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

The task of image generation from an input sketch or edge map is known as "sketch to image translation", or "sketch colorization". Sketches capture essential content of the images and they can be easily acquired. Yet, vast amount of domain difference between single channel edge maps and color images makes sketch colorization a challenging process. Lack of details in sketches especially for  background is another problem.

Sketch colorization has been investigated in numerous domains: faces~\cite{deepfacedrawing,lee2020reference,linestofacephoto,multi_density}, objects~\cite{sketchygan,texturegan,lu2018image,liuunsupervised}, animes~\cite{comicolorization,ci2018user,autopainter,zhang2018two,zhang2017style,Yuan_2021_CVPR,tip_dual_color}, art~\cite{liu2020sketch}, icons~\cite{Li_2022_CVPR} and scenes~\cite{scribbler,edgegan,zou2019language}. The majority of the approaches require user direction in the form of supplementary input, such as a reference color, patch, or image. These approaches usually generate surreal colorizations otherwise. Except for a few studies (e.g., Liu et al.~\cite{liuunsupervised}), most of the approaches follow the ``paired'' strategy, which is restricted to use datasets with a ground-truth image for each sketch.

In this study, we aim to leverage general purpose semantic image segmentation to alleviate the  aforementioned shortcomings.
We argue that an accurately colored sketch would produce a ``real'' segmentation result, i.e., a result that looks like the segmentation of a real image. Thus, for sketch based image colorization problem, we exploit semantic segmentation methods that have reached to a degree of maturity even for datasets on which they were not trained  (Section~\ref{sec:dataset}). We introduce a segmentation-based adversarial loss to be used in a GAN (Generative Adversarial Network) setup. With our approach, neither extra user instruction nor "paired" input is required.

\begin{figure*}[h]
\centering
\includegraphics[width=0.90\textwidth]{./figures/seg_model_new2.pdf}
\caption{The proposed model for sketch colorization with Adversarial Segmentation Loss (ASL). It is composed of two parts; a general purpose image translation GAN model, and an image segmentation model. During training, input sketches are first colorized using the baseline GAN model. Then, generated and ground truth color images are fed to the pre-trained panoptic segmentation model to extract fake and real segmentation maps. Finally, two additional discriminators are used to classify the segmentation maps as fake or real, respectively. The box with dashed yellow borders shows the inference stage. Red border marks the GAN model used for sketch to image translation. Here, Pix2Pix is used as an example image translation model, which could be replaced by any paired or unpaired model.}
\label{fig:model}
\end{figure*}

We introduce three models for varying levels of segmentation feedback in the sketch to image translation pipeline. Our models could be integrated into both paired and unpaired GAN models.
We illustrate the effectiveness of applying segmentation cues via comprehensive experimental analyses.
This paper extends our previous work~\cite{samhi_icip} in the following ways: (i) We apply our method to a new task: label-to-photo translation. Our experiments on two challenging datasets show that our segmentation-based adversarial loss is useful in this task, too. Again, ground-truth segmentation labels are not a requirement for our approach.
(ii) We perform experiments to set the optimal values for weights of two additional discriminators. (iii) We incorporate an outdoor dataset, Cityscapes~\cite{Cordts2016Cityscapes}, to both sketch colorization and label to photo translation tasks. (iv) We add a new metric, mean Intersection over Union (mIoU), in addition to FID score to measure the performance of all the models more reliably.

\section{Related Work}
\label{sec:related_work}

Even though the sketch and the edge map of an image are different concepts, in practice, XDoG~\cite{xdog} or HED~\cite{hed} based edge maps are considered as sketches (e.g.,~\cite{scribbler, autopainter}). Moreover, some sketch based models~\cite{sketchygan} use edge maps for data augmentation. Hence, we refer to all these models as "sketch-to-image translation" or "sketch colorization" models. Although general purpose image-to-image translation methods~\cite{cyclegan, dualgan, pix2pix, ganilla, huang2018multimodal} could be used for sketch-to-image translation tasks, the results are not satisfactory.

One widely used solution to improve the colorization performance is to employ additional  color~\cite{scribbler,autopainter,zhang2018two,ci2018user,Yuan_2021_CVPR,tip_dual_color}, patch~\cite{texturegan}, image~\cite{lee2020reference,zhang2017style,liu2020sketch,comicolorization,Li_2022_CVPR,Lee_2020_CVPR} or language guidance~\cite{zou2019language,Zou_2018_ECCV,Kim_2019_ICCV}. For instance, in color guidance, users specify their desired colors for the regions in the sketch image, and the model utilizes this information to generate the same or similar colors for these regions. Some automatic methods also utilize user guidance to improve their performance as a hybrid approach. Most of the sketch-to-image translation methods are based on ``paired'' training approach~\cite{scribbler,autopainter,sketchygan,edgegan}, but, recently unpaired methods have also been presented~\cite{liuunsupervised,liu2020sketch}.

Scribbler~\cite{scribbler} presents one of the very first paired and user guided scene sketch colorization models. In addition to pixel, perceptual and GAN losses, Scribbler uses total variation loss to encourage smoothness. XDoG is used to generate sketch images of 200k bedroom photos.
DCSGAN~\cite{tip_dual_color} uses HSV color space in addition to the RGB, for line art colorization task.
Zou et al.~\cite{zou2019language} use text inputs to progressively colorize an input sketch, in such a way that a novel text guided sketch segmenter locates the objects in the scene.
EdgeGAN~\cite{edgegan} maps edge images to a latent space during training using an edge encoder. During inference, the edge encoder is used to encode the input sketch to the latent space to subsequently generate a color image. Experiments are provided for 14 foreground and 3 background objects from COCO~\cite{coco} dataset.

EdgeGAN~\cite{edgegan} and Scribbler~\cite{scribbler} use a supervised approach where input sketches and corresponding output images exist. However, it is hard to collect sketch image pairs.
Liu et al.~\cite{liuunsupervised} propose a two stage method to convert object sketches to color images in an unsupervised (unpaired) way. They first convert sketches to gray scale images, and then to color images. Self supervision is used to complete the deliberately deleted sketch parts and clear the added noisy edges from sketch images.

In Sketch-to-Art~\cite{liu2020sketch}, an art image is generated using an input sketch, with the additional help of the target style art image. Content of the input sketch and style of the art image are encoded, and then fused to generate a stylized art image. In~\cite{Li_2022_CVPR} authors proposed a method to colorize icons which utilizes colored icon images as input in addition to the black-white icons.

The user input is valuable not only in helping the colorization network to put the right colors to indicated regions, but also in removing the color bleeding problems. In~\cite{Kim_2021_ICCV}, users draw scribbles to the regions on the generated image suffered from color bleeding artifacts for guiding the model to fix them.

Unlike these methods, our method does not require any user input to generate satisfactory colorization. Instead, we utilize adversarial segmentation guidance to improve performance.

\section{Adversarial Segmentation Consistency}
\label{sec:advsegloss_model}

Figure~\ref{fig:model} shows the overall structure of our proposed model for sketch colorization, which we refer to as Adversarial Segmentation Loss (ASL) based model. In this work, we used \textit{Pix2Pix} and \textit{CycleGAN} methods as our baselines for paired and unpaired training, respectively. This preference is made based on the effectiveness of these methods across a variety of tasks and datasets. In the figure, Pix2Pix is used to show ASL based model for paired approach. Our model could be integrated into any other paired or unpaired GAN model.

Our model consists of a baseline GAN, a panoptic segmentation network (\textit{Seg}) and two discriminators (\textit{$D_{M}$} and \textit{$D_{B}$}). Panoptic segmentation network is trained offline on the COCO Stuff~\cite{coco_stuff} dataset and
its weights are frozen during the training of our model. Fake and real images are fed to the \textit{Seg} network to get real and fake segmentation maps. Then, these two segmentation maps are given to the discriminators to classify them as fake or real. We designed three variants of our model to embed different levels of segmentation feedback to the sketch to image translation pipeline.

The first variant utilizes the full (multiclass) segmentation map of an image where all foreground and background classes (a total of 135 classes) are considered. In this model, ground-truth color image $I_{real}$ and the generated color image $I_{fake}$ are fed to \textit{Seg} which outputs full segmentation maps for both images. Then, these two outputs are given to a discriminator network \textit{$D_{M}$} to discriminate between real and fake segmentation maps. We call this model as \textit{Multi-class} in the rest of the paper.

As a higher level of abstraction, grouping objects as background and foreground alone may yield sufficient information. The second variant of our model
uses only two classes (background and foreground) in the segmentation map by grouping all foreground classes into one and all background classes into another class.
In this model, which we refer to as \textit{Binary},
binary segmentation outputs for real and fake images are fed to a discriminator network \textit{$D_{B}$} to discriminate between real and fake ones. Finally, our third variant is the union of the above two. It contains both discriminators, and is named as \textit{Combined}.

Overall loss function for our model is the sum of losses of the baseline GAN model ($L_G$) and the two additional discriminators' ($L_B$ and $L_M$).
That is, the objective function is:
\begin{equation*}
\begin{aligned}
 \label{eq:loss}
 \mathcal{L} = w_gL_G + w_bL_B + w_mL_M
  \end{aligned}
\end{equation*}

Let $Seg_B$ and $Seg_M$ correspond to the panoptic segmentation networks in \textit{Binary} and \textit{Multi-class} cases. The additional losses that we introduce, ${L}_{B}$ and ${L}_{M}$, are defined as:

\begin{equation*}
\begin{aligned}
 \label{eq:adv_loss_b}
  \mathcal{L}_{B}(G, D_B, Seg_B) = {} &
  \sum_{i}log(D_B(Seg_B(y_i))) +  \\
  & \sum_{i}log(1 - D_B(Seg_B(G(x_i)))
  \end{aligned}
\end{equation*}
\begin{equation*}
\begin{aligned}
 \label{eq:adv_loss_m}
  \mathcal{L}_{M}(G, D_M, Seg_M) = {} &
  \sum_{i}log(D_M(Seg_M(y_i))) +  \\
  & \sum_{i}log(1 - D_M(Seg_M(G(x_i)))
  \end{aligned}
\end{equation*}

Let $x_i$ be an input sketch image, and $y_i$ be the corresponding ground truth color image. When the baseline GAN model is Pix2Pix~\cite{pix2pix}, GAN loss $L_G$ is formulated as:
\begin{equation*}
 \label{eq:adv_loss_pix2pix}
   \mathcal{L}_{G}(G, D) = {}
  \sum_{i}log(D(x_i)) + \sum_{i}log(1 - D(G(x_i)) + \sum_{i} \| y_i - G(x_i)\|
\end{equation*}
When baseline is CycleGAN~\cite{cyclegan}, $L_G$ for direction $X \rightarrow Y$  is:
\begin{equation*}
 \label{eq:adv_loss_cyclegan}
 \begin{aligned}
   \mathcal{L}_{G}(G_X, G_Y, D_X) = {}  &
  \sum_{j}log(D_X(y_j)) +  \sum_{i}log(1 - D_X(G_X(x_i)) \\ & +   \sum_{i} \| x_i - G_Y(G_X(x_i))\|
  \end{aligned}
\end{equation*}
where $G_X$ maps input sketches to color images, and $G_Y$ maps the color images back to sketch domain. $D_X$ is the discriminator for domain $X$, i.e. sketches. Final $L_G$ for CycleGAN is the sum of above formulation for two directions.

We analysed the effect of each component in the objective function and, set $w_g$, $w_b$ and $w_m$ to $1$ based on the experimental analysis (see Section~\ref{sec:quant_analysis}). Note that, for the \textit{Binary} model  $w_m$, and for the \textit{Multi-class} model $w_b$ is set to 0 respectively.

\begin{figure}
\captionsetup[subfigure]{labelformat=empty}
\centering
\begin{subfigure}[b]{0.15\textwidth}
\includegraphics[width=1.0\textwidth]{./figures/seg/da_squash_and_a_squeeze_2.jpg}
\end{subfigure}
\begin{subfigure}[b]{0.15\textwidth}
\includegraphics[width=1.0\textwidth]{./figures/seg/000000010471.jpg}
\end{subfigure}
\begin{subfigure}[b]{0.15\textwidth}
\includegraphics[width=1.0\textwidth]{./figures/seg/000000038552.jpg}
\end{subfigure}
\begin{subfigure}[b]{0.22\textwidth}
\includegraphics[width=1.0\textwidth]{./figures/seg/ADE_train_00000218.jpg}
\end{subfigure}
\begin{subfigure}[b]{0.22\textwidth}
\includegraphics[width=1.0\textwidth]{./figures/seg/ADE_train_00000455.jpg}
\end{subfigure}
\caption{Sample segmentations using general purpose panoptic segmentation network on different datasets. The model generalizes well to several domains.}
\label{fig:seg-samples}
\end{figure}

\begin{figure*}[ht]
\centering
\begin{tabular}{cccccc}
\shortstack{Input \\ { }} & \shortstack{GT \\ { }} & \shortstack{CycleGAN \\ { }}  & \shortstack{C-ASL \\ (Multi-class)} & \shortstack{C-ASL\\ (Binary)} & \shortstack{C-ASL\\(Combined)} \\
\includegraphics[width=0.14\textwidth,  ,valign=m, keepaspectratio,]{./figures/visual_res/ade20k/unpaired/sketch/ADE_val_00000129_real_A.png}  \hspace*{-12pt} &
\includegraphics[width=0.14\textwidth,  ,valign=m, keepaspectratio,]{./figures/visual_res/ade20k/unpaired/gt/ADE_val_00000129.jpg} \hspace*{-12pt} &
\includegraphics[width=0.14\textwidth,  ,valign=m, keepaspectratio,]{./figures/visual_res/ade20k/unpaired/baseline/ADE_val_00000129_fake_B.png} \hspace*{-12pt} &
\includegraphics[width=0.14\textwidth,   ,valign=m, keepaspectratio,]{./figures/visual_res/ade20k/unpaired/split/ADE_val_00000129_fake_B.png} \hspace*{-12pt} &
\includegraphics[width=0.14\textwidth,   ,valign=m, keepaspectratio,]{./figures/visual_res/ade20k/unpaired/merged/ADE_val_00000129_fake_B.png} \hspace*{-12pt} &
\includegraphics[width=0.14\textwidth,   ,valign=m, keepaspectratio,]{./figures/visual_res/ade20k/unpaired/both/ADE_val_00000129_fake_B.png} \hspace*{-12pt} \\
\noalign{\vskip 1mm}
\includegraphics[width=0.14\textwidth,  ,valign=m, keepaspectratio,]{./figures/visual_res/axel/unpaired/sketch/droom_on_the_broom_15_real_A.png}  \hspace*{-12pt}&
\includegraphics[width=0.14\textwidth,  ,valign=m, keepaspectratio,]{./figures/visual_res/axel/unpaired/gt/droom_on_the_broom_15.jpg} \hspace*{-12pt}&
\includegraphics[width=0.14\textwidth,  ,valign=m, keepaspectratio,]{./figures/visual_res/axel/unpaired/baseline/droom_on_the_broom_15_fake_B.png} \hspace*{-12pt}&
\includegraphics[width=0.14\textwidth,   ,valign=m, keepaspectratio,]{./figures/visual_res/axel/unpaired/split/droom_on_the_broom_15_fake_B.png} \hspace*{-12pt} &
\includegraphics[width=0.14\textwidth,   ,valign=m, keepaspectratio,]{./figures/visual_res/axel/unpaired/merged/droom_on_the_broom_15_fake_B.png} \hspace*{-12pt} &
\includegraphics[width=0.14\textwidth,   ,valign=m, keepaspectratio,]{./figures/visual_res/axel/unpaired/both/droom_on_the_broom_15_fake_B.png} \hspace*{-12pt}\\
\midrule
\shortstack{Input \\ { }} & \shortstack{GT \\ { }} & \shortstack{Pix2Pix \\ { }}  & \shortstack{P-ASL \\ (Multi-class)} & \shortstack{P-ASL\\ (Binary)} & \shortstack{P-ASL\\(Combined)} \\
\includegraphics[width=0.14\textwidth,  ,valign=m, keepaspectratio,]{./figures/visual_res/elephant/paired/sketch/0044_real_A.png}  \hspace*{-12pt}&
\includegraphics[width=0.14\textwidth,  ,valign=m, keepaspectratio,]{./figures/visual_res/elephant/paired/gt/0044_real_B.png} \hspace*{-12pt}&
\includegraphics[width=0.14\textwidth,  ,valign=m, keepaspectratio,]{./figures/visual_res/elephant/paired/baseline/0044_fake_B.png} \hspace*{-12pt}&
\includegraphics[width=0.14\textwidth,   ,valign=m, keepaspectratio,]{./figures/visual_res/elephant/paired/split/0044_fake_B.png} \hspace*{-12pt} &
\includegraphics[width=0.14\textwidth,   ,valign=m, keepaspectratio,]{./figures/visual_res/elephant/paired/merged/0044_fake_B.png} \hspace*{-12pt} &
\includegraphics[width=0.14\textwidth,   ,valign=m, keepaspectratio,]{./figures/visual_res/elephant/paired/both/0044_fake_B.png} \hspace*{-12pt}\\
\noalign{\vskip 1mm}
\includegraphics[width=0.14\textwidth,  ,valign=m, keepaspectratio,]{./figures/visual_res/sheep/paired/sketch/0001_real_A.png}  \hspace*{-12pt}&
\includegraphics[width=0.14\textwidth,  ,valign=m, keepaspectratio,]{./figures/visual_res/sheep/paired/gt/0001_real_B.png} \hspace*{-12pt}&
\includegraphics[width=0.14\textwidth,  ,valign=m, keepaspectratio,]{./figures/visual_res/sheep/paired/baseline/0001_fake_B.png} \hspace*{-12pt}&
\includegraphics[width=0.14\textwidth,   ,valign=m, keepaspectratio,]{./figures/visual_res/sheep/paired/split/0001_fake_B.png} \hspace*{-12pt} &
\includegraphics[width=0.14\textwidth,   ,valign=m, keepaspectratio,]{./figures/visual_res/sheep/paired/merged/0001_fake_B.png} \hspace*{-12pt} &
\includegraphics[width=0.14\textwidth,   ,valign=m, keepaspectratio,]{./figures/visual_res/sheep/paired/both/0001_fake_B.png} \hspace*{-12pt}  \\
\end{tabular}
\caption{Sample results from baselines and our model with different settings. Input images on each row are from bedroom, illustration, elephants and sheep datasets, respectively. First two rows display results of unpaired training (baseline is CycleGAN), and last two rows show results for paired training (baseline is Pix2Pix).
On bedroom and elephant datasets \textit{Binary}, on illustration and sheep datasets \textit{Combined} setting gave best results for both training schemes.}
\label{fig:all-results}
\end{figure*}

\section{Datasets}
\label{sec:dataset}

We evaluated our models on five challenging datasets (see Table~\ref{tab:advsegloss_dataset}). The first dataset consists of bedroom images from the ADE20k indoor dataset~\cite{ade20k}, with $1355$ train and $135$ test images. The second dataset is Cityscapes~\cite{Cordts2016Cityscapes} dataset which contains $2975$ training and $500$ test images. The third dataset~\cite{ganilla} contains illustrations from children's books by Alex Scheffler, with $659$ train and $131$ test images. The fourth and fifth ones were curated by us from the COCO dataset. We collected images containing elephant or sheep. Note that these images may also contain other foreground/background objects such as person, animals, mountains, grass and sky. Elephant dataset contains $1800$ train and $343$ test images, and the sheep dataset has $1300$ train and $229$ test images.
Example images from these datasets and their segmentation outputs are shown in Figure~\ref{fig:seg-samples}.

\begin{table}
\caption{Statistics of the datasets used in our experiments.}
\label{tab:advsegloss_dataset}
\centering
\resizebox{0.75\columnwidth}{!}{
\begin{tabular}{lcc}
\hline
Dataset & Train Images &  Test Images \\
\hline
ADE20k Bedroom       & 1355    &  135   \\
Cityscapes          & 2975 & 500 \\
Illustration & 659    &  131     \\
COCO Elephant       & 1800    &  343     \\
COCO Sheep             & 1300    & 229     \\
\hline
\end{tabular}}
\end{table}

Edge images are extracted using the HED~\cite{hed} method. In the first two columns of Figure~\ref{fig:all-results}, we present sample natural and edge images for all the datasets. It can be seen that the images contain a variety of foreground and background objects, also it is hard to figure out the source dataset for some images.

\begin{table}
\caption{Comparison with CycleGAN~\cite{cyclegan} on \textbf{unpaired sketch-to-image translation} task in terms of \textbf{FID scores}, lower is better.}
\label{tab:advsegloss_fid_scores_unpaired_s2p}
\begin{center}
\resizebox{\columnwidth}{!}{
\begin{tabular}{ccccc}
 \toprule
\shortstack{Dataset\\ { }} & \shortstack{CycleGAN\\ { }} & \shortstack{C-ASL\\(Multi-class)} & \shortstack{C-ASL\\(Binary)} & \shortstack{C-ASL\\(Combined)} \\
 \midrule
Bedroom         & 113.1 & 111.7 & \textbf{87.1} & 93.2       \\
Cityscapes         & 62.9 & 64.1 & 64.9 & \textbf{59.1}          \\
Illustration         & 213.6 & 206.9 & 204.8 & \textbf{189.4}          \\
 Elephant        & 126.4 & 103.9 & \textbf{91.9} & 116.9      \\
Sheep                & 209.3 & 207.2 & 236.1 & \textbf{196.8}     \\
 \bottomrule
\end{tabular}}
\end{center}
\vspace{-5mm}
\end{table}

\section{Experiments}
\label{sec:exp}

We used PyTorch~\cite{pytorch} to implement our models.
We use sketch images as source domain, and color images as target domain. All training images (i.e. color and sketch images) are resized to $256\times256$ pixels. We train all models for $200$ epochs using the Adam optimizer~\cite{adam} with a learning rate of $0.0002$. We conducted all our experiments on a NVIDIA Tesla V100 GPU.

We compared our models with Pix2Pix~\cite{pix2pix} and AutoPainter (AP)~\cite{autopainter} for paired and CycleGAN~\cite{cyclegan} for unpaired setting on \textbf{sketch-to-image translation} task. We used the official implementations that are publicly available. Baseline models are trained for $200$ epochs.
Our proposed ASL model that uses Pix2Pix as the baseline GAN model is referred to as P-ASL, and similarly C-ASL refers to the model that uses CycleGAN.

\subsection{Quantitative Analysis}
\label{sec:quant_analysis}

To quantitatively evaluate the quality of generated images, we used the widely adopted Frechet Inception Distance (FID)~\cite{FID} metric. FID score measures the distance between the distributions of the generated and real images. Lower FID score indicates the higher similarity between two image sets.

\begin{table}
\caption{Comparison with AutoPainter~\cite{autopainter} and Pix2Pix~\cite{pix2pix} on \textbf{paired sketch-to-image translation} task in terms of \textbf{FID scores}, lower is better.}
\label{tab:advsegloss_fid_scores_paired_s2p}
\begin{center}
\resizebox{\columnwidth}{!}{
\begin{tabular}{cccccc}
 \toprule
\shortstack{Dataset\\ { }} & \shortstack{Auto\\{Painter}} & \shortstack{Pix2Pix\\ { }} & \shortstack{P-ASL\\(Multi-class)} & \shortstack{P-ASL\\(Binary)} & \shortstack{P-ASL\\(Combined)} \\
 \midrule
Bedroom         & 206.8 & 100.5 & 100.0 & \textbf{95.1} & 110.1      \\
Cityscapes      & 151.3  & 74.1 & \textbf{69.9} & 71.6  & 71.2          \\
Illustration    & 272.0 &180.0 & 176.9 & 178.0 & \textbf{175.7}          \\
Elephant        & 155.1 & 83.5   & 85.8   & \textbf{78.8} & 82.8      \\
Sheep           & 233.1 & 157.0 & 159.9 & 162.0 & \textbf{150.5}     \\
 \bottomrule
\end{tabular}}
\end{center}
\vspace{-5mm}
\end{table}

On Bedroom and Cityscapes datasets where ground truth segmentation maps are available, we also calculate the mean Intersection over Union (mIoU) scores on colorized images. We forward each colorized image to an off-the-shelf segmentation model trained on these two datasets separately. mIoU score measures the quality of the segmentation. We argue that better colorized images should yield higher mIoU scores.

We present FID scores for unpaired translation in Table~\ref{tab:advsegloss_fid_scores_unpaired_s2p} and  paired translation in Table~\ref{tab:advsegloss_fid_scores_paired_s2p}. FID scores are inline with the visual inspections (see Figure~\ref{fig:all-results}), for all the datasets, at least one variant of our model performed better than the baseline.

First of all, when we compare FID scores of two training schemes and baseline models, paired training (Pix2Pix) performed better than unpaired training, as expected. However, our ``adversarial segmentation loss'' affected the results of paired and unpaired cases differently. For instance, on elephant dataset our models improved baseline up to $35$ points for unpaired case, but only $5$ points for paired case.

Another crucial observation is that segmentation guidance closed the gap between unpaired and paired training results. Best FID scores for unpaired models on bedroom, illustration and elephant datasets become very close to or even better than paired training. For instance on the elephant dataset, the initial $40+$ point FID gap ($126$ vs $83$) dropped to $13$ ($92$ vs $79$) on \textit{Binary} setting. Here the only exception is the sheep dataset. Since the sheep dataset contains various complex objects, unpaired and paired models failed to generate plausible images.

\begin{table}
\caption{Comparison with CycleGAN on \textbf{unpaired sketch-to-image translation} task in terms of \textbf{mIoU scores}, higher is better.}
\label{tab:advsegloss_miou_scores_unpaired_s2p}
\begin{center}
\resizebox{\columnwidth}{!}{
\begin{tabular}{cccccc}
 \toprule
\shortstack{Dataset\\ { }} & \shortstack{CycleGAN\\ { }}  & \shortstack{C-ASL\\(Multi-class)} & \shortstack{C-ASL\\(Binary)} & \shortstack{C-ASL\\(Combined)} & \shortstack{Oracle \\ { }}\\
 \midrule
Bedroom         & 5.20 &   \textbf{6.71} & 6.58 & 6.44 &  20.62    \\
Cityscapes      & 15.67 &  14.63 & 13.56 & \textbf{15.68}  &  44.85      \\
 \bottomrule
\end{tabular}}
\end{center}
\vspace{-5mm}
\end{table}

\begin{table}
\caption{Comparison with Pix2Pix on \textbf{paired sketch-to-image translation} task in terms of \textbf{mIoU scores}, higher is better.}
\label{tab:advsegloss_miou_scores_paired_s2p}
\begin{center}
\resizebox{\columnwidth}{!}{
\begin{tabular}{ccccccc}
 \toprule
\shortstack{Dataset\\ { }} & \shortstack{Auto\\ {Painter}}  &
\shortstack{Pix2Pix\\ { }}  & \shortstack{P-ASL\\(Multi-class)} & \shortstack{P-ASL\\(Binary)} & \shortstack{P-ASL\\(Combined)} & \shortstack{Oracle \\ { }}\\
 \midrule
Bedroom         & 2.08 & 6.49 & 6.95  &  \textbf{7.39} & 6.60 &  20.62    \\
Cityscapes      & 6.02  & 18.71 & 18.63 & 18.70  & \textbf{18.74}  &  44.85      \\
 \bottomrule
\end{tabular}}
\end{center}
\vspace{-5mm}
\end{table}

\begin{table}
\caption{User Study results.}
\label{tab:user_study}
\begin{center}
\begin{tabular}{lcc}
\toprule
 Dataset & CycleGAN & C-ASL \\
 \midrule
Bedroom         & 20.0 & \textbf{80.0}  \\
Illustration        & 27.0 & \textbf{73.0}  \\
Elephant        & 39.1 & \textbf{60.9}  \\
Sheep                & 19.1 & \textbf{80.9}  \\
\hline
Cityscapes (L2P)         & 25.7  & \textbf{74.3}  \\
 \bottomrule
\end{tabular
\end{center}
\vspace{-5mm}
\end{table}

\begin{table}
\caption{Effect of changing the $w_b$ and $w_m$ values, $w_b$ is used as $1.0$ for all experiments. Using $1.0$ for both weights yields the best FID score on the ADE20k bedroom images for the task of sketch-to-image translation.}
\label{tab:advsegloss_abl}
\centering
\resizebox{0.70\columnwidth}{!}{
\begin{tabular}{lccccc}
\toprule
& \multicolumn{5}{c}{$w_b$ and $w_m$}   \\
\cmidrule(lr){2-6}
& 0.1 & 0.5 & 1.0 & 5.0 & 10.0 \\
\midrule
FID & 114.8 & 114.5 & \textbf{93.2} & 147.8 & 104.6 \\
 \bottomrule
\end{tabular}}
\vspace{-3mm}
\end{table}

We show mIoU scores for unpaired translation in Table~\ref{tab:advsegloss_miou_scores_unpaired_s2p} and paired translation in Table~\ref{tab:advsegloss_miou_scores_paired_s2p}. We also present oracle performances of the segmentation method on both datasets. On mIoU metric, again for all the datasets, at least one of the variants of our model performed better than the baseline.

When we look at the best performing settings on different datasets, structure of the dataset has an effect on the results. For instance, even though one is an indoor and the other one is an outdoor dataset, bedroom and elephant images are composed of similar structure. FG/BG ratios and placements of them in these datasets are similar across all images, i.e. walls, ceiling and floors in bedroom images are always positioned in the same places on different images. Also elephant images contain very few FG objects, i.e. only elephants most of the time, and large BG areas such as grass, trees and sky. On these two datasets, \textit{Binary} setting which considers FG/BG classes only gave the best FID score.
On the other hand, illustration and sheep images got a variety of FG objects and scenes. On such datasets, using only a FG/BG discriminator even degrades the performance.

Our model has two important parameters, $w_b$ and $w_m$, to control the effect of segmentation discriminators. To find the best possible values, we conducted experiments by training our models on the ADE20k bedroom images on the unpaired sketch to image translation task (see Table~\ref{tab:advsegloss_abl}). Using a small value like $0.1$ gives a similar score to baseline CycleGAN. On the other hand using a big value like $5.0$ increased the FID score dramatically. Setting $w_b$ and $w_m$ to $1.0$ resulted in the best FID score, thus the weights are set to $1.0$ in all experiments.

\subsection{Qualitative Analysis and User Study}

We present visual results of sketch colorization for our model and the baseline models in the Figure~\ref{fig:all-results}.
On bedroom and illustration datasets, we show results of unpaired training, and on elephant and sheep datasets we show paired training results.

On the bedroom dataset, the \textit{Binary} setting generates better images compared to baselines and other settings. Colors are uniform across the object parts in this setting. There are defective colors in the CycleGAN results such as the bottom of the bed and floor. On the illustration dataset, the baseline model performed poorly. Objects are hard to recognize and most importantly colors are not proper at all. On the other hand, \textit{Multi-class} and \textit{Combined} settings generate significantly better images i.e. generated objects and background got consistent colors. Finally, on elephant and sheep datasets although generated images are not very visually appealing for all the methods, segmentation guided images are quite appealing compared to baseline models'. On the elephant dataset \textit{Binary}, on the sheep dataset \textit{Combined} setting performed the best.

We conducted a user study to measure realism of generated images. We show two random images (at random positions, left or right) which were generated with CycleGAN and our best setting (lowest FID score) for all four datasets, and asked participants to select the more realistic one.

We collected a total of 115 survey inputs from 39 different users. We ask users to evaluate 4 S2P models and 1 L2P model. In Table~\ref{tab:user_study}, we present results of the user study in terms of preference percentages of each model.  User study results are inline with the FID score results, on all datasets, images generated by our model were preferred by the users most of the time.
On sheep and elephant datasets, users struggled to select an answer. Color distributions and shapes of FG objects are two dominant factors which lead user preferences.

\begin{figure}
\captionsetup[subfigure]{labelformat=empty}
\centering
\begin{subfigure}[b]{0.11\textwidth}
 \caption{Input}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/cityscapes/104_B_real_A.png}
 \vspace{0.05 cm}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/cityscapes/142_B_real_A.png}
 \vspace{0.05 cm}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/cityscapes/281_B_real_A.png}
\end{subfigure}
\begin{subfigure}[b]{0.11\textwidth}
 \caption{GT}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/cityscapes/104_A.jpg}
 \vspace{0.05 cm}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/cityscapes/142_A.jpg}
 \vspace{0.05 cm}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/cityscapes/281_A.jpg}

\end{subfigure}
\begin{subfigure}[b]{0.11\textwidth}
 \caption{CycleGAN}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/cityscapes/104_B_fake_B_cyclegan.png}
 \vspace{0.05 cm}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/cityscapes/142_B_fake_B_cyclegan.png}
 \vspace{0.05 cm}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/cityscapes/281_B_fake_B_cyclegan.png}

\end{subfigure}
\begin{subfigure}[b]{0.11\textwidth}
 \caption{Binary}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/cityscapes/104_B_fake_B_merged.png}
 \vspace{0.05 cm}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/cityscapes/142_B_fake_B_merged.png}
 \vspace{0.05 cm}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/cityscapes/281_B_fake_B_merged.png}

\end{subfigure}
\caption{Sample results on Cityscapes dataset for CycleGAN and our best model. In the first image, CycleGAN generates buildings instead of trees, also there are defects on the road. Other two images are blurry and lack details. On the other hand, in all cases our \textit{Binary} setting generates more visually appealing images.}
\label{fig:city-results}
\end{figure}

\begin{table}
\caption{Comparison with CycleGAN~\cite{cyclegan} on \textbf{unpaired label-to-photo translation}  task in terms of \textbf{FID scores}, lower is better.}
\label{tab:advsegloss_fid_scores_unpaired_l2p}
\begin{center}
\resizebox{0.88\columnwidth}{!}{
\begin{tabular}{ccccc}
 \toprule
\shortstack{Dataset\\ { }} & \shortstack{CycleGAN\\ { }} & \shortstack{C-ASL\\(Multi-class)} & \shortstack{C-ASL\\(Binary)} & \shortstack{C-ASL\\(Combined)} \\
 \midrule
Bedroom         & 84.2 & 86.9 & 85.6 & \textbf{78.9}       \\
Cityscapes         & 83.0 & 70.9 & \textbf{64.8} & 66.0          \\
 \bottomrule
\end{tabular}}
\end{center}
\vspace{-5mm}
\end{table}

\begin{table}
\caption{Comparison with Pix2Pix~\cite{pix2pix} on \textbf{paired label-to-photo translation} task in terms of \textbf{FID scores}, lower is better.}
\label{tab:advsegloss_fid_scores_paired_l2p}
\begin{center}
\resizebox{0.88\columnwidth}{!}{
\begin{tabular}{ccccc}
 \toprule
\shortstack{Dataset\\ { }} & \shortstack{Pix2Pix\\ { }} & \shortstack{P-ASL\\(Multi-class)} & \shortstack{P-ASL\\(Binary)} & \shortstack{P-ASL\\(Combined)} \\
 \midrule
Bedroom         & 128.1 & 118.2 & 122.3  & \textbf{110.1}     \\
Cityscapes      & 79.5 & 78.4  & \textbf{72.9} & 77.6          \\
 \bottomrule
\end{tabular}}
\end{center}
\end{table}

\subsection{Label to Photo Translation}

We also experimented with label-to-photo (L2P) translation task to show the effectiveness of our model in a different task where adversarial segmentation loss could be helpful.
In L2P task, we use ADE20k bedroom and Cityscapes datasets. Similar to S2P task, all images are resized to $256x256$ pixels. We train L2P models for $200$ epochs using the Adam optimizer~\cite{adam} with a learning rate of $0.0002$.
We show FID scores for unpaired L2P in Table~\ref{tab:advsegloss_fid_scores_unpaired_l2p} and paired translation in Table~\ref{tab:advsegloss_fid_scores_paired_l2p}. For unpaired translation our best performing method improves the baseline for more than $5$ points on Bedroom and almost $20$ points on Cityscapes datasets. Similarly on the paired translation, the improvements regard to the baseline reaches $18$ points.

In Table~\ref{tab:advsegloss_miou_scores_unpaired_l2p} and Table~\ref{tab:advsegloss_miou_scores_paired_l2p}, we present mIoU scores for unpaired and paired L2P translation, respectively. For both cases, our best performing variant outperforms the baseline method.

We present visual results in Figure~\ref{fig:city-results} for only the baseline model and our best performing setting \textit{Binary} for unpaired translation. Our model generates more photo-realistic images, also generated images comply with the input label maps better.

\begin{table}
\caption{Comparison with CycleGAN on \textbf{unpaired label-to-photo translation}  task in terms of \textbf{mIoU scores}, higher is better.}
\label{tab:advsegloss_miou_scores_unpaired_l2p}
\begin{center}
\resizebox{\columnwidth}{!}{
\begin{tabular}{cccccc}
 \toprule
\shortstack{Dataset\\ { }} & \shortstack{CycleGAN\\ { }}  & \shortstack{C-ASL\\(Multi-class)} & \shortstack{C-ASL\\(Binary)} & \shortstack{C-ASL\\(Combined)} & \shortstack{Oracle \\ { }}\\
 \midrule
Bedroom         & 5.70 & 5.88 & 6.10 & \textbf{6.69} &  20.62    \\
Cityscapes      & 20.13 & \textbf{21.45} & 20.70 & 19.61  &  44.85      \\
 \bottomrule
\end{tabular}}
\end{center}
\vspace{-5mm}
\end{table}

\begin{table}
\caption{Comparison with Pix2Pix on \textbf{paired label-to-photo translation} task in terms of \textbf{mIoU scores}, higher is better.}
\begin{center}
\resizebox{\columnwidth}{!}{
\begin{tabular}{cccccc}
 \toprule
\shortstack{Dataset\\ { }}  &
\shortstack{Pix2Pix\\ { }}  & \shortstack{P-ASL\\(Multi-class)} & \shortstack{P-ASL\\(Binary)} & \shortstack{P-ASL\\(Combined)} & \shortstack{Oracle \\ { }}\\
 \midrule
Bedroom         & 1.56 & 1.59 &  1.54 & \textbf{1.62} &  20.62    \\
Cityscapes      & 8.62 & 8.66  & \textbf{8.69} & 8.56  &  44.85      \\
 \bottomrule
\end{tabular}}
\end{center}
\label{tab:advsegloss_miou_scores_paired_l2p}
\vspace{-5mm}
\end{table}

\begin{figure}[h]
\captionsetup[subfigure]{labelformat=empty}
\centering
\begin{subfigure}[b]{0.10\textwidth}
\caption{Input}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/elephant/unpaired/sketch/000000076460_real_A.png}
\vspace{0.05 cm}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/sheep/unpaired/sketch/000000365469_real_A.png}
\end{subfigure}
\begin{subfigure}[b]{0.10\textwidth}
\caption{GT}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/elephant/unpaired/gt/000000076460.jpg}
\vspace{0.05 cm}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/sheep/unpaired/gt/000000365469.jpg}
\end{subfigure}
\begin{subfigure}[b]{0.10\textwidth}
\caption{CycleGAN}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/elephant/unpaired/baseline/000000076460_fake_B.png}
\vspace{0.05 cm}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/sheep/unpaired/baseline/000000365469_fake_B.png}
\end{subfigure}
\begin{subfigure}[b]{0.10\textwidth}
\caption{+ASL}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/elephant/unpaired/merged/000000076460_fake_B.png}
\vspace{0.05 cm}
\includegraphics[width=1.0\textwidth]{./figures/visual_res/sheep/unpaired/both/000000365469_fake_B.png}
\end{subfigure}
\caption{Sample results on elephant and sheep datasets for CycleGAN and our best model. Realism of both models are not satisfactory, however, especially colors of BG areas are better in our results. }
\label{fig:realism-res}
\vspace{-5mm}
\end{figure}

\subsection{Limitations}
\label{sec:advsegloss_limitations}

Figure~\ref{fig:realism-res} presents examples on elephant and sheep datasets where both baseline and our best performing model suffer from low visual realism. The main reason for that is these datasets contains complex foreground and background objects. However, our method performs significantly better than the baseline. Especially on the first row, colorized image using our method resembles more to the ground truth image.

\section{Conclusion}
\label{sec:conclusion}

In this study, we present a new method for the sketch colorization problem. Our method utilizes a general purpose image segmentation network and adds an adversarial segmentation loss (ASL) to the regular GAN loss. ASL could be integrated to any GAN model, and works even if the dataset does not have segmentation labels. We used CycleGAN and Pix2Pix as baseline GAN models.
We conducted extensive evaluations on various datasets including bedroom, sheep, elephant and illustration images and evaluate the performance both quantitatively (using FID and mIoU scores) and qualitatively (through a user study). We showed that our model outperforms baselines on all datasets on both FID score and user study analysis.

Regarding the limitations of our method, although we improve the baseline both qualitatively and quantitatively, especially elephant and sheep results lack realism. Even the paired training results are not visually appealing on these two datasets, most probably due to the fact that the baseline models are not very successful at generating complex scenes.

\section*{Acknowledgment}

The numerical calculations reported in this paper were fully performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources). Dr. Akbas is supported by the ``Young Scientist Awards Program (BAGEP)'' of Science Academy, Turkey.

\bibliographystyle{elsarticle-num}

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text:
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Rare Migrant Worker Strike Results in One Day Victory
Brian Hioe 05/10/2019 EnglishLaborMay 2019Social Movements
by Brian Hioe
Photo Credit: Brian Hioe
A RARE STRIKE action by migrant workers took place on May 3rd of this month. Even more unusual was the fact that the strike was not only successful but was successful after only just one day.
The strike in question took place in Xizhi in New Taipei city among Vietnamese factory workers at the Meidi Shipin Limited Corporation, specifically at a factory located in the Kangning Science and Technology Park. Workers were demonstrating that they were being forced to work between sixteen and eighteen hours per day and that they were not being paid for being illegally overworked, with this being scrubbed from legal documentation of work hours.
Migrant workers' demonstration outside the Ministry of Labor on April 28th, preceding International Workers' Day on May 1st. Photo credit: Brian Hioe
The strike began on the morning of May 3rd. Management, however, backed down relatively quickly, agreeing to pay 40 million NTD in compensation for unpaid overwork, which led to the strike ending at approximately 6 PM that day. Another contributing factor to the strike ending so quickly was that the New Taipei city government intervened relatively quickly, as did the Taiwan International Workers' Association, which has long been active in organizing and campaigning on behalf of migrant workers in Taiwan.
Workers will be paid 20,000 in compensation each and further compensation may be able to be secured in the next two years. This compensation is to be paid by May 15th. However, management wishes to secure an agreement from workers that they will not strike again, and disputes remain regarding three workers who management wishes to exclude from compensation because their employment at the factory was not more than three continuous months.
As such, it is not impossible that negotiations will break down again, necessitating further labor actions. Crucial to the success of this strike was the coordination of Vietnamese migrant workers in the factory, with 74 out of 79 Vietnamese migrant workers in the factory going on strike, but it is to be seen if any actions going forward will be as well coordinated.
Factory workers are among the largest demographics of migrant workers in Taiwan, working in jobs in traditional industries, high-tech industries, and so-called "3D" jobs that are considered "dangerous, demeaning, and dangerous". Apart from poor working conditions, low pay, and unpaid overtime, migrant factory workers also frequently face dangerous conditions not only in the workplace but with regards to their housing conditions.
As such, migrant workers have in past months been active in calling attention to factory worker deaths caused because of worker dormitories attached to factories. Factory fires are a frequent contributor to worker deaths, because these dormitories are located adjacent to factories, and may have lacking fire protection.
Photo credit: Presidential Office/Flickr/CC
Moreover, like other migrant workers in Taiwan, migrant factory workers are sometimes subject to high fees imposed on them by the employment brokers that arrange for their transportation to and from Taiwan and for their employment within Taiwan. Sometimes migrant workers are forced to take out heavy loans in order to repay their brokers and in other cases, brokers will directly deduct fees from the money they make, such as charging for board and lodging. Consequently, along with other migrant workers, migrant factory workers have called for an end to the current broker system used in Taiwan and the use of direct hiring practices, though apart from some exceptions, such as foodstuff giant I-Mei, few companies employing migrant factory workers have embraced this practice.
Indeed, migrant workers would demonstrate for both demands in a demonstration on May 6th outside the Ministry of Labor. Yet more generally, one observes that migrant factory workers are among the less organized demographics of factory workers, with domestic workers and care workers being better organized compared to migrant factory workers or migrant fishermen working on the high seas.
Namely, domestic workers, care workers, factory workers, and fishermen are the four major categories of migrant workers in Taiwan. However, it proves much more difficult for factory workers and fishermen to organize among their industries, because their workplaces are often remote, in the countryside or deep at sea—unlike domestic workers and care workers who may be located in urban areas and are able to easily gather with other migrant workers or to coordinate with migrant worker labor organizations. It remains to be seen if this will change anytime soon.
migrant factory workers in Taiwanmigrant worker labor in Taiwanmigrant worker organizing in Taiwanmigrant workers in TaiwanVietnamese migrant workers in Taiwan
Brian Hioe
Brian Hioe was one of the founding editors of New Bloom. He is a freelance writer on social movements and politics, and occasional translator. A New York native and Taiwanese-American, he has an MA in East Asian Languages and Cultures from Columbia University and graduated from New York University with majors in History, East Asian Studies, and English Literature. He was Democracy and Human Rights Service Fellow at the Taiwan Foundation for Democracy from 2017 to 2018.
丘琦欣,創建破土的編輯之一,專於撰寫社會運動和政治的自由作家偶而亦從事翻譯工作。他是出生於紐約的台裔美人。他自哥倫比亞大學畢業,是亞洲語言及文化科系的碩士,同時擁有紐約大學的歷史,東亞研究及英文文學三項學士學位。
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Fisheries Agency Working Group On Migrant Fishermen Remains Bound within Conservative Political Framework
About New Bloom
New Bloom is an online magazine covering activism and youth politics in Taiwan and the Asia Pacific, founded in Taiwan in 2014 in the wake of the Sunflower Movement. We seek to put local voices in touch with international discourse, beginning with Taiwan.
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Darussalam is een bestuurslaag in het regentschap Bireuen van de provincie Atjeh, Indonesië. Darussalam telt 842 inwoners (volkstelling 2010).

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Coronet's LS3 Luminaire highlights the modern passenger circulation space and new entrance across 8th Av. at the new Moynihan Station in NY.
SOM's design of the new West End Concourse significantly increases the concourse space in the station complex, improves pedestrian access with new station entries on Eighth Avenue, relieves congestion by providing access to the western ends of existing platforms, and transforms the travel experience for hundreds of thousands of Amtrak and Long Island Rail Road riders.
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Third, our system is deployed on the always update-to-date Linux operating system, and we follow the best practices of Linux server management. Our system is deliberately programmed securely. Our databases are backed up every day, and all important information is logged and backed up as well.


text:
Hala Jaworzyna pod Furkaską lub po prostu Hala Jaworzyna – dawna hala pasterska w polskich Tatrach Zachodnich na zachodniej stronie Doliny Chochołowskiej, pomiędzy Halą Chochołowską a Halą Krytą. Łączna powierzchnia hali wynosiła 85,05 ha, w tym pastwiska stanowiły 3,0 ha, nieużytki 11,75 ha, las 28,25 ha, kosodrzewina 42,05 ha, a dodatkowa powierzchnia serwitutów wynosiła 262,48 ha. Wypas w przeliczeniu na owce wynosił 238 sztuk, wypasano głównie bydło. Hala obejmowała obszary doliny Głębowiec i Doliny Długiej z takimi polanami, jak: Długa Polana (która potem odłączyła się od tej hali), polana Jaworzyna pod Furkaską, Polana pod Jaworki i polana Przysłop pod Furkaską. Polany te były koszone, zasadniczy wypas odbywał się w serwitutowych lasach i na północnych zboczach Bobrowca i wschodnich zboczach Parzątczaka. Powodował ogromne zniszczenia lasów i cennej roślinności alpejskiej na wapiennych zboczach Głębowca. Z rzadkich w Karpatach gatunków roślin stwierdzono występowanie złoci żółtej (na wysokości 1330 m).

W XIX i XX w. na hali tej wypasali mieszkańcy miejscowości: Ciche, Czarny Dunajec, Ratułów, Stare Bystre i Witów. Tereny te są własnością Wspólnoty Leśnej Uprawnionych Ośmiu Wsi. Gdy zostały włączone do TPN, wypas na Hali Jaworzyna został zniesiony.

Przypisy

Jaworzyna pod Furkaską


Home > Buzz	The Hills: New Beginnings? Not For The Pratts!
The Hills: New Beginnings? Not For The Pratts!
Stephanie Pratt revealed that she no longer considers her brother, Spencer, or his wife, Heidi Pratt, family!
The Hills alum, now The Hills: New Beginnings star, Stephanie Pratt, dropped off her family's dirty laundry on her podcast, Pratt Cast.
It seems that Stephanie's issues involve her brother, Spencer Pratt, and his wife, Heidi Montag Pratt….again.
Click here to listen to the podcast for yourself!
Is anyone else suspicious of the timing of this fued? Sound off in the comments!
Be the first to leave Comments for The Hills: New Beginnings? Not For The Pratts!


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State water leaders: Stormy conditions to end soon
Newsom signs executive order to aid recovery
by Olivia Wynkoop and Eli Walsh / Bay City News Foundation
Uploaded: Thu, Jan 19, 2023, 2:06 pm 0
Cars drive in the rain down El Camino Real in Menlo Park on Jan. 5, 2023. Photo by Magali Gauthier.
The parade of winter storms may be coming to an end soon, state water officials said on Monday.
After rain this week — the ninth storm in three weeks — the weather pattern will be transitioning to drier conditions, said Dr. Michael Anderson, state climatologist of the Department of Water Resources.
This is because of higher atmospheric pressure, colder temperatures and lower storm elevations, he said, which will likely bring about dry weather for the rest of the month.
"High pressure really limits the amount of moisture transported into the state, (which is) very different from what we've seen over the past three weeks," Anderson said at a press conference.
Jeremy Arrich of DWR's flood management department said that river levels have receded in most areas of the state, though there are a few areas that may face rising levels and localized flooding later this week.
Arrich said that the full California Flood Operations Center will continue to monitor flood watches in effect in the Bay Area and assist when needed.
In the past two weeks, the operations center has already deployed experts to resolve over 50 storm-related incidents like levee erosions or breaks, seepage, sloughing and sinkholes in 17 counties.
"In terms of flood system performance overall, we do expect to see a number of localized flooding incidents that we've seen over the past couple of weeks to occur across the state during such a prolonged rain event with such intense rainfall and sustained high water — incidents of levee sloughing, steeping sinkholes, minor erosion," Arrich said.
Though the end is in sight, it is still important for residents to listen closely to local law enforcement and emergency departments, he said.
"Local flood managers across the state still have some work to do as water recedes," Arrich said.
Molly White, State Water Project water operations manager at DWR, said that the rainfall has made the statewide water reserve storage at 91% of historical average, though major water reservoirs like Shasta and Trinity still maintain below-average levels.
In the East Bay, DWR will continue to prompt flood control releases from Alameda County's Lake Del Valle, as required by the U.S. Army of Engineers.
Last week, DWR warned Pleasanton residents to avoid creeks and arroyos near the lake, as there may be rapid changes in water levels.
"Based upon the current conditions and the dry and cold forecasts, we do have ample storage to still be able to conserve and gain storage over the next couple of weeks," said White.
Governor expands support in response to atmospheric river storms
Gov. Gavin Newsom signed an executive order Monday intended to expand support for those affected by the atmospheric river storms that have pelted Northern California over the last two weeks.
■ Officials respond to racial slur at San Ramon Valley High basketball game
■ Care home worker charged in death of elderly resident who drank cleaner in Walnut Creek
■ San Ramon Mayor Hudson absent indefinitely while recovering from illness
■ San Ramon City Council poised to adopt Housing Element update
■ Lafayette principal Chaplan placed on leave as district investigates allegations from her time at SRVHS
The order waives work limitations for retired annuitants in all state agencies that are responding to the storm aftermath and makes it easier for care providers and health care facilities to add staff members as needed, particularly in areas that remain heavily flooded.
The order also waives fees for people seeking to replace records like marriage and birth certificates that may have been damaged during the storms and waives school meal requirements for some elementary schools in Merced, which have been closed in recent days due to significant flooding.
"With lingering storms expected through midweek, the state continues to work with local and federal partners to prepare for and respond to flooding, debris flow and other storm-related emergencies," the governor's office said in a statement.
At least 20 people have died as a result of the storms and tens of thousands of people were evacuated after many rivers and creeks jumped their banks and flooded local neighborhoods across the state.
The storms prompted President Joe Biden to issue major disaster and emergency declarations to aid in the emergency response and recovery.
Newsom also declared a state of emergency earlier this month while many local governments did the same.
The text of Newsom's executive order can be found at gov.ca.gov.
Your support is vital to us continuing to bring you political news. Become a member today.
by Olivia Wynkoop and Eli Walsh / Bay City News Foundation /
Uploaded: Thu, Jan 19, 2023, 2:06 pm


"American University Museum is excited about the opportunities to share this rich collection with our students, scholars, the Washington community and beyond," said Jack Rasmussen, director and curator of AU Museum.
The proposed acquisition will include paintings, works on paper, photographs, sculpture and textiles. Works by history's most masterful artists are represented including Pablo Picasso and Rembrandt. Other prominent artists include 16th-century Italian painter Titian, a contemporary of Michelangelo; German Renaissance painter and printmaker Albrecht Durer; American artists Winslow Homer, John Singer Sargent and Andy Warhol; 18th-century British portraitist Thomas Gainsborough; French Impressionist Charles Francois Daubigny; and American sculptor Louise Nevelson.
"William Corcoran's dedication to presenting works by American artists led to his collecting an exceptional body of 19th-century art from Washington D.C., representing the simultaneously national and local identities of art in the emerging capital city," Rasmussen said.
Finally, the AU Museum has been offered the bulk of the Corcoran's famous works on paper collection, where it would be preserved nearly in its entirety. AU Museum's willingness to maintain the integrity of important American collections, such as that of William A. Clark, Olga Hirshhorn and many others, will provide opportunities for scholarship and exhibition for years to come.
The American University Museum at the Katzen Arts Center is open from 11 a.m. to 4 p.m. Tuesday through Sunday. Admission is free. For more information, call 202-885-1300 or look on the Web at http://www.american.edu/cas/museum.


It has happened – we are engaged. By engaged I mean committed. And by committed I mean to an act of craziness that might just save my life completely – medically and psychologically.
The Whole Life Fitness Manifesto, or WLFM, is an approach to improving one's life in many ways and it only takes 2% of your entire day to be involved. 15 minutes of intentional movement, 5 minutes of personal reflection or meditation, and 10 minutes of personal enrichment or education. Any person at any level of fitness can do the WLFM, including the "couch potato" being that I was.
"The Whole Life Fitness Manifesto is not just about physical health but whole-life fitness that blends mindfulness and personal development with equipment-free workouts." This is a free fitness resource with ongoing support in Facebook groups when you sign up on the website here. And this month the bonus is the people at Bulletproof are giving everyone who participates a free copy of Dai's book and the 7 day green smoothie plan.
Caught exercising – the laughter is part of it for us.
I am taking part in the Whole Life Fitness Manifesto to see the change it has on me. I go through periods of fitness where I work hard for a couple days, then I get distracted and stop. I am hoping that by participating in this challenge, I'll find a routine. In this routine I want to find peace and structure that I can use for the rest of my life. I am taking this challenge one day at a time, concentrating on how I feel, and I look forward to seeing where I am at the end of these 28 days.
We reconnect each morning over our daily cup of Bulletproof coffee.
I have always been interested in staying active: street hockey with friends years ago, running on a treadmill, playing with my kids when they were younger, participating in scouting group activities and working with youth in my current job.
A challenge for me has always been my "See-Food" diet. Going to a Pot Luck or Buffet, usually means trying a little bit of everything and going back for seconds of the ones I like. Also, having a "sweet mouth" (not just a sweet tooth) means that nothing in the house containing sugar is safe.
Enter the Whole Life Fitness Manifesto, (WLFM) and Bulletproof coffee. Now there is an intentional, structured plan to build better eating and exercise into my crazy schedule. I know a lot of people say they have a crazy schedule, but for me working 30 hours in a 33-hour period between two jobs is a regular occurrence during the summer.
I am participating in this program so I can continue to be active when opportunities arise. Further, there has been an unbalance in my life that this program aims to address – finances, fitness, faith and family leading to fun – I know at least two of those are out of balance on a good day for me.
Thank you Dai Manuel for putting together this program – it makes a lot of sense to me and I appreciate that there is support along the way to help us as we go.
I feel this has brought us closer together – who knew that would ever happen!
We've been participating in WLFM for a whole week now and HOLY SMOKES can I feel a difference. In my ribs, belly, legs, arms, shoulders, back… There are some places I didn't even know I had muscles that are feeling the change. I also feel less achy as the days go by. I've modified some of the movements to suit my particular needs and I feel pretty good about how things are going.
We've also entered into the world of smoothies – I know late to the game but better late than never! I plan to share some of the awesome recipes I've found and created soon. For the first time in my life I am eating all the fruits and vegetables recommended daily by the Canada Food Guide – pretty amazing news if you know me. The family is too!
There are no pretty pictures of me exercising or first thing in the morning with my Bulletproof coffee LOL.
However, in November I started drinking Bulletproof coffee for the purposes of participating in a marketing campaign. Bulletproof coffee is made up of upgraded coffee, Grass Fed Ghee and Brain Octane Oil. (For more info on that click this link here.) I have had a cup almost every day for the last 2 months. Other than the fact that the coffee this company sells is delicious, does not make me jittery or have sleep problems at the end of the day, by adding the Ghee and Brain Octane reduces my hunger pains. I find I can work for hours at a time without the distraction of hunger.
For more information on the program go to the WLFM site and for more information on the Bulletproof Life go to the Bulletproof site. My earlier post is here and it covers some more benefits I have found having Bulletproof in my life.
With the different schedules it's tricky to do the workouts together – this was one of those rare moments and we were pretty sweaty and pooped!
Join us as we get better, stronger and healthier on my Instagram account, Twitter account or Facebook page.
Congrats Carin!! Sounds like you have made some real lifestyle shifts and it looks like you're inspiring your family to do so! Kudos!
Thanks Ariana! I see you help others go on similar journeys – your website has great stuff in it! Thanks for stopping by!


Walk with Mayor and Council will be held in District 1 on September 27, 2016 starting at 5:30 pm. The walk will begin at the intersection of Houston Lake Road and Kings Chapel Road. The route will include Kings Chapel Road, Sewell Circle, North Davis Avenue and Martha Street.


meyerlemon – A Healthy Pinch.
Many people associate lemons as being part of the summer produce rotation but oddly enough they are a winter fruit, with their brightness punctuating the dreary, rainy season here in Northern California. It's really quite wonderful to see our local farmers markets now showcasing an abundance of citrus fruit such as mandarins, grapefruits and Meyer lemons alongside the heavy root vegetables that define the winter season.
If you're not familiar with the fruit, Meyer lemons are a cross between a lemon and an orange or mandarin with much of the tartness giving way to a light, floral sweetness. Therefore their bright flavor is a natural, welcome swap in a traditional lemon curd.
It's rich, silky texture is perfect as a spread on toast, scones and muffins or swirled into yogurt. So, grab some lemons and get cooking! You really can't go wrong!


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The Days Of Lounge And Chill - Tal Bergman & Tal Rom
Published: July 18 2019
Music artists are a little like explorers. Thanks to their vision and to their way of thinking outside the box, such artists are often capable to go out of their artistic comfort zone and, through time, to find themselves still able to make art at a very high level, in areas where some fans may have not expected maybe 20 years ago.
One of those artists is certainly the Israeli-born drummer and producer Tal Bergman. His exceptional talent and great versatility as a drummer has brought the Los Angeles based artist to work with the elite of the music business, through the last three decades, from Simple Minds to LL Cool J or from Rod Stewart to Billy Idol, just to mention a few.
Always keen to explore all different angles of music as an art form, Bergman has increasingly fallen in love, especially in recent times, with the idea of experimenting alternative ways to fuse music genres together, something that he succeeded to achieve extremely well through one of his latest project, the Super Group called Rock Candy Funk Party.
Bergman, though, has always been and still is a forward thinker, when it comes to music. When he met, few years ago, composer and sound engineer Tal Rom, Bergman started thinking about the possibility of working, together with Rom, on an album that would focus on genres that the drummer and producer had never approached before on a major scale.
Now, finally, the creative union of the two artists has made possible that the aforementioned opportunity would materialize in a fascinating and highly entertaining instrumental album called The Days Of Lounge And Chill.
Produced and mixed by Bergman himself, The Days Of Lounge And Chill is a breathtaking journey into the growth and expansion of genres like Lounge and Chill, through layers of sound coming from different cultures and backgrounds.
It so happens that a listener would find him/herself immersed, through the ten tracks included in the album, in a unique musical trip where Bergman and Rom create sonic pastiches of great depth, from the visionary Emotions Dans L'Espace, where echoes of Bollywood style then leave space to Peruvian layers of sound, to the delightful multi-ethnic excursus of Tabula Rasa, where Desert Music meets Lounge through a very well chiseled musical alchemy created by the two Israeli-born artists.
Bergman's impressive job on drums and percussion (entirely live recorded) and Rom's cinematic feeling created through keyboards are certainly winning factors, on this ambitious project. Throughout the whole record, there is a constant feeling of music created in a very organic way between Rom and Bergman, despite the fact that some may sometimes (unreasonably) label genres like Chill and Lounge slightly more manufactured than other more "pure" genres like Blues or Jazz.
If exploring and researching the infinite highways of music bring, as a result for Bergman and Rom, inspired and excellently played records like The Days Of Lounge And Chill proves to be, this could be just the beginning of a musical partnership that may produce even more fireworks in years to come.
The Days Of Lounge And Chill is out now and it is available via Amazon


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Scanito.com is for sale
Buy Scanito.com
Domain name: Scanito.com
Ideas for Scanito.com
A business called Scanito could develop a new type of scanning technology that would be faster and more accurate than existing scanning methods. This could be a successful business because it would offer a new, innovative way to scan documents and objects, making the process more efficient and effective.
A business called Scanito could create a mobile app that would allow users to scan QR codes and barcodes with their smartphone cameras. This could be a successful business because it would provide a convenient way for people to access information about products and services.
A business called Scanito could develop a new type of 3D scanner that would be able to scan objects in high definition. This could be a successful business because it would offer a new, innovative way to scan objects, providing results that are much more realistic and accurate than existing methods.
Why is Scanito a good name?
The name is unique and easy to remember.
It has a positive connotation associated with scanning and being able to see things clearly.
It's short, simple, and attention-grabbing.
It has the potential to become a household name.
The word "Scanito" contains seven characters and three syllables. Three syllable names are used by established brands like McDonald's, Toshiba and Delicious.
This domain was first registered in April 2016. Older domains have an advantage when ranking in search engines.
A dot-com domain is the most recognized and credible domain for businesses. It shows that you are a professional and legitimate business. Plus, customers are more likely to remember and visit a dot-com website.
Here's one suggestion for how "Scanito.com" could sound when spoken aloud:
Say Scanito.com
Michael Senefro
Brandpa is by far the best branding company I have dealt with over the years. Good aged brandable domains with clean history and reasonable prices. Overall this is a 5/5 service.
Ushir Shah
Just bought a domain through Brandpa, worked with Jowita, transaction was very smooth, quick, and emails were helpful, concise, and pertinent.
Aged Calm Friendly Hardware Image Invented Masculine Mass-market Mobile Modern Scanner Smart home Software Technology VR & AR


text:
Das Rondell ist eine Gedichtform, die aus acht Verszeilen gebildet wird. Das Rondell ist nicht zu verwechseln mit dem Rondel, welches eine französische Gedichtform ist, die aus dreizehn oder vierzehn Versen gebildet wird.

Aufbau und Varianten
Ein Rondell besteht aus acht Verszeilen, die in einer einzigen Strophe angeordnet werden. Diese unterliegen keinem strikten Reimschema oder Metrum. Das wesentliche Merkmal dieser Gedichtform ist das Wiederholen einzelner Verszeilen. Hierbei gibt es zwei unterschiedliche Varianten.

In der verbreitetsten Variante wird die erste Zeile in der vierten und siebenten wiederholt und die zweite Zeile nochmals als letzter Vers eingesetzt. Die anderen Zeilen sind beliebig, wobei manchmal auch Vers zwei und acht gleich sind.

Winter-Rondell 1
 1   Es ist kalt und winterlich,
 2   weshalb Menschen Schnupfen haben.
 3   Nässe, Schnee und Eis decken alles zu.
 4   Es ist kalt und winterlich.
 5   Leer sind Straßen, Felder, Orte.
 6   In den Häusern wohnt noch Leben.
 7   Es ist kalt und winterlich.
 8   weshalb Menschen Schnupfen haben.
Vers eins, vier und sieben beinhalten exakt denselben Wortlaut. In diesem Fall sind auch die Verse zwei und acht identisch.

Eine weitere Variante sieht vor, dass das Rondell nur eine Verszeile wiederholt. Hierbei wird der zweite Vers in der vierten und siebenten Zeile wiederholt. Alle anderen Verszeilen sind beliebig.

Winter-Rondell 2
 1   Es ist kalt und winterlich.
 2   Viele Menschen sind todkrank.
 3   Nässe, Schnee und Eis decken alles zu.
 4   Viele Menschen sind todkrank.
 5   Graue Winde ziehen durch die Welt.
 6   Leer sind Straßen, Felder, Orte.
 7   Viele Menschen sind todkrank.
 8   In den Häusern wohnt noch Leben.
Hier wiederholt sich die Strophe "Viele Menschen sind todkrank" in der zweiten, vierten und siebten Zeile.

Der Titel eines Rondells spielt eine wichtige Rolle. Er ist eine Art Oberbegriff, der den Inhalt des Gedichts bestimmt. Zumeist greifen die Verszeilen, die wiederholt werden, einen Teil des Titels auf oder beziehen sich unmittelbar auf diesen. Die anderen Verse ergänzen meist eher die Wiederholungen.

Weblinks
 Definition auf wortwuchs.net

Lyrische Form


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Apple Galaxy Note 2 iPhone 5 Mobile Phones Samsung
Apple iPhone 5 vs. Samsung Galaxy Note 2
Giants of the iOS and Android market
Apple iPhone 5 and the Samsung Galaxy Note 2 are the best in its domain. The Apple iPhone 5 is the best iOS devices while the Samsung Galaxy Note 2 is the finest among Android phones. The major share of profits and sales in the iOS market and the Android market are due to these two phones.
Apple iPhone 6S REVIEW; Specifications & Features
The Apple iPhones are generally a status symbol for many people as a result of which it has an exquisite type of design which generates a first-class feeling. The previous version of the iPhone was a little heavy compared to its size due to the all glass construction but the new iPhone 5 is much lighter as the material used is a fusion of glass and aluminum. The iPhone 5 with its 4 inch screen is a very palm friendly device. But a few scratches are still there in the design. The Samsung Galaxy Note 2 on the other hand is bigger than the iPhone 5 and narrower and slimmer. But the 5.5 inch display is a tad too big to be kept inside the pocket. The polycarbonate plastic design instead of premium metals and glass makes it much lighter for its size. The Galaxy Note 2 has a design advantage. It has a non-unibody chasis that allows for easy changing of sim cards.
The Apple iPhone 5 has an 1136 by 640 pixel resolution compared to the Samsung Galaxy Note 2's 1280 by 720 pixel resolution. The Galaxy Note 2 has a super AMOLED screen which features three sub pixels for every pixel and generates an awesome picture quality. The AMOLED screen also has a much brighter display and is easily visible even under sunlight. However, the Apple iPhone 5 has 326ppi pixel density compared to Galaxy Note 2's 256ppi. The pixel density with a 4.6% reflectance ratio make the Apple iPhone 5's screen the best for outside use. This low reflectance ratio beats the Samsung Galaxy Note 2 when it comes to viewing under sunlight.
Both the phones feature the latest operating systems among the iOS and the Android versions. The Galaxy Note 2 uses the latest Android OS version 4.1 (Jelly Bean). This OS imparts and iOS type fluidity and runs at 60 fps. The advantage of the Jelly Bean over the iOS is that it is much more user friendly and a widget system so that you can easily spread and access all the information needed on a daily basis.
The Apple iPhone 5 uses an A6 processor (dual core) which has a 1.3 GHz of memory clock speed. The A6 is a customized processor which has architecture between the Cortex A9 and the A15. The Samsung Galaxy Note 2 on the other hand uses an Exynos quad core processor having a memory clock speed of 1.6 GHz. The Samsung Galaxy Note 2 uses an ARM Mali-400 GPU while the iPhone 5 uses tri-core Power VR GPU.
About the Author – Xavier is a freelance writer who recommends you to checkout mobile phone skin.
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by Pulkit Juneja ([email protected])	 October 2, 2015
New Apple MacBook Pro 15-inch with Retina display REVIEW
by Pulkit Juneja ([email protected])	 June 12, 2015
iOS vs Android- War in 2015
by Pulkit Juneja ([email protected])	 April 30, 2015


WisBusiness.com: March 28: Medicaid expansion: Is it right for Wisconsin?
March 28: Medicaid expansion: Is it right for Wisconsin?
Medicaid expansion has become one of the key debating points in the state budget. Gov. Tony Evers proposed it in his two-year spending plan, but Republicans say it's a non-starter.
Hear the details from key players in the debate at a WisPolitics.com issues forum set for Thursday, March 28 at the Medical College of Wisconsin in Wauwatosa.
Jim Jones, the Medicaid director for Gov. Tony Evers' health department, will provide the administration's point of view. Then two key members of the Assembly Health Committee — Committee Chairman Joe Sanfelippo, R-New Berlin, and Rep. Daniel Riemer, D-Milwaukee — will provide the legislative point of view.
This event is sponsored by UW-Milwaukee, Wisconsin Academy of Global Education and Training, ELEVEN25 at Pabst, Milwaukee Police Association, The Firm Consulting, Medical College of Wisconsin and Spectrum.


Chicago is a great city year-round, but it's even better in the summer. When there's no snow to shovel, the Chicago streets are fabulous for walking. Head to Millennium Park, a beautiful landscape sandwiched between skyscrapers and Lake Michigan. Take a blanket and sit on the huge lawn and enjoy many of the free concerts offered in the summer. Walk around and enjoy the outdoor art sculptures, including Cloud Gate by Anish Kapoor, a 110-ton elliptical sculpture with a seamless series of highly polished stainless steel plates that reflect Chicago's skyline.
If you get hot, head over to the Crown Fountain, pictured above. Designed by Spanish artist Jaume Plensa, the Crown Fountain showcases images of Chicago's residents while water flows out from their open mouths. If you want some adventure by the lake, walk down to Navy Pier and ride the Ferris Wheel or just sit on the dock and enjoy the spectacular view.
Don't Miss: Garfield Park Conservatory. There's free parking and free admission, and you can waste hours with kids, a sweetheart, or with yourself in a blissfully beautiful nature experience. A great hidden gem!


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The Roselle Water Feature is a charming water feature. The LED Crystal base is enveloped within a circular arch. Made from high quality ceramic. It will enhance any room or patio. The trickling water will create a calm and tranquil atmosphere for all to enjoy.


Nutrition is a fundamental component of anyone's general health and wellness. It is imperative that your foods is operating for you at all occasions. Go through on!
Your diet regime needs protein every working day. Not only do proteins assist in muscle mass expansion, but it is also valuable for your skin and other organs. They help with your fat burning capacity and mobile procedures. In addition to these advantages, proteins also help your physique struggle off diseases. You can locate substantial quality protein in complete grains, dairy items, fish and fowl, meats, soy products, beans and legumes.
Fiber is an essential part of anyone's diet. Fiber makes you come to feel complete and will help in weight management. It can also assist in decreasing blood cholesterol. Increasing your fiber consumption reduces the chances of establishing coronary heart ailment, variety two diabetes, and specified sorts of most cancers.
Do not just rely on meat to get the protein you want. There are a excellent number of choices accessible for you to pick from. Some of these are nuts, soy, tofu, yogurt and beans. All of these food items can be used in a dish as a main course or as a facet dish. You can preserve your diet program exciting by making use of a assortment of different protein resources.
To sneak some far better nourishment into your diet regime, you can include wholesome, tasteless foods to some thing you're previously consuming. This method is undoubtedly helpful in houses with young children or very picky eaters, but every person can enhance their taking in behavior by using it. You can fortify your baked items with dry milk or pureed white beans. You can improve your family's diet this way, and no one particular has to know.
These guidelines can truly enhance your daily life. Stick to the tips presented right here in order to enhance your target, vitality ranges and reduce tension. Do not wait, try it and see the distinction of how consuming the right kind of meals could make in how you feel.
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Episode 29 includes, are new tapirs really new?, a TetZooCon round up, a quick discussion of and many a cash for question. Questions include the usefulness to eve-devo to palaeontology, the advantage of antlerage, why pterosaurs are better than birds, and the confusing mess of ratite biogeography.
John make a valiant attempt to get Darren to stop droning on and on about superheroes, but he can't be stopped. So take that, podcats listeners.
August 1, 2014 by John Conway.


text:
NUST Official Statement – Calls on Mike Ashley to return £7million to Newcastle United fans
Mike Ashley has been urged to do the right thing with Newcastle United fans.
The club owner asked to return £7million season ticket money to Newcastle United fans.
This request has come courtesy of an NUST Official Statement on Thursday morning.
The Newcastle United Supporters Trust believe that 18,000 fans have had payment taken in full for 2020/21 season tickets back in March (2020), or are still having monthly payments taken from their bank accounts for this season, with most of that (unused) season ticket now paid off.
The Government have repeatedly made clear that fans won't be allowed back into stadiums for some considerable time.
So as NUST say, there is absolutely no justifiable reason as to why Mike Ashley is not allowing fans a refund of their money if they want one.
NUST Official Statement – 15 October 2020:
NUFC Supporters Trust calls for return of fans' £7m
NEWCASTLE United is being urged to return an estimated £7m worth of season-ticket money to hard-pressed fans for games they will never see.
Newcastle United Supporters Trust (NUST) is making a public appeal to the club to "do right by fans" after previous requests fell on deaf ears.
The Trust, which has more than 14,000 members, estimates that Newcastle United currently has at least £7m in its coffers from fans who have paid for season tickets for the current 2020/21 season.
Many of these fans are on long-term deals and some fans have already paid in full for season tickets.
Other fans on long-term deals have seen NUFC continue to take money from their bank accounts every month via direct debit.
This is carrying on despite there being no realistic prospect of supporters being able to get back into stadia until March 2021 at the earliest and comes at a time when the North East has the worst unemployment rate in the country.
NUFC Supporters Trust has called the situation "morally wrong" and is urging the club to refund loyal fans money which many now desperately need.
NUST chairman Alex Hurst said:
"The club confirmed to us earlier this year they have 18,000 fans on long term season ticket deals out of 30,000 season ticket holders.
"These fans are tied in on deals which mean that many either pay for their season tickets up front or have money taken via monthly direct debits.
"With stadia being closed and no realistic prospect of fans returning any time soon, it means some of the club's most loyal supporters are hundreds of pounds out of pocket at a time when money is terribly tight.
"We calculate that a conservative estimate of money the club currently has from fans would be around £7 million or more.
"That money should be in the pockets and homes of the people of the North East, not in the bank account of a football club to be paid back at a yet unspecific date.
"The scandalous introduction of pay per view games at £14.95 means that Newcastle United fans are effectively being expected to pay twice to see their team in action.
"We at the Trust have tried to engage with the club on the matter to get a workable solution but as yet we have had no commitment to addressing the matter, so we are left with no alternative but to make a public appeal for the club to do right by its fans.
"Stop taking payments now and start repaying supporters the money which is rightfully theirs."
Newcastle United were the last club in the Premier League to repay season ticket-holding fans for games which took place behind closed doors at the end of the 2019/20 season.
In addition, no other Premier League team is currently taking money from fans for tickets for the current 2020/21 season.
Alex Hurst said:
"This cannot be allowed to carry on.
"The idea that the club need to keep charging fans a monthly payment for games they will never see is absurd and, in a time of record unemployment, morally wrong.
"The NUFC Supporters Trust is calling on fans, politicians and people of influence within football to back our call and bring pressure on Newcastle United to stop taking money from fans and refund the money owed to us as soon as possible.
"The North East is facing ever greater Covid restrictions and we need our football club to do better and to do right by its fans.
"It's time to stop taking and time to start giving back."
(To join 14,000+ other fans as members of the Newcastle United Supporters Trust go HERE)


text:
In Seriously Ill Kids, Obesity May Be Tied to Higher Death Risk: Study
But more research is needed to confirm the association, experts say
By Amy Norton
TUESDAY, March 12 (HealthDay News) -- Obese children hospitalized for certain serious illnesses may have a higher risk of dying than thinner patients, a new research review suggests.
Experts caution that the findings are just "suggestive" of a link, and do not prove that critically ill children are more likely to die if they're obese.
But the results, published online March 11 in JAMA Pediatrics, add to the list of potential risks tied to childhood obesity.
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Past studies have found that obese children face higher rates of some long-term health problems, such as type 2 diabetes, high blood pressure and asthma. They also tend to become obese adults, with all the potential health consequences that come with that, including increased risks of heart disease and certain cancers.
"This (study) suggests there may be more to childhood obesity than the risks we already know of," said lead researcher Lori Bechard, a clinical nutrition specialist at Boston Children's Hospital. "There may also be some near-term risks."
Bechard stressed, however, that the studies in her team's review had a number of limitations. They also varied widely in how they were done, and even in how they defined obesity.
"We don't feel confident that we can say there is an association" between obesity and seriously ill children's risk of dying, Bechard said. "We need more research."
Given that roughly 17 percent of U.S. children and teens are obese, this possible connection, if proven, could have significant implications.
The findings are based on data from 28 past studies of children ages 2 to 18 who were hospitalized for various reasons. Twenty-one studies looked at kids' risk of dying, and half of them found that for children with serious illnesses, obesity was linked to an increased risk of death.
The studies that did find a link tended to be larger and better done than the others. Still, Bechard said, "the evidence wasn't overwhelmingly consistent."
Besides the higher death risk in some reports, a couple of studies also found that obese children generally had a longer hospital stay. No clear link was established between obesity and the risk of contracting an infection in the hospital.
It's hard to know what to make of the results, said Dr. Patricia Vuguin, a pediatric endocrinologist at Cohen Children's Medical Center in New Hyde Park, N.Y.
"This is suggestive that there might be a relationship between obesity and higher mortality. But that doesn't tell us anything about causality," said Vuguin, who was not involved in the research.
The better-done studies tried to account for other factors -- such as the severity of a child's disease -- but there could be other reasons that obese children tended to be at greater risk of dying.
Thankfully, children rarely develop life-threatening illnesses, Vuguin said. "It's not common," she stressed. "It's unusual."
The review found no evidence that obese kids who were presumably in better health -- those having elective surgery -- had a higher-than-normal death risk. But it is so rare for children to die from elective surgery, Bechard said, it would be "very hard" for a study to detect a link with obesity, if there is one.
Vuguin said a big limitation is that nearly all studies on this issue have been retrospective, which means the researchers looked back in time at patient records. And those records may lack a lot of important information.
Bechard agreed. Prospective studies -- where researchers enroll kids when they enter the hospital, then follow them over time -- would give stronger evidence. "It would be helpful to do prospective studies, and have clearer definitions of obesity," Bechard said.
According to Vuguin, it's plausible that obesity could have some effect on how seriously ill children fare. It's thought that obesity creates a state of chronic, low-level inflammation in the body. And based on animal research, it's believed that can impair immune system function.
But it's not possible to tell from the current findings whether that explains the higher death risk seen in some obese children.
So for now, Bechard said, there are no practical implications from the results -- only more questions. "We need to keep looking at the effects of being obese in childhood," she said.
For information on helping overweight kids, visit the U.S. National Institute of Diabetes and Digestive and Kidney Diseases.
SOURCES: Lori J. Bechard, M.Ed., R.D., clinical nutrition specialist, Boston Children's Hospital; Patricia Vuguin, M.D., pediatric endocrinologist, Cohen Children's Medical Center, New Hyde Park, N.Y.; March 11, 2013, online JAMA Pediatrics


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