text:
\section{Introduction}

Reinforcement learning (RL) \citep{Sutton98} is a behavior-based approach which allows an agent, either an infant or a robot, to learn a task by interacting with its environment and observing how the environment responds to the agent's actions.
RL has been shown in robotics \citep{Kober13, Kormushev13} and in infant studies \citep{Hammerer12, Deak14} to be successful in terms of acquiring new skills, mapping situations to actions \citep{Cangelosi15}.

To learn a task, an RL agent has to interact with its environment over time in order to collect enough knowledge about the intended task.
Nevertheless, on some occasions, it is impractical to leave the agent to only learn autonomously, mainly due to time restrictions and therefore, we aim to find a way to accelerate the learning process for RL.

In domestic and natural environments, adaptive agent behavior is needed utilizing approaches used by humans and animals.
Interactive reinforcement learning (IRL) allows to speed up the apprenticeship process by using a parent-like advisor to support the learning by delivering useful advice in selected episodes.
This allows to reduce the search space and thus to learn the task faster in comparison to an agent exploring fully autonomously \citep{Suay11, Cruz15}.
In this regard, the parent-like teacher guides the learning robot, enhancing its performance in the same manner as external caregivers may support infants in the accomplishment of a given task, with the provided support frequently decreasing over time.
This teaching technique has become known as parental scaffolding \citep{Breazeal98, Ugur15}.

The parent-like teacher can be either a human user or another artificial agent.
By using artificial agents as teachers, some properties have been studied so far such as different effects of delivering advice in different episodes and with different strategies during the learning process \citep{Torrey13, Taylor14} and effects of different probabilities and consistency of feedback \citep{Griffith13, Cruz14, Cruz16}.
Nonetheless, to the best of our knowledge, there is no study so far about the implications of utilizing artificial teachers with different characteristics and different internal representations of the knowledge based on their previous experience.
Moreover, the effects when the learner ignores some of the advice has also not been studied in artificial agent-agent interaction, although some insights are given in Griffiths' work using human-human interaction with a computational interface \citep{Griffiths12}.

In this paper, we study effects of agent-agent interaction in terms of achieved learning when parent-like teachers differ in essence and when learner agents vary in the way they incorporate the advice.
We have seen differences in the performance which could lead to adaptive behavior in order to reduce interactive feedback between trainer and learner.

This paper is organized as follows: in the second section, we present background and related work about IRL from both neuroscience and computational points of view.
The third section shows the proposed IRL scenario which has been previously used but is updated here to further integrate multi-modal advice from human teachers.
In the fourth section, we present the experimental set-up and obtained results.
Finally, the fifth section gives an overall discussion including main conclusions and future work.

\section{Interactive Reinforcement Learning}
Learning in humans and animals has been widely studied by neuroscience yielding a better understanding of how the brain can acquire new cognitive skills.
We currently know that RL is associated with cognitive memory and decision-making in animals' and humans' brains in terms of how behavior is generated \citep{Niv09}.
In general, computational neuroscience has interpreted data and used abstract and formal theories to help to understand about functions in the brain.

In this regard, RL is a method used to address optimal decision-making, attempting to maximize collected reward and minimize the punishment over time.
It is a mechanism utilized by humans and in robotic agents.
In developmental learning, it plays an important role since it allows infants to learn through exploration of the environment and connect experiences with pleasant feelings which are associated with higher levels of dopamine in the brain \citep{Wise78, Gershman15}.

RL is a plausible method to develop goal-directed action strategies. During an episode, an agent explores the state space within the environment selecting random actions which move the agent to a new state. Moreover, a reward signal is received after performing an action, which may encode a positive compensation or a negative punishment.
Over time, the agent learns the value of the states in terms of future reward, or reward proximity, and how to get to states with higher values to reach the target by performing actions \citep{Weber08}.

In robotics, RL has been used to allow robotic agents to autonomously explore their environment in order to develop new skills \citep{Wiering12, Mnih15}.
To solve an RL problem means to find at least one optimal policy that collects the highest reward possible in the long run.
Such a policy is known in psychology as a set of stimulus--response rules \citep{Kornblum90}.
Optimal policies are denoted by $\pi^{*}$ and share the action-value function which is denoted by $q^{*}$ and defined as: $q^{*}(s,a) = \underset{\pi} {\mathrm{max}} ~q^{\pi}(s, a)$.
The optimal action--value function can be solved through the Bellman optimality equation for $q^{*}$:

\begin{equation}
  q^{*}(s,a) = \sum_{s'} p(s'|s,a) [ r(s,a,s')+\gamma\max_{a'} q^{*}(s',a') ]
  \label{eq:bellman}
\end{equation}
where $s$ is the current state, $a$ is the taken action, $s'$ is the next state reached by performing the action $a$ in the state $s$, and $a'$ are possible actions that could be taken in $s'$.
In the equation, $p$ represents the probability of reaching the state $s'$ given that the current state is $s$ and the selected action is $a$, and $r$ is the received reward for performing action $a$ in the state $s$ for reaching the state $s'$.
The parameter $\gamma$ is known as discount rate and represents how influential future rewards are \citep{Sutton98}.
The gray box in Fig. \ref{fig:InterativeRLScenario} shows the general description of the RL framework, where the environment is represented by domestic objects which are related to our scenario which is described in the next section.

\begin{figure}
\centering
\includegraphics[width=0.9\linewidth]{figures/InteractiveRLScenario3.png}
\caption{An interactive reinforcement learning approach with policy shaping. The agent autonomously performs action $a$ in state $s$ obtaining reward $r'$ and reaching the next state $s'$. In selected states, the trainer advises the learner-agent changing the action to be performed in the environment.}
\label{fig:InterativeRLScenario}
\end{figure}

In the learning phase, to solve equation \ref{eq:bellman}, one strategy is to allow the agent to perform actions considering transitions from state--action pair to state--action pair rather than transitions from state to state only.
Accordingly, the on-policy method SARSA \citep{Rummery94} updates every state--action value according to the equation:

\begin{equation}
Q(s,a) \leftarrow Q(s,a) + \alpha[r' + \gamma Q(s',a') - Q(s,a)]
\label{eq:SARSA}
\end{equation}
where $Q$ is the value of the state--action pair and $\alpha$ the learning rate.

Although the next action can be autonomously selected by choosing the best known action at the moment, represented by the highest state--action pair, an intuitive strategy to speed up the learning process would be to include external advice in the apprenticeship loop; early research on this topic using both humans and robots can be found in \citep{Lin91}.
When using IRL, an action is interactively encouraged by a trainer with a priori knowledge about the desired goal \citep{Thomaz05, Thomaz06, Knox13}.
In IRL, using a trainer to advise an agent on future actions is known as policy shaping \citep{Cederborg15, Amir16}.

Supportive advice can be obtained from diverse sources like expert and non-expert humans, artificial agents with perfect knowledge about the task, or previously trained artificial agents with certain knowledge about the task.
In this work, an artificial trainer-agent which was itself previously trained through RL is used to provide advice, which has been formerly used in other works.
For instance, in \citep{Cruz14} advice is given based on an interaction probability and consistency of feedback. In Taylor's works, interaction is based on a maximal budget of advice and they studied which moment is better to give advice during the training \citep{Torrey13, Taylor14}.
Fig. \ref{fig:InterativeRLScenario} shows a general overview of the agent--agent scheme where the trainer provides advice in selected episodes to the learner-agent to bootstrap its learning process.

Although interactive advice improves the learning performance of learner-agents, a problem which remains open and that can significantly affect the agent's performance is the need of a good trainer since consecutive mistakes may lead to a worse training time \citep{Cruz16}.
In principle, one may think that an expert agent with a larger accumulated reward should be a good candidate to become the trainer.
Expert agents, either human or artificial, have been used in different reinforcement learning approaches using advice (e.g.: \cite{daSilva17, Ahmadabadi02, Ahmadabadi00, Prince99}).
However, when we look into the internal knowledge representation, this may not necessarily be the best option.
On some occasions, agents with lower overall performance may be better trainers due to a possibly vast experience about less common states (i.e. states that do not necessarily lead to the optimal performance) and therefore, may give better advice in those states.
Some insights on using trainer-agents with different abilities have been discussed by \cite{Taylor11} in a simulated robot soccer domain by using a human-agent transfer approach.

\section{Domestic Robot Scenario}
In this paper, we extend a previously used RL scenario which consists of a robotic agent performing a cleaning task \citep{Cruz16}.
Here, we do not deal with contextual affordances and, therefore, we do not have to previously learn them which results in a shorter training time, in general.

The current scenario comprises two objects, three locations, and seven actions.
The robot is placed in front of a table in order to clean it up.
In this scenario, there are two objects: a \textit{cup} which is initially at a random location on the table and needs to be relocated as the table is being wiped, and a \textit{sponge} which is used by the robot in order to wipe different sections of the table.

Three locations have been defined in the cleaning scenario: \textit{left} and \textit{right} to refer to each of the two sections of the table, and one additional position called \textit{home} which is the robot's arm's initial position and the location where the sponge is placed when not being used.
Furthermore, seven domain-specific actions are allowed in this scenario defined as follows:

\begin{enumerate}[\hspace{0cm}(P1)]
	\item[(i)] \texttt{GET:} allows the robot to pick up the object which is placed in the same location as its hand.
	\item[(ii)] \texttt{DROP:} allows the robot to put down the object held in its hand. The object is placed in the same location where the hand is.
	\item[(iii)] \texttt{GO HOME:} moves the hand to the home position.
	\item[(iv)] \texttt{GO LEFT:} moves the hand to the left position.
	\item[(v)] \texttt{GO RIGHT:} moves the hand to the right position.
	\item[(vi)] \texttt{CLEAN:} allows the robot to clean the section of the table at the current hand position if holding the sponge.
	\item[(vii)] \texttt{ABORT:} cancels the execution of the cleaning task at any time and returns to the initial state.
\end{enumerate}

Each state is represented by using a state vector of four variables:

\begin{enumerate}[\hspace{0cm}(P1)]
	\item[(i)] the object held in the agent's hand (if any),
	\item[(ii)] the agent's hand position,
	\item[(iii)] the position of the cup, and
	\item[(iv)] a 2-tuple with the condition of each side of the table, i.e. whether the table surface is clean or dirty.
\end{enumerate}

Therefore, the state vector at any time $t$ is characterized as follows:

\begin{equation}
s_t = <handObject, handPosition, cupPosition, sideCondition>.
\end{equation}

\begin{figure}
\centering
\includegraphics[width=0.7\linewidth]{figures/extendedSolution3.png}
\caption{Outline of state transitions in the defined cleaning scenario. Two different paths are possible to reach a final state. Each path implies a different number of intermediate states which influence the total amount of collected reward during a learning episode. Thus path A comprises 23 states and path B 31 states.}
\label{fig:extendedSolution}
\end{figure}

As long as the agent successfully finishes the task, a reward equal to $1$ is given to it, whereas a reward of $-1$ is given if a failed-state was reached.
In this context, a failed-state is a state from where the robot cannot continue the expected task execution, for instance attempting to pick-up an object when it is already holding another object.
Furthermore, it is given a small negative reward of $-0.01$ to encourage the agent to take shorter paths towards a final state.
Therefore, the reward function can be posed as:

\begin{equation}
r(s) = \left\{
\begin{array}{r l}
  1 & \textrm{if } s \textrm{ is the final state}\\
 -1 & \textrm{if } s \textrm{ is a failed-state}\\
 -0.01 & \textrm{otherwise}
\end{array}
\right.
\label{eq:reward}
\end{equation}

At the beginning of each training episode, the robot's hand is free at the home location, the sponge is also placed at the home position, while the cup is at either the left or the right location, and both table sections are dirty.
Therefore, the initial state $s_0$ may be represented as:

\begin{equation}
s_0 = <\mathit{free, home, left | right, [dirty, dirty]}>.
\end{equation}

From the initial state, the state vector is updated every time after performing an action according to the state transition table as shown in Table \ref{tb:vectorUpdate}.
In the current scenario, considering the state vector features, there are 53 different states which represent two divergent paths to two final states.
Fig. \ref{fig:extendedSolution} depicts a summarized illustration of the transitions to reach a final state assuming the cup to be initially at the left position.
The figure also shows the number of states involved in each path.
Therefore, each path leads to a different number of transited states which in turn also leads to a different accumulated reward.

\begin{table}
\tbl{State vector transitions. After performing an action the agent reaches either a new state or a failed condition, if the latter, the agent starts another training episode from the initial state $s_0$.}
{\begin{tabular}[l]{@{}ll}\toprule
  \textbf{Action} & \textbf{State vector update} \\
\colrule
  Get & \textbf{if} handPos == \textit{home} \textbf{\&\&} handObj == \textit{cup} \textbf{then} FAILED \\
      & \textbf{if} handPos == \textit{cupPos} \textbf{\&\&} handObj == \textit{sponge} \textbf{then} FAILED \\
      & \textbf{if} handPos == \textit{home} \textbf{then} handObj = \textit{sponge} \\
      & \textbf{if} handPos == cupPos \textbf{then} handObj = \textit{cup} \\
\colrule
  Drop & \textbf{if} handPos == \textit{home} \textbf{\&\&} handObj == \textit{cup} \textbf{then} FAILED \\
       & \textbf{if} handPos != \textit{home} \textbf{\&\&} handObj == \textit{sponge} \textbf{then} FAILED \\
       & \textbf{otherwise} handObj = \textit{free} \\
\colrule
  Go $<$pos$>^{\rm *}$ & handPos = pos \\
               & \textbf{if} handObj == \textit{cup} \textbf{then} cupPos = pos \\
\colrule
  Clean & \textbf{if} handPos == cupPos \textbf{then} FAILED \\
        & \textbf{if} handPos == \textit{home} \textbf{then} FAILED \\
        & \textbf{if} handObj == \textit{sponge} \textbf{then} sideCond[handPos] = \textit{clean} \\
\colrule
  Abort & handPos = \textit{home} \\
        & handObj = \textit{free} \\
        & cupPos = random(pos) \\
        & sideCond = [dirty]*$|$pos$|$ \\
\botrule
\end{tabular}}
\tabnote{$^{\rm *}$ $<$pos$>$ may be any defined location, therefore three actions are represented by this transition, i.e.: go left, go right, and go home.}
\label{tb:vectorUpdate}
\end{table}

As defined, the same transitions may be used in scaled-up scenarios where more locations are defined on the table in a larger grid since the definition of transitions is done by only considering the object held by the robot and the hand position in reference to either the home location or the cup position.

Fig. \ref{fig:twoNICOs} shows the domestic robotic scenario with two robotic agents where one agent becomes the trainer by learning the task using autonomous RL.
The second agent performs the same task supported by the trainer-agent with selected advice using the IRL framework.

\begin{figure}
\centering
\includegraphics[width=0.75\linewidth]{figures/twoNICOs.png}
\caption{Two robotic agents performing a domestic task in the defined home scenario. The trainer-agent advises the learner-agent in selected states what action to perform next.}
\label{fig:twoNICOs}
\end{figure}

\section{Experiments and Results}

In the following subsections, the experimental set-up will be explained in detail.
Initially, we look into the internal representation and visited states of prospective advisor agents in order to explore which features may be important to act as a good trainer.
Afterward, we compare the behavior of both the advisor and the learner in terms of the internal representation, visited states, and collected reward.
Finally, we evaluate some system interaction parameters like frequency of feedback, consistency of feedback, and learner behavior.

All experiments included the training of $100$ agents through $3000$ episodes.
Q-values were randomly initialized using a uniform distribution between $0$ and $1$.
Other parameter values were learning rate $\alpha = 0.3$ and discount factor $\gamma = 0.9$.
Besides this, we used $\epsilon$-greedy action selection with $\epsilon = 0.1$.
To assess the interaction between learner and trainer-agents we used a probability of feedback of $0.25$ as a base; nevertheless, we afterward varied this parameter along with the consistency of feedback and learner behavior.
All the aforementioned parameters were empirically determined and related to our scenario.

\subsection{Choosing an Advisor Agent}

To acquire a sample of trainer-agents, autonomous RL was performed with $100$ agents, each of them a prospective trainer for the IRL approach.
In the presented scenario, there are agents with diverse behaviors which differ mostly in the path they choose until reaching a final state.
First, there are agents which most of the time choose the same path to complete the task, either path A or path B, which leads to a biased behavior due to the way the knowledge is acquired during the learning process.
From this kind of behavior and taking into account our scenario, there exist agents that regularly take the shorter path (path A) and others that take the longer one (path B); we refer to them as the specialist-A and the specialist-B agents respectively.
In both cases, agents successfully accomplish the task, although they accumulate different amounts of average reward.
Obviously, the specialist-A agents are the ones with better performance in terms of collected reward since fewer state transitions are needed to reach the final state.
Second, there are agents with a more homogeneously distributed experience, meaning that they do not have a favorite sequence to follow and have equally explored both paths.
We refer to such agents as polymath agents.

\begin{figure}
\centering
\makebox[\linewidth][c]{\includegraphics[width=1.0\linewidth]{figures/visitedTwoTeachers4.png}}
\caption{Frequency of visits per state for two agents. It is possible to observe two different behaviors. The biased (specialist-A) agent gained experience mostly on the shorter path, whereas the homogeneously-distributed (polymath) agent gained experience through most states.}
\label{fig:visitedTwoTeachers}
\end{figure}

To illustrate this, Fig. \ref{fig:visitedTwoTeachers} shows a frequency histogram of visited states for two potential trainer-agents over all training episodes.
The histogram shows two distinct distributions, one for a specialist-A agent in gray and one for a polymath agent in blue.
The specialist-A agent decided to clean the table following the shorter path most of the time and, therefore, there is an important concentration of visits among the states from $16$ to $29$ which are intermediate states to complete the task on this path.
Furthermore, there is a clear subset of states which was never visited during the learning.
In contrast, the polymath agent visited all the states and transits on both paths to a similar extent.
In the case of the specialist-B agent, there is also a concentration of visits among a subset of states, similarly to the specialist-A agent. The specialist-B agent
decided most of the time to clean following the longer path along the states from $30$ to $48$ and barely visiting states from $16$ to $29$. Therefore, we do include this agent in the results hereafter but we do not present it in some plots to make the relevant information more accessible.

To further analyze the agents' behavior we took three representative agents, one per class, that we will from now on use with the respective names: specialist-A agent with biased behavior for the shorter path, specialist-B agent with biased behavior for the longer path, and polymath agent with unbiased behavior.
The specialist-A agent visited each state with an average of $s_1=1121.21$ times, a standard deviation of $\sigma_s^1=1570.75$, an accumulated average reward of $r_1=0.11105$ per episode, and $R_1=333.15$ during the whole training.
The specialist-B agent visited each state on average $s_2=1561,15$ times obtaining a more diverse experience than the previous agent but certainly not homogeneously distributed, which can also be appreciated in the standard deviation of $\sigma_s^2=1628.70$.
The specialist-B agent accumulated an average reward of $r_2=-0.17839$ for each episode and a total of $R_2=-535.18$.
In the case of the polymath agent, each state was visited an average of $s_3=1307.51$ times with standard deviation of $\sigma_s^3=947.96$.
The accumulated average reward was $r_3=-0.00427$ per episode and the total reward was $R_3=-12.82$ during the whole training.
Table \ref{tb:performance} shows a summary of the performance of the three aforementioned agents.

\begin{table}
\tbl{Visited states, standard deviation, reward accumulated per episode, and total collected reward for three agents from classes with different behavior. The agents show different characteristics as result of the autonomous learning process.}
{\begin{tabular}[l]{@{}lllllll}\toprule
  \textbf{Agent} & $\pmb{s}$ & $\pmb{\sigma_s}$ & $\pmb{r}$ & \textbf{R} & \textbf{Characteristic} \\

\colrule
  Specialist-A agent & 1121.21 & 1570.75 & 0.11105 & 333.15 & Largest accumulated reward \\
\colrule
  Specialist-B agent & 1561.15 & 1628.70 & -0.17839 & -535.18 & Largest amount of experience\\
\colrule
  Polymath agent & 1307.51 & 947.96 & -0.00427 & -12.82 & Smallest standard deviation \\

\botrule
\end{tabular}}

\label{tb:performance}
\end{table}

Nevertheless, accumulating plenty of reward does not necessarily lead to becoming a good trainer.
In fact, it only means that the agent is able to select the shorter path most of the time from the initial state, but the experience collected in other states not involved in that route is absent or barely present and therefore, such an agent cannot give good advice in those states where it does not know how to act optimally.

For a good trainer to emerge with knowledge of most of the situations or in all possible states we suggest an agent with a small standard deviation $\sigma_s$ from the mean frequency over all visited states, which represents a better distribution of the experience during the training.
We select the trainer-agent $T^*$ computing:

\begin{equation}
  T^* = \underset{i \in A} {\mathrm{argmin}} ~\sigma_s^i
  \label{trainer}
\end{equation}
where $A$ is the set of all the trained agents and their respective visited states during the learning process.

Therefore, we propose that a good trainer is, in essence, an agent which not only collects more rewards but shows also a fairly distributed experience.
From the three agents shown above, the polymath agent has a standard deviation of $\sigma_s=947.96$ and thus might be a good advisor.
In Fig. \ref{fig:visitedTwoTeachers}, the experience distribution of such an agent is shown in blue and this experience distribution suggests that the agent has the knowledge to advise what action to perform in most of the states.
In the case of the initial state, the frequency is much higher in comparison since this state is visited every time at the beginning of a learning episode.
In fact, similar frequencies are observed in this state for a biased distribution.

\begin{figure}
\centering
\makebox[\linewidth][c]{\includegraphics[width=1.1\linewidth]{figures/QValues4.png}}
\caption{Internal knowledge representation for three possible parent-like advisors in terms of Q-values, namely the specialist-A, the specialist-B, and the polymath agent.
The specialist-A agent shown in figure a), despite collecting more reward, does not have enough knowledge to advise a learner in every situation represented by the blue box.
A similar situation is experienced by the specialist-B agent, as shown in figure b).
The polymath agent shown in figure c) has overall much more distributed knowledge which allows it to better advise a learner-agent.}
\label{fig:QValues}
\end{figure}

We also recorded the internal representation of the knowledge through the Q-values to confirm the lack of learning in a subset of states.
Fig. \ref{fig:QValues} shows a heat map of the internal Q-values of three agents, the specialist-A, the specialist-B, and the polymath agent.
Warmer regions represent a larger reward and colder regions lower values.
In fact, the coldest regions are associated with failed-states from where the agent should start a new episode, obtaining a negative reward of $r=-1$ according to Eq. \ref{eq:reward}.
In Fig. \ref{fig:QValues}, it can be observed that the specialist-A agent may be an inferior advisor since there exists a whole region uniformly in yellow, which shows no knowledge about what action to prefer.
In the case of the specialist-B agent, there exists a region which shows much less knowledge on what action to prefer when comparing it with the two other agents.
In other words, the learned policies are partially incomplete as highlighted by the blue boxes in Fig. \ref{fig:QValues}.
To the contrary, the policy learned by the polymath agent is much more complete when observing the same regions as highlighted by the green boxes.
It is important to note that the region on top is in all cases colder than the rest because it is the most distant one from the final states where a positive reward $r=1$ is given, but in spite of that, the polymath agent is still able to select a suitable action according to the learned policy.

\subsection{Comparing Advisor and Learner Behavior}

Once we had chosen trainer-agents, we were able to compare how influential such a trainer was in the learning process of a learner.
We used two agents shown in the previous subsection, the specialist-A and the polymath agent, the former with the largest accumulated reward and the latter with the smallest standard deviation.

Fig. \ref{fig:visitedRLvsIRLBadTeacher} shows the frequency with which each state was visited for $100$ learner-agents on average using the specialist-A agent with biased frequency distribution as a trainer.
We can observe a large standard deviation for visited states in IRL agents in most of the cases, which suggests diversity in terms of frequency for those states among the learner-agents.
Fig. \ref{fig:visitedRLvsIRL} shows the average frequency of visits for each state for $100$ learner-agents using the polymath agent as a trainer which has a more homogeneous frequency distribution.
It can be observed that the standard deviation for visited states in IRL agents is much lower in comparison to the previous case.
This shows a more stable behavior in terms of visiting frequency in learner-agents when using the polymath trainer-agent.

\begin{figure}
\centering
\makebox[\linewidth][c]{\includegraphics[width=1.0\linewidth]{figures/visitedRLvsIRLBadTeacher4.png}}
\caption{Visited states for the specialist-A RL trainer-agent and average state visits of IRL learner-agents. The averaged frequency for IRL agents moreover includes the standard deviation for visited states showing that in many cases the trainer-agent does not know how to advise and in consequence leads the learner-agent to dissimilar behavior.}
\label{fig:visitedRLvsIRLBadTeacher}
\end{figure}

\begin{figure}
\centering
\makebox[\linewidth][c]{\includegraphics[width=1.0\linewidth]{figures/visitedRLvsIRL4.png}}
\caption{Visited states for the polymath RL trainer-agent and average state visits of IRL learner-agents. The averaged frequency for IRL agents includes the standard deviation which in this case is considerably lower as the learners are assisted by a trainer with more knowledge about the task-space which also leads learner-agents to have more stable behavior as they are consistently advised.}
\label{fig:visitedRLvsIRL}
\end{figure}

By using the specialist-A agent as a trainer in our IRL approach the average collected reward is slightly higher in comparison with autonomous RL.
In general, the IRL approach collects the reward faster than RL but in a similar magnitude after $400$ episodes.
Fig. \ref{fig:rewardRLvsIRLBadTeacher} depicts the average collected reward during the first $500$ episodes using autonomous RL and IRL approaches with yellow and red respectively using the specialist-A agent as the trainer in the case of IRL.
The gray curves show the convoluted collected reward inside a window of 30 values to smooth the results shown.

On the other hand, by using the polymath agent as the trainer the IRL approach converges both faster and to a higher amount of reward when compared with the previous case.
This is due to the polymath agent which knows the task-space better and is able to advise correctly in more situations than the specialist agent.
In consequence, this allows the learner to complete the task faster and therefore accumulate more reward. Fig. \ref{fig:rewardRLvsIRL} shows the average collected reward in $500$ episodes for RL and IRL approaches.
Once again, the gray curves show the convoluted collected reward inside a window of 30 values to smooth the results shown.
In the following experiments, only smooth curves will be used to simplify the analysis of the results.

\begin{figure}
\centering
\includegraphics[width=0.8\linewidth]{figures/rewardRLvsIRLBadTeacher2.png}
\caption{Average collected reward by $100$ agents using RL and IRL approaches. In this case, a biased trainer (the specialist-A agent) is used to advise the learner-agents. The advice slightly improves the performance in terms of accumulated reward and convergence speed.}
\label{fig:rewardRLvsIRLBadTeacher}
\end{figure}

\begin{figure}
\centering
\includegraphics[width=0.8\linewidth]{figures/rewardRLvsIRL.png}
\caption{Average collected reward by $100$ agents using RL and IRL approaches. When using an unbiased trainer-agent (the polymath agent), the accumulated reward is higher and the convergence speed faster in comparison with the previous case using a biased agent as an advisor.}
\label{fig:rewardRLvsIRL}
\end{figure}

Therefore, IRL is in general beneficial for a learner-agent in terms of accumulated reward and convergence speed.
Nevertheless, the selection of the trainer can have significant implications on the learner's performance.
In the following subsection, we analyze the main interaction parameters in order to understand how influential they are regarding the learner's performance when being advised by a potentially good trainer.

\subsection{Evaluating Interaction Parameters}

As part of this study, we evaluated the involved interaction parameters namely probability of feedback ($\mathcal{L}$), consistency of feedback ($\mathcal{C}$), and whether the learner follows the received advice or not in order to mimic actual human-human behavior where the learner occasionally
does not follow the advice \citep{Griffiths12}.
We called this parameter \textit{learner obedience} $\mathcal{O} \in [0, 1]$, $0$ being an agent that never follows the advice and thus corresponds to a pure RL learner. Probability and consistency of feedback correspond to the frequency of giving advice to the learner and the degree to which such advice is rational in the current state respectively.

Initially, we used a fixed probability of feedback $\mathcal{L} = 0.25$, with different values of consistency.
A similar probability of feedback has been used in (Cruz et al., 2016) and therefore, we used it as a base to start the evaluation.
The idea then was to test the system over a number of different values of consistency of feedback and learner obedience.
Fig. \ref{fig:rewardf025} shows the collected reward during $500$ episodes for the different values of consistency of feedback $\mathcal{C} \in \left\lbrace0.25, 0.5, 0.75, 1.0\right\rbrace$ and learner obedience $\mathcal{O} \in \left\lbrace0.0, 0.25, 0.5, 0.75, 1.0\right\rbrace$.
In all cases, the learner obedience $\mathcal{O} = 0$, shown in black, corresponds to autonomous RL which is shown in yellow.
The collected rewards indicate generally that the more consistent the feedback, the better is the performance.
Even though that difference in the performance seems to be intuitive, it is important to note that, even with comparatively high values of consistency like $\mathcal{C} = 0.75$, the learner does not achieve significantly better performance compared to autonomous RL while on the other hand, an idealistic perfect consistency ($\mathcal{C} = 1$) allows the learner-agent to achieve much higher collected rewards than with autonomous RL even when the learner obedience is as low as $\mathcal{O} = 0.25$.
Therefore, in the current scenario, wrong advice has an important negative effect since it does not only lead to the execution of more intermediate steps but also, in many cases, leads to failed-states and thus to a high negative reward ($-1$) and the start of a new learning episode.
Further on in this section, we are going to test additional values of consistency $\mathcal{C} \in [0.75, 1.0]$ to observe how influential small variations in this parameter are.

\begin{figure
\centering
\subfloat{\includegraphics[width=0.5\textwidth]{figures/rewardsf025c025.png}}
\subfloat{\includegraphics[width=0.5\textwidth]{figures/rewardsf025c05.png}}
\\
\noindent
\subfloat{\includegraphics[width=0.5\textwidth]{figures/rewardsf025c075.png}}
\subfloat{\includegraphics[width=0.5\textwidth]{figures/rewardsf025c10.png}}
\\
\subfloat{\includegraphics[width=1.0\textwidth]{figures/label.png}}
\caption[caption]{Collected reward for different values of learner obedience using fixed probability of feedback of 0.25 and four different values for consistency of feedback between $0.25$ and $1.0$.}
\label{fig:rewardf025}
\end{figure}

In Fig. \ref{fig:rewardf025}, agents which follow the advice only $25\%$ of the time ($\mathcal{O}=0.25$), depicted in green, show much better performance when the consistency of feedback $\mathcal{C}$ is lower which is due to the agent being able to ignore the suggested wrong advice and select an action on its own.
On the contrary, agents which follow the advice all the time ($\mathcal{O}=1.0$), depicted in red color, show much better performance in presence of consistent feedback.

\begin{figure
\centering
\makebox[\linewidth][c]{%
\subfloat{\includegraphics[width=0.4\textwidth]{figures/rewardsf05c025.png}}
\subfloat{\includegraphics[width=0.4\textwidth]{figures/rewardsf075c025.png}}
\subfloat{\includegraphics[width=0.4\textwidth]{figures/rewardsf10c025.png}}%
}\\

\makebox[\linewidth][c]{%
\subfloat{\includegraphics[width=0.4\textwidth]{figures/rewardsf05c05.png}}
\subfloat{\includegraphics[width=0.4\textwidth]{figures/rewardsf075c05.png}}
\subfloat{\includegraphics[width=0.4\textwidth]{figures/rewardsf10c05.png}}%
}\\

\makebox[\linewidth][c]{%
\subfloat{\includegraphics[width=0.4\textwidth]{figures/rewardsf05c075.png}}
\subfloat{\includegraphics[width=0.4\textwidth]{figures/rewardsf075c075.png}}
\subfloat{\includegraphics[width=0.4\textwidth]{figures/rewardsf10c075.png}}%
}\\

\makebox[\linewidth][c]{%
\subfloat{\includegraphics[width=0.4\textwidth]{figures/rewardsf05c10.png}}
\subfloat{\includegraphics[width=0.4\textwidth]{figures/rewardsf075c10.png}}
\subfloat{\includegraphics[width=0.4\textwidth]{figures/rewardsf10c10.png}}%
}\\

\subfloat{\includegraphics[width=1.0\textwidth]{figures/label.png}}
\caption[caption]{Collected reward for different learner obedience levels using several probabilities and consistencies of feedback. Higher probabilities of feedback do not necessarily lead to discernible improvements in the overall performance; however, important differences can be noted as higher consistencies of feedback are used.}
\label{fig:rewardsfc}
\end{figure}

Thereupon, we modified the probability of feedback for the purpose of testing how influential different consistencies of feedback $\mathcal{C}$ and different learner obedience levels $\mathcal{O}$ are.
Fig. \ref{fig:rewardsfc} shows the accumulated reward during $500$ episodes for probability of feedback $\mathcal{L} \in \left\lbrace0.5, 0.75, 1.0\right\rbrace$ (the outcome using probability of feedback of $0.25$ is already shown in Fig. \ref{fig:rewardf025}) and consistency of feedback $\mathcal{C} \in \left\lbrace0.25, 0.5, 0.75, 1.0\right\rbrace$ using learner obedience $\mathcal{O} \in \left\lbrace0.0, 0.25, 0.5, 0.75, 1.0\right\rbrace$.

In Fig. \ref{fig:rewardsfc} the columns show the performance over different probabilities of feedback, while the rows show the performance over different values of consistency.
Observing each row, it can be seen that higher probabilities of feedback do not considerably improve the outcomes in terms of the collected reward, suggesting that often interactive feedback does not necessarily enhance the overall performance but it is rather the consistency of feedback that makes prominent differences.
In fact, observing the outcomes down the columns, thus with the same probability of feedback, different values of consistency lead to significant improvements in the collected reward and consequently, consistency of feedback has much more impact on the final learning performance.
For instance, when using the consistency of feedback $\mathcal{C} = 1.0$ (fourth row in Fig. \ref{fig:rewardsfc}), in all cases the accumulated reward is higher than $0.5$, but on the other hand, when using the consistency of feedback $\mathcal{C} = 0.75$ (third row in Fig. \ref{fig:rewardsfc}), the accumulated reward tends to slightly decrease as trainer advice increases, meaning that more interactive feedback does not help in the presence of poor consistency of feedback or, in other words, of bad advice.

Ultimately, since the consistency of feedback shows considerable sensibility in the presence of small variations, we performed one additional experiment keeping the probability of feedback fixed to $\mathcal{L} = 0.25$ as in Fig. \ref{fig:rewardf025} since we use this value as a base as aforementioned.
We tested the consistency of feedback with values $\mathcal{C} \in \left\lbrace0.8, 0.85, 0.9, 0.95\right\rbrace$ (consistency of $0.75$ and $1.0$ are already shown in Fig. \ref{fig:rewardf025}) to evaluate how these slight changes impact on the overall performance.
Fig \ref{fig:rewardsCSmall} shows the accumulated rewards for learner obedience $\mathcal{O} \in \left\lbrace0.0, 0.25, 0.5, 0.75, 1.0\right\rbrace$.
It can be seen that such small differences in the consistency of feedback can lead to dissimilar outcomes, ranging from behavior similar to autonomous RL when $\mathcal{C} = 0.8$ to behavior similar to a fully and correctly advised learner-agent when $\mathcal{C} = 0.95$.
Therefore, even a small proportion of bad advice can considerably impoverish the learning process, which shows how important it is to select trainers that can give useful advice in most states since specialised trainers, despite being more successful themselves from the initial state, have limited knowledge when it comes to states that lie outside their specialised policy.

In our approach, we have used the probability of feedback as a way to control how much advice is given to the learner-agent in terms of assistance during selected training episodes.
As mentioned above, the consistency of feedback allows to mimic the behavior of human trainer-agents who are susceptible to make mistakes during the learning process.
Nevertheless, at this point, all the instances of advice are received by the learner-agent without any discrimination between right or wrong advice.
As discussed, the inconsistent feedback may in fact lead to slow the learning process in terms of accumulated reward.
Therefore, the learner obedience parameter is an effective way for learner-agents to suppress the influence of the inconsistent feedback disregarding some wrong pieces of advice.
In this way, the learner-agents are able to accumulate more reward during the learning process.

\begin{figure
\centering
\subfloat{\includegraphics[width=0.5\textwidth]{figures/rewardsf025c08.png}}
\subfloat{\includegraphics[width=0.5\textwidth]{figures/rewardsf025c085.png}}
\\
\noindent
\subfloat{\includegraphics[width=0.5\textwidth]{figures/rewardsf025c09.png}}
\subfloat{\includegraphics[width=0.5\textwidth]{figures/rewardsf025c095.png}}
\\
\subfloat{\includegraphics[width=1.0\textwidth]{figures/label.png}}
\caption[caption]{Collected reward for different values of learner obedience using fixed probability of feedback $0.25$ and for four different cases for higher consistencies of feedback between $0.8$ and $0.95$.}
\label{fig:rewardsCSmall}
\end{figure}

\section{Conclusions and Future Work}

In this work, we presented a comparison of artificial agents that are used as parent-like teachers in an IRL cleaning scenario.
We have defined three classes of trainer-agents related to our scenario.
The agents differ in their characteristics and consequently in the obtained performance during their own learning process and in turn as trainers.
The three agents vary in their main properties which reflect in their behavior as i) the specialist-A agent with the largest accumulated reward, ii) the specialist-B agent with the largest amount of experience in terms of the number of explored states, and iii) the polymath agent with the smallest standard deviation.

It has been shown that there exists divergence in the internal representation of the knowledge of the agents through state--action Q-values since there are states in which it is not possible to distinguish what actions lead to greater reward.
Using the polymath agent as an advisor leads to both greater reward and faster convergence of the reward signal and also to a more stable behavior in terms of the state visit frequency of the learner-agents, which can be seen in the standard deviation for each visited state when compared with the case of the specialist-A agent as a trainer.

IRL generally helps to improve the performance of an RL agent using parent-like advice.
Nonetheless, it is important to take into account that higher levels of interaction do not necessarily have a direct impact on the total accumulated reward.
More importantly, the consistency of feedback seems to be more relevant when dealing with different learner obedience parameters (or a noisy or unreliable communication channel) since small variations can lead to considerably different amounts of collected reward.

Agents with a smaller standard deviation are preferred candidates to be parent-like teachers since they have a much better distribution of knowledge among the states.
This allows them to adequately advise learner-agents on what action to perform in specific states.
Agents with biased knowledge distributions collect more reward themselves, but nevertheless, have a subset of states where they cannot properly advise learners.
This leads to a worse performance in the apprenticeship process in terms of maximal collected reward, convergence speed, and behavior stability represented as the standard deviation for each visited state.

The finding that an expert in a certain domain is not necessarily a good teacher might also help the understanding of biological or natural systems in terms of assistive teaching.
For instance, a good soccer player is not necessarily a good soccer trainer.
We are not aware of studies that confirm this in biological systems or human-human interaction.
However, \cite{Taylor11} gave some interesting insights about a human-agent interaction approach.
Also, \cite{Griffiths12} studied different teacher behaviors to improve the apprenticeship in learner-agents.
Although their experiments are based on human-human interaction, they have used tutors that have mastered a given task without any classification about the level of expertise.

An important future work is to investigate how the obtained results can be scaled up to either larger discrete or continuous scenarios.
There are many real-world problems which have inherently continuous characteristics.
Many of these problems have been addressed using autonomous RL by discretizing the state-action space.
This discretization may lead to the introduction of hidden states or hidden actions for the RL agent.
However, a human trainer may not know or have access to this discrete representation and may advise actions which are not directly mapped into the discrete action-state representation used by the learner-agent.
Therefore, if the learner-agent maps the given advice into the discrete representation, it could lead to a slight error which over time could be accumulated rendering the learned policy useless.
An alternative is to address the problem directly in its continuous representation, but to the best of our knowledge, continuous IRL has not been studied yet.
It can be expected that RL agents have similar behavior in continuous scenarios compared to discrete ones since they are designed to find the optimal solution maximizing the collected reward.

Moreover, adaptive learner behavior can be explored, thus allowing to decide which advice to follow depending on the collected knowledge about the current state that the learner-agent has at a specific time.
Then, the learner-agent would act with diverse values for the learner obedience parameter, adapting it in real time.
Greater learner obedience can be expected at the beginning of the learning process, but over time the learner-agent should take its own experience more into account and therefore follow its own policy instead of the parent-like advice, leading to smaller obedience values.
In the same way, if new space is explored and consequently the reward gets worse, then parent-like advice could be used once again, leading to a dynamic learning process, taking advice into account when necessary while avoiding bad advice when possible.

\section*{Acknowledgements}
The authors gratefully acknowledge partial support by CONICYT scholarship 5043, the German Research Foundation DFG under project CML (TRR 169), the European Union under project SECURE (No 642667), and the Hamburg Landesforschungsf\"orderungsprojekt CROSS.


<nettime> Save de date: oXcars and FCForum 2011.
Subject: <nettime> Save de date: oXcars and FCForum 2011.
fertile breeding ground for such uprisings.
The #15M movement within the *#spanishrevolution* is organized online.
internet and the men and women gathering in streets and squares.
downloading) were the starting point for a larger action.
the pressing challenges that face our connected societies.
We welcome and await you all.
English translation will be provide.
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text:
First post-covid trade mission from Africa to the Netherlands
A group of 30 policy makers and captains of industry from Nigeria visited the Netherlands in the last week of March. This incoming delegation was the first trade mission from Africa to visit the Netherlands in two years' time.
6th of April 2022 News item
Following an initiative of the Dutch Consulate-General in Lagos, Nigeria's commercial capital, the group engaged in matchmaking with Dutch companies and engaged in inspiring company visits. Commissioned by the Netherlands-Enterprise Agency, the trade mission was carried out by the Netherlands-African Business Council (NABC).
Prince Ojeabulu is the COO of Rensource Energy. "I was blown away by the expertise in the field of solar energy that I found in the Netherlands," he remarked during a bus drive in between two company visits. The 30 participants were divided into 3 tracks: horticulture, circular economy, and energy transition. Ojeabulu joined the latter track, which started by visiting a floating solar park in Rotterdam. The solar panels there aren't stagnant but they 'follow' the sun, in order to generate more electricity. "It would be extremely interesting to try this out in Lagos, which is surrounded by water," Ojeabulu remarked. "Hopefully, we can work together with the Dutch company Floating Solar in order to establish that."
The group also visited the hydrogen experts at the Port of Rotterdam. The Nigerian Ambassador to the Netherlands, Dr. Eniola Ajayi, encouraged the Dutch port to also cooperate with the grand ports of Lagos or nearby Lekki. "Nigerians know how to catch up quickly: we have a young and vibrant population," the Ambassador remarked. "Our government is dedicated to living up to the promises made in the Paris Agreement and we, therefore, need to focus on renewables as well. If you weren't looking at Nigeria yet for potential partnerships, I hope you will start doing so now."
The delegation was accompanied by Arie Plieger, the deputy Consul-General in Lagos. "This visit is the direct result of the visit Dutch Prime Minister Mark Rutte made to Nigeria in 2019," Mr. Plieger remarked. "They agreed on more business cooperation between the countries. With roughly half of Nigeria's 200 million inhabitants lacking access to electricity, it is clear that the country is full of business opportunities for companies active in renewables."

redpajama_set_name: RedPajamaCommonCrawl


Samuel Gassmann handcrafts his cufflinks and shirt studs from the finest materials in his Parisian atelier. These studs are decorative fasteners for formal dress shirts. Simple, shimmering white mother-of-pearl makes a subtle, sophisticated statement.


Started in 1999, later degree in Visual Arts, He studied painting and drawing in MARGS, with Paul Porcella and human figure with Plinio Bernardt (2001 to 2003) and other courses, develops his work of figurative drawing and painting in his Studio, in your residence, researching materials and new ways of expressing their art.
Participates in the study of the human body in c. C. Mario Quintana, the MARGS, Municipal Council of culture and other entities. Elaborated in his study, many reinterpretations of works of famous painters. Today has been dedicating the human figure and abstract art (drawing, painting and printmaking) in the most varied techniques with various materials (Acrylic mass, iron filings, iron oxide, Golden powder, and other), in a sign language Expressionist.
Exposes in Brazil and abroad, with works in various museums, including Buckingham Palace. Participated in more than 100 exhibitions (collective/individual).


text:
Teenager dies in crash near Crawfordsville
Joseph Paul
jpaul@jconline.com
MONTGOMERY COUNTY, Ind. — A teenager died Thursday in a two-car crash near Crawfordsville, according to the Montgomery County Sheriff's Office.
Thomas J. Edwards, 17, of Crawfordsville was pronounced dead at a local hospital after he was taken from the scene of the crash, which occurred at 7:17 a.m. near U.S. 136 and Nucor Road, according to a news release.
Edwards, driving a 1994 Toyota, and 39-year-old Kelly J. Kruse, driving a 2010 Chevrolet, were involved in the crash. Thirty-seven-year-old Matthew Garret of Terre Haute was a passenger in the second car.
Kruse and Garrett also were taken to local hospitals and then airlifted to St. Vincent Hospital in Indianapolis.
U.S. 136 was closed for roughly four hours while crews cleared the scene.
Edwards was a senior at Crawfordsville High School, where he played on the baseball team and was a member of the French club, according to the school's Assistant Principal Mark Melton. He had aspirations to attend a four-year college and study elementary education.
Special counseling services are being offered in the wake of the death.
"Students will have varied reactions to the death of a peer," Melton said in a news release. "Any reaction is normal in the grieving process and can range from withdrawal, crying and anger."
Call J&C reporter Joseph Paul at 765-420-5339, email him at jpaul@jconline.com or follow him on Twitter: @JosephPaulJC.


The median annual salary for a Respiratory Therapist was $60,640.00 ($29.15 per hour) as of May 2016.
Do you like to help people? Do you want a job in a dynamic, growing health care profession? We have a career for you. As a Respiratory Therapist ( RT), you can provide care for patients who struggle to take their next breath. RTs never take a breath for granted.
Our Associate of Applied Science Respiratory Therapy Program is accredited by the Commission on Accreditation for Respiratory Care ( CoARC). We will prepare you for a rewarding profession that will reach patients from acute care settings to rehabilitation programs such as asthma education ( GINA). Use your specialized skills and the latest technology to make a true difference in a patient's quality of life: Become an RT at PCC.
Program graduates may sit for the National Board of Respiratory Care ( NBRC) credentialing exam which is required in the United States to work as a Respiratory Therapist.
As a graduate, you can use your experience at Pueblo Community College as a stepping stone to complete your bachelor's degree.
Respiratory Therapy specializes in the promotion of cardiopulmonary health and management.
Respiratory Therapists care for patients in all age groups, from premature infants to the elderly.
Career opportunities are excellent for Respiratory Therapists and salaries are highly competitive, according to the American Association for Respiratory Care.
Respiratory Therapists apply scientific principles to prevent, identify and treat acute or chronic cardiopulmonary diseases.
Maintain a strong working relations with surrounding hospitals and their Respiratory Therapy departments.
If you can't breathe, nothing else matters.
The Associate of Science in Respiratory Therapy program at Pueblo Community College is accredited by the Commission on Accreditation for Respiratory Care (CoARC). Click here for the Outcomes data from the Annual Report of Current Status.
THC (marijuana) is part of the required 10-panel drug screen prior to admittance into any Health Professions or Public Safety program at PCC. The passage of Amendment 64 in the State of Colorado, does not overrule Federal law, which states this is still an illegal substance. Students testing positive for THC (marijuana) will not be allowed entrance or re-entrance into a Health or Public Safety program.
According to the U.S. Bureau of Labor Statistics, employment of Respiratory Therapists is projected to grow 12% from 2017-2024, faster than the national average for all occupations.


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Journal Title: Welcome to The City of Desire.


text:
What are the 3 most important etiquette in ballroom dancing?
Dance in the line of dance.
When things go wong, apologize.
Slow dancers should move to the center of the room.
Conversation should be off the dance floor.
Cross the dance floor around the perimeter.
Clean up your mess.
Personal hygiene.
Dance at your partner's ability.
1. What are the ballroom etiquette?
1.1. Why is etiquette important in dancing?
1.2. What are the dance etiquette in social dancing?
1.3. Why do you think is it necessary to know and abide by the etiquettes of ballroom dancing?
1.4. What 5 important tips can you give someone who wants to learn ballroom dancing?
2. What are the 5 dance etiquettes?
2.1. What are the 3 important skills in social dancing?
2.2. What are the six types of dance etiquette?
2.3. What is the importance of following proper etiquette and safety in the use of facilities and equipment?
3. What are the 3 basic characteristics of ballroom dancing?
3.1. What are the three styles of ballroom dancing?
3.2. What is the importance of ballroom dancing?
3.3. What is the four main style of ballroom dancing?
3.4. What are the 2 types of ballroom dances?
3.5. What is ballroom dance in the Philippines?
3.6. What are the two principles of ballroom dancing styles Brainly?
3.7. What characteristics should be observed when performing waltz dance?
3.8. Who is a famous ballroom dancer?
3.9. Where does the male's right hand go when dancing with a lady?
3.10. Why do ballroom dancers have to connect emotionally with their dance partner?
3.11. What is social and ballroom dance?
4. Is there an etiquette to observe ballroom dancing?
What are the ballroom etiquette?
Always wear white gloves in a ballroom. Very light shades are admissible. Usually a married couple do not dance together in society, but it is a sign of unusual attention for a husband to dance with his wife, and he may do so if he wishes. Great care should be taken by a lady in refusing to dance with a gentleman.
Why is etiquette important in dancing?
Social dance etiquette is important because: It helps you fit in with the social dance crowd and makes social interactions easier. It helps you keep the peace and avoid getting into conflicts with other dancers. It helps you avoid offending or upsetting your partner and other dancers.
What are the dance etiquette in social dancing?
Line of Dance.
Appropriate Attire.
Body Odour & Hygiene.
Smiles and Thank You's.
Dance to Your Partner's Level.
Asking For & Declining Dances.
Dance with Less Experienced Dancers.
No Correcting or Teaching on the Dance Floor.
Why do you think is it necessary to know and abide by the etiquettes of ballroom dancing?
You'll be in front of a room full of people: some may be experienced and some rookies. As such, it is necessary to know how to dress up and behave. Knowing these things is essential if you don't want to be embarrassed in front of a room full of people. There are numerous ballroom dances from Country to Latin.
What 5 important tips can you give someone who wants to learn ballroom dancing?
Choose a Style of Dance.
Pick Some Music.
Dance Your Heart Out.
Practice with a Partner.
Practice, Practice, and Practice.
What are the 5 dance etiquettes?
Getting on the floor.
At the end of the dance.
Leaving the floor.
Leaving entrances free.
Sharing the floor.
Aerials and choreography.
What are the 3 important skills in social dancing?
Leading and Following. The most important skill for good social dancing is Leading and Following.
Posture and Dance Frame. If Leading and Following is a message, then good posture and dance frame are the antenna broadcasting the signal.
Multi-Tasking.
What are the six types of dance etiquette?
Asking for a dance.
Partner's ability.
Floorcraft.
Group classes.
What is the importance of following proper etiquette and safety in the use of facilities and equipment?
Practicing proper etiquette in the use of facilities shows our respect. It also reduces the risks or problems that may arise from improper use. It is also out of consideration to those who own or maintain the facilities. Above all, it ensures the safety of all users.
What are the 3 basic characteristics of ballroom dancing?
Rise and Fall and Sway are the basic characteristics. A progressive dance moving along the line of dance with no Rise and fall, utilizing Catlike movements and a Staccato Action are the basic Characteristics. A spot dance not moving along the LOD and Latin Hip Movement (Cuban Motion)are the basic characteristics.
What are the three styles of ballroom dancing?
Today, there are three main styles of Tango, the American ballroom style, the International ballroom style, and the Argentine style. Both American and International style travel around the ballroom following the line of dance.
What is the importance of ballroom dancing?
Ballroom dance allows us to experience and learn more about our emotions and learn how to let them flow and pass freely by expressing them through dancing. Learn how to be authentic. One of the greatest powers of ballroom dance is helping one get in touch with their true nature and become more authentic.
What is the four main style of ballroom dancing?
There are four styles of competitive Ballroom Dance: American Smooth, American, Rhythm, International Standard, and International Latin. While the American styles share some similarities to the International styles, the former are competed solely in the United States while the latter are competed all over the world.
What are the 2 types of ballroom dances?
There are two main schools of ballroom dance: the American style, and the International style, each with its own types of dances and competitions.
What is ballroom dance in the Philippines?
Some of the major ballroom dances done by the members of the Filipino community are: Cha-Cha-Cha, Rumba, Jive, Samba, Paso-doble, Salsa, Merengue, Bachata and Cumbia.
What are the two principles of ballroom dancing styles Brainly?
Ballroom dancing consists of two styles: the Smooth, or Standard, and the Rhythm, or Latin. The Smooth, Standard style focuses on the elegance, grace and fluidity of movement.
What characteristics should be observed when performing waltz dance?
Waltz Characteristics – Characterized by its "rise and fall" action, the Waltz includes a step, slide, and step in 3/4 time. Dancers should move their shoulders smoothly, parallel with the floor instead of up and down, and they must strive to lengthen each step.
Who is a famous ballroom dancer?
Donnie Burns is a Scottish professional ballroom dancer and currently the President of the World Dance Council. He and partner Gaynor Fairweather are 14-time World Professional Latin Champions and 11-time International Latin American Dance Champions.
Where does the male's right hand go when dancing with a lady?
Palms are touching, and fingers and thumbs are loosely clasped around each other's hands. The man's right hand rests on the woman's back, cradling her left shoulder blade. The woman's left arm rests on top of the man's right arm, and her left hand rests gently on his right shoulder (figure 5.29).
Why do ballroom dancers have to connect emotionally with their dance partner?
Both partners have to provide a counterbalance – So that the turns are safer and movements bigger, it is always important for both partners to provide a counterbalance to each other. This is another reason there must be an emotional connection between the two of you.
What is social and ballroom dance?
Is there an etiquette to observe ballroom dancing?
In traveling dances such as waltz, tango, foxtrot, samba, Viennese waltz, and quickstep,couples always move counterclockwise on the floor. Faster, more experienced dancers should always be in the outermost lane, intermediate dancers in the middle lane and inexperienced dancers in the center of the ballroom.
Leg Action Secrets for Rumba Walk – ballroom dance lessons in Los Angeles by Oleg Astakhov
9 DANCES in less then 15 MINUTES – For Absolute Beginners – Learn Fast How to Dance with any partner
Rumba most Popular basic Steps – ballroom dance lessons in Los Angeles by Oleg Astakhov
Is contemporary important for a dancer?
Do men dance garba?
Why did Arthur dance in the bathroom?
How long does it take to learn square dancing?
Who invented the welding hood?
What four books are in the Bachman books?


As is well known, no literature as such survives from the late Bronze Age Minoan-Mycenaean culture, when the Linear B script was used primarily for fiscal and book-keeping purposes, as can be seen in the approximately 5,900 extant Linear B tablets and fragments of tablets. However, this does not rule out the possibility, however remote, that there existed at least a sacral literature, as attested in other ancient scripts of that period, hieroglyphic Egyptian on papyrus, as well as Cuneiform and Hittite on clay tablets. The assumption there was no Linear B literature as such is based on the faulty premise that no literature was recorded on tablets, but it might very well have been on papyrus. The difficulty here lies in the fact that papyrus could not possibly have survived the damp Mediterranean climate of Crete, Akrotiri, Mycenae, Pylos or Tiryns or any other Minoan-Mycenaean town or settlement. So if there ever was a literature, it has long since been lost to us in the void of history. However, one inescapably significant factor mitigates in favour of such a literature, and that is the extremely frequent mention of religious and sacral rights, as well as of almost all the Homeric deities on the Linear B tablets, and this in spite of the fact that the vast majority of the Linear B tablets are primarily concerned, not with religious matters, but with fiscal administration… or so it would appear.
Of course, it is absurd to assume that, even if there were a Mycenaean literary corpus (which would have almost certainly been comprised solely of sacral and religious rites, and possibly poetry, had it existed) there would certainly not have been any haiku or anything remotely like it in such a literature. After all, Mycenaean Linear B, being contemporaneous with ancient Chinese (ca. 1,500 BC), antedated Japanese haiku by at least 3,100 years! But, hey, I can at least indulge in the fantasy.
NOTE that Linear B used a vertical bar to separate words, as we can see on second line of this haiku, where the vertical bar separates PARA & TARASA.
Interesting Richard….I am taking note.
Thank you, Raymond! Glad you like it! I sure had fun writing it.


Margaret Bernice "Bea" Woody Zollinger, 89, passed away Thursday, December 27, 2018, at Maple Lawn Medical Care in Coldwater, Michigan, with both of her daughters by her side. Bea was born to Francis Maye & Benjamin H Woody, on July 15, 1929, in West Plains, Missouri. When she was 9, the family moved to Fort Wayne, where she spent the majority of her life. She married her love, Wilfred "Bill" Zollinger, in 1948; he preceded her in death in 2009. Bea was a gifted seamstress, quilter, oil painter, and china painter. She and Bill also loved to travel near and far; in their retirement they would spend half the year in Florida, and the other half at their house on a lake in Indiana. Most of all she loved her family, her happiest days were when her family was all seated at her dining table. She is survived by her daughters, Marisue Taylor, of Coldwater, Michigan, and Karen (David) Foltz, of Naples, Florida; son, William Zollinger, of Austin, Texas. The joy of her life were her grandchildren, she was so proud of them, Jennifer (Brandy) Simon, and their children, Allison and Olivia, of Avon, Stephanie (Jason) Tebeau, and their children, Alexander and Emily, of Alpharetta, Georgia, Wade Zollinger, and his children, Bryce and Regan, of Houston, Texas. Bea was preceded in death by her husband, her parents, a grandson, Daniel Zollinger, two brothers, and three sisters.
Memorial service will be 1pm, Thursday, January 3, at Hockemeyer & Miller Funeral Home, 6131 St Joe Road, Fort Wayne, with calling two hours prior to service. Inurnment will be in Lindenwood Cemetery, Fort Wayne, at a later date.
So many wonderful memories. From high school days through the visit last fall. Bea was always smiling (unless Marisue and I were in trouble), kind and ever helpful. I always admired her artistic talents. Here china painting was amazing as were her canvases. Quilting, oh yes, what can I say. The colors and patterns were beautiful as was her stitch work. I remember so well when she taught me how to hand quilt when I visited in Florida. She was always inspiring and so much fun to be with. I will miss her, her laughter, her loving hugs, and her positive attitude. Her influence on my life will remain with me forever. I will always miss her!


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Ontario Investing in Shelter Expansion in Toronto to Protect Vulnerable Women and Children
July 15, 2019 11:30 A.M.
TORONTO — Ontario is helping to protect women and children from violence by investing $12 million in Nellie's, a Toronto-based shelter for women and children fleeing violence, s...
News release | Children, Community and Social Services
Ontario Building Skilled Workforce in Sudbury
SUDBURY — Ontario's Government for the People is helping students across the province prepare for successful careers in the skilled trades and technology sectors so that...
News release | Training, Colleges and Universities
Ontario Expanding Life-Changing Treatment for Patients with Essential Tremors
TORONTO — Approximately four per cent of Ontarians aged 65 years and older are living with essential tremors—a condition that causes uncontrollable shaking that can make it challen...
News release | Health
Ontario to Improve Crop Production by Fighting Plant Diseases
Ontario's government is investing in research to develop new practices and on-farm solutions for fruit, vegetable and field crop farmers to prevent or control costly crop diseases and pests an...
News release | Agriculture, Food and Rural Affairs
Choose Delicious Ontario Beef and Pork
TORONTO — Ontario cares about our farmers and encourages consumers to show their support for beef and pork farmers by buying and enjoying Ontario meat products.
Helping Chicken Farmers Build and Strengthen Capacity
TORONTO — The governments of Canada and Ontario are supporting a project that will build and strengthen the capacity for the chicken industry to effectively and humanely manage chickens in em...
Guelph Welcomes Canada's Agriculture Community for 2020 Conference
July 10, 2019 2:30 P.M.
GUELPH — The governments of Canada and Ontario announced today that Guelph will host the annual conference of federal, provincial and territorial agricultural ministers in 2020.
Ontario and Dnaagdawenmag Binnoojiiyag Working Together to Support Indigenous Children, Youth and Families
Keene — Today, Todd Smith, Minister of Children, Community and Social Services officially announced the designation of Dnaagdawenmag Binnoojiiyag as an Indigenous Children's Aid Society. T...
Premier Doug Ford Meets with Canada's Premiers about Internal Trade, Economic Growth
July 9, 2019 3:00 P.M.
SASKATOON — Premier Doug Ford joins Canada's provincial and territorial leaders at the Council of the Federation summer meeting in Saskatoon to advocate for removing barriers to internal trad...
News release | Office of the Premier
Government Launches Effort to Strengthen Math Skills & Improve Job Prospects
July 9, 2019 12:04 P.M.
TORONTO - Ontario's government is launching efforts to strengthen math skills for all students in Ontario - especially for students in under-served communities. This initiative will help stud...
News release | Education


Inaugural CT Shoreline Penguin Plunge: Ledyard Key Club - Special Olympics Connecticut, Inc.
The CT Shoreline Penguin Plunge raises funds for Special Olympics Connecticut' s year-round programs of sports training and athletic competition for people with intellectual disabilities.
Please support us as we prepare to dive into the icy cold waters of Ocean Beach Park on March 6, 2016 by making a contribution to Special Olympics Connecticut.
Mr. Charles R Estabrooks Jr.


text:
Independent pharmacies top the list for opioids sold in Escambia County new data reveals
New data released as part of a federal lawsuits against opioid drug manufacturers and distributors have given the public new insight to how highly addictive opioid prescription drugs entered communities.
Independent pharmacies top the list for opioids sold in Escambia County new data reveals New data released as part of a federal lawsuits against opioid drug manufacturers and distributors have given the public new insight to how highly addictive opioid prescription drugs entered communities. Check out this story on pnj.com: https://www.pnj.com/story/news/2019/08/12/independent-pharmacies-top-list-escambia-county-opioid-sales/1965700001/
Jim Little, Pensacola News Journal Published 6:00 a.m. CT Aug. 12, 2019
In 2017, the A&E Pharmacy in Warrington closed its doors as one of few remaining family-owned pharmacies in the area, but just a few years before that the quaint drug shop was in the top 3% of pharmacies in the state for selling opioid prescription pills.
New data released as part of federal lawsuits against opioid drug manufacturers and distributors have given the public new insight into how highly addictive opioid prescription drugs entered communities.
Peter Mougey with the Levin Papantonio law firm is one of the attorneys preparing for a federal trial in Ohio set for October against prescription drug distributors. The lawsuit is part of a large complex series of lawsuits that were merged into one that alleges that distributors are liable for not reporting suspiciously large orders of opioid drugs by local pharmacies as required under the federal Controlled Substance Act.
"The distributors took every penny of profit they could and pumped as many pills as they could into our communities knowing that people were dying without any regard to the impact on our community and our families," Mougey said. "We're asking the court to put an end to all of this and hold them financially responsible for what Escambia and Pensacola are paying to try to clean up the mess the defendants caused."
Mougey and other attorneys are representing more than 600 local governments including Escambia County in bringing the claims against distributors like McKesson Corporation and AmerisourceBergen and manufactures like SpecGX LLC and Actavis Pharma Inc.
As part of the lawsuit, attorneys representing the local governments were able to persuade the federal judge to release data on how opioid drugs were distributed and sold throughout the United States from 2006 to 2012.
Where does your pharmacy rank?: Find your pharmacy in interactive data released as part of federal lawsuit
Mougey said few consumers have heard of SpecGX LLC and Actavis Pharma Inc., but the data shows the two generic drug makers sold close to seven out of every 10 pills of oxycodone and hydrocodone that passed over pharmacy counters in Escambia County.
Those pills were sold to distributors like McKesson Corporation and AmerisourceBergen, who acted as middlemen between the pharmacies and drug makers. The two large pharmacy corporations of Walgreens and CVS also have their own distribution companies as well.
With the pill hydrocodone, Walgreen's and CVS had a combined 43% of the market share in Escambia County, according to the data. Mougey said the companies were responsible for reporting suspicious orders by their own pharmacies to which they distributed.
"If that's not the proverbial fox guarding the hen house, I don't know what is," Mougey said.
Attorneys for the distributors have said in court that their companies complied fully with all of the federal laws and regulations and the lawsuit should be thrown out.
But the case is set to go before a jury in a Cleveland in October unless the two sides settle.
The data released as part of the case show that in Escambia County it was the smaller pharmacies that were selling the most opioid pills.
While A&E pharmacy topped the list as far as number of pills sold at 5.8 million, for the entire seven years of data that is public, it was in second place when looking at the strength of the type of pills sold using a measurement known a morphine milligram equivalent (mme) that measures the strength of the drug compared to a milligram of morphine.
Cantonment Pharmacy, which closed its doors in 2014, topped the list selling more than 81 million mme's worth of opioid pills, while A&E Pharmacy sold more than 77 million mme.
The Walgreen Pharmacy at 6314 N. Ninth Ave. was in third with 65 million mme.
The data didn't surprise Dustin Perry, director of the medication-assisted programs at the Lakeview Center in Pensacola.
Perry said in his nine years of experience helping treat people with addiction to opioids, the majority got addicted after being prescribed an opioid from a doctor or given a pill from a friend of family member because it came from a doctor.
"It's just part of the community," Perry said. "Taking it to deal with a headache or to deal with something because mom said it was fine. Then it just spirals."
Perry said pills flooded rural communities especially as it was marketed as a pill to get people back to work.
"They flooded these rural communities and said you can work and not be addicted to these opiates," Perry said. "And unfortunately, that wasn't the case."
In 2017, more than 4,000 people died in Florida from opioid overdoses.
Perry said it's hard to tell if it's getting better or worse because data is about two years behind what is happening today.
"My phone doesn't stop ringing, so people are still struggling," Perry said. "I don't know how much of an impact we're making with the community. I know we're making some impact. People that are engaged in our services are alive. I can say that with certainty."
Top Ten Pharmacies selling opioid pills by strength in morphine milligram equivalent (mme)
Cantonment Pharmacy Inc - Cantonment, FL, 32533 - 81,938,626 mme
A and E Pharmacy Inc - 921-925 N Warrington Road, Pensacola, FL, 32506 - 77,681,283 mme
Walgreen Co. - 6314 N 9Th Ave, Pensacola, FL, 32504 - 65,122,839 mme
Marando Inc - Pensacola, FL, 32505 - 46,934,359 mme
Century Pharmacy - 7531 Mayo Street, Century, FL, 32535 - 42,145,179 mme
Walgreen Co. - 700 N Pace Blvd, Pensacola, FL, 32505 - 35,858,082 mme
The Towers Pharmacy - 1717 N E Street Suite 102, Pensacola, FL, 32501 - 35,848,765 mme
Landmark Drugs - Pensacola, FL, 32505 - 34,356,480 mme
Holiday CVS, L.L.C. - 6025 Mobile Hwy., Pensacola, FL, 32526 - 29,745,445 mme
Scott's Pharmacy LLC - Molino, FL, 32577 - 27,609,139 mme
See the full interactive list at pnj.com
Jim Little can be reached at jwlittle@pnj.com and 850-208-9827.
Read or Share this story: https://www.pnj.com/story/news/2019/08/12/independent-pharmacies-top-list-escambia-county-opioid-sales/1965700001/
Man shot Tuesday night while walking down Gulf Beach Highway
ECSO chief deputy ordered to release cellphone records
A Pace bicyclist killed after being struck by two vehicles on Woodbine Road
Sowell Farms Christmas lights display causes headaches for neighbors
Why 3 Escambia County schools were on lockdown


– Happy birthday to you Bella! I wish you a very special day, sending this Happy Birthday Bella personalized card to you. Dear Bella, I wish a bright and beautiful day to you on your special day. This named card was specially designed for Bella's birthday. Bella is also a good name to be given to newborn babies.
Bella is a diminutive of Isabella. Isabella is a variant of Isabel. Isabel originates in Latin language and means "promise of God". Ultimately, it is derived from Elizabeth, originating in Hebrew. Some sources suggest that Isabel is simply a Spanish and Portuguese form of Elizabeth. Most recently, the name Isabella was popularized by the character Isabella Swan in Twilight series written by Stephenie Meyer. Currently, it is a number one in the chart of popular feminine given names in the United States. — Famous Bellas: Bella Abzug – 20th-century writer and politician, Bella Dizhur – published author, Bella Doron – writer, Bella Freud – fashion designer. — Bella is a red-hot name right now. Thanks to the massive vampire-in-love phenomenon known as Twilight, Bella has skyrocketed in popularity in recent years. After not even being on the name radar for over seventy years, it appeared out of thin air as parents worldwide raced to name their daughters after the fair-skinned human heroine of the books (and subsequent films.) It also fares as a nickname for the ever-popular Isabella. — Short form of ISABELLA and other names ending in bella. It is also associated with the Italian word bella meaning "beautiful".
This is a personalized birthday card with name Bella, many named cards take place on this website. This is really an elegant birthday e-card for the persons whose name is Bella. Your friends will like this beautiful birthday card with Happy Birthday Bella Personalized Card theme. On this site, you can also find birthday cards and birthday gift ideas for other names outside Bella. I hope that you like this elegant Happy Birthday Bella Personalized Card.
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A beginner's guide to the dapp platform that looks set to be one of the industry's main contenders.
A blockchain platform for the development of decentralized applications (dapps), EOS is being built with the aim of creating the world's most powerful infrastructure for dapps. Regularly listed as a leading contender to Ethereum, EOS released its Dawn 3.0 update in April 2018 and quickly became one of the top five largest cryptocurrencies by market cap.
There's plenty of hype surrounding the platform and its native token, also called EOS, so let's take a closer look at how EOS works, the benefits it offers and what the future might hold for this ambitious project.
EOS offers an operating-system-like set of tools and services for the creation, hosting and execution of commercial-grade dapps. Its software includes everything from user authentication and accounts to cloud storage and server hosting, with the aim of providing an all-in-one solution for dapp developers. EOS is designed to not only ensure the quick and easy deployment of dapps, but also to potentially scale to millions of transactions per second and eliminate user fees.
What does the EOS coin do?
The EOS coin is the native currency of the EOS ecosystem. Once the EOS mainnet is live and developers can create and deploy dapps on the network, they'll need EOS coins in order to generate tokens for their own platforms. Whether or not an application will actually be accepted on the EOS platform will also be determined by a voting system only available to EOS token holders.
In a unique move, EOS's token sale began in June 2017 and is taking place over the best part of a year (341 days). The year is split into 350 periods of distribution – at the end of each period, the total number of tokens to be distributed during that period will be allocated to buyers based on the amount of Ether (ETH) they contributed as a proportion of the total ETH received during the period. Once the token sale is done, 1 billion EOS tokens will have been distributed.
The EOS.IO software introduces a new blockchain architecture designed to enable vertical and horizontal scaling of decentralized applications. This is achieved by creating an operating- system-like construct upon which applications can be built… The resulting technology is a blockchain architecture that may ultimately scale to millions of transactions per second, eliminates user fees, and allows for quick and easy deployment and maintenance of decentralized applications, in the context of a governed blockchain.
In a lot of ways, EOS aims to do what Ethereum does – but in a new way. While Ethereum uses its own programming language, EOS focuses on user-friendliness by allowing developers to create smart contracts with a host of widely used existing programming languages. While Ethereum is looking for the best solution to the scalability problems that currently limit the amount of transactions it can process at any one time, EOS uses a delegated proof-of-stake algorithm that's designed to scale much more effectively. Whether this proposed solution works or not, remains to be seen.
In short, EOS aims to offer a complete toolkit for dapp development, making it quick and easy for businesses and individuals to create blockchain-based applications.
To achieve these goals, EOS uses a delegated proof-of-stake (DPoS) consensus mechanism.
Under this system, anyone who holds tokens on a blockchain that uses the EOS software can select block producers through a continuous approval voting system. Anyone may choose to participate in block production and will be given an opportunity to produce blocks, and the software enables blocks to be produced every 0.5 seconds exactly. This is designed to not only provide the flexibility needed to upgrade the system or fix bugs, but to also allow the system to scale toward 1 million transactions per second.
EOS is being built by block.one, a Cayman Islands exempted company. 100,000,000 EOS, or 10% of the total amount of EOS tokens to be distributed will be reserved for block.one and not offered to the public as part of the token sale.
The project is led by block.one CEO Brendan Blumer and CTO Daniel Larimer, both of whom have significant tech experience. Larimer in particular has an impressive CV, having played a key role in the development of successful crypto projects Steem and Bitshares.
However, while block.one is building the EOS software, it will not configure or launch the EOS blockchain platform – this will be managed by members of the community unrelated to block.one.
In January 2018, block.one announced a joint venture with digital assets merchant bank Galaxy Digital focused on developing the EOS ecosystem and investing in projects that use EOS blockchain software. This saw the creation of a new US$325 million EOS.IO Ecosystem Fund.
In March 2018, block.one announced a US$100 million joint venture to develop projects that use EOS software with German fintech incubator FinLab AG.
Note that since the EOS mainnet launch in June 2018, EOS are no longer ERC20 tokens. As such, wallets which are ERC20-compatible do not necessarily support EOS.
In April 2018, block.one announced the first feature-complete pre-release of the EOS software, known as Dawn 3.0. You can check out this blog post for a full rundown of what the update provided, but highlights include improved scalability features, inter-blockchain communication using light clients, and security delayed transactions.
On April 6, the day of the Dawn 3.0 release, EOS was trading at US$6.10. However, excitement generated by the update, set against a background of widespread market optimism, saw its price climb above US$18 by the end of the same month.
The launch of the EOS mainnet was scheduled for June 3, 2018. This means that EOS has transitioned from the Ethereum blockchain to its own blockchain. As such, it is no longer an ERC20-comptaible token.
On June 2, 2018, at 22:59:59 UTC, shortly before the launch, EOS tokens were frozen on the Ethereum blockchain. These tokens were then converted to "real" EOS tokens on the EOS blockchain.
EOS is operating in a highly competitive corner of the cryptosphere. Not only will it need to do battle with the much larger and well established Ethereum, which does admittedly face its own challenges, but EOS will also need to compete with other highly valued projects like NEO, Lisk and Qtum.
Read our prediction for where EOS's price could be heading this year.
If you're considering buying any EOS tokens, make sure you do your own research before parting with your hard-earned cash. While EOS could one day offer a viable alternative to Ethereum and, on paper at least, has a few key benefits over the Ethereum network, the key fact to remember is that it's not yet proven. Furthermore, whilst EOS is being developed, Ethereum and other established dapp platforms continue to grow and cement their position.
No. EOS tokens cannot be mined.
Is there a minimum purchase amount when buying EOS tokens through the token sale?
The minimum contribution accepted is 0.01 ETH.
Where can I find out more about the technology behind EOS?
Take a look at the project's detailed whitepaper.
Read our complete list of upcoming EOS airdrops, including when they're scheduled to occur and details of each airdrop project.
What are the best wallets for storing, sending and receiving EOS? Find out in this comprehensive guide.
Your guide to the factors affecting the value of EOS, and an in-depth look at where the price of this cryptocurrency might be headed in coming months.


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About Sai Prema Foundation Fiji
Message from Chairman of Sai Prema Foundation Fiji
Director of the Foundation
Volunteers of the Foundation
Official Launch of Sai Prema Foundation Fiji
Speech by the Acting Prime Minister of Fiji
Speech by Fiji's Minister for Health
Sri Sathya Sai Sanjeevani Medical Centre
Sri Sathya Sai Sanjeevani Children's Heart Screening Centre
Sri Sathya Sai Sanjeevani Children's Heart Hospital Fiji
Gift of Life Celebration
Medical Scholarship Program
Health on Wheels
Sai Annapoorna
Help In Kind
Sai Prema Ashram
"Hon Prime Minister of Fiji unveiling the children's free heart screening centre"
The 2nd stage of progression for the foundation was the official inauguration of the Sri Sathya Sai Sanjeevani Children's Heart Screening Centre by the Prime Minister of Fiji, Hon. Josaia Voreqe Bainimarama on the 24th of April 2019. The child heart screening centre is located within the Sri Sathya Sai Sanjeevani Medical Centre and is once again providing absolutely free of cost service to all children who are referred there.
The screening room is filled with colorful cartoon characters on the walls to enable children to feel at ease when coming in for screening. The room contains a state-of-the-art ECHO machine which is the only one of its calibre in the South Pacific region. With a specialized pediatric probe, the unit delivers quality high-resolution ultrasound images to view a child's beating heart and the associated vessels and valves in great detail, thereby enabling a comprehensive diagnosis to be made.
The ECHO machine was first utilized in April 2019 when more than 70 children that were short listed with possible heart conditions and enabled Cardiologist Dr. Ben Anderson to be able to make a comprehensive diagnosis and provide crucial information to our International team of surgeons representing Sri Sathya Sai Sanjeevani Hospitals India to come up with a surgical treatment plan. 17 children were eventually treated successfully for congenital heart disease.
The screening centre is now regularly utilized by local screening specialist Dr. Maryanne Kora'ai who was sent on scholarship in Raipur in early 2018 by Sai Prema Foundation Fiji in association with the Sri Sathya Sai Sanjeevani Hospital. Her fellowship in non-invasive pediatric cardiology along with the newly opened child heart screening centre is making a big difference to the lives of children with congenital heart disease.
A Child Heart Screening Initiative
To assist with the "Gift of Life" initiative, the Foundation launched a new project entitled "HeartBeat", in association with the Fiji Ministry of Health. This is a national heart screening initiative for the children of Fiji to identify any potential heart conditions and ensure that they are given the deserving treatment.
To launch the project, Dr. Maryanne Kora'ai and her team travelled with the Foundation's team to the Rural villages of Fiji as part of the "Health on Wheels" initiative. 40 children were given "Echo Screening" and 4 cases were identified with potential heart disease – 3 rheumatic and 1 congenital. These children were referred for further testing at CWM Hospital in the Capital City.
Cases identified will be scheduled for screening & potential surgeries with the Gift of Life team's visit to Fiji. Children with more complex problems will be referred to the specialist Cardiac Team from the Sri Sathya Sai Sanjeevani Hospital in India.
Click on link for latest Gift of Life project publication
© SAI PREMA FOUNDATION All Rights Reserved 2017 - Website By WEBMEDIA FIJI


Born on March 9, 1930, in Fort Worth, Texas, Ornette Coleman remains one of America's greatest living jazzmen. With musical influence steeped in R & B, Coleman began playing saxophone at an early age. His first recording appeared in 1958.
Coleman experimented with various musical styles over the decades--he even collaborated with Jerry Garcia, Pat Metheny, Roland Jackson, Don Cherry, Jimmy Garrison and the Grateful Dead.
This year Coleman won a Pulitzer Prize for Sound Grammar. Recorded in one night in Italy during October of 2005, Sound Grammar emerges as Coleman's first album in a decade. Coleman plays alto sax, violin, and trumpet. Denardo Coleman plays drums, while Greg Cohen and Tony Falanga handle double-bass duties. These musical components never stray far from a rhythmic blues foundation.
Sound Grammar peaked at #13 on Billboard's Top Jazz albums. This CD contains five new Coleman compositions as well as a few older tunes that prove Coleman's musical vitality over the last 50 years.
"Jordan" stands as a frenetic tune with a driving rhythm. "Sleep Talking" finds Coleman playing violin on slow, blue jazz tune that resembles rain falling in the streets. "Turnaround" conjures traces of John Coltrane. "Matador" is an up-beat tune serving as a musical backdrop to any deadly bullfight. "Waiting For You" evokes a deep rumbling on the bottom end...like some underwater Super 8 film music. "Call To Duty" remains an unrestrained song that fits the tenacious title.
Ornette Coleman's Sound Grammar proves why he's an American music legend.
A young teacher myself at the time and still reasonably idealistic, I found Conroy's account of his first teaching experience in a one room school house on the tiny island of Yamacraw just off the coast of Beaufort, North Carolina, powerful and compelling. I wanted to know more about this fascinating author and most of all, I wanted to read more of his books.


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Q: Remove common list elements in lists of lists in R I have a list of lists, I would like to remove the common elements between these sublists.
For example
mylist = list(
c(1, 2, 3, 4),
c(2, 5, 6, 7),
c(4, 2, 8, 9)
)

becomes
mylist = list(
c(1, 3),
c(5, 6, 7),
c(8, 9)
)

I first created a list of the common elements and tried to substrate this list from the sublist but it does not work
common_elements = list(Reduce(intersect, mylist))
mylist = mylist[!(mylist %in% common_elements)]

Could you help me ? Thank you !

A: Based on the update, it is a list of vectors.  Convert the list to a two column tibble/data.frame with enframe, get the count of distinct elements to filter and split back to a list of vectors
library(dplyr)
library(tibble)
library(tidyr)
enframe(mylist) %>%
    unnest(value) %>%
    group_by(value) %>%
    filter(n_distinct(name) == 1) %>%
    with(., split(value, name)) %>%
    unname

-output
[[1]]
[1] 1 3

[[2]]
[1] 5 6 7

[[3]]
[1] 8 9

data
mylist <- list(c(1, 2, 3, 4), c(2, 5, 6, 7), c(4, 2, 8, 9))

A: A base R option
> lut <- table(unlist(mylist))

> comm <- as.numeric(names(lut[lut > 1]))

> lapply(mylist, function(x) x[!x %in% comm])
[[1]]
[1] 1 3

[[2]]
[1] 5 6 7

[[3]]
[1] 8 9

data
mylist <- list(1:4, c(2, 5:7), c(4, 2, 8, 9))

A: A tidyverse option for a list of lists.
library(tidyverse)

ls %>%
  enframe() %>%
  unnest(value) %>%
  mutate(dupes = if_else(duplicated(value) == T, as.integer(value), NA_integer_)) %>%
  filter(!value %in% dupes) %>%
  group_by(name) %>%
  mutate(X = list(value)) %>%
  ungroup() %>%
  distinct(X) %>%
  deframe()

# [[1]]
# [[1]][[1]]
# [1] 1
#
# [[1]][[2]]
# [1] 3
#
#
# [[2]]
# [[2]][[1]]
# [1] 5
#
# [[2]][[2]]
# [1] 6
#
# [[2]][[3]]
# [1] 7
#
#
# [[3]]
# [[3]][[1]]
# [1] 8
#
# [[3]][[2]]
# [1] 9

Data
ls <- list(list(1, 2, 3, 4), list(2, 5, 6, 7), list(4, 2, 8, 9))

redpajama_set_name: RedPajamaStackExchange


This handy pecan sheller will peel the shells clear off pecans. Clear out the excess pecan shells to reveal delicious, intact nut meat. This is a great tool to add to your nut cracking equipment supplies!


The following are some of the resources I've used to teach students Java over the years. Most of them were developed for A Level students (a qualification for 16-18 year olds), although they have been used by students aged from 11 upwards.
I currently use Python to begin teaching programming, I use Java once students have reached a certain level of proficiency. The reasons for this are explained elsewhere.