text:
\section{Introduction}
\label{sec::introduction}
\IEEEPARstart{S}{aliency} detection is generally  divided into two categories: salient object detection or eye fixation prediction, with the purpose of extracting the most predominant objects or predicting human eye attended locations corresponding to informative regions in an image.
With the rapid development of deep learning, saliency detection has made great progress in recent years.
As a preprocessing step, saliency detection is helpful for many applications in the computer vision tasks, such as image segmentation~\cite{Donoser2010Saliency,Li16sold,li2017weighted}, scene classification~\cite{Ren2014Region}, visual tracking ~\cite{borji2012adaptive,li2016learning,Li18pami}, and person re-identification~\cite{zhao2013unsupervised}.

\begin{figure}[htbp]
\centering
\includegraphics[width=3.5in]{edge.png}
\caption{Illustration of the importance of edge information to saliency detection.
(a) Input images.
(b) Edge maps.
(c) Saliency results without edge embeddings.
(d) Saliency results with edge embeddings.}
\label{1}
\end{figure}

The saliency of each region is usually defined as the weight of the distance between the region and other regions of the image in most traditional methods, and often based on hand-crafted features and some priors ~\cite{perazzi2012saliency, yang2013saliency, yan2013hierarchical}.
In recent years, with the success of Fully Convolutional Network (FCN) in the field of computer vision, deep learning methods has become a promising alternative to salient object detection.
Existing methods based on the FCN~\cite{LeeTK16,lee2016deep,WangLRY15,Li2016Deep,ShenW12,HouCHBTT17,Zhang2017Amulet,ChenZ0L17,Luo2017Non} have shown good performance, since the features obtained at deeper layers typically contain stronger semantic information and the global context-aware information, which are beneficial to locate salient regions even in complex scenes.
FCN promotes saliency detection by applying multiple convolution layers and pooling layers to increase the receptive field and automatically extract high-level semantic information, which plays an import role in this field.
These continuous convolution layers and pooling operations gain large receptive fields and high representation ability but reduce the size of feature maps and lose the spatial structure, and deteriorate the edges of the salient objects.
This is useful for some high-level tasks like classification and recognition, but unfortunately, it reduces the accuracy of low-level tasks which usually requires precise pixel activation, such as salient object boundaries.

Some recent works notice this problem and propose some edge-aware saliency detection methods.
For example, Zhang \emph{et al}.~\cite{Zhang2017Amulet} embed edge-aware feature maps achieved by shadow layers into the deep learning framework.
Mukherjee \emph{et al}.~\cite{mukherjee2017salprop} construct a Bayesian probabilistic edge mapping which uses low-order edge features to assign a saliency value to the edge.
However, boundary preserving still hasn't been solved well as lack of specific guidance for the boundary area.
As shown in Fig.~\ref{1}(c), the predictions around object boundaries are inaccurate as the essential fine details are lost caused by repeated strides and pooling operations.
In a word, existing FCN-based methods still have the drawback that they are difficult to maintain spatial structure like clear boundaries.

To handle this problem, we propose a novel method based on an edge-guided non-local FCN, named ENFNet, for salient object detection.
We take the FCN model in~\cite{Luo2017Non} as the baseline of our ENFNet,
and design an edge guidance block to perform feature-wise manipulation and spatial-wise transformation for effective edge embeddings.
In a specific, given the input image, we first extract the edge maps using a EdgeNet~\cite{Zitnick2014Edge}, and then employ the baseline FCN to perform salient object detection.
To embed the edge prior knowledge into the baseline FCN, we propose to use the spatially feature-wise affine transform algorithm~\cite{Wang2018Recovering} to guide feature learning.
An example of edge-guided saliency map generation by our method is shown in Fig.~\ref{1}.

In conventional models, the local contrast is widely used and plays an important role as salient objects should be different between foreground
and background areas.
While in deep models, the global context is very useful to model salient objects in the full image and complementary to the local contrast information.
To fully utilize both local contrast and global context, we design a hierarchical edge-guided non-local structure in our network.
In particular, features of each convolutional layer in FCN are first transformed into edge-guided features by edge prior knowledge, and then we employ the edge-guided features to generate local contrast features.
The global context features are computed by FCN and combined with the edge-aware local contrast features to produce the final saliency map.
We compare the proposed approach with many start-of-the-art saliency detection methods on the five benchmark datasets
including HKU-IS~\cite{li2015visual}, PASCAL-S~\cite{li2014secrets}, DUT-OMRON~\cite{yang2013saliency}, ECSSD~\cite{yan2013hierarchical} and SOD~\cite{martin2001database}, and our method achieves the best performance under all evaluation metrics.

In short, the major contributions of this work are summarized as follow:\\

\begin{itemize}

\item We propose a novel edge-guided non-local fully convolutional network for salient object detection. The proposed network is able to embed the detailed edge information in a hierarchical manner and thus generate high-quality boundary-aware saliency maps.

\item We design an edge guidance block to incorporate the edge prior knowledge in the non-local feature learning framework. The designed block is simple yet effective and thus yields a state-of-the-art performance with a little impact on execution time.

\item Extensive experiments have proved that the proposed network is effective in producing good saliency maps with clear boundaries, and outperforms all compared state-of-the-art methods in all metrics.

\end{itemize}

\section{Related Work}
We will review two aspects of salient object detection approaches that are based on deep learning techniques and edge prior information.

\subsection{Deep learning based methods}
Salient object detection can be regarded as a pixel-wise classification problem.
Although traditional salient object detection methods have their superiority, including no need training and simplicity, their overall performances are not as good as most deep learning methods.
Deep learning based methods have achieved great improvement in salient object detection as they combine local and deep features and can be trained end-to-end.
In the state-of-the-art models of convolutional neural networl (CNN), feature selection between salient and non-salient regions is automatically accomplished by gradient descent algorithm.
These models are made of a series of convolution layers and pooling until a softmax layer, which predicts the probability of each pixel belonging to objects.
Recently, these methods make a great process by deep learning methods~\cite{li2015visual,WangWLZR16,KuenWW16,LeeTK16,lee2016deep,WangLRY15,Li2016Deep,ShenW12,HouCHBTT17,Zhang2017Amulet,ChenZ0L17}, which can be divided into region-based and Fully Convolutional Networks (FCN)-based methods.

{\flushleft \bf Region-based methods.}
The region-based deep learning methods use image patches as the basic processing units for saliency prediction.
For example, Li et al.~\cite{li2015visual} utilize multi-scale features to capture contextual information and a classifier network to infer the saliency of each image segment.
Zhao et al.~\cite{Rui2015Saliency} propose a multi-context deep CNN framework benefiting from the global context of salient objects, as global context is conducive to modeling saliency of the image, while local context is conducive to estimating saliency of the region with rich features.
Li et al.\cite{Li2016Deep} propose a deep contract network to combine a piece-wise stream and a pixel-level stream for saliency detection.
And Wang et al.~\cite{Wang2015Deep} propose to train two deep neural networks to integrate global search and local estimation for salient object detection.
The work in ~\cite{LeeTK16} uses a two stream framework to separately extract high-level features from VGG-net and low-level features such as color histogram.
The fully-connected layers in CNNs is always to evaluate the saliency of every region~\cite{wang2018detect}.
The region-based methods improve the performance over the ones based on hand-crafted features to a great extent, whereas they ignore the important spatial information.
In addition, the region-based methods are time-consuming, as the network has to run many times.

\begin{figure*}[htbp]
\centering
\includegraphics[width=7.2in]{model.png}
\caption{Overall architecture of our proposed model.
Our architecture is based on VGG-16~\cite{simonyan2014very} for better comparison with previous CNN-based methods.
Herein, only three convolution blocks are listed for clarity.
The dotted-line part is an edge guidance block, where the edge features are regarded as a condition information on feature learning.}
\label{2}
\end{figure*}

{\flushleft \bf FCN-based methods.}
To improve the efficiency of CNN methods, people utilize FCN to generate a pixel-wise prediction.
The FCN-based methods abandon the fully connected layer in CNN.
They can increase the computational efficiency, but lost the spatial information.
For example, Li et al.~\cite{Li2016Deep} make use of segment-level spatial pooling stream and pixel-level fully convolutional stream to generate saliency estimation.
The work in~\cite{WangWLZR16} designs a deep recurrent FCN to incorporate the coarse estimations as saliency priors and refines the generated saliency stage by stage.
In HED~\cite{Xie2015Holistically}, the authors build skip-connections to employ multi-scale deep features for edge detection.
The edge detection task is easier than saliency detection since it does not rely on much high-level semantic information.
Thus, directly using skip-connections in salient object detection is unsatisfactory.
Then, Hou et al.~\cite{Hou2016Deeply} propose dense short connections to skip-layers within the HED architecture to obtain rich multi-scale features for salient object detection.
Another work by SRM~\cite{Wang2017A} proposes a pyramid pooling model and a stage-wise refinement model to integrate both global and local context information.
Zhang at el.~\cite{Zhang2017Amulet} aggregate high level and low level features by concatenating feature maps directly, these multi-level features are beneficial to recover local details and locate salient objects.
In addition, many methods attempt to find a way to fuse multi-scale features for better distinguishing salient and non-salient regions.
However, without edge information, they can roughly detect the targets but cannot uniformly highlight the entire objects, and the estimated saliency maps suffer from the blur boundary.

\subsection{Exploiting Edge Information}
To solve above-mentioned blurred boundary problem, some methods attempt to take advantage of edge information for generating saliency maps with clear boundaries.
Mukherjee et al.~\cite{mukherjee2017salprop} construct a Bayesian probabilistic edge mapping and assign a salient value to the edge by using low-order edge features, then they learn a conditional random field to effectively combine these features with object/non-object labels for edge classification.
The work in~\cite{Xiang2016Salient} and~\cite{wang2017edge} use edge information for detecting salient regions.
For example, in ~\cite{Xiang2016Salient}, they exploit a pre-trained edge detection model to detect the edge of objects.
Then, based on the detected edge, they segment the image into regions, and generate saliency map of every region through a mask-based Fast R-CNN~\cite{sappa2006edge}.
The model in~\cite{zhang2017deep} uses three different labels including salient objects, salient object's boundaries and background.
And in~\cite{Jiangjiang2019}, they add a edge detection branch into the pooling network, through joint training saliency model and edge detection, the details of salient objects are further sharpened.
To emphasis the accuracy of saliency boundary detection, the model~\cite{zhang2017deep} takes the extra hand-craft edge feature as a complementary to preserve edge information effectively.
Guan et. al~\cite{guan2019edge} propose an edge detection stream to combine multiple side outputs together through concatenation and uses a fusion layer that a $1\times1$ convolution to get the unified output.
With the edge information added, these above models achieve good performance in preserving the boundary of salient object.

These methods usually use edge losses to preserve the edges of salient objects or fuse shallow features and edge features for simple feature aggregation.
To make better use of edge information, from a global and local perspective, we not only add edge features in shallow layers but in deep layers.
At the same time, our edge guidance block which includes a series of operations on edge features can be implemented feature-wise affine transform, which could more effectively retain spatial structure information.
Through using parallel edge features to guide saliency detection, we could obtain more accurate saliency maps with clearer object boundaries.

\section{Edge-guided Non-local FCN}

In this section, we will introduce the architecture structure of our proposed Edge-guided Non-local Fully convolutional Network (ENFNet). The overall structure of ENFNet is shown in Fig.~\ref{2}.

\subsection{Overview of Network Architecture}
As discussed above, we combine global context information and local structure information with different resolution details, and integrate edge priors into different resolution features to better locate the boundary of the salient object.
Existing methods usually use complex backbone networks~\cite{deng2018r3net} to learn powerful features.
To improve the efficiency, we adopt the VGG-16 network~\cite{simonyan2014very} as our backbone network like~\cite{Luo2017Non}.
The image size of the input model is fixed to 352$\times$352, and the output saliency map is 176$\times$176.
To obtain the saliency map of the same size as the input image, we use the bilinear interpolation method to upsample feature maps.
However, when the foreground and background have the same contrast, and the object has a complex background, the predicted salient results are often not so good, such as unclear boundaries.
Therefore, to preserve good boundaries of salient objects, we design an edge guidance block to embed edge prior knowledge into hierarchical feature maps for effective feature representations.
The edge prior knowledge is beneficial to guide the results of saliency detection to possess good object boundaries as shown in Fig.~\ref{1}.

As shown in Fig.~\ref{2}, our ENFNet does not use the fully-connected layers because the task of saliency detection is focused on pixel-level prediction.
We select the first five blocks of VGG-16 as side outputs, where each side output is followed by a convolution block, which is designed to obtain hierarchical multi-scale features ${\{X_{1}, X_{2},  X_{3}, X_{4}, X_{5} \}}$.
Each convolution block contains two or three convolutional layers and a max pooling operation.
The last block behind the backbone network contains three convolutional layers to computes global features $X_{G}$ that is the global context of the image.

The middle layers is we proposed edge guidance block, which is located on the right side of Fig.~\ref{2}.
Details of the edge guidance block as shown in Table~\ref{tab1}.
First, we extract edge maps using the existing method~\cite{Zitnick2014Edge}, and these edge maps $X^E_{i}$ are used as input of the edge guidance block.
Second, the edge features $X^E_{i}$ and the hierarchical multi-scale features $X_{i}(i\in\{1, 2, 3, 4, 5\})$ are fused by the edge guidance block to generate edge-aware features $X^F_{i}$.
Finally, the edge features $X^F_{i}$ are through average pooling operation to obtain the contrast features $X_{i}^{C}$.
Because the salient objects are related to global or local contrast, the contrast features capture the difference between each region and its neighbors.
The last line of our model is a series of deconvolution blocks, and these deconvolution operation update the features maps from $11\times11$ to $176\times176$, for the convenience of concatenating feature maps $(X^F_{i}, X^C_{i})$ with different scales.
The local blocks contain one convolution layer to compute the local feature $X^{L}$.
In the final line we use two convolution layers and a softmax operation to compute the scores that equal to the predicted saliency map by fusing the local and global features.

\subsection{Hierarchical Non-local Structure}
Some works show that lower layers in convolutional neural networks can capture rich spatial information, while higher layers encode object-level information but are invariant to factors such as appearance and pose~\cite{pinheiro2016learning}.
Generally speaking, the receptive field of lower layers is limited, it is unreasonable to require the network to perform dense prediction in the early stage.
Performing feature extraction instead of feature classification at earlier stages, those extracted low-level features can provide spatial information for our final predictions at the bottom layer.
Therefore, the multi-scale features are quite important to improve the performance of saliency detection.
As shown in Fig.~\ref{2}, these convolution blocks below in side outputs are processed by the first five convolutional blocks of VGG-16.
The function of these convolution blocks is to obtain different scale features ${\{X_{1}, X_{2}, X_{3}, X_{4}, X_{5}\}}$.
Each of convolution block uses the kernel size of $3\times3$ and outputs features with 128 channels.

\begin{center}
\begin{table}
\caption{Details of the proposed Edge Guidance Block (EG Block).}\label{tab1}
\setlength{\tabcolsep}{2.0mm}{
\begin{tabular}{|c|c|c|c|c|c|}
\hline
Block & Layer & kernal & S & Pad & Output \\
\hline
CN&4 conv &3*3&1&Yes&176*176*128\\
\hline
EG Block-1&  2 conv &3*3 &1&Yes&176*176*128\\
\hline
EG Block-2&  2 conv &3*3 &2&Yes&88*88*128\\
\hline
EG Block-3&  2 conv &3*3 &2&Yes&44*44*128\\
\hline
EG Block-4&  2 conv &3*3,5*5 &2,4&Yes&22*22*128\\
\hline
EG Block-5&  2 conv &5*5 &4&Yes&11*11*128\\
\hline
\end{tabular}}
\end{table}
\end{center}

\subsubsection{Contrast Features}
Saliency detection is related to the global or local contrast of foreground and background.
For salient objects in an image, salient objects are the foreground that highlights the background around them.
That is to say, salient features must be evenly distributed in the foreground, and the foreground and background regions are different.
In order to capture such contrast information, we add contrast features related to each stage edge features $X^F_{i}$ outputted by the edge guidance block.
Each contrast feature is computed by subtracting $X^F_{i}$ from its local average, where the size of local neighbor region is $3\times 3$.
The average pooling operation that can reduce the errors caused by the variance of the estimated value due to the limited size of the neighborhood. Hence subtracting $AvgPool(X^F_{i})$ can retain more foreground information, and salient objects are easier to detect:
\begin{equation}
\label{E5}
X^c_{i}=X^F_{i}-AvgPool(X^F_{i}).
\end{equation}

\subsubsection{Deconvolution Features}
After the last step, the sizes of the five contrast features is gradually decreasing.
The first size of contrast feature $X^c_{1}$ is $176\times 176$, but the fifth size of contrast feature $X^c_{5}$ is $11\times 11$.
To obtain the same size of final output $176\times 176$, we need from back to front sequentially use five deconvolution (Deconv) layers to increase the sizes of  precomputed feature maps $X^F_{i}$ and $X^C_{i}$.
At each Deconv blocks, we upsample the previous feature by the stride of 2.
The result of the deconvolution feature map $D_{i}$ is computed by combining the information of edge feature $X^F_{i}$ and local contrast feature $X^C_{i}$.
We upsample the feature $D_{i}$ to obtain $D_{i+1}$.
The Deconv operation is achieved by deconvolution layer with the kernel size of $5\times 5$ and the stride of 2.
The input of this Deconv layer is the concatenation of $X^F_{i} , X^C_{i}$ and $D_{i+1}$ and the channels number of $D_{i+1}$ is sum of  $X^F_{i}$ and $D_{i+1}$,
\begin{equation}
\label{E6}
D_{i}=Deconv(X^F_{i}, X^C_{i}, D_{i+1}).
\end{equation}

\subsubsection{Local Features}
The local block uses a convolution layer with a stride of 1 and a kernel size of $1\times 1$ to obtain the final local features maps $X^{L}$:
\begin{equation}
\label{E7}
X^{L}=Conv(X^F_{1}, X^C_{1}, D_{2}),
\end{equation}
where the channel number of $X_{L}$ is the sum of sizes of $X^F_{1}$ and $D_{2}$, and the size of the local feature $X_{L}$ is $176\times 176$.
For convenience to fusion with the global feature, we need again use the Deconv operation to increase the size of the local feature $X^{L}$ from $176\times 176$ to $352\times 352$.

\subsubsection{Global Features}
A good saliency detection model not only can capture local features but also can capture global features.
Before assigning saliency tasks to a single small region, the saliency model needs to capture the global context of the image.
To achieve this purpose, we use three convolution layers after the last VGG block to computer global feature $X_{G}$.
The first two convolution layers use the kernel size of $5\times 5$, the last convolutional layer uses the kernel size of $3\times3$ and all convolutional layers have the same channel dimensions.

\subsection{Edge Guidance Block}
Five side outputs will generate five different scale features with different channels.
To effectively embed the edge feature $X^{E}_{i}$ into the feature $X_{i}$, we design a condition network to generate shared intermediate conditions in all layers for efficient computation, as shown in Fig.~\ref{2}. These generated conditions are regarded as one of inputs of the edge guidance block.

The inspiration of the edge guidance block comes from~\cite{Wang2018Recovering}, this paper employs deep spatial feature transform to recovery realistic texture in image super-resolution.
They use the possibility of semantic segmentation maps as the categorical prior, and a spatial feature transform layer is conditioned on semantic segmentation probability maps.
Different from it, as shown in Fig.~\ref{2}, our edge guidance block is composed of two stage.
In the first stage, our condition network uses four convolutional layers with the edge map as input to generate conditional features $X^{E}_{i}$, where each convolutional layer is with a kernel size of $3\times 3$ and a stride of 1.
The size of $X^{E}_{i}$ is $176\times 176$.
Note that the condition network only produces coarse edge feature $X^{E}_{i}$.
To better transferring edge features to next layers of the network and play a better guidance role, we propose to integrate the edge guidance block into hierarchical feature maps.
The edge guidance block uses two separate branches to output two features $(\gamma, \beta)$ based on the conditional features.
Then, we use $(\gamma, \beta)$ to transform $X_{i}$ into an edge-aware feature $X^{F}_{i}$ as follows:
\begin{equation}
\label{E4}
EGB(X^{F}_{i}|\gamma,\beta)=X_{i}\odot\gamma+\beta,
\end{equation}
where $\odot$ represents the element-wise product operation and $+$ represents the element-wise addition operation.
Since the spatial dimensions are preserved, the edge guidance block not only performs feature-wise manipulation but also spatial-wise transformation.

\subsection{Loss Function}
Saliency detection and image segmentation usually come down to the optimization of non-convex energy functions consisting of data items and regularization items.
A mathematical global model is the  Mumford-Shah (MS) model~\cite{Mumford1989Optimal}, which transforms the image segmentation problem into solving the minimum value of energy function.
By constructing the energy function, the curve is evolved under the driving of the minimum value of energy function and the contour curve gradually approaches the boundary of the object, and the object is finally segmented.
In~\cite{Luo2017Non}, they propose a supervised deep convolutional network, where the loss function approximates the MS functional are made of a cross entropy loss term between ground truth and predicted the saliency map and boundary loss term.
The purpose of MS functional is to minimize IOU loss by maximizing the coincidence rate between predicted boundaries and real boundaries:
\begin{equation}
\label{E3}
F_{MS}\underbrace{\approx\sum_{j}\lambda_{j}\displaystyle\int_{v\epsilon\Omega_{j}}S_{j}(y(v),\widehat{y}(v))dv}_{cross~entropy~loss}+
\underbrace{\sum_{j}\gamma_{j}(1-IoU(C_{j},\widehat{C}_{j}))}_{boundary~loss}
\end{equation}
where $v$ is the pixel location, $S_{j}$ is the total loss between ground truth $(y)$ and predicted $(\widehat{y})$ saliency map, and IoU$(C_{j}, \widehat{C}_{j})$ is the intersection over union between the true boundary and predicted boundary.
The positive constant $\lambda_{j}$ and $\gamma_{j}$ are used to tune the multi-criteria energy function in terms of data fidelity and total boundary length.

\begin{figure*}[!t]
\centering
\includegraphics[width=6.5in]{PRF_C.png}
\caption{Precision-recall curves for our model compared to MR~\cite{yang2013saliency}, DRFI~\cite{Wang2013Salient}, wCtr~\cite{zhu2014saliency}, MCDL~\cite{Rui2015Saliency}, MDF~\cite{li2015visual},DCL~\cite{Li2016Deep},DS~\cite{Li2016DeepSaliency}, DSS~\cite{Hou2016Deeply}, LEGS~\cite{Wang2015Deep}, UCF~\cite{Zhang2017Learning}, WSS~\cite{Wang2017Learning}, Amulet~\cite{Zhang2017Amulet}, NLDF~\cite{Luo2017Non}. Our model can deliver state-of-the-art performance on five datasets.}
\label{4}
\end{figure*}

\begin{center}
\begin{table*}
\caption{Maximum F-measure (larger is better) and MAE (smaller is better) of different saliency detection methods on five released saliency detection datasets.}\label{tab2}
\setlength{\tabcolsep}{4mm}{
\resizebox{\textwidth}{30mm}{
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{*}  &\multicolumn{2}{|c|}{HKU-IS}     &\multicolumn{2}{|c|}{DUT-OMRON}            &\multicolumn{2}{|c|}{PASCAL-S}         &\multicolumn{2}{|c|}{ECSSD}  &\multicolumn{2}{|c|}{SOD}\\
 \hline
                                       & max $F_{\beta}$    & MAE            &max $F_{\beta}$      &MAE                  &max $F_{\beta}$    & MAE             & max $F_{\beta}$      &MAE             &max $F_{\beta}$    & MAE\\
\hline
ENFNet         &\textbf{0.915}                          &\textbf{0.040}         &\textbf{0.779}                          &\textbf{0.065}                &\textbf{0.843}                    &\textbf{0.095}             &\textbf{0.921}      &\textbf{0.051} &\textbf{0.831}                       &\textbf{0.134}\\
\hline
NLDF~\cite{Luo2017Non}  &0.902                           &0.048           &0.753                           &0.080               &0.831                      &0.099            &0.905                            &0.063            &0.810                        &0.143\\
\hline
Amulet~\cite{Zhang2017Amulet}     &0.899                            &0.050         &0.743                            &0.098                &0.839                     &0.099             &0.915                            &0.059            &0.778                        &0.156\\
\hline
WSS~\cite{Wang2017Learning}        &0.858                            &0.079           &0.689                          &0.110                  &0.782                   &0.141               &0.856                            &0.103            &0.759                      &0.181\\
\hline
UCF~\cite{Zhang2017Learning}         &0.888                          &0.061             &0.730                         &0.120                  &0.825                   &0.115                 &0.903                          &0.069              &0.796                      &0.159\\
\hline
LEGS~\cite{Wang2015Deep}       &0.770                             &0.118           &0.669                          &0.133                   &0.756                   &0.157                &0.827                          &0.118               &0.707                     &0.215\\
\hline
DSS~\cite{Hou2016Deeply}          &0.900                             &0.050           &0.760                         &0.074                    &0.832                    &0.104                &0.908                        &0.062                 &0.807                   &0.145\\
\hline
DS\cite{Li2016DeepSaliency}            &0.864                              &0.078           &0.745                          &0.120                    &0.766                   &0.176                 &0.882                     &0.122                     &0.771                 &0.199\\
\hline
DCL~\cite{Li2016Deep}         &0.892                                &0.054           &0.733                         &0.084                     &0.815                   &0.113                &0.887                       &0.072                  &0.795                   &0.142\\
\hline
MDF~\cite{li2015visual}       &0.861                                &0.076            &0.694                          &0.092                    &0.764                   &0.145                  &0.832                     &0.105                   &0.745                  &0.192\\
\hline
MCDL~\cite{Rui2015Saliency}     &0.808                               &0.092              &0.701                       &0.089                       &0.745                   &0.146                 &0.837                     &0.101                    &0.704                &0.194\\
\hline
wCtr~\cite{zhu2014saliency}        &0.735                               &0.138                &0.588                     &0.171                      &0.664                      &0.199                 &0.726                    &0.165                      &0.639             &0.231\\
\hline
DRFI~\cite{Wang2013Salient}      &0.777                                &0.144                 &0.664                     &0.150                     &0.696                         &0.210               &0.782                     &0.170                     &0.672            &0.242\\
\hline
MR~\cite{yang2013saliency}          &0.709                              &0.174                   &0.610                      &0.187                    &0.653                       &0.232                 &0.742                   &0.186                    &0.629            &0.274\\
\hline
\end{tabular}}}
\end{table*}
\end{center}

\begin{figure*}[!t]
\centering
\includegraphics[width=\textwidth]{saliency.png}
\caption{Saliency maps produced by the MR~\cite{yang2013saliency}, DRFI~\cite{Wang2013Salient}, wCtr~\cite{zhu2014saliency}, MCDL~\cite{Rui2015Saliency}, MDF~\cite{li2015visual},DCL~\cite{Li2016Deep},DS\cite{Li2016DeepSaliency}, DSS~\cite{Hou2016Deeply}, LEGS~\cite{Wang2015Deep}, UCF~\cite{Zhang2017Learning}, WSS~\cite{Wang2017Learning}, Amulet~\cite{Zhang2017Amulet} and NLDF~\cite{Luo2017Non}. Our model can deliver state-of-the-art performance on five datasets. }
\label{3}
\end{figure*}

\subsubsection{Cross-entropy Loss}
We use two linear operators $(W_{G},b_{G})$ and $(W_{L},b_{L})$ to combine the local and global features.
And the softmax function is used to compute the probability for each pixel of  being salient or not:
\begin{equation}
\label{E8}
\hat{y}=p(y(v)=s)=\frac{e^{W^s_{L}X_{L(v)}+b^s_{L}+W^s_{G}X_{G}+b^s_{G}}}{\sum_{s\prime\in\{0,1\}}e^{W^{s\prime}_{L}X_{L(v)}+b^{s\prime}_{L}+W^{s\prime}_{G}X_{G}+b^{s\prime}_{G}}}
\end{equation}
where $p$ is the probability for each pixel of being salient, $v$ is the location of a pixel and $s$ indicates that the pixel belongs to the foreground.
The cross-entropy loss function is:
\begin{equation}
\label{E9}
G_{j}(y(v),\hat{y}(v))=-\frac{1}{N}\sum^{N}_{i=1}\sum_{s\in\{0,1\}}{(y(v_{i})=s)(\log(\hat{y}(v_{i}=s))}
\end{equation}
where $y(v)$ is the ground truth and $\hat{y}(v)$ is the predicted saliency map.

\subsubsection{IoU Boundary Loss}
This inspiration comes from the applications of the IOU boundary loss in image segmentation~\cite{Milletari2016V, Taha2015Metrics}.
IOU boundary loss computes the error between the true boundary $C_{j}$ and predicted boundary $\hat{C}_{j}$.
The boundary pixels use a Sobel operator followed by a tanh activation function and this active function predicts the gradient magnitude of saliency maps to a probability range of [0,1].
The IOU boundary loss can be represented as:
\begin{equation}
\label{E10}
IoU Loss=1-\frac{2\mid{C_{j}\cap{\hat{C}_{j}}}\mid}{\mid{C_{j}}\mid+\mid{\hat{C}_{j}}\mid}
\end{equation}

\begin{figure*}[!htbp]
\centering
\includegraphics[width=6.5in]{PRF_H.png}
\caption{For a better visual effect, we represent the Precision, Recall and F-measure value in the form of  a histogram compared to MR~\cite{yang2013saliency}, DRFI~\cite{Wang2013Salient}, wCtr~\cite{zhu2014saliency}, MCDL~\cite{Rui2015Saliency}, MDF~\cite{li2015visual}, DCL~\cite{Li2016Deep}, DS~\cite{Li2016DeepSaliency}, DSS~\cite{Hou2016Deeply}, LEGS~\cite{Wang2015Deep}, UCF~\cite{Zhang2017Learning}, WSS~\cite{Wang2017Learning}, Amulet~\cite{Zhang2017Amulet},  NLDF~\cite{Luo2017Non}. Our model is in the first column and delivers state-of-the-art performance on five datasets.}
\label{5}
\end{figure*}

\subsection{Implementation}
The pre-trained VGG-16 model~\cite{simonyan2014very} is used to initialize the weights in first five VGG Blocks.
All weights of convolution and deconvolution are initialized randomly with a constant (0.01), and the biases are initialized to 0.
The learning rate is $10^-5$ and $\lambda_{j}$,  $\gamma_{j}$ in~\eqref{E3} were set to 1.

We train our model on the MSRA-B~\cite{liu2010learning} dataset and test it on five popular benchmark datasets.
The same as the NLDF~\cite{Luo2017Non}, we use horizontal flipping as data augmentation, resulting in an augmented image set with twice large than the original one.
All training images are resized to $352\times 352$ and use the cross entropy loss and the IOU boundary loss to optimize our network.
Without further optimization, the trained model is used to predict the saliency maps in other datasets.
The whole training produces for 10 epochs with a single image batch size, and it takes about ten hours to finish the whole training.

\section{Experiments}
We have implemented our network on a single Nvidia GTX 1070Ti GPU and in Tensorflow~\cite{abadi2015tensorflow}, and our method maintains a fast runtime of 0.08 second per image.
This section will present the details of our evaluation settings, comparison with the state-of-the-art methods and impact of the proposed edge guidance block on the detection performance.

\subsection{Experimental Setup}

{\flushleft \bf Evaluation Datasets.}
We evaluate the proposed model on five public benchmark datasets including
HKU-IS~\cite{li2015visual}, PASCAL-S~\cite{li2014secrets}, DUT-OMRON~\cite{yang2013saliency}, ECSSD~\cite{yan2013hierarchical} and SOD~\cite{martin2001database}. Their details are as follows.

\begin{itemize}
\item \emph{HKU-IS:} This dataset contains 4447 images with high quality pixel labeling, and most of the images have low contrast and many salient objects.
It also contains many independent salient objects or objects touching the image boundary.

\item \emph{PASCAL-S:} This dataset has 850 natural images which are generated from the PASCAL VOC~\cite{Everingham2006The} segmentation challenge.
The ground truths labeled by 12 experts contains both pixel-wise saliency and eye fixation.

\item \emph{DUT-OMRON:} This is a large dataset with 5168 high quality images.
Each image in this dataset has one or more salient objects and a cluttered background.
Therefore, this dataset is more difficult and challenging, which provides more space for improvement for the research of saliency detection.

\item \emph{ECSSD:} This dataset contains 1000 natural and complex images with pixel-wise ground truth annotations and these images are manually selected from the Internet.

\item \emph{SOD:} This dataset has 300 images, and it was originally designed for image segmentation.
It is challenging because many images have multiple objects which with low contrast or touching the image boundary.

\end{itemize}

{\flushleft \bf Evaluation Criteria.}
We use three evaluation metrics to evaluate the performance of our model with other salient object detection methods, including
Precision-recall(PR) curves, F-measure score and mean absolute error(MAE)~\cite{Borji2012Salient}.
With continuous values normalized the saliency map to the range of 0 to 255, we compute the corresponding binary map, then compute the precision /recall pairs of all boundary maps in the dataset.
The PR curve of a dataset explains the mean precision and recall of saliency maps at different thresholds.
The F-measure is a harmonic mean of average precision and average recall, F-measure can be represented as:
\begin{equation}
\label{E11}
F_{\beta}=\frac{(1+\beta^2)\cdot{Precision}\cdot{Recall}}{\beta^2\cdot{Precision}\cdot{Recall}}
\end{equation}
where $\beta^2$=0.3 to emphasize precision over recall the same as~\cite{Achantay2009Frequency}.
The mean absolute error (MAE) is to measure the average difference between predicted saliency map S and ground truth G:
\begin{equation}
\label{E12}
MAE=\frac{1}{W\times{H}}\sum^W_{x=1}\sum^H_{y=1}\mid{S(x,y)}-G(x,y)\mid
\end{equation}
where W and H is the width and height of a given image.

\begin{figure}[htbp]
\centering
\includegraphics[width=3.2in]{result.png}
\caption{Three examples of effectiveness validation of edge information. (a) Input images. (b) Ground truths. (c) Saliency results without edge guidance. (d) Saliency results with edge guidance. }
\label{6}
\end{figure}

\subsection{Comparison with State-of-the-art Methods}
To verify the effectiveness of our experiments, we compare our method with other state-of-art ones including three conventional methods (MR~\cite{yang2013saliency}, DRFI~\cite{Wang2013Salient}, wCtr~\cite{zhu2014saliency}) and en deep learning based methods (MCDL~\cite{Rui2015Saliency}, MDF~\cite{li2015visual}, DCL~\cite{Li2016Deep}, DS~\cite{Li2016DeepSaliency}, DSS~\cite{Hou2016Deeply}, LEGS~\cite{Wang2015Deep}, UCF~\cite{Zhang2017Learning}, WSS~\cite{Wang2017Learning}, Amulet~\cite{Zhang2017Amulet}, NLDF~\cite{Luo2017Non}). It is worth mentioning that the method Amulet~\cite{Zhang2017Amulet} is also utilize edge information. Note that since some of the HKU-IS datasets are used for training in MDF~\cite{li2015visual}, we only calcute the evaluation metrics on the test dataset on HKU-IS.
Because MDF~\cite{li2015visual} also provides 200 saliency maps on the SOD dataset. Therefore, the evaluation of other methods on SOD uses only 200 saliency maps, originally 300 images.
For fair comparison, we use either the implementations with suggested parameter settings and the saliency maps provided by the authors.

\begin{center}
\begin{table}[htbp]
\caption{Effects of different number of the edge guidance blocks (EGB) on the performance.}\label{tab3}
\setlength{\tabcolsep}{1.6mm}{
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
*                                      &\multicolumn{2}{|c|}{Five EGB}     &\multicolumn{2}{|c|}{Three EGB}            &\multicolumn{2}{|c|}{Zero EGB}         \\
\hline
                                       & max $F_{\beta}$    & MAE            &max $F_{\beta}$      &MAE                  &max $F_{\beta}$    & MAE            \\
\hline
HKU-IS         &\textbf{0.915}                          &\textbf{0.040}         &0.906                          &0.046               &0.902                  &0.048            \\
\hline
DUT-OMRON  &\textbf{0.779}                           &\textbf{0.065}           &0.772                           &0.077               &0.753                      &0.080            \\
\hline
PASCAL-S  &\textbf{0.843}                           &\textbf{0.095}           &0.839                           &0.098               &0.831                      &0.099            \\
\hline
ECSSD     &\textbf{0.921}                            &\textbf{0.051}          &0.913                            &0.057              &0.905                      &0.063            \\
\hline
SOD       &\textbf{0.831}                            &\textbf{0.134}          &0.828                            &0.136               &0.810                     &0.143             \\
\hline
\end{tabular}}
\end{table}
\end{center}

We compare the proposed methods with the others in terms of F-measure scores, MAE scores, and PR-curves.
The results of quantitative comparison with compared methods are reported in Fig.~\ref{4} and Table~\ref{tab2}.
For the PR curves, we also achieve best performance compared to other methods on five datasets, as shown in Fig.~\ref{4}.
In particular, for the top-level method DCL, which uses CRF-based post-processing steps to refine the resolution, our method still achieves the better performance.
To verify the effectiveness of our ENFNet, we further report the results of F-measures and MAE scores in Table~\ref{tab2}.
From the results we can clearly see that our approach is clearly superior to competitive methods in terms of F-measures and MAE scores, especially in challenging datasets.
In terms of F-measures, our ENFNet increases the value of F-measure by 2.6\% and 2.1\% over the second best method NLDF on the two most challenging datasets DUT-OMRON and SOD respectively.
For MAE values, our method decreases 15\% and 9\% over the second best method NLDF on the two most challenging datasets DUT-OMRON and SOD respectively.
Experimental results show that the edge guidance is effective.

To express the superiority of our method more intuitively, we report the values of Precision, Recall and F-measure in the form of the histogram with five datasets and 13 methods, as shown in Fig.\ref{5}.
On the five datasets, our method can achieve best performance compared to 13 methods.

For a better visual experience, Fig.~\ref{3} provides a visual comparison of our approach and other methods.
It can be seen our method generates more clear saliency maps in various challenging images.
We select two most representative images from each dataset to display the saliency map.
These images usually contain multiple significant targets and have complex backgrounds or unclear edges.
Thus such images are more responsive to the superiority of our approach.
From Fig.~\ref{3} we can see that our method obtains the best results which are much close to the ground truth in various challenging scenarios.
To be specific, with the help of edge information guidance, the proposed method not only highlight the salient object regions clearly, but also generate the saliency maps with clear boundaries and consistent saliency values.

\subsection{Impact of Edge Guidance Block}
Because we use the edge information in our method, we compare to an edge-related method Amulet~\cite{Zhang2017Amulet} which achieves good performance.
To verify the effectiveness of our proposed edge guidance block, the inclusion of edge feature in our model as compared to baseline~\cite{Luo2017Non} for an increase in max $F_{\beta}$  and decrease in MAE on HKU-IS, DUT-OMRON, PASCAL-S, ECSSD and SOD five datasets as shown in Table~\ref{tab2}.
It can be observed visualization results that without edge guidance block in the NLDF~\cite{Luo2017Non} are shown in Fig.\ref{3}.
With the help of the edge guidance block, our approach can better localize the boundaries of most salient objects and produce more precise saliency maps.
Several visual examples are showed in Fig.~\ref{6}.

In addition, we also conduct an experiments to show the impact of the number of edge guidance blocks on the saliency detection performance.
One is that only three edge guidance blocks are used in the first three layers, and another is to remove all edge guidance blocks.
The results are shown in Table~\ref{tab3}.
From the results we can see that our complete method ENFNet achieves best than other variants, demonstrating the effectiveness of our design on the network.

\section{Conclusion}
In this paper, we have proposed a novel Edge-guided Non-local FCN for salient object detection from the local and global perspective.
We demonstrated that with saliency edge prior knowledge to perform edge-guided feature learning is beneficial to generating high-quality saliency values.
Through the edge guidance block the edge features are embedded into feature learning of our network, our method can preserve more accurate edge structure information.
Experimental results demonstrated that our proposed method consistently improves the performance on all five benchmarks and outperforms 13 state-of-the-art methods under different evaluation metrics.
In future work, we will study other prior knowledge and information like semantic priors~\cite{li19ec-cnn} and thermal infrared data~\cite{li2019rgb} to help improving the performance of salient object detection, and also extend our framework to video object saliency detection.

\ifCLASSOPTIONcaptionsoff
  \newpage
\fi

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


Quiet – The internet doesn't do quiet. It's good for a lot of things, but quiet is not one of them. The internet overflows with information. It is loud and noisy. It's a million voices. It's full of people. (Many who would never be so obnoxious in person.) This can be a great thing. Sometimes, we need sensory overload and we need to fill our brains. Only then, can we sort through it all and make sense of the world around us.
I studied under Mike Ayers in college and he used to say, "A leader needs time to sit and stare out the window." We've all got to have time to stare and imagine what life "could" be like and maybe more importantly, how it "should" be. Staring out the window gives us a chance to imagine and create a way to get to these new places, how build something new, to develop a new strategy. It allows us space to dream. Maybe daydreaming should be a bit of a discipline? When we need to make big/important decisions we need space and margin. We need quiet. Internet can't do this.
Confession: Quiet is what I need. Soccer games, and football practices, and dance classes, and church responsibilities, and work, and family, and lifegroup, and, and, and. It all just overwhelms me. My life seems like a lot of noise. Then I come home to the internet……and it's just more noise, more information, more, more, more. The stress builds and just piles on. I feel like screaming. I just want it to stop. I need quiet. I need margin and space so I am working on it. As a family, we are taking January off from sports – no soccer games or practices. We didn't sign up for basketball. We're planning to go camping. I'm also refraining from tv and much of the internet – planning to read more and write (on this blog) – to contemplate and sit and "stare out the window." Pray for me.
Anyway, I was reminded of all these ideas when I was reading Andy Stanley's book Visioneering today. I've already posted about the difference between a dreamer and a visionary (visionaries imagine themselves getting things done, dreamers just dream). "Imaginuity" is another way of speaking about vision because it too motivates one to do something. McNair Wilson worked for Disney at one time and was called an "Imagineer" – that's a pretty good term for a visionary too.
Anyway, these were just some thoughts I had today about vision.
I'll try to pull out my notes from that first convention to see what else I can remember about his "imaginuity" ideas. Maybe I'll even order the video with his teachings on the subject.


Sup Dawg's? I got some Sonic news for you.
A Sonic the Hedgehog costume…. For dogs! A Sonic the Hedgehog costume… FOR DOGS! Erm… Can I fit it on a cat?
This costume has popped up for order on halloweencostumes.com, current asking price is $20 + Postage. I can't imagine it being official but. If you fancy turning Fido into the worlds fasted hedgehog. Well hot dog, nows your chance!
Pay no attention to the fact that they have just photoshopped the costume onto the dog and not managed to get one to sit still enough to put him in one. It's a Sonic costume! For dogs! It comes in small, medium, large and extra large, though you might want to get on the dog n' bone to get more accurate measurements.
Anyone here going to order one? Regardless as to if you own a dog? You'd be barking mad not to take avantage of this find.
Edit: According to the website, this is indeed an official product.
Lets play how many sight gags can you spot… lawl.
It's back, with a vengeance… and this time, it wants your blood! Or something. Yes, the awesome Hallowe'en pumpkin stencils we offered last year are back, and now they're permanently archived in a new section in the Media section over on the sidebar. Click on 'Other Downloads' and you'll be taken to a page where our miscellaneous content will go, as and when we get it.
Rally the Cheetah lovingly made these so you can create your own Sonic pumpkins at home! And there's still a day left to go to prepare, so what perfect timing!
New additions have arrived if you don't fancy doing the same carvings as last year though, with Silver and an incredible Sonic the Werehog stencil for those looking for a challenge. You'll need a hell of a pumpkin for the Werehog one, but doesn't it look badass? Most appropriate for the season, don't you think?
You can find a direct link to the Hallowe'en Pumpkin stencils here, if you don't fancy going through that extra click, you lazy git, you. Be sure to write about your own Sonic-themed pumpkin/Hallowe'en stories in the comments box, and from me and everyone at TSS, have a ghoulish weekend!
It's generally understood that you only submit one entry into competitions. The case is no different with our currently ongoing Hallowe'en Competition for some Sonic Chronicles goodies. We can understand that one or two people may send two entries – be it accidentally (dupe-entries) or on purpose just to see if they can push their luck. But this is a bit excessive.
'datkross412' decided that one of the best ways to get our attention is send in a whopping eighteen entries, featuring almost every Sonic character under the sun. The guy even did Sonic twice. I guess the plan was to see whether one of them was our favourite character or something. There was an entry for, in order: Sonic, Chip, Werehog, Tails, Rouge, Blaze, Shadow, Knuckles, Silver, Super Sonic, Eggman, Vector, Espio, Big, Amy, Metal Sonic, Elise, and Sonic again.
So yeah. Obviously, that's taking the piss. We're not going to pick the best one for his entry, much less look at them, so 'datkross412' here's your challenge – pick one. Pick the entry you want to enter for this contest. If you don't, we'll bin them all and disqualify you. Usual contest email address, just give us the name of the character you want to keep.
And that ends today's lesson in "Don't give Svend a headache". Thanks for playing.
Thanks to Sega Europe, we have a few Sonic Chronicles goodies to give away on The Sonic Stadium. Just in time for Hallowe'en too! See that photo up there? The Chronicles tin and stylus pack? There are five of those to give away. You could win a set of them, you could. There's also a super-secret squirrel Grand Prize goody bag, which will be sent your way should you impress us the most.
Pick any Sonic the Hedgehog character, and describe to us how they would Trick or Treat someone.
It's as simple as that. Imagine a Sonic character of your choice, knocking on a house door. What does that character do when that door opens to get as much candy as possible? Funny, scary, or downright weird entries are all acceptable, so do your worst (on all accounts)!
Send your entires to dreadknux@gmail.com with your name, email address and what your chosen character does. Closing date for entries is the 7th November 2008. Good luck!
To get you into the spirit of things, SEGA have made this awesome Hallowe'en trailer for Sonic Unleashed. But it's not a trailer for the game. Nope, it's a trailer for a special animated film that SEGA are producing, to tie in with the game. So it's Sonic the Hedgehog's "Thriller" then.
It's done in a classic horror movie style, and features Sonic and new character Chip heading into a haunted house, with the blue blur unable to contain his dark secret when the full moon comes out… Almost reminded me of the intro to Luigi's Mansion until Were-Sonic started beating the snot out of some Looney Tunes-esque bull.
Give it a watch, it's ghoulishly good. Ahem. The full animated short will be available in November.
Hey you. Excited about Halloween? Good. Got a pumpkin? Good. Want to make a Sonic pumpkin? You're in luck my friend, as The Sonic Stadium has a set of pumpkin stensils that you can use to make your favourite character glow viciously in the twilight. These designs were submitted by Rally the Cheetah, who created all but the Amy pumpkin (which was created by 'The Lioness'), but feel free to print any of these onto an A4 sheet of paper and go nuts.
You've got a choice of Sonic, Tails, Knuckles, Amy, Shadow, Silver and Blaze. Click here for the stencils, along with a special tutorial made by Rally in case you're unsure as to how to create your own Sonic Pumpkin.


text:
Growing a Growth Plate
Orthopedics | January 07, 2019
Learn more about orthopedics at Children's Colorado, and how we can help you improve your practice.
See our orthopedic resources
Between operations, pediatric orthopedic surgeon Nancy Hadley-Miller, MD, sips tea and looks at X-rays. She pulls the femur and tibia of an 8-year-old girl. On the righthand head of the tibia, just below the knee joint, the clean black line of the growth plate breaks up into a smear of grey, marbled and indistinct. The fracture punched through the growth plate, and when it healed it healed as bone. Effectively sealed, the plate can't expand. The bone can't grow.
"Trampoline injury, if I'm not mistaken," she remarks.
In her 30-plus years as an orthopedic surgeon, Dr. Hadley-Miller has seen thousands of injuries like these. At Children's Hospital Colorado alone, the Orthopedics Institute sees 150 to 160 fractures a week, about 30% of which affect the growth plate. Of those, 3% might form a "bony bar" like that of Dr. Hadley-Miller's 8-year-old patient. On average, that's perhaps one a week.
"These cells are communicating, like a little city. There's a whole circle of life in there we know almost nothing about."
Nancy Hadley-Miller, MD
If the bar fuses less than half the growth plate, Dr. Hadley-Miller and surgeons like her can resect it and fill the area with a material to stop it from reforming: sometimes a fat graft, sometimes bone cement. Neither option is great. Fat degrades and melts away. Bone cement goes on hot, inflaming the surrounding tissue, and while it lasts longer, it can't grow with the patient.
That's best-case scenario.
When growth plates get injured
In many more cases, surgeons injure the counterpart limb — killing its growth plate, too. Given her age, that was the only option for the 8-year-old on Dr. Hadley-Miller's screen. Her tibia won't ever grow, but at least she won't have an angular deformity.
"There's a real need for better treatment options," says Karin Payne, PhD, Dr. Hadley-Miller's research partner and Director of the Payne Regenerative Orthopedics Lab at the University of Colorado School of Medicine, which shares both campus and faculty with Children's Colorado. "Currently there's no clinical treatment that can regenerate growth plate tissue."
Unlike bone, the growth plate comprises five distinct zones. Cartilage cells form in the top zone, proliferate in the next and hypertrophy after that. Then they die and calcify, to be broken down by osteoblasts and, finally, replaced by mineralized bone tissue. This process continues through skeletal maturity, when the plates fuse.
Engineering the environment for growth plate regeneration
The problem has to do with the mechanism of healing. Inflammatory cells invade the area and lay down the fibrous tissue necessary to stitch it back together, along, some studies have suggested, with an influx of mesenchymal stem cells. In the second week, blood vessels begin to form, which cues the stem cells to become bone.
"But if you can create an environment that promotes cartilage," says Dr. Payne, "it's possible to make those stem cells become cartilage instead."
Her lab is currently testing that idea on animal models of bony bars using a number of delivery mechanisms, in collaboration with bioengineers, biochemists and computing experts from all over the state. They're testing factors and combinations of factors that block angiogenesis, that attract stem cells, that mimic the chemical conditions under which cartilage develops and grows.
Cartilage regeneration that gels
Working with biopolymer expert Melissa Krebs, PhD, of the Colorado School of Mines, Dr. Payne's team is testing microgels made of chitosan, a biomaterial derived from crustacean cells. They're using them as a delivery mechanism for antibodies that block blood vessel formation, as well as factors that attract stem cells from surrounding tissue to encourage cartilage growth. Those studies are separate for now, but the aim of the National Institutes of Health grant that funds them is to bring them together.
Meanwhile, in a collaboration with materials science expert Stephanie Bryant, PhD, of the University of Colorado Boulder, the team is testing a biomimetic hydrogel delivery system based on polyethylene glycol, a synthetic biomaterial ideal for attaching biofactors. To this one they attach factors that promote cartilage growth and the environment it likes to grow in.
"Once we put mesenchymal stem cells into this hydrogel," says Dr. Payne, "they start producing collagen type 2 and sulfated glycosaminoglycans" — the chemical backbone, so to speak, of cartilage.
The mechanical properties of the growth plate
These projects show promise, but the project Dr. Payne is most excited about — the one she thinks is closest to clinical practice, maybe even just a few years out — is more tangible still. Conceived to replace the fat grafts or bone cement currently used to prevent bony bars, it's a personalized, 3D printed implant that blocks bony bar reformation and stabilizes the growth plate, ideally while the tissue regenerates.
For that, her lab collaborates with Dr. Bryant and CU Boulder bone mechanics expert Virginia Ferguson, PhD. Together, they're working to match the mechanical properties of the implant to each of the growth plate's five zones — an aspect of the growth plate that, so far, no research team has ever tried to deduce.
Fabricated as an interlocking system of pillars, the implants can then be infilled with cartilage-promoting hydrogels. In the lab, a cadre of grad students, research assistants and fellows resect tibia and humeri the length of thumbtacks, dry them, pack them in paraffin and chop them five microns thick. Stained, the slides provide the proof: bright blue eruptions of cartilage where, compared to samples left untreated, the bar reformed.
"It's fascinating," says Dr. Hadley-Miller, who contributes both bench and clinical work to the project. "What keeps these cells from spreading out instead of sticking together? How do the cells progress downward? What are the signals that turn them on and off? They're communicating, like a little city. There's a whole circle of life in there we know almost nothing about."
Mapping the outcomes of growth plate injury
The clinical picture of growth plate injury isn't much clearer. On that side, Dr. Hadley-Miller and her team at Children's Colorado's Musculoskeletal Research Center, or MRC, are assembling a comprehensive study — another first for the field.
Epidemiologist Patrick Carry, MS, can get a good idea of the incidence of fractures that affect the growth plate by pulling from a number of national trauma databases. But what those databases can't offer is longitude.
"As a Level 1 Trauma Center, we get a lot of kids referred with existing growth plate injuries, but we don't know how many started with a fracture that developed into a growth plate injury, or how many of those injuries resulted in these growth plate disturbances," he says. "So we're developing a strategy to answer that question."
As one of the first pediatric institutions in the U.S. to adopt an electronic medical record system, Children's Colorado has comprehensive data stretching back well more than a decade. The question is how to access and sort it.
Carry, an expert in study design, is currently working out the inclusion criteria with a targeted chart review of bones and trauma types that tend toward growth plate injury. When the charts are pulled, he and his team will comb through thousands of X-rays and diagnostic codes, a huge, time-intensive effort requiring many, many man hours.
Getting the pieces into place
That kind of effort is largely possible through the unique funding model of the MRC, which pools clinical dollars earned from surgery toward an infrastructure of 14 research assistants and two statisticians who power dozens of surgeon-directed research efforts. As the MRC's first such research assistant, Carry has been working with Dr. Hadley-Miller, the MRC's medical director, for more than ten years.
But their growth plate work started with a joint research grant opportunity between Children's Colorado and the School of Mines. At the time, Dr. Payne was working in adult cartilage regeneration, mostly focused on degenerative joint disease. She hadn't done growth plate research before. Neither, for that matter, had Dr. Hadley-Miller. Her 30-year career has been focused on the molecular genetics of scoliosis.
She heard about the grant talking to a colleague after a surgery, and she ran it past Dr. Payne, whose office was just a few doors down. As it turned out, Dr. Payne knew someone they could work with at Mines. For her, it represented a new and interesting research direction. For Dr. Hadley-Miller, it was something like a return.
"In fellowship I always wanted to do something with growth plate," she recalls. "But I didn't find a bone lab to work in, didn't have the mentorship. That's how I ended up in scoliosis. I followed another idea. It wasn't a bad way to go."
Now, though, with funding from both the Gates Grubstake Fund and the NIH, their lab is just one of a few in the world doing groundbreaking work in growth plate regeneration, and it's leading the field. For Dr. Payne, that conversation has fundamentally altered the course of her career.
Some of that is coincidence, Dr. Hadley-Miller acknowledges. But more than that, it's having the pieces in the right place.
"Patient care is our bread and butter," she says. "Here, we're using it to pay for research. But you really have to want to do it. You have to invest in the people and the infrastructure to make those connections happen."
She knocks back the rest of her tea. And with that, she's off to surgery.

redpajama_set_name: RedPajamaCommonCrawl


text:
Globalist Analysis	Previous Next
Italy After Berlusconi
Italy's political and economic future is just as shaky as it was before — maybe even more so.
By Beat Guldimann, November 11, 2011
Italy's political and economic future is just as shaky as it was before.
Italy is simply too big to fail and too big to be bailed out. All the problems we have diagnosed in Greece are magnified in Italy.
There are simply not enough purchasers of Prada shoes, Ferraris and Zegna suits in the world to lead Italy back to growth.
Markets seem to have realized that Italy's political and economic future is just as shaky as it was before — maybe even more so.
Since the end of World War II, Italy has had 13 legislatures with 60 governments, led by 24 prime ministers.
The Italians have a knack for pathos and tragedy. After all, Italy has given the world the gift of Giuseppe Verdi and Gioachino Rossini. No other culture has ever managed to match the drama and pomp of Italian opera.
What the world has witnessed in the months and weeks leading to Prime Minister Silvio Berlusconi's last curtain has been nothing short of an "opera buffa," Rossini-style. With all the gaffes and other missteps that "Il Cavaliere" ("The Knight") had to account for over his years in power, his announcement to step down is much less of a surprise than his long tenure is a miracle.
Much as Bill Clinton survived impeachment stemming from his episode with Monica Lewinksi, Berlusconi survived his trysts with Ruby Rubacuori (what a name!) and Noemi Letizia. Italians will not put down a septuagenarian prime minister for his display of virility, no matter how misguided his actions may have been.
Mr. Berlusconi also easily survived allegations of being connected to organized crime, of engaging in dubious business practices with his media empire and of abusing his power as the occupant of Palazzo Chigi, the official residence of the Presidente del Consiglio. Corruption is not unfamiliar territory in Italian politics.
It is truly remarkable that a man whose persona and performance as a politician are mired with so much controversy was able to stay in power for so long, especially seeing as how we are talking about Italy here, with its history of short-lived coalition governments.
Since the end of World War II, Italy has had 13 legislatures with 60 governments, led by 24 prime ministers, many rotating in and out of Palazzo Chigi multiple times. The average duration of Italian governments is just about 13 months, and Berlusconi's three stints as prime minister have all been above that mark. His last government so far lasted three-and-a-half years, making Italy seemingly as stable and orderly as the United Kingdom, France or Germany.
But now, at long last, Italy's political system has reached its breaking point. Members of the governing coalition, even those in his own party, have abandoned Berlusconi in droves. His government no longer has the support of the Italian parliament, and elections will have to be called as soon as practical to ring in a post-Berlusconi era.
One would think that this would give rise to a big sigh of relief not only in Italy, but also on international markets. After all, Mr. Berlusconi's stubborn reluctance to accept reality by implementing true austerity measures to deal with an astronomical sovereign debt load was seen as a major problem for Italy's credibility.
Interestingly enough, global bond markets have reacted in the opposite fashion. The uncertainty created around Berlusconi's announcement to resign has sent Italian bond yields past the critical 7% mark. Even in his disgraced exit, Il Cavaliere has yet again managed to create a stir.
As much as Berlusconi's departure is a welcome event, markets seem to have realized that Italy's political and economic future is just as shaky as it was before — maybe even more so.
While Italy had a somewhat unreasonable and sometimes comical prime minister before, at least it had a government, and one of the most long-lived in post-war history, at that. Now, Italians will have to find a new government — and nobody seems to dare make predictions as to where voters will look for leadership.
Markets hate nothing more than surprises and uncertainty. While Berlusconi's exit from Palazzo Chigi is hardly a surprise at this point, there is lots of uncertainty. Will Italy be governed by pro-business and pro-austerity conservatives, or will the next election produce a stalemate between the left and right, and a variety of special interests?
What will be the strength and role of the Lega del Nord (Northern League), which represents the bedrock of Italy's industry? And will the elected parties find a compromise quickly enough on forming a coalition government and giving it a political mandate to take the critical steps to actively deal with the crisis?
Nobody has the answers to these questions at this point, and no date has yet been set for an election. This raises the issue of what the Italian President, Giorgio Napolitano, will do to install an interim government that stays on task as the country braces itself for hitting rock bottom.
The most recent rumor is that Napolitano will ask Mario Monti to step in until an early election sometime in February 2012. Monti is a business professor at Bocconi University in Milan and a former ranking member of the EU commission. Napolitano may just pick the right person for the job.
Just a few days after the world has witnessed the great tragedy of a falling government in Greece, we are now faced with the world's eighth-largest economy in free fall. Italy is simply too big to fail and too big to be bailed out. All the problems we have diagnosed in Greece are magnified in Italy: Rome's public debt sits at over €2 trillion, a number that is significantly larger than the amounts that Germany, France and Italy (irony of all ironies) have committed to the European Financial Stability Facility.
Rome's debt represents 120% of Italian GDP. This is an unhealthy and unsustainable number, regardless of how one looks at it. But the issue is exacerbated by the fact that Italy's economy is shrinking, not growing. With austerity measures in the works, the decline of the Italian economy will be even faster, as the public sector will contribute less to the nation's GDP.
The private sector is unlikely to pick up enough steam through the next recession to make up for the difference and lead Italy back to growth. There are simply not enough purchasers of Prada shoes, Ferraris and Zegna suits in the world. And Fiat is a shadow of its former self, leaving much of the Italian car market to Volkswagen and the other German carmakers while pumping money in its Chrysler division.
Italy can neither rely on the private sector, nor on 1980s-style inflation to help it dig out of the staggering mountain of debt that has been piled up over the past few decades to fund an explosion of government programs. Structural rebirth is unavoidable. The state will need to shrink while raising taxes — and the population will have to work longer while making do with reduced pensions.
The Case for Eurobonds
Eurobonds: The Solution to the Euro Crisis?
Euro Crisis: Economic Turmoil, Political Backlash
Tags: Bill Clinton, corruption, debt, eurozone crisis, Giorgio Napoitano, Greece, Italy, Lega del Nord, Mario Monti, Northern League, organized crime, Rossini, Silvio Berlusconi, taxes, Verdi
About Beat Guldimann
Beat Guldimann is president of Tribeca Consulting Group and the author of "Inside Swiss Banking."
Full bio → | View all posts by Beat Guldimann →
Responses to "Italy After Berlusconi"


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Planning a girl's night out for the Bride-to-Be is easy when you choose from coordinating pink, black and gold party decorations. Girls Night Out – Bachelorette party theme has everything you need to coordinate an impressive event. Begin with a photo prop kit or mug shot signs so you are sure to take photos all night long. Next, add a balloon kit and banner to easily make your party space ready for the crew. Finish your party ideas with DIY bachelorette favors using die-cut shapes and you will have a Girls Night Out event no one will soon forget.


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Francisco Vicente Navarro (Mosqueruela, 1738 - Ávila, 6 de agosto de 1781) fue un compositor y maestro de capilla español.

Vida
Francisco Vicente Navarro nació en Mosqueruela, en la Provincia de Teruel. Es probable que se educase con el organista de su villa natal o en la Escuela de gramática de la cercana Cantavieja, donde también se educaron los músicos Agustín Sesé, Miguel Rambla o José Gargallo. Probablemente más tarde pasó a estudiar en La Seo de Zaragoza como infantico del coro, bajo el magisterio de Luis Serra.

Fue maestro de capilla de la iglesia de San Felipe Neri de Madrid. Gracias a una recomendación de José de Nebra, en ese momento segundo maestro de capilla de la Capilla Real de Madrid, consiguió el magisterio de la Catedral de El Burgo de Osma. Tras la partida de Fabián García Pacheco, el cabildo había consultado a Nebra sobre los maestros Navarro y García de Carrasquedo, en ese momento en el magisterio de la Catedral de Santander.

En Osma trató de revalorizar la capilla de música, recriminando a algunos músicos su actitud pasiva, denunciando la falta de algunos instrumentos y poniendo a los infantes bajo su responsabilidad. No pudo cumplir con sus responsabilidades como hubiera querido debido a enfermedades, sobre todo en 1768. Permaneció en El Burgo de Osma de 1766 a 1780, año en el que consiguió el magisterio de la Catedral de Ávila. Permanecería en Ávila hasta el 6 de agosto de 1781:

Su padre, Francisco Vicente, tuvo que pedir socorro al cabildo metropolitano, ya que a sus 73 años había quedado en la miseria y no podía pagar las deudas dejadas por su hijo. El cabildo le concedió 600 reales de limosna.

Obra
El archivo de la Catedral de El Burgo de Osma conservan numerosas composiciones litúrgicas de Navarro. En Ávila se conserva una misa a dos coros, Vísperas de San Segundo, además de varios motetes y salmos, y cinco villancicos de Navidad y Reyes.

Referencias

Nacidos en Mosqueruela
Fallecidos en Ávila
Compositores de Aragón
Compositores de España del siglo XVIII
Maestros de capilla de la Catedral de El Burgo de Osma
Maestros de capilla de la Catedral de Ávila

redpajama_set_name: RedPajamaWikipedia


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The High Court is the second highest court of law in the Marshall Islands Judiciary. The court is established by Article VI, section 3 of the republic's constitution It has appellate jurisdiction over cases originating in the lower courts as well as original jurisdiction over certain classes of cases, including some criminal and civil matters, wills, and divorces. The court consists of a Chief Justice and an optional number of Associate Justices. The current Chief Justice, Carl Ingram, a U.S. citizen who originally came to the Marshall Islands with the Peace Corps in 1979, was first appointed as an associate justice of the High Court in March 2003, then as chief justice in October 2003 and for a second 10-year term as chief justice effective October 2013.

Chief Justices

References

Marshall Islands
Law of the Marshall Islands


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Riggs Funeral Home
Send Flowers for Florine
Florine Lewis Wittkamp
March 1, 1923 ~ January 11, 2021 (age 97)
Guyton- Mrs. Florine Lewis Wittkamp, 97, passed away January 11, 2021 at St. Joseph's Hospital in Savannah, Ga surrounded by her loving family.
Mrs. Florine was born on March 1, 1923, in Ellabell, GA to the late Stephen A. and Emma C. Lewis. She lived and worked in Savannah until her marriage to the late, Harold Wittkamp. They then relocated to Effingham County where they enjoyed farming, fishing, and gardening. Mrs. Florine retired from Union Camp where she worked in bag production. In her free time, she loved gardening. She had a green thumb and loved her Daylilies. Mrs. Florine's greatest joy was spending time with her grandchildren and great-grandchildren.
She was preceded in death by her husband, Harold Wittkamp; parents, Stephen and Emma Lewis; son, James Lee Lanier; daughter, Sandra Ann Lanier; brothers, S.A. Lewis, O.B. Lewis, and J.O. Lewis; and sister Eloise "Doll" Walters.
She is survived by her daughter, Paulette Edwards (John) of Guyton; grandchildren, Kristen Blanford, and Justin Reid Edwards (Jeni); great grandchildren, Emma Grace Blanford, Bella Blanford, Scotty Blanford, Sophia Blanford, Cole Edwards, Carson Tillotson, Cooper Tillotson; and her great grandchild on the way, Cannon Reid Edwards.
The family will receive friends from 5-7pm on Wednesday, January 13, 2021 at Carlson & Riggs Funeral Home in Guyton.
A private graveside service will be held Forest Lawn Memory Gardens.
To send flowers to Florine's family, please visit our floral store.
1979 Hwy 119 South
Guyton, GA 31312
© 2021 Riggs Funeral Home. All Rights Reserved. Funeral Home website by CFS & TA | Terms of Use | Privacy Policy


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by Alan Rapp on March 7, 2014
in Movie Reviews
Title: Mr. Peabody & Sherman
IMDB: link
Based on the Mr. Peabody shorts from The Rocky and Bullwinkle Show the new full-length feature film from writer Craig Wright and director Rob Minkoff (The Lion King, The Forbidden Kingdom) may not as be as clever as the original, but it turns out to enjoyable and far more fun than I expected.
Tweaking the story of the genius dog and his adopted son Sherman who travel in time through the use of the WABAC Machine (originally constructed in the TV-series as a way to keep Sherman occupied an teach him history), Mr. Peabody & Sherman uses the machine as a linchpin of a story involving Sherman's (Max Charles) trouble with a girl at school named Penny (Ariel Winter) and the pair's unauthorized use of the time travel machine which leads to serious repercussions.
While trying to stop a nosy social worker (Allison Janney) from removing Sherman from an unsuitable home, and dealing with Penny's parents (Stephen Colbert, Leslie Mann), Mr. Peabody (Ty Burrell) takes the kids back to set things right.
The 90 minute film includes several stops through history and cameos from the likes of Marie Antoinette (Lauri Fraser), King Tut (Zach Callison), Leonardo da Vinci (Stanley Tucci), and a group of Trojan War soldiers (led by Patrick Warburton), along with cheap gags, and a fair share of puns. Stretching the the shorts on which they are based into a full-feature film, the movie delivers a well-paced adventure story along with a nice message for kids.
Burrell's performance is fine, but it lacks the dry wit and condescension of the original character voiced by Bill Scott (who also voiced Bullwinkle and Dudley Do-Right on the show among others). Of course part of this has to do with the change to make the character more emotional involved in Sherman's welfare. More about Sherman than Peabody, the movie could have used a little more of the character's original attitude.
I have fond memories of the original shorts, and I'm always hesitant on updating an original work for a new generation, but the new version kept me (and a theater packed with children of various ages) entertained for its entire running time. I may not have enjoyed Mr. Peabody & Sherman as much as Kung Fu Panda and Kung Fu Panda 2, but the film is comparable to (and even slightly better than) DreamWorks recent family-friendly releases The Croods and Turbo.
Tagged as: Rocky & Bullwinkle
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Here's something for Rik's Rarities, but Rik is on vacation in the Bahamas at the moment.
UK partygrind band Raised By Owls have released a video for the song 'Rot Steward', in which the vocalist tells him mother he joined a death metal band. The band has to say this about themselves: "We're basically a bunch of tossers writing some daft songs for you to point and laugh at." Enjoy 'Rot Steward'.


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the Hudson.
Beyond Satisfaction.
BE AT HOME
About the Apartments
LEISURE MADE
About the Amenities
A Gifted
A waterfront neighborhood in Hell's Kitchen—along the Hudson
Just off the Hudson River waterfront and minutes away from Chelsea and Midtown, the neighborhood has a range of dining and recreational activities that are unrivaled.
The Neighborhood: Hell's Kitchen
To the south are the High Line and Hudson Yards, whose parks, public spaces, and new retail sites will offer an array of additional shopping, dining, and recreational options. The No. 7 subway line adds even more convenience to the neighborhood, including crosstown access to Grand Central Station. Riverbank residents enjoy private complimentary shuttle service to Times Square and Fifth Avenue.
A Waterfront Neighborhood
Neighborhood Map: Hell's Kitchen
Romeo & Juliet Coffee Shop
555 W 42nd St, New York, NY 10036
La Vela
558 11th Ave, New York, NY 10036
Ippudo Ramen
321, 6401, W 51st St, New York, NY 10019
Nano Equadorian Kitchen
691 10th Avenue,	New York, NY, 10036
Toto Ramen
366 W 52nd St, New York NY 10019
Red Poke
600 9th Avenue, New York, NY	10036
B Side Pizza & Wine Bar
370 W 51st St., New York, NY 10019
630 10th Avenue, New York, NY 10036
Samba Kitchen
661 9th Avenue, New York, NY 10036
Hourglass Tavern & Bettibar
373 W. 46th Street, New York, NY 10036
Pocket Bar NYC
The Harrow New York
Friedman's
228 W. 47th St., New York, NY 10036
Treehaus MiMA
470 W. 42nd St., New York, NY 10036
Mom's Kitchen & Bar
Sushi of Gari 46
Mr. Biggs Bar & Grill
Lillie's Victorian Establishment
249 W. 49th St., New York, NY	10019
Tir na Nog
West Bank Café
407 W 42nd St., New York,	NY 10036
Underwest Donuts
Schmackary's
Stiles Farmers Market Inc
352 W. 52nd St, New York, NY 10019
Ramini Espresso Bar
Poseidon Greek Bakery
Los Tacos NO. 1
229 W 43rd St, New York, NY 10036
Hell's Chicken
641 10th Avenue,	New York , NY 10036
824 10th Avenue,	New York, NY 10019
Amy's Bread
Azuri Café
465 W 51st St, New York, NY 10019
Brooklyn Fare
Sullivan Street Bakery
Biz.Co.Latte
The Pony Bar
Hourglass Tavern
373 W 46th street, New York, NY 10036
Dafni Greek Taverna
Becco
Print at Ink48 Hotel
Kimpton Ink48 Hotel, 653 11th Ave, New York, NY 10036
Chez Josephine
Gotham West Market
Hell's Chicken
44 & X
Westway Diner
Ollie's
Ippudo Westside
Arriba Arriba
10019, 756 9th Ave, New York, NY 10019
Bar Catalina
Little Pie Company
BAR-TINI
Poseidon Bakery
Landmark Tavern
Chelsea Piers
62 Chelsea Piers, New York, NY 10011
Piers 92/94
Hudson River Greenway, New York, NY 10014
Playwrights Horizons
Birdland Theater
Hell's Kitchen Park
West 47th Street & West 48th Street, New York, NY 10036
Lucky Strike Manhattan
624-660 W. 42nd Street, New York, NY 10036
16th Street to 30th in Downtown Manhattan between Tenth and Eleventh Avenues
Hudson River Park
353 West St, New York, NY 10014
Fifth and Sixth Avenues between 40th and 42nd Streets
42nd Street between Ninth and Eleventh Avenues
Pier 86, W 46th St & 12th Ave, New York, NY 10036
Javits Center North
Producers Club Theaters
Museum of Arts and Design (MAD)
2 Columbus Cir, New York, NY 10019
Manhattan Kayak + SUP
315 W 44th St #5402, New York, NY 10036
(BAC)
450 W 37th St #501, New York,	NY 10018
Columbus Library
De Witt Clinton Park
Hudson River Greenway
Salons/Beauty
West Vibe Salon + Beauty
Hair 2 Stay
Ivy Nails & Spa
350 W 43rd, New York, NY	10036
Nica and Sam
743 9th Avenue, New York,	NY	10019
David Ryan Salon
Grand Cru Wine and Spirits
The Shops at Columbus Circle, 10 Columbus Cir sc101, New York, NY 10019
Couture du Jour NYC
349 W 44th St Basement Storefront	, New York, NY 10036
Muji Times Square Store
Fine and Dandy
Social Apparel
Nepenthes NY
Midtown Comics Times Square
Thrift & New
602 9th Avenue, New York,	NY 10036
Rudy's Music Shop
461 Broome St, New York, NY 10013
Enoch's Bike Shop
Delphinium Home
Riverbank Resident Shuttle - 42nd Street
Starts at Port Authority and ends at Lexington Ave at Grand Central Station
89 E 42nd St, New York, NY 10017, USA
34 Street - Herald Square Subway Station
Herald Ave, Franklin Square, NY 11003
49th St N/R/Q Subway Station
49th Street and 7th Avenue
50th Street C/E Subway Station
50th street and 8th Ave
Pennsylvania Plaza, New York, NY 10001
The Lincoln Tunnel is an approximately 1.5-mile-long set of three tunnels under the Hudson River, connecting Weehawken, New Jersey and Midtown Manhattan in New York City.
39th and 11th
Times Square Subway Station
42nd Street between 7th and 8th Avenue
Ferry NY Waterway – Midtown West 39th St.
459 12th Ave, New York, NY 10001, USA
Garage Parking – iPark located on 43rd St. Between 10th Ave. and 11th ave.
520 W 43rd St, New York, NY 10036, USA


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En tant qu'organisme à but non lucratif, les revenus annuels de CHIP FM sont constitués; d'une subvention provenant du gouvernant provincial, des bingos hebdomadaires, radiothon annuel, ventre de publicités radio, frais d'adhésions ainsi que dons.
Special feature ▼
The CISSS de l'Outaouais is preparing for an increase in hospitalizations
Hull Hospital's COVID-19 designated center expands
Article published on 14 May 2020
last modification on 13 May 2020
Translations of this article:
The COVID-19 designated center, located at Hull Hospital, will have a second unit to accommodate clients affected by the virus.
The Centre intégré de santé et de services sociaux (CISSS) de l'Outaouais has indicated that this expansion aims to separate intensive care patients from those who require a less continuous level of care.
The establishment said it had identified this additional unit "from the start of the pandemic, in anticipation of an increase in hospital cases". It is said that the use of the premises will be continuously reassessed and may be closed depending on the volume of hospitalized users, although it is effective for the moment.
«This new structure allows ours organization to be prepared in the event of a significant increase in cases. This reorganization allows us to accommodate a greater number of patients at the designated COVID-19 unit, which is currently divided into two units.», said the CISSS de l'Outaouais, via a press release.
The evolution of the situation in Outaouais continues to be monitored and the CISSS de l'Outaouais mentions that it will adjust "as and when", both at the hospital level and in the other care settings and services served by the organization.
For any information on COVID-19, the public is invited to consult the Quebec government website at www.Quebec.ca/coronavirus. Those with symptoms or concerns about COVID-19 can contact the dedicated information line at 1-877-644-4545.
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Pontiacers can now visit a new business, le Café Downtown, which is located in Fort-Coulonge, in a completely renovated building adapted to welcome customers, but also for shows. The Downtown, as owner Rémi Bertrand affectionately calls it, aims to offer an original menu, inspired by the American (...)
"All the energy is on the recovery and the recruitment of a management" -Mario Boyer
The interim director general of Île-du-Grand-Calumet, Mario Boyer, told CHIP 101.9 that the municipal council was going to await the process of the Commission municipale du Québec (CMQ) in the quote on ethics from mayor Serge Newberry and that for the moment, nothing was going to change in the (...)
The Union denounces the teleworking measures within the Quebec public service
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SADC Pontiac announced the death of a member of their team, Mario Guay, who passed away peacefully at the Shawville Hospital on January 17th. Mario Guay started working at SADC Pontiac in May 2016 as a loan and financing advisor. "Coming out of retirement to take on what started out as (...)
William Amos announces funding for the Pontiac Legions
On behalf of Minister of Veterans Affairs and Associate Minister of National Defense Lawrence MacAulay, Pontiac MP William Amos announced funding of approximately $ 33,000 from the Government of Canada to assist the Pontiac Legions ($ 10,000), Quyon ($ 11,432.30) and Wakefield ($ 11,432.30). (...)
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Campbell's Bay to complete downtown revitalization plan
The mayor of the municipality of Campbell's Bay, Maurice Beauregard, said he (...)
Interview - Maurice Beauregard - Projects for Campbell's Bay in 2021
MRC Pontiac elected officials are divided
The tension was high between elected officials during the meeting of the (...)
The Caisse Desjardins des Rivières de Pontiac still offers services to its (...)
Pontiacers can now visit a new business, le Café Downtown, which is located (...)
The interim director general of Île-du-Grand-Calumet, Mario Boyer, told CHIP (...)
The Syndicat de la function publique et parapublique du Québec (SFPQ) (...)
SADC Pontiac announced the death of a member of their team, Mario Guay, who (...)
On behalf of Minister of Veterans Affairs and Associate Minister of (...)
The CSSHBO provides details on the air quality of its establishments
The general management of the Centre de services scolaire des (...)
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Gatineau Outaouais
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