text:
\section{Approach}
The work we are presenting is built upon the VoxelNet paradigm\cite{zhou2017voxelnet}.
Our end to end framework can be split into two parts, (1) instance detection and (2) instance processing.

\begin{figure*}[htb]
\centering
\includegraphics[width=\linewidth]{overview3.png}
\caption{Overview of the Point R-CNN framework.}
\label{fig:img_framework}
\end{figure*}

\subsection{Architecture}
Our framework is outlined in \autoref{fig:img_framework}.
The architecture can be split into several modules which are (1) per-voxel feature extraction, (2) voxel features aggregation, (3) instance detection, and finally (4) instance-wise processing, \ie in our case point to point regression. In the following sections we describe these modules in detail.

\subsection{Input preprocessing}

\begin{figure}[htb]
    \centering
    \includegraphics[width=\linewidth]{input_proc.pdf}
    \caption{The input of our architecture is the concatenation of the point clouds from several sensors. During this step we associate each point of our scene to a voxel which allows us to easily access a point and his neighborhood}
  \label{fig:input_proc}
\end{figure}
The input to our algorithm is the unstructured point cloud $\vec{P}=\bigcup \vec{P}_i$, where $\vec{P}_i$ denotes the point cloud acquired from sensor $i$. The point clouds acquired from all the sensors are assumed to be time-synchronized and registered within the same world coordinate system. We further assume our world coordinate system to be axis aligned with the ground plane with the $y$-axis being the ground normal.

As a first step we define an axis-aligned cuboid working space resting on top of the ground plane. All points outside this volume are discarded at this time.

In order to reduce the impact of variable point cloud densities across the scene and to speed up processing we down-sample $\vec{p}$ using a voxel grid filter to our working space. This filter merges all points that fall within one voxel into their centroid such that after filtering each voxel contains at most one point.

We then subdivide the working space into a different regular axis-aligned grid of (larger) voxels as follows. The origin of our working space is denoted by $\vec{o}$, the dimensions of each voxel $v_x$, $v_y$ and $v_z$, and the number of voxels along each axis $N_x$, $N_y$ and $N_z$. We also choose the number $T$ of points to be considered per voxel, in our case we chose $T=64$.

Each point $\vec{p}$ of the point cloud is now assigned to the voxel it falls in, denoted by the directional voxel indices
$id_x=\lfloor{(p_x-o_x)/v_x}\rfloor$,
$id_y=\lfloor{(p_y-o_y)/v_y}\rfloor$ and
$id_z=\lfloor{(p_z-o_z)/v_z}\rfloor$, where $0 \leq id_x < N_x$ \etc .

The voxel grid is then flattened by assigning a linear index to each voxel via
$$
id = id_z + id_y * N_z + id_x * N_z * N_y .
$$

After this grouping, each point is assigned the $id$ of the corresponding voxel.

Using the $id$ we previously computed for each point, we can find the list of unique $id$s of every voxel containing at least one point.
Since we already sampled and shuffled the whole point cloud, we just have to take for each voxel the $T$ first points with this $id$ and put those $T$ points in a tensor of size $(3, T, N_v)$, 3 being our input dimension and $N_v$ being the total number of voxel in the scene $N_x*N_y*N_z$.

Instead of using the world coordinate $\vec{x}$ of each points we use the relative position $\vec{x}'$ within its corresponding voxel, \ie
$$
\vec{x}'=\vec{x}-\left(\vec{o}+\begin{pmatrix}N_xv_x\\N_yv_y\\N_zv_z\end{pmatrix}\right) .
$$
This prevents the network from learning global aspects of the scene as opposed to the desired local structures,

We pad with zeros the voxel which do not have enough points.

\subsection{Instance detection}
Now that our scene is defined, we can regress the bounding cylinder for each instance (\ie person) in the scene. Our approach to do this is inspired by previous work on 2D instance detection and segmentation \cite{girshick2015fastrcnn, he2017maskrcnn}.

\paragraph{Per-voxel feature extraction.}
\begin{figure}[htb]
    \centering
    \includegraphics[width=\linewidth]{points_feat.pdf}
    \caption{Per-voxel feature extraction pipeline.
    Each point is processed using VFE\cite{zhou2017voxelnet} and get some information from the point in the same voxel. Finally, element-wise max pooling let us retrieve a feature vector per voxel.}
  \label{fig:point_feat}
\end{figure}

The first part of our architecture is using 2 stacked \emph{voxel feature encoding} (VFE) layers \cite{zhou2017voxelnet} in order to learn a voxel-wise set of features which is invariant to point permutation.
Those VFE layers are efficiently implemented as 2D convolutions with kernels of size 1.
We use a similar notation as in \cite{zhou2017voxelnet} for the VFE layers.
To represent the i-th VFE layer, we use VFE-i$(c_{in},c_{out})$ with $c_{in}$ and $c_{out}$ being respectively the dimension of the input features and the dimension of the output features of the layer. As in \cite{zhou2017voxelnet},
the VFE layer will transform its input into a feature vector of dimension $c_{in}\times(c_{out}/2)$ before doing the point-wise concatenation, which then yields the output of dimension $c_{out}$.

The VFE are VFE-1(3, 32) and VFE-2(32, 64) followed by a fully connected layer of input and output 64 right before the element-wise max pooling.
Having the VFE instead of solely using PointNet \cite{qi2017pointnet} helps add  neighborhood information to the points (defined by being every point in this voxel).
After the max pooling of the last layer, the output size is $(64, 1, N_v)$.
We can thus reshape our output in order to retrieve a 3D image of size $(64, N_x, N_y, N_z)$.

In this work we process every voxel. However, this could be sped up as shown in \cite{zhou2017voxelnet} by only processing non-empty voxels.

In order to aggregate information from the scene and learn multi-scale features from our scene, we use a DenseUNet \cite{denseunet}.
The first step makes our network invariant to point permutation. Then, when working on voxels instead of point clouds, we go from an unstructured representation to a structured one, which is easier to apply classic deep learning methods on. By doing those two steps we provide additional neighborhood information to each point at voxel and scene level. This is more efficient than processing the whole point cloud and looking for k-closest neighbor, and it gives better results than just processing the whole point cloud as a set of patches.

\paragraph{Instance detection.}
After the feature aggregation part we take the output and feed it to two parallel heads: one for the per-voxel classification (doing the detection) and one for the per-voxel bounding cylinder regression.

With the classification branch we want to find the voxel which should be used for the bounding cylinder regression.
This is done by classifying each voxel into one of two classes: containing the top of a cylinder or not.
At the moment we assume that each voxel can only have one bounding cylinder.
This could be extended by having several bounding cylinder per voxel, or a second network for refinement.

Since there are not many point cloud datasets where the instance segmentation and/or detection are provided together with the 3D joints of each instance we decided to work on datasets that provide at least the point cloud and the joint positions. The cylinders are then defined based on the joint positions for each person.

We define each cylinder axis as being aligned with the person's neck. The top of the cylinder is at the same height as the joint which is furthest from the ground (for this person). The radius of the cylinder is determined by the distance of the joint furthest from the neck axis of this person.

During training we use the ground truth of the classification to mask the voxel that should be used for back propagation while during testing and inference time we use the output of the classification branch to retrieve the voxel which contains the desired bounding cylinders, as in the RCNN family of frameworks.

The loss for this part is defined as:
\begin{equation}
\begin{split}
L(\vec{v}, \vec{b}) = \;& \frac{1}{N_{cls}} \sum\limits_{i} L_{cls}(v_{i}, v_{i}^*) \; + \\
                        & \lambda \frac{1}{N_{reg}} \sum\limits_{i}  v_i^* L_{reg}(b_{i}, b_{i}^*)
\end{split}
\end{equation}

Here $\vec{v}$ represents the output of the head of our network doing the classification and $\vec{b}$ represents the output of the head doing the bounding cylinder regression. Their respective ground truth is represented by $\vec{v^*}$ and $\vec{b^*}$.
$L_{cls}$ is a cross-entropy loss doing the classification of voxels.
For the regression loss, we use $L_{reg}(b_i,b_{i^*}) = R(b_i - b_{i^*})$ where $R$ is the robust loss function (smooth L1) defined in \cite{girshick2015fastrcnn}.

In the case of human detection only a relatively small number of voxels contain a cylinder to regress, so we need to re-sample our data.
Instead of working with $\vec{v}$ and $\vec{b}$ which is the entire output of our networks, we use every voxel containing an object across the batch, as many non empty voxels randomly chosen across the batch and the same amount of voxel across every voxel in the batch.
In the case where there is no human in our whole batch, we just pick 32 random voxels.
That way, we maintain a bias towards "empty" voxels, we are sure to use the voxels with data in them and we do not have to compute the whole loss on every empty voxel in our batch.

The two terms of the loss are normalized by $N_{cls}$ and $N_{reg}$ and weighted by a balancing parameter $\lambda$.
In our current implementation, $N_{cls}$ and $N_{reg}$ both set to the number of voxels used after the sampling while $\lambda$ is set to 1.

We use a normalization analog to the one presented in \cite[Equation 1]{zhou2017voxelnet}, \ie the cylinder coordinates are normalized relative to the voxel size. By transforming the cylinders back into our world coordinate system we can extract a point cloud consisting of all points within this cylinder.

\subsection{Instance wise processing}
\begin{figure}[htb]
    \includegraphics[width=\linewidth]{joints_reg.pdf}
    \caption{In this last part, we use the predictions from our two branches to retrieve the bounding cylinder and extract the point cloud of each instance. These points clouds are then fed into a PointNet \cite{qi2017pointnet} in order to regress the joints.}
  \label{fig:joints_reg}
\end{figure}
Using the extracted point cloud for each cylinder, we can set up a batch of instances to be processed by the last stage of our framework. During training we take at most 32 persons across the frames in the minibatch and for each person we sample at most 1024 points. If a person does not have enough points (32 in our case), we discard this instance.

If there are no extracted point clouds at all, \ie there was no detection at all, we skip this whole part and just compute the loss previously mentioned.
In our use case, we are doing joint regression from the point cloud using PointNet \cite{qi2017pointnet}

This framework could of course be modified to replace what should be regressed and how it is regressed.
The only normalization we do on those point cloud is to put them in a sphere located at the middle of cylinder with a radius of half the height of the cylinder.
The final loss of our whole framework is:
\begin{equation}
\begin{split}
L(\vec{v}, \vec{b}, \vec{j})= \;& \frac{1}{N_{cls}} \sum\limits_{i} L_{cls}(v_{i}, v_{i}^*) \; + \\
            & \lambda \frac{1}{N_{reg}} \sum\limits_{i}  v_i^* L_{reg}(b_{i}, b_{i}^*) + \\
            & \lambda_2 \frac{1}{N_{joints}} \sum\limits_{k=0}^{N_{inst}} L_{joints\_reg}(j_{k}, j_{k}^*)
\end{split}
\end{equation}

Here, we take the loss of the first part and add the loss of the regression part.
The loss for the joint regression $L_{joints\_reg}$ we use is Mean Square Error Loss calculated between the joints predicted $\vec{j}$ and the ground truth joints $\vec{j}*$. As stated before, we sample $N_{inst}$ through our batch for the regression part, this $N_{inst}$ being 32 during training.

If there are too many persons we pick the person to regress randomly across the batch. If there are not enough persons, we pad this batch with zeros. During testing, we use every detected bounding box instead. We add another balancing term $\lambda_2$ that we set to 1, $\lambda$ being set to 1 too.

The $N_{joints}$ is the total number of joints to regress across our $N_{inst}$ instances.
We show that using an end to end training on this whole framework improves the result compared to a two-stage solution trained separately.

\section{Conclusion}

In this work we have demonstrated through extended experiments that point cloud is the natural and straightforward alternative for a multi-sensor indoor system, where the fusion of multi-sensor information is efficient. Unlike conventional methods that use complex fusion models to combine information, which tend to generalize poorly, we show through various challenging real world scenarios that the proposed algorithm can generalize well. Furthermore, we propose an end-to-end multi-person 3D pose estimation network, ``Point R-CNN'', and show that the proposed network outperforms the simply cascaded model by large margins in various experiments. The study shows that using an end-to-end network greatly improves both object detection and joint regression performance.

\section{Experiments}
To simulate real world scenarios we designed four challenging experiments and compared our results with the baseline method. In particular, we train stage-wise state-of-the-art 3D point cloud based object detection via VoxelNet\cite{zhou2017voxelnet} and a state-of-the-art point cloud based regression network, namely PointNet\cite{qi2017pointnet}.

To the best of our knowledge no end-to-end solution is available for pure point cloud based 3D multi-person pose estimation. Thus for comparison we cascade the above mentioned state-of-the-art algorithms to perform each part, \ie 3D person detection and per-person joint detection. Each part is trained separately and the best model is chosen for final evaluation.

In order to simulate camera failures in real life scenarios all datasets used in first three experiments are conducted with a random number of camera inputs, \ie we randomly drop one or more of the camera inputs. Details are discussed in the respective experiment sections.

The first three experiments are conducted on the CMU Panoptic dataset\cite{joo2017pano}, which was created to capture social interaction using several modalities.
Here several actors are interacting in a closed environment and captured by a multitude of RGB and RGBD cameras. For our experiments we use only the depth images from the ten Kinect 2 RGBD sensors and show that point clouds alone are sufficient to accurately detect people and identify their pose.

The cameras in the CMU Panoptic dataset are placed in various positions on a dome over the room, all pointing towards the center. The dataset contains several different social scenes with 3D joint annotations of the actors. For our experiments we randomly choose four scenes to conduct our evaluation. Namely, ``160224\_haggling1'', ``160226\_haggling1'', ``160422\_haggling1'', ``160906\_pizza1''. For more information about the dataset, readers are referred to \cite{joo2017pano}.

The last experiment is conducted on the MVOR dataset \cite{srivastav2018mvor}, which was acquired over 4 days at a hospital operation room with a varying number of medical personnel visible in the scene and recorded by three RGBD cameras. The dataset captures a real world operation room scenarios, including cluttered background, various medical devices, random number of people at any given time, and ubiquitous occlusion caused by medical devices. The point clouds were cropped to a 4x2x3 m size cube and sampled using a voxel grid filter with a 2.5x2.5x2.5 cm grid size.

\subsection{Metrics}
To evaluate the overall performance of the algorithm we use both 3D object detection metrics, Average Precision (AP) on Intersection of Union (IoU) > 0.5, used in KITTI Vision Benchmark Suite \cite{Geiger2012CVPR} to evaluate the instance detection part of the algorithm, and per joint mean distance metrics, denoted as DIST for per joint distance and ACC for accuracy under 10cm threshold in the following sections. When calculating the distance, we only count the joints in true positive detections. And if there is duplicated detections, we only calculate the joints difference of the highest scored detection.

\subsection{View Generalization}
In this experiment, we simulate cameras being placed in different locations in the room to demonstrate the view generalization of the algorithm. We achieve this by using different cameras at training and test time. We show that our algorithm is robust when changing the view or location of the camera.

For our training dataset we randomly choose 8 camera inputs as our training dataset, namely camera 0, 1, 2, 3, 4, 6, 7, 9. The remaining cameras 5 and 8 are used for testing. Note that in both training and testing, we choose a random number of cameras at any given time to simulate camera failures.

The first experiment is conducted on scene ``160224\_haggling1'', ``160226\_haggling1'' and ``160906\_pizza1''. The training dataset has 6858 frames, uniformly down-sampled by a factor of 3. The testing dataset has 897 frames, and is down-sampled by a factor of 30.

As we can see from \autoref{tab_exp1}, even with severe camera view changes and camera failures, the proposed method performs well. Meanwhile, our end-to-end solution outperforms the baseline by a large margin, both from detection perspective and joint regression perspective.

\begin{table}[ht]
\centering
\begin{tabular}{lccccc}
\toprule
           & \multicolumn{2}{c}{{[}1{]} + {[}16{]}} & & \multicolumn{2}{c}{Point R-CNN} \\
\cmidrule{2-3} \cmidrule{5-6}
           & DIST & ACC & & DIST & ACC \\
           & [cm] & \%  & & [cm] & \% \\
\midrule
Neck       & 5.47  & 94.8 && \textbf{4.67}  & \textbf{96.7} \\
Headtop    & 10.55 & 52.1 && \textbf{9.28}  & \textbf{63.5} \\
BodyCenter & 7.14  & 85.5 && \textbf{6.89}  & \textbf{85.6} \\
Lshoulder  & 19.00 & 5.2  && \textbf{17.00} &\textbf{ 22.9} \\
Lhip       & 11.55 & 40.4 && \textbf{10.6}1 & \textbf{52.8} \\
Lknee      & 13.41 & 33.8 && \textbf{12.80} & \textbf{40.8} \\
Lankle     & 16.36 & 28.3 && \textbf{15.75} & \textbf{30.5} \\
Rshoulder    & 18.16 & 3.4  && \textbf{16.28} & \textbf{25.7} \\
Rhip       & 12.29 & 32.1 && \textbf{11.42} & \textbf{45.6} \\
Rknee      & 14.41 & 24.7 && \textbf{13.52} & \textbf{34.8} \\
Rankle     & 17.05 & 24.4 &&\textbf{ 15.95 }& \textbf{28.2} \\
\midrule
Mean       & 13.20 & 38.6 && \textbf{12.20} & \textbf{47.9} \\
\midrule
AP         &        \multicolumn{2}{c}{81.24}& & \multicolumn{2}{c}{\textbf{81.37}}\\

\bottomrule
\end{tabular}
\caption{View generalization: per joint distance in cm and accuracy < 10 cm.}
\label{tab_exp1}
\end{table}

\subsection{Actor generalization}
In this experiment, we explore the generalization problem in terms of the objects and scenes. In particular, we train the network with scene ``160224\_haggling1'', ``160226\_haggling1'' and ``160906\_pizza1'' and tested with scene ``160422\_haggling1'' with same camera view, \ie both training and testing data uses camera  0, 1, 2, 3, 4, 6, 7, 9 with random camera failure. The training dataset has 6858 frames, uniformly downsampled by factor 3. The testing dataset has 430 frames, down-sampled with factor 30. \autoref{tab_exp2} shows that the proposed method outperforms the baseline method by a large margin, especially for shoulders, hips and Knees.

\begin{table}[]
\centering
\begin{tabular}{lccccc}
\toprule
           & \multicolumn{2}{c}{{[}1{]} + {[}16{]}} & & \multicolumn{2}{c}{Point R-CNN} \\
\cmidrule{2-3} \cmidrule{5-6}
           & DIST & ACC & & DIST & ACC \\
           & [cm] & \%  & & [cm] & \% \\
\midrule
Neck       & 7.28  & 81.0 && \textbf{5.65}  & \textbf{89.3} \\
Headtop    & 12.10 & 38.2 && \textbf{8.40 } & \textbf{75.1} \\
BodyCenter & 7.24  & 81.7 && \textbf{6.50}  & \textbf{86.5} \\
Lshoulder  & 19.83 & 0.4  && \textbf{12.96} & \textbf{42.7} \\
Lhip       & 12.26 & 28.8 && \textbf{9.24}  & \textbf{63.6 }\\
Lknee      & 13.12 & 30.3 && \textbf{10.23} & \textbf{56.6} \\
Lankle     & 16.49 & 25.8 && \textbf{13.67} & \textbf{37.4} \\
Rshoulder    & 19.00 & 5.4  && \textbf{12.49} & \textbf{44.8} \\
Rhip       & 12.85 & 27.8 && \textbf{9.42}  & \textbf{64.4} \\
Rknee      & 13.95 & 27.2 && \textbf{10.22} & \textbf{56.6} \\
Rankle     & 17.71 & 17.6 && \textbf{13.82} & \textbf{36.3} \\
\midrule
Mean       & 13.80 & 33.1 && \textbf{10.24} & \textbf{59.4} \\
\midrule
AP         &        \multicolumn{2}{c}{80.65}& & \multicolumn{2}{c}{\textbf{90.54}}\\
\bottomrule
\end{tabular}
\caption{Actor generalization: per joint distance in cm and accuracy < 10cm. }
\label{tab_exp2}
\end{table}

\subsection{View and Actor generalization}
In this study, we demonstrate the evaluation under both view change and actor changes. We trained the network with scene  ``160224\_haggling1``, ``160226\_haggling1'' and ``160906\_pizza1'' using camera 0, 1, 2, 3, 4, 6, 7, 9 and tested on scene ``160422\_haggling1'' on camera 5, 8, both with random camera failures. The training dataset has 6858 frames, uniformly downsampled with factor 3. Testing dataset has 432 frames, downsampled with factor 30. From \autoref{tab_exp3} we can see that the proposed method outperforms in all the joints, some with large margins, for example the accuracy of headtop, left and right shoulder joints.

\begin{table}
\centering
\begin{tabular}{lccccc}
\toprule
           & \multicolumn{2}{c}{{[}1{]} + {[}16{]}} & & \multicolumn{2}{c}{Point R-CNN} \\
\cmidrule{2-3} \cmidrule{5-6}
           & DIST & ACC & & DIST & ACC \\
           & [cm] & \%  & & [cm] & \% \\
\midrule
Neck       & 7.88  & 71.1 && \textbf{6.32}  & \textbf{88.4} \\
Headtop    & 9.99  & 54.9 && \textbf{8.64}  & \textbf{71.5} \\
BodyCenter & 7.20  & 80.2 && \textbf{6.29}  & \textbf{87.0} \\
Lshoulder  & 13.57 & 37.2 && \textbf{11.50} & \textbf{48.7} \\
Lhip       & 9.18  & 64.8 && \textbf{8.22}  & \textbf{73.6} \\
Lknee      & 9.28  & 63.2 && \textbf{8.12} & \textbf{74.1} \\
Lankle     & 12.15 & 45.9 && \textbf{10.99} & \textbf{52.7} \\
Rshoulder    & 14.37 & 31.5 && \textbf{11.71} & \textbf{48.1} \\
Rhip       & 10.51 & 53.3 && \textbf{8.77}  & \textbf{69.5} \\
Rknee      & 11.29 & 51.0 && \textbf{9.49}  & \textbf{64.1} \\
Rankle     & 14.43 & 35.9 && \textbf{12.62} & \textbf{44.2} \\
\midrule
Mean       & 10.90 & 53.6 &&\textbf{9.33} & \textbf{65.64}\\
\midrule
AP         &        \multicolumn{2}{c}{71.25}& & \multicolumn{2}{c}{\textbf{89.94}}\\
\bottomrule
\end{tabular}
\caption{View and actor generalization: per joint distance in cm and accuracy < 10cm. }
\label{tab_exp3}
\end{table}

\subsection{Handling background and cluttered scenes}
In this experiment, we use the MVOR dataset which shows a very cluttered room and walls in the background. Unlike previous datasets, this is a real world operation room scenario, which is naturally more complex. Furthermore, due to the size of the dataset, it is not feasible to train the network from scratch, as it is mentioned in the original paper \cite{mvor}, so in this experiment, we fine-tuned our previous model on the first 3 days of data and tested on the 4th day data with all 3 cameras used. Since the number of joints is different from the previous dataset, we only fine-tuned the joints common to both dataset's annotations. The training dataset has 513 frames and 113 testing frames. \autoref{tab_exp4} shows that our algorithm outperforms the baseline.

\begin{table}
\centering
\begin{tabular}{lccccc}
\toprule
           & \multicolumn{2}{c}{{[}1{]} + {[}16{]}} & & \multicolumn{2}{c}{Point R-CNN} \\
\cmidrule{2-3} \cmidrule{5-6}
           & DIST & ACC & & DIST & ACC \\
           & [cm] & \%  & & [cm] & \% \\
\midrule
Head    & 13.06 & 37.5 && \textbf{9.74}  & \textbf{61.9} \\
Neck    & 12.05 & 41.3 && \textbf{9.24}  & \textbf{65.0} \\
Lshoulder & 19.02 & 15.3 && \textbf{15.52} & \textbf{28.9} \\
Rshoulder & 16.90 & 21.0 && \textbf{15.12} & \textbf{27.4} \\
Lhip    & 27.93 & 7.5  && \textbf{20.27} & \textbf{14.2} \\
Rhip    & 32.34 & 8.2  && \textbf{18.50} & \textbf{19.3} \\
Lelb    & 27.60 & 6.2  && \textbf{22.03} & \textbf{12.7} \\
Relb    & 25.87 & 6.1  && \textbf{20.41} & \textbf{14.2} \\
\midrule
Mean    & 24.96 & 15.4 && \textbf{17.70} & \textbf{26.1}\\
\midrule
AP         &        \multicolumn{2}{c}{57.84}& & \multicolumn{2}{c}{\textbf{64.73}}\\
\bottomrule
\end{tabular}
\caption{MVOR dataset: per joint distance in cm and accuracy < 10cm. }
\label{tab_exp4}
\end{table}

\begin{figure}[ht]
\centering
\includegraphics[width=\linewidth]{plot_crop.png}
\caption{The mean per joint distance accuracy using different thresholds for the above four experiments. Each dotted line represents the baseline method and the solid line represents the proposed method. Experiments are color coded. }
\label{fig:img_plot}
\end{figure}

\begin{figure*}[ht]
\centering
    \includegraphics[width=\linewidth]{voxelnet.png}

    \includegraphics[width=\linewidth]{voxelnet_mvor_joints.png}

  \caption{Person detection and pose estimation results on CMU Panoptic (rows 1, 2) and MVOR (row 3). The red cylinder and skeleton are the prediction result and the green cylinder and skeleton indicate the ground truth.}
  \label{fig:img_exp1}
\end{figure*}

\subsection{Qualitative Evaluation and Discussion}
Apart from the experiments we mentioned above, we evaluated the mean of per joint distance accuracy under different thresholds. As shown in \autoref{fig:img_plot}, the proposed method consistently outperforms the baseline method under various thresholds in all four experiments. Sample testing results from all experiments are shown in \autoref{fig:img_exp1}, where the ground truth is color coded green and the prediction red.

\section{Introduction}
Point cloud analysis has been studied widely due to its important applications in autonomous driving\cite{zhou2017voxelnet,qi2017frustum,ku2017joint}, augmented reality\cite{park2008multiple}, and medical applications\cite{singh2017darwin}. However, most of the point cloud based work has been focusing on applications with only one depth sensor, or multiple sensors facing outwards, covering non-overlapping regions. Less work has been reported that tackles the 3D object detection problem in in-door multi-camera settings, which is a very common and important scenario in applications such as operating room monitoring\cite{kadkhodamohammadi2017multi}, indoor social interaction studies\cite{joo2018total}, indoor surveillance \etc.

Using multiple cameras reduces the amount of occlusions, but fusing information from multiple sensors is very challenging and still an open problem in the community. Traditionally fusing 2D and or 3D images coming from multiple sensors is handled by complex models that either process each sensor separately and fuse the decision at a later stage \cite{cho2014multi, gupta2015aligning,tulsiani2015viewpoints}, or fuse the information earlier on feature level\cite{xu2017pointfusion}. Such algorithms tend to suffer from poor generalization due to the complexity of the model and heavy assumptions. In comparison, we argue that using multiple sourced point clouds is a more straightforward and natural alternative, which provides better generalization under various challenging real world scenarios.

In this work we study the multi-person 3D pose estimation problem for indoor multi-camera settings using point cloud and propose an end-to-end multi-person 3D pose estimation network, ``Point R-CNN''.
We tested our method by simulating scenarios such as camera failures and camera view changes on the CMU panoptic dataset\cite{joo2018total}, which was collected in the CMU Panoptic studio to capture various social interaction events with 10 depth cameras. We further test our method on the challenging real world dataset MVOR\cite{mvor}, which was captured in hospital operation rooms with 3 depth cameras. The experiment demonstrates the robustness of the algorithm for challenging scenes and shows good generalization ability on multi-sensor point clouds. Furthermore, we show that the proposed end-to-end network outperforms the baseline cascaded model by a large margin.

The contributions of our work are as follows:

\begin{enumerate}
\item We propose to use point cloud as the only source for fusing data from multiple cameras and show that our proposed method is efficient and generalizes well to various challenging scenarios.
\item We propose an end-to-end multi-person 3D pose estimation network, Point R-CNN, based solely on point clouds. Through extended experiments, we show the proposed network outperforms the cascaded state-of-the-art models.
\item We present extensive experimental results simulating challenging in-door multi-camera application problems, such as repeated camera failures and view changes.
\end{enumerate}

\section*{References}

\section{Related work}

\subsection{Point cloud based approaches}
Processing point cloud data is challenging due to its unstructured nature. Typically, in order to use Convolutional Neural Network (CNN) like methods, point clouds are usually pre-processed and projected to some ordered space, such as in \cite{wu20153d,riegler2017octnet,jampani2016learning}.
Projecting point clouds is also helpful for more complex localization tasks such as 3D object detection. For example, Zhou \etal\cite{zhou2017voxelnet} proposed to use voxelized point clouds to detect 3D objects using a Region Proposal Network, which simultaneously produces voxel class labels and regression results for bounding box ``anchors''.

Alternatively, more efforts have been put into directly using point clouds. For example, Qi \etal proposed PointNet\cite{qi2017pointnet} and PointNet++\cite{qi2017pointnet++} to classify and segment point clouds in their native space. These networks are the building blocks for many later point cloud based algorithms. Recently, Ge \etal\cite{ge2018hand} proposed to directly use point clouds for hand pose estimation. We further discuss this approach in section \ref{sec:pose}.

Besides the above mentioned trends, on-the-fly point cloud transformations have also been explored. Su \etal\cite{su2018splatnet} proposed using Bilateral Convolutional Layers to filter the point cloud and further process the data without explicitly pre-processing the input point cloud. Li \etal\cite{li2018pointcnn} proposed to use $\chi$-transform to gradually transform the point clouds into a higher order representation without pre-processing steps.

Our method is inspired from the first two approaches to detect people in ordered 3D space
using voxelized input, as well as detecting human body joints on the segmented point cloud.

\subsection{Multi-sensor applications}
As discussed earlier, combining the information of multiple sensors is challenging. Conventionally the information is fused at a later stage, either by fusing the decision or fusing the feature space. Hedge \etal\cite{hegde2016fusionnet} proposed to use one network per sensor and fuse the result at a later stage to perform object recognition. Xu \etal proposed PointFusion\cite{xu2017pointfusion} to fuse point cloud features and 2D images to detect 3D bounding boxes.

However, complex fusion models present weaknesses in terms of generalization. For example, in indoor surveillance systems, the number of cameras and their position vary widely making it challenging to generalize. Hence we argue that combining multiple point cloud sources is a more natural and effective alternative where the effort of fusing information is very low in comparison. Furthermore, in case of camera failures, the structure of the input does not change, only the density, \ie number of points does. This can easily be accounted for by training the point cloud network on input cloud with variable density.

\subsection{3D human pose estimation}
\label{sec:pose}
3D human pose estimation is a challenging problem. Most recent works focused on using depth images or combining 2D and 3D information to detect landmarks for a single or for multiple persons\cite{sarafianos20163d,yang20183d,rhodin2018learning,kadkhodamohammadi2018generalizable}.

Rhodin \etal\cite{rhodin2018learning} proposed to use weakly-supervised training methods to circumvent the annotation problem. This is achieved by assuming consistency in the pose across different views. During testing the pose is estimated based on a single camera input. More recently, Moon \etal\cite{moon2018v2v} proposed to use voxelized depth images to detect 3D hands and estimate single human pose from depth images. Haque \etal \cite{haque2016pose3d} describe a view point invariant 3D human pose estimation network, where the input depth image is either a top view or a front view. They refine the landmark position using a Recurrent Neural Network and tested view transfer by training on the front view and testing in on the side view. While this relates to our problem and presents encouraging results, the assumption of having fixed viewpoints does not apply for our application.

redpajama_set_name: RedPajamaArXiv


text:
Home History Hamsterley Hall and the Sheriff's – A Brief History of the Hall...
Hamsterley Hall and the Sheriff's – A Brief History of the Hall and its Owners – Consett History
Hamsterley Hall, Hamsterley, Co Durham is located between Consett and Rowlands Gill, in a beautiful rural Hamsterley Hall estate. The Hall is believed to be originally 17th Century which was continuously updated throughout. Owned originally by the Swinburne family it stayed in their families till the early 1800s. The Hamsterley Hall estate was gifted to the Swinburne family by Charles I for their loyalty during the Civil War. In 1767 Sir John Swinburne past away leaving the hall and estate to his younger brother Henry Swinburne, a famous travel writer of the day. It was Henry who was to create the Hall we know today. Henry had studied art and literature in the Royal Academy in Turin and it was this which gave him a passion for the new movements in art and design. He actually met his wife, Martha Baker while in Paris as part of the Grand Tour and they were married there in March 1767. When they returned to England they took up residence in Hamsterley Hall.
In 1769 Henry and Martha decided to upgrade the hall, choosing to opt for a Gothic Revival-styled mansion. The mansion was transformed into a large two-story hall with four bays and castellated roof decoration. Henry had been a very lucky man and had achieved a good amount of fame and favour, however, he was dogged with bad luck in his last 10 years, losing a son, falling out of favour with the British government as well as losing vast sums of money in Jamaica. He passed away in Trinidad after catching a severe case of sunstroke in 1803. The hall stood empty for 3 years until it passed into the hands of Anthony Surtees in 1806, a member of a very old established family of County Durham. In 1838 it passed to Anthony's son Robert Smith Surtees.
Robert Smith Surtees was a well-known sportswriter, novelist and editor, best known for his loved character Jorrocks, a bumbling aristocratic. Most of Robert's books were written whilst at Hamsterley, where it is believed much of his inspiration for the halls and places within the novels of Jorrocks was taken. Robert was made Lord High Sheriff of Durham in 1856 and sat as a Justice of the Peace (JP) across the area. It was in this capacity as Lord High Sheriff and JP that he would be forever linked to the infamous Battle of the Blue Heaps which took place in 1858. Excerpts of his diary talk of what he found and witnessed during his investigation's of the incident. Robert died in Brighton in 1864 and his body brought back to be interred in the cemetery of Ebchester Church.
The Hamsterley Hall estate then passed to his daughter Eleanor who married John Vereker – 5th Viscount Gort in 1885. The hall continued to be upgraded and it was at some point around this time that an orangery was erect to the side of the hall. The hall was a living and evolving entity being added to and growing with each new owner. John Vereker died in 1902 and Eleanor died in 1933 leaving the hall to their son John Standish Surtees Prendergast Vereker – 6th Viscount Gort. John had been a major player in WWI. He was awarded numerous medals including the highest honour of all the Victoria Cross for his bravery and actions during the Battle of Canal du Nord in 1918 where he was the Field Marshall in charge. He died on 31 March 1959 in Southwark, London aged 59 years, leaving Hamsterley Hall estate to his brother Standish the 7th Viscount Gort and also another war veteran who had received the MC. Standish had actually served under his brother John during WWI. Like Surtees before him, Standish was also appointed Lord High Sheriff of Durham in 1934 as well as being a Knight of the Most Venerable Order of the Hospital of Saint John of Jerusalem, an order first constituted in 1888 by royal charter from Queen Victoria. Standish lived out his life on the Hamsterley Hall estate passing away in 1975.
After his death the hall fell into disrepair, many of the great rooms falling in on themselves. In 2010 a concerted effort was made by the new owner to rebuild the hall which is a Grade II listed building of great importance being one of the best Gothic Revival-Style building throughout Durham and the North East. As far as we are aware renovation's of this fascinating building is still ongoing and will hopefully see the hall returned to its former glory.
Consett History
Previous articleConsett Magazine – June 2019 Editorial
Next article5 Quick Small Business Marketing Tips for 2019
The Consett Magazine Editing team lovingly ❤ publishes local news from a variety of local people and sources. Please see the individual article for post specific source(s). Consett deserves good news. Submit your own story or call 01207 438292 for advertising and marketing campaigns.

redpajama_set_name: RedPajamaCommonCrawl


Eco Oxidizer's electro chemical action forms powerful hydroxyl radicals and natural peroxy compounds (H2O2) which eliminate bacteria, virus, oocysts, protozoa as well as the by-products of chlorine use – such as Crypto and Giardia. The Eco-Oxidizer's action also eliminates Chloramines and Trihalomethanes which are banned in indoor pools for their effects on humans. This means cryptosporidium is no longer a threat in your Bionized pool, because chlorine is not effective against this oocysts/protozoa unless used at very high levels.
Eco-Oxidizer does all this whilst dissolving any fatty substances such as suntan oils, urine and perspiration. Salt systems, in our opinion, are toxic, expensive and corrosive way to treat a pool. Health-conscious and aware pool owners who wish to avoid high power and chemical costs and avoid chlorine altogether choose Bionizer. Bionizer is an asthma and eczema safe pool system.
The Eco-Oxidizer will rid your pool of body oils, sunscreen and swimmer waste, without you having to manually add chemicals to your pool. The Eco-Oxidizer features a self-cleaning system for the electrodes which will save you time on cleaning and extends the life of the electrodes. Sit back and enjoy your pool while Eco-Oxidizer leaves your pool water sparkling.
Eco-Oxidizer doesn't need any chemicals to keep your pool water sparkling. With a Bionizer and Eco-Oxidizer (and pH Boss) you can enjoy your pool free from chemicals. Safe for the environment, safe for your family.


HDR End‑User Engagement Detailed Report provides a breakdown of Domestic and Overseas students undertaking various HDR end‑user engagement activities.
The following table identifies the components that are used to construct the report.
The number of students is derived from the count of unique student IDs.
Students who undertake more than one research course with different scholarships and HDR end‑user engagement activities may be counted more than once.


text:
using System.Linq;
using System.Web.Ria.Data;
using System.Windows.Ria.Data;
using Microsoft.Silverlight.Testing;
using Microsoft.VisualStudio.TestTools.UnitTesting;
using DescriptionAttribute = Microsoft.VisualStudio.TestTools.UnitTesting.DescriptionAttribute;
using System.Windows.Data.Test.Utilities;

namespace System.Windows.Ria.ApplicationServices.UnitTests
{
    /// <summary>
    /// Tests <see cref="AuthenticationBase"/> members.
    /// </summary>
    [TestClass]
    public class AuthenticationBaseTest : SilverlightTest
    {
        public class MockUser : UserBase
        {
            public override object GetIdentity() { return string.Empty; }
        }

        public class MockEntityContainer : EntityContainer
        {
            public MockEntityContainer()
            {
                this.CreateEntityList<MockUser>(EntityListOperations.Edit);
            }
        }

        public class MockAuthenticationBase : AuthenticationBase
        {
            [LoadMethod(typeof(MockUser))]
            public void LoadUser() { }

            protected override EntityContainer CreateEntityContainer()
            {
                return new MockEntityContainer();
            }
        }

        public class InvalidEntityContainer : EntityContainer { }

        public class InvalidAuthenticationBase : AuthenticationBase
        {
            protected override EntityContainer CreateEntityContainer()
            {
                return new InvalidEntityContainer();
            }
        }

        [TestMethod]
        [Description("Tests that UserType can determine the correct user type")]
        public void UserType()
        {
            MockAuthenticationBase mock = new MockAuthenticationBase();
            Assert.AreEqual(typeof(MockUser), mock.UserType,
                "UserType should be MockUser.");

            InvalidAuthenticationBase invalid = new InvalidAuthenticationBase();
            ExceptionHelper.ExpectException<InvalidOperationException>(
                () => Assert.IsNull(invalid.UserType, "UserType should throw an exception."));
        }

        [TestMethod]
        [Description("Tests that UserList can determine the correct user list")]
        public void UserList()
        {
            MockAuthenticationBase mock = new MockAuthenticationBase();
            Assert.IsNotNull(mock.UserList,
                "UserList should not be null.");

            InvalidAuthenticationBase invalid = new InvalidAuthenticationBase();
            ExceptionHelper.ExpectException<InvalidOperationException>(
                () => Assert.IsNull(invalid.UserList, "UserType should throw an exception."));
        }
    }
}

redpajama_set_name: RedPajamaGithub


I went to bed last night having let go of my faith and I woke up this morning in a pitch-dark room. I didn't know what to say. I didn't know how to walk. For someone who believes the connection to God, each other, the planet, and myself, is my spiritual journey, the light had gone out.
I am grieving this morning for all those who voted out of fear. I am grieving for those who felt like they didn't have a good choice. I am grieving for all women who have experienced the trauma of sexual assault only to be re-traumatized by this election. I am grieving for black and brown brothers and sisters who once again are feeling the danger and oppression that our culture perversely imbues their color. I am grieving for immigrants, documented and undocumented, whose humanity and security is diminished by this vote. I am grieving for Muslims whose faith automatically illicit fear and with impunity demonizes their character. I am grieving for our planet that desperately needs our stewardship and love. I am grieving all those, like me, who have lost their faith.
Kelly McGonigal, PhD., who researches compassion at Stanford University, describes my condition as moral distress—the inability to trust each other and ourselves because of a crisis in our basic belief in goodness. As this morning has progressed and by forces greater than me, I have put one foot in front of the other. Here's the thing about a pitch-dark room, the only antidote is light and the only light that I completely control is my own. So with faith the size of a mustard seed, I am turning on my light. With a timid heart afraid of being disappointed, hurt and broken, I am turning on the only light that can pierce darkness—love. I'm starting with you, my friends and family—you are easiest to love. Even though my instinct is to close my arms, I am opening my arms and reminding you and me that I see you and I love you.
I will find my way. I will find my legs again but I won't do it alone. I need you. We need each other.
Finally I leave you with this anthem by Michael Franti--I think this is what light looks like. My choir has had the blessing of singing with Michael several times and I always feel uplifted.


Custom OKI Print Solutions Overview VIDEO | Industry Analysts, Inc.
Your customers personalize and OKI print enables them. Mobile print, cloud services and more give OKI customers capabilities they used to dream of.
This entry was posted in OKI and tagged custom, oki, print by IA Staff. Bookmark the permalink.


Tape Logo T-Shirt White | The Official BALR. website. Discover the new collection.
Start your summer fresh with a brand new tee. The Tape Logo T-Shirt is the perfect option, blending comfort with style. The tee features our iconic fit and is finished with a tape logo on the chest. Wear it on shorts for the ultimate off-duty look.
Description Start your summer fresh with a brand new tee. The Tape Logo T-Shirt is the perfect option, blending comfort with style. The tee features our iconic fit and is finished with a tape logo on the chest. Wear it on shorts for the ultimate off-duty look.
The Tape Logo T-Shirt features the iconic BALR. Club logo with a taped over twist. Cut for an athletic fit and crafted from superior quality cotton, this piece is a guaranteed hit in your T-shirt rotation.


text:
New York Times Critics' Picks • 1h 34m
Directed by Nikolaus Geyrhalter • Documentary • 2016 • 94 minutes
HOMO SAPIENS is a film about the finiteness and fragility of human existence and the end of the industrial age, and what it means to be a human being.
What will remain of our lives after we're gone? Empty spaces, ruins, cities increasingly overgrown with vegetation, crumbling asphalt: the areas we currently inhabit, though humanity has disappeared. Now abandoned and decaying, gradually reclaimed by nature after being taken from it so long ago. HOMO SAPIENS is an ode to humanity as seen from a possible future scenario.
Up Next in New York Times Critics' Picks
Directed by Eric Khoo • Animation • 2014 • 98 minutes
TATSUMI celebrates the life and work of Japanese comics artist Yoshihiro Tatsumi-a manga pioneer who elevated the genre to a new level of creative expression and adult realism. A comics-crazed teenager, Tatsumi began to get published and was ...
Directed by Stephen Apkon, Andrew Young • Documentary • 2017 • 86 minutes
In a world torn by conflict — in a place where the idea of peace has been abandoned — an energy of determined optimism emerges. When someone is willing to disturb the status quo and stand for the dream of a free and secure...
Directed by Sharon Shattuck • Documentary • 2017 • 76 minutes
With her own wedding just around the corner, filmmaker Sharon Shattuck returns home to examine the mystery at the heart of her upbringing: How her transgender father Trisha and her straight-identified mother Marcia stayed together aga...


Roberts Revival iStream2 DAB/DAB+/FM Internet Radio Pastel Cream-Brand New.
Access to over 10,000 radio stations and podcasts. Play your music collection from your computer. The item roberts revival istream2 dab/dab+/fm internet radio pastel cream-brand new is in sale since Tuesday, April 25, 2017.


Find cheap Bilbao Spain to Christchurch New Zealand flights with our free search service. Compare all available fares for direct or connection flights from Bilbao to Christchurch.
What is the distance between Bilbao and Christchurch?
The average distance from Bilbao to Christchurch is 19621 kilometers (equals to 12192 miles or 10595 nautical miles). Your flight will departure from Bilbao, Spain and arrive on Christchurch, New Zealand.
How many flights fly from Bilbao to Christchurch on the daily basis?


Wall 3 gives you many options and plenty of space!
Specifically, your choice can be made between 3 different types of subjects - a double clothes rack, a rack with two shelves and a shelf system with 4 shelves. One can make shelves to make their dream walk-in closet by making a combination of two sets of clothes racks and a shelf system. Or create a functional pantry by combining 3 shelf systems.
The clothes rack is constructed of four pillars that are stabilized by different number of transverse rods, which create function and strength.
All shelves are fitted with two brackets that give them strength and ease of assembly.
All Ziito shelves get their karisma from being treated by hand if it is with heat, linseed oil or pigment.


Founded in 1984 by Managing Director, Bill Braysford, the firm began operating from a small unit in the town - before Bill was joined by his wife, Joan, and his two sons, Billy and Andrew. By 1985 the business had grown sufficiently to facilitate a move to the current premises on the Riverside Industrial Estate, the site of the former Rishton Paper Mill. This consists of an 8,000 sq ft warehouse and a yard capable of storing up to 1,000 tonnes of waste paper/recyclables. Continuing to go from strength to strength, in 2012 we won a tender with Hyndburn B C for their mixed plastics. We now employ 15 members of staff full-time.
Rishton Waste Paper Ltd is a registered carrier with the Environmental Agency.


The Monarch gown has amazing floral designs and details with lace and tule throughout the entire dress. The sheer back with floral lace is beautiful and is truly a statement piece on the dress that creates a gorgeous illusion with a long row of covered buttons. The sweeping train this dress has gives it the perfect romantic finish to the dress. The Monarch gown is a perfect blend of ivory and champagne that photographs beautifully in room and natural lighting.


text:
Q: Deserialize a JSON into a list of C# objects I'm developing a mobile game project that utilizes Firebase. I've managed to get login/registration, and sending items to the database to work just fine. However, after countless hours of banging my head against the wall, I just can't get the inventory to work. I can fetch the correct JSON data from the database, and it comes out in the following format:
{"5449000085757":{"itemLevel":82,"itemName":"Sword of Maximum Epicness","itemType":""},"6419800152996":{"itemLevel":45,"itemName":"Your Average Sword","itemType":""}}

These are all just fields for testing purposes of course. What I'm trying to do is create a list of objects, where the list is the "inventory" and the objects are the items. I've tried using Json.NET, Unity's built-in JSON utilities, all kinds of technologies, but just can't find a way. I feel kind of stupid, because obviously it can't be this difficult. No matter what example I've tried, it doesn't work.
If possible, could someone provide a quick briefing on how to create that list of objects from JSON, a simple way? I just can't figure this out on my own and it's getting really frustrating.

A: If you want a strongly typed class that you hydrate with your JSON you can do the following:

*

*Copy the JSON payload into your clipboard.

*In visual studio add a new class to your project. From the Edit menu select Paste Special / Paste JSON As Classes. This will create the class or classes that represent your JSON class.

*Open tools / Manage NuGet Packages for Solution. Add NewtonSoft.JSON library to your project.

*To hydrate your object from the JSON use something like this:
MyObject myObject = JsonConvert.DeserializeObject(jsonPayload);

A: I would first download the Newtonsoft.Json NuGet package. Ensure you import it into your class with using Newtonsoft.Json.
Then I would create an Item class:
public class Item
{
    [JsonProperty("itemLevel")]
    public long ItemLevel { get; set; }

    [JsonProperty("itemName")]
    public string ItemName { get; set; }

    [JsonProperty("itemType")]
    public string ItemType { get; set; }
}

Then you can simply deserialize your JSON into a Dictionary<string, Item> using JsonConvert.DeserializeObject:
var deserializedJson = JsonConvert.DeserializeObject<Dictionary<string, Item>>(json);

Test
var json = "{\"5449000085757\":{\"itemLevel\":82,\"itemName\":\"Sword of Maximum Epicness\",\"itemType\":\"\"},\"6419800152996\":{\"itemLevel\":45,\"itemName\":\"Your Average Sword\",\"itemType\":\"\"}}";

var deserializedJson = JsonConvert.DeserializeObject<Dictionary<string, Item>>(json);

foreach (var entry in deserializedJson)
{
    Console.WriteLine($"Key={entry.Key}, itemLevel={entry.Value.ItemLevel}, itemName={entry.Value.ItemName}, itemType={entry.Value.ItemType}");
}

Output
Key=5449000085757, itemLevel=82, itemName=Sword of Maximum Epicness, itemType=
Key=6419800152996, itemLevel=45, itemName=Your Average Sword, itemType=

A: The best move would be deserializing it as Dictionary<string, object> and Iterate through the values and casting them to your desired "Item" class.

redpajama_set_name: RedPajamaStackExchange


The Alyahodia River is a large branch of the Euphrates after the city of Al-Hindiya, also known as the Babylon Channel. In the past, its banks housed an intersection of cultures and religions which formed a thriving and integrated community. The original name of the river, Judaism River, dates back to 550 BC when Jewish prisoners were taken from its shores to Babylon by the Babylonian armies. Some remained and inhabited the area from that time until 1950, living harmoniously with neighbors of many different religions and traditions. After 1950, most of them left for Israel.
Today the river is a garbage dump, a cradle for disease and insects that putting thousand of families at risk of health and environmental problems. The Babilia Social Forum, a youth volunteer group of the Iraqi Social Forum, is now carrying out a large awareness campaign to reduce the problems of Alyahodia River and has already launched a "#Saveنهراليهودية" hashtag. The group is working to rally public opinion so that their voice can reach the government and related authorities. Posters have been distributed in order to make people aware of the bad shape of the river.
To kick off the campaign, the Babilia Social Forum held a research seminar in Hilla on the challenges currently facing the Alyahodia River. The seminar, held on 28 September at Abjad Foundation Hall, brought together university professors and environmental experts to identify problems and propose solutions for the increasingly polluted river. Dr. Dakhil Nasser from Babel University's Faculty of Science said that the river has suffered in its transition from past to present. .
The seminar witnessed fruitful collaboration between activists and experts, who presented a series of proposals and solutions aimed at solving this crisis. Dr. Mohammed Al-Zafairi, Director of the Environmental Research Center at the University of Babylon, outlined several detailed studies of toxic substances in the river's water, as well as an account of the insects and rodents which populate it. The river has become a dumping ground for sewage and toxic waste from laboratories. This is a particular problem for textile factories which burn substances that impact the air quality in surrounding residential communities.
In the next days, a large sports activity is set to take place in the framework of this campaign by the banks of the Euphrates River.


text:
Multimedia / Films
ESI in the media
Back - Perihan Magden, Turkey - Next
Perihan Magden is one of Turkey's most widely read young authors. Turkey's Literature Nobel Price winner Orhan Pamuk called her "one of the most inventive and outspoken writers of our time." She is also a regular columnist for daily Radikal, criticizing the military's influence in politics and the militarization of Turkish society.
Magden's most recent novel, "2 Girls", became a bestseller in Turkey and was translated also into English and German.
Magden is one of Turkey's most intrepid columnists, and faced numerous lawsuits for her outspoken articles. One article that brought her into confrontation with the state was her support for Mehmet Tarhan, who was jailed for his refusal to perform the mandatory military service. Turkey has not yet introduced the possibility for conscientious objectors to avoid military service:
"Every human has a right not to kill. And if you are doing your military service in Turkey there is a big chance that you might be killing someone because there is an ongoing war in the Southeast of Turkey since 30 years. So I write this article. And the head of the army made a complaint to the prosecutor."
Some of Magden's earlier trials were attended by ultra-nationalist groups: and some of those who showed up at her trials were later arrested on the charge of being part of the Ergenekon terror group. When these arrests took place in early 2008 Magden felt vindicated:
"I feel relief, I feel fairness, I feel justice. I feel like good will prevail. And I feel good for my country. This is like the cleansing of Turkey, a detox for Turkey. It was killing us, it was wrapped around our neck, it was starting to suffocate us: this fascism, this deep state. This is like breathing in and out much more comfortably."
Magden was already sentenced twice on probation and has several other pending court cases. In case of a third conviction, she would have to go to prison. This has not prevented her from stating her opinions forcefully.
Perihan Magden's columns in Radkal newspaper (in Turkish)
Interview with Perihan Magden on the Rumeli Observer blog: The virtue of boldness – Meeting Perihan Magden
Return to Europe - Documentary Project
Return to Europe Revisited
Broadcasting dates
Press reactions
Literary walk
Kosovo - Cutting the lifeline
The Albanian Renaissance
Macedonian Wedding
Bosnia - A miracle which does not shine
Serbia - Exit Europe
Romania - timisoara.com
Bulgaria - The long revolution
Greece - Alexander's shadow
Istanbul - Truth, fear and hope
Twilight of Heroes - Croatia, Europe and the International Tribunal
Katarina Radic, Belgrade
Nickolay Mladenov, Sofia
Radu Tinu, Timisoara
Ishak Alaton, Istanbul
Kire and Ruza Lazaroski, Kicevo
Tamar Beruchashvili, Tbilisi
Qefser Qahili, Lubishte
Elisa Spiropali, Tirana
Drago Josipovic, Ahmici
Enes Suljkanovic, Doboj
More portraits…
© European Stability Initiative 2019


text:
Collision Course? A Forming El Niño & The Sahel Region
May 27, 2015 • Daily Email Recap
El Niño Could Bring Drought and Famine in West Africa
See original/source content: http://www.climatecentral.org/news/el-nino-drought-west-africa-19028
A global weather phenomenon could cause a famine in the Sahel this year by combining with already dry conditions to create a "double whammy" for the region, scientists and aid groups have warned.
Professor Adam Scaife, a long term forecaster at the UK Met Office Hadley Centre, said models now agreed an El Niño event was likely and the first impacts may be felt as early as June.
El Niño is caused by a reversal of trade winds in the Pacific that allow warm water to spread east, across the ocean. The two to seven year cycle plays havoc with weather across the world.


text:
Q: Rails4+Devise: "hide" routes when user is not authenticated I am trying to make my admin back-office pages "invisible" for non authenticated users. Meaning when a user goes to something like /admin/my_controller I want to display a 404 custom page, makes him believe this is just a regular 404. And off course when the user is authenticated he will correctly access the controller.
So I got almost everything working, I'm just missing the devise/warden part. I correctly configured 404 error rescue in my Rails app (which is implemented in a controller I named ErrorsController), so when someone goes to a non existing uri such as /nothing_here, my custom 404 page correctly display.
I am trying to use ErrorsController as failure_app which is almost fully working, except that I receive an exception if I log-in with incorrect credential. I am certainly just missing one last detail. Exception is
AbstractController::ActionNotFound at /unauthenticated
The action 'create' could not be found for ErrorsController

Obviously the create action is supposed to be called from the SessionController not from the ErrorsController. And this is the last bit that is missing. How to achieve this?

Custom 404 configuration
In config/application.rb
config.exceptions_app = self.routes

In config/routes.rb
get '*dummy', to: 'errors#index'

app/controllers/errors_controller.rb
class ErrorsController < ApplicationController
  def index
    display_404
  end
end

Devise configuration
In config/initializers/devise.rb
config.warden do |manager|
  manager.failure_app = ErrorsController
end

Result

*

*Accessing a "admin" uri without being authenticated displayed correctly display my custom 404 page

*Logging-in with devise with correct credential works as expected

*Logging-in with incorrect credential raises AbstractController::ActionNotFound at /unauthenticated The action 'create' could not be found for ErrorsController

A: A more graceful way of handling errors when a user is not signed in is adding a method to your application controller. For example display_404
In your display_404 method add
raise ActionController::RoutingError.new('Not Found')

When this method is called it will raise Rails' own error page and log 'Not Found' in the log file. In development this will be shown but on screen but for production it will only be found in the log file, so adding something like 'User was not signed in' is a possibility.
Then using
before_action do
  unless admin_signed_in?
    display_404
   end
end

the error page will be shown when the visitor is not signed in.


Bad choice of words there. Very bad.
This week, I'll be starting something new with the blog. Every Thursday, there will be a small tidbit of extra information about the World of the thirteen seas, which will be placed in the World section every Tuesday. I never know what to blog about, and seeing as how my weeks are rather dull since I spend most of them working, you'll get a little something extra every week. I'm the least organized person on the planet so hopefully this will also help me improve my organizing skills.
As for this weekend, it was a quiet weekend spent watching movies for my class and eating the goodies the Easter bunny gave me. I watched Jaws and wasn't too scared, or at least I didn't get shark nightmares, and the Shawshank Redemption, which was a fantastic movie. I always wanted to see the Shawshank Redemption, but was a bit scared to because it was based on a work by Stephen King and I can't read horror without having nightmares (The Sixth Sense gave me nightmares, so I'm extra wimpy). Thankfully it wasn't enough to give me nightmares, even though there were dreadful things happening, and I really enjoyed the ending.


Training helps to expand the knowledge base and also creates opportunities for the company. Many companies conduct training programs time to time to give knowledge to employees. Generally, It is seen that employees have some weakness and training programs help to overcome those weaknesses. A popular medical imaging company CURA organised a training program in Chennai. The name of the program was Scan – 17.
In the program the company invited Dr Arvinder Singh to take the training session. Dr Arvinder Singh talked about market leadership in the session. He told that the company can become market leader by improving its services. He also told that only one person cannot achieve the goal alone. The company should provide training to all employees so that all employees can maintain quality of services.
Dr Arvinder Singh also told that the CURA Company should provide best medical services to patients in low and affordable prices. He told that competition among companies is growing day by day. To beat the competition and to become market leader, the company has to improve its services. Patients should get best quality services. All the members available in the session praised the efforts of Dr Arvinder Singh.
Dr Arvinder Singh is the CMD and CEO of Arth Diagnostics. He also received quality assurance certificate. At the centre, he provides best quality medical services to all patients. It is the reason, in just short span of time Arth Diagnostics became the best diagnostic centre. He also works as creativity and intelligence trainer. He conducted many seminars to teach simple techniques of creativity to people. The company can become market leader by using creativity.


Beautiful girls ivory daisy & organza tulle dress, suitable for a baby through to junior, perfect wedding wear for a bridesmaid / flower girl or for that special occasion including a christening etc.
Dress: Ivory sleeveless fitted matt satin bodice with a daisy flower trim on a wide waist sash that beautifully ties at the back into a large bow, with a discreet zip fastening. The skirt of the dress is just lovely ... top layer is of a sheer organza tulle with matching daisy flowers all over, satin underlining that has a tulle underlay to create fullness.
Bought this dress in 3 different sizes for our flower girls. One of the only websites that I could find that did such a wide size range in the same dress! Dresses arrived quickly and in perfect condition. The material and quality is amazing for the price, really happy with the purchase! Would recommend!
Absolutely love the dress. It is perfect. Really good quality. Really impressed with the service too. Had to exchange sizes. Was no hassle and dress arrived really quickly.


Armed with flags of Greece, Yerevan's Greek community "Ponty", headed by 57-year old Fruntik, follows the path walked each April 24. On May 19, the Greeks pay homage to their relatives who were also among the Turk's victims during the Genocide.
Each May 19 Greeks in Armenia gather at the Genocide Memorial to remember victims of the 1922 massacres.
"May 19 is the most tragic day of our nation," says Gerges Alamanos, the charge d' affaires of Greece in Armenia.
Multiple thousand Greeks residing on the south side of the Black Sea were killed in 1922, part of the same genocide that earlier devastated Western Armenia. According to some historical records, more than 350,000 residents of Pontos were among the victims.
Like the Armenians on April 24, the Greeks remember their beloved a month later with marches to the monument.
The Greek Genocide Day is shared by many Armenians, including Albert Stepanyan, director of the Armenian-Greek Cultural Center.
"Finishing the Armenian Genocide in 1915, in which (the Turks) seemed to succeed, they started the same activities against Greeks in 1919," Stepanyan says. "The existence of the Ottoman Turk government was the environment of genocide where such violence was done to different nations."
"Today April 24 and May 19 mix," says director of Armenian Genocide Museum and Institute Lavrenti Barseghyan. "On the area of this small and modest memorial two most ancient nations recall tragedies which they will never forget."
Some 5,000 residents of Greek origin call Armenia home. About 2,000 live in Yerevan. During Soviet times their numbers were several times more and included entire Greek villages.
In 1990s the Government of Greece started programs supporting the repatriation of Greeks from post-Soviet countries.
"There are several Greek families in Armenia, which are pure-blooded Greeks, and others have mixed with Armenians," says Fruntik. "Armenian and Greek people are very similar in both appearance and inner world, as well as in fate from the last century."
Nikolaidi Fruntik leads the Greek community in memorial services.
According to Fruntik the community tries to preserve all Greek traditions as far as possible. One of those traditions is the commemoration at Tsitsernakaberd.
There is no Greek Orthodox Church in Armenia, so most attend Russian Orthodox churches. But negotiations are underway to build a Greek Orthodox cathedral here.
Through a program sponsored by the Embassy of Greece, children get free Greek language lesson twice a week. And each summer Greek children ages 9-12 are given free visits to Greece.
There is also a Greek dance ensemble headed by Fruntik's Armenian wife, Agapi Khurshudyan, whose family is from the Western Armenian town of Mush.
"During the years of slaughter, only my father stayed alive of my 58-member family. Do you imagine the extent of brutality?" says Agapi. "There is a Greek movie that tells about the slaughter of Greek people in 1922. I always compare it with the movie 'Ararat', as both films tell the same phenomenon.
"I am married to a Greek and gave birth to a Greek child, and the same fate fell to my child's grandfathers' and grandmothers' lot."


Many times we may use our fingernail to scrape or peel to remove something away so that it is cleaner. Why not try our ScrapeRite plastic razor blades and save your nail?
You can lift and peel away vinyl, dirt, grime, resin, tar, gum and any other substance or contamination without damaging the surface. ScrapeRite plastic razor blades will not rust or corrode. It is gentle enough to use on delicate surfaces or tough enough to do those difficult jobs without damaging the surface.
They are made from different plastics such as Delrin, Polycarbonate, Acrylic and a New Black Industrial strength plastic. They are safer to use than a metal blade due to risk of cutting oneself. They are non- conductive for any electrical application.
Save potential surfaces from damage anywhere. In the Home, Office, Marine, Aerospace, Aviation, Manufacturing, Manufacturers, Graphic Design, Vinyl Removal, Cleaning of Carbon Fiber or Metal Molds, Yachts, Superyachts, Megayachts, Cruise Ship Maintenance, Ferry Boat Maintenance, Kitchens, Galleys, Boat and Yacht Manufacturers, Boat Builders, Yacht Builders, Shipyards, Painters, Carpenters, Metals and Metal Industry, Automotive, Bicylcles, Motorcycles and Motor Sports, Watersports, Model Making, Electrical Generating Plants, Windmill Operations and maintenance, Mastic and Silicone Removal, Label Removal, Glass and Stone care plus much more.


text:
Order Meats
Order Gear
Camille Dodero, of the Boston Phoenix, wrote in his 2004 article, " How did David Littlefield- aka The Sausage Guy-go from street meat vendor to local celebrity? "Three essential ingredients: A pair of tongs, an indefatigable work ethic and a whole lot of meat. Littlefield has gone on to be the most famous vendor in New England , if not the country."
Littlefield has fed his share of stars such as Nate Solder, Rob Gronkowski, David Ortiz,Bobby Orr, Edelman, Drew Barrymore and most importantly to him, hundreds and thousands of your average Joes and Janes. It was not always glamorous, in fact, never. He would work Downtown Crossing during the day and then go to Playoffs in Weymouth until 12:30am then throw the cart in the back of the truck and drive to Cleveland Circle's most famous dive bar Mary Ann's until about 2:30 3:00am and get home around 4am wake up and do it all over again. Frost bite came along the way working the night clubs on Lansdowne Street and then of course 88 Red Sox games, Gillette Stadium oh yeah and did we mention the 200 events a year? Littlefield likes to keep busy and he likes people.
He created a product from the voice of the people on the streets and as David Andelmen of The Phantom Gourmet stated, he should teach a class at Harvard Business School on the building of a brand with no budget, it's incredible". Dave is larger than life says chef Jasper White "he put a face and a name to his product that everybody loves." People look for Dave at events and you often here whether you are in a backyard, corporate event or on Lansdowne Street, "You're the Guy, really, like… The Guy?"
Millions of fans have come to know The Sausage Guy He's become a legendary cult figure and entertainer among celebrities and mobs of screaming fans who pop over to his cart when they get the late night urge for the "other white meat."
He has served everyone from Jerry Springer, Lenny Clark, and Jasper White to The Phantom Gourmet.
His cart is visited by sports players, Hollywood celebrities, locals, college students and baseball fans from around the world. "The Sausage Guy" has become a Boston landmark and a "must see" tourist attraction for thousands.
But who is he?
The Sausage Guy, also known as David Littlefield, can be found on Boston's famous Lansdowne St. traversing until early morning on Thursday, Friday, and Saturdays. He's also a fixture at all Red Sox games. But it wasn't always this way….
Having graduated from Curry College in 1991, David Littlefield started his first concession business in 1993. He began by selling boneless buffalo wings to Patriots fans at Foxboro stadium. After investing $600 in the cart and securing a license, the first day was a complete bust! The next week, David turned the fryolator into a steamer for hot dogs. By the next home game, he had hot dogs and the now famous sausages. He quickly became a staple of the landscape and was referred to by the masses as "The Sausage Guy," a name that has endured for more than 10 years.
After trying his hand at other professions, including radio advertising and managing a rock band, David decided to work only for himself – turning his side business as "The Sausage Guy" into a full-time gig. Suddenly, you would find "The Sausage Guy" any place a late night or lunch crowd gathered. In 1997, David secured his marquis location on Lansdowne Street. And once on Lansdowne Street, his personality took over.
It has been said, "'The Sausage Guy' has single-handedly change the vending/concession profession." His creative marking schemes and outgoing upbeat personality have continued to set him apart from the others. From a marketing relationship with WBCN, the first Sausage Guy commercial was born. He soon had a calendar, bumper stickers, hats, tee shirts, and even a T.V. commercial. In May of 2003 David secured a deal with his providers that enable him to privatize his own label. You can now purchase Sausage Guy sausages online 24-7, with shipping available anywhere in the U.S.
The Sausage Guy expands
In 1998, he took over his first restaurant, Three Clover Pizza in South Boston. Six months later, he opened Salsas down the street, creating a destination for authentic Mexican food in South Boston. And in 2004, "The Sausage Guy" opened the first Sausage Guy restaurant on Cambridge Street in Beacon Hill.
And When He's Not the Sausage Guy?
On his "off" nights, it's likely that David is home taking care of his three children, Jett, Sawyer and Grace, or spending some time with his wife Rosemary.


From picking the right flowers to ensuring everyone has received their invitation, planning a wedding can be stressful, but one of the slightly more fun tasks is picking the song you want to walk down the aisle to. There are plenty to choose from, from a traditional piece, to a romantic and beautiful song or even something more modern, we have listed our top considerations below.
This is a true classic, which many people will remember from the 'father of the bride'. This beautiful masterpiece was created back in the 19th century and is common place in churches for the special day.
Another song which became commonplace at weddings following its feature in a wedding scene of a film, All you need is love was used in Love Actually as a surprise for the couple, sung by a famous singer. The lyrics are absolutely perfect and who wouldn't want to hear this as they walk down the aisle?
They simply don't make songs like this any more. Her voice is so powerful and touches you from the very first note. Released in 1963, this is a timely classic to pull on the heartstrings.
It is hard to think of a song by Adam Sandler being the perfect song to walk down the aisle to, but it is actually both humerous and truly touching, with lyrics we can all relate to.
How could we complete a list of wedding songs without Celine Dion featuring? With over 34 million views on YouTube, this is a very popular option, albeit a little bit cliche.
This song has been re-done by so many different artists since its launch in 1939 for The Wizard Of Oz, but none have beaten Iz's version in 1993. The Hawaiian musician sadly died four years after its release, however the song has continued to grow in popularity year after year, with 175 million views on YouTube for this single video alone.
This is probably the biggest newcomer on the list, with so many of Ed Sheeran's songs feeling right for the moment. The lyrics used depict the love for someone, regardless of health and age, to love them forever through thick and thin. The video has almost reached a billion views on YouTube, making it one of the most viewed in the entire world.
This song has also been used for a number of proposals, as well as famous films and TV shows. Released in 2010, this is another new song to consider, popular in the USA yet not that well known over here in the UK.
Another new release, Marry You has been used in a number of viral videos with people proposing and recording the moment, often using flashmob dancing. The lyrics are a bit odd for a wedding song, but the tune is incredibly catchy and always makes people blush.
The overwhelming emotion people describe when hearing this song is pure love and goosebumps up and down their arms. This song is actually a cover, first released by Fleetwood Mac, which some people prefer. What partly touched people about this beautiful song was that it was actually released posthumous, as she had died two years beforehand.
Recorded in the 50's, this song was actually the B-side to 'Peggy Sue' and has actually made it onto the Rolling Stones list of the greatest songs of all time.
If you loved the 1996 romantic adaption of Romeo and Juliet, then this could be a great choice. Des'Ree is a London based RnB singer who wrote this song with Timothy Atack, utilising string instruments to great success.
If you want to feel a little like royalty, this one will certainly work. It first premièred in 1749, however it has gone on to be a common wedding song throughout the past two hundred years.
This one is perfect if you like something a bit more upbeat and fun. This is a great one to get everyone in the room smiling and cheering, so it will suit certain audiences (consider what your family is like, mine would love this).
A number of singers have had their own attempts at this song, originally made by Leonard Cohen. Whether you choose the original, Jeff Buckley or John Cale, you have a beautiful song on your hands which will bring a smile and a tear to everyone watching.
Get the groom to sing this and everyone will melt instantly (providing he has a good set of lungs). This has some great lyrics and perfectly explains one persons love for their partner, so it is automatically a great choice.
Sure, it is a bit vain, but the bride is the most important person on the day and she will look lovely, so why not demand some interest. This is the second Stevie Wonder track to make the list, however I can think of a good few more which could sneak on here.
The biggest selling artist in the world right now, Adele is breaking all kinds of records with her latest album. While a number of her tracks are based around ex's, 'make you feel my love' is actually a very romantic piece which is a top recommendation from me. It is a track that slightly slipped under the radar, however you might hear it blaring out of a few churches in the next few years.
You're spoilt for choice on various renditions and adaptions of this song, which was originally created by Secret Garden, however it was first made a globally recognised track when Josh combined his booming voice with a string quartet.
So which is your favourite? Do you have any suggestions which you feel should have made the list? Let us know in the comments below.


Please click the link below to donate to the Audio Description Association.
The Audio Description Association is a charity which relies on the support of our members and the public to maintain its work.
By making a donation to the Audio Description Association, you will be helping to ensure that blind and partially sighted people enjoy the best service that they possibly can when they attend an audio described event. It will enable Audio Describers to continue to update their skills, learn about new ideas and improve their performance whether it be in the theatre, on TV, in film or in museums and other heritage sites.
Make a donation via PayPal on this page.
Donate via a standing order.
Sign up to Give as You Live: This allows you to donate a percentage of what you spend to ADA. Go to its website and search for Audio Description Association.
Please accept our warmest thanks for the support you have provided, we greatly appreciate it.


To clean up Michigan's oil disaster, undocumented workers are being exploited after agreeing to take high-risk jobs injurious to their health.
In June, I wrote about federal immigration enforcement agents targeting workers cleaning up the the biggest oil spill in United States history to check their immigration documents. While no one was detained on site, new evidence reveals that some of the workers contracted by Enbridge Energy in Michigan to clean up the Calhoun County oil spill may not only be undocumented, but were made to work in hazardous conditions.
An investigation by Michigan Messenger (by Change.org Gay Rights blogger Todd Heywood) reveals that Hallmark Industrial — an oil spill cleanup contractor based in Texas and subcontracted by Enbridge Energy — has been busing in hundreds of allegedly undocumented workers to work on the cleanup of the Calhoun County oil spill in Michigan. Following the investigation, Hallmark was fired from the cleanup effort, leaving some of the workers unsure about whether they would be compensated for the work done.
Hiring undocumented workers as independent contractors is not main problem here. What's really concerning is that the workers were expected to work nearly 100 hours a week in unsafe conditions, with 12-14 hour shifts per day, seven days a week. Photographs obtained by the Michigan Messenger reveal numerous violations of OSHA safety standards, including evidence of workers covered in oil and mud getting food and water. The fact that workers may not possess legal right to work in the United States did not give Hallmark or Enbridge any legal right to abuse and exploit them under fair labor standards and practices.
We know from the BP oil spill cleanup efforts that prolonged exposure to similar conditions can result in a multitude of illnesses including, but not limited to, "headaches, dizziness, fatigue, nausea and problems with memory and concentration." Homes in West Michigan had to be evacuated due to the oil spill. There is little doubt that the allegedly undocumented workers were assuming a risk to their lives and health by helping clean up this oil spill for as little as $8 an hour, food on the job site, and a hotel room.
Maybe some American citizens would be willing to assume the same risks in this dire economy. But to avoid a race to the bottom with regard to worker health and safety issues, we need strictly enforce fair labor standards regardless of immigration status, so that unscrupulous employers are less willing to place the most vulnerable and desperate workers in unsafe conditions with little compensation.


Vancouver, Canada - Tinka Resources Limited (the "Company"), (TSXV: TK) (Frankfurt: TLD) (OTCPK: TKRFF), announces the closing of a private placement financing (the "Financing") announced by the Company on April 24, 2013 and May 9, 2013. The Company has raised gross proceeds of $2,575,725 through the issuance of 3,030,265 units at $0.85 per unit (the "Units"). Each Unit consists of one common share and one-half of one share purchase warrant (each whole warrant a "Warrant"). Each Warrant entitles the holder to purchase one common share at a price of $1.25 per common share for a period of eighteen months.
All securities issued under the Financing are subject to a four-month hold period that expires on September 15, 2013.
Richardson GMP Limited ("RGMP") acted as agent on a portion of the Financing placing 1,947,500 Units. In consideration for RGMP's services, the Company paid a cash fee of $165,538 and issued 194,750 compensation warrants. Each compensation warrant entitles the holder to purchase one Unit of the Company under the same terms as the Financing Units. In addition, the Company has paid a finder a cash commission of 6% of certain proceeds and issued 7,500 finder warrants, each finder warrant having the same terms and conditions as the Financing Warrants.


I've changed all the struts on an '89 and a '90 and I did not see much difference in the ride on either car. The struts will affect how much you bounce up and down after hitting a bump but they won't eliminate the "thump" when you hit one.
I had Monroe struts installed 10K ago and I'm not pleased with the ride (not like new). Lifetime warranty on Monroes but having them switched back to OEM at not charge. Would it be a wise decision to have the springs replaced also? 130K on the Corolla?
Sounds like Monroes may not be the best struts to use. I am looking for a strut/spring combination that takes the bite out of bumps as well as smooths out the roads when you drive. Shouldn't the springs help on the bumps? What is it that will help eliminate the initial thud as you go over a bump? Is it more the springs or the struts?
That's right - springs will take the brunt of most bumps and load capacity of the vehicle - but struts will also heavily influence how the car behaves over bumps and road variations. If the car is showing any signs of reduced ride height, constant bottoming with heavy loads, or wierd alignment issues affecting tire wear - this could be attributed to worn or sagging springs. Lifespan will very considerably on how the car was driven, loads it carried, and age. If the car is several years old and has 100K+ miles on it - I would not hesistate to replace the springs and struts together if there was any signs of trouble. The additional cost is very little compared to the amount of labor involved.
I think the springs are fine and the struts are worn and when I get some money and time I will probably change them, or just tolerate the ride as it is now.


MacX Free DVD to iTunes Ripper for Mac 2.0.4 Free Download - Free convert DVD to iTunes supported videos and MP3 music on Mac.
Free convert DVD to iTunes supported videos and MP3 music on Mac.
Last Updated: 9/11/2012 | File Size: 15979 KB | License: Freeware | Downloads: 286 | Developer: Digiarty Software, Inc.
MacX free DVD to iTunes Ripper for Mac is specialized in converting DVD to iTunes supported formats with ideal effect on Mac OS X. With its user-friendly interface, you can rip and convert DVD to iTunes on Mac without any difficulty. Moreover, this free Mac DVD to iTunes ripper can remove various DVD copy protections like Sony ARccOS, region protection, CSS encryption as well as extract DVD audio track to save as MP3 music file on Mac.
1. Convert and rip DVD to iTunes accepted formats on Mac, enjoy DVD movies and MP3 on iTunes free.
2. It supports ripping all kinds of DVDs, including copy-protected DVDs.
3. This free Mac iTunes ripper can extract audio from DVD as ringtone, take snapshot that you favor.
4. Equipped with high quality engine and hyper threading technology, even support multi-core CPU to get a remarkable ripping speed and the output video quality.
5. This free DVD ripper also offers flexible parameter settings to you to flexibly customize the converted videos, such as frame rate, video resolution, video bitrate, audio volume, etc.
6. Free download and trim video segment and rip DVD clips.
7.Built in Advanced technologies, this free Mac DVD to iTunes ripper is designed with features such as "Deinterlacing", "Force A/V Sync" and " Run as Safe Mode" to stabilize the conversion and get better video quality.
Statement: First, please download the orginal installation package by clicking the download link. Of course you may also download it from the publisher's website. Using MacX Free DVD to iTunes Ripper for Mac crack, key, patch, serial number, registration code, keygen is illegal. The download file hosted at publisher website. We do not provide any download link points to Rapidshare, Hotfile, Depositfiles, Mediafire, Filefactory, etc. or obtained from file sharing programs such as Limewire, Kazaa, Imesh, Ares, BearShare, BitTorrent, WinMX etc. Software piracy is theft. If you like MacX Free DVD to iTunes Ripper for Mac please buy it.


text:
Regnskap Lønn og personale Starte egen bedrift Fakturering Regnskapsprogram Rådgivning
Landås Økonomi & Regnskap
Visma.net Visma Webfaktura 24SevenOffice PowerOffice Onestop Reporting
Egenerklæring juridisk person Egenerklæring fysisk person Self-Certification legal person Self-Certification natural person
Self-Certification legal person
SELF-CERTIFICATION
Self-certification by a contact person for a customer who is a legal person.
I understand that accountants have a reporting obligation pursuant to the Norwegian Money Laundering Act and for this reason are obliged to maintain an overview of who must be regarded as an undertaking's beneficial owners and who must be regarded as politically exposed persons.
During my time as a contact person for the undertaking, I will help to keep accountants informed of any changes to the information below.
As far as I am aware, the following applies for the assignment relationship:
1) About beneficial owners
One or more natural persons who, directly or indirectly via other undertakings, either on their own or together with close family members: 2
a) Own more than 25% of the shares
b) Control more than 25% of the total number of votes in the undertaking
c) Have the right to nominate or remove more than half of the undertaking's board members or equivalent
d) Because of an agreement with owners, members, the legal person or a group, articles of associations or equivalent, can exercise control in accordance with letter a, b or c
e) In some other manner exercises control over the legal person or group
If one of more of these are ticked, please state who is or may be such a beneficial owner: . Suitable documentation must also be presented confirming their identity.
No natural persons can be regarded as beneficial owners.
2) About politically exposed persons (PEPs)
Can:
natural persons who may act on behalf of the undertaking;
natural persons with a right of disposal in relation to the undertaking's bank account or any depository; or beneficial owners 1 as mentioned above in point 1)
be regarded as a politically exposed person 2 in relation to the definition below? Are any of these close family members 2 or close business contacts of such a politically exposed person?
If yes, please provide additional information about who these are, the type of political position/office they hold and their role in the undertaking:
Definitions pursuant to the Norwegian Money Laundering Act:
1) beneficial owner: natural person who ultimately owns or controls the customer, or on whose behalf a transaction or activity is being conducted
2) close family member: parent, spouse, registered partner, co-habitant and child, as well as child's spouse, registered partner or co-habitant.
3) politically exposed person: person who serves or has served in the position or function of:
head of state, head of government, minister and assistant minister;
member of national assembly;
member of a governing body of a political party
member of a high-level judicial body, the decisions of which are not subject to further appeal, except in exceptional circumstances;
member of the board of an auditor general's office, a court of auditors or a central bank;
ambassador, charge d'affaires or high-ranking officer of the armed forces;
member of an administrative, management or supervisory body of a state-owned enterprise; or
director, member of the board or other person in the senior management of an international organisation.
Name of organisation:
Organisation number:
Sign form
Aval AS
Adresse: Laguneveien 7, 5239 RÅDAL
Epost: post@aval.no
Utvikling og webhosting av Upnode


20   * Check Digit calculation/validation error.
30       * Construct an Exception with no message.
36       * Construct an Exception with a message.
38       * @param msg The error message.
46       * the underlying cause.
48       * @param msg The error message.