text:
\section{Introduction}

The ability of dynamical systems to process information has commanded long-standing interdisciplinary research interest\cite{shaw1981strange,sinha1998dynamics,ieee}.
There are several examples of natural systems with the capability to perform different forms of \textit{intrinsic computation}\cite{crutchfield2010introduction,mainzer2007thinking,toffoli2004nothing}.
In the context of machine learning, the overarching question is how ideas from physics or physical systems can enhance existing concepts. On one hand, research directions that can enhance the performance of algorithms in handling data from dynamical systems is a very pertinent question \cite{pre2020,csfx2020,nonlineardynamics2021,lai,trichygroup1}. On the other hand, research efforts to utilize physical systems to implement machine-learning learning algorithms have serious implications for new concepts in the field of artificial intelligence. This line of enquiry also has consequences for gauging the information processing capacity of naturally-occurring or human-engineered physical, chemical, and biological systems \cite{Beniaguev613141}.

Here we consider the Reservoir Computing (RC) technique to exploit a dynamical system for machine learning. RC is a recurrent neural network (RNN) based computational framework, in which the memory capability and rich dynamics of an RNN can be used for computation without actually training the network structure itself. Instead, training the readout layer is sufficient to achieve good performance \cite{jaeger2001echo,maass2002real}. In this framework, the network is called the \textit{reservoir}, as it stores the input as a high dimensional Spatio-temporal pattern, such that a linear transformation can efficiently extract the desired output in \textit{readout}. Formally, a low-dimensional temporal input $u(t)$ is transformed into a much higher dimensional state vector $x(t)$ by the reservoir. These state vectors are processed further by the linear readout to get a desired output. For its simplicity, scalability and lower training costs, reservoir computing has attracted widespread research interest, both in terms of applications \cite{lukovsevivcius2009reservoir,lukovsevivcius2012reservoir,tanaka2019recent,nakajima2020physical,pathak2018model,zhong2021dynamic,rafayelyan2020large,ghosh2021reservoir,saha2020predicting,zhang2021learning} as well as basic development of the general framework \cite{vlachas2020backpropagation,carroll2019network,silva2021reservoir,carroll2020reservoir}.

\par
In this letter, we show through both numerical simulations and experimentation, that a surprisingly simple system, namely a single forced pendulum has sufficient richness in its dynamics to process information for intelligent computation. The central idea is that, instead of multiplexing the input in state space, we encode the inputs in the temporal patterns, effectively making it act like a high dimensional system \cite{jensen2017reservoir,appeltant2011information}.
In this work, we will assess the performance of our reservoir in
the arena of both temporal and non-temporal tasks, and demonstrate that both these classes of tasks can be performed using our minimal one-node reservoir with strikingly good performance.

\medskip

\noindent
{\bf Reservoir Dynamics:} \ Specifically, we consider a pendulum of length $l$ with a bob of mass $m$, periodically driven with a force of amplitude $F$, depicted schematically in Fig.~\ref{Fig_scheme}. Given a  damping coefficient $b$ of the medium, the equation of motion can be written as

	\begin{align}
	\frac{d^2x}{dt^2} = -\frac{g}{l}sin(x) - k\frac{dx}{dt}+ f~sign~[sin(\omega t)].
	\end{align}
	where the $sign[\cdot]$ function represents a square wave that toggles between $+1$ and $-1$ when the argument switches from positive to negative.
	Here $f = \frac{F}{m}$ and $k = \frac{b}{m}$ are the amplitude of the force and damping coefficient per unit mass. This system can yield quasiperiodicity (as depicted in Fig.~\ref{Fig_scheme}) and has been studied extensively \cite{PhysRevA.35.4404,PhysRevLett.55.2103,PhysRevA.39.2593}. With no loss of generality, we have considered $k = 5\times 10^{-2}$ and $l=1.0$ for the numerical simulations presented in this study.

    \begin{figure}
	\centering
	\includegraphics[scale=0.45]{scheme.eps}
	\caption{\textbf{(a)} Schematic of the reservoir; \textbf{(b)} dynamics of an under-damped pendulum without forcing (solid black line) with the

	period of the driving force shown by dotted red lines; \textbf{(c)} Transient dynamics of the system (solid black line)

	under periodic forcing (indicated by alternating red and blue backgrounds). In the presence of periodic driving, the dynamics is quasiperiodic here, as the frequency of force is not equal to the natural frequency of the pendulum. }
	\label{Fig_scheme}
	\end{figure}

\par The unique dynamics arising at each point in $f-\omega$ parameter space is evident from the bifurcation diagrams shown in Fig.~\ref{Fig_bif_pend}.
Unlike most studies, here we examine the temporal patterns arising, not just in the asymptotic case, but in the transient dynamics as well.
The comparative features of the transient reservoir dynamics and the asymptotic dynamics can be seen from the two columns displayed in the figure. Clearly, the {\em transient dynamics provides a richer repertoire of nonlinear patterns than the asymptotic behaviour, and we will crucially use this aspect to encode information more efficiently.}

	\begin{figure}
	\centering
	\includegraphics[scale=0.4]{pend_bif.eps}
	\caption{Bifurcation Diagram of the reservoir dynamics,  \textbf{(a-b)} with respect to amplitude $f$ of the driving force, with $\omega = 1.0$, and \textbf{(c-d)} with respect to the driving frequency $\omega$, with $f=1.5$. The first column \textbf{(a), (c)} represents the asymptotic dynamics, and the second column \textbf{(b), (d)} shows the transient dynamics starting from initial state $[x,\dot{x}]=[0,0]$. The region between dashed red lines are used to encode the input to the reservoir.}
	\label{Fig_bif_pend}
	\end{figure}

\medskip

\noindent
{\bf Input Encoding}: \ Multiplexing the input signal efficiently into the reservoir dynamics is a crucial step for reservoir computing. The complete information should be stored into the reservoir. For our case, we have three possible choices to feed the input to the system. One option is to encode the input information with the initial condition. But this is not an efficient choice. As different trajectories can evolve to the same attractor, the pre-images are not unique after transience, with different initial conditions  producing the same asymptotic trajectories. Hence this will lead to input information loss and hinder robust and consistent input encoding. Alternately, one can encode the input using the two system parameters: amplitude ($f$), and frequency ($\omega$) of the applied force. Either of these two options is a better choice than input encoding with initial conditions, as each point in $f-\omega$ space gives rise to an unique dynamical sequence.

In this work we focus on tasks involving one-dimensional inputs, and hence only one parameter needs to be varied for input encoding, while the other parameter can be kept fixed. We will consider input-encoding using both the amplitude and the frequency of  forcing,
in order to compare the efficacy of these two alternate methods of input-encoding for different tasks, including their robustness in the presence of noise.
We will denote the scheme of input-encoding using forcing amplitude $f$ as \textit{amplitude encoding}, while the scheme where inputs are encoded  using the forcing frequency $\omega$ will be simply referred to as \textit{frequency encoding}.

First consider the \textit{amplitude encoding} scheme where we multiplex the input with the amplitude of force $f$. In this scheme we need to choose a range of the parameter, say $f \in [f_{min}, f_{max}]$, and we then need to scale all input points into this range. This scaling transformation ($u \rightarrow f$) can be expressed as $f = f_{min} + (f_{max}-f_{min})u$, where, $u$ is the normalized input in range $[0,1]$. Formally, $u=\frac{min[\tilde{u}(t)]+\tilde{u}(t)}{max[\tilde{u}(t)]-min[\tilde{u}(t)]}$, $\tilde{u}(t)$ being the original input signal. Specifically, the range of $f$ for this scheme is taken to be [1,2] as shown in Fig.~\ref{Fig_bif_pend}(b). For the  \textit{frequency encoding} scheme, we can proceed in a similar fashion, with parameter $\omega$ replacing parameter $f$ in the formalism.

\par As a test-bed to gauge the performance of our system we will consider two distinct classes of tasks: one task will involve processing non-temporal signals and another task will consider processing temporal signals.
In general, Reservoir Computing has proven to be successful in solving time dependent data processing, stemming from the nonlinear memory effect of the reservoir. But for non-temporal tasks, we need to remove the memory effect. This can be achieved by resetting the reservoir to a fixed point after feeding an input data point to it. Specifically for our case, the reservoir is set to $[x,\dot{x}]=[0,0]$
after each input.

\medskip

\noindent
{\bf Reservoir State and Regression:} \ The transient dynamics of the reservoir is stored in a discretized form as the state vector. Only the transient part of the dynamics is considered as it produces richer nonlinear repertoire than its asymptotic behavior. More formally, the state vector is the set of variables $x(t)$ recorded at a fixed sampling rate $\kappa\Omega~[\kappa=1,2,3...]$, an integer multiple of the sampling cycle frequency $\Omega$, i.e. for each sampling cycle we record $\kappa$ values of $x(t)$. Thus if we store the data for $N$ cycles for each input, states to be stored can be written as $S = [x(0),~x(\tau),~x(2\tau),~....,~x(\kappa N\tau)]$, where $\tau = \frac{2\pi}{\kappa\Omega}$ is the sampling interval. For each point $\tilde{u}(t_i),~i=1,2,3,...$ of input signal, one state vector $X_i$ is formed from $S$.

Now, forming the reservoir state vector $X_i$ from stored states $S$ is different for temporal and non-temporal tasks. For non-temporal tasks,
we produce the state vector as the column matrix $X_i = [S_i]^T$, where $[\cdot]^T$ represents the transpose.
For temporal tasks, we form the state vector corresponding to any particular input with the current states as well as states corresponding to certain number of previous states, i.e. we take \ $X_i = [w_0S_{i-m},~w_1S_{i-m-1},~......,~w_{m-1}S_{i-1},~w_mS_i]^T$, where $w_j,~j=0,1,2...,m$ are the weights of previous input states, following a linear distribution in the range $[0,1]$. Here $m$ is the finite \textit{memory}, which can be considered as a hyper-parameter to be tuned for different kinds of temporal tasks, allows us to achieve the required \textit{fading memory} to process temporal data. In our numerical simulations, we have considered $m=100$.

For the two schemes of input encoding the value of $\Omega$ is different. In the \textit{amplitude encoding} scheme we take $\Omega = \omega$, the frequency of driving force and for the \textit{frequency encoding} scheme we consider $\Omega = \omega_0$, the natural frequency of the oscillator. In general, $\Omega$ can be treated as another hyper-parameter for both schemes.

 Thus, for a complete input signal $\tilde{u}(t)$ one has the reservoir state vector matrix $\Re = [X_1,~X_2,~X_3,...,X_L]$, $L$ being the length of input signal. So the matrix $\Re$ has the dimension $\kappa N\times L$ for Task - I and $m\kappa N\times L$ for Task-II, where $\kappa$ and $N$ are hyper-parameters that can be optimized for best results.

\par Now if the corresponding output for $\tilde{u}(t)$ is $\tilde{v}(t)$, the linear transformation between the output signal and the reservoir state vector matrix can be written as $\tilde{v} = W\Re,$ where, $W$ is the $1\times \kappa N$ dimensional connection matrix. This matrix can be evaluated using training data set by regression method as $W = \tilde{v}\Re^{-1}$. Specifically for this purpose, we have used the \textit{Moore-Penrose pseudoinverse} \cite{barata2012moore}.

\medskip

\noindent
{\bf Machine Learning Tasks:} \ To check the performance of the reservoir we consider two tasks. The first task is non-temporal, and involves the learning of a high-degree polynomial. The second task involves temporal data processing, and considers the difficult task of using data from one state variable to infer another state variable in a chaotic system.

Specifically, the aim of Task - I is to approximate a $7^th$ degree polynomial given by $f(x) = (x-3)(x-2)(x-1)x(x+1)(x+2)(x+3)$ in the range $x\in[-3,3]$. As this task corresponds to non-temporal input processing, one input point $x$ is necessary and sufficient to get the corresponding output $f(x)$.

Our second task (Task - II) pertains to the  reconstruction of a chaotic attractor dealing with temporal data processing. As an illustrative example, here we consider the state variable $x(t)$ of a chaotic Lorenz system $[\dot{x} = 10(y-x),~\dot{y} = x(28-z)-y,~\dot{z} = xy - 8z/3]$ as input to infer another state variable $y(t)$ in output
\cite{lorenz1963deterministic,sparrow2012lorenz}.

\medskip

\noindent
{\bf Results:} \ The efficiency of the reservoir is analysed by estimating the accuracy of the tasks it performs, quantified by root mean square error (RMSE) of the predicted output with reference to the target one. We find that the reservoir works with great accuracy for both temporal and non-temporal tasks, for both the schemes, as is clearly discernible from Fig.~\ref{Fig_result}. The success of our single-node reservoir is also evident quantitatively from Table \ref{Tab_noise}, which lists
the order of accuracy obtained for the tasks.

For Task-I, we find that a reservoir trained with only $500$ data points, can approximate the polynomial with RMSE of the order of $10^{-10}$. Further, smaller training data sets does not significantly degrade the accuracy obtained. For instance, even training data sets with
size as low as $100$, yields accuracy of the order of $10^{-6}$.

    \begin{figure}[ht]
	\includegraphics[scale=0.4]{result.eps}
	\caption{\textbf{(a)} The comparison of predicted output with target for Task-I \textbf{(a - b)} and Task-II \textbf{(c - d)}. \textbf{(a), (c)}  are the results obtained with the {\em amplitude encoding} scheme and \textbf{(b), (d)} are those obtained with {\em frequency encoding}.}
	\label{Fig_result}
	\end{figure}

\par For the case of Task-II, the reservoir was trained with data set of length $5000$, and it yielded an accuracy of the order of $10^{-3}$. For the tasks we have considered, the prediction accuracy of the trained reservoir is independent of the testing data length, and for the numerical results listed in Table~\ref{Tab_noise} we have taken the testing data length to be the same as the training data length. So from the results it is clear that even for the tasks involving intensive and complex information processing, the one-node reservoir predicts the output successfully.

\bigskip

\noindent
{\bf Performance in Presence of Noise:} \ We now assess the robustness of the performance in the presence of a noise floor. In order to examine the effect of noise on the performance, we have perturbed each state variable with a random noise, uniformly distributed in the range $[-0.01:0.01$]. The results are displayed in Table \ref{Tab_noise}. It is clear that the performance is reasonably stable even in the presence of such significantly large noise. Further, we notice that encoding inputs via the frequency of the drive is more robust and accurate than encoding inputs via the amplitude of forcing. This suggests that for optimal and most robust implementation, different control parameters for encoding information should be investigated, as the nature of the dynamics could be quite different under variation of different parameters, leading to different robustness in the presence of noise.

\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
Input encoding & Task - I & Task - II \\
\hline
$f$  & $ 10^{-10}$/\color{red}{$10^{-2}$} & $10^{-3}$/\color{red}{$10^{-3}$} \\ \hline
$\omega$ & $ 10^{-8}$/\color{red}{$10^{-3}$} & $10^{-3}$/\color{red}{$10^{-3}$} \\ \hline
\end{tabular}
    \caption{Comparison of performance, as quantified by RMSE, under two different schemes of input encoding, with one method using the forcing amplitude $f$ and the other method using forcing frequency $\omega$ to encode inputs.
The first value reports the order of the RMSE obtained from noise-free systems, while the second value (marked in red) gives the results obtained in the presence of noise.
}
    \label{Tab_noise}
\end{table}

\bigskip

\noindent
{\bf Proof-of-principle experiment:} \ We have also investigated the performance of a single-node reservoir that utilizes actual laboratory data from an experimental realization of a forced pendulum. Remarkably, even this simple experimental system yields very good performance, as seen from the results displayed in Fig.~\ref{Fig_expt}. The detailed discussion of experimental setup and procedure is listed in appendix \textit{B} of the supplementary material.

\begin{figure}
    \centering
    \includegraphics[scale=0.6]{expt_res.eps}
    \caption{Accuracy of Task-I, using the time series of a laboratory realization of a pendulum as a reservoir, demonstrating the ability of a simple experimental system to execute computational tasks. Here the \textit{frequency encoding} scheme is used.\\

    }
    \label{Fig_expt}
\end{figure}

\bigskip

\noindent
{\bf Comparison of performance with multi-node reservoirs:} \  We have also compared the efficiency of our single-node reservoir with the multi-node reservoirs utilized in earlier studies. As a representative example, we have considered the reservoir of environmentally coupled Lorenz oscillator network \cite{mandal2021achieving} to show the comparison in terms of the similar tasks performed by the two reservoirs. The tasks considered are the attractor reconstruction of chaotic R\"{o}ssler and chaotic Chua systems, and the filtering of a Mackey-Glass time series.
Table \ref{Tab_comp} lists the accuracy obtained for these tasks performed by both the reservoirs when trained with same training data. These results suggest that our single-node reservoir has the potential to perform better than a reservoir comprised of a large network of dynamical systems.

\begin{table}
    \centering
    \begin{tabular}{|c|c|c|c|}
    \hline
      Reservoir  & \ Task - I \ & \ Task - II \ & \ Task - III \ \\ \hline
 Multi-node Network    &   $10^{-9}$    &   $10^{-6}$    &    $10^{-4}$    \\ \hline
Single  Pendulum   &   $10^{-14}$    &    $10^{-6}$   &     $10^{-11}$   \\ \hline
    \end{tabular}
    \caption{Comparison of accuracy obtained from Reservoir Computing implemented by a multi-node network reservoir and a single-node pendulum reservoir, for three tasks: (I) attractor reconstruction of a chaotic R\"{o}ssler system, (II) attractor reconstruction of a chaotic Chua system, and (III) filtering of a Mackey-Glass time series.}
    \label{Tab_comp}
\end{table}

\medskip

\noindent
{\bf Duffing Oscillator:} \ In order to explore the generality of our results we have also investigated another low-dimensional nonlinear system that can be readily implemented in the laboratory,  the Duffing oscillator.
Detailed results demonstrating the successful implementation of reservoir computing with a single Duffing oscillator in different dynamical regimes, ranging  from periodic and quasi-periodic to chaotic,
can be found in appendix \textit{A} of the supplementary material. Further these results offer us a test-bed for gauging the comparative performance of systems with different dynamical complexity serving as a single-node reservoir.
The crucial feature we exploit here is that  transient periodic and quasi-periodic behaviour offers a rich repertoire of temporal sequences, while not suffering from the extreme sensitivity to initial conditions that comes alongside the complexity of chaos.
So we find that the combination of stability and complexity offered by periodic and quasi-periodic transient dynamics makes this class of dynamical behaviour most suited as a reservoir.

\medskip

\noindent
{\bf Conclusions:} \ In summary, we have successfully demonstrated that a {\em single simple dynamical system}, such as a pendulum, can be used effectively as a reservoir in Reservoir Computing. Specifically, we exploited the rich dynamics of a driven pendulum for a single-node reservoir to perform complex artificial intelligence tasks. To the best of our knowledge, a single simple pendulum working as an efficient and powerful reservoir is being reported for the first time.

In this study, we have undertaken two classes of tasks, one processing temporal signals, and the other non-temporal inputs.
One of the new directions our work suggests is the use of the transient dynamics of non-chaotic nonlinear systems as the ``reservoir'' in single-node reservoir computing, as it offers both stability and complexity. The temporal patterns embedded in the transient dynamics of a nonlinear system can thus provide a rich set of transformations for the {\em readout} layer. We present results from numerical simulations, with the parameters of the dynamical system utilized as reservoir chosen in such a way that it can be easily realized in laboratory experiments.

Importantly, this work can also be extended to deal with noisy real-world data sets containing impurities. Further, physical implementations of the idea can be potentially extended to much smaller, faster, and power-efficient systems, for instance, dynamical systems realized with integrated circuit chips. So these ideas can lead to the foundation of powerful machine-learning enabled chips.

\par In conclusion then, we have demonstrated that a single low-dimensional nonlinear dynamical system has remarkable potential for information processing, and can serve as a ``reservoir'' for Reservoir Computing. These results also open up the possibility of other simple dynamical systems for single-node Reservoir Computing, and a wide variety of natural systems can be considered as potential candidates for the reservoir. Thus this work provides a significant step forward towards the broad goal of exploiting intrinsic dynamics of natural systems for intelligent computation.

\section{Introduction}

The ability of dynamical systems to process information has commanded long-standing interdisciplinary research interest\cite{shaw1981strange,sinha1998dynamics,ieee}.
There are several examples of natural systems with the capability to perform different forms of \textit{intrinsic computation}\cite{crutchfield2010introduction,mainzer2007thinking,toffoli2004nothing}.
In the context of machine learning, the overarching question is how ideas from physics or physical systems can enhance existing concepts. On one hand, research directions that can enhance the performance of algorithms in handling data from dynamical systems is a very pertinent question \cite{pre2020,csfx2020,nonlineardynamics2021,lai,trichygroup1}. On the other hand, research efforts to utilize physical systems to implement machine-learning learning algorithms have serious implications for new concepts in the field of artificial intelligence. This line of enquiry also has consequences for gauging the information processing capacity of naturally-occurring or human-engineered physical, chemical, and biological systems \cite{Beniaguev613141}.

Here we consider the Reservoir Computing (RC) technique to exploit a dynamical system for machine learning. RC is a recurrent neural network (RNN) based computational framework, in which the memory capability and rich dynamics of an RNN can be used for computation without actually training the network structure itself. Instead, training the readout layer is sufficient to achieve good performance \cite{jaeger2001echo,maass2002real}. In this framework, the network is called the \textit{reservoir}, as it stores the input as a high dimensional Spatio-temporal pattern, such that a linear transformation can efficiently extract the desired output in \textit{readout}. Formally, a low-dimensional temporal input $u(t)$ is transformed into a much higher dimensional state vector $x(t)$ by the reservoir. These state vectors are processed further by the linear readout to get a desired output. For its simplicity, scalability and lower training costs, reservoir computing has attracted widespread research interest, both in terms of applications \cite{lukovsevivcius2009reservoir,lukovsevivcius2012reservoir,tanaka2019recent,nakajima2020physical,pathak2018model,zhong2021dynamic,rafayelyan2020large,ghosh2021reservoir,saha2020predicting,zhang2021learning} as well as basic development of the general framework \cite{vlachas2020backpropagation,carroll2019network,silva2021reservoir,carroll2020reservoir}.

\par
In this letter, we show through both numerical simulations and experimentation, that a surprisingly simple system, namely a single forced pendulum has sufficient richness in its dynamics to process information for intelligent computation. The central idea is that, instead of multiplexing the input in state space, we encode the inputs in the temporal patterns, effectively making it act like a high dimensional system \cite{jensen2017reservoir,appeltant2011information}.
In this work, we will assess the performance of our reservoir in
the arena of both temporal and non-temporal tasks, and demonstrate that both these classes of tasks can be performed using our minimal one-node reservoir with strikingly good performance.

\medskip

\noindent
{\bf Reservoir Dynamics:} \ Specifically, we consider a pendulum of length $l$ with a bob of mass $m$, periodically driven with a force of amplitude $F$, depicted schematically in Fig.~\ref{Fig_scheme}. Given a  damping coefficient $b$ of the medium, the equation of motion can be written as

	\begin{align}
	\frac{d^2x}{dt^2} = -\frac{g}{l}sin(x) - k\frac{dx}{dt}+ f~sign~[sin(\omega t)].
	\end{align}
	where the $sign[\cdot]$ function represents a square wave that toggles between $+1$ and $-1$ when the argument switches from positive to negative.
	Here $f = \frac{F}{m}$ and $k = \frac{b}{m}$ are the amplitude of the force and damping coefficient per unit mass. This system can yield quasiperiodicity (as depicted in Fig.~\ref{Fig_scheme}) and has been studied extensively \cite{PhysRevA.35.4404,PhysRevLett.55.2103,PhysRevA.39.2593}. With no loss of generality, we have considered $k = 5\times 10^{-2}$ and $l=1.0$ for the numerical simulations presented in this study.

    \begin{figure}
	\centering
	\includegraphics[scale=0.45]{scheme.eps}
	\caption{\textbf{(a)} Schematic of the reservoir; \textbf{(b)} dynamics of an under-damped pendulum without forcing (solid black line) with the

	period of the driving force shown by dotted red lines; \textbf{(c)} Transient dynamics of the system (solid black line)

	under periodic forcing (indicated by alternating red and blue backgrounds). In the presence of periodic driving, the dynamics is quasiperiodic here, as the frequency of force is not equal to the natural frequency of the pendulum. }
	\label{Fig_scheme}
	\end{figure}

\par The unique dynamics arising at each point in $f-\omega$ parameter space is evident from the bifurcation diagrams shown in Fig.~\ref{Fig_bif_pend}.
Unlike most studies, here we examine the temporal patterns arising, not just in the asymptotic case, but in the transient dynamics as well.
The comparative features of the transient reservoir dynamics and the asymptotic dynamics can be seen from the two columns displayed in the figure. Clearly, the {\em transient dynamics provides a richer repertoire of nonlinear patterns than the asymptotic behaviour, and we will crucially use this aspect to encode information more efficiently.}

	\begin{figure}
	\centering
	\includegraphics[scale=0.4]{pend_bif.eps}
	\caption{Bifurcation Diagram of the reservoir dynamics,  \textbf{(a-b)} with respect to amplitude $f$ of the driving force, with $\omega = 1.0$, and \textbf{(c-d)} with respect to the driving frequency $\omega$, with $f=1.5$. The first column \textbf{(a), (c)} represents the asymptotic dynamics, and the second column \textbf{(b), (d)} shows the transient dynamics starting from initial state $[x,\dot{x}]=[0,0]$. The region between dashed red lines are used to encode the input to the reservoir.}
	\label{Fig_bif_pend}
	\end{figure}

\medskip

\noindent
{\bf Input Encoding}: \ Multiplexing the input signal efficiently into the reservoir dynamics is a crucial step for reservoir computing. The complete information should be stored into the reservoir. For our case, we have three possible choices to feed the input to the system. One option is to encode the input information with the initial condition. But this is not an efficient choice. As different trajectories can evolve to the same attractor, the pre-images are not unique after transience, with different initial conditions  producing the same asymptotic trajectories. Hence this will lead to input information loss and hinder robust and consistent input encoding. Alternately, one can encode the input using the two system parameters: amplitude ($f$), and frequency ($\omega$) of the applied force. Either of these two options is a better choice than input encoding with initial conditions, as each point in $f-\omega$ space gives rise to an unique dynamical sequence.

In this work we focus on tasks involving one-dimensional inputs, and hence only one parameter needs to be varied for input encoding, while the other parameter can be kept fixed. We will consider input-encoding using both the amplitude and the frequency of  forcing,
in order to compare the efficacy of these two alternate methods of input-encoding for different tasks, including their robustness in the presence of noise.
We will denote the scheme of input-encoding using forcing amplitude $f$ as \textit{amplitude encoding}, while the scheme where inputs are encoded  using the forcing frequency $\omega$ will be simply referred to as \textit{frequency encoding}.

First consider the \textit{amplitude encoding} scheme where we multiplex the input with the amplitude of force $f$. In this scheme we need to choose a range of the parameter, say $f \in [f_{min}, f_{max}]$, and we then need to scale all input points into this range. This scaling transformation ($u \rightarrow f$) can be expressed as $f = f_{min} + (f_{max}-f_{min})u$, where, $u$ is the normalized input in range $[0,1]$. Formally, $u=\frac{min[\tilde{u}(t)]+\tilde{u}(t)}{max[\tilde{u}(t)]-min[\tilde{u}(t)]}$, $\tilde{u}(t)$ being the original input signal. Specifically, the range of $f$ for this scheme is taken to be [1,2] as shown in Fig.~\ref{Fig_bif_pend}(b). For the  \textit{frequency encoding} scheme, we can proceed in a similar fashion, with parameter $\omega$ replacing parameter $f$ in the formalism.

\par As a test-bed to gauge the performance of our system we will consider two distinct classes of tasks: one task will involve processing non-temporal signals and another task will consider processing temporal signals.
In general, Reservoir Computing has proven to be successful in solving time dependent data processing, stemming from the nonlinear memory effect of the reservoir. But for non-temporal tasks, we need to remove the memory effect. This can be achieved by resetting the reservoir to a fixed point after feeding an input data point to it. Specifically for our case, the reservoir is set to $[x,\dot{x}]=[0,0]$
after each input.

\medskip

\noindent
{\bf Reservoir State and Regression:} \ The transient dynamics of the reservoir is stored in a discretized form as the state vector. Only the transient part of the dynamics is considered as it produces richer nonlinear repertoire than its asymptotic behavior. More formally, the state vector is the set of variables $x(t)$ recorded at a fixed sampling rate $\kappa\Omega~[\kappa=1,2,3...]$, an integer multiple of the sampling cycle frequency $\Omega$, i.e. for each sampling cycle we record $\kappa$ values of $x(t)$. Thus if we store the data for $N$ cycles for each input, states to be stored can be written as $S = [x(0),~x(\tau),~x(2\tau),~....,~x(\kappa N\tau)]$, where $\tau = \frac{2\pi}{\kappa\Omega}$ is the sampling interval. For each point $\tilde{u}(t_i),~i=1,2,3,...$ of input signal, one state vector $X_i$ is formed from $S$.

Now, forming the reservoir state vector $X_i$ from stored states $S$ is different for temporal and non-temporal tasks. For non-temporal tasks,
we produce the state vector as the column matrix $X_i = [S_i]^T$, where $[\cdot]^T$ represents the transpose.
For temporal tasks, we form the state vector corresponding to any particular input with the current states as well as states corresponding to certain number of previous states, i.e. we take \ $X_i = [w_0S_{i-m},~w_1S_{i-m-1},~......,~w_{m-1}S_{i-1},~w_mS_i]^T$, where $w_j,~j=0,1,2...,m$ are the weights of previous input states, following a linear distribution in the range $[0,1]$. Here $m$ is the finite \textit{memory}, which can be considered as a hyper-parameter to be tuned for different kinds of temporal tasks, allows us to achieve the required \textit{fading memory} to process temporal data. In our numerical simulations, we have considered $m=100$.

For the two schemes of input encoding the value of $\Omega$ is different. In the \textit{amplitude encoding} scheme we take $\Omega = \omega$, the frequency of driving force and for the \textit{frequency encoding} scheme we consider $\Omega = \omega_0$, the natural frequency of the oscillator. In general, $\Omega$ can be treated as another hyper-parameter for both schemes.

 Thus, for a complete input signal $\tilde{u}(t)$ one has the reservoir state vector matrix $\Re = [X_1,~X_2,~X_3,...,X_L]$, $L$ being the length of input signal. So the matrix $\Re$ has the dimension $\kappa N\times L$ for Task - I and $m\kappa N\times L$ for Task-II, where $\kappa$ and $N$ are hyper-parameters that can be optimized for best results.

\par Now if the corresponding output for $\tilde{u}(t)$ is $\tilde{v}(t)$, the linear transformation between the output signal and the reservoir state vector matrix can be written as $\tilde{v} = W\Re,$ where, $W$ is the $1\times \kappa N$ dimensional connection matrix. This matrix can be evaluated using training data set by regression method as $W = \tilde{v}\Re^{-1}$. Specifically for this purpose, we have used the \textit{Moore-Penrose pseudoinverse} \cite{barata2012moore}.

\medskip

\noindent
{\bf Machine Learning Tasks:} \ To check the performance of the reservoir we consider two tasks. The first task is non-temporal, and involves the learning of a high-degree polynomial. The second task involves temporal data processing, and considers the difficult task of using data from one state variable to infer another state variable in a chaotic system.

Specifically, the aim of Task - I is to approximate a $7^th$ degree polynomial given by $f(x) = (x-3)(x-2)(x-1)x(x+1)(x+2)(x+3)$ in the range $x\in[-3,3]$. As this task corresponds to non-temporal input processing, one input point $x$ is necessary and sufficient to get the corresponding output $f(x)$.

Our second task (Task - II) pertains to the  reconstruction of a chaotic attractor dealing with temporal data processing. As an illustrative example, here we consider the state variable $x(t)$ of a chaotic Lorenz system $[\dot{x} = 10(y-x),~\dot{y} = x(28-z)-y,~\dot{z} = xy - 8z/3]$ as input to infer another state variable $y(t)$ in output
\cite{lorenz1963deterministic,sparrow2012lorenz}.

\medskip

\noindent
{\bf Results:} \ The efficiency of the reservoir is analysed by estimating the accuracy of the tasks it performs, quantified by root mean square error (RMSE) of the predicted output with reference to the target one. We find that the reservoir works with great accuracy for both temporal and non-temporal tasks, for both the schemes, as is clearly discernible from Fig.~\ref{Fig_result}. The success of our single-node reservoir is also evident quantitatively from Table \ref{Tab_noise}, which lists
the order of accuracy obtained for the tasks.

For Task-I, we find that a reservoir trained with only $500$ data points, can approximate the polynomial with RMSE of the order of $10^{-10}$. Further, smaller training data sets does not significantly degrade the accuracy obtained. For instance, even training data sets with
size as low as $100$, yields accuracy of the order of $10^{-6}$.

    \begin{figure}[ht]
	\includegraphics[scale=0.4]{result.eps}
	\caption{\textbf{(a)} The comparison of predicted output with target for Task-I \textbf{(a - b)} and Task-II \textbf{(c - d)}. \textbf{(a), (c)}  are the results obtained with the {\em amplitude encoding} scheme and \textbf{(b), (d)} are those obtained with {\em frequency encoding}.}
	\label{Fig_result}
	\end{figure}

\par For the case of Task-II, the reservoir was trained with data set of length $5000$, and it yielded an accuracy of the order of $10^{-3}$. For the tasks we have considered, the prediction accuracy of the trained reservoir is independent of the testing data length, and for the numerical results listed in Table~\ref{Tab_noise} we have taken the testing data length to be the same as the training data length. So from the results it is clear that even for the tasks involving intensive and complex information processing, the one-node reservoir predicts the output successfully.

\bigskip

\noindent
{\bf Performance in Presence of Noise:} \ We now assess the robustness of the performance in the presence of a noise floor. In order to examine the effect of noise on the performance, we have perturbed each state variable with a random noise, uniformly distributed in the range $[-0.01:0.01$]. The results are displayed in Table \ref{Tab_noise}. It is clear that the performance is reasonably stable even in the presence of such significantly large noise. Further, we notice that encoding inputs via the frequency of the drive is more robust and accurate than encoding inputs via the amplitude of forcing. This suggests that for optimal and most robust implementation, different control parameters for encoding information should be investigated, as the nature of the dynamics could be quite different under variation of different parameters, leading to different robustness in the presence of noise.

\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
Input encoding & Task - I & Task - II \\
\hline
$f$  & $ 10^{-10}$/\color{red}{$10^{-2}$} & $10^{-3}$/\color{red}{$10^{-3}$} \\ \hline
$\omega$ & $ 10^{-8}$/\color{red}{$10^{-3}$} & $10^{-3}$/\color{red}{$10^{-3}$} \\ \hline
\end{tabular}
    \caption{Comparison of performance, as quantified by RMSE, under two different schemes of input encoding, with one method using the forcing amplitude $f$ and the other method using forcing frequency $\omega$ to encode inputs.
The first value reports the order of the RMSE obtained from noise-free systems, while the second value (marked in red) gives the results obtained in the presence of noise.
}
    \label{Tab_noise}
\end{table}

\bigskip

\noindent
{\bf Proof-of-principle experiment:} \ We have also investigated the performance of a single-node reservoir that utilizes actual laboratory data from an experimental realization of a forced pendulum. Remarkably, even this simple experimental system yields very good performance, as seen from the results displayed in Fig.~\ref{Fig_expt}. The detailed discussion of experimental setup and procedure is listed in appendix \textit{B} of the supplementary material.

\begin{figure}
    \centering
    \includegraphics[scale=0.6]{expt_res.eps}
    \caption{Accuracy of Task-I, using the time series of a laboratory realization of a pendulum as a reservoir, demonstrating the ability of a simple experimental system to execute computational tasks. Here the \textit{frequency encoding} scheme is used.\\

    }
    \label{Fig_expt}
\end{figure}

\bigskip

\noindent
{\bf Comparison of performance with multi-node reservoirs:} \  We have also compared the efficiency of our single-node reservoir with the multi-node reservoirs utilized in earlier studies. As a representative example, we have considered the reservoir of environmentally coupled Lorenz oscillator network \cite{mandal2021achieving} to show the comparison in terms of the similar tasks performed by the two reservoirs. The tasks considered are the attractor reconstruction of chaotic R\"{o}ssler and chaotic Chua systems, and the filtering of a Mackey-Glass time series.
Table \ref{Tab_comp} lists the accuracy obtained for these tasks performed by both the reservoirs when trained with same training data. These results suggest that our single-node reservoir has the potential to perform better than a reservoir comprised of a large network of dynamical systems.

\begin{table}
    \centering
    \begin{tabular}{|c|c|c|c|}
    \hline
      Reservoir  & \ Task - I \ & \ Task - II \ & \ Task - III \ \\ \hline
 Multi-node Network    &   $10^{-9}$    &   $10^{-6}$    &    $10^{-4}$    \\ \hline
Single  Pendulum   &   $10^{-14}$    &    $10^{-6}$   &     $10^{-11}$   \\ \hline
    \end{tabular}
    \caption{Comparison of accuracy obtained from Reservoir Computing implemented by a multi-node network reservoir and a single-node pendulum reservoir, for three tasks: (I) attractor reconstruction of a chaotic R\"{o}ssler system, (II) attractor reconstruction of a chaotic Chua system, and (III) filtering of a Mackey-Glass time series.}
    \label{Tab_comp}
\end{table}

\medskip

\noindent
{\bf Duffing Oscillator:} \ In order to explore the generality of our results we have also investigated another low-dimensional nonlinear system that can be readily implemented in the laboratory,  the Duffing oscillator.
Detailed results demonstrating the successful implementation of reservoir computing with a single Duffing oscillator in different dynamical regimes, ranging  from periodic and quasi-periodic to chaotic,
can be found in appendix \textit{A} of the supplementary material. Further these results offer us a test-bed for gauging the comparative performance of systems with different dynamical complexity serving as a single-node reservoir.
The crucial feature we exploit here is that  transient periodic and quasi-periodic behaviour offers a rich repertoire of temporal sequences, while not suffering from the extreme sensitivity to initial conditions that comes alongside the complexity of chaos.
So we find that the combination of stability and complexity offered by periodic and quasi-periodic transient dynamics makes this class of dynamical behaviour most suited as a reservoir.

\medskip

\noindent
{\bf Conclusions:} \ In summary, we have successfully demonstrated that a {\em single simple dynamical system}, such as a pendulum, can be used effectively as a reservoir in Reservoir Computing. Specifically, we exploited the rich dynamics of a driven pendulum for a single-node reservoir to perform complex artificial intelligence tasks. To the best of our knowledge, a single simple pendulum working as an efficient and powerful reservoir is being reported for the first time.

In this study, we have undertaken two classes of tasks, one processing temporal signals, and the other non-temporal inputs.
One of the new directions our work suggests is the use of the transient dynamics of non-chaotic nonlinear systems as the ``reservoir'' in single-node reservoir computing, as it offers both stability and complexity. The temporal patterns embedded in the transient dynamics of a nonlinear system can thus provide a rich set of transformations for the {\em readout} layer. We present results from numerical simulations, with the parameters of the dynamical system utilized as reservoir chosen in such a way that it can be easily realized in laboratory experiments.

Importantly, this work can also be extended to deal with noisy real-world data sets containing impurities. Further, physical implementations of the idea can be potentially extended to much smaller, faster, and power-efficient systems, for instance, dynamical systems realized with integrated circuit chips. So these ideas can lead to the foundation of powerful machine-learning enabled chips.

\par In conclusion then, we have demonstrated that a single low-dimensional nonlinear dynamical system has remarkable potential for information processing, and can serve as a ``reservoir'' for Reservoir Computing. These results also open up the possibility of other simple dynamical systems for single-node Reservoir Computing, and a wide variety of natural systems can be considered as potential candidates for the reservoir. Thus this work provides a significant step forward towards the broad goal of exploiting intrinsic dynamics of natural systems for intelligent computation.

redpajama_set_name: RedPajamaArXiv


text:
Home › Buddhist Psychology › Natural Awakening: An Advanced Guide for Sharing Nondual Awareness
Natural Awakening: An Advanced Guide for Sharing Nondual Awareness
336 pages, paperback, 7 x 10 inches
4 Charts, 4 Appendices, Bibliography, References, Index of sections
Buddhism, Buddhist Psychology, Psychotherapy
ebook also available
By Peter Fenner, PhD
Foreword by Pir Amadon
"When crystallized identities enter a nondual field of awareness and begin to interact with a being embodying that state, suffering can't be sustained. It dissolves into a space where there is neither suffering nor its absence. Nondual awareness creates a transformational field or vortex that gently or abruptly, but irrevocably moves people into radiant awareness…. This is the paradoxical state that bodhisattvas play in for eternity."
This Guide goes behind the curtains of Dr. Peter Fenner's highly distinctive nondual teachings. It reveals in great detail and clarity the subtle and sometimes mysterious skillful methods he uses in his workshops, trainings, public presentations and coaching to swiftly awaken people to the liberating freedom of nondual, panoramic awareness.
Peter has a unique ability to reflect on the deep processes involved in his nondual transmission, especially the nuances of language, deep feelings and silence. He is well known for the rigor, precision and organic flow of his unfindability dialogs.
The skills he reveals in this Guide are many of the very same methods used by Dzogchen masters, Zen roshis and Advaita sages in "pointing out events" and "mind-to-mind transmission". Peter's teaching has been described by others as "free-form, continuous pointing to awareness."
In Buddhism nondual awareness is often called the "ultimate medicine" because no higher evolutionary accomplishment is possible for any conscious being, living anywhere, at any time. When we see everything as the seamlessly changing fabric of immutable, unfindable awareness it's impossible to be negatively touched by any environmental circumstance or inner perturbance. We move into the realm of embodied transcendence beyond disturbances and conditioned forms of peace.
In this Guide Peter often shares at the "result, or fruition level." He shows us how comprehensive awakening is our natural state, that infuses and embraces the totality of existence whenever we let go of personal striving, ambition, fantasies, hopes, fears and self-judgment. At this level our familiar preoccupations transform into a blissful and exquisite mandala that's completely free of the narrow concerns of a self-absorbed life. The neurotic energies and paranoiac projections that shape samsaric existence self-liberate into a panoramic clearing that supports evolutionary transformation throughout the universe.
In this Guide Peter's outlines everything he has discovered in 42 years of teaching. The Guide is especially useful for facilitators, therapists and coaches as it powerfully accelerates the integration on the nondual in a form that lends itself to the public sharing of pure awareness.
Part One is a virtual manual for spiritual teachers, therapists and coaches in how to introduce groups and clients to the ultimate state of healing and being—pure, pristine, timeless awareness.
Part Two traces Peter's contemporary expression of nonduality, and more especially his well-known Radiant Mind course, through to its Asian origins.
SELECTED REVIEWS
In Natural Awakening, Peter has taken the refined deconstructive practices that have liberated tens of thousands of Asian contemplatives and adapted them for effective Western use. I highly recommend it.
—Robert Thurman, Ph.D., Buddhist Scholar, Columbia University, President of Tibet House in New York, NY.
How wonderful that Peter Fenner is so generously making his masterful nondual
training manual available to the public!
—Risa Kaparo, Ph.D., author of Awakening Somatic Intelligence: The Art & Practice of Embodied Mindfulness, California.
This masterful work—lucid, keenly insightful, and beautifully written—will be of interest to beginning students and experienced teachers alike. A real gem and resource.
—John J. Prendergast, Ph.D., author of In Touch, Chief Editor of The Undivided, California.
Peter Fenner is a leader in the adaption and transmission of Asian nondual wisdom worldwide. He is a pioneer in the development of nondual therapy and creator of the 9-month Radiant Mind Course and the Natural Awakening: Advanced Nondual Training. He was a celibate monk in the Tibetan Buddhist tradition for 9 years and has a Ph.D. in the philosophical psychology of Mahayana Buddhism. Over a period of 40 years Peter has distilled the essence of traditions like Zen, Dzogchen and the Buddhist Middle Way, and adapted them to suit our post-modern culture.
He teaches in North America, Europe, Israel, India, Australia and New Zealand and has presented his work at leading universities and institutions including Columbia, Stanford, CIIS and Naropa. He has written extensively on Buddhist nondual traditions. His books include Radiant Mind: Awakening Unconditional Awareness (2007); The Edge of Certainty: Dilemmas on the Buddhist Path (2002); and The Sacred Mirror: Nondual Wisdom and Psychotherapy (ed. with John Prendergast and Sheila Krystal, 2003). Peter is the Director of Education at Timeless Wisdom: wisdom.org
A Thousand Hands: A Guidebook to Caring for Your Buddhist Community
Free the Mind
Mindful Moment: 52 Weeks of Mindfulness Exercises + Reflections
Moving From Emptiness

redpajama_set_name: RedPajamaCommonCrawl


Flowers are a perfect way to celebrate the Administrative Professionals in your life!
Enamórala con este hermoso #Bouquet 💐 tan especial y único como tu sentir por esa persona tan especial 🤩💏. .
Cotiza hoy mismo, escríbenos un mensaje 📩 o llámanos 📲, estamos para servirte al (667) 224.04.53.
Last week the magnolia blossoms were just poppin' in Central Park.
Limit your "always" and your "nevers." Follow The Simplicity Movement for more daily nature pictures and videos.
New posts everyday at 12:00, 15:00 & 18:00.
Currently showcasing the beautiful nature of Denmark.
The beauty of macro is the smallest things can come to life.
So fine in sight... yet in disguise...!!
Upojny zapach kwitnącej czeremchy... Znasz go?
Around every corner in the city is a glimpse of Spring 🌷 .


A case report of hemangioendothelioma of the hard palate. Hemangioendothelioma is a rare vascular tumour with potential for malignancy that is intermediate between that of a benign hemangioma and a malignant angiosarcoma. This tumour occurs infrequently in the head and neck regions. In this report, we describe and discuss a case of heman- gioendothelioma that originated in the hard palate of a 49-year-old woman.


text:
Name | Type | Description | Notes
------------ | ------------- | ------------- | -------------
**exchange_id** | [**STRING_32**](STRING_32.md) | Exchange identifier used to identify the routing destination. | [optional] [default to null]
**data** | [**LIST [BALANCE_DATA_INNER]**](Balance_data_inner.md) |  | [optional] [default to null]

[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)

redpajama_set_name: RedPajamaGithub


What Is a Set Off in a Florida UM Claim?
When you file a personal injury claim under Uninsured Motorist/Underinsured Motorist (UM) insurance, it is important to know about "set offs." Read on to understand what constitutes a set off in a Florida car accident claim, and how set-offs affect the overall settlement amounts in an injury claim involving UM benefits.
If you are injured in a Florida car collision in which the at-fault driver carries little or no liability insurance, you may file a claim under your UM policy. UM protects you in the event the person who causes an accident and injures you is not insured, or is insufficiently insured, or a hit-and-run driver injures you.
What is a Set Off?
In any automobile accident involving injuries, various forms of insurance often come into play. Multiple insurers may pay benefits on a single claim.
For example, when you go to the ER or the doctor's office for medical treatment for injuries, you would give each care provider all of your insurance coverage information. This may include PIP, Medpay, Worker's Compensation, Medicare and other health insurance.
When this happens, coverage sometimes overlaps. It is possible for an insured to get double recovery in certain cases.
UM providers are entitled to set off—deduct from the claimant's damages—an amount of money paid to an injured party from some other coverages or other transactions. Otherwise, a claimant would stand to collect twice on an award of benefits.
Fla. Stat. § 627.727 says that UM benefits are intended to be over and above, but not duplicating the benefits available to you and your insureds under various forms of insurance or other coverage. It is up to the UM provider to show duplication of benefits.
What Set-Offs Can You Expect in a Florida UM Claim?
PIP and MedPay are allowed to be set off against an award of UM benefits. An exception to the rule is, in cases where PIP or MedPay benefits are pending award and are still available, the UM provider cannot set off for anticipated or "likely" coverage.
Workers' Compensation benefits that have been paid, or are due and payable, may be set off against UM coverage.
However, the UM provider cannot set off for "likely" coverage. As portrayed in USAA Cas. Ins. Co. v. McDermott, the Florida Court of Appeals held that a UM insurance provider is not entitled to a setoff for workers' compensation benefits that were likely to be available or payable to the employee in the future.
Bodily injury limits of the uninsured, at-fault motorist's automobile liability policy.
Medicare benefits will not be offset from a UM award. Medicare is a Federal health insurance program targeting a specific set of people for their general welfare: individuals over age 65, those under age 65 with certain disabilities, and people with permanent kidney failure needing dialysis or a kidney transplant.
Therefore, Medicare cannot be compared to UM insurance coverage, which compensates an insured for injuries triggered by an automobile accident caused by an uninsured vehicle.
If you were recently injured in an Orlando car accident, you need precise, insightful answers to questions about insurance coverage and many other details you have never before faced.
We urge you to call Kramer Law for straightforward, practical advice based on the facts of your case. It costs nothing to have your case reviewed by our injury lawyers.


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text:
DieselBeagle
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Rebecca Shapiro graduated from Otis College of Art and Design. She has worked for Dreamworks Animation and currently works in Los Angeles on Motion Graphics design by day. By night she works in freelance artistry as well as fighting crime with her Dog, Bran.
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Last edited by Nit
Saturday, April 25, 2020 | History
2 edition of Administration of social education in Japan. found in the catalog.
Administration of social education in Japan.
Japan. Shakai KyoМ"ikukyoku.
Published 1961 by Social Education Bureau, Ministry of Education, Government of Japan in [Tokyo] .
Civics, Japanese.
Contributions Japan.
LC Classifications H62.5.J3 J35 1961
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Habeas corpus in the federal court
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veteran comes back
1848, historical revelations, inscribed to Lord Normanby.
Geology of Dorothea, Sandra, and Irwin townships
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Maya nationalisms and postcolonial challenges in Guatemala
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Translated into English for the first time, the Japanese language version of Education Reform and Social Class in Japan won the first Osaragi Jirō Prize for Commentary sponsored by the Asahi shinbun.
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His home country is Japan. Pat Nickell is an assistant professor and Sherry L. Field is an associate professor in the Department of Social Science Education at the University of Georgia. Nationally-accreditedProfessionals Involved in Social Education Japan has systematized the designation ofvarious specialized professionals involved in social education by establishing nationally-accreditedqualifications for Social Educa tion Coordinators (Social Education Directors), librarians and museum curators.
Number. Books at Amazon. The Books homepage helps you explore Earth's Biggest Bookstore without ever leaving the comfort of your couch. Here you'll find current best sellers in books, new releases in books, deals in books, Kindle eBooks, Audible audiobooks, and.
The importance of education is established at an early age in Japan. Almost all Japanese children attend some form of pre-school, either kindergarten (yochien) or education-based daycare centres (hoikuen).
These schools help prepare children for entry into the larger school system. Public education is the norm in Japan. Education in Japan is compulsory at the elementary and lower secondary levels. Most students attend public schools through the lower secondary level, but private education is popular at the upper secondary and university levels.
Education prior to elementary school is provided at kindergartens and day-care centers. The programmes for those children aged 3–5 resemble Primary languages: Japanese.
Japan should receive exactly the same education as Japanese children. In Japanese schools, a child's grade is determined by the student's age. (Children who are born between April 2nd to April 1st of the following year will be in the same grade.) The Japanese grade might be different from the grade in the student's home Size: 41KB.
Established inJapan College of Social Work is a private higher education institution located in the large town of Kiyose (population range of 50, inhabitants), Tokyo. This policy profile on education in Japan is part of the Education Policy Outlook series, which presents comparative analysis of education policies and reforms across OECD File Size: 1MB.
Japan NGO Network for Education (JNNE) 6. Association of Nationwide Evening Junior High Schools 7. Monthly J ournal Social Education 8. Japan Association for Promotion of Social Educ ation (JAPSE) 9. Social Education, our peer-reviewed, flagship journal, contains a balance of theoretical content and practical teaching award-winning resources include techniques for using materials in the classroom, information on the latest instructional technology, reviews of educational media, research on significant social studies-related topics, and lesson plans that can be applied to.
Undeniably, this personnel policy is a disadvantage to cultivating the professionalism of social work in Japan. Thirdly, the career development within social work is segmented and enclosed.
There is a gap between the government and private sectors and mobility beyond sector boundaries is almost completely non-existent (Yoshimura, ). Educational Administration in Japan and the Role of Local Governments Nagaki KOYAMA Associate Professor Graduate School of Library, Information and Media Studies University of Tsukuba Introduction If we once ask what education is, we can see immediately that the field is.
In some countries, there is dissatisfaction with the administration of social security, and calls for reform involve a review of the role of the State, the responsibilities of the social partners and the desirability of greater participation of the private Size: KB. Primary Geography Education in Japan: Curriculum as Social Studies, Practices and Teachers' Expertise Takashi SHIMURA1 Joetsu University of Education, Niigata, JAPAN Introduction The book, Geography Education in Japan (Ida et al.
), was published by geography education researchers in Japan. Written in English, this book is aCited by: 1. According the the OECD, in Japan, 80 percent of three-year-olds and 94 percent of four-year-olds attend daycare or preschool.
However, only 46 percent of the cost of early childhood education and care (ECEC) in Japan is covered by public funds—the lowest of all OECD countries. The remaining 54 percent is borne by families. Search the world's most comprehensive index of full-text books.
My library. Welcome to the Center for US-Japan Comparative Social Studies, an Internet-based nonprofit organization. We founded the Center in for the purpose of: Providing the general public with information about education, society and culture of Japan in English and about education, society and culture of the United States in Japanese.
Management and Administration in Education: What do school heads do. A focus on primary school heads in one district in Zimbabwe. By Wellington Samkange Zimbabwe Open University, P.O.
Box MPMount Pleasant, Harare, Zimbabwe Abstract A study was conducted in twenty primary schools in Chegutu Education District of Zimbabwe. The purpose of theMissing: Japan. Japan - Japan - Government and society: Japan's constitution was promulgated in and came into force insuperseding the Meiji Constitution of It differs from the earlier document in two fundamental ways: the principle of sovereignty and the stated aim of maintaining Japan as a peaceful and democratic country in perpetuity.
The emperor, rather than being the. Introduction The Japanese education system is modeled on and heavily influenced by its American counterpart.
The Fundamental Law of Education, passed in under American occupation, introduced the 6+3+3+4 structure of Japanese education: six years of elementary education, three years at lower secondary school, three at upper secondary. "A surprise for observers who view Japan as a developmental state, run by a powerful central bureaucracy and aligned with a conservative party whose policies often override public interest, Another Japan is Possible casts new light on a neglected but vital aspect of Japan's emerging political economy.
A remarkable group of scholars Format: Paperback. Education - Education - Education in the Tokugawa era: In a shogunate was established by a warrior, Tokugawa Ieyasu, in the city of Edo (present Tokyo). The period thence to the year —the Tokugawa, or Edo, era—constitutes the later feudal period in Japan.
This era, though also dominated by warriors, differed from former ones in that internal disturbances finally. Start studying 7th grade social studies chapter 8 Japan. Learn vocabulary, terms, and more with flashcards, games, and other study tools.
The Meiji Restoration of ended six centuries of feudal military rule by establishing a divine Emperor and clam oligarchy. Compulsory schooling and military service soon followed. Until WWII both institutions engaged in moral training that was both Confucian and nationalistic in its emphasis on absolute loyalty to the state and Emperor.
The ethical instruction in the school Author: James H. Buck. Christian social theology Christian church history Christian denominations & sects Other & comparative religions: Social sciences: Language: Natural sciences & mathematics: General statistics Political science Economics Law Public administration Social services; associations Education.
Content Japan Society Junior Fellows Program. Immerse pdf in Japanese culture and connect with future leaders from the U.S. and Japan this summer with Japan Society's Junior Fellows Program.Social Work Education Network. Menu Skip to content. Home; Search for: February 4, Novem U.S.
Online Course Listings. February 4, U.S. Based Grants and Scholarships for Students. Worldwide Directory of Social Work Programs.
Proudly powered by WordPress • Theme.The Japanese government provides free elementary ebook middle school education for all children with disabilities under the School Education Law.
Inthere werestudents, percent of the student population, who received special education in elementary and middle schools (Naikakufu b).
hotseattleseahawksjerseys.com - Administration of social education in Japan. book © 2020


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Artificial intelligence is a hot topic right now. Driven by a fear of losing out, insurers across Europe have been heavily investing in AI-focused initiatives. Unfortunately, most of these efforts will fail.


text:
Hopefully Coming Soon – A "Conflict-Free" Supply Chain
The image above is from a photoessay by photographer Mark Craemer, about the harvesting of minerals in the Democratic Republic of the Congo. You may find the images disquieting.
The Dodd-Frank Consumer Protection Act was signed into law by President Obama on July 21st, 2010. Most of it deals with things like bank and credit regulation and Wall Street reform. But there is also a provision (section 1502) that would require companies registered with the SEC to identify that their products are "conflict-free".
Gary Nevison at Element14 writes (doc):
Over 800 pages into the US Consumer Protection Act (the Act), signed into law by President Obama on 21 July 2010, section 1502 lays the foundations for US Securities and Exchange Commission (SEC) registered companies being required to make "Disclosures relating to Conflict minerals originating in the Democratic Republic of the Congo" (DRC) This will identify those products manufactured by a company that can be proved "DRC Conflict-Free" or otherwise.
A growing number of investigative reports over the past few years have linked the finances generated by the extraction of minerals in and around the DRC to the ongoing extreme conflicts between armed paramilitary organisations, particularly in the eastern regions of the country.
The Act defines the following minerals and their derivatives as "conflict minerals":
Columbite-Tantalite/"Coltan" (the most common source of Tantalum)
Wolframite (the primary source of Tungsten),
Cassiterite (Tin ore),
Gold.
The Act leaves scope for the Secretary of State to amend the definition of 'conflict mineral' at a later date.
At the moment, it is very nearly impossible to determine the exact origin of the minerals used in electronic components. For electronics hobbyists, the most likely questionable component would be tantalum capacitors, which are popular due to their low leakage and high capacitive density (more microfarads per cubic millimeter). While it's true that very few hobbyists are registered with the SEC and subject to the law stated above, I suspect we will all benefit from it.
Ultimately, hobbyists and big companies get their parts from the same component manufacturers. The big companies will be putting pressure on these manufacturers to start identifying their products as conflict-free (or not) so that they can know how to label their own products. In the end, the DIY folks also win, because they will get the same information.
mitpatterson
i wonder if they should have included diamonds as well, ever heard of "blood diamonds". One question, don't know if you guys know, but do companys have to explicity say that it "is not confict-free" or is it just they can say if it is conflict free, and if no mention of conflict free is mentioned then it is assumed that is not conflict free? that make any sense
johngineer
The provision addresses 'blood diamonds' as well. Pretty much any 'conflict mineral' from that part of the world.
Presumably yes, if it's not explicitly stated to be 'conflict free', then it is assumed to be otherwise. The law says that products sold by companies registered with the SEC must be certified 'conflict free', and that the companies must be able to prove it is so. In my opinion, this makes it a simple yes or no issue.
Filed under: EE — by johngineer
"It is not enough to have a good mind; the main thing is to use it well" - Rene Descartes


GEM file extension | How can I open or convert this file?
Many people share .gem files without attaching instructions on how to use it. Yet it isn't evident for everyone which program a .gem file can be edited, converted or printed with. On this page, we try to provide assistance for handling .gem files.
The GEM graphic files are related to XnView. GEM file is a GEM Metafile. GEM (Graphical Environment Manager) was Digital Research's GUI (or a WIMP in the original), written in the 1980s. GEM is known primarily as the graphical user interface (GUI) for the Atari ST series of computers, and was also supplied with a series of IBM PC-compatible computers from Amstrad.
The GEM project files are related to Amulet GEMstudio. GEM file is an Amulet GEMstudio Project. GEMstudio is used to create the Graphical User Interface (GUI) and program the Amulet module. The Amulet module is a touch screen LCD device.
The GEM archive files are related to RubyGems. GEM file is a RubyGems Gem Package. The RubyGems software allows you to easily download, install, and use ruby software packages on your system. Ruby is a dynamic, reflective, object-oriented, general-purpose programming language.
Naturally, other applications may also use the .gem file extension. Even harmful programs can create .gem files. Be especially cautious with .gem files coming from an unknown source!
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The .gem file extension is often given incorrectly!
If you have useful information about the .gem file format, then write to us!


Andiroba Oil is a natural moisturizer that can be used alone or as an ingredient in skincare and haircare products. It comes to you from the Amazon rainforests where it is extracted from a type of palm plant.
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text:
La griva americana  (Turdus migratorius) és un au migratòria de la família dels túrdids.

Hàbitat i distribució
Es distribueix àmpliament a Amèrica del Nord des del nord del Canadà i Alaska. Passa l'hivern en els estats de Florida i Califòrnia (Estats Units); a Mèxic, Guatemala i Belize.

Taxonomia
Alguns autors han considerat que la subespècie pròpia del sud de la Baixa Califòrnia és en realitat una espècie de ple dret:
 Turdus confinis Baird, SF, 1864 - griva de San Lucas.

Referències

Turdus

redpajama_set_name: RedPajamaWikipedia


text:
Q: Creating a python wrapper for an API using classes I am trying to make a Python Wrapper for an API. I have been able to create scripts that work fine but don't use classes. I want to make a real wrapper of that API using classes. I am new to OOP in Python.
Following was my attempt but I am stuck at how to convert it to an OO type.
import urllib2
from urllib import urlencode
import json

class apiclient:
    def __init__(self,
                request_url,
                hmh_api_key,
                client_id,
                grant_type="password",
                username="username",
                password="password"):

        values = {
                "client_id": client_id,
                "grant_type": grant_type,
                "username": username,
                "password": password
            }

        data = urlencode(values)

        req = urllib2.Request(request_url, data)
        req.add_header("Api-Key", api_key)
        response = urllib2.urlopen(req)

        response_header = response.info().dict
        response_body = response.read()
        json_acceptable_string = response_body.replace("'", "\"")
        response_body_dict = json.loads(json_acceptable_string)

        return response_body_dict ## this is the response

if __name__ == "__main__":

    API_KEY = "75b5cc58a5cdc0a583f91301cefedf0c"
    CLIENT_ID = "ef5f7a03-58e8-48d7-a38a-abbd2696bdb6.hmhco.com"
    REQUEST_URL = "http://some.url"

    client = apiclient(request_url=REQUEST_URL,
                        api_key=API_KEY,
                        client_id=CLIENT_ID)

    print client

Without classes, I get the response JSON as response_body_dict but with classes I get TypeError: __init__() should return None. How should I start designing my program.
I have shown only a part of the whole program, there are a lot many similar scripts that send requests to URLs and get JSON responses.
Thanks!

A: You should not return something from __init__ function.
EDIT:
If you need that value you should use the response_body_dict as a class member and get him from other method:
import urllib2
from urllib import urlencode
import json

class apiclient:
    def __init__(self,
                request_url,
                api_key,
                client_id,
                grant_type="password",
                username="username",
                password="password"):

        values = {
                "client_id": client_id,
                "grant_type": grant_type,
                "username": username,
                "password": password
            }

        data = urlencode(values)

        req = urllib2.Request(request_url, data)
        req.add_header("Api-Key", api_key)
        response = urllib2.urlopen(req)

        response_header = response.info().dict
        response_body = response.read()
        json_acceptable_string = response_body.replace("'", "\"")
        self.response_body_dict = json.loads(json_acceptable_string)

    def get_response_body(self):
        return self.response_body_dict

if __name__ == "__main__":

    API_KEY = "75b5cc58a5cdc0a583f91301cefedf0c"
    CLIENT_ID = "ef5f7a03-58e8-48d7-a38a-abbd2696bdb6.hmhco.com"
    REQUEST_URL = "http://some.url"

    client = apiclient(request_url=REQUEST_URL,
                        api_key=API_KEY,
                        client_id=CLIENT_ID)
    response = client.get_response_body()

    print client

redpajama_set_name: RedPajamaStackExchange


text:
Stone Arabia Battlefield
Route 10, Stone Arabia, NY
Battle of Stone Arabia
On October 19, 1780 Sir John Johnson led an army of 900 Loyalist and British Regulars on an invasion of the Mohawk Valley. After following a circuitous route across southern New York State they swept north up the Schoharie Valley entering the Mohawk Valley at Fort Hunter where the army turned west towards the Canajohary/Palatine Districts. The invaders camped the evening of October 18th near the Noses, a geological feature which forms the great divide in the Appalachian Mountain chain. The following day they crossed the Mohawk River at Keator's Rift and burned Jellis Fonda's mill before continuing on to attack the settlement of Stone Arabia located a mile and half north of the Mohawk River in the Palatine District. Colonel John Brown of the Massachusetts's Militia marched his 380 militiamen out of Fort Paris to meet the enemy on a field about a mile from the fort. Outnumbered nearly 3 to 1 he was shot from his horse and his men fled the field. Stone Arabia was completely destroyed by the enemy including the twin Dutch Reformed and German Lutheran Churches. Both churches were rebuilt after the war ended and still stand today. The Battlefield is marked with a NYS historical marker sign. Further interpretation of the Battle of Stone Arabia can be seen at the Fort Plain Museum.
Palatine Church 3.9 miles
Arkell Museum 4.3 miles
Trinity Lutheran Church and Cemetery (Stone Arabia Churches)1.9 miles
Fort Plain Museum & Historical Park4.2 miles
Fort Klock Historic Restoration7.3 miles
First Annual Conference American Revolution in the Mohawk Valley May 1, 2015
Sir William Johnson establishes trading post at Warrenbush on Mohawk River


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Nancy is a passionate freelance writer and blogger. She has more than 8 years of experience in writing for different online mediums. Nancy believes in the magic of written words to inspire and motivate. Visit her new blog Onedeblog about web design and photography.


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text:
To Realize Your Dream Or Not
by Jennifer on Oct.11, 2012, under Motivation
How do you go from having an unrealized dream to realizing your dream? I explore this concept in my upcoming book, "Change Your Story, Change Your Life: A Path To Your Success." Both in fiction and in life, you have a choice: you can be an active hero in your story or you can be a passive and reactive one. My book guides the reader how to become the author of their life and become an active hero in pursuit of their dreams. Through story tools, I show the reader how, in any story, we empathize with a central character at the beginning because their world is suddenly turned upside down. By the end of the story, after watching the central character move through a series of obstacles and escalating obstacles, we see he/she learn, grow and realize what they need to do in order to achieve their goal. You can do this in your life as well. You can take a dream that is unrealized and use basic story tools to materialize that dream. On this note, I recently saw two noteworthy documentaries that deal with the making of a dream. One is "Kingdom Come" and the other is "Dumbstruck."
"Kingdom Come" follows a first-time director (Daniel Gillies) as he tries to raise a million dollars to finance his first film, "Broken Kingdom." This emotionally-charged journey is interwoven with over 30 rare interviews from acclaimed indie actors including Mark Ruffalo, Illeana Douglas, Don Cheadle, Kevin Smith, Edward Burns, Tim Roth, Morgan Spurlock, Selma Blair, Robert Townsend, Bill Pullman, and many more. It was co-directed by Paiman Kaleyeh and John Lyons Murphy. I was so moved by this film. It took so much courage by the filmmakers and Daniel Gillies to reveal everything that went into the making of the dream. It shows the good, the bad and the ugly of what it takes. It is a realistic view of what artists go through to turn the unrealized dream into one that gets realized. I highly recommend this film and "Broken Kingdom" the film that is the subject of this powerful documentary. For more information on this film and to purchase your copy, go to this link.
"Dumbstruck" is a funny, touching and richly human documentary about professional ventriloquists. The five personalities featured in this film are a vivid and endearing ensemble attempting to achieve their dreams of success in a form of entertainment too often disregarded and disrespected in the simultaneously foreign and familiar world of Middle America. If your parents were to ask you what you want to be when you grow up, and you answered 'a ventriloquist,' chances are they would a) question your sanity, b) have you locked up, or c) be supportive because they'd just seen this delightful new documentary, "Dumbstruck." I loved that this film demonstrates what goes into the making of a dream and that it shows one of its characters, Terry Fator, going from a guy who paints houses and mows lawns in Corsicana, Texas to scrape by, to winning the million dollar grand prize on "America's Got Talent." A few months later, the director, Mark Goffman, finds himself filming Terry in the CEO's office at the Mirage Hotel, where he signs an unimaginable $100,000,000 headliner deal. I love that within this film, we see a dream being realized. I love the heart that goes into the process of recognizing a dream that isn't mainstream. It touches the inner nerd in each of us and makes us root for the success of this group of characters. I also admire what Mark Goffman had to say about what inspired his documentary: "It all started with a toast at my wedding. My mother-in-law shocked our 200 friends and family when she held up her white-gloved hand and it began to speak. The white-gloved hand delivered a moving, heartfelt toast, with humor, charm and grace. I know how hard it is for her to speak in public, and to move the room to tears, with essentially a sock puppet, well, so began my adventure into the world of ventriloquism." This is a touching film that will inspire you to believe that the dream is possible. You just have to put the work into making it happen. For more information on this film, go to this link.
I love that these films found their way onto my radar as I was writing about the same theme in my book. Creating a story that sends a message about how you can take an unrealized dream and go through the journey of realizing that dream is what it's all about. As long as you are alive on earth, you can make your dream happen. It's all up to you. You can be an active hero in your story and have your story reflect a life that you want to be living. You create your destiny.
:daniel gillies, documentary, dreams, dumbstruck the movie, john lyons murphy, lindsay goffman, mark goffman, motivational, paiman kalayeh, rachel leigh cook


text:
Per sick - today's live show jeopardized
Written by tevensso on June 24, 2003 to Per Gessle.
STOCKHOLM - Per has caught a bad case of bronchitis, due to the cold and wet midsummer in Halmstad, according to a report published in Aftonbladet today.
"I sound like Louis Armstrong, it's really troublesome. If I can't sing, what do I do?" says a worried Per, who is scheduled to perform tonight on the live TV show "Allsång på Skansen."
Whether he will perform his two songs together with his band "Mazarinerna" or not, no one knows yet. "The doctor said that this must heal itself and that the only thing that helps is a good dose of rest," Per tells Aftonbladet.
The show's producer, Meta Bergkvist, says they don't have a contingency plan. "We'll see tomorrow" (read: today). "If he's not better then, we'll have to solve it somehow," she says.
Aftonbladet's story (In Swedish)
Happy birthday, Per (January 12, 2011)
Per tells TDR that he and Marie are "thrilled" with what's to come in 2011 (January 3, 2011)
Radio Halland's annual interview with Per Gessle today (December 28, 2010)
Macca at the Apollo (December 14, 2010)
Per Gessle one of Sweden's most played songwriters (October 8, 2010)
Magellan said on June 24, 2003 09:07:
I like Louis Armstrong. Don't wory Per. Everything will bee o.k.
wonderful_balloon said on June 24, 2003 09:26:
PER - TAKE THE TIME YOU NEED! :) We' ll see us again in Halmstad!
Katuska said on June 24, 2003 09:31:
Drink some mulled wine with cinnamon and clove :-)
daniel_alv said on June 24, 2003 09:38:
can't someone tell us if he will sing or not...
DesmondChild said on June 24, 2003 13:15:
Øh, Did Louis Armstong sometimes go swimming when he was drunk?
Well, just a thought...
But as long as Per doesn't sound like René Dif from Aqua I'm still happy...
Liesebieke said on June 24, 2003 18:10:
Per is currently performing live on Tv1 in rainy Stockholm and actually he looks good for being ill... and the voice? I hear no difference!!!! He can even whistle without any problems!
Eloy said on June 24, 2003 18:14:
Oh boy! GET WELL SOON!
LOVE YA MAN!
harriej said on June 24, 2003 19:09:
Per,
Take your time to get better!
Don't worry, everything will be fine.
Dmitry said on June 25, 2003 05:07:
this summer is really nasty(I mean the weather), that it makes people fall ill. I'm really sorry for Per has got sick. Per, get well soon!! If you want I can give you an old russian recipe that has been experiensed many times by people: take vodka- probably you know-it's such a russian cooper, or if you don't have it, I think you can use whisky instead as well - and mix it with honey, just dissolve honey in the liquid - and drink it - you'll get well very soon, I promise you!
No treatment at the pharmacy!