text:
\section{Introduction}
Angiography is a visualization technique whereby injecting a dye into the circulatory system an image of the inside of blood vessels can be obtained. A common method through which to obtain angiographic data is computed tomography (CT). A variety of fields have benefited from recent advances in CT angiography (CTA), one of them being the treatment of acute ischemic stroke.

Given that for stroke patients the elapsed time since the stroke onset is critical to recovery, fast and precise imaging of affected brain regions is crucial. A common first step in the diagnosis of affected tissue is vessel segmentation. A bottleneck to this process is in the localization and evaluation of regions of interest, which often relies on an emergency radiologist having to manually go over the volumetric data. It comes to no surprise that automation techniques dedicated to speeding up this process have been seeing significant development in recent years.

\subsection*{Imaging Techniques}
A variety of methods exist to capture volumetric angiographic information. X-ray based methods like computed tomography angiograhy (CTA) tend to be more commonplace over magnetic resonance angiography (MRA). Technical reasons for this include higher spatial resolution, although there are also practical reasons such as not having to check if the patient is susceptible to MR.

Image processing techniques have been developed to segment target regions in volumetric data. However, the design of robust techniques is non-trivial. There are structures in the cranial cavity with equal intensity values as the contrast material which often result in false positives under naive segmentation approaches. Furthermore, determining the exact boundaries of vessels that are located close to bone structure is a challenge of its own. Methods in clinical use tend to revolve around the use of an atlas, which base decisions given density models created from manually annotated examples~\cite{atlas1, atlas2, atlas3}. However, atlas based methods are limited in their ability to generalize to variability in individual patient anatomy.

\subsection*{Deep Learning}
With the increase of annotated data, supervised learning techniques have in recent years become prevalent in medical imaging fields. Convolutional neural networks (CNNs) have shown great promise in diagnosis from visual information. In recent studies, these approaches have began to surpass domain experts in terms of early detection and overall accuracy~\cite{breast_cancer}. With enough data, these networks have in some cases even managed to discover underlying correlations previously unknown to experts \cite{iris}. It thus comes with no surprise that there is a growing incentive to make use of the apparent ability of these models to discover and utilize features in the data that are unperceivable to humans.

One obvious downside to deep models is the amount of data required to tune their parameters. Commonly used CNNs tend to have numbers of parameters in the order of hundreds of thousands, if not millions. However, access to large amounts of data in many medical imaging fields is often impossible. One obvious reason to this scarcity is rarity of certain conditions. However, other roadblocks are present. Patient anonymity concerns make it impossible for data sets to be compiled and distributed. Furthermore, data labelling entails lots of tedious manual labour. This is worsened by the fact that domain experts are commonly the only ones qualified to perform such tasks.

\subsection*{Contributions}
This paper aims to explore data efficiency techniques that can prove useful for training a deep model for brain vessel segmentation tasks in CTA scans.

First, we propose a synthetic vessel generation algorithm than can be used to pretrain a CNN model. The generated vascular structure aims to mimic the appearance of the arterial system observed in real CTA scans. This allows the use of unlabeled CTA scans by blending generated synthetic vascular structures into them.

Second, we explore whether the recreation of the underlying noise in the scans can serve as a form of augmentation. The process consists on denoising a scan and applying noise similar in nature to CT noise.

\section{Previous research}

\subsection*{Synthetic Vessel Generation}

The arterial system of the human body is a complex branching structure used to deliver blood from the heart to different parts of the body. The physiology of arterial trees has evolved, over the course of millions of years, to efficiently supply tissue with oxygen and nutrients, as well as removing metabolic end products~\cite{karch2000staged}.

First attempts to incorporate these principles into discrete computational models include~\cite{2d_vessels_1991, vascular_trees, 2d_vessels_1996}. The produced 2-D vascular structures by their early angiogenesis models, although milestones at the time, were artificial and simplistic in appearance.

With an increase in biological data, newer computational models of arterial trees have been developed in a range of research fields, each accounting for influencing variables at different size scales. Approaches such as the one taken by~\cite{karch2000staged} and~\cite{schneider2012tissue} aim to account for macroscopic influencers such as oxygen concentration in surrounding tissue and fluid flow resistance within the generated vessel structure. Other approaches such as~\cite{milde2008hybrid} attempt to simulate microscopic influencing variables such as the formation of endothelial tip cells and the influence that proteins and hormone densities in the extracellular matrix have on tip cell trajectories.

A radically different approach involves generating vascular structures using a learned distribution from a data set. A common choice for such tasks are Generative Adversarial Networks (GAN)~\cite{GAN}. Although these methods are only capable of creating an implicit model of the data they are presented, they have been shown to produce anatomically convincing vascular structures~\cite{retina_GAN, retina_GAN_segmentation}.

\subsection*{Vessel segmentation}
Early approaches to vessel segmentation tended to revolve around the application of unsupervised computer vision techniques. A common approach to help highlight vascular structures involves multi-scale analysis, whereby local second-order information (Hessian) is examined~\cite{hessian}. A similar approach is that of matched filtering~\cite{staal2004ridge, retina_vesselseg}, where a variety of Gaussian based filters (such as Gabor filters) are convolved with the image to enhance features. Another example of a used approached is that of vessel tracking. This approach involves tracing a vessel based on local structure~\cite{region_growing}. Vessel tracking however requires root positions to be predefined.

In recent years, supervised approaches have shown dominance over the aforementioned methods thanks to their ability to make use of ground truth data. Convolutional neural networks (CNN) have shown great promise due to their hierarchies of features learnt directly from the data. Approaches such as~\cite{retina_CNN, retina_CNN2} were among the first to show the potential of CNNs to vessel segmentation tasks on retina images. Following advances in 3D CNNs,~\cite{tetteh2018deepvesselnet} proposed approaches to efficiently treat large CT and MR scan volumes for vessel segmentation of the head. These included replacing 3D kernels by 1D cross-hair filters and pretraining a model on synthetic vessel data. More recent state-of-the-art research on volumetric data has made use of U-Net architectures~\cite{U-net}. Skip-connections in the architecture allow for local information to be preserved across feature extraction layers. This makes the U-nets ideal for localization of small anatomical structures~\cite{DBLP:journals/corr/abs-1809-04430, roth2018application}.

\section{Methodology}
\subsection*{Synthetic Vessel Generation}
The synthetic vessel generation approach employed in this research was in many ways similar to the rooted tree branching structure found in~\cite{schneider2012tissue}. A 3D tree structure grows from a root node, where new inter nodes can grow from current leaf nodes, and inter nodes can spawn new leaf nodes to form bifurcations. The core idea behind our approach is to generate two trees such that each root node is approximately located at the coordinates of different internal carotid artery so as simulate a synthetic arterial system branching into the brain.

In a given tree, each node has a three-dimensional position vector $\vec{p}$ and a direction vector $\vec{d}$. For all nodes except for the root node, the starting position vector $\vec{p}$ is the tip of its parent's direction vector ($\vec{p}_p + \vec{d}_p$). The root node's position vector $\vec{p}$ is a predefined coordinate given by a internal carotid's average entry point to the brain given by the subjects in our data set. The direction vector of the root node was set to point straight up so as to have the vessel structure grow upwards into the brain (since the root's position is in the base of the brain). The direction vector of a child node $\vec{d}_c$ depended on whether the node's parent was a leaf-node or if the new node was a bifurcation.

In the simple case that a child node was spawned from a leaf node, the child's direction vector is initialized to be equal to it's parent ($\vec{d}_c = \vec{d}_p$) and subsequently perturbed in two ways. The first perturbation is performed by adding Gaussian noise $N(0, \sigma)$ to the angle component of the polar coordinates, where $\sigma$ was empirically set to $\pi/32$ radians. The second perturbation is in the direction of a sampled target point in order to guide the leaf node towards points of interest (more on this later). The post-perturbation vector $\vec{d_c^*}$ is always adjusted in magnitude so as to satisfy $\|\vec{d}_c\| = \|\vec{d}_p\|$. This will be ensured by modifying $\vec{d_c^*}$ in the following manner:
\begin{equation}
    \vec{d}_c = \|\vec{d_p}\| \frac{\vec{d_c^*}}{\|\vec{d_c^*}\|}
    \label{inter_node_radius}
\end{equation}

In the case that the child node was a product of a bifurcation, the direction vector was determined similar to~\cite{schneider2012tissue}, where the bifurcation angle is calculated in accordance to fluid dynamics described in~\cite{Fung_Biomechanics} and the minimum work and energy dissipation proposed in~\cite{Murray299} and~\cite{Optimality_principles}:
\begin{equation}
    \cos \left(\phi_{c}\right)=\frac{r_{p}^{4}+r_{c}^{4}-r_{e}^{4}}{2 r_{p}^{2} r_{c}^{2}}
    \label{child_angle}
\end{equation}
where $\phi_{c}$ is the bifurcation angle of the new child, and $r_{p}$, $r_{c}$, $r_{e}$ are the radius of the parent, new child, and existing child nodes respectively.

\begin{figure}[b!]
    \centering
    \includegraphics[width=\columnwidth]{img/Child_rotation.png}
    \caption{Representation of direction vectors when a new child is created in a bifurcation node.}
    \label{fig:child_rot}
\end{figure}

The new child's direction vector $\vec{d}_c$ is coplanar to $\vec{d}_p$ and $\vec{d}_e$. Once the angle is obtained using eq.~\ref{child_angle}, a new direction vector $\vec{d}_n$ is calculated using Rodrigues' rotation formula, where a copy of $\vec{d}_e$ is rotated by ($\phi_{c} + \phi_{e}$) around the pivot vector $\vec{d}_p\times\vec{d}_e$ that is normal to the plane containing $\vec{d}_p$ and $\vec{d}_e$ (Figure~\ref{fig:child_rot}). The resulting $\vec{d}_n$ has magnitude $\|\vec{d}_n\| = \|\vec{d}_e\| = \alpha\|\vec{d}_c\|$, where $\alpha$ is the ratio between magnitudes of $r_c$ and $r_e$. The vector $\vec{d}_n$ has to be subsequently scaled in order to obtain $\vec{d}_c$ (more on this below).

The radius of the vessel is tied to the direction vector of it's nodes. Each node is represented as a sphere with center at a given node's position vector $\vec{p}$ and with radius proportional to the direction vector magnitude $r = \beta\|\vec{d}\|$. With enough overlap between spheres ($\beta > 1.0$), a tube-like structure is approximated. The binary vessel structure can be obtained in 3-dimensional pixel space through rasterization of the tree nodes into voxel space. The value of $\beta$ not need be much larger than $1.0$ for the rendered vessels to appear realistic, as the resolution of a CTA scan is always too low compared to the size scale of the imperfections arising from this approximation. We empirically set a node's starting $\beta$ value to 3.0, as even in branches that had their radii reduced due to bifurcations taking place in earlier nodes (while having parent-child distances preserved), no visible width disturbances could be observed (see Figure~\ref{fig:trees}).

\begin{figure}[h]
  \begin{subfigure}{0.49\columnwidth}
    \includegraphics[width=\linewidth]{img/no_target_vessels.png}
  \end{subfigure}
  \hfill
  \begin{subfigure}{0.49\columnwidth}
    \includegraphics[width=\linewidth]{img/rodrigues_vessels.png}
  \end{subfigure}
  \caption{Generated vessel structures with no spatial target growth incentive. The right figure was grown in a small spatial volume relative to the root width, while the left was grown unconstrained by volume.}
  \label{fig:trees}
\end{figure}

Given that we determine a node's radius to be $r = \beta\|\vec{d}\|$, if we know the radius of a bifurcation's new child $r_c$, the direction magnitude $\|\vec{d}_c\|$ can be obtained. The vector $\vec{d}_c$ is the calculated by scaling the normalized $\vec{d}_n$ vector by $\|\vec{d}_c\|$:

\begin{equation}
    \vec{d}_c = \frac{\vec{d}_n}{\|\vec{d}_n\|} \frac{r_c}{\beta}
    \label{bifurcation_direction}
\end{equation}

The radius of a new child node also depends on whether the parent is a leaf node becoming an inter node or an inter node becoming a bifurcation. If the parent node is a leaf node, the new child will be assigned the same $\beta$ as the parent so as to preserve the same radius along the vessel. However, in the case of a bifurcation node, the children will have radii conforming Murray's law~\cite{Murray299}:

\begin{equation}
    r_{p}^{\gamma}=r_{l}^{\gamma}+r_{r}^{\gamma}
    \label{murray_lay}
\end{equation}
where $\gamma$ denotes the bifurcation exponent. A value of $\gamma = 3.0$ was chosen for our model, although values of $2.0 < \gamma \leq 3.0$ have been reported in the literature~\cite{schneider2012tissue, vascular_trees, canine_coronary_artery}.

To determine the radius assigned to the new child in a bifurcation, we follow the heuristic proposed in \cite{schneider2012tissue}. According to Murray's law for a fully symmetric bifurcation ($r_c = r_e$), the "expected" radius $r_\mu$ of distal segments is defined as $r_c = 2^{-1/\gamma}r_p$. The new child radius is sampled from the narrow normal distribution $\mathcal{N}\left(\mu=r_{\mu}, \sigma=r_{\mu} / 32\right)$ and clipped between $0.0<r_c<r_p$. Once $r_c$ is known, the existing child's radius is subsequently modified by rearranging equation 3.4:

\begin{equation}
    r_{e}=\left(r_{p}^{\gamma}-r_{c}^{\gamma}\right)^{1 / \gamma}
    \label{murray_lay_re}
\end{equation}

After a bifurcation, if any of the two children have a smaller radius than $r_{min}$, the child is pruned. In the case of pruning, the remaining child will then have its radius become equal as its parent $r_p$ (thus the parent becoming an inter node). If the existing child happened to be the one pruned and had children of his own, the entire child's branch is recursively removed. Similarly, if the none of the two children in the bifurcation were pruned, the existing child's branch will have its radius recursively reduced by the ratio $r_{e\_new}/r_{e\_old}$. If any of the subbranches happened to contain nodes with radius $r<r_{min}$, those subbranches are recursively removed.

\begin{figure*}[t]
  \begin{subfigure}{.66\columnwidth}
    \includegraphics[width=\linewidth]{img/axial_3D_vessels.png}
    \caption{Axial (top-down) view.}
    \label{fig:tree_target_axial}
  \end{subfigure}
  \hfill
  \begin{subfigure}{0.66\columnwidth}
    \includegraphics[width=\linewidth]{img/coronal_3D_vessels.png}
    \caption{Coronal (back-front) view.}
    \label{fig:tree_target_coronal}
  \end{subfigure}
   \hfill
  \begin{subfigure}{0.66\columnwidth}
    \includegraphics[width=\linewidth]{img/sagittal_3D_vessels.png}
    \caption{Sagittal (left-right) view.}
    \label{fig:tree_target_sagittal}
  \end{subfigure}
  \caption{Generated vessel structures with spatial target growth incentive. The target volume was a vessel density atlas created by averaging the ground truth of patients in our data set. }
  \label{fig:hemisphere_trees}
\end{figure*}

When growing the tree, two separate lists of nodes were kept updated. One list contained all current leaf nodes, while the other contained all inter nodes capable of undergoing bifurcation. A predefined bifurcation probability was used to determine whether the next created child node would be a product of a leaf-node becoming an inter node or an inter node becoming a bifurcation. Depending on which of the two cases was decided, a random node was sampled from the corresponding list.

The sampling was performed under a uniform distribution. Previous tests dealing with weighted sampling based on a node's region within the brain (more on this below) lead to single branches growing disproportionately while others leaf nodes never had a chance to grow. Also, certain requirements are needed to be satisfied for an inter node to be considered as capable of bifurcation. First, the radius of the node had to be at least $2r_{min}$ (otherwise one of the children was guaranteed to be pruned right away). Second, the node couldn't be a leaf node. Lastly, the node had to be no closer than a predefined number of nodes away along a vessel from an existing bifurcation.

One key point to note is that, unlike in~\cite{schneider2012tissue}, we do not back-propagate through the entire tree in order to optimize every node's angles and widths when a bifurcation takes place. Such an approach would increase the computational complexity of the algorithm significantly, and subsequently the time required to generate these structures. Our main motivation for building these vessel structure is to optimize the performance of our segmentation algorithm through pretraining, and as such, extensive improvements to realism start to bring diminishing returns. Furthermore, the resolution at which the vessels are rendered into the scans is likely to help hide minor imperfections. It is sufficient that the generated vessel structures appear to be plausible to the feature extraction layers of our convolutional network.

An approach was also introduced to make the global vessel distribution mimic the one found in the brain. The brain arterial system is designed to supply the entire brain, yet the current structure described so far grows away from the root node and is unlikely to meaningfully fill the volume of the brain once rendered. To solve this issue, a density atlas was created out of the patients scans in our data set to assign a volume with probabilities of an artery being present.

This volume was turned into binary values depending on whether a region had a probability density above a certain threshold. This transformation proved to be helpful to prevent all neighbouring vessels within a region to cluster into the same high-probability regions, and instead spread evenly out. When a leaf node was created (except in the bifurcation case), the surrounding region to the nodes position was sampled in the atlas. The sampled point with the highest value $\vec{s}_{max}$ was then used to create a target vector $\vec{t} = \vec{s}_{max} - \vec{p}_c$ towards which to rotate the new child's $\vec{d}_c$. After the rotation, the resulting vector is normalized to have the same length as its parent:
\begin{equation}
    \vec{d}_{c\_new} = \| \vec{d}_{p}\|\frac{\vec{d}_{c\_old} + \lambda(\vec{t} - \vec{d}_{c\_old})}{\|\vec{d}_{c\_old} + \lambda(\vec{t} - \vec{d}_{c\_old})\|}
    \label{target}
\end{equation}
where $\lambda$ determines how much to rotate $\vec{d}_{c\_old}$ towards $\vec{t}$.

To incentivize vessels to spread out, every time a node is created the neighboring region around the node in the atlas was reduced in value. The neighboring region was in the form of a sphere around the node's position $\vec{p}_c$. The neighborhood radius was linearly proportional to the radius of the node $r_c$.

Another use of the vessel atlas is to determine if a node is out of bounds. This is done by checking if the atlas has a value of 0 at the node's position. If a leaf node is too far out of the brain, the branch is discontinued (the leaf will never be selected to spawn a child). On top of this, in order to make the overall structure more realistic, a different atlas was used for each of the two hemisphere root nodes. Such atlas would only have non-zero values for one brain hemisphere. This causes the resulting vessel structures to no longer cross over between hemispheres. A resulting example structure can be seen in Figure \ref{fig:hemisphere_trees}.

\subsection*{Data sets}
Available to us was a set of 4D CTA scans belonging to 57 stroke patients. Out of these, 24 had labeled artery ground truths, while the other 33 were unlabeled. Given that the goal of this research was to improve the segmentation on a single 3D scan, we treated each of the patient's frames in the time dimension as independent scans.

The labeled data set was one where each individual 4D scan had one single 3D ground truth. The labelling of each patient 4D scan was performed by hand by moving along the time dimension and marking the volume highlighted by the contrast material over time. The 3D time frames that made up our training, validation, and test sets were manually selected based on presence and clarity of the vessels. We explicitly aimed to use images from peak arterial, equilibrium, and peak venous phases of the 4D CTA scan. Time steps from a 4D scan's early arterial phase were manually discarded as insufficient contrast material had reached the brain and no vessels were visible. Similarly, late time steps belonging to late venous phases were discarded due to the contrast being too diffused. The final training set contained 243 3D scans originating from 20 patients. The validation set consisted of 26 3D scans originating to two different patients. Lastly, the test set consisted of 22 3D scans belonging to yet two other different patients. A given patient's set of 3D scans were purposely not split across multiple of these data sets (train, validation, and test) to prevent the model from using learned features specific to individual patient's anatomy.

For each of the selected 3D frames, a specific ground truth was created to correspond with the visible contrast in the frame. A threshold was applied to the general ground truth so as to remove segmentation of arteries not yet highlighted on a given 3D frame. Furthermore, since the human segmentation had occasional under-segmentation in some frames, the ground truth was enhanced by expanding the segmentation to neighboring pixels in the upper and lower axial slice if such pixels had intensity values within range of typical contrast values ($95 <= I < 450$).
\begin{figure*}[t]
\vspace{-1.5cm}
\centering
    \begin{adjustbox}{width=\textwidth}
    \begin{tikzpicture}{width=12cm}
    \centering
        \node[align=left] at (0,-0.2) {\includegraphics[width=2.0cm]{img/noise_S_region.png}};
        \node[text width=1.85cm, align=center] at (0,0.6) {Sagittal};
        \node[align=left] at (2.2,0) {\includegraphics[width=2.0cm]{img/noise_S_denoised.png}};
        \node[align=left] at (4.4,0) {\includegraphics[width=2.0cm]{img/noise_S_new.png}};
        \node[align=left] at (6.6,0) {\includegraphics[width=2.0cm]{img/noise_S_original.png}};

        \node[align=left] at (0,1.9) {\includegraphics[width=2.0cm]{img/noise_C_region.png}};
        \node[text width=1.85cm, align=center] at (0,2.6) {Coronal};
        \node[align=left] at (2.2,2.0) {\includegraphics[width=2.0cm]{img/noise_C_denoised.png}};
        \node[align=left] at (4.4,2.0) {\includegraphics[width=2.0cm]{img/noise_C_new.png}};
        \node[align=left] at (6.6,2.0) {\includegraphics[width=2.0cm]{img/noise_C_original.png}};

        \node[align=left] at (0,3.9) {\includegraphics[width=1.5cm]{img/noise_A_region.png}};
        \node[text width=1.85cm, align=center] at (0,4.8) {Axial};
        \node[align=left] at (2.2,4.) {\includegraphics[width=2.0cm]{img/noise_A_denoised.png}};
        \node[align=left] at (4.4,4.) {\includegraphics[width=2.0cm]{img/noise_A_new.png}};
        \node[align=left] at (6.6,4.) {\includegraphics[width=2.0cm]{img/noise_A_original.png}};

        \node[text width=2.0cm, align=center] at (2.2,5.2) {\small{Denoised}};
        \node[text width=2.0cm, align=center] at (4.4,5.5) {\small{Generated }};
        \node[text width=2.0cm, align=center] at (4.4,5.2) {\small{Perlin noise}};
        \node[text width=2.0cm, align=center] at (6.6,5.2) {\small{Original noise}};
    \end{tikzpicture}
    \end{adjustbox}
    \caption{CTA slices showing the lateral ventricles. The maps on the left indicate the region being displayed. The left-most column shows the denoised version of the CTA slices. The natural CT noise (right column) appears to have similar local noise patterns to the generated Perlin noise (middle column).}
    \label{fig:noise}
\end{figure*}
In order to avoid the pretraining phase to learn characteristics of individual patient's anatomy, we refrained from rendering the synthetic vessels into any of the scans in the labeled data set. To create the synthetic vessel data set the first time frame from each unlabeled patient was taken, since in those frames the contrast material had not yet reached the region of the scan.

Finally, both the synthetic vessel data set and the labeled data set were augmented through rotations, translations and mirroring. The amount of rotation and translation was performed by sampling a tuple of values from a uniform distribution. Rotations were in the range of $[-20, 20]$ degrees along the axial plane and $[-10, 10]$ along the coronal and sagittal planes. Translations were in the ranges $[-3\%, 3\%]$ of the scans vertical dimension along the z-axis and $[-5\%, 5\%]$ of the axial plane dimensions along the x and y axis. Mirroring only happened along the sagittal plane (with 50\% probability), as we aimed to keep the patient facing in the original direction and have the body retain the same orientation.

\subsection*{Synthetic Noise}

Recent studies have pointed out CNN approaches to image recognition are not as reliant on object shapes as was previously hypothesised, and instead these networks tend to develop large biases to textures present in the training data~\cite{cnn_textures}. Given that the CTA data set available to us is rather limited in number of examples (as is across most medical fields), it is not far-fetched to think that a CNN could learn patterns in the noise textures of the data set. We thus take the approach of denoising each 3D scan so as to later add new generated noise. The performance on the ´new noise´ data set is then compared to the model trained on scans where the original noise is left as is.

The denoising algorithm used was a variation of the `Non-local means` algorithm which, instead of using the mean of large pixel neighbourhoods of an individual image, the mean was computed based on the given pixel's value through time. The denoising process worked under the assumption that individual regions across the time dimension have independent noise from one-another. Denoised images such as the ones seen in Figure \ref{fig:noise} are obtained.

Extensive literature on noise in CTA scans exists~\cite{Omar_noise, Kijewski_1987, Polacin_1992}. Whenever a deeper understanding of underlying noise distributions is gained, better denoising and reconstruction algorihtm follow. Such studies however, attempt to identify approximate noise models by analysing inherent properties of noise distributions, most commonly the distances of individual pixel values to local regions of the image. Gaussian noise is often suggested as a fitting candidate. In computer graphics however, there is a clear distinction between ´value noise´ and ´gradient noise´ models. ´Value noise´ has the characteristic of lacking natural looking texture gradients. Despite this, the approximate distribution of pixel values on both of these types of noise models is practically identical.

Perlin noise~\cite{Perlin} is a form of ´gradient noise´ which shows resemblances to the observed textures in CTA scans while displaying global value distributions remarkably similar to Gaussian noise. In real 3D CTA scans pixel noise values are not independent of neighboring pixels. Noise gradients are consistent across neighboring 2D slices (regardless of orientation of the slices). The noise distribution can form local patterns that, at small enough scales, are practically indistinguishable from small brain vessels. To replicate this phenomenon, an approach similar to \cite{Perlin_data_augmentation} is used. A 3D volume of Perlin noise is added to the denoised scan in order to make the noise structure along each dimension consistent. We hypothesise that the consistency of the 3D noise across space might help the model better distinguish structures produced by noise.

It is also possible to generate this noise at multiple scales. Adding octaves of noise together can form fractal structures, which make the noise less geometric in appearance (see Figure~\ref{fig:octaves}). For this research we used 2 octaves. An example of the resulting scans can be seen in Figure~\ref{fig:noise}.
\begin{figure}[!t]
  \begin{subfigure}{0.49\columnwidth}
    \includegraphics[width=\linewidth]{img/octave1.png}
  \end{subfigure}
  \hfill
  \begin{subfigure}{0.49\columnwidth}
    \includegraphics[width=\linewidth]{img/octave3.png}
  \end{subfigure}
  \caption{2D slices of the generated 3D Perlin noise. The single octave version is quite geometric in appearance (left), while the 3 octave version (right) has a more natural appearance.}
  \label{fig:octaves}
\end{figure}

This addition of noise acts as a form of data-augmentation, from which we can generate any arbitrary number of new data points.

\subsection*{Network Architecture}
This choice of architecture for this research is a U-Net variant with similar parameters to the one posed by~\cite{DBLP:journals/corr/abs-1809-04430}. In their research, the architecture was tuned for a segmentation task of the neck and head anatomy. Despite the segmentation regions found in their tasks being significantly larger than the regions specific to the vessel segmentation task in ours, their architecture showed promise during initial tests.

The input to the network consists of 5 subsequent horizontal slices of a CTA scan. The network is trained to predict the ground truth for the middle slice out of the given 5. The 2 lower and 2 upper slices are thus meant to provide the network with useful context. The residual 3D U-net architecture consisted of 7 residual convolutional blocks in the downward path, having a fully-connected block at the bottom, and then followed by 7 residual convolutional block in the upwards path. The first 5 downwards layers performed 2D convolutions, while the latter 2 were 3D. As characteristic to U-Nets, the input to each upwards block was the output of the previous upwards block concatenated with the residual connection from the corresponding downwards block. The original x and y-dimensions of the input slices were preserved across all layers by using a stride of 1 and applying zero-padding where necessary. The output layer responsible for assigning a segmentation value to each pixel (in the original resolution of the input) was a 1x1x1 convolutional layer with sigmodial activation. Specific training hyperparameters used in this research can be found in the Appendix.

During training, a Dice-coefficient loss function was used. In preliminary experiments, this loss function proved to converge much faster and with much lower variance than a focal binary cross entropy loss function. To speed up convergence, the Adam optimizer \cite{adam} was used to compute adaptive learning rates for each parameter.

\section{Results, and Discussion}
\subsection*{Experimental Results}

When pre-training the model on generated vascular structures, 1000 unique scans were created. This was the case for both generation conditions (original CT noise and new Perlin noise). This translated to roughly 12500 training batches for each condition. None of the slices in the pre-training set were reused. In the other hand, when training and fine-tuning on real patient data, 5 epochs of iteration over the training set were performed. On a NVIDIA Titan RTX GPU, the pre-training procedure took roughly 16 hours per model, while the fine-tuning process was an additional 25 hours.

\begin{table*}[!t]
\caption{Average test set performances with corresponding standard deviations.}
\begin{tabular}{c|c|c|c|c|c|}
\cline{2-6}
          & \hfil Real data only & \begin{tabular}[c]{@{}l@{}}\hfil Pretrain\\\hfil original noise\end{tabular} & \begin{tabular}[c]{@{}l@{}}\hfil Fine-tune\\\hfil original noise\end{tabular} & \begin{tabular}[c]{@{}l@{}}\hfil Pretrain\\\hfil new noise\end{tabular} & \begin{tabular}[c]{@{}l@{}}\hfil Fine-tune\\\hfil new noise\end{tabular} \\ \hline
\multicolumn{1}{|l|}{\begin{tabular}[c]{@{}l@{}}\hfil Mean Dice\\ $\mu$ ($\pm$ $\sigma^ 2$)\end{tabular}} & \small 0.691 ($\pm$ 0.020) & \small 0.498 ($\pm$ 0.006) & \small 0.710 ($\pm$ 0.019) & \small 0.498 ($\pm$ 0.016) & \small 0.709 ($\pm$ 0.025)  \\ \hline
\end{tabular}
\label{Tab:results}
\end{table*}

The results seen in Figure \ref{fig:pretrain_results} show the validation scores of the model trained on real patient scans side-by-side with the validation scores of models pre-trained on generated vascular structures. The results reported are averages across 3 training runs. Pre-training on generated scans where Perlin noise was applied appears to achieve similar maximum scores as models pre-trained on generated scans where the original CT noise was intact. This can also be seen in the test set scores in Table \ref{Tab:results}. The two pre-trained model conditions had no difference in Dice value in the test set, having a mean Dice value of 0.710 and 0.709. Meanwhile, the condition were models were not pre-trained had a mean Dice of 0.691.

\begin{figure}[!h]
    \centering
    \includegraphics[width=1.0\columnwidth]{img/Mean_plot_pretrain_crop.png}
    \caption{Dashed lines show validation Dice scores during pre-training process on generated vascular structures. For comparison, the solid line shows validation scores of the model trained exclusively on real images. Each line depicts the average across 3 separate training runs. The shaded area denotes one standard deviation from the mean.}
    \label{fig:pretrain_results}
\end{figure}
\begin{figure}[!h]
    \centering
    \vspace{-0.5cm}
    \includegraphics[width=\columnwidth]{img/Mean_plot_finetune_zoom_crop.png}
    \caption{Dashed lines show validation Dice scores during fine-tuning processes. For comparison, the solid line shows validation scores of the model trained exclusively on real images. Each line depicts the average across 3 separate training runs.}
    \label{fig:finetune_results}
\end{figure}

Across all validation steps in each individual pre-training run, the best performing checkpoint in terms of Dice score was tracked. The model weights for this checkpoint were then used as a start point in the fine-tuning procedure.

Figure \ref{fig:finetune_results} shows the validation scores of the fine-tuning progress alongside the ones obtained by the condition with no model pre-training. For the first 8000 training steps, the fine-tuning validation scores appear to surpass that of the model trained exclusively on real data by at least 0.02. This difference appears to diminish with subsequent training set iterations. Furthermore, the model pre-trained on synthetic vascular data with Perlin noise, appears to have a performance on par with the model pre-trained on scans that had the original CT noise untouched. The mean test set performances can be seen in Table \ref{Tab:results}.

\subsection*{Qualitative Results}
After fine-tuning, the two pre-train conditions appear to have little or no difference in terms of qualitative performance at the vessel segmentation task. However, when compared to the models trained only on real data, there are noticeable differences. Throughout Figures~\ref{fig:segmentation1}-\ref{fig:segmentation7}, the model pre-trained on scans with original CT noise is to be taken as representative of segmentation performed by the Perlin noise pre-training condition. Images showing the hand-labeled ground truth, as well as the unlabeled slice, are presented for comparison.

\begin{figure*}[!ht]
    \vspace{-0.5cm}
  \begin{subfigure}{.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_noseg_7.png}
  \end{subfigure}
  \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_seg_7.png}
  \end{subfigure}
   \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_pre_7.png}
  \end{subfigure}
    \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_gt_7.png}
  \end{subfigure}
  \caption{Segmentation on internal carotid arteries. Unaltered image (left). No pretrain model (mid-left). Fine-tuned model (mid-right). Hand-labeled ground truth (right).}
  \label{fig:segmentation1}
\end{figure*}

\begin{figure*}[!ht]
  \begin{subfigure}{.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_noseg_2.png}
  \end{subfigure}
  \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_seg_2.png}
  \end{subfigure}
   \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_pre_2.png}
  \end{subfigure}
    \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_gt_2.png}
  \end{subfigure}
  \caption{Segmentations on regions near internal carotid arteries. Unaltered image (left). No pretrain model (mid-left). Fine-tuned model (mid-right). Hand-labeled ground truth (right).}
  \label{fig:segmentation2}
\end{figure*}

\begin{figure*}[!ht]
  \begin{subfigure}{.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_noseg_1.png}
  \end{subfigure}
  \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_seg_1.png}
  \end{subfigure}
   \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_pre_1.png}
  \end{subfigure}
    \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_gt_1.png}
  \end{subfigure}
  \caption{Segmentations on a central slice. Unaltered image (left). No pretrain model (mid-left). Fine-tuned model (mid-right). Hand-labeled ground truth (right).}
  \label{fig:segmentation3}
\end{figure*}

\begin{figure*}[!ht]
  \begin{subfigure}{.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_noseg_6.png}
  \end{subfigure}
  \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_seg_6.png}
  \end{subfigure}
   \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_pre_6.png}
  \end{subfigure}
    \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_gt_6.png}
  \end{subfigure}
  \caption{Segmentations on a central slice. Unaltered image (left). No pretrain model (mid-left). Fine-tuned model (mid-right). Hand-labeled ground truth (right).}
  \label{fig:segmentation4}
\end{figure*}

\begin{figure*}[!ht]
  \begin{subfigure}{.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_noseg_3.png}
  \end{subfigure}
  \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_seg_3.png}
  \end{subfigure}
   \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_pre_3.png}
  \end{subfigure}
    \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_gt_3.png}
  \end{subfigure}
  \caption{Segmentations on regions near the top of the skull. Unaltered image (left). No pretrain model (mid-left). Fine-tuned model (mid-right). Hand-labeled ground truth (right).}
  \label{fig:segmentation5}
\end{figure*}

\begin{figure*}[!ht]
  \begin{subfigure}{.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_noseg_4.png}
  \end{subfigure}
  \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_seg_4.png}
  \end{subfigure}
   \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_pre_4.png}
  \end{subfigure}
    \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_gt_4.png}
  \end{subfigure}
  \caption{Side view of segmentations on regions near the left side of the skull. Unaltered image (left). No pretrain model (mid-left). Fine-tuned model (mid-right). Hand-labeled ground truth (right).}
  \label{fig:segmentation6}
\end{figure*}

\begin{figure*}[!ht]
  \begin{subfigure}{.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_noseg_5.png}
  \end{subfigure}
  \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_seg_5.png}
  \end{subfigure}
   \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_pre_5.png}
  \end{subfigure}
    \hfill
  \begin{subfigure}{0.5\columnwidth}
    \includegraphics[width=\linewidth]{img/seg/Seg_gt_5.png}
  \end{subfigure}
  \caption{Side view of segmentations. Unaltered image (left). No pretrain model (mid-left). Fine-tuned model (mid-right). Hand-labeled ground truth (right).}
  \label{fig:segmentation7}
\end{figure*}

For vessels in the center of the head, all model types appear to accurately segment vessels. In certain cases, the models appear to learn to correctly avoid segmenting pieces of bone that could, in terms of shape and contrast, easily be confused with large vessels. An example of this can be seen in the frontal section of Figure~\ref{fig:segmentation4}.

The models appear to have a hard time segmenting vessels close to the skull surface. The model trained exclusively on patient data appears to struggle far more for these types on conditions than the fine-tuned models. Figure~\ref{fig:segmentation6} shows an example of the fine-tuneds model having close to no trouble segmenting vessels near the left side of the skull, while the baseline model suffers heavily from false negatives. To lesser degree, this effect can also be observed in the frontal lobe of Figure~\ref{fig:segmentation7} In the other hand, Figure~\ref{fig:segmentation5} presents an example of both models failing to segment vessels near the top of the skull.

In the other hand, it should be noted that fine-tuned models suffered from false positives more often than models with no pre-training. The fine-tuned models appeared to occasionally segment regions near the skull, which although similar in intensity to vessels, had no resemblance in terms of shape. Examples of this are seen in Figure~\ref{fig:segmentation2} near the occipital bone and near the right temporal bone.
\clearpage
Examples of oversegmentation were observed to happen commonly around the internal carotid arteries. This is likely due to the amount of contact surface between the artery and the surrounding bone. There was also a tendency for all model conditions to segment bone regions that were similar in shape to large vessels (Figures~\ref{fig:segmentation1} and~\ref{fig:segmentation2}). Bone structures in such regions have similar pixel intensities to the arteries transporting contrast material, which could explain the source of confusion for a model.

To our surprise, the models were able to occasionlly segment the shape of the internal carotid arteries correctly despite no boundary being visible to the naked eye between the vessel wall and the surrounding bone structure. An example of this can be seen in the fine-tuned model in Figure~\ref{fig:segmentation1}.

\subsection*{Discussion}

Even though the results show pretraining having relatively small improvements over the non-pretrained model, qualitative analysis showed notable differences between conditions in terms of false positives and false negatives. Is it thus hard to tell how much of the error is caused by each error type. Valuing one training condition over the other should be weighted by the intended use of the model.

As mentioned in the previous section, there are cases where the models fail to segment vessels close to the skull wall. However, we observed plenty of examples in the training set where such types of vessels were either not fully labeled, or not labeled altogether in the ground truth. Better labelling could help in reducing these sources of error.

Furthermore, segmentation of vessel structures such as the internal carotid arteries is a non-trivial task, even for a human. A lot of the vessel volume can be seen as easily segmentable through simple contrast features. However, correct segmentation of vessels around the base of the brain are likely to require complex hierarchical features that use surrounding structures. Data sets with larger varieties of patients could help supervised models develop a better understanding of such regions.

The models trained on scans where the CT noise was replaced with new Perlin noise provided no visible improvement over the other pretaining counterpart. One plausible argument is that the effect of overfitting to such reocurring noise structures in the repeated scans might simply be insignificant. Alternatively, it is possible that the rotations and translations performed during data augmentation (together with the interpolation process that comes with these) disrupt the reocurring noise patterns enough for them to not be an issue in the end.

Finally, when evaluating the generalizability of the presented results, it should also be noted that the CT scanner type and scanning protocol can vary across data sets. Although a variation in performance is expected under different technical factors of other CT scanners and image calibrations, the extent of the variation has not been explored in this study.

\section{Conclusions}
High quality training data for vessel segmentation is scarce. There are two main reasons to this. The process of segmenting good ground truth training examples by hand is an expensive process in terms of the time required. Additionally, privacy laws make it hard for large patient data sets to become available for these kinds of research purposes.

In this paper we explored the use of synthetic vessel structures as a way to artificially increase the amount of training data. A computational model was created that generates volumetric vascular structures, which can then be blended into CT scans.

Pre-training on such synthetic vascular structures and then fine-tuning on real data proved to improve the final Dice score by 0.019. The replacement of the original CT noise in the pre-training set had no impact on the performance.

Qualitative analysis of the segmentation performed by fine-tuned models had notable differences from models trained exclusively on real data. While the regular (not pretrained) models had issues segmenting small vessels near the skull, the fine-tuned models proved capable of accurate segmentation of such regions. In the other hand, the fine-tuned models commonly displayed more false positives than the counterpart, specially on structures near the base of the brain.

\subsection*{Future Work}
As evident from this study, a downfall of computational vascular models is the difficulty to generate synthetic large-scale structures that conform, both visually and structurally, to those observed in CTA data. For purposes where realistic appearance is preferred over the explainability of the computational model, generative approaches similar to \cite{retina_GAN, retina_GAN_segmentation} could prove helpful under the condition that enough data is available.

An investigation on the ability of generative approaches to create convincing detail could be of interest, specially at the level of the image noise distribution. Furthermore, the approach discussed in this paper of applying synthetic Perlin noise to an image does not have the capability to generate the artifacts in the noise pattern observed in real CT scans. Examples of such imperfections include beam hardening and scatter artifacts, as well as reconstruction artifacts. These could potentially be learned in the generative distributions.

The effect of large class imbalances common to volumetric imaging was also not investigated during this research. Addressing this issue could help improve segmentation in errors such as the oversegmentation of the skull around the carotid arteries or the false negatives on vessels near bone regions.

\subsection*{Acknowledgements}
Special thanks to Renan Sales Barros, Aashish Venkatesh, Efstratios Gavves, as well as the rest of the Nico.lab team, without whom this project would not have been possible.

\bibliographystyle{plain}

redpajama_set_name: RedPajamaArXiv


text:
Q: BlackBerry Packager for Android Apps I'm trying to get my Android app ready for the BlackBerry by using this online packager: https://bdsc.webapps.blackberry.com/android/bpaa/
So the first time I used it, it worked, but ever since I get unspecific errors during the signing process. ("Some error occured...")
Is it me or is it the packager? What is the easiest alternative to pack an Android app for the BlackBerry?

A:
What is the easiest alternative to pack an Android app for the BlackBerry?

I think you should try one of the other tools, because the online tool is not working for you and also because there are better ways.
I guess they created the online version to entice devs with a super simple option, but I don't think it is the best way to do it.  As well as the unhelpful error message that you got is the fact that it doesn't address testing...
As well as the online packager, they offer an Eclipse plug-in and a set of command-line tools:
https://bdsc.webapps.blackberry.com/android/tool
I used the Eclipse plug-in and I found it to be very simple and unobtrusive.  The support forum for the plug-in is here:
http://supportforums.blackberry.com/t5/BlackBerry-Plug-in-for-Android/bd-p/adt
The only thing that took me time was setting up the 'BAR signer', and I don't think there is any way around this.  To do that you need to follow the steps listed here:
https://bdsc.webapps.blackberry.com/android/documentation/bar_signing_1904696_11.html
Hope this helps.

A: If you have already packaged it and signed it, i believe it will throw an error if you try again, unless you change the version number to a higher value.

A: I don't know if you're still looking for an answer, but I ran into similar problems and managed to get it working by using an older version of the SDK. The repackager tool doesn't work with the new v22 platform tools, so I had to downgrade my SDK to the older version before trying to run it through the online repackager.
You can manually download older versions of the SDK here:
http://www.hariadi.org/android/manually-download-of-android-sdk-tools-and-sdk-platform-tools/
Hope that helps.

redpajama_set_name: RedPajamaStackExchange


Sears filed for Chapter 11 bankruptcy protection from creditors Monday, The Wall Street Journal reports. And CEO Eddie Lampert has stepped down. It wasn't clear last week which type of bankruptcy the department store giant would file, but it didn't choose Chapter 7 liquidation. Instead, Sears reached a deal with lenders to keep many stores open while it restructures.
Defense contractors L3 and Harris announced a merger over the weekend, the Washington Business Journal reports. If the deal passes muster, the new company would create the sixth-largest aerospace and defense prime contractor, the Journal reports. It would combine to form a military communications and defense electronics company with about $16 billion in revenue and a $34 billion market cap.
The world's oil demand will grow at a slower pace this year and next, the International Energy Agency said in a forecast. The new outlook cites challenges like increased trade tensions and higher oil prices.
And finally, an economic update: The U.S. dollar dipped Monday after retail sales data for September missed economists' expectations, Reuters reported.


text:
Beyond School Walls: Combining Mentorship with On-the-Job Learning
Roger Turner, Fleet Operations, Cox Enterprises
© 2019 Beyond School Walls
Last year, Cox and Big Brothers Big Sisters of Metro Atlanta (BBBSMA) joined forces to launch a new approach to mentoring with a program called Beyond School Walls. Though other BBBS affiliates had tried the program, this was the first of its kind for Atlanta. This program combines traditional mentorship with an immersive educational experience. The one simple, but very important element that makes this program different than traditional mentoring programs is location.
Read more about Beyond School Walls: Combining Mentorship with On-the-Job Learning
Digital Empowers: Elevating Disability Inclusion at Work
Alexa Miller
© Photo by Ian Wagreich / © U.S. Chamber of Commerce
The Digital Empowers campaign, a partnership between the U.S.
Read more about Digital Empowers: Elevating Disability Inclusion at Work
Wellbeing in the Workplace: Shifting Culture in Corporate America
Elyse Cohen
When it comes to improving mental health and wellness, employers are uniquely suited to help. Adults spend most of their time working than performing any other activity.
Read more about Wellbeing in the Workplace: Shifting Culture in Corporate America
Engineering Excellence: CofC and STEM Businesses Look to Bridge Regional Skills Gap
Chief Etheridge
By 2020, one of the city's higher education institutions, in partnership with some of the city's major STEM businesses, will look to make a bridge of their own to address a skills gap that is prominent across the country. In the fall of 2020, the College of Charleston (CofC) will officially introduce its Bachelor of Science in Systems Engineering degree program after being approved by the state's Commission on Higher Education this summer.
Read more about Engineering Excellence: CofC and STEM Businesses Look to Bridge Regional Skills Gap
A Civics Lesson from Thomas Jefferson
© Photo by Ian Wagreich / © U.S. Chamber of Commerce Foundation
On November 7, 2019, Monticello's Thomas Jefferson joined the Chamber Foundation for a forum to discuss the state of civic knowledge, civic engagement, and civil discourse in America. This is an excerpt from his remarks to the audience.
Read more about A Civics Lesson from Thomas Jefferson
"A country that lends itself to bigness": The Texas-Size Importance of Higher Ed's Role in Rural Workforce Development
© Lubbock Economic Development Alliance
Lubbock is home to Texas Tech University (TTU), which has eight satellite campuses located within the "bigness" of the West Texas region: Abilene, Amarillo, El Paso, Fredericksburg, Highland Lakes, Junction, Midland, and Odessa. While you may not be familiar with these towns, you are probably familiar with the problems that face their workforces.
Read more about "A country that lends itself to bigness": The Texas-Size Importance of Higher Ed's Role in Rural Workforce Development
Civic Literacy, Good Governance, and Prosperity
John Raidt
© NASA Earth Observatory images by Joshua Stevens, using Suomi NPP VIIRS data from Miguel Román, NASA Goddard Space Flight Center
A strong business community matters to the nation because the private sector produces the goods, services, and jobs that society needs to survive and thrive, individually and collectively. Moreover, the private sector-led economic activity provides the revenue to fund the public sector in its vital role in the country.
Read more about Civic Literacy, Good Governance, and Prosperity
The Business Case for Civics
George Washington once described "the education of our youth in the science of government" as one of the most important priorities for the young republic.
Read more about The Business Case for Civics
Transforming Global Health: Cross-Sector Partnership to Advance the SDGs
Lawrence Bowdish, PhD
Yagmur Cosar
The UN Sustainable Development Goals (SDGs) are a global effort to address some of the most pressing challenges facing the world.
Read more about Transforming Global Health: Cross-Sector Partnership to Advance the SDGs
From John Legend to Apprenticeship's John Lennon: A Wrap-up of Talent Forward 2019
Jaimie Francis
Each year, as the Chamber Foundation hosts the Talent Forward national workforce conference, we focus on talent—educating and training, talent tech and data, recruiting, hiring, and upskilling. With no shortage of talent challenges, the opportunities for informative discussion are endless. While a day devoted to dissecting talent-related topics could focus solely on the problems, Talent Forward is all about the solutions.
Read more about From John Legend to Apprenticeship's John Lennon: A Wrap-up of Talent Forward 2019

redpajama_set_name: RedPajamaCommonCrawl


Tucked cozily away in Vancouver's Downtown Eastside neighbourhood sits a building that is hard to miss. With ornate aboriginal designs and a majestic totem pole on its roof, this cultural oasis is different from anything else in the city — or even the country, for that matter.
The Skwachays Aboriginal Hotel & Gallery opened its doors in August, showcasing aboriginal culture to the city — and the rest of the world.
"There are so many different layers for how this positively affects the aboriginal community, but I think for the greater community at large, they get to have a great experience and exposure to aboriginal art," Maggie Edwards, Skwachays' general manager, told The Huffington Post B.C. in an interview.
"It's such a beautiful place to stay in the hotel; we are getting tremendous exposure to the greater, wider community of Vancouver and travellers in general. I think it's just a win-win on so many levels."
Skwachays is more than a good-looking boutique hotel; it has two additional functions. First, 24 units are saved for low-cost aboriginal artist housing; the lower level is being renovated into a workshop. Artists live at Skwachays on a three-year contract, during which they produce pieces that are sold in the hotel's lobby gallery. Skwachays' goal is to help the artists become financially independent by the end of their contract.
Second, the remaining 18 rooms are designated as the hotel, each uniquely designed by one of six aboriginal artists. These rooms, as well as the gallery sales, fund the artist housing, resulting in a self-sustaining social enterprise.
Aside from the sheer beauty of its design, hotel guests can immerse themselves in aboriginal culture. All food is produced by a local aboriginal catering company.
Responses to "Fascinating First Nation Art Hotel Is Unlike Anything Else In Canada"


text:
There are worries about a resurgence of the practice of female genital mutilation in the chaos that has followed the overthrow of Burkina Faso's President Compaore in 2014. Photo: А traditional surgeon holding razor blades used to carry out female genital mutilation
Source: Voice of America, October 15, 2015
Mission of the UN Children's Fund (UNICEF): "UNICEF is mandated by the UN General Assembly to advocate for the protection of children's rights, to help meet their basic needs and to expand their opportunities to reach their full potential. UNICEF is guided by the Convention on the Rights of the Child and strives to establish children's rights as enduring ethical principles and international standards of behaviour towards children. UNICEF mobilizes political will and material resources to help countries, particularly developing countries, ensure a "first call for children" and to build their capacity to form appropriate policies and deliver services for children and their families. UNICEF is committed to ensuring special protection for the most disadvantaged children - victims of war, disasters, extreme poverty, all forms of violence and exploitation and those with disabilities. UNICEF responds in emergencies to protect the rights of children. In everything it does, the most disadvantaged children and the countries in greatest need have priority." (UN Children's Fund website, "UNICEF's Mission Statement")
Burkina Faso's Record on Children:
"The most significant human rights problems included ... violence and discrimination against ... children, including female genital mutilation/cutting (FGM/C)... child marriage was a problem. Almost 50 percent of girls were married before the age of 18. According to a 2010 government survey, 23.5 percent of girls between the ages of 15 and 19 were married... FGM/C ... was practiced widely, particularly in rural areas, and usually performed at an early age... 76 percent of girls and women between the ages of 15 and 49 and 13 percent of girls under the age of 15 reported being subjected to FGM/C..." (US State Department's Country Report on Human Rights Practices 2014, Burkina Faso)


text:
Kerry James Marshall artist biography | original signed artworks & limited edition prints for sale
Inspired heavily by the events of the Civil Rights movement in addition to the Black Power movement events,Kerry James Marshall is an American painter and sculpture artist who seeks to relay his life experiences growing up through the use of dark abstraction like paintings. While his art style can best be described as a modern realist artist, Kerry James Marshall is often related to other artists' styles via the likes of Amy Hill, Mark Jacobson, and several others. Throughout the course of Kerry James Marshall's artistic career, he has shared his artwork with the public through canvas paintings and elaborate sculpture works. He has been published many times and many of his works are on exhibit in many of the greatest known galleries. While many of his works were done during the 1980s and 90s, Marshall's best-known works include a superhero comic book completely based on African mythology, Voyager (1992 painting with a focus on race), and many other works based on American events. Less
Inspired heavily by the events of the Civil Rights movement in addition to the Black Power movement events,Kerry James Marshall is an American painter and sculpture artist who seeks to relay his life experiences growing up through the use of dark abstraction like paintings. While his art style can best be described as a modern realist artist, Kerry James Marshall is often related to other artists' styles via the likes of Amy Hill, Mark Jacobson, and several others. Throughout the course o More
United States, 1955 | male artist | Figurative art | Lettrism | Share on Facebook and Twitter
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What is Lettrism?
Lettrism is an art form which uses letters, words and symbols to create an artwork. The art form was popularized in the 1950s in America, but was established in 1940s Paris. Lettrisme is the French spelling of the movement, and the French word for letter. Less
Lettrism is an art form which uses letters, words and symbols to create an artwork. The art form was popularized in the 1950s in America, but was established in 1940s Paris. Lettrisme is the French spelling of the movement, and the French More
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text:
Now displaying: April, 2020
A Plan for Ending New York's Shutdown 0
Arpit Gupta joins Brian Anderson to discuss how New York City can safely restart its economy and allow people to resume normal activities—the subject of his new Manhattan Institute issue brief (coauthored with Dr. Jonathan Ellen), "A Strategy for Reopening New York City's Economy."
As the U.S. city most affected by the coronavirus, New York faces unique challenges in its road to recovery. The key question remains: how can the city's economy reopen safely? The issue brief provides a strategic blueprint for doing that, with two key components: effective measures to reduce the risks of new infection and a phased approach that protects vulnerable populations.
Cities and Pandemics: A Long History 0
Edward L. Glaeser joins Brian Anderson to discuss the implications of the Covid-19 pandemic on city life in America, the connection between urban density and contagious disease, how to prepare for the threat of future outbreaks, and the economic-policy response of leaders in Washington.
As New York enters its second month under effective lockdown, Glaeser reminds us that "density and connection to the outside world—the defining characteristics of great cities—can also turn deadly." Contagious disease has always been the enemy of urban life; overcoming it in the past has required massive investments in sanitary infrastructure. The current pandemic could prove a long-run disaster for urban residents and workers unless public fear is alleviated.
Good Samaritans vs. Covid-19 0
Rev. Franklin Graham, president and CEO of Samaritan's Purse and son of the late evangelical leader Billy Graham, joins Howard Husock to discuss his organization's response to the coronavirus pandemic, the volunteers behind these efforts, and how secular Americans can better understand faith-inspired philanthropic work.
In New York City's Central Park, Graham's disaster-relief organization set up a field hospital to treat patients overflowing from nearby Mount Sinai Hospital. Since the facility opened, its medical teams have treated more than 100 patients. Graham notes that he's following in his grandfather's footsteps, providing medical help not only in New York but also in China, where Samaritan's Purse has donated supplies and personal protective equipment. "American civil society," writes Husock, "diverse and self-organized, still responds to need."
Prospects for a Coronavirus Vaccine 0
Virologist and investor Peter Kolchinsky joins Brian Anderson to discuss a coronavirus vaccine, the critical genetic differences between Covid-19 and the flu, and his proposals to reform the pharmaceutical industry.
As millions of Americans approach a month of living under stay-at-home orders, scientific teams across the globe are racing to find a vaccine for the coronavirus. According to Kolchinsky, several vaccines are already in development, and concerns that the virus will mutate and evade them are overblown. But until a treatment is made widely available, he warns, we will have to maintain a level of social distancing to prevent the health-care system from being overwhelmed. Kolchinsky is the author of The Great American Drug Deal: A New Prescription for Innovative and Affordable Medicines.
Covid-19 in New York 0
Seth Barron and Nicole Gelinas discuss the latest developments in New York City's fight against the coronavirus, the impact of the city's lockdown on future growth, and the response of state and local leaders.
As New York continues under lockdown, the effects of the coronavirus outbreak are becoming evident: the city's death toll has passed 1,000, with more than 40,000 confirmed cases. In addition to health-care professionals, essential public employees like the city's transit workers and NYPD officers are falling ill at a troubling rate. Mayor Bill de Blasio and Governor Andrew Cuomo have responded to the crisis with varying degrees of effectiveness, but the outbreak has revealed a lack of preparedness for a public-health emergency of this scale.
To follow City Journal's ongoing coverage of the coronavirus pandemic and its impact on New York, the United States, and the world, click here.


George Lucas's 1971 feature debut THX 1138 has never felt more prescient in its depiction of a future dystopia in which the populace are kept as docile workers by a ruthless authority manifested as faceless androids who beat the weak and vulnerable miscreants while proclaiming, "We only want to help you". Pretty much spot on for much of the 99% populating this miserable western world in the aftermath of our own (financial) apocalypse.
Asian Dub Foundation have previous when it comes to providing live soundtracks for particularly provocative pieces of cinema. In recent years, they've also performed to Mathieu Kassovitz's La Haine and Gillo Pontecorvo's The Battle of Algiers. However, these two films are notable for the prominent radical political message they promote. THX 1138 can be read as a piece heavily influenced by Orwell's 1984 nightmare vision come true, but it can also be seen as a forerunner to the escapist fantasy fictions of Lucas's later work which will forever define him as a film-maker.
Just seeing THX 1138 on a big screen is a treat, the film looks wonderful with pin-sharp cinematography and future design motifs which seem to have perfectly predicted the sleekly functional and violently inhuman architectural spaces of the 21st century metropolis. ADF's soundtrack rattles and pulses ominously throughout the film's first half, unfortunately drowning out much of the movie dialogue spoken by Robert Duvall and Donald Pleasance's protagonists. While it's a film easily followed through visuals and accompanying music, it does feel like the verbal nuances have been overlooked. At times it feels like ADF are playing a concept concert of mutant punk techno to backing images of cybernetic police states and social suppression.
Ultimately, it's the film which wins as Lucas's images are so searingly dazzling. But as the final half hour of THX 1138 detonates into a furiously fast-cut extended action sequence which has more than stood the test of 44 years, ADF respond by blazing away in kind. It's the perfect union of film and live performance demonstrating that for all the political determination present in ADF's presentation of this piece, what really gets people going is a full-pelt car chase with relentlessly charged musical accompaniment.


We are delighted to announce the 6th annual Big T Triathlon, taking place on the 6th May 2019 in Trowbridge. The venue once again is the Sports Centre, Frome Rd, Trowbridge BA14 0DN.
You can choose between a sprint distance (400m / 25km / 5km) and our Enduro, olympic distance (600m / 48km / 10km) and Adult Aquathon (400m /5km). There are also Little t Junior races.
Transition is open from 06:30 on the Monday. The first wave for the Enduro event starts at 07:15 and the sprint event at approx. 08:50.
Click the button to download the race manual (3MB download). You will need a PDF viewer to read the manual.
Forms are available. Please don't delete any information, just add what is necessary. Thanks!
Get the 2018 Race Manual: it won't change much for the 2019 event.
You can enter at RyderHQ events by clicking the button below and choosing your distance.


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10 Words for the Angry and Upset
Easily create your own lists to study with!
Is It 'Feel Bad' or 'Feel Badly'?
We feel good about answering this question
Feel bad is the grammatically correct version when describing that you don't feel well physically or emotionally. "Feel," like all other sense verbs, can double as an action verb or a "linking verb," where it connects the subject with a clause describing the subject. Something can "look delicious" but not "look deliciously," and we "feel sad" and not "feel sadly." Feel badly is likely heard often due to the adverbial use of "bad" in examples like "it hurts badly."
"I feel so badly about correcting their grammar."
OR WAIT: "I feel so bad about correcting their grammar."
Which one is it? Feel is a verb, so shouldn't what comes after it be—and look like—an adverb?
The answer is: no.
Like 'be' or 'look', 'feel' is a linking verb. That means that "I feel bad" is correct—just like "I feel sad" (rather than "I feel sadly") or "that looks delicious" (rather than "that looks deliciously").
Linking Verbs
Feel is a particular kind of verb called a linking verb. (Another term for linking verb is copula or copulative verb.) Linking verbs are not like regular action verbs. They function only to connect the subject of a sentence or clause with words that describe or identify that subject. And those words are either adjectives (or adjective phrases) or nouns (or noun phrases).
There are a number of linking verbs in English, among them be, become, seem, and all of the sensory verbs: smell, look, taste, sound, and feel. And while each of these can also be used as an action verb, it's easy to tell when they're being used as linking verbs, as in these cases involving the sensory linking verbs:
Those pies smell delicious. NOT: Those pies smell deliciously.
The pies look perfect. NOT: The pies look perfectly.
The crust doesn't taste too sweet. NOT: The crust doesn't taste too sweetly.
Their chewing sounds loud. NOT: Their chewing sounds loudly.
I feel bad that you didn't get any pie. NOT: I feel badly that you didn't get any pie.
Except let's stop at that last one again because, unlike the other "NOT" versions, we do, in fact, hear and read it—and often from people who know their way around an English sentence.
Why do People Say "Feel Badly?"
The common wisdom is that it's a case of "hypercorrection": we apply a rule of grammar in a situation that isn't quite the right one, and the result is a nonstandard linguistic form or construction. In this particular case, people who have learned to favor "it hurt badly" over "it hurt bad," and "need it badly" over "need it bad," use "feel badly" because they assume it is similarly superior to "feel bad."
If this is the case, though, why don't we also hear "I feel sadly" or "I feel angrily"? And why don't we also hear constructions like "smell awfully" and "look deliciously"?
As we said above, most linking verbs can also be used as regular old action verbs. Pies look delicious (where look is a linking verb), but we can also look at pies (where look is an action verb). Feel can also be an action verb, and when it's functioning as an action verb it's commonly followed by an adverb:
I feel strongly that the pies should be shared equally.
And that's where the goofy grammarian's joke about people who feel badly being unable to experience tactile sensations or being capable only of apathy comes from.
But some people make a considered distinction between feel bad and feel badly, choosing feel bad when feel is about physical health and feel badly when feel is about an emotional state. Others switch them with just as much intention. These uses are established enough that some dictionaries (including Merriam-Webster Unabridged) cover badly as an adjective; it is, after all, following a linking verb.
There was a time, mostly in the 19th century and mostly because bad was thought to only mean "somewhat evil," that people were advised to use feel badly for both physical health and emotional states. Modern advisers recommend feel bad in both contexts.
Interestingly, the same folks typically approve the use of feel well to describe one's health and feel good to sing along to James Brown. Feel good is also approved for both. Well and good are both adjectives in these cases. Which is, of course, all well and good.
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I want to make something.
I discovered a little Etsy shop called sarah&bendrix through a well-know HGTV designer, Emily Henderson. I fell in love with their art (like the one above) and have wanted to make something like it ever since I first saw it. I don't think I'm going to copy their hearts, but do something a little different. The plan is to head to Michael's this weekend (really wish there was a HobbyLobby closer to me though because I love that place) and see what I can find. I'm thinking something to do with an animal…and I'm sure you can probably guess which one if you just look at a certain Pinterest board of mine (hint, hint).
My First Before & After!


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Home » TV » FX Greenlights 'Fosse/Verdon' With Lin-Manuel Miranda, Sam Rockwell, Michelle Williams
FX Greenlights 'Fosse/Verdon' With Lin-Manuel Miranda, Sam Rockwell, Michelle Williams
Posted by Mary Anne Butler July 24, 2018 Comment
FX has just greenlit a new series called Fosse/Verdon based on Sam Wasson's Bob Fosse biography. This alone is exciting, considering how deep into the dance genre and culture of performance art they've been going lately with Pose (which got picked up for a second season).
The Hollywood Reporter tells us that Lin-Manuel Miranda is re-teaming with "Hamilton" and "In The Heights" director Thomas Kail for this 8-part series, and they're bringing some top notch friends with them. Kail will be directing, and the pair will be executive producing.
Editorial credit: Andrea Raffin / Shutterstock.com
Michelle Williams will play Gwen Verdon, the 4-time Tony winner and Fosse collaborator who was also his wife and muse. This is pretty solid casting, and I cannot wait to see how she brings the movement to the role. Oscar winning actor Sam Rockwell will play Fosse.
Editorial credit: Featureflash Photo Agency / Shutterstock.com
Steven Levenson is showrunning, and he said in a joint statement with Miranda and Kail:
"Bob Fosse ignited a revolution in American dance, theater and film. But, in contrast to the well-worn myth of the visionary artist working in solitude, Fosse's work would not have been possible without Gwen Verdon, the woman who helped to mold his style — and make him a star. We are honored to work with the incredible team at FX and Fox 21, and these two extraordinary actors, to tell the story of this remarkable couple, and the complicated, fascinating relationship between them."
Nicole Fosse (daughter of Fosse and Verdon) will serve as co-exec producer and will oversee the production for The Verdon Fosse Legacy. She offered her thoughts:
"My mother and father have one of the greatest love stories ever known. They were extremely complex people with an indestructible bond, loyalty and trust that endured both fantasy and reality. Finally, we have a creative team with the talent and wisdom to tell the story."
Reportedly, the series will start production in the fall, and is currently eyeing a 2019 release.
About Mary Anne Butler
Bleeding Cool News Editor Mary Anne Butler (Mab, for short) has been part of the fast-paced world of journalism since she was 15, getting her start in album reviews and live concert coverage for a nationally published (print) music magazine. She eventually transitioned to online media, writing for such sites as UGO/IGN, ComicsOnline, Geek Magazine, Ace of Geeks, Aggressive Comix (where she is still Editor-in-Chief), and most recently Bleeding Cool.
Over the past 10 years, she's built a presence at conventions across the globe as a cosplayer (occasionally), photographer (constantly), panelist and moderator (mostly), and reporter (always).
Interviews, reviews, observations, breaking news, and objective reporting are the name of the game for the founder of Harkonnen Knife Fight, a Dune-themed band with an international presence.
Though she be but little, she is fierce. #MabTheProfessional
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(Last Updated July 24, 2018 7:59 pm )
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Mark Simpson (journalist)
For the BBC journalist, see Mark Simpson (Ireland correspondent).
This article may rely excessively on sources too closely associated with the subject, potentially preventing the article from being verifiable and neutral. Please help improve it by replacing them with more appropriate citations to reliable, independent, third-party sources. (July 2019) (Learn how and when to remove this template message)
Mark Simpson
Writer, reporter, commentator
marksimpson.com
Mark Simpson is an English journalist, writer, and broadcaster specialising in popular culture, media, and masculinity. Simpson is the originator of the term and concept metrosexual. He has been described by one critic as "the skinhead Oscar Wilde".[1]
Simpson has written for numerous publications around the world, including The Times, The Guardian, Salon, Arena Homme +, GQ Style, Vogues Hommes International, The Independent on Sunday, Têtu, the Seattle Stranger, and Dutch Playboy. In December 2007, GQ Russia placed him in their 'Top Ten Things That Changed Men's Lives'.[2]
1 The term metrosexual
2.1 Male Impersonators
2.2 Anti-Gay
2.3 It's a Queer World
2.4 The Queen is Dead
2.5 Sex Terror
2.6 Saint Morrissey
2.7 Metrosexy
The term metrosexual[edit]
Mark Simpson is credited with coining the term metrosexual in a 1994 article for The Independent.[3][4] He also introduced the word to the US in 'Meet the Metrosexual', a much-quoted essay on Salon.com in 2002, leading to the global popularity of the term.[5] This was also the first citation of the UK footballer David Beckham as the ultimate example of the type. Simpson was later credited with introducing the term 'retrosexual' (in the sense of the anti-metrosexual) in 2003.[6]
Described as one of the "Ideas of the Year" by The New York Times in 2006, Simpson wrote on the issue of sport and advertising increasingly using homoerotic imagery, in a process he dubbed "sporno" ("the place where sport and porn meet and produce a gigantic money shot").[7][8] Simpson wrote about "sporno" for Out magazine commenting that: "whole new generation of young bucks, from twinky soccer players like Manchester United's Alan Smith and Cristiano Ronaldo to rougher prospects like Chelsea's Joe Cole and AC Milan's Kak', keen to emulate their success, are actively pursuing sex-object status in a postmetrosexual, increasingly pornolized world."[9] The London Times newspaper also featured sporno in their 'Year in Ideas' list.[10]
In 2010, the global trend spotting website Science of the Time described Simpson as "the world's most perceptive writer about masculinity". The Times of India included 'metrosexual' in their review of the most important words of the last thirty years, commenting: "Much has been written about metrosexuals, but no one has done it as well as the man credited with coining the term, Mark Simpson."
In 2014, Simpson proposed that the age of the metrosexual has passed and is evolving into a new kind of man called the spornosexual. Spornosexual is a neologism, also coined by Simpson, combining the words "sport," "porn," and "metrosexual."[11] It describes an aesthetic adopted by some men who consume both sports and pornography. Such men, in addition to possessing certain metrosexual attributes, carry their own bodies as accessories and are not shy at showcasing them in public. The spornosexual style emphasises heavy, lean musculature, and certain kinds of tattooing. Spornosexuals spend much money and time on physical fitness and upkeep.[12] Some commentators argue that the rise of spornosexuals is another indication of society becoming evermore narcissistic.[13][14]
Male Impersonators[edit]
Simpson's first book Male Impersonators (1994) provided the background for his theory of metrosexuality by examining the way men were represented in popular culture – movies, ads, mags, music, male stripping, and comedy – and showing how 'unmanly' passions such as homoeroticism, male narcissism, and male masochism were not excluded but rather exploited, albeit semi-secretly, in voyeuristic virility.
Returning to Freud's theory of universal bisexual responsiveness, Simpson also 'outed' what he saw as the homoerotic subtext of masculinity itself. In particular, Simpson analysed the way movies, ads, pop music, and bodybuilding, had replaced 'real' masculinity, if it ever existed, with something 'sexy and simulated'. In his chapter on Marky Mark and his (then) recent Calvin Klein ads, Simpson argued that the rapper's appearance on billboards in Times Square and on the side of buses 'in his prime and in his underwear', grabbing his 'package' to shift product, graphically demonstrated how the commodification of the male body – 'and gay men's love for it' – had become 'eyepoppingly' mainstream.
The book included a chapter arguing that the real romance in Top Gun was between Maverick (Tom Cruise) and Iceman (Val Kilmer). Quentin Tarantino made a cameo appearance in the film Sleep with Me later the same year as a party-guest making a similar argument.
Anti-Gay[edit]
Simpson's collection Anti-Gay (1996), described on the jacket as "the shameful antidote to feelgood politics"', according to The Independent, "divided the gay community". Led by Simpson, various 'non-heterosexual' contributors, such as Bruce LaBruce and Glenn Belverio, John Weir, Peter Tatchell, Lisa Power, and Anne-Marie Le Ble, voiced their criticism of the gay "one-size-fits-all" identity and the gay media's intolerance of anything that wasn't "glad" and "clap happy". Anti-Gay was one of the first "post-gay" books, appearing a year or so before a series of largely conservative American gay books critical of gay culture, such as The Rise and Fall of Gay Culture (D Harris, 1997) and Life Outside (M Signorile, 1997), Sexual Ecology (G Rotello, 1998).[15]
It's a Queer World[edit]
It's a Queer World published the same year, described on the dust jacket as "hilariously perverse" and "taking a warped look at fin-de-siecle pop culture where nothing is as straight – or gay – as it seems", collected Simpson's popular columns of the same name which appeared in Attitude magazine, and showed how gay and straight culture were converging, a decade before this became a common theme.[citation needed][16]
The Queen is Dead[edit]
The Queen is Dead (1998) collected his colourful and confessional correspondence with American writer Steven Zeeland. According to the jacket blurb: "A chance letter sparks off an hilariously doomed transatlantic literary romance involving Marines, glory holes, cats, intellectuals, transsexuals and a bizarre love-triangle rivalry with gay serial-killer and Gianni Versace's assassin Andrew Cunanan." Despite its openly offbeat subject matter, it was reviewed positively: "Something of a masterpiece" wrote Roger Clarke in The Independent.[17]
Sex Terror[edit]
Sex Terror: Misadventures in pop culture is a collection of essays, interviews and articles from Simpson's late 1990s journalism, including writings in Attitude magazine and The Guardian. Interview subjects include Henry Rollins, Julie Burchill and Dana International.
Saint Morrissey[edit]
Saint Morrissey was Simpson's "psycho-bio" of the former Smiths front man, written at a low point of the singer's career, and published the year before Morrissey's 2004 comeback. The book prompted some comparisons with the subject's style: "Simpson is funny, clever, honest, irreverent and egotistical: quite the match for Morrissey. More biographies should be written this way."[18] (Laurence Phelan, Independent on Sunday Books of the Year')[19]
Metrosexy[edit]
Simpson's ebook, Metrosexy (2011), collects his writings on the subject of metrosexuality, offering a kind of biography of the subject, which he describes as a "Frankenstein monster with flawless skin". The book follows the term from its origin in an article of Simpson's for The Independent newspaper in 1994, to its international adoption in the early 2000s, the backlash against it and its subsequent Jersey Shore resurgence, to the point where Simpson argues that metrosexuality had come to be seen as simply "normal".
Footnotes[edit]
^ Philip Hensher Archived 4 January 2007 at the Wayback Machine, The Independent: "Mark Simpson is a skinhead Oscar Wilde, his bon mots are both alarming and amusing, getting up people's noses and inside their trousers with equal aplomb."; quote used to promote Simpson's books "Sex Terror" and "The Queen is Dead"
^ Mark S (11 December 2007). "Simpson Tops Arnie And Freud in GQ Spread". Marksimpson.com. Retrieved 11 September 2011.
^ Simpson, Mark (1994). "Here come the mirror men". The Independent.
^ Here come the mirror men, MarkSimpson.com
^ Simpson, Mark (2002). "Meet the metrosexual". Salon.
^ Paul McFedries (6 October 2004). "retrosexual". Word Spy. Retrieved 27 June 2014.
^ Haskell, David (10 December 2006). "THE 6th ANNUAL YEAR IN IDEAS; Sporno". The New York Times. Retrieved 11 January 2007.
^ palu (18 December 2006). "Metrosexual man invents Sporno". In The Mix. Archived from the original on 10 January 2007. Retrieved 11 January 2007.
^ Simpson, Mark (19 June 2006). "Sporno". Out Magazine. Retrieved 14 June 2014.
^ "Sporno". The Times. London. 30 December 2006. p. 18.
^ "The metrosexual is dead. Long live the 'spornosexual'". The Daily Telegraph. Retrieved 27 June 2014.
^ "What is a 'spornosexual'? It's the new wave of metrosexuals, journalist writes". NY Daily News. 12 June 2014. Retrieved 27 June 2014.
^ Merz, Thoe (9 July 2014). "It's not easy being a spornosexual". UK Daily Telegraph.
^ "'Spornosexuality': an evolutionary step backwards for men". The Daily Telegraph. Retrieved 27 June 2014.
^ "Anti-Gay". Marksimpson.com. Archived from the original on 27 September 2011. Retrieved 11 September 2011.
^ "It's a Queer World". Marksimpson.com. Archived from the original on 27 September 2011. Retrieved 11 September 2011.
^ "The Queen is Dead". Marksimpson.com. Retrieved 11 September 2011.
^ "Saint Morrissey". Marksimpson.com. 24 August 2004. Retrieved 11 September 2011.
^ Granger, Ben (2004). "This Alarming Man". Spike Magazine.
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