text:
\section{INTRODUCTION}
Robot learning~\cite{yu2017preparing, peng2018sim, andrychowicz2020learning} has been testified to successfully work not only in simulation but also on real-world robotic control. In this domain, reinforcement learning (RL)~\cite{sutton1998introduction, duan2016benchmarking, dong2020deep} methods are typically applied for robotic control via sim-to-real transfer~\cite{peng2018sim, yu2019sim, valassakis2020crossing}. As one of the most widely applied methods in sim-to-real research literature, domain randomization (DR) can help to learn policies that are more general and robust to be applied in various environments which do not permit direct access to their underlying dynamics. System identification (SI) is another main category of methods that can help to bridge the domain gap. SI methods usually configure the physical parameters from historical transition data, either in an explicit or implicit manner. Both approaches have been proven to be feasible for some control tasks~\cite{andrychowicz2020learning, yu2017preparing}, when encountering the sim-to-real transfer problems, or more generally, domain transfer problems. However, a successful execution of the task does not necessarily indicate that an optimal control strategy is achieved, which leaves the space for further improvement based on solving the existing defects of above approaches.

Problems of existing methods are investigated previously. Conventional DR methods~\cite{tobin2017domain} are able to provide a policy with better generalization in various environments, but not the optimal policy for the settings in a specific environment. The optimization objective is additionally taken with respect to an expectation of the distribution of system dynamic properties, rather than directly on the true dynamic settings of the testing environment. Therefore, being aware of the true dynamics of a new environment is necessary for accomplishing optimal control on a task, which is lacking for DR in present works.

SI can help with bridging this gap via an active inferring of system dynamics with the necessary data collected.  In \cite{yu2017preparing}, an online system identification (OSI) module is used for explicitly configuring the dynamics parameters of environments, with a subsequent universal policy (UP) conditioned on the identified parameters to generate adaptive actions in new environments. However, both the UP and OSI modules can be infeasible in practice for general robot learning tasks according to the discussions in \cite{valassakis2020crossing}. OSI may not be feasible since the trajectories may not provide enough information for accurately predicting the dynamics parameters in a point-estimation manner, therefore distributions can better represent the uncertainty in the dynamics identification. The problem can be ill-posed to directly predict dynamics parameters, according to \cite{zhu2018efficient}. Multiple potential dynamics models can lead to the same trajectories as a non-injective function, due to the entanglement of dynamics parameters. For UP, it can be hard to train when the dimension of dynamics parameters is high.

Present methods for system dynamics identification usually require aligned trajectories for constructing the contrastive loss~\cite{zhu2018efficient, chebotar2019closing}, which we find not to be a necessity. In previous methods, the trajectories alignment can usually be achieved with setting same initial states and executing the same policy, or rollout the simulation environment on a give state and certain action for a single timestep at each time. However, not all simulators can support a direct setup to be any state in the environments. Setting up the initial states only will lead to compounding errors on the trajectories due to the dynamics difference or different random seeds, or requires undesirable engineering efforts. Our proposed method solves the trajectories alignment procedure by leveraging on a well-trained forward dynamics prediction model.

This paper makes the following contributions:
1) We demonstrate the limited performances of typical DR methods when the dynamics of testing environment is uncertain or various, compared against our proposed method;
2) We propose a method to handle dynamics uncertainty and unmodeled effects~\cite{andrychowicz2020learning} separately, with SI module for implicitly identifying the dynamics parameters, as well as DR for unmodeled effects involving observation noise, observation delay and action noise;
3) For system dynamics identification, our method does not depend on aligned trajectories collected in the source and target domains, but with randomly sampled trajectories carried out by the same control policy;
4) Our method learns a regularized dynamics embedding rather than applying the oracle dynamics parameters as the representation of system dynamics, to handle the entangled effects or redundant information within dynamics parameters. A universal policy conditioned on the learned dynamics embedding is trained with DR for both dynamics and unmodeled effects.

\section{RELATED WORK}
Existing methods for bridging the domain transfer gap (\emph{e.g.}, sim-to-real) can be broadly categorized in to the following classes: 1). Domain Randomization~\cite{tobin2017domain} randomizes either visual features~\cite{james2017transferring} or dynamics parameters~\cite{peng2018sim} in a source domain to train policies with better adaptability to the target domain. The branch of works also include the recent process of structured DR~\cite{prakash2019structured} and active DR~\cite{mehta2020active}. 2). System Identification usually requires an extensive data collection and calibration process to mitigate the gap between the source and target environments. Explicit SI can be incorporated with a universal policy~\cite{yu2017preparing} for achieving an adaptive control of the real robot for various environment settings. Implicit SI usually uses recurrent units like long short-term memory (LSTM) networks with sequential inputs to preserves information about environment dynamics~\cite{peng2018sim, andrychowicz2020learning}. 3). Domain Adaptation (DA)~\cite{wang2018deep, tanwani2020domain} applies transfer learning techniques to match the distribution of source domain data with the target domain data, often applied in visuomotor control with images.  4). Strategy Optimization (SO)~\cite{yu2018policy, yu2019sim, yu2020learning} requires evaluating a family of policies (called \textit{strategy}) in the target domain and selecting the one with the best performance, usually with sampling-based methods like Bayesian optimization (BO)~\cite{mockus2012bayesian} and CMA-ES~\cite{hansen1995adaptation}.

Our works focus on the SI approach with universal policies trained for domain transfer. DR in general cannot provide optimal policy due to the induced noise for increasing model generality. The optimized objective for the control policy is additionally taken with respect to an expectation of randomized parameters in environments. Thus only sub-optimal policies can be performed in the target domain even for the case that target sample data falls in the domain of randomized distributions of the source. Traditional explicit SI methods~\cite{yu2017preparing} directly configure the system parameter, which is testified to be an ill-posed problem~\cite{zhu2018efficient,valassakis2020crossing} due to that the entangled effects of multiple parameters will lead to non-unique identification results. This problem no longer exists for implicit SI~\cite{peng2018sim, andrychowicz2020learning}. However, the true system parameters in the source domain are not leveraged for implicit SI, which hinders the learning efficiency of this approach. To this end, we found the embedding of system dynamics parameters can be achieved with encoders to improve the above approaches.

\section{PRELIMINARIES}
\subsection{Notations}
A typical formulation of RL problems follows a standard Markov decision process (MDP), which can be represented as $(\mathcal{S}, \mathcal{A}, R, \mathcal{T}, \gamma)$, where $\mathcal{S}$ and $\mathcal{A}$ are feasible sets of state and action, and $R$ is reward function $R(s,a)$: $\mathcal{S}\times \mathcal{A}\rightarrow \mathbb{R}$. $\mathcal{T}$ defines the transition probability from current state $s$ and action $a$ to a next state $s^\prime$ based on a \textbf{fixed} dynamics setting: $\mathcal{T}(s^\prime|s,a)$, and $\gamma\in(0,1)$ is a discount factor. However, it is not sufficient for a learned policy to be applied in a transferred domain. We consider a partially observable MDP (POMDP) with \textbf{randomized} dynamics as $(\mathcal{S}, \mathcal{A}, \Theta, \mathcal{T}, \mathcal{O}, \mathcal{P}_{\mathcal{O}}, R, \gamma)$, with additional $\Theta$ as the $\mathbb{R}^d$ space of dynamics parameters ($d$ is the number of dynamics parameters), $\mathcal{O}$ as the observation space and $\mathcal{P}_{\mathcal{O}}$ as the emission probability distribution: $\mathcal{P}_{\mathcal{O}}(o|s)$. Moreover, the transition probability $\mathcal{T}$ will then be different from the standard MDP, and it further depends on the dynamics parameters $\theta$ as $\mathcal{T}(s^\prime|s,a,\theta), \theta\in\Theta$. Note that here we define $\mathcal{P}_{\mathcal{O}}$ to be independent on dynamics parameters $\theta$, since we disentangle the observed transition process into the transition $\mathcal{T}$ in underlying state space $\mathcal{S}$ and the omission $\mathcal{P}_{\mathcal{O}}$ of observations from states, so only the transition of underlying states $\mathcal{T}$ depends on the dynamics parameters $\theta$.

\section{METHODOLOGY}
\begin{figure*}[htbp]
    \begin{center}
        \includegraphics[scale=0.26]{img/overview.png}
    \end{center}
    \caption{Overview of our method, including four steps: (a) training the dynamics prediction model with inputs of observation $o$, action $a$, and embedding $\alpha$ of system dynamics parameter $\theta$; (b) training the universal policy with a learned encoder for providing the embedding of system dynamics; (c) optimization in the embedding space with data from a target domain using Bayesian optimization and the learned forward prediction model; (d) inference in the target domain environment with identified dynamics embedding and the universal policy. The blue shaded arrows indicate the gradient flows during model training. The red arrows indicate a trained/fitted models or parameters are applied (with fixed weights/values) somewhere else. }
    \label{fig:overview}
\end{figure*}
In this paper, we propose the method for domain transfer with not only DR but also SI in an embedding space. The overview of our method is shown in Fig.~\ref{fig:overview}. Three key components are involved in the framework:
 \begin{center}
 \textbf{Dynamics Encoder}: $\mathbf{E}(\theta) \mapsto \alpha$  \\
 \textbf{Forward Dynamics Predictor}: $\mathbf{F}(o,a,\alpha)\mapsto o^\prime$ \\
 \textbf{Universal Policy}: $\pi(o, \alpha) \mapsto a$ \\
 \end{center}

The \textbf{Dynamics Encoder} $\mathbf{E}$ is an embedding network to generate low-dimensional embedding $\alpha$ from the system parameters $\theta$. In practical robotic learning tasks, dozens of system parameters can be involved in simulation~\cite{valassakis2020crossing, andrychowicz2020learning}, which would severely increase the difficulty of universal policy learning. Moreover, explicit identification of each parameter value is neither necessary nor impossible for general cases~\cite{zhu2018efficient}. For example, an increment of friction coefficients on a specific joint may be counteracted by a decrease of mass of some link bodies when we simply look at the trajectories executed by the robot with the same torques applied. The entangled effects of system parameters are considerable in SI process. The \textbf{Forward Dynamics Predictor} $\mathbf{F}$ is learned to mirror both the transition function $\mathcal{T}(s^\prime|s,a,\theta)$ of the environment with randomized dynamics $\theta\in\Theta$ and the observation emission function $\mathcal{P}_{\mathcal{O}}(o|s)$ for transferring states $s,s^\prime$ to observations. By leveraging the conditional dependency on embedding $\alpha$ in function $\mathbf{F}$, we propose to directly optimize $\alpha$ in the embedding space using Bayesian optimization when given the data from the target domain, for achieving the embedding SI. The \textbf{Universal Policy} $\pi(o, \alpha)$ basically follows the standard settings in \cite{yu2017preparing}, where a concatenation of observations and identified parameter embedding is taken as policy inputs. By conditioning the policy on the system properties, it is capable of adaptively selecting optimal actions in different system settings and randomized parameters $\theta$. We will show details in later sections.

\subsection{Theoretical Insights}

In the following, we provide the theoretical justification of our method. Since we assume the observation emission process has no impact on the dynamics of underlying state space, the analysis below will be based on a fully observable MDP with randomized dynamics but no observations. Apart from this, another difference of our implementation in practice from the theoretical analysis is the usage of dynamics embedding, \emph{i.e.}, using $\alpha$ instead of $\theta$. These two differences will not cause the loss of generality for our analysis.

Following a similar setup in \cite{yang2019imitation}, we extend the definitions of different occupancy measures for MDP with randomized dynamics as in Table~\ref{tab:occupancy}.

\begin{center}
\begin{table*}[htbp]
\captionof{table}{Different occupancy measure for MDP with randomized dynamics}
\begin{tabular}{ |c|c|c|c|c| }
\hline
Occupancy Measure & State-Action  & State Transitions & Joint w/o Dynamics & Joint w/ Dynamics \\
\hline
Denotation & $\rho_\pi(s,a, \theta)$ &  $\rho_\pi(s,s^\prime)$ & $\rho_\pi(s,a,s^\prime)$ & $\rho_\pi(s,a,\theta,s^\prime)$  \\ \hline
Support & $\mathcal{S}\times\mathcal{A}\times\Theta$ & $\mathcal{S}\times\mathcal{S}$ & $\mathcal{S}\times\mathcal{A}\times\mathcal{S}$ & $\mathcal{S}\times\mathcal{A}\times\Theta\times\mathcal{S}$  \\  \hline
Definition & $\rho_\pi(s,\theta)\pi(a|s,\theta)$ & $\int_{\mathcal{A}\times\Theta}\rho_\pi(s,a,\theta)\mathcal{T}(s^\prime|s,a,\theta)\text{d} a \text{d}\theta$ &
$\int_{\Theta}\rho_\pi(s,a,\theta)\mathcal{T}(s^\prime|s,a,\theta)\text{d}\theta$ & $\rho_\pi(s,a,\theta)\mathcal{T}(s^\prime|s,a,\theta)$  \\ \hline
\end{tabular}
\label{tab:occupancy}
\end{table*}
\end{center}

\begin{customdef}{1}[Estimated Dynamics]
The estimated dynamics distribution is defined as the distribution of dynamic parameters estimated from given the transition tuples $\{(s,a,s')\}$, and it can be calculated as:
\begin{equation}
    p(\theta|s,a,s') = \frac{\rho(s,a,\theta,s')}{\rho(s,a,s')} = \frac{\mathcal{T}(s'|s,a,\theta)\rho(s,a,\theta)}{\int_{\Theta}\mathcal{T}(s'|s,a,\bar{\theta})\rho(s,a,\bar{\theta})\text{d}\bar{\theta}}
\end{equation}
\end{customdef}
In practice, the estimated dynamics can be represented as a parameterized model with learnable parameters $\psi$ and optimized with dataset $\mathcal{D}^s$ from the source domain. During training or evaluation, the predicted distribution and true distribution are denoted as $p^s_\psi(\theta|s,a,s')$ and $p^t(\theta|s,a,s')$ respectively. The optimization objective for SI can be formulated as minimizing the discrepancy of estimated and true dynamics distributions, \emph{e.g.}, $\min_\psi \mathbb{D}_\text{KL}[p^s_\psi(\theta|s,a,s')||p^t(\theta|s,a,s')]$. We will prove that the method of leveraging the dynamics prediction model to achieve SI (step (c). in the scheme as in Fig.~\ref{fig:overview}) is a valid approach for this objective.

\begin{customlemma}{2}
Given the same dataset distribution $\rho^s(s,a,s')=\rho^t(s,a,s')$, the difference between the estimated dynamics distribution $p^s$ and the true distribution $p^t$ can be characterized as:
\begin{align}
&\mathbb{D}_\text{KL}[p^s(\theta|s,a,s')||p^t(\theta|s,a,s')] \\

    &=\mathbb{D}_\text{KL}[\rho^s(s,a,\theta,s')||\rho^t(s,a,\theta,s')]
\end{align}
\label{the:1}
\end{customlemma}

\begin{customdef}{3}[Forward Dynamics Prediction]
The forward dynamics prediction model is defined as the distribution of next state $s'$ given the current tuple $(s,a,\theta)$ in a MDP with randomized dynamics, as an approximation of the true underlying dynamics in the source/target domain:
\begin{equation}
    f(s'|s,a,\theta)\approx \mathcal{T}(s'|s,a,\theta)=\frac{\rho(s,a,\theta,s')}{\rho(s,a,\theta)}
\end{equation}
\end{customdef}

In practice, this model approximation process can be achieved with optimization on collected dataset in the source domain. We denote the parameters of forward dynamics prediction as $\Phi$, which are learned in the approximation process with objective $\min_\Phi \mathbb{D}_\text{KL}[f^s_\Phi(s'|s,a, \theta)||f^t(s'|s,a, \theta)]$ (step (a).), where $\{(s,a,\theta)\}$ are given and $f^t(s'|s,a, \theta)$ are the true distributions of the forward prediction. After training, $f(s'|s,a,\theta)$ is evaluated on the testing dataste (\emph{e.g.}, the target domain dataset), with its predicted distribution denoted as $f^s_\Phi(s'|s,a,\theta)\approx\frac{\rho^s(s,a,\theta,s')}{\rho^s(s,a,\theta)}$ and true distribution as $f^t(s'|s,a,\theta)\approx\frac{\rho^t(s,a,\theta,s')}{\rho^t(s,a,\theta)}$. In our proposed method leveraging the forward dynamics prediction, the SI process is characterized as $\min_\theta \mathbb{D}_\text{KL}[f^s_\Phi(s'|s,a, \theta)||f^t(s'|s,a, \theta)]$ (step (c).), where $\{(s,a)\}$ and $\Phi$ are given. In conventional SI, without $f(s'|s,a,\theta)$, this optimization objective actually becomes $\mathbb{D}_\text{KL}[\mathcal{T}^s(s'|s,a, \theta)||\mathcal{T}^t(s'|s,a, \theta)]$, where $\mathcal{T}^s$ and $\mathcal{T}^t$ are the transition functions of the source and target domains respectively, and it requires the simulator to set the same $(s,a)$ as the real world does all the time for a sim-to-real transfer example.

Now we have another lemma of discrepancy between $f^s(s'|s,a,\theta)$ and $f^t(s'|s,a,\theta)$ as follows.
\begin{customlemma}{4} The distance of the distribution from the forward dynamics prediction and the true distribution can be formulated with the KL-divergence, it thus follows:
\begin{align}

\mathbb{D}_\text{KL}[f^s||f^t]\approx \mathbb{D}_\text{KL}[\rho^s||\rho^t] -\mathbb{D}_\text{KL}[\rho^s(s,a,\theta)||\rho^t(s,a,\theta)]
\end{align}
\label{the:2}
where $f^{s,t}$ are shorten for $f^{s,t}(s'|s,a, \theta)$, and $\rho^{s,t}$ are shorten for $\rho^{s,t}(s,a,\theta,s')$.
\end{customlemma}

\begin{customthm}{5}
The optimization of forward dynamics prediction is increasing the lower bound of the optimization objective for improving the estimated dynamics, i.e.,

\begin{equation}
    \mathbb{D}_\text{KL}[p^s||p^t]\geq  \mathbb{D}_\text{KL}[f^s||f^t]
\end{equation}
where $p^{s,t}$ are $p^{s,t}(\theta|s,a,s')$ and $f^{s,t}$ are $f^{s,t}(s'|s,a,\theta)$.
\label{thm:lb}
\end{customthm}
The above theorem tells us that a direct optimization of estimated dynamics for parameters $\phi$ can be approximately achieved with optimization of parameters $\Phi$ for forward dynamics prediction then leverage it to optimize $\theta$.
Proofs of above theorems and lemmas are all provided in App.~\ref{appsec:proof}.

In further analysis, we note that step (a) and (c) in our method are actually a SI process, not in original parameter space but an embedding space. As proposed in \cite{tanwani2020domain}, the domain-invariant representation learning can be achieved by both minimizing the marginal discrepancy (called \textit{marginal distributions alignment}) and minimizing the conditional discrepancy (called \textit{conditional distributions alignment}) for an object classification task. Here we try to achieve a domain-invariant forward prediction model by optimizing the embedding of system dynamics. We will show how our method satisfies the process of minimizing conditional discrepancy. The reason that marginal distribution alignment is not achieved in current settings is due to the lack of true system dynamics parameters in the target domain, which are required in marginal distribution alignment.

For conditional distributions alignment, we first modify the definition in \cite{tanwani2020domain} with additional conditional variable. Then we narrow down the definition for general domain-transfer models to the specific forward dynamics prediction model in our method.
\begin{customdef}{6}[Conditional Distributions Alignment (modified from \cite{tanwani2020domain})]
Given two domains $D^s=\{x^s_i, y^s_i, z^s_i\}^{N^s}_{i=1}$ and $D^T=\{x^t_i, y^t_i, z^t_i\}^{N^t}_{i=1}$ drawn from random variables $(X^s\times Y^s\times Z^s)$ and $(X^t\times Y^t\times Z^t)$ with different output conditional probability distributions $Pr(Y^s|X^s, Z^s)\neq Pr(Y^t|X^t, Z^t)$, conditional alignment corresponds to finding the transformation $X\times Z \overset{g(X)}{\longrightarrow} E\times Z \overset{f(E,Z)}{\longrightarrow} Y$ such that the discrepancy between the transformed conditional distributions is minimized, \emph{i.e.}, $Pr(Y^s|g(X^s), Z^s)=Pr(Y^t|g(X^t), Z^t)$. Note that the number of additional variables $Z$ is not limited to be one.

\end{customdef}
In our case with forward dynamics prediction model $\mathbf{F}(o,a,\alpha)$ and dynamics encoder $\mathbf{E}(\theta)$, we have $Pr(o^{t \prime}|o^t, a^t, \theta^t)\neq Pr(o^{s \prime}|o^s, a^s, \theta^s)$, and conditional alignment for the forward dynamics prediction model optimizes the dynamics embedding $\alpha$ such that, based on the optimized dynamics embedding, the resulting prediction distributions are minimized, \emph{i.e.}, $Pr(o^{s \prime}|o^s,a^s;\mathbf{E}(\theta^s))=Pr(o^{t \prime}|o^t,a^t;\alpha^t)$. In the source domain the embedding $\alpha^s=\mathbf{E}(\theta^s)$ is optimized via back-propagating through the dynamics encoder $\mathbf{E}$, while in the target domain $\alpha^t$ is directly optimized with methods like BO due to the lack of true dynamics parameters. From above analysis, we show that our method (specifically step (a) and (c)) accomplishes an extended conditional distribution alignment for identifying the dynamics embedding.

\subsection{Universal Policy with Embedding System Identification}
In previous methods, SI is usually achieved with aligned trajectories~\cite{zhu2018efficient, chebotar2019closing}, which is found to be unnecessary in our method. We will detail the formulation of two approaches as follows. Suppose the datasets in the source and target domains are as $\mathcal{D}^s=\{(o^s, a^s, o^s{}^\prime)\}$ and $\mathcal{D}^t=\{(o^t, a^t, o^t{}^\prime)\}$, $\mathcal{D}^s$ is collected with dynamics randomization (without observation noise and action noise). Apart from deriving actions from the same policy as $a^s=\pi(o^s)$ and $a^t=\pi(o^t)$, most present methods assume the underlying states to be the same in the source and target domains, as $o^t\sim\mathcal{P_O}(\cdot|s)$ and $o^s\sim\mathcal{P_O}(\cdot|s)$ for $\forall o^t\in \mathcal{D}^t, o^s\in\mathcal{D}^s$, so as to define a loss function as $L(\{o^s{}^\prime, o^t{}^\prime\})$  for optimizing the identified system dynamics parameters. The trajectory in underlying state space $\tau=(s_{T=0}, s_{T=1}, s_{T=2}, ..., s_{T=n})$ is required to be aligned in the source domain and the target domain to form a valid loss function, especially for the initial state $s^{T=0}$. In our experiments, we find this assumption in dataset collection both unnecessary and inconvenient. On the one hand, due to the potential dynamics differences, the underlying states may not be well aligned for the dataset collected in source and target, \emph{e.g.}, $\tau^s=(s_{T=0}^s, s_{T=1}^s, s_{T=2}^s, ..., s_{T=n}^s)\neq \tau^t=(s_{T=0}^t, s_{T=1}^t, s_{T=2}^t, ..., s_{T=n}^t)$ especially for the latest states in trajectories. On the other hand, manually set the states for the source domain to match with the target domain may not be feasible or inconvenient for some practical cases, \emph{e.g.}, hard to set a simulator to a certain state, etc. Therefore, in our proposed method, the trajectory alignment is no longer required for the SI process. Specifically, we assume that a forward dynamics prediction model $\mathbf{F}$: $\hat{o^\prime}=\mathbf{F}(o,a;\theta)$ trained with source domain dataset $\mathcal{D}^s$ will accurately predict the next observations in the target domain dataset $\mathcal{D}^t$ only when the input dynamics embedding $\alpha$ of the target environment is accurate. Therefore we can construct a loss/score function for optimizing the dynamics embedding based on the forward prediction results in the target domain.

In our method, the forward dynamics prediction model $\mathbf{F}$: $\hat{o}^\prime=\mathbf{F}(o,a;\alpha)$, is trained with $\mathcal{D}^s$ and further applied on $\mathcal{D}^t$ to fit $\alpha$, assuming that the target data is within the distribution of source data. This can be achieved by increasing the randomization ranges in the source domain until satisfactory. For learning the forward prediction model $\mathbf{F}_\Phi$ and dynamics encoder $\mathbf{E}_\psi$, we have the following objectives:
\begin{align}
    &\min_\Phi \mathbb{E}_{(o,a,o^\prime, \theta)\sim\mathcal{D}^s}[L_1(\mathbf{F}_\Phi(o,a;\mathbf{E}(\theta)), o^\prime)] \label{eq:forward_prediction} \\
    &\min_\psi \mathbb{E}_{(o,a,o^\prime, \theta)\sim\mathcal{D}^s}[L_1(\mathbf{F}(o,a;\mathbf{E}_\psi(\theta)), o^\prime)+\lambda L_2(\mathbf{D}(\mathbf{E}_\psi(\theta)))] \label{eq:dynamics_encoder}
\end{align}
where both $L_1$ and $L_2$ are mean squared error (MSE) loss function in our experiments, and $\lambda$ is a trade-off coefficient for balancing the dynamics prediction performance and the reconstruction of dynamics parameters through encoding and decoding. This is corresponding to step (a) in Fig.~\ref{fig:overview}.

The objective for optimizing the embedding $\alpha$ with the learned forward dynamics prediction function $\mathbf{F}_{\Phi^*}$ is:
\begin{equation}
    \alpha^* = \arg \min_\alpha \mathbb{E}_{(o,a,o^\prime)\sim\mathcal{D}^t}[L_3(\mathbf{F}_{\Phi^*}(o,a;\alpha), o^\prime)]
    \label{equ:embed}
\end{equation}
where $L_3$ is also MSE in our experiments. This is corresponding to step (c) in Fig.~\ref{fig:overview}.

The universal policy $\pi$: $a=\pi(o;\alpha)$ in our method is trained in the source domain and applied for inference in the target domain. During the training process of the universal policy, observation noise and action noise are applied with randomized parameters, together with the randomization of dynamics parameters. The objective for learning the universal policy is:
\begin{equation}
    \phi^* = \arg \max_\phi \mathbb{E}_{\theta}[J_\theta(\pi_\phi(\cdot|\mathbf{E}(\theta)))]
    \label{equ:up}
\end{equation}
where $\phi$ are parameters of the policy $\pi$. This is corresponding to step (b) in Fig.~\ref{fig:overview}.

Combining above objectives as in Eq.~\eqref{equ:embed} and \eqref{equ:up}, our method is capable of achieving the optimal policy inference for any dynamics setting via optimized embedding SI:
\begin{equation}
    \pi^* = \pi_{\phi^*}(\cdot|\alpha^*)
\end{equation}
which corresponds to step (d) in Fig.~\ref{fig:overview}. The pseudo-code for the entire UPESI algorithm is shown as Alg.~\ref{alg:upesi}.

\begin{algorithm}[htbp]
\SetAlgoLined
 \# (a). TRAIN DYNAMICS PREDICTION MODEL\;
 Initialize data buffer $\mathcal{D}^s$, prediction model $\mathbf{F}_\Phi$ and encoder $\mathbf{E}_\psi$, and a pretrained policy $\pi_\text{ini}$\;
 \While{$i<N_1$}{
    $\theta\in\Theta$\;
    \While{$j<N_2$}{
    Execute policy $\pi_\text{ini}$ on robot to collect samples $(o,a,o^\prime)$ under dynamics $\theta$;
    $\mathcal{D}^s=\mathcal{D}^s\bigcup (o,a,o^\prime, \theta)$\;
    }
 }
 \While{$i<N_3$}{
    Sample a batch of data $\mathcal{D}^b$ from $\mathcal{D}^s$\;
    Optimize $\mathbf{F}_\Phi$ and $\mathbf{E}_\psi$ with Eq.~\eqref{eq:forward_prediction} and Eq.~\eqref{eq:dynamics_encoder} respectively using $\mathcal{D}^b$ \;
    }

 \# (b). TRAIN UNIVERSAL POLICY\;
    Fix encoder parameters $\mathbf{E}_\psi$\;
    Initialize universal policy $\pi_\phi$\;
    \While{$i<M$}{
    $\theta\in\Theta$\;
    $\alpha=\mathbf{E}_\psi(\theta)$\;
    \While{$j<\text{MaxSteps}$}{
    $a=\pi_\phi(o;\alpha)$\;
    $o^\prime, r$=env.step($a$;$\theta$)\;
    Update $\pi_\phi$ with Eq.~\eqref{equ:up}\;
    }
    }

 \# (c). FIT DYNAMICS EMBEDDING\;
    Fix dynamics prediction model $\mathbf{F}_\Phi$\;
    Collect data buffer $\mathcal{D}^t$ in the target domain following the same way as the dynamics training data buffer $\mathcal{D}^s$\;
    \While{$i<M$}{
    Bayesian optimize the embedding $\alpha$ with $\mathcal{D}^t$ as Eq.~\eqref{equ:embed};
    }

 \# (d). INFERENCE IN A TARGET DOMAIN\;
    Rollout the universal policy $\pi$ to generate action: $a=\pi(o;\alpha)$.
 \caption{Universal Policy with Embedding System Identification (UPESI)}
 \label{alg:upesi}
\end{algorithm}

\section{EXPERIMENT}
\subsection{Comparison Methods}
The comparison involves the following methods:

(1). \textbf{No DR.} A conservative policy is trained in a certain environment with DR, as a comparison baseline.

(2). \textbf{DR only.} A general policy is trained with randomized environments on specified parameters and distributions.

(3). \textbf{DR+UP (True).} An adaptive universal policy is trained with true system parameters as additional inputs in randomized environments.

(4). \textbf{DR+UP+SI.} The policy is trained in the same manner as (3), but tested with a learned SI module to predict dynamics parameters from historical transitions.

(5). \textbf{DR+UP+Encoding (BO).} As the proposed method, an adaptive universal policy is trained with the embedding of system parameters as additional inputs in randomized environments, also with a BO process for configuring the embedding in new environment using the learned forward prediction model.

(6). \textbf{DR+UP+Encoding (True).} As an oracle for the proposed method, the universal policy is trained in the same way as (5), but the embedding is given by the true system parameters going through the learned encoder.

Method (1) works as a baseline for all other methods, which basically represents the optimal policy for a certain training environment and also the most conservative policy when testing in various environments. The comparison of (3) and (4) will imply the potential effects caused by the deficiency in SI module. The comparison of (5) and (6) will show the effects of embedding configuration based on BO when there are no true system parameters but only with samples of transition provided.

\subsection{Experimental Setup}
\begin{figure}[htbp]
	\centering\includegraphics[width=8.5 cm]{img/invertedpendulum.pdf}
	\caption{Different configurations for the environment \textit{InvertedDoublePendulum-v2}.}
	\label{fig:different_configs_pendulum}
\end{figure}
Environments \textit{InvertedDoublePendulum-v2}, \textit{HalfCheetah-v3} in OpenAI Gym MuJoCo are used for testing  the above methods.
For \textit{InvertedDoublePendulum-v2}, the environment is randomized for its five important dynamics parameters, including damping, gravity, two geometry lengths and density, which are detailed in App.~\ref{appsec:rand} Tab.~\ref{tab:pendulum}. The encoded latent space is chosen to have a dimension of 2 as the disentangled compact representation of system dynamics. Fig.~\ref{fig:different_configs_pendulum} shows visualization of three different configurations for the task scene with randomized parameters. For \textit{Halfcheetah-v3}, 13 dynamics parameters are randomized as detailed in App.~\ref{appsec:rand} Tab.~\ref{tab:halfcheetah}, with an embedding space of dimension 4. For training both the conservative or adaptive universal policies, we use the twin delayed deep deterministic policy gradient (TD3) algorithm~\cite{fujimoto2018addressing}, with 4 MLP layers for both the policy and the value networks. The dynamics networks and SI networks have 4 MLP layers. SI model predicts the system dynamics parameters with a stack of 5 frames of transitions, using the same training data as in the training of dynamics prediction models. In our experiments, each method is tested with three runs with different random seeds.

\subsection{Experimental Results}
\begin{figure}[htbp]
	\centering\includegraphics[width=9 cm]{img/learning_curve2.png}
	\caption{Average learning performances of different methods for two environments. Methods include no DR (black dashed lines), DR only, DR with UP, DR with UP and encoding. }
	\label{fig:learning_curve}
\end{figure}

Fig.~\ref{fig:learning_curve} shows the average learning performances in terms of episode rewards in training for all methods on two environments. The policies trained without DR are evaluated in their specific training environments as optimal baselines. The shaded areas indicating the standard deviations. Each run takes 2000 or 25000 episodes of training the policies with TD3 algorithm for \textit{InvertedDoublePendulum-v2} and \textit{HalfCheetah-v3} respectively. The universal policies with either embedding SI or true system parameters significantly outperforms the DR baselines in two environments, although not as optimal as the no-DR policies since the episode reward is directly evaluated in the training environments (\emph{i.e.} non-randomized for no-DR case but randomized for others).

\begin{figure}[htbp]
	\centering\includegraphics[width=9 cm]{img/bo.png}
	\caption{Bayesian optimization on two-dimensional dynamics embedding space for the environment \textit{InvertedDoublePendulum-v2}. Two cases with different dynamics parameters are displayed. The blue "crosses" are the query points in BO process, the red "stars" are the best points after BO, and the green "triangles" are the true values for dynamics embedding derived from the true system parameters. Note that the ``triangles" and ``stars" are almost overlapped.}
    \label{fig:bo_pendulum}
\end{figure}
For the embedding SI process, we take the \textit{InvertedDoublePendulum-v2} as an example. Fig.~\ref{fig:bo_pendulum} shows the BO process for configuring the embeddings of two sets of randomly sampled system parameters following the step (c) in our proposed method (as in Fig.~\ref{fig:overview}), with the dynamics prediction model trained with 10000 episodes of policy rollouts and BO for each parameter configuration with 1000 episodes of data. After 500 iterations of BO for embedding configuration, the best query point is already very close to the true embedding values as shown in Fig.~\ref{fig:bo_pendulum}. Results are similar for \textit{HalfCheetah-v3} just with a higher dimensional embedding. 2000 episodes of data are leveraged for training the dynamics prediction model and 100 episodes of data are used for BO in identifying each new testing environment.
\begin{figure}[htbp]
	\centering\includegraphics[width=8 cm]{img/compare_methods.png}
	\caption{Test performances of different methods for two environments. The white dots are the mean values, and the black bold lines indicate the 25\%-75\% quantiles.}
	\label{fig:compare_methods}
\end{figure}

\begin{table}[htbp]
\begin{center}
\captionof{table}{Test performances: mean and standard deviations of episode rewards for different methods on two environments.}
\begin{tabular}{ |c|c|c|c| }
\hline
Env & Method &  Episode Reward \\
\hline
\multirow{6}{*}{\textit{pendulum}} & No DR & $5168.9\pm{4324.4}$  \\ \cline{2-3}
 & DR only & $2791.1\pm{2960.8}$\\ \cline{2-3}
 & DR+UP (True) & $7586.8\pm{3435.7}$  \\ \cline{2-3}
 & DR+UP+SI & $2153.9\pm{3741.0}$\\ \cline{2-3}
 & \textbf{DR+UP+Encoding (BO)} & $\mathbf{7303.9\pm{3574.2}}$ \\ \cline{2-3}
 & DR+UP+Encoding(True) & $7474.0\pm{3454.7}$ \\ \hline

 \multirow{6}{*}{\textit{halfcheetah}} & No DR & $3213.6\pm{2917.9}$  \\ \cline{2-3}
 & DR only & $7594.2\pm{2571.1}$\\ \cline{2-3}
 & DR+UP (True)& $8956.7\pm{2279.9}$  \\ \cline{2-3}
  & DR+UP+SI & $5679.7\pm{2994.4}$\\ \cline{2-3}
 & \textbf{DR+UP+Encoding (BO)} & $\mathbf{8051.5\pm{2646.6}}$ \\ \cline{2-3}
 & DR+UP+Encoding(True) & $8194.5\pm{2385.5}$ \\
\hline
\end{tabular}
\label{tab:test}
\end{center}
\end{table}

Fig.~\ref{fig:compare_methods} and Tab.~\ref{tab:test} display the test performances of different methods on randomly sampled system parameters within the same distributions of DR, as the step (d) in Fig.~\ref{fig:overview}. We show the episode reward distributions of different methods as a violin plot in Fig.~\ref{fig:compare_methods}. Policies for each method are tested on 10 randomly sampled environment dynamics for 10 episodes each, as a total of 100 episodes for each method. We can see that although the method without DR can achieve good rewards in training since there is no randomized dynamics but a fixed one, in test cases it has a heavy allocation for both the head and tail on the performance distribution. The reason is that the lack of randomized dynamics show some unseen cases incapable of being handled well by the policy. The method with only DR also does not work well because of the hardness in training an optimal policy in randomized environments, which is testified to be an ill-posed problem. For both environments, the heavy tails in no-DR method are greatly alleviated for methods with UP, due to its awareness of system dynamics. However, the true dynamics parameters of the testing environment are usually not accessible, which requires a SI module to configure. Our experiments show that the SI process with a stack of 5 frames of historical transitions (see similar settings in~\cite{yu2017preparing}) is not capable of providing an accurate estimation of the system dynamics,  which severely degrades the performances for UP methods. For both environments, our proposed method with embedding SI using BO process shows advantageous performances over other methods, even as good as the one with true system parameters.

\section{CONCLUSIONS and DISCUSSIONS}
We propose to optimize the dynamics embedding rather than directly configuring the system parameters from historical transitions, and demonstrate its advantageous performances over standard DR and UP methods for a general domain transfer setting, with some primary tests on both a low-dimensional and a high-dimensional simulated environments. The deficiency for DR policies of not being able to achieve optimal actions and the difficulties within normal SI methods for directly configuring the system parameters are revealed. Our future work involves extending the current framework to more complex robot learning tasks, as well as its application on sim-to-real transfer problem, which is a subset of the domain transfer in general. The difficulty of UP learning due to the expanded input spaces with high-dimensional system parameters are not exposed in current experiments, therefore some higher-dimensional tasks will be investigated as well.

\section*{APPENDIX}
\subsection{Theoretical Proofs}
\label{appsec:proof}
\begin{customlemma}{2}
Given the same dataset distribution $\rho^s(s,a,s')=\rho^t(s,a,s')$, the difference between the estimated dynamics distribution $p^s$ and the true distribution $p^t$ can be characterized as:
\begin{align}
&\mathbb{D}_\text{KL}[p^s(\theta|s,a,s')||p^t(\theta|s,a,s')] \\

    &=\mathbb{D}_\text{KL}[\rho^s(s,a,\theta,s')||\rho^t(s,a,\theta,s')]
\end{align}
\end{customlemma}
\begin{proof}
\begin{align}
\text{RHS}&=\mathbb{D}_\text{KL}[\rho^s(s,a,\theta,s')||\rho^t(s,a,\theta,s')]\\
&-\mathbb{D}_\text{KL}[\rho^s(s,a,s')||\rho^t(s,a,s')] \\
&=\int_{\mathcal{S}\times\mathcal{A}\times\Theta\times\mathcal{S}}\rho^s(s,a,\theta,s')\log(\frac{\rho^s(s,a,\theta,s')}{\rho^t(s,a,\theta,s')} \\ &\times\frac{\rho^t(s,a,s')}{\rho^s(s,a,s')})\text{d}s\text{d}a\text{d}\theta\text{d}s' \\
&=\int_{\mathcal{S}\times\mathcal{A}\times\Theta\times\mathcal{S}}\rho^s(s,a,\theta,s')\log\frac{p^s(\theta|s,a,s')}{p^t(\theta|s,a,s')}\text{d}s\text{d}a\text{d}\theta\text{d}s' \\
&=\int_{\Theta}p^s(\theta|s,a,s')\log\frac{p^s(\theta|s,a,s')}{p^t(\theta|s,a,s')}\text{d}\theta \\
&=\mathbb{D}_\text{KL}[p^s(\theta|s,a,s')||p^t(\theta|s,a,s')] = \text{LHS}.
\end{align}
\end{proof}

\begin{customlemma}{4} The distance of the distribution from the forward dynamics prediction and the true distribution can be formulated with the KL-divergence, it thus follows:
\begin{align}

\mathbb{D}_\text{KL}[f^s||f^t]\approx \mathbb{D}_\text{KL}[\rho^s||\rho^t] -\mathbb{D}_\text{KL}[\rho^s(s,a,\theta)||\rho^t(s,a,\theta)]
\end{align}
where $f^{s,t}$ are shorten for $f^{s,t}(s'|s,a, \theta)$, and $\rho^{s,t}$ are shorten for $\rho^{s,t}(s,a,\theta,s')$.
\end{customlemma}

\begin{proof}
Similar as the proof of Lemma~\ref{the:1}, we have,
\begin{align}
    \text{RHS}&=\mathbb{D}_\text{KL}[\rho^s(s,a,\theta,s')||\rho^t(s,a,\theta,s')] \\
    &- \mathbb{D}_\text{KL}[\rho^s(s,a,\theta)||\rho^t(s,a,\theta)] \\
&=\int_{\mathcal{S}\times\mathcal{A}\times\Theta\times\mathcal{S}}\rho^s(s,a,\theta,s')\log(\frac{\rho^s(s,a,\theta,s')}{\rho^t(s,a,\theta,s')} \\
&\times\frac{\rho^t(s,a,\theta)}{\rho^s(s,a,\theta)})\text{d}s\text{d}a\text{d}\theta\text{d}s' \\
&\approx\int_{\mathcal{S}\times\mathcal{A}\times\Theta\times\mathcal{S}}\rho^s(s,a,\theta,s')\log\frac{f^s(s'|s,a,\theta)}{f^t(s'|s,a,\theta)}\text{d}s\text{d}a\text{d}\theta\text{d}s' \\
&=\int_{\Theta}f^s(s'|s,a,\theta)\log\frac{f^s(s'|s,a,\theta)}{f^t(s'|s,a,\theta)}\text{d}s' \\
&=\mathbb{D}_\text{KL}[f^s(s'|s,a,\theta)||f^t(s'|s,a,\theta)] = \text{LHS}
\end{align}
\end{proof}

\begin{customthm}{5}
The optimization of forward dynamics prediction is increasing the lower bound of the optimization objective for improving the estimated dynamics, i.e.,

\begin{equation}
    \mathbb{D}_\text{KL}[p^s||p^t]\geq  \mathbb{D}_\text{KL}[f^s||f^t]
\end{equation}
where $p^{s,t}$ are $p^{s,t}(\theta|s,a,s')$ and $f^{s,t}$ are $f^{s,t}(s'|s,a,\theta)$.
\end{customthm}

\begin{proof}

With Lemma~\ref{the:1} and \ref{the:2}, we have,
\begin{align}
    \text{LHS}&=\mathbb{D}_\text{KL}[\rho^s(s,a,\theta,s')||\rho^t(s,a,\theta,s')]\\
    &=\mathbb{D}_\text{KL}[\rho^s(s,a,\theta,s')||\rho^t(s,a,\theta,s')] \\
    &- \mathbb{D}_\text{KL}[\rho^s(s,a,\theta)||\rho^t(s,a,\theta)] + \mathbb{D}_\text{KL}[\rho^s(s,a,\theta)||\rho^t(s,a,\theta)]\\
    &\approx \mathbb{D}_\text{KL}[f^s(s'|s,a,\theta)||f^t(s'|s,a,\theta)] \\
    &+ \mathbb{D}_\text{KL}[\rho^s(s,a,\theta)||\rho^t(s,a,\theta)]\\
    &\geq \mathbb{D}_\text{KL}[f^s(s'|s,a,\theta)||f^t(s'|s,a,\theta)]
\end{align}
\end{proof}
The last line of above proof is due to that $\mathbb{D}_\text{KL}[\rho^s(s,a,\theta)||\rho^t(s,a,\theta)]$ is non-negative.

\section{Randomized Parameters}
\label{appsec:rand}
\begin{table}[h!]
\begin{center}
\captionof{table}{Randomized dynamics parameters (5-dimensional) for \textit{InvertedDoublePendulum-v2}}
\begin{tabular}{ |c|c|c| }
\hline
\textbf{Variable} & \textbf{Range} \\
\hline
damping & [0.02, 0.3] \\ \hline
gravity & [8.5, 11.0] \\ \hline
length1& [0.3, 0.9] \\ \hline
length2 & [0.3, 0.9] \\ \hline
density & [0.5, 1.5] \\
\hline
\end{tabular}
\label{tab:pendulum}
\end{center}
\end{table}

\begin{table}[h!]
\begin{center}
\captionof{table}{Randomized dynamics parameters (13-dimensional) for \textit{HalfCheetah-v3}}
\begin{tabular}{ |c|c|c| }
\hline
\textbf{Variable} & \textbf{Range} \\
\hline
gravity & [5.5, 14.0] \\ \hline
bthigh damping & [3.0, 9.0] \\ \hline
bshin damping & [1.5, 7.5] \\ \hline
bfoot damping & [1.0, 5.0] \\ \hline
fthigh damping & [1.5, 7.5] \\ \hline
fshin damping & [1.0, 5.0] \\ \hline
ffoot damping & [0.2, 2.8] \\ \hline
bthigh stiffness & [100, 380] \\ \hline
bshin stiffness & [20, 340] \\ \hline
bfoot stiffness & [10, 230] \\ \hline
fthigh stiffness & [20, 340] \\ \hline
fshin stiffness & [20, 220] \\ \hline
ffoot stiffness & [10, 110] \\
\hline
\end{tabular}
\label{tab:halfcheetah}
\end{center}
\end{table}


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US nuclear plants vulnerable to terrorists
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Commercial and research nuclear facilities across the U.S. are inadequately protected against the threat of terrorism, according to the results of new study released by the Nuclear Proliferation Prevention Project at the University of Texas at Austin's LBJ School of Public Affairs.
FEMA: New dispute resolution pilot program for public assistance applicants
The Federal Emergency Management Agency on Aug. 20 announced a new pilot program that allows public assistance grant applicants an added option of arbitration by an independent review panel to resolve disagreements related to public assistance projects for disaster-related emergency protective measures; infrastructure repair and replacement work; or debris removal. This Dispute Resolution Pilot Program is a result of the Sandy Recovery Improvement Act (SRIA) of 2013. The added option allows applicants, including state, local, tribal, and territorial governments as well as certain non-profits, to file for arbitration, which will be conducted by an independent review panel, instead of pursuing a second appeal under FEMA's Public Assistance (PA) Program. Complete details are available here.
DHS cybersecurity appointment seen as government-industry bridge
CSO via CIO
Phyllis Schneck, the McAfee executive appointed to head the Department of Homeland Security's cybersecurity efforts, is the right choice to build cooperation between government and the private sector in protecting the nation's critical infrastructure, security experts say.
Report: Sandy recovery spending must account for future climate change
E&E Publishing LLC
The Hurricane Sandy Rebuilding Task Force, a president-appointed group in charge of coordinating rebuilding efforts for states devastated by Hurricane Sandy, issued a report stressing the need to take climate change into account when investing federal funds in disaster recovery.
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FEMA publishes two documents related to communications during and after nuclear events
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FEMA's Communicating During and After a Nuclear Power Plant Incident. This FEMA document provides communications guidance for domestic nuclear power plant (NPP) incidents, including sample text and suggested answers to anticipated public and media questions. This document also provides background information explaining roles and responsibilities across all levels of government during an NPP incident. While primarily created for federal leaders who will speak to the public, this document should also complement the routinely exercised communication materials used by state, local and tribal officials.
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TRB: Guidebook on Integrating GIS in Emergency Management at Airports
The Transportation Research Board's Airport Cooperative Research Program has published "Report 88: Guidebook on Integrating GIS in Emergency Management at Airports," designed to help airports identify needs and assess current capabilities with respect to using geographical information systems (GIS) in emergency management. The report discusses GIS and emergency management and provides a road map for airports to move from their current state of GIS implementation (even if they do not yet have any form of GIS) to the point of integrating with emergency management and coordinating with mutual aid partners. Download guidebook here.
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N.C. Central University is partnering with the Federal Emergency Management Agency this September to help North Carolina businesses, campuses and local government agencies prepare for disasters.
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Social media creates new opportunities for emergency management every day. That's the message that will be delivered during a new portion of the upcoming annual conference for the National Association of Government Web Professionals in Louisville, Ky. On Sept. 27, speakers will discuss emergency communication and how ideas like MCUrgent — a social media aggregation service created by association Treasurer Carol Spencer — can bring emergency managers up to speed with the technical elite.
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A magnitude 6.1 earthquake shook southern Mexico, Aug. 21, and rattled buildings as distant as Mexico City — around 250 miles away — but there were no reports of damage or injuries. The epicenter was 11 miles northwest of the town of Ayutla de los Libres in the poor southern state of Guerrero, the United States Geological Survey said, revising down an initial reading of 6.4.
Pakistan monsoon, floods leaves 139 dead
The Associated Press via ABC News
The National Disaster Management Authority of Pakistan has confirmed that flooding from torrential downpours has affected millions and left 139 dead — 47 of those killed were confirmed as residents of the Punjab province.
Forecasters: Hurricane season 2013 is no bust
David Sinclair, who makes his living on the shore of the Atlantic Ocean, has some words of advice for anyone taking solace in a seemingly slow start to the 2013 hurricane season: "Don't ever drop your guard until we're through September," says Sinclair, who owns the waterfront Ocean Grill and Tiki Bar in Carolina Beach, N.C. "Now through September is when the bigger ones really start flying off the hook."

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text:
Q: Add recommended extensions in portable vscode how can I add recommended extensions in portable vscode? The idea is to start vscode in any workspace and get the popup for recommendations.
It does not work to put them in data/extension.json.
I tried to put the
{   "recommendations": ["dbaeumer.vscode-eslint", "esbenp.prettier-vscode"] }
this line in data/extensions.json also in data/extensions/extensions.json, but there are no recommended extensions visible in VSCode.

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In 2014, the Building and Development Control Committee, (BDCC), undertook a review of the current planning system to assess as to whether it was fit for purpose for the 21st century. As part of that review the States of Alderney, (SOA) engaged ARUP as independent consultants to compile a detailed report into the island's planning system1. This brought forward a number of recommendations for improving the operation of the States' Planning department for the future and identified a number of issues that needed addressing which included the creation of a robust policy framework to improve the quality of BDCC decision making in the future.
Alongside this report the BDCC had become aware of a number of inconsistencies between the island's existing Land Use Plan, (LUP), and the Building and Development Control (Alderney) Law, 2002 ("the Law") under which it is made. The BDCC were also aware of the statutory requirement for a review of the Land Use Plan (LUP) to be take place in 2016 and in addition, the long-standing aim of the SOA that the main parts of the Law and the LUP should apply to the States and development carried out by them.
It was recognised, after consultations between the BDCC, the Law officers and Arup, that the review of policy would have to be dealt through the LUP process including a public planning inquiry. This would allow for the formation of a robust policy base upon which BDCC could make adjudications in the future and would be a transparent and open process through which the public could engage. It was decided that associated amendments to legislation would be progressed at the same time but the LUP process would recognise that certain amendments to the LUP were dependent on amendments to legislation being approved.
Therefore, the BDCC decided to pursue reform of certain parts of the Law by ordinance such as amendments to the designated area provisions consistent with policies in the LUP and applying the main parts of the Law and the LUP to the States and public utilities bringing cohesion to the Law and the LUP; the amendments also, deal with certain other matters raised by the ARUP report.
As such, the BDCC began the LUP review process in autumn 2015. It was quickly identified that due to the timescale and volume of work required that it would not be possible to conduct the full reform of the planning system in the statutory timescale required for the review of the 2011 LUP. Therefore, the decision was taken to split the process into 2 phases.
The Land Use Plan 2016 (Phase 1 of the review) established a vision for the Island and updated policies in respect of residential development. It was subject to a LUP Inquiry during spring 2016 and was subsequently approved by Full States in July 2016.
It was proposed that Phase 2 of the review would further update the LUP to take account of economic, natural environment and built heritage considerations (plus any updates to the contents of the Phase 1 review as required) to produce a consolidated revised Land Use Plan. Phase 2 of the LUP was also subject to a period of public consultation ahead of the Land Use Plan Inquiry as had been the case with Phase 1.
In parallel, the BDCC also released for public consultation a proposed draft amending ordinance and green paper explaining the amendments to the Law. This dealt with the inconsistencies between the Law and the LUP that had been previously identified, applied the main parts of the Law and the LUP to the States and public utilities and proposed amendments to the designated area provisions. The designated area amendments were mainly to permit the BDCC to consider planning applications for essential States and public utility works in that area and other development of strategic importance and to achieve consistency between the LUP and the Law.
The draft LUP was presented to full public inquiry in September 2017 chaired by an independent inspector and his report was subsequently published in November 2017.
Consultation of the draft amending ordinance was undertaken until the end of November 2017. Over 320 representations were received in relation to the consultation on the Ordinance and green paper. These representations (redacted of personal information) may be accessed via the SOA website.
The Committee requested Mr Young, our former Planning Officer acting for the Committee to compile an analysis of the responses. It was found that while there was support for the requirement for the island to have flexibility to consider its future infrastructure needs, the overwhelming response was to raise concerns in relation to the proposed changes. These responses fell into two distinct categories.
a. Some of the concerns were fundamental, based upon long standing public perceptions particularly about the function of the "Designated Area".
b. Some were detailed implementation issues raising concerns about how the proposed law changes would operate in reality, particularly in relation to the major projects policy S7 proposed within the LUP.
1. Opposition in principle to consideration of any development of strategic importance being considered by BDCC in the "designated area" on environmental and heritage grounds.
2. That the proposals were motivated by a desire to over develop Alderney for private financial gains.
3. That the amendments were being rushed through, motivated entirely by a desire to facilitate the FAB project.
4. That the amendments would leave the island exposed to inappropriate short-term decision making by the States, due to frequent turnover of members.
5. That there was a lack of procedural safeguards in relation to the process for the Policy and Finance Committee (P&F) making a recommendation to the States under the proposed exception in the Ordinance for development or other work of strategic importance.
6. That any P & F recommendation in relation to development it considers to be of strategic importance needs to be supported by evidence, and should be based on objective, independent judgement rather than a member's personal whims.
7. That there was insufficient clarity in the wording of the criteria which had to be satisfied for P&F and the States to be satisfied that work is of strategic importance and a lack of clarity as to what amounts to work of strategic importance.
8. That any proposed developments under S7 should be subject to prior public consultation or a plebiscite.
9. That there should be a requirement for a public inquiry process for a major planning application, which is currently absent in Alderney planning law.
10. That any legal changes should be affected by a review of the entire Law by Projet de Loi and not selectively by Ordinance.
11. There was a perception that BDCC members are not seen to be independent, as they are also P & F members and that the Planning Office cannot be impartial as it is part of the SOA.
12. The SOA has inadequate resources and the Planning Office lacks expertise to support the BDCC to ensure large scale applications are subjected to proper objective independent scrutiny.
Having carefully considered the representations, and undertaken discussions with ARUP, the Law officers and Mr John Young, the BDCC have concluded that there were a number of points with which the Committee has great sympathy for, or agrees with. Therefore, the Committee has decided upon a number of changes to be made to the final legislation to address some of the points raised within the consultation.
Introduction of new procedural requirements applicable to P & F Committee recommendations that development is of strategic importance. P&F would be required to include with any recommendation to the States;o a description of the proposed development and its location including by reference to a map.
o P & F's reasons for making the recommendation.
o a reference to the evidence P&F has relied upon including any SOA, approved policy document it is required to take into account under the legislation.
o express requirement for P & F to base their opinion on robust and credible and objective evidence.
o evidence relied upon by P&F should be publicly available (subject to commercial confidentially considerations).
SOA procedural rules for Billets should be changed so that any proposed recommendation is published no less than 28 days before the States are due to debate any such proposal. This should ensure there is an opportunity for the public to comment through their elected representatives.
In the event that such evidence is not published for 28 days before the States debate, the item would be would be deferred until such time as it had.
A power for the BDCC to request a Planning Inquiry into any application in any area of the Island for development or other work which is very significant for the island and where objections have been made by the public to the application. This extends the original proposal for an additional inquiry power beyond proposals in the designated area.
The inspector of any such inquiry shall be required to consider the application and objections to it and then produce a report with recommendations to the BDCC which would include whether or not the application departs from/is consistent with the LUP.
In the event of a departure/inconsistency with the LUP the inspector will be required to determine whether this can be considered as a minor departure from the LUP, or whether it is a more significant departure from the LUP. In the latter case, this would require a second planning inquiry under the Law to enable the States to approve the change to the LUP.
Where relevant, the inspector will consider, as part of his report, whether a proposal in the designated area is substantially the same as that described in the States resolution that the development is of strategic importance.
Planning applications which are subject to a planning inquiry will have two opportunities for public representations to be submitted, firstly when the application is submitted (following the current procedures) and secondly, when the timetable for the Inquiry is announced. There will be minimum notice periods which will not run concurrently to ensure there is adequate time for public representations.
In addition, to these adjustments to the proposed amending ordinance, the Committee has also under taken additional actions to address some of the additional matters raised by the consultation.
The Planning fee structure will be revised to increase the fees for larger or more significant applications to ensure BDCC has the resources to contract in the support required to evaluate objectively and professionally such major applications with all their complexities. At the moment such applications place the department's single planning officer and BDCC under unacceptable pressure. The maximum fee will be £125,000.
The draft Major Projects statutory guidance will be revised to ensure pre - application consultation and to take into account the proposed amendments and particularly the new Planning Inquiry provisions for planning applications.
The States also debated at the January 2018 States meeting, at the Committee's request, the option of appointing non- States members to BDCC. The BDCC obtained expressions of interest from quality persons who could had added much to BDCC decision making capability in terms of independence and expertise. However, at the present time there seems to be is insufficient public or States Member support for this. This proposal will therefore be held in abeyance for the time being.
A) Matters which fall outside the scope of the law consultation.
Policy S7. The Policy S7 for the designated area was considered in detail at the 2017 LUP Public Inquiry and the Inspector was generally in support of the policy, but suggested some recommendations in detail. These recommendations have been taken into account in proposed revised wording of the supporting text to the policy in the final LUP and are explained in the Committee's response to the Inspectors report.
The suggestions that the proposals were motivated by the FAB project.
The BDCC has strived since 2015 to ensure that its focus is on formation of appropriate and fair planning policies, procedures and legislation whilst ensuring that it retains an open mind, as planning authority, in relation to any potential planning application for FAB link or any other major project in Alderney. The BDCC has sought to keep this work separate from the P & F functions in relation to the FAB link project. Separate advice has been provided to the BDCC by external advisers, the Law officers and the planning officer, to ensure that the proper split between the functions has been maintained. The major projects policy, S7 stood on its own merits and was endorsed by an independent planning inspector at the LUP enquiry. It now appears that the French regulator has effectively put the main FAB link project on hold while the Brexit negotiations continue, this should disperse any perception that this is a factor in progressing the LUP and legislation changes.
The suggestion that the law changes should be progressed by Projet de Loi.
The BDCC considers that the Law requires major change. The BDCC has sought to improve the law by modification by ordinance on 4 occasions and it has been guided in these matters by the Law Officers throughout. If the law changes were postponed, the BDCC are advised that it would take around two years to conclude a Projet de Loi. This could be further delayed by external issues relating to demands on law officer time in relation to Brexit. From the experience of other jurisdictions, reviewing an entire planning law is demanding, complex often controversial and very time consuming. This huge task would be likely to take many more years and with it incur substantial extra cost.
It would mean many of the policies of the LUP could not be implemented and the full benefit of the significant investment already made could be lost. It would also depart from the original course of action to improve the planning system as set out in the recommendations proposed by ARUP in 2014.
Many of the critical public comments expressed issues which relate to the governance of Alderney which is at present under review. These issues fall beyond the scope of BDCC.
The Committee have instructed the Law Officers on the changes outlined in this policy paper and intend to lodge the revised draft amending Ordinance on the billet by 23 Feb 2018 for debate at the March 2018 States Meeting in conjunction with the proposed, finalised Land Use Plan.


WELLINGTON, New Zealand (Radio New Zealand International, November 14, 2016) – Australia has reached a resettlement deal with the United States for refugees being held on Papua New Guinea's Manus Island and Nauru, Prime Minister Malcolm Turnbull said on Sunday.
Under Australia's tough border security laws, asylum seekers intercepted trying to reach the country by boat are sent for processing at detention camps (regional processing centres) on Manus island and the tiny South Pacific island nation of Nauru.
Some 1,300 people are currently held in the camps, many of whom have been held for more than three years.
About 900 men are in camp on Manus Island while about 400 people are in the camp on Nauru with roughly the same number living in the community.
"I can now confirm that the Government has reached a further third country resettlement arrangement for refugees presently in the regional processing centres. The agreement is with the United States," Turnbull told a press conference in Canberra.
He said the agreement, to be administered with the United Nations Refugee Agency, is available only to those currently in the processing centres and will not be repeated.
It will also apply to 370 refugees from Nauru and Manus currently receiving medical care in Australia.
Turnbull would not say exactly how many refugees would be resettled in the United States, but said women, children and families would be prioritised.
Detainees who have had applications for refugee status rejected should return to their countries, he said.
Australia's offshore detention policy for 'boat people' has drawn widespread condemnation both at home and overseas amid reports of harsh conditions and systemic child abuse.
The deal relieves a major headache for Turnbull, whose poll numbers fell to a 14-month low last week, raising speculation of fresh political turbulence in a country that has had four leaders in six years.
Abdul Aziz, 24, who has spent more than three years on Manus Island after fleeing his home in Sudan in 2013, was very happy.
"All the refugees were smiling and hugging each other. This nightmare is going to end finally, but the main question is when and how and how we get out of here," he told Reuters.
"We in the United States have agreed to consider referrals from UNHCR on refugees now residing in Nauru and in Papua New Guinea."
The election of Donald Trump, who has threatened to ban Muslims from entering the United States and championed anti-immigration policies, has injected uncertainty into the deal.
But Turnbull said the "agreement was reached some time ago. There is a great deal of preparation and planning that has gone into it and, indeed, in leading up to this announcement."
No timeline was given for the process.
Amnesty International last month said as many of 410 men, women and children held on Nauru are being driven to the brink as a result of treatment that amounted to "torture".
Papua New Guinea has said it will close the Manus Island centre, but the Nauru facility will remain open.
Asylum seekers who refuse offers to resettle in a third country or to return home will be offered a 20-year visa to stay on Nauru, but no financial support, Immigration Minister Peter Dutton said.
Once rich in phosphate, Nauru has limited economic resources and the Australian-funded detention centre provides the tiny island state's most significant revenue stream.


I believe every photographer, when editing the day's harvest, would for some images ponder the decision whether an image would look better in color or monochrome. For me, this happens mainly with photographs that have a high contrast range, like this scene I took in the Lobby of the Ice Bar Berlin (ever had a drink in an arctic environment?). I was attracted by the harmony of the colors, in addition to the playful lighting effects), so I shot this in color with my PEN-F and the 12mm f/2 prime lens (image specs 1/80 sec @ f/2.2 and ISO 2000). When processing the photo in Lightroom CC Classic, I was seeing the potential for a monochrome conversion, and applied a high contrast preset, that also introduced some grain, mimicking an analogue film (as you see above, the PEN-F has no noise issues at ISO 2000, at least if you keep the image at a reasonable size). I like both version, but if I need to pick one I tend to take the colored version. I would be interested in your views, please comment below.
This past weekend I have spent in Berlin together with Street Photo Legend Eric Kim, his wife Cindy, his sister Annette and a great crew of 12 other like minded Street Photographers who joined Eric's "Conquer Your Fears In Street Photography" workshop in our Nation's capital. It was a truly unforgettable experience. Mainly for meeting a bunch of fantastic and fun people. Thanks so much, Eric, Cindy, Annette and all of my co-students!! And it did the trick. Normally being rather "unobtrusive" in my street photography, I was blown away by how much fun it actually is to simply walk up to interesting looking people, asking them if I can "make" their portrait and share a few life stories with them. So the workshop really delivered on its promise, thanks to Eric.
Already back in the treadmill of the job that pays the bills, I just wanted to quickly share the success message and a couple of my street portraits taken during the workshop, all with my Olympus PEN-F and the mZuiko 25mm/F1.8 prime lens.
Here is the link to the post on Eric's blog on the workshop results, check it out! There were so many great photos that came out of that two-day session in Berlin, they deserve some appreciation.
Once I find time to sort through all my photos I will do a proper workshop review with some more of my workshop results and a few "making of" images. Stay tuned !
After a totally crazy week at work, where the job that pays the bills has pretty much sucked up all my time and energy, I'm at the doorsteps of a hopefully nice (only contents-wise, the weather forecast is gruesome) weekend in our Nation's capital Berlin, where I will attend a workshop with Street Photography Guru Eric Kim, a gift from my family and friends on the occasion of my half century birthday I had earlier this year.
Curious about what will await me I'm posting a street photograph I took during a visit to Berlin back in May, which is my entry into this week's Word Press' Weekly Photo Challenge, that has the theme "temporary".
What is more temporary than a fleeting moment of life captured in a street photo. In the end, all street images are snapshots of life as it happens in that exact instant, and that never will be repeated. And it is especially true for this photograph of a father sharing an intimate moment with his little son.
Taken with the Olympus OM-D E-M1 and the mZuiko 14-150mm F/4-5.6 travel zoom. Image specs 1/13 sec @ f/5.6, ISO 1600 and 120mm focal length. Monochrome conversion in Lightroom Classic CC.
I wish everyone a great and creative weekend!
Just got back in from our marvelous Berlin weekend. My bag is already repacked for another quick trip to Portland. My alarm will go off at 3:30 am, then I'll drive to Munich for a 7am flight to Amsterdam and then on to PDX.
The photo above was my personal favorite from the weekend, a shot I took in staircase of the Otto Bock Science Center in Berlin. Taken with the PEN F and the mZuiko 14-150mm F4-5.6 travel zoom. Image specs are 1/60 sec @ f/4.5 and ISO 320, focal length was 22mm.
Will sort through the rest of my Berlin photos on the plane tomorrow and post from the US.
Before heading back to the Pacific Northwest before sunrise on Monday morning, the significant other and myself took the road for our Nation's Capital on Friday afternoon to visit close friends, do some shooting and shopping in the city and attend a Whiskey tasting I got as present for my 50th birthday back in January. The photographs shown here are taken out of the moving car in a combination of slow shutter speeds (both were shot at 1/13 sec) and panning the camera backwards against the driving direction. The conversion to monochrome was done in Lightroom CC with the pre-set mimicking a TRI-X analogue film.
For more tips and inspirations published on the Streets of Nuremberg visit my free Learning Center.


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Sovereign Gold Bond Series II opens today: 10 things to know about the scheme
Amid the coronavirus lockdown, the demand for the precious metals is rising as investors have taken a backseat from equities in such a volatile market. The yellow metal is expected to trade as a safe investment during volatile times.
Rupa Burman Roy
This is the second tranche of the six tranches of Sovereign Gold Bonds issues announced by the government last month
The second tranche of government-run Sovereign Gold Bond (SGB) scheme 2020-21 will open for subscription today.
Series II of the sovereign gold bond scheme will close for subscription on May 15. The issue price of the Series II has been fixed at Rs 4,590 per gram. This is the second tranche of the six tranches of Sovereign Gold Bonds issues announced by the government last month.
Here are 10 things to know about the latest Sovereign Gold Bond Scheme 2020-21 Series II:
1. The issue price of sovereign gold bonds is based on recent closing price of gold as published by the India Bullion and Jewellers Association Ltd for gold of 999 purity. "The nominal value of the bond based on the simple average closing price (published by the India Bullion and Jewellers Association Ltd) for gold of 999 purity of the last three business days of the week preceding the subscription period, works out to Rs 4,590 per gram of gold," RBI said in the statement earlier.
2. This is the second tranche of gold bonds of this fiscal year, issued by Reserve Bank of India (RBI) on behalf of the government. The first tranche of sovereign gold bond in the financial year 2020-2021 was open for subscription from April 20 to 24.
3. The first trance for the fiscal year saw a record subscription of 17.73 lakh units worth Rs 822 crore, the highest ever subscription since the October 2016 issue (35.98 lakh units of gold bonds issued worth Rs 1,081 crore), as per RBI data. The yellow metal was priced at Rs 4,639 for the first tranche of FY20.
4. Investors can buy the SGB schemes through commercial banks, post offices, stock exchanges Bombay Stock Exchange and National Stock Exchange, and the Stock Holding Corporation. Investors who can invest in the SGBs include individuals, trusts, charitable organizations, Hindu Undivided Families (HUFs) and universities.
5. The minimum investment limit in gold bonds is one gram of gold, where one unit of the bond is equal to one gram of gold. They have a maturity period of eight years, although investors can opt to exit after the fifth year.
6. The maximum limit of subscription shall be 4kg for individuals and HUFs, and 20kg for trusts and similar entities per fiscal year.
7. As per the RBI's directive, buyers applying online and making payment through digital mode will be provided with a Rs 50 discount.
8. Considered as a safe haven commodity, price of gold futures extended gains on the commodity exchange MCX and touched Rs 47K mark, on the back of spread of COVID-19 virus and the resulting lockdown.
9. RBI has also released a timeline of issuance of gold bonds for the first six months of the year. Following are the remaining gold bond schemes to be issued in the next four months:
2020-21 Series III : Subscription on June 08-12, 2020; Issued on June 16, 2020
2020-21 Series IV: Subscription on July 06-10, 2020; Issued on July 14, 2020
2020-21 Series V: Subscription on August 03-07, 2020; Issued on August 11, 2020
2020-21 Series VI: Subscription on August 31-September 04, 2020; Issued on September 08, 2020
10. SGB scheme was launched in November 2015 with an objective to shift the purchase of gold into financial savings. The yellow metal is considered to be less risky asset during times of economic turmoil.
Published on: May 11, 2020, 12:44 PM IST
Posted by: Rupa Roy, May 11, 2020, 12:44 PM IST


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Q: Heroku: LoadError: cannot load such file -- mimemagic/overlay I'm getting an error when uploading a release to heroku.
!     LoadError: cannot load such file -- mimemagic/overlay
I successfully ran the 'bundle exec rake -P' command as advised but could not see any mimemagic in the output. I've never had to think about mimemagic before. I think its pulled in by other gems, notably 'carrierwave'. I've found a conversation (https://github.com/rails/rails/issues/41757) where them gem has been amended in the last few days and wonder whether I've been caught by a breaking update.

A: Mimemagic is a dependency of Marcel which is a dependency of a dependency of ... ActiveStorage. The maintainers just changed the license (I think this week), which caused a ruckus.
You want mimemagic at a version between 3.6 and 3.9. You could set it in your Gemfile gem "mimemagic", "~> 0.3.6". Then run bundle update marcel mimemagic. If you're on a mac, run brew install shared-mime-info first.

A: in my case, just update rails version to
gem "rails", "~> 5.2.5"

then bundle update

A: What worked was updating rails in my gemfile to gem 'rails', '~> 5.2.5' and then running bundle update as @asharijuang mentioned above.
Then I was able to finally push to Heroku after being stuck on this for a few days
A few of the things I tried unsuccessfully:
gem install mimemagic
brew install shared-mime-info then bundle update marcel mimemagic

A: In the end is was quite simple.
Include:-
gem 'mimemagic', '~> 0.3.10'
in the Gemfile. The entry specifically requires a set version, thus avoiding the versions that got pulled. At some point in the future I'll replace 'carrierwave' with the vanilla 'ActionStorage'.


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Event Type: Music
Peebles Concert Band
Sun 21 October
£10, FREE for under 15
Enjoy a musical Sunday afternoon with the thirty musicians of Peebles Concert Band as they perform a varied programme that takes in everything from musicals to movie themes, classical pieces to marches. Enjoy the huge sound of this local band conducted by Vaughan Fleischfresser
The Roy Orbison Story Date: 07/11/2019 Time: 7:30 pm - 9:30 pm Price: £22, £20 Barry Steele put the rock back into The Roy Orbison Story® as he commemorates the legendary Bog O in concert.
Patsy Cline & Friends Date: 03/10/2019 Time: 7:30 pm - 9:30 pm Price: £18 Sue Lowry captures the velvet country voice of Patsy Cline. Her powerful rendition is a celebration of pure country music.
CREATIVE PEEBLES FESTIVAL: Horse Flying Solo Date: 23/08/2019 Time: 7:30 pm - 9:30 pm Price: The iconic singer/songwriter, with her voice described as 'a finely honed insturment' opens Creative Peebles Festival 2019
The Fureys Date: 17/03/2020 Time: 7:30 pm - 9:30 pm Price: £22, £11 under 15 Expect an evening of music, fun and great stories from the legendary band.
3hattrio Date: 11/09/2019 Time: 7:30 pm - 9:30 pm Price: £15, £13, £6 under 15 Banjo, guitar, double bass, foot percussion, lush vocals and violin - 3hattrio deliver an exhilarating evening.
Lonesome Ace Stringband Date: 18/09/2019 Time: 7:30 pm - 9:30 pm Price: £15, £13, £6 under 15 feel the energy of three passionate musicians playing the bejesus out of those instruments and loving every minute.
The Counterfeit Sixties Date: 18/10/2019 Time: 7:30 pm - 9:30 pm Price: £18, £16, £6 under 15 Enjoy a full multimedia experience centred on the greatest songs from the most creative period in pop music.
Gnoss Date: 14/11/2019 Time: 7:30 pm - 9:30 pm Price: £15, £13, £6 under 15 This dynamic young Scottish folk band was nominated Up and Coming Artist of the Year in 2017.
Borders Big Band The Happy Prince


Bike to School Week is a celebration of cycling and active transportation. This year students and teachers across the Greater Toronto and Hamilton Area (GTHA) will celebrate the first-ever joint Bike to School Week, May 25th - 29th.
To participate, visit www.biketoschoolweek.ca and register your school's event.
Downloadable posters to assist in the promotion of your event.
Win Bike Racks for your School!
TDSB Schools that complete a short and easy follow-up survey following the completion of their event will receive a certificate of participation and will be entered to win bike racks for their school. Four schools will be selected to receive bike racks based on need and the percentage of their school population that participated in the event.


"Don't ever say it won't happen to you"
On New Year's Day Linda Thorne emerges from her home holding a doll. It's one of few personal belongings that survived water rushing through a levee on the Meramec River in Missouri. "Is this the wake up call? Yes, it's a wake up call. It's the worst bad dream that I've ever had," she says as she cries openly in front of her neighbors on Butler Court. Some lived high on the hill in their trailer homes. Others, like Linda, lived low, too low on the hill to escape with much else than their lives. And like so many people, she's relying on the Red Cross to keep her safe, warm and fed while she figures out her next step. Linda smiles and jokes a lot, which surprises the other residents at the shelter. "How can you? they ask. You've lost so much. I tell them we have a whole new year." Grateful for what she has and for what's coming--the birth of a her first great grandchild--Linda no longer denies that going back to live in her old home will be possible. During the coming days the Red Cross shelter will be her refuge. It's a safe haven, she says, allowing her to be the positive person she tends to be. "I want to thank the Red Cross from the bottom of my heart because if it wasn't for the Red Cross, I wouldn't have a place to go."


subordination--an Indian always executes an order with religious fidelity.
Recommended Reading: Storm in the Mountains: Thomas' Confederate Legion of Cherokee Indians and Mountaineers (Thomas' Legion: The Sixty-ninth North Carolina Regiment). Description: Vernon H. Crow, Storm in the Mountains, dedicated an unprecedented 10 years of his life to this first yet detailed history of the Thomas Legion. But it must be said that this priceless addition has placed into our hands the rich story of an otherwise forgotten era of the Eastern Cherokee Indians and the mountain men of both East Tennessee and western North Carolina who would fill the ranks of the Thomas Legion during the four year Civil War. Crow sought out every available primary and secondary source by traveling to several states and visiting from ancestors of the Thomas Legion to special collections, libraries, universities, museums, including the Museum of the Cherokee, to various state archives and a host of other locales for any material on the unit in order to preserve and present the most accurate and thorough record of the legion. Crow, during his exhaustive fact-finding, was granted access to rare manuscripts, special collections, privately held diaries, and never before seen nor published photos and facts of this only legion from North Carolina. Crow remains absent from the text as he gives a readable account of each unit within the legion's organization, and he includes a full-length roster detailing each of the men who served in its ranks, including dates of service to some interesting lesser known facts.
Storm in the Mountains, Thomas' Confederate Legion of Cherokee Indians and Mountaineers is presented in a readable manner that is attractive to any student and reader of American history, Civil War, North Carolina studies, Cherokee Indians, ideologies and sectionalism, and I would be remiss without including the lay and professional genealogist since the work contains facts from ancestors, including grandchildren, some of which Crow spent days and overnights with, that further complement the legion's roster with the many names, dates, commendations, transfers, battle reports, with those wounded, captured, and killed, to lesser yet interesting facts for some of the men. Crow was motivated with the desire to preserve history that had long since been overlooked and forgotten and by each passing decade it only sank deeper into the annals of obscurity. Crow had spent and dedicated a 10 year span of his life to full-time research of the Thomas Legion, and this fine work discusses much more than the unit's formation, its Cherokee Indians, fighting history, and staff member narratives, including the legion's commander, Cherokee chief and Confederate colonel, William Holland Thomas. Numerous maps and photos also allow the reader to better understand and relate to the subjects. Storm in the Mountains, Thomas' Confederate Legion of Cherokee Indians and Mountaineers is highly commended, absolutely recommended, and to think that over the span of a decade Crow, for us, would meticulously research the unit and present the most factual and precise story of the men, the soldiers who formed, served, and died in the famed Thomas Legion.
From Publishers Weekly: Beginning with the flight of the Creeks into Union territory pursued by Confederate forces (including many of Stand Watie's Cherokees), this popular history recounts grim, bloody, lesser-known events of the Civil War. Hatch (Clashes of the Cavalry) also describes the most incredible incidents.... Kit Carson, who fought Apaches and Navajos under the iron-fisted Colonel Carleton, arranged the Long Walk of the Navajos that made him infamous in Navajo history to this day. The North's "Captain" Woolsey, a volunteer soldier, became a brutal raider of the Apaches. General Sibley, a northerner and first Governor of Minnesota, oversaw the response to the Sioux Uprising of 1862 that left several hundred dead. The slaughter of Black Kettle's Cheyennes at Sand Creek in 1864 by Colorado volunteers under Colonel Chivington, a militant abolitionist whose views on Indians were a great deal less charitable, "forms a devastating chapter." Hatch, a veteran of several books on the Indian Wars that focus on George Armstrong Custer, has added to this clear and even-handed account a scholarly apparatus that adds considerably to its value.
He examines the rise of abolition and secession, and then advances into the Civil War. Early in the conflict, Union sympathizers burned a number of railroad bridges, resulting in occupation by Confederate troops and abuses upon the Unionists and their families. The author documents in detail the 'siege and relief' of Knoxville. Although authored by a Unionist, the work is objective in nature and fair in its treatment of the South and the Confederate cause, and, complete with a comprehensive index, this work should be in every Civil War library.