text:
\section{Introduction}
\vspace{-0.10cm}

Scene segmentation is a challenging and fundamental task that aims to assign semantic categories to every pixels of scene images. The key of scene segmentation refers to parsing and segmenting a scene image into a range of semantic coherent regions. Therefore, it is critical to improve the feature similarity of the same object while keeping the feature discrimination of different objects. To this end, on the one hand, we explore to propagate features throughout the images to share features and harvest context information, which is beneficial for improving the feature similarity. One the other hand, in order to keep the discriminative power of features belonging to different objects, we propose to make use of boundary information to control the information flow during propagation progress. In a word, we propose a boundary-aware feature propagation module to build strong connections within the same segment and weak connections between different segments, as shown in Figure \ref{Figure:Introduction}. This module requires two components: boundary detection and graph construction.

\begin{figure}
\begin{center}
  \includegraphics[width = 0.486\textwidth]{introduction.pdf}
\end{center}
\vspace{-0.24cm}
\caption{(Best viewed in color) The boundary-aware feature propagation module builds stronger connections within the same segment and weaker connections between different segments, which helps to enhance the similarity of features belonging to the same segment while keeping discrimination of features belonging to different segments.}
\vspace{-0.36cm}
\label{Figure:Introduction}
\end{figure}

First, boundary detection, which is an implicit task in scene segmentation, is important for meticulous dense prediction. However, in existing segmentation methods, boundary detection did not attract due attention since boundary pixels only account for a small portion of the entire image and it has little contribution to the performance improvement. In this work, we try to find a way to simultaneously achieve segmentation and boundary detection, and further make use of the learned boundary information to enhance the segmentation performance. With regards to this, we propose to generate the boundary label of semantic objects from the existing object class labels given in the segmentation datasets and define it as an additional class for learning and classification. By doing so, concise boundaries are well learnt and inferred as one additional class because the characteristics of pixels on boundary are different from those of most pixels off boundary. And the parsing of pixels among disputed area (i.e., near the boundary) is enhanced. Moreover, taking boundary as an additional class requires little change on network but makes the network be aware of the boundary layout that could be further used for segmentation improvement.

Second, graphic model is needed to create the order rules for feature propagation. Convolutional methods~\cite{chen2016deeplab, yu2015multi} are popular in scene segmentation, but they usually consume large computation resources when aggregating features from grand range of receptive fields. Moreover, the convolution kernels could not vary with input resolutions, thus cannot ensure a holistic view of the overall image. Recently, DAG-RNN~\cite{shuai2017scene} proposes to use four directed acyclic graphs (DAGs) with different directions to model the function of undirected cyclic graphs (UCGs), which structurize the image by building pixel-by-pixel connections throughout whole image. However, DAGs require lots of loops to scan the images pixel by pixel. Thus it is very slowly even on low-resolution feature maps, which limits its application on ``dilated FCN''~\cite{chen2016deeplab, Zhao_2017_CVPR, Zhang_2018_CVPR} and on high resolution datasets like Cityscapes~\cite{cordts2016cityscapes}. To address this issue, we propose a more efficient graphic model to achieve faster feature propagation. We find that each DAGs adopted by~\cite{shuai2017scene} could be alternatively replaced by two Unidirectional Acyclic Graphs (UAGs), in which the pixels of the same row or column are dealt in parallel with 1D convolution. The proposed UAGs greatly speed up the feature propagation process. Moreover, different from the DAGs that are extremely deep, the proposed UAGs are much shallower and thus alleviate the problem of propagation vanish~\cite{pascanu2013difficulty}.

Finally, based on the UAG-structured image and the learned boundaries information, we build a boundary-aware feature propagation (BFP) module. In the BFP, local features of the same segment are shared via unimpeded connections to exchange information that achieves feature assimilation, while features of different segments are exchanged under controlled connections with the guidance of learned boundaries.
There are several advantages of our proposed boundary-aware feature propagation (BFP) network. First, since the proposed UAGs deal with pixels of the same row or column in parallel, we achieve the propagation process in a high speed. And the UAGs contain much fewer parameters than convolutional methods. Second, as we express boundary detection as classification of a semantic class, lots of parameters and complex module for boundary detection are saved. Third, with the advice of boundary confidence, the local features are propagated in a more motivated way, enhancing the similarity of features belonging to the same segment while keeping the discrimination of features belonging to different segments.

The main contributions of this paper can be summarized as follows:
\begin{itemize}
  \vspace{-0.26cm}
  \item We show that the boundary can be learned as one of semantic categories, which requires little change on network but obtains essential boundary information.
  \vspace{-0.26cm}
  \item We propose some unidirectional acyclic graphs (UAGs) to propagate information among high-resolution images with a high speed.
  \vspace{-0.26cm}
  \item We propose a boundary-aware feature propagation module to improve the similarity of local features belonging to the same segment region while keeping the discriminative power of features belonging to different segments.
  \vspace{-0.26cm}
  \item We achieve new state-of-the-art performance consistently on PASCAL-Context, CamVid, and Cityscapes.
  \vspace{-0.36cm}
\end{itemize}

\begin{figure*}
\begin{center}
  \includegraphics[width = 0.976\textwidth]{Framework.pdf}
\end{center}
\vspace{-0.36cm}
\caption{An overview of the proposed approach. We use the ResNet-101 (CNN)  with the dilated network strategy~\cite{chen2016deeplab} as backbone and the proposed boundary-aware feature propagation (BFP) module is placed on the top of CNN. The supervisor of loss 2 is the new ground truth of N+1 classes with an additional boundary class generated from the original ground truth of N classes.}
\vspace{-0.24cm}
\label{Figure:Framework}
\end{figure*}

\vspace{-0.10cm}
\section{Related work}
\label{Section:ReviewFCN}

\subsection{Scene Segmentation}
 Scene segmentation (or scene parsing, semantic segmentation) is one of the fundamental problems in computer vision and has drawn lots of attentions. Recently, thanks to the great success of Convolutional Neural Networks (CNNs) in computer vision~\cite{krizhevsky2012imagenet, szegedy2015going, Wang_ECCV2016ksr, liu2019feature, gu2018unpaired, wang2018detect, gu2019graph, ZengY_2018_CVPR, gu2019unpaired}, lots of CNNs based segmentation works have been proposed and have achieved great progress~\cite{he2019dynamic, fu2019adaptive, zeng2019joint, zhang2018bi, Zhang2019VOS, wang_iccv2019lfsd, rota2018place}. For example, Long et al.~\cite{long2015fully} introduce the fully convolutional networks (FCN) in which the fully connected layers in standard CNNs are transformed to convolutional layers. Noh et al.~\cite{noh2015learning} propose deconvolution networks to gradually upsample the coarse features to high resolution. Chen et al.~\cite{chen2016deeplab} propose to remove some pooling layers (or convolution stride) in CNNs and adopt dilated convolution to retain more spatial information. And some works focus on lightweight network architectures~\cite{badrinarayanan2017segnet, li2017not} and real-time segmentation~\cite{Zhao_2018_ICNet, paszke2016enet, Yu_2018_BiSeNet, romera2018erfnet}.

Context aggregation is a hot direction in scene segmentation. For example, Chen et al.~\cite{chen2016deeplab} propose an atrous spatial pyramid pooling (ASPP) module to aggregate multi-scale context information. Yu et al.~\cite{yu2015multi} employ multiple dilated convolution layers after score maps to exercise multi-scale context aggregation. Zhao et al.~\cite{Zhao_2017_CVPR} introduce pyramid spatial pooling~(PSP) to exploit context information from different scale regions.
Zhang et al.~\cite{Zhang_2018_CVPR} encode semantic context to network and stress class-dependent feature maps. He et al.~\cite{Hejun_2019_CVPR} propose adaptively pyramid context module to capture global-guided local affinity. Fu et al.~\cite{fu2019dual} integrate local and global dependencies with both spatial and channel attention. Ding et al.~\cite{ding2019semantic} employ semantic correlation to infer shape-variant context.

Graphic models have a long history in scene segmentation. Early works construct the graphic model with hand-crafted features~\cite{gould2009decomposing, liu2011sift, yang2014context, tighe2013finding}. Markov Random Fields (MRF)~\cite{gould2009decomposing, kumar2010efficiently, larlus2008combining} and Conditional Random Fields (CRF)~\cite{krahenbuhl2011efficient, russell2009associative, chen2016deeplab, liu2015semantic} build the dependencies according to the similarities of neighboring pixels. Liang et al.~\cite{liang2016semantic} propose to construct graph topology based on superpixel nodes and incorporate long-range context with Graph LSTM. Shuai et al.~\cite{shuai2017scene} adopt undirected cyclic graphs (UCGs) to formulate an image and decompose the UCGs with directed acyclic graphs (DAGs). Byeon et al.~\cite{Byeon_2015_CVPR} propose to divide an image into non-overlapping windows and employ a 2d LSTM to construct local and global dependencies. However, most of graph-based method are time-consuming and computationally expensive as they require candidate pre-segments, superpixels, or lots of loops.

In this work, we propose unidirectional acyclic graphs (UAGs), based on which the local features are quickly propagated in parallel. And to construct strong dependencies within the same segment and weak dependencies among different segments, we exploit the learned boundary information to guide the feature propagation within the UAG-structured image.

\subsection{Boundary Detection}
\vspace{-0.10cm}

Boundary detection is a fundamental component for many computer vision tasks and has a long history~\cite{arbelaez2011contour, dollar2013structured, konishi2003statistical, isola2014crisp}. For example, Lim et al.~\cite{lim2013sketch} propose sketch tokens (ST) and Doll{\'a}r~\cite{dollar2015fast} et al. propose structured edges (SE) based on fast random forests to deal with boundary detection as local classification problem. Recently the success of CNNs have great improve the performance of boundary detection~\cite{bertasius2015deepedge, bertasius2015high, hwang2015pixel, shen2015deepcontour, xie2015holistically}. Xie et al.~\cite{xie2015holistically} employ features from the middle layers of CNNs for boundary detection. Shen et al.~\cite{shen2017multi} propose multi-stage fully convolutional networks for boundary detection of electron microscopy images. These methods target at optimizing the accuracy of boundary detection instead of generating semantic boundary information for high-level tasks. Boundary information could be used for improving segmentation performance. For example, Bertasius et al.~\cite{bertasius2015high}, Hayder et al.~\cite{hayder2017boundary}, Chen et al.~\cite{chen2016semantic} and Kokkinos~\cite{kokkinos2015pushing} employ the binary edges to improve the segmentation performance. However, they all employ an additional branch of network for edge detection, which requires more resources and deal with segmentation and boundary detection as two detached tasks. Different from~\cite{bertasius2015high, hayder2017boundary, chen2016semantic}, our goal is not to detect the clearly binary boundaries, but to infer a boundary confidence map that represents the probability distributions of high-level boundary layout.

\section{Approach}
\vspace{-0.10cm}

Due to the diverse style and complex layout of scene images, it is necessary to classify every pixel using holistic context information but protect its differentiation from overwhelming by global scene. In this respect, we propose a boundary-aware feature propagation module to arm the local features with a holistic view of contextual awareness but keep the discriminative power of features for different objects. The overall architecture is shown in Figure \ref{Figure:Framework}. We use the dilated FCN (subsampled by 8) based on ResNet-101~\cite{he2016deep} as backbone. The supervisor of loss 2 is the boundary-aware ground truth (N+1 classes) that are generated from the original ground truth (N classes).

\subsection{Semantic Boundary Detection}
\vspace{-0.10cm}
Boundary delineation is favourable for meticulous scene parsing. However, because of the various semantic labels and complex layout of objects in segmentation datasets, parsing pixels in the boundary area is always difficult and results in confused prediction. In this work, instead of directly assigning semantic labels to pixels in boundary area, we explore to infer the boundary layout first and improve the segmentation performance with the learned boundary information. Lots of works have contributed to boundary detection~\cite{bertasius2015deepedge, bertasius2015high, hwang2015pixel, shen2015deepcontour, xie2015holistically}, but most of them focus on edges that sketch the objects. Different from them, we only focus on the boundaries of semantic objects that are predefined in segmentation datasets. We have observed that boundaries have the property of dramatic changes in RGB and feature information. And the boundary label is easy to be generated from the existing ground truth. Consequently, we assume that the boundary could be viewed as an additional semantic category and simultaneously learned with other existing object categories.
As shown in Figure~\ref{Figure:Framework}, we obtain a new ground truth (Loss 2, N+1 classes) from the original ground truth (Loss 1, N classes) and utilize the new ground truth for supervising the network to learn and infer the boundary layout.

Different from previous boundary detection works that aim to boundary delineation only or deal with segmentation and boundary detection as two detached tasks, our proposed semantic boundary detection is embed with semantic object parsing. Our boundary detection module only targets at boundaries of semantic objects predefined in the training data and generate concise boundary information under interaction with segmentation. These two tasks are combined into one, and they benefit each other. By training them together, the scene segmentation classes help suppress the edges within objects that are not semantic boundary of objects, e.g., edges of eyes. Scene segmentation helps the boundary detection to filter out noise and delineate well-directed boundary, while boundary detection makes the scene segmentation be aware of the important boundary layout information.

\begin{figure}[t]
\begin{center}
  \includegraphics[width = 0.5\textwidth]{UCG_DAG.pdf}
\end{center}
\caption{Each point of the DAGs has three different directions. Thus, the DAGs have to scan the image pixel by pixel and consumes lots of time with many loops. We decompose the four DAGs to six Unidirectional Acyclic Graphs (UAGs). Each of UAGs propagates information toward a single direction, which deals with pixels of each row in parallel and then deals with pixels of each column in parallel. For example, the UAG${_S}$ is in the south direction only and the UAG$_{{S.E}}$ is in the east direction only (based on UAG${_S}$).}
\label{Figure:UCG_DAG}
\end{figure}

\begin{figure*}
\begin{center}
  \includegraphics[width = 0.996\textwidth]{propagation_probability.pdf}

  \scriptsize{\hspace{5em} \textbf{(a)}  \hfill \hspace{0em} \textbf{(b)} \hfill \hspace{0em} \textbf{(c)} \hfill \hspace{0em} \textbf{(d)} \hfill \hspace{0em} \textbf{(e)} \hspace{5em} } \\
\end{center}
\vspace{-0.36cm}
\caption{(a) Image; (b) Original ground truth; (c) New generated ground truth: add a boundary class generated from the original ground truth; (d) Boundary confidence map: the probability distribution of the boundary layout; (e) Propagation confidence map: the confidence distribution of propagation.}
\vspace{-0.24cm}
\label{Figure:ImageExample}
\end{figure*}

\subsection{Unidirectional Acyclic Graphs}

Context is designed to aggregate wide range of surrounding information, thus it desires a holistic view of the overall image without regard to resolution. One popular way is to employ stacked convolution or dilated convolution to enlarge the receptive field~\cite{chen2016deeplab, yu2015multi, Ding_2018_CVPR}, but this consumes large computation resources. The work of~\cite{shuai2017scene} proposes DAG-RNN to capture long-range context based on directed acyclic graphs (DAGs). As shown in Figure \ref{Figure:UCG_DAG}, pixels are locally connected to form a undirected cyclic graph (UCG) to build propagation channels among the whole image. To overcome the loopy property of UCG, the UCG is decomposed to four DAGs with different directions (southeast, southwest, northeast, northwest). However, since each pixel of the DAGs has three different directed connections, the feature propagation based on DAG-structured images has to scan the image pixel by pixel and requires lots of loops. Thus it is very slowly even on low-resolution feature maps, which limits its application on ``dilated FCN''~\cite{chen2016deeplab, Zhao_2017_CVPR, Zhang_2018_CVPR} and on high resolution datasets like cityscapes~\cite{cordts2016cityscapes} and CamVid~\cite{brostow2008segmentation}. To address this issue, we explore to reduce the number of loops and propagate information in parallel. Herein, we propose some Unidirectional Acyclic Graphs (UAGs) as shown in Figure \ref{Figure:UCG_DAG}, which deal with pixels of each row in parallel and then deals with pixels of each column in parallel. Each DAGs adopted by~\cite{shuai2017scene} could be alternatively replaced by two UAGs. For example, the DAG$_{SE}$ is decomposed to UAG$_{S}$ and UAG$_{S.E}$, where UAG$_{S}$ is south directed that deals with pixels of the same row in parallel and UAG$_{S.E}$ is east directed (after UAG$_{S}$) that deals with pixels of the same column in parallel. As a result, the number of loops for each DAGs is reduced from \emph{H$\times$W} to \emph{H$+$W}, where $H$ and $W$ are the height and width of feature maps. The proposed UAGs greatly speed up the feature propagation process, which is economic and desired in practice, especially for applications that require high resolution and big eyeshot (e.g., self-driving vehicle). Moreover, due to the pixel-by-pixel operation, the recursive layers in DAGs are very deep that could be unrolled to thousand layers. This causes the problem of propagation vanish~\cite{pascanu2013difficulty}. The proposed UAGs are much shallower than DAGs and thus alleviate the the problem of propagation vanish.

\vspace{-0.10cm}
\subsection{Boundary-Aware Feature Propagation}
\vspace{-0.10cm}

 However, unselective propagation would make the features assimilated, which results in smooth representation and weakens the features' discrimination. To classify features in different objects and stuff in scene segmentation, it is beneficial to improve the feature similarity of the same object while keeping the feature discrimination of different objects. Therefore, we introduce the boundary information into feature propagation to control the information flow between different segments. As shown in Figure \ref{Figure:Introduction}, with the learned boundary information, we build strong connections for pixels belonging to the same segment but weak connections for different segments. During propagation, more information is passed via strong connections within the same segment and less information flows crossing different segments. In such a way, pixels get more information from other pixels of the same objects and less information from pixels of other objects. Consequently, features of different objects could keep their discrimination while features of the same object trend to be cognate, which is desired for segmentation. The detailed process of proposed boundary-aware feature propagation is presented below.

\begin{figure}
\begin{center}
  \includegraphics[width = 0.396\textwidth]{RNN-Gate.pdf}
\end{center}
\caption{As our UAGs are unidirectional and in parallel, we show the propagation process in 1D here for clarity. $i_{t}$ represents the feature of pixel at position $t$, $h_{t}$ is output (hidden state), $p_{t}$ is the propagation confidence.}
\label{Figure:Boundary-Aware-DAG}
\end{figure}

 As our UAGs are unidirectional and in parallel, we formulate the propagation process in 1D here for notation clarity. Extension to 2D/3D is straightforward. We denote the feature of pixel at position $t$ as $i_{t}$, and the corresponding output (hidden state) is denoted as $h_{t}$. The standard propagation process based on our UAG-structured image is formulated as following:
\vspace{-0.07cm}
 \begin{equation}
\begin{aligned}
& h_{t} = g(U*i_{t}+W*h_{t-1}+\delta)\\
\end{aligned}
\end{equation}
\vspace{-0.07cm}
where $*$ is 1D convolution operation, $U, W$ are learnable parameters of 1D convolution and $\delta$ is learnable bias. $g$ is element-wise nonlinear activation function (we use ReLU).

For the boundary-aware propagation, we first extract the boundary confidence map from the $(N+1)$ segmentation confidence maps, as shown in Figure \ref{Figure:Framework}. We denote the boundary confidence of  pixel $t$ as $b_{t}$, corresponding to $i_{t}$. Based on the boundary confidence map, we generate the propagation confidence map:
\vspace{-0.07cm}
\begin{equation}
p_{t} = 1-\beta f(\alpha b_{t}-\gamma)
\end{equation}
\vspace{-0.07cm}
where $p_{t}$ is the propagation confidence that decides how much the information of pixel $t$ to be passed to next region. $\alpha$=$20$ and $\gamma$=$4$ are constant chosen by experience, $f$ is sigmoid function to enhance the boundary, $\beta$ is a learnable parameter. With the propagation confidence, the propagation process can be reformulated as below:
\vspace{-0.07cm}
\begin{equation}
\begin{aligned}
& h_{t} = g(U*i_{t}+W*h_{t-1}p_{t-1}+\delta)\\
\end{aligned}
\end{equation}
\vspace{-0.07cm}
as shown in Figure~\ref{Figure:Boundary-Aware-DAG}, the propagation is controlled by the boundary and thus models boundary-aware context feature for better parsing of different segments.

For the UAGs that have ``two directions'', they are also unidirectional and in parallel. For example, UAG$_{S.E}$ is formulated below:
\vspace{-0.07cm}
\begin{equation}
h_{t}^j = g(U*i_{t}^j+W*h_{t-1}^jp_{t-1}^j+ \hat{W}*h_{t-1}^{j-1}p_{t-1}^{j-1} +\delta)
\end{equation}
\vspace{-0.07cm}
There are two hidden states, $h_{t-1}^j$ and $h_{t-1}^{j-1}$, input to the current cell, where $t$ and $j$ are the denotations of horizontal and vertical axis, respectively. Finally, the hidden states of the corresponding positions in four UAGs (i.e., UAG$_{S.E}$, UAG$_{S.W}$, UAG$_{N.E}$, UAG$_{N.W}$) are fused together to generate the final output, as shown in Figure \ref{Figure:UCG_DAG}.

One example of boundary confidence map and propagation confidence map is shown in Figure \ref{Figure:ImageExample}. We learn the boundary confidence map under the supervise of new generated ground truth with additional boundary class. To control the progress of feature propagation, propagation confidence map is generated from boundary confidence map. If pixel $i_{t}$ is in boundary region, then it has a higher boundary probability $b_{t}$ and hence a smaller propagation probability $p_{t}$. Thus, the feature propagation is suppressed and weak signals are passed to next pixel. Otherwise, it has a strong propagation to spread its features to next pixel.

\section{Experiments}

\subsection{Implementation Details}
\label{Section:implementation_detail}

Our Network is implemented based on the public platform Pytorch. We use ResNet-101~\cite{he2016deep} with the dilated network strategy~\cite{chen2016deeplab} (subsampled by 8) as our backbone.
In detail, the convolution strides for downsampling in last two blocks are reset to 1 and the convolutions of last two blocks are dilated with dilation rates of 2 and 4 receptively. $Pool~5$ and layers after it are discarded.
The network is trained with mini-batch, batchsize is set to 12 for PASCAL-Context and 8 for Cityscapes and CamVid.
Following deeplab-v2~\cite{chen2016deeplab}, we use the ``poly'' learning rate policy, where current learning rate $ Lr_{c} $ depends on the base learning rate $ Lr_{b} $ and iterations: $Lr_{c}=Lr_{b}$$\times$$(1-\frac{iter}{total\_iter})^{0.9}$. Momentum and weight decay are fixed to 0.9 and 0.0001 respectively
~We adopt random horizontal flipping and random resizing between 0.5 and 2 to augment the training data.

Most of the scene segmentation datasets do not provide boundary layout, we use the provided segmentation ground truth to generate boundary-aware ground truth, as shown in Figure \ref{Figure:ImageExample} (c). As we adopt the dilated FCN as our backbone, the spatial size is downsampled 8 times in encoding process. Thus to avoid the loss of boundary information in feature maps with smallest spatial size, pixels with distance smaller than 9 pixels (i.e., trimap of 18 pixels) to boundary are defined as boundary pixels and their ground truth labels are set to $N+1$, where N is the number of classes in datasets. In our experiments, the over-wide boundary (e.g., trimap of 50 pixels) squeezes small objects and weakens the function of boundary in feature propagation. We evaluate our network with mean Intersection-over-Union (\textbf{mIoU}). Mathematical definition of mIoU please refer to~\cite{long2015fully}.

\subsection{Efficiency Analysis}

To evaluate the speed of the proposed UAGs, we report in Table~\ref{Table:DAGvsUAG} the inference time of UAGs and compared it with DAGs~\cite{shuai2017scene} on different resolution of input images, based on dilated FCN (subsampled by 8). The number of loops is also recorded. Different from DAGs that have to scan the image pixel by pixel, the proposed UAGs deal with pixels of each row/column in parallel, hence they save a lot of loops. As shown in Table \ref{Table:DAGvsUAG}, the UAGs contain much less loops than DAGs and consequently they are much faster than DAGs. Especially with high resolution (e.g., 960$\times$720), DAGs are very slow and time-consuming. The speed of DAGs is highly related with the input resolution that determines the number of loops, thus DAGs are not suitable of high-resolution datasets (e.g., Cityscapes~\cite{cordts2016cityscapes}) and FCN with dilated network strategy~\cite{chen2016deeplab}. Besides the inference time, training of DAGs based on dilated FCN requires one hundred times more GPU hours than our proposed UAGs, which also shows the high efficiency of our approach. To quantitatively compare the segmentation performances of DAGs and proposed UAGs, we evaluate them on VGG-16~\cite{simonyan2014very} with encoder-decoder strategy, exactly in the same way like that in~\cite{shuai2017scene}. DAGs and UAGs achieve almost the same results on PASCAL-Context (UAGs 43.0\% vs.~DAGs 42.6\%). This shows that the proposed UAGs realize the same function as DAGs but with a much faster speed.

\begin{table}[t]
\footnotesize
\begin{center}
\begin{tabular}{lccc}
\toprule
Methods & Input Resolution&\# Loops & Time (s)\\
\midrule
\midrule
FCN& $480\times 360$ &none &0.35\\%(2.39Hz)
DAGs& $480\times 360$&10800 &17.92\\%&12.92\\
UAGs& $480\times 360$&300 &0.47\\
\midrule
FCN& $960\times 720$&none &0.42\\%(2.39Hz)
DAGs& $960\times 720$&43200 &56.97\\%&46.97\\
UAGs& $960\times 720$&600 &0.76\\
\bottomrule
\end{tabular}
\end{center}
\vspace{-0.24cm}
\caption{Inference speed comparison of FCN~(baseline), DAGs, and UAGs on dilated ResNet-101 with different resolution inputs.}
\vspace{-0.24cm}
\label{Table:DAGvsUAG}
\end{table}
\subsection{Ablation Studies}

We show the detailed results of ablation studies of the proposed approach in Table~\ref{Table:AblationStudy}. The proposed UAGs harvests local features and propagate them throughout the whole image to achieve a holistic context aggregation, which greatly improves the segmentation performance from the baseline (dilated FCN). Then, based on the UAGs, we learn the boundary information and inject it to the propagation process to control the information flow between different regions. With the boundary information, the UAGs build stronger connections of pixels within the same segment and weaker connections between different segments. Thus, features of the same segment become more similar but features of different segments remain discriminative.

We also visualize some examples of the inferred boundary confidence map in Figure \ref{Figure:Boundary_demos}. As shown in Figure \ref{Figure:Boundary_demos}, the inferred boundary map mainly involves boundaries between the semantic segments predefined in datasets instead of other edge information. Therefore, it contains the desired boundary layout of semantic objects and could be used for control of the feature propagation throughout image. The results in Table~\ref{Table:AblationStudy} show the effectiveness of the boundary-aware feature propagation (BFP) network. Following~\cite{kohli2009robust, chen2016semantic}, we evaluate the performance of BFP near boundaries, as shown in Figure~\ref{Figure:trimap}. We compute the mIoU for the regions within different bands of boundaries.

DT-EdgeNet~\cite{chen2016semantic} is the most related to BFP. However, BFP learns the boundary as one of semantic classes and is trained end-to-end, while DT-EdgeNet learns edge with additional EdgeNet and requires a two-step training process for DeepLab and EdgeNet. As shown in Figure~\ref{Figure:Boundary_demos}, Our learned boundaries response less to object interior edges than DT-EdgeNet.

BFP is proposed to perform feature propagation that is some kind of contextual feature modeling, while DT is used to refine the segmentation scores. We use the DT to filter the segmentation scores of BFP and this brings 0.7\% performance gain, which shows that the DT and BFP are complementary.
\begin{figure}[t]
\centering
  \scriptsize{\hspace{4em} Images  \hfill \hspace{1em} Boundary Confidence \hfill  Ground Truth\hspace{1.9em} } \\
 \includegraphics[width=0.487\textwidth]{boundary_demos2_compressed.pdf}
\vspace{-0.24cm}
\caption{Qualitative examples of inferred boundary map.}
\vspace{-0.26cm}
\label{Figure:Boundary_demos}
\end{figure}

\begin{figure}[t]
\begin{center}
  \includegraphics[width = 0.266\textwidth]{trimap2.pdf}
\end{center}
\vspace{-0.36cm}
\caption{Segmentation performance within band (trimap) around boundaries.}
\vspace{-0.16cm}
\label{Figure:trimap}
\end{figure}

\begin{table}[t]
\footnotesize
\begin{center}
\begin{tabular}{lccccc}
\toprule
Methods&Backbone & UAGs& Boundary & MS &mIoU \\
\midrule
\midrule
FCN & ResNet-50& & & &41.2\\
BFP & ResNet-50 & \Checkmark& & &49.8\\
BFP & ResNet-101 & \Checkmark& & &50.8\\
BFP & ResNet-101 & \Checkmark& \Checkmark& &52.8\\
BFP & ResNet-101& \Checkmark& \Checkmark& \Checkmark&53.6\\
\bottomrule
\end{tabular}
\end{center}
\vspace{-0.16cm}
\caption{Ablation Study of Boundary-aware Feature Propagation (BFP) Network on PASCAL-Context. Baseline is dilated FCN and MS means multi-scale testing.}
\vspace{-0.1cm}
\label{Table:AblationStudy}
\end{table}

\subsection{Comparison with the State-of-the-Art Works}
\label{Section:BenchMarkResults}

\textbf{PASCAL-Context}~\cite{mottaghi_cvpr14} provides pixel-wise segmentation annotation for the whole scene image. There are 4998 training images and 5105 testing images in PASCAL-Context. Following~\cite{mottaghi_cvpr14}, we use the most common 59 classes for evaluation. Testing accuracies of PASCAL-Context are shown in Table \ref{Table:resultsContext}, which shows that the proposed BFP outperforms the state-of-the-art works by a large margin.

\begin{table}[t]
\footnotesize
\begin{center}
\begin{tabular}{p{3.76cm}|p{1.276cm}<{\centering}}
\toprule
Methods & mIoU \\
\midrule
\midrule
O2P~\cite{carreira2012semantic}& 18.1 \\
FCN-8s~\cite{shelhamer2016fully} & 39.1 \\
BoxSup~\cite{dai2015boxsup} & 40.5\\
HO-CRF~\cite{arnab2016higher}& 41.3 \\
PixelNet~\cite{bansal2016pixelnet} & 41.4 \\
DAG-RNN~\cite{shuai2017scene} & 43.7 \\
EFCN~\cite{shuai2019toward}& 45.0 \\
DeepLab-v2+CRF~\cite{chen2016deeplab} & 45.7 \\
RefineNet~\cite{Lin_2017_CVPR}& 47.3 \\
MSCI~\cite{lin2018multi} & 50.3 \\
CCL+GMA~\cite{Ding_2018_CVPR} & 51.6 \\
EncNet~\cite{Zhang_2018_CVPR} & 51.7 \\
\midrule
BFP (ours) & \textbf{53.6} \\
\bottomrule
\end{tabular}
\end{center}
\vspace{-0.24cm}
\caption{Testing accuracies on PASCAL-Context.}
\vspace{-0.02cm}
\label{Table:resultsContext}
\end{table}

\begin{table}[t]
\footnotesize
\begin{center}
\begin{tabular}{p{3.76cm}|p{1.276cm}<{\centering}}
\toprule
Methods & mIoU \\
\midrule
\midrule
DeconvNet~\cite{noh2015learning} &48.9  \\
SegNet~\cite{badrinarayanan2017segnet}  &50.2 \\
FCN-8s~\cite{long2015fully}  &52.0 \\
DeepLab~\cite{chen2015semantic}  &54.7 \\
DilatedNet~\cite{yu2015multi} &65.3 \\
Dilation+FSO~\cite{kundu2016feature}&66.1 \\
G-FRNet~\cite{islam2017gated} &68.0\\%\colorbox{mygray}{68.6}\\
Dense-Decoder~\cite{Bilinski_2018_CVPR} &70.9\\%\colorbox{mygray}{68.6} \\
\midrule
BFP (ours) &\textbf{74.1}  \\
\bottomrule
\end{tabular}
\end{center}
\vspace{-0.2cm}
\caption{Testing accuracies on CamVid.}
\vspace{-0.36cm}
\label{Table:resultsCamVid}
\end{table}

\begin{table*}[t]
\footnotesize
\begin{center}
\begin{tabular}{p{1.8cm}|p{0.3cm}<{\centering}p{0.3cm}<{\centering}p{0.3cm}<{\centering}p{0.3cm}<{\centering}p{0.3cm}<{\centering}p{0.3cm}<{\centering}p{0.3cm}<{\centering}
p{0.3cm}<{\centering}p{0.3cm}<{\centering}p{0.3cm}<{\centering}p{0.3cm}<{\centering}p{0.3cm}<{\centering}p{0.3cm}<{\centering}p{0.3cm}<{\centering}p{0.3cm}<{\centering}p{0.3cm}<{\centering}p{0.3cm}<{\centering}
p{0.3cm}<{\centering}p{0.5cm}<{\centering}|p{0.76cm}<{\centering}
\toprule
Methods & \rotatebox{90}{road}&\rotatebox{90}{sidewalk}&\rotatebox{90}{building}&\rotatebox{90}{wall}&\rotatebox{90}{fence}&\rotatebox{90}{pole}&\rotatebox{90}{traffic light}&\rotatebox{90}{traffic sign}&\rotatebox{90}{vegetation}& \rotatebox{90}{terrain} & \rotatebox{90}{sky} & \rotatebox{90}{person} & \rotatebox{90}{rider} & \rotatebox{90}{car} & \rotatebox{90}{truck} & \rotatebox{90}{bus} & \rotatebox{90}{train} &\rotatebox{90}{motorcycle} &\rotatebox{90}{bicycle}&mIoU \\
\midrule
\midrule
FCN~\cite{shelhamer2016fully}&97.4 &78.4 &89.2 &34.9 &44.2 &47.4 &60.1 &65.0 &91.4 &69.3 &93.9 &77.1 &51.4 &92.6& 35.3 &48.6 &46.5 &51.6 &66.8& 65.3 \\
DPN~\cite{liu2015semantic}& 97.5 &78.5& 89.5 &40.4 &45.9 &51.1 &56.8 &65.3& 91.5 &69.4 &94.5& 77.5 &54.2 &92.5 &44.5& 53.4& 49.9 &52.1 &64.8& 66.8 \\
LRR~\cite{ghiasi2016laplacian}& 97.7 &79.9 &90.7 &44.4 &48.6 &58.6 &68.2 &72.0& 92.5 &69.3 &94.7 &81.6 &60.0 &94.0 &43.6 &56.8 &47.2 &54.8 &69.7& 69.7 \\
Deeplabv2~\cite{chen2016deeplab}&97.9 &81.3 &90.3 &48.8 &47.4 &49.6 &57.9 &67.3 &91.9 &69.4 &94.2 &79.8 &59.8 &93.7& 56.5& 67.5 &57.5& 57.7& 68.8&70.4 \\
RefineNet~\cite{Lin_2017_CVPR}& 98.2& 83.3 &91.3 &47.8 &50.4 &56.1 &66.9 &71.3 &92.3 &70.3 &94.8 &80.9 &63.3 &94.5 &64.6& 76.1 &64.3 &62.2 &70.0 & 73.6 \\
DepthSet~\cite{kong2018recurrent}& -& -& -& -& -& -& -&-& -& -& -& -& -& -& -& -& -&-&- & 78.2 \\
ResNet-38~\cite{wu2019wider}& 98.5 &85.7 &93.1& 55.5 &59.1& 67.1& 74.8 &78.7 &93.7 &72.6 &95.5 &86.6 &69.2& 95.7 &64.5 &78.8 &74.1 &69.0& 76.7& 78.4 \\
PSPNet~\cite{Zhao_2017_CVPR}& 98.6& 86.2& 92.9& 50.8& 58.8& 64.0& 75.6& 79.0& 93.4& 72.3& 95.4& 86.5& 71.3& 95.9& 68.2& 79.5& 73.8&69.5&77.2 & 78.4 \\
AAF~\cite{Ke_2018_ECCV}& 98.5& 85.6& 93.0& 53.8& 59.0& 65.9& 75.0& 78.4& 93.7& 72.4&\textbf{95.6}& 86.4& 70.5& 95.9& 73.9& 82.7& 76.9& 68.7& 76.4& 79.1\\
DFN~\cite{yu2018learning}& -& -& -& -& -& -& -&-& -& -& -& -& -& -& -& -& -&-&- & 79.3\\
PSANet~\cite{zhao2018psanet}& -& -& -& -& -& -& -&-& -& -& -& -& -& -& -& -& -&-&-& 80.1 \\
DenseASPP~\cite{yang2018denseaspp}& 98.7 &\textbf{87.1} &93.4 &\textbf{60.7}& 62.7 &65.6 &74.6& 78.5& 93.6 &72.5 &95.4& 86.2& 71.9 &96.0 &\textbf{78.0}& \textbf{90.3} &80.7 &\textbf{69.7} &76.8 & 80.6 \\
\midrule
BFP (ours) & \textbf{98.7}& 87.0& \textbf{93.5}& 59.8& \textbf{63.4}& \textbf{68.9}& \textbf{76.8}& \textbf{80.9}& \textbf{93.7}& \textbf{72.8}& 95.5& \textbf{87.0}& \textbf{72.1} & \textbf{96.0} & 77.6 &89.0 & \textbf{86.9} &69.2&\textbf{77.6}& \textbf{81.4} \\
\bottomrule
\end{tabular}
\end{center}
\caption{Category-wise performance comparison on the Cityscapes test set. Note that the DenseAspp~\cite{yang2018denseaspp} uses stronger backbone DenseNet-161~\cite{Huang_2017_CVPR} than Resnet-101~\cite{he2016deep} we adopt as backbone.}
\label{Table:Cityscapes_results_comparison}
\end{table*}

\textbf{CamVid}~\cite{brostow2008segmentation} is a road scene image segmentation dataset which provides dense pixel-wise annotations for 11 semantic categories.  There are 367 training images, 101 validation images and 233 testing images. The testing results are shown in Table \ref{Table:resultsCamVid}. It shows again that the proposed BFP outperforms previous state-of-the-arts by a large margin.

\textbf{Cityscapes}~\cite{cordts2016cityscapes} is a recent street scene dataset which contains 5000 high-resolution (1024$\times$2048) images with pixel-level fine annotations. There are 2975 training images, 500 validation images and 1525 testing images. 19 classes (e.g.,~roads, bicycles and cars) are considered for evaluation on the testing sever provided by the organizers. The  Category-wise results are shown in Table \ref{Table:Cityscapes_results_comparison}. Our BFP is only trained on fine annotations, while~\cite{chen2018encoder} also uses coarse annotations for training. Some segmentation examples on Cityscapes are shown in Figure \ref{Figure:QualitativeResults_Cityscapes}.

\vspace{-0.24em}
\section{Conclusion}
\vspace{-0.376em}
In this work, we address the challenging issue of scene segmentation. In order to improve the feature similarity of the same segment while keeping the feature discrimination of different segments, we explore to propagate features throughout the image under the control of inferred boundaries. Towards to this, we first propose to learn the boundary as an additional semantic class to enable the network to be aware of the boundary layout. Then, in order to structurize the image to define the order rules for feature propagation, we propose some unidirectional acyclic graphs (UAGs) to model the function of undirected cyclic graphs (UCGs) in a much more efficient way than DAGs. Based on the proposed UAGs, holistic context is aggregated via harvesting and propagating the local features throughout the whole image efficiently. Finally, we propose a boundary-aware feature propagation (BFP) network to detect and utilize the boundary information for controlling the feature propagation of the UAG-structured image. The proposed BFP is capable of improving the similarity of local features belonging to the same segment region while keeping the discriminative power of features belonging to different segments. We evaluate the proposed boundary-aware feature propagation network on three changeling semantic segmentation datasets, PASCAL-Context, CamVid, and Cityscapes, which show that the proposed BFP achieves new state-of-the-art segmentation performance consistently.

\begin{figure}[t]
\centering
  \scriptsize{\hspace{4em} Images \hfill \hspace{0.2em} Ours \hfill  Ground Truth \hspace{2.5em} } \\
 \includegraphics[width=0.476\textwidth]{demos_cityscapes.pdf}
\caption{Qualitative segmentation examples on Cityscapes.}
\label{Figure:QualitativeResults_Cityscapes}
\end{figure}

\section*{Acknowledgement}
\begin{spacing}{0.90}
\footnotesize{This research is supported by Singapore Ministry of Education Academic Research Fund AcRF Tier 3 Grant no: MOE2017-T3-1-001, and by BeingTogether Centre, a collaboration between Nanyang Technological University and University of North Carolina at Chapel Hill. BeingTogether Centre is supported by National Research Foundation, Prime Minister Office, Singapore under its International Research Centres in Singapore Funding Initiative.}
\end{spacing}
\hyphenpenalty=100
\tolerance=10
{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
Q: Rails 4, img_tag as form_for control? I'm trying to build a form where the user is able to pick an avatar image for his user profile. I have a table named avatars(which has id(INT) and url(string), and another named users(with a bunch of stuff, and img(INT)).
I want to create a preview of all the avatar images, and then enable the user to click on the one he wants to pick it (preferably this would highlight the image border), and then write the avatar id into the user table under img. I think I've set up the proper associations, but now I've hit a wall on how to handle on click events on the img_tag. Is this the correct way to do this, and if not how should I be doing it?
my view so far:

    <%= form_for @user do |f| %>
        <%if @user.errors.any? %>
            <% for message in @user.errors.full_messages %>

<%= message %>

            <% end %>
        <% end %>
        <%= f.label :username %>
        <%= f.text_field :username, class:'form-control', placeholder:'MightyWizard5578' %>

        <%= f.label :email %>
        <%= f.text_field :email, class:'form-control', placeholder:'example@topdeckandwreck.com' %>

    Pick your avatar:

    <% @avatars.each do |avatar| %>
    <%= image_tag(avatar.url, height: '75', width: '75') %>
    <% end %>

    <%= f.label :bio %>
    <%= f.text_area :bio, class:'form-control', placeholder:'Tell us a bit about yourself!' %>

    <% end %>

my controller:
class UsersController < ApplicationController

  ...

  def edit
    @avatars = Avatar.all
    @user = current_user
  end

  def update
  end

  def user_params
    params.require(:user).permit(:username, :email, :password, :password_confirmation, :img, :bio, :auth_token)
  end
end

application controller:
class ApplicationController < ActionController::Base
  # Prevent CSRF attacks by raising an exception.
  # For APIs, you may want to use :null_session instead.
  protect_from_forgery with: :exception

  helper_method :current_user

  private
    def current_user
        @current_user ||= User.find_by_auth_token!(cookies[:auth_token]) if cookies[:auth_token]
    end
end

associations
class User < ActiveRecord::Base
    belongs_to :avatar
end

class Avatar < ActiveRecord::Base
    has_many :users
end

A: This is what is done in rails:

    Pick your avatar:

    <% @avatars.each do |avatar| %>

             <%= f.radio_button :avatar, avatar.id %>
             <%= image_tag(avatar.url, height: '75', width: '75') %>

    <% end %>

So you can pick avatar from the list. Everything else (like hiding radio button, highlighting border and so on) is done in CSS

redpajama_set_name: RedPajamaStackExchange


A calm and tranquil oasis in the midst of metro Niagara. Located on the main street of Thorold, Ontario (yoga classes close to St Catharines, Welland, Niagara Falls and Fonthill) in a dedicated practice room. Classes offered six days a week, morning and evenings. Taught by compassionate, professional teachers with an emphasis on yoga being accessible for "every body". Special events include workshops, seminars, meditation classes and cooking classes.
To send an email message to Yoga Centre of Niagara, click the envelope on the right.
A calm and tranquil oasis in the midst of metro Niagara. Located on the main street of Thorold, Ontario (yoga classes close to St Catharines, Welland, Niagara Falls, Fonthill) in a dedicated practice room. Classes offered six days a week, morning and evenings. Taught by compassionate, professional teachers with an emphasis on yoga being accessible for "every body". Special events include workshops, seminars, meditation classes and cooking classes.
Style of yoga is considered hatha based, with an emphasis on alignment, breath and flow of postures. Beginners welcomed anytime. Many versions of each posture are offered so practitioners of all levels can be challenged and also practise at their personal comfort level. Specialty classes like beginner, pilates, mom & baby, back care and better sleep are part of our regular program. Please check our website at www.yogacentreniagara.com for all the latest schedule information.
A faster paced yoga class for participants with some experience. For example, familiarity with Downward Facing Dog would be appropriate.
A slow, careful class perfect for a deep stretch, building stability in joints and gaining balance/confidence. Suitable for older adults, those with chronic pain or illness, or those just wanting stress relief.
A beginner friendly yoga class with careful stretching, deep breathing and lots of coaching to deepen your study of yoga. Suitable for every body.
A mat based class, friendly to beginners and experienced practitioners.
A faster paced yoga class for those with some experience. For example, comfort in Downward Facing Dog would be helpful. A vinyasa style class with depth added by focusing on intention, or chakras, or some other aspect of yoga philosophy.
Ditch the workout and join the party. Dance and laugh to burn up to 600 calories per hour. A chance to leave your work day stresses behind and slim down at the same time.
A meditative class featuring a study and exploration of the chakras. Includes meditation, chanting or other yoga practices to enhance our experience of the energetic body.
Accessible for almost anyone, this careful class offers deep breathing, good stretching and strength building to all. And don't dismiss this as too "easy". You'd be amazed what interesting positions we can access using a chair or other props. Keep strong and fit your whole life through!
A physically challenging class for experienced students. Be prepared to sweat. The pace is quick in the vinyasa style with different weekly emphasis.
A thorough class to explain the postures, breathing techniques and why we do the things we do in class. Recommended not only to beginners but to those who wish to deepen their understanding of the complete yoga practice.
A yoga class for every body that focuses on careful stretching, deep breathing and lots of core awareness to increase functional strength in the back and provide a full stretch.
A yoga class for every body that focuses on careful stretching, deep breathing and lots of coaching from the instructor so you can deepen your practice.


Yiup, in America, you can get away with wearing a 'camou' outfit, and even here in the UAE, but not in Nigeria.
Back home, the Nigerian soldiers feel insulted when a civilian rocks this 'army camouflage'.
US based former Nollywood actress and beauty queen, Regina Askia stuns in a 'camou' jumpsuit.
Na only the first pixel weh I like.


We'll start with a root note, examine the necessary intervals for our chord (available on the seventh chord wiki page) and then build out accordingly.
Per the interval pattern, we can start by adding a major third and perfect fifth. The major third is four semitones above the root while the perfect fifth is going to be seven semitones above the root.
If you count straight up, seven spots from the second fret on the sixth string, the note you fall on is C#. That means the same C# note at the fourth fret on the fifth string will suffice as our perfect fifth. The same reasoning can be applied to the major third (third string and third fret).
We can use the same counting tactic to place our major seventh interval.
Our major seventh interval (an F) falls on the fourth string at the third fret. How did we get there?
If we know that a major seventh interval falls 11 semitones from the root note (from this graphic), which is an F sharp, we count up 11 frets giving us our F, which can also be played at the fourth string on the third fret.
Start with the following root note.
Per the interval pattern, we add a minor third and perfect fifth.
The perfect fifth is easy, since it forms a power chord shape (fifth string, seventh fret) with our root note. Since a minor third on the fifth string falls at the third fret (three semitones above the root) we can use the octave of that note on the third string at the fifth fret, to grab our minor third.
Lastly we add our minor seventh interval, falling ten frets up from the root.
Ten frets up from the root note (A) would be a G, which can be played by your pinky finger on the second string at the eighth fret.
Some other tertian seventh chords would include the dominant, diminished and half-diminished, all of which are covered in the full article. Now that you know how to build a seventh chord, it'll be a great deal easier to understand and memorize others.


text:
using System.Text.Json;
using Azure.Core;

namespace Azure.Management.Compute.Models
{
    public partial class ResourceSkuCosts
    {
        internal static ResourceSkuCosts DeserializeResourceSkuCosts(JsonElement element)
        {
            string meterID = default;
            long? quantity = default;
            string extendedUnit = default;
            foreach (var property in element.EnumerateObject())
            {
                if (property.NameEquals("meterID"))
                {
                    if (property.Value.ValueKind == JsonValueKind.Null)
                    {
                        continue;
                    }
                    meterID = property.Value.GetString();
                    continue;
                }
                if (property.NameEquals("quantity"))
                {
                    if (property.Value.ValueKind == JsonValueKind.Null)
                    {
                        continue;
                    }
                    quantity = property.Value.GetInt64();
                    continue;
                }
                if (property.NameEquals("extendedUnit"))
                {
                    if (property.Value.ValueKind == JsonValueKind.Null)
                    {
                        continue;
                    }
                    extendedUnit = property.Value.GetString();
                    continue;
                }
            }
            return new ResourceSkuCosts(meterID, quantity, extendedUnit);
        }
    }
}

redpajama_set_name: RedPajamaGithub


text:
Home›News›The Long Read: Midlothian 2020
The Long Read: Midlothian 2020
By the end of this article, you won't recognise west Midlothian
[infobox]Article updated 23 August 2016[/infobox]
"Environmental improvements alone will not be sufficient to overcome the competition from Straiton and elsewhere", section 8.3.38 Midlothian Local Development Plan 2015.
Midlothian Council's cabinet has approved the latest Midlothian Local Development Plan (LDP), expressing their vision for the county in the next five years and beyond. Unlike previous masterplans, this one is focused heavily on creating a "Midlothian Gateway", or to you and I, a shopping and leisure development to rival the likes of Fort Kinnaird. If you are looking for a focus on regenerating town centres you may be left a little disappointed. In this one off analysis of the forthcoming LDP, we will take a journey through the planned housing, educational and infrastructural changes. From the new double size Roslin, to the A701 bypass and the new Penicuik High School, everything you need to know now that the Scottish Government have been asked to ratify the plan.
Every five years, Midlothian Council are obligated to set forth their plans for development within the county. Within the report they will allocate land to housing, promise to build new infrastructure, whilst additionally throwing in a smattering of economical, educational and policy changes. The last report was published in 2008 and since then a wider report into the east of Scotland has also been compiled, duly named the SES (South East Scotland) Plan. This local plan therefore echoes many of the requirements set in the SES Plan but goes into greater detail on how these requirements will be met. A draft main issues report was consulted on last year and land owners were given the chance to submit their land for consideration for housing allocation. The public were also given the chance to comment on the plans, which included the controversial A701 realignment. Taking into consideration this feedback, the council have now compiled a draft plan. Councillors approved this plan late 2014, with a few amendments. It went to public consultation in 2015 and was approved with no major changes in June 2016. The Scottish Government must now rubber-stamp the proposals. In the meantime it is available to view and download here.
The Contents
It is a substantial report, comprised of 172 pages of aspirations. From the outset this is a document with a clear plan, to deliver a realigned A701 and thus open up greenbelt land to developers to create a growth generating "Midlothian Gateway". The jury is still out on whether or not the report's headline act is worthy of applause, but please do share your views below once we've explained the plans in depth. For ease, the rest of this article will be broken into three parts. First, the major housing and commercial developments will be analysed. This includes (click to go to) West Straiton, the A701 corridor and finally, Penicuik. Next is the education report, which will be divided into primary and secondary schooling. To finish, the major infrastructure projects will be explored: the A701 bypass, the A702/Mauricewood Road roundabout, the north Penicuik link road and the Edinburgh Orbital Bus Route.
Housing & Other Developments
West Straiton
West Straiton? What is West Straiton? I'm glad you asked. West Straiton is that undulating land you can see to the west of the Straiton Park and Ride. Previously home to quarries and mines, the land has been viewed as too unstable to build on and thus it has been left as part of Edinburgh's vast greenbelt. However with ambitious plans for thousands of new homes throughout the A701 corridor, Midlothian Council believes the time has come to create an A701 bypass. The A701 bypass itself is nothing new. The idea for it has been circulating since the late twentieth century. It is important to note though that it has never got to this stage in planning. Now, it is proposed that it is formally approved, at least in terms of policy, with compulsory purchase orders being planned to pave the way for the route. Delivery is many years away however, as you will see later on. This means that there will need to be concrete plans for development around the route before it can raise the capital required to be constructed. Developers throughout the rest of the A701 corridor as far south as Penicuik will also need to put in a bob or two to fund the delivery, which could be off-putting, especially for those wishing to develop in the far south of Penicuik.
The new realigned A701 will likely follow the green route you can see on the map below. However there are a further two possible routes not on the map. The original blue route is deemed too costly to build, due to the presence of mine workings ,and will not be chosen for further studies. It also has limited land to the east of it on which developers could build, scuppering any chance of building the "Midlothian Gateway" retail and leisure park. The green route is therefore preferable, running from an altered junction on the A720 to the A720 just west of Easter Bush. Thick woodland will flank the route to the west, with strict landscaping rules being drawn up to keep the development land green, including "tree lined avenues". I wouldn't be surprised to see a roof terrace or two either, considering the site is highly visible from the bypass. Landowners Morton Assets have already revealed their plans for 100,000 sq.ft of offices on the site, creating a "Yours Business Park". Those of course will still have to face planners. there is now question mark of the development by Morston Assets, after the company entered administration. KPMG say it is too early to say what will happen to their assets. Other developments planned include a hotel on the current site of the Park and Ride. A new facility would be created several hundred metres from the current site. Midlothian Council are to also actively campaign for a Cinema chain to develop on the land, viewed as an accompaniment to the planned film studio. Leader of the council Owen Thompson did tease our readers on Facebook by saying "A cinema? Watch this space". Further shops and restaurants will also likely join the development. Housing is not included at this stage however could be included in the next plan. On that matter, the future of the current caravan site, located off the A701, is not mentioned in the document.
Route options © Midlothian Council
What will become of the current A701? It is to become the route for public transport, cyclists and pedestrians. The council say it would be landscaped to accommodate such. Naturally cars would also still travel on it, but unless you live in Bilston or Loanhead, you would be more likely to utilise the bypass.
The A701 Corridor
First, a definition. The A701 corridor, in the sense of the article, is defined as being Loanhead, Bilston and Roslin.
Unlike the new Straiton development, there a few noteworthy changes for the A701 corridor. In Loanhead, housing is not an important feature, at least not in this iteration of the plan. There are currently several committed development sites, like Ashgrove, which is due to be a Taylor Wimpey development. This will deliver 170 houses before 2024. The 2015 plan is proposing a new allocation off Edgefield Road. This is council owned and will likely become a 25 property social housing development. Another site noted is Burghlee, although it is not allocated to housing in this plan. However, should a developer come along who is willing to build the 175 houses possible, then the council may be minded-to-grant. Apart from that, this development plan pays little more attention to Loanhead. They will of course be getting a new Paradykes Primary School (previously announced). By moving the school to beside the leisure centre, a new community mixed use building will be created.
Down the road and into Bilston we see more housing allocations. The land currently occupied by Pentland Plants has been allocated to 75 properties. The business' intentions are unknown. Taylor Wimpey are already building their "Cameron Gardens" development and this looks like it could be extended by an additional 350 homes, however the exact positioning of the new A701 bypass will effect how many can be built within the new greenbelt boundary. Due to these additional dwellings, Bilston has received its own new primary school (opened August 2016). Further land has been allocated to develop the Bush science park, 14.5 hectares to be exact. Midlothian Council are keen to make this site into a world class development, and with the University of Edinburgh continuing to construct class-leading facilities, their vision can easily come to fruition. Bilston may also get a health centre, although this is mentioned only briefly.
To the east, Roslin. Roslin is unique in Midlothian due to it being protected by a conservation order. Therefore any new housing must be of a higher quality than the average new build. Locally sourced materials must be used to make sure the development does not alter the visual appeal of the current village. It already has committed development land to the south west. Fifty houses can be built to the west of the health centre. It is believed the developer of this could be close to submitting an application, after purchasing the site last year from the University. New allocations include the former Roslin Institute site. This can accommodate 250 homes and must be designed to be high quality, with avenue trees etc. Another site, deemed the Roslin expansion, rather misleadingly considering the others are also expansions, can accommodate 260 dwellings. Just over one hundred of these could be delivered by 2024. Roslin will also benefit from an expansion of their Pavilion. A Roslin Glen tourist information centre is also planned to capture the history of the village. A new primary school is under construction.
That's about it for the A701 corridor, except the major development we didn't mention. Surprisingly Midlothian Council propose creating an A701 corridor secondary school to replace Beeslack. The "relocated Beeslack" would be located somewhere in the Bilston region, or as part of West Straiton. A feasibility study will be undertaken to investigate where the new school could be sited. It would serve Roslin, Bilston and Loanhead, though it's still unknown whether the catchment areas would be altered to include the entirety of Loanhead. Skip ahead to the education section to read more on this.
Penicuik
Finally, we are on to our home town. Surprisingly, or maybe not, there is little to add that hasn't already been planned. The latest development plan notes that there is disappointment that the north west Penicuik developments have not yet come to fruition. Some of these allocated sites have had minded-to-grant permission since 2003 but due to the economic downturn etc. they haven't materialised. However now in 2014, there is movement. Slow, tedious movement, but movement none the less. The plan has revealed the root of the problems though. It cites the planned new roundabout on the A702 as being the major sticking point. In short, there seems to have been a disagreement as to who would pay for the upgrade. On Christmas Eve 2015, Taylor Wimpey agreed to fund the roundabout's construction, though they will attempt to claw back some funds from other developers.
Due to the lack of ongoing work, Midlothian Council has not allocated any further sites in the north west to housing. The University of Edinburgh did want to offload a site capable of taking six hundred dwellings but this has been knocked back, at least for the next five years anyway. Before moving onto the major local development at Auchendinny, it is important to note the smaller allocations in the town. A site off Eastfield Farm Road has been allocated for construction of ten social houses. Furthermore a site at Kirkhill will accommodate twenty homes. The latter is likely subject to objections due to the proposals to construct on the currently utilised tennis courts. A campaign group has been established to fight this proposal. Moving on, and 25 homes could be built at Belwood Crescent, although this development is not safeguarded by the plan due to it not currently being available. Should it become available within the plan period, it may be built on, subject to planning application of course. Another site not included due to unavailability is Pomathorn Mill. The former paper-making building could become up to fifty homes should the businesses using it vacate. The developer will have to develop a high quality scheme as it is visible to the surrounding region. CALA Homes are believed to be actively pursuing this site. Finally, the now defunct Wellington School could be turned into fifty to sixty dwellings, although the developer must agree to road improvements to proceed. All of these developments would require additional primary school accommodation, with planned extensions to Cuiken, Cornbank and Mauricewood schools.
Into Auchendinny, and Miller Homes wishes to add a further 350 homes. Their wish has been granted by the council with the allocation of the land to the north and east of the new Glencorse Centre. It is believed that they could construct 260 of these residences by 2024. Due to the size of the development a new primary school will be required. The Auchendinny Primary School will be developer funded and shall replace the axed Glencorse Primary School, which is to close as soon as the Auchendinny school is operational. The image below shows an artist's visualisation of the site:
© Miller Homes – Artist's Visualisation
That's it for housing, so here's a depressing line in the report: "Environmental improvements alone will not be sufficient to overcome the competition from Straiton and elsewhere". Yes, we are onto Penicuik's town centre. Midlothian Council acknowledge in the report that there is desire for improvement and they promise to support business encouraging efforts, like those of the Business Improvement District, Penicuik First. They also pledge support to a shopfront improvement scheme. However as the town centre is not owned by the council, there is little they can do to significantly alter the current retail proposition. A heritage centre does get a mention in the development plan, along with the need for parking improvements.
"The current and planned level of housing in the Penicuik area is insufficient to sustain two secondary schools each with a minimum pupil roll of 750 pupils."
The above quote is one which summarises Midlothian's new education report. It is very much a story of 'out with the old and in with the new', with plans for six new schools and three renovations in total. Whilst we've come to expect the odd surprise in documents such like this, 2015's master plan offers a true overhaul of the school estate.
Primary Schooling
In 2015, Midlothian Council had three new "A701 corridor" Primary Schools in planning: a new Bilston Primary School and replacements to Paradykes and Roslin Primary Schools. The new Bilston Primary School opened to pupils in August 2016. Paradykes is set to move to beside Loanhead Leisure Centre, where a new mixed use facility will be created. The old land will likely be used for housing. Roslin's Primary School will also be replaced and should be operational by August 2017. Both Paradykes and Roslin's new primary schools are under construction. Regardless, that was a prerequisite to what is actually in the report.
Glencorse Primary is to be closed. After many years of uncertainty, the allocation of new housing land at Auchendinny will lead to the eventual closure of Glencorse Primary School. With a roll of 29 pupils in 2015, but a capacity of 100, the school has lacked financial viability for many years. It is proposed that Miller Homes, who plan to construct 350 homes north of the Glencorse Centre, will fund, or partially fund, a new Auchendinny Primary School to serve the village and Milton Bridge. Pupils, who currently attend Glencorse, will likely be given the choice on whether to attend the new school or Mauricewood Primary School, which will be extended courtesy of Taylor Wimpey. It should be noted that Miller must still obtain permission for their development, though it is believed to be in a late stage of planning. Should the local development plan be approved unchanged by the government, Miller could submit immediately afterwards and aim for a late 2017 commencement of construction. They plan to have 260 properties standing by 2024, so this timescale seems most likely.
In summary, plans include: three replacement schools at Bilston, Roslin and Paradykes, one completely new school at Auchendinny, one closure at Glencorse and three extensions at Mauricewood, Cornbank and Cuiken. Funding by the Scottish Government's "Schools for the Future" programme will go to Bilston, Roslin and Paradykes, whilst developers' contributions will part or wholly fund a new Auchendinny school along with extensions at Mauricewood, Cuiken and Cornbank.
Secondary Schooling
The population in the region is ageing, thus the school rolls are falling. This is creating a substantial problem in Penicuik, due to the pace of development not matching the time taken for the rolls to fall. Both Beeslack CHS and Penicuik High School are operating below capacity, with Penicuik High School at a critical low point. Beeslack, which is associated with Mauricewood, Glencorse and Roslin Primaries, also serves as an overflow to Loanhead. It has a capacity of 860 but is operating, this year, with 739 pupils, equating to 85.9% occupancy. This occupancy is greater than that of Dalkeith and Newbattle High Schools, although their rolls are forecast to hold steady or rise. Penicuik High School is a different can of worms, with the lowest occupancy of all the non-denominational secondary schools in Midlothian, substantially so. The building has a capacity of 945 but is operating with only 565 pupils, equating to 59.8% occupancy.
Newly calculated projections of the school rolls, should nothing change, were published as part of the report. Beeslack is forecast to continue its decline, reaching a critical low of approximately 500 pupils in 2025 before starting to gain pupils again. Penicuik High's roll is forecast to continue upwards from 2015 to 700 pupils before plateauing thereabouts for roughly ten years. The report says (section 5.2, page 7):
The planned and proposed housing development will bring more pupils into the area but what is planned will not be enough to sustain two secondary schools with the desirable level of pupils. In the meantime the pupil roll across both schools will continue to fall and we estimate that it will take until 2030 to begin to see any significant increase in the number of secondary pupils in the Penicuik area.
As the paragraph states above, two secondary schools are no longer sustainable in the town. This "desirable level of pupils" is a new quantifiable policy created by the council. For a secondary school to be fully successful, Midlothian believes that it must be served by at least 6,000 homes, or about 750 students. Including current, planned and proposed housing stock, both schools will still operate below this critical level. Beeslack would be associated to 5,672 homes and Penicuik High to 5,840. It is for that reason, four options are to be considered for the future of the Midlothian's secondary schooling. These are detailed below:
Option 1 – Maintain six secondary schools in the county (including New Shawfair) – No change to schooling in Penicuik
Option 2 – Reduce estate to five schools (inc. New Shawfair) – Create single site Penicuik High School which would serve every primary school in Penicuik, Roslin, Bilston and Loanhead.
Option 3 – Maintain six secondary schools (inc. New Shawfair) – Create a new Penicuik High School to serve primary schools in Penicuik, but relocate Beeslack to serve Bilston, Roslin and Loanhead.
Option 4 – Increase estate to seven schools (inc. New Shawfair and New Gorebridge) – Same plans for Penicuik as per Option 3 with addition of a school at Gorebridge.
Option one seems unlikely due to the falling rolls. It would require a county wide catchment review to be undertaken frequently to best maximise the schooling estate. Should funding from the government not succeed, this would be the only option. Option two would create a so called "Super School" in Penicuik. This could have a roll near 1800 come 2040, a figure deemed unsustainable by many councils. A single site would be selected within Penicuik, most likely the vacant land at Beeslack or the playing fields at Penicuik High School. The report states that an alternative site has not yet been found in Penicuik. Option three is a new one, and a surprising one too. This option would replace the ageing Penicuik High School, rated a 'C' on the building condition scale, but also create a new secondary school somewhere up the A701. The "relocated Beeslack" could be located in the Bush Science Park or the new West Straiton development, although a study will investigate available sites further. It would serve Loanhead, Bilston and Roslin, which alone are set to generate enough pupils to run a successful secondary school (6,159 houses). For option three, funding would need to be sought for two secondary schools at once, totalling approximately £40m. A report from a council seminar on 11 November 2014 remarks:
Fundamental assumption in this option [#3] was that Beeslack would be relocated. In the context of SFT [Schools for the future] funding, it was thought unlikely that both Penicuik and Beeslack schools would be successful in the same round of SFT funding. With that in mind, and given the respective condition ratings for both schools (Penicuik was a C condition rating and Beeslack a B), it was thought likely that SFT would prioritise investment in Penicuik. However, the relocation of Beeslack at the earliest possible date would be key to realigning the catchment areas.
So, if option three was to emerge as the preferred option, Beeslack would likely continue to operate until about 2025, whilst Penicuik High could move in to their new school by 2020. Option two would prove quickest to implement. Should a funding application be granted in the next 12 months, construction could get underway in 2018, for an August 2021 opening date. This means that a pupil starting in first year in 2016 could end their school career in a new building.
A £10,000 feasibility study is to be undertaken to further investigate the options. This will probably lead to a public consultation sometime in 2016. As of August 23, it is still forthcoming. If you are interested in the secondary school situation, click here to read our analysis of the possible secondary school site.
The A701 Bypass
Since the creation of Midlothian Council, there have been talks to create an A701 bypass. Councillors would suggest the development, the public would hold meetings, the media would get into a frenzy and … nothing would happen. Why should it be any different this time? There is no reason to suggest it will be any different, in fact the main land owner, who would subsidise the road's construction, is in administration. That aside, the current councillors are keen to get policy support for the bypass. To do this, the road is included within the Local Development Plan (LDP) and the associated "Action Plan". Controversially, it comes with the allocation of one hundred hectares of greenbelt land to commercial development, a move the council says is necessary to fund the road. Here are the important details:
Cost – Ranging from £7.18m to £13.5m – dependent on the exact route and whether the road would be a single or dual carriageway
Length – 2.8km to 3km
Funding – Mainly from developer contributions from those building throughout the A701 corridor (as far south as Penicuik). Small council contribution and government funding possible.
Construction impact – Limited impact to commuters initially, however there will be disruption when constructing the end roundabouts. The community of Damhead could face years of disruption depending on the chosen route.
Benefits – Technically it should remove the pressure on the current A701 and the A702, however the road is to inspire growth in the region. With additional houses, come additional commuters and as such the route could become saturated. Careful planning and public transport incentives would be required to make sure this does not happen. Furthermore, the City Bypass is operating well over capacity (130%). Until this is resolved, the connecting roads will remain congested.
Impacts – Loss of greenbelt land, increased pollution from additional car journeys and the potential distruction of known wildlife habitats.
Deliverability – The road could be operational by early next decade.
The A701 bypass is very much a work in progress and the exact route is not yet known. Four options exist for the routing, along with a further four depending on whether the road would be a single or dual carriageway. The two more expensive options (£13m+) give more land to the east for development, but would be more difficult to construct, thus delaying the opening date. It should be noted that developers behind film studio proposals at Straiton, say their plans would be "fatally damaged" by rerouting of the A701 through their site. Only one option proposes not to do this but it is not viewed favourably by the council. A willingness for development is required to get the project off the ground but an over-reliance on developers could prove troublesome.
A702/Mauricewood Road Roundabout
This is the intervention the region will see first. The proposal is to upgrade the A702/Mauricewood Road T-Junction to a major three-arm roundabout. To do this, the junction will be moved north east slightly, changing the routing of Mauricewood Road. A substantial roundabout will then be constructed over the current route of the A702, creating a new junction. Improvement works will continue down Mauricewood and Belwood Roads, widening the carriageway and adding full length pavements. At the bottom of Mauricewood Road, traffic signals will be added. Details:
Cost – £4.7m
Size – The roundabout will be a comparable size to those on the A702 at Lothianburn. Street lighting will be required.
Funding – Taylor Wimpey will fund the upgrades.
Construction Impact – There will be many months of disruption. Developers could create a relief road to allow the A702 to remain open during the entire build, however, should they not do this, the positioning could result in complete closure of the A702 for at least part of the works.
Benefits – The arrangement may make it easier for traffic to leave the Mauricewood Road junction more regularly. A roundabout should reduce the number of road traffic accidents.
Impacts – HGVs could struggle on the approach during wintry conditions. There will be a high impact during construction.
Deliverability – Under construction. End of 2017.
North Penicuik Link Road
This development will be a relief road between Mauricewood Road and Rullion Road, serving the new housing development to be constructed by a consortium of Cala Homes and Avant Homes. It will run between the Mauricewood Steadings and just south of Deanburn. Technically it will be a residential road, so speed calming measures like speed humps will be likely and roundabouts and access points to "home zones" could also feature. Details:
Cost – £1.5m to £3m
Length – About 1km
Funding – Developer only
Construction Impact – Little to none. The road would be built before housing construction commences and would be connected to the current roads by t-junction. Some disruption is possible during the connection phases.
Benefits – Those who live on Rullion Road or in the Cornbank/Cuiken areas of town will notice a marked reduction in journey times.
Impact – The new road could inspire additional journeys via the A702. Mauricewood Road would see an increase in peak hour traffic.
Deliverability – It depends on when a planning application is submitted on behalf of the housing consortium. This was meant to happen at the end of 2014 but has been delayed until Autumn 2016. If the application process isn't as stinted as Greenlaw Mill, construction could start by mid 2017. The road would, therefore, be operational by early 2018.
Edinburgh Orbital Bus Route & Lothianburn Park and Ride
Much like the A701 bypass, the Edinburgh Orbital Bus Route (EOBR) has been discussed for many years now. According to feasibility studies undertaken by SESTrans back in 2009, the key to delivering the project was to have a full length hard-shoulder on the A720 City Bypass. This would allow an express bus to run along the side of the trunk road without affecting the other traffic. The buses would then leave the bypass, using their own segregated busways to enter Park and Rides. One Park and Ride is yet to be built, the Lothianburn Park and Ride. This would be located on land north of the Esso petrol filling station. A road would then be built between the Straiton and Lothianburn sites, allowing buses to travel between the two in just under two minutes. Compare this to the car travellers' fifteen minutes at peak times and you can see why the EOBR would be popular. Plans are at a preliminary stage, however they go as far to say that buses would run between Newbridge and Queen Margaret University every five minutes. Notable stopping points include the ERI and Edinburgh Airport. The feasibility studies are linked in the sources section at the end of this article should you wish to study the proposals further. Details:
Cost – £10m+ – Segregated busways (blue route on map) and other supporting infrastructure would be expensive. Plans also say modified train-buses would be used. These 'bendy' buses would cost several hundred thousand each. Approximately twenty buses would be needed, so there would be substantial initial outlay.
Length – About 20 miles – Exact routing to be agreed on
Funding – West Lothian, Midlothian, East Lothian and Edinburgh councils would contribute along with a small government fund. Developers in south Edinburgh may contribute and those at Shawfair in Midlothian.
Construction Impact – It's hard to say at the moment due to the lack of recent plans, the most recent were published in 2010, but disruption would be likely, especially in the east of Edinburgh. Upgrading of the city bypass' hard shoulders could also cause delays, though work should be undertaken overnight.
Benefits – Travellers from Midlothian would be able to reach the west of Edinburgh in just one bus journey. This would likely be faster than taking the car, depending on the time of day. The bus could provide onward connections to several rail routes at Edinburgh Park and Shawfair.
Impact – Non-segregated busways could utilise current private transport lanes. This would likely lead to further congestion. Segregated busways will use greenbelt land and would have an environmental impact.
Deliverability – A detailed study needs undertaken, then funding needs sought, construction undertaken and a service provider secured. This all takes time and so the route will probably not be operational until early next decade. Development Plans for the councils state that the route should be delivered by 2030.
Other Infrastructure Projects to Note
A design process is ongoing to ascertain what the future Sheriffhall roundabout will look like. Options include two smaller separate roundabouts, a bypass overfly and a roundabout overfly. This project is high impact and any construction would cause months of disruption. However traffic should flow better along the A720 once operational. Finally we must note the reinstatement of Penicuik's heavy railway line. Two studies have been undertaken for this. It is believed they have been taken up to the route selection point, so Midlothian Council should have a final route along with the associated costings. The outcome of these reports was due to be reported to the cabinet but has not yet been released, despite being completed in June last year. Our initial Freedom of Information submission to Midlothian Council was refused on the grounds they did not own the reports. Earlier in 2016, we obtained the reports and published them on this website for you to read. It is unlikely the route could be operational until 2030 and it would probably have to extend to Peebles to be profitable. An extension to the Borders Railway may be considered for ease.
Conclusion & Final Analysis
Midlothian is set to expand rapidly within the next fifteen years and it is clear it needs a solid infrastructure to support it. Be this a strong network of schools or a strong road network, there must be plans to mitigate the extreme growth. Whether the residents like it or not, Midlothian is changing. By 2030 it will be unrecognisable, should the plans materialise. One thing is for sure, there is no way back from what is to come.
For your reference, here are the sources for the information in this article:
A701 Bypass:
Midlothian Council's Technical Notes (including route selection feasibility studies) – http://penic.uk/1Iifumn
Midlothian Council's Draft Local Development Plan – http://penic.uk/1AHIlSZ.
A702/Mauricewood Road Bypass:
Taylor Wimpey's Greenlaw Mill Transport Assessment – http://penic.uk/1CKdZg5
Taylor Wimpey's Greenlaw Mill Case Study – http://penic.uk/1vjjKS2
SESTrans feasibility studies – http://penic.uk/1vHEZI4
P&R Planning Application – http://penic.uk/1LcDt80
Sheriffhall Roundabout:
Transport Scotland junction design options – http://penic.uk/1G561z5
What do you think? Let us know your view below or join the conversation on Facebook or Twitter.
TagsEducationEnvironmentHousingMidlothian CouncilPenicuikRoslinStraitonTransport
Museum Plans Shortlisted for Funding
Local Defibrillator Map Unveiled
The link road from Mauricewood to Rullion Road is worrying – it will increase traffic on Rullion Road substantially, decrease the residential nature of Cornbank and reduce air quality.
Kenny Deas
Anything to reduce the accidents at the bottom of Mauricewood Road and clear the awful congestion. I am quite certain the air quality of Cornbank has been more affected by bus transport.

redpajama_set_name: RedPajamaCommonCrawl


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text:
namespace v8 {
namespace internal {

Address IC::address() const {
  // Get the address of the call.
  Address result = Assembler::target_address_from_return_address(pc());

#ifdef ENABLE_DEBUGGER_SUPPORT
  Debug* debug = isolate()->debug();
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  // of the call.
  if (!debug->has_break_points()) return result;

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  // break point locations are updated correctly.
  if (debug->IsDebugBreak(Assembler::target_address_at(result))) {
    // If the call site is a call to debug break then return the address in
    // the original code instead of the address in the running code. This will
    // cause the original code to be updated and keeps the breakpoint active in
    // the running code.
    return OriginalCodeAddress();
  } else {
    // No break point here just return the address of the call.
    return result;
  }
#else
  return result;
#endif
}

Code* IC::GetTargetAtAddress(Address address) {
  // Get the target address of the IC.
  Address target = Assembler::target_address_at(address);
  // Convert target address to the code object. Code::GetCodeFromTargetAddress
  // is safe for use during GC where the map might be marked.
  Code* result = Code::GetCodeFromTargetAddress(target);
  ASSERT(result->is_inline_cache_stub());
  return result;
}

void IC::SetTargetAtAddress(Address address, Code* target) {
  ASSERT(target->is_inline_cache_stub() || target->is_compare_ic_stub());
  Heap* heap = target->GetHeap();
  Code* old_target = GetTargetAtAddress(address);
#ifdef DEBUG
  // STORE_IC and KEYED_STORE_IC use Code::extra_ic_state() to mark
  // ICs as strict mode. The strict-ness of the IC must be preserved.
  if (old_target->kind() == Code::STORE_IC ||
      old_target->kind() == Code::KEYED_STORE_IC) {
    ASSERT(Code::GetStrictMode(old_target->extra_ic_state()) ==
           Code::GetStrictMode(target->extra_ic_state()));
  }
#endif
  Assembler::set_target_address_at(address, target->instruction_start());
  if (heap->gc_state() == Heap::MARK_COMPACT) {
    heap->mark_compact_collector()->RecordCodeTargetPatch(address, target);
  } else {
    heap->incremental_marking()->RecordCodeTargetPatch(address, target);
  }
  PostPatching(address, target, old_target);
}

InlineCacheHolderFlag IC::GetCodeCacheForObject(Object* object,
                                                JSObject* holder) {
  if (object->IsJSObject()) {
    return GetCodeCacheForObject(JSObject::cast(object), holder);
  }
  // If the object is a value, we use the prototype map for the cache.
  ASSERT(object->IsString() || object->IsSymbol() ||
         object->IsNumber() || object->IsBoolean());
  return PROTOTYPE_MAP;
}

InlineCacheHolderFlag IC::GetCodeCacheForObject(JSObject* object,
                                                JSObject* holder) {
  // Fast-properties and global objects store stubs in their own maps.
  // Slow properties objects use prototype's map (unless the property is its own
  // when holder == object). It works because slow properties objects having
  // the same prototype (or a prototype with the same map) and not having
  // the property are interchangeable for such a stub.
  if (holder != object &&
      !object->HasFastProperties() &&
      !object->IsJSGlobalProxy() &&
      !object->IsJSGlobalObject()) {
    return PROTOTYPE_MAP;
  }
  return OWN_MAP;
}

JSObject* IC::GetCodeCacheHolder(Isolate* isolate,
                                 Object* object,
                                 InlineCacheHolderFlag holder) {
  Object* map_owner =
      holder == OWN_MAP ? object : object->GetPrototype(isolate);
  ASSERT(map_owner->IsJSObject());
  return JSObject::cast(map_owner);
}

} }  // namespace v8::internal

#endif  // V8_IC_INL_H_


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At the end of the day, a civil litigator is a lawyer who will help you navigate the law when dealing with civil disputes. Though civil disputes may not carry the threat of criminal penalties, paying out damages can be just as costly.
What Are the Parts of a Civil Lawsuit?
Everything begins with a "complaint". Once a complaint is filed, it is up to the defendant to respond to the complaint. However, the court through which you file your complaint will depend on the nature of your case and the amount of money being requested for damages.
The dollar limit for Maryland Small Claims Cases is $5,000, which means any case requesting more than $5,000 must be filed elsewhere.
Are You Looking for a Essex, MD Civil Lawyer?
No matter which side you're on during the case, having a trusted legal team on your side is going to make a big difference. Call on The Law Office of Ross W. Albers to settle your civil disputes and see the difference in action.


text:
David Kirby, Ph.D.
Assistant Professor of Chemistry
Administration Bldg. – Room 448
Academic:
Ph.D. The Pennsylvania State University
B.S. Saint Francis University
dkirby@ndc.edu
Nirmala Krishnamurthy, M.Sc., Ph.D.
Administration Building, 4th Floor, Room 435
Ph.D. University of Utah, Salt Lake City, UT.
M.S. Indian Institute of Technology-Madras (IITM), India
B.S. University of Madras, India
nkrishnamurthy@ndc.edu
David Orosz, Ph.D.
Marie Goetz Geier Distinguished Professor of STEM, Professor of Biochemistry, Department Head of Chemistry
Admin. Bldg. Rm. 449
Ph.D. Medical College of Ohio, Toledo, Ohio
B.A. Hiram College
dorosz@ndc.edu


text:
Spring Training - Houston Astros at New York Mets
Clover Park - Port St. Lucie, FL
Spring Training - St Louis Cardinals at New York Mets
Spring Training - Miami Marlins at New York Mets
FriMar 04202201:10 PM
Spring Training - Washington Nationals at New York Mets
Spring Training - Miami Marlins at New York Mets (Split Squad)
Spring Training - Atlanta Braves at New York Mets
Spring Training - Pittsburgh Pirates at New York Mets
Spring Training - St Louis Cardinals at New York Mets (Split Squad)
ThuMar 0320221:10PM
Buy Spring Training - Miami Marlins at New York Mets Tickets at Clover Park on Thursday, March 3, 2022. TicketCity, Trusted since 1990.
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⚾ Spring Training - Miami Marlins at New York MetsSpring Training - Miami Marlins at New York Mets
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2022-03-03T13:10:00Thursday, March 3, 2022 1:10PM
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Spring Training - Miami Marlins at New York Mets at Clover Park
Buy tickets for Spring Training - Miami Marlins at New York Mets at Clover Park in Port St. Lucie, FL on Mar 3, 2022 at 1:10PM local time. All New York Mets tickets and schedule are available at TicketCity. Only 1,274 tickets left for this Mar 3 event.
When do the gates open for Spring Training - Miami Marlins at New York Mets, Mar 3 2022?
Gates for all MLB games typically open 1 hour before first pitch. Holders of seats with access to premium club areas and suites may be eligible for even earlier access.
What is the Clover Park MLB child ticket policy?
Children under 2 or under 30" in height do not require a separate ticket at all MLB stadiums. Some teams have exceptions for children under 4 years old and up to 36" in height. Children that do not require a ticket to attend will not have a seat. They are expected to sit on the lap of a caregiver during the game without obstructing the view of other fans.
What are the best seats at Clover Park?
Generally speaking seats along the infield, in close proximity to the field tend to be the most desired. However, there is value to be had at all price points with outfield seats offering close proximity to the field and upper level seats offering clean site lines.
What is the Clover Park bag policy?
Clover Park follows MLB's league mandated bag policy. All bags must be under 16"x16"x8" in size. Backpacks are only permitted if they are of the single compartment drawstring type. Purses, diaper bags and medical bags are allowed, but must be within the size limitations. All teams reserve the right to further restrict what can be brought into the stadium. Clear bags are always preferred.
How will I receive my Spring Training - Miami Marlins at New York Mets, Mar 3 2022 tickets?
The delivery method will be disclosed in the ticket notes and/or at checkout. Most seats are electronic transfer only. These require the use of an iOS or Android device for claiming your seats and for entry scanning at the gate. Traditional hard stock tickets will ship via UPS.