text:
\section{Introduction\label{sec:intro}}

Modeling and forecasting complex dynamical systems is a major
challenge in domains such as environment and climate~\citep{rolnick2019tackling}, health science~\citep{choi2016retain
, and in many industrial
~applications~\citep{toubeau2018deep}
~Model Based (MB) approaches typically rely on partial or ordinary differential equations (PDE/ODE) and stem from a deep understanding of the underlying physical phenomena. Machine learning (ML) and deep learning methods are more prior agnostic yet have become state-of-the-art for severa
~spatio-temporal prediction tasks~\citep{shi2015convolutional,wang2018predrnnta,oreshkin2019n,dona2020pde}
~and connections have been drawn between deep architectures and numerical ODE solvers
~e.g.~ neural ODEs~\citep{chen2018neural,ayed2019learning}
~However, modeling complex physical dynamics is still beyond the scope of pure ML methods, which
often cannot properly extrapolate to new conditions as MB approaches do.

Combining the MB and ML paradigms is an emerging trend
to develop the interplay between the two paradigms. For example, \cite{brunton2016discovering,long2018pde}
learn the explicit form of PDEs directly from data, \citet{raissi2017physics,sirignano2018dgm} use NNs as implicit methods for solving PDE
, \cite{seo2020} learn spatial differences with a graph network, \cite{ummenhofer2020} introduce continuous convolutions for fluid simulations, \cite{de2017deep} learn the velocity field of an advection-diffusion system,
\cite{greydanus2019hamiltonian,chen2019symplectic}
enforce conservation laws in the network architecture or in the loss function.

The large majority of aforementioned MB/ML hybrid approaches assume that the physical model adequately describes the observed dynamics. This assumption is, however, commonly violated in practice
~This may be due to various factors, e.g.~ idealized assumptions and difficulty to explain processes from first principles \citep{gentine}, computational constraints prescribing a fine grain modeling of the system \citep{epnet}, unknown external factors, forces and sources which are present \citep{Large2004}.
In this paper, we aim at leveraging prior dynamical ODE/PDE knowledge in situations where this physical model is incomplete, i.e.~ unable to represent the whole complexity of observed data. To handle this case, we introduce a principled learning framework to Augment incomplete PHYsical models for ideNtIfying and forecasTing complex dYnamics~(APHYNITY). The rationale of APHYNITY, illustrated in Figure~\ref{fig:comparison_data_phys_coop} on the pendulum problem, is
to \textit{augment} the physical model when---and only when---it falls short.

Designing a general method for combining MB and ML approaches is still a widely open problem, and a clear problem formulation for the latter is lacking \citep{Reichstein2019}. Our contributions towards these goals are the following:
\begin{itemize}

\item We introduce a simple yet principled framework for combining both approaches. We decompose the data into a physical and a data-driven term such that the data-driven component only models information that cannot be captured by the physical model. We provide existence and uniqueness guarantees~(Section~\ref{subsec:decomp}) for the decomposition given mild conditions, and show that this formulation ensures interpretability and benefits generalization

\item We propose a trajectory-based training formulation~(Section~\ref{subsec:learning}) along with an adaptive optimization scheme~(Section~\ref{subsec:optim}) enabling end-to-end learning for both physical and deep learning components. This allows APHYNITY to \textit{automatically} adjust the complexity of the neural network to different approximation levels of the physical model, paving the way to flexible learned hybrid models.

\item We demonstrate the generality of the approach on three use cases (reaction-diffusion, wave equations and the pendulum) representative of different PDE families (parabolic, hyperbolic), having a wide spectrum of application domains, e.g.~ acoustics, electromagnetism, chemistry, biology, physics~(Section~\ref{sec:expes}). We show that APHYNITY is able to achieve performances close to complete physical models by augmenting incomplete ones, both in terms of forecasting accuracy and physical parameter identification. Moreover, APHYNITY can also be successfully extended to the partially observable setting~(see discussion in Section \ref{discussion}).

\end{itemize}

\begin{figure}
\centering
\vspace{-0.6cm}
\begin{tabular}{ccc}
 \hspace{-0.5cm} \includegraphics[height=3.7cm]{figs/data-driven.png} &  \hspace{-0.5cm} \includegraphics[height=3.7cm]{figs/physical.png} &
 \hspace{-0.5cm}
 \includegraphics[height=3.7cm]{figs/P3.png}\\
(a) Data-driven Neural ODE  & (b) Simple physical model & (c) Our APHYNITY framework
\end{tabular}
    \caption{Predicted dynamics for the damped pendulum vs. ground truth (GT) trajectories $\nicefrac{\diff^2 \theta}{\diff t^2} + \omega_0^2 \sin \theta + \alpha \nicefrac{\diff \theta}{\diff t} = 0$. We show that in (a) the data-driven approach~\citep{chen2018neural} fails to properly learn the dynamics due to the lack of training data, while in (b) an ideal pendulum cannot take friction into account. The proposed APHYNITY shown in (c) augments the over-simplified physical model in (b) with a data-driven component. APHYNITY improves both forecasting (MSE) and parameter identification (Error $T_0$) compared to (b).}
    \label{fig:comparison_data_phys_coop}
\end{figure}

\section{Related work}
\label{sec:related-work}

\paragraph{Correction in data assimilation} Prediction under approximate physical models has been tackled by traditional statistical calibration techniques, which often rely on Bayesian methods~\citep{pernot2017critical}.
Data assimilation techniques, e.g.~ the Kalman filter~\citep{kalman1960new,becker2019recurrent}, 4D-var \citep{courtier1994strategy},  prediction errors are modeled probabilistically and a correction using observed data is applied after each prediction step. Similar residual correction procedures are commonly used in robotics and optimal control \citep{chen2004disturbance,li2014disturbance}. However, these sequential (two-stage) procedures prevent the cooperation between prediction and correction.  Besides, in model-based reinforcement learning, model deficiencies are typically handled by considering only short-term rollouts \citep{janner2019trust} or by model predictive control \citep{nagabandi2018neural}.
The originality of APHYNITY is to leverage model-based prior knowledge by augmenting it with neurally parametrized dynamics. It does so while ensuring optimal cooperation between the prior model and the augmentation.

\paragraph{Augmented physical models} Combining physical models with machine learning (\textit{gray-box or \textit{hybrid}} modeling) was first explored from the 1990's: \cite{psichogios1992hybrid,thompson1994modeling,rico1994continuous} use neural networks to predict the unknown parameters of physical models. The challenge of proper MB/ML cooperation was already raised as a limitation of gray-box approaches but not addressed. Moreover these methods were evaluated on specific applications with a residual targeted to the form of the equation.
In the last few years, there has been a renewed interest in deep hybrid models bridging data assimilation techniques and machine learning to identify complex PDE parameters using cautiously constrained forward model \citep{long2018pde,de2017deep}, as discussed in introduction.
~Recently, some approaches have specifically targetted the MB/ML cooperation. HybridNet~\citep{long2018hybridnet} and PhICNet~\citep{saha2020phicnet} both use data-driven networks to learn additive perturbations or source terms to a given PDE. The former considers the favorable context where the perturbations can be accessed, and the latter the special case of additive noise on the input. \citet{wang2019integrating,neural20} propose several empirical fusion strategies with deep neural networks but lack theoretical groundings. PhyDNet \citep{leguen20phydnet} tackles augmentation in  partially-observed settings, but with specific recurrent architectures dedicated to video prediction.

 ~Crucially, all the aforementioned approaches do not address the issues of uniqueness of the decomposition or of proper cooperation for correct parameter identification. Besides, we found experimentally that this vanilla cooperation is inferior to the APHYNITY learning scheme in terms of forecasting and parameter identification performances~(see experiments in Section~\ref{sec:results}).

\section{The APHYNITY Model}
\label{sec:model}

In the following, we study dynamics driven by an equation of the form:
\begin{equation}
\label{eq:ode}
\frac{\diff X_t}{\diff t} = F(X_t)
\end{equation}
defined over a finite time interval $[0,T]$, where the state $X$ is either vector-valued, i.e.~ we have $X_t\in\mathbb{R}^d$ for every $t$, (pendulum equations in Section \ref{sec:expes}), or $X_t$ is a $d$-dimensional vector field over a spatial domain $\Omega\subset\mathbb{R}^k$, with $k\in\{2,3\}$, i.e.~ $X_t(x)\in\mathbb{R}^d$ for every $(t,x)\in[0,T]\times\Omega$ (reaction-diffusion and wave equations in Section \ref{sec:expes}).
We suppose that we have access to a set of observed trajectories ${\mathcal{D}} = \{X_\cdot:[0,T]\rightarrow{\mathcal{A}} \ |\ \forall t\in[0,T], \nicefrac{\diff X_t}{\diff t} = F(X_t)\}$, where ${\mathcal{A}}$ is the set of $X$ values (either $\mathbb{R}^d$ or vector field). In our case, the unknown $F$ has ${\mathcal{A}}$ as domain and we only assume that $F\in{\mathcal{F}}$, with $({\mathcal{F}}, \|\cdot\|)$ a normed vector space.

\subsection{Decomposing dynamics into physical and augmented terms\label{subsec:decomp}}

As introduced in \Secref{sec:intro}, we consider the common situation where incomplete information is available on the dynamics, under the form of a family of ODEs or PDEs characterized by their temporal evolution $F_p\in{\mathcal{F}}_p\subset{\mathcal{F}}$. The APHYNITY framework leverages the knowledge of ${\mathcal{F}}_p$ while mitigating the approximations induced by this simplified model through the combination of physical and data-driven components. ${\mathcal{F}}$ being a vector space, we can write:
\[
F = F_p + F_a
\]
where $F_p\in{\mathcal{F}}_p$ encodes the incomplete physical knowledge and $F_a\in{\mathcal{F}}$ is the  data-driven augmentation term complementing $F_p$
~The incomplete physical prior is supposed to belong to a known famil
, but the physical parameters~(e.g.~ propagation speed for the wave equation) are unknown and need to be estimated from data. Both $F_p$ and $F_a$ parameters are estimated by fitting the trajectories from ${\mathcal{D}}$.

The decomposition $F = F_p + F_a$ is in general not unique. For example, all the dynamics could be captured by the $F_a$ component. This decomposition is thus ill-defined, which hampers the interpretability and the extrapolation abilities of the model. In other words, one wants the estimated parameters of $F_p$ to be as close as possible to the true parameter values of the physical model and $F_a$ to play only a complementary role w.r.t $F_p$, so \textit{as to model only the information that cannot be captured by the physical prior}. For example, when $F\in{\mathcal{F}}_p$, the data can be fully described by the physical model, and in this case it is sensible to desire $F_a$ to be nullified; this is of central importance in a setting where one wishes to identify physical quantities, and for the model to generalize and extrapolate to new conditions. In a more general setting where the physical model is incomplete, the action of $F_a$ on the dynamics, as measured through its norm, should be as small as possible.

This general idea is embedded in the following optimization problem:
\begin{equation}
\label{eq:opt}
\underset{F_p\in{\mathcal{F}}_p, F_a\in{\mathcal{F}}}{\min} ~~~\left\Vert  F_a  \right\Vert ~~~
\mathrm{subject~to} ~~~~ \forall X\in{\mathcal{D}}, \forall t, \frac{\diff X_t}{\diff t} =(F_p+F_a)(X_t)
\end{equation}The originality of APHYNITY is to leverage model-based prior knowledge by augmenting it with neurally parametrized dynamics. It does so while ensuring optimal cooperation between the prior model and the augmentation.

A first key question is whether the minimum in \eqref{eq:opt} is indeed well-defined, in other words whether there exists indeed a decomposition with a minimal norm $F_a$. The answer actually depends on the geometry of ${\mathcal{F}}_p$, and is formulated in the following proposition proven in Appendix~\ref{app:proof}:
\begin{prop}[Existence of a minimizing pair]\label{prop:exist_unique}
If ${\mathcal{F}}_p$ is a proximinal set\footnote{\label{fn:proximal-chebyshev}A proximinal set is one from which every point of the space has at least one nearest point. A Chebyshev set is one from which every point of the space has a unique nearest point. More details in Appendix~\ref{app:chebyshev}.}, there exists a decomposition minimizing \eqref{eq:opt}.
\end{prop}
Proximinality is a mild condition which, as shown through the proof of the proposition, cannot be weakened. It is a property verified by any boundedly compact set. In particular, it is true for closed subsets of finite dimensional spaces. However, if only existence is guaranteed, while forecasts would be expected to be accurate, non-uniqueness of the decomposition would hamper the interpretability of $F_p$ and this would mean that the identified physical parameters are not uniquely determined.

It is then natural to ask under which conditions solving problem \eqref{eq:opt} leads to a unique decomposition into a physical and a data-driven component. The following result provides guarantees on the existence and uniqueness of the decomposition under mild conditions. The proof is given in Appendix~\ref{app:proof}:
\begin{prop}[Uniqueness of the minimizing pair]\label{prop:unique}
If ${\mathcal{F}}_p$ is a Chebyshev set\textcolor{red}{\footnotemark[1]}, \eqref{eq:opt} admits a unique minimizer. The $F_p$ in this minimizer pair is the metric projection of the unknown $F$ onto ${\mathcal{F}}_p$.
\end{prop}
The Chebyshev assumption condition is strictly stronger than proximinality but is still quite mild and necessary. Indeed, in practice, many sets of interest are Chebyshev, including all closed convex spaces in strict normed spaces and, if ${\mathcal{F}} = L^2$, ${\mathcal{F}}_p$ can be any closed convex set, including all finite dimensional subspaces. In particular, all examples considered in the experiments are Chebyshev sets.

Propositions \ref{prop:exist_unique} and \ref{prop:unique} provide, under mild conditions, the theoretical guarantees for the  APHYNITY formulation to infer the correct MB/ML decomposition, thus enabling both recovering the proper physical parameters and accurate forecasting.

\subsection{Solving APHYNITY with deep neural networks\label{subsec:learning}}
In the following, both terms of the decomposition are parametrized and are denoted as $F_p^{\theta_p}$ and $F_p^{\theta_a}$
~Solving APHYNITY then consists in estimating the parameters $\theta_p$ and $\theta_a$. $\theta_p$ are the physical parameters and are typically low-dimensional, e.g.~ 2 or 3 in our experiments for the considered physical models. For $F_a$, we need sufficiently expressive models able to optimize over all ${\mathcal{F}}$: we thus use deep neural networks, which have shown promising performances for the approximation of differential equations~\citep{raissi2017physics,ayed2019learning}.

When learning the parameters of $F_p^{\theta_p}$ and $F_a^{\theta_a}$, we have access to a finite dataset of trajectories discretized with a given temporal resolution $\Delta t$: ${\mathcal{D}}_{\text{train}} = \{(X^{(i)}_{k\Delta t})_{0\leq k\leq \left \lfloor{\nicefrac{T}{\Delta t}}\right \rfloor} \}_{1\leq i\leq N}$. Solving \eqref{eq:opt} requires estimating the state derivative $\nicefrac{\diff X_t}{\diff t}$ appearing in the constraint term. One solution is to approximate this derivative using e.g.~ finite differences as in \citep{brunton2016discovering,greydanus2019hamiltonian,cranmer2020lagrangian}. This numerical scheme requires high space and time resolutions in the observation space in order to get reliable gradient estimates. Furthermore it is often unstable, leading to explosive numerical errors as discussed in Appendix~\ref{app:der_superv}. We propose instead to solve \eqref{eq:opt} using an integral trajectory-based approach: we compute $\widetilde{X}
^i_{k\Delta t, X_0}$ from an initial state $X^{(i)}_0$ using the current $F_p^{\theta_p} + F_a^{\theta_a}$ dynamics, then enforce the constraint $\widetilde{X}^i_{k\Delta t, X_0}=X^i_{k\Delta t}$. This leads to our final objective function on $(\theta_p, \theta_a)$:
\begin{equation}
\label{eq:opt_final}
\underset{\theta_p, \theta_a}{\min} ~~~\left\Vert  F_a^{\theta_a}  \right\Vert ~~~
\mathrm{subject~to} ~~~~ \forall i, \forall k, \widetilde{X}^{(i)}_{k\Delta t} = X^{(i)}_{k\Delta t}
\end{equation}
where  $\widetilde{X}^{(i)}_{k\Delta t}$ is the approximate solution of the integral $ \int_{X_0^{(i)}}^{X_0^{(i)}+k \Delta t} (F_p^{\theta_p} + F_a^{\theta_a})(X_s) \diff X_s$ obtained by a differentiable ODE solver.

In our setting, where we consider situations for which $F^{\theta_p}_p$ only partially describes the physical phenomenon, this coupled MB + ML formulation leads to different parameter estimates than using the MB formulation alone, as analyzed more thoroughly in Appendix~\ref{app:alt_methods}.
Interestingly, our experiments show that using this formulation also leads to a better identification of the physical parameters $\theta_p$ than when fitting the simplified physical model $F^{\theta_p}_p$ alone~(Section~\ref{sec:expes}). With only an incomplete knowledge on the physics, $\theta_p$ estimator will be biased by the additional dynamics which needs to be fitted in the data.
Appendix~\ref{app:ablation} also confirms that the integral formulation gives better forecasting results and a more stable behavior than supervising over finite difference approximations of the derivatives.

\subsection{Adaptively constrained optimization\label{subsec:optim}}

The formulation in \eqref{eq:opt_final} involves constraints which are difficult to enforce exactly in practice.
We considered a variant of the method of multipliers~\citep{constrained_optim} which uses a sequence of Lagrangian relaxations $\mathcal{L}_{\lambda_j}(\theta_p, \theta_a)$:
 \begin{equation}
 \label{eq:opt_final_relaxed}
\mathcal{L}_{\lambda_j}(\theta_p, \theta_a) = \|F_a^{\theta_a}\| + \lambda_j \cdot \mathcal{L}_{traj}(\theta_p, \theta_a)
 \end{equation}
where $\mathcal{L}_{traj}(\theta_p, \theta_a) = \sum_{i=1}^N\sum_{h=1}^{T/\Delta t} \|X^{(i)}_{h\Delta t} -  \widetilde{X}^{(i)}_{h\Delta t}   \|$.

\begin{wrapfigure}{r}{0.435\textwidth}
\vspace{-5pt}
\begin{algorithm}[H]
\SetAlgoLined
Initialization: $\lambda_0\geq0, \tau_1 >0, \tau_2>0$\;
\For{epoch = $1:N_{epochs}$} {
\For{iter in $1:N_{iter}$}{
\For{batch in $1:B$}{
$\theta_{j+1} = \theta_j - \tau_1 \nabla \left[ \lambda_j\mathcal{L}_{traj}(\theta_j) + \left\Vert F_a \right\Vert  \right]  \;$
}
 }
 $\lambda_{j+1} =  \lambda_j +$ $\tau_2\mathcal{L}_{traj}(\theta_{j+1}) \;$
 }
  \caption{\label{alg:optim} APHYNITY}
\end{algorithm}
 \vspace{-10pt}
\end{wrapfigure} This method needs an increasing sequence $(\lambda_j)_j$ such that the successive minima of $\mathcal{L}_{\lambda_j}$ converge to a solution~(at least a local one) of the constrained problem \eqref{eq:opt_final}. We select $(\lambda_j)_j$ by using an iterative strategy: starting from a value $\lambda_0$, we iterate, minimizing $\mathcal{L}_{\lambda_j}$ by gradient descent\footnote{Convergence to a local minimum isn't necessary, a few steps are often sufficient for a successful optimization.}, then update $\lambda_j$ with:
$\lambda_{j+1} = \lambda_j + \tau_2\mathcal{L}_{traj}(\theta_{j+1})$, where $\tau_2$ is a chosen hyper-parameter and $\theta = (\theta_p, \theta_a)$. This procedure is summarized in Algorithm~\ref{alg:optim}. This adaptive iterative procedure allows us to obtain stable and robust results, in a reproducible fashion, as shown in the experiments.

\section{Experimental validation\label{sec:expes}}

We validate our approach on 3 classes of challenging physical dynamics: reaction-diffusion, wave propagation, and the damped pendulum, representative of various application domains such as chemistry, biology or ecology (for reaction-diffusion) and earth physic, acoustic, electromagnetism or even neuro-biology (for waves equations).
~The two first dynamics are described by PDEs and thus in practice should be learned from very high-dimensional vectors, discretized from the original compact domain. This makes the learning much more difficult than from the one-dimensional pendulum case. For each problem, we investigate the cooperation between physical models of increasing complexity encoding incomplete knowledge of the dynamics~(denoted \textit{Incomplete physics} in the following) and data-driven models. We show the relevance of APHYNITY~(denoted \textit{APHYNITY models}) both in terms of forecasting accuracy and physical parameter identification.

\subsection{Experimental setting\label{subsec:exp_sett}}

We describe the three families of equations studied in the experiments. In all experiments, ${\mathcal{F}}=\mathcal{L}^2({\mathcal{A}})$ where ${\mathcal{A}}$ is the set of all admissible states for each problem, and the $\mathcal{L}^2$ norm is computed on ${\mathcal{D}}_{train}$ by: $\|F\|^2 \approx \sum_{i,k}\|F(X^{(i)}_{k\Delta t})\|^2$. All considered sets of physical functionals ${\mathcal{F}}_p$ are closed and convex in ${\mathcal{F}}$ and thus are Chebyshev. In order to enable the evaluation on both prediction and parameter identification, all our experiments are conducted on simulated datasets with known model parameters. Each dataset has been simulated using an appropriate high-precision integration scheme for the corresponding equation. All solver-based models take the first state $X_0$ as input and predict the remaining time-steps by integrating $F$ through the same differentiable generic and common ODE solver~(4th order Runge-Kutta)\footnote{This integration scheme is then different from the one used for data generation, the rationale for this choice being that when training a model one does not know how exactly the data has been generated.}. Implementation details and architectures are given in Appendix~\ref{app:implementation}.

\vspace{-0.2cm}
\paragraph{Reaction-diffusion equations}
We  consider a 2D FitzHugh-Nagumo type model~\citep{klaasen1984fitzhugh}. The system is driven by the PDE
\(
        \frac{\partial u}{\partial t} = a\Delta u + R_u(u,v; k),
    \frac{\partial v}{\partial t} = b\Delta v + R_v(u,v)\)
where $a$ and $b$ are respectively the diffusion coefficients of $u$ and $v$, $\Delta$ is the Laplace operator. The local reaction terms are $R_u(u,v; k) = u - u^3 - k - v, R_v(u,v) = u - v$. The state is $X=(u,v)$ and is defined over a compact rectangular domain $\Omega$ with periodic boundary conditions. The considered physical models are: \begin{itemize*}
    \item \textit{Param PDE ($a,b$)}, with unknown ($a,b$) diffusion terms and without reaction terms: ${\mathcal{F}}_p = \{F_p^{a,b}:(u,v) \mapsto (a\Delta u, b\Delta v)\ | \ a\geq a_{\min} >0, b\geq b_{\min}>0\}$;
    \item \textit{Param PDE ($a,b,k$)}, the full PDE with unknown parameters: ${\mathcal{F}}_p = \{F_p^{a,b,k} : (u,v)\mapsto \linebreak (a\Delta u + R_u(u,v;k),   b\Delta v + R_v(u,v)\ |\ a\geq a_{\min} > 0, b\geq b_{\min} > 0, k\geq k_{\min} > 0\}$.
\end{itemize*}

\vspace{-0.25cm}
\paragraph{Damped wave equations}
We investigate the damped-wave PDE:
\(
    \frac{\partial^2 w}{\partial t^2} - c^2\Delta w + k \frac{\partial w}{\partial t}=0
\)
where $k$ is the damping coefficient. The state is $X=(w, \frac{\partial w}{\partial t}
)$ and we consider a compact spatial domain $\Omega$ with Neumann homogeneous boundary conditions. Note that this damping differs from the pendulum, as its effect is global. Our physical models are:
\begin{itemize*}
    \item \textit{Param PDE ($c$)}, without damping term: ${\mathcal{F}}_p = \{F^c_p: (u, v) \mapsto (v, c^2 \Delta u) \ |\ c\in[\epsilon,+\infty) \text{ with } \epsilon>0\}$;
    \item \textit{Param PDE ($c,k$)}: ${\mathcal{F}}_p = \{F^{c,k}_p:(u, v) \mapsto (v, c^2 \Delta u - kv)\ | \ c, k\in[\epsilon,+\infty) \text{ with } \epsilon>0\}$.
\end{itemize*}

\vspace{-0.25cm}
\paragraph{Damped pendulum}
The evolution
follows the ODE $\nicefrac{\diff^2\theta}{\diff t^2} + \omega_0^2 \sin \theta  +\alpha \nicefrac{\diff\theta}{\diff t} = 0$, where $\theta(t)$ is the angle, $\omega_0
~the proper pulsation ($T_0$ the period)
~and $\alpha$ the damping coefficient. With state $X = (\theta, \nicefrac{\diff\theta}{\diff t})$, the ODE is
$F_p^{\omega_0, \alpha}: X \mapsto (\nicefrac{\diff\theta}{\diff t},  - \omega_0^2 \sin \theta  - \alpha \nicefrac{\diff\theta}{\diff t})$. Our physical models
are:
\begin{itemize*}
    \item \textit{Hamiltonian~}\citep{greydanus2019hamiltonian},
    a conservative approximatio
    , with ${\mathcal{F}}_p = \{F^{\mathcal{H}}_p: (u,v) \mapsto (\partial_y \mathcal{H}(u,v), -\partial_x \mathcal{H}(u,v))\ |\ \mathcal{H}\in H^1(\mathbb{R}^2)\}$, $H^1(\mathbb{R}^2)$ is the first order Sobolev space.
    \item \textit{Param ODE ($\omega_0$)}, the frictionless pendulum: ${\mathcal{F}}_p = \{F^{\omega_0,\alpha=0}_p | \ \omega_0\in[\epsilon,+\infty) \text{ with } \epsilon>0\}$
    \item \textit{Param ODE ($\omega_0, \alpha$)}, the full pendulum equation: ${\mathcal{F}}_p = \{F^{\omega_0,\alpha}_p | \ \omega_0, \alpha\in[\epsilon,+\infty)\text{ with } \epsilon>0\}$.
\end{itemize*}

\vspace{-0.25cm}
\paragraph{Baselines}
As purely data-driven baselines, we use Neural ODE~\citep{chen2018neural} for the three problems and PredRNN++~(\citealp{wang2018predrnnta}, for reaction-diffusion only) which are competitive models for datasets generated by differential equations and  for spatio-temporal data. As MB/ML methods, in the ablations studies (see Appendix \ref{app:ablation}), we compare for all problems, to the vanilla MB/ML cooperation scheme found in \citep{wang2019integrating,neural20}. We also show results for \textit{True PDE/ODE}, which corresponds to the equation for data simulation~(which do not lead to zero error due to the difference between simulation and training integration schemes). For the pendulum, we compare to Hamiltonian neural networks \citep{greydanus2019hamiltonian,toth2019hamiltonian} and to the the deep Galerkin method (DGM, \citealp{sirignano2018dgm}). See additional details in Appendix~\ref{app:implementation}.

\subsection{Results}
\label{sec:results}

\begin{table}[t!]
    \centering
    \vspace{-0.6cm}
    \footnotesize
\setlength{\tabcolsep}{8.5pt}
    \caption{Forecasting and identification results on the (a) reaction-diffusion, (b) wave equation, and (c) damped pendulum datasets. We set for (a) $a=1\times 10^{-3}, b=5\times 10^{-3}, k=5\times 10^{-3}$, for (b) $c=330$, $k=50$ and for (c) $T_0=6$, $\alpha=0.2$ as true parameters. $\log$ MSEs are computed respectively over 25, 25, and 40 predicted time-steps. \%Err param. averages the results when several physical parameters are present.  For each level of incorporated physical knowledge, equivalent best results according to a Student t-test are shown in bold. n/a corresponds to non-applicable cases.
    \label{tab:pendulum}}
    \begin{tabular}{cclccc}
    \toprule
Dataset & & Method & $\log$ MSE & \%Err param. & $\|F_a\|^2$ \\
    \midrule
\multirowcell{8}{\parbox{0.7cm}{\centering\tiny (a) Reaction-diffusion}} & \multirowcell{2}{\parbox{0.9cm}{\centering\tiny Data-driven}} &
Neural ODE
& -3.76$\pm$0.02 & n/a & n/a \\
&& PredRNN++
& -4.60$\pm$0.01  & n/a & n/a\\\cmidrule{2-6}
& \multirowcell{2}{\parbox{0.9cm}{\centering\tiny Incomplete physics}} &Param PDE ($a,b$) & -1.26$\pm$0.02 & 67.6 & n/a\\
&& APHYNITY Param PDE ($a,b$) & \textbf{-5.10$\pm$0.21} & \textbf{2.3} &  67 \\\cmidrule{2-6}
&\multirowcell{4}{\parbox{0.9cm}{\centering\tiny Complete physics}}&  Param PDE ($a,b,k$) & \textbf{-9.34$\pm$0.20} & 0.17 & n/a\\
&&  APHYNITY Param PDE ($a,b,k$) & \textbf{-9.35$\pm$0.02} & \textbf{0.096} & 1.5e-6\\
  \cdashline{3-6}\noalign{\vskip 0.2ex}
&&  True PDE & -8.81$\pm$0.05 & n/a  & n/a\\
&&  APHYNITY True PDE & \textbf{-9.17$\pm$0.02} & n/a & 1.4e-7\\ \midrule
\multirowcell{8}{\parbox{0.8cm}{\centering\tiny (b)\\ Wave equation}} &\centering\tiny Data-driven & Neural ODE
& -2.51$\pm$0.29 & n/a  & n/a  \\ \cmidrule{2-6}
&\multirowcell{2}{\parbox{0.9cm}{\centering\tiny Incomplete physics}} & Param PDE ($c$) & 0.51$\pm$0.07 & 10.4 & n/a  \\
&& APHYNITY Param PDE ($c$) & \textbf{-4.64$\pm$0.25} & \textbf{0.31} & 71. \\ \cmidrule{2-6}
&\multirowcell{4}{\parbox{0.8cm}{\centering\tiny Complete physics}} & Param PDE $(c,k)$ & -4.68$\pm$0.55 & 1.38 & n/a \\
&& APHYNITY Param PDE $(c, k)$ & \textbf{-6.09$\pm$0.28} &  \textbf{0.70} & 4.54 \\ \cdashline{3-6}\noalign{\vskip 0.2ex}
&& True PDE  & -4.66$\pm$0.30 & n/a & n/a \\
&& APHYNITY True PDE & \textbf{-5.24$\pm$0.45} & n/a & 0.14 \\\midrule
\multirowcell{9}{\parbox{1cm}{\centering \tiny (c) \\Damped pendulum}} & \centering\tiny Data-driven & Neural ODE
& -2.84$\pm$0.70 & n/a & n/a   \\ \cmidrule{2-6}
&\multirowcell{5}{\parbox[c]{1cm}{\centering \tiny Incomplete physics}}  & Hamiltonian
& -0.35$\pm$0.10  & n/a & n/a \\
&& APHYNITY Hamiltonian   & \textbf{-3.97$\pm$1.20}    & n/a & 623 \\ \cdashline{3-6}\noalign{\vskip 0.2ex}
&& Param ODE ($\omega_0$) & -0.14$\pm$0.10  & 13.2  & n/a   \\
&& Deep Galerkin Method ($\omega_0$)  & -3.10$\pm$0.40 & 22.1 & n/a \\
&& APHYNITY Param ODE ($\omega_0$) &  \textbf{-7.86$\pm$0.60}  & \textbf{4.0}  &132  \\ \cmidrule{2-6}
&\multirowcell{5}{\parbox{0.8cm}{\centering\tiny Complete physics}} & Param ODE ($\omega_0, \alpha$)  & \textbf{-8.28$\pm$0.40}  & \textbf{0.45}   & n/a    \\
&& Deep Galerkin Method ($\omega_0,\alpha$)  & -3.14$\pm$0.40 & 7.1 & n/a \\
&& APHYNITY Param ODE ($\omega_0, \alpha$)  & \textbf{-8.31$\pm$0.30} & \textbf{0.39} & 8.5  \\ \cdashline{3-6}\noalign{\vskip 0.2ex}
&& True ODE  & \textbf{-8.58$\pm$0.20}  & n/a & n/a \\
&& APHYNITY True ODE   & \textbf{-8.44$\pm$0.20}  & n/a & 2.3  \\
\bottomrule
\end{tabular}
    \label{tab:results-for-all}
    \vspace{-0.2cm}
\end{table}

We analyze and discuss below the results obtained for the three kind of dynamics. We successively examine different evaluation or quality criteria. The conclusions are consistent for the three problems, which allows us to highlight clear trends for all of them.

\vspace{-0.25cm}
\paragraph{Forecasting accuracy}
The data-driven models do not perform well compared to \textit{True PDE/ODE} (all values are test errors expressed as $\log$ MSE): -4.6 for PredRNN++ vs. -9.17 for reaction-diffusion, -2.51 vs. -5.24 for wave equation, and -2.84 vs. -8.44 for the pendulum in Table~\ref{tab:results-for-all}.
The Deep Galerkin method for the pendulum in complete physics \textit{DGM ($\omega_0,\alpha$)}, being constrained by the equation, outperforms Neural ODE but is far inferior to APHYNITY models. In the incomplete physics  case, \textit{DGM ($\omega_0$)} fails to compensate for the missing information. The \textit{incomplete physical models}, \textit{Param PDE ($a,b$)} for the reaction-diffusion, \textit{Param PDE ($c$)} for the wave equation, and \textit{Param ODE ($\omega_0$)} and \textit{Hamiltonian models} for the damped pendulum, have even poorer performances than purely data-driven ones, as can be expected since they ignore important dynamical components, e.g.~ friction in the pendulum case. Using APHYNITY with these imperfect physical models greatly improves forecasting accuracy in all cases, significantly outperforming purely data-driven models, and reaching results often close to the accuracy of the true ODE, when APHYNITY and the true ODE models are integrated with the same numerical scheme~(which is different from the one used for data generation, hence the non-null errors even for the true equations), e.g.~ -5.92 vs. -5.24 for wave equation in Table~\ref{tab:results-for-all}. This clearly highlights the capacity of our approach to augment incomplete physical models with a learned data-driven component.

\vspace{-0.25cm}
\paragraph{Physical parameter estimation}
Confirming the phenomenon mentioned in the introduction and detailed in Appendix~\ref{app:alt_methods}, incomplete physical models can lead to bad estimates for the relevant physical parameters: an error respectively up to 67.6\% and 10.4\% for parameters in the reaction-diffusion and wave equations, and an error of more than 13\% for parameters for the pendulum in Table~\ref{tab:results-for-all}. APHYNITY is able to significantly improve physical parameters identification: 2.3\% error for the reaction-diffusion, 0.3\% for the wave equation, and 4\% for the pendulum. This validates the fact that augmenting a simple physical model to compensate its approximations is not only beneficial for prediction, but also helps to limit errors for parameter identification when dynamical models do not fit data well. This is crucial for interpretability and explainability of the estimates.

\vspace{-0.25cm}
\paragraph{Ablation study}
  We conduct ablation studies to validate the importance of the APHYNITY augmentation compared to a naive strategy consisting in learning $F=F_p+F_a$ without taking care on the quality of the decomposition, as done in \citep{wang2019integrating,neural20}. Results shown in Table \ref{tab:results-for-all} of Appendix \ref{app:ablation} show a consistent gain of APHYNITY for the three use cases and for all physical models:  for instance for \textit{Param ODE ($a,b$)} in reaction-diffusion, both forecasting performances ($\log \text{MSE}=$-5.10 vs. -4.56) and identification parameter (Error$=$ 2.33\% vs. 6.39\%) improve. Other ablation results are provided in Appendix \ref{app:ablation} showing the relevance of the the trajectory-based approach described in Section~\ref{subsec:learning}~(vs supervising over finite difference approximations of the derivative $F$).

\vspace{-0.25cm}
\paragraph{Flexibility}
When applied to complete physical models, APHYNITY does not degrade accuracy, contrary to a vanilla cooperation scheme (see ablations in Appendix \ref{app:ablation}). This is due to the least action principle of our approach: when the physical knowledge is sufficient for properly predicting the observed dynamics, the model learns to ignore the data-driven augmentation. This is shown by the norm of the trained neural net component $F_a$, which is reported in Table~\ref{tab:results-for-all} last column: as expected, $\|F_a\|^2$ diminishes as the complexity of the corresponding physical model increases, and, relative to incomplete models, the norm becomes very small for complete physical models~(for example in the pendulum experiments, we have $\Vert F_a \Vert= 8.5$ for the APHYNITY model to be compared with 132 and 623 for the incomplete models). Thus, we see that the norm of $F_a$ is a good indication of how imperfect the physical models ${\mathcal{F}}_p$ are. It highlights the flexibility of APHYNITY to successfully adapt to very different levels of prior knowledge. Note also that APHYNITY sometimes slightly improves over the true ODE, as it compensates the error introduced by different numerical integration methods for data simulation and training (see Appendix~\ref{app:implementation})

\begin{figure}[t!]
\vspace{-0.8cm}
    \centering
    \subfloat[Param PDE ($a$, $b$), diffusion-only
    ]{
    \includegraphics[width=0.32\textwidth]{figs/reaction-diffusion/physical.png}}
      \hfill
    \subfloat[APHYNITY Param PDE ($a$, $b$)
    ]{
    \includegraphics[width=0.32\textwidth]{figs/reaction-diffusion/affinity.png}}\hfill
    \subfloat[Ground truth simulation
    ]{
    \includegraphics[width=0.32\textwidth]{figs/reaction-diffusion/ground-truth.png}}
    \caption{Comparison of predictions of two components $u$ (top) and $v$ (bottom) of the reaction-diffusion system. Note that $t=4$ is largely beyond the dataset horizon ($t=2.5$).
    \label{fig:reaction-diffusion-demo}
    }
\end{figure}

\begin{figure}[t!]
\vspace{-0.6cm}
    \centering
    \subfloat[Neural ODE
    ]{
    \includegraphics[width=0.32\textwidth]{figs/wave_damped/neural_ode.png}}
    \hfill
    \subfloat[APHYNITY Param PDE ($c$)
    ]{
    \includegraphics[width=0.32\textwidth]{figs/wave_damped/affinity.png}}\hfill
    \subfloat[Ground truth simulation
    ]{
    \includegraphics[width=0.32\textwidth]{figs/wave_damped/truth.png}}
    \caption{Comparison between the prediction of APHYNITY when $c$ is estimated and Neural ODE for the damped wave equation. Note that $t+32$, last column for (a, b, c) is already beyond the training time horizon ($t+25$), showing the consistency of APHYNITY method.\label{fig:wave-damped-demo}
    }
\end{figure}

\vspace{-0.25cm}
\paragraph{Qualitative visualizations}
Results in Figure~\ref{fig:reaction-diffusion-demo} for reaction-diffusion show that the incomplete diffusion parametric PDE in Figure~\ref{fig:reaction-diffusion-demo}(a) is unable to properly match ground truth simulations: the behavior of the two components in Figure~\ref{fig:reaction-diffusion-demo}(a) is reduced to simple independent diffusions due to the lack of interaction terms  between $u$ and $v$. By using APHYNITY in Figure~\ref{fig:reaction-diffusion-demo}(b), the correlation between the two components appears together with the formation of Turing patterns, which is very similar to the ground truth. This confirms that $F_a$ can learn the reaction terms and improve prediction quality. In Figure~\ref{fig:wave-damped-demo}, we see for the wave equation that the data-driven Neural ODE model fails at approximating $\nicefrac{\diff w}{\diff t}$ as the forecast horizon increases: it misses crucial details for the second component $\nicefrac{\diff w}{\diff t}$ which makes the forecast diverge from the ground truth. APHYNITY incorporates a Laplacian term as well as the data-driven $F_a$ thus capturing the damping phenomenon and succeeding in maintaining physically sound results for long term forecasts, unlike Neural ODE.

\vspace{-0.25cm}
\paragraph{Extension to non-stationary dynamics}
We provide additional results in Appendix \ref{app:additional} to tackle datasets where physical parameters of the equations vary in each sequence. To this end, we design an encoder able to perform parameter estimation for each sequence. Results show that APHYNITY accommodates well to this setting, with similar trends as those reported in this section.

\paragraph{Additional illustrations}
We give further visual illustrations to demonstrate how the estimation of parameters in incomplete physical models is improved with APHYNITY. For the reaction-diffusion equation, we show that the incomplete parametric PDE underestimates both diffusion coefficients. The difference is visually recognizable between the poorly estimated diffusion (\Figref{fig:comp-diffusion}(a)) and the true one (\Figref{fig:comp-diffusion}(c)) while APHYNITY gives a fairly good estimation of those diffusion parameters as shown in \Figref{fig:comp-diffusion}(b).

\begin{figure}[h]
\centering
\subfloat[$a=0.33\times 10^{-3}, b=0.94\times 10^{-3} $, diffusion estimated with Param PDE $(a,b)$]{\includegraphics[width=0.32\textwidth]{figs/reaction-diffusion/physical.png}}\hfill
\subfloat[$a=0.97\times 10^{-3}, b=4.75\times 10^{-3}$, diffusion estimated with APHYNITY Param PDE $(a,b)$]{\includegraphics[width=0.32\textwidth]{figs/reaction-diffusion/aphynity_phys_est.png}} \hfill
\subfloat[$a=1.0\times 10^{-3}, b=5.0\times 10^{-3}$, true diffusion]{\includegraphics[width=0.32\textwidth]{figs/reaction-diffusion/aphynity_phys_est.png}}

\caption{Diffusion predictions using coefficient learned with (a) incomplete physical model Param PDE $(a,b)$ and (b) APHYNITY-augmented Param PDE$(a,b)$, compared with the (c) true diffusion\label{fig:comp-diffusion}}
\end{figure}

\section{Conclusion}
 \label{discussion}
In this work, we introduce the APHYNITY framework that can efficiently augment approximate physical models with deep data-driven networks, performing similarly to models for which the underlying dynamics are entirely known. We exhibit the superiority of APHYNITY over data-driven, incomplete physics, and state-of-the-art approaches combining ML and MB methods, both in terms of forecasting and parameter identification on three various classes of physical systems. Besides, APHYNITY is flexible enough to adapt to different approximation levels of prior physical knowledge.

An appealing perspective is the applicability of APHYNITY on partially-observable settings, such as video prediction.
Besides, we hope that the APHYNITY framework will open up the way to the design of a wide range of more flexible MB/ML models, e.g.~ in climate science, robotics or reinforcement learning. In particular, analyzing the theoretical decomposition properties in a partially-observed setting is an important direction for future work.

\section*{Acknowledgements:}
Funding (P. Gallinari), Chaires de recherche et d'enseignement en intelligence artificielle (Chaires IA), DL4Clim project.

\bibliographystyle{iclr2021_conference}

redpajama_set_name: RedPajamaArXiv


text:
General Events (0) History (30)
2007/0024(NLE) EC/Russia Agreement: agreed principles of the modernisation of the existing system of utilisation of the Transsiberian routes
Progress: Preparatory phase in Parliament
Lead TRAN
Former Responsible Committee TRAN
Former Responsible Committee TRAN VAN DALEN Peter ( ECR)
Committee Opinion AFET
Committee Opinion INTA
Former Committee Opinion AFET
Former Committee Opinion INTA
TFEU 100-p2, TFEU 218-p6a
3.20.15 Transport agreements and cooperation
6.40.04.02 Relations with Russian Federation
EP - Former Responsible Committee
all - Additional information
The Lisbon Treaty, which entered into force on 1 December 2009, amended the EU's two core treaties, the Treaty on European Union (TEU) and the Treaty establishing the European Community (EC Treaty). The latter was renamed the Treaty on the Functioning of the European Union (TFEU).
These changes had various consequences for many ongoing procedures. First of all, the articles of the TEU and of the old EC Treaty that constitute the legal basis of all the proposals founded on those Treaties were renumbered in accordance with the table of equivalences mentioned in Article 5 of the Lisbon Treaty.
In addition, some proposals underwent a change to their legal basis going beyond a mere change to their numbering, and this resulted in changes to the type of procedure .
The Lisbon Treaty also introduced new concepts of decision-making procedure . The old "codecision procedure" was extended to new areas and renamed the "ordinary legislative procedure". A new "consent procedure" replaced the old "assent procedure". New interinstitutional procedures were also set up for the adoption of certain non-legislative acts, for example the conclusion of some international agreements.
The ongoing proposals concerned by these changes were formally modified by the Commission in a Communication published on 2 December 2009 (COM(2009)0665).
In the case of the proposal for a Decision of the Council and the Representatives of the Governments of the Member States of the European Union, meeting within the Council on the conclusion of the Agreement in the form of an Exchange of Letters on "Agreed Principles of the Modernisation of the existing system of utilisation of the Transsiberian routes" between the European Community and its Member States, on the one hand, and the Russian Federation, on the other hand, the entry into force of the Lisbon Treaty had the following impacts :
· the old legal basis – Treaty/EC/Art.80(2), Art.300(2) first para, (3) first para and (4). – became Art 100(2), Art 218 (6)(a) and (7) of the TFEU. Please note that the numbering of the old legal basis corresponds to the consolidated version of the Treaty that was applicable immediately before the entry into force of the Lisbon Treaty, and may differ from the references in the original Commission proposal ;
· the proposal, which had previously fallen under the old consultation procedure (CNS), was classified as an interinstitutional non-legislative procedure (NLE).
CSL - Council Meeting
PURPOSE: to conclude an agreement on the modernisation of the existing system of utilisation of the Transsiberian routes between the European Community and its Member States, on the one hand, and the Russian Federation, on the other hand.
PROPOSED ACT: Decision of the Council and the representatives of the governments of the EU Member States meeting within the Council.
BACKGROUND: this draft agreement aims to solve the issue of Siberian overflight payments requested by the Russian Federation from EC carriers for overflights over Russian territory which were
an important stumbling block in EU-Russia relations. The payments are an EU-Russia trade irritant which has to be solved before Russia's accession to WTO.
The agreement will strengthen transport cooperation in the framework of the EU-Russia Common Economic Space. It lays down general principles which will have to be implemented in bilateral air services agreements between Member States and the Russian Federation.
Based on the negotiating directives of the mandate, the text of the agreement was agreed ad referendum at the final negotiating round on 24 November 2006 between Vice-president Barrot and Mr. Levitin, Transport Minister of the Russian Federation, in the margins of the EU-Russia summit in Helsinki.
CONTENT: the agreement contains key principles which must be implemented in the bilateral agreements between Member States and the Russian Federation and includes the following results:
all payments after 1st January 2014 shall be cost-based, transparent and non-discriminatory between foreign carriers and in line with the Chicago Convention; the provisions of bilateral agreements between EU Member States and the Russian Federation requiring prior conclusion of commercial agreements for Transsiberian routes shall be terminated; all new frequencies granted by the Russian side during the transition period and beyond shall be free of payments and will not need the prior conclusion of a commercial agreement; all frequencies currently leased by EC carriers from Russian carriers will be grandfathered. In return, the Member States will have the choice either of increasing overflight frequencies for the Russian side or agreeing bilaterally with the Russian side on any other means; the current level of payments will be reduced in 2010 for certain parts of the payments.
In a separate letter, the Russian Minister Levitin stated the Russia Federation's readiness to increase overflight frequencies for EC carriers in future bilateral negotiations with Member States. The increase of overflight frequencies to Asian destinations will be made taking into account the traffic rights obtained by EU Member States to destinations in the Far East.
Lastly, it should also be noted that b ilateral air services agreements will be amended, but will remain in force.
COM(2007)0055
events/0/docs/0/url
http://www.europarl.europa.eu/registre/docs_autres_institutions/commission_europeenne/com/2007/0055/COM_COM(2007)0055_EN.pdf
http://www.europarl.europa.eu/RegData/docs_autres_institutions/commission_europeenne/com/2007/0055/COM_COM(2007)0055_EN.pdf
committees/1/date/0
committees/1/rapporteur
VAN DALEN Peter
European Conservatives and Reformists
all payments after 1st January 2014 shall be cost-based, transparent and non-discriminatory between foreign carriers and in line with the Chicago Convention;
the provisions of bilateral agreements between EU Member States and the Russian Federation requiring prior conclusion of commercial agreements for Transsiberian routes shall be terminated;
all new frequencies granted by the Russian side during the transition period and beyond shall be free of payments and will not need the prior conclusion of a commercial agreement;
all frequencies currently leased by EC carriers from Russian carriers will be grandfathered. In return, the Member States will have the choice either of increasing overflight frequencies for the Russian side or agreeing bilaterally with the Russian side on any other means;
the current level of payments will be reduced in 2010 for certain parts of the payments.
Lastly, it should also be noted that bilateral air services agreements will be amended, but will remain in force.
celexid
CELEX:52007PC0055(02):EN
http://ec.europa.eu/info/departments/mobility-and-transport_en
KALLAS Siim
Transport, Telecommunications and Energy
meeting_id
In addition, some proposals underwent a change to their legal basis going beyond a mere change to their numbering, and this resulted in changes to the type of procedure.
The Lisbon Treaty also introduced new concepts of decision-making procedure. The old "codecision procedure" was extended to new areas and renamed the "ordinary legislative procedure". A new "consent procedure" replaced the old "assent procedure". New interinstitutional procedures were also set up for the adoption of certain non-legislative acts, for example the conclusion of some international agreements.
In the case of the proposal for a Decision of the Council and the Representatives of the Governments of the Member States of the European Union, meeting within the Council on the conclusion of the Agreement in the form of an Exchange of Letters on "Agreed Principles of the Modernisation of the existing system of utilisation of the Transsiberian routes" between the European Community and its Member States, on the one hand, and the Russian Federation, on the other hand, the entry into force of the Lisbon Treaty had the following impacts:
· the old legal basis – Treaty/EC/Art.80(2), Art.300(2) first para, (3) first para and (4). – became Art 100(2), Art 218 (6)(a) and (7) of the TFEU. Please note that the numbering of the old legal basis corresponds to the consolidated version of the Treaty that was applicable immediately before the entry into force of the Lisbon Treaty, and may differ from the references in the original Commission proposal;
committees/0/associated
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committees/2/rapporteur/0
KOCH Dieter-Lebrecht
LIBERADZKI Bogusław
TELIČKA Pavel
KONEČNÁ Kateřina
TAYLOR Keith
EFD
PAKSAS Rolandas
ARNAUTU Marie-Christine
Committee Opinion
Former Committee Opinion
committees/10
http://register.consilium.europa.eu/content/out?lang=EN&typ=SET&i=SMPL&ROWSPP=25&RESULTSET=1&NRROWS=500&DOC_LANCD=EN&ORDERBY=DOC_DATE+DESC&CONTENTS=2791*&MEET_DATE=22/03/2007
https://eur-lex.europa.eu/smartapi/cgi/sga_doc?smartapi!celexplus!prod!DocNumber&lg=EN&type_doc=COMfinal&an_doc=2007ν_doc=55
Transport agreements and cooperation
Relations with Russian Federation
activities/0/docs/0/celexid
activities/0/commission/0/DG/url
http://ec.europa.eu/dgs/transport/index_en.htm
committees/2/shadows/4/mepref
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4f1ac457b819f25896000007
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http://www.ipex.eu/IPEXL-WEB/dossier/dossier.do?code=NLE&year=2007νmber=0024&appLng=EN
Treaty on the Functioning of the EU TFEU 218-p6a
Consent by Parliament
geographical_area
EC/Russia Agreement: agreed principles of the modernisation of the existing system of utilisation of the Transsiberian routes

redpajama_set_name: RedPajamaCommonCrawl


I am a professionally qualified stone carver, with a First Class degree in Art and Design (Sculpture and Ceramics) and an MA with distinction in Fine Art. I have a studio in Birmingham and have been sculpting for over 20 years. I have taught art, sculpture and ceramics at the University of Wolverhampton and Birmingham City University and continue to teach privately. I have worked on commissions across the world as well as in the UK.
I am passionate about what I do and this is reflected in the work I create.


text:
Research Now Hires George Pappachen to Fill New Position of EVP Corporate Development & Strategy
Research Now
DALLAS, April 20, 2017 /PRNewswire/ -- Research Now, the global leader in digital research data and data solutions for better insights and business decisions, announces that George Pappachen has joined the company in the newly created position of Executive Vice President, Corporate Development & Strategy.
Pappachen will play a pivotal role in developing strategies for the organization, both corporate-wide and within business units to drive accelerated growth. The business development strategies will build on the company's core asset of high-quality first-party research panel data, and will expand Research Now's reach into research and data-related technology platforms and marketing services to address new opportunities presented by the dynamic global marketing environment.
Among his responsibilities are ensuring the company stays on the leading edge of the rapidly evolving data-driven and multichannel marketing and market research landscape; evaluating new geographies and target markets; identifying and pursuing potential acquisitions and partnerships; and orchestrating new initiatives across various company functions, including sales, marketing, and product development.
Pappachen brings substantial experience in technology, data, research, analytics, and global investments. Most recently, he served as President of Global Products and EVP, Strategy and Business Development at Kantar Media. Before that, he was Global Strategy and Business Development Director for WPP's Data Alliance. Earlier in his career at WPP, Pappachen served as WPP's first Global Chief Privacy Officer.
Pappachen received his Juris Doctor (JD) from St. John's University in 1996 and has a Bachelor of Arts in Political Science from the University of Florida. He is based in Research Now's New York office.
About Research Now
Research Now Group, Inc., is the global leader in digital research data for better insights and business decisions. Founded in 1999, the company was a pioneer in originating online data sampling and created the first B2B panel. The company provides world-class research data solutions that enable better decisions and better results for its 3,000 market research, consulting, media, and corporate clients through access to over 11 million deeply-profiled business professionals and consumers in more than 40 countries. Research Now operates globally with locations in the Americas, Europe, the Middle East, and Asia-Pacific, and is recognized as the quality, scale, and customer satisfaction leader in the market research industry. For more information, please go to www.researchnow.com.
SOURCE Research Now
http://www.researchnow.com


text:
Entering a pre-alpha open-world raid.[1]
Pre-alpha Brood Queen raid boss.[2]
Raids will either be based on triggered events or more traditional systems. Traditional world bosses will change based on node development.[3]
We want things to be more fluid and one of the ways that we make them more fluid is through our triggered event system. That is one of the methods by which we make these more spontaneous. We make them more accessible to a wider audience. You can be just in the city and not normally be a part of a raid team, but now get to participate in this organic event that's kind of popped up around the city and if you are not participating then buildings are gonna get destroyed and NPCs are going to die... It's not just how fast can we do it, how will we get it done in its schedule, but oh my god this thing popped up if we don't kill it we're not going to access to our stables this week and we can't get our animal husbandry certificates... It's that risk versus reward that gives you a sense of adrenaline.[4] – Steven Sharif
1 Schlachtzugstrategien
2 Schlachtzug-Mechaniken
2.1 Telegraphen
3 Fortschritt von Schlachtzügen
3.1 Schlachtzugsstufen
3.2 Bosse
3.3 Metriken
3.4 Trophäen-Park
3.5 Bestenlisten
4 Plündern
4.1 Beute-Tabellen
4.2 Beutemarkierung
5 Raid voice controls
6 Offene Welt
7 Adaptive Inhalte
8 Gruppengrößen
8.1 Alpha-1 Gruppengrößen
9 Zergs
10 Zugehörigkeiten
11 Kunstwerke
Schlachtzugstrategien
Raids will have elements that can be pre-planned.[5]
Required composition of DPS, healers and support.[5]
Key positioning.[5]
Raids will also have dynamic elements that can change from session to session.[5]
The types and numbers of bosses and mobs present in the raid and their skill repertoire.
Variables are manifested based on node progression.[5]
The number of metropolis nodes developed.
The types of metropolis nodes developed.
One of the design elements that we're implementing into our raids is that the raid will not be exactly the same every single time. You're going to have variables that can't necessarily be pre-planned out for. You can pre-plan out for a lot of the raid like how many DPS do you need and healers and support; where the key position and all that kind of stuff; but I think the compelling aspect of Ashes raiding will be the difficulty in achieving this content and having that content change from session to session as well. We want there to be variables that get manifested by you know what type of node got developed elsewhere. Is he going to have acolytes or cultists? What will the acolytes have skills [available] to them? What kit is the boss gonna have? What available skill repertoire will the boss be able to [wield]? ... A lot of those systems are influenced obviously by world development. So the raid kind of takes into account at what stage has the world developed: Are there two metropolises now available in the world? Okay well let's activate this skill in this skill. Now you have five metropolises, well now all these skills have been activated. Are there are they all economic nodes? Are they all military nodes? That we can change things based on that stuff. And it really is a threat assessment from the environment against the players.[5] – Steven Sharif
Schlachtzug-Mechaniken
Raids contain intricate combat mechanics.[6]
Multiple phase boss fights.
Adds.
Random oriented skill usage.
Telegraphed animations, but no obvious telegraphed templates on the ground.
Fights will require location, mobility and strategy.
Combat itself will be pretty intricate mechanics-wise. We're going to have different phases of the bosses, there's going to be a lot of adds stuff, there's going to be random oriented skill usage. We're not going to have telegraphed templates on the ground, but we will have telegraphed animations, so it's going to be location, mobility, strategic. It will be something that can not be repeatable in the exact same way from raid to raid, but has a variance between the combat, so raiders are going to have to be fluid in thinking on their feet.[6] – Steven Sharif
Telegraphen
In specific circumstances there will be telegraphs (tells) to communicate something that's happening.[7]
Animation tells.
Templates.
Fortschritt von Schlachtzügen
Schlachtzugsstufen
Tiers of raid difficulty are in accordance with the world's node structure.[8][9]
The more developed a zone the more challenging the content; and we feel that's a good progression when it comes to actual adventuring class, because it will match both world development and it will also continue to allow for participation for new players, if they may go into a zone that's recently been reset by either a siege or some other event.[9] – Steven Sharif
Particular world bosses have stages of progression, such as defeating acolytes, then lieutenants, then generals, then the world boss itself.[10]
At each stage there is increasing difficulty.[10]
This may be parsed out into different time periods and be behind a wall of development.[10]
A single digit percentage of the population will be capable of defeating certain content.[10]
There will be some in-depth raiding that has multiple stages that will be extremely difficult and... It would definitely be in the single digits of population that will be capable of defeating certain content... It doesn't mean that there won't be content available for the larger percentages as well... There should be a tiered level of content that players can constantly strive to accomplish. If there is no ladder of progression and everything is flat and all content can be experienced, then there is no drive to excel.[10] – Steven Sharif
Metriken
Ashes of Creation gathers tracking data to facilitate achievements, titles and other accolades on a server.[11]
Number of successful siege defenses.[11]
Number of successful siege assaults.[11]
Number of PvP kills.[11]
Raid participation points.[11]
Number of firsts.[11]
Visibility of those types of things on the server help to promote a competitive atmosphere. If there is an attainable impression that you can work towards, so that others know your success, you have more drive to succeed.[11] – Steven Sharif
Trophäen-Park
Trophäen-Park is an area within Town, City or Metropolis nodes that has available spots for trophies. These trophies show the character names of players who are the first to complete world bosses and dungeon bosses that were spawned as the result of the development of that particular node.[12]
Server announcements and achievements are designed to encourage groups to experience new content.[12]
There will be dungeon and raid leader boards.[13]
Some leader boards will be across all servers.[14]
Plündern
Ashes of Creation intends to use traditional loot rules, selected by the party leader.[15]
Lootmaster
A master looter (or lootmaster) is a player designated by the party leader to decide how loot is distributed in a dungeon or raid party.
With round-robin looting, party members take turns looting.
Need before greed
This is a traditional need before greed system based on dice rolls.
Bidding system
Intrepid are investigating a potential bidding system, which allows players to bid on items instead of rolling for them.[15]
The highest bidder wins the item.
The gold then goes into a pool that is split among the rest of the party members.
Beute-Tabellen
Mobs and Bosse will drop Gold, Items and crafting materials based on the type of mob or boss. For example, a wolf is not going to be carrying a sack of gold. It may instead drop "pelts" that can be traded for gold.[16]
Loot tables will favor dropping crafting materials as opposed to trash items.[16]
Loot tables are disabled for player controlled monsters.[17]
The developers intend to test dynamic loot tables during the test phases.[18]
Beutemarkierung
Gatherers may have the ability to "spoil" a boss' loot.[19]
If you want to be able to get those mats from the boss, they're just not going to drop for anybody. They will only drop for a master gatherer who is capable of extracting those resources from the creature itself... The reason why we say there's a lot of interdependencies between the crafters of the world and the raiders of the world and our PvPers of the world is because we don't want to house the capability to attain these things all within the adventuring class. We want there to be an influence necessary from the craftsmen's guild to come in to either come and participate and spoil the boss and gather the goods or something along those lines.[19] – Steven Sharif
The dynamics of who has the authority to gather the loot will be decided based on testing.[19]
Party leader.
First tagging raid.
Most damage done.
With regards to the dynamics of authority, you know, who gathers those things, exactly what is that: The party leader of the first tagging raid. Is that the most damage dealing. Is that the you know whatever: Those are things we've got a test in testing and see how they turn out.[19] – Steven Sharif
The loot tagging system is still being finalized.[19]
Raid voice controls
Raid leaders are able to control Voice chat.[20]
Toggle (global) mute on the entire raid.[20]
Assign speaker roles allowing party members to globally mute other members.[20]
Raid members will be able to (locally) mute other members.[20]
If you are joined in raid where you have multiple squads that have connected into a raid for the castle sieges, the leaders of those squads will be able to communicate across all members of squad. So let's say you have five teams of four, for a total of twenty people within a particular raid... there are five that have global speech capacity; and then in those individual parties they'll also have their own inter-voice communication. So party leader will be able to hear their party members and the party members will hear each other, but they'll also be able to hear other party leaders speak as well.[21] – Steven Sharif
Alpha-0 flood plains environment.[22]
Ashes of Creation will be a seamless open-world experience.[23]
No loading time or loading screens between regions.
There will be open world dungeons and raids. The aspiration is to maintain the open world feel while being able to capitalize on the benefits of instanced mechanics.[24]
Instancing is only going to happen in certain dungeons where the desire is to have greater narrative appeal. Outside of these and arenas there will not be too much instancing anywhere else.[23]
We're probably going to do instancing only in certain dungeons and in arenas. You probably won't see instancing too much anywhere else. What you see is gonna be what you get.[23] – Jeffrey Bard
The PvP flagging system presents an opportunity for open conflict.[25]
Adaptive Inhalte
New points of interest (such as dungeons and world bosses) spawn as nodes develop.[26] This content adapts to the development of the zone it is in.[27]
Populations will change.[27]
Content difficulty will change.[27]
The content may be different altogether, depending on what is developed and how.[27]
Different types of antagonists with different story lines.[27]
Some dungeons will only be unlocked if nodes are developed to certain stages.[28]
Storyline objectives for players inside dungeons will depend on the story arc paths chosen through the node system.[28]
Drop tables in areas and dungeons will be tied into the progression of certain areas.[28]
Certain dungeons and other points of interest across the map will all be affected by the server's node development. Some dungeons will only be unlocked if nodes are developed to certain stages. The storyline objectives for players inside dungeons will also be dependent on the story arc paths chosen through the node system. The drop tables in area and dungeons will also be tied into the progression of certain areas. For example, let's say that the humans have developed a node in Region A, and a storyline has opened up that leads players to inspect the ruins (dungeon) of a nearby area. And let's say that this node was developed in a scientific (crafting) zone… Well before the node developed, this dungeon was accessible… But now the dungeon has propagated new monster assets that include a drop table catering to a crafting emphasis because of the development of that scientific node. And perhaps, a new boss appears in different rooms of the dungeon that includes different adventure quest starts, like a mysterious item with a storyline that can only be progressed if a node develops to the metropolis stage in a certain region, across the world. Our system is so vast, when it comes to interconnectivity and how the world reacts to the players.[28]
Gruppengrößen
The minimum goal is 500 players on a single battlefield. I think we will be able to hit 500vs500 but we shall see.[29] – Steven Sharif
Ashes of Creation is designed for solo players as well as large and small groups.[30]
The party size is eight (8) players for a single group.[15]
Raids will have 40 man groups.[31]
Content will be tailored for 40, 16 and 8 person group sizes.[18]
Arenas will have 1 man, 3 man, 5 man and possibly 20 man Free-For-All (Deathmatch) group sizes.[32]
There won't specifically be guild vs guild arenas but team-based matchmaking allows teams to face other teams.[33]
Castle sieges are expected to have at minimum 250x250 players to be on a single battlefield, with the possibility of increasing this to 500x500 over time.[29]
The idea behind an 8-person group is to allow us to really amplify party roles, and to create a need for each of the archetypes in every party.[15]
Content isn't locked behind guilds.[34]
Mechanics exist within larger conflicts for small groups and even solo players to impact the battle.[35]
This includes systems that will likely focus on single group sized participants while the larger battle is occurring.
Alpha-1 Gruppengrößen
Gruppengrößen in Ashes of Creation Apocalypse.[36]
Ashes of Creation Apocalypse battle royale will have party sizes of up to 5.[36]
Guild mode will have parties of raids of up to 20.[36]
Ashes of Creation Apocalypse castle sieges will have parties of 5 within a raid, with 5 raids for each of the two teams.[36]
Ashes of Creation Apocalypse horde mode will have a party size of 5 with a total of ten groups, making for 50 players.[36]
There will be an option in the lobby where you are capable of joining a guild based match that you can link parties together up to a maximum of twenty individuals.[36] – Steven Sharif
Zergs
For every play, there should be a counterplay. We often see Castle Sieges turn into zerg-fests, which really doesn't capture the epic, back-and-forth battles we wish to see in Ashes of Creation. One of our key pillars is that Choice Matters[37], and that applies to tactics and strategy as well. A well-formulated battle plan should win out over stat sheets and bodies. Zergs will generally be difficult to pull off, and will be eminently counterable through siege weapons, traps, and other battlefield tricks.[38]
Zergs are empowered by fast-travel. Meaningful travel times are intended to prevent zerg play from being so much of an influence.[39]
Encounters are designed to have meaning in terms of how boss skills and abilities relate to group compositions, tactics and strategies. Zerging is not experiencing content.[40]
Raid and dungeon bosses have specific mechanics that players need to learn and react to. Zergs that are not aware of these mechanics or react to them appropriately will be wiped.
There is a give-and-take for guilds that want to see a larger number of members as opposed to a more focused group that may be a part of an alliance.[41]
We're very cognizant of the fact that we don't want to see zerging be a mechanic that's utilized by guilds to accomplish content or just to steamroll over sieges... There are specific mechanics that we are working on that will be seen through the testing phases that relate to a degree of understanding of certain systems that can't just be overrun with numbers.[41] – Steven Sharif
Mechanics that encourage political intrigue will play a role in destabilizing zergs.[42]
The best way that I found in games I played previously to take down a zerg is to cause drama from within... It's that conflict inside of the politicking that happens in that big organization. If we provide opportunities for division to occur then it also provides stability to keep a server healthy away from that zerg mentality also... If we approach the castle siege and we've destroyed the walls and we're in the throne room and we're about to cast on the penultimate thing and I at this time am just so excited from what's happening that I'm like "screw it I'm gonna go for it". It's gonna be mine. I'm gonna take the taxes for the next month. I'm gonna take all the gear from the castle. I'm gonna take everything... We kind of want that political intrigue to be present in the game.[42] – Steven Sharif
Objective-based game play helps to balance the zerg mentality.[43]
I always feel that if you balance based on groups that in your balance focus is to incorporate features that play well from a player versus player perspective as well as a player versus environment perspective: Having support classes, having DPS and tanks that can obstruct movement and/or create you know bottlenecks on the field and stuff like that. I think a well-rounded raid it will perform better against a non well-rounded raid, however then you incorporate the second aspect of numbers; and that's where again mobility, organization, leadership tactics. Having objectives in gameplay that make those important helps to balance the zerg mentality that a lot of guilds can tend to have.[43] – Steven Sharif
An affiliation tree is under development that determines which entities can participate in attacks against other entities within its hierarchy.[44]
Allianzen.[44]
Citizenship.[44]
Guilds.[44]
Parties.[44]
Raids.[44]
Religion.[44]
Society.[44]
There is guild affiliation, there's party affiliation, there's a raid affiliation, alliance affiliation, there's a citizenship affiliation, there's society affiliation, there's religious affiliation. All of these things have some hierarchy; and within that hierarchy there's the ability to participate within certain systems. So for example, if you have a node that has fallen under your vassal state and you're a citizen of the parent node, then you could participate in a siege against the vassal node but if you're a citizen of the vassal node you could not participate as an attacker against the parent node; so there's a hierarchy, unless you were to renounce your citizenship.[44] – Steven Sharif
↑ Livestream, 4 May 2018 (20:00).
↑ Podcast, 23 April 2018 (27:14).
↑ 5.0 5.1 5.2 5.3 5.4 5.5 Podcast, 9 July 2018 (22:24).
↑ 6.0 6.1 Livestream, 8 April 2018 (PM) (1:14:01).
↑ 9.0 9.1 Podcast, 4 August 2018 (1:39:47).
↑ 10.0 10.1 10.2 10.3 10.4 Podcast, 4 August 2018 (1:42:14).
↑ 11.0 11.1 11.2 11.3 11.4 11.5 11.6 Podcast, 4 August 2018 (1:47:21).
↑ 12.0 12.1 Podcast, 4 August 2018 (1:35:58).
↑ Livestream, 18 July 2017 (58:50).
↑ 15.0 15.1 15.2 15.3 Group dynamics blog.
↑ 18.0 18.1 February 8, 2019 - Questions and Answers.
↑ 20.0 20.1 20.2 20.3 Interview, 20 October 2018 (5:30).
↑ Interview, 6 December 2018 (2:57).
↑ 23.0 23.1 23.2 Livestream, 22 May 2017 (20:59).
↑ 27.0 27.1 27.2 27.3 27.4 Livestream, 17 November 2017 (18:29).
↑ 28.0 28.1 28.2 28.3 MMOGames interview, January 2017
↑ 29.0 29.1
↑ 36.0 36.1 36.2 36.3 36.4 36.5 Interview, 17 August 2018 (18:53).
↑ Blog: 10 facts about castle sieges in the MMORPG.
↑ 42.0 42.1 Interview, 11 May 2018 (44:20).
↑ 43.0 43.1 Interview, 8 August 2018 (11:52).
↑ 44.0 44.1 44.2 44.3 44.4 44.5 44.6 44.7 44.8 Interview, 11 May 2018 (58:07).
Abgerufen von "https://ashesofcreation.wiki/index.php?title=Raids&oldid=33928"
Spielerinteraktionen
Diese Seite wurde zuletzt am 6. September 2019 um 01:15 Uhr bearbeitet.


text:
Q: Two networks of type C on one interface i need some help configuring routes.
This is the situation:
I have one ISP who is providing me with two /27 networks.
ISP_NET1 -> 40.40.40.32/27 (ISP GATEWAY 40.40.40.33)
ISP_NET2 -> 40.40.50.32/27 (ISP GATEWAY 40.40.50.33)
On my gateway (Ubuntu machine) i have one NIC which is connected to the ISP. It is configured to use 40.40.40.34 with default gateway 40.40.40.33 .
Second gateway NIC is connected to a Layer 2 switch on to which there are multiple other servers connected. Each server has one public IP out of ISP_NET1 or a small range like /29.
Right now servers on the L2 switch using ISP_NET1 works as expected. However i am not sure how i should configure ISP_NET2 on gateway so that "clients" having an address of ISP_NET2 will go though it's origin gateway (40.40.50.33).
I hope my description is understandable.

redpajama_set_name: RedPajamaStackExchange


text:
Christendom's Greatest Cathedral to Become a Mosque
By Raymond Ibrahim 2013-06-18T03:00:28
The Hagia Sophia at night.
While unrest in Turkey continues to capture attention, more subtle and more telling events concerning the Islamification of Turkey -- and not just at the hands of Prime Minister Erdogan but majorities of Turks -- are quietly transpiring. These include the fact that Turkey's Hagia Sophia museum is on its way to becoming a mosque.
Why does the fate of an old building matter?
Because Hagia Sophia -- Greek for "Holy Wisdom" -- was for some thousand years Christianity's greatest cathedral. Built in 537 A.D. in Constantinople, the heart of the Christian empire, it was also a stalwart symbol of defiance against an ever encroaching Islam from the east.
After parrying centuries of jihadi thrusts, Constantinople was finally sacked by Ottoman Turks in 1453. Its crosses desecrated and icons defaced, Hagia Sophia -- as well as thousands of other churches -- was immediately converted into a mosque, the tall minarets of Islam surrounding it in triumph.
Then, after the dissolution of the Ottoman Empire, as part of several reforms, secularist Ataturk transformed Hagia Sophia into a "neutral" museum in 1934 -- a gesture of goodwill to a then-triumphant West from a then-crestfallen Turkey.
Thus the fate of this ancient building is full of portents. And according to Hurriyet Daily News, "A parliamentary commission is considering an application by citizens to turn the Hagia Sophia in Istanbul into a mosque…. A survey conducted with 401 people was attached to the application, in which more than 97 percent of interviewees requested the transformation of the ancient building into a mosque and afterwards for it to be reopened for Muslim worship."
Even lesser known is the fact that other historic churches are currently being transformed into mosques, such as a 13th century church building -- portentously also named Hagia Sophia -- in Trabzon. After the Islamic conquest, it was turned into a mosque. But because of its "great historical and cultural significance" for Christians, it too, during Turkey's secular age, was turned into a museum and its frescoes restored. Yet local authorities recently decreed that its Christian frescoes would again be covered and the church/museum turned into a mosque.
Similarly, the 5th century Studios Monastery, dedicated to St. John the Baptist, is set to become an active mosque. And the existence of the oldest functioning Christian monastery in the world, 5th century Mor Gabriel Monastery, is at risk. Inhabited today by only a few dozen Christians dedicated to learning the monastery's teachings, the ancient Aramaic language spoken by Jesus, and the Orthodox Syriac tradition, neighboring Muslims filed a lawsuit accusing the monks of practicing "anti-Turkish activities" and of illegally occupying land which belongs to Muslim villagers. The highest appeals court in Ankara ruled in favor of the Muslim villagers, saying the land that had been part of the monastery for 1,600 years is not its property, absurdly claiming that the monastery was built over the ruins of a mosque -- even though Muhammad was born 170 years after the monastery was built.
Turkey's Christian minority, including the Orthodox Patriarch, are naturally protesting this renewed Islamic onslaught against what remains of their cultural heritage -- to deaf ears.
The Muslim populace's role in transforming once Christian sites into mosques is a reminder of all those other Turks not protesting the Islamization of Turkey, and who if anything consider Erdogan's government too "secular."
Their numbers are telling. In May 2012, Reuters reported that:
Thousands of devout Muslims prayed outside Turkey's historic Hagia Sophia museum on Saturday [May 23] to protest a 1934 law that bars religious services at the former church and mosque. Worshippers shouted, "Break the chains, let Hagia Sophia Mosque open," and "God is great" [the notorious "Allahu Akbar"] before kneeling in prayer as tourists looked on. Turkey's secular laws prevent Muslims and Christians from formal worship within the 6th-century monument, the world's greatest cathedral for almost a millennium before invading Ottomans converted it into a mosque in the 15th century.
The desire to turn Hagia Sophia into a mosque is not about Muslims wanting a place to pray -- as of 2010, there were 3,000 active mosques in Istanbul alone. Rather, it's about their reveling, and trying to revivify, the glory days of Islamic jihad and conquest: Reuters added that Muslims "staged the prayers ahead of celebrations next week marking the 559th anniversary of the Ottoman Sultan Mehmet's conquest of Byzantine Constantinople." According to Salih Turhan, a spokesman quoted by Reuters, "As the grandchildren of Mehmet the Conqueror, seeking the re-opening Hagia Sophia as a mosque is our legitimate right."
https://pjmedia.com/blog/hagia-sophia-to-become-mosque/


text:
The Stooges' Sax Player Steve Mackay in Critical Condition
by Junior Wells / Sep 10, 2015
Steve Mackay, best known for his saxophone work with the Stooges, is in critical condition at a California hospital with a life-threatening infection. The 65-year-old musician, who later worked with the Violent Femmes, is battling sepsis, a complication that can lead to multiple organ failure.
Hospital officials at Seton Medical Center in Daly City said that Mackay is surrounded by family members in the intensive care unit, according to KQED.
"Our thoughts and prayers go out to Steve and [his wife] Patti for the best possible outcome," Stooges guitarist James Williamson said in a written statement. "Steve was always an important part of our band's live sound, and last year I worked with him again when he played on a couple of songs on my solo album. His expression was always unique. It was all his own."
Mackay rose to fame for his work on the Stooges' 1970 album Fun House. He later performed on the Stooges' comeback albums, 2007′s Ready to Die and 2013′s The Weirdness. The Stooges were inducted into the Rock and Roll Hall of Fame in 2010.
Mackay and Stooges frontman Iggy Pop are the only two surviving members of the Fun House band. Bassist Dave Alexander died in 1975, followed by guitarist Ron Asheton in 2009, and Asheton's drummer brother Scott in 2014. The 1970 album was later expanded into a gigantic, seven-disc box set in 1999 titled The Complete Fun House Sessions.
Over the years, Mackay also collaborated with Commander Cody and a string of bands in the Bay Area, where he has lived since the mid-'70s.
You can hear Steve's legendary sax in "LA Blues" below:
Punk Rock Karaoke Upcoming Shows
Flash Music Reviews for September 2015
"The duty of youth is to challenge corruption." ― Kurt Cobain


text:
en.huanqiu.com
China ushers in a new era of service economy
2019年8月29日 Huanqiu.com
The service sector accounted for 52.2 percent of China's total GDP in 2018, which suggests that China has entered an era of service economy, according to a research group with the Chinese Academy of Social Sciences.
(Photo/Xinhua)
Xia Jiechang, head of the research team, made the remarks at a recent seminar on the development and opening-up of the country's service industry in the new era.
The service sector has become a pillar of China's economy, Xia added, noting that the tertiary industry attracted 46.3 percent of the country's workforce and 56 percent of fixed-asset investment.
This was attributed to the high priority China places on developing its service industry as it has issued several documents in this regard, including a paper on promoting quality and expansion of capacity in the housekeeping service sector published by the State Council this June.
The country's service industry still has its shortcomings, such as shortage of private services in education, healthcare and elderly care, structural imbalance between supply and demand, and backward management system.
Data showed that the average government education budget for general junior school students in Shanghai was over 30,000 yuan in 2016, while the figure in central China's Henan province was only 7, 811 yuan.
To pursue high-quality development of the modern service sector, China needs to improve its market environment, support policies as well as laws and regulations on the protection of rights and interests of investors, market access, credit systems and governance, Xia noted.
China enjoys coordinated regional development
China has made steady progress in implementing its coordinated regional development strategy, contributing to its stable and sound economic growth and high-quality development.
The strategy includes advancing development in the western region, revitalizing the northeast, energizing the central region, integrating the development of the Beijing-Tianjin-Hebei region, the development of the Yangtze Economic Belt, the integrated development of the Yangtze River Delta and the planning and building of the Guangdong-Hong Kong-Macao Greater Bay Area.
As a result, regional development has become more balanced. Calculated with constant prices, the average annual growth rate of per capita GDP in the eastern, central, western, and northeastern regions in China between 1953 and 2018 was 7.2 percent, 8.2 percent, 8.5 percent, and 6.1 percent, respectively.
Coordinated regional development is a crucial step to tap into development potential and foster growth drivers.
China will create a medium-high-end consumption market of nearly 500 million people in the Beijing-Tianjin-Hebei region, the Yangtze River Delta and the Guangdong-Hong Kong-Macao Greater Bay Area during the 14th Five-Year Plan period (2021-2025), which will significantly expand the country's consumption demands, according to a report issued by the Academy of Macroeconomic Research (AMR) under the country's National Development and Reform Commission.
Zhang Yan, an associate researcher at AMR, said that the Guangdong-Hong Kong-Macao Greater Bay Area, a world-class bay area, will play an important role in driving economic growth and scientific and technological innovation, adding that the country will build it into a global innovation hub.
To further promote coordinated regional development, China needs to work faster to remove barriers between urban and rural areas and enhance the level of equal access to basic public services to boost domestic demand, Zhang noted.
The country also needs to address the infrastructural gap between the eastern and western region that hinders coordinated development. To this end, support for the western region will be intensified to build more railways and graded highways and improve the internet penetration rate.
Beijing Daxing International Airport deploys service robots
A bird's-eye view of Beijing Daxing International Airport. [Photo/Xinhua]
The Beijing Daxing International Airport has deployed 10 virtual robots and 10 physical robots inside its terminal halls, delivering excellent performance results during their trial operation, according to the airport.
To improve passenger experience, the robots will interact with passengers to provide help with flight information, services, airport facilities and other information on how best to pass through security.
With functions such as indoor positioning and autonomous navigation, the physical robots are able to avoid obstacles automatically. They can also interact with passengers and provide information, including important airport activities and news.
These service robots are the epitome of the newly built smart airport. Reports confirm that the airport has applied smart technologies to develop 68 systems on 19 platforms to improve its operational efficiency and quality of service.
Mobile payment vitalizes small and micro-businesses
(Photo/Zhang Guorong)
Driven by mobile payment, China's small and micro-businesses currently show great vitality and potential, continuing to fuel economic growth.
A report released on Aug. 6 suggests that in the first half of 2019, 90 percent of street vendors saw a growth in sales after they introduced mobile payment options.
Frequency of consumption at market stalls increased by 30 percent, and sales rose by 2.5 percent, said the report.
These free digital payment tools reduce trading time and operating costs for the vendors. Alipay, a mobile and online payment platform, plans to cut total costs of at least 50 billion yuan for owners of small businesses in the next three years.
Alipay also introduced online payment discounts and enabled users to borrow loans on the platform to stimulate consumption.
Mobile payment allows the positive influence of small businesses to be recognized within the national economy, said Yu Fenghui, an adjunct professor with Huazhong University of Science and Technology.
China has a total of 100 million small and micro-businesses. They consolidate the foundation of national economic development as a part of the private economy, which contributes to over 60 percent of GDP.
Yu said it is essential to provide more digital business tools to cut costs and optimize operation for vendors so that they can play a more significant role in the digital economy.
570 million people in China now use their mobile phones to pay, 80 percent of whom pay by scanning two-dimensional barcodes, according to a report released by China UnionPay in January.
China hopes to benefit the world with 5G
China is developing 5G technology to benefit the world, rather than threaten it. The country has always been engaged in global collaboration in the construction of 5G standards and the development of 5G trial networks.
Chinese telecommunications company Huawei has helped build 5G networks around the world. By July 18, the company had secured more than 50 5G commercial contracts across the globe, including 28 in Europe.
Statistics show that Huawei's products and solutions have saved 55 billion euros in 5G network construction costs for European countries alone.
Back in 2013, when 5G remained in the research stage, China established the IMT-2020 (5G) promotion group and invited foreign companies like Ericsson, Nokia, and Qualcomm to join the team. The group members joined hands to research 5G, formulate technology standards and construct trial networks.
China Unicom recently formed a 5G international cooperation alliance with eight telecom operators from Spain, Germany, Japan, France, the UK and the US, among others, to promote 5G development.
China's domestically developed regional jet completes highland test flight
ARJ21, China's domestically developed regional jet, completed its first highland test flight in southwest China on Aug. 19.
Designed to adapt to the highland conditions with high anti-crosswind capabilities, ARJ21 can take off and land at the highland airports and avoid obstacles along the complicated air routes in southwest China.
China's civil aviation industry has developed fast since 1949. The airport facilities of civil aviation have gradually improved, the coverage of the airline network has dramatically increased, and air transportation capabilities have significantly advanced.
By the end of 2018, China's regularly scheduled flights had traveled a total distance of 8.4 million kilometers.
Social e-commerce shows great potential
(Photo/Pixabay.com)
The value of China's social e-commerce reached 1.2 trillion yuan in 2018, accounting for 14 percent of the country's online retail market, according to a report released by the Internet Society of China in July.
The market scale is expected to reach 2 trillion yuan in 2019, an increase of 63.2 percent year-on-year, said the report.
Social e-commerce is a subset of e-commerce that involves using social media to assist in the online buying and selling of products and services.
An online consumer, who prefers to buy daily necessities on the e-commerce platform Pinduoduo, said she saves money by using discounts offered by the platform or inviting friends to help cut prices.
The report said that the number of consumers engaged in social e-commerce reached 500 million in 2019.
Since last year, many social e-commerce enterprises such as Pinduoduo and Yunji have completed initial public offerings. Meanwhile, traditional e-commerce companies like Jing Dong and Taobao started to introduce social e-commerce services.
It is a trend that traditional e-commerce gradually develops into social e-commerce, said Cheng Xusen, a professor in information management and e-commerce with the University of International Business and Economics.
Social e-commerce is more user-oriented, said Cheng. He believes the user networks enable products to be accurately directed to consumers with strong purchasing power.
Food production increases steadily in China
(Photo/The Economic Daily)
China's food production has maintained a healthy trend toward stable growth. In 2019, the country's summer grain output reached 141.8 billion kilograms, underpinning the sufficient supply of major agricultural products.
Stable food production and food safety lay a solid foundation for economic and social development and give the country more confidence to cope with external challenges and risks, said Han Changfu, the Chinese Minister of Agriculture and Rural Affairs.
China's per capita grain possession now exceeds 470 kilograms, higher than the world's average. The country's self-sufficiency rates of rice, wheat, and corn all stand above 98 percent.
China plays an active role in safeguarding global food safety, as the country can feed 20 percent of the world's population with only 9 percent of the world's arable land and 6.4 percent of its water resources.
The country has taken various measures to protect farmland, including designating permanent basic cropland, cultivating high-quality farmland and expanding trials in crop rotation.
The country aims to build 800 million mu (53 million hectares) of high-quality farmland by 2020. By the end of 2018, about 640 million mu of high-quality farmland had already been cultivated.
Technology has helped to improve grain production. China has increased technological input, applied modern technologies to farming, such as the Internet of Things (IoT) and artificial intelligence, and intensified farmer training. As a result, the overall level of mechanization in plowing, sowing, and harvesting is growing substantially.
Last year, the advances in agricultural science and technology contributed to 58.3 percent of China's agricultural production. The grain yield per mu increased to 375 kilograms in 2018 from 170 kilograms 40 years ago. Moreover, about 90 percent of China's agrarian brands are domestically developed.
The country encourages farmers to work together to reduce costs, increase production and embrace the market. Currently, the country has 2.2 million registered agricultural cooperatives. Last year, it allocated 42.8 billion yuan to encourage farming in certain provinces.
China experiences huge changes in transportation, postal services, communication in 70 years
(Photo/Chinanews.com)
On Tuesday, China's National Bureau of Statistics (NBS) released data revealing the drastic changes the country has made since the founding of the People's Republic of China 70 years ago, especially after the implementation of reform and opening up in 1978.
According to NBS, China's total operating mileage of high-speed rails had hit 30,000 kilometers by the end of 2018, accounting for over two-thirds of the global total and ranking first in the world.
The country is also home to 143,000 kilometers of expressways, with an annual growth rate of 25.8 percent. Among the world's top 10 container ports regarding handling capacity last year, seven were in China.
Over the past 70 years, China has continuously expanded its network of air routes and airports. By the end of 2018, the country had 4,945 regular air routes, 412.1 times the number in 1950.
Since the founding of the People's Republic of China, the country has also been working hard to improve and reform its postal system. From a country where no express delivery service existed, it has become the world's largest express delivery market. Statistics indicated that by the end of 2018, the number of postal business outlets stood at 275,000, 10.4 times more than in 1949.
The country handled a total of 5.07 billion postal parcels last year, up from 1.53 million in 1988. The express delivery industry of the country generated revenue of 603.8 billion yuan last year, accounting for 76.4 percent of the total revenue of postal services.
Moreover, China also witnessed considerable improvements in communication. Starting in 2014, the country quickly established the world's largest and broadest-covering 4G network in just a few years. By the end of 2018, the country had 1.17 billion 4G users, accounting for 74.4 percent of total mobile users.
The number of internet users also grew 40.9 percent annually from 620,000 in 1997 to 830 million in 2018.
China's construction industry grows rapidly since 1949
China's construction industry has seen rapid growth since 1949, according to a report issued by the country's National Bureau of Statistics (NBS).
The output value of the industry totaled 23.5 trillion yuan in 2018, 4,124 times that of 1952. The average annual growth rate reached 13.4 percent, said the report.
The added value accounted for 8.2 percent of the country's GDP last year, 6.8 percentage points higher than in 1979.
Construction enterprises have increased alongside the booming industry, with more than 1.2 million enterprises in the sector last year.
Meanwhile, corporate strength has improved significantly. In 2018, 69 Chinese mainland companies were included in the list of the top 250 international contractors released by Engineering News-Record (ENR), an American weekly magazine on the construction industry, ranking first among global countries for the fourth time in a row.
The fast-growing industry has boosted employment. By the end of 2018, 55.63 million people were employed in the sector, accounting for 7.2 percent of the nation's total, an increase of 5.6 percentage points over the 1980 figure.
With the maturing of technologies and an improvement in skills, Chinese enterprises have completed world-class construction projects at home, such as the Three Gorges Dam and the Qinghai-Tibet Railway.
They have also expanded their business overseas. China-Myanmar oil pipelines and the Mohammed VI Bridge in Morocco and other facilities built by Chinese enterprises have won widespread praise in recent years.
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