text:
\section{Conclusion, limitation, and future work}
\label{sec:future}

We have introduced BranchGAN, a novel, progressive training procedure for unconditional GANs which enables multi-scale image manifold learning and manipulation. The key idea is to not only progressively increase network depth by adding layers, but also increase the network width by creating multiple, progressively activated training branches triggered by different sub-vectors of the network input. Each sub-vector corresponds to, and is trained to learn, image representations at a particular scale, leading to a scale-disentangled learning scheme.
Experimental results on several well-known high-quality image datasets verify the effectiveness of our method in disentangling image manifolds by scales. We also demonstrated new and improved applications by GANs via BranchGAN training.

BranchGAN is scale-aware, but not feature-aware. This is a major limitation to progressive training using images at a selected set of resolutions. One reason is that not all image features are well represented in this selected set of training images. Another reason is that similar or repeated features in an image may not always be in the same scale, \i{e.g.}, due to perspective projection. While such features are often manipulated as a collection during editing, they are difficult to learn using the current BranchGAN. In addition, the scale disentanglement afforded by our current approach is only a partial one, since adding a new training layer, which corresponds to a newly activated sub-vector, can still impact weights learned for the preceding sub-vectors. As a result, all learned weights may  be correlated with image features across multiple scales. Overall, while the controllability enabled by BranchGAN training for image manipulation has been improved, it is still inherently limited.

In future work, we would like to extend BranchGAN to feature- or semantic-aware progressive training, where sub-vector designation can be based on more meaningful or more visually apparent image features; this would add more meaning to sub-vector manipulation for image editing and synthesis. We believe that the progressive training paradigm introduced by BranchGAN is a generic approach and can be tuned for different forms of disentanglement by adjusting the training targets. In addition, we shall explore potential values of scale-disentangled image manifolds in tasks such as image compression, filtering, and denoising. Finally, it is a curious question whether branch suppression exists in other ``multi-branch'' neural networks, such as ResNet~\cite{he2016deep}, DenseNet~\cite{huang2017densely}, and capsule networks~\cite{hinton2011transforming}. We are interested in whether this phenomenon may offer insights to the training of other convolutional and/or generative networks.

\section{Results, evaluation, and applications}
\label{sect:exp}

We have tested and evaluated BranchGAN on three datasets: \textit{church\_outdoor} from LSUN~\cite{yu15lsun}, \textit{celeba\_{hq}} \cite{karras2017progressive}, and \textit{car}. The original \textit{car} dataset has 800 $\times$ 600 pixel resolution. To speed up the training, we used downsampled versions of \textit{celeba\_hq} (256 $\times$ 256) and \textit{car} (400 $\times$ 300). We trained our models on a GTX TITAN XP GPU, which took roughly 20 $\sim$ 40 hours per (full) dataset.

\subsection{Evaluation of scale disentanglement}

\begin{figure}[!t]
\begin{center}
\includegraphics[width=\linewidth]{figures/face256-1}
\caption{ {\em Effects on generated images for $celeba\_hq$ dataset by varying individual sub-vectors.\/} We first initialize ${\mathbf{z}}$ randomly, and then replace one of the sub-vectors ${\mathbf{z}^t}$, $t=0,\ldots,4$, by $p{\bf I}$, where $p=-0.8,-0.4,0,0.4,$ or $0.8$ and ${\bf I}$ is the all-one vector of length $|{\mathbf{z}^t}|$, while holding all the other sub-vectors
fixed. Columns $1$ to $5$ show images generated by BranchGAN and the last column shows a variance image for the five generated images on the left, where lightness reflects pixel variance. From top to bottom, changing ${\mathbf{z}^t}$ leads to smaller and smaller image variations, as reflected by intensity drop in the variance images. Sub-vector ${\mathbf{z}^0}$ dominates the overall color, ${\mathbf{z}^1}$ controls some facial features, while the rest bring minor changes near ear, mouth, and hair.}
\label{fig:face256-1}
\vspace{-15pt}
\end{center}
\end{figure}

\begin{figure}[!t]
\begin{center}
\includegraphics[width=\linewidth]{figures/car}
\caption{{\em Effects on generated images for $car$ dataset by varying individual sub-vectors.\/} The output setting is the same as in Figure~\ref{fig:face256-1}. The first row basically reflects the property of the $car$ dataset: it contains left-views and right-views, but no front-views. GAN was not able to learn a smooth interpolation between the two views, resulting in a messy image in the middle of the first row. However, once ${\mathbf{z}^0}$ is fixated with a view, the other sub-vectors can generate smooth interpolations. ${\mathbf{z}^1}$ appears to alter the view angles slightly, ${\mathbf{z}^2}$ impacts the front parts of the car, while changing the other sub-vectors influences more minor details.}
\label{fig:car}
\vspace{-5pt}
\end{center}
\end{figure}

\begin{figure}[!t]
\begin{center}
\includegraphics[width=\linewidth]{figures/lsun}
\caption{ {\em Effects on generated images for $church\_outdoor$ dataset by varying individual sub-vectors.\/} The output settings is the same as in Figure~\ref{fig:face256-1}. Similarly, as more clearly reflected in the variance images, the sub-vectors ${\mathbf{z}^t}$, with $t$ from 0 to 4, appear to control higher-level to finer image details.}
\label{fig:lsun}
\vspace{-15pt}
\end{center}
\end{figure}

\paragraph{Qualitative evaluation.}
We wish to show how each designated sub-vector affects images generated via BranchGAN training. To this end, we vary the values of each sub-vector while holding the other sub-vectors fixed, as shown in Figures~\ref{fig:face256-1}, \ref{fig:car}, and \ref{fig:lsun}. From visual examination and as detailed in the figure captions, we can observe that $\mathbf{z}^0$ affects the output images most significantly as it mainly controls large-scale structures, which contrasts the effects of $\mathbf{z}^4$ or $\mathbf{z}^5$ as they are tied more to smaller-scale details. This suggests that scale disentanglement by splitting the training to progressively activated sub-vectors has been achieved. That being said, it is also clear that we do not have direct control for feature localization or image semantics.

\vspace{-10pt}

\begin{figure}[!t]
\begin{center}
\includegraphics[width=\linewidth]{figures/fusion}
\caption{{\em Cross-scale image fusion.\/} The notations and synthesis setup are the same as in Figure~\ref{fig:teaser}. From the first three columns, we can see that the sub-vector $\mathbf{x}^1$ mainly controls the facial features while $\mathbf{x}^2$ controls the face shape. By swapping $\mathbf{x}^1$ and $\mathbf{x}^2$, a face swap may be achieved. $\mathbf{x}^3$ and $\mathbf{x}^4$ have less significant impacts, such as lighting, shading, and minor changes in hair and ear. More such examples can be found in the supplementary material.}
\label{fig:fusion}
\vspace{-25pt}
\end{center}
\end{figure}

\begin{figure*}
\centering
\includegraphics[width=\linewidth]{figures/statistic}
\caption{ {\em Distributions of VBS over specific frequency ranges or image scales.\/} The $\VBS$ of DCGAN~\cite{radford2015unsupervised}, PGGAN \cite{karras2017progressive} and InfoGAN \cite{chen2016infogan} predominantly falls into the interval $(0.5, 1.5)$, whereas the $\VBS$ of BranchGAN spans over a wider range of $(0.1, 2.5)$.}
\label{fig:VBS_histo}
\end{figure*}

\begin{figure}
\centering
\includegraphics[width=\linewidth]{figures/branchgan-stat}
\caption{{\em Plot of $\VBS$ values for sub-vectors $\mathbf{z}^0, \mathbf{z}^1, \ldots, \mathbf{z}^4$ against frequencies.\/} Peak VBS values indicate maximal impact. For example, sub-vector $\mathbf{z}^0$ exhibits higher impact over the lowest frequency range, which corresponds to larger image scales.}
\label{fig:VBS_peak}
\end{figure}

\paragraph{Quantitative evaluation.}
To examine how each dimension of the latent manifold space impacts appearance of the output images, we designed a metric to evaluate the variance of the output images when the latent vector is manipulated. The metric, which we refer to as {\em variance by scale\/} or {\em VBS}, denoted by $\mathcal V$, measures the variation of output images with respect to {\em any sub-vector\/}  $\mathbf{z'}$ of $\mathbf{z}$, at a specific scale, as reflected by a frequence interval $[f_1,f_2]$. That is, $\mathbf{z'}$ can correspond to a single dimension of $\mathbf{z}$ or to one of the designated sub-vectors ${\mathbf{z}^t}$, $t=0,\ldots,4$. Specifically,
\begin{equation}
\small
\label{eq:is1}
\begin{gathered}
V_{f_1}^{f_2}(\mathbf{z'}) = \sum_{h,w,d}  \mathop{\mathbb{E}}_{\mathbf{c} \sim U(-1,1)} \sigma_{\mathbf{z'} \sim  U(-1,1), \overline{\mathbf{z'}} \leftarrow \boldsymbol{c}}, \big(\DFT_{f_1}^{f_2}(G({\mathbf{z}}))\big), \\
\text{and    } {\mathcal V}_{f_1}^{f_2}(\mathbf{z'}) = V_{f_1}^{f_2}(\mathbf{z'}) / \mathop{\mathbb{E}}_{\mathbf{z'} \subseteq \mathbf{z}} V_{f_1}^{f_2}(\mathbf{z'}),
\end{gathered}
\end{equation}
where $\overline{\mathbf{z'}}$ is the set of dimensions of $\mathbf{z}$ excluding $\mathbf{z'}$. $\sigma_{\mathbf{z'} \sim  U(-1.0,1.0), \overline{\mathbf{z'}} \leftarrow \boldsymbol{c}} f(\mathbf{z})$ refers to the deviation of the value of $f(\mathbf{z})$ when $\mathbf{z'}$ follows the uniform distribution $U(-1.0,1.0)$ and $\overline{\mathbf{z'}}$ is fixed as a constant vector $\mathbf{c}$. $G(\mathbf{z})$ is the output image of the generator $G$ given $\mathbf{z}$. $h,w,d$ are the height, width, and depth of images (or layer activations). $\mathop{\mathbb{E}}(\boldsymbol{\cdot})$ is the expectation operator. In Eq.~(\ref{eq:is1}), $\DFT_{f_1}^{f_2}(\boldsymbol{\cdot})$ refers to the discrete Fourier transform of an image, and $(f_1,f_2)$ is a frequency range. In order to avoid the impact of image size, $\VBS$ is further normalized by division over their expected values. Intuitively, a larger value of $\VBS$ implies a greater impact of a manifold dimension (or a subset of manifold dimensions) on the output.

To examine the distributions of $\VBS$ at specific scales, we split the frequency domain into five ranges: $(0,1/16)$, $(1/16, 1/8)$, $(1/8, 1/4)$, $(1/4, 1/2)$, and $(1/2, 1)$, which roughly correspond to increasingly fine image scales. We then visualize the $\VBS$ distributions for various GANs using histogram plots, as shown in Figure~\ref{fig:VBS_histo}. To produce the histrograms, we randomly sampled 10 $\mathbf{z}$ vectors to generate 10 images using the trained model for the $celeba\_hq$ dataset. For each $\mathbf{z}$ vector, which is of dimension 150, and for each dimension, we vary it while keeping all the other dimensions fixed. This results in a set of varied images, from which we compute the VBS value for the selected dimension. Overall, we collect $10 \times 150 = 1,500$ VBS values to form the histograms, for each GAN option and for each frequency range.
As can be observed from Figure~\ref{fig:VBS_histo}, the $\VBS$ values of BranchGAN exhibit a much greater variance (\i{i.e.}, wider histogram) than those of traditional GANs, such as DCGAN~\cite{radford2015unsupervised}, PGGAN \cite{karras2017progressive}, and InfoGAN \cite{chen2016infogan}. Specifically, the $\VBS$ values of these traditional GANs at all scale levels mainly fall into the range of $[0.5, 1.5]$, implying that the corresponding image representations are more scale-entangled, in comparison to BranchGAN, whose $\VBS$ values vary over a larger interval $[0.1, 2.5]$.

Finally, to examine whether the image manifolds learned by BranchGAN are disentangled by the designated sub-vectors $\mathbf{z}^t, t = 0,\ldots,4$, we show how the $\VBS$ of each $\mathbf{z}^t$ varies against frequencies in Figure~\ref{fig:VBS_peak}. These VBS values were obtained in the same way as for the histogram plots in Figure~\ref{fig:VBS_histo}. As we can observe, $\mathcal V(\mathbf{z}^0)$ sees its peak value in the $(0, 1/16)$ range, implying that $\textbf{z}^0$ mainly controls larger-scale structures of the generated images. The remaining sub-vectors $\mathbf{z}^1$, $\mathbf{z}^2$...$\mathbf{z}^4$ show their peaks at frequency intervals which reflect their respective controls over increasingly finer image features or structures.

\subsection{New applications}

\paragraph{Coarse-to-fine image synthesis.}
We show that the scale disentanglement afforded by BranchGAN facilitates coarse-to-fine image synthesis. To this end, we developed a new interactive application; see Figure~\ref{fig:app1} (a). A user can select best-matching faces from randomly-generated ones displayed on the right panel. At the coarsest scale, the images are mapped from different $\mathbf{z}^0$ values with other sub-vectors set to zero. If the user is satisfied with a coarse-level image, then he/she can select it and move on to the next scale. Then, the value of $\mathbf{z}_0$ will be fixed and images mapped from different $\mathbf{z}^1$ values will be displayed for selection. As a result, the user can progressively improve the appearance of a synthesized face, as shown in Figure~\ref{fig:app1} (b).

\begin{figure}[!t]
\begin{center}
\includegraphics[width=\linewidth]{figures/app1-all}
\caption{{\em Coarse-to-fine image synthesis.\/} (a): GUI of the application. (b): Sequences of images selected in a coarse-to-fine manner. Bounding boxes with the same color highlight the changes made by the user at each step: yellow box $\rightarrow$ thinner face, red box $\rightarrow$ less dimple, and blue box $\rightarrow$ red lip.}
\label{fig:app1}
\end{center}
\end{figure}

\vspace{-10pt}

\paragraph{Cross-scale image fusion.}
Scale disentanglement facilitates another new application: {\em cross-scale image fusion}, where latent codes representing different scales are joined to create hybrid images. Figures~\ref{fig:teaser} and~\ref{fig:fusion} show some examples of such image fusion, which are synthesized by integrating coarse-scale features of one image with fine-scale features of another. Through swapping sub-vectors representing different scales, our approach can achieve coarse-level fusion, such as face swap, as well as fine-level fusion, such as expression and face shape transfer.

\subsection{Improving interactive image editing (iGAN)}

We show how BranchGAN can improve the performance of iGAN~\cite{zhu2016generative}, an interactive image editing tool. In the original paper, DCGAN~\cite{radford2015unsupervised} was adopted. We now compare different choices of GANs as replacement for DCGAN, including PGGAN~\cite{karras2017progressive},
InfoGAN~\cite{chen2016infogan}, and BranchGAN.

In the iGAN framework, a user makes interactive edits (\i{e.g.}, scribbles, warping) to an existing image. The edits may be unprofessional and lead to various artifacts. The tool can automatically adjust the output image to keep all user edits as natural as possible. We conduct our experiment using a modified iGAN workflow, as shown in Figure~\ref{fig:overview}.
Specifically, we abandoned ``edit transfer''~\cite{zhu2016generative} to improve performance for images with cluttered backgrounds, as is the case for the $celeba\_hq$ dataset. On the other hand, we enriched the available operations in the GUI, as shown in Figure \ref{fig:ui-igan}. We also removed the smoothness term from the original optimization objective to ensure more faithfulness to user inputs. Aside from these changes, the modified iGAN workflow is the same as the original iGAN implementation~\cite{zhu2016generative} and has the following objective consisting of a color term and an edge term:
\begin{equation}
\label{eq:combined}
\begin{gathered}
\mathbf{z}^*(\mathbf{C}, \mathbf{M},\mathbf{E}) = \arg\min_{\mathbf{z}} |\mathbf{C} - G(\mathbf{z})|\cdot \mathbf{M}/|\mathbf{M}|+ \\
\alpha |HOG(G(\mathbf{z})) - HOG(\mathbf{E})|,\\
\end{gathered}
\end{equation}
where $\mathbf{C}$ is the color map (edited image) and $\mathbf{M}$ is the mask. $\mathbf{M}(i,j)=1$ if pixel $(i,j)$ has color assigned and $\mathbf{M}(i,j)=0$ if $(i,j)$ has color erased. $|\mathbf{M}|$ denotes the sum of elements in $\mathbf{M}$. $\mathbf{E}$ is the edge map drawn with the edge tool and $\HOG(\cdot)$ is the differentiable $\HOG$ operator \cite{dalal2005histograms} which maps an image to a $\HOG$ descriptor. The parameter $\alpha$ is used to balance the two terms in the objective function. We set $\alpha=10$ throughout our experiment. Note that if the user does not provide an edge map, the edge term is disabled.

\begin{figure}[!t]
\begin{center}
\includegraphics[width=1.0\linewidth]{figures/framework-igan}
\caption{ {\em Overview of modified iGAN workflow.} As in \cite{zhu2016generative}, the encoder that projects an image onto a manifold needs to be trained in advance. Once the user makes an edit to the image, the edited image
is mapped to a latent code in the manifold space, which is assigned as the initial value ($\mathbf{z}_0$) of the latent vector. Then the latent vector $\textbf{z}$ is optimized to minimize the objective in Eq.~(\ref{eq:combined}).} \label{fig:overview}
\end{center}
\end{figure}

\begin{figure}[!t]
\begin{center}
\includegraphics[width=0.9\linewidth]{figures/ui}
\caption{ {\em GUI of the modified iGAN tool.} The main window includes an edit zone (left) and a display zone (right). The edit zone provides various tools and a canvas to help edit the color map and mask or produce the edge map.
The display zone shows the result generated by iGAN based on the edits. See video in the supplemental material for more details.} \label{fig:ui-igan}
\end{center}
\end{figure}

The modified iGAN framework can be integrated with the generator of any unconditional GAN. To examine which GAN manifold serves iGAN better, we used the minimum optimization value of the objective in Eq.~(\ref{eq:combined}) as the metric. The minimum optimization loss indicates to what extent the output image fits user inputs. Smaller objective value represents greater effectiveness of the GAN manifold in avoiding local-minima traps. We compared the minimum optimization loss of DCGAN~\cite{radford2015unsupervised}, PGGAN \cite{karras2017progressive}, InfoGAN \cite{chen2016infogan}, and our BranchGAN, given the same set of user inputs. To simplify the comparison, we used 60 images from the training dataset as the user inputs. Table \ref{table:error} shows that BranchGAN has the lowest minimum objective value than traditional GANs, implying that the scale-disentangled manifolds learned by BranchGAN can better fit user inputs.

\tabcolsep=0.1cm
\begin{table}[!t]
\begin{center}
\begin{tabular}{c||c|c|c|c}
Dataset   & \multicolumn{2}{c|}{$celeba\_hq$} &  car  & $church\_outdoor$ \\
\hline
Image size & $256^2$ & $512^2$ & $400 \times 300$  & $256^2$  \\
		\hline
		\hline
DCGAN  & 0.24      &   0.26 &  0.29 & 0.27\\
\hline
PGGAN  &     0.18   &  0.17  &  0.19 & 0.22\\
\hline
InfoGAN &  0.23      &   0.25 &  0.29 & 0.24    \\
\hline
Ours &  \textbf{0.15}        & \textbf{0.14}   &   \textbf{0.17}  & \textbf{0.18} \\
\end{tabular}
\caption{Average minimum optimization loss of iGAN using different GANs. DCGAN was used by the original iGAN.}
\label{table:error}
\end{center}
\end{table}

Figure~\ref{fig:edit-face} shows a few image editing examples using different GAN manifolds as the latent space of iGAN. The visual comparison between BranchGAN and other GANs indicates that BranchGAN generally performs better in fitting both coarse-level structures and fine-scale features.

\begin{figure}[!t]
\begin{center}
\includegraphics[width=\linewidth]{figures/edit-face}
\caption{{\em Comparison of iGAN results when using different GAN manifolds as the latent space.\/} Original images were edited by users in different ways: (a) no edit, (b) face lightened, (c) hair erased, (d) adding edge map. Notably, none of the results perfectly fit the edited images, as patterns do exist in images generated by the same GAN model. Other than this, we observe that in (a) and (b), the head poses rendered by BranchGAN and PGGAN fit the inputs better. In terms of smaller-scale image features, BranchGAN generally performs better than other GANs, which could be observed in regions highlighted in red bounding boxes.} \label{fig:edit-face}
\end{center}
\end{figure}

\section{Introduction}
\label{sec:intro}

Unconditioned
GANs~\cite{goodfellow2014generative} have been intensively studied as
a means for unsupervised learning and data synthesis. Compared to their conditional counterparts~\cite{mirza2014conditional,yan2016attribute2image,odena2016conditional,reed2016generative,zhang2017stackgan,isola2017image,zhu2017unpaired,yi2017dualgan},
unconditioned GANs place less burden on the training data but are less steerable at the same time. In an unconditional GAN, a well-trained generator could synthesize novel data by sampling a random noise vector from the learned manifold as input and altering values
``parameterizing'' the dimensions of the manifold.
However, this synthesis process is typically uncontrollable and counterintuitive, since we have little understanding how each manifold
dimension impacts the synthesized output.

\begin{figure}[!t]
\centering
\includegraphics[width=\linewidth]{figures/teaser}
\caption{{\em Cross-scale image fusion\/} by directly combining coarse-scale features in one image with finer-scale features from another. Please note that $\mathbf{x}^0 (\mathbf{x} \in \{\mathbf{a},\mathbf{b}\})$ encodes image-wide structures and $\mathbf{x}^t (t \in \{1,2,3,4\})$ encodes increasingly fine-scale features. Given a pair of images, we compose new images by cross-combining coarse-scale structures and fine-scale features of the two, accomplishing expression transfer (a) and face swap (b).}
\vspace{-15pt}
\label{fig:teaser}
\end{figure}

\begin{figure*}[!t]
\centering
\includegraphics[width=0.95\linewidth]{figures/framework}
\caption{{\em Training pipeline of BranchGAN.\/} We start with both the generator (G) and discriminator (D) having a low spatial resolution. During the first training period, we feed $\mathbf{z}^0$ with random vectors of uniform distribution and $\mathbf{z}^t$ ( $t > 0$) with zero vectors $\mathbf{0}$, and make those linear layers corresponding to $\mathbf{z}^t$ ( $t > 0$) untrainable. As the training advances, we incrementally add layers to G and D, thus increasing the spatial resolution of the generated images. Meanwhile, we ``de-freeze'' more $\mathbf{z}$ vectors for training by feeding them with non-zero uniform-random vectors. This process is repeated until the target resolution is reached. During training, ``branch suppression'' (see Section~\ref{sec:method}) happens as $\mathbf{z}^0$ has well encoded large-scale structures and will maintain its dominance in coarse-level encoding. When $\mathbf{z}^1$ is de-frozen, it is suppressed in terms of coarse-level encoding but has the chance to encode finer-scale features.}
\vspace{-10pt}
\label{fig:framework}
\end{figure*}

For manifold learning of images or other visual forms, the notion of {\em feature scales\/} is of great importance. An ability
to learn multi-scale or scale-invariant features often leads to a deeper and richer understanding of representations and distributions
of images. In the last few years, {\em scale-aware unconditioned GANs\/} have been developed, \i{e.g.}, StackGAN~\cite{zhang2017stackgan}, LPGAN~\cite{denton2015deep} and PGGAN~\cite{karras2017progressive}, where correlated GANs are trained in a coarse-to-fine manner, using lower- and then higher-resolution images, with the goal of improving the quality of the final full-resolution images. However, factors which impact image features at various scales remain entangled in PGGAN networks. In StackGAN and LPGAN, factors (dropout layer or noisy perturbation) were added at various scales to learn scale-independent image features, though they are neither explicit nor controllable.
Under the setting of conditional GANs, several recent works~\cite{donahue2018,yan2016attribute2image}, including DNA-GAN~\cite{xiao2018dnagan} and InfoGAN~\cite{chen2016infogan}, aim to disentangle the latent codes which correspond to different image attributes. Namely, the image structures are disentangled by attributes rather than scales.

In this paper, we introduce a novel training method that enables unconditioned GANs to learn image manifolds in a ``{\em scale-disentangled\/}'' manner,
aiming to improve the controllability of image synthesis and editing. The key novel feature of our learning paradigm is that each noise vector, as input to the
generator, is {\em explicitly\/} split into a prescribed number of sub-vectors, \i{e.g.}, 5 for learning $256^2$ images and 6 for $512^2$ images, where each
sub-vector corresponds to, and is trained to learn, image representations at a particular scale. A consequence of such a sub-vector designation
is that we can {\em directly\/} manipulate and even combine latent (sub-vector) codes modeling different feature scales, leading to novel applications of
unconditional adversarial learning that were not possible before. Figure~\ref{fig:teaser} shows an example of {\em cross-scale image fusion\/}, where we
intentionally synthesize an image by integrating the coarse-scale features of one image with finer-scale features of another.

At the high level, our learning method employs the standard GAN framework, which comes with an unconditioned generator and a discriminator, and follows the standard GAN training paradigm as described in~\cite{goodfellow2014generative,radford2015unsupervised}. To achieve scale-disentangled learning, our network is trained {\em progressively\/}, bearing some similarity to Karras et al.~\cite{karras2017progressive}. However, instead of progressing only on network depth (adding network layers as the resolutions of training images increase), our network training also progresses over network {\em width\/} by progressively activating sub-vectors that correspond to different feature scales; see Figure~\ref{fig:framework} and more details in Section~\ref{sec:method}. As a result, we explicitly designate dimensions of the image manifold to different image scales, leading to scale-specific ``{\em training branches\/}''. Hence, we refer to our network as {\em BranchGAN\/}.

By disentangling the image scales, BranchGAN enables multi-scale learning of image manifolds and more controllable image editing and synthesis, without requiring extra labels.
We tested our novel training method on several high-quality image datasets to verify its effectiveness in learning scale-disentangled image representations, compared with alternative
GAN training schemes. We show that BranchGAN enables new applications of GANs to coarse-to-fine image synthesis and scale-aware image fusion. We further demonstrate improved
performance of iGAN~\cite{zhu2016generative}, an interactive image generation method, when the generator is trained by BranchGAN in place of DCGAN.

\section{BranchGAN training}
\label{sec:method}

\begin{figure}[!t]
\centering\includegraphics[width=\linewidth]{figures/framework2}
\caption{{\em Training sub-procedure at one scale level.\/} After a new layer is added to the generator, we first only train the last layer of the generator while holding other layers untrainable (Stage I). After the last layer is well-trained, we then de-freeze all pre-trained layers (the branches in green) plus the newly-added branch for training (Stage II). To avoid ``sudden shock'' to the well-trained layers, we feed the newly de-frozen $\mathbf{z}$ sub-vector with a random vector following uniform distribution $U(-\alpha,\alpha)$, where $\alpha$ increases smoothly from $0.0$ to $1.0$ throughout Stage II.} \label{fig:framework2}
\end{figure}

As shown in Figure~\ref{fig:framework}, we start with training only the sub-vector corresponding to the coarsest level features, \i{i.e.}, using the lowest-resolution images, while keeping the other sub-vectors ``frozen''. Then we progressively ``de-freeze'' the sub-vectors, one at a time, as a set of higher-resolution images is employed for training and more network layers are added. When training for a finer scale, the network weights learned from the previous coarser scales are used for initialization. Note that after training, these weights are often changed to adapt to the new training data.

What had motivated our branched GAN idea and what made it work effectively is a phenomenon that we observed during our experiments with multi-branch data generators and coined as ``{\em branch suppression\/}''. Roughly speaking, we found that when multiple noise vectors, with their respective training branches are at play, GAN training typically results in one dominant branch while the other branches are either fully or partially {\em suppressed\/}. In other words, the already-trained weights (branches) will have priority in maintaining their role in encoding the image structures that are already encoded and suppress the other branches.

Branch suppression does happen to the proposed branched training method. When de-freezing one sub-vector during progressive training, branch suppression helps inhibit the ability of the newly defrozen branch in the network to encode coarser-scale structures, thus ``encouraging'' it to encode the finer-scale structures in the new set of higher-resolution
training images. Note that the inhibition or suppression is not absolute; the network weights in previously trained branches are still altered.

BranchGAN progressively adds the depth (or layers) and breadth (or $\mathbf{z}$ sub-vectors) to the generator, and then start training with images of higher-resolution. During the process, branch suppression helps encourage the newly-added sub-vector to encode the finer-scale structures, as shown in Figure~\ref{fig:framework}. At each scale, a two-staged sub-procedure is used to avoid sudden shock to already well-trained, smaller-resolution layers: see Figure~\ref{fig:framework2}. Note that all already-added layers of the discriminator are trainable throughout the sub-procedure. More details about hyper-parameters are available in the supplementary material.

The architecture of the generator is the same as shown in Figure~\ref{fig:framework}. For $256\times 256$ image generation, we use $5$ $\mathbf{z}$ sub-vectors. The number of $\mathbf{z}$ sub-vectors is subject to change according to the resolution of output images. We use generator and discriminator networks that are mirror images of each other and always grow in synchrony, and use the standard non-saturated loss as in DCGAN~\cite{radford2015unsupervised} for training.

\section{Related work}
\label{sec:related}

\subsection{Multi-scale image representation}

An inherent property of visual objects is that they only exist as meaningful entities over certain ranges of scales in an image. How to describe image structures at multiple scales remains an essential and challenging problem in image analysis, processing, compression, as well as image synthesis. Early methods for multi-scale image representing such as Discrete Fourier/Cosine Transforms (DFT/DCT)~\cite{ahmed1974discrete} and Discrete Wavelet Transformation (DWT)~\cite{shapiro1993embedded} are widely used in decomposing small-scale details and large-scale structures. In our paper, DFT is employed as a metric for scale disentanglement.

Another scale-independent representation of images is the layer activations of a well-trained Convolutional Neural Network (CNN)~\cite{lecun1998gradient,krizhevsky2012imagenet,simonyan2014very}. In a CNN, top activation layers roughly represent large-scale image structures such as objects and scenes, while bottom activations represent small-scale details such as edges, colors, or textures. Other than CNNs, stacked models such as Deep Belief Network (DBN)~\cite{hinton2006fast}, Stacked AutoEncoders (SAE)~\cite{vincent2010stacked,vincent2008extracting} or multi-scale sparse Coding~\cite{sallee2003learning} can also be utilized to retrieve multi-scale representations of images, though the effectiveness could be limited.

\subsection{Coarse-to-fine image synthesis}

Scale-aware image synthesis has been explored in StackGAN~\cite{zhang2017stackgan}, LPGAN~\cite{denton2015deep}, and PGGAN~\cite{karras2017progressive},
as we discussed in Section~\ref{sec:intro}. The goal of these methods is to synthesize higher-quality images, rather than to learn multi-scale image manifolds.
We extend the idea of progressive training from progressively adding layers to progressively growing both layers and branches.
In addition, instead of training multiple GANs~\cite{zhang2017stackgan,denton2015deep}, our method trains only one GAN.

\subsection{Controllability in image synthesis}

One of the most frequently adopted approaches to improving the controllability of image generation is conditional modeling. For example, conditional GANs or semi-conditional GANs can condition the image synthesis task on image attributes~\cite{yan2016attribute2image,donahue2018,xiao2018dnagan, chen2016infogan}, classes~\cite{odena2016conditional}, input texts~\cite{reed2016generative,zhang2017stackgan}, or images~\cite{isola2017image,zhu2017unpaired,yi2017dualgan}. These methods either require extra labels or paired training images, or need strict inherent relations between the priors and the outputs. Our method, as a type of unconditional GAN, conditions image generation on random noise of uniform distribution and does not require any extra labels or priors.

For unconditional GANs, the method known as iGAN~\cite{zhu2016generative} provides a way for users to synthesize or manipulate realistic images in a more controllable way. In iGAN, users could add certain constraints on the appearance of desired images (\i{e.g.}, draw edges, add color strokes or set an exemplar image) and the latent code is then optimized to satisfy these constraints. Nonetheless, a sophisticated optimization method is required, as gradient descent is particularly vulnerable to local minima. We demonstrate through experiments that the scale-disentangled latent spaces learned with our method can help iGAN avoid falling into local minima and hence boost its performance.

\section{Appendix}
\subsection{Networks architecture and hyperparameters}

Code for the models is available will be made available. The detailed information about the networks architecture of generators and discriminators are presented in Table \ref{table:arc256-g}, \ref{table:arc256-d}, \ref{table:arc400-300-g}, \ref{table:arc400-300-d}. The non-architecture hyper-parameters are listed in Table \ref{table:hyper}. Please note that lrelu is leaky relu layer.

\noindent
\tabcolsep=0.11cm
\begin{table}
\begin{center}
\begin{tabular}{c||c|c}
\hline
    &  activation size &   filter  size   \\
		\hline
				\hline
input &   [30], [30],  &    NA   \\
& [30], [30], [30] &\\
\hline
concat &       [150]   &   NA  \\
linear &    [32768]             &     [32768,150]\\
reshape &    [8,8,512]       &  NA \\
deconv+instanceNorm+lrelu   &    [16,16,256]      &   [5,5,512,256]  \\
deconv+instanceNorm+lrelu   &    [32,32,128]      &   [5,5,256,128]  \\
deconv+instanceNorm+lrelu  &    [64,64,64]      &   [5,5,128,64]  \\
deconv+instanceNorm+lrelu   &    [128,128,64]      &   [5,5,64,64]  \\
deconv+sigmoid (output)   &    [256,256,3]      &   [5,5,64,3]  \\
\hline
\end{tabular}
\caption{Network architecture of the generator for $256 \times 256$ image synthesis.} \label{table:arc256-g}
\end{center}
\end{table}

\tabcolsep=0.11cm
\begin{table}
\begin{center}
\begin{tabular}{c||c|c}
\hline
    &  activation size &   filter  size   \\
		\hline
		\hline
input &   [256,256,3]  &    NA   \\
deconv+instanceNorm+lrelu   &    [128,128,64]     &   [5,5,3,64]  \\
deconv+instanceNorm+lrelu   &    [64,64,64]      &   [5,5,64,64]  \\
deconv+instanceNorm+lrelu  &    [32,32,128]      &   [5,5,64,128]  \\
deconv+instanceNorm+lrelu   &    [16,16,256]      &   [5,5,128,256]  \\
deconv+instanceNorm+lrelu   &    [8,8,512]      &   [5,5,256,512]  \\
reshape   &    [32768]      &   NA  \\
linear &      [1]    &    [32768,1]  \\
\hline
\end{tabular}
\caption{Network architecture of the discriminator for $256 \times 256$ image synthesis.} \label{table:arc256-d}
\end{center}
\end{table}
\noindent

\tabcolsep=0.11cm
\begin{table}
\begin{center}
\begin{tabular}{c||c|c}
\hline
    &  activation size &   filter  size   \\
		\hline
		\hline
input &   [30], [30], &    NA   \\
& [30], [30], [30] &\\
\hline
concat &       [150]   &   NA  \\
linear &    [17920]             &     [17920,150]\\
reshape &    [5,7,512]       &  NA \\
deconv+instanceNorm+lrelu   &    [10,13,256]      &   [5,5,512,256]  \\
deconv+instanceNorm+lrelu   &    [19,25,128]      &   [5,5,256,128]  \\
deconv+instanceNorm+lrelu  &    [37,50,64]      &   [5,5,128,64]  \\
deconv+instanceNorm+lrelu   &    [75,100,64]      &   [5,5,64,64]  \\
deconv+instanceNorm+lrelu  &    [150,200,64]      &   [5,5,64,64]  \\
deconv+sigmoid (output)   &    [300,400,3]      &   [5,5,64,3]  \\
\hline
\end{tabular}
\caption{Network architecture of the generator for $400 \times 300$ image synthesis.} \label{table:arc400-300-g}
\end{center}
\end{table}

\noindent
\tabcolsep=0.11cm
\begin{table}[!t]
\begin{center}
\begin{tabular}{c||c|c}
\hline
    &  activation size &   filter  size   \\
		\hline
		\hline
input &   [300,400,3]  &    NA   \\
deconv+instanceNorm+lrelu   &    [150,200,64]     &   [5,5,3,64]  \\
deconv+instanceNorm+lrelu   &    [75,100,64]      &   [5,5,64,64]  \\
deconv+instanceNorm+lrelu  &    [38,50,64]      &   [5,5,64,64]  \\
deconv+instanceNorm+lrelu   &    [19,25,128]      &   [5,5,64,128]  \\
deconv+instanceNorm+lrelu   &    [10,13,256]      &   [5,5,128,256]  \\
deconv+instanceNorm+lrelu   &    [5,7,512]      &   [5,5,256,512]  \\
deconv+instanceNorm+lrelu   &    [17920]      &   NA \\
linear &      [1]    &    [17920,1]  \\
\hline
\end{tabular}
\caption{Network architecture of the discriminator for $400 \times 300$ image synthesis.} \label{table:arc400-300-d}
\end{center}
\end{table}

\tabcolsep=0.11cm
\begin{table}[!t]
\begin{center}
\begin{tabular}{c||c}
\hline
name &  value   \\
		\hline
		\hline
Optimizer&  AdamOptimizer       \\
learning rate &  0.0002   \\
beta1 &       0.5         \\
beta2 &    0.999       \\
		\hline
\#sub-vector   &  5 for 256 $\times$ 256 images      \\
 &   6 for 512 $\times$ 512 or 400 $\times$ 300 images        \\
		\hline
\#epoch/scale   &   20 for 256 $\times$ 256 images       \\
  &   12 for 512 $\times$ 512 or 400 $\times$ 300 images     \\
			\hline
\#batch/epoch  &   subject to dataset size and batch size    \\
  &   we use full dataset for each epoch \\
			\hline
batch size &   20 for 256 $\times$ 256 images    \\
&  12 for 512 $\times$ 512 or 400 $\times$ 300 images     \\
\hline
\end{tabular}
\caption{Non-architecture training hyperparameters.} \label{table:hyper}
\end{center}
\end{table}
\noindent

\subsection{Initialization of neural weights, ``freeze'' and ``defreeze''}

For the untrained linear or deconv/conv/linear layers, the filter weights are initialized with normally random numbers $N(\mu,\sigma)$ and biases are initialized with $0$. For instance normalization layer, we initialize the $scale$ with 1.0 and assign the $center$ with 0.0.

We ``freeze'' certain branches (or weights) by feeding the corresponding $\mathbf{z}$ vector with $\mathbf{0}$. Here we intend to explain the reason why feeding zero vector makes the corresponding weights untrainable.

 Therefore, the activations of linear layer when fed with $\mathbf{0}$ are given by $f(\mathbf{0}) \equiv  \mathbf{\theta}\mathbf{0}+\mathbf{0}  \equiv \mathbf{0}$, where $\mathbf{\theta}$ are the linear weights. The activations of conv/deconv layer are given by $f(\mathbf{0})  \equiv \mathbf{\theta}  \circledast  \mathbf{0} + \mathbf{0}  \equiv \mathbf{0}$ (or $\mathbf{\theta}  \circledast  \mathbf{0} \equiv \mathbf{0}$ after concatenation), where $\mathbf{\theta}$ are filter weights. So we have the gradients $\nabla f_\mathbf{\theta}(\mathbf{0}) \equiv \mathbf{0}$. For instance normalization layer and leaky relu layer, $g(\mathbf{0})  \equiv lrelu((\mathbf{0}-\mathbf{0})  \cdot  \mathbf{1.0} + \mathbf{0.0})  \equiv \mathbf{0}$. We have the gradients $g_\mathbf{\beta}(\mathbf{0}) \equiv \mathbf{0}$, where $\beta$ is the scale.

In this way, the branches (or weights) could be ``frozen'' when fed with $\mathbf{0}$.
To ``defreeze'' these branches (or weights), simply feed them with non-zero vectors.

\subsection{Branch suppression}
\label{subsec:supp}
We observed ``branch suppression'' in all kinds of multi-branch generators as shown in Figure~\ref{fig:ps}, among which some are fully suppressed, some are partially suppressed. In ``branch suppression'', the already-trained weights (branches) will have priority in maintaining their role in encoding the image structures that are already well encoded and suppress the other branches. To explain it in more details, we present a few examples of branch suppression in Figure~\ref{fig:ps}.
\noindent
\begin{figure*}[!t]
\begin{center}
\includegraphics[width=\linewidth]{figures/ps2}

\includegraphics[width=\linewidth]{figures/ps3}
\caption{Two examples of branch suppression in GANs. In these examples, we employ the training loss and discriminator of dcgan~\cite{radford2015unsupervised}. Here we change the architecture of the generator a bit by conditioning image generation on split $\mathbf{z}$ vectors ($\mathbf{z}^t, t \in \{1,2,3\}$).
In the upper row, the left branch is already well trained for image generation, and the middle and right branches are initialized randomly (see more details about the initialization in the supplementary material). Then we train the GAN by following the standard GAN training procedure as in~\cite{radford2015unsupervised}. After the training converges, the left branch dominates the output while the other are fully suppressed, as seen from the variance image on the right (see Fig. \ref{fig:face256-1} for the meaning of variance image). In the lower row, the generator architecture is the same as traditional GAN except that the $\mathbf{z}$ vector is split. We train the left branch till converging, then de-freeze the middle branch for training till converging, and finally the right branch. Note that the number of training steps for each stage are equal and the pre-trained weights (or branches) are not frozen even after new branches are de-frozen. As a result, the middle branch is slightly suppressed and the right branch is severely suppressed as seen from the rightmost variance images.} \label{fig:ps}
\end{center}
\end{figure*}

\subsection{More image editing results with BranchGAN and iGAN}
Figure \ref{fig:face512-branch} shows the outputs of BranchGAN on $celeba\_hq$ 512 $\times$ 512 dataset.

Figures \ref{fig:fusion4} shows more results of cross-scale image fusion.

Figures \ref{fig:face256}, \ref{fig:face512}, \ref{fig:igan-face256}, \ref{fig:car-igan} and \ref{fig:lsun-igan} show more results of iGAN using our multi-scale image manifold as the latent codes.

\begin{figure*}[!t]
\begin{center}
\includegraphics[width=0.8\linewidth]{figures/fusion_examples/2_0}
\includegraphics[width=0.8\linewidth]{figures/fusion_examples/2_1}
\includegraphics[width=0.8\linewidth]{figures/fusion_examples/2_2}
\includegraphics[width=0.8\linewidth]{figures/fusion_examples/2_3}
\includegraphics[width=0.8\linewidth]{figures/fusion_examples/2_4}
\includegraphics[width=0.8\linewidth]{figures/fusion_examples/2_5}
\caption{Results of {\em Cross-scale image fusion.\/} The notations and synthesis setup are the same as in Figure~\ref{fig:fusion}.}
\label{fig:fusion4}
\vspace{-25pt}
\end{center}
\end{figure*}

\noindent
\begin{figure*}[!t]
\begin{center}
\includegraphics[width=0.8\linewidth]{figures/face512-1}
\caption{{\em Effects on generated images for $celeba\_hq$ 512 $\times$ 512 dataset by varying individual sub-vectors.\/} The output setting is similar as in Figure~\ref{fig:face256-1} except that there is one more sub-vector $\mathbf{z}^5$ and each row is generated independently. From top to bottom, changing ${\mathbf{z}^t}$ ($t \in \{0, 1, 2, 3, 4, 5\}$) leads to smaller and smaller image variations, as reflected by intensity drop in the variance images. Similar to Figure~\ref{fig:face256-1}, sub-vector ${\mathbf{z}^0}$ dominates the overall color, ${\mathbf{z}^1}$ controls some facial features and hair features, while the rest bring minor changes near ear, mouth, and hair.} \label{fig:face512-branch}
\end{center}
\end{figure*}
\noindent

\noindent
\begin{figure*}[!t]
\begin{center}
\includegraphics[width=0.95\linewidth]{figures/face256}
\caption{The edge maps and color maps drawn by users and the corresponding image generation results with improved iGAN.} \label{fig:face256}
\end{center}
\end{figure*}

\begin{figure*}[!t]
\begin{center}
\includegraphics[width=\linewidth]{figures/edit-face-obsolete}
\caption{Edits by users and the corresponding results generated by improved iGAN. (a) face erased. (b) face slimmed. (c) mouth replaced with a patch from another image. (d) hair darkened. (e) mouth closed. (f) hair turned brown. (g) hair turned brown, eye shadowed, and lips reddened. (h) face whitened  and lips reddened.} \label{fig:igan-face256}
\end{center}
\end{figure*}

\begin{figure*}[!t]
\begin{center}
\includegraphics[width=\linewidth]{figures/512}
\caption{Face image (512x512) generation and editing results with improved iGAN. (a)-(b), results based on edge maps. (c)-(d), results based on masked color maps. (e)-(h), image editing results.} \label{fig:face512}
\end{center}
\end{figure*}

\begin{figure*}[!t]
\begin{center}
\includegraphics[width=\linewidth]{figures/car-igan}
\caption{Car image generation and editing results with improved iGAN: (a-b) results based on edge maps; (c-d) results based on masked color maps; (e-h) manipulation of existing images, including erasing license plate (e), changing body color (f \& h), and adding extra edge map (g).}
\label{fig:car-igan}
\end{center}
\end{figure*}

\begin{figure*}[!t]
\begin{center}
\includegraphics[width=\linewidth]{figures/lsun-igan}
\caption{Church image generation and editing results with improved iGAN: (a-b) results based on edge maps; (c-d) results based on masked color maps; and (e-h) image editing results.} \label{fig:lsun-igan}
\end{center}
\end{figure*}

\subsection{Consistency of $VBS$ with human perception}

We are not aware of universally accepted metrics to assess variation of outputs ``by scale", as Frechet Inception Distance and Inception Score do not. We proposed VBS as objective metrics for variation-by-scale. To assess consistency of the metric with human perception, we conducted a user study. We selected 48 pairs of result images (18 for each dataset) that were produced by controlling the value of different latent vectors of BranchGAN. We hired 20 Turkers to rate each pair in terms of level of variation. In the test, three options with elaborate explanations were shown to the Turkers: (a) large-scale variation; (b) median-scale variation; or (c) small-scale variation. The label with the most votes is treated as ground-truth. Then we compare the human-labeled results with those estimated by VBS (the scale level with highest score is used) and compute the percentages of agreements is 85,4\%. The agreement rates of VBS and human perception are quite high from this preliminary study (significantly better than random), serving as an initial validation. Further explorations are certainly warranted.

\subsection{Experiments with LAPGAN \cite{denton2015deep}}

LAPGAN \cite{denton2015deep} is very similar to BranchGAN in terms of coarse-to-fine image synthesis and adding noise at multiple scale level, though the noise is added though dropout layer, which is neither controllable nor explicit. We did re-implement LAPGAN and attempt to add noise vectors explicitly as inputs to generators at each step. The results showed that the noise vectors are not responsible for any variation of the residual images. The reason could be that by using strictly paired training data, the upscaled conditioning image would deterministically generate the residual image.

redpajama_set_name: RedPajamaArXiv


Get your Vikings Tickets and join the crowd with cheers of "Let's Go Vikings" LIVE at TCF Bank Stadium! Buy your Minnesota Vikings tickets today!
The Minnesota Vikings are a member of the NFC North Division in the National Football Conference. The Vikings franchise was established in 1961. The Minnesota Vikings will move in their brand new stadium in 2016!
If you're looking for tickets to upcoming Vikings games, or Vikings playoff games, VIPSEATAS.COM is the place to look!
VIPSEATS.COM is a great source to buy Minnesota Vikings tickets. We've been buying and selling Minnesota Vikings tickets since 1994. Buy direct! VIPSEATS.COM invests heavily in NFL Tickets! In fact, VIPSEATS.COM invests in Minnesota Vikings season tickets, therefore, we can offer cheap Minnesota Vikings tickets . Many times our Vikings tickets are less expensive than the Minnesota Vikings tickets Individual game prices offered at the Hubert H. Humphrey Metrodome box office! VIPSEATS.COM's Minnesota Vikings Tickets investments allow us access to Minnesota Vikings Playoff tickets, Minnesota Vikings Super Bowl tickets and all Hubert H. Humphrey Metrodome tickets .
View our Event Listing for available games in the Minnesota Vikings Schedule. Refer to the Minnesota Vikings seating chart when selecting a quantity of seats, and click the BUY button. All Prices are in US Dollars.
Minnesota Vikings tickets are shipped via FedEx. Most Vikings Tickets orders are processed and shipped within 24 hours of your order. If your Minnesota Vikings tickets have not been printed or distributed by the Hubert H. Humphrey Metrodome Box Office at the time of your order, they will be shipped as soon as they are received by VIPSEATS.COM. Last minute Minnesota Vikings tickets orders can be emailed directly to you or left at the Minnesota Vikings Box Office at the Hubert H. Humphrey Metrodome.
You have finally found a Minnesota Vikings tickets source you can trust!
Buyers- VIPSEATS.COM tands behind our Minnesota Vikings Tickets purchases with our 100% Guarantee! Minnesota Vikings tickets guaranteed to be authentic. Fast confirmations and prompt, professional Minnesota Vikings tickets delivery always in time for the event. 100% refund (minus shipping) for any Minnesota Vikings event canceled and not rescheduled.
VIPSEATS.COM protects your privacy. You will never be contacted directly by a buyer or prospective buyer. You can adjust your Minnesota Vikings tickets prices to reflect marketplace value at any time prior to a sale. You will receive prompt payment from VIPSEATS.COM for all Minnesota Vikings tickets orders that you confirm and fulfill.
VIPSEATS and VIPSEATS.COM are not affiliated with the Minnesota Vikings, the Minnesota Vikings official site, or the Minnesota Vikings box office. VIPSEATS is a ticket broker that sells Minnesota Vikings tickets, often at above the printed face value. Any use of trademarked team or venue names on this site is used expressly for descriptive purposes.
Click Here to join our email list for the latest offers on Minnesota Vikings tickets and Minnesota Vikings Packages.


It is the policy of Red Rock Family Practice, P.C. to provide essential services regardless of the patient's ability to pay. Discounts are offered depending upon household income and size. A sliding fee schedule is used to calculate the basic discount and it will be updated each year using the federal poverty guidelines. A minimum payment of 3.65 will be required for all office visits. Once approved, the discount will be honored for one year, after which the patient must reapply.
A completed application, including documentation of household income and insurance coverage must be on file. Adults living in homes with others must include income for all those within the household that contribute support to the applicant.
Required documentation is last year's income tax return, last three pay stubs, a Medicaid denial letter, and any other form of documented income. Any insurance or insurance reimbursement programs must be disclosed to Red Rock Family Practice. Zero income responses will not be accepted. Failure to provide proper documentation within 30 days will result in the application being denied.
The discount will apply to all medical services given by physicians at Red Rock Family Practice. Office visits will be limited to three per month. Additional visits will be decided based upon the individual doctor's discretion. Pharmacy samples will be provided when available without charge to the patient.


text:
REDEFINING FEMININE AND MASCULINE TO HUMAN.
Gender stereotyping, biases, and discrimination cost dreams and lives every day. From gun violence to climate change, gender stereotypes are at the center of the world's most pressing issues.
DREAM EQUAL is working towards a world in which all people are empowered to realize their full potential, regardless of gender.
Inspired by her personal experiences with gender stereotyping, 19-year-old Ina Bhoopalam founded DREAM EQUAL to combat the gender-based issues she faced growing up. While researching ways to take action against gender inequality, Ina discovered many unaddressed problems within the current gender equality movement and decided to found DREAM EQUAL to confront them. Since its founding as a local Nebraskan non-profit, DREAM EQUAL has spread to over 35 cities and is serving youth all around the world.
OUR PILLARS
We strive to empower children to become capable young leaders who are confident in their goals and dreams.
We work to advocate for a more gender-sensitive education system and world.
We seek to educate our communities about the negative effects of gender stereotypes and how to effectively combat them.
year from start
fundraised
chapter members
DREAM EQUAL is REVOLUTIONARY,tackling gender inequality in ways no one has ever done before, with intersectionality at its core.
WHY WE ARE
We created an organization that focuses on gender stereotypes in youth even before they develop, trying to prevent their negative effects for future generations. Our aim is to build a better future, rather than simply lessen the problems that already exist in the present.
Gender inequality affects everyone: men, women, and non-binary people. That's why we fight for all genders at DREAM EQUAL, not just cis girls.
We seek to center all gender identities, sexual orientations, socioeconomic statuses, races, and ability levels in our fight for gender equality.
The movement for a gender-sensitive world needs YOU.
Consider subscribing to our newsletter below!
Join the fight, and DREAM EQUAL with us.
Thanks for submitting! We respect your privacy.
DREAM EQUAL is a 501(c)3 nonprofit.
dreamequalinc.org
ABOUT US
DREAM CHAMPIONS
WE SUPPORT UN SDG 5
PRIVACY POLICY | © by Dream Equal, Inc.

redpajama_set_name: RedPajamaCommonCrawl


Mario was born in Zagreb in 1973 to a catholic family. During the war, in 1991, he began his theological studies at Thelogocial Seminary Matija Ilirik in Zagreb, Croatia. During his second year a christian befriended him and began witnessing to him. At this time his grandmother become ill and so he asked his friends to pray for her. God answered this prayer and she was healed of her severe pain. The answered prayer softened his heart and that same night as he read the book of John he said to himself, " This is truth. I find myself in every word. This is what I need." And afterward he experienced great change.
Mario completed a two year course in Practical Theology at CFNI, Dallas,TX. He completed his theological studies, on the field of Christian Counseling , on the theme of " Sexual Abuse of Children" . He post-graduated in "Effective Christian Leadership. In 2012 Mario completed his doctoral studies in Practical Theology with special emphasis on leadership.
Mario become pastor and president of Crkva cjelovitog evandelja in the fall of 2001. In addition to his vision to work and serve both in Zagreb and the entire country of Croatia, as the pastor, Mario has formed many ministries in the church, initiated civil organizations, and various practical church ministries with the goal of effective ministry and positive influence of society. Mario married Bonnie Hackett, who's also alumnus of CFNI. She leads the contemporary band "Bez Kompromisa".


Darvocet is a drug which is composed of two medications: acetaminophen and propoxyphene. Acetaminophen is a non-prescribed pain reliever and a medication that reduces fever and is also available in medications like Tylenol. Propoxyphene is an opioid drug that is mainly taken to suppress the pain a person is experiencing and also as a cough suppressant. Acetaminophen also assists with the pain-relieving effects of the propoxyphene. The drug is mainly manufactured to treat mild to moderate pain that occur in people with or without the symptoms of fever. All this you'll also know if you buy Darvocet online and take the drug with the help of a prescription.
Since we already told you when you came here looking for information to buy Darvocet online in the U.S. that the main person of the drug Darvocet is to relieve a person from the pain he is experiencing, and propoxyphene, which is one of the main components of the drug, is an opioid drug, it effectively suppresses the pain. However, the drug also produces a feeling of sedation, well-being, and euphoria. When taken for a long time, a person can become physically and psychologically dependent on the drug.
If a person starts taking higher doses of the drug, he will automatically get used to the presence of the drug at a higher level in his system. This results in the development of a potential withdrawal syndrome when the intake of the drug is stopped abruptly because the system is already used to the presence of the drug. That's why it is important that once you start taking the drug, you should start with lower doses first, and if the need for better amount of doses is felt, the prescribed dose of the drug will be increased. But to prevent yourself from the drug's withdrawal symptoms, it is important that a person should slow down the intake of the drug and then stop taking it. It is also important that before a person decides to buy Darvocet online, one should make sure that he takes the drug with the help of a prescription which is provided along with the prescribed dose of the drug. If you're still not ready to do this, then you are putting your own life in danger.


Elke week deelt Story de mooiste, ontroerendste en bijzonderste foto's die voorbijkomen op Instagram. Een mooi kijkje in het leven van de sterren.
Homemade presents are sometimes the most thoughtful of all. Don't you agree?
Here I am for the 30th Anniversary of Rock in Rio in 2015. I must have shown too late to see the Girl from Ipanema and too early for the Olympics! At least I have the Gold for Guinness's Largest Rock Concert Attendance ever in '94 on Copacabana.??


Cutting edge design meets pioneering technology in our second generation Aquapulse Max goggle, now offering an even better fit and easier adjustment. Delivering on style and performance, it includes our most advanced IQfit™ technology for unparalleled accuracy and our most secure fit yet.
Enjoy the ultimate precision-fit, leak-free performance with our pioneering IQfit™ goggle.


text:
Home Analysis Rebalancing China and Bracing for the Trade War
Rebalancing China and Bracing for the Trade War
The only thing more ambitious than Beijing's goals is its time frame.
Economic Reform
https://geopoliticalfutures.com/author/porchard/
Phillip Orchard is an analyst at Geopolitical Futures. Prior to joining the company, Mr. Orchard spent nearly six years at Stratfor, working as an editor and writing about East Asian geopolitics. He's spent more than six years abroad, primarily in Southeast Asia and Latin America, where he's had formative, immersive experiences with the problems arising from mass political upheaval, civil conflict and human migration. Mr. Orchard holds a master's degree in Security, Law and Diplomacy from the Lyndon B. Johnson School of Public Affairs, where he focused on energy and national security, Chinese foreign policy, intelligence analysis, and institutional pathologies. He also earned a bachelor's degree in journalism from the University of Texas. He speaks Spanish and some Thai and Lao.
Obama, Trump and Biden: Consistency in Foreign Policy
Taiwan and the Geopolitics of Microchips
Why India and Pakistan Avoided Nuclear War
Phillip Orchard, Xander Snyder - March 7, 2019


text:
using System;

namespace Mozu.Api.Contracts.SiteSettings.Order
{
		///
		///	Settings used when checking out an active order.
		///
		public class CheckoutSettings
		{
			///
			///Settings for the checkout login. Choose whether or not shoppers must first login before a purchase can be processed. Choose the option for guests to purchase without logging in, prompt guests to login, or require them to login before a purchase can be completed.
			///
			public CustomerCheckoutSettings CustomerCheckoutSettings { get; set; }

			///
			///Credit card payment authorization setting defined for the site for order processing.
			///
			public OrderProcessingSettings OrderProcessingSettings { get; set; }

			///
			///Payment gateway settings defined for the site. A payment gateway is an eCommerce application service provider that authorizes payments for eBusinesses, online retailers, and other stores that have an online commerce component.
			///
			public PaymentSettings PaymentSettings { get; set; }

		}

}

redpajama_set_name: RedPajamaGithub


text:
Joan Cooper may refer to:

 Joan Cooper (1922–1989), British actress
 J. California Cooper, the pen-name of Joan Cooper (1931–2014), American playwright and author
 Joan Cooper (social worker) (1914–1999), English civil servant and social worker

redpajama_set_name: RedPajamaWikipedia


text:
Standard-Examiner
Western Wasatch
Beyond Bars
National Commentary
Ogden Raptors
Police Fire
Login (isd-chatterbox.com)
Pelicans gather on an island on Farmington Bay near the Great Salt Lake on Tuesday, June 29, 2021, in Farmington. The Great Salt Lake in Utah has been shrinking for years, and a drought gripping the American West could make this year the worst yet. The receding water is already affecting nesting pelicans that are among millions of birds dependent on the largest natural lake west of the Mississippi River.
A map of the Great Salt Lake in the context of state lines and the outline of Paleo Lake Bonneville, shown in dark blue.
Rick Bowmer, Associated Press
Editor's note: This article is published through the Great Salt Lake Collaborative, a solutions journalism initiative that partners news, education and media organizations to help inform people about the plight of the Great Salt Lake — and what can be done to make a difference before it is too late. Read all of our stories at greatsaltlakenews.org.
The Great Salt Lake, already vulnerable to climate fluctuations over the ages, has been "set up to fail" by human impacts, but some of those who are making such assessments nevertheless see hopeful signs in the state's new enthusiasm to save the vital inland sea.
The lake is known as a terminal lake, meaning it has no outlet and therefore is exposed to major climate-driven changes. In the 1980s, the lake rose 4 meters, causing flooding and even prodding the state to build giant pumps to send the extra water into Utah's west desert. But now, amid a Western megadrought, the lake is dipping to historic lows, made more significant by upstream diversions and consumption.
"It is normal for a terminal lake to fluctuate, but that makes it easy to excuse what is happening," said Bonnie Baxter, a Westminster College biology professor who has studied the lake's biological characteristics and the growing population's impacts. "Now there really are stark differences and indicators that humans are having an impact on what would be a normal fluctuation."
However, the deepening lake emergency moved the Utah Legislature this year to create a $40 million program to look at ways to preserve and restore the lake. Baxter, who heads Westminster's Great Salt Lake Institute, said she sees signs of a groundswell of support for saving the lake.
Image supplied, Great Salt Lake Institute
"I think people are waking up to the fact that it might go away," Baxter said. One indicator is that more people are realizing the lake has individual importance — the scenery, depicted by amateur artists who now wistfully remember closer shores; hunters prowling the lake's fringes for generations; workers who see their livelihoods potentially evaporating; sailboat owners whose craft sit idle, unable to launch because there's not enough water; and so on.
"I'm not a person that is prone to drama; I am motivated by the straightforward science," Baxter said. "But I am seeing people moved by fear for the lake along the Wasatch Front. I am motivated by that."
The lake, the eighth largest saline lake in the world, has an estimated $1.5 billion economic value and supports about 10 million waterbirds, of about 250 species.
A 2017 Utah State University study estimated than Utahns every year divert 3.3 trillion liters of water from the rivers and streams that feed the lake.
Baxter and others in academia have traced the Great Salt Lake's natural and human history, hoping that their work will help to inform the ongoing conversation about the lake's fate.
"We've done this experiment before," Baxter said, referring to other terminal lakes, like the Aral Sea, that have shrunk by catastrophic degrees. "It starts with water diversions to feed people, to house people, and then you have a time of drought and the lakes are no longer able to bounce back."
The Great Salt Lake's geologic history shows a much larger view than the disappearance of the freshwater Lake Bonneville, which lost enormous volume about 15,000 years ago, its remnant being the current stressed saltwater lake. Baxter said that was only the most recent stupendous transformation — over the last 800,000 years, "large lake episodes have been the norm." Water in the Bonneville basin over geologic time has risen and fallen repeatedly.
In her 2018 study "Great Salt Lake microbiology: a historical perspective," Baxter describes humans' impacts on the lake and how the body of water's nature has been altered by upstream consumption, diversions such as Farmington Bay and Willard Bay, and industry.
Humans likely have been in Utah since the Pleistocene epoch, between 15,000 to 20,000 years ago, the high point of Lake Bonneville, according to Baxter's manuscript, which in part draws upon the work of numerous scientists and historians.
Lake Bonneville would have given the region's inhabitants an abundant source of freshwater fish. As the lake changed over time, humans would have moved in tune with the changing shorelines, hunting, fishing and foraging.
The Fremont native peoples buried their dead during their time around the Great Salt Lake's wetlands and along the Bear River in the period of 400 to 1000 AD, as shown by anthropology and archeology studies. The Shoshone and Utes lived on the lake's north side and the Goshutes roamed along the lake's southern reaches.
In 1824, explorer Jim Bridger floated down the Bear River and into the Great Salt Lake, claiming its modern discovery. But historians have reported that French-Canadian trapper Etienne Provost beat Bridger to the Great Salt Lake by a few months.
The John C. Fremont expedition in 1843 mapped and described the region's topography, including the lake's islands — one of which bears his name — and reported on its mineral and biological content. In 1849, civil engineer Howard Stansbury's team conducted a wider study of the lake's geography, natural history, minerals and water chemistry. Stansbury also is immortalized by an island named after him.
The 1847 arrival of the pioneers of The Church of Jesus Christ of Latter-day Saints heralded what would become today's significant population growth and associated human impacts on the lake.
The pioneers noted that the Salt Lake Valley's geography paralleled the Holy Land's in the Middle East. According to the church in a 1997 citation, the similarity generated a sense that Salt Lake was a land for a "chosen people, just as the Holy Land was seen as the promised land" in biblical times.
Mineral extraction, agriculture diversions and shortcuts for the railroad eventually would affect the previously pristine lake as the settlement grew. Minerals obtained today are used for road and softener salt, magnesium chloride for steel production, and potassium sulfate for fertilizer.
Those industries diverted and dammed and created evaporation ponds. Other damming projects created vital bird habitats, but they too diverted water that before would have reached the lake.
The brine shrimp industry began booming in the 1970s, but increasing salinity in the shrinking lake threatens the enterprise. However, Baxter pointed to cooperation between the industry and the Utah Division of Wildlife Resources to ensure the lake is not overharvested.
With the combination of climate change and assorted human impacts, "Now we've got this lake set up to fail," Baxter said. "But I'm really buoyed by what happened in the Legislature. It was unanimous, bipartisan. There was a lot of lake love going on. It was beautiful."
She urged people to look at the array of reasons they should care about the lake. "If you don't care about the migratory birds, you might care about what the dust will do to air quality. We all need to be doing not one thing, but everything we can do to value water getting into this lake."
Join thousands already receiving our daily newsletter.
Defense mitigation specialist testifies of Emile's 'tumultuous' life
Chamber Orchestra Ogden holding Red Dress Concert on Saturday
Farmers Market Ogden's Winter Market opens on Saturday at Union Station
Copyright © Standard-Examiner | www.standard.net | 332 Standard Way, Ogden, UT 84404


They are prehistoric creatures living in a modern world, and they just keep coming back. The dinosaurs couldn't stop them, and the army couldn't stop them. Nothing seems to stop them. No I'm not talking about creatures in some 1950's B-movie; I'm talking about cockroaches. But like most movies (even "The Attack of the 50-Foot Cockroach") yours can have a happy ending. If you try to get rid of cockroaches but they keep coming back—you, like a movie hero, need to change your methods to win.
Chances are if cockroaches make your kitchen a perpetual feed station, you're trying to get rid of them with bug spray. Or maybe catnip, soapy water, or beer-soaked bread. While any of those methods might repel, kill (or drunken) cockroaches already in your house, they don't kill the roaches that live outdoors or inside your walls. And if you love horror movies, consider this: in some parts of the South, there are up to 250,000 Asian cockroaches per acre. If you're so inclined to look down a single manhole, you'll see evidence of thousands of American cockroaches feeding of feces and sipping up the sewer water.
But you never have to see any cockroaches in your house if you kill them where they live. And that can be accomplished easy as one-two-three. One: Go to the store and buy Combat Roach Killing Bait Strips or Source Kill for Roaches. Two: Distribute the bait stations around the house—under the sink, around floor drains, and by the garbage cans. Three: Rest assured that the cockroaches are carrying the bait back to their nests and it is killing off all the roaches in the area.
Unless you kill the roaches where they live you will just repeat the cycle of getting rid of cockroaches only to find them coming back again. The choice is easy: either live in a bad B-movie or use Combat for a happy ending.


text:
Termit je naziv za pirotehničku smjesu koja sporo izgara i daje velike količine toplinske energije, u tzv. aluminotermijskoj reakciji.

Sastav
Aluminotermijska reakcija kao redukcijsko sredstvo koristi aluminij, a kao oksidacijsko može iskoristiti mnoštvo metalnih oksida:

3FeO + 2Al → 3Fe + Al2O3 + 783 kJ (2500 °C)
Fe2O3 + 2Al → 2Fe + Al2O3 + 760 kJ (2960 °C)
3CuO + 2Al → 3Cu + Al2O3 + 1153 kJ (4865 °C)
3Cu2O + 2Al → 6Cu + Al2O3 + 1090 kJ (3183 °C)
3NiO + 2Al → 3Ni + Al2O3 + 865 kJ (3171 °C)
Cr2O3 + 2Al → 2Cr + Al2O3 + 2288 kJ (2977 °C)
3MnO + 2Al → 3Mn + Al2O3 + 1687 kJ (2427 °C)
3MnO2 + 4Al → 3MnO + 2Al2O3 + 4358 kJ (2771 °C)

Reakcije oslobađaju dovoljno topline da se reducirani metal dobije kao talina.

Svojstva
Termitne smjese se uglavnom vrlo teško pale i stabilne su na zraku.

Uporaba
Najčešće korištena termitna smjesa je ona s praškastim željezovim(III) oksidom i aluminijem. Koristi se za termitno zavarivanje, primjerice tračnica vlakova, tramvaja, itd.

Pirotehnika


As part of the celebrations for 500 years from Cosimo I's birth, father of modern Tuscany, the Laurentian Library shows his magnificent books.
Cosimo I De 'Medici, son of the commander Giovanni delle Bande Nere and Maria Salviati,, was born on June 12, 1519 and on January 9, 1537 he became Duke of Florence; with the extension of the Dukedom of Florence to Grand Dukedom of Tuscany he has been the first Grand Duke since 1569 to his death in 1574 and it was a dukedom rich in transformations and "festina lente", that is going on carefully but straightaway.
Fifty-four precious books from the library of Cosimo I are now shown in this very important exhibit, including private manuscripts and those for Michelangelo's Library.
The exhibition thus is divided into two sections, the private collection and the public one: in one you can admire the books he kept in his private library, these are his biographies, donations, manuscripts he collected, all works that focus his various and multiple interests, ranging moreover from architecture to maths, from music to geography, here further represented by the two armillary spheres for the study of astronomy.
The second section is dedicated to the great increase of manuscripts for the Public Library, of which Michelangelo had planned the building and which was opened in 1571. During fifty years the Medici library collected from 1000 codes to more than 3000, with five different origins, purchase or transfer from other libraries, both Florentine and from other parts of Tuscany.
The exhibit also shows a never seen painting by Jacopo Ligozzi, the sketch of one of the paintings in the northern part of Salone dei Cinquecento in Palazzo Vecchio, which also completed the decoration requested by Cosimo I.
Also on display is the autograph manuscript of the three-choir mass composed by Costanzo Porta for the Grand Duke and visitors can listen to these XVI century music during the visit.


Blue Area is Islamabad's most elite area. Its tall, shiny buildings and sprawling mansions speak of its inhabitant's opulence.
On one of its pavements, however, sits a highly skillful woman who spends her day sewing footballs with her hands, which she then puts up for sale.
Abida Parveen says hard times forced her to take up this task. She is a mother of four, and her husband is unable to earn bread for the family due to his illness.
Her day's labour provides her with Rs. 400-500, barely enough to put food on the dinner table. The family is also under a debt of Rs. 800,000.
But she doesn't beg from anyone; it's she and her needles which ensure the family doesn't sleep hungry.
A poster next to her narrates her heartwrenching story to the people who pass by. Her only wish is that someone gets her a small house and pays-off her debts which would remove a massive burden off her.


text:
Супаяджи (; 1854 — 25 февраля 1912) — предпоследняя главная королева-консорт Бирмы из династии Конбаун (1878—1879), одна из жен и сводная сестра последнего короля Бирмы Тибо Мина.

Ранняя жизнь
Супаяджи, родившаяся в 1854 году под именем Хтейксу Пхаягьи (ထိပ်စုဖုရားကြီး), была старшей из трех дочерей короля Бирмы Миндона (1808—1878) и Синпьюмашин (1821—1900). Она была родной сестрой Супаялат и Супаялай. Она получила удел город Мон Наун и поэтому была известна как принцесса Мон Наун с королевским титулом Сусириратанаманггаладеви.

Коронация
Честолюбивая Синпьюмашин, посадив Тибо на королевский трон в 1878 году, предложил своей старшей дочери Хтейк Супаяджи стать его королевой. Во время королевской коронации Супаялат (1859—1925) встала рядом со своей старшей сестрой, чтобы быть помазанной королевой одновременно, нарушив древний обычай. Брак её сестры так и не был завершен, и Супаялат, как говорили, впервые и в последний раз в истории принудила к моногамии бирманского короля, хотя Тибо впоследствии также женился на их младшей сестре Хтейк Супаялай (1863—1912), принцессе Яметин.

Ссылка

Правление королевской семьи длилось всего семь лет (1878—1885), когда Тибо Мин потерпел поражение в третьей англо-бирманской войне и под давлением британцев был вынужден отречься от королевского престола в ноябре 1885 года. 25 ноября 1885 года их увезли в крытом экипаже из Мандалайского дворца через Южные ворота окруженного стеной города по улицам, вдоль которых выстроились британские солдаты и их плачущие подданные, к реке Иравади, где их ждал пароход под названием «Турия» (Солнце). Супаяджи и королеву-мать Синпьюмашин отправили в Тавой. Она умерла 25 февраля 1912 года в Мингуне после своей матери, которая умерла в 1900 году. Её останки были захоронены в южной части Пагоды Шведагон в современном Янгоне.

Примечания

История Мьянмы


Amy Winehouse, the singer, has been found dead at her home at the age of 27.
Miss Winehouse, who came to fame with her debut album Frank in 2003, was found at her flat in north London this afternoon, the Metropolitan Police have confirmed.
The Norwegian charged with going on a killing spree in which at least 91 people died is a former member of a populist anti-immigration party who wrote blogs attacking multiculturalism and Islam.
The suspect, detained after 84 people were gunned down at a youth camp and another 7 killed in a bomb attack on Friday, has been identified by Norwegian media as Anders Behring Breivik.
Website entries under Breivik's name criticized European policies of trying to accommodate the cultures of different ethic groups, and claimed a significant minority of young British Moslems back radical Islamic militancy.
"When did multiculturalism cease to be an ideology designed to deconstruct European culture, traditions, identity and nation-states?" said one entry, posted on February 2, 2010 on the right-wing website http://www.document.no.
"According to two studies, 13 percent of young British Muslims aged between 15 and 25 support al Qaeda ideology," said another entry dated February 16 last year.
Police searched an apartment in an Oslo suburb on Friday, but neighbors said the home belonged to Breivik's mother, whom they described as a nice lady.
Deputy Police Chief Roger Andresen would not speculate on the motives for what was believed to be the deadliest attack by a lone gunman anywhere in modern times. But they said the man in custody had described himself on his Facebook page as leaning toward right-wing Christianity.
Breivik had also been a member of the Progress Party, the second largest in parliament, the party's head of communications Fredrik Farber said. Breivik was a member from 2004 to 2006 and in its youth party from 1997-2006/2007.
And as Deacon Greg Kandra (HT) rightly asks, is this the new face of terror.
Wikipedia already has a page on Anders Behring Breivik here.
UPDATE: You can check out his hate-filled Manifesto here.
The Roman Catholic Diocese of Orange said Friday it has made a $50-million offer to buy the bankrupt Crystal Cathedral.
The cash proposal would allow the Garden Grove church to lease portions of the property for 36 months. At the end of the lease, the ministry would have to find a new location, with the help of the diocese. The lease for each building on the property would be negotiated separately.
"We wanted to be as flexible as possible given their situation," said Alan Martin, a bankruptcy attorney for the diocese.
The Crystal Cathedral, founded by the Rev. Robert H. Schuller and the home of the weekly "Hour of Power" broadcast, filed for bankruptcy in October, citing more than $50 million in debt.
As the bankruptcy unfolds, the cathedral has received several offers for the property, which includes the glass cathedral, designed by famed architect Phillip Johnson, and a 234-foot prayer spire.
A real estate group has offered $46 million for the 40-acre site in Garden Grove, as did Chapman University. Both bids would allow the church to lease back its core buildings with the possibility of purchasing the cathedral back.The proposal by the Catholic Church would allow creditors to be paid back in full before the end of the year and would preserve the cathedral's cemetery."We think it would be as quick as a resolution as possible," Martin said.
Orange Bishop Tod Brown has said the diocese needs a worship space for the more than 1.2 million parishioners in the area and the 3,000-seat cathedral is a less expensive alternative to building a new cathedral. The diocese has considered a project in Santa Ana that would cost at least $100 million.In recent weeks, the Catholic Church has made it clear that an offer would be solidified. On Friday, the diocese submitted formal proposals to both the Crystal Cathedral board and the creditor's committee.
Marc Winthrop, an attorney for the cathedral, declined to comment Friday.The diocese said it would work with the cathedral to find an "acceptable alternative worship space," possibly another Garden Grove diocese venue, upon expiration or early termination of its lease.The Catholic Church would renovate portions of the cathedral to bring it in line with worship practices."It's in their hands at this point," said Steve Bohannon, a spokesman for the Catholic Church.
A spokesman for the Crystal Cathedral could not be reached.


Thieves pulled off a brazen daytime theft of a piece of heavy equipment worth $40,000 at a construction site in Kelowna.
TNC Excavating president Troy Chapman believes the 2015 John Deere 26G excavator was stolen from a job site at Dilworth Drive and Enterprise Way on Sunday, between 10 a.m. and 3 p.m.
"We don't know where to go with the (all the) theft in Kelowna anymore. We are just constantly fighting the thieves," he said.
Chapman believes the thieves entered the site when one of the workers left temporarily. He alleges they drove the excavator onto a dirt pile to get it inside their getaway vehicle.
RCMP Cpl. Jesse O'Donaghey says evidence at the scene suggests the suspect vehicle used to commit the crime fled northbound on Dilworth Drive.
This isn't the first time the company has had something stolen. Earlier this year, another machine was taken, along with fuel.
Investigators are asking businesses in the area to check their video surveillance of parking lots and nearby roads.
"RCMP are also encouraging motorists, who may have been in that area during that time to review any dash camera footage that may assist in our investigation," said O'Donaghey.
Chapman said the company does have insurance, but he's fed up with the number of equipment thefts.
Anyone with information is asked to contact Const. Regan Donahue at 250-762-3300 and keep an eye out for the machine, with serial number 1FF026GXCFK260346.


text:
Used 2018 AudiA5 2.0T Premium Sportback
VIN: WAUANCF59JA041911
Stock: MB2904
Sportback/5 seats
quattro all-wheel drive
2.0L 2.0-liter TFSI four-cylinder engine
Convenience Package$900
Alarm System w/Motion Sensors
Audi Advanced Key
Heated Auto-Dimming Power/Memory Exterior Mirrors
SiriusXM All Access Service
Heated Front Seats$500
Smart device integration: Audi smartphone interface
Drive type: quattro all-wheel drive
Compression ratio: 9.60 to 1
Forward collision: Audi pre sense basic mitigation
Power adjustable front head restraints
1-OWNER**FACTORY WARRANTY STILL REMAINS**PREMIUM QUATTRO ALL WHEEL DRIVE**2.0L TFSI TURBO ENGINE**CONVENIENCE PACKAGE**LEATHER SEATS**HEATED FRONT SEATS**PANORAMIC SUNROOF**REAR BACKUP CAMERA**KEYLESS PUSH START**HID HEADLAMPS**POWER REAR LIFTGATE**STEERING WHEEL CONTROLS**KEYLESS ENTRY**BLUETOOTH, HANDS-FREE**AWD** Complimentary 6 Month/6,000 Mile Warranty with every Car! #WeArePrestige Our Mission is to make your convenience our priority, with aggressive pricing and the most dynamic inventory! We take all trades including dirt bikes, motorcycles, 4wheelers, boats, jetski's and more! We offer financing as low as 1.9%, first time buyer programs, and second chance loans for those with a challenged history! Over the phone appraisals, out of state shipping, local airport pickup, walk around videos and additional photos at your request to allow this to be easiest purchase experience you've ever had! Prestige Auto Mall is proud and dedicated to offering genuine customer care. We welcome all out of state customers and provide airport pickups. We will even help arrange shipping for you to have car delivered to your door. At our dealership you will find 150+ vehicles including Audi, Acura, BMW, Cadillac, Infiniti, Jaguar, Land Rover, Mercedes, Volvo, Chevrolet, Dodge, Chrysler, Fords and many more brands. We are confident that we have what you are looking for. All of our vehicles have been through a multi-point inspection. We have ASE certified mechanics on site. We offer some of the best warranties in the industry. Including a complimentary 6month/6,000 mile warranty! We strongly believe in our inventory and our employees. Do not miss out on the opportunity to purchase your next vehicle at a wholesale price. Shop our inventory today! The Prestige Family wants to earn your business. We can finance you with bad credit, no credit, bankruptcies and first time buyers! Our Finance Team has the tenure and experience to get you APPROVED and Driving the car, truck, Van or SUV you are looking for. We are open 6 days a week. We understand and respect your busy schedule and appreciate the opportunity to accommodate. Walk-ins welcome Monday- Friday 9am-7pm Saturday- 10am-6pm Sunday- By appointment only Disclaimer Pricing Excludes tax, tag, title and registration . Dealer documentation fee of $250 not included. While we make every effort to ensure the data listed here is correct, there may be instances where some of the options or vehicle features or price may be listed incorrectly, as we get data from multiple data sources. PLEASE MAKE SURE to confirm the details of this vehicle with the dealer to ensure its accuracy. Dealer cannot be held liable for data that is listed incorrectly. **Please Note: We turn our inventory daily, please check with the dealer to confirm vehicle availability
First time Audi owner, now BIG time Audi fan, but
By Jnaga | on Sunday, January 20, 2019
With the exception of my very first car, a used '67 VW Bug, I've exclusively purchased Japanese cars all my life. I have done so primarily because I'm a Consumer Reports kind of nerd and have always placed a high premium on reliability, practicality and affordability. But after several methodical test drives, I was absolutely wowed by and ultimately fell in love with the new A5 Sportback. Even now, after 14 months of ownership, I still get that boyish sense of exhilaration nearly every time I get behind the wheel. But, in the spirit of full disclosure, I do have a few small quibbles that I will dispense with prior to elaborating on my many likes. First, rear and side visibility leaves something to be desired, an issue that is further exacerbated by its sloped rear window design. In addition, the side view mirrors are also fairly small and the view out the rear side passenger windows is similarly compromised. Luckily, it has a well-designed blind spot monitoring system to help compensate for those deficiencies. Nevertheless, when making lane changes, I often feel like I'm executing them more on faith than on confident visibility of a clear and safe travel path. Second, upfront interior storage is nearly nonexistent. There appears to have been only a halfhearted attempt to provide in-console storage, which amounts to a tiny, shallow bin that has only a partially enclosing lid—one that doesn't fully conceal the few articles one is able to fit in it. And unlike other cars in this class, there's no dedicated compartment in which to store sunglasses, forcing you to be creative with where you choose to stow them. On top of all that, the cup holders are placed so close to the center dash controls that they can barely hold anything larger than a small water bottle or a medium size drink before interfering with the use of those controls. So no Big Gulps allowed in this A5's cozy cockpit! The interior is sleek and stylish, if not a bit spartan. And while definitely upscale, it doesn't seem to me to have quite the same luxurious feel of its Japanese or domestic rivals. Just the same, it's easy to find a comfortable driving position and the leather seats are pleasantly supportive, if not a bit on the firm side. The moonroof has a mesh screen closure that lets filtered sunlight through, which on occasion can be both annoying and a bit uncomfortable when the sun is hovering directly overhead in the summer. Personally, I would have preferred something completely opaque. Now for the positives. I absolutely love driving it! For a 2.0 liter 4-cylinder turbo, it has plenty of pep yet gets commendable gas mileage for a car in this class. It grips the road exceptionally well, moves smoothly and effortlessly through traffic and is supremely confident in its handling. Aesthetically, this is a sublimely beautiful car, especially in the Brilliant Black exterior finish I opted for, which has subtle metallic overtones. In my opinion, the hatchback version of the A5 is one of the sharpest looking vehicles in Audi's current "accessibly priced" lineup, save perhaps for the A7, which was unfortunately out of my personal price range. Admittedly, a lot of my interior quibbles would likely have been addressed in the A7, but that would have set me back another $25-30k! As for rear storage, this hatchback is amazing! The rear seats lay nearly complely flat (no large transitional hump, as with most other sedans) and the rear opening is cavernous, making our frequent trips to Sam's Club, and the attendant need to stuff various jumbo-sized items in the back, a real breeze. Rear storage in the hatchback version of the A5 is truly amazing for a car this size and really helped to bridge my craving for the svelte aesthetics of a sports sedan while appeasing my desire for the practicality of an SUV. One small quibble here is that you can't close the rear hatch from inside the cabin. A peculiar oversight to be sure, especially since such functionality is commonplace in virtually all SUVs outfitted with a power lift gate. As for the optional Bang and Olufsen sound system, I have just two words to say to you fellow audiophiles out there—GET IT! It sounds absolutely amazing! I don't recall how much I paid for it, but I don't really care, because whatever it cost, it was worth it. It turns my commute to and from work as well as our frequent long car trips around east Texas into truly joyful listening experiences. My only beef here, and it's small, is that there is no midrange control for the equalizer—yet, curiously, the Audi sound engineers saw it fit to incorporate separate controls for both the bass and subwoofer. Go figure. In addition to an adjustable control to finely modulate the surround sound effect, it also has an additional sound modification feature known as "3D Effect", which allows you to further augment the ambiance of your music and give it even more of a concert hall feel. At least I think that was its purpose. Lastly, the tech in this car, including the "virtual cockpit" option (which I highly recommend!), is fantastic! Those Audi design engineers have thoroughly thought this car through and made it one of the most forward thinking cars on the market today. One peculiar oversight, however, and for me this was more than a bit of an annoyance considering the price point of this car, is the lack of a digital readout for tire PSI. A bit disappointing for a car that seems to address nearly every conceivable technophile's whim—and then some. For lack of time and space, I can't do justice to all the amazing and worthwhile tech and safety features this car has incorporated into it, but trust me, it's mind boggling! It has truly been an awe-inspiring journey of discovery. In point of fact, the tech features in this car can sometimes be so engrossing that it can be downright distracting—if you let it. Just the same, if you're in the market for a performance-oriented sports sedan yet yearn for the greater practicality of an SUV, I would highly encourage you to test drive this car—repeatedly. It'll take more than a casual romp or two to truly appreciate the wealth of features this exceptional vehicle has to offer. But be sure to find a knowledgeable salesperson who can properly demonstrate the myriad thoughtful design features this car has to offer. Then, compare it to other similarly priced Japanese, German and domestic sports sedans out there. I promise, you won't be disappointed.…
6th Audi
By Jay | on Saturday, March 14, 2020
20" tires, sport suspension, sport seats, S-Line Pkg. Bang & O stereo; AWD: the best out of the 6 Audi's I have owned. Not a family car, but if you want a sports car with german engineering, understated class, awesome handling and addictive sound system' at this price, nothing compares.…
Very comfortable, stylish, and nice performing car
By Mr. T | on Thursday, September 17, 2020
My A5 is the new for 2018 Sportback. My wife and I have loved this car. It is a combination of fun, comfort, style. Ours is loaded with everything and we use it all. The car's interior tech systems are very comprehensive and intuitive. It's fairly quiet for a low-to-the-road car. I used to have a BMW and this car is total comparable as a roadster. We have to set an alarm to go off at 85 mph or you'll get going pretty fast before you know it. My BMW was that way but my Audi will get to the speed a lot quicker. Trunk is great & rear seats fold down if you need more room. Paint is the one fault for me as I have a black car and they show gravel chips at lot more that others. This car does chip and Audi accepts no responsibility. My car could be a one-in-a-thousand or not. I don't know because I have not seen another 1000 black A5 Sportback's to compare to.…
Based on 5 consumer ratings for 2018–2021 models. | Privacy
2015 Ford Fusion SE Sedan
2016 Chevrolet Impala LTZ w/2L…
2008 Mercedes-Benz E-Class Sed…


slipcover sectional sofa my sectional 2 years later with a busy household.
double rod closet organizer double rod wardrobe double rod freestanding closet cover double rod wardrobe with 5 shelves.
recycling led bulbs fluorescent light bulbs.
20 inch lamp shade the lamp shades sold by com are available in a full range of colours.
industrial look furniture vintage industrial style furniture vintage industrial style furniture.
decorative wall lights park lane chrome wall light and white shade buy decorative wall lights online india decorative wall lights bangalore.
pantry storage cabinets pantry door spice rack organizer kitchen cabinet storage organizers pantry door spice rack organizer kitchen cabinet kitchen cabinet storage.
fire wood rack cover firewood rack cover adjustable firewood rack bracket kit firewood rack cover round firewood rack cover.
appalachian tree service tree service of inc.
low profile king bed frame low rise bed frame rise wood bed frame low profile wooden king high rise platform bed.


text:
Image/Top Cow Reviews
Paradiso #3
By Former Contributor
On 17 February 2018
By Ram V, Devmalya Pramanik, Dearbhla Kelly, Aditya Bidikar, Lizzie Kaye, and Shanna Matuszak
Issue to issue, Paradiso is the series that refuses to be put in a category when it comes to telling its story. As more knowledge about its world is given, even more is shrouded in mystery, serving as a trail of bread crumbs that invite the reader to dive even deeper into its strange universe. But while this may be irritating to some, there's a lot to love within its beautifully detailed pages as the pieces click into place.
Paradiso #3 is an interesting chapter filled with little bits of pay-off built up over the last two issues. It's a little surprising to remember just how much Ram V has established in such little time, especially in creating a wide range of characters with such varying roles and desires. So much in fact, that the book's events serve as a bit of cathartic relief. Seeing them interact in meaningful ways, whether it be in forming a larger group or engaging in combat, brings a nice level of closure to open story threads. This is also all really well-paced, giving the action time to breathe between dramatically resonant scenes.
Jack, Paradiso's main character, has also begun to have a much more intriguing relationship to the overall plot. Aside from being the caretaker of the technologically advanced pneumas, some revelations about him will place his role in a different light. It becomes easier to be more sympathetic to his situation and to his story in general, even though there is much more to be told. Other developments, such as the backstory of a settlement within the city, are delivered in greater detail. While it may seem a little odd, the pacing of this story matches up perfectly with the actions of the characters in the moment. These kinds of details weaved within are what make this series worth the second look.
Devmalya Pramanik once again wows in creating visual wonder. From floating buildings to intense fight sequences, there's rarely a page that doesn't inspire. The attention to detail in a scene when a robot meets a magnetic weapon is just one example where the gritty post-apocalyptic setting meshes well with futuristic elements. Dearbhla Kelly's colors give technology an ominous feeling, opting to fill dark rooms with the contrasting lights of monitors. Aditya Bidikar's lettering brings the mechanical voice heard in previous issues to life, covering panels with menacing glitching text. When all of these talents are combined, the resulting instances of technologically dark imagery bring Paradiso right in line with the tone of its subject matter.
Paradiso #3 continues to top itself in both artistic quality and character investment. The unveiling of information on the world gets better as the series goes on, giving readers enough to think about for the next installment while also providing enough to not feel detached. There's plenty more to expect from Paradiso, whether it be what's in store for its cast or what kind of new visual storytelling element will be used in the future.
Aditya BidikarDearbhla KellyDevmalya PramanikImageImage ComicsJonathan BaldessariLizzie KayeParadisoParadiso #3Ram VreviewShanna Matuszak
Previous Post X-Men Gold #21
Next Post Black Panther: Long Live the King #5
About The Author Former Contributor
Former All-Comic.com Contributor