text:
\section{Introduction}

\section{Introduction}

Hand detection is fundamental and extremely useful in human-computer interaction and robotics. It helps computers and robots to understand human intentions\cite{yang2015grasp}, and provides a variety of clues, e.g. force, pose, intention, for high level tasks. Aside from locating hands in an image, determining the in-plane rotation of the hand is also important as it is usually considered as initialization for other tasks such as hand pose estimation\cite{wang2009real} and gesture recognition\cite{wang20116d}. While generic object detection benchmarks have been refreshing over the last decade, hand detection from a single image, however, is still challenging due to the fact that hand shapes are of great appearance variation under different wrist rotations and articulations of fingers\cite{mittal2011hand}\cite{li2013pixel}.

In this paper, we propose to solve hand detection problem jointly with in-plane rotation estimation.
Fig.~\ref{fig0} shows the general pipeline of our system.
Inspired by the RCNN \cite{girshick2014rich} framework, we first extract a number of rectangle regions that are more likely to contain a hand (Fig.~\ref{fig0}(a)). Due to the clutter of the image and the articulated shape of the hand, we propose a discriminative proposal generation algorithm, which significantly outperforms the state-of-the-art region proposal methods such as selective search\cite{uijlings2013selective} and objectness\cite{alexe2012measuring} in term of the recall.
The rotation network then estimates the in-plane rotation that align the input hand, if there is, to the upright direction (Fig.~\ref{fig0}(b)). The input data are rotated according to this estimated rotation and then fed into the detection network to perform a binary classification (Fig.~\ref{fig0}(c)).

Our model is trained jointly with multiple tasks simultaneously, which has been demonstrated to be very successful for many vision tasks. In our case, hand detection and in-plane rotation are closely related and could benefit each other. Calibrating training data under different rotation to upright position results in rotation invariant feature, which relieves the burden of the detection/classification model. While in return, detection model can verify if the rotation estimation is reasonable. However, due to the nature of the convolutional neural networks, rotation invariance is more difficult to achieve than translation invariance, which prevents us from an end-to-end optimization. As a result, previous works \cite{rowley1998rotation}  usually handle transformation estimation and detection separately or in a iterative fashion, which may not achieve a global optima.

We design a derotation layer, which explicitly rotates a feature map up to a given angle. This allows us to jointly optimize the network for two tasks simultaneously (See Fig.~\ref{fig1} for the network structure). Recently, spatial transformer networks (ST-CNN) \cite{jaderberg2015spatial} also presented a differentiable module to actively spatially transform feature maps with CNN. However, their transformation is learned unsupervised such that could be any arbitrary rotation that are not directly interpretable(The discussion that ST-CNN may not be the ideal hand detection model are shown in the appendix). Also, the transformation space is typically huge and would require much more data and time to converge. Comparatively, our rotation estimation network is aimed for upright alignment, such that the output can be directly used for related tasks, e.g. hand pose estimation. It is also trained supervised, which is more likely to converge.

\begin{figure*}[t]
\centering
\includegraphics[width=\textwidth]{./pdf/fig1new.pdf}
\caption{\textbf{Pipeline of our system: Joint hand detection and rotation estimation.}
We first generate region proposals from the input image and feed them into the neural network. The in-plane rotation is estimated by the rotation network, and applied back to the input proposal. The aligned data are then fed into the detection network. Thanks to the derotation layer, two tasks are jointly optimized end-to-end.}
\label{fig0}
\end{figure*}

The contributions of this paper are mainly in four aspects. First, we propose, by our knowledge, the first framework that jointly estimates the in-plane hand rotation and performs detection. Experiment shows that we achieve significant better performance than state-of-the-art on widely used benchmark. Second, we design the derotation layer, which allows end-to-end optimization with two tasks simultaneously. The rotation estimation network is trained with strong supervision, which converges more efficiently. Third, we propose a hand shape proposal generation algorithm with significantly improved recalls and Mean Average Best Overlap score(MABO) outperforming the state-of-the-art selective search\cite{uijlings2013selective} and objectness\cite{alexe2012measuring}. Last but not least, our model is much more efficient than previous work. Thanks to the rotation estimation network, we do not need to brute force search for all possible angles and thus reduce the detection time to $1/7$.

\section{Related Work}
Recent hand detection methods from a single image can be classified into four categories:

\noindent \textbf{Skin Detection Method.} These methods build a skin model with either Gaussian mixture models \cite{sigal2004skin}, or using prior knowledge of skin color from face detection\cite{viola2001robust}. However, these methods often fail to apply to hand detection in general conditions due to the fact that complex illuminations often lead to large variations in skin color and make the skin color modelling problem challenging.

\noindent \textbf{Template Based Detection Method.} These methods usually learn a hand template or a mixture of deformable part models. They can be implemented by Harr features like Viola and Jones cascade detectors \cite{dalal2005histograms}, HOG-SVM pipeline\cite{dalal2005histograms}, mixtures of deformable part models(DPM) \cite{mittal2011hand}. A limitation of these methods is their use of weak features (usually HOG or Harr features). There are also methods that detects human hand as a part of human structure, which uses the human pictorial structure as spatial context for hand position \cite{karlinsky2010chains}. However, these methods require most parts of human are visible, and occlusion of body parts makes hand detection difficult \cite{ghiasi2014parsing}.

\noindent \textbf{Per-pixel Labeling Detection Method.} A pixel labeling approach \cite{li2013pixel} has shown to be quite successful in hand detection in ego-centric videos. In \cite{zhu2014pixel}, the pixel labeling approach is further extended to a structured image labeling problem. However, these methods require time-consuming pixel-by-pixel scanning for whole image.
\iffalse
Hand bounding box detection methods can help to reduce the pixel-by-pixel scanning regions, thus per-pixel labeling methods and bounding box detection methods can benefit each other.
\fi

\noindent \textbf{Detection Method with Pose Estimation.} These methods can be classified as two types: 1) first estimate the object pose, and then predict the object label of the image derotated with the object pose;
Rowley, Baluja, and Kanade\cite{rowley1998rotation} proposed a seminal rotation invariant neural network-based face detection. The system employs multiple networks: the first is a rotation network which processes each input window to determine its orientation, and then uses this information to prepare the window for one or more detector networks. 2) simultaneous pose estimation and detection. He, Sigal and Sclaroff \cite{he2014parameterizing} proposed a structured formulation to jointly perform object detection and pose estimation. Fidler et. al.\cite{fidler20123d} proposed a 3D object detection and viewpoint estimation with a deformable 3D cuboid model. As far as we know, less attention is paid on using convolutional neural networks to jointly model object detection and rotation estimation problems for 2D images.

\section{Approach}
We present an end-to-end optimized deep learning framework for joint hand detection and rotation estimation with a single image.
The overall pipeline is illustrated in Fig.~\ref{fig1}.
We first extract proposals for regions that are likely to contain a hand. To particularly take the advantage of the strong local characteristic of hand shape and color, we train a multi-component SVM using features from Alexnet\cite{krizhevsky2012imagenet} for region proposal rather than simple segmentation based algorithm.
The proposals are fed into convolution layers to exact shared features that will be used for both rotation estimation and detection afterward.
The rotation network performs a classification task to estimate an in-plane rotation that could align the hand in the input image, if any, to the upward direction. Then, the shared feature is explicitly rotated according to the result from the rotation network, and pass through the detection network for a confidence score.
Since the feature is supposed to be well aligned, the detection network does not need to handle the alignment and thus performs more reliably.
The rotation transformation is done by the derotation layer, which allows back propagation and enable an end-to-end training. Different to ST-CNN\cite{jaderberg2015spatial}, both the rotation network and detection network are trained under supervision, therefore the output of the rotation network is guaranteed for the desired data alignment.

\begin{figure*}[t]
\centering
\includegraphics[width=\textwidth]{./pdf/pipeline_new5.pdf}
\caption{\textbf{Overview of our model.} The network consists of four parts: 1) a shared network for learning features to benefit both rotation estimation and detection tasks; 2) a rotation network for estimating the rotation of a region proposal; 3) a derotation layer for rotating inputted feature maps to a canonical pose; 4) a detection network for classifying the derotated proposal. These modules are jointly used to learn an end-to-end model for simultaneous hand detection and rotation estimation.}
\label{fig1}
\end{figure*}

\subsection{Proposal Generation}
A variety of category-independent region proposals are proposed including selective search\cite{uijlings2013selective}, objectness\cite{alexe2012measuring}, and category independent object proposals\cite{endres2010category}.
However, due to the cluttered background and articulated shape of the hands, previous region proposals (especially segmentation based) can no longer guarantee recall while keep the number of proposals to be manageable(Table 1 from experiment section shows that selective search and objectness are both not good at hand detection.).

We adopt a discriminative approach to generate hand region proposal. Inspired by deformable parts model(DPM)\cite{felzenszwalb2010object}, we cluster the training data to 8 subgroups based on the aspect ratio of the image patches, and train one linear SVM using pooled conv5 layer feature extracted from the Alexnet \cite{krizhevsky2012imagenet} for each group. We learn the threshold on validation set such that 100 percent of the positive data is covered with at least 0.5 Intersection over Union(IOU). Fig.\ref{figproposala} shows that our method ensures significantly higher recall while keeps the number of proposal per image comparable.

\subsection{Rotation Aware Network}
In this section, we introduce the rotation aware neural network to decide if a region proposal contains a hand. The detailed network structure is in supplementary material.
\subsubsection{Network Structure}
\iffalse
The network starts with 3 convolution+relu+pooling to extract features from input
image proposals. The resolution of the input feature map is $13\times13$  due to the strides but still maintains the spatial information. Built upon this feature, the rotation network consists of 2 convolution layers followed by 3 fully connected layer and estimate the angles to rotate such that the hand, if any, in the proposal could be aligned to the upward direction.
We formulate the rotation estimation problem into a classification problem. We divide the 360 degrees into 18 bins such that each bin accounts for 20 degrees, and the purpose of the rotation network is to predict the right bin in which the ground truth rotation falls in. Although this classification formulation loses the prediction accuracy up to the scale of a bin and the continuous relations between neighboring bins, in practice we find this formulation is more stable than a regression on continuous angular value, and the estimated precision is sufficient for the detection network.
Afterward, a derotation layer rotates the feature from the shared network according to the estimated in-plane angle from the rotation network. The rotated feature is then fed into 2 convolution layers and 3 fully connected layers to perform a binary classification, telling if the proposal contains a hand.
Since the derotation layer is differentiable, the whole network can be optimized end-to-end, and two tasks, rotation estimation and hand detection, can be jointly optimized.
\fi
The network starts with 3 convolution+relu+pooling to extract features from input proposal patches. The resolution of the input feature map is $13\times13$  due to the strides but still maintains the spatial information. Built upon this feature, the rotation network consists of 2 convolution layers followed by 3 fully connected layer and estimate the angles to rotate such that the hand, if any, in the proposal could be aligned to the upward direction.
We formulate the rotation estimation problem into a regression problem. Given an rotated hand, the rotation network performs as a regressor and outputs a two dimensional rotation estimation vector $\mathbf{l} = (\cos\alpha,\sin\alpha)$. The estimated $\mathbf{l}$ will then be sent to the derotation layer to rectify the orientation of training patches. Afterward, a derotation layer rotates the feature from the shared network according to the estimated in-plane angle from the rotation network. The rotated feature is then fed into 2 convolution layers and 3 fully connected layers to perform a binary classification, telling if the proposal contains a hand. Since the derotation layer is differentiable, the whole network can be optimized end-to-end, and two tasks, rotation estimation and hand detection, can be jointly optimized.

\subsubsection{Derotation Layer}
Derotation layer is a layer which applies a rotation transformation to a feature map during a single forward pass.
In our scenario, the input of a derotation layer is the feature map computed from the original image and a in-plane rotation angle predicted from either the rotation network or ground truth, and the output of this layer is the derotated feature map under the given rotation angle, while supposedly under the canonical upright hand pose (Refer to Fig.~\ref{fig3}).
\begin{figure}[t]
\centering
\includegraphics[width=0.4\textwidth]{./pdf/derotate2.pdf}
\caption{Illustration of applying derotation transformation to input feature map. The derotation layer aims to warp the input image to a canonical hand pose by $\mathbf{R}_\alpha$. In this work, the canonical hand pose is an upright hand pose as shown in the right part of this figure.}
\label{fig3}
\end{figure}

Specifically, if $\alpha$ is the in-plane rotation angle we want to apply, the derotation transformation is
\begin{equation}
\label{eq4}
\left[ {{\begin{array}{*{20}c}
 x' \\
 y'
\end{array} }} \right] = \underbrace{\left[ {{\begin{array}{*{20}c}
 \cos\alpha & -\sin\alpha\\
 \sin\alpha & \cos\alpha
\end{array} }} \right]}_{\mathbf{R}_\alpha}\left[ {{\begin{array}{*{20}c}
 x \\
 y
\end{array} }} \right]
\end{equation}
\noindent where $[x',y']$ is the target coordinates of the regular grid in the output feature map under the canonical upright hand pose, $[x,y]$ is the source coordinates of the regular grid in the input feature map.

In our implementations, we use inverse mapping to get the output feature map. In other word, for each pixel $[x',y']$ of the output we get the corresponding position $[x, y]$ in the input feature map. Since $[x, y]$ is often not located on a regular grid, we calculate the feature by averaging the values from its four nearest neighbor locations. We pad zero to $[x, y]$, which is outside of the regular grid. An alternative choice is to pad with feature outside the box, which is not implemented here due to efficiency issue.

The back-propagation can be done with a record of the mapping between coordinates between feature map before and after the derotation layer. Updating value on $[x', y']$ is backward propagated to the coordinates from which its value comes, which is in a similar fashion as some pooling layer and ROI layer in \cite{girshick2015fast}\cite{ren2015faster}.

\subsection{Loss Layer}
Our model overall has two losses, the rotation network loss and the detection network loss.

\noindent \textbf{Rotation loss.} For rotation estimation, we use $L_2$ loss. We get ground true hand bounding boxes and use them to train a network that can
do regression on the hand's rotation, formulated as a two-dimensional vector $\mathbf{l} = (\cos\alpha,\sin\alpha) \triangleq (\frac{c}{\sqrt{c^2 + s^2}},\frac{s}{\sqrt{c^2 + s^2}}) $. Here, $c,s$ are outputs of the final convolutional layer in rotation network, $\mathbf{l}$ is enforced as a normalized pose vector by normalizing $c,s$, and thus we can enforce $\mathbf{R}_\alpha$ in Eq.(\ref{eq4}) as a rotation matrix. More exactingly, if $\mathbf{l}$
and $\mathbf{l}^{*}$ are the predicted and ground truth rotation estimation vectors, the rotation loss is
\begin{equation}
\label{eq5}
L_{rotation}(\mathbf{l},\mathbf{l}^{*}) = ||\mathbf{l} - \mathbf{l}^{*}||_2^2
\end{equation}

It is easy to compute the partial derivative of $L_{rotation}$ w.r.t $\mathbf{l} = (\cos\alpha$, $\sin\alpha)$. To deduce the backward algorithm of rotation loss, we need to compute the partial derivative of $\mathbf{l} = (\cos\alpha,\sin\alpha)$ w.r.t. $c,s$, which can be calculated as follows:
\begin{eqnarray}
\nonumber \frac{\partial \cos\alpha}{\partial c} &=& (c^2 + s^2)^{-\frac{1}{2}} - c^2(c^2 + s^2)^{-\frac{3}{2}}\\
\nonumber \frac{\partial \cos\alpha}{\partial s} &=& -cs(c^2 + s^2)^{-\frac{3}{2}}\\
\nonumber \frac{\partial \sin\alpha}{\partial c} &=& -cs(c^2 + s^2)^{-\frac{3}{2}}\\
\nonumber \frac{\partial \sin\alpha}{\partial s} &=& (c^2 + s^2)^{-\frac{1}{2}} - s^2(c^2 + s^2)^{-\frac{3}{2}}
\end{eqnarray}

\noindent \textbf{Detection loss.} For detection task, we use a simple cross-entropy loss. Denote $D^{*}$  to be the ground truth object labels, and we use
the detection loss as follows
\begin{equation}
\label{eq6}
L_{detection}({D},{D}^{*}) = - \frac{1}{n} \sum_i \sum_j D_i^{*} \log(D_i)
\end{equation}
\noindent where $D_i = {e^{z_j^i}}/{\sum_{j = 0}^1e^{z_j^i}}$ is the prediction of class $j$ for proposal $i$ given the output $z$ of the final convolutional layer in detection network, $n$ is the training proposal number.

\subsection{Training Schema}
The rotation aware network contains two pathways that interact with each other through the derotation layer. To train the model, we adopt a divide and conquer strategy. We first initialize the shared network and the rotation network with the model pretrained on ImageNet, and only fine tune on the rotation estimation task. Then, we fix these two networks but enable the detection network, and fine tune for the hand binary classification task. After the network parameters converge to reasonable good local optima, we enable all the network and optimize in a end-to-end manner for both tasks.

We take only the region proposals from proposal generation section as the training data. Depending on the IOU with ground truth, a region proposal is considered to be positive if the IOU is larger than 0.5; negative if the IOU is smaller than 0.5; discarded otherwise, which ends up with about 10K positives and 49 million negatives. Since the number of positive and negative data is extremely imbalanced, we use all the positives and randomly sampled 30 million negatives. We also ensure the ratio between positive and negative data in each mini-batch to be 1:1. For the negative data, they do not contribute any gradient during the back propagation from the rotation network.

\subsubsection{Data Augmentation}
Since the pretrained Alexnet on ImageNet does not encode rotation related information, we found that training with the limited number of positive data results in poor generalization capability. We relieve the overfitting by augmenting the size of the training data. We horizontally flip the training image, which allows invariance against left/right hand. We also rotate each positive proposals to 36 times (10 degree once) around the center of the patch, and take each of them as a training data. This dramatically increases the size of training data to 36 times bigger, and our training data eventually contains over 6 million proposals from 4096 images.

\subsubsection{Hard Negative Mining}
Inspired by DPM\cite{felzenszwalb2010object}, we perform hard negative mining to suppress confusing false alarms. After training a model from the initial training data, we run the model on the whole training proposals. We add the negatives with high score ($\geq0.4$) to the training data and train a model again. Generally speaking, 2-3 times of hard negative mining are sufficient to improve the performance to be almost saturated.

\iffalse
In practice, we find that skin area, e.g. face, arm, are more likely to be misunderstood as hand due to similar skin color. To make the hard negative more effective, we run a skin area detection\cite{skinmodel} and intentionally add negative proposals from the skin area into the training data.
\fi

\section{Experiments}

\subsection{Dataset and Evaluation}
The proposed method is evaluated on widely-used Oxford hand dataset\cite{mittal2011hand} and EgoHands dataset\cite{egohands2015iccv}. The Oxford hand dataset contains 13050 hands annotated with bounding box and rotation from images collected from various public image datasets. The dataset is considered to be diverse as there is no restriction imposed on the pose or visibility of people, and background environment. Oxford hand dataset has much cluttered background, more viewpoint variations and articulated shape changes than other popular hand dataset such as Signer\cite{Signer} and VIVA\cite{VIVA}. The EgoHands dataset \cite{egohands2015iccv} contains 48 Google Glass videos of complex, first-person interactions between two people, which are also annotated with bounding box and rotation. This dataset is mainly used to recognize hand activities in first-person computer vision.

To demonstrate the performance of the whole system, we evaluate the region proposal generation, the rotation estimation, and final detection performance respectively.
For region proposal generation, we measure the percentage of positive data that is covered by any proposal with an IOU larger than 0.5. To further show the localization precision, we also calculate the Mean Average Best Overlap (MABO)\cite{uijlings2013selective}, which is a standard metric to measure the quantity of the object hypotheses. For rotation estimation, we measure the difference between the estimation and the ground truth. For the detection, we use the typical average precision(AP) and precision-recall curve with a threshold 0.5 on IOU.

\subsection{Performance on Oxford Hand Dataset}
\subsubsection{Region Proposal Generation}
Fig. \ref{figproposalb} and Table \ref{tab2} show comparisons between our SVM based approach to the traditional segmentation based algorithms such as selective search\cite{uijlings2013selective} and objectness\cite{alexe2012measuring}. We achieve nearly 100\% recall and a significantly higher MABO with only about half the number of proposals (7644 vs. 13000+) used in selective search and objectness. Qualitatively, selective search fails due to the fact that it relies much on over-segmentation and may not be suitable for complex scenarios with cluttered background and many connected skin-like regions, while our method could take advantage of the discriminative power of the articulated local shape of the hand and generate reliable proposals.
\begin{figure}[h]
 \centering
\begin{minipage}{0.49\linewidth}
\begin{center}
\subfigure[]{\includegraphics[width=\textwidth]{./pdf/recallmaboa3.pdf}\label{}}
\end{center}
\end{minipage}
\begin{minipage}{0.49\linewidth}
\begin{center}
\subfigure[]{\includegraphics[width=\textwidth]{./pdf/recallmabob3.pdf}\label{}}
\end{center}
\end{minipage}
\caption{Trade-off between recall(a) and MABO(b) of the object hypotheses in terms of bounding boxes on the Oxford hand test set.}
\label{figproposala}
\end{figure}

\iffalse
\begin{figure}[b]
\centering
\includegraphics[width=0.55\textwidth]{./pdf/proposalcompare3.pdf}
\caption{Comparison between our proposal generation approach to the traditional segmentation based algorithms.
Examples of locations for objects whose Best Overlap score is around MABO of our method, objectness and selective search. The green boxes are the ground truth. The red boxes are created using our proposal generation method.}
\label{figproposalb}
\end{figure}
\fi

\begin{figure}[b]
 \centering
 \begin{tabular}{cc}
\includegraphics[width=0.6\linewidth]{./pdf/ours.pdf} & Ours \\
\includegraphics[width=0.6\linewidth]{./pdf/objectness.pdf} & Objectness\\
\includegraphics[width=0.6\linewidth]{./pdf/selectivesearch.pdf} &Selective search
\end{tabular}
\caption{Comparison between our proposal generation approach to the traditional segmentation based algorithms. Examples of locations for objects whose Best Overlap score is around MABO of our method, objectness and selective search. The green boxes are the ground truth. The red boxes are created using our proposal generation method.}
\label{figproposalb}
\end{figure}

\begin{table}[htbp]
\caption{Proposal generation performance on the hand test data. \#win means the window numbers.} \centering
\begin{tabular}{c|ccc}
  \hline

  {Method} & Recall & MABO &  \#win\\
  \hline
  selective search &  46.1\% &  41.9\% & 13771 \\
  objectness &  90.2\% &  61.6\% & 13000 \\
  our proposal method &  99.9\% &  74.1\% & 7644\\
  our proposal method &  100\% &  76.1\% & 17489\\
  \hline
\end{tabular}
\label{tab2}
\end{table}

\subsubsection{Rotation Estimation}
We first demonstrate that the rotation network can produce reasonable in-plane rotation estimation. Table \ref{tablerotation} shows the performance of the rotation estimation(Refer to only rotation model). We can see that the prediction for 45.61\% of the data falls in 10 degree around the ground truth, and 70.13\% for 20 degree, 79.79\% for 30 degree. Examples of hand rotation estimation results on test images are also shown in Fig. \ref{fig:rotation}. We see that our rotation model leads to excellent performance.
\begin{figure*}[t]
\centering
\includegraphics[width=0.75\textwidth]{./pdf/rotationexamples2.pdf}
\caption{Examples of hand rotation estimation results on proposals of test images. The red and green arrows indicate the estimated and ground truth rotation angles, respectively.}
\label{fig:rotation}
\end{figure*}

\begin{table}
\caption{Rotation estimation performance on the hand test data. Rotation is correct if the distance in degree between prediction and ground truth is less than $\delta_\alpha = 10^\circ, 20^\circ, 30^\circ$. We compare the rotation estimation results on the hand test data with only rotation model, and joint rotation and detection model. } \centering
\begin{tabular}{c|ccc}
  \hline

  {Method} & $\leq 10^\circ$ & $\leq 20^\circ$ & $\leq 30^\circ$ \\
  \hline
  Only rotation model &  45.61\% & 70.13\% & 79.79\%\\
  Joint model    &  47.84\% & 70.88\% & 80.24\%\\

  \hline
\end{tabular}
\label{tablerotation}
\end{table}

\subsubsection{Detection Performance}
We compare our model to several state-of-the-art approaches such as R-CNN\cite{girshick2014rich}, DPM-based method\cite{mittal2011hand}, DP-DPM\cite{girshick2015deformable} and ST-CNN\cite{jaderberg2015spatial}, the first three of which do not explicitly handle rotation. Fig. \ref{precision_recall}(a) shows the precision recall curves, and the number after each algorithm is the average precision(AP).
Our model(seperated) means that the shared 3 convolution layers are kept unchanged, and the rotation and detection networks are trained separately with shared network not tuned, and our model(joint) means that the network is end-to-end trained. Our average precision on Oxford hand dataset is 48.3\% for our model(joint), which is significantly better (11.5\%, 6\% higher) than the state of the art\cite{mittal2011hand}, in which AP = 36.8\% is reached with DPM trained with hand region, and AP = 42.3\% is reached with additional data such as hand context and skin color model(We do not use such additional data). Our models, joint or separated, is advantageous over seminal CNN-based methods, AP of our separated model is 4.9\% higher than R-CNN, 6.6\% higher than ST-CNN. This demonstrates that data alignment with rotation is very critical for the classification model in the detection network. In Fig. \ref{figresults}, we show some results of our method on test image from Oxford hand dataset, in which both detection bounding boxes and rotation estimation results are shown. The discussion that ST-CNN may not be the ideal hand detection model is shown in the appendix.

\begin{figure*}[htbp]
 \centering
 \begin{tabular}{cc}
\includegraphics[width=0.45\linewidth]{./pdf/ap1new.pdf} & \includegraphics[width=0.45\linewidth]{./pdf/ap2new.pdf} \\
 (a) & (b)
\end{tabular}
\caption{Precision-Recall curve comparing the baseline and final results. (a) Comparison with baselines. DPM means the results with hand shape detector in \cite{mittal2011hand}. (b) Comparison with detection with ground truth rotation, a performance upper bound.}
\label{fig:precision_recall}
\end{figure*}

\begin{figure*}[htbp]
\centering
\includegraphics[width=0.9\textwidth]{./pdf/results6.pdf}
\caption{Examples of high-scoring detection on Oxford hand database. The rotation estimation is illustrated with red arrows.}
\label{figresults}
\end{figure*}

We give more experimental results of our hand detector on Oxford hand dataset. Fig. \ref{figoxfordoutdoor} and Fig.\ref{figoxfordindoor} are examples of high-scoring detection on Oxford hand database for outdoor and indoor images, respectively. Obviously, our method works well for both outdoor and indoor images, for images with multiple and single hands. We give examples of false alarm detection in Fig.\ref{figoxfordfalse}, which indicates that skin areas(such as face, arm, foot) are more likely to be misunderstood as hand due to similar skin color, and some non-skin-like regions are also easy to be misclassified. We believe that we can make the hard negative more effective by running a skin area detection\cite{skinmodel} and intentionally add negative proposals from the skin area into the training data.
\begin{figure*}[htbp]
\centering
\includegraphics[width=0.5\textwidth]{./pdf/outdoor.pdf}
\caption{Examples of high-scoring detection on Oxford hand database(outdoor images). The rotation estimation is illustrated with red arrows.}
\label{figoxfordoutdoor}
\end{figure*}
\begin{figure*}[t]
\centering
\includegraphics[width=0.8\textwidth]{./pdf/indoor.pdf}
\caption{Examples of high-scoring detection on Oxford hand database(indoor images). The rotation estimation is illustrated with red arrows.}
\label{figoxfordindoor}
\end{figure*}
\begin{figure*}[htbp]
\centering
\includegraphics[width=0.8\textwidth]{./pdf/falsealarm.pdf}
\caption{Examples of false alarm detection on Oxford hand database. (a) false alarm with skin-like region. (b) false alarm with non-skin region.}
\label{figoxfordfalse}
\end{figure*}

\subsubsection{Efficiency}
We compare the running time with the previous state-of-the-arts method\cite{mittal2011hand}, R-CNN\cite{girshick2014rich}, DP-DPM\cite{girshick2015deformable} in Table \ref{tabdetectiontime}. The time efficiency of our method is superior to that of the method in\cite{mittal2011hand}, and it is comparable to that of R-CNN and DP-DPM. The running time is about 8 seconds per image of $500\times 400$ pixels on a quad-core 2.9GHz PC with Nvidia Titan X, while previous method in \cite{mittal2011hand} takes about 55 seconds per image. Our method is more efficient due to the use of region proposal instead of sliding window, and derotating only once with estimated angle instead of brute force rotating in \cite{mittal2011hand}. We believe that our method can be more time efficient by leveraging more advanced region proposal method such as region proposal networks\cite{ren2015faster} and sharing convolutional feature maps of an image for all proposals by using pooling methods such as ROI pooling\cite{girshick2015fast}.
\begin{table}[t]
\caption{Average time (second/image) to detect hands. Comparison are made with state-of-the-arts DPM-based method\cite{mittal2011hand}, R-CNN\cite{girshick2014rich}, DP-DPM\cite{girshick2015deformable}, and our joint model. Our method is superior to \cite{mittal2011hand} in running time.} \centering
\begin{tabular}{c|cccc}
  \hline

  {} & DPM & R-CNN & DP-DPM & Joint model\\
  \hline
  running time &  55  & 9  & 2 & 8\\
    \hline
\end{tabular}
\label{tabdetectiontime}
\end{table}

\subsection{Model Analysis}
\subsubsection{Is The Model Well Optimized?}
In order to understand if the model is properly optimized with explicit rotation estimation, we train a detection network with the ground truth rotation. The precision-recall curve is shown in Fig. \ref{fig:precision_recall}(b). The average precision is 50.9\%, which can be considered as a performance upper bound under the current network topology. Again, it shows that aligning data to supervised orientation could great benefit the detection model. Also, our performance is only 2.6\% lower than this upper bound, which indicates our system is well optimized.

\subsubsection{Does Joint Training Help?}
Conceptually, it is beneficial to train a network by jointly optimizing over multiple related tasks. We investigate this issue here by comparing a model without jointly training to our joint model. To obtain a non-jointly optimized model, we still follow the divide and conquer fashion of parameter initialization, but allow the rotation network and the detection network to have shared first 3 layers for feature extraction. This results in 2\% drop on average precision(Refer to Fig. \ref{fig:precision_recall}(b)) and about 1\% drop on rotation estimation(Refer to Table \ref{tablerotation}). Overall, we demonstrate that joint training allows multiple tasks to share mutually useful information, and provides a compact and efficient network topology.

\subsection{Performance on EgoHands dataset}
\begin{figure*}[t]
\centering
\includegraphics[width=0.8\textwidth]{./pdf/ego2.pdf}
\caption{Examples of high-scoring detection on Egohands database. The rotation estimation is illustrated with red arrows.}
\label{figegomoreresult}
\end{figure*}
\begin{figure}[htbp]
\centering
\includegraphics[width=0.5\textwidth]{./pdf/ap-ego1.pdf}
\caption{Precision-Recall curve comparing the baseline and final results on EgoHands dataset\cite{egohands2015iccv}.}
\label{fig_ego_pccurve}
\end{figure}
In order to show the generalization power of our method, we test our pipeline on EgoHands dataset\cite{egohands2015iccv}. Fig.\ref{fig_ego_pccurve} shows precision-recall curve comparing the baseline and final results on EgoHands dataset, and the number after each algorithm is the average precision(AP). Our model(seperated) means that the shared 3 convolution layers are kept unchanged, and the rotation and detection networks are trained separately with shared network not tuned, and our model(joint) means that the network is end-to-end trained. Fig. \ref{figegomoreresult} shows examples of high-scoring detection on Egohands database. The rotation estimation performance on Egohands dataset are shown in Tab.\ref{tableegorotation}.

We compared our pipeline with the state-of-the-art detection algorithm in \cite{egohands2015iccv}. We implement the state-of-the-art hand detector on this dataset with the network prototxt and Caffemodel provided by \cite{egohands2015iccv}. For more rigorous evaluation, we compare detection performance of the method in  \cite{egohands2015iccv} and our method with the same region proposals, NMS and the other experiment setting. The average precision with our seperated model (AP:75.7\%) is higher than the results with baseline (AP:73.3\%)(Refer to Fig.\ref{fig_ego_pccurve}), which indicates that rotation information is helpful to improve the detection results. We then compare the rotation estimation and detection results with separated and joint models. We can see that the rotation estimation results with our joint model is slightly better than the results with only rotation model. Separated model results in 1.4\% drop on average precision than joint model. Therefore, we again demonstrate that joint training allows multiple tasks to share mutually useful information, and provides a compact and efficient network topology.
\begin{table}[t]
\caption{Rotation estimation performance on Egohands dataset.} \centering
\begin{tabular}{c|ccc}
  \hline

  {Method} & $\leq 10^\circ$ & $\leq 20^\circ$ & $\leq 30^\circ$ \\
  \hline
  Only rotation model &  48.63\% & 76.56\% & 87.26\%\\
  Joint model    &  49.01\% & 76.68\% & 87.09\%\\

  \hline
\end{tabular}
\label{tableegorotation}
\end{table}

\section{Conclusion}
Hand detection and pose estimation are important tasks for interaction applications. Previous works mostly solved the problem as separated tasks. In this paper, we explore the feasibility of joint hand detection and rotation estimation with CNN, which is based on our online derotation layer planted in the network. Our experimental results demonstrate that our method is capable of state-of-the-art hand detection on widely-used public benchmarks. The detection network can be extended to use hand context and more sophisticated rotation model.

\ifCLASSOPTIONcompsoc

  \section*{Acknowledgments}
\else

  \section*{Acknowledgment}
\fi
The authors would like to thank Dr. Peng Wang and Dr. Jiewei Cao from University of Queensland, and Dr. Sven Bambach from Indiana University Bloomington for helpful discussions.
The authors acknowledge the support of NVIDIA with the donation of the GPUs used for this research.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


Philipp is CEO & Founder of OneShore Energy GmbH in Berlin, a company focused on PV diesel hybrids for islands and remote locations. Since May 2017 OneShore is part of BayWa r.e., a European leader in renewable energy. Before he worked for NexTracker, Solaria Corp, Invest in Germany in Palo Alto, CA, Monitor Company and gained experience in banking with UBS and HypoVereinsbank.
As a true islander with his family originating from Chania / Crete, he is committed to drive the transition to a more sustainable generation of energy on islands. With OneShore, he has deployed first hybrid systems in Greece and Africa and now as part of BayWa re is working on the development of large scale hybrids with a focus on Greece and Southeast Asia.
Philipp leads the task force for start-ups and innovative business models at the Berlin-Brandenburg Energy Network and served as member of the Industry Advisory Committee of the Solar Europe Industry Initiative.
He studied at London School of Economics in London and ESADE in Barcelona and holds a M.Sc. degree in Management.
Michalis Verroiopoulos was born in 1964 in Athens. He holds a degree in Civil Engineering, University of Patras (1989). In 2002 he obtained a Master of Business Administration (MBA) from the Economic University of Athens. In 2000 he acquired a Diploma in "Quality Management System" by the European Organization of Quality (EOQ) and the HMA. Since November 2015, Mr. Verroiopoulos is Secretary General of Energy and Mineral Resources at the Ministry of Environment & Energy of the Hellenic Republic.
From June 2015 to November 2015 was Chief Executive Officer of the company PPC Renewables. From 2010 to May 2015 he worked at the company Conergy Energy Systems as Project Manager in Greece and the United Kingdom. From 1999 to 2010 he worked at Athens International Airport SA "Eleftherios Venizelos" where he was Design and Construction Manager. From 1991 to 1999 he worked as Project Manager in the Design, Construction, Commissioning and Operation on Building and infrastructure projects. In 2014 he was elected City Councillor in the Municipality of Vrilissia. He is married and has a son and a daughter.
Takis SARRIS is a Mechanical Engineer with an MSc in Energy Production from CHP plants. He has 18 years of experience in the energy industry, 10 years in executive level positions developing and executing operational strategies. In the early '00s, as Project Manager of TEMA Energy SA, he successfully implemented the first natural gas tri-generation CHP plant in Greece. In 2006 he joined the RES world as Project Manager of Suntechnics GmbH and Conergy GmbH, and in 2008 he joined juwi Hellas SA as CTO. In 2012, as CTO of juwi Hellas, the company was declared by Fortune magazine as the 7 th fastest developing company in Greece. In 2014, as CTO of juwi Hellas, he participated in the establishment of the company in the emerging Turkish RES market, where he successfully implemented one of the first PV plants that was ever connected in the Turkish grid. In 2016, as MD of juwi Hellas, he participated successfully in the pilot Greek Tender for new Solar projects, which was the beginning of the new era in the Greek solar industry. Since December 2014 he is the President and Managing Director of juwi Hellas SA, developing the appropriate policies to meet juwi Group's mission of being one of the top RES developers worldwide.
Dimitris Varlamis is member of JinkoSolar since 2013 and he is responsible for sales in SouthEastern Europe. He is managing also Key Global Accounts of Jinko, investors, utilities and EPC, with projects all over the world. He has been working in the PV sector since 2010 with strong sales background. His deep knowledge of PV industry, his sales skills in particular regarding large scale projects, makes him a valued expert in this field.
Charilaos Lazanis received his Diploma in Electrical Engineering in 1994 from the University of Patras, Greece. He holds a Master in Business Administration degree from Cardiff Business School, UK.
During the years 2000 - 2002 he worked at Cyprus Leasing SA in Athens as Credit & Marketing Officer. Since 2003 has been working in DAPEEP SA (former LAGIE SA) in the Renewable Energy Sources Department. His primary duties as deputy director are signing the Power Purchase Agreements (PPA) on behalf of DAPEEP, issuing the statements for the monthly settlement of the RES producers in the Interconnected System and applying all the relevant legislation regarding contract management and pricing of the various renewable energy technologies. Furthermore, his professional experience and expertise involves the integration and implementation of the new energy policies in the field of RES support schemes and the legal issues of RES Power Purchase Agreements.
He has represented LAGIE in numerous workshops and conferences regarding photovoltaics. He is also member in various committees regarding the national policy measures towards the transition to the new RES support scheme, and the reform of licensing procedures of renewable energy technologies in Greece.
Charilaos Lazanis is member of the Technical Chamber of Greece.
Diomidis joined Foresight in 2009 and currently holds the Investment Director position in the Infrastructure team. Based in Rome, Diomidis is heavily involved in the group's energy infrastructure investments in Southern Europe. He has been key founding team member of ForVEI, a leading solar PV investment platform of ca. € 350.0 M value, that raised investment capital from 5 different institutional investors. In 2018 it completed a full investment cycle by reaching an exit and delivering mid-teen returns.
He is primarily focused in leading Foresight's activities in the infrastructure debt space structuring and arranging Green project bonds across the renewable energy and energy efficiency sectors.
Foresight Group is a leading independent infrastructure and private equity investment manager which has been managing investment funds on behalf of institutions and retail clients for over 30 years. With current assets under management of £2.8 billion, raised from UK and international private and high net-worth individuals, family offices, pension funds and other institutional investors, Foresight strives to generate increasing dividends and capital appreciation for its investors over the long term. Foresight has offices in UK, USA, Italy and Australia.
Stelios Loumakis was born in Athens in 1969 and graduated Chemical Engineer, National Technical University of Athens (NTUA), in 1992. At post-graduate level he has been awarded the degree of Master of Business Administration (MBA) from Portsmouth University of U.K. At present he is PhD Candidate NTUA elaborating a doctoral thesis in the field of Electricity Market Operation. He is a member of TEE (Technical Chamber of Greece) since 1993.
He started his professional career at late '80s in parallel with his studies in the family business, for decades involved in exports of garments to the European markets. During '90s he developed a new department trading OTC para-pharmaceuticals, diagnostic tests and health products both in Greece and Cyprus. Later on he worked for five years as an executive in Marketing and Sales in the leading multinational company Sara Lee Household & Body Care Hellas AE and later to the Greek industrial bread and food company Karamolegos AE. Back in business he was involved for five years in the rapidly expanding sector of organized bread and food retail in co-operation with the food and bread industrial company Katselis ABEE. In 2006 he entered the field of power generation through photovoltaic (PV) technology. He pioneered in Greece developing centralized PV plants. His company's first centralized PV plant was one of the first of its kind in the country and was connected to the grid in April 2008.
Overall, he enjoys valuable engineering and consulting experience in the field of wholesale electricity market and renewables and is the President of the Hellenic Association of Photovoltaic Energy Producers (www.spef.gr) since December 2011. SPEF is the official business and scientific association founded in 2009 that represents in Greece power generating organizations from PVs. Due to this role he enjoys extensive experience and public presence with articles, technical studies, law proposals, participation in public consultations and speeches in several highly esteemed conferences.
Fotis Gakis received his Diploma in Electrical Engineering in 2005 from the National Technical University of Athens. He holds a Master of Science in Energy Production and Management and a Master in Business Administration.
From 2007 he works in the energy sector and since 2016 he works as a Policy Engineer in the Hellenic Electricity Distribution Network Operator (HEDNO), being involved with regulatory issues for grid-connected users. His main activities include self-consumption support schemes, requirements for generators, storage, demand response and energy communities.
Xanthi is a lawyer, admitted in Athens Bar in 2013, and an associate of MStR Law Firm since 2014. She is a graduate of the Law Faculty of the National University of Athens and holds a postgraduate degree in Public Law from the same university. Her practice area focuses on administrative law, corporate law & contracts and energy law, on advisory and court level, acting for both domestic and international clients. In the field of energy market, Xanthi has participated in litigations involving RES, projects' transfers / acquisitions -share or asset deals- and the performance of due diligence as well, inter alia, in relation to one of the biggest photovoltaic projects in Greece. She also regularly advises enterprises of the energy sector on various issues regarding their daily operation and the drafting, reviewing & negotiation of related contracts (EPC, lease agreements etc.).
Theodoros is a lawyer and an associate of MStR Law Firm since 2017. He is a graduate of the Law Faculty of the National University of Athens and holds a postgraduate degree in Private International Law and International Transactions from the same university. His practice areas focuses on commercial law and transnational disputes. He has participated in due diligence projects related to energy sector and in several litigations involving RES.
Christos Pronios is a Mechanical Engineer with 30 years of experience in leading edge ICT solutions for engineering and industrial applications. During the last decade he has worked on energy efficiency and renewable energy with the bulk of activities in solar PV and diesel+solar solutions. He is currently the New Applications Manager in the EUROSOL group of companies, in charge of both R&D and commercial projects involving micro-grids, hybrid power systems and energy storage.
Current R&D activities include research on digitization and management of the demand-side in micro-grids, development of cyber-physical system components for distributed micro-grid integration in a virtual or physical smart-grid, and utilization of IoT design patterns components in micro-grids, while current commercial project activities are concentrated in the MENA region.
Fields: Energy Policy, Renewable Energy Technologies, Energy Economics, Energy Financing, Energy Market Mechanisms, Energy Efficiency Applications, Formulation of Energy Support and Financing Programmes.
Prof: >15yrs in the Energy Sector. Continuous professionally active in International, European and National energy related issues with focus on RES and EE. For the past 5yrs Senior Expert in the Technical Assistance for the Reform of the Renewable Energy Sector and Energy Efficiency in Greece, based at MinEnvEn.
Member of several national and European Committees and WGs on RES & EE, Energy Planning & Modelling, Support Schemes, Long term energy plans.
Thanassis Petmezas is the Chief Executive Director of cosmoONE. He is a graduate of the Electrical Engineering Dept. of the University of Patras/Greece and also holds an MBA from Athens University of Economics and Business.
In 2000, he was appointed as head of cosmoONE, the first B2B marketplace established in Greece by OTE/COSMOTE to develop the electronic procurement services market in Greece. Since then, Mr. Petmezas is steering the company in the Greek market and abroad, by developing and delivering leading-edge sourcing applications for the Public and Private Sector.
In the past, he has served as Director of Major Accounts and Director of Enterprise Solutions and Services at Compaq Computers SA (1996-2000), as Senior Sales Executive for large Public organizations at INTRASOFT (1994- 1996), as Sales Manager of Large Accounts, Partners and Multinational/Global Accounts at Hewlett Packard Hellas (1987-1994) and Head of Sales for CAD / CAM & GIS systems at TEKTRONIX (1983-1987).
During his career, he has been appointed as member and as the Vice President of the BoD of the Greek eCommerce Association (GRECA), member of the BoD of the Federation of Hellenic Information Technology & Communications Enterprises (SEPE) and he has joined committees, organizations and initiatives, to promote IT solutions and electronic commerce B2B services.
Yezdan has over 20 years of experience in structured and project finance.
Prior to RECOM, she headed up the structured finance team of Phanes Group which is a German solar PV developer specialized in Africa. She led deals in Sub Saharan Africa specifically Nigeria and Malawi solar programs. Prior to that she was the Head of Structured Finance in SunEdison which was the largest renewable energy developer worldwide (NYSE quoted company). She covered Middle East, North Africa and Turkey for SUNE. She worked in Egypt (1st round and second round), Jordan, UAE, Turkey solar programs.
Prior to joining SunEdison, she spent five years as the Director of Finance in Habboush Group, the operating partner of Goldman Sachs ('GS') for GS's renewable energy portfolio of 1.8 GWs in Turkey. Habboush Group is an Iraqi-American company with headquarters in Istanbul and offices in Dubai, Basrah and Baghdad. Ms. Akcacakir has led numerous business initiatives in the region for Habboush Group including establishing JV businesses with AAA rating international companies (i.e/ Halliburton, Fluor, Petrofac, Hanwha, SK) to start new businesses in Iraq.
Current role – Dimitris joined Alpha Bank in 2013 and is a Director in Structured Finance Division.
Relevant experience – Dimitris has a circa 20-year banking experience with Alpha Bank and Emporiki Bank. The last 13 years he arranges but also advises PF transactions. Before that, he was evaluating and auditing subsidized SMEs investment projects (3 years) and prior to that he worked at the Investment Banking Division (Valuations, IPOs). The current area of his responsibilities includes structuring/origination, negotiation and execution of structured finance / project finance transactions; finance and project documents negotiation and syndication; financial modelling; management and monitoring of legal documentation and legal and technical due diligence; Dimitris has a comprehensive knowledge of the power sector and related market, technical and regulatory aspects, while amongst others, he has arranged financings for several wind parks with total installed capacity of over 300MW, 4 CCGTs (over 1600MW) and 3 cogeneration projects (over 150MW) and quite a few PVs..
Background – Dimitris holds an MSc in Finance from Lancaster University (UK), a Bachelor's degree in Economics from University of Athens (Greece) and is a CFA Charterholder.


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text:
Home Scores Bracket Rankings Standings Stats Video Final Four History NIT
Mike Lopresti | NCAA.com | March 9, 2017
Behind the scenes of a crazy two days for Michigan
Donning practice jerseys, Michigan beat Illinois by 20 on Thursday.
WASHINGTON – What the basketball coach for Michigan remembered 24 hours later, thinking back on the terrifying moment his team had to flee a shattered jet, was the noise of the escape.
"The engine is still running, there's gas fumes coming in on everybody," John Beilein was saying Thursday. "When the engine finally shut down, there was a big pop. I said, `Oh, my goodness.' But everybody was away from the plane. We were good."
By Thursday afternoon, it was time for them to reflect and give thanks, while the rest of us looked on in wonder at how a team that had just endured so much could show up at Verizon Center – almost literally at the last minute – and beat Illinois by 20 points in the Big Ten tournament.
"At this time 24 hours ago we were aborting a takeoff at a couple of hundred miles an hour," Beilein said. "What these guys have been through the last 24 hours has been incredible."
It is worth a revisit. This day in the lives of the Michigan Wolverines that they will never, ever forget.
MORE: Conference tournament scores
It started in darkness Wednesday afternoon. The Wolverines were in the middle of practice back in Ann Arbor, when the lights went out. A power outage, apparently caused by high winds.
"We had to go out in the practice gym and find natural light, and go basically halfcourt," senior forward Mark Donnal said. But no big deal. The workout soon ended and the team hopped on the bus for the airport.
There were 119 on board the team plane that taxied out for takeoff. Coaches, players, cheerleaders, band members, school officials and family members. Most of the players had their headphones on, lost in music, or already dozing.
The plane accelerated down the runway, but then something began to go wrong. Those high winds. The pilot decided to abort at the last minute and tried to stop the plane. But the plane wasn't stopping.
HAIL!! #GoBlue #B1GTourney #HTTV pic.twitter.com/pQBNa8HhVd
— Michigan Basketball (@umichbball) March 9, 2017
Junior forward D.J. Wilson: "I had my head phones on and I was just chilling. Then I felt him hit the brakes. In the back of my mind I knew we had only so much runway left. Then I saw ahead of me a fence and a ditch and we ran through the fence. I thought, `Oh no, the ditch is coming.'"
Television analyst Dan Dakich, whose son Andrew is a senior guard: "He told me some guys were flying around because they didn't have their seat belts on. He said `I'll never have my seat belt not on.' He said it was little bit of chaos but everybody was pretty calm."
The plane finally rumbled to a stop. Evacuation time. Donnal was in the emergency seat, which meant he had to help with the door. He put all 6-9, 240 pounds into it. "I've been on a plane hundreds of times and never thought I'd have those duties and actually try to pull out the door. The adrenaline kind of hit me pretty quickly."
MARCH MADNESS SHOP
He and teammate Jon Teske got it open, and Wolverines started pouring out. Senior guard Duncan Robinson was in an aisle seat. "Sean Lonergan (a senior teammate) was actually sitting on the window on the other side of me. He was one of the first people out. I still don't understand how he was. He was crawling over seats, doing everything he could to get out."
Sophomore forward Moritz Wagner: "I'm never going to sit in an exit row, ever. I don't know what I would have done. I jumped off the wing. I ran. You've seen the movies, right? You're a 19-year-old naïve kid, (you're thinking) this plane behind us is going to blow up. That fear moment. I ran as far as I could."
Beilein: "We had players know enough to open the emergency doors and get everybody out. We've got people jumping onto the wings, we've got cheerleaders jumping on the wings, and the band getting out of there."
No one was hurt. Many quickly got on phones to contact family.
Junior guard and team star Derrick Walton Jr.: "The first person I thought about was my mom. It put a lot of things in perspective for me personally. Life itself is more important. I was able to step back and look at things differently."
Senior guard Zak Irvin called his mother. "It was so windy, she could hardly hear me. I just told her I loved them."
They were safe, but what to do now? The Wolverines were scheduled to play Illinois the next day at noon in Washington, D.C. The team collected in a hotel, and counselors were called in to address any anxiety.
We love this song! #Hail #GoBlue 〽 pic.twitter.com/awhkmcHDnG
"We care deeply about what these kids are going through, and our staff, so it was important to do that," athletic director Warde Manuel said. "We will continue to monitor it. We just want to make sure everything is good with them. Games are something we do, but it's not life."
The players met among themselves. Michigan could either try to find another plane and get to Washington the next morning, or just forfeit if too many were too upset to travel. It was the kind of decision suddenly rattled college athletes never expect to have to make.
Beilein: "I didn't want to be a part of that. I made sure they could tell their feelings to some counselors. I told them, `I'm with you, either way.'''
Wilson: "We had a long discussion multiple times throughout the day. I know we had a couple of guys who were hesitant. But we all grouped together and decided to get on the plane."
Irvin: "My whole thing was, I know it was going to be tough, but we've got to get on a plane eventually."
Robinson: "There were some different ideas thrown out, like driving and then stopping and then waking up and driving again. We kind of all decided if we banded together and did it as a team..."
A Plan B jet had to be found. Who stepped forward to help? The Detroit Pistons. Their plush charter jet would be available. The players would get a kick out of that.
There was still Illinois to prepare for at the hotel Wednesday night. The normal routine would be a film session, a walkthrough, lots of game plan discussion.
"I walked in there and said, `We aren't doing this," Beilein said. "We have to let time take care of this a little bit.'
The Wolverines were sent to bed, since the morning would be early. They'd have to get going before dawn. "It was tough to fall asleep," Robinson said. "But we were waking up at 6, anyway."
We are SO proud of our team as today's win brings @JohnBeilein's total wins at Michigan to 210, the most in school history! #GoBlue pic.twitter.com/ZrTKXEDbkW
They were on the way to the airport by 6:45 a.m., ready for takeoff an hour later, which was the moment of truth for many of them. Some staff had elected not to fly because of what had happened. Beilein's wife, too.
Wagner: "I'm usually not scared, but today I was a little scared."
Irvin: "The hardest part for all of us was getting back on the plane. I think it definitely helped we were on the Pistons plane. It helped everybody's spirit.
"Once we landed, everybody was fine."
Next obstacle: The weekday District of Columbia traffic. The Wolverines landed at Dulles, a long haul from the Verizon Center. They arrived with 80 minutes to spare before the scheduled tipoff. No practice, no game uniforms – those were still in the belly of the jet – not much in the way of clothes.
"We're here, bare-bones," Beilein said.
They made a request – immediately granted by the Big Ten – to push the game 20 minutes. But even then, would they be ready?
Turned out, that was not a problem.
Irvin: "I felt like we were going to be amped up. Once we got off the plane, everybody was ready to go. We were just excited to get out and play basketball. It was a time to get away from everything."
MARCH MADNESS 2017 -- PLAN TODAY
Final Four in Phoenix
Walton: "I thanked God on several occasions. He gave me this day, so I just wanted to maximize it to the best of my ability.
"Once we got out on the court, we just got back to doing what we do every day."
Beilein studied his locker room: "It was very quiet everywhere. I wasn't going to shout at them, `What's wrong with you guys? It's a big game.' It wasn't a big game anymore. It was an important game, but being safe...
"After the anthem, they came back in, I could tell we were going to be ready.
"I said, `We just need to make one shot and we'll be fine.' Because it seemed like a whole new season, and it is. It was as unique an experience as anybody could ever have.
How ready were the Wolverines? They made 12 of their first 18 shots, blew away to a 20-point lead — the poor Illini, a desperate bubble team suddenly relegated to the bad guys in this drama — and never looked back.
MORE: Full NCAA.com basketball coverage
The scoreboard didn't make sense. The team ahead by 20, those were the guys who the day before had run through a field, their lives flashing before their eyes
Beilein: "They played connected today, like they were connected yesterday when we got a hundred-some people off an in airplane it seemed like in two minutes.
Wilson: "It's been crazy, but at the same time I think it's brought us closer. We've got each other's back."
If the scoreboard didn't say it, you could tell what the Wolverines had gone through by their attire. Gold tops and blue bottoms, casual practice gear, fine for a scrimmage but hardly suitable to wear for a Big Ten tournament game. But nobody cared.
And nobody knew if their uniforms would make it in time for Friday's game with Purdue. "We have washers and dryers here in D.C." Beilein said. And the practice uniforms had worked out pretty well. "We might ride that wave," Robinson said.
It ended 75-55. Walton had 19 points. Indeed, he had made the most of the day. Irvin had 18. It also was Beilein's 210th victory at Michigan, making him the winningest coach in Wolverine's history. What a moment to get it.
"I don't know what to think right now," he said. "I was not emotional at all in this game until the end -- when I saw my daughter."
That's when the feelings flooded over him, like it did at some point for all of them, thinking of family, and how this story could be turned out so, so different. Back in Michigan, a plane still rested off a runway in a heap. They would be back to play again Friday. Their 24 hours were over.
Mike Lopresti is a member of the US Basketball Writers Hall of Fame, Ball State journalism Hall of Fame and Indiana Sportswriters and Sportscasters Hall of Fame. He has covered college basketball for 43 years, including 39 Final Fours. He is so old he covered Bob Knight when he had dark hair and basketball shorts were actually short.
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ASOS is going the extra step of working with the labels it represents by creating a collection—the ASOS Black x Puma range will likely be the start of other ventures.
While ASOS has worked with labels before in terms of distribution—American Apparel sees ASOS strategically—the Puma deal comprises a capsule of 60 sportswear pieces.
The products feature embossed rubberized branding with the ASOS Black x Puma logo, and the launch itself shows the company's ambition. It's not a distribution partnership but one which sets the scene for ASOS as a brand in its own right.
The collection blends technical elements with classic shapes, mixing function with great design.
The look book was photographed by Jamie Morgan, using young artists. The film follows on from this, with the São Paulo art scene as a backdrop.
The Os Pixadores graffiti collective stars in the campaign. The collective, made up on gangs of five to six, uses street art, bombing the city and working at night. The style of graffiti, pixacão, derives from political slogans painted on government buildings in Brazil to protest its dictatorship.
The artists have become legendary in the city, and ASOS sees them as being 'perfect ambassadors' for the new range's innovative designs.
The Pixadores were best known for a protest during the 2008 São Paulo Biennale, where they painted on the Pavilion's blank walls. One activist, Caroline Pivetta de Mota, was arrested.
The film has been made by director Ben Newman, who previously shot an Urban Tour campaign featuring roller-bladers in Paris.
The campaign encourages people to be creative by sharing their art, hashtagged on Twitter or via Instagram with #Beacreator. The film and collection will be launched on September 25.
This post has been sponsored by ASOS.


text:
MURALS TO SAVE THE SOUL OF A COLOMBO NEIGHBOURHOOD
In conversation with Firi Rahman (#WeAreFromHere) by Ester van Steekelenburg (Urban Discovery)
Colombo, Sri Lanka's capital, is undergoing a make-over. After years of civil war, the city is set to become a Singapore-like economic powerhouse. Chinese and Indian-fuelled investment has poured into the country to make this dream come true. But at what cost? Slave Island, an old neighbourhood in the heart of the city, is where the skyscrapers are going up faster than one can imagine. What used to be a unique and vibrant urban district is transformed beyond recognition. The quarter is quickly being swallowed up by the shiny new apartment buildings rising around it. Artist Firi Rahman has lived his whole life in Slave Island, and as more buildings in his neighbourhood are threatened by demolition, he has gathered up his artist friends and started a campaign to save its soul. #WeAreFromHere is a unique artistic initiative that maps stories of ordinary and extraordinary locals to highlight the unique multicultural community of Slave Island.
Slave Island — locally known as Kompannaveediya — has lived many lives. In the colonial days, when it was still an island in a crocodile-infested lake, the Portuguese and later the Dutch considered it the ideal location for a slave prison. The name stayed long after the slaves were set free, and the area grew into what it is today: a hub of vibrant activity where African, Indian, Javanese, Burgher, Moor and most prominently, Malay heritage are visible at every street corner. You can hear it in the music, see it in the colours and taste it in the food. No longer an island, it is a diverse community made up of people from all faiths and walks of life, which is unique in Colombo.
Slave Island is about to undergo another reincarnation. The location, just south of the city's Central Business District Fort, is triple-A, so the small alleyways have become valuable property. The eviction of residents in an area of approximately 160 acres in the heart of Colombo 02, began in 2012. Some 70,000 households are now being relocated to newly constructed apartment-style housing in other parts of the city. It is part of an ambitious $287 million 'City of Colombo Urban Regeneration Project' spearheaded by the Urban Development Authority. The scale and speed of the project is mind-boggling, with some streets being completely erased from the map, and with them their social fabric and memories of place. Java Lane is one of them. Here, the only thing the developers left standing is the green and white neighbourhood mosque, now surrounded by nothing but apartment blocks, disconnected from its community.
AN ARTISTS' INITIATIVE
Local artists Firi Rahman and Vicky Shahjahan started #WeAreFromHere in 2012, a visual project portraying the people that make up the unique neighbourhood of Slave Island. Their wall murals feature familiar faces from the community: sportspeople, street vendors, mechanics, musicians, actors and artists. Firi shares: "#WeAreFromHere showcases the people who make Slave Island a fascinating, unique and diverse place. There are many places that have been demolished or damaged already, like the popular Castle Hotel and the Java Lane mosque. There are many more places, of which we don't know whether they'll still be here in a few years' time."
But more than just the buildings, the project wanted to document the local community. They collected stories from ordinary and extraordinary residents and turned them into wall murals. Firi adds: "It's a time of rapid change for us, and we're all adjusting. I want to give people the feeling that this place — its heritage and culture — has value. With all these new buildings they may feel that theirland and their properties can be bought just like that. I tell the stories of the community, to make people feel proud of their place."
Firi started by recording voice cuts of the conversations he was having, and he was soon joined in his artistic journey by local artists Parilojithan Ramanathan and Vicky Shahjahan. Wanting to make it more interactive, the trio decided to draw portrait murals of the people around Slave Island. "We first wanted to make one big mural, but then decided to spread them through the neighbourhood to better blend in with the urbanfabric, create less disruption and get more traction," says Vicky.
One of the persons we 'meet' on the walls is Fazil, a respected member of the local society. His portrait tells the story of how here in Slave Island, funerals are occasions of togetherness for people, irrespective of their faith. We then meet 'the captain': a popular car repairman, Rifakath: a known rugby player and Milan: a street cart vendor, each with their own story that we learn through a set of accompanying cards. We later meet Milan in real life and buy a faluda from his cart. "It's like a treasure hunt, but with people! You get a card of a person, and you have to find the respective wall mural. The idea is not just to get to know a person, but also to find out how they matter to the community."
❝The effect of the project was that Slave Island's many little laneways were uplifted and connected with an invisible artistic thread.❞
THE VALUE OF PLACE
The mural project proved a trigger point, an outlet for many concerned citizens and an opportunity for them to communicate the value of this place to others. What followed was a series of #WeAreFromHere walks, talks, exhibitions, gatherings and celebrations. The effect of the project was that Slave Island's many little laneways were uplifted and connected with an invisible artistic thread. What started as an art project has become the face of resistance against the whims of big developers, and the voice of a unique community.
Use art to amplify the voices of the community.
Bring together different segments in a multicultural community.
Generate interest from international agencies and festivals.
Educate administrators and developers about the value of place.
Don't stay away from complex and sensitive heritage. When used correctly, art can reveal and connect different layers in your district.
Introduction - Rejuvenation of underused spaces into places
Place Management
The Carpet Man of Kampong Gelam


The reason why we have Bitcoin is because of the 2008 crisis. Satoshi Nakamoto, the individual or the group behind Bitcoin designed a cryptocurrency that isn't tied to any economy. More than a decade after Bitcoin was created, there are thousands of other cryptocurrencies out there. And now, even countries that are struggling with their economy have now relied on blockchain.
Last year, Iran was kicked out of the SWIFT network. It has crippled the country's ability to trade in an efficient manner. Russia along with other countries are working on an alternative which is the blockchain.
By taking a closer look at the current value of Iranian Rial, it is now equivalent to $0.000024. It means that you will need millions of rials just to buy a phone or any gadget for that matter.
But of course, the question is if it is going to work? This isn't the first time that we have a country that resorted to blockchain hoping to solve economic problems. Venezuela has been dealing with hyperinflation. It is even projected that their inflation rate would reach one million percent. Under the Maduro administration, the government decided to have their cryptocurrency named Petro in order to continuously trade despite US sanctions.
However, the cryptocurrency was controversial. According to the Maduro administration, the Petro is backed by oil. However, critics were questioning that the oil to back the Petro can't be seen anywhere. So far, Venezuela's Petro adoption is being forced. The Petro has also been used to create a new currency called the Sovereign Bolivar.
As for Iran, they launched a cryptocurrency that is backed by gold. This cryptocurrency is known as Peyman. There are four partner banks that worked together with Ghoghnoos Company to make this a reality. For Valiolla Fatemi who works as Ghoghnoos director, "The token can function like a wallet and channel the excess assets into the economic cycle. We seek to extend the technology for accelerating the pace of bank transactions".
For the initial stages, there will be one billion units of the cryptocurrency that will be made available. And also, in the near future, the cryptocurrency can be purchased on Irana Fara Bourse. Peyman has been launched after speculations that Iran is about to have a state-backed crypto called crypto-rial.
Is this a timely solution? Iran has been having a difficult time to transact with other countries as of the moment since they've been kicked off the SWIFT network. Could this be the best solution to Iran's problem?
Iran recently announced that it is restricting the use of cryptocurrencies. Bitcoin and other top cryptocurrencies can't be used for payment in the country. Is it going to limit the use of crypto in order to increase the adoption of Peyman?
Previous post Bitcoin Is Headed to $2K?
Bitcoin Mining Equipment Are No Longer Profitable?
Cryptocurrency Exchange in Canada Hacked or an Exit Scam?


text:
Q: jQueryUI Tooltips are competing with Twitter Bootstrap I have been able to get some tool tips to work finally with the following code:
Hover over me

and then

The problem I'm running into is that it is using jQueryUI tooltips (no arrows, big ugly tooltips) instead of Bootstraps.
What do I need to do to make use of the Bootstrap Tooltips instead of jQueryUI?

A: A simple solution is to load bootrap.js after jquery-ui.js, so that the bootstrap .tooltip method takes precedence.

A: In case you must load jquery-ui.js after bootstrap.js here is how to do it:

This will override jquery-ui's tooltip and always use bootstraps.

A: This worked for me:
If you need to use JQuery-UI but you want to use bootstrap's tooltip, simply get a customized version of JQuery-UI from this website.
Simply uncheck the "Tooltip" option, and download it (it will be something like "jquery-ui-1.10.4.custom.js").
Simply add it to your project instead of the version you are currently using now and, you are ready to party. This will avoid the conflict. It worked like a charm to me!

A: try using jQuery.noConflict() and see if that helps you at all. It looks like bootstrap and jQuery are using the same namespace, and perhaps the bootstrap and jQuery tooltips get loaded into the same function, or one gets loaded first.
You can also check to see which library is loaded first.  Usually if you declare the same function twice in javascript the second one is the one that is used.
Thirdly, check the jQueryUI package (on their website) and see if you can download a version that does not have the tooltips included (I checked and this is totally doable).

A: There is a easy and good solution, just rearrange your bootstrap and jquery ui file link like this:

Just load jQueryui before loading boostrap js file. It's work for me.

A: Assuming that UI will called after bootsrap initialized
Store the bootstrap function just before the UI is initialized
jQuery.fn.bstooltip = jQuery.fn.tooltip;

Then call it as following
$('.targetClass').bstooltip();

A: Both jQuery UI and Bootstrap use tooltip for the name of the plugin. Use $.widget.bridge to create a different name for the jQuery UI version and allow the Bootstrap plugin to stay named tooltip (trying to use the noConflict option on the Bootstrap widget just result in a lot of errors because it does not work properly; that issue has been reported here):
$.widget.bridge('uitooltip', $.ui.tooltip);

So the code to make it work:

JQuery tooltips can then be instantiated in the following way:
$('.someclass').uitooltip();

Nice copy paste code that also handles the button conflict:

A: How about just using Bootstrap popovers instead? BS popovers do not create the same conflict although they require the same tooltip.js component. Yes, they are more stylized but doesn't cause my JQuery UI buttons to go wonky.
I'm using Jquery UI only for custom autocomplete/comboboxes (so can't claim other JQ-UI controls are ok) and none of the above worked to fix the CSS issue on the combobox buttons becoming "unstyled" when invoking BS Tooltips. Switching to BS Popovers provided essentially the same effect with no conflicts, no reordering of my script imports, and no explicit bridge statements needed.

A: This solution seems to work well for me.
// handle jQuery plugin naming conflict between jQuery UI and Bootstrap
$.widget.bridge('uibutton', $.ui.button);
$.widget.bridge('uitooltip', $.ui.tooltip);

A: An easy way is to load bootstrap.js after jquery-ui.js, so that the bootstrap .tooltip overrides jquery UI's. If you're using a module loader like requirejs, then you could make bootstrap dependent upon jquery-ui, as such:
requirejs.config({
    baseUrl: 'js',
    waitSeconds: 30,
    shim: {
        'bootstrap': {
            deps: [ 'jquery', 'jquery-ui' ]
        },...

A: In case you...

*

*Don't want to load bootstrap after jQuery UI and

*$.widget.bridge and noConflict don't work for you and

*You don't want to override jQuery UI tooltips

then another way of re-enabling bootstrap tooltips is as follows:

and then you can initialize bootstrap tooltips this way:
// initialize tooltips
bsTooltip.call( $( "[data-toggle='tooltip']" ) );

A: The other way is to download a custom build of jquery UI.
Exclude what is not needed, in this case, tooltip.
Can download it from here https://jqueryui.com/download/
This way you can include it anywhere

A: Assuming that UI will called after Bootsrap initialized (and you cannot change the order Bootstrap >> UI), put after bootstrap import:
jQuery.fn.bstooltip = jQuery.fn.tooltip;

Then after all:
jQuery.fn.uitooltip = jQuery.fn.tooltip;
jQuery.fn.tooltip = jQuery.fn.bstooltip;

So when you'll use $(".selector").tooltip() it would be the Bootstrap ones. And you can still use the UI version with $(".selector").uitooltip().

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Foundation in Leon - What Are The Costs?
$1,280.34 - the typical costs to install concrete foundations in Leon Iowa.Commercial costs for 200 square feet of concrete foundation installation - inside $4.83 & $7.98.Read more about costs to install concrete foundations in Leon Iowa.
Charges for all Leon tools transporting to the business site.
Hourly charges, assignment setup and compilation efforts that are currently affiliated with concrete foundation installation work.
Concrete Foundation Installation in Leon control dues for general contractors.
It cannot include permissionsinspection payments that the Leon dept of constructing necessitates.


text:
Throw Back Thursday: Glimpse Into The World Of Lost Los Angeles
Home > Blog > Throw Back Thursday: Glimpse Into The World Of Lost Los Angeles
The Los Angeles cityscape as we know it could have been much more dynamic had a slew of visionary projects, dreamed up by legendary architects, actually progressed past the drawing board. On view at LA's A+D Museum until September 29, a landmark exhibition titled Never Built: Los Angeles explores these architectural contenders and the what-ifs that they call to mind. Here, cocurators Greg Goldin and Sam Lubell take us on a mini tour of the would-be utopia.
Downey Mixed-Use Office Building, 2009
Big idea: An office building in Downey, designed by LA firm B+U for local financier Jesse Flores. Imagine this: A three-level structure enveloped by a network of silicon-dipped, nanogel-insulated fiberglass fabric panels resembling the flames of a fire. Two cents: "The city of Downey would have scored a big hit with this building," says Goldin. "This innovative, illuminated landmark would have been a huge draw and proven that it is not only desirable, but also possible, for great architecture to crop up anywhere, even in small cities."
Courtesy of B+U Architects
Pacific Ocean Park Redevelopment, 1969
Big idea: An offshore hotel and shoreline condo complex by Anthony Lumsden, chief of design at Daniel, Mann, Johnson and Mendenhall (DMJM). Imagine this: A cylindrical glass tower rising 30 stories out of Santa Monica Bay. Two cents: "Though the building was not an oil rig, the comparison would have been inevitable," says Goldin. "The image of the tower rising out of the water is quite powerful."
LAX Original Master Plan, 1952
Big idea: The main terminal for Los Angeles International Airport by Pereira and Luckman (opening image). Imagine this: A central, circular terminal covered by a giant glass dome. Six "finger" passageways radiating from its periphery leading to boarding gates. Two cents:"Although it may have been a challenge to pull off, this bold design would have set a high standard," says Lubell. "Such airports as JFK and Dulles International Airport executed much more daring designs in future years, leaving LA in the dust again."
Courtesy of Eric Lloyd
Civic Center, 1925
Big idea: A monumental civic center plan for downtown LA by Lloyd Wright that included blocks of buildings flanked by terraced walkways progressing up Bunker Hill (above).Imagine this: Persian temple complex meets Art Deco styling. Two cents: "The project's ambitious scale may have wiped out the fine-grain character of the area—but this happened anyway in Bunker Hill 30 years later," says Lubell. "Still, this level of planning and design is something the city has rarely seen."
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About The Altman Brothers
The Altman Brothers are one of the premiere real estate groups locally, nationally and internationally, specifically dealing with high-end properties and exclusive clientele. Partnered with Douglas Elliman the Altman Brothers are full service real estate experts whose clients benefit from the custom tailored, hands on service while receiving all the exclusive amenities and resources of one of the most established and respected firms in the business.


text:
Tromikosoma uranus is een zee-egel uit de familie Echinothuriidae.

De wetenschappelijke naam van de soort werd in 1877 gepubliceerd door Charles Wyville Thomson.

Echinothurioida

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It's an interesting experience releasing a book. It's both gratifying and frustrating.
Understand that I'm not blaming them for not reading the book because from the start, it's obvious they have quite a negative view on property views that are, unfortunately 'set in stone' and can't change, no matter what. And that's a shame.
But interestingly, the journalists and commentators who took the time to read the book have given great reviews.
In any case I thought I would take the time to address their misconceptions about my advice in the book. The following points also go to the very fabric of what we're about here at Gladfish.
I am not proposing that you should focus solely on property investment as a means to your pension. I am definitely saying that you shouldn't just rely on your traditional pension. Especially if you realise that it's not working for you.
I have never said that it is definite that prices will double every 7-10 years. I do say that it is the biggest assumption we make as investors. Additionally, I also believe that even if it doesn't double in 7 to 10 years then the principles that I talk about in The 3+1 Plan and my website will still work it will just take longer.
I have never said that property investment is an easy way to riches. The opposite in fact. You are going to have to do some work, change the way you think and even go through some sleepless nights. If anyone tells you that you'll be an overnight millionaire with property investment, I suggest that you run the other way.
I have never said that I will make people millionaires. I do say that I help people with become empowered to make decisions that will change their life and that one of the by-products may be that you become a millionaire, although this will depend on your individual goals.
I am not claiming to be a "property guru". Instead, I am a guy who has spent more than 15 years working and investing in property. You can do the same as me given enough time and doing the same I do, which is buy property and hold it. Rather simple really when you give it enough time. I also tend to have the ability to rant on about the subject of property investment which makes sense to most people.
Gladfish is not a company focused on selling you property at all cost. In fact, when you speak to many of our clients you'll find that we actually stop you from buying many times depending on your individual circumstances. That's why we say '1,2 STOP' and '5 and HOLD'. These strategies are aimed at stopping you at various points to get everything in order before you continue investing.
I do hope this makes some sense and clears up any misconceptions you may have about The 3+1 plan or the subjects discussed within, but of course, the best way to understand what I'm really saying is just to read the book!
If you want further explanations or clarifications, give the team a call on +44 (0)207 923 6100.