text:
\subsection{Style}

\section{Introduction}
Learning the underlying dynamical representation of some observed variables ${\mathrm{x}}_{t} \in \mathbb{R}^n$, where $t \in \left\{t_0,...,T\right\}$ is the temporal sampling and $n$ the dimension of our observation space is a key challenge in various scientific fields, including control theory, geoscience, fluid dynamics, economics; for applications ranging from system identification to forecasting and assimilation issues \cite{ident_control,Abarbanel1996,jeong_hussain_1995,iden_economics}.

For fully-observed systems, {\em i.e.} when observed variables ${\mathrm{x}}_{t}$ relate to some underlying deterministic states ${\mathrm{z}}_{t}$ according to a diffeomorphic mapping possibly corrupted by some noise, recent advances \cite{brunton_discovering_2016,fablet_blin_ieee,nguyen2019emlike} have shown that one can identify the governing equations of the dynamics of ${\mathrm{z}}$ from a representative dataset of observations $\{{\mathrm{x}}_{t_i}\}_i$. When the observed variables ${\mathrm{x}}_{t}$ only relate to some but not all the components of underlying states ${\mathrm{z}}_{t}$, these approaches do not apply since no ODE or, more generally, no one-to-one mapping defined in the observation space can represent the time evolution of the observations. In this context, Takens's theorem states the conditions under which a delay embedding, formed by lagged versions of the observed variables, guarantees the existence of governing equations in the embedded space \cite{takens_theorem}.

Takens's theorem has motivated a rich literature of machine learning schemes to identify dynamical representations of partially-observed systems using a delay embedding. This comprises both non-parametric schemes based on nearest-neighbors or analogs \cite{Abarbanel_model_chaos} as well as parametric schemes which include polynomial representations \cite{paduart_identification_2010}, neural network models \cite{NN_takens}, Support Vector Regression (SVR) models \cite{SVM_takens}. For all these approaches, the identification of the appropriate delay embedding is a critical issue \cite{takens_params_1,takens_params_2}

In this work, we do not rely explicitly on a delay embedding. We rather aim to identify an augmented space of higher dimension than the manifold spanned by the observed variables, where the dynamics of the observations can be fully described by an ODE. Numerically, the joint identification of the augmented space and the governing ODE for a given observation dataset exploits neural-network representations. We report experiments for linear and chaotic dynamics, which illustrate the relevance of the proposed framework compared to state-of-the-art approaches, and then further discuss the key features of this framework with respect to state-of-the-art dynamical systems identification tools such as Koopman operator theory \cite{KoopmanGeneral}.

\section{Background and Related Work}
\label{sec:related_works}
Let us consider an unobserved state variable $\boldsymbol{\mathrm{z}}$ governed by an autonomous system of $s$ differential equations $\Dot{{\mathrm{z}}}_{t} = f({\mathrm{z}}_{t})$. Let us also assume that this system generates a flow $\Phi_{t_i}({\mathrm{z}}_{0}) = \int_{t_0}^{t_i}f({\mathrm{z}}_{u})du \in \mathbb{R}^s$ with trajectories that are characterized by an asymptotic limit-cycle $L$ of dimension $d$ contained in $\mathbb{R}^s$. We further assume that we are provided with a measurement function $\mathcal{H}$ that maps our state variable $\mathrm{z}$ to our observation space $\mathrm{x}_t = \mathcal{H}(\mathrm{z}_t) \in \mathbb{R}^n$.

When considering the data-driven identification of a dynamical mapping that governs some observation data, we first need to evaluate if the dynamics expressed in our observation space are deterministic and thus, can be described using a smooth\footnote{The word smooth here stands for continuously differentiable or $\mathcal{C}^1$.} ODE. Another way to tackle this question is to find the conditions under which the deterministic properties of the unobserved limit-cycle $L$ are preserved in the observation space in $\mathbb{R}^n$ so one can reliably perform forecasts in the observation space.

The general condition under which a mapping $\mathcal{H}$ will preserve the topological properties of the initial limit-cycle involves a differential structure. Assuming that $L$ is a smooth compact differential manifold, the topological properties of $L$ are preserved through a projection $\mathcal{H}$ in $\mathbb{R}^n$ if $\mathcal{H}$ is one-to-one and an immersion of $L$ in $\mathbb{R}^n$. Under these conditions our observation mapping is called an embedding \cite{Sauer1991}.

The simplest example of an embedding is the case where our observation operator is an identity matrix. With such embedding we have direct access to the state variable $z$ which is governed by a deterministic ODE. This particular case has been widely studied in the literature, parametric representations based on augmented state formulations have been for decades the most popular models due to their simplicity and interpretability \cite{paduart_identification_2010}, \cite{brunton_discovering_2016}. In the last years, neural network and deep learning have enriched the state-of-the-art of parametric representations \cite{wiewel2018latentspace}, \cite{raissi2018multistep}. In particular, the link between residual networks \cite{chen2018neural}, \cite{ResINN} and numerical integration schemes have opened new research avenues for learning extremely accurate dynamical models even from irregularly-sampled training data.

However, for a wide range of real-world systems, we are never provided with an observation operator that forms an embedding of the latent state variables. The key issue is that we do not have any guarantees on the existence of a smooth ODE governing the temporal evolution of our observations in $\mathbb{R}^n$.

From this point of view, the question of finding an appropriate dynamical representation of some observed data may not be this straightforward. The fact that our data may come from some higher-dimensional unobserved governing equation may restrict the above-mentioned state-of-the-art algorithms. The main difficulty is how to go from a low-dimensional observation space to higher-dimensional spaces that provides at least a {one-to-one} mapping between two successive observations. From a geometrical point of view, the time delay theorem \cite{takens_theorem} provides a way to build higher dimensional spaces that preserve the topological properties of the true (unobserved) dynamics limit-cycle from a lower-dimensional phase space. A generalization of this theorem \cite{Sauer1991} shows that we can reconstruct topologically similar limit-cycles using any appropriate smooth composition map of the observations. The derivation of a dynamical system from such representations on the other hand encountered large disparities since no explicit relationships between the defined phase space and an ODE formulation have been clearly made. Classical state-of-the-art techniques such as polynomial representations \cite{brunton_discovering_2016} and K-Nearest Neighbors (KNN) algorithms were proposed but they often fail to achieve both accurate short-term forecasting performance and topologically similar reconstructed limit-cycles.

In this work, we address the identification of a latent embedding that fits an ODE representation of the underlying dynamics of our observations. As detailed in the next Section, the core idea of this work is to replace the augmented observable vector formulated in \cite{takens_theorem} by augmented states that are outputs of an optimization problem with respect to an ODE formulation.

\section{Learning latent representations of partially-observed dynamics}
\label{sec:hrep}

\textbf{Augmented latent dynamics}: Let us assume a continuous $s$-dimensional dynamical system $\mathrm{z}_t$ governed by an autonomous ODE $\Dot{{\mathrm{z}}}_{t} = f({\mathrm{z}}_{t})$ with $\Phi_t$ the corresponding flow $\Phi_t(z_{t_0}) = \int_{t_0}^{t}f(z_{u})du$. In many applications, one cannot fully access to state $\mathrm{z}$ and the observed variables, also called observables, only relate to some components of state $\mathrm{z}$. Formally, we can define an observation function $\mathcal{H}: \mathbb{R}^s \longrightarrow \mathbb{R}^n$ such that observed state $\mathrm{x}_t$ follows $\mathrm{x}_t = \mathcal{H}(\mathrm{z}_t)$

The key question arising here is the extent to which the dynamics expressed in the observation space $\mathbb{R}^n$ reflect the true underlying dynamics in $\mathbb{R}^s$, and consequently, the conditions on $\mathcal{H}$ under which the predictable deterministic dynamical behavior of the hidden states is still predictable in the observation space. To illustrate this issue, we may consider a linear dynamical system in the complex domain governed by the following linear ODE:
\begin{equation}
\left\{
\begin{aligned}
&\Dot{{\mathrm{z}}}_{t} = \alpha{\mathrm{z}}_{t}\\
&{\mathrm{z}}_{t_0} = {\mathrm{z}}_{0}
\label{eq:trig_ODE_exp}
\end{aligned}
\right.
\end{equation}
with ${\mathrm{z}} \in \mathbb{C}$ a state variable and $\alpha \in \mathbb{C}$ a complex imaginary number.
The solution of this problem is
\begin{align}
&{{\mathrm{z}}}_{t} = Ke^{\alpha t}
\label{eq:sol_trig}
\end{align}
with $K$ a constant depending on ${\mathrm{z}}_{0}$. Assuming that we are only provided the real part as direct measurements ${\mathrm{x}}_t \in \mathbb{R}$ of the true states ${\mathrm{z}}_t$, no smooth autonomous ODE model in the scalar observation space can describe the trajectories of the observed states as the mapping between two successive observed states is not one-to-one. For example, assuming that $\mathrm{z}_{t_0}$ and $\mathrm{z}_{t_1}$ correspond to two states that have the same real part but distinct imaginary parts, the associated observed states are equal
$\mathrm{x}_{t_0} = \mathrm{x}_{t_1}$. However, the time evolution of the states $\mathrm{z}_{t_0}$ and $\mathrm{z}_{t_1}$ differ if they have different imaginary parts, such that the observed states $\mathrm{x}_{t_0+\delta}$ and $\mathrm{x}_{t_1+\delta}$ after any time increment $\delta$ are no more equal. As a consequence, a given observation may have more than one future state and this behavior can not be represented by a smooth ODE in the observation space.

For a given observation operator $\mathcal{H}$ of a deterministic underlying dynamical system governing $\mathrm{z}_{t}$, Takens's theorem guarantees the existence of an augmented space, defined as a delay embedding of observed state series, in which a one-to-one mapping exists between successive time steps of the observation series \cite{takens_theorem}. Rather than exploring such delay embeddings, we aim to identify an augmented latent space, where latent dynamics are governed by a smooth ODE and can be mapped to observed states. Let us define $\mathrm{X}_t \in \mathbb{R}^{d_E}$ the $d_E$-dimensional augmented latent state with $d_E>n$ as follows:
\begin{align}
{\mathrm{X}_t}^T = [\mathrm{x}_{t}^T, \mathrm{y}_{t}^T]
\label{eq:aug_vect}
\end{align}
with $\mathrm{y}_{t} \in \mathbb{R}^{d_E-n}$ the unobserved component of latent state $\mathrm{X}_t$. We aim to define the augmented latent space according to the following state space model:
\begin{equation}
\left\{
\begin{aligned}
&\dot{{{\mathrm{X}}}}_{t} = f_\theta({\mathrm{X}}_{t})\\
&{{\mathrm{x}}}_{t} = G({\mathrm{X(t)}})
\label{eq:aug_ode}
\end{aligned}
\right.
\end{equation}
where the dynamical operator $f_\theta$ belongs to a family of smooth operators (in order to guarantee uniqueness \cite{coddington1955theory}) parametrized by a parameter vector $\theta$. We typically consider a neutral-network representation with Lipschitz nonlinearities and finite weights. As detailed in the next sections, we address the identification of the operator $f_\theta$ and associated latent space $\mathrm{X}$ from a dataset of observed state series $\{\mathrm{x}_0,\ldots,\mathrm{x}_T\}$ as well as the exploitation of identified latent dynamics for the forecasting of the time evolution of the observed states, for instance unobserved future states  $\{\mathrm{x}_{T+1},\ldots,\mathrm{x}_{T+N}\}$.

\textbf{Learning scheme}: Given an observation time series $\{\mathrm{x}_0,\ldots,\mathrm{x}_T\}$, we aim to identify the state-space model defined by (\ref{eq:aug_ode}), which amounts to learning the parameters $\theta$ of the dynamical operator $f_\theta$. However, as the component $\mathrm{y}_{t}$ of the latent state ${\mathrm{X}_t}$ is never observed, this identification requires the joint optimization of the model parameters $\theta$ as well as the hidden component $\mathrm{y}_{t}$. Formally, this problem is then stated as the following minimization of the forecasting error on observed variables:
\begin{equation}
\begin{array}{c}
\hat{\theta} = \arg \displaystyle \min_{\theta}
    \displaystyle \min_{\{y_t\}_{t}} \sum_{t=1}^T \| \mathrm{x}_{t} - G\left (\Phi_{\theta,t} \left(X_{t-1}) \right) \right ) \|^2 \\~\\
    \mbox{Subject to  }
    \left \{
\begin{array}{lcl}
    X_t& =& \Phi_{\theta,t}(X_{t-1})\\~\\
    G(X_{t})& =& \mathrm{x}_{t}\\
    \end{array}\right.
    \end{array}
\label{eq:opti_crit}
\end{equation}
with  $\Phi_{\theta,t}$ the one-step-ahead diffeomorphic mapping associated with operator $f_\theta$ such that:
$$\Phi_{\theta,t}(X_{t-1}) = X_{t-1} + \int_{t-1}^{t}f_\theta(X_{u})du$$
In (\ref{eq:opti_crit}), the loss to be minimized involves the one-step-ahead forecasting error for the observed variable $\mathrm{x}_{t}$. The constraints state that the augmented state $\{X_t\}_{t}$ is composed of observed component $G(X_{t})$ and
should be a solution of ODE (\ref{eq:aug_ode}). Here, we numerically minimize the equivalent formulation:
\begin{equation}
 \displaystyle \min_{\theta}
    \displaystyle \min_{\{\mathrm{y}_{t}\}_{t}} \sum_{t=1}^T \| \mathrm{x}_{t} - G\left (\Phi_{\theta,t} \left(X_{t-1}) \right) \right ) \|^2
    + \lambda \| X_t -  \Phi_{\theta,t}(X_{t-1})\|^2 \\~\\
\label{eq:opti_crit 2}
\end{equation}
where $X_{t}^T=[\mathrm{x}_{t}^T,\mathrm{y}_{t}^T]$ and $\lambda$ a weighting parameter. The term $ \| X_t -  \Phi_{\theta,t}(X_{t-1})\|^2$ may be regarded as a regularization term such that the inference of the unobserved component $\mathrm{y}_{t}$ of the augmented state $X_{t-1}$ is not solved independently for each time step.

Using a neural-network parametrization for the ODE operator $f_\theta$, the corresponding forecasting operator $\Phi_{\theta,t}$ is also stated as a neural network based on a numerical integration scheme formulation (typically a $4^{th}$-order Runge-Kutta scheme). This architecture, very much similar to a ResNet \cite{he_deep_2015}, allows very accurate identification of ODE models \cite{fablet_blin_ieee,ResINN}. Hence, for a given observed state series $\{x_0,\ldots,x_T\}$, we minimize ($\ref{eq:opti_crit 2}$) jointly w.r.t. $\theta$ and unobserved variables $\{\mathrm{y}_0,\ldots,\mathrm{y}_T\}$. In the experiments reported in Section \ref{sec:exp}, we consider bilinear architectures \cite{fablet_blin_ieee}. The proposed framework applies to any neural-network architecture.

\textbf{Application to forecasting}: We also apply the proposed framework to the forecasting of the observed states $\mathrm{x}_t$. Given a trained latent dynamical model (\ref{eq:aug_ode}), forecasting future states for  $\mathrm{x}_t$ relies on the forecasting of the entire latent state $X_t$. The latter amounts to determining an initial condition of the unobserved component $\mathrm{y}_t$ and performing a numerical integration of the trained ODE (\ref{eq:aug_ode}).

Let us denote by $\mathrm{x}^n_{t},$ $t \in \left\{t_0,...,T\right\}$ a new series of observed states. We aim to forecast future states $\mathrm{x}^n_{t},$ $t \in \left\{T+1,...,T+\delta T\right\}$. Following (\ref{eq:opti_crit 2}), we infer the unobserved component $\widehat{\mathrm{y}}_{T}$ of latent state $X^n_{T}$ at time $T$ from the minimization of:
\begin{equation}
\hat{\mathrm{y}}^n_{T} = \arg \min_{\mathrm{y}^n_{T}}  \displaystyle \min_{\{\mathrm{y}^n_{t}\}_{t<T}} \sum_{t=T+1}^{T+\delta T} \| \mathrm{x}^n_{t} - G\left (\Phi_{\theta,t} \left(X^n_{t-1}) \right) \right ) \|^2
    + \lambda \| X^n_t -  \Phi_{\theta,t}(X^n_{t-1})\|^2
\label{eq:opti_crit 3}
\end{equation}
Here, we only minimize w.r.t. latent variables $\{\mathrm{y}^n_{t}\}$ given
the trained forecasting operator $\Phi_{\theta,t}$. This minimization relates to a variational assimilation issue with partially-observed states and known dynamical and observation operators \cite{Lynch2010}. Similarly to the learning step, we benefit from the neural-network parameterization of operator $\Phi_{\theta,t}$ and from the associated automatic differentiation tool to compute the solution of the above minimization using a gradient descent.

We may consider different initialization strategies for this minimization problem.
Besides a simple random initialization, we may benefit from the information gained  on the manifold spanned by the unobserved components during the training stage. The basic idea comes to assume that the training dataset is likely to comprise state trajectories which are similar to the new one. As the training step embeds the inference of the whole latent state sequence, we may pick as initialization for minimization (\ref{eq:opti_crit 3}) the inferred augmented latent state in the training dataset which leads to the observed state trajectory that is the most similar (in the sense of the L2 norm) to the new observed sequence $\mathrm{x}^n_{t},$. The interest of this initialization scheme is two-fold: (i) speeding-up the convergence of minimization (\ref{eq:opti_crit 3}) as we expect to be closer to the minimum; (ii) considering an initial condition which is  in the basin of attraction of the reconstructed limit-cycle. The latter may be critical as we cannot guarantee that the learnt model does not involve other limit-cycles than the ones truly revealed by the training dataset, which may lead to a convergence to a local and poorly relevant minimum

\begin{comment}

\begin{algorithm}[t]
\caption{Estimating an initial condition given new observation sequence}
\label{alg:VODEN}
    \begin{algorithmic}[1]
    \State {Estimation}$(\vect{\mathrm{x}}^n_{1:T}, \Phi_{\theta,t}, \vect{\mathrm{X}}_{1:T})$:
    \State \mathrm{x}^{n,0}_0 = \Phi_{\theta,T}(X_1)

    \While {not converge}
        \State \% E step
        \State gradient\_step = 0
        \If {gradient\_step < $N_E$}
            \State Compute $\vect{x}^q_{1:T-1} = q_{\phi}(\vect{y}_{1:T})$
            \For {$t \in \{1,..,T-1\}$}
                \State Compute $\vect{x}^f_{t+1} = f(\vect{x}_t)$
            \EndFor
            $loss_E = \sum_{t=2}^T\big(\Phi_t(||\mathcal{H}(\vect{x}^f_t) - \vect{y}_t||) + ||\vect{x}^f_t - \vect{x}^q_t||\big)$
            \State Optimise $\phi$ to minimise $loss_E$
            \State gradient\_step = gradient\_step + 1
        \EndIf

        \State \% M step
        \State gradient\_step = 0
        \If {gradient\_step < $N_M$}
            \State Optimise $\theta$ to minimise $\sum_{t=1}^{T}||f(\vect{x}_t)-\vect{x}_{t+1}||$
            \State gradient\_step = gradient\_step + 1
        \EndIf
    \EndWhile

    \end{algorithmic}
\end{algorithm}
\end{comment}

\section{Numerical experiments}
\label{sec:exp}

\textbf{Application to a linear ODE}: In order to illustrate the key principles of the proposed framework, we consider
the following linear ODE in the complex domain:
\begin{equation}
\left\{
\begin{aligned}
&\Dot{{\mathrm{z}}}_{t} = \alpha{\mathrm{z}}_{t}\\
&{\mathrm{z}}_{t_0} = {\mathrm{z}}_{0}
\label{eq:trig_ODE}
\end{aligned}
\right.
\end{equation}
with $\alpha = -0.1-0.5j$, $j^2 = -1$ and $\mathrm{z}_{0} = 0.5$.
As $\alpha \in \mathbb{C}$ with $Real(\alpha)<0$ and $\mathrm{z}_{0} \neq 0$, the solution of this ODE is an ellipse in the complex plane (Fig. \ref{fig:forecast_lin}).

We assume that the observed state is the real part of the underlying state, {\em i.e.} the observation function $\mathcal{H} : \mathbb{C} \longrightarrow \mathbb{R}$ is given by $\mathrm{x}_t = Real(\mathrm{z}_t)$. This is a typical example, where the mapping between two successive observations is not a one-to-one  mapping since all the states that have the same real part lead to the same observation. As explained in section \ref{sec:hrep}, one cannot identify an autonomous ODE model that will reproduce the dynamical behavior of the observations in the observations space.

We apply the proposed framework to this toy example. We consider a 2-dimensional augmented state $\mathrm{X}_t = [\mathrm{x}_{t}, \mathrm{y}^1_{t}]$. As neural-network parametrization for operator $f_\theta$, we consider a neural network with a single linear fully-connected layer. We use an observation series of $10000$ time steps as training data. As illustrated in Fig.\ref{fig:forecast_lin}, the inferred latent state dynamics, though different from the true ones, depicts a similar spiral pattern.
This result is in agreement with the geometrical reconstruction techniques \cite{takens_theorem} of the latent dynamics up to a diffeomorphic mapping. Overall, our model learns a dynamical behavior similar to the true model represented by an elliptic transient and an equilibrium point limit-set.

\begin{figure}[h]
\centering
  \includegraphics[clip,width=0.9\columnwidth,height=5cm]{figs/phase3.png}%
\caption{{\bf Inference of latent dynamics for a 2-dimensional linear ODE}: (left) true (blue) and inferred (orange) 2-dimensional latent states, (right) true observation sequence (blue) and predicted observation sequence from the inferred initial condition using the sequence of previous observations (orange). The considered 2-dimensional linear ODE is given by (\ref{eq:trig_ODE}).
}
\label{fig:forecast_lin}
\end{figure}

We also illustrate the forecasting performance of the proposed framework in Fig. \ref{fig:forecast_lin}. Given the same initial condition over the observable state, we compare the forecasting of the observed state by the true equation and the proposed model. For the proposed model, the initial condition over the unobservable component results from minimization (\ref{eq:opti_crit 3}). The resulting forecast of the observation sequence is similar to the true observation sequence (mean square error < 1E-5).

\textbf{Lorenz-63 dynamics}: Lorenz-63 dynamical system is a 3-dimensional model that involves, under some specific parametrizations \cite{lorenz_deterministic_1963}, chaotic dynamics with a strange attractor. We simulate chaotic Lorenz-63 state sequences with the same model parameters as proposed in \cite{lorenz_deterministic_1963} using the LOSDA ODE solver \cite{odepack} with an integration step of 0.01. We assume that only the first Lorenz-63 variable is observed $\mathrm{x}_t = \mathrm{z}_{t,1}$. We apply the proposed framework to this experimental setting using a training sequence of $10000$ time-steps.

For benchmarking purposes, we perform a quantitative comparison with state-of-the-art approaches using delay embedding representations \cite{takens_theorem}. The parameters of the delay embedding representation, namely the lag $\tau$ and the dimension $d_E$ of the augmented space were computed using state-of-the-art techniques. Specifically, the lag parameter was computed using both the mutual information and correlation  techniques \cite{takens_params_1}, respectively denoted as $\tau_{MI}$ and  $\tau_{Corr}$. Regarding the dimension of the embedding representation, we used the Takens embedding condition $d_{E} = 2d+1$ with $d$ the dimension of the hidden limit-cycle. The delay embedding dimension was also computed using the False Nearest Neighbors (FNN) method \cite{takens_params_2}. We also tested arbitrary parameters for the delay embedding dimension. Given the delay embedding representation, we tested two state-of-the-art data-driven representations of the dynamics. The Analog Forecasting technique (AF) which is based on the nearest neighbours algorithm \cite{lguensat_analog_2017} and the Sparse Regression (SR) method on a second order polynomial representation of the delay embedding states.
Regarding deep learning models, we evaluate the Latent-ODE model as proposed in \cite{chen2018neural} for the modeling of time series. The architecture of the Latent-ODE model is the following: The recognition network is an RNN with 100 hidden units. The dimension of the latent space is 4 and the dynamical model is a fully-connected network with bilinear layers as proposed in \cite{fablet_blin_ieee} (the same as used in our model). The decoder is a fully connected network with one layer and 15 units.

Regarding the proposed framework, referred to as Neural embedding for Dynamical Systems (NbedDyn), we tested the model for different dimensions of the augmented state space, namely from 3 to 6. The neural-network parametrization for operator $f_{\theta}$ is a fully-connected architecture with bilinear layers as proposed in \cite{fablet_blin_ieee} \footnote{The code will be available on GitHub soon.}.

\begin{table}[h]
\centering
\begin{adjustbox}{width=4.9in,totalheight=2.8in}
\begin{tabular}{lll*{3}c}
\toprule
\multicolumn{3}{c}{Model} &$t_0+h$ &$t_0+4h$  & $\lambda_1$  \\
\midrule \midrule
\multirow{4}{*}{AF}
& $\tau_{MI}$ =16 & $d_E(FNN) = 3$ &$7.5E-4$  & $2.4E-3$ &$1.16$  \\
& $\tau_{MI}$ =16 & $d_E(Takens) = 6$ &$6.4E-5$  & $1.6E-4$ &$NaN$  \\
& $\tau_{Corr}$ =29 & $d_E(FNN) = 3$ &$0.0011$  & $0.0021$ &$1.19$  \\
& $\tau_{Corr}$ =29 & $d_E(Takens) = 6$ &$0.0025$  & $0.0063$ &$NaN$  \\
& $\tau$ = 6 & $d_E = 3$ &$2.9E-4$  & $5.5E-4$ &$0.95$  \\
& $\tau$ = 10 & $d_E = 3$ &$1.2E-4$  & $0.00104$ &$0.84$  \\
\midrule
\multirow{4}{*}{SR}
& $\tau_{MI}$ =16 & $d_E(FNN) = 3$ &$0.038$  & $0.299$ &$0.084$  \\
& $\tau_{MI}$ =16 & $d_E(Takens) = 6$ &$0.1076$  & $0.2497$ &$NaN$  \\
& $\tau_{Corr}$ =29 & $d_E$(FNN) = 3 &$0.218$  & $1.005$ &$-0.73$  \\
& $\tau_{Corr}$ =29 & $d_E$(Takens) = 6 &$0.1344$  & $0.7841$ &$NaN$  \\
& $\tau$ = 6 & $d_E = 3$ &$0.00185$  & $0.00256$ &$NaN$  \\
& $\tau$ = 10 & $d_E = 3$ &$0.00733$  & $0.06179$ &$0.2535$  \\
\midrule
\multirow{1}{*}{Latent-ODE}
&   &  &$0.0801$  & $0.520$ &$NaN$  \\
\midrule
\multirow{4}{*}{NbedDyn}
&   & $d_E = 3$ &$8.14E-4$  & $2.29E-3$ &$0.82$  \\
&   & $d_E = 4$ &$1.891E-4$  & $6.106E-4$ &$0.960$  \\
&   & $d_E = 5$ &$2.206E-4$  & $1.869E-3$ &$0.822$  \\
&   & $d_E = 6$ &$\textbf{6.820E-6}$  & $\textbf{6.5125E-5}$ &$\textbf{0.87}$  \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption {{\bf  \em Forecasting performance on the test set of data-driven models for Lorenz-63 dynamics where only the first variable is observed}: first two columns : mean RMSE for different forecasting time steps, third column : largest Lyapunov exponent of a predicted series of length of 10000 time-steps (The true largest Lyapunov exponent of the Lorenz 63 model is 0.91 \cite{Sprott_chaos}).}
\label{tab:for_63}
\vspace{-0.3cm}
\end{table}

\begin{figure}[h]
\includegraphics[clip,width=\columnwidth,height=7cm]{figs/train_att.png}
     \caption{{{\bf \em Evolution of the learnt latent space}: starting from a random initialization of the augmented states $\mathrm{y}_i$, the latent space is optimized according to minimization \ref{eq:opti_crit 2}} to form a  limit-cycle similar to the true Lorenz 63 attractor. We depict 3-dimensional projections of the learnt latent space for the proposed model with different embedding dimensions from $d_E=3$ to $d_E=6$.}
     \label{fig:latent_train}
\end{figure}

Fig. \ref{fig:latent_train} illustrates the learning process for the latent space from the initialization to the last training epoch. We also report the analysis of short-term forecasting performance as well as the long-term asymptotic behavior characterized by the largest Lyapunov exponent of the benchmarked models in Tab \ref{tab:for_63}. The proposed model leads to significant improvements in terms of short term forecasting performance with respect to the other approaches. Surprisingly, The Latent-ODE model lead to the poorest performance both in terms of forecasting error and asymptotic behavior. This is mainly due to the fact that the latent space is seen as a non linear projection of the observed variables through the optimization of the ELBO loss \cite{krishnan_structured_2016}. By contrast, our latent embedding formulation optimizes the latent states to forecast the observed variables which explicitly constrain the latent space to be an embedding of the true underlying dynamics. The SR model seems to lead to better short term forecast, however, it does not capture well the chaotic patterns, which are associated to a positive largest Lyapunov exponent. This may suggest the combination of the SR model and a delay embedding may require additional investigation as a good geometrical reconstruction of the phase space as stated in Takens' theorem does not guarantee the existence of a parametric ODE model based on the corresponding delay embedding variables. Better performance is reported using an analog forecasting approach. The performance however greatly varies depending on the considered definition of the delay embedding. Using ad hoc parameters ($\tau_{MI}$ = 6, $d_E = 3)$, one may retrieve the expected long-term chaotic behavior ($\lambda_1=0.84$) with a relatively low short-term forecasting error (1.2e-4 for a one-step-ahead forecast). When considering the proposed model, we report for all parametrizations, augmented space dimensions from 3 to 6, performance at least in the same range as the best analog forecasting setting. Besides, when increasing the dimension of the augmented space, we significantly decrease short-term forecasting errors (<1.e-5 for a one-step-ahead forecast when considering $d_E=6$, i.e. one order of magnitude compared to the second best model) while keeping an appropriate chaotic long-term pattern ($\lambda_1=0.87$).

\textbf{Modeling Sea Level Anomaly (SLA)}: The data driven identification of dynamical representations of real data is an extremely difficult task especially when the underlying processes involve non stable behaviors such as chaotic attractors. This is mainly due to the fact that we  do not have any exact knowledge of the closed form of the equations governing the temporal evolution of our variables. Furthermore, the measured quantity may depend on other unobserved variables which makes the exploitation of data-driven techniques highly challenging.

In this context, we report an application to SLA (Sea Level Anomaly) dynamics, which relate to upper ocean dynamics and are monitored by satellite altimeters \cite{Calmant2008}. Sea surface dynamics are chaotic dynamics and clearly involve latent processes, typically subsurface and atmospheric processes. The dataset used in our experiments is a SLA time series obtained using the WMOP product \cite{WMOP_soc}. The spatial resolution of our data is a $0.05\degree$ and the temporal resolution $h=1$ day. We use the data from January 2009 to December 2014 as training data and we tested our approach on the last month of the year 2014. The considered region is located on south Mallorca ($2.5\degree E$ to $4.25\degree E$, $37.25\degree N$ to $39.5\degree N$). The spatial scales of the data were decomposed through a PCA decomposition of dimension $N_E=15$, which amounts to capture $92\%$ of the total variance

\begin{wraptable}{r}{0.5\linewidth}
\centering
\begin{adjustbox}{width=2.6in,totalheight=1.1in}
\begin{tabular}{ll*{6}c}
\toprule
\multicolumn{1}{c}{Model} & &$t_0+h$ & $t_0+2h$ & $t_0+4h$ \\
\midrule \midrule
\multirow{2}{*}{Latent-ODE}
 &RMSE & 0.025 & 0.032 & 0.048 \\
 &Corr & 89.25\% & 89.85\% & 89.07\% \\
\midrule
\multirow{2}{*}{AF}
&RMSE& 0.046  & 0.062 & 0.073 \\
&Corr& 95.56\%  & 86.11\% & 69.87\% \\
\midrule
\multirow{2}{*}{NbedDyn}
& RMSE &  0.002 & 0.007 & 0.027 \\
& Corr &  99.99 \% & 99.90\% & 98.95\% \\

\bottomrule
\end{tabular}
\end{adjustbox}
\caption {{\bf  \em SLA Forecasting performance on the test set of data-driven models}: RMSE and correlation coefficients for different forecasting time steps.}
\label{tab:for_SLA}
\end{wraptable}

We report forecasting performance for our model and include a comparison with analog methods and a neural ODE setting in Tab. \ref{tab:for_SLA}. For our model, we consider the following parameterization: a latent space with $d_E = 60$ and a fully connected bilinear architecture with 60 bilinear neurons and 4~fully-connected layers for the dynamical model $f_\theta$. Our model clearly outperforms the three benchmarked schemes with a very significant gain for the forecasting performance at one day (relative gain greater than 90 \%) and two days (relative gain greater than 90 \%). For a 4-day-ahead forecasting, our model still outperforms the other ones though the gain is lower (relative gain of 40\%). We let the reader refer to the Supplementary Material for a more detailed analysis of these experiments, including visual comparisons of the forecasts.

\section{Discussion}
\label{sec:disc}

In this work, we address the data-driven identification of latent dynamics for systems which are only partially observed, {\em i.e.} when some components of the system of interest are never observed. The reported forecasting performance for Lorenz-63 dynamics is in line with the forecasting performance of state-of-the-art learning-based approaches for a noise-free and fully-observed setting. This is of key interest for real-world applications, where observing systems most often monitor only some components of the underlying systems. As a typical example, the SLA forecasting experiment clearly motivates the proposed framework in the context of ocean dynamics for which neither in situ nor satellite observing systems can provide direct observations for all state variables (e.g., subsurface velocities, fine-scale sea surface currents).

We may also further discuss how the proposed framework relates to state-of-the-art dynamical system theory approaches. Most of these approaches rely on delay embedding, as Takens's theorem states the existence of a delay embedding in which the topological properties of the hidden dynamical system are equivalent to those of the true systems up to a diffeomorphic mapping. Hence, state-of-the-art approaches typically combine the selection of a delay embedding representation within classic regression models to represent the one-step-ahead mapping in the considered embedding. Here, we consider latent dynamics governed by an unknown ODE (\ref{eq:aug_ode}) but we do not explicitly state the latent space. This is however implicit in our forecasting framework. By construction, the considered forecasting model relies on the integration of the learnt ODE (\ref{eq:aug_ode}) from an initial condition given as the solution of minimization (\ref{eq:opti_crit 3}). Let us consider the following embedding $\psi$ such that:
\begin{equation}
    \psi \left( \{\mathrm{x}_{t}\}_{t_0:T}\right) =
    \arg \min_{\mathrm{X}_{T}}  \displaystyle \min_{\{\mathrm{X}_{t}\}_{t<T}} \sum_{t=1}^T \| \mathrm{x}_{t} - G\left (\Phi_{\theta,t} \left(X_{t-1}) \right) \right ) \|^2
    + \lambda \| X_t -  \Phi_{\theta,t}(X_{t-1})\|^2
\end{equation}
Given this embedding, the resulting one-step-ahead forecasting for the observed variable may written as:
\begin{equation}
\mathrm{x}_{T+1} = G\left ( \Phi_{\theta,t} \left( \psi \left( \{\mathrm{x}_{t}\}_{t=t_0:T}\right) \right ) \right )
\end{equation}
Hence, $\psi$ defines a delay embedding representation implicitly stated through minimization (\ref{eq:opti_crit 3}). In this embedding, the dynamics of the observed system $\mathrm{x}$ is governed by the composition of observation operator $G$ and forecasting operator $\Phi_{\theta,t}$. Regarding the literature on Koopman operator theory, most approaches rely on the explicit identification of eigenfunctions and eigenvalues of the Koopman operator \cite{KoopmanGeneral,brunton2016koopman,DMD_koopman}. Our framework relates to the identification of the infinitesimal generator $f_\theta$ of the one-parameter subgroup defined by Koopman operator through the ODE representation (\ref{eq:aug_ode}). By construction, the Koopman operator associated with the identified operator $f_{\hat{\theta}}$ is also diagonalizable, such that the identification of infinitesimal generator $f_{\hat{\theta}}$ provides an implicit decomposition of the Koopman operator of the underlying and unknown dynamical system onto the eigenbasis of the learnt latent dynamics governed by ODE (\ref{eq:aug_ode}).

Future work will further explore methodological aspects, especially the application to high-dimensional and stochastic systems. In the considered framework, operator $G$ is stated as an identity operator on the observed component of state $X_t$. For high-dimensional systems, operator $G$ may be stated as an encoder to keep the latent space low-dimensional and make minimization (\ref{eq:opti_crit 2}) tractable. The combination of the proposed framework with the variational setting considered in  the Latent-ODE model \cite{chen2018neural} also appears as an interesting direction for future work.
The extension to stochastic systems through the identification of a Stochastic ODE is also of key interest, for instance for future applications of the proposed framework to geophysical random flows, especially to the simulation and forecasting of ocean-atmosphere dynamics in which stochastic components naturally arise \cite{chapron2018large}.

\begin{comment}
As discussed in section \ref{sec:related_works}, the applications for which our observation mapping preserves the topological properties of the true hidden dynamics are quite rare. From this point of view, the question of how to go from an imperfect observation of the hidden phase space to a reconstruction space where the topological properties of the hidden dynamical system are preserved was widely studied in the context of dynamical systems. Theorems such as delay embedding provide a mean to go from a partial knowledge of a hidden dynamical system to a geometrical reconstruction of the unobserved limit-cycle. The idea here is, given a scalar observation of a true state trajectory $x_t = h(\Psi(z_t)) \in \mathbb{R}, t = 0, ..., T$, to define a delay coordinates map as follow
\begin{align}
{\mathrm{F}}(h,\Psi,T) = [h(z(t)), h(\Psi_{-T}(z(t))), h(\Psi_{-2T}(z(t))), ..., h(\Psi_{-(n-1)T}(z(t)))]
\label{eq:aug_gen_tekens}
\end{align}
The dimension $n$ and the function $g$ are chosen to assure that the mapping from the true unobserved space to the augmented space is smooth so we can reproduce the predictable deterministic behaviour in the unobservable dynamical model in the augmented space (supposing that our observation mapping $h$ is continuously derivable or $\mathcal{C}^1$). The general choice of these parameters was initially formulated by Takens in \cite{} as the delay embedding coordinates and it has been shown that a dimension equals to $n = 2d+1$ is sufficient to geometrically reconstruct the true underlying limit-cycle.

This result gained a lot of popularity in dynamical system reconstruction. The fact that one can understand the behaviour of a potentially complex high dimensional system only using scalar measurements is quit strong. Unfortunately, the main issues with such approaches is that they are graphical, or in an other word, they doesn't provide a direct mean to identify and forecast the observed variables.

This work can be seen as an alternative to the delay embedding techniques where our mapping is parametrised by an ODE. Formally, we can formulate our augmented representation introduced in section \ref{sec:related_works} as an embedding ${\mathrm{F}}(h,\Psi,\theta)$ of the true limit-cycle governed by an ODE $f_\theta$. Indeed, given a scalar observation of a true state trajectory $x_t = h(\Psi(z_t)) \in \mathbb{R}$ and assuming that $n> 2d$, the Whitney theorem \cite{} assures that almost any smooth map $F$ from $\mathbb{R}^s$ to $\mathbb{R}^n$ is an embedding of the hidden limit-cycle. Based on this condition, we can define a dynamical embedding as follow :
\begin{align}
{\mathrm{F}}(h,\Psi,\theta) = [x(t), y_1(t), y_2(t), ..., y_{n-1}(t)]
\label{eq:aug_gen_tekens}
\end{align}
where the augmented states $y_i, i = 1, ..., n-1$ are defined as solutions of the optimization problem :
\begin{equation}
\begin{array}{c}
    \displaystyle \min_{y_i, i = 1,n-1} \sum_{n=1}^T \| \lambda (x_t - G(\Phi_t(X_{t-1}))) + (1-\lambda)(X_t - \Phi_t(X_{t-1})\| \\~\\
    \end{array}
\label{eq:opti_crit__}
\end{equation}
with $\Phi_t(X_{t-1}) = \int_{t-1}^{t}f_\theta(X_{u})du$,  ${\boldsymbol{\mathrm{X}}} = [x(t),y_1(t),y_2(t), ..., ,y_{d_E-1}(t)]$, $\theta$ the ODE parameters and $G$ an observation function.

We can prove the smoothness of our dynamical embedding function $F$ as follow : assuming that our $f_\theta$ is a $C^1$ function, $\Phi$ is a diffeomorphism and sins $h$ ang $G$ are smooth functions, the solution of our optimization problem is unique. (en quoi ça fait de F est un embedding ?)

PS : F embedding si n>2d et si F smooth (au sens $C^1$ ou au sens difféo je ne suis pas sur)

In practice, the ODE parameters $\theta$ are also optimized jointly with the augmented states, as follow :
\begin{equation}
\begin{array}{c}
    \displaystyle \min_{\theta,y_i, i = 1,n-1} \sum_{n=1}^T \| \lambda (x_t - G(\Phi_t(X_{t-1}))) + (1-\lambda)(X_t - \Phi_t(X_{t-1})\| \\~\\
    \end{array}
\label{eq:opti_crit_}
\end{equation}

unicité issue ?
\subsubsection*{Acknowledgments}

Use unnumbered third level headings for the acknowledgments. All acknowledgments
go at the end of the paper. Do not include acknowledgments in the anonymized
submission, only in the final paper.

\end{comment}

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Paraparawa – twórca rolnictwa w mitach Indian Trio (Tiriyó) z Surinamu i Brazylii.

Według mitu, przed Paraparawą, ludzie żywili się wyłącznie mięsem zwierząt. Paraparawa złowił rybę i gdy miał ją zjeść, ta zmieniła się w piękną kobietę. Paraparawa poprosił ją by została jego żoną, a ona zgodziła się. Na weselną ucztę, z głębin rzeki, przybył jej ojciec pod postacią aligatora (w innej wersji mitu pod postacią anakondy). W pysku trzymał naręcza owoców: banany, owoce jukki, bataty i pochrzyny. Żona poradziła Paraparawie by ten zamiast zjeść dary, zasadził je w ziemi.

Paraparawa podzielił owoce na mniejsze części i zakopał. Z każdego kawałka wyrosły nowe krzewy, wydając od razu owoce. Plon był tak obfity, że ucztować mógł cały lud Paraparawy. Gdy po skończeniu uczty, Paraparawa zasadził resztki, ponownie wyrosły z nich krzewy z owocami. Tak w szczepie Trio narodziło się rolnictwo.

Bibliografia

Mitologia Indian

redpajama_set_name: RedPajamaWikipedia


Customers need to contact the Utility Clerk to start, transfer, or cancel utility services with the Cottage Grove Water &Sewer Utility.
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Get your passports ready! During the spring meeting at the New England Conservatory (NEC) in March, outgoing chapter chair Jared Rex shared the exciting news that our fall meeting would be held in Montreal jointly with the New York State/Ontario chapter of MLA and the Quebec chapter (SQACBM) of the Canadian Association of Music Libraries, Archives and Documentation Centres (CAML). Jared had announced that the Greater New York chapter of MLA would also participate, but they have since decided to drop out. The meeting will be held at McGill University's New Residence on Thursday, November 8 and Friday, November 9, which we chose because it leads into the long Veteran's Day weekend observed in the U.S.
We realize that this will be a more expensive meeting for our members for a variety of reasons, but we are working on ways to make it affordable, starting with making it possible to stay only a single night in Montreal. The event will begin Thursday morning with French-language presentations, but bilingual welcome messages and English-language presentations won't start until Thursday afternoon. The event will wrap up mid-afternoon on Friday. The registration price, $60 US, will include breakfast on Thursday and Friday, a reception at the end of the Thursday program, Friday lunch, and numerous catered coffee breaks. We will have a block of discounted rooms ($179 CAD) at the Hilton Garden Inn Montreal Centre-Ville Hotel, right across from McGill's music library. Most importantly, we have travel grants available for up to three people, described in greater detail elsewhere in the newsletter.
To balance the expense and inconvenience of the Montreal meeting, the NEMLA board decided when we signed on that our spring 2019 meeting should be in Boston to be accessible to as many of our members as possible. I am happy to announce that my own institution, Boston University, will host the Spring 2019 meeting. We still have a lot to focus on before then, so we have not yet set a date.
We expect there will be a competitive process for selecting presentations. Don't be shy about responding to the call for proposals. I hope NEMLA will be well-represented among the presenters.
To recap election announcements from Jared, I offer congratulations to our winners, Sarah Funke Donovan of the Boston Symphony Orchestra as our new Vice-Chair/Chair-Elect and Patricia B. M. "Tish" Brennan of Rhode Island College as our new Member-at-Large. I thank Laura Stokes, Past Chair, and Jonathan Manton, Member-at-Large, for their work as they cycle off the Board, and Jared Rex for all his guidance as he transitions to Past Chair. I thank Ruthann McTyre, Elizabeth Berndt-Morris and Sara White for their willingness to run for our elected positions. Finally, I thank Lisa Wollenberg of the University of Hartford as our new web editor, and I thank Sarah Hunter of Boston University as she steps down from that role, one she continued to hold even when her job duties moved away from music librarianship. As program chair for the NEC meeting, I am proud of the well-attended and informative event. I particularly appreciate Alan Karass, who pulled double duty as both our Secretary/Treasurer and our site host. It is clear why Alan was eager to show off NEC's new library and other spaces, and the whole library staff was extremely hospitable.
I look forward to serving you as the chapter chair, and I am excited about all we have lined up.
Following introductory remarks, the first session, "Bound for Glory: Cataloging Bound Sheet Music as Collections" was given by Robert Cunningham of the Boston Public Library and Andrea Cawelti of Harvard University. Attendees were led through the steps of a project to create effective and efficient cataloguing workflow for bound collections of (mostly) sheet music. The descriptive problems presented by these often miscellaneous groupings of compositions– whose only common element may be that they had a single owner who chose to have them bound together in a volume– were described with enthusiasm and wry humor by the team of Cunningham and Cawelti. The creator/owners of these unique collections sometimes turn out to be people of import or significance in some realm of endeavor (perhaps even music!) such as Julia Ward Howe, but others are more obscure and will require research effort beyond established works of authority. Robert specifically mentioned the use of Ancestry (Library Edition) as well as Family Search (sponsored by the Church of the Latter-Day Saints) as useful personal identification resources. Following the meeting, it has been confirmed by Andrea that Harvard's collection records are on ArchiveGrid, but that this would be dependent on the searcher's individual institutional profile. Those interested in complete details of these cataloguing best practices are encouraged to contact Andrea [ cawelti@fas.harvard.edu ] and/or Robert [ rcunningham@bpl.org ] for the latest copy of the procedures.
The next presentation was given by Jean Wald of Stetson University. In "Pianists Speak: The Interviews of Robert Dumm, 1959-2008," Jean outlined the challenges of working with a special collection in the absence of dedicated archival staff, lack of time away from primary duties for the non-archivist, adequate secure space for research and storage, and lack of funds for materials and staff. Despite the bumpy road, Jean enriched our picture of the pianistic world by working with nearly 400 oral history interviews making up the principle part of the Robert Dumm Collection at Stetson University. Dumm (1928-2012), once Dean at Boston Conservatory in his early career, spent over 40 years interviewing a wide array of "pianistic" personalities, mainly performers but also composers, piano pedagogues and their students both famous and obscure, competition winners, piano makers, piano tuners, and various others. The renown of these individuals varied from luminaries like Van Cliburn and Nicolas Slonimsky, to local piano teachers who had attended master classes Dumm held throughout the US and Canada. Regardless of the logistical and support challenges, Jean continues to move forward with arranging, cataloguing, and digitizing these recordings and their transcriptions for future students and scholars. View the collection description here.
After these presentations, Jared Rex opened the business meeting by welcoming the first-time attendees and announcing the current openings on NEMLA committees. Jonathan Manton then reported on his activities as Member-at-Large including the administration of the MLA Annual Meeting Travel Grant and the Public Library Panel at the Spring 2017 Meeting at MIT. Sofía Becerra-Licha put out a call for institutions willing to host the NEMLA Archives. Alan Karass submitted the Treasurer's report (provided below). Marci Cohen put out a call for mentees for the NEMLA First-Time Attendee program. Jared then discussed the possibility of transforming the current committee structure to a communities-of-interest model. He also shared preliminary information on the Fall Meeting in Montreal—a joint meeting with NYSO and SQACBM. He announced the election results including our new Vice-Chair/Chair-Elect, Sarah Funke Donovan and new Member-at-Large, Patricia B. M. "Tish" Brennan. Jared also announced that Lisa Wollenberg will be assuming the role of Webpage Editor and thanked Sarah Hunter for her years of service in that position. The gavel was then smoothly passed from Jared Rex, now Past Chair, to Marci Cohen as incoming chapter Chair, who promptly adjourned the meeting for lunch.
Upon our return to NEC, a panel discussion was held titled, "Migrating to a New Integrated Library System (ILS) and Discovery Layer: Perspectives from the Trenches." Stage-setting for the panel topic was provided by Anne Adams, Chair of the NEMLA Technical Services Committee (TSC), who explained that numerous libraries in New England have migrated from one library services platform to another in the recent past. The TSC wanted to provide chapter members with an overview of the process as well as words of advice (and possibly warning) from those recently in the trenches. Panelists included Michael Rogan of Tufts (migrated June 2017 from III a.k.a. Innovative to ExLibris Alma/Primo), Tish Brennan of Rhode Island College (migrated December 2016 from Innovative to OCLC Worldshare Management System (WMS)/Discovery in a consortium environment), Rebecca McCallum & Alec McLane of Wesleyan (migrated July 2017 from Voyager to ExLibris Alma/Primo in a consortium environment) and Andrea Cawelti of Harvard University (scheduled for migration July 2018 from HOLLIS Classic (Aleph) to ExLibris Alma/Primo). Some aspects of each institution's experience including pre-migration planning and preparation, data clean-up, and post-migration "chestnuts", including handling of material types, faceting, searching EBSCO databases, and authorized access points as well as the user interface were discussed. The wealth of experiential information provided by the panel was formidable. Some panel members expressed that there is and will be more data clean-up than they first imagined or plan for, including not just bibliographic data, but acquisitions & financial data and patron data. They advised considering a year of mapping and clean-up, a year of testing, implementation and training, and at least a year of post-migration surprises and further clean-up. Ideally, one would imagine in advance all possible anomalies in how a new system will translate technical services information into public services information in a way different from a familiar old system to ensure patrons do not lose access to essential features or information on which they have come to rely. This is unfortunately virtually impossible to attain. Each of the panelist's institutions experienced post-migration "Yikes!" discoveries including data which did not migrate properly, data which was not indexed or is otherwise now unsearchable or un-browsable, data which was not displaying as anticipated, faceting working counterintuitively to patron expectations, "discovery" software obscuring essential catalog information or unable to harness the robust metadata of the underlying databases creating inaccurate results for patrons, and other problematic issues. All the panelists agreed that music librarians need to make their voices heard to their library system vendors, not just as individual customer institutions, but collectively through the MLA User Group structure. Librarians should also take advantage of mechanisms provided by the vendor (such as a community center for requesting changes/fixes) to vote for improvements and enhancements. There is greater power to effect change in collective action so librarians should not be shy about asking fellow music users of that vendor to vote or endorse their requests.
The afternoon continued with tours of the Blumenthal Family Library and the SLPC followed by a concert including setar Improvisation by Nima Janmohammadi, and a performance of Ciaccona (2015) by Cody Forrest, violin. The concert ended with Invocation (2017) for String Quartet by Cody Forrest, performed by Chu Chun Jean Huang and Shuxiang Yang, violins, Daniel Orsen, viola, and Joseph Gotoff, cello. The audio fidelity of YouTube can only present a glimmer of these exciting recent compositions. The meeting closed with a reception.
Recordings of the proceedings for this meeting may be found on NEMLA's YouTube channel.
NEMLA is going to Canada! The New England (NEMLA) and New York State/Ontario Chapters (NYSO) of the Music Library Association, together with the Québec Chapter of the Canadian Association of Music Libraries, Archives and Documentation Centres (SQACBM), are now accepting panel, poster, and presentation proposals for our fall meeting at McGill University, Montreal, Quebec on November 8 and 9, 2018. We are pleased to announce that the MLA Board of Directors will also be joining us for this international conference. Please be advised that attendees from the U.S. will need a passport to attend.
Registration is $60 (US). On Thursday, the day will end with a reception at the Marvin Duchow Music Library. More details on the meeting including hotel information, etc. will be forthcoming.
NEMLA is now accepting applications for grant support to attend the Fall chapter meeting in Montreal, Quebec, Canada. The $400 of support may be awarded to a single individual or divided among as many as three awardees at the discretion of the NEMLA Board.
Preference will be given to current or recent MLS graduate students or new library professionals.
Applications are due Wednesday, August 15, 2018. To apply, please complete the form at https://goo.gl/forms/AzvMyMEY8HoVCL6i2.
The proposal deadline is Wednesday, August 1, 2018. Accepted presenters will be notified of their status by August 15, 2018.
NEMLA Archives needs a permanent home! The NEMLA chapter's archives comprises 18.5 linear feet of historical materials that include oral histories, newsletters, business records, photographs, and more. The bulk of the collection is currently stored (unofficially) at the Boston Public Library, plus a few linear feet in the national MLA archives at the University of Maryland (which we, along with most chapters, have been asked to withdraw and house locally). Therefore, we are in search of a new home for this collection in the New England region. Please contact NEMLA chapter archivist Sofía Becerra with any questions or leads: sbecerralicha@berklee.edu. Thank you!


text:
LEGISLATIVE OVERVIEW
Michigan Council on Crime and Delinquency
Raising the age for juvenile offenders
Originally Published Here
LANSING, Mich. (WILX) - On Wednesday, Michigan Senators introduced a bipartisan plan to raise the age of legal adulthood from 17 years old to 18.
Currently, 17-year-olds can be prosecuted as adults, however, this new proposal would consider 17-year-olds as juveniles in most criminal offenses.
This change would give juveniles a second chance at life in some eyes, and instead of going through the adult justice system they would go through the juvenile justice system.
But, any 17-year-olds convicted of violent criminal offenses will still be charged as adults.
Michigan is one of four remaining states that treat 17-year-olds as adults.
States with similar raise-the-age laws have seen a significant decrease in juvenile court referrals.
Lawmakers are strongly pushing for this new law and say it can only benefit the state.
"Raising the age is just the best step and the best direction for the state," Senator Pete Lucido (R) of Shelby Township said. "We all talk about criminal justice reforms - today we're done talking."
A similar bill was introduced two years ago but failed because it didn't include a funding mechanism.
The new proposal suggests a reimbursement aspect that would give the money back to the counties if their juvenile cases cost them more with this new bill.
Michigan Radio: Gov. Whitmer signs bills raising age of Michigan's juvenile offenders
Governor Whitmer Signs Raise the Age!
Michigan Radio: Raise the Age bills on their way to governor's desk

redpajama_set_name: RedPajamaCommonCrawl


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What's your favorite way to celebrate Valentine's Day??
Share your thoughts with us – inquiring minds would love to know!
Welcome to the Social Media Boost Of The Week! Each week, you'll have the opportunity to grow a different social network. This week, we're boosting Pinterest!
IMPORTANT: ONLY Add PINTEREST Links to this Social Media Boost list!
If possible I would love to go out to eat early, I have in mind Mimi's, I love the atmosphere and to me is so romantic!!
Happy Friday! Thank you for hosting! Our favourite way to celebrate Valentine's Day is with a special dinner for two. This year it's at one of our favourite French restaurants. I've shared some Tips on How to Keep The Romance Alive in Your Relationship for Valentine's Day and Beyond as well as the Hearth and Soul Link Party, where everyone is welcome. This week, not surprisingly, we are celebrating love! Have a wonderful weekend!
Ramona, hope you're having a great week. Thanks for hosting.
Favorite way to celebrate Valentine's Day?
Hmmm . . . whatever comes along is fun and fine!
I am not a fan of Valentine's Day. I don't like getting things or going out just because there's one day set aside for it. I did enjoy getting heart-shaped boxes of chocolates from my granddaddy when he was still alive but not from boyfriends or the husband. If the latter ones can't show it every day, I don't care to see it on Valentine's Day. LOL Thank you for hosting and have a most wonderful weekend!
Thank you for hosting this blog party! I am very simple when it comes to Valentine's Day! I hope you have a wonderful weekend!
Thank you for hosting! Have a fabulous weekend!
Just spending time with my loved one.
Thanks for hosting and I hope you have a wonderful weekend.
Thanks for hosting and for sharing with my party as well Ramona!


Several speech-language activities centered around a grid of differently decorated easter eggs. This is a word document that allows you flexibility to design your own activities.
The present downloadable worksheet is a great resource for elementary school students at Beginner (pre-A1) and Elementary (A1) level. It is a useful material for practising Prepositions in English, and was designed for developing your class' Listening and Speaking skills. It is about the vocabulary topic of Easter.
pgreig is from/lives in United States and has been a member of iSLCollective since 2012-05-02 18:20:02. pgreig last logged in on 2013-01-16 03:23:14, and has shared 1 resources on iSLCollective so far.


text:
Q: Spring Data JDBC org.springframework.data.mapping.MappingException: Could not read value rental_movie from result set How to select query filter in one-to-one relationship with Spring Data JDBC ?
Schema looks like this, basically 2 tables where Rental references Movie
drop table if exists rental;
drop table if exists movie;

create table movie
(
    id          serial primary key,
    title       text,
    description text
);

create table rental
(
    movie    integer primary key references movie (id),
    duration text,
    price    integer
)

And my code looks like this
@Query("select * from movie where title = :title ")
fun findByTitle(@Param("title") title: String): List<Movie>

But got an exception org.springframework.data.mapping.MappingException: Could not read value rental_movie from result set!
The example project on GitHub.
P.S I am quite new to this and followed this video to learn basics, please help me to do it in proper way
Solution # 1
Use the @Query like this, but still not so good since there can be a lot of columns inside second table
SELECT movie.*,
       rental.price    AS rental_price,
       rental.duration AS rental_duration,
       rental.movie    AS rental_movie
FROM movie
         LEFT OUTER JOIN rental ON rental.movie = movie.id
where movie.title = 'Matrix'

A: Your solution #1 is currently the correct way to do this.
Query must return columns for all simple properties of the aggregate root, and for all embedded or referenced entities.
If you don't want to do that you can alway specify your own RowMapper or ResultSetExtractor
Let's assume the following classes (similar to those you probably have):
class Movie {

    @Id Long id;
    String title;

    @Embedded
    Actor with;

    Rental rental;
}

class Actor {
    String name;
}

class Rental {
    Integer price;
}

Your select needs to return the following columns:

*

*id for the id property of Movie

*title for the title property of Movie

*rental_price for the price property of Rental. Note the prefix rental comes from the property namerentalnot from the class nameRental`.

*rental_movie this column is an artificial id for Rental used to determine if there is a Rental at all or if Movie.rental is null.
The value is irrelevant except for the fact if it is null or not.
This column is not required if Rental has an id column.

*name for the property name of the class Actor.
Note: There is no prefix here. If you want a prefix, e.g. because a class is embedded multiple times you have to put that in the @Embedded annotation.
_Note #1: There is no artificial id here.
For embedded classes there is the onEmpty attribute of the @Embedded annotation to control if when all properties are null, if the whole embedded class is null or if the embedded class gets instantiated with all properties set to null.
With Spring Data JDBC 1.x a missing column causes an exception as you have seen.
From Version 2.0 a missing column will be silently ignored and the property not set.
There is an issue to provide an easier way to define by just defining the where clause: https://jira.spring.io/browse/DATAJDBC-225

A: data class Movie(
        @Id
        val id: Long?,
        val title: String,
        val description: String,
        val rental: Rental
)

I don't use Kotlin, but I think if you want to query Movie and Rental together, you have to use @OneToOne annotation. Something like(java):
public class Movie {
@OneToOne(mappedBy = "moive", cascade = CascadeType.ALL,
              fetch = FetchType.LAZY, optional = false)
private Rental rental;
}

And your Rental class is not right, need movieId.
public class Rental {

   ...
   @OneToOne(fetch = FetchType.LAZY)
   @JoinColumn(name = "movie_id")
   private Movie movie;

}

you could check some code example.

redpajama_set_name: RedPajamaStackExchange


text:
New polls show Americans support impeachment inquiry but aren't sold on removing Trump
By Grace Sparks, CNN
Updated 6:33 PM EDT, Tue October 8, 2019
US President Donald Trump speaks during a signing of a US-Japanese trade agreement in the Roosevelt Room of the White House October 7, 2019, in Washington, DC. (Photo by Brendan Smialowski / AFP) (Photo by BRENDAN SMIALOWSKI/AFP via Getty Images)
PHOTO: BRENDAN SMIALOWSKI/AFP/AFP via Getty Images
Poll: Majority support impeachment inquiry of Trump
Nuclear codes Biden Trump
From Trump to Biden: How transfer of nuclear codes will work
PHOTO: CNN
Sen. Graham/Erin Burnett split
PHOTO: Fox News/CNN
US President Donald Trump waves to the media as he makes his way to board Air Force One before departing from Andrews Air Force Base in Maryland on January 12, 2021. - Trump is traveling to Texas to review his border wall project. (Photo by MANDEL NGAN / AFP) (Photo by MANDEL NGAN/AFP via Getty Images)
PHOTO: Mandel Ngan/AFP/Getty Images
Trump mostly in seclusion during final days of presidency
PHOTO: KamalaHarris/Twitter
PHOTO: @FLOTUS
Watch Melania Trump's farewell speech
UNITED STATES - JANUARY 4: Rep. Lauren Boebert, R-Colo., is seen during a group photo with freshmen members of the House Republican Conference on the House steps of the Capitol on Monday, January 4, 2021. (Photo By Tom Williams/CQ-Roll Call, Inc via Getty Images)
PHOTO: Tom Williams/CQ-Roll Call, Inc/Getty Images
GOP lawmaker accused of giving 'reconnaissance' tour prior to Capitol riot
Andrew McCabe 0118
Andrew McCabe compares Capitol attack to Benghazi attack
Avlon: Here's a way to turn down the 'MAGAphone'
Lawmaker slams riot: 'Damned if I'll let it' shake faith in democracy
Raskin: I'm not losing my son in 2020 and my country in 2021
Sen. King: Cutting off Trump's intel access should be easy decision for Biden
A sign for the National Security Agency (NSA), US Cyber Command and Central Security Service, is seen near the visitor's entrance to the headquarters of the National Security Agency (NSA) after a shooting incident at the entrance in Fort Meade, Maryland, February 14, 2018. - Shots were fired early Wednesday at the ultra-secret National Security Agency, the US electronic spying agency outside Washington, leaving one person injured, officials said. Aerial footage of the scene from NBC News showed a black SUV with numerous bullet holes in its windshield crashed into concrete barriers at the main entrance to the NSA's headquarters in Fort Meade, Maryland. (Photo by SAUL LOEB / AFP) (Photo by SAUL LOEB/AFP via Getty Images)
PHOTO: Saul Loeb/AFP/Getty Images
Christopher Miller orders NSA chief to install Trump loyalist as agency's top lawyer
A trio of polls released Tuesday show Americans support an investigation into accusations that President Donald Trump asked Ukraine to probe his 2020 rival Joe Biden but they are not sold on the idea of the President being removed from office.
Almost three-in-five Americans support House Democrats' move to start an impeachment inquiry of the President, according to a Washington Post-Schar School poll released Tuesday. The inquiry centers around Trump's phone call with Ukrainian President Volodymyr Zelensky after a whistleblower filed a complaint about the call. A transcript of the conversation released by the White House shows Trump repeatedly pushed Zelensky to investigate former Vice President Joe Biden and his son, Hunter. There is no evidence of wrongdoing by either Joe or Hunter Biden.
The Post's poll found nearly half of those polled approved of the inquiry into impeachment and want Trump removed from office, while 6% approve of the proceedings but don't want him removed and 38% don't agree with the proceedings to begin with.
Other polls released later on Tuesday afternoon found less support for Trump's impeachment and removal from office.
A Quinnipiac poll, out later on Tuesday, found slightly lower support for impeachment than the Post. Forty-five percent of those polled said the President should be impeached and removed from office, 49% said he shouldn't be. Quinnipiac's poll was among registered voters, rather than all adults, but often question wording makes a big difference, with their poll specifically referencing Trump's removal from office. More (53%) approve of the House's formal impeachment inquiry, according to the Quinnipiac poll.
And an NBC/WSJ poll that was released Tuesday evening found even less support for impeaching and removing Trump – 43% of those polled said they were in favor and 49% said they were against impeachment and removal.
Both Quinnipiac's and The Washington Post's polls found growing support for impeachment.
In September of this year, Quinnipiac reported only 37% said Trump should be removed from office, up to 45% now. And in July of this year, a Washington Post/ABC News poll found that only 37% of Americans said Congress should begin impeachment proceedings, the lowest it had been since they began tracking the question in August 2018, when 49% wanted impeachment proceedings to begin. Now, 58% said Congress should have begun the inquiry.
The allegation that Trump held back military aid from Ukraine before his request to Zelensky is seen as a big deal in the Washington Post poll – 58% said this matters "a lot" or "a great deal," compared to 37% who said it matters "not so much" or "not at all."
Only 38% of Americans in the NBC/WSJ poll said Trump has been honest and truthful when it comes to the congressional investigation, and 58% said he hasn't. Similarly, in the Quinnipiac poll, 55% of registered voters reported they believe the President abuses the power of his office. And only 34% of voters approve of how he's responding to the impeachment inquiry.
Despite an onslaught of negative headlines, Trump's approval rating has remained steady. In the NBC/WSJ poll, Trump's approval holds at 43%, the exact same as it was in August. In the Quinnipiac poll, 40% of voters approve of the job he's doing as president, the same as it was in mid-September, pre-inquiry.
In The Washington Post-Schar School poll, a majority of Americans (61%) said that Democrats in Congress are making a necessary stand against Trump's actions by beginning impeachment proceedings. Over half (53%) said they're acting to uphold their constitutional duties. Forty-one percent said this was an overreaction while 55% said it isn't. Another half of Americans said that Democrats are distracting Congress from more important issues.
Both NBC/WSJ and Quinnipiac's polls find that around half of people trust the investigation – 51% in NBC/WSJ said it was a serious allegation to be fully investigated, while 51% said it was legitimate in Quinnipiac, only 44% in each who said it was "politically motivated" (NBC/WSJ) or a "political witch hunt" (Quinnipiac).
The Washington Post and the Schar School of Policy and Government at George Mason University poll was conducted by telephone between October 1 and 6 among a random national sample of 1,007 adults, 69% of whom were reached on cellphones and 31% on landlines. The margin of sampling error is plus or minus 3.5 percentage points; the error margin is larger for results among subgroups.
The Quinnipiac University poll was conducted by phone October 4-7, surveying 1,483 self-identified registered voters nationwide with a margin of error of +/- 3.1 percentage points, including the design effect.
The NBC/WSJ poll was conducted October 4-6 of 800 adults — more than half via cell phone — and it has an overall margin of error of plus-minus 3.5 percent points.
This story has been updated after the release of the Quinnipiac and NBC polls.


Local Private Tour Guide | Durban, South Africa | Paul K.
Hello and welcome to Durban! My name is Paul and I'm a true Durbanite!
Sun, great warm sea and Golden beaches, what more can you ask for?
I love the warm climate we have and the proximity to so many great tour venues.
I've been living in Durban since I was 13. I've grown up with a love for the outdoors. Camping, fishing, cooking are a few of the things that excite me. I'm a great Sports enthusiast with Rugby at the top of my list.
I'm a nature specialist and my passion goes way back to my childhood. I attended a boarding school in the mountains in amongst the farms and quickly learnt about wildlife. All things natural are special to me from grasses and insects to the magnificent Elephants and Mountains.
Although I can follow a routine I like to be flexible, especially with private groups.
I'm passionate about the Durban Safaris I run but also the Durban Day tours like Durban city Tours and Valley Of 1000 Hills.
Guiding to me is a passion, not just a cool way to make a living!
Come take a ride with Me and we will discover a few things, new and magical, together.
Paul went out of his way to make certain my adventure was spectacular. A wonderful guide!
My vehicle holds up to 8 passengers but it's more comfortable with 6.
Open Safari vehicle comes with qualified drivers so cannot drive them.
I'm only fluent in English, although I can have a conversation in Afrikaans.
Durban "Big 5" Safari !


text:
<?php

switch($_GET['action']) {

#########################################################################################################################
// Page d'inscription par défaut //
#########################################################################################################################
default:

		// Nbre d'inscrits
		$sql_nb=mysql_query("SELECT count(id_membre) as nbid FROM members");
		$datanb=mysql_fetch_object($sql_nb);
		$nb_members=$datanb->nbid;

		$add='';

head($add);

echo 'Rejoignez sans plus attendre les '.$nb_members.' utilisateurs de Mon-Look.com !
';

	// Si on a une erreur à afficher.
	if (isset($_SESSION['erreur'])) {
		echo '

			 Une erreur est survenue dans la vérification du formulaire :

'.$_SESSION['erreur'].'
';
		unset($_SESSION['erreur']);
	}

echo'	<form name="myform" action="?p=inscription&action=verif" method="post" onsubmit="return checkifvalid();">

 » Vos identifiants :

			Pseudo
			<input type="text" name="pseudo" style="background-image:url(images/formulaires/pseudo.png);" maxlength="50" onKeypress="return block(event,3);"> *

			Adresse Email
			<input type="text" name="email" style="background-image:url(images/formulaires/email.png);"  maxlength="255" onKeypress="return block(event,5);"> *

			Mot de Passe
			<input type="password" name="pass1" style="background-image:url(images/formulaires/pass.png);" maxlength="50" onKeypress="return block(event,3);"> *

			Confimer Mdp
			<input type="password" name="pass2" style="background-image:url(images/formulaires/pass.png);" maxlength="255" onKeypress="return block(event,3);"> *

 » Vous :

			Prénom
			<input type="text" name="prenom" class="input_inscript"  maxlength="50" style="background-image:url(images/formulaires/nom.png);  onKeypress="return block(event);""> *

			Nom
			<input type="text" name="nom" class="input_inscript" style="background-image:url(images/formulaires/nom.png); " maxlength="50" onKeypress="return block(event);"> ( privé )

			Pays
			<select name="country">
					  <option value="Canada">Canada</option>
					  <option value="France">France</option>
					  <option value="Belgique">Belgique</option>
					  <option value="U.S.A">U.S.A</option>
					  <option value="Autre">Autre</option>
					</select>  *

			Age
			<input type="text" name="age" style="width:60px" class="input_date" maxlength="2"  onKeypress="return block(event,4);"> * ! 13 ans minimum !

			Sexe
			<select name="gender">
				  <option value="h">Homme</option>
				  <option value="f">Femme</option>
				</select>  *

			Tel Mobile
			<input type="text" name="tel" class="input_inscript" style="background-image:url(images/profil/mobile.png); " maxlength="50" onKeypress="return block(event,4);">

					   Confidentiel

Nous vous invitons à entrer votre num de mobile pour que les autres membres de M-L puissent vous envoyer des message via le site, c\'est à dire sans avoir un accés direct à votre numéro !

 » Motivations :

			Je recherche
			<select name="cherche">
				  <option value="h">un homme</option>
				  <option value="f">une femme</option>
				  <option value="hf">un homme ou une femme</option>
				  <option value="p">personne</option>
				</select> *

			Type de relation

			 	<input name="amitie" type="checkbox" value="1" style="width:20px" style="border:0px; background-color:#B4E4E6"> Développer une amitié

				<input name="activites" type="checkbox" value="1" style="width:20px"style="border:0px; background-color:#B4E4E6"> Partenaires d\'activités

				<input name="court" type="checkbox" value="1" style="width:20px"style="border:0px; background-color:#B4E4E6"> Une courte relation

				<input name="long" type="checkbox" value="1" style="width:20px"style="border:0px; background-color:#B4E4E6"> Une longue relation

				<input name="amusement" type="checkbox" value="1" style="width:20px"style="border:0px; background-color:#B4E4E6"> L\'amusement

				<input name="sexe" type="checkbox" value="1" style="width:20px"style="border:0px; background-color:#B4E4E6"> Relation sexuelle

Règlement :

- Vous devez etre agé de plus de 13 ans

- Aucune photo vulgaire

- Le spam est interdit

- Vous devez mettre une photo de vous seulement

<input type="checkbox" name="regagree" id="agree" value="valeur" onClick="ChangeStatut(this.form)" style="width:20px; margin-left:20px; border:0px; background-color:#B4E4E6""/> J\'accepte le reglement

Terminer l\'inscription

</form>';
foot();

break;
#######################################################################################################################
// Page vérifant les infos donnés + Insertion MYSQL + Envoie mail confirmation
#######################################################################################################################
case "verif":

	$pseudo=strtolower(htmlspecialchars($_POST['pseudo']));
	$email=strtolower(htmlspecialchars($_POST['email']));
	$pass1=strtolower(htmlspecialchars($_POST['pass1']));
	$pass2=strtolower(htmlspecialchars($_POST['pass2']));
	$prenom=strtolower(htmlspecialchars($_POST['prenom']));
	$nom=strtolower(htmlspecialchars($_POST['nom']));
	$age=strtolower(htmlspecialchars($_POST['age']));
	$gender=strtolower(htmlspecialchars($_POST['gender']));
	$cherche=strtolower(htmlspecialchars($_POST['cherche']));
	$amitie=strtolower(htmlspecialchars($_POST['amitie']));
	$activites=strtolower(htmlspecialchars($_POST['activites']));
	$court=strtolower(htmlspecialchars($_POST['court']));
	$long=strtolower(htmlspecialchars($_POST['long']));
	$amusement=strtolower(htmlspecialchars($_POST['amusement']));
	$sexe=strtolower(htmlspecialchars($_POST['sexe']));
	$tel=strtolower(htmlspecialchars($_POST['tel']));
	$country=strtolower(htmlspecialchars($_POST['country']));
	$password_encrypt = crypt( md5($pass1) , CLE );
	$key=GenKey();

	$sql1=mysql_query("SELECT count(id_membre) as nbpseudo FROM members WHERE username='$pseudo'");
	$data1=mysql_fetch_object($sql1);
	if ($data1->nbpseudo!=0) $err="- Le pseudo choisi est déjà utilisé.
";

	/* ON tri les pseudo bannis */
	if ($pseudo==base64_decode('a2lyaWtpcmk4NA==')) $ban=1;
	else $ban=0;

	/* On gère le num de téléphone international */
	$newtel="";

	if ($country=="france") { if (strlen($tel)==10) $newtel="+33".substr($tel,1,9); }
	if ($country=="canada" || $country=="u.s.a") $newtel="+3".$tel;
	if ($country=="belgique") $newtel="+32".$tel;

	$sql2=mysql_query("SELECT count(id_membre) as nbemail FROM members WHERE email='$email'");
	$data2=mysql_fetch_object($sql2);
	if ($data2->nbemail!=0) @$err.="- Nous n'autorisons qu'une seule inscription par adresse email.
";

	if (!empty($err)) {
		$_SESSION['erreur']=$err;
		rediriger('?p=inscription');
	}

	$sql=mysql_query("INSERT INTO `members` ( `username` , `password` , `email` , `age` , `country`, `portable` ,`gender` , `joindate`,`active` , `fname` , `lname` , `ip` , `validcode` , `cherche` , `amitie` , `activites` , `relationct` , `relationlt` , `amusement` , `sexe`, `admin` )
					VALUES ('$pseudo', '$password_encrypt', '$email', '$age', '$country', '$newtel', '$gender', NOW( ), '0' , '$prenom', '$nom', '".ip()."', '$key' , '$cherche', '$amitie' , '$activites' , '$court' , '$long' , '$amusement' , '$sexe', '$ban')") or die(mysql_error());

			$mail_body = "

				Bonjour,

Vous venez de vous inscrire sur Mon-Look.com, pour confirmer votre inscription, vous n'avez qu'à utiliser les informations inscrites ci-dessous.

				Vos identifiants de connexion sont :

				Login: $pseudo

				Password: $pass1

				Vous devez maintenant valider votre inscription en cliquant sur le lien ci-dessous :)

				".URL."?p=inscription&action=valid&c=".$key."&l=".$pseudo."

				Nous vous souhaitons de bonnes rencontres sur Mon-Look !

		";
		$mail_object = "Finalisation de l'inscription à Mon-Look.com";
		$headers  = "MIME-Version: 1.0\n";
		$headers .= "Content-type: text/html; charset=iso-8859-1\n";

		@mail( $email, $mail_object, $mail_body, $headers );

	head();
	echo "
Votre inscription s'est déroulée avec succés.

	    Vous devez impérativement finaliser votre inscription en cliquant sur le lien présent dans le mail que nous venons de vous envoyer.

		L'équipe de Mon-Look.com
";
	foot();

break;
#######################################################################################################################
// Vérification du code d'activation du compte //
#######################################################################################################################
case "valid":

	$code=htmlspecialchars(addslashes($_GET['c']));
	$pseudo=htmlspecialchars(addslashes($_GET['l']));

	$sql=mysql_query("SELECT id_membre, validcode FROM members WHERE username='$pseudo' AND active='0'");
	$data=mysql_fetch_object($sql);
	if ($data->validcode==$code) {
		$sql2=mysql_query("UPDATE members SET active='1' WHERE username='$pseudo'");
		head();
		echo "
Merci d'avoir activé votre compte.

			  Vous pouvez maintenant vous connecter à Mon-Look.com et faire de nombreuses rencontres.

			  Bon surf

";
		echo '<form name="identification2" method="post" action="?p=fonctions/identification">

					<input type="text" name="pseudo" style="background-image:url(images/formulaires/pseudo.png);" maxlength="50" class="input_log" value="Login" onclick="this.value=\'\'">

					<input type="password" name="pass" style="background-image:url(images/formulaires/pass.png); margin-top:10px" maxlength="50" class="input_log" value="password"  onclick="this.value=\'\'">

Se connecter

			</form>
			';

		foot();
	} else {
		head();
		echo "
Erreur : Code Incorrect

			  Une erreur est survenue durant l'activations.

			  Si le problème persiste, veuillez contacter l'administrateur

			  ";
		foot();
	}

break;
}
?>

redpajama_set_name: RedPajamaGithub


Free economy U.S. shipping applies automatically to orders of $100 or more at checkout. Shipping rate is calculated from merchandise subtotal (less discounts, if applicable) and does not include tax or purchases of gift certificates/cards. Valid on only one shipping address per order. Promotion not valid on express shipping or for shipments to AK, HI, APO/FPO locations or U.S. territories. Promotion not valid in retail stores. Not valid for cash. No shipping adjustments on previous purchases. Offer is non-transferable. Janie and Jack reserves the right to change or cancel this promotion at any time.
Promotions are special offers we make to all our customers. They are valid for select merchandise, while supplies last, and for a limited time only. Prices and quantities may vary in stores and online. In stores discounts will be taken at the register. Some promotions have coupon codes that must be entered at checkout to receive the discount.
Janie and Jack promotions may not be combined with any other discounts or promotions other than Janie and Jack in-store and online promotions that do not require a specific coupon or authenticated code.
Cannot be combined with any other offers except as described on janieandjack.com.
Not valid at Janie and Jack Outlet.
Janie and Jack reserves the right to change or cancel promotions at any time.
May not be applied to purchases in any store closing sale.
Valid only at Janie and Jack stores in the U.S. and online at janieandjack.com.


text:
class TreeBuilderInfraNetworking < TreeBuilder
  has_kids_for ManageIQ::Providers::Vmware::InfraManager, [:x_get_tree_provider_kids]
  has_kids_for EmsCluster, [:x_get_tree_cluster_kids]
  has_kids_for Switch, [:x_get_tree_switch_kids]
  has_kids_for EmsFolder, [:x_get_tree_folder_kids]

  private

  def tree_init_options(_)
    {
      :leaf     => "Switch",
      :open_all => true,
      :full_ids => true
    }
  end

  def set_locals_for_render
    locals = super
    locals.merge!(:id_prefix => 'nt_', :autoload => true)
  end

  def root_options
    [t = _("All Distributed Switches"), t]
  end

  def x_get_tree_roots(count_only, _options)
    objects = Rbac.filtered(ManageIQ::Providers::Vmware::InfraManager.order("lower(name)"))
    objects.each do |item|
      item[:load_children => true]
    end
    count_only_or_objects(count_only, objects)
  end

  def x_get_tree_provider_kids(object, count_only)
    count_only_or_objects(count_only,
                          Rbac.filtered(EmsCluster.where(:ems_id => object[:id])),
                          "name")
  end

  def x_get_tree_cluster_kids(object, count_only)
    hosts = object.hosts
    switch_ids = hosts.collect { |host| host.switches.pluck(:id) }
    count_only_or_objects(count_only, Rbac.filtered(Switch, :where_clause => ["shared = true and id in(?)", switch_ids.flatten.uniq]))
  end

  def x_get_tree_host_kids(object, count_only)
    count_only_or_objects(count_only,
                          Rbac.filtered(object.switches.where(:shared =>'true')).sort,
                          "name")
  end

  def x_get_tree_switch_kids(object, count_only)
    objects = count_only_or_objects(count_only,
                                    object.lans.sort,
                                    "name")
    objects.each do |item|
      item[:load_children => true]
      item[:cfmeNoClick => true]
    end
  end
end


text:
/* -*- Mode: C++; tab-width: 8; indent-tabs-mode: nil; c-basic-offset: 2 -*- */
/* vim: set ts=8 sts=2 et sw=2 tw=80: */
/* This Source Code Form is subject to the terms of the Mozilla Public
 * License, v. 2.0. If a copy of the MPL was not distributed with this
 * file, You can obtain one at http://mozilla.org/MPL/2.0/. */

#ifndef mozilla_dom_HTMLAnchorElement_h
#define mozilla_dom_HTMLAnchorElement_h

#include "mozilla/Attributes.h"
#include "mozilla/dom/Link.h"
#include "nsGenericHTMLElement.h"
#include "nsIDOMHTMLAnchorElement.h"
#include "nsDOMTokenList.h"

namespace mozilla {
class EventChainPostVisitor;
class EventChainPreVisitor;
namespace dom {

class HTMLAnchorElement final : public nsGenericHTMLElement,
                                public nsIDOMHTMLAnchorElement,
                                public Link
{
public:
  using Element::GetText;
  using Element::SetText;

  explicit HTMLAnchorElement(already_AddRefed<mozilla::dom::NodeInfo>& aNodeInfo)
    : nsGenericHTMLElement(aNodeInfo)
    , Link(this)
  {
  }

  // nsISupports
  NS_DECL_ISUPPORTS_INHERITED

  // CC
  NS_DECL_CYCLE_COLLECTION_CLASS_INHERITED(HTMLAnchorElement,
                                           nsGenericHTMLElement)

  virtual int32_t TabIndexDefault() override;
  virtual bool Draggable() const override;

  // Element
  virtual bool IsInteractiveHTMLContent(bool aIgnoreTabindex) const override;

  // nsIDOMHTMLAnchorElement
  NS_DECL_NSIDOMHTMLANCHORELEMENT

  // DOM memory reporter participant
  NS_DECL_SIZEOF_EXCLUDING_THIS

  virtual nsresult BindToTree(nsIDocument* aDocument, nsIContent* aParent,
                              nsIContent* aBindingParent,
                              bool aCompileEventHandlers) override;
  virtual void UnbindFromTree(bool aDeep = true,
                              bool aNullParent = true) override;
  virtual bool IsHTMLFocusable(bool aWithMouse, bool *aIsFocusable, int32_t *aTabIndex) override;

  virtual nsresult PreHandleEvent(EventChainPreVisitor& aVisitor) override;
  virtual nsresult PostHandleEvent(
                     EventChainPostVisitor& aVisitor) override;
  virtual bool IsLink(nsIURI** aURI) const override;
  virtual void GetLinkTarget(nsAString& aTarget) override;
  virtual already_AddRefed<nsIURI> GetHrefURI() const override;

  nsresult SetAttr(int32_t aNameSpaceID, nsIAtom* aName,
                   const nsAString& aValue, bool aNotify)
  {
    return SetAttr(aNameSpaceID, aName, nullptr, aValue, aNotify);
  }
  virtual nsresult SetAttr(int32_t aNameSpaceID, nsIAtom* aName,
                           nsIAtom* aPrefix, const nsAString& aValue,
                           bool aNotify) override;
  virtual nsresult UnsetAttr(int32_t aNameSpaceID, nsIAtom* aAttribute,
                             bool aNotify) override;
  virtual bool ParseAttribute(int32_t aNamespaceID,
                                nsIAtom* aAttribute,
                                const nsAString& aValue,
                                nsAttrValue& aResult) override;

  virtual nsresult Clone(mozilla::dom::NodeInfo *aNodeInfo, nsINode **aResult) const override;

  virtual EventStates IntrinsicState() const override;

  virtual void OnDNSPrefetchDeferred() override;
  virtual void OnDNSPrefetchRequested() override;
  virtual bool HasDeferredDNSPrefetchRequest() override;

  // WebIDL API
  void GetHref(nsAString& aValue, ErrorResult& rv)
  {
    GetHTMLURIAttr(nsGkAtoms::href, aValue);
  }
  void SetHref(const nsAString& aValue, mozilla::ErrorResult& rv)
  {
    SetHTMLAttr(nsGkAtoms::href, aValue, rv);
  }
  // The XPCOM GetTarget is OK for us
  void SetTarget(const nsAString& aValue, mozilla::ErrorResult& rv)
  {
    SetHTMLAttr(nsGkAtoms::target, aValue, rv);
  }
  void GetDownload(DOMString& aValue)
  {
    GetHTMLAttr(nsGkAtoms::download, aValue);
  }
  void SetDownload(const nsAString& aValue, mozilla::ErrorResult& rv)
  {
    SetHTMLAttr(nsGkAtoms::download, aValue, rv);
  }
  // The XPCOM GetPing is OK for us
  void SetPing(const nsAString& aValue, mozilla::ErrorResult& rv)
  {
    SetHTMLAttr(nsGkAtoms::ping, aValue, rv);
  }
  void GetRel(DOMString& aValue)
  {
    GetHTMLAttr(nsGkAtoms::rel, aValue);
  }
  void SetRel(const nsAString& aValue, mozilla::ErrorResult& rv)
  {
    SetHTMLAttr(nsGkAtoms::rel, aValue, rv);
  }
  nsDOMTokenList* RelList();
  void GetHreflang(DOMString& aValue)
  {
    GetHTMLAttr(nsGkAtoms::hreflang, aValue);
  }
  void SetHreflang(const nsAString& aValue, mozilla::ErrorResult& rv)
  {
    SetHTMLAttr(nsGkAtoms::hreflang, aValue, rv);
  }
  void GetType(DOMString& aValue)
  {
    GetHTMLAttr(nsGkAtoms::type, aValue);
  }
  void SetType(const nsAString& aValue, mozilla::ErrorResult& rv)
  {
    SetHTMLAttr(nsGkAtoms::type, aValue, rv);
  }
  // The XPCOM GetText is OK for us
  void SetText(const nsAString& aValue, mozilla::ErrorResult& rv)
  {
    rv = SetText(aValue);
  }

  // Link::GetOrigin is OK for us

  using Link::GetProtocol;
  using Link::SetProtocol;

  // Link::GetUsername is OK for us
  // Link::SetUsername is OK for us

  // Link::GetPassword is OK for us
  // Link::SetPassword is OK for us

  using Link::GetHost;
  using Link::SetHost;

  using Link::GetHostname;
  using Link::SetHostname;

  using Link::GetPort;
  using Link::SetPort;

  using Link::GetPathname;
  using Link::SetPathname;

  using Link::GetSearch;
  using Link::SetSearch;

  using Link::GetHash;
  using Link::SetHash;

  // The XPCOM URI decomposition attributes are fine for us
  void GetCoords(DOMString& aValue)
  {
    GetHTMLAttr(nsGkAtoms::coords, aValue);
  }
  void SetCoords(const nsAString& aValue, mozilla::ErrorResult& rv)
  {
    SetHTMLAttr(nsGkAtoms::coords, aValue, rv);
  }
  void GetCharset(DOMString& aValue)
  {
    GetHTMLAttr(nsGkAtoms::charset, aValue);
  }
  void SetCharset(const nsAString& aValue, mozilla::ErrorResult& rv)
  {
    SetHTMLAttr(nsGkAtoms::charset, aValue, rv);
  }
  void GetName(DOMString& aValue)
  {
    GetHTMLAttr(nsGkAtoms::name, aValue);
  }
  void SetName(const nsAString& aValue, mozilla::ErrorResult& rv)
  {
    SetHTMLAttr(nsGkAtoms::name, aValue, rv);
  }
  void GetRev(DOMString& aValue)
  {
    GetHTMLAttr(nsGkAtoms::rev, aValue);
  }
  void SetRev(const nsAString& aValue, mozilla::ErrorResult& rv)
  {
    SetHTMLAttr(nsGkAtoms::rev, aValue, rv);
  }
  void GetShape(DOMString& aValue)
  {
    GetHTMLAttr(nsGkAtoms::shape, aValue);
  }
  void SetShape(const nsAString& aValue, mozilla::ErrorResult& rv)
  {
    SetHTMLAttr(nsGkAtoms::shape, aValue, rv);
  }
  void Stringify(nsAString& aResult, ErrorResult& aError)
  {
    GetHref(aResult, aError);
  }

protected:
  virtual ~HTMLAnchorElement();

  virtual void GetItemValueText(DOMString& text) override;
  virtual void SetItemValueText(const nsAString& text) override;
  virtual JSObject* WrapNode(JSContext *aCx, JS::Handle<JSObject*> aGivenProto) override;
  nsRefPtr<nsDOMTokenList > mRelList;
};

} // namespace dom
} // namespace mozilla

#endif // mozilla_dom_HTMLAnchorElement_h


Brasserie Artisanale L'Amère à Boire - What's new !
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