text:
\section{Introduction}
Video semantic segmentation, i.e., the task of assigning a semantic label to every pixel in video frames, is crucial for the success of many computer vision applications, such as  video summarization and autonomous navigation. In this context, fully-supervised methods~\cite{featurespace,voxel2voxel,clockwork,tripathi,predective} have made great progress, particularly with the advent of deep learning. These methods, however, inherently rely on having access to large amounts of training videos with pixel-level ground-truth annotations in every frame. Unfortunately, such annotations are highly time-consuming and expensive to obtain, and generating realistic synthetic data to obtain annotations~\cite{synthia,playgame} is a challenging task in itself.
While semi-supervised techniques~\cite{supervoxel,viaflow,viastroke} mitigate this issue by leveraging partial annotations, they still require some pixel-level ground-truth.

\begin{figure}[t!]
\centering
\includegraphics[width=0.45\textwidth]{method.png}
\caption{\textbf{Overview of our framework}. Given only video-level tags, our weakly-supervised video semantic segmentation network jointly leverages classifier heatmaps and motion information to model both multiple foreground classes and multiple background classes. This is in contrast with most methods that focus on foreground classes only, thus being  inapplicable to scenarios where differentiating background classes is crucial, such as in autonomous driving.}
\label{fig:method}
\end{figure}

By contrast, weakly-supervised semantic segmentation methods~\cite{webscale,multi-class,viadetection,discriminative,domain_adapt,obj_track,movingobject,fast,saleh,sec,ccnn,stc,whatspoint,weaklyandsemi} rely only on tags. When working with still images~\cite{saleh,sec,ccnn,stc,whatspoint,weaklyandsemi}, tags are typically assumed to be available in each image, whereas for video-based segmentation~\cite{webscale,multi-class,viadetection,discriminative,domain_adapt,obj_track,movingobject,fast}, tags correspond to entire videos or video snippets.
While recent years have seen great progress in weakly-supervised semantic segmentation, most existing methods, whether image- or video-based, have a major drawback: They focus on foreground object classes and treat the background as one single entity. However, having detailed information about the different background classes is crucial in many practical scenarios, such as autonomous driving, where one needs to differentiate the road from a grass field.

In this paper, we introduce an approach to weakly-supervised video semantic segmentation that treats all classes, foreground and background ones, equally (see Fig~\ref{fig:method}). To this end, we propose to rely on class-dependent heatmaps obtained from classifiers trained for image-level recognition, i.e., requiring no pixel-level annotations. These classifier heatmaps provide us with valuable information about the location of instances/regions of each class. We therefore introduce a weakly-supervised loss function that let us exploit them in a deep architecture.

In particular, we develop a two-stream deep network that jointly leverages appearance and motion. Our network fuses these two complementary sources of information in two different ways: A trainable early fusion, which puts in correspondence the spatial and temporal information and learns to combine it into a spatio-temporal stream, and a late fusion further leveraging the valuable semantic information of the spatial stream to merge it with the spatio-temporal one for final prediction. Altogether, our approach constitutes the first end-to-end framework for weakly-supervised semantic segmentation to handle both multiple foreground and background classes.

To the best of our knowledge, only two weakly-supervised video semantic segmentation approaches~\cite{multi-class,co_parsing} can potentially handle multiple background classes. However,~\cite{multi-class} relies on a simple similarity measure between handcrafted features, and thus does not translate well to complex scenes where multiple instances of the same class have significantly different appearances.
While~\cite{co_parsing} relies on more robust, pre-trained deep learning features, it exploits additional, pixel-wise annotations to train a fully-convolutional network for scene/object classification. Furthermore, none of these two methods offer an end-to-end learning approach, which has proven key to the success of many other computer vision tasks.

Our experiments demonstrate the benefits of our approach in several scenarios. First, it yields accurate segmentations on challenging outdoor scenes, such as those depicted by the CamVid~\cite{camvid} and CityScapes~\cite{Cityscapes} datasets, for which methods modeling foreground classes only do not apply. Furthermore, it outperforms the state-of-the-art methods that, as us, rely only on video-level tags on the standard YouTube Object~\cite{youtube} dataset.

\section{Related Work}
Over the years, many approaches have tackled the problem of video semantic segmentation. In particular, much research has been done in the context of fully-supervised semantic segmentation, including methods based on CNNs~\cite{clockwork,voxel2voxel,predective} and on graphical models~\cite{featurespace,activeinference,tripathi}. Here, however, we focus the discussion on the methods that do not require fully-annotated training data, which is typically expensive to obtain.

In this context, semi-supervised approaches have been investigated. In particular,~\cite{viaflow,supervoxel} proposed to propagate pixel-level annotations provided in the first frame of the sequence throughout the entire video. While this still requires complete annotations in one frame per video,~\cite{viastroke} relied on user scribbles to define foreground and background regions. None of these methods, however, consider background classes. Furthermore, they still all make use of some pixel-level annotations.

By contrast, weakly-supervised semantic segmentation methods tackle the challenging scenario where only weak annotations, e.g., tags, are given as labels. Much research in this context has been done for still images~\cite{social,tellme,graph-based,multi-image-model,mil,structured,weaklyandsemi,saleh,ccnn,sec,stc,learning,distinct_saliency,fromimagelevel,exploiting}. In particular, most recent methods build on deep networks by making use of objectness criteria~\cite{whatspoint}, object proposals~\cite{fromimagelevel,learning,augmented}, saliency maps~\cite{distinct_saliency,stc,mining,exploiting}, localization cues~\cite{diverse,sec}, convolutional activations~\cite{saleh}, motion cues~\cite{motioncues} and constraints related to the objects~\cite{ccnn,weaklyandsemi}. Since the basic networks have been pre-trained for object recognition, and thus focus on foreground classes, these methods are inherently unable to differentiate multiple background classes.

\begin{figure}[!t]
\centering
\small
\begin{tabular}{c c c}

\scriptsize{Road} & \scriptsize{Tree} &  \scriptsize{Building} \\
\includegraphics[width=.14\textwidth]{heatmap/road.jpg} &
\includegraphics[width=.14\textwidth]{heatmap/tree.jpg} &
 \includegraphics[width=.14\textwidth]{heatmap/building.jpg} \\

\scriptsize{Bus} & \scriptsize{Bicycle} & \scriptsize{Sign}\\
\includegraphics[width=.14\textwidth]{heatmap/bus.png} &
\includegraphics[width=.14\textwidth]{heatmap/bicycle.png} &
\includegraphics[width=.14\textwidth]{heatmap/sign.png} \\

\end{tabular}
\caption{Classifier heatmaps for some of the foreground and background classes of the CityScapes dataset. Note that these heatmaps give a good indication of the location of foreground instances and background regions.}
\label{fig:heatmap}
\end{figure}

\begin{figure*}[!h]
\centering
\includegraphics[width=0.88\textwidth]{proposed_loss.png}
\caption{\textbf{Proposed Network Structure}. Our two-stream semantic segmentation network leverages both image and optical flow to extract the features. These features are fused in two stages. An early, trainable fusion that puts in correspondence the spatial and temporal information, and a late fusion that merges the resulting spatio-temporal stream with the appearance one for final prediction.}
\label{fig:net}
\end{figure*}

Similarly, most weakly-supervised video semantic segmentation techniques also focus on modeling a single background class. In this context~\cite{webscale,discriminative} work in the even more constrained scenario, where only two classes are considered: foreground vs. background. By contrast, to differentiate multiple foreground classes, but still assuming a single background,~\cite{fast} relied on motion cues and~\cite{webcrawled} made use of a huge amount of web-crawled data (4606 videos with 960,517 frames).

In the same setting of multiple foreground vs. single background, several methods have proposed to rely on additional supervision. For instance,~\cite{viadetection} relied on the CPMC~\cite{cpmc} region detector, which has been trained from pixel-level annotations, to segment foreground from background. In~\cite{domain_adapt} and~\cite{movingobject}, object proposal methods trained from pixel-level and bounding box annotations, respectively, were employed. Similarly,~\cite{obj_track} relied on an object detector trained from bounding boxes. The method of~\cite{co_segment} utilized the FCN trained on PASCAL VOC in a fully-supervised manner to generate initial object segments.

All the weakly-supervised approaches discussed above assume to observe a single background class. In many cases, such as autonomous navigation, however, it is crucial to differentiate the multiple background classes. To the best of our knowledge, only two methods are able to handle this scenario. In~\cite{multi-class}, a nearest-neighbor-based label transfer technique was introduced, which relies on a simple distance between handcrafted features. While this strategy would work well for classes such as grass or sky in which appearance variations are limited, it translates poorly to more challenging and complex scenes, such as urban ones, where individual classes can depict a large range of appearances. As a consequence, this method was only demonstrated on simple scenes containing at most one or two instances of a few classes. In~\cite{co_parsing}, more advanced, deep learning features were exploited. However, this method makes use of  pixel-level supervision to train an FCN to label pixels as either scene vs. object, or multiple scene classes vs. object.

By contrast, we introduce a method that handles multiple foreground and background classes, but only relies on video-level tags. To this end, we introduce a loss function based on classifier heatmaps, and exploit it to train a
two-stream network jointly leveraging complementary spatial and temporal information in an end-to-end manner.

\section{Our Approach}

In this section, we introduce our approach to weakly-supervised video semantic segmentation. First, we introduce the classifier heatmaps that allow us to model both multiple foreground and background classes. We then introduce our two-stream architecture, which jointly leverages motion and appearance, and discuss our learning scheme, including our loss based on the classifier heatmaps.

\subsection{Classifier Heatmaps}
\label{sec:heatmap}
One of the main challenges when working with tags only for weakly-supervised semantic segmentation is that the annotations do not provide any information about the location of the different classes. While mitigated in the presence of only few foreground classes and a single background one, this problem becomes highly prominent when dealing with complex urban scenes containing many instances of each foreground class and several background classes, such as road, grass, buildings. Existing weakly-supervised methods are dedicated to handle multiple foreground objects, but cannot handle multiple background ones, typically because they inherently rely on object recognition networks, which only tackle foreground classes. To address this, we propose to extract class-specific heatmaps that localize the different classes. Our goal here is to achieve this for both foreground and background classes, and without requiring any pixel-level or bounding box annotations.

Prior work has shown that ConvNets trained with a classification loss can yield remarkable localization results~\cite{oquab2015object,zhou2016learning}. Hence, similarly, for foreground classes, we make use of the VGG-16 network~\cite{vgg} trained on the standard 1000 ImageNet classes. Specifically, we transform the VGG-16 model into a fully-convolutional network by converting its fully-connected layers into convolutional ones, while keeping the trained weights. In other words, the output of the last layer of the transformed model becomes a $W \times H \times 1000$ tensor, and passing an image through the network yields a map showing the activation of each class at each pixel in a low-resolution version of the input image. In practice, we can then access the activations of the foreground classes of interest by only considering a subset of the 1000 ImageNet classes.

The standard 1000 ImageNet classes, however, do not include background. To this end, we collected iconic background images by crawling the background classes on the ImageNet website~\cite{image-net}. We then trained one-vs-all VGG-16 models (pre-trained on the standard 1000 ImageNet classes) for these background classes and followed the same strategy as for the foreground ones to obtain heatmaps. More details are provided in section~\ref{sec:dataset}.

In Fig.~\ref{fig:heatmap}, we show the heatmaps for some of the foreground and background classes of the CityScapes~\cite{Cityscapes} dataset. Note that, while sometimes a bit coarse, the heatmaps still provide valuable information about the location of these classes. In the next section, we introduce our two-stream network that jointly leverages appearance and motion, and show how our heatmaps can be used to train it.

\subsection{Weakly-supervised Two-stream Network}
\label{sec:netStructure}

Videos have two intrinsic features: \emph{Appearance} and \emph{Motion}. To leverage these two sources of information, inspired by the approach of~\cite{2stream} for action recognition, we develop the two-stream network depicted in Fig.~\ref{fig:net}. One stream takes an RGB image as input, and the other optical flow. Compared to taking a series of images as input, explicitly using optical flow to represent motion has the advantage of relieving the network from having to estimate motion implicitly. Below, we discuss how we encode optical flow and describe our fusion strategy. We then introduce our weakly-supervised learning framework.

\vspace{-0.3cm}
\paragraph{Encoding Optical Flow.}
Dense optical flow~\cite{optical-flow} can be represented as a displacement vector field between a pair of frames at time $t$ and $t + 1$. The horizontal and vertical components of the displacement vector field can be thought of as image channels, which makes them well suited to act as input to a convolutional network, such as the one shown in the upper stream of the model in Fig.~\ref{fig:net}. To represent the motion across a video clip, we stack the flow channels corresponding to both directions (vertical and horizontal) of $L$ consecutive frames, in range $(t - \floor{\frac{L}{2}},t + \floor{\frac{L}{2}}]$, to form a total of $2L$ input channels.

\vspace{-0.3cm}
\paragraph{Fusing Appearance and Motion.}
As can be seen in Fig.~\ref{fig:net}, the appearance and motion streams both consist of a series of convolutional layers, following the VGG-16 architecture~\cite{vgg}. The outputs of these streams are then fused at two different levels. In particular, fusion occurs after the fifth convolutional layer (Conv5-3) of each stream, which has been shown to contain a rich semantic representation of the input~\cite{saleh,deepedge}. The first, early fusion puts in correspondence the activations of both streams corresponding to the same pixel location. As~\cite{2stream}, instead of performing sum- or max-fusion, we rely on a convolutional fusion strategy. This gives more flexibility to the network and allows it to learn which channels from the motion and appearance streams should be combined together. The second, late fusion of our network merges the spatio-temporal stream resulting from early fusion with the appearance stream. This fusion is achieved at the point where each stream predicts class scores. The rationale behind this is that the appearance stream provides valuable semantic information on its own, and should thus be propagated to the end of the network. The resulting scores are then passed through a deconvolution layer to obtain the final, full-resolution, semantic map.

\subsubsection{Weakly-Supervised Learning}
We now introduce our learning algorithm for weakly-supervised semantic segmentation. We first introduce a simple loss based on image tags only, and then show how we can incorporate the localization information of our classifier heatmaps to the loss function.

Intuitively, given image tags, one would like to encourage the image pixels to be labeled as one of the classes that are observed in the image, while preventing them to be assigned to unobserved classes. Note that this assumes that the full set of tags available cover all the classes depicted in the image, which is a common assumption in weakly-supervised semantic segmentation~\cite{ccnn,weaklyandsemi,mil,whatspoint,sec,saleh}.

Formally, given an input video $V$, let $\mathcal{L}$ be the set of tags associated to $V$ and $\bar{\mathcal{L}}$ the class labels that are not among the tags. Furthermore, let us denote by $s_{i,j}^k(\theta)$ the score produced by our network with parameters $\theta$ for the pixel at location ($i,j$) and for class $k$, $0\leq k < N$, in the current input video frame $I$. Note that, in general, we will omit the explicit dependency of the variables on the network parameters. Finally, let $S_{i,j}^k$ be the probability of class $k$ obtained after a softmax layer, i.e.,
\begin{equation}
S_{i,j}^k = \frac{\exp(s_{i,j}^k)}{\sum_{c=1}^N\exp(s_{i,j}^c)}\;.
\end{equation}

Encoding the above-mentioned intuition can then simply be achieved by designing a loss of the form
\begin{equation}
L_{tag} = -\frac{1}{|\mathcal{L}|}\sum_{k\in \mathcal{L}} \log{S^k} - \frac{1}{|\bar{\mathcal{L}}|}\sum_{k\in \bar{\mathcal{L}}}\log(1-S^k) \;,
\label{eq:loss_mil}
\end{equation}
where $S^k$ represents a candidate score for each class in the input frame. In short, the first term in Eq.~\ref{eq:loss_mil} expresses the fact that the present classes should be in the input frame, while the second term penalizes the pixels that have high probabilities for the absent classes. In practice, instead of computing $S^k$ as the maximum probability (as previously used in~\cite{mil,whatspoint}) for class $k$ over all pixels in the input frame, we make use of the convex Log-Sum-Exp (LSE) approximation of the maximum (as previously used in~\cite{fromimagelevel,saleh}), which can be written as
\begin{equation}
\tilde{S}^k = \frac{1}{r}\log\left[\frac{1}{|I|}\sum_{i,j \in I}\exp(rS_{i,j}^k)\right]\;,
\label{eq:LSE}
\end{equation}
where $|I|$ denotes the total number of pixels in the input frame and $r$ is a parameter allowing this function to behave in a range between the maximum and the average. In practice, following~\cite{fromimagelevel,saleh}, we set $r$ to 5.

The loss of Eq.~\ref{eq:loss_mil} does not rely on any localization cues. As a consequence, minimizing it will typically yield poor object localization accuracy. To overcome this issue, we propose to make use of the classifier heatmaps introduced in Section~\ref{sec:heatmap}. To this end, we first generate binary masks $B_k$ for each class $k$. These binary masks are obtained by setting to 1 the values that are above 20\% of the maximum value in the heatmap of class $k$, and to 0 the other ones.

Our goal then is to encourage the model to have, for each class, high probability at pixels inside the corresponding binary mask. To this end, we introduce the loss function
\begin{equation}
L_{heatmap} = -\frac{1}{|\mathcal{L}|}\sum_{k\in \mathcal{L}}\frac{1}{|{B_k}|}\sum_{{i,j}\in{B_k}} \log{S_{i,j}^k}\;,
\label{eq:loss_heatmap}
\end{equation}
which we use in conjunction with the loss of Eq.~\ref{eq:loss_mil}.

While this heatmap-based loss significantly helps localizing the different classes, the heatmaps typically only roughly match the class boundaries. To overcome this, we follow the CRF-based strategy of~\cite{sec}. Specifically, we construct a fully-connected CRF, with
unary potentials corresponding to the probability scores predicted by our segmentation network, and image-dependent Gaussian pairwise potentials~\cite{dcrf}.
We then add another term to the loss function, corresponding to the mean KL-divergence between the outputs of the network and the outputs of the fully connected CRF. This term encourages the network prediction to coincide with the CRF output, which produces segmentations that better respect the image boundaries.

Altogether, our network can handle multiple foreground and background classes, and, as discussed in more detail in Section~\ref{sec:details}, can be trained in an end-to-end fashion.

\section{Experiments}
In this section, we first describe the datasets used in our experiments and provide details about our learning and inference procedures. We then present the results of our model and compare it to state-of-the-art weakly-supervised semantic segmentation methods.

\subsection{Datasets}
\label{sec:dataset}
To demonstrate the effectiveness of our approach, and evaluate the different components of our model, we use the challenging  CityScapes~\cite{Cityscapes} and CamVid~\cite{camvid} road scene datasets. Furthermore, to compare to the state-of-the-art, we make use of YouTube-Objects~\cite{youtube}, which most weakly-supervised video semantic segmentation methods report on. Note that, although different annotation types are provided in each of these datasets, we only make use of tags, indicating which classes are present in each video clip.

\begin{table}[!t]
\centering
\small
\caption{Background classes used to train our classifiers (Section~\ref{sec:heatmap}) for the CityScapes and CamVid datasets.}
\label{tab:background_samples}
\begin{tabular}{| l|c c c c c c|}
\hline
Class & \rotatebox[origin=c]{60}{\scriptsize{road}} & \rotatebox[origin=c]{60}{\scriptsize{sidewalk}} & \rotatebox[origin=c]{60}{\scriptsize{building}} & \rotatebox[origin=c]{60}{\scriptsize{vegetation}} & \rotatebox[origin=c]{60}{\scriptsize{terrain}} & \rotatebox[origin=c]{60}{\scriptsize{sky}}\\
 \hline

\#Samples & 126 & 306 & 670 & 176 & 190 & 180\\
\hline
\end{tabular}
\vspace{-0.2cm}
\end{table}

\textbf{CityScapes}: Cityscapes~\cite{Cityscapes} is a recently released large-scale dataset, containing high quality pixel-level annotations for 5000 images collected in street scenes from 50 different cities. The images of Cityscapes have resolution 2048$\times$1024, making it a challenge to train very deep networks with limited GPU memory. We therefore downsampled the images by a factor of 2.
The annotations correspond to the 20$^{th}$ frame of 30-frame video snippets.
We then extracted optical flow from 10 consecutive frames, from the 16$^{th}$ to the 25$^{th}$, and used the RGB frames and image-level tags in conjunction with these optical flows to train our model.
We made use of the standard training/validation/test partitions, containing 2975,500, and 1525 images, respectively. Following the standard evaluation protocol~\cite{Cityscapes}, we used 19 semantic labels (belonging to 7 super categories: ground, construction, object, nature, sky, human, and vehicle) for evaluation (the void label is not considered for evaluation).

\textbf{CamVid}: CamVid dataset consists of over 10 minutes of high quality 30 Hz footage. The videos are captured at $960\times 720$ resolution with a camera mounted inside a car. Three of the four sequences were shot in daylight, and the fourth one was captured at dusk. This dataset contains 32 categories. In our experiments, following~\cite{camvid11,featurespace,segnet}, we used a subset of 11 classes. The dataset is split into 367 training, 101 validation and 233 test images. As for CityScapes, ground-truth labels are provided every 30 frames. We extracted optical flow in 10 frames around the labeled ones, and used them with the RGB frames for training.

\textbf{Iconic Data}:
The background classes and number of samples per class, extracted from the background images of the ImageNet website, as mentioned in Section~\ref{sec:heatmap}, and used to train our background classifiers for CityScapes and CamVid are given in Table~\ref{tab:background_samples}. Note that, in the standard 1000 classes of ImageNet, there is no general \emph{person} class, which appears in both datasets. To handle this class, we therefore proceeded in a similar manner as for the background classes, but making use of a small subset of the samples (1300 samples) from~\cite{inria,nicta-ped}.

\textbf{YouTube-Objects}: The YouTube-Objects dataset is composed of videos collected from YouTube by querying for the names of 10 object classes of the PASCAL VOC Challenge. It contains between 9 and 24 videos per class. The duration of each video ranges from 30 seconds to 3 minutes. The videos are weakly annotated, with each video containing at least one object of the corresponding queried class. In the dataset, the videos are separated into shots.
For our experiments, we randomly extracted 6-8 frames from each shot to obtain a total of 13800 frames out of 700,000 ones available in the dataset.
We again made use of snippets of 10 frames to encode optical flow.

For evaluation, we used the subset of images with pixel-level annotations provided by~\cite{mask-yto}. Note that there is no overlap between this subset and the shots from which we extracted the training data.

\subsection{Implementation Details}
\label{sec:details}
To train our two-stream network, introduced in Section~\ref{sec:netStructure}, we relied on stochastic gradient descent with a learning rate starting at $10^{-5}$ with a decrease factor of $10$
every $10k$ iterations, a momentum of $0.9$, a weight decay of $0.0005$, and mini-batches of size $1$. Similarly to recent weakly-supervised segmentation methods~\cite{saleh,whatspoint,mil,weaklyandsemi,sec}, the weights of our two-stream network were initialized with those of the 16-layer VGG classifier~\cite{vgg} pre-trained for 1000-way classification on the ILSVRC 2012~\cite{ILSVRC}. Hence, for the last convolutional layer, we used the weights corresponding to the classes shared by the datasets used here and in ILSVRC. For the background classes, we initialized the weights with zero-mean Gaussian noise with a standard deviation of $0.1$.
At inference time, given only the test image and optical flow, the network generates a dense prediction as a complete semantic segmentation map.

For both CamVid and CityScapes, we used the GPU implementation of~\cite{optical-flow} to generate the stack of optical flow for each snippet of length 10. For YouTube-Objects, we used the optical flow information pre-computed by~\cite{flow-yto}. Note that neither of these methods relies on any learning strategy, and thus they can be directly applied to our input images.
We used C++ and Python (the Caffe framework~\cite{caffe}) for our implementation. As other methods~\cite{sec,weaklyandsemi,saleh,ccnn,stc}, we further applied a dense CRF~\cite{dcrf} to refine this initial segmentation.
To this end, we used the default CRF parameter values as in the original paper~\cite{dcrf}.

\subsection{Experimental Results}

Below, we first evaluate the different components of our method on the validation set of the two challenging road scene datasets. We then provide results of our complete framework on their respective test sets. Finally, we compare our approach to state-of-the-art weakly-supervised segmentation methods on YouTube-Objects.

\vspace{-0.3cm}
\subsubsection{Ablation Study}
To evaluate the influence of the different components of our approach, we designed the following baselines. \emph{No-Heatmap} corresponds to a single-stream model exploiting the RGB image only, without exploiting our heatmap-based loss of Eq.~\ref{eq:loss_heatmap},
i.e., using the loss of Eq.~\ref{eq:loss_mil} and the CRF loss. \emph{Foreground-Heatmap} consists of a similar single stream network, additionally using the loss of Eq.~\ref{eq:loss_heatmap}, but only for the foreground classes extracted from the VGG-16 network pre-trained on ILSVRC. \emph{Our-Heatmap} corresponds to using all our heatmaps, i.e., for foreground and background classes, with a single-stream network. Finally, \emph{Ours} corresponds to our two-stream network with all the loss terms.

We report the results of these different models in Table~\ref{tab:cityscapes_setups} for Cityscapes and in Table~\ref{tab:camvid_setups} for CamVid. In particular, we report the mean Intersection over Union (mIoU), the average per-class accuracy and global accuracy. The general behavior is the same for both datasets: Exploiting heatmaps for foreground class improves over not using heatmaps at all. However, also relying on heatmaps for background classes gives a significant boost in performance. Finally, jointly leveraging appearance and motion in our two-stream network further improves segmentation accuracy. As can be observed in Table~\ref{tab:eval_heatmap_camvid}, which provides the per-class intersection over union for CamVid, our heatmaps and our two-stream network add significant improvement to the baselines for most of the classes, especially in background classes, e.g., sky and road.

\begin{table}[!t]
\renewcommand{\arraystretch}{1.4}
\centering
\scriptsize
\caption{Influence of our heatmaps and of optical flow. These results were obtained using the CityScapes validation set.}
\label{tab:cityscapes_setups}

\begin{tabular}{l| c | c | c }
\hline
 Setup & Mean IOU & Mean Class Acc. & Global Acc. \\
 \hline
 No-Heatmap & \small{8.4\%} & \small{18.8\%} & \small{20.9\%}  \\
 ImageNet-Heatmap & \small{11.4\%} & \small{33.2\%} & \small{22.0\%} \\
 Our-Heatmap & \small{20.6\%} & \small{40.6\%} & \small{54.0\%} \\
 Our Two-Stream & \small{23.6\%} & \small{40.3\%} & \small{63.9\%} \\
 \hline
\end{tabular}
\end{table}

\begin{table}[!t]
\renewcommand{\arraystretch}{1.4}
\centering
\scriptsize
\caption{Influence of our heatmaps and of optical flow. These results were obtained using the CamVid validation set.}
\label{tab:camvid_setups}

\begin{tabular}{l| c | c | c }
\hline
 Setup & Mean IOU & Mean Class Acc. & Global Acc.\\
 \hline
 No-Heatmap & \small{10.2\%} & \small{24.9\%} & \small{19.5\%} \\
 ImageNet-Heatmap & \small{11.0\%} & \small{25.8\%} & \small{28.9\%} \\
 Our-Heatmap & \small{29.5\%} & \small{49.7\%} & \small{62.6\%} \\
 Our Two-Stream &  \small{31.1\%} & \small{50.2\%} & \small{67.4\%} \\
 \hline
\end{tabular}
\end{table}

\begin{table*}[!t]
\renewcommand{\arraystretch}{1.2}
\centering
\small
\caption{Influence of our heatmaps and of optical flow. Per-class IoU for the CamVid validation set.}
\label{tab:eval_heatmap_camvid}
\scalebox{0.85}
{
\hspace{-0.2cm}
\begin{tabular}{l | c c c c c c c c c c c}
\hline
Setup & \rotatebox[origin=c]{90}{\scriptsize{building}} & \rotatebox[origin=c]{90}{\scriptsize{vegetation}} & \rotatebox[origin=c]{90}{\scriptsize{sky}} & \rotatebox[origin=c]{90}{\scriptsize{car}} & \rotatebox[origin=c]{90}{\scriptsize{sign}} & \rotatebox[origin=c]{90}{\scriptsize{road}} & \rotatebox[origin=c]{90}{\scriptsize{pedestrian}} & \rotatebox[origin=c]{90}{\scriptsize{fence}} & \rotatebox[origin=c]{90}{\scriptsize{pole}} & \rotatebox[origin=c]{90}{\scriptsize{sidewalk}} & \rotatebox[origin=c]{90}{\scriptsize{cyclist}}\\
 \hline

 No-HeatMap & 37.0 &   33.0 &   0.0 &  28.6 &   7.8 &    4.6 &   0.0 &   0.1 &   0.7 &   0.4 &   0\\
ImageNet-HeatMap & 29.8 &  0.0 &    0.1 &   14.1 &    7.5 &   53.4 &    4.9 &    4.9 &    0.2 &    0.0 &    6.2\\
Our-HeatMap & 54.1 &   76.1 &   86.3 &   19.4 &    6.6 &   56.3 &    9.0 &    0.9 &    0.5 &    6.0 &    9.0\\
 Our Two-Stream & 63.4 &   72.2 &   84.2 &   19.3 &    8.9 &   60.6 &   14.3 &    0.0 &    0.0 &    4.1 &   15.2\\
\hline

 \end{tabular}
 }
 \end{table*}

\begin{table*}[!tp]
\renewcommand{\arraystretch}{1.2}
\centering
\small
\caption{Comparison to fully-supervised semantic segmentation methods on the CamVid test set. While we use the weakest level of supervision, the difference to fully supervised methods, especially in background classes (sky, building, road and tree) is remarkably low.
}
\label{tab:eval_detail_camvid}
\scalebox{0.9}
{
\hspace{-0.2cm}
\begin{tabular}{l | l | c c c c c c c c c c c  | c }
\hline
 Method & Supervision & \rotatebox[origin=c]{90}{\scriptsize{building}} & \rotatebox[origin=c]{90}{\scriptsize{vegetation}} & \rotatebox[origin=c]{90}{\scriptsize{sky}} & \rotatebox[origin=c]{90}{\scriptsize{car}} & \rotatebox[origin=c]{90}{\scriptsize{sign}} & \rotatebox[origin=c]{90}{\scriptsize{road}} & \rotatebox[origin=c]{90}{\scriptsize{pedestrian}} & \rotatebox[origin=c]{90}{\scriptsize{fence}} & \rotatebox[origin=c]{90}{\scriptsize{pole}} & \rotatebox[origin=c]{90}{\scriptsize{sidewalk}} & \rotatebox[origin=c]{90}{\scriptsize{cyclist}} &  \rotatebox[origin=c]{0}{mIOU}\\
 \hline
SegNet~\cite{segnet} & pixel level annotation & 68.7 & 52.0 & 87.0 & 58.5 & 13.4 & 86.2 & 25.3 & 17.9 & 16.0 & 60.5 & 24.8  & 46.4\\
Liu and He~\cite{activeinference} & pixel level annotation& 66.8 & 66.6 & 90.1 & 62.9 & 21.4 & 85.8 & 28.0 &17.8 & 8.3 & 63.5 & 8.5 & 47.2 \\
\hline
Ours & image-level tags & 58.9 &   46.4 & 83.8 &   26.5 &   12.0 &   64.4 &    8.0 &   11.3 & 3.1 &   1.1  & 11.0 & 29.7\\
 \hline
 \end{tabular}
 }
 \end{table*}

\begin{table*}[!t]
\renewcommand{\arraystretch}{1.3}
\centering
\scriptsize
\caption{Comparison to fully-supervised semantic segmentation methods on the CityScapes test set. As on CamVid, while we use the weakest level of supervision, the gap with fully supervised methods is quite low, particularly on background classes.
}
\label{tab:eval_detail_cityscapes}
\scalebox{0.8}
{
\hspace{-0.2cm}
\begin{tabular}{l| l | c c c c c c c c c c c c c c c c c c c | c | c }
\hline
Method & Supervision & \rotatebox[origin=c]{90}{road} & \rotatebox[origin=c]{90}{sidewalk} & \rotatebox[origin=c]{90}{building} & \rotatebox[origin=c]{90}{wall} & \rotatebox[origin=c]{90}{fence} & \rotatebox[origin=c]{90}{pole} & \rotatebox[origin=c]{90}{traffic-light} & \rotatebox[origin=c]{90}{traffic-sign} & \rotatebox[origin=c]{90}{vegetation} & \rotatebox[origin=c]{90}{terrain} & \rotatebox[origin=c]{90}{sky} & \rotatebox[origin=c]{90}{person} & \rotatebox[origin=c]{90}{rider} & \rotatebox[origin=c]{90}{car} & \rotatebox[origin=c]{90}{truck} & \rotatebox[origin=c]{90}{bus} & \rotatebox[origin=c]{90}{train} & \rotatebox[origin=c]{90}{motorcycle}& \rotatebox[origin=c]{90}{bicycle} & \rotatebox[origin=c]{90}{mIOU Class}& \rotatebox[origin=c]{90}{mIOU Category}\\
 \hline

 FCN-8s~\cite{FCN}& pixel-level annotation & 97.4 & 78.4 & 89.2 & 34.9 & 44.2 & 47.4 & 60.1 & 65.0 & 91.4 & 69.3 & 93.9 & 77.1 & 51.4 & 92.6 & 35.3 & 48.6 & 46.5 & 51.6 & 66.8 & 65.3 & 85.7\\
 Deeplab~\cite{deeplab}& pixel-level annotation &97.3 &	77.6 &	87.7 &	43.6 &40.4 &	29.7 &	44.5 &	55.4 &	89.4 &	67.0 &	92.7 &	71.2 &	49.4 &	91.4 &	48.7 &	56.7 &	49.1 &	47.9 & 58.6 & 63.1 & 81.2\\
 SegNet~\cite{segnet}&pixel-level annotation & 96.4	& 73.2 &	84.0 &	28.4 &	29.0 &	35.7 &	39.8 &	45.1 &	87.0 &	63.8 &	91.8 & 	62.8 &	42.8 &	89.3 &	38.1 &	43.1 &	44.1 &	35.8 &	51.9 & 56.9 & 79.1 \\
 \hline
 Ours& image-level tags &78.5 &	2.7 &	45.0 &	6.6 &	9.8 &	5.4 &	0.7 &	2.1 &	63.3 &	22.0 &	71.5 &	17.6 &	8.0 &	43.6 &	16.0 &	15.5 &	33.0 &	17.9 &	13.6 & 24.9 & 47.2\\
 \hline
 \end{tabular}
 }
 \end{table*}

\begin{table*}[h!]
\renewcommand{\arraystretch}{1.2}
\small
\centering
\caption{Comparison to the state-of-the-art on the YouTube-Objects dataset. We report the per-class and mean IoU. Note that our two-stream network significantly outperforms the state-of-the-art baselines.}
\label{tab:yto}
\scalebox{0.99}
{
\hspace{-0.2cm}\begin{tabular}{ l|c c c c c c c c c c |c }
\hline
Method & \rotatebox[origin=c]{0}{aeroplane} & \rotatebox[origin=c]{0}{bird} & \rotatebox[origin=c]{0}{boat} & \rotatebox[origin=c]{0}{car} & \rotatebox[origin=c]{0}{cat} & \rotatebox[origin=c]{0}{cow} & \rotatebox[origin=c]{0}{dog} & \rotatebox[origin=c]{0}{horse} & \rotatebox[origin=c]{0}{motorbike} & \rotatebox[origin=c]{0}{train} &  \rotatebox[origin=c]{0}{mIOU}\\
 \hline

Papazoglou et al.~\cite{fast} & 67.4 & 62.5 & 37.8  & 67.0 & 43.5 & 32.7 & 48.9 & 31.3 & 33.1 & 43.4& 46.8\\
Tang et al.~\cite{discriminative} & 17.8 & 19.8 & 22.5 & 38.3 & 23.6 & 26.8 & 23.7 & 14.0& 12.5 & 40.4 & 23.9\\
Ochs et al.~\cite{long-term-video}&13.7 & 12.2 & 10.8 & 23.7 & 18.6 & 16.3 & 18.0 & 11.5 & 10.6&  19.6 & 15.5\\
\hline

 Ours &   67.6 &   72.3 &   58.1 &   60.1 &   59.8 &   42.6 &   60.1 &   46.3 &  53.6 &   12.4 & 53.3 \\

\hline
\end{tabular}
}
\end{table*}

Furthermore, we evaluated the influence of the CRF on our results. On CityScapes, our two-stream network without the CRF loss achieves 20.3\% mIOU vs 23.6\% with the CRF, thus showing that the CRF helps, but is not the key to our results.

Regarding runtimes, the average inference time of our method per image on CityScapes given optical flow is 0.56s without CRF inference as post-processing and 3.6s with CRF inference. This matches the runtimes reported in other papers that worked on CityScapes, although in the fully-supervised setting, such as~\cite{FCN} (0.5s without CRF) and~\cite{deeplab} (4s with CRF).

\subsubsection{Results on Test Sets}
We then evaluated our complete approach on the test sets of CamVid and CityScapes. In Table~\ref{tab:eval_detail_camvid} and Table~\ref{tab:eval_detail_cityscapes}, we compare the results of our weakly-supervised approach to those of fully-supervised methods. Note that, while these methods make use of much stronger supervision during training, thus making the comparison unfair to us, the gap in accuracy with our method, especially for background classes (sky, building, road and tree) is remarkably low. This further illustrates the strength of our approach, which, despite using only tags, yields good segmentation accuracy.

Qualitative results of our two-stream network on samples from CityScapes and CamVid are also depicted in Fig.~\ref{fig:result}.

\subsubsection{Comparison to the State-of-the-Art}
To further show the effectiveness of our method, we compare it with other weakly-supervised video semantic segmentation baselines on the standard YouTube-Objects dataset. Note that, here, all the classes correspond to foreground objects, with a single background class, which makes this dataset a less attractive candidate for our method. This comparison, however, lets us evaluate the performance of our two-stream network with respect to the state-of-the-art in weakly-supervised video semantic segmentation. As shown in Table~\ref{tab:yto}, our results significantly outperform the state-of-the-art on this dataset, thus again showing the benefits of our approach (see Fig.~\ref{fig:result} for qualitative results).

Note that other approaches that make use of additional supervision, such as object detectors trained from pixel-level~\cite{viadetection} or bounding box~\cite{obj_track} annotations, have also reported results on this dataset. While we only exploit tags, our approach yields results comparable to those of these methods (53.3\% for our method versus 54.1\% for~\cite{viadetection} and 55.8\% for~\cite{obj_track}).

\begin{figure*}[p!]
\centering
\small
\begin{tabular}{c c c c }
\multicolumn{4}{c}{CityScapes Dataset}\\
\includegraphics[width=.21\textwidth]{results/frankfurt_000001_029600_leftImg8bit.png}&
\includegraphics[width=.21\textwidth]{results/lindau_000002_000019_leftImg8bit.png}&
\includegraphics[width=.21\textwidth]{results/lindau_000014_000019_leftImg8bit.png}&
\includegraphics[width=.21\textwidth]{results/munster_000041_000019_leftImg8bit.png}\\
\includegraphics[width=.21\textwidth]{results/frankfurt_000001_029600_gtFine_color.png}&
\includegraphics[width=.21\textwidth]{results/lindau_000002_000019_gtFine_color.png}&
\includegraphics[width=.21\textwidth]{results/lindau_000014_000019_gtFine_color.png}&
\includegraphics[width=.21\textwidth]{results/munster_000041_000019_gtFine_color.png}\\
\includegraphics[width=.21\textwidth]{results/frankfurt_000001_029600_ours.png}&
\includegraphics[width=.21\textwidth]{results/lindau_000002_000019_ours.png}&
\includegraphics[width=.21\textwidth]{results/lindau_000014_000019_ours.png}&
\includegraphics[width=.21\textwidth]{results/munster_000041_000019_ours.png}\\
\multicolumn{4}{c}{CamVid Dataset}\\
\includegraphics[width=.21\textwidth]{results/01TP_001890_img.png}&
\includegraphics[width=.21\textwidth]{results/05VD_000780_img.png}&
\includegraphics[width=.21\textwidth]{results/05VD_003630_img.png}&
\includegraphics[width=.21\textwidth]{results/16E5_007995_img.png}\\
\includegraphics[width=.21\textwidth]{results/01TP_001890.png}&
\includegraphics[width=.21\textwidth]{results/05VD_000780.png}&
\includegraphics[width=.21\textwidth]{results/05VD_003630.png}&
\includegraphics[width=.21\textwidth]{results/16E5_007995.png}\\
\includegraphics[width=.21\textwidth]{results/01TP_001890_ours.png}&
\includegraphics[width=.21\textwidth]{results/05VD_000780_ours.png}&
\includegraphics[width=.21\textwidth]{results/05VD_003630_ours.png}&
\includegraphics[width=.21\textwidth]{results/16E5_007995_ours.png}\\
\multicolumn{4}{c}{YouTube-Objects Dataset}\\
\includegraphics[width=.21\textwidth]{results/00080487.jpg}&
\includegraphics[width=.21\textwidth]{results/00080497.jpg}&
\includegraphics[width=.21\textwidth]{results/00080507.jpg}&
\includegraphics[width=.21\textwidth]{results/00080517.jpg}\\
\includegraphics[width=.21\textwidth]{results/00080487.png}&
\includegraphics[width=.21\textwidth]{results/00080497.png}&
\includegraphics[width=.21\textwidth]{results/00080507.png}&
\includegraphics[width=.21\textwidth]{results/00080517.png}\\
\includegraphics[width=.21\textwidth]{results/00080487_ours.png}&
\includegraphics[width=.21\textwidth]{results/00080497_ours.png}&
\includegraphics[width=.21\textwidth]{results/00080507_ours.png}&
\includegraphics[width=.21\textwidth]{results/00080517_ours.png}\\
\end{tabular}
\caption{Qualitative results on CityScapes, CamVid, and YouTube-Objects. Note that for each dataset, from top to bottom, there is the RGB frame, Ground-truth and the prediction of our two-stream network.}
\label{fig:result}
\end{figure*}

\section{Conclusion}
In this paper, we have proposed the first weakly-supervised video semantic segmentation approach that considers both multiple foreground and background classes. To this end, we have introduced a two-stream network that leverages optical-flow and RGB image, trained using a loss based on classifier heatmaps. Our experiments have demonstrated the benefits of using such heatmaps and of exploiting optical flow on challenging urban datasets. Furthermore, our two-stream network has also outperformed the state-of-the-art weakly-supervised video semantic segmentation methods on the standard YouTube-Object benchmark. In the future, we plan to investigate other fusion strategies within our two-stream formalism. Moreover, we will aim to leverage depth information from stereo images, which does not require any additional annotations.

{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


text:
Taibaiella helva is a Gram-negative, strictly aerobic, oval-shaped, non-spore-forming and motile bacterium from the genus of Taibaiella which has been isolated from farmland soil from Qianshan in China.

References

Chitinophagia
Bacteria described in 2019

redpajama_set_name: RedPajamaWikipedia


text:
Where is Hillhead?
Hillhead is located in the county of Hampshire, South East England, three miles north of the town of Cowes, seven miles south-east of the major city of Southampton, and 71 miles south-west of London. Hillhead lies three miles north of the Isle of Wight border. Hillhead falls within the district council of New Forest, under the county council of Hampshire. It is in the SO45 postcode district. The post town for Hillhead is Southampton.
Hillhead on a map
Bounding box showing extent of Hillhead
Location of Hillhead within the UK
Which county is Hillhead in?
Hillhead is in the ceremonial county of Hampshire, the historic county of Hampshire, and the administrative county of Hampshire.
Where is Hillhead in Hampshire?
Hillhead is situated towards the southernmost extremity of Hampshire, and roughly centrally between the easternmost and westernmost extremities of Hampshire.
Location of Hillhead within Hampshire
What is the postcode for Hillhead?
The nearest postcode to the centre of Hillhead is SO45 1BY.
Where is Hillhead in SO45?
Hillhead is situated towards the southernmost extremity of the SO45 postcode district, and towards the easternmost extremity of the SO45 postcode district.
Location of Hillhead within SO45
What is the post town for Hillhead?
The post town for Hillhead is Southampton. Hillhead is in the SO45 postcode district, which corresponds to the post town of Southampton.
What is the latitude and longitude of Hillhead?
The centre of Hillhead is located at position 50.80972° latitude and -1.32192° longitude.
What is the easting and northing of Hillhead?
The centre of Hillhead is located at the grid reference easting 447872 and northing 101349 within the British National Grid (OSGB36) system.
What are the nearest towns to Hillhead?
Position of Hillhead relative to nearby towns. Distance is measured from the centre of Hillhead to the centre of the town.
Hillhead is 3 miles north of Cowes
Hillhead is 4 miles north-west of East Cowes
Hillhead is 7 miles south-west of Fareham
Hillhead is 7 miles south of Hedge End
Hillhead is 8 miles north of Newport
Hillhead is 9 miles south-west of Wickham
Hillhead is 9 miles west of Gosport
Hillhead is 9 miles north-west of Ryde
Hillhead is 10 miles east of Lymington
Hillhead is 10 miles south-east of Totton
What are the nearest cities to Hillhead?
Position of Hillhead relative to nearby cities. Distance is measured from the centre of Hillhead to the centre of the city.
Hillhead is 8 miles south-east of Southampton
Hillhead is 10 miles west of Portsmouth
Hillhead is 17 miles south of Winchester
Hillhead is 24 miles west of Chichester
Hillhead is 27 miles south-east of Salisbury
Hillhead is 52 miles west of Brighton and Hove
Hillhead is 60 miles south-east of Bath
Hillhead is 64 miles south-east of Wells
Hillhead is 65 miles south of Oxford
Hillhead is 71 miles south-west of City of Westminster

redpajama_set_name: RedPajamaCommonCrawl


Great news for people who plan ahead for their holidays. The European Coastal Airlines schedule and prices until September 30 is now available, and you can book your summer flight online right now, with a nice 20% discount if you book 60 days in advance.
Prices from the sea port of Resnik near Split Airport vary according to the time of year, and there will be as many as five flights a day to and from the airport in peak season….
← European tourism finishes strong and begins shaky….


We have created this page to inform you about the latest news about educator jobs in Punjab. All the latest breaking news related to 33000 educator jobs in Punjab 2018 will be uploaded on this page. Hopefully you know that Punjab government has assigned NTS to take the entry test for the posts of educators. Bad news is for Commerce graduates that they are not eligible to apply for these posts. We strongly condemn this as large number of Commerce graduates are unemployed in the country and they must be allowed to appear in entry test for the posts of educators.
Yousuf Abbasi education reporter and columnist of Daily Express had written a column on this issue. He gave strong arguments in favor of Commerce graduates. According to him all federal and provincial departments accept equivalent degrees then why school education department is not inviting applications from Commerce graduates, who have equivalent degrees to BA and B.Sc. He demanded that recruitment policy should be revised as B.Com, BBA degree holders are also graduates and quite eligible to teach many subjects in schools. Commerce and technology graduates should also be given equal playing field. No one is demanding for separate quota for Commerce graduates just allow them to appear in entry test as test can show that how much talent they have. You can read his column Umeed Bihar in Urdu on this page about this issue. Affected candidates can contact provincial ombudsman or High Court for relief. Read a news of last year about High Court Decision in this regard below this page.
We have written a comprehensive article for guiding you about the preparation of NTS test for recruitment of educators in Punjab. You must read it for comprehensive guidance. Stay in touch with your own website and specially with this page for latest updates and breaking news about educator jobs in Punjab 2018. We wish you best of luck for your future.


Sutherland, the process transformation company, announced a 3-year agreement with Egypt's Information Technology Industry Development Agency (ITIDA) to expand its business presence in Egypt, and enhance the service portfolio offered to the region.
According to the agreement, signed on the 3rd day of GITEX Dubai 2018, Sutherland will add more than 750 full-time jobs in its service delivery centre in Alexandria.
The centre, which was opened in 2010, provides a full range of integrated back-office and client facing front-office services to the company's clients in Europe, Africa, the Middle East, Asia, and North America.
Maha Rashad, acting CEO of ITIDA, said that Egypt has been taking all the required steps to develop its thriving IT& IT-enabled services industry. She noted that the industry has witnessed great progress in terms of the number of services offered, and the growth of the market, especially with the talent outflow, and the annual 550 university graduates.
According to the International Data Corporation (IDC), the combined IT, BPO, and KPO export market in Egypt is forecasted to grow from $3.25bn in 2017, to $4.7bn in 2020.
"Sutherland Egypt is planning for great expansions over the next few years. New Middle Eastern and multinational accounts are currently approaching Sutherland Egypt. Thus, the plan is to open up in Cairo, Egypt too," commented Julie Clarke, Sutherland Vice President for Human Resources EMEA region.


text:
Tag Archives: coal
by Christopher Collins
The Creature of Martin Creek Lake Won't Die
The Martin Lake coal plant in East Texas is the biggest sulfur dioxide polluter in the nation. And unlike other regional super-polluters, this one is still chugging along.
Mon, Sep 23, 2019 |
From above, the Creature takes the shape of a spider: Its eyes are smokestacks, its legs spindly railways stretching from the semicircle of clearcut pines into ...Read More
Report: Power Plants are Leaking Cancer-Causing Toxins into Groundwater Across Texas
An environmental group found that all of Texas' coal-fired power plants have polluted groundwater with multiple chemicals, including arsenic, which was detected at 13 of the 15 sites.
Thu, Jan 17, 2019 |
In 1975, Alonzo Peeler Jr. figured that selling a small section of his ranchland to a power company was a sage financial move. Now 78, the lifelong rancher says...Read More
Despite Trump and Rick Perry's Best Efforts, Another Coal Plant Eats the Dust in Texas
The impending closure of the 650-megawatt Oklaunion plant is more writing on the wall for the state's power generators.
Thu, Oct 4, 2018 |
Chalk this one up as another loss for the White House. Last month, American Electric Power (AEP) announced that it would close its 650-megawatt power plant in V...Read More
by Naveena Sadasivam
After Failing to Prop Up Coal in Texas, Rick Perry is Trying Again Nationwide
As energy secretary, Perry is proposing to guarantee profits for uncompetitive coal plants in the Northeast, Mid-Atlantic and Midwest.
Wed, Oct 18, 2017 |
In late 2005, then-Governor Rick Perry was in the middle of a protracted battle with a coalition of environmentalists, renewable energy advocates, mayors and lo...Read More
Facing Tougher Pollution Regulations, Texas Coal Plants Want Old EPA Rule Back
In 2011, Texas sued to block an EPA rule. Now nine Texas coal plants are facing more stringent regulations as a result.
Wed, Jan 11, 2017 |
Texas' latest attempt to dodge federal pollution regulations appears to have backfired, as the Environmental Protection Agency is now pushing more stringe...Read More
by Forrest Wilder
Editorial: Pressing On in a Media Landscape Under Threat
Even in these tumultuous times, the Observer hasn't strayed from its mission to produce truth-speaking journalism that inspires change from the top....Read More
Report: Seven Texas Coal Plants Could Close in Coming Years
Some plants are likely to lose hundreds of millions of dollars, Public Citizen says.
The plants will struggle to turn a profit due to low natural gas prices and increasing competition and regulations, according to a new report....Read More
Green Groups Urge Lawmakers: Don't Leave Taxpayers on the Hook for Coal Cleanup
Tue, Aug 23, 2016 |
Following an Observer investigation into loose coal regulations that could put taxpayers on the hook for mine cleanup, green groups are demanding change....Read More
With little more than a promise, the Texas Railroad Commission is trusting a struggling coal industry to pay for the cost of cleaning up old mines.
Thu, Aug 18, 2016 |
In mid-2014, Texas' biggest utility, Energy Future Holdings, was about to file for bankruptcy, and Texas regulators suddenly had a big problem on their hands...Read More


text:
Medioimages/Photodisc/Photodisc/Getty Images
Can You Be a Doctor of Anthropology?
Susan Sherwood
How to Become an Oceanographer
Anthropologists study the behavior, culture and traditions of groups of people, often exploring major world issues. A graduate degree in anthropology lets you understand and perhaps improve people's lives. A master's degree is common, but a doctoral degree will let you become a leader in the field.
Many jobs are open to an anthropologist with a master's degree. To be accepted into a master's program, you don't have to major in anthropology as a undergrad. You must choose a specific area of concentration, such as archeology, ancient technology or linguistic, biological, medical or sociocultural anthropology. It takes an average of two years to complete a master's degree. You'll probably need to pay for it yourself or take out student loans because master's programs don't receive much outside funding.
If you want to work at a university, run a museum or lead projects, you'll need a doctorate in anthropology. Ph.D. programs prepare students to do research in fields such as globalization, media, race, popular culture and traditions. You spend about three years taking classes before starting work on your dissertation, an original research project. Doing the research and collecting data takes a year or two, and then you'll need a year or more to write about your work. Finally, you present your dissertation to a faculty committee and answer their questions.
Government agencies, schools, corporations and nonprofit groups employ anthropologists to look into issues important to the specific organizations. Jobs are varied, including university professor and researcher, museum curator, public health worker, social services administrator, forensic anthropologist, management consultant, law enforcement personnel, human rights activist, historical preservationist and market researcher. Anthropologists work all over the world in urban, suburban, rural and abandoned or damaged areas.
In 2010, the annual median pay for anthropologists was $54,230. The federal government is one of the field's largest employers. It also paid the best: $70,800 per year. Anthropologists often work as consultants in science, management or technology, but their average yearly income, $46,280, is considerably less. The Bureau of Labor Statistics predicts that anthropology will be a growing field between 2010 and 2020. Jobs will increase more than 20 percent, which is faster than average. Most of the increase will come from businesses that hire anthropologists. More research is needed as companies move into new markets and want to understand different types of consumers.
2016 Salary Information for Anthropologists and Archeologists
Anthropologists and archeologists earned a median annual salary of $63,190 in 2016, according to the U.S. Bureau of Labor Statistics. On the low end, anthropologists and archeologists earned a 25th percentile salary of $48,240, meaning 75 percent earned more than this amount. The 75th percentile salary is $81,430, meaning 25 percent earn more. In 2016, 7,600 people were employed in the U.S. as anthropologists and archeologists.
How to Become an Archaeologist
What Can I Do With a Geography Degree
List of Top Colleges for Becoming an Archaeologist
What is a Historian
How Long Does It Take to Become a Psychologist?
The Advantages of a Ph.D. in Biotechnology
The Amount of College Education Requirements For a Marine Biologist
What Can I Do With a Sociology Degree?
UC Berkeley: Anthropology: Graduate Programs
New York University: Department of Anthropology: Graduate Program
Bureau of Labor Statistics: Anthropologists and Archeologists
Duke University: Cultural Anthropology: Graduate Study at Duke
Cornell University: Department of Anthropology: Doctoral Program in Anthropology at Cornell
American Anthropological Association: What do Anthropologists do?
University of South Florida: Department of Anthropology: Margaret Mead
U.S. Bureau of Labor Statistics: Occupational Outlook Handbook: Anthropologists and Archeologists
Career Trend: Anthropologists and Archeologists
The New York Times: How Napoleon Chagnon Became Our Most Controversial Anthropologist
Living in upstate New York, Susan Sherwood is a researcher who has been writing within educational settings for more than 10 years. She has co-authored papers for Horizons Research, Inc. and the Capital Region Science Education Partnership. Sherwood has a Ph.D. in curriculum and instruction from the University at Albany.


text:
__version__ = '$Id$'

class EulerMovement(object):
	"""System that applies entity movement to position using Euler's method

	:param position_component: Name of :class:`grease.component.Position`
		component to update.
	:param movement_component: Name of :class:`grease.component.Movement`
		component used to update position.
	"""

	def __init__(self, position_component='position', movement_component='movement'):
		self.position_component = position_component
		self.movement_component = movement_component

	def set_world(self, world):
		"""Bind the system to a world"""
		self.world = world

	def step(self, dt):
		"""Apply movement to position"""
		assert self.world is not None, "Cannot run with no world set"
		for position, movement in self.world.components.join(
			self.position_component, self.movement_component):
			movement.velocity += movement.accel * dt
			position.position += movement.velocity * dt
			position.angle += movement.rotation * dt

redpajama_set_name: RedPajamaGithub


text:
The OpenAIRE Research Graph Expansion and the new Content Acquisition Policy: widening the scope
Pedro Príncipe - University of Minho, Paolo Manghi, Alessia Bardi - ISTI - CNR, Jochen Schirrwagen - University of Bielefeld and André Vieira - University of Minho
This poster is about the OpenAIRE Research Graph Expansion and the new Content Acquisition Policy - CAP released in October 2018, which defines the conditions under which metadata of scientific products collected from content providers in OpenAIRE will be considered for inclusion in the OpenAIRE information space.
The OpenAIRE service infrastructure harvests metadata about scholarly communication products (literature, datasets, software, and other research products) and links between such products from a range of institutional or subject repositories, national and institutional research information portals, aggregators, e-journals, data repositories, and software repositories. In addition, it infers links between literature and such products via advanced text and data mining techniques. The resulting information graph (i.e. interlinked sets of objects) is intended to favour monitoring of open science and open science publishing workflows (e.g. science reproducibility and transparent assessment).
The aim is to build a high quality, trusted, public, open science graph on top of which different types of services can be built, for example for monitoring research impact and research assessment.
The new CAP have an important role on the Research Graph expansion, taking into account that defines the conditions under which metadata of scientific products, collected from content providers in OpenAIRE will be considered for inclusion in the information space.
The major update in the new CAP is that metadata describing Open Access and non-Open Access material that will be included and the links to other products, that will be resolved whenever possible (i.e. the provided PIDs have a resolver). Therefore, OpenAIRE is expanding the scope to include non-OA content too. Specifically, OpenAIRE accepts the metadata records of all scientific products whose structure respect the model and semantics as expressed by the OpenAIRE guidelines.


We pride ourselves here at NJS Joinery Ltd on our handcrafted bespoke joinery products. We have years of craftsmanship and experience and we have invested in the most up to date machinery which enables us to ensure each cut is as precise as it could possibly be. With our cutting edge machinery and our skilled craftsman, NJS Joinery Ltd provides top quality products efficiently created and finished by our team of experienced Joiners. We offer a wide range of products and work closely with local businesses around Ipswich and its surrounding areas to make sure that all our materials are of the best quality for our clients.
Below are a few examples of our Joinery projects. To view more projects, please see our gallery.


Seemiller Grip - Front View. (c)2006 Greg Letts, licensed to About.com, Inc.
In the Seemiller grip, the racket is held similarly to the shakehand grip, but with a 90 degree turn so that the thumb and index finger are used to grip the sides of the bat. Both forehand and backhand are played with the same side of the bat, although the bat can be turned to use the other side. It is typically used with a combination bat.
This grip is named after Dan Seemiller, who first popularized the grip in the 1970's, and enjoyed world level success with it.
The Seemiller grip allows good wrist movement on the forehand stroke, giving a powerful forehand topspin. It is also good for blocking on both sides.
Because the one side of the bat is used for both forehand and backhand, the grip does not have the problem of a crossover point that the shakehands grip has.
Most players will put a long pimpled or antispin rubber on the back of the bat and occasionally twiddle the bat to provide extra variation in their returns.
The amount of wrist movement is hampered on the backhand side, limiting the ability to topspin the ball heavily, or hit with great power.
Also, since the introduction of the two-color rule, the advantages gained by twiddling the racket are much less than before.
What Type of Player Uses This Grip?
This grip is commonly used by attacking style players who prefer to play with a strong forehand topspin and steady backhand, with occasional variations in play caused by twiddling the racket to use the rubber on the back of the bat. Players who prefer to block and counter hit from both sides can also find this grip to their liking.
The Seemiller grip is relatively out of favor at the highest levels of the game in recent years.


The Town Council introduced the Citizens' Awards in 2001, as a way of recognising the tremendous work done by the 'unsung heroes' of our community. There are an enormous number of people whose caring work goes unrecognised so if there is someone you know, young or old, who you feel is deserving of recognition, please complete and send in a nomination form.
Nominees could be involved in caring for children, elderly, sick or disabled people; perhaps they do voluntary work or work with young people, or are simply just a good neighbour. We have a youth category for nominees aged 19 or under. All nominations will be considered at a Council Committee meeting in January.
You can complete an online form or download a nomination form below or contact Woodley Town Council on 0118 969 0356 if you would like one sent to you, alternatively you can pick one up at the Oakwood Centre, Woodford Park Leisure Centre or Woodley Library. Completed forms should be returned to Woodley Town Council, Citizens Awards, The Oakwood Centre, Headley Road, Woodley, RG5 4JZ.


We will have a holiday for The Labors Day from 1st to 3rd May. and will get back to work on 4th. During this time, all our sale department will have limit access to check emails or others.
Thanks for your always support.