text:
\section{Introduction}
While reinforcement learning (RL) has demonstrated success in various sequential decision making settings \cite{barto1990sequential, shortreed2011informing}, challenges remain in the personalized domain. For example, we may be interested in recommending treatments to a cohort of patients, where individuals may respond to the same treatment differently, even while conditioning on the same health state. From an RL framework, each patient can be modeled as an entity whose individual characteristics parameterize a distinct Markov decision process (MDP). With this framing, one could, in principle, train a separate policy from scratch for each individual. However, this approach is impractical in settings where exploration is costly and individual trajectory data are limited. As a result, there is an increasing need to think about how to leverage prior data in sophisticated manners to develop few-shot learning approaches for deployment in unseen environments.

In this work, we take a meta-learning approach to this problem and propose a method tailored to few-shot learning across a \emph{diverse} set of environments. Our approach builds on model agnostic meta-learning (MAML), a paradigm for learning a single prior initialization of policy gradient parameters that can adapt quickly to a set of observed tasks. We extend this approach by leveraging the notion that certain environments are more similar to each other than others in personalized settings, and accordingly their corresponding experiences should be given higher attention during adaptation. We thus propose generalizing this approach by learning several different potential initializations, and choosing an appropriate initialization to use for a given environment at test time.

Our proposed algorithm--cluster adaptive meta-learning (CAML)--explicitly seeks to determine which past parameters should be factored in during adaptation. Because the core idea of our adaptation relies on being able to quickly identify the most relevant past trajectories, we seek to characterize these experiences through a distance metric that establishes a notion of similarity between policies, borrowing from related work in inverse reinforcement learning (IRL) and determining policy divergence. We rely on whole trajectories$-$which provide a richer source of information over alternatives used in previous work \cite{DBLP:journals/corr/abs-1803-11347, DBLP:journals/corr/FinnAL17, DBLP:journals/corr/abs-1812-07671}$-$and would like to call upon a diverse set of environment interactions for reference. Accordingly, we retain an episodic model-free approach in contrast to more recent model-based online-learning methods where each timestep is considered a new task \cite{DBLP:journals/corr/abs-1803-11347}. Our contributions center around how to (1) study personalization effectively in simulated contexts, (2) characterize the divergence of policies across a population of environments, and (3) learn effectively across different and previously unseen environments with minimum exploration cost. We introduce a $K$-medoids-inspired algorithm for policy adaptation and few-shot learning across multiple environments, and demonstrate approach's successes over existing methods in a 2D testbed.

\section{Background}

\subsection{Personalized Markov decision processes (MDPs)}
We consider a modified version of a typical continuous state-space MDP, parameterized specifically by an individual entity type $\mathcal{T}_i$ among a population of possible types $\mathcal{T}$. For lexical consistency, we refer to the agent as the actual policy or decision-maker, and define an ``entity'' to be the object through which an agent interacts with the larger environment (the ``patient'' in our working example). Each entity type then introduces an MDP  $\mathcal{M}^i \triangleq (\mathcal{S}, \mathcal{A}, P^i, r^i, \gamma, s_0^i)$ with unique transition probabilities $P^i: \mathcal{S} \times \mathcal{A} \times \mathcal{S} \rightarrow \mathbb{R}$. Given shared state and action spaces $S$ and $A$, we thus allow for diversity in behavior across entities from different types.

For any two different entity types $\mathcal{T}_i \neq \mathcal{T}_j$, $P^i$ may not necessarily differ from $P^j$, but crucially at the onset of training for some subset of unseen types $\mathcal{T}_i \subseteq \mathcal{T}$ we make no assumption that these transition dynamics are the same. Finally, $\gamma$ is a discount factor assumed to be the same for all agents, and $s_0^i$ is the initial state distribution for environment type $i$. Our goal across a population of entity types is then to learn some function $f : \mathcal{T} \rightarrow \Pi$ mapping from entity types to policies. For each entity type given $s^i_0$ and a minimal number of timesteps $t$, we can then output an optimal policy $\pi_\theta^i$ with regard to maximizing the cumulative discounted reward $\sum_{t=0}^{T - 1} \gamma^t r(s_t, a_t)$ for $T$-length episodes, where $\theta$ denotes the parameters for personalized policy $\pi_i$ taking action $a_t \in \mathcal{A}$ given state $s_t \in \mathcal{S}$.

\subsection{Meta-reinforcement learning}
In the typical meta-learning scenario, we are interested in automatically learning learning algorithms that are more efficient and effective than learning from scratch \cite{DBLP:journals/corr/abs-1803-11347}. To do so, instead of looking at individual data points, a meta-learning model is trained on a set of tasks, treating entire tasks as training examples with the goal being to quickly adapt to new tasks using only a small number of examples at test time. We can consider a task to be any type of desirable machine learning activity, such as correctly classifying cat images in a supervised learning scenario, or learning how to walk forwards at a certain velocity in RL. Expanding on this latter example, meta-RL tries to learn a policy for new tasks using only a small amount of experience in the test setting. More formally, each RL task $\tau_i$ contains an initial state distribution $s_0^i$ and transition distribution $P^i(s_{t+1} | s_t, a_t)$. In the few-shot RL setting parameterized by $K$ shots, after training on train tasks $\{\tau_\text{train}\}$, we are allowed to use $K$ rollouts on test task $\tau_\text{test}$, trajectory $(s_0, a_0, \ldots s_T)$, and rewards $R(s_t, a_t)$ for adapting to $\tau_\text{test}$.

\subsection{Model-agnostic meta-learning for RL}

More specifically, our approach builds on model-agnostic meta-learning (MAML) for reinforcement learning \cite{DBLP:journals/corr/FinnAL17}, which optimizes over the average reward of multiple rollouts. Doing so we wish to learn an initial set of policy gradient parameters capable of quickly adapting to a new RL task in a few gradient descent steps. Given $n \in N$ meta-training environments, and initial policy parameters $\theta$, MAML first samples an MDP $\mathcal{M}^i$, $i \in \{1, 2, \ldots, n\}$. For each sampled MDP, we then train a policy given $K$ rollouts, arriving at task-adapted parameters $\theta_i'$ through gradient descent $\theta_i' = \theta - \alpha \nabla_\theta \mathcal{L}_{\mathcal{T}_i}(\pi_\theta)$ (described as the inner training loop). $\alpha$ is our learning rate and $\mathcal{L}_{\mathcal{T}_i}(\pi)$ describes the inverse reward from following policy $\pi$ with entity type $\mathcal{T}_i$.
Feedback through the reward generated on the $(K+1)^{\text{st}}$ rollout is then saved. After $n$ tasks are sampled, we perform a meta-update using each saved reward, updating our initialization parameters $\theta'$ with $\theta' \leftarrow \theta - \beta \nabla_\theta \sum_{\mathcal{T}_i \sim p(\mathcal{T})} \mathcal{L}_{\mathcal{T}_i}(\pi_{\theta_i'})$, where $\beta$ is the meta-learning rate. MAML thus seeks to leverage the gradient updates for each task during the inner loop of training, reaching a point in parameter space relatively close to the optimal parameters of all potential tasks. We then evaluate performance by sampling $\mathcal{M}^j$, $j \in N \setminus n$, observing the rewards generated from the $(K+1)^{\text{st}}$ trajectory after adapting with $K$ rollouts and policy parameters $\theta'$.

\subsection{Characterizing policy divergence}

Aside from gaining further knowledge with respect to how policies adapt to their environments over time, we also seek to establish a valid distance metric between various policies to inform policy parameter initialization in few-shot RL. Conceptually we can imagine that given pairs of MDPs and policies $(\mathcal{M}^i, \pi^i)$ for types $\mathcal{T}_i \in \mathcal{T}$, the policies start with an indistinguishable parameter initialization and diverge when adapting to their personalized environments after $t$ timesteps of training. If adapted policies $\pi_i'$ and $\pi_j'$ are still similar to each other, then this suggests that their environments $\mathcal{M}^i$ and $\mathcal{M}^j$ may be similar as well, and accordingly we can use this information to form a representation of optimal policy parameter clusters, making sure to only reference relevant prior experiences when adapting to a new environment.

To begin our search for such a distance metric we look to the field of imitation learning, and specifically inverse reinforcement learning (IRL). Here the goal is to learn a cost function explaining an expert policy's behavior \cite{Ng:2000:AIR:645529.657801}. However, while typical IRL methods first learn this cost function and then optimize against it to train a policy to imitate the expert, we are content with the cost function alone, whose components we can borrow to define our own quantifiable metric defining the distance between two policies. As noted in \cite{DBLP:journals/corr/HoE16}, we can uniquely characterize a policy by its occupancy measure given by
\begin{equation}
\rho_\pi(s, a) = \pi(a |s) \sum_{t= 1}^{T} P(S_t = s | \pi), \text{ where } \rho_\pi : \mathcal{S} \times \mathcal{A} \rightarrow \mathbb{R}
\end{equation}
We essentially view this metric as  the state-action distribution for a policy. Using a symmetric measure such as the Jensen-Shannon (JS) divergence \cite{lin1991divergence}, we can thus compare the occupancy measures of multiple policies at specific time steps over training as a metric into policy divergence over time. While we study a very different problem from traditional IRL–where we do not explicitly try to minimize policy divergence–such measures of divergence allow for both descriptive analysis in tracking divergence and informing new learning algorithms. In principle we can then characterize policies based on their observed trajectories, and later show that this is enough to measure similarities across their respective environments as well.

\section{Related Work}
While there is much work summarizing the general meta-learning literature \cite{DBLP:journals/corr/abs-1803-11347,DBLP:journals/corr/abs-1810-02334, DBLP:journals/corr/abs-1803-00676}, we focus on initialization-based methods for RL. Here we wish to learn a set of optimal model parameters such that after meta-learning, the policy is initialized to an optimal position in parameter space to adapt to new environments relatively quickly. Finn et al. demonstrate this paradigm in MAML, which achieves meta-reinforcement learning through gradient-based optimization \cite{DBLP:journals/corr/FinnAL17}. They introduce the generalizable notion of doing well across a variety of tasks, where each task in the RL setting is a similar goal such as trying to reach a certain point in 2D space. Given training on reaching a certain support set of points, we would like to be able to quickly learn how to reach a new batch of unseen query points. Instead of trying to generalize across strictly different tasks, we adopt this framework by modeling different entity types encountered$-$each harboring their own potentially unique rewards and transition probabilities given the same states and actions$-$as different tasks.

Implementation-wise, although modern day software packages make MAML's computation relatively straightforward, it's reliance on unrolling the computation graph behind gradient descent and taking second derivatives has motivated follow-on work on simpler methods, such as first-order MAML and Reptile, which avoid doing so with first-order approximations and direct gradient movements respectively \cite{DBLP:journals/corr/FinnAL17, DBLP:journals/corr/abs-1803-02999}. We focus on learning algorithms with similar computational dependencies due to their comparable performance with vanilla MAML.

Learning similarities between tasks through clustering or other distance-based  metrics has also been explored before in non-RL few-shot settings, where a central motivation lies in being able to achieve comparable results with alternatives while maintaining much simpler inductive biases. Snell et al. propose prototypical networks for few-shot classification \cite{DBLP:journals/corr/SnellSZ17}, which aims to learn an embedding where data points cluster around a single prototype representation in each class. Accordingly, given a learned transformation between input features and some vector space, we can simply use the computed prototypes on unseen data points for classification. Although we do not explore learning further embeddings on our proposed distance metric, we nonetheless demonstrate that calculating similarities given observed occupancy measures is enough to identify similar environments, drawing upon a similar intuition for RL.

\section{Personalizing policy learning}

We now present our approach for (1) measuring and interpreting divergence of policies in personalization, and (2) defining few-shot meta-learning algorithms for online adaptation to new entity types. We also introduce a testbed to study the effects of personalization across entities on training. As outlined in Section 2.1, we work with a set of personalized MDPs representing a population of entity types.

\subsection{A personalized particle environment for 2D navigation}
\begin{wrapfigure}{r}{0.5\textwidth}
  \vspace{-13.5pt}
  \begin{center}
    \includegraphics[width=0.48\textwidth]{figures/2d_nav.png}
  \end{center}

  \caption{\textbf{2D personalized particles.} Policy $\pi$ tries to move entity
 (blue, red, yellow) to target (gray), but entities behave in different ways unknown to the policy. Right: Two remapping schemes, where some cardinal direction e.g. `up' corresponds to a different transition vector. Code available upon request.}
 \vspace{-13.5pt}
\end{wrapfigure}
We begin our study of divergence in personalizing policies with a 2D continuous episodic gridworld environment, where a point agent must move to a target position in a constrained amount of time. The state space is given by the agent's current 2D position, and at each timestep the agent may choose to go left, right, up, or down one pixel unit. Following these commands, the agent tries to maximize its reward, calculated to be the negative squared distance to the goal after each timestep. Finally episodes end every $100$ timesteps, or when the agent is within $0.01$ units of the goal. Particles start at origin $(0, 0)$, and try to reach target at $(1, 1)$ before the episode ends. $\mathcal{S}$ is defined by the coordinates of the particle at timestep $t \in \{0, 1, \ldots T\}$. Actions are discrete $a$, corresponding to moving one unit left, right, down, up, i.e.
$a_t \in \{(-1, 0), (1, 0), (0, -1), (0, 1)\}$.

We personalize this environment with the introduction of a population of entities, where we may either stick with one throughout the policy's entire training run or randomly introduce new entities into the world at the start of every episode. Each agent is initialized with a personalization function $\mathcal{F}: \mathcal{A} \rightarrow \mathcal{A}'$, which first remaps the cardinal directions of each action and then imposes additional variance. For example, given default action $a_r = (1, 0)$, telling a default agent to go right, our policy may encounter an agent who upon receiving action input $(1, 0)$ actually moves with action $(0.42, -1)$. This is achieved by first remapping $a_r$ to $(0, -1)$, and then imposing further variance on the x-coordinate. We can think of this as having the same set of interventions for all agents, but facing different responses in the form of varied actual transition outcomes.

\subsection{Practical divergence estimation}
As introduced in Section 2, by characterizing policies through their occupancy measures, we derive a similarity metric and quantitatively analyze their divergence. Furthermore, individual policies may move closer or further to others over time, forming new neighbors that reflect personalization over time. However, as discussed in \citep{DBLP:journals/corr/HoE16}, simply using the occupancy measure as presented and matching on all states and actions is not practically useful.
Instead of looking at an occupancy measure defined over all states as above then, we propose an alternative measure reliant only on a sampled subset of states. We observe that two policies may intuitively be different if for each of various given states, they differ in their action distributions. However, the states must have a non-zero chance of occurring in both policies of interest. Accordingly, in the $K$-shot $N$-ways setting, where for each of $N$ MDPs $\mathcal{M}_i$ we get $K$ trajectories $\{\tau^1_i, \ldots, \tau^K_i\}$ to observe before updating and an additional trajectory $\tau'_i$ after the update, we can calculate a kernel density estimate (KDE) $\hat{q}(\tau_{1: N})$ based on the observed trajectories $\tau_{1: N}' = \{\tau'_1, \tau'_2, \ldots \tau'_N\}$. States $\hat{s}_j \sim \hat{q}(\tau_{1: N}')$ can then be sampled, with probability density $\hat{q}(\tau_{1: N}')(\hat{s}_j)$.

Referring back to (1), for any policy $\pi_i$, we estimate the conditional probability $\pi(\cdot | s)$ by feeding in $\hat{s}_j$ to our model network and obtaining the probability vector $\pi'_i(\hat{s}_j) = (p_1^j, \ldots, p_n^j)$ where $p^j_a$ denotes the probability of taking action $a$ given state $s_j$. Towards estimating $\sum_{t=1}^{T} P(S_t = s | \pi)$, we again calculate the KDE over our observed trajectories, but now only consider $\tau'_i$ for MDP $\mathcal{M}_i$.

Accordingly, for any set of policies we first obtain a respective observed batch of trajectories, calculate an estimated state-marginalized variant of occupancy measure $\hat{\rho}_s^i$ for $s \in \mathcal{S}$ and entity types $\mathcal{T}_i$, and compute pairwise symmetric divergences across entity types. In our case we use the Jensen-Shannon divergence \cite{lin1991divergence}.
\begin{equation}
    D_{\text{JS}}(\rho^{i}_s, \rho^{j}_s) \triangleq D_{\text{KL}}\Big(\rho^{i}_s\; \big{|}\big{|}\; \frac{\rho^{i}_s + \rho^{j}_s}{2}\Big) + D_{\text{KL}}\Big(\rho^{j}_s\; \big{|}\big{|}\;\frac{\rho^{i}_s + \rho^{j}_s}{2}\Big) \text{ for } i \neq j
\end{equation}
where $D_\text{KL}(\cdot, \cdot)$ is the Kullback–Leibler divergence.
As a final measure, we employ $K$-medoids clustering to group our policies using the pairwise $D_{\text{JS}}(\cdot, \cdot)$ as a distance metric. The overall procedure is summarized in Algorithm 1.
Besides describing our policies, we use these metrics to inform algorithms for few-shot learning, as described in the next section.

\begin{algorithm}[b]
\caption{Policy Divergence Estimation through Observed Trajectories}
\label{alg1}
\begin{algorithmic}[1]
\REQUIRE Distribution over entity types $p(\mathcal{T})$, initial policies $\pi^i_\theta \in \Pi$ and episode horizon $T$
\REQUIRE Initialize update counter $t = 0$, and batch size $n$ for computing estimates
\WHILE{not done}
    \STATE Sample entity type $\mathcal{T}_i \sim p(\mathcal{T})$ and initialize new policy $\pi_\theta^i$
    \STATE Sample $K$ trajectories $\tau^i = \{s_1, a_1, \ldots, s_T\}$ following $\pi^i_\theta$
    \STATE Update $\theta'$ through vanilla policy gradient with REINFORCE \cite{NIPS1999_1713}
    \STATE Save trajectory $\tau'_i = \{s_1, a_1, \ldots, s_T\}$ using updated
    $\pi_{\theta'}^i$
    \STATE Increment $t \leftarrow t + 1$
    \IF{$t = n$}
        \STATE Compute type-specific KDE $\hat{q}(\tau_i')$ for all saved $\tau_i^{'}$
        \STATE Obtain concatenated updated trajectories $\tau_{1:N}' = \{\tau'_1, \ldots, \tau'_N\}$

        \STATE Compute overall KDE $\hat{q}(\tau_{1: N})$ and generate samples $s \sim \hat{q}(\tau_{1: N}) $
        \FORALL{$s$}
             \STATE Calculate $\pi^i(a | s)$ for all policies $\pi^i$ and compute state probability densities $\hat{q}(\tau_i')(s)$
             \STATE Estimate $\hat{\rho}_s^i = \pi^i(a | s)\hat{q}(\tau_i')(s)$ for given state $s$
        \ENDFOR
    \STATE Compute time-indexed pairwise Jensen-Shannon divergences
    $D_{\text{js}}(\hat{\rho}_s^i, \hat{\rho}_s^j)$
    for all $\mathcal{T}_i, \mathcal{T}_j \in \mathcal{T}$
    \STATE Obtain divergence metric $\sum_{s \sim \hat{q}(\tau_{1:N}')}D_{\text{js}}(\hat{\rho}_s^i, \hat{\rho}_s^j)\hat{q}(\tau_{1:N}')(s)$
    \STATE Reset $t = 0$
    \ENDIF
\ENDWHILE
\end{algorithmic}
\end{algorithm}

\subsection{Cluster-adapting meta-learning}
Given our policy divergence estimators, we hope to better organize our previous experiences for adaptation in new environments. In this section, we describe a $K$-medoids-inspired meta-learning algorithm called cluster-adapting meta-learning (CAML). Similar to serial versions of meta-learning algorithms such as MAML, CAML iteratively learns an initialization for parameters of a neural network model, such that given new MDP $\mathcal{M}^i$, after a few trajectory rollouts and a single batch update during test time, our policy performs competitively to those pretrained on the same environment. However, while previous algorithms update a single set of optimal parameters drawn from parameter space, CAML maintains parameters representative of larger clusters.

In setting up CAML, we consider the following design choices: (1) a valid distance metric to compare past trajectories and their corresponding policies in a model-free setting, (2) the clustering algorithm to organize past experiences, and (3) an initialization method to select the most appropriate policy parameters at test time. We consider (1) to be the most important contribution, and included details in Section 4.2. As described, we implement (2) using the $K$-medoids algorithm \cite{kaufmann1987clustering}, although in practice any clustering method may be used.
Finally with regard to $(3)$ at the end of our training iterations we obtain $k$ medoid policies, and during evaluation we view fast-adaptation to each individual MDP as a bandit problem. Given $k$ available arms (the policies) and $K$ arm pulls (the few number of shots, i.e. episodes allowed), we want to maximize the corresponding reward from following the arm policy for an entire episode. While more sophisticated multi-armed bandit methods exist \cite{DBLP:journals/corr/abs-1902-03657}, we found that simply sampling policies and saving their cumulative associated rewards for each rollout $r \in \{1, 2, \ldots, K\}-$then initializing with the medoid policy parameters corresponding to the highest reward$-$performed competitively against baselines. The full method is described in Algorithm 2. Following these considerations, we found that in few-shot settings ($K = 10$ trajectory rollouts) CAML performs favorably to alternatives.

\begin{algorithm}[H]
\caption{Training with CAML ($K$-medoids version)}
\label{alg1}
\begin{algorithmic}[1]
\REQUIRE Distribution over entity types $p(\mathcal{T})$, initial policy parameters $\theta$, episode length $T$
\REQUIRE Number of medoids $k$, batch size to cluster with $n$
\FOR{initial iterations $1, \ldots, n$}
    \STATE Sample entity type $\mathcal{T}_i \sim p(\mathcal{T})$ and initialize new policy $\pi_\theta^i$
    \STATE Obtain and save updated $\pi^i_{\theta'}$ and $\tau_i'$ through Steps $3, 4, 5$ in Algorithm 1
\ENDFOR
\STATE Compute distance matrix $D$ with pairwise distances on the saved $\tau_i'$ using Algorithm 1.
\STATE Perform $K$-medoids clustering on $D$, saving set of $k$ corresponding medoid policies $\Pi_\theta^k$ and trajectories $\tau^k_{1:N}$. Save trajectories $\tau^k \in \tau^k_{1:N}$ to compute next iteration of occupancy measures.
\FOR{iterations $n+1, n+2 \ldots$}
    \STATE Sample entity type $\mathcal{T}_i \sim p(\mathcal{T})$ and randomly sample medoid policy $\pi_\theta \in \Pi^k$
    \STATE Obtain and save updated $\pi^i_{\theta'}$ and $\tau_i'$ through Steps $3, 4, 5$ in Algorithm 1
    \IF{number of saved policies equals $n$}
        \STATE Repeat steps $5$ and $6$, updating $k$ medoid policies and trajectories.
    \ENDIF
\ENDFOR
\end{algorithmic}
\end{algorithm}

\section{Experiments}
In our experimental evaluation, we validate if: (1) clustering on our occupancy measure estimate reasonably characterizes divergence of policies over time, (2) our environments suggest that personalization is necessary for fast adaptation, and (3) building on these metrics can inspire effective few-shot learning algorithms. To focus on the effect of our algorithm we use vanilla policy gradient (VPG) for both meta-learning and evaluation.

\subsection{Matching policy divergence to training environments}
Towards evaluating both the dynamics of our testbed and the strength of our policy divergence distance metric, we first sought to see if clustering on trained VPG trajectories could recover their corresponding initial environmental dynamics. To do so, we first initiated six latent entity types by remapping controls completely (e.g. the yellow particle in Figure 1). Afterwards, for each latent type, we generated four variants by further introducing uniform variance for given entity and action while preserving the defining cardinal direction (red particle in Figure 1). In total we ended up with $24$ entity types. To eludidate divergence across optimal policies, for each type we then trained an individual VPG policy from scratch over $40$ updates performing a batch parameter update every $10$ iterations, and calculating estimated occupancy-measure-based distances according to Algorithm 1.

\begin{figure}[t]
  \centering
  \begin{subfigure}{.33\columnwidth}
    \centering
    \includegraphics[width=\linewidth]{figures/checkpoints-0.png}

  \end{subfigure}%
  \hfill
  \begin{subfigure}{.33\columnwidth}
    \centering
    \includegraphics[width=\linewidth]{figures/checkpoints-20.png}

  \end{subfigure}%
  \hfill
  \begin{subfigure}{.33\columnwidth}
    \centering
    \includegraphics[width=\linewidth]{figures/checkpoints-40.png}

  \end{subfigure}
  \caption{\textbf{Policy divergence over time.} T-SNE visualization of policy divergences over $40$ training updates. Colors denote original latent group. Computing pairwise distances using our divergence metric leads to noticeable grouping over time.}
  \vspace{-12pt}
\end{figure}

As observed in Figure $2$, based on estimated occupancy measure, trained policies show increasing signs of neighboring with members of their respective groups over training updates. One takeaway is that our occupancy measure-based divergence is effective at measuring policy divergence, where directed through their respective environments, we can thus evaluate how different two policies are based on their trajectories.
Accordingly, in the few-shot RL setting, one valid strategy for producing an optimal policy without having to train from scratch would be to identify prior expert policies trained on similar environments. Taking this one step further, because our occupancy measures directly derive from $\pi(a | s)$ which is itself derived from a policy network's parameters, this provides another interpretation into the effectiveness of finding nearby parameters in policy space.

\subsection{Evaluating fast adaptation to personalized environments}

\begin{figure}[b]
  \centering
  \begin{subfigure}{.3\columnwidth}
    \centering
    \includegraphics[width=\linewidth]{figures/eval_graph-1-3.png}

  \end{subfigure}%
  \hfill
  \begin{subfigure}{.3\columnwidth}
    \centering
    \includegraphics[width=\linewidth]{figures/eval_graph-2-3.png}

  \end{subfigure}%
  \hfill
  \begin{subfigure}{.39\columnwidth}
    \centering
    \includegraphics[width=\linewidth]{figures/eval_graph-3-3.png}

  \end{subfigure}
  \caption{\textbf{Evaluation of learning algorithms on personalized environments.} CAML performs comparatively well across various evaluation entity types. }
\end{figure}

Given training on a set support types $\mathcal{T}^S$, our learning algorithms should quickly perform well on unseen query type set $\mathcal{T}^Q$. In-line with $K$-shot RL, for each support type $\mathcal{T}_i^S \in \mathcal{T}^S$, our policies are allowed $K$ rollouts for adaptation during each training iteration. Towards evaluation, for each learning algorithm we train with $100$ iterations using its policy-specific training algorithm. All underlying gradients were calculated with VPG, updating with batch size $10$. For evaluation, due to the on-policy nature of policy gradient, we first allow $K$ rollouts to collect new samples from unseen type $\mathcal{T}_i^Q \in \mathcal{T}$. Allocating these trajectories as an initialization period, for each $\mathcal{T}_i^Q$ we then evaluate fast adaptation by comparing rewards across policies for up to five updates with fine-tuning via VPG.

We compare CAML to five other methods: (1) pretraining a policy by randomly sampling from all support environments during training, similar to joint training \cite{DBLP:journals/corr/abs-1709-07979}, (2) pretraining a policy in the same query environment that we use for evaluation, (3) pretraining a policy in a different environment, (4) training with Reptile, a meta-learning algorithm comparable to MAML in performance but much simpler in computation that performs $K > 1$ steps of stochastic gradient descent for randomly sampled support environments before directly moving initialization weights in the direction of the weights obtained during SGD \cite{DBLP:journals/corr/abs-1803-02999}, and (5) randomly initializing weights.

The results in Figure 3 suggest that CAML is able to quickly adapt to unseen entity types after one update during evaluation. We note that given three different environments, CAML consistently outperforms a support type-pretrained policy and a randomly-initialized policy, and also seems to outperform Reptile to varying degrees as well. The results also hint at differences in the environments dynamics. For example, entities with evaluation types $1$ or $2$ demonstrate instances where jointly trained and models pretrained on different environments initialize in poor states, suggesting that they require heavier personalization for success. On the other hand, evaluation type $3$ represents a scenario where entity types may be representative of the larger population. While the matched pretrained policy serves as an upper bound in performance for all cases, we observe that the joint trained and unmatched pretrained policy model also seem to effectively transfer to the new environment.

Lastly we acknowledge the potential challenges of pretraining to personalized environments. Observing the ending coordinates of various policies in Figure 4, we note that in certain situations, such as when the query environment is not representative of a policy's past experiences, methods such as joint training or pretraining with another environment may initiate detrimental behavior such as moving in the wrong direction.

\section{Discussion and Future Work}
We tackle meta-learning for fast adaptation in personalized environments, where environments may share the same reward, but differ dramatically in state transition probabilities. Using ideas from characterizing policy divergence, we introduce
competitive improvements to a promising meta-learning algorithm paradigm, better initializing to a set of optimal policy parameters by better organizing past experiences into relevant clusters. While we believe further baselines and experiments are needed, our results also show preliminary support that meta-learning to minimize total distance between all potential optimal parameters may be sub-optimal when trying to adapt to diverse-enough environments. Our method builds on this original motivating curiosity, and suggests one possible alternative to better approach personalized reinforcement learning.

\begin{figure}[t]
  \centering
  \begin{subfigure}{.33\columnwidth}
    \centering
    \includegraphics[width=\linewidth]{figures/final_dest-136.png}

  \end{subfigure}%
  \hfill
  \begin{subfigure}{.33\columnwidth}
    \centering
    \includegraphics[width=\linewidth]{figures/final_dest-245.png}

  \end{subfigure}%
  \hfill
  \begin{subfigure}{.33\columnwidth}
    \centering
    \includegraphics[width=\linewidth]{figures/trajectories-0.png}

  \end{subfigure}
  \caption{\textbf{Performance on 2D navigation.} Qualitative comparisons marking the ending location of particles at the end of episodes for the first evaluated type, with attention to performance after initialization and a few updates. Only the matched pretrained model and CAML reach positions noticeably closer to the target coordinate $(1, 1)$ in $5$ updates (left). Visualization of example trajectories taken by pretrained and random policies (right).}
  \vspace{-5pt}
\end{figure}

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\bibliographystyle{plainnat}

redpajama_set_name: RedPajamaArXiv


When people think of "rentals", a large number visualize smaller-sized housing—such as apartments and townhouses that typically don`t offer residents their own identity. Anyone relocating to Belle Plaine, however, really should recognize that there are also houses for rent in the area. An array of reasons exist for researching houses for rent in Belle Plaine—instead of apartments or even purchasing a house—and by making use of the proper tools for the job it`s possible to very quickly trim down a list of properties and identify the best place for you.
A lot of individuals prefer houses for rent in Belle Plaine because they provide far more space, in contrast with the majority of apartments or condominiums. Furthermore, rental homes allow residents a great deal more flexibility with lease obligations less than or equal to a year (most often) and not a lot of accountability for maintenance. Landlords or management companies are ordinarily those that will handle repairs and maintenance, but study your lease agreement thoroughly to make sure that there won`t be any contention over which party is responsible for what in the event that anything breaks.
When you search in Belle Plaine, houses for rent are located primarily in outer-lying neighborhoods, as opposed to the downtown area. Bear in mind, if you`re serious about living downtown, there are always some exceptions. Houses for rent in Belle Plaine offer a great deal of added space when compared to apartments or condominiums, so they`re wonderful for families with children and groups of multiple roommates. Whereas most other forms of housing alternatives have communal amenities and may or may not have a backyard, single-family houses sit independently from adjoining residences and have their own individual facilities such as laundry or even a home office or game area. Houses for rent in Belle Plaine, Kansas still are also found within easy travel distance to many of the city`s places of work, destinations and entertainment areas.
RentFinder gives you the ability to quickly see available houses for rent in Belle Plaine that are suited to your desires. Should you have children—to give an example—RentFinder will allow you to have a look at primary or secondary schools and child-friendly venues in the local area over on the right-side map on this web page. Do you have cats or dogs that are parts of your family unit? You`ll be able to look for pet-friendly parks too. If it`s important to ensure you live in a spot where it`s easy to grab your fix of pizza, this web page`s right-hand area can help you do just that.
So, once you do spot that undeniably flawless house for rent in Belle Plaine, Kansas in the midst of your web search, you should not transfer any kind of upfront cash for it without ever seeing the property prior to that point. This means planning to view the home in person, and not simply browsing photos forwarded to you from the real estate agent or landlord, or even seen through the web.
When the opportunity comes to stop by a house, be mindful of the small things that show wear and tear. The owner of the place is ordinarily responsible for maintaining it, so its existing shape will offer a hint as to how they will treat it if you end up being a tenant. Does it look like the yard has been kept up in nice shape? Do you observe any inside areas that are in disrepair and badly cared for everywhere you look? Do virtually all of the appliances it comes with (washing machine, range, refrigerator, or microwave) power on? Do all of the doorways pointing to the outside lock securely? Furthermore, it is a good idea to get a glimpse at the nearby houses in the immediate area. Even though most will belong to separate individuals, their appearances and all around condition are very enlightening about what that location is really like.
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text:
Animal Planet » Programs » Ray Mears Wild About Britain
Ray Mears Wild About Britain
Ray Mears takes viewers on an incredible journey to beautiful locations around Britain in 'Ray Mears Wild Britain'. Watch the wildlife expert uncover some of the finest habitats and fascinating wildlife of the UK, showing viewers how we can all find our own relationship with the wild, even in places close to our homes. Each episode of the series is themed around a different habitat such as Woodland, Wetlands and Farmland. Highlights of the series include; Ray explores the Forest of Dean in Gloucestershire, using his tracking skills and expert wildlife and plant knowledge to reveal its secrets to viewers and Ray explores Britain's shingle shore in East Anglia which covers 10,000 miles of coastline and meets a local fisherman, who has dug lugworms for bait for 40 years. Overall, Ray's passion and knowledge invites viewers to discover a hidden world which lies just beyond their doorstep as he explores each location and delves into the heart of each natural habitat.

redpajama_set_name: RedPajamaCommonCrawl


This two-day course prepares current Scrum Masters for their leadership role in facilitating Agile team, program, and enterprise success in a Scaled Agile Framework (SAFe®) implementation. Attending the class prepares you to take the SAFe® 4 Advanced Scrum Master exam and become a certified SAFe® Advanced Scrum Master (SASM).
SAFe® Advanced Scrum Master training course is provided under Scaled Agile, Inc. Partner Program Agreement.
A SAFe Advanced Scrum Master (SASM) is a servant leader who has a broader, systems view of the Scrum Master role and provides integrated practices for facilitating multiple team, program, and enterprise success in larger programs. Advanced Scrum Masters understand Agile and Scrum anti-patterns and facilitate cross-team interactions to support program execution and relentless improvement. Providing this experience and servant leadership helps teams improve flow and quality of work, resulting in cohesive, high-performing Lean-Agile teams that deliver continuous value.
Attending the class prepares individuals to take the exam and achieve certification for SAFe® Advanced Scrum Master (SASM).
SAFe® Advanced Scrum Master training course offered by Architecture Center Ltd is provided under Scaled Agile, Inc. Partner Program Agreement and complies with the requirements for Scaled Agile, Inc. Courseware License Agreement.
To obtain the SAFe® 4 Advanced Scrum Master (SASM) certificate, it is necessary to obtain a positive result on the SAFe 4 Agile exam.
A certified SAFe Advanced Scrum Master (SASM) is a Scrum Master responsible for improving team results through the facilitation of cross-team interactions and relentless improvement. Attending the class prepares you to take the exam and become a SAFe® 4 Advanced Scrum Master (SASM).
The first step toward becoming a SASM is to attend the SAFe Advanced Scrum Master course. This is a requirement, because attending the course provides access to all the study materials and the exam. Note that attending the course does not guarantee passing the exam.


This prestige tower is made up of retail, commercial and luxury residential zones and occupies the last prime CBD Brisbane riverside site. The luxury apartments crowning the tower featured up to 5m cantilevered balconies in a highly sculptured form offering panoramic views up both reaches of the Brisbane River.
The building features a central core utilising only the external ring of the core and the external building frame for lateral stability. This solution offered maximum flexibility in core planning for the multi-use tower together with simplicity in construction.
Detailed analysis and innovative temporary works structures allow the lift and stair core to span over alluvial deposits from an ancient creek bed, a heritage drain structure and the principal telecommunications trunk main connecting Brisbane and Sydney.
Detailed 3D stress analysis and produced documentation that allowed the builder to form and construct the columns with a structurally efficient and simple reinforcement layout.


Without these sponsors, we would not be able to provide you with all we intend to. The best way to say thank you is to pay them a visit and see what they have to offer you. It just may be what you're looking for.
You can sponsor our academy, the Aiki Jutsu Do brand, one of our Youtube videos, sponsor us by providing us with your products (f.e. clothing, gear, shoes, and other training items that we will endorse in our training and course videos), or even get such a message in one or more levels of our online course. Students view lessons for an average of 30 minutes and at least one or more months to complete one level. During that time, they can absorb your brand message in a clearly visible yet friendly manner. Needless to say that the audience is highly targeted which allows you to reach the highest value your marketing can achieve.


text:
Q. How Old was General Hood at Gettysburg?
Posted in Media, Memory by Andy Hall on June 26, 2010
A. Thirty-two.
Hood as portrayed by (l. to r.) Patrick Gorman in Gettysburg (1993) and Gods and Generals (2003), Levon Helm in In the Electric Mist (2009) and himself in real life (c. 1865).
Picking up a thread of an idea from David Woodbury at Of Battlefields and Bibliophiles, I've been thinking about how film and television typically portrays Civil War figures. Most often they're depicted substantially older than they really were. I'm not necessarily speaking of the actor's actual age versus character's (because actors routinely play much younger characters), but more generally his apparent age — how he's made to look. It's easy to see why this would be the case. When an actor portraying a Civil War figures actually is about the right age for the character, it's often jarring for the viewer, to whom it doesn't "feel" right even if, in fact, it is. Matthew Broderick was 25 or 26 when he shot Glory; his character, Robert Gould Shaw, was exactly that age at the time of the events depicted in the film. Nonetheless, although the actor and the role were perfectly matched for age, it still didn't "look right" for a lot of people, and probably harmed the overall public reception of the movie. People just couldn't see someone that young, in that role. (Being known at the time primarily for his role as as Ferris Bueller didn't help.)
But this just highlights something that we might easily forget — the vast majority of these men, from private to general, were very young by modern standards. At the beginning of the war, Ulysses S. Grant was 39. George Meade was 45. George McClellan was 36. George Pickett was 36. James Longstreet was 40, as was John George Walker. Stonewall Jackson was 37. William Tecumseh Sherman was 41, and so on. Robert E. Lee was 54, the "old man," not just because of the senior position he held, but because he was, by the standard of the day, objectively and factually old.
There are lots of exceptions, of course — Albert Sidney Johnston was nearly 60 when he got plinked at Shiloh — but still it amazes me how young these men were when so much rested on their shoulders.
Update: Argghh! Dimitri Rotov has stolen a march on me on this very topic over at Civil War Bookshelf.
Tagged with: Albert Sidney Johnston, George McClellan, George Meade, George Pickett, Gettysburg, Glory, Gods and Generals, In the Electric Mist, James Longstreet, John George Walker, Levon Helm, Matthew Broderick, Patrick Gorman, Robert E. Lee, Robert Gould Shaw, Shiloh, Stonewall Jackson, Ulysses S. Grant, William Tecumseh Sherman


This is a image galleries about 1996 Dodge Ram Wiring Diagram. You can also find other images like wiring diagram, parts diagram, replacement parts, electrical diagram, repair manuals, engine diagram, engine scheme, wiring harness, fuse box, vacuum diagram, timing belt, timing chain, brakes diagram, transmission diagram, and engine problems. But we don't host any of these image files according to 1996 Dodge Ram Wiring Diagram on this site. We never store the image file in our host. We just links any topics about 1996 Dodge Ram Wiring Diagram to many other sites out there. If you need to remove any file or want to see the images, please contact original image uploader or browse out there.
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text:
For The SPARQ Bias In Financial Markets Program
General Support/Program
Stanford SPARQ (Social Psychological Answers to Real-world Questions) is a center in the social psychology area of the Stanford University Department of Psychology. Practitioners across sectors — from government, business, and nonprofits — work with SPARQ to solve communities' most pressing problems, with the mission of creating and sharing social psychological insights with people working to improve society. This program grant will help SPARQ in its next phase of research on implicit bias in financial markets with respect to race and gender — conducted in partnership with Illumen Capital and the Stanford Global Projects Center. The grant will help the partnership design an intervention toolkit and set of evaluation tools, where bias may be measured directly before/after training, and then several months later to measure progress.
siepr.stanford.edu
366 Galvez Street, Stanford, CA, 94305, United States
for a new paradigm for utility wildfire safety in California
The Stanford Woods Institute for the Environment's mission is to produce breakthrough environmental knowledge and solutions that sustain people and planet today and for generations to come. This grant supports the institute's ability to identify more effective utility wildfire safety solutions in California, which is critical to achieving both wildfire resilience and climate goals. (Substrategy: Wildfire)
for the Toward a World Free of Nuclear Weapons project
Former secretary of state George Shultz's Toward a World Free of Nuclear weapons project has renewed and catalyzed worldwide interest in reestablishing the vision of, and taking steps toward, significant reductions in global nuclear arsenals and their ultimate elimination. In the coming year, Secretary Shultz, renowned physicist Sidney Drell, and former Ambassador James Goodby plan to enlist the support of countries that have nuclear weapons capabilities and those that are potential nuclear weapon states to encourage step-by-step progress toward a reduction in the role of nuclear weapons worldwide. Such steps could include beginning joint aerial monitoring for nuclear weapons testing and developing methods to encourage active participation of the nine nuclear weapons states in accelerating disarmament.
for the Woods Institute Leopold Fellowship
This grant to the Woods Institute at Stanford University would provide communication training to outstanding scientists working in a broad range of environmental fields. The goal is to impart the skills necessary for scientists to translate their knowledge in non-academic settings in a manner that is understandable to decision makers, media, and the public.
Latino leaders on the power of community-led conservation
How communities of color lead on climate solutions — and ways to support them
Statement on the signing of the Inflation Reduction Act


text:
The 50 worst rap lyrics of all time: 50-41
Noah Hubbell March 4, 2013 5:30AM
47. Lil' Wayne - "Goulish"
"Fuck Pusha T and anybody that love him/His head up his ass, I'mma have to head-butt him"
This is the rhyme that Wayne began his Pusha T diss with, so no matter what he said after, he had already lost. Wordplay for the sake of wordplay is like chewing for the sake of chewing.
46. Notorious B.I.G. - "The Ten Crack Commandments"
"Rule nombre uno"
Big is one of the greatest American rappers of all time, but did you know that he's also one of the greatest Spanglish rappers of all time? Sure, "nombre" means "name," but, come on, this is America. If it sounds Spanish and you know what he's trying to say then, functionally, you're speaking the language.
45. Game - "Dreams"
"It's kinda hard to imagine, like Kanye West coming back from his fatal accident to beatmaking and rapping."
Game is referring to the car accident that nearly killed Kanye years ago, the one he talks about in "Through the Wire." But, apparently, Game doesn't know what fatal means. But wait, they do call Kanye Yeesuz... Maybe Game knows something about resurrection that we don't.
Noah Hubbell
Meow Wolf Introduces Party Portal, a Series of Immersive Concerts


The Village of Lombard is currently accepting applications for its 2018 Youth Leadership Program. High school students who live in Lombard and are interested in learning about local government and public service are encouraged to apply.
Participating students will receive interactive presentations and tours from leaders in the Village Manager's Office, Finance, Community Development, Police, Fire and Public Works Departments.
Feb. 15, from 6:30-7 p.m. and will continue with a regularly scheduled Village Board Meeting, from 7 – 9 p.m. Students will be recognized by Lombard's Village President and Board of Trustees, and will be presented with a certificate of completion for the program.
The class size will be kept to a maximum of 10 students, with a minimum of 7. All high school students between the ages of 14 and 18 are invited to apply, however Lombard residents will be given preferential admittance. Applications are available at www.villageoflombard.org/youthleadership and can be dropped off at Village Hall or emailed to meadea@villageoflombard.org by 4:30 p.m. on Monday, Jan. 15, 2017. Students chosen for the program will be notified by Tuesday, Jan. 16, 2017.
Anyone with questions regarding the program or application process can contact Lombard's Communications Coordinator Avis Meade at (630)620-5718 or meadea@villageoflombard.org.


The school district supports our Early Childhood Program by providing and maintaining our child care facility and utilities. However, tuition is the primary funding source for staff salaries and the purchase of equipment and supplies. A non-refundable registration fee of $25.00 is required with your enrollment packet.
If you qualify for D.E.S. subsidy, you will be required to pay a co-payment (if applicable) and any difference between the D.E.S. subsidy amount and the center's fee schedule. If you are receiving D.E.S. childcare assistance and your child is absent, you will be charged accordingly (D.E.S. only reimburses 2 absences per month).
Invoices for tuition will be distributed on the district paydates and must be paid in advance for the following two weeks of services. If payment is not received, your child will not be able to attend the center. Children who are absent from the center for more than 1 week without advance notice may be withdrawn from the center. All payments must be made by check or money order only and payable to Tiny T-Birds. Parents presenting checks for payment will need to show a current driver's license or proof of ID to the Center Director. All payments should be placed in the secure payment box located on the door of the Director's office. Payments can also be made through the Web Store on the CRUHSD website for an additional fee of $2.50, be sure to forward your payment confirmation to the Assistant Director at dwilliams@crsk12.org to ensure payment is posted to your account. Cash will not be accepted. No refunds will be given.
NSF CHECKS: Beginning July 1, 2007, the check writer will be notified by the Mohave County Treasurer of returned checks for non-sufficient funds, and asked to pay the full amount with fees, within 12 days of receipt of the notice, to the Mohave County Treasurer. Unless this amount is paid in person within the 12 days, with a cashier's check or money order, all information relating to this incident will be reported to the Mohave County Attorney's Office for criminal prosecution. If your check is returned, we will require all future payments to be made by money order.
Tuition scholarships are funded by First Things First (FTF) and administered through Valley of the Sun United Way. We receive a limited number of scholarships to be distributed to eligible families based off of family eligibility criteria formed by FTF and will be awarded to eligible families based on the center enrollment priority.


Custom Field Suite (CFS) lets you add custom fields to your posts. It's lightweight and battle-tested (there's not much to break).
We do not provide support.
This is a free plugin. We're not selling anything.
CFS includes 14 field types. There are no plans to add more.
If you want all the bells-and-whistles, use ACF.
Better than ACF, because this is compatible with the standard WP postmeta system. I was waiting for WP5.x compatible version, so now I'm very glad. great job!
This is very compatible plugin.
Your plugin works like a charm! It could be even better with more user-friendly loop element. Concept of loop is very good but it slightly inferior to native gallery (there's no multiple upload).
Dear developers! The plugin is just wonderful!
This is the easy, fast, flexible and free plugin. Field repeater is one of the best available functions it have. I am in love with CFS.
Thanks for this wonderful plugin .
Перевести «Custom Field Suite» на ваш язык.


text:
Consumer Telematics Systems Market: By Vehicle Type (Truck, Semi-Trailer, Motor Coach, Taxicab, Trailers & Others); By Components (Communication, Sensors, Control Unit, Audio/Video); By Services (Safety & Security, Navigation, Others); By Geography Forecast (2018-2023)
Global consumer telematics is valued $16.6 billion in the year 2017 and is anticipated to grow with a CAGR of 26.54% from the year 2018 to 2023.
Format Type: PDF+ Excel DatabaseReport Code: ATR 0012
Commercial Automotive Telematics Market: By Vehicle Type (Truck, Semi-Trailer, Motor Coach, Taxicab, Trailers & Others); By Components (Communication, Sensors, Control Unit, Audio/Video); By Services (Safety & Security, Navigation, Others); By Geography - Forecast(2018-2023)
Telematics is anticipated to grow at a CAGR of 9.1% during 2018-2023 and generate revenues worth $31.09 Billion by 2023. The increasing demand for connected vehicles for better safety management is expected to further drive the market
Driver Monitoring Systems Market: By Offering Type, By Key Components, By Monitoring Type, By Vehicle Type, By Vehicle Drive Type, By Geography - Forecast (2018-2023)
The current global driver monitoring systems market is estimated to be around $2.2 billion, and with the growing consumer interest in this market, driven by government legislations (Europe) and other regulatory body requirements at rise
Intelligent Transport System Market: Technologies & Standards (Cloud Service, GPS, Others) Components (Sensor, Interface board, others) Systems (ATMS, ATIS, Others) Application (Fleet Management, Traveler Information, Others) & Geography - Forecast(2019 - 2025)
With both ecological and economic adverse effects pertaining to traffic, intelligent transport system (ITS) market based on advanced telecommunication and information technology such as GPS and cloud service is gaining traction as an imperative asset for the transportation sector.
Connected Cars Market: By Connectivity Type (4G LTE, 3G, Wifi, Others), By Type of Interaction (Vehicle-to-Vehicle, Vehicle-to-Infrastructure, Others), By Features (Navigation, System Diagnosis, Safety & Security, Others) - Forecast(2019 - 2025)
Abundant scope of opportunities from numerous economies aggressively developing their transportation infrastructure for improved routes and transit has driven the connected cars market size to be $63.38 billion as of 2018.


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A realtor is someone who is a professional in the real estate business who is also a member of the National Association of Realtors, which is an association of professionals. Realtors always include those residential and commercial real brokers who work as agents. They do play an important role in the real estate business and that us why their presence in the real estate industry is always much appreciated. Without them there are very many things that are bound to go wrong and become messier in the real estate. The following are some of the roles that they play making them very important to you as a real estate owner. Learn more about top real estate service , go here.
They help keep the real estate business up to date. It is with the realtors that you will be able to know what is trending in the real estate business industry. They are always up to date and will make sure that they update you on what is going on around you and even worldwide that could be beneficial to your real estate business. With the information that they will provide, you will be able to know what is good for your business and what is threatening your business. This will help you make the necessary changes to make it better. Find out for further details on top agents in Caledon right here.
They will always look for clients to occupy your newly built or vacant residential houses. They will always try to convince those who want to move in to settle at your place. Some of them usually go to the extent of advertising the vacant houses and moving to places to find people to occupy them. Without them it can take very long for you to get a client while they will only take a few days to get one. This means that they will always keep your business moving.
Realtors always keep your real estate business in a professional standard. This makes your business become more trusted by those who are willing to take part. Since they belong to a professional association, they are certified and qualified to do that job. It is their area of specialization and anyone who sees that you employ the services of a realtor will definitely know that your business is legitimate.
Realtors will always do all the heavy work for you. As a real estate owner you will only be left with doing the financials. You might be a very busy person such that you cannot follow up o what is going on with your houses all the time. You can get a realtor who will always visit your residential houses and help to check and keep you up to date. He or she will inform you of what is going ion t help keep you up to date.


You may be an expanding business or you may just happen to operate in an area where multiple languages are spoken. Either way, you have to be able to convert your site into something that's readable in more than one language. It's not easy to successfully code this into a WordPress website, but never fear because our team of expert coders is here to provide you with a professional and stable WordPress multilingual website.
It's not just a matter of installing an add-on to transform your site into something that can be read in multiple languages. Extensive coding is necessary to get it right and to keep the site stable. And even then have you thought about how your audience is going to be able to switch between languages? A special button is going to be needed to switch between languages so you are giving your customers a choice in how they meet your business or organization.
We are in business because of our customers. It's been the case since the very beginning and that's why we are dedicated towards increasing the level of customer satisfaction amongst our entire client base.
That's why when you get in touch with us you will be put into contact with one of our master coders straight away. A rough quote can then be given to you on just a few basic details of your project. Quotes are kept as low as possible to make sure that we are accessible to as many people as humanly possible. Quality doesn't always have to come at a high price.
Better results are provided to all clients due to the fact that we create and share our detailed plans with you. These plans are to demonstrate to you exactly how your project will be carried out and in what time period you can expect the job to be completed in.
If you are tired with services that put no emphasis on customer satisfaction then grab a change of scenery and get in touch with us today!


text:
package dao

import (
	"context"
	"fmt"

	"encoding/json"
	"go-common/app/interface/main/web/model"
	"go-common/library/cache/redis"
	"go-common/library/log"
)

const (
	_keyCardFmt = "ac_%d"
)

func keyCard(mid int64) string {
	return fmt.Sprintf(_keyCardFmt, mid)
}

// SetCardBakCache set card data to cache.
func (d *Dao) SetCardBakCache(c context.Context, mid int64, rs *model.Card) (err error) {
	var bs []byte
	key := keyCard(mid)
	if bs, err = json.Marshal(rs); err != nil {
		log.Error("json.Marshal(%v) error(%v)", rs, err)
		return
	}
	err = d.commonSetBakCache(c, key, bs)
	return
}

//CardBakCache get card data from cache.
func (d *Dao) CardBakCache(c context.Context, mid int64) (rs *model.Card, err error) {
	key := keyCard(mid)
	conn := d.redisBak.Get(c)
	defer conn.Close()
	var values []byte
	if values, err = redis.Bytes(conn.Do("GET", key)); err != nil {
		if err == redis.ErrNil {
			err = nil
			log.Warn("CardBakCache (%s) return nil", key)
		} else {
			log.Error("conn.Do(GET,%s) error(%v)", key, err)
		}
		return
	}
	if err = json.Unmarshal(values, &rs); err != nil {
		log.Error("json.Unmarshal(%v) error(%v)", values, err)
	}
	return
}

func (d *Dao) commonSetBakCache(c context.Context, key string, bs []byte) (err error) {
	conn := d.redisBak.Get(c)
	defer conn.Close()
	if err = conn.Send("SET", key, bs); err != nil {
		log.Error("conn.Send(SET,%s,%s) error(%v)", key, string(bs), err)
		return
	}
	if err = conn.Send("EXPIRE", key, d.redisCardBakExpire); err != nil {
		log.Error("conn.Send(EXPIRE,%s,%d) error(%v)", key, d.redisCardBakExpire, err)
		return
	}
	if err = conn.Flush(); err != nil {
		log.Error("conn.Flush error(%v)", err)
		return
	}
	for i := 0; i < 2; i++ {
		if _, err = conn.Receive(); err != nil {
			log.Error("conn.Receive(%d) error(%v)", i, err)
		}
	}
	return
}

redpajama_set_name: RedPajamaGithub


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Portland Project Services Pty Ltd full data: finance, accounts, vacancies. Download bank accounts, tenders, credit history, taxes and fees Portland Project Services Pty Ltd.
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Portland Project Services Pty Ltd is a limited by shares Australian proprietary company. Located at VIC 3305 since 2000-04-30 the company is, as the updated on 2015-07-12 ABN database shows, registered. The company has been registered for Goods & Services Tax since 2000-07-01. Known company numbers for Portland Project Services Pty Ltd are as follows: ACN - 086149046, Australian Business Number - 21086149046. Portland Project Services Pty Ltd has been the company name since 2004-10-05. Otway Solutions is an example of a business name Portland Project Services Pty Ltd used. The complete list includes 1 solitary business name. Otway Solutions was the company's business name from 2006-03-28 until 2012-03-29. Their official company name changed on 2000-01-05 to Australian Drag Bike Association Pty Ltd. The company had used this name up until 2004-10-05. We found 0 trademarks that reference this company.


text:
using System;

namespace Shouldly
{
    [Flags]
    public enum StringCompareShould
    {
        IgnoreCase = 1,
        IgnoreLineEndings = 2
    }
}


Walmart will continue its buying spree of digital brands in 2018 as it aims to build and differentiate its online inventory in competing against Amazon. Marc Lore, eCommerce head of the world's largest retailer, revealed that there's a bigger strategy involved with Walmart's bullish acquisitions during the Shoptalk conference in Las Vegas on last Tuesday.
The retailer's acquisitions are targeted to expand its reach into new demographics—a younger and hipper clientele of millennials. Jet was acquired for more than $3 billion in 2016, while online startups ModCloth and Bonobos were purchased in 2017 for about $50-$75 million and $310 million, respectively.
Lore said that Walmart is prepared to spend about $50 million to $300 million, or more, for future acquisitions. Since Jet appeals to affluent millennials in urban areas like New York and San Francisco, adding more digital brands to its roster makes it all the more attractive.
While the aggressive acquisition of independent brands allows Walmart to learn about merchandise expertise in specific categories, these online startups will also benefit from the retailer's supply chain infrastructure.
"The concept is let's cross pollinate talent. Let's cross pollinate learnings and let's have a common backbone and backend through Walmart's supply chain infrastructure. This wasn't about let's figure out how to rip out costs. It's about how to play offense," Andy Dunn, founder of Bonobos and Walmart's SVP of digital consumer brands, emphasized.
Dunn joined Lore on stage to refute reports that his colleague was being ousted following Walmart's disappointing online growth in the last quarter of 2017. Despite the holiday shopping season, online sales growth decelerated to 23 percent from previous quarter's 50 percent. Moreover, Walmart's fourth-quarter results missed Wall Street's forecast earnings, causing shares to drop by more than 10 percent.
He downplayed the speculations of an early exit as well, reiterating his commitment to staying for five years, and possibly more.


We are Meteor testing experts. We can help you up your skills by working with you directly on the problems in your codebase. We find this is the best kind of training.
We've delivered multiple projects from Mobile/Web, through Backend and Low-level hardware drivers to financial-analysis software for European Investment Bank.
We will help you decide and polish the feature requirements, not only produce the code.
Contributors to webdriverio, cucumberjs, react-testing-library, testdoublejs and many others test-related tools. In general - we not only use the tools. We build and improve them. If you miss a functionality in the tooling - we will be able to help.
We've built blaze renderer (blazeRenderer / blazeRendererExample) to allow React-like testing of your Blaze templates.
We've made testable Meteor package that allows using modern JS testing tooling with meteor (think: jest, WallabyJS, etc).
CPU usage and number of connections before and after our improvements.


Upper: Textile mesh, PU support. High breathability and waterproof.
Toe Cap: Non Metallic 200 J.
Available: 1.100 N Textile puncture – resistant midsole (S3+CI+SRC).
Available: 1.100 N Textile puncture – resistant midsole (S3+CI+SRC+WR).


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Notre Dame and Northwestern Announce Home and Home Series
April 14, 2011 By paulmbanks Leave a Comment
Northwestern and Notre Dame, two schools that first met in college football 1889, are renewing their rivalry in 2014. The announcement came this morning on ESPN's Mike & Mike in the Morning radio show today by Mike Greenberg, a Northwestern alum, and Mike Golic, a Notre Dame alum.
The Wildcats last played the Fighting Irish in 1995, winning a massive upset that kicked off that magical Rose Bowl season. They'll travel to South Bend on Nov. 15, 2014 and then the Fighting Irish will visit Ryan Field during the 2018 season.
Northwestern and Notre Dame have played one another 47 times in the last 121 years, which makes ND the most popular nonconference opponent in NU program history.
"This is really an exciting time for Chicago's Big Ten team as we continue to upgrade our future non-conference schedule," said Phillips. "With schools such as Boston College, California, Syracuse and Vanderbilt visiting Ryan Field in future years, combined with the addition of Nebraska to our division in the Big Ten, there's no better time to be a Wildcat football fan.
The last time Notre Dame visited Evanston was 1976. The schools have met just four times since then–from 1992 to '95. For the 2014 game in South Bend, Northwestern has secured an additional number of visiting team tickets for Wildcat fans.
Paul M. Banks is CEO of The Sports Bank.net , a Midwest webzine. He's also a regular contributor to Chicago Now, Walter Football.com, Yardbarker, and Fox Sports
He does a regular guest spot each week for Chicagoland Sports Radio.com You can follow him on Twitter @thesportsbank and at Facebook here
ESPN College Gameday: How it Went From Transcendent to Unwatchable
2022 NFL Mock Draft 4-28-22 Final Edition
Six Leading Candidates to be the Next Notre Dame Head Coach
#10 Notre Dame Fighting Irish vs Purdue Boilermakers Game Preview
Quick Guide to the Top College Football Teams of 2021
Why Notre Dame QB Ian Book Fits in with the New Orleans Saints
Notre Dame to ACC? Jim Phillips Could be the Guy to Make it Happen
Notre Dame QB Coach Tommy Rees is Ideal for Northwestern OC Job
2020 NFL Mock Draft 9-18-19
Notre Dame Football: Who is the Biggest Remaining Obstacle to a CFP Berth?
Filed Under: College Football, college sports, Notre Dame Tagged With: Fighting Irish football, Irish, irish-wildcats, nd football, northwestern vs notre dame, northwestern-notre dame rivalry, Notre Dame, notre dame and northwestern home and home, notre dame and northwestern renew rivalry, notre dame football, notre dame vs northwestern, notre dame-northwestern rivalry


Langkah Syabas offers a total of 25 tastefully appointed chalets, all with a private varendah. Our 7 new rooms have absolute sea views and surround a new swimming pool. The chalets are of various sizes and configurations with an approximate average size of 37 square meters, with some individual units and others, semi-detached.
All chalets come with a range of standard facilities to make your stay comfortable. Our chalets are built facing a swimming pool with a tropical garden, approximately 100 meters from the sea.


This thread had become too unwieldy at 78 pages in length, spanning across a decade, where most of the submissions had been implemented many years ago. I recently reviewed each and every posting in this thread, verified what had been implemented in-product, and which had not as yet been. I then logged any outstanding submissions which had not yet been implemented to case CORE-9799 where we are tracking them for inclusion in an upcoming release. With that done, it's time to lock this thread and start anew. This time, providing a bit more guidance along the way to ensure everyone is successful in providing the necessary information required to properly identify these devices.
If you would like to submit the SysObjectID for your device so that it's properly identified by Orion, then please head over to our newly start thread at the link below.
1. Re: Tell us your "Unknown" devices!
2. Re: Tell us your "Unknown" devices!
3. Re: Tell us your "Unknown" devices!
The only Unknown devices I have are devices that do not support SNMP and I only ping them. Could there be a label created for these device types that said something like "SNMP Not Supported"? That way they would not show up unknown.
4. Re: Tell us your "Unknown" devices!
These are not showing up as "Unknown" but it would be nice to have something other than "Cisco"....all of my equipment is Cisco.
5. Re: Tell us your "Unknown" devices!
What type of device for each of these IDs?
6. Re: Tell us your "Unknown" devices!
It'd be cool to be able to choose an icon for "unknown" devices, such as BSD or Linux machines. Penguins and demon babies are always fun to see on a webpage.
7. Re: Tell us your "Unknown" devices!
8. Re: Tell us your "Unknown" devices!
Could you provide SysObjectIDs for the specific devices?
9. Re: Tell us your "Unknown" devices!
Juniper Netscreen 5GT / 1.3.6.1.4.1.3224.1.14 / Netscreen Technologies, Inc.
Accedian EtherNID VE100 / 1.3.6.1.4.1.22420.1.1 / accedian, inc.
10. Re: Tell us your "Unknown" devices!
11. Re: Tell us your "Unknown" devices!
12. Re: Tell us your "Unknown" devices!
13. Re: Tell us your "Unknown" devices!
One things that would be nice, is the ability to add a vendor/company name to an unknown device.
We have a few devices that we need to monitor but can ony do it via ICMP, and to go in and edit the database is OK, but it would be better if we could do this from the system manager.
14. Re: Tell us your "Unknown" devices!
15. Re: Tell us your "Unknown" devices!
16. Re: Tell us your "Unknown" devices!
SysObjectID (which you can find in Node Details) in System Manager.
17. Re: Tell us your "Unknown" devices!
I believe I provided all this info, albeit using slightly different terminology ?!?
I actually added this info to my Node Details View.
18. Re: Tell us your "Unknown" devices!
Sorry, wasn't singling you out, Guru. My original instructions were vague, so I edited them, and reposted to make sure the next poster was likely to read them.
19. Re: Tell us your "Unknown" devices!
All support for Brocade SAN switches. The top level OID exists, but there's nothing below it.
20. Re: Tell us your "Unknown" devices!
It monitors temperature with remote temperature probs.
21. Re: Tell us your "Unknown" devices!
22. Re: Tell us your "Unknown" devices!
You should probably open up a support case if it's changed between releases. This list is meant for devices we've never included.
23. Re: Tell us your "Unknown" devices!
24. Re: Tell us your "Unknown" devices!
Re: UnDP Unversal Alerting - is it possible?


So true. There is nothing but therapy for you and me, the Narc thinks he/she is superior and normal; at least thats what they want us to believe. I read, pray everyday because thats how powerful this disorder is. You and I will never understand completely without help like this. Its an addiction just like any other addiction and recovery is a dailt work in progress. The Narc is SHAMELESS and THIS is SHAMEFUL!!


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2018 - MIAMI - USA March 24, 2018
Radwanska stuns Halep in Miami comeback win
2012 champion Agnieszka Radwanska stunned Simona Halep at the Miami Open, coming back from a set down to knock out the World No.1.
By Stephanie Livaudais
MIAMI, FL, USA - 2012 champion Agnieszka Radwanska stunned Simona Halep at the Miami Open, coming back from a set down to knock out the World No.1.
Despite not posting back-to-back victories since January, No.30 seed Radwanska found her form to win 3-6, 6-2, 6-3, handing Halep her first ever defeat in Miami to a player ranked outside the Top 20.
Read more: Pliskova outfoxes tricky Hsieh in Miami marathon
"I expected a tough match from Simona, so I'm just glad I could win a match like this," Radwanska said afterwards. "There were pretty tricky conditions and some weird shots, but I'm very, very happy with the way I played, especially at the end. And to beat the No.1 in the world, obviously."
.@Simona_Halep 's serving is 👌 today!
She takes the first set 6-3 against Radwanska at the @MiamiOpen pic.twitter.com/bikHHAlNp5
— WTA (@WTA) March 24, 2018
The pair kicked off their eleventh career meeting by trading breaks, with Radwanska recovering from 0-2 down. Halep broke again to get ahead 4-1, but an error strewn game from her side of the court put them right back on serve.
Parity didn't last too long, though, as Radwanska - who only held her serve once in the the opening set - wasn't able to consolidate the break. Ten of her 18 unforced errors came in the first set alone too, and Halep took advantage of the free points to snatch away the set.
This match has flown by! @ARadwanska takes the second set 6-2 against Halep at the @MiamiOpen pic.twitter.com/E3Xq1EsmaG
It was a different story in the second set as Radwanska began on a tear, dropping just four points in the opening four games to break twice and open up a 4-0 lead. As Halep's frustration mounted, so did her unforced errors, as the Romanian tried to close out points too quickly and ended up overcooking her shots.
Halep was able to grab one of the breaks back at 4-1, but found herself immediately under pressure again, facing two break points. Radwanska broke again late in the second to take the set, sending the match into a third.
The Romanian got off to a break lead to start the final set 2-0, but her frustration and unforced errors - 44 in total against 31 winners - proved to be her undoing. Radwanska broke straight back, reeling off six of the last seven games of the match to claim the victory after an hour and 48 minutes.
.@ARadwanska is through to the @MiamiOpen Round of 16!
She knocks out World No.1 Halep 3-6, 6-2, 6-3. pic.twitter.com/OY6EtiG3VY
The victory is Radwanska's second career win over a World No.1, and the first since 2012. She extends her great record in Miami, too; she's now reached the round of 16 or better in ten of her 12 appearances.
Up next, Radwanska will take on former World No.1 Victoria Azarenka, who took down No.20 seed Anastasija Sevastova. Azarenka holds a 12-5 lead in the pair's head-to-head record.
"We haven't played for a very long time, but I've played so many matches against her," Radwanska recalled. "I think we've played on every possible surface and in every tournament!
"She's one of the best players in the world and she's always going to be. Playing her, I really have nothing to lose so that's going to push me 100%."


What a day it has been! We finally have our 2.3 Gingerbread release and everyone is happier than a boy on Christmas morning.
While all this joy is in the air, Droid-Life has been exclusively gifted with a Gingerbread Keyboard Port! Thanks to Peter Alfonso of Bugless Beast, we are sitting on some 2.3 goodness. So after the jump, we will have instructions and the 'how to' on getting this on your phone!
In the subject I mentioned this is for rooted devices only. Why? Because stock phones don't have the rights to install .libs. Sorry non-roots! But hey, time to start looking into the subject of rooting?
Update: Check out our hands-on of the new Gingerbread keyboard!
*Should work on ALL rooted 2.2 Android devices.
*Won't work on 2.1. You need the newest Voice Search app from the market.
*Good idea to make a back up of your LatinIME.apk from your /system/app folder in case something goes wrong.
2. Place on root of SD Card and boot into Recovery.
3. Install .zip as you would themes etc.
4. Reboot and select "Android Keyboard" under Input Method and you are good to go.
*Note* – DX users, "In Clockwork, go to mounts and storage>mount system and then back to install .zip from sd card".
I hope you all enjoy everyone! Anyone wanna take a gander at what else is next from 2.3?
Please show love to Peter by following his work on his Twitter.
And be sure to check out all of his work on his website! We love this guy!
LauncherPro Update Coming This Week?


We will be participating in the Frankfort Park District Super Dog Egg Hunt with our dogs, collecting treat-filled eggs!
It will take place on Saturday, April 13th at 2:30 PM at the Bark Park at Commissioners Park in Frankfort, IL at Sauk Trail and 80th Ave.
This is a free event, open to the public, and all are welcome.
Please call us directly at 708-377-5695 for more information or if you have any questions.
We hope to see you and your pets!
Board Your Pet with Us during the Holidays And Save!
Planning on going away for the upcoming holidays? We believe both you and your pet should enjoy a relaxing vacation!
Lincolnway Animal Hospital offers boarding for dogs and cats so that you can leave your pet in total comfort while you are away. In addition, book your pet's extended stay with us this season and get holidays FREE! Includes free boarding Thanksgiving day, Christmas day and New Year's day when you book 2+ nights.
Call us today at (708) 720-2400 to book your pet's vacation. Quick - spots are filling fast!
Do you want to save $25 on your pet's next visit to us? Now, it's simple! Just tell a friend or family member about our hospital and ask them to mention your name when they book a new client appointment. We will instantly credit both of your accounts with $25 for use on your pet's next visit.
As always, we appreciate the confidence you've shown in us by telling your friends about our hospital and love having you as part of the family!


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Home / News / Supercars
America's Elation announces plans for Freedom electric hypercar
Viknesh Vijayenthiran November 19, 2020 Comment Now!
A Silicon Valley-based company by the name of Elation has announced plans for a hypercar.
The company was founded by Carlos Satulovsky and Mauro Saravia, and aims to be America's preeminent hypercar marque, though it has some catching up to do in this regard with SSC. There's also Hennessey Performance Engineering and Czinger developing hypercars to challenge Europe's best.
Elation is unique in that its hypercar, to be called the Freedom, should it get around to being built, will offer the choice of internal-combustion or battery-electric power.
Elation Freedom
The internal-combustion setup will consist of a 5.2-liter V-10, 7-speed dual-clutch transmission, and all-wheel-drive system, suggesting that it's been borrowed from an Audi R8 or Lamborghini Huracan, both of which have the same setup. Elation is asking $2.3 million for a Freedom with this setup.
The electric setup is more interesting and actually the more affordable option. It's priced to start from $2 million and will offer a three-motor setup with 1,414 horsepower in standard guise. A more potent guise that will feature four electric motors will deliver as much as 1,903 hp, according to Elation. Like the Porsche Taycan, the Freedom will use a two-speed transmission at the rear axle to help it achieve a high top speed.
Performance claims for the top model include a 0-60 mph time of 1.8 seconds and top speed of 260 mph.
Powering the electric motors will be a T-shaped battery forming part of the vehicle structure. There will be 100- and 120-kilowatt-hour options, with the latter claimed to deliver 400 miles of range with moderate driving. According to Elation, the Freedom will be able to drive with only one motor at lower speeds or when cruising on the highway to conserve energy. Supplying many of the electronics is Cascadia Motion, a unit of automotive supplier BorgWarner, which has supplied motors for Formula E and energy recovery systems for Formula One.
All of the hardware will sit in a lightweight carbon-fiber monocoque structure, and the car will also have F1-style pushrod suspension and a low coefficient of drag of 0.28 Cd.
Elation plans to show the Freedom in production guise at the 2022 Geneva International Motor Show. Should there be sufficient demand, the company will start production shortly after the Geneva debut and build no more than 25 examples per year.
High-Res Gallery: Elation Freedom
Electric Cars Supercars
McLaren Artura's V-6 sounds off in teaser video, full reveal Feb. 16
Lamborghini launches track-focused Huracan STO
One-make race series for the Ford GT40 starts in 2021
Bugatti CEO Stephan Winkelmann to also oversee Lamborghini
All 9 US Bugatti Chiron Pur Sports recalled due to tires cracking Supercars January 17, 2022
Bollinger B1 and B2 electric truck and SUV shelved, focus shifts to... Industry January 14, 2022
Fisker teases electric supercar Supercars January 14, 2022
Subaru STI E-RA concept: Electric track machine targets 6:40 'Ring time Auto Shows January 14, 2022


Works on the implementation of very special stairs are coming closer and closer to their completion. On Monday, 16/04/2018 The HIDE restaurant at 85 Piccadilly, London will be open to the public, and so will be the StairStalk stairs - the result of our long-term cooperation with the designers from Atmos Studio http://www.atmosstudio.com. Please, visit our gallery, in which the successive stages of the StairStalk project will be presented: from conceptual works, through factory and assembly works, to the final completion.
We have just introduced new, very interesting materials into our offer. Now, we offer Zig-Zag stairs and cantilevered stairs in BLAUSTAHL and CORIAN. Examples of implementation of the new materials can be seen in our stair gallery under numbers ST887 and ST990.
For the 7th time in a row we are exhibiting at the Grand Designs Live show at London ExCel, 29 of April -07 of May. If you are looking for a bespoke staircase for your project come along to discuss what we can do for you. Look forward to seeing you there!! We are on stand B260.
It gives me great pleasure to invite you to the Made Expo Fair, to be held in Milan from the 8-th to the 11th of March We are located in Hall 7, stand number F18. I look forward to seeing you at the venue.
We are exhibiting at BUDMA 2017 Fair. You are cordially invited to meet us on stand no. 47, hall 3A, on 07-10.02.2017.
We are happy to inform that for the first time our stairs will be presented in Italy.
You are cordially invited to the SAIE3 Exhibition in Bologne, stand no. C103 on 19-22.09.2016.
IMPLEMENTATION OF THE "INTERNATIONALIZATION OF TRĄBCZYŃSKI COMPANY" PROGRAM.
We are pleased to announce that on the 29th of April we signed an agreement with the Board of the Wielkopolska Region for the implementation of the "INTERNATIONALIZATION OF TRĄBCZYŃSKI COMPANY" program on the UK and Italian markets.
The program will cover 2016-2017. Please contact Mikolaj Trabczynski, schody@trabczynski.com.plfor more information on the progress of the program.
We invite you to make yourself acquainted with product specification document for our ZIGZAG staircases with glass balustrades.
We thought you would be interested to hear that we are back at the Grand Designs Live exhibition at the Excel centre in London, from 2nd to 10th of May 2015.
If you would be interested in meeting us and hearing more about the exciting projects we have completed and the exceptional level of service we can offer, please come and see us.
Find us in the Grand Build section, stand no. 389.
We are exhibiting at Homebuilding & Renovating show at London Olympia, 26-28 of September. If you are looking for a bespoke staircase for your project come along to discuss what we can do for you. Look forward to seeing you there!! We are on stand C72.
The introduction of the new catalog Trąbczyński Stairs at Budma Fair Poznań 2014.
Downloadable PDF version of the catalogue available in our website gallery.
TRĄBCZYŃSKI product offering has responded to the very latest design modes.
We now offer new variations on the theme of contemporary bespoke spiral staircase solutions.
We cordially invite for visiting our stand at GRAND DESIGNS LIVE exhibition. The Exhibition will take place in London ExCel between 04th and 12th of May. Our stand no. is B236. We will be presenting new line of stainless steel balustrades in ARTDECO style and also BOLEFLOOR unique flooring which has been recently added to our offer.
( up to 6.0m for special orders ).
( 450mm for special orders ). The Bolefloor boards are suitable for underfloor heating.
GDS company extends their offer with highest quality multilayer engineered flooring- ROYAL FLOOR. The thickness of the boards is 20mm , the width is between 200mm and 300mm and the length is up to 6.5m. The flooring gets factory finishing with hardwax oil or high quality lacquer. ROYAL FLOOR is suitable for underfloor heating.
( Poland ) was the place where we presented new and very attractive materials used in our staircases. We presented curved Zig-Zag stair made in smoked oak with balustrade made of Corten Steel and brass . The handrail was finished with high quality leather.


At the Washington Auto Show The National Science Foundation (NSF) showed off one of the projects they funded. The NSF funds more than 3,000 autonomous vehicle research projects. The organization is dedicated to funding projects in science and engineering research and education that will directly impact our world.
A great example is a Collaborative Research project – Control of Vehicular Traffic Flow via Low-Density Autonomous Vehicles. $239,971.00 was awarded to Rutgers University, Temple University, The University of Arizona, and Vanderbilt University.
Through this project, it was realized that you only need 5% of the vehicles driving in a constant pattern and those cars can change the flow of traffic and reduce fuel consumption by up to 40%.
Listen as Emily from Vanderbilt University explains how the project works and the findings that could transform your world.


Support your local businesses ~ - Order By Design Inc.
I received a call from a client I hadn't worked with in quite some. She wanted assistance with some organizing, paperwork and general clutter which had accumulated over the past 4 months.
At this point I felt so compelled to reassure her those are her thoughts, not mine. My life's work is to serve with my gifts not to place judgement.
Back to our conversation. She shared with me her mom's response " Honey, you're thinking about this a little too much. I'm sure Heidi is just happy to have the business." Wow, I couldn't have said it any better! She nailed it. I was simply happy to hear from her, just like all of my return clients.
Dear clients: I am honored you believe in me and have the faith to know I will get you to your ultimate goal. In addition to this, I hope you feel good knowing you are supporting your local small businesses! Thank you all for your continued patronage.


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Die Bismarckjugend (eigentlich: Bismarckjugend der Deutschnationalen Volkspartei, ab 1929 Bismarckbund) war die Jugendorganisation der Deutschnationalen Volkspartei (DNVP) in der Weimarer Republik.

Geschichte
Die deutschnationale Berliner Bismarckjugend wurde bereits 1920 gegründet. Die DNVP war die letzte Partei im Reichstag, die eine eigene Jugendorganisation gründete. Im Jahr 1922 schlossen sich verschiedene der DNVP nahestehende Jugendgruppen in Hannover zum Reichsverband der Jugendgruppen der Deutschnationalen Volkspartei zusammen, der schon im Herbst 1922 in Bismarckjugend der Deutschnationalen Volkspartei (kurz: Bismarckjugend) umbenannt wurde. Die Bismarckjugend war politisch vollständig von der DNVP abhängig.

Zu Beginn wurde die Bismarckjugend von Wilhelm Kube geführt. Im März 1923 übernahm Hermann Otto Sieveking (* 24. März 1891, † 4. September 1931), seit 1922 Vorsitzender ihrer Hamburger Landsmannschaft, den Vorsitz der Bismarckjugend. Unter seiner Leitung bekam die Organisation einen paramilitärischen Charakter. Zudem begann die Bismarckjugend, jährliche nationale Jugendtreffs zu veranstalten. Nachdem Sieveking im September 1931 im Alter von nur 40 Jahren gestorben war, wurde im Dezember 1931 Herbert von Bismarck (1884–1955) zu seinem Nachfolger im Vorsitz des Bismarckbundes gewählt.

Mitte 1923 zerfiel die Bismarckjugend immer stärker, da die DNVP mit der gesellschaftlichen Ordnung des Kaiserreichs identifiziert wurde und deshalb unpopulär unter Jugendlichen wurde.

Im Juli 1928 zelebrierte die Bismarckjugend im Rahmen ihres siebten nationalen Jugendtreffs in Friedrichsruh ein Gedenken zum dreißigsten Todestag Otto von Bismarcks. Friedrichsruh ist der Standort des Bismarck-Mausoleums. Das Treffen hatte eine große Bedeutung für die Mutterpartei, denn damit konnte die DNVP ihre Stärke zeigen, trotz ihres schlechten Ergebnisses bei den Reichstagswahlen im selben Jahr. Gegen Ende der 1920er Jahre hatte sich die Bismarckjugend erholt und ihre Mitgliederzahl wieder vergrößert.

Nach der Gleichschaltung durch die NSDAP im Jahr 1933 wurden alle anderen Parteien entweder aufgelöst oder verboten. Am 21. Juni 1933 wurden die Jugendorganisationen der DNVP wegen angeblicher kommunistischer Unterwanderung verboten. 1935 wurden schließlich die Uniformen der Bismarckjugend per Gesetz verboten.

Name
Nach Gründung der Organisation lautete ihr Name zunächst Reichsverband der Jugendgruppen der Deutschnationalen Volkspartei. Im Herbst 1922 wurde der Name in Bismarckjugend der Deutschnationalen Volkspartei geändert, die man dann kurz Bismarckjugend nannte. Ab 1929 wurde der Name Bismarckbund geführt.

Der Name verweist auf den deutschen Kanzler Otto von Bismarck und sollte die Organisation mit dem historischen Erbe Bismarcks verbinden. Bismarcks Enkel Otto Fürst von Bismarck erlaubte der Jugendorganisation, den Namen seines Großvaters zu benutzen.

Mitgliedschaft
Mitglieder konnten Frauen und Männer im Alter zwischen 14 und 25 Jahren werden. Die Organisation hatte 800 lokale Unterorganisationen in ganz Deutschland. Ihre ersten Ortsgruppen entstanden in den industriellen Gebieten Deutschlands. Später expandierte sie aber auch in die ländlichen Gebiete im Osten des Landes. Generell war die Bewegung in den protestantischen Gebieten Deutschlands stärker ausgebildet als in den katholischen. Hochburgen waren Berlin, Magdeburg, Hessen, Thüringen, Niedersachsen, Pommern, Württemberg und Hamburg.

Die meisten Mitglieder kamen aus bürgerlichen oder adeligen Familien. Dennoch war die Bismarckjugend Berlin, die größtenteils aus Mitgliedern der Arbeiterklasse bestand, die größte Teilorganisation. Der Berliner Ableger der Bismarckjugend wurde bereits 1920 gegründet. Im Jahr 1922 hatte die Bismarckjugend Berlin um die 6.000 Mitglieder, von denen 80 % der Arbeiterklasse entstammten.

Die größte Mitgliederzahl wurde mit 42.000 erreicht, was die Bismarckjugend zur stärksten Parteijugendorganisation nach der Sozialistischen Arbeiter-Jugend der SPD machte.

Zeitschrift
Die Bismarckjugend veröffentlichte die Zeitschrift Deutsches Echo.

Einzelnachweise

Jugendverband (Deutschland)
Paramilitärische Organisation (Weimarer Republik)
Gegründet 1922
Aufgelöst 1933
Deutschnationale Volkspartei
Politische Organisation (Weimarer Republik)
Otto von Bismarck als Namensgeber

redpajama_set_name: RedPajamaWikipedia


Location of Sloviansk on a map.
Sloviansk is a city found in Donetsk, Ukraine. It is located 48.87 latitude and 37.62 longitude and it is situated at elevation 61 meters above sea level.
Sloviansk has a population of 124,800 making it the 6th biggest city in Donetsk. It operates on the CEST time zone, which means that it follows the same time zone as Donetsk.


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Home Celebrity Q&AWhat is 'Grimm' Star Silas Weir Mitchell's Favorite Creature?
What is 'Grimm' Star Silas Weir Mitchell's Favorite Creature?
Celebrity Q&A, Featured Article
https://americanprofile.com/articles/what-is-grimm-star-silas-weir-mitchells-favorite-creature/
By Paulette Cohn on May 7, 2013
GRIMM -- "Island of Dreams" Episode 115 -- Pictured: Silas Weir Mitchell as Monroe -- (Photo by: Scott Green/NBC)
https://americanprofile.com/wp-content/uploads/2013/04/silas-weir-mitchell-grimm-150x150.jpg
I am a fan of "Grimm" and all the great creatures they have on the show. I especially like the actor who plays Monroe. Does he have any favorite creatures?
—Gregory Agos, Lexington, Ky.
Not surprisingly, Silas Weir Mitchell, 43, who plays Monroe, would have to say the Blutbad, because he morphs into the wolf-like being. But he does have other favorites among the NBC cop drama's mythical creatures, or "Wesen," as they are called.
"I just like their names," he says. "Like the Volksthiville, which is sort of a Rumpelstiltskin character. It's just a fun name. I did like the bat creature—the Murciélago—that I really thought looked good."
And while the majority of the work of turning the human actors into their Wesen counterparts is done by computer effects, the Philadelphia-born Mitchell says that there are things he does as an actor to help it appear more realistic.
"If Monroe is scared versus angry, because there are several situations under which a Wesen will reveal himself, and one of them is an aggressive, angry, purposeful morph," he explains. "Then sometimes if you're really, really sad, or scared or nervous, your inner self will burst out because your guard is down.
"So in the event that you do it one way versus the other, you're going to have a different aspect. And the CGI is able to capture that. So it's not just a big thing that goes on top of you and obfuscates your emotional life. It's prime enough that it can see what you're feeling. It can react to your face."
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