text:
\section{Introduction}

As mobile robots become more capable of executing a variety of tasks, they are becoming increasingly likely to be deployed in environments co-located with people. This step demands seamless and safe integration into spaces with pedestrians such as buildings, homes and offices. In the path towards socially compliant mobile robots, research on human-robot co-navigation
has made progress on planning and learning approaches
for robots that can handle local interactions with moving pedestrians among crowds in open spaces.
Navigation in indoor environments presents additional challenges due to space constraints that limit maneuverability around the pedestrians and create layout-dependent patterns of pedestrian interaction.
These spaces, such as corridors, doors and intersections pose  hard problems for any existing navigation solution. For these types of spaces, there is a need for new interactive navigation models for tight spaces with constraints imposed by obstacles, walls, doors and other common structural elements, based only on on-board sensor information. Fig. \ref{fig:intro3} illustrates some of the basic layouts and the common interactions that emerge in these geometries from typical indoor environments.

\begin{figure}[t]
\centering
\includegraphics[width=1\linewidth]{./im/summary.png}
\caption{
Illustration of the problem of robot navigation around pedestrians in constrained indoor environments. The robot follows a global path (red dotted line) while maneuvering to circulate seamlessly around pedestrians (blue dashed line example).
The robot's policy is trained on a small set of simple walls-world environments representative of common indoor layouts and the emerging interactions, such as in corridors and intersections (left). We show generalization to two types of unseen environments (right): more complex compositional walls-worlds (top right) and 3D reconstructions of real-world sites (bottom right).
\vspace{-5mm}
}
\label{fig:intro3}
\end{figure}

In this paper we present a novel robot learning approach and training strategy focusing in the ability to navigate around pedestrians in typical indoor environments
in which spatial constraints are sufficiently important to influence the feasibility of the robot's behavior.
The proposed architecture takes advantage of the complementary strengths of
motion planning and reinforcement learning (RL),
in which the RL
component learns to handle the local interactions with pedestrians as it pursues the globally planned trajectory, and
adapts to the current conditions of the environment
as observed with on-board sensors, even in constrained indoor spaces such as corridors and doors.
We observe that typical indoor environments exhibit common geometric elements and diverse composition of these elements. With the aim of generalization to novel realistic spaces, we explore a compositional principle for multi-layout training, in which a policy is trained in a small number of simple, canonical, layouts, such as a corridor, or a single door exit (Fig. \ref{fig:intro3} left). We analyze the capability of this approach to generalize to more complex layouts with multiple composed and combined navigation challenges not seen during training (Fig. \ref{fig:intro3} top right), and find experimentally that policies trained on a set simple layouts generalize better when those represent the elements of the geometries and interactions that conform to the target layout.
This insight results particularly relevant for the   built environment, which is typically composed by combinations of basic layouts templates, such as hallways, rooms, door exits, and crosswalks.
We then demonstrate transfer of the policies learned on walls-worlds (Fig. \ref{fig:intro3} left) to unseen 3D reconstructions of two real-world environments (Fig. \ref{fig:intro3} bottom right), such as a supermarket and an apartment.

We instantiate our navigation approach and train it to control a simulated non-holonomic wheeled robot in multiple environments on the interactive Gibson simulator \cite{xiazamirhe2018gibsonenvCVPR}\cite{48697interactivegibsonRAL}, which leverages the Pybullet physics engine~\cite{coumans2016pybullet} for realistic physics simulation and supports virtual sensing (lidar) for our multi-agent setup.
The environment includes  simulated pedestrians driven by the Optimal Reciprocal Collision Avoidance (ORCA) model ~\cite{van2011reciprocalORCA} based on a social forces model~\cite{helbing1995socialForcesModel}.
Fig. \ref{fig:wallsmesh} illustrates the two types of simulation environments used in this paper:
the simple walls-worlds layouts implemented using simple shapes, denominated by the prefix WALLS- (Fig. \ref{fig:wallsmesh} left), and the real-scale 3D reconstructed environments implemented using textured meshes from the Gibson dataset \cite{xiazamirhe2018gibsonenvCVPR} (Fig. \ref{fig:wallsmesh} right), denominated by the prefix MESH-.

In summary, this paper presents the following contributions:
(1) a \textbf{learning approach and a simulation environment}
for the problem of goal-oriented robot navigation around moving pedestrians,
that unifies ideas from reinforcement learning, planning and multi-agent simulation
to enable human-robot co-navigation
in constrained environments representative of real human indoor spaces;
(2) \textbf{a compositional multi-layout training regime} using canonical walls-worlds layouts that generalizes to more complex environments;
and (3) a \textbf{demonstration of transfer} of the learned policy on simplified walls-world \textbf{to unseen 3D reconstructions of two real-world environments} represented by scanned 3D textured meshes. These results support the compositional principle applicability to real-world environments and indicate promising frontiers for the emerging topic of agent simulation within reconstructed environments for tasks that involve human-robot interaction. Accompanying the paper, we present a \href{https://ai.stanford.edu/~cdarpino/socialnavconstrained/}{video} \cite{PaperURLandVideo2020} with demonstrations of the robot behaviors obtained in the experiments.

\section{Related Work}
\label{sec:relatedwork}

\textbf{\textit{Navigating safely among humans}} is a prerequisite for the deployment of mobile robots.
This has motivated researchers in autonomous navigation and human-robot interaction to study different aspects of the navigation problem, such as navigation within crowds~\cite{chen2019crowd},
communicating implicit and explicit navigation information with haptic devices~\cite{che2020implicitexplicitdorsaokamura},
co-navigation~\cite{khambhaita2020viewingRachid} using anticipatory signals~\cite{unhelkar2015human},  navigation cues in human gaze~\cite{8990034_navCNNattentionhumangaze},
and the effects of distinct robot navigation strategies on pedestrians~\cite{mavrogiannis2019effectsnavstrategiesHRI}.
Despite this long tradition, truly social mobile robots that are safe, efficient and fluent when deployed in human environments are still a research challenge.

Early solutions \textbf{\textbf{for navigation in human environments}} combine a motion planner~\cite{lavalle2006planning} with a controller that executes the plan~\cite{kunchev2006path}.
These approaches suffer from the ``freezing robot problem''~\cite{trautman2010unfreezing,trautman2015robot}, in which the planner finds all solutions to be below certain threshold of safety or feasibility, causing the robot to stop and continuously replan while the pedestrians move. This problem is more severe in domains with complex geometry and large number of pedestrians.
To overcome these limitations, researchers have resort to machine learning approaches~\cite{otte2015survey}. Multiple approaches have proposed to use imitation learning to match human navigation patterns~\cite{pomerleau1989alvinn}\cite{ muller2006off}\cite{bojarski2016end}\cite{pokle2019deep}. The limitation of imitation learning is that the agent does not generalize well beyond what has been seen in the demonstrations, resulting on potentially unsafe actions when the robot reaches states out of distribution.
Reinforcement learning (RL)~\cite{sutton2018reinforcement} with a self-exploratory loop in simulation is a technique that naturally provides further coverage of the state space.

\begin{figure}[t]
  \centering
  \includegraphics[width=0.48\textwidth]{./im/wallsmesh.png}
  \caption{Implemented multi-agent simulation environments. Layouts with the prefix \textbf{WALLS-} are simple walls-worlds (left). The prefix \textbf{MESH-} is used for 3D reconstructions from real sites (right). \vspace{-7mm}}
  \label{fig:wallsmesh}
\end{figure}

Previous works pioneered the use of \textit{\textbf{deep reinforcement learning} in navigation around pedestrians} and developed multi-agent collision avoidance systems focused on optimizing path efficiency (CADRL)\cite{chen2017decentralized}
 and using the reward function to induce social norms such as passing on the right (SA-CADRL)\cite{chen2017sociallyawareDRLmit_iros17}.
Deep RL has also been successfully used for crowd navigation with recursive architectures that model interactions among pedestrians in the crowd to improve the model's performance as the number of pedestrians becomes large in open spaces (SARL)\cite{chen2019crowd}.
These approaches use pedestrians positions as input to the network (either ground-truth in simulation or detection/tracking from vision and lidar),
but do not explicitly consider the presence of geometric constraints in the layout and are rather tested on spacious environments with no sensor input that reflects environmental constraints for the local interactions. This paper proposes a lidar-based framework
with an architecture and training regime that enables the policy
to acquire the behaviors needed
to perform in constrained indoor environments.

Previous solutions have explored how to \textit{combine \textbf{motion planning and learning}} for navigation around dynamic obstacles. The idea is to leverage the motion planner to provide guidance to a learned controller, which adapts the plan to the current surrounding conditions.
This multi-level strategy to combine the long-term planning (global path in the map) and short-term decision making also resembles the principles of how humans perform spatial navigation tasks \cite{epstein2017cognitive}.
\cite{pokle2019deep} and \cite{pmlr-v78-gao17a} showed this structure in imitation learning outperforms a single monolithic end-to-end architecture with no planning guidance, and \cite{sacnavicra2020} combined it with RL and active learning in order to learn human preferences for speed. We build on these ideas
and propose and architecture and training regime to use RL instead of imitation learning to avoid being limited to the regions of the state space covered in the expert demonstrations.

Finally, our learning procedure \textit{trains on simple instances} of a task with the goal of generalizing to task instances with the true complexity. Similar idea is behind methods on curriculum learning~\cite{Bengio2009CurriculumL} that create a series of increasingly complex task instances to facilitate learning~\cite{Matiisen2019TeacherStudentCL}\cite{graves2017automated}\cite{florensa2017reverse}. Differently, we assume that the challenges of our original navigation task cannot be learned progressively; they are the result of combining a finite set of complex patterns of layout-motion of dynamic agents (pedestrians). Therefore, we propose to learn to overcome these complex patterns separately in independent but intertwined training episodes, which will enables us to transfer eventually to a navigation task with the full combinatorial complexity.

\section{Learning to Navigate in Pedestrian Environments}
\label{sec:citations}

We propose to address the navigation problem in pedestrian environments with a combination of a sampling-base motion planner~\cite{lavalle2006planning} and a reactive low level sensorimotor policy learned with RL and implemented as a deep neural network. We assume that our solution can make use of a 2D map approximating the layout of static elements in the environment (e.g. furniture and walls) and that it can localize on it. Each episode the robot will start at a different location and is queried to navigate to a new final goal defined as a location in the map. We query the motion planner once at the beginning of the episode for a shortest path to the goal location.
This path information is then used to define the low-level sensorimotor task that we describe next, followed by a description of the simulation environments we create to train our low-level policy.

\subsection{Reactive Navigation to Follow a Path}

We model the problem of navigating safely among humans following a given path as a Partially Observable Markov Decision Process (POMDP) defined by the tuple $\mathcal{M} = (\mathcal{S},\mathcal{A},\mathcal{O}, \mathcal{T},\mathcal{R},\gamma)$. Here, $\mathcal{S}$ is the state space; $\mathcal{A}$ is the action space; $\mathcal{O}$ is the observation space; $\mathcal{T}(s'|s,a), s\in\mathcal{S}, a\in\mathcal{A}$, is the state transition model defining a probability over the next state $s'$ after taking action $a$ in state $s$; $\mathcal{R}(s) \in \mathbb{R}$ is the reward at state $s$; $\gamma \in [0,1)$ is the discount factor. We assume that the state is not directly observable and learn a policy $\pi(a|o)$ conditioned on observations $o \in \mathcal{O}$ instead of latent states $s \in \mathcal{S}$.
The agent following the policy $\pi$ obtains an observation $o_t$ at time $t$ and performs an action $a_t$, receiving from the environment an immediate reward $r_t$ and a new observation $o_{t+1}$. Assuming the policy is parameterized by $\theta$, a policy gradient algorithm optimizes $\theta$ to maximize that the expected future return:
\begin{equation}
\theta^* =  \underset{\theta}{\arg\max}\,J(\theta)= \underset{\theta}{\arg\max}\, E_\pi\left[\sum_{t}\gamma^t r_t\right]
\end{equation}

More concretely, the observation space, $\mathcal{O}$, includes the elements \mbox{$o = \{goal, lidar, waypoints\}$}, where $goal$ is the episodic navigation goal, represented by the 2D coordinates in robot's reference frame, $lidar$ contains the 128 range measurements from a 1D LiDAR sensor in robot sensor reference frame, and $waypoints$ contains a $n=6$ waypoints  computed by a global planner with access to a map of the environment. The action space, $\mathcal{A}$, defines actions \mbox{$a = \{v_x, v_y, \omega\}$}, where $(v_x, v_y)$ is the  commanded linear velocity, and $\omega$ is the commanded angular velocity for the mobile robot.

The reward function is composed of multiple terms that encourage the policy to reach the final goal, minimizing time, following the given global path but deviating from it to avoid the penalized collisions:
\begin{equation}
R = R_{goal} + R_{timestep} + R_{collision} + R_{potential} + R_{waypoint}
\end{equation}
where $R_{goal}=+1$ is a sparse reward assigned upon reaching the goal within a distance $d_g=0.5m$, $R_{timestep}=-0.001$ is a small negative reward with value per time step to encourage minimizing the task time, $R_{collision}=-1$ is assigned upon collision and terminates the episode, $R_{potential}$ is a dense reward assigned per time step as a function of the distance to the next desired waypoint.

We use the Soft Actor-Critic (SAC) algorithm~\cite{SAC_ICML_18,SAC_algapp_arxiv} to learn both a value function (critic) and a policy (actor) approximated by deep neural networks. SAC optimizes for a combined objective of the expected sum of rewards and the entropy.
The policy network maps from observation space to action space, providing velocity references to a low level velocity controller. The model architecture we propose for the policy network is depicted in Fig.\ref{fig:model}. The critic network presents a similar architecture with a different implementation after the shared featurizing head. Through extensive ablation studies, we tested a number of other variants for the observation space by including other inputs to the policy network, such as observation stacking with consecutive lidar frames, robot odometry, pedestrian detection (coordinates of pedestrians) and time to collision. These variants did not offer significant performance advantages in our domains and we opted for the simplest model that solves the task. However, these other inputs are expected to become relevant when considering explicit aspects of social interactions, such as pedestrian prediction, semantics, human-like social patterns, preferences and safety guaranties. We believe this is an interesting avenue for future research that can build on the presented model and leverage our multi-agent simulation environment.

\begin{figure}[t]
  \centering
  \includegraphics[width=0.48\textwidth]{./im/policynetwork.png}
  \caption[Placeholder]{Network Architecture. \vspace{-0.5cm}}
  \label{fig:model}
\end{figure}

\subsection{Simulated Environments}
\label{sec:sim}

We created a multi-agent simulation environment for the general problem of navigation around pedestrians, in which a simulated robot agent can collect experience and train according to the described POMDP. We use the Interactive Gibson Simulator (iGibson) \cite{48697interactivegibsonRAL} that runs on top of the pyBullet physics engine~\cite{coumans2016pybullet}.
Figure \ref{fig:layoutsdefinition} shows a top-down view of the environments used for training and testing.  These layouts were designed to capture the essential geometric properties of many indoor environments such as corridors, crossing hallways and office spaces with doorways.  Many indoor spaces can be viewed as a composition of these simpler components.

Pedestrian behaviors are simulated using ORCA \cite{van2011reciprocalORCA}.
This model is often used to simulate pedestrian motion for crowd simulation and robot social navigation research \cite{chen2019crowd}.
ORCA uses a joint optimization and a centralized controller that guarantees that pedestrians will not collide with each other or any other objects added to a list of known obstacles, an useful property for driving the simulated pedestrians and provide a training environment for the robot.

Based on a standard RL episodic training loop, a collision between the robot and any object results in termination of the episode and environment reset, which generates new random samples for the start and goal location of the robot and all pedestrians. Success is recorded if the robot gets to within 0.5 meters of the goal.  A timeout is recorded if it takes the robot longer than 125 seconds to get to the goal.  The personal space radius for each pedestrians was set to $10cm$.

\section{Experimental Evaluation and Results}
\label{sec:evaluation}

We conduct a series of experiments in simulation using the environments in Fig.\ref{fig:layoutsdefinition}
\footnote{{\small We have previously tested that policy networks that output linear and angular velocity commands can smoothly drive our robot platform and we expect successful real robot deployment in the future. In terms of sensing, the profile of the simulated lidar accurately reflect that of the real robot's.}}
\footnote{{\small We thank the Stanford HAI-AWS Cloud Credits for Research program for its support with compute resources.}}
. We design the experiments to study the following questions:
\textbf{(Sec. \ref{sec:eval1})} Does the proposed approach enable a robot agent to learn to navigate around pedestrians in constrained environments?; and
does multi-layout training on a small set of canonical environments (WALLS-ABCDF) generalize to navigation on more complex layouts (WALLS-GHI) that exhibit composition of the basic geometric elements?
\textbf{(Sec. \ref{sec:eval2})} How does the proposed method compare with a planning-only approach; and with
\textbf{(Sec. \ref{sec:evalSARL})}
previous work on RL for a feasible domain?
\textbf{(Sec. \ref{sec:eval3})} Can a policy trained in the simplified environments (WALLS-ABCDF) transfer to more complex 3D reconstructions of indoor environments (MESH)?

The evaluation and comparison is conducted using a set of eight independently-trained policy networks. Two policies are trained using a \textbf{single layout} (\textbf{$\Pi_{WALLS-B}$}, \textbf{$\Pi_{WALLS-F}$}). Four policies are training using the proposed multi-layout training regime, in which the agent is randomly located in one of the layouts for each episode. The \textbf{multi-layout} polices are \textbf{$\Pi_{T1}$} trained on WALLS-ABF, \textbf{$\Pi_{T2}$} trained on WALLS-ABD, \textbf{$\Pi_{T3}$}  trained on  WALLS-ADF, and \textbf{$\Pi_{T4}$} trained on WALLS-ADE. Note that $\Pi_{T1}$ has been exposed to a corridor (A), door exit (B) and crosswalk (F), whereas $\Pi_{T2}$ doesn't see the crosswalk, $\Pi_{T3}$ doesn't see the door exit, and $\Pi_{T4}$ doesn't experience the crosswalk or door configurations during training. Finally, the MESH environments are used to train the policies \textbf{$\Pi_{MESH-MARKET}$} and \textbf{$\Pi_{MESH-HOME}$}, separately using the supermarket and apartment 3D reconstructions, respectively.

For each experimental session, we train the policy for 200,000 training steps using 4 parallel simulation environments. We use the SAC implementation available on TFagents \cite{guadarrama2018tfagents}
and the available software integration with iGibson \cite{48697interactivegibsonRAL} and openAI Gym \cite{brockman2016openai}.

\textbf{Metrics:}
The following metrics are used to evaluate the objective performance of the system.
\textbf{Success rate (S)}: percentage  (\%) of episodes where the robot reaches its goal without colliding or timing out;
\textbf{Collision rate (C)}: percentage (\%) of episodes during which the robot collides with either a pedestrian or static object such as a wall;
\textbf{Collision with pedestrians (PC)}: percentage of episodes involving collisions with pedestrians;
\textbf{Collision with obstacles (OC)}: percentage (\%) of episodes involving collisions with static obstacles such as walls;
\textbf{Timeout rate (TO)}:  percentage (\%) of episodes where the robot runs out of time before reaching its goal;
\textbf{Personal Space Overlap (PSO)}: distance (cm) between the robot and pedestrians for which the robot is closest than the specified personal space threshold, aggregated across all pedestrians and averaged over length of the episode.

\subsection{Experiments}

\subsubsection{\textbf{Single- and Multi-Layout WALLS- Training}} \label{sec:eval1}
The single-layout and multi-layout policies are deployed in unseen layouts WALLS-G, WALLS-H and WALLS-I. Layout WALLS-H represents the generalization target with most compositional elements on it (corridors, doors, obstacles, intersections). The results are summarized in Tables \ref{table:singlelayout} and \ref{table:multilayout}, respectively. All presented Tables contain the average results over three experimental runs of 100 episodes each. Success rate below $90\%$ are highlighted in orange.

First, we note that policies from single-layout training, which achieve $S>90\%$ when tested in the same training environment, have a performance drop when tested on more complex layouts. Both $\Pi_{WALLS-B}$ and $\Pi_{WALLS-F}$ had lower performance in layout WALLS-H. This indicates the need for a larger body of experience during training, considering other arrangements of constraints and the patterns of pedestrian motion that arise in these configurations. Unlike single-layout training, using multiple canonical environments for training policy $\Pi_{T1}$ resulted in consistent generalization performance to all three test environments with average success rate of $95.16\%$ ($93.5\%$ WALLS-I, $96\%$ WALLS-H and $96\%$ WALLS-G) (Table \ref{table:multilayout}).

\begin{figure*}[t]
  \centering
  \includegraphics[width=0.6\textwidth]{./im/layoutsdefinition2.png}
  \caption{Definition of the layouts used in training and testing environments in simulation. \vspace{-3mm}}
  \label{fig:layoutsdefinition}
\end{figure*}

\input{table_singlelayouttrain}
\input{table_multilayout}

However, not all multi-layout policies generalize equally well to the compositional test environments. While $\Pi_{T1}$ and $\Pi_{T3}$ produce consistent transfer performance (on WALLS-G, WALLS-H, and WALLS-I), $\Pi_{T2}$ and $\Pi_{T4}$, which haven't experience the crosswalk or door configurations, have significant performance drop in WALLS-H which challenges the agent the most. $\Pi_{T4}$ also has a performance cost on WALLS-I. This results supports the importance of relevant geometric configurations in enabling transfer to a compositional domain, as a key element on learning beyond simply large collection of varied experience.

\begin{figure}[b]
  \centering
  \includegraphics[width=0.5\textwidth]{./im/trainningcurvesT1seeds.png}
  \caption{$\Pi_{T1}$ training curves: Average return as a function of environment steps and the success rate for interleaved evaluations every 10,000 training steps.}
  \label{fig:T1seeds}
\end{figure}

The multi-layout approach results in consistent training curves as shown in Figure \ref{fig:T1seeds} for  $\Pi_{T1}$ training with 4 random seeds. The resulting behavior of the policy can be observed in a video accompanying this paper  \href{https://ai.stanford.edu/~cdarpino/socialnavconstrained/}{linked here} \cite{PaperURLandVideo2020}.

\subsubsection{\textbf{Comparison with a planning-based approach}} \label{sec:eval2}

Planning approaches are suitable for application in our problem set up as they can also perform in arbitrary layouts (maps) without increasing complexity as a function of the layout geometry or size in an intractable manner.
We compare the performance of the learned policy $\Pi_{T1}$ with the planner available with the ROS Navigation framework (ROS-NAV-STACK)
.
The planner in the ROS-NAV-STACK is a layered costmap method \cite{lu2014layered} that also uses two components: a global planner
using Dijkstra's algorithm and a local controller
that uses the dynamic window approach \cite{fox1997dynamicDWA}.
The ROS navigation parameters were tuned to optimize the performance of the planner in the WALLS layouts. The tests are conducted on WALLS-I with an increasing number of pedestrians in comparison with $\Pi_{T1}$. Results are summarized on Table \ref{table:rospedsawp} and the success rate as a function of the number of pedestrians is shown in Fig. \ref{fig:pedswapplot}. The planner resulted in lower success rate across all tests caused mainly due to timeouts. This robot-freezing problem \cite{trautman2010unfreezing} becomes more frequent as the pedestrian density increases and the planner fails to find feasible plans in the dynamic scene. While the  combination of planning and learning in $\Pi_{T1}$ has a performance that also degrades with the number of pedestrians, the failure cases are due to collisions and not timeouts. Note that real deployment can count with an additional sensing-based safety stop.

\input{table_meshtrain}
\input{table_ROSNAVbenchmark}

The comparison with a motion planner (no learning) is a proxy to quantify the benefit added by the RL components (Fig. \ref{fig:pedswapplot}). The planner is less successful at accomplishing the goal because it suffers frequently from the robot freezing problem  (large timeouts (TO) and low collisions -- due to not moving -- in Table \ref{table:rospedsawp}), which means that it continuously fails to find a feasible path and keeps re-planning. In contrast, out method resulted in no timeouts.

\begin{figure}[t]
  \centering
  \includegraphics[width=0.4\textwidth]{./im/rosnavpedswap_h.png}
  \caption{Performance of T1 and ROS-NAV tested on WALLS-I with increasing number of pedestrians. \vspace{-6mm}}
  \label{fig:pedswapplot}
\end{figure}

\subsubsection{\textbf{Comparison with a Reinforcement Learning Approach}} \label{sec:evalSARL}

Since our target domain in constrained environments differs from previous works in open spaces, previous RL methods would require significant modifications to include the additional needed perceptual input to be comparable in our domain (refer to RL in Section \ref{sec:relatedwork}). To establish a baseline comparison with an existent RL architecture we
test our policy $\Pi_{T1}$ on the domain
provided by \cite{chen2019crowd}, where SARL achieved state of the art results for crowd navigation
(including in comparison with relevant methods such as CADRL \cite{chen2017decentralized}).
SARL performs in open spaces and offers an open source implementation \cite{chen2019crowd}, including a widely-used comparative domain that consists of
a circle crossing scenario in which pedestrians starts at randomized positions on a circle with $4m$ radius and navigate to the antipodal point in the circle. The SARL policy is trained and tested on this domain \cite{chen2019crowd}, whereas we deploy our original policy $\Pi_{T1}$ which has not seen the circle domain. Both SARL and our policy achieve 100\% success rate in this domain, which establishes that our system is equally able to solve previous RL domains in the literature for the case of cooperative ORCA pedestrians.
Analogous to SARL experiments in \cite{chen2019crowd}, Figure \ref{fig:episodes} shows some illustrative examples of $\Pi_{T1}$ navigation episodes. The robot lower speeds while pedestrians cross and then advances with increased speed. The policy allows linear velocities in the range $(-0.2,1)m/sec$, which enable stopping and backwards motions to accommodate pedestrians.

\subsubsection{\textbf{Cross-domain generalization with WALLS and MESH environments}} \label{sec:eval3}

We investigate if an agent trained in the simplified representation WALLS- is capable of navigating around pedestrians in realistic 3D scanned scenes (MESH), such as a supermarket (MESH-MARKET) and an apartment (MESH-HOME), illustrated in Fig. \ref{fig:layoutsdefinition} and shown in the accompanying video \cite{PaperURLandVideo2020}.
First we measure the performance of training and testing on MESH-MARKET and MESH-HOME (i.e. MESH to MESH), and compare to that of $\Pi_{T1}$  in the mesh environments (i.e. multi-layout training on WALLS to MESH), and viceversa. The results are presented on Table \ref{table:mesh}.

The average success rate obtained by training and testing on the same mesh results in 79.5\% (MESH-MARKET =75\%; MESH-HOME=84\%), superior to the average transfer from WALLS to MESH of 71.5\% ($\Pi_{T1}$ in MESH-MARKET =77\%; $\Pi_{T1}$ in MESH-HOME=66\%). This is consistent with the expected performance of training in the target environment vs. policy transfer. However, transfer from MESH training to any other environments exhibits a significant performance drop compared to $\Pi_{T1}$. This indicates that the MESH performance is the product of overfitting to the environment, while WALLS training performs more consistently across domains. This result supports both the layout compositional argument and the approach of training on abstracted walls worlds \vspace{-2mm}.

\begin{figure}[t]
  \centering
  \includegraphics[width=0.35\textwidth]{./im/pedTRAJ_SARL.png}
  \caption{Example test episodes of $\Pi_{T1}$ in the circular open domain. \vspace{-5mm}}
  \label{fig:episodes}
\end{figure}

\section{Conclusion}
\label{sec:conclusion}

We present an approach based on reinforcement learning to learn robot policies for navigation around pedestrians in constrained environments. The proposed model uses a motion planner that provides a globally planned trajectory, whereas the reinforcement component handles the local interactions needed for on-line adaptation to pedestrians. The learned policy exhibits interesting behaviors such as slowing down, maneuvering around pedestrians and going backwards to accommodate different common interactions that emerge as a consequence of the constrained layouts.

The analysis of the use of a compositionality principle for the proposed multi-layout training regime showed (1) the ability to train on simplified model abstractions and deploy on a more complex unseen environment with composition of the basic layouts; (2) that policies trained on relevant geometric configurations enable better generalization than those trained on layouts that don't exhibit the geometry and inherent pedestrians interactions present in the target layout; and (3) policies trained on walls-worlds are able to generalize to unseen 3D reconstructions from real environments (such as an apartment and a supermarket). These results offer a practical advantage by removing the need to train on target environments with high reconstruction fidelity.

\section*{Supplementary Materials}

\setcounter{section}{0}
\setcounter{subsection}{0}

\section{Simulation Environment Implementation}

\paragraph{\textbf{Simulation}}
The robot collects experience in the simulated environment while being tasked to navigate from a set of initial to target positions. A policy network is learned using the off-policy Soft Actor Critic reinforcement algorithm (SAC) in continuous action space of robot velocity. The policy network receives guidance from a motion planner that provides waypoints to follow a globally planned trajectory, whereas the reinforcement component handles the balance with the local interactions. The RL agent is rewarded with a balance between following the waypoints but also deviating to accommodate for the presence of pedestrians. This framework results in policies that can adapt to arbitrary environments by generating a navigation motion plan, and leaves the local reactive interaction with nearby pedestrians to be learned by reinforcement. Robot perception is based on a lidar sensor, which raw data resulted in enough information for obstacles and pedestrians avoidance in the tested environments.

The simulation environment is built based on iGibson and the PyBullet physics engine. The learning agent is a differential drive mobile robot. These non-holonomic constraints are included in the simulation.
The policy network outputs the desired values of linear and angular velocity for the robot $(V,w)$, which are passed to a low level velocity controller in wheel speed. This software architecture in simulation follows the architecture on the real robot in terms of input commands.

\paragraph{\textbf{Robot Behavior}}
The policy network receives as input a set of observations \mbox{$o = \{goal, lidar, waypoints\}$}, where $goal$ is the episodic navigation goal (2D polar coordinates of the goal in robot frame), $lidar$ contains 1D LiDAR sensor measurements in robot frame, and $waypoints$ contains a number of \textit{reference waypoints}  computed by a global planner with access to a map of the environment. The waypoints are selected from the collision-free shortest path between the start and goal points of the episode. The \textit{reference waypoints} are updated as the robot makes progress along the desired path. A \textit{waypoint resolution} is used to define the distance between consecutive waypoints, and a \textit{waypoint tolerance} value is used to define the minimum proximity from the robot to a waypoint to receive the waypoint reward and update the next desired waypoint. Parameters for the robot behavior are summarized on Table \ref{tab:sim}.

\input{table_simparam}

\paragraph{\textbf{Simulated Pedestrians}}
The simulated pedestrians follow the ORCA social forces model. The used parameters are summarized on Table \ref{tab:sim}. The ORCA model jointly optimizes the trajectories of all agents (pedestrians) to collaboratively avoid collisions. This property is guaranteed if all agents on the scene participate in the optimization. The ORCA model that drives all pedestrians has access to the state of the robot and considers it a participant agent. This produces the behaviors of the pedestrians to be collaborative with the robot under the expectation that the robot will be reciprocally collaborative in avoiding collisions. However, the robot follows a policy different from ORCA (either the learned policy or the ROS-NAV-STACK planner). This simulation strategy results in an approximation to mutual collaboration between the pedestrians and the robot. While ORCA offers an efficient model to simulate multiple pedestrians, it is limited in the types of trajectories the pedestrians can follow and does not adapt well to trajectory that require turns, concave obstacles or trajectories with lack of visibility to the goal. To address this limitation, each layout includes a definition of allowed initial and end areas (2D locations and  allowed radius shown in Fig. \ref{fig:wallslayoutsdetails} as shaded areas) to require underlying pedestrian trajectories that are feasible for the ORCA model to generate. For each episode, locations are randomly sampled from this allowed set. Pedestrians are represented in the simulator interchangeable as either a cylinder shape or a rigid mesh. We observe that improvements on the behavioral simulation of pedestrians at the macro level would potentially unlock greater adaptability of the learned behaviors to the real world.
For example, in the simulation of MESH- worlds, which follows the same principles as the WALLS- environments,
the simulation of pedestrians becomes more challenging as the specific context becomes relevant for sampling the start and end points for the ORCA-driven pedestrians: (e.g. walking from the kitchen to the bedroom).
We expect that improvement on pedestrian simulation alone will result on better robot performance overall,
without additional modifications to the method, as it would produce more fluent pedestrians trajectories. We leave these improvements as subject of future work.

\paragraph{\textbf{Multi-Layout Training}}
We train a set of policies on selected combinations of simple layouts WALLS-. These combinations are denominated $\{T1,T2,T3,T4\}$. For each $T_i$, the robot is randomly located in one of the included WALLS- layouts at the beginning of each training episode.

\paragraph{\textbf{Simulation Design}}
Using the proposed approach and generally on simulation-based robot learning, the engineering effort centers on designing a simulation environment that properly exhibits the dynamics and properties of the target domain. In robotics, this effort differs from planning techniques, in which the effort is rather centered in explicitly defining algorithmic solutions within some problem properties. Both approaches require significant design and engineering, while the difference lies in specifying the environment (the problem) or the algorithm (an explicit solution).

\begin{figure}[h]
  \centering
  \includegraphics[width=0.4\textwidth]{./im/grid2.png}
  \caption{Locations on WALLS- domains. Each layout has dimensions $20m x 20m$.}
  \label{fig:wallslayoutsdetails}
\end{figure}

\section{Hyperparameters}

Table \ref{tab:sacparam} reports the hyper-parameters used for the reinforcement learning algorithm SAC. All the reported policies were learned using the same set of hyper-parameters.

We tuned the parameters of the ROS-NAV-STACK planner to optimize its performance in the tested environments. Adjusting these values reduces planner timeouts and increases likelihood of finding feasible solutions in the footprint of the tested layouts. Table \ref{tab:rosnavparamlist} reports the used parameters.
\input{table_SAC}
\input{table_rosnavparam}

redpajama_set_name: RedPajamaArXiv


text:
jasonboredd 2022-01-12 20:19:29 UTC #1
Before you even start your comic or novel, how do you go about creating your characters? What tool do you use to make sure each one is unique?
DokiDokiTsuna 2022-01-12 21:02:28 UTC #2
jasonboredd:
What tool do you use to make sure each one is unique?
At first glance, I feel like that's kind of a weird question, because what's the point of creating multiple characters if they all act the same...? ^^; I would think some level of uniqueness would be the default choice, not something you have to 'make sure' of...
But then I guess the issue here is that people have different motivations for creating characters...for instance, an amateur might create Character B for the sole purpose of being Character A's best friend, and B's personality or what they add to the story is secondary, if not completely irrelevant.
Or a more advanced (but still misguided) writer might differentiate characters ONLY through character flaws, so as the story goes on and they overcome these flaws, they devolve into a bland, uniform hero hive-mind (* cough* RWBY * cough*).
But I still feel like you could simply...just not do that? ^^; Like, if you have enough emotional intelligence to recognize that people have different personalities, you could just...give your characters different personalities...
Like, let's say Character A is shy and reserved...so let's make their best friend Character B also reserved, but NOT shy. They are as quiet as A (a common thread to build their friendship on!), but only because they prefer not to interact with people, not because of any social anxieties. They are more than willing to take action and defend A when necessary (now A and B have a dynamic!).
On their adventure, they go to a new land and meet Character C...to highlight that this is a novel experience, let's make C really different from the first two and have them be outgoing and loud. Maybe B will dislike C, but the more sensitive A will see their good qualities. Maybe C will tease A at first, prompting them to start standing up for themselves (character development!). The clash of personalities is an opportunity to do a lot of interesting interactions, establish new dynamics, and flesh out our characters.
Lots of stuff will happen, and eventually A, B, and C, will have become friends, and stronger characters as well. But we ain't done yet-- they still need to meet Character D.
Last time we highlighted the new character's personality by giving them something different from the established group: quiet characters encounter a loud one. But now that the established group has more variety, we have to take a different approach if we want to do this again. What do the members of Team ABC all have in common right now...?
Well...they're all nice people. So let's make Character D a mean person!
Now we have a real writing challenge~. The process of Team ABC's friendship may have been eventful, but it could easily be called inevitable since they're all kind, decent people working towards the same goal...no matter what, they'd probably end up seeing eye-to-eye eventually.
Character D, however, may not even want to help ABC at first. Maybe they're even trying to hurt ABC and need to be convinced to switch sides (the classic redemption arc!).
Maybe D's presence in the group will cause friction between ABC as they disagree on how to deal with it-- B might be against even having them around, and C might think they can 'reform' them with enough time. A might not know what to think, and become stressed from watching their oldest friend and newest friend fight and argue.
And of course, D can have thoughts on this, too-- maybe B's distrust offends them and they act out. Maybe B is right and the fact that they're deceiving C makes them feel a little guilty. Maybe they gravitate to A for an 'objective' opinion, and A and D grow together as characters. Maybe all of these things at once. ^^;
TL;DR Just give each new character some noticeable difference from the characters that come before them, and then USE that difference (very important) to develop them all as a cast.
BLTX 2022-01-12 21:05:36 UTC #3
DokiDokiTsuna:
I, for example, have two characters who are both based off pretty much the same part of my own personality a few years back, and I had to take measures to make sure they wouldn't turn out too similar.
Aeneas1 2022-01-12 21:10:49 UTC #4
I use no other tool but my mind when it comes to designing and creating characters.
YS_og 2022-01-12 21:14:21 UTC #5
I create the story line and setting, first. Filling in the roles needed to tell the story already acts as one big step to creating unique characters with personalities and traits/abilities or contributions that fill out the story elements you need accomplished. The rest will likely happen organically as you begin writing, as trying to plan out too much might make the character-plot relationship stiff.
darthmongoose 2022-01-12 21:23:58 UTC #6
When I create a cast, I don't really use a tool so much as things like pencils, paper and text documents to flesh them out.
My aim is always to create characters who aren't just interesting individually, but create interesting interactions when you put them together, so I tend to start with a protagonist and then think "who would be a funny or interesting person to put next to this character?"
So with Errant, which originated as a comic called Fan Dan Go back when I was a teenager, I started with Rekki. She's this little, angry, driven, spikey, energetic person themed around the colour red who started life as a sort of "female version of a shounen manga hero". Then I created two characters who balance her to challenge and help her grow: Subo, a big, chilled out, scruffy hippie guy themed around green, and Sarin, a lofty, cautious, intellectual perfectionist themed around blue.
Who do I add to that? Okay, well, those three are all very "what you see is what you get" so let's add somebody a bit more inscrutible to challenge them all! So I added Jules; stealthy, funny, unpredictable, prone to saying inappropriate things themed around purple, and [name redacted], this ethereal, fey, whimsical character with deep connections to the lore, themed around white or rainbow.
If I made the cast now. I'd probably keep it smaller. Like I'm much more likely to work around a trio in my modern comics. But I do really like all those characters. You can pair up any two characters from Errant and get an interesting interaction. That's sort of my litmus test to make sure I haven't made two characters in a cast who are too similar and could be merged into a single entity, or if one of them needs to be changed. If you can't write an interesting scene where any two characters hang out, look into how you can find a contrast between them.
BLTX:
I can understand that-- as the one person I actually know fairly well, I base 90% of my MCs on different parts of my own personality. But the key word there is 'different'-- I know characters will start getting 'samey' if I base them all on the same traits (especially if they're in the same story) so I try to avoid doing that.
But if you do base characters on the same elements of your personality, then it's your responsibility to fill in the gaps that remain in order to make different characters.
Like, I have two characters in separate stories that enjoy people the way I do. That's their shared trait: 'likes to interact with people', or at least to be around them.
One of them is heavily extrapolated into a full-blown extravert: they like people so much that they are constantly inserting themselves into the other characters' lives, trying to help any way they can and ecstatic to spend time with any of their 'friends', even those who clearly don't like them. ^^;
The other, however, is more cautious (and a bit more accurate to myself)...they like people, but they don't feel like they belong with them, so they usually stay away, instead opting to watch over them from a distance. When they DO get close with someone, they feel like they finally have 'permission' to open up more and share more, and only then does the audience realize how much they treasure everyone in their lives.
So, the same trait, but vastly different takes on it...because as I said, making characters unique is kinda the whole point. That's still relevant even when you start with the same base concept.
CommandanteLemming 2022-01-12 21:49:07 UTC #8
I love building characters, and there's lots of different methods. Sometimes it's just as I feel them. That said, one method that produces really reliably good characters is to start with a central contradiction - two aspects of a person that pull them in opposite directions, create internal conflict, and force you as an author to figure out how this is even possible.
To use an example not in my current book, I have an unfinished book built around a fallen Valkyrie who has spent the last 900 years living on Earth as a convert to Orthodox Judaism (It came to me sitting in a Wagner opera performance during Passover week lol - I was literally munching on my matzah outside listening to hardcore fans talk unprompted about how they didn't think he was all that bad of an anti-Semite.) Anyway, that led to a really hard central question I had to answer - which is how does an immortal being that's literally seen Asgard end up practicing a religion other that the one where she's tangibly part of the pantheon. Figuring out the answer fleshed out not only her character but all the rules of the world building.
Moesakuara 2022-01-12 22:28:36 UTC #9
Ooh, this is fun. I usually start with a cliche or simple archetype and build a story around them, thinking up a plot where the character's traits or biggest failings are what needs to be overcome by the end and are, in many ways, the actual catalyst of the entire plot. To flesh them out I will add quirks and nuances from my own experiences, people I know and strangers.
For Fields, my lead Jules is a tough vigilante on a righteous quest. She's fiery, daring and tends to hold on to a grudge. I used the "hard on the outside soft on the inside/anti hero with a heart of gold" trope when I created her. Because really, she holds a grudge because she is a sensitive person, who cares deeply about the people she loves and struggles to let go. I also like pairing two opposing traits together. Again, with my main character in Fields: she's fairly quiet and more reserved. Yet she is also very opinionated.
I think mixing and matching those kind of things can help add a lot of depth to characters.
Aims 2022-01-12 22:29:34 UTC #10
TBH I just create what characters I feel like creating when they're needed.
I start by their role and their story, and then their base personality and looks (these sometimes are the hardest part, but if the characters are friends, family, or share a past in general then I can overlook the existence of many similarities), and then I finally go for the names, etc. Normally I use a name dictionary to make sure the name fits the character's nationality or the nationality of whoever named them, although sometimes their backstory or personality just makes it so nationality isn't a factor anymore, for example, Amy and MC, neither cared about the significance of names so one gave herself the first name she could think of when someone asked what her name was, and the other named himself Molotov Cocktail. And when they're from a fictional country, I just make one up, unless it makes sense for them to have an actual name that exists.
But if you're looking for tools to make characters, I'm sure there are plenty of random character generators in the app store. But don't worry too much about "making every character unique". If the characters have a connection between them, like love, friendship, families ties, etc. then them having similarities between them is totally normal, and can make their connections feel natural.
TheLemmaLlama 2022-01-12 23:07:20 UTC #11
Another approach you could take is first create whatever characters you want and not worry about similar they are, and then minimise the scale of your universe/multiverse (something you might want to do anyway once you realise that creating a consumable piece of media is a pain in the ass and takes forever :P). You might find yourself 'merging together' characters that are too similar, leaving you with only characters that are different
allenT 2022-01-13 00:38:29 UTC #12
Not really development but how i make my characters more stand out.
When my character doesn't have strong personality, having foil characters around the protagonist makes them vivid (through contrast) & give different kinds of chemistry. Also character design: gotta have enough differences.
Also when possible, create some less-likely scenarios, it makes you(writer) work out other routes and not following overused routes, something readers will find them interesting.
WOSTR 2022-01-13 00:51:33 UTC #13
The tools for me are the plot and the opposition.
How did one character find themselves in the situation they are in, what does this individual do about it and what are their interactions with those they're connected to like.
Expanding further with explanations, victories or consequences and what they spell is development for me, people believe that a character has to change their ways each time, I don't think that's a requirement.
system 2022-02-12 00:51:43 UTC #14

redpajama_set_name: RedPajamaCommonCrawl


Reem Al-Nasser, Qeyama Series, 2013.
Reem al Nasser's (Saudi Arabia) works are influenced by her personal story and position in society, observing and exploring themes of religious, cultural, social behaviours and beliefs of the people from her community.
In her recent works, Reem has been exploring the notion of death as a tactic used by authorities to scare people and the idea of movement and mobility in societies. For six years, Reem has researched the rituals of the burial and grave digging, questioning how would we change if death could be separated from its stereotyped religious and cultural connotations.
Reem graduated in Economics at Jazan University in 2011 and is part of Shift exhibition at the Mosaic Rooms (1/7/17 to 2/9/17).


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Welcome to the Waitara Buildings basket - note that this basket is still under construction.
If you would like to add images, documents, or recollections about any of the buildings featured, please register with Kete or contact staff at the Taranaki Research Centre.
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Waitara Police Station (Former), 3 Parris Street (circa 1940)
This building was constructed about 1940 as the Waitara Police Station - It served this purpose until the 1980s when the property was sold.
This property was purchased by the Police Department from W. Wilkes in December 1910; at the time of purchase there was a large villa residence on the property which was retained.
During 1911 alterations were made to the villa residence, with a new lock-up (now
7 Warre Street (circa 1880s)
The exact construction date of this house is unclear, although it was in existence on this site by at least 1895, with the house present in a photograph taken that year (Puke Ariki C.3.11).
The house has been substantially modified, with the exterior now plastered, the veranda enclosed, and original windows replaced with aluminium.
See: CT TN2/41
Drynan's Building, 51 McLean Street (circa 1890s)
This two-storied timber building was likely constructed during the 1890s by Thomas Shaw Drynan, who had owned the property (Pt Section 1 of Block 96 Waitara West) since 1878.
At various times Drynan was a builder, undertaker, farmer, storekeeper and mayor of Waitara.
The 1990s NPDC heritage inventory notes a construction date of 1897, although it gives no evidence for this date. Photographs show the building was certainly in existence
Waitara Hotel (1867-2008)
1913 view by Frederick George Radcliffe, Auckland City Libraries Collection
Established during 1867 with Thomas Leedom as proprietor, the Waitara Hotel was originally located at the corner of High and Queen Streets (Section 2 of Block 65 Waitara West), near the heart of Waitara's original commercial centre.
In June 1872 the first Waitara Bridge was completed and Waitara's commercial centre began to shift towards McLean Street - the main road north from New
Joll's Boarding House, Cnr West Quay and Whittaker Streets (constructed 1879/1882 - removed 1920)
Detail from circa 1890s view of Waitara
The southern portion of this building was constructed for Samuel Joll in 1879 as a butcher's shop.
The northern portion of the building was added in 1882 as an addition to the original butchery shop - The building was later used as a boarding house.
The architect for the additions was Ed Wickham and the builder a man named 'Wilson' - the construction cost was £390.
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RAF challenges sexist stereotypes with provocative ad juxtaposing clichéd ad slogans with front line combat
by Pay Per Clicked Agency
in Digital Marketing
The Royal Air Force (RAF) is launching a thought-provoking campaign, 'Women should be defined by actions, not clichés', challenging sexist stereotypes and the clichéd portrayal of women in advertising.
Developed by RAF's creative agency Engine, the campaign mocks the gender stereotypes that frequently appear in mainstream advertising, juxtaposing them with the RAF's own talent serving in front line roles.
Those stereotypes include phrases such as: "Fine lines and wrinkles? I want to combat the signs of ageing"; "Strong healthy heart, what's my secret?"; "With my busy life I don't have time to slow down"; "I can't believe we turned up wearing the same thing"; "I want a lip gloss that stays on whatever life throws at me"; "All day protection now with wings so I can handle anything".
The RAF's campaign is the third winner of Channel 4's annual Diversity in Advertising Award, which last year invited brands and agencies to pitch campaigns that would challenge ingrained stereotypes and the objectification and sexualisation of women. It beat shortlisted campaigns from Cadbury Milk Tray, eBay and Flybe to take the award.
The RAF wins £1m of airtime on Channel 4, while the other three finalists are offered match-funded commercial airtime of up to £250,000 if they launch their ads. Since the scheme began in 2016, only one brand that has been shortlisted but not won, Ford, has gone on to run its ads.
"The Royal Air Force has been delighted to have worked on this venture with Channel 4 and Engine, allowing us to showcase the vast range of exciting opportunities available, regardless of gender," says air vice-marshal Chris Elliot, chief of staff personnel and air secretary at the RAF.
"As an inclusive employer, the Royal Air Force continues to support each and every one of our personnel, noting that they are as unique as the roles they fill. Through teamwork we move forward in breaking down stereotypical barriers and hope to encourage others to do so through this campaign."
The campaign comes after a new survey by Channel 4 found that the main problem with ads featuring women was not the levels of representation but the roles in which women are portrayed. Early findings show that in the 1,000 most watched TV ads over a four week period, more than 40% showed women in clichéd "homemaker" or "house wife" roles.
"This RAF advert clearly illustrates the difference between how women are portrayed in advertising, compared to the realities of everyday life for a woman serving in the RAF," says Matt Salmon, Channel 4's head of agency and client sales.
"It is a worthy recipient of £1m of our airtime and given our research shows that women are typically shoehorned into derogatory or stereotypical roles, campaigns such as this are long overdue. We hope that other brands now follow the RAF's lead in considering how women are portrayed within advertising."
Channel 4 has committed to running the award until at least 2020, with the next iteration opening for entries in June 2019.
In 2016, the competition focused on improving the representation of disability and was won by Mars and AMV for their Maltesers campaign, while in 2017 the focus was on non-visible disabilities and was won by Lloyds Bank's #GetTheInsideOut campaign on mental health.
The post RAF challenges sexist stereotypes with provocative ad juxtaposing clichéd ad slogans with front line combat appeared first on Marketing Week.
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Giving customers and suppliers confidence in our ability to fulfill extensive commitments over the long term.
To meet tight CAPEX and OPEX schedules, large pipeline construction projects and operations often require substantial upfront investments for mobilization and localized services.
With a strong balance sheet supporting our potential for significant future growth, Shawcor enjoys a distinct competitive advantage in our ability to fund significant internal and external opportunities—particularly within today's cyclical energy market.
Shawcor has led the industry in innovative problem solving for 85 years—and we're not stopping now. Stay on the lookout for continued growth in operations, technologies and capacity over both the short and long term.


Australia's leading independent law firm, Corrs Chambers Westgarth, has advised Carter Holt Harvey on the sale of its NSW sawmilling operations.
Following a competitive process, Carter Holt Harvey has signed an agreement with Associated Kiln Driers to sell all its shares in Carter Holt Harvey Timber Pty Ltd (CHH Timber). CHH Timber owns Carter Holt Harvey's Tumut sawmill, Gilmore treatment facility and a distribution centre in Berkeley Vale. It also has a 50% interest in the Highland Pine Products joint venture in Oberon.
New Zealand-owned Carter Holt Harvey produces and distributes a comprehensive range of wood-based building products and has been a key player in the Australian and New Zealand timber industries for over a century.
Corrs has a long-standing relationship with Carter Holt Harvey, having worked with the company for more than a decade on a number of private M&A transactions.
"It has been a pleasure to work with Carter Holt Harvey on this important transaction. It was a great team effort and we look forward to continuing to work with Carter Holt Harvey in the future," said Corrs Partner Sandy Mak.
Sandy, the lead partner on the transaction, was supported by Special Counsel Nicole Graham and Lawyer Tatiana Stein in Corrs' Corporate Advisory team. The complex transaction also required the knowledge and technical expertise of a number of other Corrs teams, including those led by Partners Eugenia Kolivos (IPT&C), Christine Covington (Environment and Planning), Stephen Price (Workplace Relations), Mark Payne (Tax) and Rommel Harding-Farrenberg (Banking and Finance).


text:
S&P 500 hits new record high, TSX declines
The Toronto stock market was lower Thursday while traders prepared to take in the release of job creation data Friday and European leaders considered sanctions against Russia
Mar 06, 2014 • Last Updated March 6, 2014 • 2 minute read
TORONTO — The Toronto stock market was lower Thursday while traders prepared to take in the release of job creation data Friday and European leaders considered sanctions against Russia.
The S&P/TSX composite index declined 17.65 points to 14,286.52.
The Canadian dollar gained 0.59 of a cent to 91.19 cents US amid a better than expected read on building permits in January.
U.S. indexes were higher with the Dow Jones industrials up 78.52 points to 16,438.7, the Nasdaq gained 6.08 points to 4,364.05 and the S&P 500 index was ahead 6.77 points to 1,880.58, a new record high.
Harsh winter conditions have crimped job creation, and expectations for the February U.S. non-farm payrolls report are muted. Economists expect the report to show around 145,000 new positions were created last month.
There was positive news out ahead of that data. Weekly applications for U.S. unemployment benefits, a proxy for layoffs, declined to 323,000 last week from 348,000, the lowest level in three months.
In Canada, analysts expect the report to show the economy created about 15,000 jobs last month, according to Thomson Reuters.
Markets were monitoring developments in Ukraine following a rocky start to the week after Russia invaded the Crimean peninsula where it has major military installations and many people are Russian speaking.
European leaders said Thursday that Russia will face sanctions unless it withdraws its troops from Crimea or engages in credible talks to defuse the situation.
But analtysts point out that markets are trying to take a pragmatic approach to the issue.
"It doesn't matter economically," said John Stephenson, portfolio manager at First Asset Funds Inc.
"The reality is there is no economic pie to fight over. If the Russians rolled into Iran, different story because you're talking oil, you're taking something economic that is of interest in the West. As opposed to wheat and potatoes."
On the corporate front, Canadian Natural Resources Ltd. (TSX:CNQ) said its quarterly adjusted net income came in at 52 cents per share, four cents below estimates. Cash flow per share was $1.64, which was 10 cents below the estimate. Its quarterly dividend will rise to 22.5 cents per shares, up two cents and its shares slipped 12 cents to $40.58.
Engineering firm SNC-Lavalin Group (TSX:SNC) reported a quarterly profit of $92.54 million or 61 cents a share, down from $93.84 million a year ago, missing forecasts by a penny. Revenue fell to $2.12 billion in the fourth quarter, down $300 million from a year earlier and its shares fell $1.41 to $46.96.
Auto parts maker Linamar Corp. (TSX:LNR) posted quarterly net earnings of $68.7 million or $1.06 per share, compared with $30.7 million or 47 cents in the same 2012 period. Revenue increased to $926.1 million from $756.5 million and Linamar increased its quarterly dividend by 25 per cent to 10 cents a share. Its shares ran up $1.97 to $51.27.
The base metals sector led advancers, up two per cent with May copper one cent higher at US$3.21 a pound.
April crude was down 13 cents to US$101.32 a barrel and the energy sector was 0.15 per cent ahead.
The gold sector was slightly higher while April bullion rose $5.30 to US$1,345.40 an ounce.
Techs led decliners, down 0.7 per cent.


Interview with photographer Stephanie Mortimore, a recent graduate from Hereford College of Arts.
When did you first become aware of photography?
I grew up with an awareness of photography with my parents documenting my childhood on their Fujifilm Full Automatic DL-150 film camera. Throughout my life I have always had a huge love for the arts and from a young age I knew that I wanted to be an artist. Until I began experimenting with photography at the age of eleven, taking photographs in the garden using my parents' film camera, I had always considered photography as a tool for documentation and had anticipated that I'd grow up creating art using iconic media, since my focus remained on drawing. Though, as I continued to experiment with photography, I came to realise the true magic of the medium and my fascination grew.
Whilst studying for my degree at Hereford College of Arts I developed a love for critical theory, and through my reading of and engagement with theory I became fascinated with photography as a medium, which embalms time and acts as a form of posthumous communication. My reading of theory transformed the manner in which I perceive and engage with photography, enabling the realisation that the creation of photographs was not limited to the taking of them.
Through my practice I primarily explore themes of time, memory and transience in my engagement with 'lost' photographs and the 'lost' histories they imbue.
What I find most beautiful and haunting about photographs is the manner in which they embalm time. When I look at a photograph I feel as though I am looking at a ghost or a distant constellation. In the case of the 'old' photographs that I collect and work with, I consider looking at a photograph to be no different from looking at a ghost. In a photograph I am able to see an apparition of a dead person, a nebulous image which has become manifest to me through the medium of photography. In this sense, photography also has a relationship with my spiritual fascination and this is apparent in my work.
My practice is primarily driven by my engagement with photographic theory and my fascination with the medium's ontological qualities.
My reading of 'Camera Lucida' by Roland Barthes has been highly influential to my photographic practice and engagement with photography as a medium. 'Camera Lucida' is a reflection on photography in which Barthes explores the relationship between photography and life, death and history and the relationships that we have with photographs.
I have also been heavily influenced by the work of Tom Butler, whose work focuses upon the transformation of the mundane to the fantastical. Working with 19th century cabinet cards, Butler's work is often presented within the classic proportions of portraiture, juxtaposed with abstraction, and is focused on visually revealing a sense of unconscious. Butler has a fascination with the scientific and spiritual landscape in which his source material might have been created, which influenced the spiritual and otherworldly aspect of his work. The domestication of cabinet cards was contemporaneous to that of séances and, at the time, even seeing their own images reproduced must have been somewhat magical in itself.
My working process is very instinctive. In producing 'Gemini', I worked with a series of graduation portraits purchased online in the form of a 'high school yearbook' from Norway, Maine. I felt an instant connection with the sitters in the collection in that they were graduating students and I was soon to graduate myself. Since these portraits were to be used for work presented at my Degree Show, this was a synchronicity I couldn't ignore.
When working on 'Gemini', the relationship between each constellation and each individual sitter was directed by what I considered an intrinsic link between the two. My engagement with each sitter enabled me to create a body of work which portrays an instinctive, yet purposeful, reflection of my relationship with photographic imagery and critical theory. In producing the work, it was so beautiful to see light emanating through the pinhole constellations pierced through the surface of each image, immediately transforming the formal portrait into something fantastical.
What do you intend viewers to perceive from your images?
Through my practice and engagement with photography I hope to grant visibility to lost imagery and the lost histories they embody.
'Gemini' was heavily influenced by the Roland Barthes quote: "The photograph is literally an emanation of the referent. From a real body, which was there, proceed radiations which ultimately touch me, who am here; the duration of the transmission is insignificant; the photograph of the missing being, as Sontag says, will touch me like the delayed rays of a star." From my reading and interpretation of this quote, I began considering the anonymous sitters in the original collection as 'constellatory bodies' who exist, as stars do, as delayed projections of light into the future.


DICE has released a Battlefield 4 Q&A touching upon vehicular combat.
According to the firm, "all-new Chinese vehicles" in the game will include but won't be "limited to" the following: Type 99 MBT, ZFB-05 Armored Car, Z-9 Haitun Transport Heli, Z-10W Attack Heli, and DV15 Interceptor Attack Boat.
One-handed weapons and gadgets can also be used while in water should things get a bit hairy.
A shot of the Chinese Z-10W Attack Heli is below.
Battlefield 4 will be available October 29 for PC, PS3 and Xbox 360 and will also be made available for PS4 and Xbox One once the consoles launch.


text:
(4374) Tadamori es un asteroide perteneciente al cinturón de asteroides, descubierto el 31 de enero de 1987 por Kenzo Suzuki y el también astrónomo Takeshi Urata desde el Toyota Observatory, Japón.

Designación y nombre
Designado provisionalmente como 1987 BJ. Fue nombrado Tadamori En honor al samurái Taira no Tadamori.

Características orbitales
Tadamori está situado a una distancia media del Sol de 2,222 ua, pudiendo alejarse hasta 2,601 ua y acercarse hasta 1,843 ua. Su excentricidad es 0,170 y la inclinación orbital 4,846 grados. Emplea 1210 días en completar una órbita alrededor del Sol.

Características físicas
La magnitud absoluta de Tadamori es 13,3. Tiene 5,687 km de diámetro y su albedo se estima en 0,388. Está asignado al tipo espectral S según la clasificación SMASSII.

Véase también
 Lista de asteroides del (4301) al (4400)
 Cuerpo menor del sistema solar

Referencias

Asteroides del cinturón principal
Objetos astronómicos descubiertos por Kenzo Suzuki
Objetos astronómicos descubiertos por Takeshi Urata
Objetos astronómicos descubiertos desde el Toyota Observatory
Objetos astronómicos descubiertos en 1987
Wikiproyecto:Asteroides/Artículos de asteroides

redpajama_set_name: RedPajamaWikipedia


Japa means the repetition of ancient mantras to raise a person's vibration in order to provide healing to the body, mind and spirit.
During the classes you will be introduced to basic mantra meditations and guided through the philosophy of Japa and how it can be implemented into your daily routine. These simple techniques can help quiet your mind, focus your attention on the areas that matter, and maximise your potential. The practice of chanting mantra's ultimately balances your mind, expands your awareness and facilitates personal transformation.
Eileen offers quality Holistic Health Care with over 16 therapies available at the centre, including allergy testing Bio-Resonance Medicine Complex Homeopathy Louise Hay Workshops and Coaching. Reiki Reflexology and Training of Heal Your Life Workshop Leaders.


A profoundly simple approach that reveals greater happiness and fulfillment. Award-winning author, Garret Biss, shares remarkable insight about the source of stress and unhappiness in our lives. A 15-year study of personal growth and success led Biss to the revolutionary life approach he shares - one that is certain to help you reveal a happier, more productive and more fulfilled version of you! In this book, you will discover: · Seven tools that reveal the positive in every event you face · One technique to regain hours of lost time and energy each day · How to invest your time and energy in what matters most · One thing that will improve every relationship you have · How to drastically improve your impact on the world · Five things you worry about that happy people don't · One simple technique that allows you to enjoy everyone more · A quick way to dissolve the remorse or regret you hold SPECIAL BONUSES: Easy-to-take quizzes to help you master your spheres, and optional assignments to help you achieve your happiest life. BUY THIS BOOK NOW to break free from the stress holding you back and discover the happier, more fulfilled, version of you!


text:
Home > USDA-APHIS > USDA Wildlife Services > 2431
U.S. Department of Agriculture: Animal and Plant Health Inspection Service
USDA Wildlife Services - Staff Publications
Avian Influenza A Viruses Reassort and Diversify Differently in Mallards and Mammals
Ketaki Ganti, Emory University School of MedicineFollow
Anish Bagga, Emory UniversityFollow
Juliana DaSilva, Center for Disease Control and Prevention, Influenza Division, Atlanta, GAFollow
Samuel S. Shepard, Center for Disease Control and Prevention, Influenza Division, Atlanta, GAFollow
John R. Barnes, Center for Disease Control and Prevention, Influenza Division, Atlanta, GAFollow
Susan A. Shriner, USDA Wildlife ServicesFollow
Katia Koelle, Emory UniversityFollow
Anice C. Lowen, Emory UniversityFollow
Ketaki Ganti https://orcid.org/0000-0002-9687-3505
Susan Shriner https://orcid.org/0000-0002-0254-6141
Katia Koelle https://orcid.org/0000-0002-0254-6141
Viruses 2021, 13, 509.
https://doi.org/10.3390/v13030509
Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Reassortment among co-infecting influenza A viruses (IAVs) is an important source of viral diversity and can facilitate expansion into novel host species. Indeed, reassortment played a key role in the evolution of the last three pandemic IAVs. Observed patterns of reassortment within a coinfected host are likely to be shaped by several factors, including viral load, the extent of viral mixing within the host and the stringency of selection. These factors in turn are expected to vary among the diverse host species that IAV infects. To investigate host differences in IAV reassortment, here we examined reassortment of two distinct avian IAVs within their natural host (mallards) and a mammalian model system (guinea pigs). Animals were co-inoculated with A/wildbird/California/187718-36/2008 (H3N8) and A/mallard/Colorado/P66F1-5/2008 (H4N6) viruses. Longitudinal samples were collected from the cloaca of mallards or the nasal tract of guinea pigs and viral genetic exchange was monitored by genotyping clonal isolates from these samples. Relative to those in guinea pigs, viral populations in mallards showed higher frequencies of reassortant genotypes and were characterized by higher genotype richness and diversity. In line with these observations, analysis of pairwise segment combinations revealed lower linkage disequilibrium in mallards as compared to guinea pigs. No clear longitudinal patterns in richness, diversity or linkage disequilibrium were present in either host. Our results reveal mallards to be a highly permissive host for IAV reassortment and suggest that reduced viral mixing limits avian IAV reassortment in a mammalian host.
Natural Resources and Conservation Commons, Natural Resources Management and Policy Commons, Other Environmental Sciences Commons, Other Veterinary Medicine Commons, Population Biology Commons, Terrestrial and Aquatic Ecology Commons, Veterinary Infectious Diseases Commons, Veterinary Microbiology and Immunobiology Commons, Veterinary Preventive Medicine, Epidemiology, and Public Health Commons, Zoology Commons
USDA Wildlife Services Website


•	Schedule transportation, interpreters, readers and independent living services for disabled consumers.
Additional Comments CONDITIONS OF EMPLOYMENT: This will be a contingent permanent appointment. Promotions and transfers may change appointees' negotiating unit. Applicants should be aware that changes in negotiating units may affect their salary, insurance and other benefits. Limited travel will be required. Verification of degree and/or transcripts will be required for all candidates at the time of interview.
APPLICATION: Qualified candidates should send a resume and letter of interest by February 6, 2019 to accesjobs@nysed.gov (E-mail submissions are preferred). You must include the Box number (ACS-2187/43065) of the position in the subject line of your email and cover letter to ensure receipt of your application.
Notes on ApplyingAPPLICATION: Qualified candidates should send a resume and letter of interest by February 6, 2019 to accesjobs@nysed.gov (E-mail submissions are preferred). You must include the Box number (ACS-2187/43065) of the position in the subject line of your email and cover letter to ensure receipt of your application.


text:
The American Political Dilemma (1)
February 13 2017 By Abiodun Giwa
The United States is home to Democracy, government by the people and for the people. Americans are proud of their system of government, with checks and balances, between the Executive, the Legislature and the Judiciary.
The pride is manifest in the government's zeal at encouraging other countries to accept their own type of democracy. American founding fathers have done whole lots of foundation works, to ensure that the ordinary people are not left behind, and successive governments have worked, to build on the good work of the founding fathers.
The Constitution is structured about how the people will vote their executive without disenfranchising the people in the rural areas, often referred to as the battleground states. The founding fathers knew that the major cities population will always or sometimes outpace the population in the rural areas. Therefore, they ensured that the battleground states have more voting power in the decision of choosing the executive, rather than leaving it to the rich and wealthy politicians in the cities and their supporters, so that people in the battleground states are not left out.
The world witnessed that part of the Constitution at work with the election of Donald Trump as president with the voting power of the people in the battleground states, while Hillary Clinton, who won majority of the votes in major states like New York and California lost the election, because she failed to get required votes from the battleground states.
The mammoth protests and demonstrations, following Trump's emergence as president, attests to the organizing power of the rich and the wealthy in the liberal groups that are concentrated mostly in the cities. The puzzle is whether these protests and demonstrations can affect the way the sitting president goes about fulfilling his election promises, which favor the voters and people in the battleground states than people in the cities.
Yes, people say Trump is supposed to carry all Americans along to avoid disenfranchising people in the cities. But Conservative Republicans call this good argument, but they are quick to point out the differences between the Liberal and the Conservative political and economic agenda.
The most salient question at this juncture is: How did the Liberals managed to rule for eight solid years and forgot about the battleground states and the role the states could play at ensuring their remaining in power?


For me, a wish list serves as things one could wish to have and hope to have. By preparing such a list, it helps one to declutter and help us prioritise what we would really like (and want to do).
My wish list for beauty products for now is pretty limited for the time being, as I still have a stash I need to go through for now, and I did actually buy the things I wanted.
1. Hair oil for maintenance, as my ends can be rather dry. Please people, send me hair products to try.. my hair needs maintenance.
3. More makeup-- and skincare products.
4. Baking classes - really would like to learn, but not exactly a priority now due to lack of time. I would like to learn how to bake my favourite western pastries, cakes and pies like meat pies, apple pie, etc.
I make do for now by working on basic essential cooking skills for asian dishes when I can manage to find the time.
5. Sewing classes- actually higher on my list of priorities as it serves a more useful purpose for me than baking >_<, but I cant do anything until I complete my make up classes.
These are things I currently aspire to have, and writing and sharing about my journey makes one more feel more passionate about it, is it not?
They are the same company that retails Swatches, but currently that is only what my budget can afford for the time being. *sob sob*.
9. A BoyFriend. The type that makes your heart go fluttery and wants to spend time with you. Enough said.
Last edited: 24th June, 2013.


text:
StreamHub-Android-SDK
=====================

Make Android apps powered by Livefyre StreamHub

Read the docs: http://livefyre.github.com/StreamHub-Android-SDK/

This StreamHub-Android-SDK can be used for both Eclipse and Android Studio

**Steps to use in Eclipse:**

Get the fresh StreamHub-Android-SDK from Github

1.  Start with an existing project or create a new one

1. 	To import StreamHub-Android-SDK into your workspace go to: File > Import Project > General > Existing Project into Workspace
2. 	Browse and select StreamHub-Android-SDK; it should now show in the package explorer

3.	Right click on your project and select properties then select Android tab

4.	Under the Library section, select Add button then select StreamHub-Android-SDK from the list of libraries

5.  Click on Apply and OK.

____________

**Steps to use in Android Studio:**

Get the fresh StreamHub-Android-SDK from Github

1.  Start with an existing project or create a new one

1.	Right click on your project and select 'Open Module Settings'

2.	Select the **'+'** button on the top left corner of window

3.	Select "Import Existing Module"

4.  Browse and select StreamHub-Android-SDK
 * Android Studio may request to convert the SDK to gradle version; if this occurs, select next and finish

5.  Add the following dependency by going to project folder -> "app" folder -> "build.gradle" file under dependencies as follows:

```
dependencies {
  compile project(':streamHubAndroidSDK')
}
```
**Make sure that the following line is in your project folder -> settings.gradle file**

```
include ':streamHubAndroidSDK'
```

Note: You can customize configurations from within   [Config.java](https://github.com/Livefyre/StreamHub-Android-SDK/blob/master/src/livefyre/streamhub/Config.java).

# Sample App

* Reviews Demonstrative Example: https://github.com/Livefyre/StreamHub-Android-Reviews-App

* Comments Demonstrative Example: https://github.com/Livefyre/StreamHub-Android-SDK/tree/master/examples/commentstream

# SDK Client Classes

The StreamHub Android SDK exposes several Client classes that can be used to request StreamHub APIs.

* [`AdminClient`](http://livefyre.github.com/StreamHub-Android-SDK/com/livefyre/streamhub_android_sdk/AdminClient.html) - Exchange a user authentication token for user information, keys, and other metadata

* [`BootstrapClient`](http://livefyre.github.com/StreamHub-Android-SDK/com/livefyre/streamhub_android_sdk/BootstrapClient.html) - Get recent Content and metadata about a particular Collection

* [`StreamClient`](http://livefyre.github.io/StreamHub-Android-SDK/com/livefyre/streamhub_android_sdk/StreamClient.html) - Poll a stream for a collection to retrieve new, updated, and deleted content

* [`WriteClient`](http://livefyre.github.io/StreamHub-Android-SDK/com/livefyre/streamhub_android_sdk/WriteClient.html) - Post content, flag content, like content in a collection

# License

Copyright 2013 Livefyre Inc.

Licensed under the MIT License

redpajama_set_name: RedPajamaGithub


Kylie, my 7 year old, has an admirer. Friday night, 2 of the girls' friends stayed the night. It got a bit wild with 6 kids (a large and quite heavy mirrored closet door was seriously injured), but overall they were good. Saturday morning while we were enjoying pecan pancakes and cantaloupe, our doorbell rang. I answered it…surprise, surprise…no one was there. (We have been the victims of 'Ding, Dong, Ditch' quite often lately…I now know the culprits.) I looked down and discovered this.
Anyways, I find it quite disturbing that I am having to discuss 'dating' with my 7 year old. Granted, I have already had to have 'the talk' with my 9 year old because of some quite graphically horrifying questions posed to me, but to have the 'You're not dating…until you're 18′ talk so soon? I have avoided 'the talk' with the 2 younger girls because I thought their biological mother may want to have that discussion, but I don't think I can really put it off anymore. So, I'm praying that God will give me the right words, at the right time, and it not cause some huge drama down the line…wishful thinking.
what a proud day to remember!!
loose teeth gross me out! I don't look forward to those days!! She looks SO PROUD!!!
$1.42??? That amount cracks me up!
How cute! The tooth fairy leave gold dollar coins here. Happy WW!


text:
Cycloheptanon is een cyclisch keton. Het is een kleurloze, ontvlambare vloeistof met een fruitige geur. Ze is vrijwel onoplosbaar in water.

Synthese
De Franse scheikundige Jean-Baptiste Boussingault isoleerde deze verbinding in 1836 voor het eerst uit calciumsuberaat, dit is het calciumzout van kurkzuur (octaandizuur). Door dit te verwarmen verkreeg hij calciumcarbonaat en cycloheptanon:

Ca(O2C(CH2)6CO2)  ->  CaCO3 + (CH2)6CO

Cycloheptanon kan ook bereid worden door de Dieckmann-condensatie van kurkzuur of esters van kurkzuur, gevolgd door decarboxylering.

Een andere manier is de ringvergroting van cyclohexanon met behulp van stikstofhoudende verbindingen als diazomethaan.

Men kan cycloheptanon ook verkrijgen door de omlegging van het corresponderende epoxide 1,2-epoxycycloheptaan of cyclohepteenoxide in dimethylsulfoxide of dimethylformamide, met natriumjodide als katalysator.

Toepassingen
Cycloheptanon wordt gebruikt als tussenproduct in de bereiding van andere organische verbindingen, waaronder bencyclaan (een vasodilator) en pimelinezuur.

Keton
Cyclische verbinding
Ontvlambare stof


text:
Study & Continuing education
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Yahya Wardak from Afghanistan
Yahya Wardak, Kabul, Afghanistan
Name: Yahya Wardak
Lives in: Kabul, Afghanistan
Period in Germany: 1992 – 2010 in Heidelberg, Hamburg and Bonn
Education and research institutions: University of Heidelberg, Bernhard Nocht Institute for Tropical Medicine Hamburg
I already had good contacts in Germany, which is what ultimately led me to go and spend some time living there. During my time in Hamburg and Bonn, I studied health care and health care management and worked in tropical medicine. I was supported by InWEnt.
'Studying independently was new to me.'
I think the biggest difference between Germany and Afghanistan is the work ethic. Studying independently and having to take responsibility for everything was new to me. I have taken on a number of the positive traits that result from this approach. If a German person gives you his or her word, then you can rely on it. This is a quality that I have taken to heart.
My time in Germany has strongly influenced my professional life. I have acquired a great deal of expertise, including practical and theoretical knowledge that has benefited and continues to benefit me greatly in my work in my own country.
'Now, rather than simply treating individual patients, I can do something for the health of the population as a whole.'
I have now gone in another direction, leaving clinical medicine behind in favour of health care. This has completely changed my professional life. Now, rather than simply treating individual patients, I can do something for the health of the large municipalities and the population as a whole.
When I think back to my time in Germany, I remember studying hard, working hard and everything running in an orderly fashion. In the end, these efforts pay great dividends.
I missed the sun and the clear distinction between the seasons when I was in Germany and I didn't like the long German winters and the dark autumn days.
'I still have personal and professional contacts in Germany'
I work together with my friends and colleagues on a number of projects. I am also in touch with many of the alumni in Kabul. I currently work as an administrative specialist (CIM expert) at the Afghan Ministry of Higher Education in Kabul and have published 137 new medical textbooks to date. Funding is provided by the German Academic Exchange Service (DAAD) and German Aid for Afghan Children. Additionally, I have developed a concept for sustainable health care and established the first interdisciplinary day clinic, which is due to open shortly. I am planning to develop the Afghan National Textbook Programme for all subjects and also intend to set up similar clinics elsewhere.
My life has been marked by three simple but incredibly strong passions: searching for love, striving for knowledge and feeling unbearably burdened by the Afghan's people's suffering.
Dr Yahya Wardak on the Alumniportal Deutschland
The Alumniportal enables me to find jobs and training opportunities, as well as allowing me to develop contacts and strengthen my own social network. I haven't made much use of the portal so far, but certainly intend to do so in future.
Looking ahead, I intend to set up the Alumni Network Afghanistan (ANA). This network will serve to establish and strengthen links between former students and between these students and their alma maters, and to maintain their ties with Germany.
It will provide a forum for personal, professional and academic dialogue. The network will be open to anyone who has studied in Germany or spent more than two years practising their profession in the country. The time has come to set up an alumni network for Afghanistan to enable networking among these experts and to pool their potential.
See Yahya Wardak's profile in the Community
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The new sporty side by side AVALANCHE 800 XT, yours starting at 13,995* fully loaded!
Fun is not optional with the 2018 Avalanche 800 XT! Its powerful 800cc V twin engine is providing intense feelings throughout acceleration that will bring you up to 120 km/h. Its well designed cockpit and standard adjustable seats provide great comfort to both driver and passenger. The 58'' extended version, is bringing more stability while riding fast on rough trails and its additional 5 cm travel on each a-arm is providing even more comfortable ride than the standard version. Front and rear fenders extensions are giving an amazing look to our new sporty side by side and protecting passengers against everything nature will bring on! With the Avalanche 800 XT you can drive wherever you want to go for a fraction of competitors pricing!
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Essential Function's Clients are all business, but my target market is setting up buying groups of SME's & large corporate multi-site operations.
Due to the nature of my business, if the message / logo screams utilities, its usually ignored, hence the cog idea. We can incorporate more utility type photos as we build the website.
Personal - i'm 45, been in sales all my life, im a Libran, hence lots of ideas, but when we nail it, im sure it will lok amazing. Want a long term relationshp with designer, as wanting to establish more brandign moving forwards.
hi ch when you will come back?
My entry #15 has 2 logo please check both.
Hi i have alread submited an entry for your Essential logo design.


text:
RAINBOW BRIDGE An Illustrated History
By: Hassell, Hank
Quantity: 10 available
Brand new softcover book!
; The morning of August 14, 1909, marked the official discovery of the magnificent natural bridge, which spans 275 feet and towers 291 feet above the stream bed below it. Its fame was soon widespread, but for many years its visitors would be few, their numbers restricted by the long, arduous trail around Navajo Mountain to the site. The bridge's story included such western figures as trader Louisa Wetherill, wife of John and a Navajo speaker who was the first Anglo to hear of the bridge; Barry Goldwater, who for a time owned and operated Rainbow Lodge; Zane Grey, who wrote about the bridge; and David Brower, the Sierra Club leader who got wrapped up in the intersection of the Rainbow Bridge story with that of Glen Canyon Dam. Its construction and the filling of Lake Powell behind it made Rainbow Bridge a battleground, key territory in the larger war over water and conservation in the West. The remote, hard-to-reach national monument was supposed to define a limit to Colorado River reclamation but instead was inundated by Lake Powell and the tide of visitors who then could reach the foot of the bridge by boat. Though it is now easily and frequently visited and National Park Service amenities are in place, access to Rainbow Bridge is still an evolving and controversial issue. ; 8.5" x 11"; 173 pages; 0874212669
Title: RAINBOW BRIDGE An Illustrated History
Author Name: Hassell, Hank
Categories: Utah,
Publisher: Logan, UT U. S. A., Utah State University Press: 1999
Keywords: Golden Arches Rainbow Bridge Golden Arches National Parks Utah,
The Official System of Contract Bridge - The Official Book on the Official System
MARJUM PASS [Rainbow Valley, Utah]
By: Sanford, Dee S.
The Bridge to France - The Inside Story of the Wartime Shipping Board
By: Hurley, Edward N. (Wartime Chairman of the U.S. Shipping Board) *SIGNED by autho
Open At the Arms and Firm At the Feet - Unlocking the Power of the Spirit
By: Gray, Campbell
Cedar City [Utah], gateway to rainbow land A community portrait
By: Larson, Gustive Olof
THE DEVIL'S RAINBOW
By: Furnas, J. C.
LEGACY OF LOVE
By: Rosvall, Patty
Heal Our Land - Piano/vocal
By: Hatch, Orrin G. - Perry, Janice Kapp