}^{\mathsf{N}%
+%% %% }\delta (E_{k},\check{E}_{k})} \textrm{ is once more the Hamming distance}, \\
+%% %% \displaystyle{d_{s}(S,\check{S})} & = & \displaystyle{\dfrac{9}{\mathsf{N}}%
+%% %% \sum_{k=1}^{\infty }\dfrac{|S^k\Delta {S}^k|}{10^{k}}}.%
+%% %% \end{array}%
+%% %% \right.
+%% %% \end{equation}
+%% where $|X|$ is the cardinality of a set $X$ and $A\Delta B$ is for the symmetric difference, defined for sets A, B as
+%% $A\,\Delta\,B = (A \setminus B) \cup (B \setminus A)$.
+
+
+%% \begin{proposition}
+%% The function $d$ defined in Eq.~\ref{nouveau d} is a metric on $\mathcal{X}$.
+%% \end{proposition}
+
+%% \begin{proof}
+%% $d_e$ is the Hamming distance. We will prove that $d_s$ is a distance
+%% too, thus $d$, as being the sum of two distances, will also be a distance.
+%% \begin{itemize}
+%% \item Obviously, $d_s(S,\check{S})\geqslant 0$, and if $S=\check{S}$, then
+%% $d_s(S,\check{S})=0$. Conversely, if $d_s(S,\check{S})=0$, then
+%% $\forall k \in \mathds{N}, |S^k\Delta {S}^k|=0$, and so $\forall k, S^k=\check{S}^k$.
+%% \item $d_s$ is symmetric
+%% ($d_s(S,\check{S})=d_s(\check{S},S)$) due to the commutative property
+%% of the symmetric difference.
+%% \item Finally, $|S \Delta S''| = |(S \Delta \varnothing) \Delta S''|= |S \Delta (S'\Delta S') \Delta S''|= |(S \Delta S') \Delta (S' \Delta S'')|\leqslant |S \Delta S'| + |S' \Delta S''|$,
+%% and so for all subsets $S,S',$ and $S''$ of $\llbracket 1, \mathsf{N} \rrbracket$,
+%% we have $d_s(S,S'') \leqslant d_e(S,S')+d_s(S',S'')$, and the triangle
+%% inequality is obtained.
+%% \end{itemize}
+%% \end{proof}
+
+
+%% Before being able to study the topological behavior of the general
+%% chaotic iterations, we must first establish that:
+
+%% \begin{proposition}
+%% For all $f:\mathds{B}^\mathsf{N} \longrightarrow \mathds{B}^\mathsf{N} $, the function $G_f$ is continuous on
+%% $\left( \mathcal{X},d\right)$.
+%% \end{proposition}
+
+
+%% \begin{proof}
+%% We use the sequential continuity.
+%% Let $(S^n,E^n)_{n\in \mathds{N}}$ be a sequence of the phase space $%
+%% \mathcal{X}$, which converges to $(S,E)$. We will prove that $\left(
+%% G_{f}(S^n,E^n)\right) _{n\in \mathds{N}}$ converges to $\left(
+%% G_{f}(S,E)\right) $. Let us remark that for all $n$, $S^n$ is a strategy,
+%% thus, we consider a sequence of strategies (\emph{i.e.}, a sequence of
+%% sequences).\newline
+%% As $d((S^n,E^n);(S,E))$ converges to 0, each distance $d_{e}(E^n,E)$ and $d_{s}(S^n,S)$ converges
+%% to 0. But $d_{e}(E^n,E)$ is an integer, so $\exists n_{0}\in \mathds{N},$ $%
+%% d_{e}(E^n,E)=0$ for any $n\geqslant n_{0}$.\newline
+%% In other words, there exists a threshold $n_{0}\in \mathds{N}$ after which no
+%% cell will change its state:
+%% $\exists n_{0}\in \mathds{N},n\geqslant n_{0}\Rightarrow E^n = E.$
+
+%% In addition, $d_{s}(S^n,S)\longrightarrow 0,$ so $\exists n_{1}\in %
+%% \mathds{N},d_{s}(S^n,S)<10^{-1}$ for all indexes greater than or equal to $%
+%% n_{1}$. This means that for $n\geqslant n_{1}$, all the $S^n$ have the same
+%% first term, which is $S^0$: $\forall n\geqslant n_{1},S_0^n=S_0.$
+
+%% Thus, after the $max(n_{0},n_{1})^{th}$ term, states of $E^n$ and $E$ are
+%% identical and strategies $S^n$ and $S$ start with the same first term.\newline
+%% Consequently, states of $G_{f}(S^n,E^n)$ and $G_{f}(S,E)$ are equal,
+%% so, after the $max(n_0, n_1)^{th}$ term, the distance $d$ between these two points is strictly less than 1.\newline
+%% \noindent We now prove that the distance between $\left(
+%% G_{f}(S^n,E^n)\right) $ and $\left( G_{f}(S,E)\right) $ is convergent to
+%% 0. Let $\varepsilon >0$. \medskip
+%% \begin{itemize}
+%% \item If $\varepsilon \geqslant 1$, we see that the distance
+%% between $\left( G_{f}(S^n,E^n)\right) $ and $\left( G_{f}(S,E)\right) $ is
+%% strictly less than 1 after the $max(n_{0},n_{1})^{th}$ term (same state).
+%% \medskip
+%% \item If $\varepsilon <1$, then $\exists k\in \mathds{N},10^{-k}\geqslant
+%% \varepsilon > 10^{-(k+1)}$. But $d_{s}(S^n,S)$ converges to 0, so
+%% \begin{equation*}
+%% \exists n_{2}\in \mathds{N},\forall n\geqslant
+%% n_{2},d_{s}(S^n,S)<10^{-(k+2)},
+%% \end{equation*}%
+%% thus after $n_{2}$, the $k+2$ first terms of $S^n$ and $S$ are equal.
+%% \end{itemize}
+%% \noindent As a consequence, the $k+1$ first entries of the strategies of $%
+%% G_{f}(S^n,E^n)$ and $G_{f}(S,E)$ are the same ($G_{f}$ is a shift of strategies) and due to the definition of $d_{s}$, the floating part of
+%% the distance between $(S^n,E^n)$ and $(S,E)$ is strictly less than $%
+%% 10^{-(k+1)}\leqslant \varepsilon $.
+
+%% In conclusion,
+%% %%RAPH : ici j'ai rajouté une ligne
+%% %%TOF : ici j'ai rajouté un commentaire
+%% %%TOF : ici aussi
+%% $
+%% \forall \varepsilon >0,$ $\exists N_{0}=max(n_{0},n_{1},n_{2})\in \mathds{N}
+%% ,$ $\forall n\geqslant N_{0},$
+%% $ d\left( G_{f}(S^n,E^n);G_{f}(S,E)\right)
+%% \leqslant \varepsilon .
+%% $
+%% $G_{f}$ is consequently continuous.
+%% \end{proof}
+
+
+%% It is now possible to study the topological behavior of the general chaotic
+%% iterations. We will prove that,
+
+%% \begin{theorem}
+%% \label{t:chaos des general}
+%% The general chaotic iterations defined on Equation~\ref{general CIs} satisfy
+%% the Devaney's property of chaos.
+%% \end{theorem}
+
+%% Let us firstly prove the following lemma.
+
+%% \begin{lemma}[Strong transitivity]
+%% \label{strongTrans}
+%% For all couples $X,Y \in \mathcal{X}$ and any neighborhood $V$ of $X$, we can
+%% find $n \in \mathds{N}^*$ and $X' \in V$ such that $G^n(X')=Y$.
+%% \end{lemma}
+
+%% \begin{proof}
+%% Let $X=(S,E)$, $\varepsilon>0$, and $k_0 = \lfloor log_{10}(\varepsilon)+1 \rfloor$.
+%% Any point $X'=(S',E')$ such that $E'=E$ and $\forall k \leqslant k_0, S'^k=S^k$,
+%% are in the open ball $\mathcal{B}\left(X,\varepsilon\right)$. Let us define
+%% $\check{X} = \left(\check{S},\check{E}\right)$, where $\check{X}= G^{k_0}(X)$.
+%% We denote by $s\subset \llbracket 1; \mathsf{N} \rrbracket$ the set of coordinates
+%% that are different between $\check{E}$ and the state of $Y$. Thus each point $X'$ of
+%% the form $(S',E')$ where $E'=E$ and $S'$ starts with
+%% $(S^0, S^1, \hdots, S^{k_0},s,\hdots)$, verifies the following properties:
+%% \begin{itemize}
+%% \item $X'$ is in $\mathcal{B}\left(X,\varepsilon\right)$,
+%% \item the state of $G_f^{k_0+1}(X')$ is the state of $Y$.
+%% \end{itemize}
+%% Finally the point $\left(\left(S^0, S^1, \hdots, S^{k_0},s,s^0, s^1, \hdots\right); E\right)$,
+%% where $(s^0,s^1, \hdots)$ is the strategy of $Y$, satisfies the properties
+%% claimed in the lemma.
+%% \end{proof}
+
+%% We can now prove the Theorem~\ref{t:chaos des general}.
+
+%% \begin{proof}[Theorem~\ref{t:chaos des general}]
+%% Firstly, strong transitivity implies transitivity.
+
+%% Let $(S,E) \in\mathcal{X}$ and $\varepsilon >0$. To
+%% prove that $G_f$ is regular, it is sufficient to prove that
+%% there exists a strategy $\tilde S$ such that the distance between
+%% $(\tilde S,E)$ and $(S,E)$ is less than $\varepsilon$, and such that
+%% $(\tilde S,E)$ is a periodic point.
+
+%% Let $t_1=\lfloor-\log_{10}(\varepsilon)\rfloor$, and let $E'$ be the
+%% configuration that we obtain from $(S,E)$ after $t_1$ iterations of
+%% $G_f$. As $G_f$ is strongly transitive, there exists a strategy $S'$
+%% and $t_2\in\mathds{N}$ such
+%% that $E$ is reached from $(S',E')$ after $t_2$ iterations of $G_f$.
+
+%% Consider the strategy $\tilde S$ that alternates the first $t_1$ terms
+%% of $S$ and the first $t_2$ terms of $S'$:
+%% %%RAPH : j'ai coupé la ligne en 2
+%% $$\tilde
+%% S=(S_0,\dots,S_{t_1-1},S'_0,\dots,S'_{t_2-1},S_0,$$$$\dots,S_{t_1-1},S'_0,\dots,S'_{t_2-1},S_0,\dots).$$ It
+%% is clear that $(\tilde S,E)$ is obtained from $(\tilde S,E)$ after
+%% $t_1+t_2$ iterations of $G_f$. So $(\tilde S,E)$ is a periodic
+%% point. Since $\tilde S_t=S_t$ for $t<t_1$, by the choice of $t_1$, we
+%% have $d((S,E),(\tilde S,E))<\epsilon$.
+%% \end{proof}
+
+
+
+
+%%RAF : mis en supplementary
\section{Statistical Improvements Using Chaotic Iterations}
-
\label{The generation of pseudorandom sequence}
+The content is this section is given in Section~\ref{A-The generation of pseudorandom sequence} of the annex document.
+
+
+%% \label{The generation of pseudorandom sequence}
+
+
+%% Let us now explain why we have reasonable ground to believe that chaos
+%% can improve statistical properties.
+%% We will show in this section that chaotic properties as defined in the
+%% mathematical theory of chaos are related to some statistical tests that can be found
+%% in the NIST battery. Furthermore, we will check that, when mixing defective PRNGs with
+%% chaotic iterations, the new generator presents better statistical properties
+%% (this section summarizes and extends the work of~\cite{bfg12a:ip}).
+
+
+
+%% \subsection{Qualitative relations between topological properties and statistical tests}
+
+
+%% There are various relations between topological properties that describe an unpredictable behavior for a discrete
+%% dynamical system on the one
+%% hand, and statistical tests to check the randomness of a numerical sequence
+%% on the other hand. These two mathematical disciplines follow a similar
+%% objective in case of a recurrent sequence (to characterize an intrinsically complicated behavior for a
+%% recurrent sequence), with two different but complementary approaches.
+%% It is true that the following illustrative links give only qualitative arguments,
+%% and proofs should be provided later to make such arguments irrefutable. However
+%% they give a first understanding of the reason why we think that chaotic properties should tend
+%% to improve the statistical quality of PRNGs.
+%% %
+%% Let us now list some of these relations between topological properties defined in the mathematical
+%% theory of chaos and tests embedded into the NIST battery. %Such relations need to be further
+%% %investigated, but they presently give a first illustration of a trend to search similar properties in the
+%% %two following fields: mathematical chaos and statistics.
+
+
+%% \begin{itemize}
+%% \item \textbf{Regularity}. As stated in Section~\ref{subsec:Devaney}, a chaotic dynamical system must
+%% have an element of regularity. Depending on the chosen definition of chaos, this element can be the existence of
+%% a dense orbit, the density of periodic points, etc. The key idea is that a dynamical system with no periodicity
+%% is not as chaotic as a system having periodic orbits: in the first situation, we can predict something and gain a
+%% knowledge about the behavior of the system, that is, it never enters into a loop. A similar importance for periodicity is emphasized in
+%% the two following NIST tests~\cite{Nist10}:
+%% \begin{itemize}
+%% \item \textbf{Non-overlapping Template Matching Test}. Detect generators that produce too many occurrences of a given non-periodic (aperiodic) pattern.
+%% \item \textbf{Discrete Fourier Transform (Spectral) Test}. Detect periodic features (i.e., repetitive patterns that are close one to another) in the tested sequence that would indicate a deviation from the assumption of randomness.
+%% \end{itemize}
+
+%% \item \textbf{Transitivity}. This topological property previously introduced states that the dynamical system is intrinsically complicated: it cannot be simplified into
+%% two subsystems that do not interact, as we can find in any neighborhood of any point another point whose orbit visits the whole phase space.
+%% This focus on the places visited by the orbits of the dynamical system takes various nonequivalent formulations in the mathematical theory
+%% of chaos, namely: transitivity, strong transitivity, total transitivity, topological mixing, and so on~\cite{bg10:ij}. A similar attention
+%% is brought on the states visited during a random walk in the two tests below~\cite{Nist10}:
+%% \begin{itemize}
+%% \item \textbf{Random Excursions Variant Test}. Detect deviations from the expected number of visits to various states in the random walk.
+%% \item \textbf{Random Excursions Test}. Determine if the number of visits to a particular state within a cycle deviates from what one would expect for a random sequence.
+%% \end{itemize}
+
+%% \item \textbf{Chaos according to Li and Yorke}. Two points of the phase space $(x,y)$ define a couple of Li-Yorke when $\limsup_{n \rightarrow +\infty} d(f^{(n)}(x), f^{(n)}(y))>0$ et $\liminf_{n \rightarrow +\infty} d(f^{(n)}(x), f^{(n)}(y))=0$, meaning that their orbits always oscillate as the iterations pass. When a system is compact and contains an uncountable set of such points, it is claimed as chaotic according
+%% to Li-Yorke~\cite{Li75,Ruette2001}. A similar property is regarded in the following NIST test~\cite{Nist10}.
+%% \begin{itemize}
+%% \item \textbf{Runs Test}. To determine whether the number of runs of ones and zeros of various lengths is as expected for a random sequence. In particular, this test determines whether the oscillation between such zeros and ones is too fast or too slow.
+%% \end{itemize}
+%% \item \textbf{Topological entropy}. The desire to formulate an equivalency of the thermodynamics entropy
+%% has emerged both in the topological and statistical fields. Once again, a similar objective has led to two different
+%% rewritting of an entropy based disorder: the famous Shannon definition of entropy is approximated in the statistical approach,
+%% whereas topological entropy is defined as follows:
+%% $x,y \in \mathcal{X}$ are $\varepsilon-$\emph{separated in time $n$} if there exists $k \leqslant n$ such that $d\left(f^{(k)}(x),f^{(k)}(y)\right)>\varepsilon$. Then $(n,\varepsilon)-$separated sets are sets of points that are all $\varepsilon-$separated in time $n$, which
+%% leads to the definition of $s_n(\varepsilon,Y)$, being the maximal cardinality of all $(n,\varepsilon)-$separated sets. Using these notations,
+%% the topological entropy is defined as follows: $$h_{top}(\mathcal{X},f) = \displaystyle{\lim_{\varepsilon \rightarrow 0} \Big[ \limsup_{n \rightarrow +\infty} \dfrac{1}{n} \log s_n(\varepsilon,\mathcal{X})\Big]}.$$
+%% This value measures the average exponential growth of the number of distinguishable orbit segments.
+%% In this sense, it measures the complexity of the topological dynamical system, whereas
+%% the Shannon approach comes to mind when defining the following test~\cite{Nist10}:
+%% \begin{itemize}
+%% \item \textbf{Approximate Entropy Test}. Compare the frequency of the overlapping blocks of two consecutive/adjacent lengths ($m$ and $m+1$) against the expected result for a random sequence.
+%% \end{itemize}
+
+%% \item \textbf{Non-linearity, complexity}. Finally, let us remark that non-linearity and complexity are
+%% not only sought in general to obtain chaos, but they are also required for randomness, as illustrated by the two tests below~\cite{Nist10}.
+%% \begin{itemize}
+%% \item \textbf{Binary Matrix Rank Test}. Check for linear dependence among fixed length substrings of the original sequence.
+%% \item \textbf{Linear Complexity Test}. Determine whether or not the sequence is complex enough to be considered random.
+%% \end{itemize}
+%% \end{itemize}
+
+
+%% We have proven in our previous works~\cite{guyeux12:bc} that chaotic iterations satisfying Theorem~\ref{Th:Caractérisation des IC chaotiques} are, among other
+%% things, strongly transitive, topologically mixing, chaotic as defined by Li and Yorke,
+%% and that they have a topological entropy and an exponent of Lyapunov both equal to $ln(\mathsf{N})$,
+%% where $\mathsf{N}$ is the size of the iterated vector.
+%% These topological properties make that we are ground to believe that a generator based on chaotic
+%% iterations will probably be able to pass all the existing statistical batteries for pseudorandomness like
+%% the NIST one. The following subsections, in which we prove that defective generators have their
+%% statistical properties improved by chaotic iterations, show that such an assumption is true.
+
+%% \subsection{Details of some Existing Generators}
+
+%% The list of defective PRNGs we will use
+%% as inputs for the statistical tests to come is introduced here.
+
+%% Firstly, the simple linear congruency generators (LCGs) will be used.
+%% They are defined by the following recurrence:
+%% \begin{equation}
+%% x^n = (ax^{n-1} + c)~mod~m,
+%% \label{LCG}
+%% \end{equation}
+%% where $a$, $c$, and $x^0$ must be, among other things, non-negative and inferior to
+%% $m$~\cite{LEcuyerS07}. In what follows, 2LCGs and 3LCGs refer to two (resp. three)
+%% combinations of such LCGs. For further details, see~\cite{bfg12a:ip,combined_lcg}.
+%% Secondly, the multiple recursive generators (MRGs) which will be used,
+%% are based on a linear recurrence of order
+%% $k$, modulo $m$~\cite{LEcuyerS07}:
+%% \begin{equation}
+%% x^n = (a^1x^{n-1}+~...~+a^kx^{n-k})~mod~m .
+%% \label{MRG}
+%% \end{equation}
+%% The combination of two MRGs (referred as 2MRGs) is also used in these experiments.
-Let us now explain why we have reasonable ground to believe that chaos
-can improve statistical properties.
-We will show in this section that chaotic properties as defined in the
-mathematical theory of chaos are related to some statistical tests that can be found
-in the NIST battery. Furthermore, we will check that, when mixing defective PRNGs with
-chaotic iterations, the new generator presents better statistical properties
-(this section summarizes and extends the work of~\cite{bfg12a:ip}).
-
-
-
-\subsection{Qualitative relations between topological properties and statistical tests}
+%% Generators based on linear recurrences with carry will be regarded too.
+%% This family of generators includes the add-with-carry (AWC) generator, based on the recurrence:
+%% \begin{equation}
+%% \label{AWC}
+%% \begin{array}{l}
+%% x^n = (x^{n-r} + x^{n-s} + c^{n-1})~mod~m, \\
+%% c^n= (x^{n-r} + x^{n-s} + c^{n-1}) / m, \end{array}\end{equation}
+%% the SWB generator, having the recurrence:
+%% \begin{equation}
+%% \label{SWB}
+%% \begin{array}{l}
+%% x^n = (x^{n-r} - x^{n-s} - c^{n-1})~mod~m, \\
+%% c^n=\left\{
+%% \begin{array}{l}
+%% 1 ~~~~~\text{if}~ (x^{i-r} - x^{i-s} - c^{i-1})<0\\
+%% 0 ~~~~~\text{else},\end{array} \right. \end{array}\end{equation}
+%% and the SWC generator, which is based on the following recurrence:
+%% \begin{equation}
+%% \label{SWC}
+%% \begin{array}{l}
+%% x^n = (a^1x^{n-1} \oplus ~...~ \oplus a^rx^{n-r} \oplus c^{n-1}) ~ mod ~ 2^w, \\
+%% c^n = (a^1x^{n-1} \oplus ~...~ \oplus a^rx^{n-r} \oplus c^{n-1}) ~ / ~ 2^w. \end{array}\end{equation}
+%% Then the generalized feedback shift register (GFSR) generator has been implemented, that is:
+%% \begin{equation}
+%% x^n = x^{n-r} \oplus x^{n-k} .
+%% \label{GFSR}
+%% \end{equation}
-There are various relations between topological properties that describe an unpredictable behavior for a discrete
-dynamical system on the one
-hand, and statistical tests to check the randomness of a numerical sequence
-on the other hand. These two mathematical disciplines follow a similar
-objective in case of a recurrent sequence (to characterize an intrinsically complicated behavior for a
-recurrent sequence), with two different but complementary approaches.
-It is true that the following illustrative links give only qualitative arguments,
-and proofs should be provided later to make such arguments irrefutable. However
-they give a first understanding of the reason why we think that chaotic properties should tend
-to improve the statistical quality of PRNGs.
-%
-Let us now list some of these relations between topological properties defined in the mathematical
-theory of chaos and tests embedded into the NIST battery. %Such relations need to be further
-%investigated, but they presently give a first illustration of a trend to search similar properties in the
-%two following fields: mathematical chaos and statistics.
+%% Finally, the nonlinear inversive (INV) generator~\cite{LEcuyerS07} has been studied, which is:
-\begin{itemize}
- \item \textbf{Regularity}. As stated in Section~\ref{subsec:Devaney}, a chaotic dynamical system must
-have an element of regularity. Depending on the chosen definition of chaos, this element can be the existence of
-a dense orbit, the density of periodic points, etc. The key idea is that a dynamical system with no periodicity
-is not as chaotic as a system having periodic orbits: in the first situation, we can predict something and gain a
-knowledge about the behavior of the system, that is, it never enters into a loop. A similar importance for periodicity is emphasized in
-the two following NIST tests~\cite{Nist10}:
- \begin{itemize}
- \item \textbf{Non-overlapping Template Matching Test}. Detect generators that produce too many occurrences of a given non-periodic (aperiodic) pattern.
- \item \textbf{Discrete Fourier Transform (Spectral) Test}. Detect periodic features (i.e., repetitive patterns that are close one to another) in the tested sequence that would indicate a deviation from the assumption of randomness.
- \end{itemize}
-
-\item \textbf{Transitivity}. This topological property previously introduced states that the dynamical system is intrinsically complicated: it cannot be simplified into
-two subsystems that do not interact, as we can find in any neighborhood of any point another point whose orbit visits the whole phase space.
-This focus on the places visited by the orbits of the dynamical system takes various nonequivalent formulations in the mathematical theory
-of chaos, namely: transitivity, strong transitivity, total transitivity, topological mixing, and so on~\cite{bg10:ij}. A similar attention
-is brought on the states visited during a random walk in the two tests below~\cite{Nist10}:
- \begin{itemize}
- \item \textbf{Random Excursions Variant Test}. Detect deviations from the expected number of visits to various states in the random walk.
- \item \textbf{Random Excursions Test}. Determine if the number of visits to a particular state within a cycle deviates from what one would expect for a random sequence.
- \end{itemize}
-
-\item \textbf{Chaos according to Li and Yorke}. Two points of the phase space $(x,y)$ define a couple of Li-Yorke when $\limsup_{n \rightarrow +\infty} d(f^{(n)}(x), f^{(n)}(y))>0$ et $\liminf_{n \rightarrow +\infty} d(f^{(n)}(x), f^{(n)}(y))=0$, meaning that their orbits always oscillate as the iterations pass. When a system is compact and contains an uncountable set of such points, it is claimed as chaotic according
-to Li-Yorke~\cite{Li75,Ruette2001}. A similar property is regarded in the following NIST test~\cite{Nist10}.
- \begin{itemize}
- \item \textbf{Runs Test}. To determine whether the number of runs of ones and zeros of various lengths is as expected for a random sequence. In particular, this test determines whether the oscillation between such zeros and ones is too fast or too slow.
- \end{itemize}
- \item \textbf{Topological entropy}. The desire to formulate an equivalency of the thermodynamics entropy
-has emerged both in the topological and statistical fields. Once again, a similar objective has led to two different
-rewritting of an entropy based disorder: the famous Shannon definition of entropy is approximated in the statistical approach,
-whereas topological entropy is defined as follows:
-$x,y \in \mathcal{X}$ are $\varepsilon-$\emph{separated in time $n$} if there exists $k \leqslant n$ such that $d\left(f^{(k)}(x),f^{(k)}(y)\right)>\varepsilon$. Then $(n,\varepsilon)-$separated sets are sets of points that are all $\varepsilon-$separated in time $n$, which
-leads to the definition of $s_n(\varepsilon,Y)$, being the maximal cardinality of all $(n,\varepsilon)-$separated sets. Using these notations,
-the topological entropy is defined as follows: $$h_{top}(\mathcal{X},f) = \displaystyle{\lim_{\varepsilon \rightarrow 0} \Big[ \limsup_{n \rightarrow +\infty} \dfrac{1}{n} \log s_n(\varepsilon,\mathcal{X})\Big]}.$$
-This value measures the average exponential growth of the number of distinguishable orbit segments.
-In this sense, it measures the complexity of the topological dynamical system, whereas
-the Shannon approach comes to mind when defining the following test~\cite{Nist10}:
- \begin{itemize}
-\item \textbf{Approximate Entropy Test}. Compare the frequency of the overlapping blocks of two consecutive/adjacent lengths ($m$ and $m+1$) against the expected result for a random sequence.
- \end{itemize}
-
- \item \textbf{Non-linearity, complexity}. Finally, let us remark that non-linearity and complexity are
-not only sought in general to obtain chaos, but they are also required for randomness, as illustrated by the two tests below~\cite{Nist10}.
- \begin{itemize}
-\item \textbf{Binary Matrix Rank Test}. Check for linear dependence among fixed length substrings of the original sequence.
-\item \textbf{Linear Complexity Test}. Determine whether or not the sequence is complex enough to be considered random.
- \end{itemize}
-\end{itemize}
+%% \begin{equation}
+%% \label{INV}
+%% \begin{array}{l}
+%% x^n=\left\{
+%% \begin{array}{ll}
+%% (a^1 + a^2 / z^{n-1})~mod~m & \text{if}~ z^{n-1} \neq 0 \\
+%% a^1 & \text{if}~ z^{n-1} = 0 .\end{array} \right. \end{array}\end{equation}
-We have proven in our previous works~\cite{guyeux12:bc} that chaotic iterations satisfying Theorem~\ref{Th:Caractérisation des IC chaotiques} are, among other
-things, strongly transitive, topologically mixing, chaotic as defined by Li and Yorke,
-and that they have a topological entropy and an exponent of Lyapunov both equal to $ln(\mathsf{N})$,
-where $\mathsf{N}$ is the size of the iterated vector.
-These topological properties make that we are ground to believe that a generator based on chaotic
-iterations will probably be able to pass all the existing statistical batteries for pseudorandomness like
-the NIST one. The following subsections, in which we prove that defective generators have their
-statistical properties improved by chaotic iterations, show that such an assumption is true.
-\subsection{Details of some Existing Generators}
+%% \begin{table}
+%% \renewcommand{\arraystretch}{1.3}
+%% \caption{TestU01 Statistical Test Failures}
+%% \label{TestU011}
+%% \centering
+%% \begin{tabular}{lccccc}
+%% \toprule
+%% Test name &Tests& Logistic & XORshift & ISAAC\\
+%% Rabbit & 38 &21 &14 &0 \\
+%% Alphabit & 17 &16 &9 &0 \\
+%% Pseudo DieHARD &126 &0 &2 &0 \\
+%% FIPS\_140\_2 &16 &0 &0 &0 \\
+%% SmallCrush &15 &4 &5 &0 \\
+%% Crush &144 &95 &57 &0 \\
+%% Big Crush &160 &125 &55 &0 \\ \hline
+%% Failures & &261 &146 &0 \\
+%% \bottomrule
+%% \end{tabular}
+%% \end{table}
-The list of defective PRNGs we will use
-as inputs for the statistical tests to come is introduced here.
-Firstly, the simple linear congruency generators (LCGs) will be used.
-They are defined by the following recurrence:
-\begin{equation}
-x^n = (ax^{n-1} + c)~mod~m,
-\label{LCG}
-\end{equation}
-where $a$, $c$, and $x^0$ must be, among other things, non-negative and inferior to
-$m$~\cite{LEcuyerS07}. In what follows, 2LCGs and 3LCGs refer to two (resp. three)
-combinations of such LCGs. For further details, see~\cite{bfg12a:ip,combined_lcg}.
-Secondly, the multiple recursive generators (MRGs) which will be used,
-are based on a linear recurrence of order
-$k$, modulo $m$~\cite{LEcuyerS07}:
-\begin{equation}
-x^n = (a^1x^{n-1}+~...~+a^kx^{n-k})~mod~m .
-\label{MRG}
-\end{equation}
-The combination of two MRGs (referred as 2MRGs) is also used in these experiments.
+%% \begin{table}
+%% \renewcommand{\arraystretch}{1.3}
+%% \caption{TestU01 Statistical Test Failures for Old CI algorithms ($\mathsf{N}=4$)}
+%% \label{TestU01 for Old CI}
+%% \centering
+%% \begin{tabular}{lcccc}
+%% \toprule
+%% \multirow{3}*{Test name} & \multicolumn{4}{c}{Old CI}\\
+%% &Logistic& XORshift& ISAAC&ISAAC \\
+%% &+& +& + & + \\
+%% &Logistic& XORshift& XORshift&ISAAC \\ \cmidrule(r){2-5}
+%% Rabbit &7 &2 &0 &0 \\
+%% Alphabit & 3 &0 &0 &0 \\
+%% DieHARD &0 &0 &0 &0 \\
+%% FIPS\_140\_2 &0 &0 &0 &0 \\
+%% SmallCrush &2 &0 &0 &0 \\
+%% Crush &47 &4 &0 &0 \\
+%% Big Crush &79 &3 &0 &0 \\ \hline
+%% Failures &138 &9 &0 &0 \\
+%% \bottomrule
+%% \end{tabular}
+%% \end{table}
-Generators based on linear recurrences with carry will be regarded too.
-This family of generators includes the add-with-carry (AWC) generator, based on the recurrence:
-\begin{equation}
-\label{AWC}
-\begin{array}{l}
-x^n = (x^{n-r} + x^{n-s} + c^{n-1})~mod~m, \\
-c^n= (x^{n-r} + x^{n-s} + c^{n-1}) / m, \end{array}\end{equation}
-the SWB generator, having the recurrence:
-\begin{equation}
-\label{SWB}
-\begin{array}{l}
-x^n = (x^{n-r} - x^{n-s} - c^{n-1})~mod~m, \\
-c^n=\left\{
-\begin{array}{l}
-1 ~~~~~\text{if}~ (x^{i-r} - x^{i-s} - c^{i-1})<0\\
-0 ~~~~~\text{else},\end{array} \right. \end{array}\end{equation}
-and the SWC generator, which is based on the following recurrence:
-\begin{equation}
-\label{SWC}
-\begin{array}{l}
-x^n = (a^1x^{n-1} \oplus ~...~ \oplus a^rx^{n-r} \oplus c^{n-1}) ~ mod ~ 2^w, \\
-c^n = (a^1x^{n-1} \oplus ~...~ \oplus a^rx^{n-r} \oplus c^{n-1}) ~ / ~ 2^w. \end{array}\end{equation}
-Then the generalized feedback shift register (GFSR) generator has been implemented, that is:
-\begin{equation}
-x^n = x^{n-r} \oplus x^{n-k} .
-\label{GFSR}
-\end{equation}
-Finally, the nonlinear inversive (INV) generator~\cite{LEcuyerS07} has been studied, which is:
-\begin{equation}
-\label{INV}
-\begin{array}{l}
-x^n=\left\{
-\begin{array}{ll}
-(a^1 + a^2 / z^{n-1})~mod~m & \text{if}~ z^{n-1} \neq 0 \\
-a^1 & \text{if}~ z^{n-1} = 0 .\end{array} \right. \end{array}\end{equation}
-
-
-
-\begin{table}
-\renewcommand{\arraystretch}{1.3}
-\caption{TestU01 Statistical Test Failures}
-\label{TestU011}
-\centering
- \begin{tabular}{lccccc}
- \toprule
-Test name &Tests& Logistic & XORshift & ISAAC\\
-Rabbit & 38 &21 &14 &0 \\
-Alphabit & 17 &16 &9 &0 \\
-Pseudo DieHARD &126 &0 &2 &0 \\
-FIPS\_140\_2 &16 &0 &0 &0 \\
-SmallCrush &15 &4 &5 &0 \\
-Crush &144 &95 &57 &0 \\
-Big Crush &160 &125 &55 &0 \\ \hline
-Failures & &261 &146 &0 \\
-\bottomrule
- \end{tabular}
-\end{table}
-
-
-
-\begin{table}
-\renewcommand{\arraystretch}{1.3}
-\caption{TestU01 Statistical Test Failures for Old CI algorithms ($\mathsf{N}=4$)}
-\label{TestU01 for Old CI}
-\centering
- \begin{tabular}{lcccc}
- \toprule
-\multirow{3}*{Test name} & \multicolumn{4}{c}{Old CI}\\
-&Logistic& XORshift& ISAAC&ISAAC \\
-&+& +& + & + \\
-&Logistic& XORshift& XORshift&ISAAC \\ \cmidrule(r){2-5}
-Rabbit &7 &2 &0 &0 \\
-Alphabit & 3 &0 &0 &0 \\
-DieHARD &0 &0 &0 &0 \\
-FIPS\_140\_2 &0 &0 &0 &0 \\
-SmallCrush &2 &0 &0 &0 \\
-Crush &47 &4 &0 &0 \\
-Big Crush &79 &3 &0 &0 \\ \hline
-Failures &138 &9 &0 &0 \\
-\bottomrule
- \end{tabular}
-\end{table}
-
-
-
-
-
-\subsection{Statistical tests}
-\label{Security analysis}
-
-Three batteries of tests are reputed and regularly used
-to evaluate the statistical properties of newly designed pseudorandom
-number generators. These batteries are named DieHard~\cite{Marsaglia1996},
-the NIST suite~\cite{ANDREW2008}, and the most stringent one called
-TestU01~\cite{LEcuyerS07}, which encompasses the two other batteries.
-
-
-
-\label{Results and discussion}
-\begin{table*}
-\renewcommand{\arraystretch}{1.3}
-\caption{NIST and DieHARD tests suite passing rates for PRNGs without CI}
-\label{NIST and DieHARD tests suite passing rate the for PRNGs without CI}
-\centering
- \begin{tabular}{|l||c|c|c|c|c|c|c|c|c|c|}
- \hline\hline
-Types of PRNGs & \multicolumn{2}{c|}{Linear PRNGs} & \multicolumn{4}{c|}{Lagged PRNGs} & \multicolumn{1}{c|}{ICG PRNGs} & \multicolumn{3}{c|}{Mixed PRNGs}\\ \hline
-\backslashbox{\textbf{$Tests$}} {\textbf{$PRNG$}} & LCG& MRG& AWC & SWB & SWC & GFSR & INV & LCG2& LCG3& MRG2 \\ \hline
-NIST & 11/15 & 14/15 &\textbf{15/15} & \textbf{15/15} & 14/15 & 14/15 & 14/15 & 14/15& 14/15& 14/15 \\ \hline
-DieHARD & 16/18 & 16/18 & 15/18 & 16/18 & \textbf{18/18} & 16/18 & 16/18 & 16/18& 16/18& 16/18\\ \hline
-\end{tabular}
-\end{table*}
-
-Table~\ref{NIST and DieHARD tests suite passing rate the for PRNGs without CI} shows the
-results on the two first batteries recalled above, indicating that all the PRNGs presented
-in the previous section
-cannot pass all these tests. In other words, the statistical quality of these PRNGs cannot
-fulfill the up-to-date standards presented previously. We have shown in~\cite{bfg12a:ip} that the use of chaotic
-iterations can solve this issue.
-%More precisely, to
-%illustrate the effects of chaotic iterations on these defective PRNGs, experiments have been divided in three parts~\cite{bfg12a:ip}:
-%\begin{enumerate}
-% \item \textbf{Single CIPRNG}: The PRNGs involved in CI computing are of the same category.
-% \item \textbf{Mixed CIPRNG}: Two different types of PRNGs are mixed during the chaotic iterations process.
-% \item \textbf{Multiple CIPRNG}: The generator is obtained by repeating the composition of the iteration function as follows: $x^0\in \mathds{B}^{\mathsf{N}}$, and $\forall n\in \mathds{N}^{\ast },\forall i\in \llbracket1;\mathsf{N}\rrbracket, x_i^n=$
-%\begin{equation}
-%\begin{array}{l}
-%\left\{
-%\begin{array}{l}
-%x_i^{n-1}~~~~~\text{if}~S^n\neq i \\
-%\forall j\in \llbracket1;\mathsf{m}\rrbracket,f^m(x^{n-1})_{S^{nm+j}}~\text{if}~S^{nm+j}=i.\end{array} \right. \end{array}
-%\end{equation}
-%$m$ is called the \emph{functional power}.
-%\end{enumerate}
-%
-The obtained results are reproduced in Table
-\ref{NIST and DieHARD tests suite passing rate the for single CIPRNGs}.
-The scores written in boldface indicate that all the tests have been passed successfully, whereas an
-asterisk ``*'' means that the considered passing rate has been improved.
-The improvements are obvious for both the ``Old CI'' and the ``New CI'' generators.
-Concerning the ``Xor CI PRNG'', the score is less spectacular. Because of a large speed improvement, the statistics
- are not as good as for the two other versions of these CIPRNGs.
-However 8 tests have been improved (with no deflation for the other results).
-
-
-\begin{table*}
-\renewcommand{\arraystretch}{1.3}
-\caption{NIST and DieHARD tests suite passing rates for PRNGs with CI}
-\label{NIST and DieHARD tests suite passing rate the for single CIPRNGs}
-\centering
- \begin{tabular}{|l||c|c|c|c|c|c|c|c|c|c|c|c|}
- \hline
-Types of PRNGs & \multicolumn{2}{c|}{Linear PRNGs} & \multicolumn{4}{c|}{Lagged PRNGs} & \multicolumn{1}{c|}{ICG PRNGs} & \multicolumn{3}{c|}{Mixed PRNGs}\\ \hline
-\backslashbox{\textbf{$Tests$}} {\textbf{$Single~CIPRNG$}} & LCG & MRG & AWC & SWB & SWC & GFSR & INV& LCG2 & LCG3& MRG2 \\ \hline\hline
-Old CIPRNG\\ \hline \hline
-NIST & \textbf{15/15} * & \textbf{15/15} * & \textbf{15/15} & \textbf{15/15} & \textbf{15/15} * & \textbf{15/15} * & \textbf{15/15} *& \textbf{15/15} * & \textbf{15/15} * & \textbf{15/15} \\ \hline
-DieHARD & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} & \textbf{18/18} * & \textbf{18/18} *& \textbf{18/18} * & \textbf{18/18} *& \textbf{18/18} * \\ \hline
-New CIPRNG\\ \hline \hline
-NIST & \textbf{15/15} * & \textbf{15/15} * & \textbf{15/15} & \textbf{15/15} & \textbf{15/15} * & \textbf{15/15} * & \textbf{15/15} *& \textbf{15/15} * & \textbf{15/15} * & \textbf{15/15} \\ \hline
-DieHARD & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} *& \textbf{18/18} *\\ \hline
-Xor CIPRNG\\ \hline\hline
-NIST & 14/15*& \textbf{15/15} * & \textbf{15/15} & \textbf{15/15} & 14/15 & \textbf{15/15} * & 14/15& \textbf{15/15} * & \textbf{15/15} *& \textbf{15/15} \\ \hline
-DieHARD & 16/18 & 16/18 & 17/18* & \textbf{18/18} * & \textbf{18/18} & \textbf{18/18} * & 16/18 & 16/18 & 16/18& 16/18\\ \hline
-\end{tabular}
-\end{table*}
-
-
-We have then investigated in~\cite{bfg12a:ip} if it were possible to improve
-the statistical behavior of the Xor CI version by combining more than one
-$\oplus$ operation. Results are summarized in Table~\ref{threshold}, illustrating
-the progressive increasing effects of chaotic iterations, when giving time to chaos to get settled in.
-Thus rapid and perfect PRNGs, regarding the NIST and DieHARD batteries, can be obtained
-using chaotic iterations on defective generators.
-
-\begin{table*}
-\renewcommand{\arraystretch}{1.3}
-\caption{Number of $\oplus$ operations to pass the whole NIST and DieHARD batteries}
-\label{threshold}
-\centering
- \begin{tabular}{|l||c|c|c|c|c|c|c|c|}
- \hline
-Inputted $PRNG$ & LCG & MRG & SWC & GFSR & INV& LCG2 & LCG3 & MRG2 \\ \hline\hline
-Threshold value $m$& 19 & 7 & 2& 1 & 11& 9& 3& 4\\ \hline\hline
-\end{tabular}
-\end{table*}
-
-Finally, the TestU01 battery has been launched on three well-known generators
-(a logistic map, a simple XORshift, and the cryptographically secure ISAAC,
-see Table~\ref{TestU011}). These results can be compared with
-Table~\ref{TestU01 for Old CI}, which gives the scores obtained by the
-Old CI PRNG that has received these generators.
-The obvious improvement speaks for itself, and together with the other
-results recalled in this section, it reinforces the opinion that a strong
-correlation between topological properties and statistical behavior exists.
-
-
-The next subsection will now give a concrete original implementation of the Xor CI PRNG, the
-fastest generator in the chaotic iteration based family. In the remainder,
-this generator will be simply referred to as CIPRNG, or ``the proposed PRNG'', if this statement does not
-raise ambiguity.
+%% \subsection{Statistical tests}
+%% \label{Security analysis}
+
+%% Three batteries of tests are reputed and regularly used
+%% to evaluate the statistical properties of newly designed pseudorandom
+%% number generators. These batteries are named DieHard~\cite{Marsaglia1996},
+%% the NIST suite~\cite{ANDREW2008}, and the most stringent one called
+%% TestU01~\cite{LEcuyerS07}, which encompasses the two other batteries.
+
+
+
+%% \label{Results and discussion}
+%% \begin{table*}
+%% \renewcommand{\arraystretch}{1.3}
+%% \caption{NIST and DieHARD tests suite passing rates for PRNGs without CI}
+%% \label{NIST and DieHARD tests suite passing rate the for PRNGs without CI}
+%% \centering
+%% \begin{tabular}{|l||c|c|c|c|c|c|c|c|c|c|}
+%% \hline\hline
+%% Types of PRNGs & \multicolumn{2}{c|}{Linear PRNGs} & \multicolumn{4}{c|}{Lagged PRNGs} & \multicolumn{1}{c|}{ICG PRNGs} & \multicolumn{3}{c|}{Mixed PRNGs}\\ \hline
+%% \backslashbox{\textbf{$Tests$}} {\textbf{$PRNG$}} & LCG& MRG& AWC & SWB & SWC & GFSR & INV & LCG2& LCG3& MRG2 \\ \hline
+%% NIST & 11/15 & 14/15 &\textbf{15/15} & \textbf{15/15} & 14/15 & 14/15 & 14/15 & 14/15& 14/15& 14/15 \\ \hline
+%% DieHARD & 16/18 & 16/18 & 15/18 & 16/18 & \textbf{18/18} & 16/18 & 16/18 & 16/18& 16/18& 16/18\\ \hline
+%% \end{tabular}
+%% \end{table*}
+
+%% Table~\ref{NIST and DieHARD tests suite passing rate the for PRNGs without CI} shows the
+%% results on the two first batteries recalled above, indicating that all the PRNGs presented
+%% in the previous section
+%% cannot pass all these tests. In other words, the statistical quality of these PRNGs cannot
+%% fulfill the up-to-date standards presented previously. We have shown in~\cite{bfg12a:ip} that the use of chaotic
+%% iterations can solve this issue.
+%% %More precisely, to
+%% %illustrate the effects of chaotic iterations on these defective PRNGs, experiments have been divided in three parts~\cite{bfg12a:ip}:
+%% %\begin{enumerate}
+%% % \item \textbf{Single CIPRNG}: The PRNGs involved in CI computing are of the same category.
+%% % \item \textbf{Mixed CIPRNG}: Two different types of PRNGs are mixed during the chaotic iterations process.
+%% % \item \textbf{Multiple CIPRNG}: The generator is obtained by repeating the composition of the iteration function as follows: $x^0\in \mathds{B}^{\mathsf{N}}$, and $\forall n\in \mathds{N}^{\ast },\forall i\in \llbracket1;\mathsf{N}\rrbracket, x_i^n=$
+%% %\begin{equation}
+%% %\begin{array}{l}
+%% %\left\{
+%% %\begin{array}{l}
+%% %x_i^{n-1}~~~~~\text{if}~S^n\neq i \\
+%% %\forall j\in \llbracket1;\mathsf{m}\rrbracket,f^m(x^{n-1})_{S^{nm+j}}~\text{if}~S^{nm+j}=i.\end{array} \right. \end{array}
+%% %\end{equation}
+%% %$m$ is called the \emph{functional power}.
+%% %\end{enumerate}
+%% %
+%% The obtained results are reproduced in Table
+%% \ref{NIST and DieHARD tests suite passing rate the for single CIPRNGs}.
+%% The scores written in boldface indicate that all the tests have been passed successfully, whereas an
+%% asterisk ``*'' means that the considered passing rate has been improved.
+%% The improvements are obvious for both the ``Old CI'' and the ``New CI'' generators.
+%% Concerning the ``Xor CI PRNG'', the score is less spectacular. Because of a large speed improvement, the statistics
+%% are not as good as for the two other versions of these CIPRNGs.
+%% However 8 tests have been improved (with no deflation for the other results).
+
+
+%% \begin{table*}
+%% \renewcommand{\arraystretch}{1.3}
+%% \caption{NIST and DieHARD tests suite passing rates for PRNGs with CI}
+%% \label{NIST and DieHARD tests suite passing rate the for single CIPRNGs}
+%% \centering
+%% \begin{tabular}{|l||c|c|c|c|c|c|c|c|c|c|c|c|}
+%% \hline
+%% Types of PRNGs & \multicolumn{2}{c|}{Linear PRNGs} & \multicolumn{4}{c|}{Lagged PRNGs} & \multicolumn{1}{c|}{ICG PRNGs} & \multicolumn{3}{c|}{Mixed PRNGs}\\ \hline
+%% \backslashbox{\textbf{$Tests$}} {\textbf{$Single~CIPRNG$}} & LCG & MRG & AWC & SWB & SWC & GFSR & INV& LCG2 & LCG3& MRG2 \\ \hline\hline
+%% Old CIPRNG\\ \hline \hline
+%% NIST & \textbf{15/15} * & \textbf{15/15} * & \textbf{15/15} & \textbf{15/15} & \textbf{15/15} * & \textbf{15/15} * & \textbf{15/15} *& \textbf{15/15} * & \textbf{15/15} * & \textbf{15/15} \\ \hline
+%% DieHARD & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} & \textbf{18/18} * & \textbf{18/18} *& \textbf{18/18} * & \textbf{18/18} *& \textbf{18/18} * \\ \hline
+%% New CIPRNG\\ \hline \hline
+%% NIST & \textbf{15/15} * & \textbf{15/15} * & \textbf{15/15} & \textbf{15/15} & \textbf{15/15} * & \textbf{15/15} * & \textbf{15/15} *& \textbf{15/15} * & \textbf{15/15} * & \textbf{15/15} \\ \hline
+%% DieHARD & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} * & \textbf{18/18} *& \textbf{18/18} *\\ \hline
+%% Xor CIPRNG\\ \hline\hline
+%% NIST & 14/15*& \textbf{15/15} * & \textbf{15/15} & \textbf{15/15} & 14/15 & \textbf{15/15} * & 14/15& \textbf{15/15} * & \textbf{15/15} *& \textbf{15/15} \\ \hline
+%% DieHARD & 16/18 & 16/18 & 17/18* & \textbf{18/18} * & \textbf{18/18} & \textbf{18/18} * & 16/18 & 16/18 & 16/18& 16/18\\ \hline
+%% \end{tabular}
+%% \end{table*}
+
+
+%% We have then investigated in~\cite{bfg12a:ip} if it were possible to improve
+%% the statistical behavior of the Xor CI version by combining more than one
+%% $\oplus$ operation. Results are summarized in Table~\ref{threshold}, illustrating
+%% the progressive increasing effects of chaotic iterations, when giving time to chaos to get settled in.
+%% Thus rapid and perfect PRNGs, regarding the NIST and DieHARD batteries, can be obtained
+%% using chaotic iterations on defective generators.
+
+%% \begin{table*}
+%% \renewcommand{\arraystretch}{1.3}
+%% \caption{Number of $\oplus$ operations to pass the whole NIST and DieHARD batteries}
+%% \label{threshold}
+%% \centering
+%% \begin{tabular}{|l||c|c|c|c|c|c|c|c|}
+%% \hline
+%% Inputted $PRNG$ & LCG & MRG & SWC & GFSR & INV& LCG2 & LCG3 & MRG2 \\ \hline\hline
+%% Threshold value $m$& 19 & 7 & 2& 1 & 11& 9& 3& 4\\ \hline\hline
+%% \end{tabular}
+%% \end{table*}
+
+%% Finally, the TestU01 battery has been launched on three well-known generators
+%% (a logistic map, a simple XORshift, and the cryptographically secure ISAAC,
+%% see Table~\ref{TestU011}). These results can be compared with
+%% Table~\ref{TestU01 for Old CI}, which gives the scores obtained by the
+%% Old CI PRNG that has received these generators.
+%% The obvious improvement speaks for itself, and together with the other
+%% results recalled in this section, it reinforces the opinion that a strong
+%% correlation between topological properties and statistical behavior exists.
+
+
+%% The next subsection will now give a concrete original implementation of the Xor CI PRNG, the
+%% fastest generator in the chaotic iteration based family. In the remainder,
+%% this generator will be simply referred to as CIPRNG, or ``the proposed PRNG'', if this statement does not
+%% raise ambiguity.
\subsection{First Efficient Implementation of a PRNG based on Chaotic Iterations}
\subsection{Practical Security Evaluation}
\label{sec:Practicak evaluation}
-
-Pseudorandom generators based on Eq.~\eqref{equation Oplus} are thus cryptographically secure when
-they are XORed with an already cryptographically
-secure PRNG. But, as stated previously,
-such a property does not mean that, whatever the
-key size, no attacker can predict the next bit
-knowing all the previously released ones.
-However, given a key size, it is possible to
-measure in practice the minimum duration needed
-for an attacker to break a cryptographically
-secure PRNG, if we know the power of his/her
-machines. Such a concrete security evaluation
-is related to the $(T,\varepsilon)-$security
-notion, which is recalled and evaluated in what
-follows, for the sake of completeness.
-
-Let us firstly recall that,
-\begin{definition}
-Let $\mathcal{D} : \mathds{B}^M \longrightarrow \mathds{B}$ be a probabilistic algorithm that runs
-in time $T$.
-Let $\varepsilon > 0$.
-$\mathcal{D}$ is called a $(T,\varepsilon)-$distinguishing attack on pseudorandom
-generator $G$ if
-
-\begin{flushleft}
-$\left| Pr[\mathcal{D}(G(k)) = 1 \mid k \in_R \{0,1\}^\ell ]\right.$
-\end{flushleft}
-
-\begin{flushright}
-$ - \left. Pr[\mathcal{D}(s) = 1 \mid s \in_R \mathds{B}^M ]\right| \geqslant \varepsilon,$
-\end{flushright}
-
-\noindent where the probability is taken over the internal coin flips of $\mathcal{D}$, and the notation
-``$\in_R$'' indicates the process of selecting an element at random and uniformly over the
-corresponding set.
-\end{definition}
-
-Let us recall that the running time of a probabilistic algorithm is defined to be the
-maximum of the expected number of steps needed to produce an output, maximized
-over all inputs; the expected number is averaged over all coin flips made by the algorithm~\cite{Knuth97}.
-We are now able to define the notion of cryptographically secure PRNGs:
-
-\begin{definition}
-A pseudorandom generator is $(T,\varepsilon)-$secure if there exists no $(T,\varepsilon)-$distinguishing attack on this pseudorandom generator.
-\end{definition}
-
-
-
-
-
-
-
-Suppose now that the PRNG of Eq.~\eqref{equation Oplus} will work during
-$M=100$ time units, and that during this period,
-an attacker can realize $10^{12}$ clock cycles.
-We thus wonder whether, during the PRNG's
-lifetime, the attacker can distinguish this
-sequence from a truly random one, with a probability
-greater than $\varepsilon = 0.2$.
-We consider that $N$ has 900 bits.
-
-Predicting the next generated bit knowing all the
-previously released ones by Eq.~\eqref{equation Oplus} is obviously equivalent to predicting the
-next bit in the BBS generator, which
-is cryptographically secure. More precisely, it
-is $(T,\varepsilon)-$secure: no
-$(T,\varepsilon)-$distinguishing attack can be
-successfully realized on this PRNG, if~\cite{Fischlin}
-\begin{equation}
-T \leqslant \dfrac{L(N)}{6 N (log_2(N))\varepsilon^{-2}M^2}-2^7 N \varepsilon^{-2} M^2 log_2 (8 N \varepsilon^{-1}M)
-\label{mesureConcrete}
-\end{equation}
-where $M$ is the length of the output ($M=100$ in
-our example), and $L(N)$ is equal to
-$$
-2.8\times 10^{-3} exp \left(1.9229 \times (N ~ln~ 2)^\frac{1}{3} \times (ln(N~ln~ 2))^\frac{2}{3}\right)
-$$
-is the number of clock cycles to factor a $N-$bit
-integer.
+This subsection is given in Section~\ref{A-sec:Practicak evaluation} of the annex document.
+%%RAF mis en annexe
+
+
+%% Pseudorandom generators based on Eq.~\eqref{equation Oplus} are thus cryptographically secure when
+%% they are XORed with an already cryptographically
+%% secure PRNG. But, as stated previously,
+%% such a property does not mean that, whatever the
+%% key size, no attacker can predict the next bit
+%% knowing all the previously released ones.
+%% However, given a key size, it is possible to
+%% measure in practice the minimum duration needed
+%% for an attacker to break a cryptographically
+%% secure PRNG, if we know the power of his/her
+%% machines. Such a concrete security evaluation
+%% is related to the $(T,\varepsilon)-$security
+%% notion, which is recalled and evaluated in what
+%% follows, for the sake of completeness.
+
+%% Let us firstly recall that,
+%% \begin{definition}
+%% Let $\mathcal{D} : \mathds{B}^M \longrightarrow \mathds{B}$ be a probabilistic algorithm that runs
+%% in time $T$.
+%% Let $\varepsilon > 0$.
+%% $\mathcal{D}$ is called a $(T,\varepsilon)-$distinguishing attack on pseudorandom
+%% generator $G$ if
+
+%% \begin{flushleft}
+%% $\left| Pr[\mathcal{D}(G(k)) = 1 \mid k \in_R \{0,1\}^\ell ]\right.$
+%% \end{flushleft}
+
+%% \begin{flushright}
+%% $ - \left. Pr[\mathcal{D}(s) = 1 \mid s \in_R \mathds{B}^M ]\right| \geqslant \varepsilon,$
+%% \end{flushright}
+
+%% \noindent where the probability is taken over the internal coin flips of $\mathcal{D}$, and the notation
+%% ``$\in_R$'' indicates the process of selecting an element at random and uniformly over the
+%% corresponding set.
+%% \end{definition}
+
+%% Let us recall that the running time of a probabilistic algorithm is defined to be the
+%% maximum of the expected number of steps needed to produce an output, maximized
+%% over all inputs; the expected number is averaged over all coin flips made by the algorithm~\cite{Knuth97}.
+%% We are now able to define the notion of cryptographically secure PRNGs:
+
+%% \begin{definition}
+%% A pseudorandom generator is $(T,\varepsilon)-$secure if there exists no $(T,\varepsilon)-$distinguishing attack on this pseudorandom generator.
+%% \end{definition}
+
+
+
+
+
+
+
+%% Suppose now that the PRNG of Eq.~\eqref{equation Oplus} will work during
+%% $M=100$ time units, and that during this period,
+%% an attacker can realize $10^{12}$ clock cycles.
+%% We thus wonder whether, during the PRNG's
+%% lifetime, the attacker can distinguish this
+%% sequence from a truly random one, with a probability
+%% greater than $\varepsilon = 0.2$.
+%% We consider that $N$ has 900 bits.
+
+%% Predicting the next generated bit knowing all the
+%% previously released ones by Eq.~\eqref{equation Oplus} is obviously equivalent to predicting the
+%% next bit in the BBS generator, which
+%% is cryptographically secure. More precisely, it
+%% is $(T,\varepsilon)-$secure: no
+%% $(T,\varepsilon)-$distinguishing attack can be
+%% successfully realized on this PRNG, if~\cite{Fischlin}
+%% \begin{equation}
+%% T \leqslant \dfrac{L(N)}{6 N (log_2(N))\varepsilon^{-2}M^2}-2^7 N \varepsilon^{-2} M^2 log_2 (8 N \varepsilon^{-1}M)
+%% \label{mesureConcrete}
+%% \end{equation}
+%% where $M$ is the length of the output ($M=100$ in
+%% our example), and $L(N)$ is equal to
+%% $$
+%% 2.8\times 10^{-3} exp \left(1.9229 \times (N ~ln~ 2)^\frac{1}{3} \times (ln(N~ln~ 2))^\frac{2}{3}\right)
+%% $$
+%% is the number of clock cycles to factor a $N-$bit
+%% integer.
-A direct numerical application shows that this attacker
-cannot achieve its $(10^{12},0.2)$ distinguishing
-attack in that context.
+%% A direct numerical application shows that this attacker
+%% cannot achieve its $(10^{12},0.2)$ distinguishing
+%% attack in that context.
