THE EUROPEAN Union (EU) will pay for the construction of three highway projects in Slovakia.
The Ministry of Transport, Construction and Regional Development has been delivered a decision by the European Commission, according to which the initial phases of projects D1 Hričovské Podhradie-Lietavská Lúčka; D1 Hubová-Ivachnová; and R2 Zvolen Východ (East)-Pstruša will definitely be covered by European resources.
The two sections of D1 highway and the Zvolen stretch of R2 expressway belong among the so-called phased projects, financed under two financial European fund frameworks. The EÚ will cover costs in total of more than €150 million in Slovakia, according to the ministry.
The projects are to be completed under the Integrated Infrastructure Operational Programme within the next three years.


gramercy global media Celebrates Website Launch with Champagne - Duration: 63 seconds.
DAF Interview with Troy at the Stock Exchange Bulding - Duration: 6 minutes, 8 seconds.
What is search engine marketing (SEM)? - Duration: 2 minutes, 36 seconds.
Was ist search engine marketing (SEM)? - Duration: 2 minutes, 36 seconds.


Thanks to all the artists, activists and others who contributed to 52 Weeks!
Guggenheim Appetizer, newspaper cut-out, 2014.
Guests, SD Video, 2007-14 (video still). Guests consists of footage shot in Dubai and Abu Dhabi from 2007.
guests from Pat McElnea on Vimeo.


Schramsberg's history dates back to the 19th century, when German immigrant Jacob Schram first made his home and started his winery on Diamond Mountain.
Over one hundred years later Jack and Jamie Davies would restore the Schramsberg estate in 1965. They set out to produce sparkling wine – specifically, "America's most prestigious, select and admired sparkling wine; chosen for special guests, special gifts, pampering one's self and expressing one's taste in unique products." Through the decades, this spirit of innovation earned Schramsberg and the Davies worldwide praise and recognition.
Schramsberg has also played a role in world history. The Blanc de Blancs was used for President Nixon's 1972 "Toast to Peace" with China's Premier Zhou Enlai (see timeline). Schramsberg's sparkling wines have been served at official State functions by every U.S. Presidential administration since.
Schramsberg's founders, Jack and Jamie Davies were determined to seek out a simpler, more meaningful life. In 1965, after a yearlong search, they discovered a run-down winery on the mountainside above St. Helena. The 19th century Victorian mansion presided over the tangled remains of once stately gardens and underground cellars. The historic Schramsberg property had been abandoned for years, but the Davies were looking for a challenge. On that day they decided Schramsberg would be brought back to life.
The Davies had an ambition to make wine, but not just any wine. They were setting out to produce a world-class sparkling wine, and it was a lofty goal. Later that year, the 1965 Blanc de Blancs came to be, and would be the first commercial use of Chardonnay in American sparkling wine.
Schramsberg sparkling wines are produced from Pinot Noir and Chardonnay grapes grown in a broad array of cool-climate North Coast vineyards located in Napa, Mendocino, Sonoma and Marin counties. The winemakers draw from more than 110 individual vineyard sites to create over 250 base wines each year. These wines are then artfully blended to create Schramsberg's 12-14 distinctive sparkling wines each year.
Wall Street Journal Good/Very Good. "Despite the Brut in the name, we'd recommend this to people who prefer their bubblies a little sweeter and softer than most. Relaxed and relaxing and quite approachable. Good as an aperitif or with cookies, so maybe it's just the thing for a noon inauguration."


No flashlight or cell phone.
Not permitted to touch the actors and property of the mansion.
No refund will be made if you are escorted out of the mansion for not respecting rules.
No refund is possible as soon as you enter the mansion.
Enter at your own risks in the haunted house!
Not responsible for accidents and injuries.
NIt is not suggested for people with heart disease, pregnant women, people with claustrophobia and / or have any health problems.


text:
\section{Dataset: Refinement of bounding box annotations.}

For those datasets where annotations are provided in the form of bounding boxes, we refine these to pixel-wise
annotations suitable for DN training by adopting a similar technique to~\cite{Papandreu15iccv}:
we use the GrabCut algorithm~\cite{Rother04sg} to segment the foreground object pixels from the background. We constrain the central area of the bounding box ($\alpha \%$ of pixels within the box) to be foreground, while the pixels
outside the bounding box of the object of interest and the pixels inside the remaining bounding boxes of the other annotated objects in the
image are constrained as background. \SI{what is alpha?}

We perform additional refinement on the CBCL dataset, which consists of 3,547 images with polygonal bounding boxes for 9 object categories (car,
pedestrian, bicycle, building, tree, sky, road, sidewalk and store). It was created for object detection and contains missing
labelled objects, ambiguous labelling situations and objects that are not tightly bound within the polygon annotations. We re-labelled and
extended the categories for 584 of the images containing dense pixel-wise annotations and produced semi-supervised annotations for the
remaining 2,963 images by applying a CRF classifier~\cite{Ladicky10eccv} combined with the polygon-based ground truth.

\subsection{Results}

Images are initially normalized using spatial contrast normalization, independently applied to each channel $f$ as $\bar{X}_{i,j,f} = X_{i,j,f} / (1 + \alpha  \frac{1}{k^2}\sum_{j'=0}^{k-1}{\sum_{i'=0}^{k-1}{x^2_{i+i',j+j',f}}})^\beta$. Here $k=7$ stands for the kernel size and $\alpha = 1, \ \beta=0.5$.

\begin{table*}[]
	\centering
	\caption{Semantic segmentation quantitative results on the testing dataset, training with the Sparse domain.}
	\vspace{-3mm}

		\scriptsize
		\tabcolsep=0.06cm
		\begin{tabular}{@{}lcccccccc@{}}
			\toprule
			Method    & other & pole & car & sign & pedestrian & cyclist & \textbf{per-class} & \textbf{global} \\ \midrule
			FCN  	  & 94.1 & 1.0 & 92.8 & 10.8 & 77 & 77.5 & 58.9 & 93.1\\ \midrule
			P-Net     & 86.9 & 0 & 81.3 & 11.5 & 80 & 56.8     & 52.8 & 85.7\\ \bottomrule
		\end{tabular}%

	\label{tab:sparse_results}
\end{table*}

\begin{table*}[]
	\centering
	\caption{Evaluation of Knowledge Transference to P-Nets of different sizes.}
	\vspace{-3mm}
	\resizebox{\textwidth}{!}{%
		\scriptsize
		\tabcolsep=0.06cm
		\begin{tabular}{@{}lccccccccccccc@{}}
			\toprule
			Method    & sky & building & road & sidewalk & fence & vegetation & pole & car & sign & pedestrian & cyclist & \textbf{per-class} & \textbf{global} \\ \midrule
			T-Net $\rightarrow$ P-Net 6B (Probs)   & 87.3   	& 75.7     & 87.4   & 28.1      & 9.4     & 83.8    & 6.8    & 89.4   & 24.5    & 55.4         & 6.1      & 50.3                  & 75.3\\ \midrule

			T-Net $\rightarrow$ P-Net 8B (Probs)       & 85.1   	& 65.1     & 87.5   & 21.1      & 35.7     & 85.3    & 6.6   & 90.0   & 45.2    & 53.2         & 55.6     & 57.3                 & 70.8               \\ \midrule

			T-Net $\rightarrow$ P-Net 10B (Probs)   & 84.1   	& 79.9     & 84.9   & 31.0      & 22.9     & 78.7    & 3.1    & 88.6   & 31.0    & 51.6         & 15.3      & 51.9                 & 75.7 \\ \midrule
			\midrule
			T-Net $\rightarrow$ P-Net 6B (Probs-drop)   & 87.3   	& 75.7     & 87.4   & 28.1      & 9.4     & 83.8    & 6.8    & 89.4   & 24.5    & 55.4         & 6.1      & 50.3                  & 75.3\\ \midrule

			T-Net $\rightarrow$ P-Net 8B (Probs-drop)   & 87.6   	& 75.9     & 79.3   & 43.2      & 27.1     & 80.8    & 4.0    & 86.9   & 19.9    & 68.5         & 14.0      & 53.4                  & 74.8\\ \midrule

			T-Net $\rightarrow$ P-Net 10B (Probs-drop)   & 84.1   	& 79.9     & 84.9   & 31.0      & 22.9     & 78.7    & 3.1    & 88.6   & 31.0    & 51.6         & 15.3      & 51.9                 & 75.7 \\ \midrule

		\end{tabular}%
	}
	\label{tab:multiblock}
\end{table*}
\section{Supplementary Material}
\label{app:AppendixA}

Images are initially normalized using spatial contrast normalization, independently applied to each channel $f$ as $\bar{X}_{i,j,f} = X_{i,j,f} / (1 + \alpha  \frac{1}{k^2}\sum_{j'=0}^{k-1}{\sum_{i'=0}^{k-1}{x^2_{i+i',j+j',f}}})^\beta$. Here $k=7$ stands for the kernel size and $\alpha = 1, \ \beta=0.5$.

\begin{table*}[]
	\centering
	\caption{Semantic segmentation quantitative results on the testing dataset, training with the Sparse domain.}
	\vspace{-3mm}

		\scriptsize
		\tabcolsep=0.06cm
		\begin{tabular}{@{}lcccccccc@{}}
			\toprule
			Method    & other & pole & car & sign & pedestrian & cyclist & \textbf{per-class} & \textbf{global} \\ \midrule
			FCN  	  & 94.1 & 1.0 & 92.8 & 10.8 & 77 & 77.5 & 58.9 & 93.1\\ \midrule
			P-Net     & 86.9 & 0 & 81.3 & 11.5 & 80 & 56.8     & 52.8 & 85.7\\ \bottomrule
		\end{tabular}%

	\label{tab:sparse_results}
\end{table*}

\begin{table*}[]
	\centering
	\caption{Evaluation of Knowledge Transference to P-Nets of different sizes.}
	\vspace{-3mm}
	\resizebox{\textwidth}{!}{%
		\scriptsize
		\tabcolsep=0.06cm
		\begin{tabular}{@{}lccccccccccccc@{}}
			\toprule
			Method    & sky & building & road & sidewalk & fence & vegetation & pole & car & sign & pedestrian & cyclist & \textbf{per-class} & \textbf{global} \\ \midrule
			T-Net $\rightarrow$ P-Net 6B (Probs)   & 87.3   	& 75.7     & 87.4   & 28.1      & 9.4     & 83.8    & 6.8    & 89.4   & 24.5    & 55.4         & 6.1      & 50.3                  & 75.3\\ \midrule

			T-Net $\rightarrow$ P-Net 8B (Probs)       & 85.1   	& 65.1     & 87.5   & 21.1      & 35.7     & 85.3    & 6.6   & 90.0   & 45.2    & 53.2         & 55.6     & 57.3                 & 70.8               \\ \midrule

			T-Net $\rightarrow$ P-Net 10B (Probs)   & 84.1   	& 79.9     & 84.9   & 31.0      & 22.9     & 78.7    & 3.1    & 88.6   & 31.0    & 51.6         & 15.3      & 51.9                 & 75.7 \\ \midrule
			\midrule
			T-Net $\rightarrow$ P-Net 6B (Probs-drop)   & 87.3   	& 75.7     & 87.4   & 28.1      & 9.4     & 83.8    & 6.8    & 89.4   & 24.5    & 55.4         & 6.1      & 50.3                  & 75.3\\ \midrule

			T-Net $\rightarrow$ P-Net 8B (Probs-drop)   & 87.6   	& 75.9     & 79.3   & 43.2      & 27.1     & 80.8    & 4.0    & 86.9   & 19.9    & 68.5         & 14.0      & 53.4                  & 74.8\\ \midrule

			T-Net $\rightarrow$ P-Net 10B (Probs-drop)   & 84.1   	& 79.9     & 84.9   & 31.0      & 22.9     & 78.7    & 3.1    & 88.6   & 31.0    & 51.6         & 15.3      & 51.9                 & 75.7 \\ \midrule

		\end{tabular}%
	}
	\label{tab:multiblock}
\end{table*}
\section{Training Strategies for DN architectures}\vspace{-2mm}
\label{sec:training}
In this section we describe the different approaches used to train our networks on the challenging MDRS3 dataset
described in section~\ref{sec:datasets}. The approaches explored are (i) ``e2e"---standard end-to-end training over various subsets of the
multi-domain training data; (ii) ``BGC"---which uses Balanced Gradient Contribution to generate stable gradient directions for end-to-end
training; (iii) ``Flying-Cars"---dynamic domain adaptation of the sparse training data; and (iv) ``Ensemble"---the ensembling of models
trained on separate domains. The performance of these methods is evaluated in section~\ref{sec:exps}.

Each training strategy was initialised identically. Contraction blocks of the architecture under study were assigned the weights of classification networks pre-trained on ImageNet -- VGG-16~\cite{Simonyan15iclr} in the case of FCN and VGG-F~\cite{Chatfield14bmvc} in the case of T-Net. Adjustments to the shape of the weights were performed where dimensions do not match. Expansion blocks were initialized using the method of He \emph{et al.~}\cite{He15iccv}.

Optimisation was performed via standard backpropagation using Stochastic Conjugate Gradient Descent (S-CGD), endowed with a bounded line-search strategy and backtracking with Armijo's rule~\cite{LeNCLPN11}. To avoid overfitting, the number of line-search iterations was bounded to 3
This proved to converge faster to good solutions than stochastic gradient descent without manual tweaking of learning rates.

\vspace{-2mm}
\subsection{End-to-End Training (e2e)}\vspace{-1mm}
\label{sec:training_endtoend}

Our simplest training approach, end-to-end (e2e) training, consisted of standard mini-batch training on random samples (with replacement) from the mixed dense and sparse training set (\emph{i.e.~} all data in). To achieve reasonable per-class accuracy, the use of weighted cross-entropy (WCE) as training loss was found to be essential. WCE re-scales the importance of each class, $l \in [1, \dots, L]$, according to its inverse frequency $f^{l}(\mathcal{X})^{-1}$ in the training data set $\mathcal{X}$, \emph{i.e.~}:

\vspace{-2mm}
\begin{equation}%
\mbox{\normalsize\( %
\text{Loss}_\text{WCE}(x^n, y^n) = -\sum_{ijl}^{HWL} \omega(y^n_{ijl}) y^n_{ijl} \text{log}(\mathcal{F}(x^n, \theta))_{ijl},%
\)} %
\end{equation}
\noindent where $\mathcal{F}$ refers to the network, $x^n$, $y^n$ stand for the $n$-th training image and ground truth image respectively, and
the weighting function is given by
\begin{equation}
\mbox{\normalsize\( %
w(y_{ijl}^n) = \text{max} \left\lbrace \frac{f^l(\mathcal{X})^{-1}}{\sum_{i=1}^L f^{i}(\mathcal{X})^{-1}  \text{ min} \{   f^{1}(\mathcal{X})^{-1}, \dots,  f^{L}(\mathcal{X})^{-1}\} }, \epsilon \right\rbrace\)}, \text{ for } \epsilon = 10^{-5}.
\end{equation}

In this way, WCE helped the networks to account for class frequency imbalances, a common phenomenon exposed in Table~\ref{tab:class_distribution}, which were otherwise observed to reduce a network's attention to rare but important classes such as pedestrians or bicycles during training.

End-to-end training was applied to learn separate models for the dense and sparse domains as well as a combined model on both data domains. However, when this approach is used naively on the combined data, we observed an unstable oscillatory behaviour of the objective and eventually divergence of the system. This phenomenon is due to the strong difference between the statistics of both distributions, which give rise to very noisy descent directions during optimisation. Thus, in order to exploit all the information available in both domains one needs to stabilize the training process, via alternatives such as those proposed in the following sections.

\vspace{-3mm}
\subsection{Balanced Gradient Contribution (BGC)}
\label{sec:training_balanced}
The severe statistical difference between the domains induces a large variance in gradients for a sequence of mini-batches. Data from the
dense domain is more stable and suitable for structural classes, but less informative in general. Data from the sparse domain is highly
informative, with critical information about dynamic classes, but very noisy. To deal with these aspects we propose to compute search
directions using the directions proposed by the dense domain under a controlled perturbation given by the sparse domain as shown in
(\ref{eq:bgc}).

\vspace{-3mm}
\begin{equation}
\text{Loss}_{\text{BGC}}(\mathcal{X}, \mathcal{Y}) = \text{Loss}_\text{WCE}(\mathcal{X}^D, \mathcal{Y}^D) + \lambda
\text{Loss}_\text{WCE}(\mathcal{X}^S, \mathcal{Y}^S),
\label{eq:bgc}
\end{equation}

\noindent where $\mathcal{X}, \mathcal{Y}$ stand for a subset of samples and their associated labels, drawn from the dense (D) or sparse
(S) domains. This procedure can be seen as the addition of a very informative regularizer controlled by the parameter $\lambda$, but an
analogous effect can be achieved by generating mini-batches containing a carefully chosen proportion of images from each domain, such that
$|\mathcal{X}^D| >> |\mathcal{X}^S|$. This modification of the training procedure leads to superior results and a stable behaviour, as
reported in section~\ref{sec:exps}.

\subsection{Flying Cars (FC): Domain Adaptation by Data Projection}
\label{sec:training_fc}

Another alternative to solve the problem caused by the combination of incompatible domains is to project or transfer one domain into another. In our case, the noisy sparse domain is projected to the dense domain, using ideas from domain adaptation~\cite{VLP2011b}. This can be achieved, for instance, by selecting random images from the dense domain and using them as backgrounds in which to inject the objects and labels of the sparse domain. This approach was recently used in the ``Flying Chairs" approach of~\cite{Dosovitskiy15} to train DNs for optical flow from synthetic data. It can be seen as a way of performing highly informative data augmentation over the dense domain. Similarly to~\cite{Dosovitskiy15}, we use a naive approach which does not provide a hard constraint on the spatial context of the objects being inserted into the scene, hence the name ``Flying Cars" (FC).

\vspace{-3mm}
\subsection{Ensemble of Sparse and Dense Domains}\vspace{-1mm}
\label{sec:training_ensemble}
Finally, it is possible to think about the domains as two different tasks: one consisting of recognizing $L_D = 11$ classes from
finely-annotated data and the other recognizing $L_S = 6$ classes, \emph{i.e.~} foreground, traffic signs, poles, cars, pedestrians and cyclists; from noisy sparse annotations. The model trained on the dense domain,  $\theta_D$, is better at structural elements such as roads,
buildings and sidewalks; while the model trained on the sparse domain, $\theta_S$, is extremely good at segmenting dynamic objects
such as pedestrians and cyclists. These models can be combined as part of a larger network which adds several new trainable blocks
to perform a consensus from the output of the original models. In our experiments the ensemble is performed by fixing the original
networks and adding a convolutional block and four residual-blocks~\cite{He15cvpr} to estimate a consistent output. This is shown in
Fig.~\ref{fig:cnn_archs}(iv). Residual-blocks were used as they were found to lead to better generalization than simple convolutions in practice. The current configuration, 4 blocks, one with $128$ features and three with $64$ features, was the best configuration we found that did not lead to clear overfitting. This approach is further analysed in section~\ref{sec:exps}.

\vspace{-2mm}

\section{Transferring Knowledge across Deconvolutional Networks}
\label{sec:transfer}
\vspace{-1mm}
We use the training methods described in section~\ref{sec:training} to train both FCN and T-Net architectures. The results are reported in
Table~\ref{tab:training}. For all training methods explored, FCN is observed to consistently outperform the smaller T-Net.
Moreover, among the different approaches for training, the most outstanding in terms of per-class
accuracy is the multi-domain ensemble.

Despite the high accuracy of the FCN ensemble, its large number of parameters makes it unsuitable for embedded applications, in the context of road scene segmentation. We next investigate whether it is possible to boost a more compact model such as T-Net to have an equivalent performance.
Our hypothesis is that the capacity of T-Net is sufficient to produce results at the level of FCN and FCN-ensemble, but due to specific details of its training and architecture, such as batch normalization and noise within the training data, the methods of section~\ref{sec:training} cannot exploit its full potential.
We therefore examine an alternative training approach for T-Net. We adopt the FCN ensemble as a Source Network (S-Net) and attempt to emulate its behaviour with (\emph{i.e.~} transfer its knowledge to) the T-Net. We describe three approaches to transfer knowledge: (i) via labels (TK-L), (ii) via soft-max probabilities (TK-SMP),
and (iii) via soft-max probabilities with weighted-cross-entropy (TK-SMP-WCE).

\paragraph{\bf Transferring Knowledge Through Labels (TK-L).}\label{sec:transfer_tktl}
\vspace{-2mm}
This strategy aims to distill the knowledge of the S-Net directly from its predicted labels, in the spirit of~\cite{Bucilua06kdd}. We use both dense and sparse domains of training data described in section~\ref{sec:datasets}, ignoring their original annotations.
The benefit of this approach is that the multi-modality of the data has been filtered by the S-Net and some distractors are ignored, so the information reaching T-Net is simpler, leading to a smoother search space and making it easier to find good solutions. In our setup we included extra training data from a large unlabelled Google Street View (GSV) dataset~\cite{Zamir14pami}, taken of street scenes from multiple cities in the US. We remove the upward facing camera and took a random crop from each image to produce 51,715 images. We combined previous and new training data using BGC to train the T-Net with a standard cross-entropy loss. Here, BGC is used as an important mechanism to control the influence of the GSV data and prevent from drift.

\paragraph{\bf Transferring Knowledge Through SoftMax Probabilities (TK-SMP).}\label{sec:transfer_tktsp}
\vspace{-2mm}
The strategy uses additional information from S-Net during transfer, by considering the probability distributions produced by the softmax classifier, which contains information about how different classes are correlated~\cite{HintonVinyals15}. To this end, we train a T-Net using standard cross-entropy between the probability distributions of S-Net and T-Net as our loss function. As in the previous strategy, the training makes use of BGC to control the influence of GSV data to bound its contribution. This second approach leads to a notable improvement of the network per-class accuracy as shown in Table~\ref{tab:transfer} (i)-(ii).

A variation of this method consists of adding drop-out blocks to the T-Net during the transference process. In practice, this addition behaves as in end-to-end training, helping to improve the generalization of the net. See Table~\ref{tab:transfer}(iii).

\paragraph{\bf Transferring Knowledge Through SoftMax Probabilities with WCE (TK-SMP-WCE).}\label{sec:transfer_tktspwce}
\vspace{-2mm}
One of the problems with the previous approaches of TK-TL and TK-SMP is that they do not account for class imbalance during transfer. In practice this means that the resulting models are biased towards the dominant classes and producing models with higher per-class accuracy requires a higher number of epochs during training. We propose to solve this problem by controlling the influence of each softmax sample with WCE, in the same way that the influence of different datasets is controlled by BGC. This simple modification, in combination with the use of dropout in the T-Net, leads to models that have virtually the same per-class accuracy as the S-Net, \emph{i.e.~} an ensemble of FCNs; see Table~\ref{tab:transfer} (iv). In this way the full potential of the T-Net is unlocked, giving rise to an accurate and memory-efficient model, convenient for embedded systems and automotive applications.

\section{Experimental Results}\label{sec:exps}
\setlength{\medmuskip}{0mu}
\vspace{-2mm}

We evaluate the performance of the proposed training methodology with respect to a set of state-of-the-art baselines.
Special emphasis is set on the performance of our TK-SMP-WCE transfer technique when used in combination with Balanced Gradient
Contributions (BGC).

\vspace{-2mm}\paragraph{\bf Experiment Setup.}
All our experiments are carried out on the MDRS3 dataset (Section~\ref{sec:datasets}), testing on the combination of U-LabelMe
and CBCL (1,526 images overall). Due to time and resource constraints, we subsample the original images to a resolution of
$240\times180$ in all our experiments. This speeds up training and evaluation of models but makes certain classes, such as
sidewalks, poles and traffic signs, systematically harder to recognize for all models due to the low resolution. Nevertheless,
this factor is consistent across all the experiments and does not affect the conclusions obtained when comparing different training
approaches and models. Images are initially normalized using spatial contrast normalization, independently applied to each
channel. Afterwards, zero-mean and data re-scaling in the range [-127,127] are applied. In practice we observed that this
normalization speeds-up convergence.

Results are evaluated according to the average per-class accuracy (\textbf{per-class}) and the global accuracy (\textbf{global}). Given the number of pixels, $n_{i,j}$, belonging to class $i$ and classified as class $j$, and assuming $L$ is the number of classes, then \textbf{per-class} is evaluated as $\frac{1}{L}\sum_i n_{i,i}/\sum_{j=1}^{L} n_{i,j}$ and \textbf{global} as
$\sum_i n_{i,i}/N$ where $N$ is the total number of pixels in the evaluation set. Due to the intrinsic unbalanced nature of the class frequencies in urban scenes,
we consider the average per-class to be more significant to assess the recognition and generalisation capabilities of the models. Within parenthesis we report the difference between the results of the current method and the FCN model at Table~\ref{tab:training}(i) as a reference (improvements are highlighted in blue, diminishments in red).

\vspace{-2mm}
\subsection{Assessing Multi-Domain Training}\label{sec:exps:1}

\setlength{\textfloatsep}{10pt plus 10.0pt minus 20.0pt}
\setlength{\floatsep}{10pt plus 10.0pt minus 20.0pt}
\setlength{\intextsep}{12.0pt plus 20.0pt minus 20.0pt}

\paragraph{\bf End-to-End training.}

In Table~\ref{tab:training} (i)-(iii), we first evaluate the performance of T-Net against the FCN network~\cite{Long15cvpr} and ALE~\cite{Ladicky10eccv}, a classical semantic segmentation framework based on hand-crafted features. These models are trained using the dense domain
only, with the end-to-end approach described in section~\ref{sec:training_endtoend}. As Table~\ref{tab:training} (i)-(iii) shows, for this initial setup T-Net underperforms both FCN, by 11.2 points per-class, and ALE, by 1.9 points

We extended this first evaluation by adding the sparse domain to the end-to-end training. However, as shown in Table~\ref{tab:training} (iv) and (viii) the
training diverged in both cases. This phenomenon was commented on in section~\ref{sec:training_endtoend} and is attributed to the gradient noise
introduced by the sparse domain when its contribution is unbounded. This reinforces our claim that control over the distribution and the complexity of the data is required to produce competitive training results.

\paragraph{\bf Flying Cars, BGC \& Ensemble.}
 When the end-to-end training is replaced by methods implementing policies to control the contribution of each domain, the improvement in accuracy is notable. Table~\ref{tab:training} (v)-(vii) shows that for all the techniques,  controlled training improves the per-class of the standard FCN. FC and BGC methods, although not achieving the top performance, have the advantage of requiring just one training stage; while the ensemble requires training individual models first (per domain) and then merging them. Yet, since the ensemble of FCN achieves the highest performance we use it as our S-Net, and try to match its performance with T-Net. The outcome of applying FC, BGC and ensemble on the T-Net are analogous to the previous case; and again, the ensemble renders the best results in terms of per-class accuracy (see Table~\ref{tab:training} (ix)-(xi)).

\subsection{Evaluation of Knowledge Transfer Methods}\label{sec:exps:3}
As summarized in Table~\ref{tab:transfer}, results of previous training approaches on T-Net are dramatically improved when applying knowledge
transfer methods. For all the transference method we added the unlabelled data from the Google Street View Data Set~\cite{GMCP} in order to
increase the variability of the S-Net responses during the process, which helps capturing the behaviour of S-Net.

Here we see that the evolution of the transferring techniques is directly correlated to the improvement of the T-Net performance. A simple
transfer of labels (TK-L) from the S-Net produces a T-Net model that is already 2.9 points better than FCN (used here as a reference). When
the transfer is based on the softmax probability distribution over the classes, as in TK-SMP, accuracy is boosted up to 57.3 (6.7 points
better than FCN). It is worth noticing than, when dropout is included in the TK-SMP transference (TK-SMP-Drop), it improves global accuracy
in 3.2 points compared to FCN. We observed this effect when using dropout at expense of some loss in per-class accuracy.

Finally, Table~\ref{tab:transfer} (iv) shows that when the S-Net softmax distributions are weighted according to their
relevance in the dataset (\emph{i.e.~} less abundant more relevant), the transference of this knowledge reaches the maximum performance found so far,
59.3\% of per-class accuracy. Thus, the TK-SMP-WCE approach produces a T-Net 9.1 points better than FCN in per-class and 0.2 in global
accuracy, almost reaching the results of the S-Net, \emph{i.e.~} an ensemble of two FCN which has $200\times$ more parameters. Visual results of this evolution
are shown in Fig.~\ref{fig:examples} for testing examples. Notice how the T-Net obtained from TK-SMP-WCE can sometimes produce better
results than the S-Net. We believe that these results give strong evidence to render knowledge transfer methods and in particular TK-SMP-WCE
as preferred methods to train memory constrained DNs.

\begin{table}[t!]
	\vspace{2mm}
	\centering
	\caption{Semantic segmentation quantitative results for FCN and T-Net on the testing dataset for the training methods under study, \emph{i.e.~} end-to-end (dense, D; sparse, S; and both, D+S), BGC, Flying cars (FC) and net ensemble (Ens.).}
	\vspace{-3mm}
	\resizebox{\textwidth}{!}{%
		\scriptsize
		\tabcolsep=0.06cm
		\begin{tabular}{@{}lcccccccccccll@{}}
			\toprule
			& \includegraphics{figs/sky.eps} & \includegraphics{figs/building.eps} & \includegraphics{figs/road.eps} & \includegraphics{figs/sidewalk.eps} & \includegraphics{figs/fence.eps} & \includegraphics{figs/trees.eps} & \includegraphics{figs/pole.eps} & \includegraphics{figs/car.eps} & \includegraphics{figs/traffic.eps} & \includegraphics{figs/pedestrian.eps} & \includegraphics{figs/cyclist.eps} & \\
			Method    & sky & building & road & sidewalk & fence & vegetat. & pole & car & sign & pedest. & cyclist & \textbf{per-class} & \textbf{global} \\ \midrule
			(i) FCN~\cite{Long15cvpr} (D)					& 77.8    & 67.6     & 86.1   & 35.9      & 35.1     & 89.6    & 8.9    & 86.2   & 41.3    & 17.6         & 10.6      	& 50.6              & 71.6              \\ \midrule
			(ii) T-Net (D)  									& 80.6    & 62.0     & 85.2   & 20.6      & 4.4     & 84.5         & 9.4    & 70.0  & 6.4   & 7.6         & 2.3       & 39.4 \UP{-11.2}       & 66.6 \UP{-5.0}         \\ \midrule

			(iii) ALE~\cite{Ladicky10eccv} (D) 				& 85.0  	& 69.0      & 94.0   & 8.0       & 19.0    & 83.0         & 3.0    & 74.0  & 13.0    & 5.0        & 1.0       & 41.3 \UP{-9.3}              & 72.1   \UP{0.5}   \\
			\midrule\midrule
			(iv) FCN~\cite{Long15cvpr} e2e (D+S)  	& \multicolumn{13}{c}{\textbf{training diverged}}\\ \midrule
			(v) FCN~\cite{Long15cvpr} FC 	  				& 85.3   	& 71.4     & 87.0   & 26.4      & 19.8     & 86.5    & 10.6   & 89.3   & 45.4    & 58.8         & 7.0       & 53.4  \UP{2.8}                & 73.2 \UP{1.6}              \\ \midrule
			(vi) FCN~\cite{Long15cvpr} BGC 	  				& 80.3   	& 73.5     & 82.6   & 49.5      & 39.6     & 91.6    & 11.6    & 87.3   & 50.8    & 44.1         & 19.6      & 57.3  \UP{6.7}                & 75.5   \UP{3.9}            \\ \midrule
			(vii) FCN~\cite{Long15cvpr} Ens. (\textbf{S-Net}) 	& 77.4   	& 71.9     & 85.0   & 27.8      & 40.8     & 85.8    & 8.0    & 93.4   & 43.0    & 80.4         & 60.6      & 61.3   \UP{10.7}               & 73.4    \UP{1.8}           \\ \midrule \midrule
			(viii) T-Net e2e (D+S) 	  & \multicolumn{13}{c}{\textbf{training diverged}}\\ \midrule
			(ix) T-Net FC   									& 77.5    & 67.2     & 77.7   & 34.4      & 18.4     & 86.3         & 8.0    & 80.0  & 18.8   & 25.9         & 4.8       & 45.3  \UP{-5.3}             & 69.1	\UP{-2.5}			\\ \midrule
			(x) T-Net BGC   									& 58.9    & 64.5     & 81.6   & 21.5      & 4.8     & 83.1         & 11.0    & 82.3  & 21.2   & 31.3         & 9.3       & 42.7   \UP{-8.3}            & 64.9		\UP{-6.7}		\\ \midrule
			(xi) T-Net Ens.   								& 89.0    & 57.4     & 85.5   & 22.9      & 0.3     & 92.2         & 11.4    & 86.3  & 14.6   & 56.6         & 16.9      & 46.9   \UP{-3.7}            & 65.5	\UP{-6.1}			\\ \midrule

		\end{tabular}%
	}
	\label{tab:training}
\end{table}

\begin{table*}[t!]
\vspace{-2mm}
	\centering
	\caption{Evaluation of the proposed Knowledge Transfer techniques for $\text{S-Net} \rightarrow \text{T-Net}$.}
	\vspace{-3mm}
	\label{tab:exp:3}
	\resizebox{\textwidth}{!}{%
		\scriptsize
		\tabcolsep=0.06cm
		\begin{tabular}{@{}lccccccccccccc@{}}
			\toprule
			& \includegraphics{figs/sky.eps} & \includegraphics{figs/building.eps} & \includegraphics{figs/road.eps} & \includegraphics{figs/sidewalk.eps} & \includegraphics{figs/fence.eps} & \includegraphics{figs/trees.eps} & \includegraphics{figs/pole.eps} & \includegraphics{figs/car.eps} & \includegraphics{figs/traffic.eps} & \includegraphics{figs/pedestrian.eps} & \includegraphics{figs/cyclist.eps} & \\
			Method    & sky & building & road & sidewalk & fence & vegetat. & pole & car & sign & pedest. & cyclist & \textbf{per-class} & \textbf{global} \\ \midrule

		(b) baseline: FCN~\cite{Long15cvpr} (D)					& 77.8    & 67.6     & 86.1   & 35.9      & 35.1     & 89.6    & 8.9    & 86.2   & 41.3    & 17.6         & 10.6      	& 50.6              & 71.6              \\ \midrule
			(i) T-Net (TK-L)    & 88.7   	& 50.6     & 68.8   & 45.4      & 48.7     & 77.2    & 18.6    & 73.1   & 19.4    & 68.8         & 29.3      & 53.5 \UP{2.9}                 & 62.9 \UP{-8.7}              \\ \midrule
			(ii) T-Net (TK-SMP)       & 85.1   	& 65.1     & 87.5   & 21.1      & 35.7     & 85.3    & 6.6   & 90.0   & 45.2    & 53.2         & 55.6     & 57.3  \UP{6.7}            & 70.8    \UP{-0.8}           \\ \midrule

			(iii) T-Net (TK-SMP-Drop)   & 87.6   	& 75.9     & 79.3   & 43.2      & 27.1     & 80.8    & 4.0    & 86.9   & 19.9    & 68.5         & 14.0      & 53.4  \UP{2.8}                & 74.8  \UP{3.2}             \\ \midrule

			(iv) T-Net (TK-SMP-WCE)   & 87.4 & 66.9 & 82.0 & 33.0 & 37.9 & 83.3 & 14.1 & 89.4 & 40.0 & 78.6 & 40.2 & 59.3 \UP{9.1} & 71.8  \UP{0.2}            \\ \midrule

		\end{tabular}%
	}
	\label{tab:transfer}

\end{table*}

\begin{figure}[t!]
	\centering
	\includegraphics[width=0.95\textwidth]{figs/examplesfinal2b.eps}
	\vspace{-4mm}
	\caption{Qualitative results on test images for different training methods. Our proposed method of training T-Net via transfer learning results in visually good segmentations, in some cases providing better results than the FCN ensemble and even noisy ground truth.}
	\label{fig:examples}
\end{figure}

\section{Multi-Domain Road Scene Semantic Segmentation (MDRS3)}\label{sec:datasets}

\begin{figure*}[t]
	\centering
	\resizebox{\textwidth}{!}{%
		\scriptsize
		\tabcolsep=0.042cm
		\begin{tabular}{c c c c c}
		\toprule
			\textbf{DENSE:} & CamVid (2007)~\cite{Brostow09prl,Brostow08eccv} & KITTI-S (2012)~\cite{Geiger13ijrr} & *Urban LabelMe (2008)~\cite{Russell08ijcv} & 			*CBCL (2013)~\cite{Bileschi07cbcl} \\ \midrule
			\textit{Example(s)} &
				\includegraphics[width=0.09\textwidth]{figs/samples/camvid/rgb/1.eps}
				\includegraphics[width=0.09\textwidth]{figs/samples/camvid/rgb/3.eps} &
				\includegraphics[width=0.18\textwidth]{figs/samples/kitti/rgb/2.eps} &
				\includegraphics[width=0.10\textwidth]{figs/samples/ulabelme/rgb/2.eps}
				\includegraphics[width=0.10\textwidth]{figs/samples/ulabelme/rgb/3.eps} &
				\includegraphics[width=0.09\textwidth]{figs/samples/cbcl/rgb/2.eps}
				\includegraphics[width=0.09\textwidth]{figs/samples/cbcl/rgb/6.eps} \\

			&
				\includegraphics[width=0.09\textwidth]{figs/samples/camvid/gt/1.eps}
				\includegraphics[width=0.09\textwidth]{figs/samples/camvid/gt/3.eps} &
				\includegraphics[width=0.18\textwidth]{figs/samples/kitti/gt/2.eps} &
				\includegraphics[width=0.10\textwidth]{figs/samples/ulabelme/gt/2.eps}
				\includegraphics[width=0.10\textwidth]{figs/samples/ulabelme/gt/3.eps} &
				\includegraphics[width=0.09\textwidth]{figs/samples/cbcl/gt/2.eps}
				\includegraphics[width=0.09\textwidth]{figs/samples/cbcl/gt/6.eps} \\
			\textit{\# Images} & \textbf{600} (Cambridge, UK) & \textbf{547} (Karlsruhe, GER) & \textbf{942} (Various) & \textbf{3,547} (Boston, USA)\\

			\textit{Used for} & Training & Training & \textbf{\textcolor{ForestGreen}{Test}} & \textbf{\textcolor{ForestGreen}{Test}} \\

			\toprule
			\textbf{SPARSE:} & *ETH-RMPTMP (2009)~\cite{Ess09pami} & *GTSRB (2013)~\cite{Houben13ijcnn} & M-COCO (2014)~\cite{Lin14eccv} & *KITTI-O (2012)~\cite{Geiger13ijrr} \\ \midrule
			\textit{Example(s)} &
				\includegraphics[width=0.09\textwidth]{figs/samples/eth/rgb/1.eps}
				\includegraphics[width=0.09\textwidth]{figs/samples/eth/rgb/3.eps} &
				\includegraphics[width=0.09\textwidth]{figs/samples/signs/rgb/2.eps}
				\includegraphics[width=0.09\textwidth]{figs/samples/signs/rgb/3.eps} &
				\includegraphics[width=0.10\textwidth]{figs/samples/coco/rgb/1.eps}
				\includegraphics[width=0.10\textwidth]{figs/samples/coco/rgb/2.eps} &
				\includegraphics[width=0.18\textwidth]{figs/samples/kitti-o/rgb/1.eps} \\

			&
				\includegraphics[width=0.09\textwidth]{figs/samples/eth/gt/1.eps}
				\includegraphics[width=0.09\textwidth]{figs/samples/eth/gt/3.eps} &
				\includegraphics[width=0.09\textwidth]{figs/samples/signs/gt/2.eps}
				\includegraphics[width=0.09\textwidth]{figs/samples/signs/gt/3.eps} &
				\includegraphics[width=0.10\textwidth]{figs/samples/coco/gt/1.eps}
				\includegraphics[width=0.10\textwidth]{figs/samples/coco/gt/2.eps} & \includegraphics[width=0.18\textwidth]{figs/samples/kitti-o/gt/1.eps} \\
			\textit{\# Images} & \textbf{14,056} (Zurich, CH)& \textbf{740} (Various, GER) & \textbf{3,262} (Various) & \textbf{7,481} (Karlsruhe, GER)\\

			\textit{Used for} & Training & Training & Training & Training \\ \bottomrule
		\end{tabular}%
		}
		\vspace{-4mm}
	\caption{Constituent datasets of the Multi-Domain Road Scene Semantic Segmentation (MDRS3) dataset, containing dense and sparse labels. Datasets marked with * were upgraded from coarse labels or bounding boxes to pixel-wise annotations. The test set consists of two dense datasets (CBCL, Urban LabelMe) to better evaluate generalisation performance. Best viewed on screen.
	\label{fig:datasets}

\end{figure*}

Acquiring data suitable for training road scene semantic segmentation is expensive and time-consuming. The process of densely labelling an image with 10-20 classes can take up to 30 minutes for a typical, cluttered perspective street-view image and so existing datasets tend to be relatively small. In addition, datasets are often confined to localised geographic regions and trained and tested on in isolation. In our work we consider using numerous datasets to create one aggregate dataset, which we refer to as the Multi-Domain Road Scene Semantic Segmentation dataset (MDRS3), to take advantage of all of the relevant training data available.

\paragraph{\bf Dataset composition.}
Fig.~\ref{fig:datasets} details the constituent datasets of our MDRS3 dataset.
We consider popular road scene semantic segmentation datasets with dense pixel-wise annotations such as
CamVid~\cite{Brostow09prl,Brostow08eccv} and KITTI Semantic~(KITTI-S)~\cite{Geiger13ijrr,Kundu14eccv,Ros15wacv}.
However, as shown in Table~\ref{tab:class_distribution}, these dense datasets contain a large imbalance in the frequency of occurrence of various classes: structural classes such as road, sky or building are several orders of magnitude more frequent than important non-structural classes such as cars, pedestrians, road-signs or cyclists.
To boost the recognition of the latter, we include specific detection and recognition datasets where annotations are available in the form of bounding-boxes or segmentation masks: KITTI Objects (KITTI-O)~\cite{Geiger13ijrr}, a filtered set of Microsoft COCO (M-COCO)~\cite{Lin14eccv} containing pedestrians, cyclists, road signs and cars in urban environments, ETH Robust Multi-Person Tracking from Mobile Platforms (ETH-RMPTMP)~\cite{Ess09pami} for pedestrians and the German Traffic Sign Recognition Benchmark (GTSRB)~\cite{Houben13ijcnn} for road signs.
The distribution of classes for our MDRS3 train and test sets (final two rows of Table~\ref{tab:class_distribution}) illustrate how training data in our dataset includes many more instances of important rare classes compared to existing dense datasets.

\vspace{-2mm}
\begin{table*}[ht]
	\centering
	\caption{Class distribution (\% of total pixels) for the MDRS3 dataset constituents and test/train splits. The ``void" class has been removed for clarity.}\vspace{-3mm}
	\label{tab:data1}
	\resizebox{\textwidth}{!}{%
		\scriptsize
		\tabcolsep=0.21cm
		\begin{tabular}{@{}lr|rrrrrrrrrrrr@{}}
			\toprule
			          & &
			          \includegraphics{figs/sky.eps} & \includegraphics{figs/building.eps} & \includegraphics{figs/road.eps} & \includegraphics{figs/sidewalk.eps} & \includegraphics{figs/fence.eps} & \includegraphics{figs/trees.eps} & \includegraphics{figs/pole.eps} & \includegraphics{figs/car.eps} & \includegraphics{figs/traffic.eps} & \includegraphics{figs/pedestrian.eps} & \includegraphics{figs/cyclist.eps}\\
			Dataset    & \# Image & sky & build. & road & side. & fence & veg. & pole & car & sign & ped. & cycl.\\ \midrule

			CamVid~\cite{Brostow09prl,Brostow08eccv} & 600 & 15.7 & 24.4 & 33.4 & 6.2 & 2.6 & 11.4 & 0.4 & 4.8 & 0.5 & 0.4 & 0.5 \\ \midrule
			KITTI-S~\cite{Geiger13ijrr} & 547 & 6.2 & 25.9 & 17.2 & 7.0 & 3.7 & 28.7 & 0.5 & 9.9 & 0.4 & 0.2 & 0.2 \\ \midrule
			*U-LabelMe~\cite{Russell08ijcv} & 942 & 13.2 & 39.9 & 19.1 & 8.1 & 0.3 & 11.1 & 0.5 & 5.8 & 0.3 & 1.1 & 0.5 \\ \midrule
			*CBCL~\cite{Bileschi07cbcl} & 3,547 & 5.4 & 26.4 & 28.2 & 6.9 & 0.7 & 17.9 & 1.3 & 11.8 & 0.3 & 0.8 & 0.2 \\ \midrule
			*ETH-RMPTMP~\cite{Ess09pami} & 14,056 & - & - & - & - & - & - & - & - & - & 100.0 & - \\ \midrule
			*GTSRB~\cite{Houben13ijcnn} & 740 & - & - & - & - & - & - & - & - & 100.0 & - & - \\ \midrule
			M-COCO~\cite{Lin14eccv} & 3,262 & - & - & - & - & - & - & 1.0 & 63.7 & 11.4 & 16.6 & 7.3 \\ \midrule
			*KITTI-O~\cite{Geiger13ijrr} & 7,481 & - & - & - & - & - & - & - & 90.7 & - & 7.4 & 1.9 \\ \midrule	\midrule
			MDRS3-Train & 26,686 & 5.4	& 12.1 & 12.5 & 3.1 & 1.5 & 9.2 & 0.5 & 36.6 & 3.4 & 13.2 & 2.5 \\ \midrule
			MDRS3-Test & 4,489 & 10.0 & 34.4 & 22.8 & 7.6 & 0.5 & 14.0 & 0.8 & 8.3 & 0.3 & 1.0 & 0.3 \\
			 \bottomrule
				\end{tabular}%
	}
	\label{tab:class_distribution}
\end{table*}

\setlength{\textfloatsep}{10pt plus 1.0pt minus 12.0pt}
\setlength{\floatsep}{10pt plus 1.0pt minus 12.0pt}
\setlength{\intextsep}{12.0pt plus 2.0pt minus 8.0pt}
\vspace{-9mm}

\paragraph{\bf Refinement of sparse annotations.}
For constituent datasets where annotations are provided in the form of bounding-boxes (marked with an asterisk in Table~\ref{tab:class_distribution}), we perform refinement to pixel-wise annotations by adopting a similar GrabCut-based approach to~\cite{Papandreu15iccv}.
For the CBCL dataset, which is labelled with polygonal bounding-boxes for 9 object categories and contains many void areas, we enlarge the category set to 11 and extend existing labels to missing areas using a CRF classifier~\cite{Ladicky10eccv}. We provide further detail of this process in the accompanying supplementary material.

\paragraph{\bf Test dataset.}
For evaluation, we maintain a separation between datasets used for training and testing. We use a combination of different domains with
dense and sparse annotations for training, while for testing we use two separate datasets with dense pixel-wise annotations: a new subset of the LabelMe dataset~\cite{Russell08ijcv} with urban images from different cities, referred here to as Urban LabelMe (U-LabelMe) and a processed subset of the CBCL StreetScenes Challenge
Framework~\cite{Bileschi07cbcl}.
These two datasets are more challenging compared to CamVid and KITTI, containing a larger variety of scenarios with different viewpoint and illumination conditions (compared to the forward-looking camera viewpoint in CamVid and KITTI).
Our test dataset thus provides a better measure of the generalisation performance of the trained network at test time, especially compared to the common practice of using subsets of the same sequence for training and testing.
\section{Network Architectures for Semantic Segmentation}
\label{sec:networks}
\vspace{-2mm}
We consider two DN architectures and the trade-off they achieve between task performance and memory footprint. The two selected state-of-the-art networks are: the fully convolutional network (FCN)~\cite{Long15cvpr} and the DeconvNet~\cite{Noh15iccv}. We do not consider models that are extended with a CRF such as~\cite{Zheng15iccv}, since such extensions do not alter the intrinsic model capacity and smoothing can be added as a post-processing step if desired.

The DeconvNet and FCN architectures are shown in Fig.~\ref{fig:cnn_archs}(ii)-(iii), respectively. Both DNs expand on
VGG-16~\cite{Simonyan15iclr}, but the DeconvNet is much deeper than FCN (75\% more parameters), making it harder to train and unsuitable for embedded
applications. The depth of these networks is justified for the task of semantic segmentation of general scenes (which contain thousands of classes of objects), but shallower
networks may suffice for constrained urban environments. Another difference between these architectures is in the
upsampling philosophy: the FCN combines outputs of different layers to achieve better localization accuracy, while the DeconvNet stores
pooling indices to re-use later for guiding feature map upsampling. This last strategy has been shown to improve localization accuracy for different problems~\cite{Zeiler10cvpr}.

\begin{figure*}[t!]
\centering
\includegraphics[width=\textwidth]{figs/networks.eps}\\
\vspace{-4mm}
\caption{Summary of network architectures. From top to bottom: (i) a key to basic building blocks; (ii) DeconvNet~\cite{Noh15iccv} (251M parameters); (iii) FCN~\cite{Long15cvpr} (134M parameters); (iv) our constrained Target Net or T-Net (1.4M parameters); and (v) our dense/sparse FCN ensemble or chosen S-Net (269M parameters). All convolutions assume stride 1 unless otherwise specified. Descriptions of these networks can be found in sections~\ref{sec:networks} and \ref{sec:training}.}
\label{fig:cnn_archs}
\end{figure*}

\vspace{-4mm}
\subsection{Source Network (S-Net) Architecture}\label{sec:target}\vspace{-1mm}

The Source Network (S-Net) is selected by choosing the best possible performing network, disregarding memory or computational constraints.
Our choice of S-Net consists of an ensemble of two FCN networks trained on different
data modalities, \emph{i.e.~} dense and sparse data modality---see Fig.~\ref{fig:cnn_archs}(iv)---, which was found to be the best performing unconstrained network as reported in section~\ref{sec:exps}.
Although this ensemble contains more parameters than a DeconvNet (269M versus 251M), it leads to better results and is faster to train, reason why we do not use DeconvNet and favour FCN-based approaches instead for S-Net. Further detail is contained in section~\ref{sec:training_ensemble}.

\vspace{-2mm}
\subsection{Target Network (T-Net) Architecture}\label{sec:production}\vspace{-1mm}
The T-Net, shown in Fig.~\ref{fig:cnn_archs}(v), consists of a model based on the pooling-unpooling principle of the
DeconvNet~\cite{Noh15iccv} but simplified to suit an embedded system (similar to the ``basic" SegNet version
proposed in~\cite{Badrinarayanan15arxiv}).

T-Net consists of 4 \textit{contraction} blocks, followed by 4 \textit{expansion} blocks, with a total of
$1.4$ M parameters. This reduced size offers a good compromise between memory requirement and  performance. Contraction blocks serve to create a rich representation that allows for recognition as in standard
classification CNNs. Expansion blocks are used to improve the localization and delineation of label
assignments. Both contraction and expansion blocks use $7{\times}7$ kernels with a stride of 1 pixel and a fixed number of 64 feature maps. Batch normalization is added prior to ReLU to reduce internal covariate shift~\cite{Ioffe15icml} during training and improve convergence. Upsampling in expansion blocks is carried out by storing and retrieving pooling indices for current activations. This helps to produce sharp edges in the final output, avoiding blocky results~\cite{Noh15iccv}. A linear classifier performs the final label estimation at the pixel level. The choice of 4 blocks is motivated by empirical analysis, offering the best trade-off between model compactness and good performance (see supplementary material).

\section{Related Work}
\label{sec:related}
\vspace{-1mm}

Our work spans the topics of semantic segmentation, training with limited data, and knowledge transfer. We briefly recapitulate related literature from each.

\paragraph{\bf Semantic Segmentation.}
The task of semantic segmentation involves the estimation of a function $\mathcal{F}$ which maps an input image, such as $\mathcal{I} \in
[0,\dots,255]^{H \times W}$, to an output label image $\mathcal{J} \in [1,\dots,L]^{H \times W}$, where the labels $1,\dots,L$
index the semantic class of the input at that pixel (\emph{e.g.~} road, sidewalk, sky, vegetation, pedestrians, etc.).
It is a popular problem in computer vision and has been tackled for various environments from
indoors~\cite{Silberman12,Handa16cvpr} to outdoors~\cite{Kundu14eccv,Tighe10eccv}, as well as for specific tasks such as road
scene perception~\cite{Sengupta13icra}. For the latter, which is the focus of our work, semantic segmentation is expected to play
a key role as part of the local planning and obstacle avoidance sub-systems of future semi-autonomous and autonomous vehicles.

Classical tools for addressing the problem include pipelines based on a combination of hand-crafted features (\emph{e.g.~} SIFT, HOG) and
region-based classifiers (\emph{e.g.~} SVM, ADABoost), with probabilistic graphical models such as Conditional Random Fields~(CRFs) used to
produce structured predictions~\cite{Ros15wacv,Sengupta13icra,Hu13icra,Kohli09ijcv,Ladicky10eccv,Valentin13}. With the arrival of
deep convolutional neural networks (CNNs), hand-crafted features were substituted  by learned CNN representations, which worked at
the level of image patches~\cite{Alvarez12eccv,Girshick14rcnn}. This trend continued with the introduction of DNs, which naturally
perform the process of recognition and whole-image segmentation, producing a dense inference at a pixel
level~\cite{Long15cvpr,Noh15iccv,Badrinarayanan15arxiv,Silberman12}. Recently, this trend has culminated in the addition of
structured prediction by employing messaging passing between the net and an MRF~\cite{icml2015_chenb15} or adopting the use of
Recurrent Nets with equivalent behaviours to a CRF~\cite{Zheng15iccv}.

\paragraph{\bf Training with Limited Data.}
One key problem with DNs is that when applied to certain domains, such as automotive environments, there is a lack of suitably large and varied training data.
Two recent approaches~\cite{Dai15iccv,Papandreu15iccv} propose means of mitigating this problem by augmenting an existing semantic segmentation dataset (\emph{i.e.~} consisting of pixel-wise labels) with additional data from object detection and image classification datasets, which are weakly annotated with bounding boxes or text captions. Both approaches are directly applied on the augmented datasets to train DNs in an end-to-end fashion and both report subsequent improvements in accuracy for the PASCAL-VOC dataset~\cite{Everingham15}.
However, obtaining significant improvements in this manner is possible only when the existing and additional datasets are similar in nature---in this case, both consisted of annotations of simple objects, ignoring the architectonic elements composing the background, \emph{e.g.~} road, sidewalk, etc. As we show in this work, the application of these strategies when dealing with urban imagery and architectonic classes fails to produce the most competitive results in automotive scenarios.
Furthermore, while the issue of training data scarcity is likely to diminish with time, as new larger datasets are released (\emph{e.g.~} the recent releases of the Cityscapes~\cite{Cordts2015Cvprw} dataset, containing 5,000 fine-labelled images and 20,000 coarse-labelled images, and the SYNTHIA dataset~\cite{Gros2016}, containing 250,000 synthetic fine-labelled images),  we believe that our approach will remain useful for training resource-constrained segmentation networks.

\vspace{-2mm}
\paragraph{\bf Model Compression by Knowledge Transfer.}
While the recent trend in deep learning has been to strive for even deeper models~\cite{He15cvpr}, the preference for deep versus shallower models is not because shallower models have been shown to have limited capacity or representational power, but rather that learning and regularization procedures used to train shallow models are not sufficiently powerful~\cite{Ba14nips}.
One reason for this is that, counterintuitively, the likelihood of falling into poor quality local minima increases with decreasing network size~\cite{Choromanska15aistats}.
Various approaches to extract better performance from shallow networks have been proposed in the literature.
In~\cite{Bucilua06kdd}, an ensemble of classifiers, trained on a small but representative subset of a larger dataset, is used to label the larger unlabelled dataset. The large ensemble-labelled dataset is then used to train a network, demonstrating improved performance versus training on the original ground truth for the smaller dataset.
More recently, \citep{Ba14nips} shows that shallow neural nets can be trained to achieve performances previously only reachable by deep models, by training a large teacher ensemble and transferring knowledge from it to a shallow but wide model by training it to match the logit activations of the teacher.
Hinton \emph{et al.~}~\cite{HintonVinyals15} confirm these findings and propose to address the problem by exploiting the ``dark knowledge" available in the teacher ensemble, referring to the full probability distribution produced by the soft-max classifier. This knowledge is transferred to a compact student network using relaxation of cross-entropy. This approach was extended in~\cite{Romero15-iclr}, showing that it is possible to reduce the number of parameters by creating deeper-and-thinner students out of shallower-wider teachers, at the possible expense of increasing computation time. A further recent line of relevant work on network compression focuses on applying sophisticated engineering tools to reduce the network size. Examples include \cite{HanMD15}, which uses  pruning, trained quantization and Huffman coding to further compression, and ~\cite{Iandola16arxiv}, in which these engineering tools are combined with a novel architecture to produce very compact classification networks.
In this paper we extend previous research on knowledge transfer to a novel problem and the recently proposed architecture of DNs.

\section{Introduction}\label{sec:intro}
\vspace{-2mm}
Deconvolutional Networks (DNs) are a class of neural network which have achieved notable recent success on the task of semantic segmentation, in which image recognition is performed at the resolution of individual pixels~\cite{Farabet13pami,Long15cvpr,Noh15iccv,Badrinarayanan15arxiv}. They have consequently become an attractive architecture for road scene segmentation---a useful component in many autonomous driving or advanced driver assistance systems. However, several limitations exist when trying to apply state-of-the-art DNs in practice.

Firstly, they are inefficient in terms of memory footprint. While commercial chips targeting the automotive industry are becoming increasingly parallel, the small size of fast-access on-chip SRAM memories remains limited (\emph{e.g.~} 512 KB for the Mobileye EyeQ2\footnote{\url{http://www.mobileye.com/technology/processing-platforms/eyeq2/}}\footnote{\label{footnote:disclaimer}All product names may be trademarks of their respective companies.} chip and 1-10 MB for the Toshiba TMPV 760 Series\footnote{\url{http://toshiba.semicon-storage.com/ap-en/product/automotive.html}}\textsuperscript{\ref{footnote:disclaimer}} chip family). In contrast, the popular FCN-8s network~\cite{Long15cvpr} with 134.5 M parameters requires more than 500 MB of memory.
Although more efficient architectures have been proposed, such as SegNet~\cite{Badrinarayanan15arxiv}, they still contain tens of millions of parameters (29.5 M for~\cite{Badrinarayanan15arxiv}) and are yet to demonstrate accuracy on a par with the larger FCN-8s.

Secondly, since DNs are typically trained in a supervised manner, their performance benefits from access to a large amount of training data with corresponding per-pixel annotations. Producing such annotations is an expensive and time-consuming process. Hence, while datasets for tasks such as image classification can reach $\mathcal{O}(10^7)$ images in scale~\cite{Lin14eccv,Russakovsky15ijcv,MITPLACES}, popular semantic road scene segmentation datasets such as CamVid~\cite{Brostow09prl} or KITTI~\cite{Geiger13ijrr} contain $\mathcal{O}(10^3)$ images. The scarcity of data results in a lack of samples for rarer but important classes such as pedestrians and cyclists, which can make it difficult for models to learn these concepts without overfitting. Furthermore, data scarcity implies poor coverage over the true distribution of possible road scenes: datasets are typically captured in one or a few localised regions under relatively homogeneous road conditions. Understanding how best to incorporate knowledge from new domains as training data becomes available is an important problem to ensure the best general task performance given available data.

To address these limitations, we propose an approach which draws on ideas from domain adaptation~\cite{Chen15cvpr,Tzeng15iccv} and model
compression by transfer learning~\cite{HintonVinyals15}. We begin by collating numerous publicly available datasets from different domains
and modalities which are useful for the task of semantic road scene segmentation. We refer to our aggregated dataset as the Multi-Domain
Road Scene Semantic Segmentation (MDRS3) dataset. In contrast to existing work in road scene semantic
segmentation~\cite{Kundu14eccv,Ros15wacv}, we select two of the constituent datasets in their entirety as the test set for MDRS3. This
means that training and testing for MDRS3 are not carried out on subsets of the same original dataset and performance is a better indication
of task generalisation. We then examine methods for training on different modalities of data to create the best possible model, ignoring
time and memory constraints. We discover that ensembling networks trained on distinct domains leads to much improved performance, and create
a best-performing network containing 269 million parameters, which we refer to as the Source Network (S-Net). Finally, we explore
methods for transferring knowledge from the unconstrained S-Net to a memory constrained architecture, which we refer to as the
Target Network (T-Net). We demonstrate that by this approach, we can meet the desired constraints for embedded applications while achieving
a higher accuracy than is otherwise impossible through existing training strategies. Concretely, we show that the performance of a
state-of-the-art FCN~\cite{Long15cvpr} can be bettered using a deconvolutional network with 1\% of the memory and comparable run-time.
Fig.~\ref{fig:summary} summarises our experimental findings.
For reproducibility, we plan to make publicly available all of our trained models and MDRS3 dataset upon publication

\begin{figure}[t!]
	\centering
	\includegraphics[width=1.\textwidth]{figs/header3b.eps}
	\vspace{-7mm}
	\caption{\textit{Left:} Model performance versus model size. Blue points indicate the baseline performance of the two different network architectures we compare. Green points indicate performance using different training strategies explained in Sec.~\ref{sec:training}. Red point denotes the performance of our constrained network after knowledge transfer, explained in Sec.~\ref{sec:training}. Our approach outperforms the per-class accuracy of a state-of-the-art fully convolutional network (FCN e2e (D)) with 1\% of the model size. \textit{Right:} Segmentation output on a test image, illustrating the qualitative improvement of our method. Best viewed on screen.}

	\label{fig:summary}
		\vspace{-4mm}
\end{figure}

\section{Conclusions and Future Work}\label{sec:conclusions}\vspace{-2mm}
In this work we have described a training strategy for DNs to be used in resource-constrained applications such as road scene segmentation. We showed that training relatively shallow target networks via regular end-to-end approaches on a challenging aggregate dataset leads to underperformance versus state-of-the-art deep models. One likely cause for this is that shallow models are much harder to optimize and, when trained directly on noisy or multi-modal data, have difficulty in navigating local minima.
To overcome this, we extended the idea of knowledge transfer to DNs. We first explored various means of producing a best-performing source network, by relaxing resource constraints and ensembling networks across different data domains.
We then demonstrated that by using the source network as a guide, it was possible to train a target network which satisfied all constraints while giving better performance than a state-of-the-art FCN network and almost the same performance than an ensemble of FCNs, with a memory footprint that is just $0.5\%$ of the ensemble.
We believe that our findings will be very useful for training DNs not just in automotive settings but also in context where labelled data is limited and practical constraints exist on model size.

redpajama_set_name: RedPajamaArXiv


text:
Harris Co. makes 24 citations in first day targeting paper plates
Lifestyle // Health
'This is what we dread': Disaster expert Angela Blanchard on COVID-19 and the Houston economy
Lisa Gray, Staff writer
April 3, 2020 Updated: April 4, 2020 9:08 a.m.
Angela Blanchard, one of Houston's leading experts on disaster response, is pictured.
Courtesy Angela Blanchard
Angela Blanchard, one of Houston's leading experts on disaster response, began discussing the Houston area's need to respond fast to the coronavirus while the Houston Rodeo was still open. We've checked back in with her frequently.
As CEO of Neighborhood Centers/Baker Ripley, Blanchard helped Houston respond to many disasters, including the surge of people fleeing Katrina. Now she consults with business clients about long-term disaster responses, and at Brown University teaches a graduate-level public-policy class, "Disaster and Displacement."
This interview has been edited for clarity and length. You can hear a longer version — as well as Blanchard's Cajun accent — in the Chronicle's new podcast, Coronavirus Chronicle.
We hear a lot of talk about a "surge" of demand on hospitals. But right now we have plenty of capacity. Are you still worried?
Yes. That surge is hard to grasp, but a brilliant student of mine has a great analogy: Picture hospitals' bed capacity as a pond. In the pond are two lily pads, and those lily pads are doubling each day. At 40 days, the pond is going to be covered with lily pads; the beds will be used up.
So, what is the day when the pond is half covered? We're used to thinking it's 20 days. We imagine that these things develop in some loping-along manner. But in fact, it's Day 39 that is half-covered. And on Day 40 all the beds are gone.
We've watched countries elsewhere go from OK one day to a catastrophic emergency the next. That's what surge looks like. That's how it works.
And then we continue with doubling from there. This is why we're all making sacrifices: to avoid that moment.
Where do you think we are now in this crisis?
Twenty-three days ago we were all vaguely aware there was a virus. Now, in 23 days, we've all become epidemiologists and disease-spread experts and market watchers. We're all checking regularly to see the price of a barrel of oil.
What's happening today in the minds of us who think all the time about disasters — the anticipators, I call us — is, this is what we dread, the one thing that triggers the other thing. And then at some point, you're not sure which of the two is more devastating.
We've heard this terrible and painful conversation around coronavirus and jobs: Do we really sacrifice our economy, our worldwide economy, to save lives? Country by country, city by city, individual by individual, we're all making that decision. We're deciding whether to stay home, whether to forego economic activity in favor of protection for ourselves and families and to save the lives of people we may never meet.
That creates its own painful and terrible set of consequences for people whose livelihoods are at stake.
What makes this catastrophe different from others Houston has faced?
In Houston, we already know what to do for hurricanes. If it was a hurricane, we'd all be watching our screen.
Eric Holthaus would be telling us: Here's the size of it. Here's the speed of it. This is what the European model says, and here's what the American model says. We're so hurricane-literate, we can all just tell you what the dirty side looks like and what it means when the eye passes. We've got it down.
But we're struggling to understand, what's the equivalent of wind speed for a virus? What's the COVID equivalent of the dirty side of the storm?
ON HOUSTONCHRONICLE.COM: Portraits of Houstonians, through our windows
The other thing that's really challenging is, we can see a storm. We see it on our screens, and we see it when the trees are bending at 45-degree angles. Then we see the aftermath and we see the debris piled in the streets. When suffering is visible, the challenges are palpable.
Where we are now, is the trees have begun to blow. We're not crystal-clear on the wind speed or the progress of this thing. We don't quite understand its size. We're looking at the places it's already hit, and it looks really, really bad.
What indicators are you tracking?
I keep a dashboard on my computer desktop: the 10 things I'm looking at every single day to see the shape and size of this disaster.
What I keep on my screen is this: How many cases? How much testing? What's the supply? What's the anticipated need? And what's the supply of PPEs and other equipment that our first responders and health professionals will need?
On one side of the equation, we're looking at the virus itself and the disease spread. What will a surge look like in this region? How many infected? How many seriously ill people will constitute our surge? What's the area's hospital-bed capacity? It's important to know when we'd run out.
Now, on the economic side of my dashboard, I'm looking at how many people have already let landlords know that they're going to have trouble with their rent, how many people have already been laid off.
We hear more talk about low-income families and the people that are working for low wages to begin with; they're in acute crisis. But there are also two-income families that will become one income families because of the oil and gas crash. So we're looking at two kinds of major shifts in employment and what that means.
How many people have filed for unemployment? What do the filings look like for small businesses and medium-sized businesses for bankruptcy? How many of them are prepared and understand how to move forward? That dashboard is extremely important.
Then there's the nonprofit sector, which everybody in the disaster-relief arena knows to watch. Anna Babin at the United Way here has got her finger on the pulse. In the past month they've had this massive increase in 2-1-1 calls to United Way. And what do you think the top questions were? Number one, they want to understand the virus, to know where to go if they get sick. Number two, rent assistance. Number three, food.
That means the hurricane has landed and people are feeling it. The rain is falling, even if we can't see it.
ALISON COOK: Fear and loathing in the grocery store
It's also extremely important to pay attention to emergency hotlines. What are people asking for? What are people calling mental-health hotlines experiencing?
If Houston's leaders have that dashboard, they can see, This is the shape of what we're facing.
If we can all embrace that information and its complexity, and understand how each of these things impacts the others, we have a shot at having more say about our destiny than if we passively wait, hunkered down in our own siloed arenas, focused only on our own institutions.
Can I get a copy of that dashboard? Is it available widely? Or is somebody working on it?
My dashboard exists mostly on my desktop and in my brain at the moment. But I'm not the only leader that's thinking about these metrics.
Brett Perlman, the CEO of Center for Houston's Future, and I did some brainstorming about what that would look like, and where to get good information.
(Let me give a shout-out here to the Chronicle for keeping us on top of some of the critical developments and giving us the stories as well as the pictures and numbers.)
We can be sure that some things are going to be important. We know, for example, that as we do more testing, we'll get a better idea of the size of the affected population. Then we'd be able to know right away when we start to have a decline in new infections. And if we understand when we have a decline, then we can make a projection about when we can reopen businesses.
If we can make this invisible disaster visible on the screen, and give us all a picture of what we're dealing with and who's in charge of each of these elements, then we can be more consciously coordinated in how we move through it.
I think next week is going to bring a real surge of connection between sectors and leaders reaching out across institutions.
I've said to a lot of my business clients, Look, you won by competing well. But you'll win this one by collaborating well, because some of the people you competed with a month ago might be your best source of understanding and insight for what's ahead.
You're talking about action on a local and even personal level, not a state or federal level?
There was a time when we expected institutions that we built to show up and rescue us when things got too heavy, too difficult and overwhelming. Starting with Katrina, we've seen that those institutions haven't evolved quickly enough, and aren't responsive enough, to deal with what we face now and the speed with which it unfolds.
So I tell people: No one's coming. Not as a message of despair, and not because we should feel abandoned, but because we should be reminded that we're who's coming. We're here. This is our city, our county, our region. It's time for us to step up. We have to own this one.
Eating somebody's cooking besides my own.
Really, I have nothing to whine about, not a thing in the world. But when I'm tempted to, it's because I'm facing another one of Angela Blanchard's culinary creations. There's a miniature disaster in the pot every day at my house.
Seriously, though, history around the world tells us this: The human spirit is not extinguishable. If you're alive today, you descended from all the people that survived the plagues, the genocides, the disasters, the floods, the droughts.
Within us we have the capacity to do two things. One is to rise to the challenge of working through this, of seeing our way to the other side together.
The deeper and more spiritual challenge is coming to terms with the suffering. One loss that we're going to face is a loss of livelihoods. The businesses and the restaurants we love that won't all be there when we go back. We're going to lose venues and companies.
We'll see ruined plans. Promises that parents have made to children, like paying for college, won't be realized because the income is not there to do it.
With those great losses comes a deep grief. These are losses that that we should mourn.
And with the grief, we need gratitude: to notice the things we're grateful for. At every milestone, with every disaster, I remind people to take note of both their gratitude and grief. They'll have both. And they have to live side by side in our hearts together.
lisa.gray@chron.com, @LisaGray_HouTX
Goldfinches are back in Houston. Better late than never
Lisa Gray
Reach Lisa on
Lisa Gray is a senior writer on the features desk. Previously, she's held many of the Chronicle's most interesting jobs: Senior editor for digital, features enterprise editor, member of the editorial board, acting op-ed editor, columnist--and, most fun of all, founding editor of Gray Matters, the Chronicle site named "Best Blog" in Texas three years in a row.
Email her at lisa.gray@chron.com. Or follow her on Facebook, where she spends way too much time.

redpajama_set_name: RedPajamaCommonCrawl


If you want us to consider you for our station don't just send a link. That's plain rude in our opinion!
Introduce yourself, tell us why you want to become part of Safehouse Radio?
What it is about music and your chosen genre(s) etc that drives and inspire you?
If you really feel like it what you had for breakfast! I'm afraid we have no where for you to post a picture of it unfortunately.
Basically be nice and we'll be nice back!


text:
Young Hero is More Than Just A Cancer Survivor
** In recognition of Stand Up To Cancer Day, OGTV is sharing this story of a young girl with the courage, spirit, and strength to overcome multiple surgeries and treatments while still smiling and an encouraging other kids to keep up the fight. **
Julia Katharine Alexander is a one-of-a-kind little girl, who is rarely seen without a super bright smile on her face. She's just so full of joy, love, and compassion for others. Everyone who knows her can recall something profound, inspirational, or just plain funny that Julia's shared with them. At home, they are known as "Julia-isms." Regardless of your encounter with this sweet girl, you leave feeling a little warmer and happier inside.
On December 14, 2014, Julia was diagnosed with a Grade II Ependymoma brain tumor. Since then, she has bravely endured three major brain surgeries, five minor surgeries, eight rounds of chemotherapy, and two months of radiation therapy. While she is not considered NED (No Evidence of Disease), the people in her life are so grateful that her tumor remains stable.
With no coaching from her parents, Julia has volunteered for nearly 10 organizations and participated in even more philanthropic activities in the past year. Now with a stable tumor, Julia uses her giving spirit and story of survivorship to help other kids affected by cancer through her role as an Ambassador for the largest private funder of childhood research grants, St. Baldrick's Foundation.
This foundation is a volunteer-powered charity that funds more in childhood cancer research grants than any organization except the U.S. Government. Senior Director Danielle Leach considers the selection and development of the Ambassadors to be a critical part of the success of the organization. "Every year St Baldrick's picks 5 children as Ambassadors, 4 who have survived and one who has passed," she explains. "We do this in order to represent the actual childhood cancer experience. We take these children to the brink of death in order to survive. While the statistics may say they are surviving, but are they really thriving? That's what we are trying to make happen."
Julia loves her role and the opportunity it allows her to help others. "When I became an Ambassador," she says proudly, "it was the happiest day of my life." Watch as Julia attends their annual "Brave The Shave" head shaving event to help and support volunteer head shavers and other children who are going through treatment. When you see her energy and all the ways that she supports her peers, you too will fall in love with her tremendous spirit of giving.
Information about this organization can be found at: https://www.stbaldricks.org/
To see more OnlyGood TV WELLNESS videos, visit us at: http://bit.ly/OnlyGoodTVYTWELLNESS
Watch an exclusive collection of stories featuring PETS, FAMILY, WELLNESS, and COMMUNITY, including unique series and Live Specials that shine a spotlight on everyday people, making the world a better place. #CancerSurvivor #KidAmbassador #Philanthropy #OGTV


There are various ways to sell a classic car, and we are motivated to find the most suitable and profitable option for you. We at Lemacc will advise you on the advantages and disadvantages of the respective sales platforms. Before selling your vehicle, we will examine it together to assess the overall condition and discuss options for dealing with any damages. After reviewing the costs, we will find a specialist workshop to refurbish your vehicle. After the vehicle has been technically and optically refurbished, a professional photo shoot completes the preparation for selling. Subsequently, your vehicle will be posted to our website and other platforms if agreed upon. We will take care of the sale of your vehicle. This way you can remain anonymous and do not have to deal with areas of conflict or stress with potential buyers.


text:
Duncan Geere
Minecrafting 152: Burning Flesh
Phew, what a week. The early rumbles over Mojang restricting how server owners can make money have turned into a bit of an earthquake. But now there's some clarity over exactly what you are allowed to do and what you're not.
The rest of the community, however, has been just getting on with being its usual amazing self. This week, we've got a single-player world powered by flesh, a great tutorial on A-frames, some D-Day inspired terrain and an island with a secret. Shall we begin?
It's a completely normal thing for companies to stop other people making money off something they've created. Until this week, the only exception to that rule in Minecraft was videos, but now Mojang has opened up a little more – allowing people to make money off servers.
In a post on the official Mojang blog, Owen has explained exactly what circumstances allow you to make money from Minecraft servers. In short, it's totally cool for you to charge for access or for in-game items, as well as show advertising and accept donations, but you can't create two tiers of players or sell anything that gives one player an advantage over another. "We hate the idea of server hosts restricting Minecraft's features to players who have already bought our game! It seems really mean," says Owen.
Meanwhile, there's no snapshot this week but Jeb's been hard at work on his underwater biomes. He's published an image of the texture for the sea lantern block, as well as teasing some details about water dungeons – which sounds very exciting. Not quite as exciting, however, as the changes planned for sponge blocks. I'm very much looking forward to seeing what the community does with those.
If you're looking to add some new life to your Minecrafting, you should check out ourtwenty best Minecraft mods.
Speaking of the community, its outpouring of creativity shows no signs of stopping. I really enjoyed this tutorial on how to use A-frames in your construction to create tall, imposing buildings. It's a great way to put together Viking-like structures, but it can also be used for modernist greenhouses as /u/patbarnslow demonstrates amply. You can see some of the principles in action here, too. Really nice work.
Meanwhile /u/sparks_00 has built a power plant for his singleplayer world that runs on human flesh. Creepy. "I wanted a challenge," he says. "Every redstone device is now hooked up to my rotten-flesh burning power plant. If I run out of flesh, everything stops working." It's entirely mod-free and everything. Impressive.
If you're interested in some new terrain to play on, this landscape inspired by WW2 Normandy, released to commemorate D-Day, is beautiful. I also loved this cute little island with a hidden secret and this modernist mansion. Oh, and to cap things off for this week, here's how you use a compass effectively in the nether.
That's it for today. If you've seen anything in the Minecraft community that you think I should be highlighting, tell me about it by dropping me an email. I'll be back with another roundup this time next week.
Minecraft console commands


Mr. Suresh Krishna is the Chariman and Managing Director of Sundram Fasteners Ltd. He is a Nominee of the Finance Insititution at Tata Steel. Mr. Krishna serves as the Chairman of several other companies including Sundram Clayton Ltd., Upaasana Finance Ltd., Sundram Telematics Ltd., Sundram Non-Conventional Energy Systems Ltd., Sundram Numeric Ltd.and Unipaat TVS Ltd. Considering his excellent managerial skills Mr. Krishna has also been appointed as a director in Lucas TVS Ltd., T V Sundram Iyengar & Sons Ltd. and TVS Sewing Needles Ltd.


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Gift encourages young women to pursue mathematics
A young student's dream of becoming a mathematics teacher has come a step closer thanks to a generous philanthropic donation for a one-off prize.
UQ student, Kathryn Stephan who is studying a Bachelor of Education majoring in mathematics, was thrilled to be the recipient of this generous $1000 gift aimed at encouraging young women to study mathematics.
Former UQ student Wendy Robson donated the gift in honour of her mother, who was discouraged from studying mathematics as a young girl. Ms Robson's gift will keep giving to future generations of women to strive towards their dreams.
Miss Stephan said that in a field as challenging and dynamic as mathematics, the donation was affirmation that she had made the right choice.
"I find that while I enjoy maths, it is still a challenge, but this prize has inspired me to continue to do the very best that I can," she said.
Miss Stephan said she was fortunate to have parents and teachers who supported her in her decision to study education and mathematics.
"It wasn't until high school that I developed a passion for maths, through the help of many fabulous teachers, and my parents have always been supportive of my decision to study education and attend The University of Queensland.
"I don't think I would be where I am today without their support and encouragement."
When she is a teacher, Miss Stephan wants to encourage students to pursue their dreams.
"I hope that my career in education will allow me to inspire students to pursue whatever career they desire."
You too can empower students to pursue their dreams by donating to the UQ Future Scientist Scholarship Fund and removing some of the financial barriers they may face.
Help encourage more young women to pursue their dreams in science.
UQ Homepage
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Authorised by: UQ Advancement


text:
Sat, 9 April 2016
067 Killing The Passive Will
Sometimes we discover that we just plain want to hang on porn. We want to be passive and put the responsibility of moving forward on someone else. But it's time to make a decision.
In Episode 66, I asked the question, "Do you really want to fix this?"
This episode struck a nerve with the audience. I received more email then I ever have before. Some guys were worried I was quitting podcasting. Several guys thought my sharp comments were directly at them. And many more shared the action they were taking as a result from this show.
So I thought I would go deeper into what set me off. It's the passive will and a failure to take responsibility.
In this episode, you'll learn:
- The quote that almost got me in a fight
- What we discover about our struggle
- What the passive will is
- About the decision we must make
- What it means to decide
"Sometimes we discover that we just plain want to hang on to the sinful behavior, and some of us even have to face the fact that we then hope to blame God for not "healing" us. We see right away when we are dealing with a passive will, someone who looks lumpishly to a counselor or minister of God to do what only he himself can do. All such things as these come to the fore, along with the memories of where the heart opened itself to envy, bitterness, lust and so on. Then in full confession of our sins of God, we acknowledge our rank foolishness and our propensities to pride and rebellion, and we make a decision about sin."
- Leanne Payne, The Healing Presence
1-on-1 Coaching With Matt
http://recoveredman.com/coaching
Links and show notes at http://recoveredman.com/67
https://www.facebook.com/dobberco
http://twitter.com/MattDobschuetz
https://plus.google.com/+MattDobschuetz
http://recoveredman.com/contact-matt/
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Direct download: pfr67.mp3


After ten years at Kotidanga, I have seen God fulfill the missionary dream. We've watched as people have accepted Christ, grown, and gone on to tell others about Him. We've seen Christ's command of Matthew 28:19-20 take fruit in the form of churches started and people discipled. And now…the Kamea are sending their own. It's the ultimate missionary dream.
For the last two years, I've known that God was going move our family somewhere. At the same time, my heart still breaks for the remote areas that remain unreached. I've been praying that God would send 38 missionary families to continue reaching the Gulf Province's other 19 remote tribes. To step away from this, and be a pastor in America, my heart would break. This job is too big to step away from it.
I could move to a new location in a remote area and start over. Theoretically, in ten years, I could duplicate what has already been accomplished here at Kotidanga. But at this rate, we'll never complete the task of reaching these tribes in this generation. And that's when it hit me: Why not train educated Papua New Guineans to have a heart for their own people?
Obviously Paul had not led every single person in Asia Minor to Christ, but he definitely gave it his best attempt. Walking for days, ship-wrecks, stonings, slanders, and occasionally getting to watch a Thessalonian church grow in spite of his absence, he now ponders back upon his years of service. In verse 23 he proclaims, "…now having no more place in these parts…" The Apostle had completed his task in that part of the world, and was moving on to Rome and then Spain.
This feeling floods my soul as I look back on the last 10 years here in Kamea-land (not the stonings or ship-wrecks, thankfully). Numerous churches have been started. Many men have been trained for the ministry. The national leadership has stepped up to the task of evangelizing their own. There is work to be done. Just as Paul needed Timothy, Titus, Apollos, and others to continue working in those parts, I still see a huge need for other missionaries, like my parents and others to continue here among the Kamea–Bible translation and medical ministries continue to open doors. More churches need to be started and more men need training. Yet I have that unsettledness that Paul felt–his job was complete, and it was time for him to go somewhere else and do it again.


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Gundert Bungalow was the home of the German missionary, scholar and lexicographer, Dr. Herman Gundert, who has given invaluable contributions to the Malayalam language. Dr. Gundert stayed at the bungalow, at Illikkunnu, Thalassery, for two decades. It was in 1839 that he settled down here.
Dr. Gundert was instrumental in bringing out the first Malayalam dictionary in 1859 and the first Malayalam newspapers, Rajyasamacharam, which was followed by Paschimodayam. It was while he stayed at this bungalow that he laboured long and hard at these dream projects. He also started a school in the building in 1839.
The bungalow has wide verandahs, huge doors and other features of a typical colonial mansion. However, it is its history more than its architecture that attracts visitors.
Location : Koduvally, Thalassery, 19 kms from Kannur
Nearest major Railway Station : Thalassery
Nearest major Bus Station : Thalassery
Nearest Airports : Kozhikode (96 kms), Mangalore(174 kms).
Historical Places Places to Visit in Kannur
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War in a faroff kingdom is waged by the king who leaves the maiden to embark on his own journey. He leaves his wife with his mother for safekeeping for she is with child. She gives birth to a baby, whom she calls Joy.


Does reality arise from information? What does this even mean? Anton Zeilinger explains the impact of John Wheeler's radical idea.
Does reality arise from information? What does this even mean? We explore the impact of John Wheeler's revolutionary ideas.


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Home » Articles » Regenerative Medicine: Evidence Suggests Human Body Best Resource for Healing
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Regenerative Medicine: Evidence Suggests Human Body Best Resource for Healing
The human body may be its own best ally when it comes to the treatment of injuries, ulcers and deformities, especially when it concerns helping a patient's foot or ankle to heal faster.
"One such advance is using a patient's own human cells/tissues to help them heal (regenerative medicine)," says Stephen A. Brigido, DPM, FACFAS, a Pennsylvania foot and ankle surgeon and Fellow Member of the American College of Foot and Ankle Surgeons. "An area of continued study among foot and ankle surgeons, using platelet-rich plasma (PRP), shows particular promise for patients needing quick recovery times from a foot injury or surgery," he added.
During a doctor's visit, a patient's own plasma is enriched with their platelets, by taking blood out, almost as if they are donating blood. The blood is then separated. Two healing parts of the blood (platelets and plasma) are injected back into the patient right where they need it most—the injury.
All kinds of patients can benefit from the use of PRP, but some injuries and conditions, or those who are on their feet often, may benefit more from using regenerative medicine techniques.
"Achilles tears, for example, are a common injury for those constantly on their feet," says Dr. Brigido. "The Achilles can be injected with platelet-rich plasma during a regular office visit and the injection is almost as quick as getting a flu shot. The 'healing qualities' of the platelet-rich plasma can turbo-boost healing," he adds.
Patients living with diabetes are also more prone to ulcers on their feet, wounds that can be very difficult to heal due to poor blood circulation. "The use of PRP treatments can help these patients by bringing healing platelet-rich plasma to a part of the body that is deficient in healthy oxygen-rich blood," says Dr. Brigido. "The great thing about PRP is it gives patients like this the extra nudge they need to get their own body to start to heal itself," he added.
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Foot Diagram
Cartilage Regeneration Benefits Athletes & Boomers
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Ankle Replacement Transforms Hopeless Situations into Active Lives
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You don't have to be a professional athlete to benefit from a new procedure called platelet-rich plasma (PRP) therapy.Used in Pittsburgh Steelers wide receiver Hines Ward and Major...
Founded in 1942, the American College of Foot and Ankle Surgeons is a specialty medical society of more than 7,400 foot and ankle surgeons. All Fellows of the College are board certified by the American Board of Foot and Ankle Surgery.
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Chad Noreuil is an award-winning Clinical Professor of Law at Arizona State University, where he has been teaching since 2001. Prior to joining the ASU faculty, Professor Noreuil taught at Washington University College of Law and, prior to that, was an Assistant Attorney General for four years, where he litigated numerous federal and state jury trials.
Professor Noreuil has authored two books on passing the bar exam (The Zen of Law School Success and The Arizona Bar Exam: Pass it Now) and a book on how to succeed in law school (The Zen of Law School Success). He is a certified yoga instructor and has also written on the parallels between yoga practice and the practice of law, as well as the parallels between Zen and legal success. Professor Noreuil lectures nationally on multiple subjects for Barbri Bar Review and teaches a seminar on passing the bar exam at law schools across the country.