text:
\section{Conclusion}
\label{sec:con}
The lack of large-scale datasets of paired RGB and depth images is one of the major bottlenecks for improving $3$D hand pose estimation.
To address this limitation, we propose a conditional GAN-based  model called DGGAN to bridge the gap between RGB images and depth maps.
DGGAN synthesizes depth maps from RGB images to regularize the $3$D hand pose prediction model during training, eliminating the need of paired RGB images and depth maps conventionally used to train such models.

The proposed DGGAN is integrated into a $3$D hand pose prediction framework, and is trained end-to-end together for $3$D pose estimation.
DGGAN not only generates more realistic hand depth images, which can be used in many other applications such as $3$D shape estimation but also results in significant improvement in $3$D hand pose estimation, achieving new state-of-the-art results.

\paragraph{Acknowledgement.}
This work
was supported in part by Ministry of Science and Technology
(MOST) under grants MOST 107-2628-E-001-005-MY3 and MOST 108-2634-F-007-009.

\section{Experimental Settings}
\label{sec:exp}

This section introduces our experimental settings.
The selected benchmark datasets for performance evaluation are first given.
The evaluation metric and training details are then presented.

\subsection{Datasets for Evaluation}
\label{sec:dataset}

We conduct the experiments on three benchmark datasets, including the stereo hand tracking benchmark (STB)~\cite{zhang20163d}, the render hand pose dataset (RHD)~\cite{zb2017hand}, and the multi-view hand pose (MHP) dataset\cite{gomez2017large}.

The STB dataset is a dataset of real hands.
It contains two different subsets called SK and BB.
The images in SK are captured by Point Grey Bumblebee2 stereo camera while images in BB are from a depth sensor.
In our experiments, we use the BB subset for DGGAN training, and leverage the SK subset for unpaired testing.

RHD is a synthetic dataset.
Zhang~\etal~\cite{zhang20163d} use a $3$D simulator, Maya, to render the images from $20$ different characters doing $39$ actions.
Each data entry consists of an RGB image and the corresponding depth image, and both $2$D/$3$D annotations.
This dataset is challenging since its images are captured with various view points and of many different hand shapes.

The MHP dataset provides color hand images as well as the bounding boxes of hands and the $2$D and $3$D location of each joint.
It consists of hand imaegs of $21$ people with different hand movements.
For each frame, it provides the images from four different angles of view.
The $2$D and $3$D annotations are obtained by Leap Motion Controller.

Before training, we first crop the hand regions from the original canvas to make sure that hand parts have dominating proportion in the frame.
Notice that the STB and MHP datasets use the center of a palm rather than a wrist as one of its hand keypoints.
Hence, we revise the annotation to move the center of the palm to the wrist in the same way performed in \cite{cai2018weakly}.

\subsection{Evaluation Metric}

Following the previous works \cite{cai2018weakly,chen2018generating,zb2017hand}, we evaluate the results of hand pose estimation by using 1) the \textit{area under the curve} (AUC) on \textit{percentage of correct keypoints} (PCK) between threshold $20$mm and $50$mm (AUC $20$\_$50$) and 2) the \textit{end-point-error} (EPE): the distance between predicted $3$D joint locations and the ground truth.
In Table \ref{table:3Dresult}, we report the AUC $20$\_$50$ as well as the mean and the median of EPE over all hand keypoints.

\subsection{Training}

During training, we first initialize the weights of the depth-map reconstruction and hand pose estimation modules in the proposed DGGAN.
Both modules are initialized by fitting the STB dataset (see \ref{sec:dataset}) but trained separately.
Then, we connect the two modules and fine-tune the whole network in an end-to-end manner.
For training with the RHD and STB dataset, the discriminator is derived to distinguish the $G_{S\rightarrow T}(x_s)$ and $x_t$, a randomly chosen depth-map from the respective dataset.
For the MHP dataset, we simply randomly assign a depth-map from RHD dataset as $x_t$ because the MHP dataset does not contain any dense depth maps.

\begin{figure*}[t]
	\begin{center}
		\begin{tabular}{cccccc}
		\includegraphics[width=0.14\textwidth]{1_real_A.png}
		\includegraphics[width=0.14\textwidth]{1_real_B.png}
		\includegraphics[width=0.14\textwidth]{1_fake_B.png}
		\includegraphics[width=0.14\textwidth]{2_real_A.png}
		\includegraphics[width=0.14\textwidth]{2_real_B.png}
		\includegraphics[width=0.14\textwidth]{2_fake_B.png}

		\end{tabular}
	\\
	\begin{tabular}{cccccc}
		\includegraphics[width=0.14\textwidth]{3_real_A.png}
		\includegraphics[width=0.14\textwidth]{3_real_B.png}
		\includegraphics[width=0.14\textwidth]{3_fake_B.png}
		\includegraphics[width=0.14\textwidth]{epoch022_real_A.png}
		\includegraphics[width=0.14\textwidth]{epoch022_real_B.png}
		\includegraphics[width=0.14\textwidth]{epoch022_fake_B.png}

		\end{tabular}
    \\
    \begin{tabular}{cccccc}
		\includegraphics[width=0.14\textwidth]{epoch013_real_A.png}
		\includegraphics[width=0.14\textwidth]{epoch013_real_B.png}
		\includegraphics[width=0.14\textwidth]{epoch013_fake_B.png}
		\includegraphics[width=0.14\textwidth]{epoch019_real_A.png}
		\includegraphics[width=0.14\textwidth]{epoch019_real_B.png}
		\includegraphics[width=0.14\textwidth]{epoch019_fake_B.png}

		\end{tabular}
	\end{center}
	\caption{Comparison between the generated and ground-truth depth maps on the RHD dataset.
	%
	The first and fourth columns show the RGB images.
	%
	The second and fifth columns display the real depth maps.
	%
	The third and sixth columns give the generated depth maps.}
	\label{fig:RHDdepthcmp}
\end{figure*}
\begin{figure*}[]
	\begin{center}
	\begin{tabular}{cccccc}
		\includegraphics[width=0.14\textwidth]{epoch023_real_A.png}
		\includegraphics[width=0.14\textwidth]{epoch023_real_B.png}
		\includegraphics[width=0.14\textwidth]{epoch023_fake_B.png}
		\includegraphics[width=0.14\textwidth]{epoch108_real_A.png}
		\includegraphics[width=0.14\textwidth]{epoch108_real_B.png}
		\includegraphics[width=0.14\textwidth]{epoch108_fake_B.png}

		\end{tabular}
		\begin{tabular}{cccccc}
		\includegraphics[width=0.14\textwidth]{epoch008_real_A.png}
		\includegraphics[width=0.14\textwidth]{epoch008_real_B.png}
		\includegraphics[width=0.14\textwidth]{epoch008_fake_B.png}
		\includegraphics[width=0.14\textwidth]{epoch016_real_A.png}
		\includegraphics[width=0.14\textwidth]{epoch016_real_B.png}
		\includegraphics[width=0.14\textwidth]{epoch016_fake_B.png}

		\end{tabular}
		\begin{tabular}{cccccc}
		\includegraphics[width=0.14\textwidth]{epoch051_real_A.png}
		\includegraphics[width=0.14\textwidth]{epoch051_real_B.png}
		\includegraphics[width=0.14\textwidth]{epoch051_fake_B.png}
		\includegraphics[width=0.14\textwidth]{epoch065_real_A.png}
		\includegraphics[width=0.14\textwidth]{epoch065_real_B.png}
		\includegraphics[width=0.14\textwidth]{epoch065_fake_B.png}

		\end{tabular}
	\end{center}
	\caption{Comparison between the generated and ground-truth depth maps on the STB dataset.
	%
	The first and fourth columns show the RGB images.
	%
	The second and fifth columns display the real depth maps.
	%
	The third and sixth columns give the generated depth maps.}
	\label{fig:STBdepthcmp}
\end{figure*}
\section{Introduction}
\label{sec:intro}

Vision-based $3$D hand pose estimation ($3$D HPE) aims to estimate the $3$D keypoint coordinates of a given hand image.
$3$D HPE has drawn increasing attention owing to its wide applications to human-computer interaction (HCI)~\cite{antoshchuk2018gesture, lin2013airtouch}, sign language understanding~\cite{zafrulla2011american}, augmented/virtual reality (AR/VR)~\cite{mueller2018ganerated, hung2016re}, and robotics~\cite{antoshchuk2018gesture}.
RGB images and depth maps are two the most commonly used input data for the $3$D HPE task.
An example of a hand image and its corresponding depth map is shown in Figure~\ref{fig:rgb_vs_d}(a).
Depth map can provide $3$D information related to the distance of the surface of human hands.
Training networks with depth maps has been proven to achieve significant progress on the $3$D HPE task~\cite{cai2018weakly, iqbal2018hand}.
In addition, with the depth information provided by the depth maps, the hand segmentation task can be effectively solved.
Unfortunately, capturing depth maps often requires specific sensors (\eg~Microsoft Kinect, RealSense), which limits the usability of those state-of-the-art methods based on depth maps.
Commercial depth sensors are usually much more expensive than RGB cameras.
On the other hand, RGB images are the most commonly used input data in the HPE task because it can be easily captured by abundant low-cost optical sensors such as webcams and smartphones.
However, $3$D HPE from RGB images is a challenging task.

\begin{figure}[t]
\hspace{-0.3cm}
\begin{tabular}{cc}
\centering
\fbox{
\includegraphics[width=0.2\textwidth]{pair.png}\label{fig:pair}
}
&
\fbox{
\includegraphics[width=0.2\textwidth]{unpair.png}\label{fig:unpair}
}
\\
(a)&(b)
\end{tabular}
\caption{Training examples in a generic $3$D HPE dataset: (a) paired RGB and depth images; (b) unpaired RGB and depth images. Our work does not rely on paired training data and therefore is applicable to both RGB-only and depth-only $3$D HPE tasks.}
\label{fig:rgb_vs_d}
\end{figure}
In the absence of depth information, estimating $3$D hand pose from a monocular RGB image is intrinsically an ill-posed problem.
To address this issue, the state-of-the-art methods such as~\cite{cai2018weakly, ge2019handshapepose}
leverage both RGB hand images and their paired depth maps for the $3$D HPE task.
Their $3$D hand pose inference process takes an RGB image and the paired depth information into account.
They first regress $3$D hand poses on RGB images, and then utilize a separate branch to regularize the predicted $3$D hand pose by using the paired depth maps.
The objective of the depth regularizer is to make the predicted $3$D keypoint positions consistent with the provided depth map.
It results in two major advantages: 1) training networks with depth maps can efficiently improve the hand pose estimator by using the depth information to reduce the ambiguity and 2) enabling $3$D HPE based on merely RGB images during the inference stage.
These approaches require paired RGB and depth training images.
Unfortunately, most existing hand pose datasets only contain either depth maps or RGB images, instead of both.
It makes the aforementioned approaches not applicable to such datasets.
Besides, the unpaired RGB and depth training images cannot be explorited for them.
Figure~\ref{fig:rgb_vs_d}(b) shows an example of unpaired RGB and depth map images.

To tackle this problem, we propose a novel generative adversarial networks, called {\em Depth-image Guided GAN} (DGGAN).
Our network contains two modules: {\em depth-map reconstruction} and {\em hand pose estimation}.
The main idea of our approach is to directly reconstruct the depth map from an input RGB hand image in the absence of paired RGB and depth training images.
Given an RGB image, our depth-map reconstruction module aims to infer its depth map.
Our hand pose estimation module takes RGB and depth information into account to infer the $3$D hand pose.
In the hand pose estimation module, we infer the $2$D hand keypoints on the input RGB image, and regress the $3$D hand pose by using the inferred 2D keypoints.
The depth map is then used to regularize the inferred $3$D hand pose.
Unlike most existing $3$D HPE models, the real depth maps used to train our DGGAN model do not require any paired RGB images.
Once DGGAN is learned, the proposed HPE module directly infers the hand pose by using an RGB image and guided (regularized) by a DGGAN-inferred depth map.
Since the depth-map can be inferred by our depth-map reconstruction module, the proposed DGGAN no longer requires paired RGB and depth images.
Our DGGAN jointly trains the two modules in an end-to-end trainable network architecture.
Experimental results on multiple benchmark datasets demonstrate that our DGGAN not only reconstructs the depth map of an input RGB image, but also significantly improves the $3$D hand pose estimator via an additional depth regularizer.

The main contributions of this study are summarized as follows:
 \begin{enumerate}

    \item We propose a depth-map guided adversarial neural networks (DGGAN) for $3$D hand pose estimation from RGB images. Our network can jointly infer the depth information from input RGB images and estimate the 3D hand poses.

     \item We introduce a depth-map reconstruction module to infer the depth maps from input RGB images while learning to predict $3$D hand poses. Our DGGAN is trained on readily accessible hand depth maps that are not paired with RGB images.

     \item Experimental results demonstrate that our approach achieves new state-of-the-art in $3$D hand pose prediction accuracy on three benchmark datasets, including the RHD, STB, and MHP datasets.
 \end{enumerate}

\section{Our Approach}
\label{sec:method}

Our goal is to estimate the $3$D hand pose from a monocular RGB hand image.
Although the existing state-of-the-art methods~\cite{boukhayma20193d,icppso, Yuan_2018_CVPR} have shown that training networks with RGB and depth images can improve the $3$D hand pose estimators, few $3$D hand pose datasets consist of paired RGB and depth images.
To deal with the lack of paired data issue, we propose a novel adversarial neural network, called depth-map guided generated adversarial networks (DGGAN) illustrated in Figure \ref{fig:GAN}, which can jointly learn to infer the depth map from an RGB image of hand and to estimate $3$D hand pose.
In the following, we give an overview of the proposed DGGAN and describe the two major modules of DGGAN in detail.

\subsection{Overview of DGGAN}

The proposed DGGAN consists of two major modules, a {\em depth-map reconstruction} module and a {\em hand pose estimation} module.
Its network architecture is shown in Figure~\ref{fig:GAN}.

Given an RGB hand image $\mathbf{I}$, we want to estimate the $K$ $3$D hand joint locations $\mathbf{J}^{xyz}\in \mathbb{R}^{3 \times K}$.
Each column in the $3 \times K$ matrix is a vector of size $3$ and represents the $(x,y,z)$ coordinates of a joint, i.e., $\mathbf{J}^{xyz} = [J^{xyz}_1, J^{xyz}_2 , \ldots , J^{xyz}_K ]$.

\begin{figure}[t]
\hspace{-0.5cm}
		\begin{tabular}{c}
		\includegraphics[width=0.48\textwidth]{depth_module.png}

		\end{tabular}
	\caption{Network architecture of the depth-map reconstruction module.}
	\label{fig:depth_module}
\end{figure}

The two modules in the proposed DGGAN $G$ are trained by using the GAN loss $\mathcal{L}_{GAN}$ and the task loss  $\mathcal{L}_{task}$, respectively.
The objective of learning $G$ is formulated as a min-max game:
\begin{equation}
   G^* = \arg \min_{G}\max_{D}(\lambda_{t} \mathcal{L}_{task} + \lambda_{g} \mathcal{L}_{GAN}),
\end{equation}
where $\lambda_{t}$ and $\lambda_{g}$ control the relative importance of these two loss terms.

\begin{figure*}[t]
	\begin{center}
		\begin{tabular}{c}
		\includegraphics[width=0.9\textwidth]{task.png}

		\end{tabular}
	\end{center}
	\caption{Architecture of the hand pose estimation module.
	%
	This module takes paired RGB images and inferred depth maps as inputs.
	%
	$2$D CPM consumes an RGB image as input and produces the hand joint heatmap.
	%
	The joint heatmap is fed to the regression network to estimate the $3$D joint locations with the aid of a depth regularizer.
	%
	The depth regularizer reconstructs the depth map from $3$D joint locations and is trained using L1 loss and the GAN-synthesized depth map as guidance.}
	\label{fig:task}
\end{figure*}

Given an RGB hand image, our depth-map reconstruction module tries to generate its corresponding depth map.
A set of unpaired training depth images is adopted to train the depth-map reconstruction module so that its inferred depth maps are similar to real ones.
To achieve that, the discriminator in this module works on distinguishing real depth maps from fake (generated) ones.
Section~\ref{sec:depth_module} describes the details of depth-map reconstruction.
The depth map inferred from the depth-map reconstruction module together with the input RGB image are fed to the hand pose estimation module for estimating the $3$D hand pose.
In the hand pose estimation module, the input RGB image is used to regress the $3$D hand pose.
The inferred depth-map is adopted to regularize the predicted $3$D hand pose.
The loss for hand pose estimation $\mathcal{L}_{task}$ is adopted for optimization.
Section~\ref{sec:3dpose} describes the details.

\subsection{Depth-map Reconstruction Module}
\label{sec:depth_module}

The depth-map reconstruction module aims at relaxing the requirement of paired RGB and depth images during training.
This module is constructed via an adversarial network that infers the depth map according to an input RGB image.
Figure~\ref{fig:depth_module} shows the network architecture of this module.
In the training phase, our network requires both depth and RGB training images.
Nevertheless, the RGB and depth images do not need to be paired.
We consider the process of inferring depth map from its corresponding RGB image as an unsupervised adaptation problem, where the RGB modality $S$ and depth modality $T$ are both provided.
We are given a set of RGB images $X_S$ and a set of real depth maps $X_T$.
To translate from $S$ to $T$, we adopt an encoder-decoder architecture $G_{S\rightarrow T}$.
The generator $G_{S\rightarrow T}$ is trained to generate a realistic depth map to fool the discriminator $D$ while $D$ id derived to distinguish the real data $x_t$ and generated fake data $G_{S\rightarrow T}(x_s)$.
The loss for the depth-reconstruction modules is as follows:
\begin{equation}
\begin{split}
\mathcal{L}_{GAN}&(G_{S\rightarrow T}, D, X_S, X_T) = \\ &\mathbb{E}_{x_t\sim X_T}[\log D(x_t)] + \\
&\mathbb{E}_{x_s\sim X_S}[\log(1 -  D(G_{S\rightarrow T}(x_s)))].
\end{split}
\end{equation}

This loss also provides semantic constraints to force the generator to produce more realistic depth maps.
By taking as input unpaired RGB and depth images, our depth-map reconstruction module becomes applicable to vastly more hand pose datasets.
Furthermore, we can train the network with a large amount of unpaired RGB and depth images.

\subsection{Hand Pose Estimation Module}
\label{sec:3dpose}

Given an inferred depth map computed by the depth-map reconstruction module, we combine it with the input RGB image and feed both to the hand pose estimation module.
The network architecture of the hand pose estimation module is shown in Figure~\ref{fig:task}.
The hand pose estimation module calculates the task loss $\mathcal{L}_{task}$, which is composed of two terms $\mathcal{L}_{task} = \mathcal{L}_{2D}+\mathcal{L}_z$.
The $3$D hand regression loss $\mathcal{L}_{2D}$ and depth regularization loss $\mathcal{L}_z$ are described in section~\ref{sec:regrssion} and~\ref{sec:depth}, respectively.

\subsubsection{3D Hand Pose Regression}
\label{sec:regrssion}

Previous studies~\cite{cai2018weakly} show that depth information can be used to build a powerful regularizer.
We leverage the depth regularizer for improving the result of $3$D HPE.
Unlike most previous works where the ground-truth depth maps are needed, our model uses a synthetic depth map generated by the depth-map reconstruction module.
Our experimental results show that training with such synthetic depth maps substantially helps improve the result of direct regression.

$3$D hand pose regression takes an RGB image and an inferred depth map as input and outputs joint locations in two steps.
In the first step, we adopt a popular variant of the CPM architecture~\cite{cao2017realtime, wei2016convolutional} as the $2$D joint location predictor.
This predictor consists of six stages.
Each stage contains seven convolutional layers followed by a Rectified Linear Unit (ReLu).
It predicts $K$ heatmaps $\{H_s^k\}_{k=1}^K$ for $K$ different hand joints.
The pixel value in $k^{th}$ heatmap at stage $s$, $H^k_s$, indicates the confidence that the $k^{th}$ joint is located at this position.
Following the convention \cite{wei2016convolutional}, the ground-truth heatmap is denoted as $\{H_*^k\}_{k=1}^K$.
Each $H^k_*$ is the Gaussian blur of the Dirac-$\delta$ distribution centered at the ground-truth location of $k^{th}$ joint.
We train this part of Hand Pose module by standard backpropogation and the mean square error (MSE) loss.
In addition to the MSE loss, we add the intermediate supervision for each stage.
The final loss for $2$D location prediction is
\begin{equation}
    \mathcal{L}_{2D} =\frac{1}{6K} \sum_{s = 1}^{6} \sum_{k = 1}^K||H_s^k - H_*^k||_F^2.
\end{equation}

In the second step, the regression network takes the heatmap from CPM as input, and outputs the relative depth.
Its architecture is a mini-CPM (one stage instead of six) followed by three fully connected layers.
${Z} \in \mathbb{R}^{K \times 1}$ denotes the relative depth of each hand joint.
We employ smooth L1 loss between ${Z}$ and the ground-truth ${Z}^*$.
The loss of depth regression $\mathcal{L}_z$ is summarized as follows:
\begin{equation}
    \mathcal{L}_{z} =  \frac{1}{K} \sum_{k = 1}^K
    \left\{
    \begin{aligned}
       \frac{1}{2}({Z}_k - {Z}_k^*)^2, \mbox{ if } |{Z}_k - {Z}_k^*| \le 0.5\\
       |{Z}_k - {Z}_k^*|, \mbox{ otherwise}.
    \end{aligned}
    \right.
\end{equation}

\subsubsection{Depth Regularizer}
\label{sec:depth}

To provide supervision on every pixel on a depth map, we employ the depth regularizer (DR) proposed in \cite{cai2018weakly}.
The depth regularizer takes the relative depth as input and predicts a relative depth map $D$.
It reshapes $Z \in \mathbb{R}^{K \times 1}$ to a $K \times 1 \times 1$ tensor, which is considered as a $K$-channel image input.
We then up-sample this image from $K$-channel with resolution $1 \times 1$ to $1$-channel with the original depth map resolution ($n \times m$) through the 6 layers of transposed CNN.

We take L1 norm between $D$ and the ground-truth relative depth map $D^*$ as depth regularizer loss $\mathcal{L}_{dep}$, i.e.,
\begin{equation}
    \mathcal{L}_{dep} = ||D - D^*||,
\end{equation}
where $D^*$ is obtained by input depth map $\hat{D}^*$ as follows
\begin{equation}
D^* = \frac{\hat{D}^* - \min \hat{D}^*}{\max \hat{D}^* - \min \hat{D}^*}.
\end{equation}
Note that, we only use the ground-truth depth map $\hat{D}^*$ during the initialization stage.
It would be replaced by DGGAN-generated depth maps once the initialization stage ends.

Combining the loss terms described in Section \ref{sec:3dpose} and Section \ref{sec:depth}, we summarize the loss function for the hand pose estimation module as
\begin{equation}
    \mathcal{L}_{task} =\lambda_{z} * \mathcal{L}_{z} + \lambda_{2D} * \mathcal{L}_{2D} + \lambda_{dep} * \mathcal{L}_{dep},
\end{equation}
where $\lambda_{z}$, $\lambda_{2D}$, $\lambda_{dep}$ control the importance of three different loss terms, respectively.

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

Research topics related to this work are discussed below.

\subsection{3D HPE from Depth Images}

$3$D HPE from depth mapshas been extensively studied.
Existing approaches in this field make noticeable advances~\cite{wan2018dense, Yuan_2018_CVPR, deng2017hand3d, Wu18HandPose,ge2018hand, Ge_2018_ECCV,li2018point}.
Wan~\etal~\cite{wan2018dense} propose a dense regression approach to fit the parameters of a deformed hand model.
Ge~\etal~\cite{ge2018hand, Ge_2018_ECCV} present PointNet\cite{Qi_2017_CVPR} to extract hand features and regress hand joint locations by referring to the extracted features.
Wu~\etal~\cite{Wu18HandPose} adopt the intermediate dense guidance map supervision to generate hand heatmaps.
Although the existing methods achieve very accurate estimation results, they typically rely on the hand data captured by high-precision depth sensors, which are still expensive to have in practice and usually require data collection in a lab environment.
Different from the models in the aforementioned methods,  our model performs inference on RGB data without the need of depth maps.

\begin{figure*}[h]
	\begin{center}
		\begin{tabular}{c}
		\includegraphics[width=0.7\textwidth]{GAN.png}

		\end{tabular}
	\end{center}
	\caption{Overview of the proposed DGGAN.
	%
	DGGAN consists of two modules, a {\em depth-map reconstruction module} shown in Figure~\ref{fig:task} and a {\em hand pose estimation module} shown in Figure~\ref{fig:task}.
	%
	The former module trained using the GAN loss aims at inferring the depth map of a hand based on the input RGB image and making the generated depth map looks realistic.
	%
	The latter module trained using the task loss estimates hand poses from the input RGB and the GAN-reconstructed depth images.
	%

	}
	\label{fig:GAN}
\end{figure*}

\subsection{3D HPE from Monocular RGB Images}

Due to the wide availability of RGB cameras, $3$D HPE from monocular RGB images is becoming increasingly popular in computer vision applications.
Many recent methods aim at estimating hand joint locations directly from a single RGB image \cite{cai2018weakly,iqbal2018hand,ge2019handshapepose,zb2017hand,mueller2018ganerated,chen2018generating,yang2018disentangling,boukhayma20193d,zhe2019Geo, tekin2019h+}.
Zimmermann \etal \cite{zb2017hand} use $2$D convolutional neural networks (CNN) to extract features from an RGB image, and regress the $3$D hand joint locations.
However, their method suffers from depth ambiguity due to the absence of depth information.
Developing the methods upon the work by Zimmermann~\etal, Iqbal~\etal~\cite{iqbal2018hand} and Cai~\etal~\cite{cai2018weakly} inherit and adopt a similar $2$D CNN architecture for extracting image features.
Iqbal~\etal use depth maps as intermediate guidance while Cai~\etal treat depth maps as a regularizer in a weakly supervised manner.
Though these two methods make substantial progress in terms of estimation accuracy, there currently exist few datasets that fulfill their requirement of paired depth maps and RGB images.
Ge~\etal~\cite{ge2019handshapepose} take one step further by predicting the hand mesh from an RGB image and then the 3D hand joint locations based on the mesh.
However, their method requires paired mesh information which is even rarer among all existing datasets.

Compared with these methods, our method also uses depth information during training, but it does not require any paired RGB images and depth maps.
Thus, it is much more flexible since it can consume RGB images and depth maps from different datasets or sources.

\subsection{3D Mesh Estimation from RGB Images}

To further enhance $3$D HPE~\cite{baek2019pushing,boukhayma20193d,ge2019handshapepose,joo2018total}, hand mesh estimation can be included. %
Namely, the model estimates not only the hand joints but also the hand surface mesh.
However these methods such as \cite{ge2019handshapepose} have a common drawback:
They require additional mesh annotations which are even more expensive to obtain than joint locations.
Thus, they are typically trained on synthetic datasets due to this limitation.
Seungryul~\etal~\cite{boukhayma20193d} introduce an iterative learning method to refine mesh shapes and achieve very good performance.
However, like $3$D hand joint locations, hand meshes highly rely on additional supervision from hand segment maps which are typically not available in nowadays hand pose datasets.
The method by boukhayma~\etal~\cite{boukhayma20193d} is the only extra-data-free method, but its performance is limited.

\subsection{GAN-based Image Translation}

Generating images using generative adversarial networks (GAN)~\cite{goodfellow2014generative} has gained remarkable progress.
Many approaches explore how to better manipulate images by applying GAN models \cite{hoffman2017cycada,isola2017image,zhu2017unpaired,Chen_2019_CrDoCo}.
Isola~\etal~\cite{isola2017image} propose the Pix2Pix network which translates label or edges maps to synthesized photos, reconstructs objects from edge maps, or colorizes images.
Zhu~\etal~\cite{zhu2017unpaired} introduce the cycle-consistent generated adversarial network (CycleGAN). %
CycleGAN uses the cycle consistency loss to disentangle the input and output pair and therefore does not need paired input.
Hoffman~\etal~\cite{hoffman2017cycada} propose cycle-consistent adversarial domain adaptation (CyCADA).
Compared to CycleGAN, CyCADA contains a segmentation loss. %
As a result, CyCADA not only translates images from one modality to another but also deals with a specific visual task.

Applying the generative adversarial model to RGB hand images for hand pose estimation is also gaining popularity.
Muller~\etal~\cite{mueller2018ganerated} introduce the geometry consistent GAN (GeoConGAN) to generate synthetic image data for training.
Chen~\etal~\cite{chen2018generating} propose the tonality-alignment generative adversarial networks (TAGAN) for producing more realistic images from synthetic images for hand pose estimator training.
However, these methods only focus on generating RGB images.
None of them generates depth maps for assisting hand pose estimator training.

\section{Experimental Results}
\label{sec:res}

For evaluation on the STB dataset, we choose PSO~\cite{pso}, ICPPSO~\cite{icppso}, and CHPR~\cite{chpr} as the baselines.
In addition, we select the state-of-the-art approaches, Z\&B~\cite{zb2017hand} and that by Cai~\etal~\cite{cai2018weakly} for comparison.

On the RHD dataset, we compare our method with Z\&B~\cite{zb2017hand} and that in~\cite{cai2018weakly}.
Also, on the MHP dataset, we compare our method to that in~\cite{cai2018weakly}.
Note that Cai~\etal~\cite{cai2018weakly} have not released their code yet.
We re-implement their method and report the results according to our implementation.

\subsection{Ablation Study}

For analyzing the effectiveness of the proposed DGGAN, we conduct ablation studies for DGGAN on three different datasets.
The detailed results are summarized in Table~\ref{table:3Dresult}.
Specifically, we conduct the experiments for the following three different settings:
\begin{enumerate}
\item Regression: It represents training the regression network only on RGB images and without any depth regularizer.
\item Regression + DR + DGGAN: We learne the depth-regularized regression network using RGB images with the depth maps generated by DGGAN.
\item Regression + DR + true depth map: We derive the depth-regularized regression network using RGB images with their paired true depth maps.
\end{enumerate}

To measure the effectiveness of the generated depth maps, we compare settings Regression and Regression + DR + DGGAN.
As illustrated in Table~\ref{table:3Dresult}, using the generated depth map significantly boosts the performance of the model in Regression.
The AUC $20$\_$50$ is improved by \textbf{0.043}, \textbf{0.024}, \textbf{0.011} on the RHD, STB, and MHP datasets, respectively.
The EPE mean is also considerable reduced by \textbf{13.2\%} and \textbf{19.7\%} and \textbf{7.3\%} on the RHD, STB and MHP datasets respectively.

To compare the generated depth map with the real depth maps, we conduct two more experiments.
Comparing results of Regression + DR + true depth map and Regression + DR + DGGAN shows that the generated depth maps are a key factor of performance boosting.
On the RHD dataset, training with the generated depth maps is only slightly worse than the true RHD depth maps by $0.02$ in AUC $20$\_$50$ and $1$ mm in EPE mean.
However, on the STB dataset, the results of training with generated depth maps are even better than training with the real depth maps (by $0.006$ in AUC $20$\_$50$ and $0.94$ mms in EPE mean).
This result is probable due to the fact that the depth maps collected from depth sensors are less stable and noisier than the depth maps collected from a $3$D simulator.
By training the DGGAN with unpaired high-quality depth maps from RHD, our generator can potentially reduce the noise, and further benefit the training in the hand pose estimation module.
It is worth noting that Regression + DR + true depth map requires the paired depth and RGB image.

In addition to the quantitative analysis, Figure~\ref{fig:RHDdepthcmp} and Figure~\ref{fig:STBdepthcmp} provide some examples for visual comparison between the generated and true depth maps on the RHD and STB datasets, respectively.
We can see that the generated depth maps are visually very similar to the ground-truth ones.
\subsection{Comparison with State-of-the-arts}
We select the state-of-the-art approaches~\cite{boukhayma20193d,cai2018weakly, panteleris2018using,Spurr_2018_CVPR,zhang20163d,mueller2018ganerated,zb2017hand} for comparison.
The comparison results are reported in Figure~\ref{fig:teaser} and Table~\ref{table:stoa}.
As shown in Figure~\ref{fig:teaser} and Table~\ref{table:stoa}, our approach outperforms all existing state-of-the-art methods.
Although the results of the method by Cai~\etal~\cite{cai2018weakly} come close to ours, we emphasize that our DGGAN has an crucial advantage of \emph{not} requiring any paired RGB and depth images.

redpajama_set_name: RedPajamaArXiv


Adult Education refers to training and teaching of adults. This type of education involves various settings such as a large classroom full of students at a university or small groups of professionals in the corporate world.
Most adult education programs are offered as master's degree. You must have a bachelor's degree to enroll in the master's program. However, your bachelor degree need not be in adult education.
Usually, aspirants of a graduate study in adult education have knowledge and experience in a particular field. An MS degree program builds upon this fundamental knowledge and makes you eligible not only to work in that particular field, but also teach skills related to it.
Students in an adult education masters degree program come from diverse backgrounds. Some have experience teaching in community colleges and providing educational counseling while others have provided continuing professional education in fields such as social services, health care, natural resources management and financial services.
To pursue a master degree in adult education, you must have a love for teaching, and strong interpersonal skills with diverse people.
The program is designed to help you teach adult students better. A master's degree in adult education includes subjects such as adult psychology, adult growth and development, adult education administration, adult education in social context, and program planning and development.
The employment outlook for adult educators is very good. These days many employers demand high levels of academic skills and like their employees to be up to date on the latest market trends. Many adults join these classes for personal enrichment.
After completing your graduate degree in adult education, you can work in various fields such as post secondary education (vocational schools, colleges, and universities), continuing professional education, adult basic education, corporate training and development, and many others.
In fact, an MS degree in adult education is a minimum requirement to teach in most schools and colleges these days.
As an adult education teacher, you can teach evening classes to people who are too old to attend high school. You can also opt to work part time in community groups, and private and religious organizations.
Adult education workers are often paid by the hour. According to Bureau of Labor Statistics, median pay of an adult educator is around $14.85 per hour and the national average is $ 49,627. The salary varies according to the subject you teach and the place where you teach.
An adult education graduate degree has excellent prospects. If you have a basic skill and you want to teach, consider enrolling for a masters' degree in adult education program.
A school that offers a good master's degree program in adult education is University of Phoenix. You can do on campus, online, or both. University of Phoenix is probably the best innovation in online learning. You can get their free no obligation information by clicking on the link.


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NEW YORK--(BUSINESS WIRE)--Fitch Ratings has affirmed its 'A' rating on the Greater Orlando Aviation Authority (GOAA), Florida's approximately $56 million special purpose facility taxable revenue bonds, series 2009 (the Rental Car Facility Project bonds). The Rating Outlook is Stable.
--Sizable underlying origination and destination (O&D) market: Orlando benefits from a sizable O&D market (95% of total enplanements), and is the largest rental car market in the U.S. by gross revenues. Visiting traffic is driven by the geographically dispersed nature of Orlando's business centers and theme parks, making rental cars an attractive and necessary transportation link. However, there is some vulnerability of demand for car rentals from exposure to the leisure market and more general declines in activity due to the ongoing economic downturn.
--Competitive customer facility charge (CFC) rate: The airport has a competitive $2.50 CFC rate with no anticipated increases through debt maturity in 2017. GOAA has the flexibility to modify its CFC rate at any time without the need for outside approval.
--Fully executed agreements: GOAA's facility benefits from car rental facility agreements executed by all rental car operators serving at the airport. The agreements run concurrent with the maturity of the CFC bonds.
--Conservative debt structure: GOAA has level debt service obligations with a short maturity of seven years, with coverage at or above 2 times (x) with no dependence on transaction growth. The bonds benefit from strong liquidity reserves, with $45 million in cash reserves as of Sept. 30, 2011 (up 55% from original review). No additional bonding is expected. However, there is some vulnerability from the limited nature of car rentals as the sole revenue stream available to bondholders.
--A considerable drop in airport traffic operations or in associated rental car transactions could adversely affect pledged revenue levels, and, without an increase in the CFC rate, could lead to degradation in coverage to levels inconsistent with the current rating level.
--Additional leverage on the CFC revenue stream to cover additional improvements to the consolidated rental car facility (CONRAC) could dilute bondholders and have a negative impact on the rating. However, additional capital needs are modest (current balances in the CFC account are sufficient to cover 70% of all estimated future project costs), and additional leverage is viewed as unlikely.
The 2009 bonds are a special limited obligation, payable solely from CFC payments received by GOAA. Bonds are also secured by interest earnings on available funds and other pledged funds. Revenues and other airport funds of GOAA are not pledged to the payment of the 2009 bonds.
Orlando's consolidated rental car facility was completed on-time and on-budget, and has been operational since April 2010. Orlando's CFC rate of $2.50 continues to compare favorably to CFCs charged at other large airports, and Fitch believes this rate level is more than sufficient to meet GOAA's special facility debt obligations going forward at current traffic levels. Orlando's CFC revenues totaled $22 million in fiscal 2010, up 1% from fiscal 2009. For the first 11 months of fiscal 2011 through August, CFC revenues are up 5.7% over a year prior. Under various stress scenarios, envisaging declines in transaction days of up to 15% over the next three fiscal years with modest 1% recovery per annum thereafter, pledged revenues generated by the CFC are sufficient to provide 2.1x or greater debt service coverage. Including the fully funded coverage account of $2.4 million, coverage increases to 2.3x. This stable, elevated level of coverage provides room for substantial downside risk, and is consistent with an 'A' category rating. GOAA's ability to increase the CFC with board approval only (no need to consult with County officials or concessionaires) provides further downside protection for bondholders.
The structural elements of GOAA's special facility bonds provide protection for bondholders. The bonds benefit from $45 million in fund balances, equaling 80% of the total amount of bonds currently outstanding. This includes a $5 million CFC stabilization fund (funded with CFCs already collected at time of issue), $2.4 million coverage fund (funded at time of issue with bond proceeds), $9.5 million debt service reserve fund (funded at time of issue with bond proceeds), and a $28.3 million facility improvement fund (funded with CFCs collected on a go-forward basis, up from $14.6 million a year prior). Furthermore, the existence of car rental facility agreements executed by all operators serving the airport also provides some certainty that most, if not all, car rental operators will serve at the airport facility. In the event of mergers between any of the car rental companies, (particularly involving DTG at present), there will be minimal impact on revenues for the CONRAC, as current contracts remain in place through 2017 (coterminous with bond maturity).
While the short maturity, low debt quantum, and structural protections of the special facility bonds allow for a relatively high rating ('A' being the highest rating Fitch has assigned to a car rental facility bond issue), inherent risks to this type of structure place upper limits on the rating. Concerns include the limited nature of car rentals as the only revenue stream available to service debt. Without recourse to airport revenues, it is difficult for the rating to rise above the 'A' category. Furthermore, demand for car rentals is exposed to the variability of discretionary spending in the current economic downturn. This is particularly true in Orlando, which is dependent on tourism and leisure activity for much of its visiting traffic.
--'Rating Criteria for Airports', dated Nov. 29, 2010.


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text:
Q: Concurrency in java 8 I am trying to learn - concurrency in java 8. Below are few versions with their performance-
List<Class1> getSequential(List<String> numList) {
    return numList
        .stream()
        .map(num -> getOneObjofClass1(num))
        .collect(Collectors.toList());
}

Sequential Time: 31.0 seconds
List<Class1> getConcurrentWithES(List<String> numList) {
    List<Class1> obj = new ArrayList<>();
    ExecutorService executorService = Executors.newFixedThreadPool(100);

        List<Future<Class1>> futures =
            numList
                .stream()
                .map(num -> executorService.submit(() -> getOneObjofClass1(num)))
                .collect(Collectors.toList());

        for (Future<Class1> future : futures) {
            obj.add(future.get());
    return obj;
}

Concurrent Time: 6.0 seconds
List<Class1> getConcurrentWithCompletableFutures(List<String> numList) {
    List<Class1> obj = new ArrayList<Class1>();
    numList
        .stream()
        .map(num ->
            CompletableFuture.supplyAsync(() -> getOneObjofClass1(num)).thenApply(obj::add))
        .forEach(CompletableFuture::join);

    return obj;
}

Concurrent Time With Completable Futures: 21.0 seconds
List<Class1> getConcurrentWithParallelStreams(List<String> numList) {
    return numList
        .parallelStream()
        .map(num -> getOneObjofClass1(num))
        .collect(Collectors.toList());
}

Concurrent Time With Parallel Streams: 11.0 seconds.
Why are versions with completable futures and parallel streams not giving me good performance as compared to concurrency with Executor service. (Size of input list is 20 values)

redpajama_set_name: RedPajamaStackExchange


Mozilla Firefox 3.0.11 0 out of 5 based on 0 ratings.
Fixed an issue where, in some specific cases, the bookmarks database would become corrupt.
See the Firefox 3.0.10 release notes for changes in previous releases.


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text:
All office and service activities of Bangladesh Road Transport Authority (BRTA) are being re-opened from tomorrow.
An office order issued by the BRTA on Sunday said that considering the current situation of coronavirus, the cabinet department has included the departments and agencies involved in revenue collection of the government under emergency services and strictly maintained all departmental offices and circle offices as per the instructions of the road transport and highways department. Necessary steps have to be taken to launch the service activities of.
A BRTA official told The Daily Star that services could be limited to areas where there is a risk of infection.
He also said that a notification in this regard is likely to be issued tomorrow.
Police have stopped more than 100 Dhaka-bound buses in Sirajganj

redpajama_set_name: RedPajamaCommonCrawl


Buccaneers General Manager Jason Licht took some heat for trading into the second round to take a kicker.
But part of the reason Licht didn't mind is that he thought Florida State's Roberto Aguayo was a first-round talent. Then again, Licht's confident in his drafts as a whole, as he thinks he ended up with three first-rounders this year and seven the last two years.
In an interview with Scott Reynolds of Pewter Report, Licht explained that they had first-round grades on not just cornerback Vernon Hargreaves III, but defensive end Noah Spence and Aguayo as well. Coupled with four players from last year's draft he had rated that highly (quarterback Jameis Winston, left tackle Donovan Smith, guard Ali Marpet and linebacker Kwon Alexander), the Bucs think they struck it rich.
Of course, it's possible that Licht just values the guys he chooses differently than others. But of the lot of them, the one that would raise the most eyebrows is Aguayo, not because he's a bad kicker, but because he's a kicker and they traded up to get him.
By picking up an extra fourth-rounder by trading down with the Bears, the Bucs had the ammunition to make the move for the Florida State kicker. And even though he got what he thought was a first-rounder with last year's fourth, not everyone is so successful, and Licht pointed to those numbers.
"In the 2005-15 drafts in the fourth rounds, there were 390 players drafted in the fourth round," Licht said. "Twenty percent of those went on to become starters or Darren Sproles-type players. I consider Sproles as a starter even though he doesn't really start. He's a key player. Thirty-seven percent are career backups like Louis Murphy.
If you go by that logic, his move makes a degree of sense, but it's clear that the Bucs were also trusting their guts on this one. When they're all first-round talents, you might as well.
I think I won the lottery this year.
Licht opened himself up to a storm of controversy by drafting Aguayo. He's going to be on display every week and people are going to expect a Gostkowski / Vinatieri type performance on day one.
We'll know in a few years if he's right.
Of course he will probably be gone by then.
……Licht will always be LINKED to Aguayo forever …..THAT will be HIS legacy …not Winston or Kwon but by a pretty darn good placekicker…….
@jonathankrobinson424, I'm pretty sure most people will forget about the kicker in a year or so, his legacy is tied in with Winston for sure. But even if everyone went back to picking a kicker in the 2nd rd. from what I've heard about the guy he wouldn't care if the team was winning anyway.
@Kelly's Chronicles: Yeah, those 4th rounders were the reason you won consistently, not because of Favre and Rodgers. I get the point you're trying to make, those aren't bad players you listed, we all just grow tired of hearing what a genius Ted Thompson is, because he isn't. Ron Wolf was a great GM, Thompson is average. Need proof? Just think back to when A-Rodge was hurt for a few games and the entire team was hot garbage. It's easy for players to look better than they are when there's a HOFamer manning the helm.
His first 4 picks last year were all starters with 3 potential pro-bowlers. I expect at least 2 maybe 3 this year so I agree with him. For those still calling him a backstabber, even Ronde Barber is on record as saying it was the right move to can Lovie.
Time for a random drug test.
I'm thinking Licht is an easy grader. If you give a first round grade to 70 players every year it's pretty easy to get 3 or 4 "1st rounders" every year. Will be fun to look back at how many multi-year starters (for other teams) that he passed on to pick a kicker in round 2.
That's like 127%…. What a clown.
Really hoping jdphx was just having math-fun with all of us or it's a "shake my head" moment for humanity. 20% starters; 37% career backups; 43% out of the NFL (with 27% never found the NFL field of THOSE last 43%).
2014 – Mike Evans, Sefarian Jenkins, Charles Sims.
Bash him all you want, but his track record since day 1 has been great.
I'm sure they also drafted a long snapper they felt was right on the cusp of a first round grade, he just didn't want to brag.
Jason is the man, you must not truly pay attention to bucs football if you think differently.
Every GM thinks they got good picks or they wouldn't have picked them. Winston was legit #1 pick last year, and Hargreaves was a easy first round talent. Those were the only picks I thought were first round type guys. In 2014 I loved Mike Evans, and I thought the RB Sims was a steal. Anyone can become a starter on a bad team, so I can't judge by that. Let's see how the wins and losses turn out.
No, the goal is to put together a winning program. It doesn't matter how many first round guys you got unless you win with them. Ryan Leaf and Trent Richardson were first round guys, that doesn't mean much..
win games with your kicker, not just survive them. not drafted a round too high; trading picks questionable. Go Bears.


text:
"Persians," An Ancient Play Remade for the 21st Century
Director Anne Bogart on remaking the Western world's oldest play for the 21st century.
Ralph Flores | July 1, 2014
Seeking Shelter: A Story of Greek Refugees and the Virgin Episkepsis
What dramatic stories could this Byzantine icon tell?
Maria G. Psara | May 15, 2014
Five-Ton Wheel Takes Center Stage for "Prometheus Bound"
A five-ton steel wheel has craned into the Getty Villa for this fall's outdoor theater production.
Annelisa Stephan | July 17, 2013
Getty Voices: Backstage at the Villa
What does it take create live theater?
Valerie Tu | July 15, 2013
In Search of Euripides' Helen
A video discussion on the dramaturgy of Euripides' Helen.
Mary Louise Hart | September 21, 2012
An Interview with the Creative Team behind Euripides' "Helen" at the Getty Villa
Video interviews of the stars, director, and playwright behind the production of Euripides' Helen.
Desiree Zenowich | August 30, 2012
Unmasking Scandal at Villa Theater Lab
Villa Theater Lab invites performers to work in residence at the Getty Villa for two weeks, workshopping new theater pieces and presenting them in...	Read More
Steve Saldivar | March 21, 2012
Fire and Ice: Artists Get Ready for the Pacific Standard Time Festival
From January 19 to 29, the Pacific Standard Time Performance and Public Art Festival will present more than 30 new public art commissions and...	Read More
Glenn Phillips | January 12, 2012
A Living Artifact: "Trojan Women (after Euripides)" Premieres Tonight
Tonight at 8:00 p.m., the Getty Villa becomes a stage for the premiere of Trojan Women (after Euripides). It's the culmination of years of...	Read More
Desiree Zenowich | September 8, 2011
Nothing Happens, Everything Happens: Perspectives on "Trojan Women (after Euripides)"
"People don't understand why Trojan Women is such a great play, because they say nothing happens," says director Anne Bogart, explaining why SITI Company...	Read More
SITI Company on "Trojan Women (after Euripides)" at the Getty Villa
SITI company premieres a newly commissioned adaptation of Euripides' Trojan Women at the Getty Villa on September 8. "We've been working for 20 years to...	Read More
Reimagining Euripides: A 21st-Century "Trojan Women" at the Getty Villa
First performed over 2,400 years ago, Euripides' Trojan Women is one of the most enduring and moving of classical dramas—and one of the greatest...	Read More


Stone retaining wall systems over three feet in height generally consist of a concrete masonry retaining wall system that is simply faced with large natural stones or stacked boulders.
If you are seeking a solution more attractive than plain modular block walls for instance, and your budget affords it, stone retaining walls will enhance the aesthetics of the surrounding environment.
But be aware that definitions of precisely what constitutes a stone retaining wall vary from company to company; nearly as much as the building standards each employs. This makes choosing the best retaining wall company to install your new or replacement retaining wall paramount.
Some retaining wall companies will claim stone retaining walls must be made from cut stones and then pieced together to form a wall, while others define stone retaining walls as anything faced with a stone or having a stone-like veneer. That includes retaining walls with flagstone, synthetic stones and stones which have been cut in half then mortared to a CMU wall.
Stone retaining walls are among the most expensive to build; so, to ensure a satisfactory outcome with such a significant and permanent fixture is to thoroughly investigate the retaining wall companies from whom you request estimates.
Carefully examine their image gallery of work. Request to visit one of the retaining walls from their image gallery that is most like one you want built.


The Metéora (Greek: Μετέωρα, pronounced [mɛˈtɛoɾɐ], lit. "middle of the sky", "suspended in the air" or "in the heavens above" — etymologically related to meteorology) is one of the largest and most important complexes of Greek Orthodoxmonasteries in Greece, second only to Mount Athos. The six monasteries are built on natural sandstone rock pillars, at the northwestern edge of the Plain of Thessaly near the Pineios river and Pindus Mountains, in central Greece. The nearest town is Kalambaka. The Metéora is included on the UNESCO World Heritage List under criteria.
The Theopetra caves 5 kilometres (3.1 mi) south of Meteora had inhabitants fifty millennia ago.The oldest known example of a man-made structure was found within a prehistoric cave in central Greece, according to the Greek culture ministry. The structure is a stone wall that blocked two-thirds of the entrance to the Theopetra cave near Kalambaka on the north edge of the Thessalian plain. It was constructed 23,000 years ago, probably as a barrier to cold winds. "An optical dating test, known as Optically Stimulated Luminescence, was applied on quartz grains nested within the stones. We dated four different samples from the sediment and soil materials, and all provided identical dates," Nikolaos Zacharias, director of the laboratory of archaeometry at the University of Peloponnese, told Discovery News. According to a statement by the ministry of culture, "the dating matches the coldest period of the most recent ice age, indicating that the cavern's inhabitants built the stone wall to protect themselves from the cold." Excavated since 1987, the Theopetra cave is well known to palaeontologists as it was used and inhabited continuously from the Palaeolithic period onwards (50,000 to 5,000 years ago).
In the 9th century, an ascetic group of hermit monks moved up to the ancient pinnacles.
They were the first people to inhabit Metéora. They lived in hollows and fissures in the rock towers, some of which reach 1800 ft (550m) above the plain. This great height, combined with the sheerness of the cliff walls, kept away all but the most determined visitors. Initially the hermits led a life of solitude, meeting only on Sundays and special days to worshipand pray in a chapel built at the foot of a rock known as Dhoupiani. As early as the 11th century AD hermit monks were believed to be living among the caves and cutouts in the rocks.
The exact date of the establishment of the monasteries is unknown. By the late 11th and early 12th centuries, a rudimentarymonastic state had formed called the Skete of Stagoi and was centered around the still-standing church of Theotokos (mother of God). By the end of the 12th century, an ascetic community had flocked to Metéora.
In 1344, Athanasios Koinovitis from Mount Athos brought a group of followers to Metéora. From 1356 to 1372, he founded the greatMeteoron monastery on Broad Rock, which were perfect for the monks; they were safe from political upheaval and had complete control of the entry to the monastery. The only means of reaching it was by climbing a long ladder, which was drawn up whenever the monks felt threatened.
At the end of the 14th century, the Byzantine Empire's 800-year reign over northern Greece was being increasingly threatened byTurkish raiders who wanted control over the fertile plain of Thessaly. The hermit monks, seeking a retreat from the expandingTurkish occupation, found the inaccessible rock pillars of Meteora to be an ideal refuge. More than 20 monasteries were built, beginning in the 14th century. Six remain today.
In 1517, Nectarios and Theophanes built the monastery of Varlaám, which was reputed to house the finger of St John and the shoulder blade of St Andrew.
In 1921, Queen Marie of Romania visited Meteora, becoming the first woman ever allowed to enter the Great Meteoron monastery. In the 1920s there was an improvement in the arrangements. Steps were cut into the rock, making the complex accessible via a bridge from the nearby plateau. During World War II the site was bombed. Many art treasures were stolen.
Until the 17th century, the primary means of conveying goods and people from these eyries was by means of baskets and ropes.
Six of the monasteries remain today. Of these six, four were inhabited by men, and two by women. Each monastery has fewer than 10 inhabitants. The monasteries are now tourist attractions.
Average elevation is 313 metres (1,027 ft).
Studies suggest that the pinnacles were formed about 60 million years ago during the Paleogene Period. Weathering and earthquakes then shaped them into their present shape.
Beside the Pindos Mountains, at the western region of the Thessaly, in the middle of northern Greece, these sandstone rocks rise from the ground. The rocks are composed of a mixture of sandstone and conglomerate. They were formed about 60 million years ago. A series of earth movements pushed the seabed upwards, creating a high plateau and causing many fault lines to appear in the thick layer of sandstone.
Continuous weathering by water, wind and extremes of temperature turned them into huge rock pillars, marked by horizontal lines which geologists maintain were made by the waters of a prehistoric sea. Greek historian Herodotus wrote in the 5th century BC that local people believed the plain of Thessaly had once been a sea. If this was accurate, there was most probably an inundation at the end of the last Ice Age, around 8000 BC. However, he failed to mention the rocks of Metéora and they are not recorded in the writings of other ancient Greek authors. This has led to the belief that the pinnacles did not exist 2000 years ago; a theory dismissed by modern geologists.
The cave of Theopetra is located 4 kilometres (2.5 mi) from Kalambaka. Its uniqueness from an archeological perspective is that in it contains, within a single site, the records of two greatly significant cultural transitions: The replacement ofNeanderthals by modern humans, and the later transition from hunter-gathering to farming after the end of the last Ice Age. The cave consists of an immense 500 square metres (5,400 sq ft) rectangular chamber at the foot of a limestone hill, which rises to the northeast above the village of Theopetra, with an entrance 17 metres (56 ft) wide by 3 metres (9.8 ft) high. It lies at the foot of the Chasia mountain range, which forms the natural boundary between Thessaly and Macedonia prefectures, while the Lithaios River, a tributary of the Pineios River, flows in front of the cave. The small Lithaios River flowing literally on the doorsteps of the cave meant that cave dwellers had always easy access to fresh, clean water without the need to cover daily long distances to find it. Excavations and research and have discovered petrified diatoms, which have contributed to understanding the Palaeo-climate and climate changes.Radiocarbon dating evidences human presence dating back 50,000 years. The cave is open to the public.


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Uiha è un'isola ed un distretto delle Tonga della divisione di Haʻapai con  abitanti (censimento 2021).

Località
Di seguito l'elenco dei villaggi del distretto:

Uiha -  abitanti
Felemea -  abitanti
Lofanga -  abitanti

Note

Collegamenti esterni

Uiha
Isole delle Tonga

redpajama_set_name: RedPajamaWikipedia


The Philippines is known for their stunning beaches, like Boracay and Palawan! But when these famous beaches get too stuffy, there are also hidden beaches in our country which are just as beautiful!
As an archipelagic country, the Philippines has a wide variety of natural wonders, some are even world-class. Tourists love visiting the country to experience the magnificent land and water forms our country has to offer.
The Philippines is known for its gorgeous world-class beaches like Boracay, Puerto Princesa, and El Nido. Summer or not, foreign and local tourists spend their vacation on these islands.
But if you prefer some peace and quiet time by the beach, away from the crowded shores, or just want to explore some hidden treasures the country has to offer, you might enjoy staying at beaches that are less known.
Aurora is famous for its fine beaches. The province hosts thousands of tourists who enjoy the great beaches. Casapsapan Beach is the hidden gem waiting to be discovered. The roads might be a bit rough, but the beautiful scenery that awaits you is definitely worth it.
Casapsapan Beach's white sand and serene environment will surely entice tourists to stay. It has a long shoreline and fine, white sand at par with Boracay's. Expect the locals to be hospitable (and may even ask you to drink with them).
How to get there: You can have a deluxe ride at Genesis Bus Line to have an air-conditioned bus ride with reclining seats, free snacks, blankets, and onboard restroom. You can enjoy this no stopover ride to Baler, Aurora and take a mini-bus or a van to Casiguran.
Dubbed as the "Boracay of the North," Anguib Beach in Cagayan will mesmerize you with its clear waters and fine, white sand. Catch the waves and hit the beach with the sea facing the Pacific Ocean. The North offers the insta-worthy backdrop of the blue ocean, especially when it's sunset.
As you arrive at the beach, no entrance fee is charged. Although there are no outside activities or water sports available, this place makes you appreciate all the humble things in life.
How to get there: You can hire a van to climb to Sta. Ana, Cagayan, and drop by San Vicente Port to get on a boat to the beach. You can also ride a bus bound for Sta. Ana, Cagayan then take a tricycle from the town center to San Vicente Port. From there you can ride a boat to Anguib Beach.
Travelling on a budget is still enjoyable, especially when you go to Tambobong Beach. Unlike the crowded and commercialized beach in Bolinao, there are no expensive resorts on this beach. You can even camp out anywhere and enjoy the shade of the trees after a long drive on rough roads.
How to get there: Ride the bus to Alaminos, Pangasinan. Board another bus to Sta. Cruz, Zambales, and ask the driver to drop you off at Burgos Market. Ride the trick to Tambobong.
Masasa beach might probably be a familiar name. After all, it's just located in Batangas –just hours away from Manila. This beach is quite accessible to those who just want to get away from the city. The beach is famous for diving, aside from a great swimming spot. You can also camp out (even by the beach), or just rest it out and just sunbathe all you want.
How to go there: Ride a bus to Batangas City grand terminal. From there, take the jeepney to Anilao and get off at the Anilao Port or Talaga Port. Take the public boat to Tingloy Port/ Masasa Beach.
Found at the southernmost end of Oriental Mindoro is Tambaron Island. This secluded getaway is your complete island experience. You won't have an infinity pool and air-conditioned suites but you'll have the entire cove to yourselves. Enjoy the fresh seafood and do island hopping on the nearby island as well.
How to get there: Take a bus to Bantangas City Pier and board the RoRo vessel to Calapan. Ride a van to Roxas and from Roxas, take a van or bus to Bulalacao. A motorized boat will take you to Tambaron Island.
Even if the province is constantly struck with typhoons and located at the easternmost part of its region facing the sea, you can enjoy this Batanes-like cove in Catanduanes. Cagnipa Cove in Pandan not only provides a perfect place for skinny-dipping and swimming but also a great place to hike –because the cove is nearby Cagnipa Hills that will surely make you have a Batanes feels.
How to get there: Ride an airplane from Manila to Virac and ride a jeepney to Pandan. You can also ride a bus from Manila to Tobacco, Albay and ride a ferry from Tobacco seaport to Virac seaport to ride the jeepney to Pandan after.
Aside from the popularity of their mangoes (which are the sweetest in the country), Guimaras Island is also famous for Guisi beach. If you're looking for a private and secluded beach to enjoy for yourself, this might be for you. Spending a laid-back afternoon near the beach and eating mangoes make the experience worth it. This might be Guimaras' best-kept secret. You can also visit their historic lighthouse while you're at it.
How to get there: Ride the airplane to Ilo-Ilo. From the Ortiz Port in Ilo-ilo City, ride a ferry to Joran Wharf Guimaras then, take a jeepney ride to Guisi Beach.
We have a lot of beaches with different colors and kinds of sand. We have the fine and rough cream, white, even black sands, but we also have pink!
If there's the Pink Beach in Zamboanga, there's also the Sila Beach in Northern Samar. The pink-colored sand came from the pulverized remains of crab shells and coral skeletons damaged by weathering that blended in the white sand. You can also find rock formations nearby the emerald green top turquoise color of the sea.
How to get there: Book a flight to Tacloban, Leyte. Ride a van to Calbayog City which lasts for five hours and another van to San Isidro, Northern, Samar. From there, you will ride a boat to San Vicente.
Named after the towering karst cliffs in Busuanga that made the island look black, Black Island offers exciting adventures that you won't forget. Aside from the fine, white sand and waters that are so clear, you'll admire their coral garden –which Palawan is also famous for.
In addition, you can go inside the magnificent cave that boasts its stalactites and stalagmites that was enveloped by white silica. It also has its own natural pool inside.
How to get there: Book flight to Coron, Palawan. Take a van bound for the town proper from the airport.
Head down South, you can enjoy another beautiful paradise. Tawi-Tawi might give you chills because of its reputation, but rest assured this island will make you feel safe. Locals dubbed it as "Virgin Island" which is a place not known to many tourists (but is worth the visit).
Panampangan Island offers its powdery white sand and rows of coconut trees, along with its long sandbar. Although it is advisable you should only stay just for a day. You need to leave mainland early to enjoy your stay to the fullest because you need to go back before dusk.
How to get there: Ride an airplane to Zamboanga International Airport, and book another flight from there to Sanga-Sanga Airport in Bongao, Tawi-Tawi or take the ferry from Zamboanga to Bongao.
Where is your favorite among these hidden treasures? Let us know in the comments below!


T H E S I G N E T C O L L E C T I O N is inspired by British Heritage and tradition. This palm hand design was hand drawn by Reo herself and engraved onto the surface of a round signet ring.
M A T E R I A L S : This piece is made of sterling silver. All pieces are stamped with my REO Makers mark.


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Fair Pay to Play Act
Cade Clark, Staff Writer
On Monday September 30th, the Governor of California, Gavin Newsom signed a Fair Pay to Play Act. This means for the first time college athletes can be paid for their name and image. Although, with the act being passed, it does not guarantee colleges will pay their athletes, but they can get an agent who can manage their deals.
This goes against an NCAA ban where players are not allowed to receive any compensation besides their scholarships.
Now the Fair Pay to Play Act is making more of a national impact with U.S Representative Anthony Gonzalez. He is speculated to propose a national law that would give all college athletes the opportunity to make money from endorsements. Also other states like Colorado are following in California's footsteps. Colorado is working on creating a bill for next year that will make students eligible for earnings off their name and likeness.
Many athletes who are already playing at the pro level do not agree with the bill that are already at the pro level. They think that college should remain with no pay because they're still at college to pursue something, and making it pro should be the point where they begin to make money.
As Tim Tebow said in an interview with ESPN, "If I could support my team, support my college, support my university, that's what it is all about. But now we're changing it from 'us'…from being an alumni where I care, which makes college sports special, to then okay it's not about 'us,' it is not about 'we.' It is just about 'me.'"
Tebow added, "It changes what is special about college football. We turn it into the NFL, where who has the most money, that is where you go."
Tebow is basically saying that if colleges start paying them they will get more of a self-centered image than a team as a whole.
cade clark
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MV softball 2019


Conceptually, the three main screens are Television, Mobile and the Internet. The fourth screen – the most important – represents the world of brands as seen by the consumer across all media platforms. We also discuss what it means to be Customer-Centric, Moments of Truth, Customer Satisfaction, Customer Value Management and Inside-Out business management practices.
4ScreensMedia helps sort out the complexity of the traditional media mix and the social and digital media platforms of the Age of the Internet.
Our intent is to provide opinions and generate intellectual capital in the areas of strategy, process design, social (earned) media, WOM (word-of-mouth) marketing, business insight and transformation. 4ScreensMedia's sweet spot is where marketing and technology intersect.


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Пётр Гергардович Дик (1 января 1939, Глядень, Алтайский край — 14 августа 2002, Ворпсведе, Германия) — советский художник-график; член Союза художников России (1977), народный художник России (1999).

Биография
Родился 1 января 1939 года в селе Глядень, в Благовещенском районе Алтайского края в исторически сложившемся немецком поселении.

В 1962 году окончил Свердловское художественное училище им. И. Д. Шадра, а в 1973 году Московское высшее художественно-промышленное училище.

Дети: Александр 1965 г.р. Оставлен на попечение бабушки.

С конца 1960-х годов жил во Владимире. Работал в оригинальная технике — пастель и уголь по наждачной бумаге.

В 1991 году присвоено звание заслуженный художник РСФСР, а в 1999 году — народного художника России, а в 2001 году был удостоен российско-германской премии в области культуры.

Скончался скоропостижно 14 августа 2002 года в городе Ворпсведе, в Германии, накануне открытия своей персональной выставки. Похоронен во Владимире.

Творчество

С 1992 года работы художника экспонируются в Дюссельдорфе, Берлине, Мюнхене, Санкт Аугустине, Гамбурге, Поммерсфельдене и Нюрнберге, а в Эрлангене состоялись четыре его персональные выставки. Работы художника приобретены Баварским государственным фондом художественных коллекций в Мюнхене.

Произведения художника находятся в собраниях Третьяковской галереи, Русского музея, Владимиро-Суздальского историко-архитектурного художественного музея-заповедника, музеев Новосибирска, Омска, Орла, Твери, Тулы, Тюмени, в коллекции редакции журнала «Наше наследие», в частных собраниях в России и за рубежом.

Примечания

Литература
 Петр Дик. Художественное наследие. [Альбом] / Текст В. С. Турчина. Дизайн А. А. Рюмина. Москва, 2005
 Петер Дик. Территория тишины: [Каталог выставки] / Государственный Русский музей. Тексты А. Боровского, Л. Вострецовой. Дизайн Й. Киблицкого. Санкт-Петербург, 2012
 Петр Дик. Диалог с пастелью: [Каталог выставки] / Московский музей современного искусства, галерея «Г.О.С.Т.». Текст Е. Н. Судаковой. Дизайн С. Н. Андриевича. Москва, 2009

Ссылки
 Петр Дик — официальный сайт
 Дик Пётр Гергардович 1.01.1939 — 14.08.2002
 Дик Петр Гергардович (1939—2002)

Выпускники Свердловского художественного училища
Выпускники МГХПА имени С. Г. Строганова
Графики СССР
Графики России
Художники Владимира
Члены Союза художников СССР
Народные художники Российской Федерации
Похороненные во Владимире


ISLAMABAD: The government on Tuesday formally notified an immediate ban on import of furnace oil (FO) and ordered all the refineries to utilise billions of rupees in annual deemed duty collected on petroleum products to upgrade their refining facilities.
The government also ordered immediate reduction in the FO production to a minimum, and to enter into commercial agreements with power producers for utilisation of their capacity for FO storage, a statement issued by the Petroleum Division said.
In future, all refineries would ensure that FO production was the minimal by-product of Crude Oil processing, the government ordered. The oil refineries should undertake production of additional storage facilities as well as utilisation of the proceeds of deemed duty for up gradation/modernisation of their facilities.
In the notice, the government imposed ban on import of FO with immediate effect, except for the K-Electric.
Previously, in similar instances, the government, via the Economic Coordination Committee (ECC) and the cabinet, has allowed the K-Electric to run their plants on residual furnace oil (RFO) instead of gas from the Sui Southern Gas Company, and has picked up the difference of cost as subsidy under the Gas Load Management Plan (GLMP).
In light of the current situation, sources said the ECC and cabinet have been requested that decision for diversion of gas to other sectors should be reviewed and urged to maintain the allocated priority of gas to the power sector.
It has been requested, that in case gas was required to be diverted, running cost of equivalent MW of generation of RFO be subsidised, the sources added.
It is worth noting that the minister for power division has seen and authorised the submission of the summary to the ECC.
The decision taken to cut the import bill by banning fuel oil import would also impact the fragile power sector of the country.
In Pakistan, 54 percent of total power generation depends on oil or gas, while of these, 26 percent operate on re-liquified natural gas (RLNG).
"With the introduction of RLNG in the power system as fuel, sector dependency on imported RLNG as a fuel for generating electricity has increased manifold," sources said, adding that since water was not available in reservoirs, after coal, nuclear, and local gas, power plants were despatched RLNG.
They informed that no RFO generation had been envisaged for the months of November 2018 to March 2019.
However, they added that to maintain strategic reserve at RFO-based power plants, and to handle the high inventory of furnace oil at refineries, RFO and Low Sulphar Furnace Oil (LSFO) supplies were requested at Muzaffargarh, Jamshoro, HUBCO, KAPCO, at a steady rate. "This was communicated to the Petroleum Division on November 22, 2018," sources added.
The gas requirements presented during the CCOE meetings held on November 28, December 5, and December 26, 2018 were based on the forgoing parameters, and were 393, 495, 405, 857, and 1,043 MMCFD, they said.
But, during December 2018, only 180 to 200 MMCFD gas was allocated to the power sector, which was sufficient for generating only 1,200MW average instead of 2,600MW planned and projected.
This, the sources said resulted in a daily average of 1,600MW RFO-based generation through RLNG between December 6, 2018 and January 1, 2019.
Sources said the RFO consumption which started from 4,200 MT/day to about 18,000 MT/day till January 1, 2019, has adversely impacted the stock strategic building exercise for plants running during the high demand months.
This situation they cautioned would further aggravate with the diversion of gas required by the power sector to the other sectors, which were at low priority for gas.
"This diversion with the differential that has already been accrued due to running of RFO-based power plants will result in about Rs10 billion of differential to be passed on the end consumer in form of Fuel Price Adjustment till January 12, 2019," sources said.


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Illinois deer study reveals human preferences
Ralph Loos Editor
Chicago — A study on white-tailed deer and humans' perceptions of the animal has wrapped up with findings that say as much about us as it does about them.
For starters, people in northern Illinois who were asked about their preferred manner of controlling the deer population tended to pick archery hunting as the most acceptable and least controversial – no matter if they lived in an urban area or a rural area, and no matter if they felt they were sharing space with too many deer or with too few deer.
Also, the same people who said they were most open to archery being used to manage deer desired public participation in the deer management process.
"Whether they hunt them, just like them or don't like them at all, most people feel some sort of connection with deer," said Rachael Urbanek, who led the study, "Ecological and societal impacts of suburban white-tailed deer: a case study in the Chicago Metropolitan Area," as a doctoral candidate at Southern Illinois University-Carbondale. She studied and worked under the guidance of Dr. Clay Nielsen, associate professor of Forest
Wildlife in the SIUC Cooperative Wildlife Research Laboratory and Department of Forestry.
The four-year study, which wrapped up at the end of 2011, was based in suburban Chicago – mostly in McHenry and Lake counties – and utilized a multi-faceted approach to investigate common obstacles in suburban deer management.
"The main goal was to provide information that can be useful for wildlife management, for deer management," Nielsen explained. "We really wanted to provide deer managers information that will help them better manage deer."
More than 600 residents of McHenry County were surveyed about their thoughts on deer. The respondents were overwhelmingly cooperative.
"When you are working with deer in Illinois, it's obviously an issue that brings out emotion," Nielsen said. "In this study, we were dealing more with people and how they perceived deer. We weren't capturing or handling deer, which creates even more emotion."
Urbanek, who earned her Ph.D. and will continue her career this fall as assistant professor of wildlife science at Arkansas Tech University, pointed to results of the Illinois study that urges deer managers to be forward-looking.
"In my findings, I discuss the need for managers to examine suburban deer populations and management issues at a broader scale – countywide versus single community – and the promotion of proactive deer management in lieu of the conventional tendency of beginning management only when deer populations have become overabundant."
The timing of the study was extraordinary – battles over the management of deer in northern Illinois ratcheted up over the past two years as urban areas and forest preserves became overrun with deer while hunters in more rural areas complained about seeing fewer deer.
Sharpshooting and other culling programs run by DNR to manage the herd and monitor chronic wasting disease have been under attack by residents who fear human deer management has over-stepped its bounds.
The varying perceptions the public has about deer populations was another interesting aspect of the study. Urbanek said managers require lots of information as to why methods are acceptable or unacceptable by the public.
"Simply put, people preferring a decrease in deer density experienced heavy damage to personal property and enjoyed deer, but were concerned about the amount of deer," she said. "Respondents who enjoyed deer, and were not concerned about the amount of deer, were less likely to prefer a decrease."
There aren't a lot of tangibles when it comes to the public's perception of deer.
"Why would on person say there is too many deer and the guy next door say there are too few?" asked Urbanek. "That's the kind of question that's hard to answer. But it's a topic deer managers should consider when they are making management decisions. Basically, there is no way to predict. You really do have to survey. In other words, ask."
The SIUC study recommends that deer managers conduct surveys that incorporate conflict and perceived outcomes of deer management methods "to gain information that may guide public education and resolve management disputes."
Another aspect of the study, which was funded by the Lake County Forest Preserve District and the McHenry County Conservation District, was to develop recommendations to improve vegetation and deer monitoring programs.
From a deer management perspective, the study also analyzed the practicality and costs of deer density estimations – deer counts. The study looked at aerial and pellet-based distance sampling surveys.
"Wildlife biologists require density estimates for deer to properly manage them," Urbanek said. "Aerial surveys are often used to obtain density estimates, but are subject to problems."
Problems with bias and precision exist with both methods, Nielsen noted.
"Given accurate pellet decay and deposition rates and a large sample size of pellet groups, pellet-based may be advantageous due to less bias in density estimates, less-expensive survey costs, and no need for elaborate equipment," he said.
Urbanek's research included a look at the relationship between landscape characteristics and deer density.
"This information can be utilized to determine suburban lands that may be prone to high deer densities and inform land management practices," she said.
The deer study began years ago with a lot of background research.
SIUC's team surveyed deer biologists at state conservation agencies to compare opinions regarding urban and suburban deer management with those found in public surveys.
"We emailed a survey to deer biologists in 41 states to investigate what agencies are doing to control urban deer, which management techniques have been used in the past and are currently being used, and which techniques are believed to be most effective," Urbanek explained.
The replies?
Urban and suburban deer populations were increasing in three-fourths of the states and most biologists believed that urban and suburban deer were a problem in their state.
Sixty-five percent of biologists indicated they had not surveyed local communities for their opinions on deer and deer management. Nielsen said the study is 177 pages. Urbanek has already published a half-dozen papers from the research.
"There's a lot of work in those pages and a lot of information that we hope will be put to good use," he said. "Deer management isn't easy, and it's especially difficult in urban and suburban areas. One of the key factors is society – people."
Categories: Hunting News, Hunting Top Story, Social Media
Your Daily Wisconsin Outdoor News Update – January 15, 2021
Your Daily Minnesota Outdoor News Update – January 15, 2021
So you want to be an ice angler


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News National Madonna King: Baby Mia's death shows why we need a sensible solution for birds that attack
6:00am, Aug 13, 2021 Updated: 8:43pm, Aug 12
Madonna King: Baby Mia's death shows why we need a sensible solution for birds that attack
Baby Mia's tragic death must be a catalyst for change when it comes to managing birds, writes Madonna King. Image: TND
Madonna King
When I was a kid growing up in Central Queensland, we'd be made to wear ice-cream buckets on our heads to school.
My brothers and I would dawdle off, feeling protected from the magpies that made a vacant allotment in Roma their home for a couple of months each year.
On the back, Mum would paint a face – big eyes, a nose and a mouth – knowing that they would nosedive from behind, but aim for the back of the head.
I'm not sure if it worked or not. But this week, after a five-month-old Brisbane girl was killed as her mother ran from a magpie attack in a suburban park, the war waged on magpies needs to work.
Little Mia isn't the only death caused by a 300-gram, nose-diving magpie.
A few years ago, a Queensland boy was killed when ran into the path of car while fleeing a nose-diving magpie.
And twice in the past 24 hours, I've seen cyclists, in an 800-metre radius from my house, almost killed as they swerved on a busy road to avoid their attacker.
Am I wrong to want revenge? To want to track down the magpie responsible for Mia's death and ensure it doesn't have the chance to hurt anyone else?
"No,'' says one of the nation's top magpie experts Professor Emeritus Darryl Jones from Griffith University. "That's what anyone would think, especially when it involves children.''
He says that bird, however, had the same perspective; it simply wanted to protect its children.
But when a dog attacks a child, it is taken away and put down.
When a shark takes a surfer as prey, it is hunted. When a crocodile takes the life of a swimmer, who has invaded its habitat, it is shot. Why are magpies so special?
They're not, says Professor Jones, a behavioural ecologist.
Magpies were simply saved by calling city streets and suburban parks their home; it wasn't feasible to start shooting birds out of trees, or as they chased cyclists down streets.
Professor Jones has a point there. But he says taking magpies away from their home territory – as long as it was more than 70 kilometres away – had the same effect. They'd never find their way back.
Baby Mia's death should be a catalyst to do something.
In this Brisbane case, there had been numerous alleged complaints about the same magpie. In another local incident, the same magpie returns each year to wage war on four and five-year-olds walking to an inner-city school.
No one is saying magpies need to die (although before talking to Professor Jones that was my preferred position).
But authorities, responsible for heavily populated city areas, need to have a magpie management plan.
Parents and children – like Mia and her Mum – should be able to use a suburban park, without fear of attack.
This week, we've been saturated with cries that this is not the bird's fault. That we have encroached on their natural habitat.
Rubbish. Shouldn't a park, filled with kids swings and yoga lessons and walking tracks, be a young parent's natural habitat?
And what about a busy city road? Are the magpie apologists suggesting we sell our homes, and leave the murderous magpies to move in?
We are not talking about ridding the nation of a bird some people love. Only about seven per cent of magpies "even look sideways at someone'', according to Professor Jones.
The ferocity of attacks has not increased over time, and nor has the 'attack' season, which is the same from Hobart to Cairns each year.
Of that tiny fraction that attack people, about half will focus on pedestrians, and the rest are made up of "cyclist-only magpies and even postie-only magpies''.
"They'll hammer the posties up and down the street and not look at anyone else sometimes,'' Professor Jones says.
And they're clever. Although they will always come from behind, they may turn and aim for a cheek or an ear to avoid a cyclist's helmet.
I saw that repeatedly yesterday.
The ice-cream bucket might not work any more.
But removing offending birds from city scapes and "relocating'' them 70 kilometres away, research shows, will work.
It won't bring Mia back. But it should be the simple and sensible policy we implement to prevent another family going through the hell Mia's parents are now living.
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