text:
Giant Featherback – Freshwater Fish Species in Thailand
Species: Chitala lopis.
Common names: Giant Featherback, Indonesian Featherback, Black Featherback and Knifefish. Thai Name: Pla Satu.
Distribution: Giant Featherback is found in Southeast Asia, including Indochina, Borneo, Malay Peninsula, Sumatra and Java in Indonesia.
Habitat: Found in lakes and the slow running water found at river mouths and also in forest-covered streams. Where there hiding among submerged roots and rocks!
The Giant Featherback
Is a member of the Notopteridae species (meaning "back fin"). Featherback or knifefish are a family of bony-tongues (Osteoglossiformes). The bony-tongues are a primitive but highly successful family of fishes which have been on earth for 200 million years or more. Their shape gives them their common name Knifefish. Chitala lopis or the Giant Featherback is a fish of the genus Chitala found in Southeast Asia.
With the denotation of "knifefish" the Notopteridae should not be confused with Gymnotiformes, the electric knifefish. Although their manner of swimming is similar, the two groups are not closely related. The name is from Greek noton meaning "back" and pteron meaning "fin". They have a slender, elongated, bodies, which giving them a knife-like appearance, the body color is silver to bronze body with a dark or brown back, long brown caudal fin and a silvery white belly. The caudal fin is small and fused with the anal fin, which runs most of the length of the body. Where present, the dorsal fin is small and narrow; giving rise to the common name of "Featherback". The fish swims by holding its body rigid and rippling the anal fin to propel itself forward or backwards.
The Notopterids species
Have specialized swim bladders, its organ extends throughout the body and even into the fins in some cases; although the swim bladder is not highly vascularised. It can absorb oxygen from air and also functions to produce sound; by squeezing air through a narrow passage into the pharynx. Some species prepare nests and guard the eggs until they hatch.
Size: It can be up to about 25 kg. (55lbs.) and 1.5 m. in length, the world record is 9.8 kg (21,5lbs)
Fishing method: Giant Featherback can be caught by fishing with live bait, small fish, shrimp, but also lure fishing with small minnows, spinners and spoons.
Sexing: Males have a larger and slimmer body, compared to the females. During the spawning period females have a more rounded abdomen.
Source: Wikipedia – Fishbase
Back to Fish Species


text:
The Churchill Barriers are four causeways in the Orkney islands with a total length of .  They link the Orkney Mainland in the north to the island of South Ronaldsay via Burray and the two smaller islands of Lamb Holm and Glimps Holm.

The barriers were built between May 1940 and September 1944, primarily as naval defences to protect the anchorage at Scapa Flow, but since 12 May 1945, serve as road links between the islands. The two southern barriers, Glimps Holm to Burray and Burray to South Ronaldsay, are Category A listed.

History

On 14 October 1939, the Royal Navy battleship HMS Royal Oak was sunk at her moorings within the natural harbour of Scapa Flow, by the  under the command of Günther Prien.  U-47 had entered Scapa Flow through Holm Sound, one of several eastern entrances to Scapa Flow.

The eastern passages were protected by measures including sunken block ships, booms and anti-submarine nets, but U-47 entered at night at high tide by navigating between the block ships.

To prevent further attacks, the First Lord of The Admiralty Winston Churchill ordered the construction of permanent barriers.  Work began in May 1940 and the barriers were completed in September 1944 but were not officially opened until 12 May 1945, four days after Victory in Europe Day.

Construction

The contract for building the barriers was awarded to Balfour Beatty, although part of the southernmost barrier (between Burray and South Ronaldsay) was sub-contracted to William Tawse & Co.  The first Resident Superintending Civil Engineer was E K Adamson, succeeded in 1942 by G Gordon Nicol.

Preparatory work on the site began in May 1940, while experiments on models for the design were undertaken at Whitworth Engineering Laboratories at the University of Manchester.

The bases of the barriers were built from gabions enclosing 250,000 tonnes of broken rock, from quarries on Orkney. The gabions were dropped into place from overhead cableways into waters up to  deep.  The bases were then covered with 66,000 locally cast concrete blocks in five-tonne and ten-tonne sizes.  The five-tonne blocks were laid on the core, and the ten-tonne blocks were arranged on the sides in a random pattern to act as wave-breaks.

Labour

A project of this size required a substantial labour force, which peaked in 1943 at over 2,000.

Much of the labour was provided by over 1,300 Italian prisoners of war who had been captured in the desert war in North Africa; they were transported to Orkney from early 1942 onwards.

The prisoners were accommodated in three camps, 600 at Camp 60 on Little Holm and the remaining 700 at two camps on Burray.

In 1943, those at Camp 60 built an ornate Italian Chapel, which still survives and has become a tourist attraction.

Ecological impact
Research by the University of York published in 2012 showed significant changes to the ecology of the area, and that behind the barriers an eutrophic environment dominated due to the loss of the natural throughflow of water.

Deterioration
In October 2011, the Orkney Islands Council took control of the barriers from the Ministry of Defence. Since then, with increasingly erratic weather events and rising sea levels as a result of global climate change, the barriers have begun to deteriorate. Of the four barriers, only Barrier No. 2, from Lamb Holm to Glimps Holm, is at high risk for needing to be replaced, according to the Scottish Environment Protection Agency. Replacing even one of the causeways is extremely unpopular in Orkney due to their historical significance. The Council was as of February 2021 exploring options that would preserve all of the causeways.

Gallery

References

External links
 Burray and The Barriers
 Undiscovered Scotland: The Churchill Barriers
 Our Past History: The Churchill Barriers
 Okneypics.com: photos of the barrier

Orkney
Buildings and structures in Scotland
Civil engineering
Category A listed buildings in Orkney
Causeways in Europe

redpajama_set_name: RedPajamaWikipedia


text:
\section{Introduction}
\noindent The primary goal of learning methodologies is to imbue intelligent agents with the capability to autonomously and successfully perform complex tasks, when \emph{a priori} design of the necessary behaviors is intractable.
Most tasks of interest, especially those with real-world applicability, quickly exceed the capability of designers to handcraft optimal or even successful policies.
It can even be infeasible to construct appropriate objective or reward functions in many cases.
Instead, learning techniques can be used to empirically discover the underlying objective function for the task and the policy required to satisfy it, typically utilizing state, action, or reward data.
Several classes of these techniques have yielded promising results, including learning from demonstration, learning from evaluation, and reinforcement learning.

Reinforcement learning has been proven to work on scenarios with well-designed reward functions and easily available interactions with the environment \cite{Mnih2015a}.
However, in real-world robotic applications, explicit reward functions are non-existent, and interactions with the hardware are expensive and susceptible to catastrophic failures.
This motivates leveraging human interaction to supply this reward function and task knowledge, to reduce the amount of high-risk interactions with the environment, and to safely shape the behavior of robotic agents.

Learning from evaluation is one such way to leverage human domain knowledge and intent to shape agent behavior through sparse interactions in the form of evaluative feedback, possibly allowing for the approximation of a reward function \cite{Knox2009,MacGlashan2017,Warnell2018}.
This technique has the advantage of minimally tasking the human evaluator and can be used when training behaviors they themselves cannot perform.
However, it can be slow to converge as the agent can only identify desired or even stable behaviors through more random exploration or indirect guidance from human negative reinforcement of unwanted actions, rather than through more explicit examples of desired behaviors.

In such a case, learning from demonstration can be used to provide a more directed path to these intended behaviors by utilizing examples of the humans performing the task.
This technique has the advantage of quickly converging to more stable behaviors.
However, given that it is typically performed offline, it does not provide a mechanism for corrective or preventative inputs when the learned behavior results in undesirable or catastrophic outcomes, potentially due to unseen states.
Learning from demonstration also inherently requires the maximal burden on the human, requiring them to perform the task many times until the state space has been sufficiently explored, so as to generate a robust policy.
Also, it necessarily fails when the human is incapable of performing the task successfully at all.

Learning from interventions, where a human acts as an overseer while an agent is performing a task and periodically takes over control or intervenes when necessary, can provide a method to improve the agent policy while preventing or mitigating catastrophic behaviors \cite{Saunders2017}.
This technique can also reduce the amount of direct interactions with the agent, when compared to learning from demonstration.
Similar to learning from evaluation, this technique suffers from the disadvantage that desired behaviors must be discovered through more variable exploration, resulting in slower convergence and less stable behavior.

Most of these human interaction methods have been studied separately, and there is very little work combining multiple modalities to leverage strengths and mitigate weaknesses.
In this paper, we work towards our conceptual framework that combines multiple human-agent interaction modalities into a single framework, called the Cycle-of-Learning for Autonomous Systems from Human Interaction \cite{Waytowich2018}.
Our goal is to unify different human-in-the-loop learning techniques in a single framework to overcome the drawbacks of training from different human interaction modalities in isolation, while also maintaining data-efficiency and safety.

In this paper, we present our initial work towards this goal with a method for combining learning from demonstrations and learning from interventions for safe and efficient training of autonomous systems. We seek to develop a real-time learning technique that combines demonstrations as well as interventions provided from a human to outperform traditional imitation learning techniques while maintaining agent safety and requiring less data. We validate our method with an aerial robotic perching task in a high-fidelity simulator using a quadrotor that has continuous roll, pitch, yaw and throttle commands and a downward facing camera. In particular, the contributions of our work are twofold:
\begin{enumerate}[\indent (1)]
	\item  We propose a method for efficiently and safely learning from human demonstrations and interventions in real-time.
	\item We empirically investigate both the task performance and data efficiency associated with combining human demonstrations and interventions.
\end{enumerate}
We show that policies trained with human demonstrations and human interventions together outperform policies trained with just human demonstrations while simultaneously using less data. To the best of our knowledge this is the first result showing that training a policy with a specific sequence of human interactions (demonstrations, then interventions) outperforms training a policy with just human demonstrations (controlling for the total amount of human interactions), and that one can obtain this performance with significantly reduced data requirements, providing initial evidence that the role of the human should adapt during the training of safe autonomous systems.

\begin{figure}[!t]
    \centering
    \includegraphics[width=1.0\linewidth]{figures/col_diagram2_colorblind.png}
    \caption{Cycle-of-Learning for Autonomous Systems from Human Interaction: a concept for combining multiple forms of human interaction with reinforcement learning. As the policy develops, the autonomy independence increases and the human interaction level decreases. This work focuses on the first two components of the cycle (dashed box): Learning from Demonstration and Learning from Intervention.}
    \label{fig:col_diagram}
\end{figure}

\section{Background and Related Work}
\subsection{Learning from Demonstrations}
Here we provide a brief summary of Learning from Demonstrations (LfD); a more comprehensive review can be found in \cite{Argall2009}.
Learning from Demonstrations, sometimes referred to as \emph{Imitation Learning}, is defined by training a policy $\pi$ in order to generalize over a subset $\mathcal{D}$ of states and actions visited during a task demonstration over $T$ time steps:
\begin{align*} \label{eq:demonstration_dataset}
\mathcal{D} = \left\{\vec{a}_0, \vec{s}_0, \vec{a}_1, \vec{s}_1, ... , \vec{a}_T, \vec{s}_T\right\}.
\end{align*}
This demonstration can be performed by a human supervisor, optimal controller, or virtually any other pre-trained policy.

In the case of human demonstrations, the human is implicitly trying to maximize what may be represented as an internal reward function for a given task (Equation \ref{eq:imitation_rew}), where $\pi^*(\vec{a}^*_t | \vec{s}_t)$ represents the optimal policy that is not necessarily known, in which the optimal action $\vec{a}^*$ is taken at state $\vec{s}$ for every time step $t$.

\begin{equation}\label{eq:imitation_rew}
\max_{\vec{a}_0,...,\vec{a}_T} \sum_{t=0}^{T} r_t(\vec{s}_t, \vec{a}_t) = \sum_{t=0}^{T} \log p (\pi^*(\vec{a}_t^* | \vec{s}_t))
\end{equation}

Defining the policy of the supervisor as $\pi_{sup}$ and its estimate as $\hat{\pi}_{sup}$, imitation learning can be achieved through standard supervised learning, where the parameters $\theta$ of a policy $\pi_\theta$ are trained in order to minimize a loss function, such as mean squared error, as shown in Equation \ref{eq:sup_mse}.

\begin{equation}\label{eq:sup_mse}
\hat{\pi}_{sup} = \argmin_{\pi_\theta} \sum_{t=0}^{T} ||\pi_\theta(\vec{s}_t) - \vec{a}_t ||^2
\end{equation}

There are many empirical successes of using imitation learning to train autonomous systems. For self-driving cars, \citeauthor{Bojarski2016} successfully used human demonstrations to train a policy that mapped from front-facing camera images to steering wheel commands using around one hundred hours of human driving data \cite{Bojarski2016}.
Similar approaches have been taken to train small unmanned air system (sUAS) to navigate through cluttered environments while avoiding obstacles, where demonstration data was collected by human oracles in simulated \cite{Goecks2018} and real-world environments \cite{Giusti2015}.

\subsection{Learning from Interventions}
In \emph{Learning from Interventions} (LfI) the human takes the role of a supervisor and watches the agent performing the task and intervenes (i.e. overriding agent actions with human actions) when necessary, in order to avoid unsafe behaviors that may lead to catastrophic states.
Recently, this learning from human intervention concept was used for safe reinforcement learning (RL) that could train model-free RL agents without a single catastrophe \cite{Saunders2017}. Similar work has proposed using human interaction to train a classifier to detect unsafe states, which would then trigger the intervention by a safe policy previously trained based on human demonstration of the task \cite{Hilleli2018}. This off-policy data generated by the safe policy is aggregated to the replay buffer of a value-based reinforcement learning algorithm (Double Deep Q-Network, or DDQN \cite{Hasselt2015}). The main advantage of this method is being able to combine the off-policy data generated by the interventions to update the current policy.

\subsection{Related Work}

Several existing works have studied, in isolation, the use of different human interaction modalities to train policies for autonomous systems, either in the form of demonstrations \cite{Akgun2012}, \cite{Argall2009}, interventions \cite{Akgun2012a}, \cite{Saunders2017} or evaluations \cite{Knox2009}.  However, there has been relatively little work on how to effectively combine multiple human interaction modalities into a single learning framework. Several cases include the combination of demonstrations and mixed initiative control for training robot polices \cite{Grollman2007} as well as the recent work by \citeauthor{Hilleli2018} where imitation learning was combined with interactive reward shaping in a simulated racing game  \cite{Hilleli2018} and the recent work \cite{Peng2018} where deviation from the expert demonstration is added to a reward function to be optimized with reinforcement learning.

Another example of work that attempts to augment learning from demonstrations with additional human interaction is the Dataset Aggregation (DAgger) algorithm \cite{Ross2011}.
DAgger is an iterative algorithm that consists of two policies, a primary agent policy that is used for direct control of a system, and a reference policy that is used to generate additional labels to fine-tune the primary policy towards optimal behavior. Importantly, the reference policy's actions are not taken, but are instead aggregated and used as additional labels to re-train the primary policy for the next iteration. In \cite{Ross2013} DAgger was used to train a collision avoidance policy for an autonomous quadrotor using imitation learning on a set of human demonstrations to learn the primary policy and using the human observer as a reference policy.
There are some drawbacks to this approach that are worth discussing. As noted in \cite{Ross2013}, because the human observer is never in direct control of the policy, safety is not guaranteed, since the agent has the potential to visit previously unseen states, which could cause catastrophic failures.
Additionally, the subsequent labeling by the human can be suboptimal both in the amount of data recorded (perhaps recording more data in suboptimal states than is needed to learn an optimal policy) as well as in capturing the intended result of the human observer's action (as in distinguishing a minor course correction from a sharp turn, or the appropriate combination of actions to perform a behavior).
Another limitation of DAgger is that the human feedback was provided \emph{offline} after each run while viewing a slower replay of the video stream to improve the resulting label quality.
This prevents the application to tasks where real-time interaction between humans and agents are required.

\section{Proposed Methodology: Cycle-of-Learning}
This work demonstrates a technique for efficiently training an autonomous system safely and in real-time by combining learning from demonstrations and interventions. It is the first part of the Cycle-of-Learning concept (Figure~\ref{fig:col_diagram}) which aims to combine multiple forms of human-agent interaction for learning a policy that mimics the human trainer in a safe and efficient manner. Although this paper focuses on the first two parts of the Cycle-of-Learning, for brevity, we will refer to the algorithm presented here as the Cycle-of-Learning (CoL).

The CoL starts by training an initial policy $\pi_0$ from a set of task demonstrations provided by the human trainer using a standard supervised learning technique (regression in this case since the action-space for our task is continuous).
Next, the agent is given control and executes $\pi_0$ while the human takes the role of overseer and supervises the agent's actions.
Using a joystick controller, the human intervenes whenever the agent exhibits unwanted behavior that diverges from the policy of the human trainer, and provides corrective actions to drive the agent back on course, and then releases control back to the agent.
The agent then learns from this intervention by augmenting the original training dataset with the states and actions from the intervention, and then fine-tuning $\pi_0$.
The agent then executes the new policy $\pi_n$ while the human continues to oversee and provides interventions as necessary.
In practice, the human trainer can easily switch between providing demonstrations and interventions by switching control between the human and the agent as shown in Figure \ref{fig:airsim_col_diagram_compact}.
Combining demonstration and intervention data in this way should not only improve the policy over what learning from demonstration can do alone but also require less training data to do so.
The intuition is that the agent will inevitably end up in states previously unexplored with the original demonstration data which will cause it's policy to fail and that intervening from those failure states allows the agent to quickly learn from those deficiencies or "blind spots" in its own policy in a more targeted fashion than from demonstration data alone \cite{Ramakrishann2018}.
In this way, we learn only from the critical states, which is more data efficient, instead of using all states for training as is done in DAgger \cite{Ross2011}.

\subsection{Data Efficiency}
A demonstration is defined as a human-produced trajectory of state-action pairs for the entire episode, while an intervention is defined as a trajectory of state-action pairs for only the subset of the episode where corrective action is deemed necessary by the human.
Thus, the amount of data provided via intervention is nearly always less than the amount provided via demonstration.
Training routines that incorporate more episodes utilizing learning from intervention rather than learning from demonstration will in general be more data sparse, assuming comparable task performance.
Therefore, by utilizing components of the CoL to learn from both demonstration and intervention, we can train with less data than if demonstrations had been used in isolation for an equivalent number of episodes, resulting in a more efficient training framework.
This concept generalizes to the full CoL, as the agent naturally requires less input from the human as its policy develops, its task proficiency increases, and it becomes more autonomous (indicated in Figure \ref{fig:col_diagram}).

\subsection{Safe Learning}
The notion of \emph{safe} learning here refers to the ability of a human oracle to intervene in cases where catastrophic failure may be imminent.
Thus, the agent is able to explore higher risk regions of the state space with a greater degree of safety.
This approach leverages human domain knowledge and ability to forecast such boundary states, which the agent cannot do early in the training process when the state space is less explored.
By allowing the policy to explore less seen regions and then provide training data of how to correct from those states, human interventions provide a richer dataset that improves the policy in those regimes.
This is contrasted to a method based solely on demonstration, which may only see states and observations along a nominal trajectory and have a policy poorly fit to data outside that envelope.
The result is a policy that is more robust, through greater data diversity, while not risking damage to the agent that is typical with methods that rely on random exploration of the state space.
This provides a method to safely train an autonomous system.

\subsection{Real-Time Interaction}
The utility of the demonstrated approach is partially linked to the ability of the agent to consume data as it is provided by the human oracle and update its policy online.
The current system accomplishes this by storing all subject state-action pairs in the training dataset, which is queried in real-time to update the policy, and then fine-tuning that policy whenever new samples are added to the dataset.
During intervention, this allows for interaction with an agent using a policy trained on the most recent corrective actions provided by the human.
The short time between novel human intervention data and behavioral roll-outs from the agent policy prevents significant delay in this feedback loop that might result from more infrequent, batch learning.
As in closed loop systems, large temporal delays between feedback inputs and their resultant output behaviors can lead to instability.
In this context, that would manifest as unstable training as the human oracle would need to correct for undesired actions for significantly longer before seeing any effect on the agent behavior.
This shortcoming was exhibited in DAgger, where policy correction was a delayed, offline process.

\begin{figure}[!t]
    \centering
    \includegraphics[width=1.0\linewidth]{figures/airsim_col_diagram_compact3.png}
    \caption{Flow diagram illustrating the learning from demonstration and intervention stages in the CoL for the quadrotor perching task.}
    \label{fig:airsim_col_diagram_compact}
\end{figure}

\section{Implementation}
The next sections address the experimental methodology used to evaluate the proposed approach and the implementation of the learning algorithm (shown in Figures \ref{fig:airsim_col_diagram_compact} and Algorithm \ref{alg:algo}).

\subsection{AirSim Environment}
We tested our CoL approach (Figure~\ref{fig:airsim_col_diagram_compact}) in an autonomous quadrotor perching task using a high-fidelity drone simulator based on the Unreal Engine called AirSim developed by Microsoft \cite{Airsim2017}.
AirSim provides realistic emulation of quad-rotor vehicle dynamics while the Unreal Engine allows for the development of photo-realistic environments.
In this paper, we are concerned with training a quadrotor to autonomously land on a small landing platform placed on top of a ground vehicle (see Figure \ref{fig:Screenshot}).

\begin{figure}[!t]
    \centering
    \includegraphics[width=1.0\linewidth]{figures/Screenshot.png}
    \caption{Screenshot of AirSim environment and landing task. Inset image in lower right corner: downward-facing camera view used for extracting the position and radius of the landing pad which is part of the observation-space that the agent learns from. }
    \label{fig:Screenshot}
\end{figure}

The current observation-space consists of vehicle inertial and angular positions, linear and angular velocities, and pixel position and radius of the landing pad extracted using a hand-crafted computer vision module (15 dimensional continuous observation-space).
The vehicle is equipped with a downward-facing RGB camera that captures 320x240 pixel resolution images.
The camera framerate and agent action frequency is 10.5 Hz, while the human observer views the video stream at approximately 35 Hz.
The action-space comprises the four continuous joystick commands (roll, pitch, yaw, and throttle), which are translated to reference velocity commands (lateral, longitudinal, heading rate, and heave) by the vehicle autopilot.

For the perching task, the goal is to land the quadrotor as close to the center of the landing pad as possible. We define a landing a success if the quadrotor lands within 0.5m radius of the center of the platform and a failure otherwise. At the beginning of each episode, the quadrotor starts in a random x,y location at a fixed height above the landing pad and the episode ends when either the quadrotor reaches the ground (or landing pad) or after 500 time-steps have elapsed.

\subsection{Cycle-of-Learning Algorithm}

\begin{algorithm}[!tb]
\caption{Combining Human Demonstrations and Interventions (Cycle-of-Learning)}\label{alg:algo}
\begin{algorithmic}[1]

\Procedure{Main}{}
    \State Initialize agent's policy $\pi$
    \State Initialize human dataset $\mathcal{D}_H$
    \State Initialize \emph{Update Policy} procedure
    \State Define performance threshold $\alpha$
    \While {task performance $< \alpha$}
        \State Read observation $o$
        \State Sample action $a_{\pi} \sim \pi$
        \If {Human Interaction ($a_H$)}:
            \State Perform human action $a_H$
            \State Add $o$ and $a_H$ to $\mathcal{D}_H$
        \Else
            \State Perform $a_{\pi}$
        \EndIf
        \If {End of Episode}
            \State Evaluate task performance
        \EndIf
    \EndWhile
\EndProcedure

\Procedure{Update Policy}{}
    \State Spawn separate thread
    \State Initialize $loss$ threshold $loss_{TH}$
    \While {\emph{Main} procedure running}
        \State Load human dataset $\mathcal{D}_H$
        \If {New Samples}:
            \While {$loss > loss_{TH}$ or $n < n_max$ }
                \State Sample $N$ samples $o, a$ from $\mathcal{D}_H$
                \State Sample $\hat{a} \sim \pi$
                \State Compute $loss =
                \frac{1}{N} \sum_{i}^{N} (\hat{a}_i - a_i)^2$
                \State Perform gradient descent update on $\pi$
            \EndWhile
        \EndIf
    \EndWhile
\EndProcedure

\end{algorithmic}
\end{algorithm}

As shown in Algorithm~\ref{alg:algo} the main procedure starts by initializing the agent's policy $\pi$, the human dataset $\mathcal{D}_H$, the \emph{Update Policy} subroutine, and task performance threshold.
The main loop consists of either executing actions provided by the agent or actions provided by the human. The agent reads an observation from the environment and an action is sampled based on the current policy. At any moment the human supervisor is able to override the agent's action by holding a joystick trigger. When this trigger is held, the actions performed by the human $a_h$ are sent to the vehicle to be executed and are added to the human dataset $\mathcal{D}_H$ to update the policy according to the \emph{Update Policy} subroutine.

The agent's policy $\pi$ is a fully-connected, three hidden-layer, neural network with 130, 72, and 40 neurons, respectively. The network is randomly initialized with weights sampled from a normal distribution. The policy is optimized by minimizing the mean squared error loss using the Root Mean Square Propagation (RMSProp) optimizer with learning rate of 1e-4.
Unless defined otherwise, the human dataset $\mathcal{D}_H$ is initialized as an empty comma-separated value (CSV) file. Its main goal is to store the observations and actions performed by the human.
The procedure to update the policy in real-time spawns a separate CPU thread to perform policy updates in real-time while the human either demonstrates the task or intervenes.
This separate thread continuously checks for new demonstration or intervention data based on the size of the human dataset.
If new samples are found, this thread samples a minibatch of 64 samples of observations and actions from the human dataset and is used to perform policy updates based on the mean squared error loss until it reaches the loss threshold of 0.005 or maximum number of epochs (in this case, 2000 epochs).
This iterative update routine continues until the task performance threshold $\alpha$ is achieved, which can vary from task to task depending on the desired performance. For this work, we set $\alpha$ to 1 and only stop training after a pre-specified number of episodes defined in our experimentation methodology to empirically evaluate our method over a controlled number of human interactions, here defined as either human demonstrations or human interventions.

\subsection{Experimental Methodology}
Using the AirSim landing task, we tested our proposed CoL framework against several baseline conditions where we compared against using only a single human interaction modality (i.e. only demonstrations or only interventions) using equal amounts of human interaction time for each condition. By controlling for the human interaction time, we can assess if our method of utilizing multiple forms of human interaction provides an improvement over a single form of interaction given the same amount of human effort.

Each human participant (n=4) followed the same experimental protocol: given an RGB video stream from the downward-facing camera, the participant controlled the continuous roll, pitch, yaw, and throttle of the vehicle using an Xbox One joystick to perform 4, 8, 12 and 20 complete episodes of the perching task for three experimental conditions: demonstrations only, interventions only, and demonstrations plus interventions with the CoL method, with each condition starting from a randomly initialized policy. For the CoL condition, participants performed an equal number of demonstrations and interventions to match the total number of episodes for that condition.
For example, given 4 episodes of training, our CoL approach would train with learning from demonstrations in the first 2 episodes and then switch to learning from interventions for the last 2 episodes.
We compared this to learning from demonstration for all 4 episodes as well as learning from interventions for all 4 episodes.
This was repeated for 8, 12 and 20 episodes to study the effect of varying amounts of human interaction on task performance.
Following the diagram in Figure \ref{fig:airsim_col_diagram_compact} and Update Policy procedure on Algorithm \ref{alg:algo}, the agent's policy is trained on a separate thread in real-time, and a model is saved for each complete episode together with the human-observed states and the actions they performed.
These saved models are later evaluated to assess task performance according to our evaluation procedure described in the next section.

We also compared our approach to a random agent as well as an agent trained using a state-of-the-art reinforcement learning approach. The reinforcement learning agent used a publicly available implementation of Proximal Policy Optimization (PPO) \cite{Schulman2017} with a four degree-of-freedom action space (pitch, roll, throttle, yaw) and was trained for 1000 episodes, using only task completion as a binary sparse reward signal. To investigate the effect of action-space complexity on task performance, we also implemented the PPO where only two actions (pitch and roll) and three actions (pitch, roll and throttle) were available; for both cases, all other actions were held to a constant value. For the two actions condition (pitch and roll), the agent was given constant throttle and descended in altitude at a constant velocity. For both conditions yaw was set to 0. Training time of the reinforcement learning agent was limited to the simulated environment running in real-time.

\section{Results}

We evaluated our method in terms of task completion percentage, defined as the number of times the drone successfully landed on the landing pad over 100 evaluation runs, for each training method as well as for different amounts of human training data. Additionally, We compared the number of human data samples, i.e. observation-action pairs, used during training for each condition.

\begin{figure}[t]
\centering
  \includegraphics[width=1.0\linewidth]{figures/figure4.pdf}
\caption{Performance comparison in terms of task completion with Interventions (Int), Demonstrations (Demo) and the Cycle-of-Learning (CoL) framework for (A) 4 human interactions, (B) 8 human interactions, (C) 12 human interactions and (D) 20 human interactions, respectively. Here, an interaction equates to a single demonstration or intervention and roughly corresponds to the number of episodes. Error bars denote 1 standard error of the mean.  We see that CoL outperforms Int and Demo across nearly all human interaction levels.}
\label{fig:performance_all}
\end{figure}

Figure \ref{fig:performance_all} compares the performance of the models trained using only interventions (Int), the models trained using only demonstrations (Demo), and the models trained using the Cycle-of-Learning approach (CoL).
We show results for only these conditions as the random policy condition and the RL condition trained using PPO with the full four degree-of-freedom action space were not successful given the small amount of training episodes, as explained later in this section.
Barplots show the task completion performance from each condition averaged over all participants with error bars representing 1 standard error.
Subpanels show the performance for varying amounts of human interaction: 4, 8, 12 and 20 episodes.
For the 4 human interaction condition (Figure \ref{fig:performance_all}A), all methods show similar task completion conditions.
However, for the 8, 12 and 20 human interaction conditions, we see that the CoL approach achieves higher task completion percentages compared to the demonstration-only and intervention-only conditions, with the intervention condition performing the worst.

For the final condition of 20 episodes our proposed approach achieves 90.25\% ($\pm$ 5.63\% std. error) task completion as compared to 76.25\% ($\pm$ 2.72\% std. error) task completion using only demonstrations.
In comparison, for the 8 episodes condition, our proposed approach achieves 74.75\% ($\pm$ 9.38\% std. error) task completion in contrast to 54.00\% ($\pm$ 8.95\% std. error) task completion when using only demonstrations.

\begin{figure}[t]
\centering
  \includegraphics[width=1.0\linewidth]{figures/figure5.pdf}
\caption{Comparison of the number of human samples used for training with Interventions (Int), Demonstrations (Demo) and the Cycle-of-Learning (CoL) framework for (A) 4 human interactions, (B) 8 human interactions, (C) 12 human interactions and (D) 20 human interactions, respectively. Error bars denote 1 standard error of the mean. We see that CoL uses less data than the demonstration-only condition and only slightly more data than the intervention-only condition.}
\label{fig:samples_all}
\end{figure}

Figure \ref{fig:samples_all} compares the number of human data samples used to train the models for the same conditions and datasets as in Figure \ref{fig:performance_all}.
For the final condition of 20 episodes our proposed approach used on average 1574.50 ($\pm$ 54.22 std. error) human-provided samples, which is 37.79\% fewer data samples when compared to using only demonstrations.
Note that the policies generated from this sparser dataset were able to increase task completion by 14.00\%.
These results yield a CoL agent that has 1.90 times the rate of task completion performance per sample when compared to learning from demonstrations alone.
This value is computed by comparing the ratios of task completion rate to data samples utilized between the CoL agent and the demonstration-only agent, respectively.
Averaging the results over all presented conditions and datasets, the task completion increased by 12.81\% ($\pm$ 3.61\% std. error) using 32.05\% ($\pm$ 3.25\% std. error) less human samples, which results in a CoL agent that overall has a task completion rate per sample 1.84 times higher than its counterparts.

\begin{figure}[t]
\centering
  \includegraphics[width=1.0\linewidth]{figures/ppo_curve.png}
\caption{Performance comparison between the Cycle-of-Learning (CoL) with four continuous actions and the Deep Reinforcement Learning algorithm Proximal Policy Optimization (PPO) trained for three different task complexities using 2, 3, and 4 continuous actions.}
\label{fig:ppo_curve}
\end{figure}

Figure \ref{fig:ppo_curve} shows a comparison between the performance of the CoL method as well as the PPO baseline comparisons using a 2, 3 and 4 degree-of-freedom action space. For PPO with two actions, the agent was able to achieve 100\% task completion after 500 episodes, on average. However, when the action space complexity increases to three actions, the PPO agent performance was significantly reduced, now completing the task less than 5\% of the time after training for 1000 episodes. As expected, the PPO agent with the full four degree-of-freedom action space fails to complete the task after training for 1000 episodes. In contrast, the CoL method, with the same four degree-of-freedom action space, achieves about 90\% task completion in only 20 episodes, representing significant gains in sample efficiency compared to PPO.

\section{Discussion and Conclusions}

Learning from demonstrations in combination with learning from interventions yields a more proficient policy based on less data, when compared to either approach in isolation.
It is likely that the superior performance of the CoL is due to combining the two methods in sequence so as to leverage their strengths while attenuating their deficiencies.

Having been initialized to a random policy, learning from interventions alone produced more random behaviors, making convergence to a baseline behavior much slower.
Overall performance is thus slower to develop, resulting in lower percent completion for the same number of interaction episodes.
Conversely, learning from demonstrations alone was quicker to converge to stable behavior, but it was consistently outperformed by the CoL across varying numbers of interactions, while having more training data to utilize.
This seems initially counter-intuitive as more training data should result in a more accurate and presumably more proficient policy.
However, as the demonstrations typically follow stable trajectories, the agent is less likely to encounter regions of the state space outside these trajectories.
When enacting the policy at test time, any deviations from these previously observed states is not captured well in the policy, resulting in poor generalization performance.
By allowing the agent to act under its current policy, in conjunction with adaptively updating the policy with corrective human-provided actions needed to recover from potentially catastrophic states, the dataset and subsequent policy is improved.
Thus, the CoL allows for both rapid convergence to baseline stable behavior and then safe exploration of state space to make the policy more refined \emph{and} robust.

The results shown in Figure \ref{fig:samples_all} confirm the expectation that the combination of learning from demonstrations and interventions requires less data than the condition of learning from demonstrations alone, for the same number of episodes.
This supports the notion that the CoL is a more data efficient approach to training via human inputs.
When additionally considering the superior performance exhibited in Figure \ref{fig:performance_all}, the data efficiency provided by this technique is even more significant.
This result further supports the notion that a combinatorial learning strategy inherently samples more data rich inputs from the human observer.

It should be emphasized here that rather than providing an incremental improvement to a specific demonstration or intervention learning strategy, this work proposes an algorithmically agnostic methodology for combining modes of human-based learning.
The primary assertion of this work is that learning is made more robust, data efficient, and safe through a fluid and complementary cycling of these two modes, and would similarly be improved with the addition of the later stages of the CoL (i.e. learning from human evaluation and reinforcement learning).

As seen in Figure~\ref{fig:ppo_curve}, the PPO baseline comparison method was tested across varying complexity with different numbers of action dimensions. A striking result that can be seen is the significant drop-off in performance when going from two-actions, in which the drone had a constant downward throttle and only controlled roll and pitch, to three and four actions, in which the drone also had to control its own throttle. An obvious characteristic of a successful policy for the perching task is that the drone needs to descend in a stable and smooth manner, which is already provided in the two-action condition, as the downward throttle was set \textit{apriori}. This makes the task of solving for an optimal policy much simpler. In the three and four action condition, however, this behavior must be learned from a sparse reward signal (success or failure to land), which is very difficult given limited episodes.

When implementing the CoL in real world environments, catastrophic failures may be seriously damaging to the autonomous agent, and thus unacceptable.
Having a human observer capable of intervening provides a mechanism to prevent this inadmissible outcome.
Further, techniques that might be applied to enforce a similar level of safety automatically might limit the exploration of the state space, yielding a less robust or less capable policy.
Analogously to the shift of policy design from roboticists or domain experts to human users and laypersons, which is yielded by human-in-the-loop learning, the technique of learning from interventions shifts the implementation of system fail safes away from developers toward users.
This shift leverages human abilities to predict outcomes, adapt to dynamic circumstances, and synthesize contextual information in decision making.
\subsection{Current Limitations and Future Directions}

Our current implementation is limited to the first two stages of the CoL: learning from demonstrations and interventions.
Our planned future work will include adding in more components of the CoL; for example, learning from human evaluative feedback as done in \cite{Knox2009,MacGlashan2017,Warnell2018}.
Additionally, we aim to incorporate reinforcement learning techniques to further fine-tune the learning performance after learning from human demonstrations, interventions and evaluations using an actor-critic style RL architecture \cite{Sutton1998}.

A second limitation of the current implementation is that it requires the human to supervise the actions taken by the agent at all times.
Future work aims to incorporate confidence metrics in our learning models so that the autonomous system can potentially halt its own actions when it determines it has low confidence and query the human directly for feedback in a mixed-initiative style framework \cite{Grollman2007}, similar to active learning techniques.
Furthermore, our results clearly indicate that a two-stage process - with a primary stage with a large proportion of human-provided actions followed by a secondary stage with a smaller proportion of those actions - outperforms processes with uniformly large or small amounts of human data throughout.
This suggests there is perhaps an optimal point in the learning process at which to vary in the amount of human input from full demonstrations to interventions.
Figure \ref{fig:performance_all} illustrates this notion across the varying number of interactions shown in the subfigures, i.e. through the change in relative performance between the three conditions.
In future work we will examine if such an optimal mixture or sequencing of demonstrations and interventions exists, such that learning speed and stability are maximized, and if so, whether it is operator dependent.
Rather than having a predetermined staging of the demonstrations and interventions that is potentially suboptimal, a mixed initiative framework could determine this optimal transition point.
This could further reduce the burden on the human observer, allow for faster training, and even provide a mechanism to generate more robust policies through guided exploration of the state space.

This work demonstrates the first two stages of the CoL in a simulation environment with the goal of eventually transitioning to physical systems, such as an sUAS.
The CoL framework was implicitly designed for use in real world systems, where interactions are limited, and catastrophic actions are unacceptable.
As can be seen in Figure~\ref{fig:ppo_curve}, our method learns to perform the perching task in several orders of magnitude less time than traditional RL approaches, potentially allowing for feasible on-the-fly training of real systems.
Therefore, we expect that the application of the CoL to sUAS platforms, or other physical systems, should operate in effectively the same manner as demonstrated in this work.
Future efforts will focus on transitioning this framework onto such physical platforms to study its efficacy in real world settings.
One critical hurdle that must be overcome, is the implementation of the learning architecture on embedded hardware, constrained by the limited payload of an sUAS.

Additionally, given that we are utilizing a relatively high fidelity simulation environment, i.e. AirSim, it may be beneficial to bootstrap a real world system with a policy learned in simulation.
Although there are numerous challenges in transferring a policy learned in simulation into the real world, the CoL itself should allow for significantly smoother transfer due to its cyclic nature in which the user can revert to more direct and user intensive inputs at any point during the learning to allow for adaptation to previously unobserved states.
This capability inherently provides a method of transfer learning in the case of disparities between simulated and real world properties of the vehicle, sensors, and environment.
For example, if the perching behavior learned in simulation was transferred to an actual sUAS, the vehicle dynamics may have unmodeled non-linearities, the imagery may have dynamic range limitations, or the environment may present exogenous gust disturbances.
In such cases, the baseline policy would be monitored and corrected via learning from intervention, if these discrepancies yielded undesirable or possibly catastrophic behaviors.

\section{Acknowledgments}

Research was sponsored by the U.S. Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-18-2-0134 . The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

\bibliographystyle{aaai}

redpajama_set_name: RedPajamaArXiv


WinRAR Archiver 2018 is software used for compressing and extracting ZIP files of many formats like .rar. WinRAR Archiver is available as free download in full version, and it's effortless to use with a simple interface. WinRAR is supported on Windows 7, Windows 8 and Windows Vista operating systems. Support for Android was also released in 2015 when the use of RAR files started to increase, so they decided to move to smartphone areas by even introducing it to iPhone and Linux. WinRAR also developed different format extensions called RAR4 and RAR5 which are more stable and have extended compatibility.
File archive's like WinRAR are very useful as many computers do not have sufficient space store big files, so they are compressed and are reliable as they are safe and are not damaged or corrupted for future use. RAR files are elementary to create and takes less than 2 minutes depending on your CPU processing speed a quad-core processor will do the work fast. There are no limits on archiving files it can be any necessary file from 10 to 50 gigabytes. Winrar does some of the same functions as PowerISO, and Daemon Tools except it does not support burning and mounting of virtual discs onto the hard drive.
How to Extract Files with WinRAR 2018?
The latest version of Winrar has a new feature called 256-bit encryption to the files which uses a potent combination of passwords to protect a record by someone who does not have access to it. Unpacking ACE, ARJ, ZIP, 7Z, and ISO are possible now. The self-extracting file option comes in very handy when the other user does not know how to unzip a .rar folder. Later on, it also released a stable release for MAC OS X because lots of users were demanding it because no such other software can do this process simpler and faster like WinRAR does. UTF-8 is used for some files as so they are human readable.
Overall a great piece of software which saves all the files in NTFS instead of FAT 32 so modern day hard drives can read and edit it. There is an option in the software which can show a list of the recently processed files.


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Home » News & Updates » Tennessee Court Must Consider Jurisdictional Issue in Child Custody Case, Appeals Court Holds
Tennessee Court Must Consider Jurisdictional Issue in Child Custody Case, Appeals Court Holds
Posted by Martin Heller Potempa & Sheppard, PLLC on April 7, 2015
Matters of custody and visitation can be contentious in any divorce proceeding. However, they become even more complicated issues when one or both of the parents end up moving to another state. In these cases, there is a special set of laws that determine which states have jurisdiction over the matter, but the analysis of the laws can be complex. The Court of Appeals of Tennessee recently examined an issue of custody involving several states. In Taylor v. McClintock, the appeals court ruled that Tennessee had jurisdiction to determine custody issues under the particular set of facts in the case.
The background of the case involved a parenting order that was entered in Florida. The parties had obtained their divorce in Florida, where the mother was designated as the primary residential parent of the parents' only child. Shortly afterward, the father moved to Tennessee. The parenting order stated that should the father relocate, he would get alternate parenting time consisting of one week per month. After several years' time and a number of parenting disputes, the Florida court allowed the child to move to Tennessee in order to give the father "make-up" parenting time. At the same time, the child's mother moved from Florida to Alabama. The Florida court, in granting the mother's motion to relocate, stated that the father would be permitted to exercise the rights of a primary residential parent for the period of time in which the child resided with him.
The child lived in Tennessee with the father for six months, at which point the father petitioned a Tennessee court to modify the Florida parenting order. The father sought to be designated as the child's primary residential parent. However, the Tennessee court held that it did not have jurisdiction to modify the Florida order since Florida was the child's home state and retained jurisdiction.
On appeal, the child's father argued that the trial court erred in concluding that it did not have jurisdiction to modify the parties' parenting order. Specifically, he argued that his child had lived in Tennessee for a period of six months, which made Tennessee the child's home state. The appeals court looked to the Uniform Child Custody Jurisdiction and Enforcement Act (UCCJEA), which applies in cases of interstate custody issues. The appeals court noted that, at the time the father petitioned for modification, none of the parties were living in Florida. Likewise, there were no pending modifications or issues in the Florida court. Therefore, the state of Florida did not have exclusive, continuing jurisdiction over the parties' custody dispute.
The Court of Appeals also found that Tennessee was the child's home state when the father initiated legal proceedings. The child had lived in the state of Tennessee for at least six consecutive months immediately before the father petitioned the court for modification. Therefore, under definitions found in the UCCJEA, the appeals court held that Tennessee had home state jurisdiction to make a custody determination in the matter. The court also noted that it appeared that none of the parties would even be returning to Florida since the father resided in Tennessee and the mother resided in Alabama.
Since the appeals court held that the Tennessee trial court did, in fact, have jurisdiction, it reversed and remanded the case to the trial court to consider whether it would decline jurisdiction. Under the appropriate law, a court may decline jurisdiction if it is an inconvenient forum, or another state would be a more appropriate forum.
If you are dealing with child custody or visitation challenges, or need advice on any other related matter, contact the Nashville family law attorneys at Martin Heller Potempa & Sheppard. Our legal team can guide you through the process of dissolution with compassion and competence. For an initial consultation, contact one of our attorneys today by emailing info@mhpslaw.com, or call (615) 800-7096.
Lawyering is about asking the right questions. It's about not asking the wrong ones, too.
Do not use the authority granted to you in a Power of Attorney to transfer property to yourself. Just don't.
Think of legal forms companies like the plumbing section of Home Depot and ask yourself, "Do I really know what the hell I'm doing, or should I hire a pro?"
Fighting over a dog during a divorce makes sense to me. But a cat? Cats have agendas.

redpajama_set_name: RedPajamaCommonCrawl


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A new IHOP restaurant being built in Mt. Juliet is scheduled to open Dec. 18.
A new IHOP restaurant being built in Mt. Juliet is scheduled to open Dec. 18, according to a company representative.
The future restaurant on Pleasant Grove Road at The Paddocks at Mt. Juliet will seat 122, IHOP representative Austin Lewis said.
Job seekers can apply in person at the IHOP at 1728 Old Fort Parkway in Murfreesboro or online at nowhiring.com/ihopmtjuliet.
The restaurant expects to eventually employ about 100 workers, property owner Alex Ikbariah has said.
The restaurant will be part of an approximate 11,000-square-foot building.
Initial plans were for IHOP to use about 3,400 square feet and the remaining space would be available for lease.
Camp Bow Wow is scheduled to open in December under franchise owner Tyler Keith.
Dog day care services, grooming and boarding will be available at the former Grace United Methodist Church Family Life Center building at 3085 N. Mt. Juliet Road.
The new Mt. Juliet location will include 54 cabins with cots, six luxury suites, four indoor and outdoor play areas and equipment, dog pools and 24-hour monitoring.
The new TriStar Summit Medical Center Mt. Juliet ER at 100 Wilfong Parkway, off Interstate 40 and Beckwith Road, is scheduled to start seeing patients at 8 a.m. Dec. 3, officials said.
The approximate 8,900-square-foot facility will have eight emergency treatment rooms and will be open 24 hours a day. Adult and pediatric patients will be accepted.
The project is an $11 million investment.
Work is underway on an At Home superstore that also will be in The Paddocks area near Academy Sports + Outdoors.
The approximate 88,000-square-foot home decor store is expected to open sometime in 2019, according to information through the company. No other details were available.
A new Slim Chickens restaurant approved at 4161 N. Mt. Juliet Road now hopes to start construction in early 2019 with hopes to open in late spring or early summer, said Harold Wallace of Life Changing Hospitality.
Slim Chickens has more than 70 locations in 11 states. Its menu includes chicken tenders, wings, grilled wraps and handmade dipping sauces.


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There are many complexities and challenges coping with an evacuation. One challenge evacuees are often not prepared for is that people who have not experienced an evacuation often don't understand the enormity of the situation. For example, people who have not experienced this type of crisis and disaster may say things like "well at least you didn't lose your house or belongings," indicating that the impact on you was small or insignificant. When a community, or parts of a community experience loss, so do the people within the community. It is likely that the forest fires may create a feeling of lack of safety for you. This is also a form of loss.
Old losses from the past, even ones you thought you had moved through, coped with and resolved.
Impatience, irritability and less tolerance than you had before the fires.
Look for other areas in your life where you can reduce stress by reducing your expectations… be kind to yourself.
Practice good self-care. Get lots of sleep (you may feel like you want naps during the day because the emotional stress can be very tiring). Eat healthy, stay hydrated by drinking lots of water, and try and maintain some routines. Make sure you get some physical exercise such as walking, biking, and doing physical activities.
Recognize that an evacuation is traumatic, and contacting a counsellor or crisis line may be an important part of your coping. You can dial 2-1-1 on your phone to contact and Information and Referral Specialist who can help connect you to the appropriate community resources.
You can also call the confidential 24 hour crisis line 780-743-HELP (4357).


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Led by the tribe but attended by thousands of others over its two days, the Nanticoke Powwow in Millsboro serves as both fundraiser and festival. It's a tribal coming together and a chance to ensure their culture endures for the coming generations.
"Powwow for me is a family reunion. It's who I am," said Tribal Secretary Kayleigh Vickers. "This is what we need to keep alive to keep our stories alive."
It was Vickers' first year coordinating the powwow, which was themed "The sacred fire which continues to burn within us" to honor generations gone and still to come.
Tribal dancers in full regalia are part of the way the Nanticoke pass their traditions on throughout the year and especially at powwow, said Herman Jackson, a former tribal councilman who drummed and danced over the weekend.
"All our dances and drumming, it's about our creator and we give thanks back to him," Jackson said. "We dress this way because we are Native Americans. That's why we call it regalia and not a costume. A costume, you wear to be something you're not."
The powwow is an event open to the public and resembles more of a fair than a spiritual or familial gathering. Dozens of food and crafts vendors are scattered throughout the grounds and dances and music are centerpiece displays.
And though Odessa's Denise Ashton-Dunkley has made her gourdwork, dreamcatchers, regalia and artwork an important part of her financial life, it's also kept her connected to her Nanticoke Lenni-Lenape roots.
"It keeps me grounded. It keeps me in touch with who I am. I get this satisfaction from people coming and appreciating what I do through my art. It keeps it alive for me," Ashton-Dunkley said. "They can get an idea of who we are and our identity here in Delaware."
More:On 9/11, Delaware company got all its workers out of Tower 2
The gathering also is the primary fundraiser for the Nanticoke Indian Association, the nonprofit that supports the tribe and operates the museum near the powwow grounds.
Raising those funds helps them to thrive as a tribe, Vickers said, and the event's open-to-the-public posture reminds those who come that the tribe's members are their neighbors.
"It's so people know that we are here, that we will continue to be here and that we will always be here," Vickers said. "One of my elders just told me today that you can have all the degrees you want, but until you get to know people and their culture and their heritage and who they really are, that is when you'll be complete as a person."
More:President Donald Trump supporters rally in Georgetown
Contact Adam Duvernay at aduvernay@delawareonline.com or (302) 324-2785. Follow on Twitter @DuvinDE.


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Jared Leto dives deep as Joker in Suicide Squad
The Academy Award winner takes method to another level in the upcoming summer release
By Nicole Sperling
April 15, 2016 at 08:35 PM EDT
Clay Enos
It's no easy feat to take on a comic book character that's been immortalized in cinema by the likes of Jack Nicholson and Heath Ledger with performances so riveting, Ledger's turn landed him a posthumous Oscar. So it was only with a deep commitment that Jared Leto, himself an Oscar winner for his role in Dallas Buyers Club, would take on the Joker part in Warner Bros.' upcoming summer release Suicide Squad, a part he and director David Ayer redefined for today's audience.
"The Joker has a lot of different looks, sort of built from the looks throughout the history of DC Comics but with a new sort of flair and flavor on him so he does feel like a modern-day gangster, because… he's always been a gangster," says Ayer.
Leto's eccentric approach has already become the stuff of legend. We chatted with the actor for EW's Summer Movie Preview Issue, which is on stands Friday. Leto hasn't talked much yet about the role in the August release. Here are some choice excerpts from our interview.
ENTERTAINMENT WEEKLY: What were your original thoughts when you were approached about taking on a character that has become so iconic?
JARED LETO: You take a deep breath. There is a responsibility I suppose that you can feel but it's also incredibly exciting. The work that's been done on this character by so many people before me has been so impactful, so incredible, so much fun, so profound, so risky, that it's a very special thing to be asked to take on that responsibility.
What did you want to do with him to make him feel original in comparison to what had been done before?
We knew we had to strike new ground. There had been such great work that we knew we had to go in a different direction. So you had a kind of direction from the very beginning, knowing that you can't go that way, so you have to head this way. That was really helpful. But the Joker is fantastic because there are no rules. The Joker operates from instinct.
What kind of conversations did you have with David about who this guy was going to be?
David and I had conversations and I think he trusted that I was going to go out and experiment and explore and come back with something for him to continue the collaboration with. And it was nice to have that trust from him. He really let me lose and encouraged me. That was a priceless thing when you're working with a director to have that faith and trust.
So what did you do to create this guy? What did you go and do? Who did this guy become to you?
He became a real person. I don't know if person is the right word. I think the Joker lives in between reality and another plane. Kind of a shaman in a way. It's a very intoxicating role to take on. You have permission to break rules and to challenge yourself and anyone around you in a really unique way.
I first started at the beginning, educating myself, researching, reading as much as I could, going back to the source material. And then at a certain point, I knew I had to stop doing that. Because the Joker has been redefined, reinvented many times before. I think the fun thing about it is when people have done it in the past, there is some spirit of the Joker essence that they keep, but they either build upon something or tear something down and start again at the beginning. For me, I knew once I had gone through the process of educating myself, I had to throw everything away and start from the beginning and really build this from the ground up. It was a transformative process. There was a physical transformation. There was a physical conditioning.
What, specifically, did you do?
There are a lot of things. It's probably better to not get into it but to the Joker, violence is a symphony. This is someone who gets an extreme reward from the act of violence and manipulation. Those are the songs he sings and he is very in tune with what makes people tick. I did meet with people that were experts, doctors, psychiatrists that dealt with psychopaths and people who had committed horrendous crimes, and then I spent some time with those people themselves, people who have been institutionalized for great periods of time. I guess when you take on a role, any role, you become part detective, part writer, and for me that's my favorite time of the entire process, the discovering, the uncovering, and the building of a character. Yeah, it's really fun.
Now, how difficult was it staying in that character for the entire shoot?
It was challenging but it was also fun. He has a great sense of humor, depending on who you ask. [Laughs]
Like your castmates?
Yeah, you can ask them about that. But for me, I knew I had to be committed as much as possible. I had to be committed beyond belief. And I did what I needed to do to deliver the best I possibly could. There was a lot on the line and I want to do justice for all the work that has been done before. It's not a part-time job. It's immersive. It takes over your life and that's what I needed to do for myself. Other people can show up and are genius but I did what I needed to do to deliver. And we had a good time with it. I think it was exactly what was needed for me, at least.
Tell me about the video you made for your castmates before your henchman dropped a pig on the table in the middle of the rehearsal room. How did that whole thing go down?
I think in the beginning it was important to set up and define our relationship, so to speak. [laughs] There were a lot of things. It was fun. You've got to remember doing these things isn't just about the result, it's the process. It's working with Mr. Frost, who is the Joker's henchman. It's making the choice and the decision of how am I going to introduce the Joker to the very first people who will meet him. So it becomes an exercise as much for me as it is for anyone else. And it's as much about the process as it is about the results. How do you go about these sorts of things? How do you work with the people around you? It was a lot of fun. The attention to detail and the process that we went through helped to bring a life to the character for me. Those actions and those gestures—the joker loves a grand gesture. Those were really important. And they were fun. The Joker is someone who doesn't take things too seriously.
Where did you get the pig?
If I told you, you wouldn't believe me. Nah, I can't tell you. But there was a method to the madness and a point for all of it. I wouldn't have changed it for the world. It was a really wonderful experience. I really loved the opportunity and I have a lot of gratitude for the chance to take this on and to share it with a new generation of younger people and people who may have been familiar, to revisit it in a different way.
How involved were you in creating the Joker's look?
David had some very specific ideas. As far as the look of the Joker, it was a collaborative process. There were things I brought to the table and things David brought to the table and it was a mashup of both of our sick and twisted minds. There were specific things that he liked and wanted and I was there to help bring them to life in the best way I could.
What specific things did you bring to it?
I think the most important thing I brought was probably not to do with the outside but with the internal. But you know David was specific with tattoos. They were his idea but there was a lot to play with. Who knows what will end up in the final film?
I think for the Joker and the process, it's probably best to talk to the other people. I don't mean that dismissively, but I think it will be more interesting. What I think is interesting is what did the crew think, what did the DP think? They must have thought I was completely f—in crazy, but I know this, every time I walked on the set, I saw a lot of smiles and people were so glad to have smiley back and that was really touching and a nice thing that they got a sense of joy out of whatever was happening. I was happy to make everyone smile… even if I had to cut their tongues out.
Suicide Squad opens on Aug. 5.
For more from our Summer Movie Preview, pick up the new issue of Entertainment Weekly, on newsstands today, or buy your choice of four collectible covers here – and subscribe now for more exclusive interviews and photos, only in EW.
David Ayer
Joker director says the Joaquin Phoenix movie 'doesn't follow anything' from the comics
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Suicide Squad: Jared Leto's Joker gets extended look in new promo
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Matt Smith joins Jared Leto in Morbius movie
Jared Leto becomes the Living Vampire in first Morbius teaser photo
Suicide Squad: Jared Leto reveals many Joker scenes were cut
Will Smith won't be back as Deadshot in Suicide Squad sequel
Suicide Squad director explains the Joker's cut role
Suicide Squad star Jared Leto to lead Sony's Morbius movie
Jason Reitman's Ghostbusters sequel, Jared Leto's Morbius cement 2020 releases
Suicide Squad: Jared Leto shares new Joker, Harley Quinn photos
Suicide Squad: Jared Leto's Joker is no laughing matter
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Jared Leto shaves beard for Sony's upcoming Morbius film
James Gunn is 'enormously excited' about directing The Suicide Squad
Todd Phillips shares new Joker image of Joaquin Phoenix, confirms film's R rating
Jared Leto to star in standalone Joker movie
Summer Movie Preview


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TIFF 2016: A United Kingdom
Posted on September 13, 2016 September 13, 2016 by Courtney SmallIn Reactions, Reviews, TIFF2 Minutes Read
Amma Asante's latest film, A United Kingdom, tells the real-life story of Seretse Khama (David Oyelowo), a prince who nearly lost his throne and country simply because he wanted to follow his heart. A member of the royal family in his native Bechuanaland (now known as Botswana), Seretse meets and falls in love with Ruth Williams (Rosamund Pike), a white woman with no royal lineage, while studying in London in 1947. Despite the racial climate in London and Africa, South Africa was on the eve of launching the apartheid movement, Serestse and Ruth ignored the warnings from those around them and proceeded to get married. Thus making Ruth the queen of a country she had never even set foot in. As one can imagine, an interracial marriage, let alone one of this magnitude, sent shockwaves through both of their lands.
At the request of Seretse's uncle, who disapproved of their union, and worried about their partnership with South Africa, who secretly was trying to strip Bechuanaland of its natural resources, the British government did everything they could to deny Seretse of his birth right. Claiming that they were merely trying to stop a pending war between those who supported Seretse and those backing his uncle, the government withheld documents proving Seretse was fit to lead, tried to lure Ruth away from her husband and even imposed a five-year exile from his homeland on Seretse. Despite all of this, the love between Seretse and Ruth never faltered. Instead it grew stronger and ultimately changed the course of an entire country in the process.
Crafting a crowd-pleasing love story that does not neglect the cultural significance at its core, the film marks another confident leap forward for Asante. While her talents were evident in Belle, she really hits her stride with this emotionally charged tale. Asante does a wonderful job of giving equal weight to the us against the world romance and the political storm that occurred as a result. In highlighting the extraordinary lengths that the British government went to trying to force Seretse to do their bidding, Asante not only strengthens the overall impact of the love story, but also offers some biting commentary regarding the overreaching hand of Western governments. The biggest gut-punch that Seretse receives is not from his uncle's actions, but the symbolic one from the likes of Winston Churchill and other politicians who used his pain for their political advancements.
Asante also deserves credit for how well she subverts the outsider narrative by making Ruth the stranger in a foreign land. Rosamund Pike is wonderful in the role of Ruth. Providing the character with both vulnerability and inner strength, Pike expertly conveys the complexities of a woman who has been thrown into a racially charged situation that has far greater ramifications than she could ever imagine. Her chemistry with Oyelowo, himself a force to be reckoned with in the film, seems almost effortless.
Avoiding many of the common tropes that are often found in narratives like this, A United Kingdom is a film that is not afraid to have a conversation about race relations, while simultaneously championing those who fought for love and equality for all.
Saturday, September 17, 4:30 PM, Winter Garden Theatre
Tickets can be purchased at tiff.net
Published by Courtney Small
The founder of Cinema Axis, Courtney is a Rotten Tomatoes approved critic who can frequently be heard discussing film as co-host of Frameline on Radio Regent. Courtney has contributed to several publications including Leonard Maltin, That Shelf, POV Magazine, In the Seats, That Shelf, Black Girl Nerds, and Comix Asylum. He also celebrates diversity in cinema as host of the Changing Reels podcast and is a member of the Online Film Critics Society and the African American Film Critics Association.	View all posts by Courtney Small
Tags: 2016, A United Kingdom, Amma Asante, Arnold Oceng, Charlotter Hope, David Oyelowo, Drama, Jack Davenport, Jack Lowden, Jessica Oyelowo, Laura Carmichael, Merveille Lukeba, Nicholas Lyndhurst, Rosamund Pike, TIFF, TIFF16, Tom Felton
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Wow. I have got to see this! Thanks for the review and clueing me into a film I might not ever have heard about otherwise.
ninvoid99 says:
Amma Asante definitely won me over with Belle as I'm definitely intrigued to see this as I'm happy that Rosamund Pike is getting some damn good film projects.
dbmovies says:
Great review. I am really looking forward to seeing this film. I am in love with Pike since seeing Pride & Prejudice and an Education, and I think she is the kind of an actress who should be given more lead roles.
Courtney Small says:
After seeing Gone Girl, I am convinced Pike can handle any role. She once again confirms this with her work in A United Kingdom.


Congratulations to our talented national medalists!
This fall and winter, students across the country and from places abroad submitted nearly 320,000 works of art and writing to the 2016 Scholastic Art and Writing Awards.
Nearly 180,000 works earned regional Gold Keys and advanced to NYC for national adjudication. Of those Gold Key works, just 2,400 received national medals.
Multiple students from SHS earned medals and represent the top 1 percent of art pieces submitted to the competition.


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Q: Get Json data from controller I need to create dinamic select into my form.
I have wrote this code to get it dinamically from my controller (CodeIgniter) that get data from model.
This is the script:
<script type="text/javascript">
   $(document).ready(function(){
                    $("select#regione").change(function(){
                        $.getJSON("http://test:8888/ricerca/test",{id: $(this).val()}, function(dati){
                            var options = '';
                            for (var i = 0; i < dati.length; i++) {
                                options += '<option value="' + dati[i].valore + '">' + dati[i].etichetta + '</option>';
                            }
                            $("#provincia").html(options);
                            $('#provincia option:first').attr('selected', 'selected');
                        });
                    });
                });
            </script>

This is the dropdown form:
<form action="base_url().'ricerca'" method="post">
<select name="regione" id="regione">
<option value="">No option</option>
<option value="Lombardia">Lombardia</option>
<option value="Lazio">Lazio</option>
</select>

<select name="provincia" id="provincia">
</select>
</form>

and this is the controller:

class Ricerca extends CI_Controller
{
 public function test()
{
 $dati = array();
         array_push($dati,array("valore"=>"ciao", "etichetta"=>"ciao"));
         array_push($dati,array("valore"=>"mamma", "etichetta"=>"mamma"));
         return json_encode($dati);
}
}

Before to do my query in the model I would see if my script works but if I select an option in the first select called "Regione" the script does nothing

A: you have to echo json data from controller function not to return data
public function test()
{
 $dati = array();
         array_push($dati,array("valore"=>"ciao", "etichetta"=>"ciao"));
         array_push($dati,array("valore"=>"mamma", "etichetta"=>"mamma"));
         echo json_encode($dati);
}

or you can do
public function test()
    {
     $dati = array();
             array_push($dati,array("valore"=>"ciao", "etichetta"=>"ciao"));
             array_push($dati,array("valore"=>"mamma", "etichetta"=>"mamma"));
             $this->output
    ->set_content_type('application/json')
    ->set_output(json_encode($dati));
    }

redpajama_set_name: RedPajamaStackExchange


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Employee presence tracking system will help to be certain most workers and employees are fulfilling their obligations and duties. In just about any organization organization the vast majority of people that we hire are good workers who admire company's schedule but there are always a couple employees and workers, who arrive for office and also have a propensity to leave early. A worker attendance monitoring system helps HR department to see who's clocked in and at what time. It is possible to be certain that you just pay your employees for the period that they work. Employee attendance system gives an accurate picture of their organization's labor cost. It's absolutely essential of HR department. Employee presence tracking system allows you along with your HR department to track employee attendance and time with preciseness.Importance of worker attendance tracking method to get Administrator. It isn't possible for the secretary to monitor his team in working all of the time. He or she cannot act as dictator or babysitter, nonetheless it is very crucial that you be certain that his employees are still coming into work on time and taking days away correctly. He assesses his employees for their ability and time, so if they are unproductive as they are sick, or they are coming from overdue his business will suffer. It is vital to keep an eye on everybody in your organization and their demands.
The use of an employee presence monitoring system can save yourself company's time and reduce errors that are made while calculating attendance.Benefits of employee presence tracking system Satisfy Employees & boost employee productivity:Employee attendance tracking system can help satisfy employees of their organization. Employees will be more happy because time and attendance applications guarantees timely and accurate cover. In the event the employees of the organization are satisfied and motivated they will continue to work more as a result there'll probably be great enhancement in productivity.Reduce errors & Saves money:There is always a chance that something will go wrong as you're manually records the employee's attendance. Employee attendance tracking system is a modern computer and electronic aided method of keeping employee records. It keeps employee records quickly, true. The typical employee presence monitoring system has an error rate of less than one percent, which is much less than expected while the duty is performed by hand. A member of staff attendance monitoring system is a handy tool for company to improve efficiency cut costs. Implementing a technology based time and attendance solution with bio metric devices will directly a way help reduce company's labor costs.