text:
\subsection{Generative Adversarial Networks}

\subsection{Domain Adaptation}

Domain adaptation is a method that tries to adapt data from one domain (\textit{i}.\textit{e}.,  the source domain) into another domain (\textit{i}.\textit{e}.,  the target domain), while classification task performance is preserved in a target domain \cite{transfer}.
This is considered a prior knowledge transfer of the source domain to the target domain for dealing with tasks in the target domain.
Formally, we assume that there is a joint probability distribution of input data, $x$, with its label, $y$, in each domain: $P_\mathrm{S}(x,y)$ in the source domain and $P_\mathrm{T}(x,y)$ in the target domain. The two distributions are defined on $\mathcal{X} \times \mathcal{Y}$, where $\mathcal{X}$ and $\mathcal{Y}$ are the input data space and the label space, respectively.
Given the labeled source domain data, {$(x_s, y_s)$ $\sim P_\mathrm{S}(x,y)$}, and unlabeled target domain data, ${x_t}$, which come from the marginal distribution, $P_\mathrm{T}(x)$ of $P_\mathrm{T}(x,y)$, domain adaptation learns a classifier, ${h} : {\mathcal{X}} \rightarrow {\mathcal{Y}}$, which properly fits on the posterior distribution, $P_\mathrm{T}(y|x)$ of $P_\mathrm{T}(x,y)$.

The simplest way to conduct domain adaptation is to just not care about differences in the distributions of the two domains, which are called a domain discrepancy \cite{domaindisc}. It  trains a model only with the source domain data and applies the model to the target domain without modifications. In many cases, this method does not work well because of the domain discrepancy \cite{domaindisc}. A popular way to overcome this is to reconstruct a common representation space where the two domain distributions are projected \cite{DANN,domaindisc,ARDA}. This approach attempts to obtain a domain invariant representation space where a projected distribution is invariant from domain to domain. Additionally, it trains a model with projected data from the source domain distribution. It is considered that, as distributions of two domains on the representation space become closer, the domain discrepancy becomes smaller and the learned model performs satisfactorily on the target domain \cite{DANN}.

There are some approaches to find the common representation space. \citet{transfer1} and \citet{transfer2} proposed methods that performed reweighting of samples from the source domain. \citet{transfer3} and \citet{transfer4} aimed to find a feature space transformation that maps a source domain distribution to a target domain distribution.
While their methods attempted to match the feature distributions from the source and the target domain by reweighting and geometric transformation, there were some trials that adversarially applied a GAN framework to reconstruct the common representation space \cite{DANN,unsupervised,ARDA}.
These methods focused on leading the feature representations to be indistinguishable between the two domains while maintaining the capability of the task classifier via adversarial learning with a discriminator.

\subsection{Style transfer}
Style transfer is to

\subsection{Setup}
We used the ROBOTIS Turtlebot3, a commercial RC car robot for our experiment. We mounted three webcams facing the left, forward and right directions.
We gathered approximately 5000 pairs of images and steering labels in a simulator. To alleviate the covariate shift~\cite{Dagger} and improve the generalization, we added noise while collecting the data and performed data augmentation by perturbing contrast and saturation of the images randomly as in ~\cite{Rccar1}. Aside from the simulator images for the source images, we gathered approximately 5,000 pairs of images for the target images. A few examples of these images are shown in Fig.\ref{env}.

 \begin{figure}[t]
 \centering
   \begin{minipage}[t]{.24\textwidth}
   \includegraphics[width=\linewidth, height=3.5cm]{figures/env}
   \caption{Source and target images}
   \label{env}
   \end{minipage}
   \hspace{0.2cm}
   \begin{minipage}[t]{.72\textwidth}
   \centering
   \includegraphics[width=\linewidth, height=3.5cm]{figures/courses.pdf}
   \caption{Course photograph. (left) Course 1-red line and course 2-purple line (center) Course 3 (right) Course 4-red and purple line. S is a start point and D is a destination.}
   \label{course}
   \end{minipage}
 \end{figure}

\subsection{Comparison on Transferred Images} \label{compare}
The core of our method is how to reduce the domain discrepancy effectively through adversarial learning. In this section, we argue that using DRN for the generator, cycle loss, and style loss together is essential for our objective by comparing the quality of the transferred images.

\begin{figure}[t]
\vskip 0.1in
\begin{center}
\centerline{\includegraphics[width=\columnwidth,height=9cm]{figures/transferred_images}}
\caption{The source images and the transferred images by five models.}
\label{transferred_images}
\end{center}
\vskip -0.1in
\end{figure}

We compare five models categorized by: 1) with/without a cycle; (The model without a cycle transfers the source images to the target images but not the opposite.) 2) DRN/UNET in the generator; and
3) with/without style loss.

Fig.\ref{transferred_images} shows the source images (first column) and the corresponding transferred images obtained by five models. 1) As shown in the figure, the model without a cycle generates realistic images (second column) but fails to preserve the content of the source images. In particular, this model changed the direction and shape of the road, which is the most important part for navigation. 2) Among the models with a cycle, the models with UNET in the generator (third, fourth columns) seem to change only the color and preserve the content of the source image overly strictly compared with the models with DRN in the generator (fifth, sixth columns). The input image has a short path to the transferred image and thus to the reconstructed image in the UNET architecture. We argue that this short path may allow the model retain low level information and thus not change much from one domain to the other in order to reduce the cycle loss. In addition, the UNET with style loss model (fourth column) seems to portray textures but repeating artifacts exist. 3) Finally, the style loss appears to be critical for allowing the transferred image to have realistic textures. For example, the model with style loss successfully converted the grass plane in the source image into realistic grass with volume. In addition, the building in the simulator appears to have glass windows. More images are shown in the supplementary material S3.

We only shows one of the four possible combination for the model without a cycle, the model with DRN in the generator, style loss and without a cycle in Fig.\ref{transferred_images}. We empirically checked that the model without a cycle always fails to preserve the content. In summary, the quality of the transferred images supports our argument that using cycle loss, style loss, and DRN for the generator together is critical for the performance.

\subsection{Outdoor Navigation Test Results}
\ctable[
caption = \textbf{Average number of human interrupts to finish per a trial},
width = 1.01\textwidth,
star,
pos=t,
topcap,
label=navigation_result
]{c|cccccccc}{
}{
\toprule
\multirow{2}{*}{\small Model} &
\multicolumn{2}{c}{\small course1} &
\multicolumn{2}{c}{\small course2} &
\multicolumn{2}{c}{\small course3} \\
\cline{2-9}
 & \small{intervention} & \small{failed place} & \small{intervention} &\small{failed place} & \small{intervention} & \small{failed place} \\
\midrule
\small Source SL &\small $>$5 &\small X &\small $>$5 &\small X &\small $>$5 &\small X \\ \hline
\small UNET w/o style &\small 1.6 &\small 0.6 &\small 1.3 &\small 0.6 &\small 1.0 &\small 0.0  \\ \hline
\small UNET w/ style &\small 3.0 &\small 3.0 &\small 1.5 &\small 0.5 &\small 1.0 &\small 1.0  \\ \hline
\small DRN w/o style &\small 3.6 &\small 2.0 &\small 4.0 &\small 1.3 &\small 1.0 &\small 1.0  \\ \hline
\small\textbf{DRN w/ style} &\small 0.0 &\small 0.0 &\small 0.6 &\small 0.0 &\small 0.0 &\small 0.0  \\ \hline
}
\begin{table} [t]
\caption{\textbf{Success rate to pass the complex intersections}}\label{intersection_success_rate}
\centering
		\begin{tabular}{ccccc}\hline
			 &\small UNET w/o style &\small UNET w/ style & \small DRN w/o style & \small \textbf{DRN w/ style} \\ \hline
			\small Success rate &\small 3/5 &\small 0/5 & 2/5 & 5/5 \\ \hline
		\end{tabular}
\end{table}

In the outdoor test, we measure how well our navigational system finished various courses using a protocol similar to that in ~\cite{Rccar1}. First, we measure the number of failed places and interventions for three courses. If a vehicle goes off the road or turns against a given command, we intervene in the vehicle driving (increase the number of intervention) and then reposition the vehicle in order to try again. If the vehicle fails again, this increases the number of the failed places (but not counted as an intervention) and we reroute the vehicle manually. We also measure the rate of success in passing a complex intersection under the given command.

We tested our model on four courses. Each course is illustrated in Fig.\ref{course}. Courses 3 and 4 are unseen during the training. We do not take measurements on course 4 because this course does not contain an intersection. Instead, on course 4, we demonstrate our model's ability to navigate an unseen course as this course still requires acceptable navigational skill.

We compared the performance of five models. We exclude the model without a cycle in Sec.\ref{compare} because it changes the content and distorts the road in particular. We included the model trained with the source images and labels (supervised learning model--- named Source SL). Tab.\ref{navigation_result} shows the average number of interventions and failed places for each model while finishing the courses.

The Source SL model failed to finish the course within reasonable trials. Given the performance of the Source SL model, we could conclude that the application of the model trained only with simulator data in outdoor environments is likely to fail. This means that domain adaptation is essential. In addition, the model with DRN in the generator, a cycle and style loss exhibited the best performance. Only this model finished all the courses without an intervention. Along with upper bound in Sec.\ref{md} and the result in the previous section, we may argue that generating a realistic image (\textit{i}.\textit{e}.,  reducing the domain discrepancy) using our method is crucial. In addition, we confirmed that our proposed model navigated on course 4 well. From the results obtained for courses 3 and 4, we may argue that our model learn generality for unseen courses. Our navigation video is available at \textcolor{red}{https://www.youtube.com/watch?v=BQXtX14e-0M}.

We tested the success rate of passing a complex intersection under the given commands. We consider failure as the case in which the aforementioned human intervention is required.
We omit the Source SL owing to its low performance as shown in Tab.\ref{navigation_result}. As shown in Tab.\ref{intersection_success_rate}, only the model with DRN in the generator, a cycle and style loss successfully navigated in all situations.

\section{Introduction}
\input{Introduction}

\section{Related Work} \label{related}
\input{RelatedWork}

\section{Method}
\input{Method}

\section{Experiments} \label{experiment}
\input{Experiment}

\section{Discussion}\label{discussion}
\input{Discussion}

\bibliographystyle{plainnat}

\subsection{Model Components}\label{model_details}

An overview of our model is illustrated in Fig.~\ref{fig:overview}. Our model contains
two generators $G_{T\rightarrow S}$ and $G_{S\rightarrow T}$, two discriminators $D_{T}$ and $D_{S}$, and a task classifier $C$ with the trainable parameters $\theta_{g^T}, \theta_{g^S}, \theta_{d^T}, \theta_{d^S}, \theta_{c}$ respectively. The subscript $S$ and $T$ indicate the domains of the input image (source/target). The generator converts the input image from one domain to fit to the other domain. The discriminator takes the transferred images and real images and distinguishes between them. The classifier, which has branches at the last layer \cite{Rccar1} to perform a conditional navigation task, takes the transferred image and the ground-truth label of the source image, and then learns to classify.

We design the generator with residual blocks \cite{he2016deep}. We replace some down-sampling layers with the dilated convolution \cite{drn} not to lose spatial information without reducing receptive fields, which is crucial for scenery understanding. We call this architecture as dilated residual network (DRN) \cite{drn} in the generator. We adopt the dilated convolution in the classifier for the same purpose.

In addition, we attempt to apply UNET \cite{unet} to our generator, which is a dominant model for pixel-to-pixel image generation tasks such as image segmentation \cite{unet,cciccek20163d} and style transfer \cite{pix2pix}. By including skip connections, UNET can utilize the low level features of the input image. We compare the quality of the transferred images in Sec.\ref{compare}.

We adopt the patchGAN in \cite{pix2pix} for the discriminator to generate high-resolution image. The patchGAN has multiple discriminators for local patches of the image to capture local high-frequency parts of the image. We empirically observed that the use of patchGAN increases a resolution of the transferred image compared with a single discriminator as the size of the transferred image increases. Details regarding its implementation are described in supplementary material S1.

\subsection{Training Loss}\label{loss}

Our objective function contains three loss terms: adversarial loss, style loss, and task loss.

1) \textbf{Adversarial loss.} We adopt the loss terms of the Fisher GAN \cite{FISHERGAN} inspired from the Fisher linear discriminant analysis \cite{flda}. The Fisher GAN belongs to the integral probability metrics (IPMs) framework \cite{ipm}, which is known to be a strongly and consistently convergent metric family \cite{ipmfdiv,gansurvey}.
Moreover, the Fisher GAN is more computationally beneficial than other IPMs such as those in \cite{wasserstein, wgp} as it does not require the calculation of second derivatives for estimating the distance \cite{FISHERGAN}
while maintaining a stable training property.

In addition, we adopt L1 cyclic consistency loss between the input and reconstructed images based on \cite{cyclegan,disco,CYCADA}. We anticipate that the use of this loss can aid in preserving the content of an image, especially the road, which is important for training an accurate navigational model.

The adversarial loss for the target input image is defined as follows:
\begin{align}
L_A^T = \Phi(\theta_{d}^T, \theta_{g}^T) + \lambda(1-\Omega(\theta_d^T, \theta_g^T))-\frac{\rho}{2}(\Omega(\theta_d^T, \theta_g^T)-1)^2 +\E_{x\sim P_\mathrm{T}} \Vert G_{S\rightarrow T}(G_{T\rightarrow S}(x)) - x \Vert_1
\label{fisherfirst}
\end{align}
where $\lambda$ is the Lagrange multiplier, $\rho>0$ is the quadratic penalty weight coefficient \cite{FISHERGAN}, and $P_\mathrm{S}$ and $P_\mathrm{T}$ are the source and target image distributions, respectively. $\Phi(\theta_d, \theta_g)$ and $\Omega(\theta_d, \theta_g)$ are equations derived from augmented Lagrangian \cite{augmented,FISHERGAN} and defined as follows:
\begin{align}
\Phi(\theta_d^T, \theta_g^T) &= \E_{x\sim P_\mathrm{T}}D_{T}(x)-\E_{x \sim P_\mathrm{S}}D_{T}(G_{S\rightarrow T}(x)) \label{fishersec} \\
\Omega(\theta_d^T, \theta_g^T) &= \frac{1}{2}[\E_{x\sim P_\mathrm{T}}D_{T}^2(x)+\E_{x\sim P_\mathrm{S}}D_{T}^2(G_{S\rightarrow T}(x))] \label{fisherlast}
\end{align}
The adversarial loss for the source input image $L_A^S$ can be derived in a similar manner.

2) \textbf{Style loss.} As the source and target images differ in their style and texture, we adopt a style loss in order to allow the transferred image to have a similar style to the target images. We are motivated by style transfer which is, when given two images, to generate an image containing the content of one image and the style of the other image. To obtain the representation of a style, we use the gram matrix which indirectly represents feature distribution as in \cite{style2,style3,style4}. Instead of using a pretrained network, we utilize the middle layers' features of the discriminator to obtain the gram matrix, which works surprisingly well, as shown in Sec.\ref{experiment}.

To match the styles of the real and transferred images, we attempt to reduce the squared Frobenius norm of the gram matrix of the real image, $A^l$ and that of the fake image, $B^l$ in the $l_{th}$ layers of the discriminator. The style loss is defined as follows:
\begin{align}
L_S = \sum_{l=1}^L \frac{1}{(n^l)^2 (c^l)^2} \sum_{i,j} (A_{ij}^l-B_{ij}^l)^2 \label{style loss}
\end{align}
where $n^l$ and $c^l$ represent the product of the batch size and the area of the feature map and the number of the feature map, respectively, in the layer $l$. $L$ is the number of layers in the discriminator.

3) \textbf{Task loss.} We divide the steering command into five intervals to assign class labels and train the classifier using typical cross entropy loss. It should be noted that instead of strict labels for each class, we use soft labels that assign a small portion of the label to adjacent classes \cite{salimans2016improved}. It provides the classifier with the relation between the adjacent classes and prevents the classifier from producing extremely sharp results. \citet{NVIDIA} showed that an overly sharp output may hinder navigation. As compared with the use of the negative entropy and swap penalty terms proposed in \cite{NVIDIA}, our method is simpler and produces the same desired result.

\subsection{Theoretical Analysis} \label{md}

We address the lack of labels in the target domain by making the source images resemble the target images, and giving them the labels of the source images. Intuitively, if a classifier works well for the transferred images and the transferred images are similar to the target images, then the classifier may fit to the target images. In this section, we argue that our intuition can be explained mathematically by presenting the upper bound of the trained model's performance under a mild assumption.

We refer to the image and label spaces as $\mathcal{X}$ and $\mathcal{Y}$, respectively. For an image $x \in \mathcal{X}$ and a label $y\in \mathcal{Y}$, $P_\mathrm{T}(x,y)$ and $P_\mathrm{F}(x,y)$ defined on $\mathcal{X} \times \mathcal{Y}$ denote the joint probability on the target and the transferred domains, respectively. $P_\mathrm{T}(x)$ and $P_\mathrm{T}(y|x)$ are the marginal distribution and conditional probability for the joint probability, $P_\mathrm{T}(x,y)$, respectively. The same is true for $P_\mathrm{F}(x)$ and $P_\mathrm{F}(y|x)$. Below is the list of a definition and assumption used in our analysis.

\begin{defn}\label{def1}
An error rate $e(x,y)$ denotes the inaccuracy of the trained classifier for an image $x$ and its label $y$. The range of the error rate is [0,1]. Then an average error in the target domain is the average error rate on the target domain, \textit{i}.\textit{e}.,
\begin{equation} \label{eq:errorgdd}
E_\mathrm{T} = \int_{\mathcal{X} \times \mathcal{Y}} e(x,y)P_\mathrm{T}(x,y)dxdy.
\end{equation}
An average error in the transferred domain, $E_\mathrm{F}$, is defined in the same manner. It should be noted that we use integral $\int\cdot$ for the discrete space $\mathcal{Y}$ for convenience.
\end{defn}

We can interpret $E_\mathrm{T}$ as the measure of the trained model's inaccuracy on the target domain. The same is true for $E_\mathrm{F}$.

\begin{assump}\label{assump1}
The class information has been transferred correctly, \textit{i}.\textit{e}.,  $P_\mathrm{F}(y|x) \approx P_\mathrm{T}(y|x)$ in $supp(P_\mathrm{T})\bigcup supp(P_\mathrm{F}) \subset \mathcal{X} \times \mathcal{Y}$, where $supp(\cdot)$ indicates the support of a probability distribution.
\end{assump}

Assumption~\ref{assump1} can be supported with practical implementations such as the cyclic loss in Sec.\ref{loss}.
We can then obtain the upper bound of the error of the classifier on the target domain as follows:

\begin{thm}\label{thm1}
$E_\mathrm{T} \leq 4 \sqrt{X_2^2(P_\mathrm{T}(x),P_\mathrm{F}(x))} + E_\mathrm{F}$ where $X_2^2$ is the Chi-squared distance.
\end{thm}
\begin{proof}
See supplementary material S2.
\end{proof}

According to Theorem 1.1 in \citet{FISHERGAN}, the Fisher IPM is equivalent to the Chi-squared distance, $X_2^2(P_\mathrm{T}(x),P_\mathrm{F}(x))$. It should, thus, be small if we perform the Fisher GAN training successfully. $E_\mathrm{F}$ should also be small if we train the classifier correctly. Along with our training result, this analysis explains why our empirical results in Sec.\ref{experiment} show excellent performance.

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Please prepare submission files with paper size ``US Letter,'' and
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Fonts were the main cause of problems in the past years. Your PDF file
must only contain Type 1 or Embedded TrueType fonts. Here are a few
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\item You should directly generate PDF files using \verb+pdflatex+.

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\end{itemize}

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\subsection{Margins in \LaTeX{}}

Most of the margin problems come from figures positioned by hand using
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\subsubsection*{Acknowledgments}

Use unnumbered third level headings for the acknowledgments. All
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\section*{References}

References follow the acknowledgments. Use unnumbered first-level
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size to \verb+small+ (9 point) when listing the references. {\bf
  Remember that you can use more than eight pages as long as the
  additional pages contain \emph{only} cited references.}
\medskip

\small

[1] Alexander, J.A.\ \& Mozer, M.C.\ (1995) Template-based algorithms
for connectionist rule extraction. In G.\ Tesauro, D.S.\ Touretzky and
T.K.\ Leen (eds.), {\it Advances in Neural Information Processing
  Systems 7}, pp.\ 609--616. Cambridge, MA: MIT Press.

[2] Bower, J.M.\ \& Beeman, D.\ (1995) {\it The Book of GENESIS:
  Exploring Realistic Neural Models with the GEneral NEural SImulation
  System.}  New York: TELOS/Springer--Verlag.

[3] Hasselmo, M.E., Schnell, E.\ \& Barkai, E.\ (1995) Dynamics of
learning and recall at excitatory recurrent synapses and cholinergic
modulation in rat hippocampal region CA3. {\it Journal of
  Neuroscience} {\bf 15}(7):5249-5262.
\subsection{Learning for Autonomous Navigation}
The majority of the learning based autonomous navigation models are trained using supervised learning. \citet{carNvidia} realized autonomous highway driving and some studies improved them by changing learning method \cite{Dagger,PLATO,carDagger, Rccar1, carend} or learning driving state associated with driving actions \cite{deepdriving,guisti,NVIDIA}. However, these methods require real labeled data which is usually expensive.

Some studies exploited a simulator to train an autonomous navigation model for some advantages: 1) Ability to measure any physical property and change model configuration at a very low cost. 2) No safety issues. However, it is difficult to use the model learned from the simulator directly in reality. To address this issue, \citet{Depthnet} and \citet{RCcar_transfer} exploited input data, which is less variant across the two environments instead of raw images. However, their methods depend on additional sensors or complex subsystems. \citet{CAD2RL} suggested the use of randomization with widely varying features. Although they demonstrated the performance of  their navigation model in an indoor environment, it may be challenging to apply it to an outdoor environment owing to the high complexity involved \cite{RCcar_transfer}. \citet{drivingGAN} and \citet{fuck} are works that are the most similar to ours and use generative adversarial networks (GANs) \cite{GAN,lee2017seqgan,yoo2017energy,gansurvey,hwang2017disease} to adapt the simulator to reality; however, they did not demonstrate actual driving in an outdoor environment. Moreover, details to improve domain adaptation performance in their works are different from ours.

\subsection{Domain Adaptation}
Domain adaptation is a method that involves the adaptation of data from one domain (\textit{i}.\textit{e}.,  a source domain) into another domain (\textit{i}.\textit{e}.,  a target domain) while the classification task performance is preserved in a target domain \cite{transfer}. By doing so, we can train a classifier to work satisfactorily for the target domain data with plentiful labeled source domain data and unlabeled target domain data.

The important part of domain adaptation is the reduction of the differences in the two domains' distributions, which is called a domain discrepancy \cite{domaindisc}. One method to achieve this is to construct a common representation space wherein the two distributions are projected indistinguishably from each other \cite{DANN,domaindisc,ARDA,transfer_survey2}, and then train a task model with the projected source domain data. Intuitively, as the two projected distributions approach each other, the domain discrepancy becomes smaller and the task model performs satisfactorily in the target domain \cite{DANN}.
In order to find a common representation space, several methods such as using re-weighting \cite{transfer1,transfer2} and finding a feature space/subspace transformation \cite{transfer3,transfer4,transfer5} have been proposed. Recently, some trials have been performed, in which a GAN framework was used to make the feature representations indistinguishable between the two domains while maintaining the ability of the task classifier \cite{DANN,unsupervised,ARDA,CYCADA}.

Domain adaptation has been applied to various tasks. \citet{mav} learned transferable policies for UAV navigation using the maximum mean discrepancy \cite{MMD, mmd1,mmdGAN} of a predefined kernel.
There are also several studies that employed domain adaptation to exploit synthesized data for eye-gaze estimation, pose estimation and other robotics tasks \cite{Berkeley,Apple,DAex1,DAex2,DAex3}.

\section{Implementation details}\label{implementation details}

We use the Adam optimizer \cite{adam} to minimize the objective function with a learning rate of 0.0001. We do not decay a learning rate. All the weights are randomly initialized from a truncated normal distribution with a standard deviation of 0.02, and we use a batch size of 8 for the DRN generator and 4 for the UNET generator. We use a data augmentation technique such as image contrast, brightness, saturation and injecting Gaussian noise with a probability 0.5 to diverse sample distribution. In the GAN training, the discriminator often overpowers the generator, thus hampering the generator from learning effectively. We add additive Gaussian noise and a dropout \cite{srivastava2014dropout} to the hidden layers of the discriminator to balance the power between the generator and discriminator.

We adapt our architecture from \cite{isola2017image,unsupervised}. Tab.\ref{gen} shows the architecture details of each component of our model. We denote the filter size, stride, number of the feature map and dilation rate as $\textbf{F}$, $\textbf{S}$, $\textbf{C}$, and $\textbf{D}$, respectively. For example, $\textbf{F3S1C64}$ denotes a convolution layer wherein 3$\times 3$ spatial filters are applied with a stride of 1, generating 64 feature maps. If $\textbf{D}$ is appended, the dilation convolution is applied with a corresponding dilation rate. In Tabs.\ref{drng} and \ref{classifier}, layers with two multi rows involve a residual connection. In Tab.\ref{unetg}, the arrow represents concatenation to the layers of the generator that are indicated. In Tab.\ref{disc}, $Lrelu$ denotes a leaky relu function with a slope of 0.2. It should be noted that we do not apply non-linear activations to the last layer of each component.

We implemented the classifier as a branched architecture in which each branch shares the classifier until the $14_{th}$ layer as shown in Tab.~\ref{classifier}. We have five output nodes for each command that produce probabilities for five steering command intervals.

We apply pixel normalization \cite{karras2017progressive} for the generator, which normalizes the feature vector to the unit length so that local response of the feature map is well maintained. We empirically verify that the generator with pixel normalization produces a sharper result than other normalization techniques such as instance normalization \cite{style1,huang2017arbitrary} and batch normalization \cite{ioffe2015batch}.

We apply instance normalization to the discriminator as in the case of other style transfer and domain adaptation approaches \cite{CYCADA,shu2018dirt,huang2017arbitrary, cyclegan,style1}. We use a small batch size owing to the memory capacity, and the performance of the batch normalization degenerates as the batch size decreases, as the small number of samples in a batch does not represent the entire dataset \cite{wu2018group}. We, thus, adopt group normalization \cite{wu2018group} for the classifier, which normalizes the input tensor with respect to a group of channels in order to guarantee the classification performance even with a small batch size. It should be noted that we do not apply normalization to the last layer of each component.

We send an angular velocity command to steer the robot at 4Hz. We divide the angular velocity in [-1.4,1.4]rad/s into five intervals and set a middle value of the range as the representative value for each class. During the navigational test in the real world, we send a weighted average angular velocity calculated with the representative values and our model's predicted class probability as the weight.

\clearpage

\begin{table}[h]\caption{Architecture details of the generator, discriminator and the classifier.}
     \begin{subtable}{.5\linewidth}
       \centering
         \caption{DRN generator}
         \begin{tabular}{c|c}
           Layer Index & {DRN} \\ \hline
           Input & 96$\times$96$\times$3 Image \\
           \hline

 		  	L - 1 & Conv, F7S1C64, Relu \\\hline
           	\multirow{2}{*}{L - 2} & Conv, F3S1C64, Relu \\
           	 & Conv, F3S1C64, Relu \\ \hline
           	L - 3 & 2$\times$2 max-pool, S2 \\ \hline
           	\multirow{2}{*}{L - 4} & Conv, F3S1C128, Relu \\
             & Conv, F3S1C128, Relu \\ \hline
           	\multirow{2}{*}{L - 5} & Conv, F3S1C128, Relu \\
           	 & Conv, F3S1C128, Relu \\ \hline
            \multirow{2}{*}{L - 6} & Conv, F3S1C256D1, Relu \\
             & Conv, F3S1C256D2, Relu \\ \hline
            L - 7 & Conv, F3S1C128, Relu \\ \hline
            L - 8 & Conv, F3S1C128, Relu \\ \hline
            L - 9 & Deconv, F3S2S64, Relu \\ \hline
            L - 10 & Deconv, F7S1S3
         \end{tabular} \label{drng}
     \end{subtable}
     \begin{subtable}{.5\linewidth}
       \centering
         \caption{UNET generator}
         \begin{tabular}{c|c}
           Layer Index & {UNET} \\ \hline
           Input & 256$\times$256$\times$3 Image \\
           \hline

 		  	L - 1 & Conv, F4S2C64, Relu \tikzmark{a1} \\ \hline
           	L - 2 & Conv, F4S2C128, Relu \tikzmark{a2}\\ \hline
           	L - 3 & Conv, F4S2C256, Relu \tikzmark{a3}\\ \hline
           	L - 5 & Conv, F4S2C512, Relu \tikzmark{a4}\\ \hline
            L - 6 & Conv, F4S2C512, Relu \tikzmark{a5}\\ \hline
           	L - 7 & Conv, F4S2C512, Relu \tikzmark{a6} \\ \hline
           	L - 8 & Conv, F4S2C512, Relu \tikzmark{a7} \\ \hline
            L - 9 & Conv, F4S2C512, Relu \\ \hline
            L - 10 & Deconv, F4S2C512, Relu \tikzmark{b1}\\ \hline
            L - 11 & Deconv, F4S2C512, Relu \tikzmark{b2}\\ \hline
            L - 12 & Deconv, F4S2C512, Relu \tikzmark{b3}\\ \hline
            L - 13 & Deconv, F4S2C512, Relu \tikzmark{b4}\\ \hline
            L - 14 & Deconv, F4S2S256, Relu \tikzmark{b5}\\ \hline
            L - 15 & Deconv, F4S2C128, Relu \tikzmark{b6}\\ \hline
            L - 16 & Deconv, F4S2C128, Relu \tikzmark{b7}\\ \hline
            L - 17 & Deconv, F4S2C3
         \end{tabular} \label{unetg}
         \begin{tikzpicture}[overlay, remember picture, yshift=.25\baselineskip, shorten >=.5pt, shorten <=.5pt]
    \draw [->] ({pic cs:a7}) [bend left] to ({pic cs:b1});
    \draw [->] ({pic cs:a6}) [bend left] to ({pic cs:b2});
    \draw [->] ({pic cs:a5}) [bend left] to ({pic cs:b3});
    \draw [->] ({pic cs:a4}) [bend left] to ({pic cs:b4});
    \draw [->] ({pic cs:a3}) [bend left] to ({pic cs:b5});
    \draw [->] ({pic cs:a2}) [bend left] to ({pic cs:b6});
    \draw [->] ({pic cs:a1}) [bend left] to ({pic cs:b7});
  		 \end{tikzpicture}
     \end{subtable}
          \begin{subtable}{.5\linewidth}
       \centering
         \caption{Discriminator}
         \begin{tabular}{c|c}
           Layer Index & {Discriminator} \\ \hline
           Input & 96$\times$96$\times$3 Image \\
           \hline

 		  	\multirow{3}{*}{L - 1} & Conv, F4S1C64, Lrelu \\
           	 & Dropout, $\rho=0.5$ \\
           	 & Gaussian noise, $\sigma=0.2$ \\ \hline
           	\multirow{3}{*}{L - 2} & Conv, F4S2C128, Lrelu \\
             & Dropout, $\rho=0.5$ \\
           	 & Gaussian noise, $\sigma=0.2$ \\ \hline
           	\multirow{3}{*}{L - 3} & Conv, F4S2C256, Lrelu \\
             & Dropout, $\rho=0.5$ \\
             & Gaussian noise, $\sigma=0.2$ \\ \hline
            \multirow{3}{*}{L - 4} & Conv, F4S2C512, Lelu \\
             & Dropout, $\rho=0.5$ \\
             & Gaussian noise, $\sigma=0.2$ \\ \hline
            L - 14 & Conv, F4S2C1024, Lrelu \\ \hline
            L - 15 & Conv, F1S1C1
         \end{tabular}\label{disc}
     \end{subtable}
          \begin{subtable}{.5\linewidth}
       \centering
         \caption{Classifier}
         \begin{tabular}{c|c}
           Layer Index & {Classifier} \\ \hline
           Input & 96$\times$96$\times$3 Image \\
           \hline

 		  	L - 1 & Conv, F7S2C64, Relu \\\hline
           	\multirow{2}{*}{L - 2} & Conv, F3S1C64, Relu \\
           	 & Conv, F3S1C64, Relu \\ \hline
            \multirow{2}{*}{L - 3} & Conv, F3S1C64, Relu \\
           	 & Conv, F3S1C64, Relu \\ \hline
           	L - 6 & 2$\times$2 max-pool, S2 \\ \hline
           	\multirow{2}{*}{L - 4} & Conv, F3S1C128, Relu \\
             & Conv, F3S1C128, Relu \\ \hline
           	\multirow{2}{*}{L - 5} & Conv, F3S1C128, Relu \\
           	 & Conv, F3S1C128, Relu \\ \hline
            \multirow{2}{*}{L - 6} & Conv, F3S1C256D1, Relu \\
             & Conv, F3S1C256D2, Relu \\ \hline
            \multirow{2}{*}{L - 7} & Conv, F3S1C256D2, Relu \\
             & Conv, F3S1C256D2, Relu \\ \hline
            \multirow{2}{*}{L - 8} & Conv, F3S1C512D2, Relu \\
             & Conv, F3S1C512D4, Relu \\ \hline
            \multirow{2}{*}{L - 9} & Conv, F3S1C512D4, Relu \\
             & Conv, F3S1C512D4, Relu \\ \hline
            L - 10 & Conv, F3S1C512D2, Relu \\ \hline
            L - 11 & Conv, F3S1C512D2, Relu \\ \hline
            L - 12 & Conv, F3S1C512, Relu \\ \hline
            L - 13 & Conv, F3S1C512, Relu \\ \hline
            L - 14 & Global average pooling \\ \hline
             & Branches for command\\ \hline
            L - 15 & FC, C5, Softmax
         \end{tabular} \label{classifier}
     \end{subtable}
 \label{gen}
\end{table}

\clearpage

\section{Proof of Theorem 1 in Sec.3.3}\label{proof of thm}
\begin{thm}
$E_\mathrm{T} \leq 4 \sqrt{X_2^2(P_\mathrm{T}(x),P_\mathrm{F}(x))} + E_\mathrm{F}$ where $X_2^2$ is the Chi-squared distance.
\end{thm}

Before we prove the theorem, we go through following two propositions.

\begin{prop}\label{prop1}
For $P_T(x)$ and $\frac{P_T(x)+P_F(x)}{2}$, the following inequality holds:
\begin{equation} \label{eq:tve}
\begin{aligned}
\scriptstyle{TV(P_{T}(x)||\frac{P_{T}(x)+P_{F}(x)}{2})^2 \leq \frac{1}{4}X_2^P(P_{T}(x),\frac{P_{T}(x)+P_{F}(x)}{2})}
\end{aligned}
\end{equation}
where $TV$ is the total variation distance on the probability space defined by $TV(\mathbb{P}||\mathbb{Q}) = \frac{1}{2}\int |\mathbb{P}-\mathbb{Q}| d\mu$ for given measure $\mu$ and two probability distributions, $\mathbb{P}$ and $\mathbb{Q}$. $X_2^P$ is the Pearson divergence between $\mathbb{P}$ and $\mathbb{Q}$.
\end{prop}

\begin{proof}
\citet{choosing} showed that $TV(\mathbb{P}||\mathbb{Q})^2 \leq \frac{1}{4}X_2^P(\mathbb{P},\mathbb{Q})$ holds if $\mathbb{P}$ is dominated by $\mathbb{Q}$ where $\mathbb{P}$ and $\mathbb{Q}$ are probability distributions.

Because the support of $P_\mathrm{T}(x)$ is contained in the support of $\frac{P_\mathrm{T}(x)+P_\mathrm{F}(x)}{2}$, we can derive Equation~\ref{eq:tve} by substituting $P_{T}(x)$ for $\mathbb{P}$ and $\frac{P_\mathrm{T}(x)+P_\mathrm{F}(x)}{2}$ for $\mathbb{Q}$.
\end{proof}

\begin{prop}\label{prop2}
\begin{equation}\label{eq:tvep}
TV(P_T(x)||P_F(x))<2 \sqrt{X_2^2(P_{T}(x),P_{F}(x))}.
\end{equation}
\end{prop}

\begin{proof}

the relationship between $X_2^2$ and the Pearson divergence, $X_2^P$, such that $X_2^2(\mathbb{P},\mathbb{Q})=\frac{1}{4}X_2^P(\mathbb{P},\frac{\mathbb{P}+\mathbb{Q}}{2})$ holds \cite{FISHERGAN}, gives us the following relation:

\begin{align} \label{eq:finaltv}
\scriptstyle TV(P_{T}(x)||P_{F}(x)) &\scriptstyle= \frac{1}{2}\int |P_{T}(x)-P_{F}(x)| dx \\
&\scriptstyle= \int |P_{T}(x)- \frac{P_{T}(x)+P_{F}(x)}{2}|dx \\
&\scriptstyle=2TV(P_{T}||\frac{P_{T}+P_{F}}{2}) \\
&\scriptstyle \,<\, 2\sqrt{\frac{1}{4}X_2^P(P_{T},\frac{P_{T}+P_{F}}{2})} \label{ineqlast} \\
&\scriptstyle=2\sqrt{X_2^2(P_{T},P_{F})}.
\end{align}
The inequality of Eq. \ref{ineqlast} comes from Prop.~\ref{prop1}.
\end{proof}

We now show the theorem using Prop.~\ref{prop2} as follows:
\begin{align}
\scriptstyle E_\mathrm{T} &\scriptstyle= \int_{\mathcal{X} \times \mathcal{Y}} e(x,y)P_\mathrm{T}(x,y)dxdy \label{eq:start} \\
&\scriptstyle= \int_{\mathcal{X} \times \mathcal{Y}} e(x,y)(P_\mathrm{T}(x,y)-P_\mathrm{F}(x,y)+P_\mathrm{F}(x,y))dxdy \\
&\scriptstyle \leq \int_{\mathcal{X} \times \mathcal{Y}
e(x,y)|P_\mathrm{T}(x,y)-P_\mathrm{F}(x,y)|dxdy + E_\mathrm{F} \\
&\scriptstyle \leq \int_{\mathcal{X} \times \mathcal{Y}
|P_\mathrm{T}(x)P_\mathrm{T}(y|x)-P_\mathrm{F}(x)P_
\mathrm{F}(y|x)\big|dxdy + E_\mathrm{F} \label{thm2}\\
&\scriptstyle \approx  \int_\mathcal{X
|P_\mathrm{T}(x)-P_\mathrm{F}(x)|dx\int_\mathcal{Y} P_\mathrm{T}(y|x)dy + E_\mathrm{F} \label{thm3}\\
&\scriptstyle = 2TV(P_\mathrm{T}||P_\mathrm{F}) + E_\mathrm{F}\, <\, 4\sqrt{X_2^2(P_{T},P_{F})} + E_\mathrm{F} \label{eq:end}
\end{align}
where we apply $e(x,y)\leq 1$ to Eq.~\ref{thm2}, and Assumption 1 to Eq. \ref{thm3}.

\section{Additional Transferred Images}

We show additional transferred images in Fig.\ref{supple_tr} in addition to Fig.4. Our proposed model generates the most realistic images while preserving the content of the source image.

\clearpage

\begin{figure*}[h]
\begin{center}
\centerline{\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{figures/transferred_images_supp}}
\caption{Source images and transferred images from five models.}
\label{supple_tr}
\end{center}
\end{figure*}

\clearpage
\section{Embedding by Other Methods}
We used other embedding methods to determine if the transferred images are actually similar to the target images, in addition to t-SNE in Sec.5. We used isomap \cite{isomap}, principal component analysis (PCA) \cite{pca} and kernel PCA \cite{kpca}. Fig.\ref{PCAcosine}, \ref{PCA}, and \ref{isomap} represent the embedding results obtained using PCA with cosine kernel, PCA, and isomap respectively. The transferred images moved toward the target images in all figures. $\mathtt{S, T}$, and $\mathtt{F}$ in each figure, indicate the median of the source, transferred, and target domain embedding images, respectively.

\begin{figure}[!htb]
\minipage{0.5\textwidth}
  \includegraphics[width=\linewidth]{figures/kpca_cosine}
  \caption{Cosine kernel PCA result.}\label{PCAcosine}
\endminipage\hfill
\minipage{0.5\textwidth}
  \includegraphics[width=\linewidth]{figures/pca2d}
  \caption{Standard PCA result.}\label{PCA}
\endminipage\hfill \\
  \centering
  \includegraphics[width=0.5\textwidth]{figures/isomap}
  \caption{Isomap result.}\label{isomap}
\end{figure}

\clearpage

\section{Additional Experiment}
In addition to our navigation experiment, we performed an additional task to classify the direction in which the camera is heading with respect to the road in the local trail environment. It could be stated that this task has a strong relation with the road following navigation task because the model that learns for the task could perform UAV road following as shown in \cite{NVIDIA,guisti}.

\begin{figure}[h]
\begin{center}
\centerline{\includegraphics[width=\columnwidth]{figures/gwanak_images_supp}}
\caption{Source images, transferred images and target images for local trail.}
\label{supple_gwanak}
\end{center}
\end{figure}

\ctable[
caption = Classification accuracy for three models,
center,
star,
pos=h,
topcap,
label=accuracy
]{c|ccc}{
}{
\toprule
\multirow{2}{*}{Model} &
\multicolumn{3}{c}{Ground truth} \\
\cline{2-4}
& {left} & {center} & {right} \\
\midrule
Source SL & 87.9\%& 66.3\% & 82.7\%  \\
Target SL & 99.2\% & 96.0\% & 99.5\% \\
\textbf{Our model} & 91.3\% & 94.2\% & 86.7\%  \\ \hline
}

\ctable[
caption=Confusion matrix for three models,
star,
pos=h,
topcap,
label=cf_mtx
]{cc|ccc|ccc|ccc}{
}{
\toprule
& & \multicolumn{3}{c}{Source SL output} & \multicolumn{3}{c}{Target SL output} & \multicolumn{3}{c}{\textbf{Our model output}} \\
\cline{3-11}
& & left & straight & right & left & straight & right & left & straight & right \\
\multirow{3}{*}{\rotatebox[origin=c]{90}{\parbox[t]{0.8cm}{Ground\\ truth}}} & left & 87.9\%& 8.3\% & 3.8\% & 99.2\% & 0.45\% & 0.33\% & 91.3\% & 8.2\% & 0.5\%\\
& straight & 15.1\%& 66.3\%& 18.6\% & 0.32\% & 96.0\% & 3.7\% & 3.0\% & 94.2\% &2.8\%\\
& right & 13.0\% & 4.3\%& 82.7\% &0.03\% & 0.48\% & 99.5\% & 2.1\% & 11.2\% & 86.7\% \\ \hline
}

We set target domain images as local trail images. We then developed a simulator that contains road and low-poly trees, fences, and rocks as target images. Fig.\ref{supple_gwanak} shows the source images and transferred images obtained using our model with DRN in the generator, a cycle and style loss, and the target images. The fences and bushes became more realistic in the transferred images. This shows that our proposed model is capable of generating realistic images in different environments from the one described in the body of the paper even with a low-poly simulator.

We trained three models that classify where the camera is heading for given trail images in three classes (left/straight/right with respect to the road). The first is Source SL (Supervised Learning) model that trains only using source images and corresponding labels in a supervised manner. The second is Target SL model that uses target images and their correct labels during the training. The third is our proposed model that described in the body of the paper.

Tab.\ref{accuracy}\ref{cf_mtx} shows the classifying accuracy and its confusion matrix. Our model improved the accuracy as compared with the Source SL model. In addition, the dangerous mistake that switches left class to right class and the opposite decreased in our model compared with Source SL model. These results support that our domain adaptation method is helpful in learning a navigational system using a simulator and makes us anticipate that our model may perform lane following task well in the real world despite the gap between the accuracy of our model and that of the Target SL.

redpajama_set_name: RedPajamaArXiv


text:
AFP South Africa
AFP / Phill Magakoe
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No, Johannesburg has not banned informal waste collectors
Published on Tuesday 2 July 2019 at 18:13
A tweet shared more than a thousand times shows a road sign -- apparently erected in Johannesburg -- that bans informal waste collectors on the street. However, there is no such ban in place and Mayor Herman Mashaba has said the sign was not put up by city authorities. It's not clear who made the sign, but Mashaba has asked residents to report any similar signs spotted around the city.
No, this image does not show a South African government official playing solitaire on her computer while clients wait
An image shared hundreds of times on Facebook purports to show how a Durban-based home affairs department employee let people wait in a queue while she played cards on her computer. The image is real and went viral in 2012 -- however, it was not taken in South Africa, but at a bank in India.
No, this video does not show a recent attack on illegal immigrants in South Africa
Published on Friday 28 June 2019 at 17:43
A video being shared on Facebook purports to show the badly burned victims of an attack on illegal immigrants in South Africa. In fact, we know that the video does not show a recent attack in South Africa -- it was very likely filmed in Nigeria. We're still trying to find out the full story behind it. Can you help us?
This is a private family home in east Johannesburg. It's not owned by Julius Malema
Published on Friday 28 June 2019 at 16:55Updated on Tuesday 2 July 2019 at 10:43
The residents of a luxury home in Johannesburg continue to fear for their safety as a result of a 2018 Facebook post which falsely claims the mansion belongs to Julius Malema, leader of South Africa's leftist opposition Economic Freedom Fighters (EFF) party. The claim is so widely shared that a quick internet search might lead you to believe the claim is true. In fact, reports have been falsely linking the house to Malema since 2010. The home's owner told media at the time that the property had no connection to Malema. In December, a member of the family issued a plea for members of the public to leave them alone.

redpajama_set_name: RedPajamaCommonCrawl


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The former Minister of Police Affairs, Mr Jelili Adesiyan, has warned against rigging in the September 22 governorship election in Osun State, saying, the Social Democratic Party (SDP) will resist any action that will tamper with the victory of its candidate, Senator Iyiola Omisore, at the polls.
Speaking during a Petals FM programme, The Interview, on Monday, Adesiyan said, "Aregbesola is ruling with decrees in Osun State, against the constitution of the Federal Republic of Nigeria. Since he became the governor, there was no local government election in Osun State, he only created area offices, and added more to the problems he had created in the state," as a result, on assumption into office, Omisore of SDP will normalise the situation.
The Aregbesola-APC government is trying to perpetuate itself in government for the next 30 years when some of us will not be interested in government again to cover his atrocities, that is why he has gone to bring a surrogate to take after him, that won't work, the people of Osun will resist it.
"We have also been told that part of the plans of the opposition is to lure people with money, and buy their votes, this also will not work. We have already mobilised our people to be at alert, and ensure they monitor proceedings at polling centres. They should check peoples PVCs and ensure that the photographs match up with the faces of the bearers.
"The here is, there won't be peace in Osun State if there is no justice during the coming election. there won't be peace in Osun if there is no justice," Adesiyan, said, while he warned the Independent Electoral Commission (INEC), and other election umpires to be just and not be biased before, during and after the election.
He, however, expressed confidence that the SDP, being the "most popular and most acceptable party loved by all and sundry in Osun State, will will the September 22 governorship election, by the grace of God," adding that, "the people are already prepared and are ready to vote for Senator Iyiola Omisore, as the only man who can rescue Osun People, and retire the lost glory of the state.


Quavo is a staple of the Atlanta community so it only makes sense that he takes every opportunity to give back. One of those initiatives is his Huncho Day football game, which started last year. On Sunday, the second annual game was played and had a star-studded cast of NFLers, including Cam Newton, Alvin Kamara, Saquon Barkley, and Josh Norman. One of the biggest names there though was Colin Kaepernick who hasn't been seen on a football field in a long time.
Kaepernick helped draw a huge crowd the flag football game and he made sure to put on a show as he threw a touchdown pass to Barkley. Kap even made sure he connected with Quavo himself on a two-point conversion. The fans were definitely in for a big treat with that play.
The former 49ers star hasn't been in the NFL since the 2016 season when he was essentially blackballed after refusing to stand for the National Anthem. Kap recently settled his lawsuit against the NFL where it is rumored that he made upwards of $10 million. Since then though, Kaepernick hasn't confirmed whether or not he will try to make a return to the NFL.
Below is a video of the crowd that followed both Kaepernick and Ed Reid onto the field yesterday.


text:
Queensland bottled water 'infused with love' voted world's best
By Megan Gorrey
March 1, 2018 — 8.49pm
Infused with frequencies of love, the moon or rainbow, Sturt Hinton's bottled water sounds more like a magic potion than a source of hydration.
Mr Hinton prefers to describe his Townsville-made drink, Frequency H2O, in marketing material as the ''ultimate elixir of life and the most dynamic beverage in the universe''.
His water won top honours at the Berkeley Springs International Water Tasting Awards in the US on Sunday, when 12 judges sipped about 100 different waters.
Frequency H20 founder Sturt Hinton started his business from scratch. Credit:Wolter Peeters
The organisation said the gold medal-winning water ''paid tribute to both love and hope''. Bottled waters from the US and Canada rounded out the top three.
Mr Hinton came up with the concept for his water after a battle with depression. He arrived in Sydney with two boxes of his product and without a home.
It's now stocked in more than 500 stores in Australia and has reportedly caught the attention of celebrities including Katy Perry, Ruby Rose, The Veronica's and wellness guru Lola Berry.
Singer Katy Perry is said to enjoy a drop of Frequency H2o water. Credit:Nick Moir
The Frequency H2o website describes the water, which costs $3.30 per litre, as ''a fusion of science and art'' which ''harnesses the incredible natural alchemy of energised molecules in order to promote health, balance and harmony''.
Mr Hinton said he developed ''technology'' to infuse the natural spring water sourced from North Queensland with different sound and light frequencies. ''The category of water that it's in is structured water. It replicates a natural setting where water is running over rocks or in vortexes.''
The water is available in love, moon or rainbow frequencies.
''I just thought the concept of sipping on a rainbow was delicious. It's fun to bring something dynamic and fun to the market.''
Mr Hinton, of Middle Cove in Sydney, said he was ''humbled'' to win the award at what he described as the ''Olympics of water''. ''It was amazing, I just feel so blessed that I got to live my dream and follow my passion, as wild and radical as it might seem.''
He said consumers were right to be sceptical about many bottled water brands and encouraged sippers to look at a product's type analysis.
Debate has long raged about the cost, benefits and environmental consequences of bottled water, however, it continues to be a popular choice among consumers.
About 5.3 million Australians above age 14 said they drank bottled water in an average week in 2015, an 8 per cent jump on the previous year's figure, market research firm Roy Morgan found.
International water sommelier Martin Riese said samplers looked for "taste characteristics".
"A water which has a unique taste will get from me a high score, a water which almost doesn't have a taste and I could not easily remember gets a lower score from me,'' he said.
However, Mr Riese said he didn't think there was a "best water". "Everybody has a different taste and my job as a water sommelier is to find the right water for my guests.
"So the best tasting water is the water my guests like."
Mr Riese predicted bottled water would mirror the organic food movement in recent years and look to go "back to nature" with minimal processing.
"People will understand that water should come from nature and not from a factory. So the next big thing will be water with minerals, like spring water."
Mr Riese hadn't tried Frequency H2O but liked the fact it was a spring water.
His "biggest surprise" last year was an Australian branded water, Aqui-Live, which he described as "thick on your palate, like olive oil".
Megan Gorrey
Megan Gorrey is the Urban Affairs reporter at The Sydney Morning Herald.


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text:
Midleton Educate Together National School first opened its doors on the 28th of August 2008, with the greatly appreciated assistance and dedication of our patron and our start-up group who worked hard for us to be sanctioned.
Since then, the school has grown from strength to strength, and in August 2017, we received the keys to our wonderful new permanent home in Broomfield West. Our new building is well-equipped and spacious, and will enable us to continue to grow as a school and a community.
Our school now has over 500 pupils. Within that number, we are lucky to have a unit catering for children with autism, with five classrooms, including two early intervention classrooms. Many of the students who attend this unit integrate into the mainstream setting at their class levels when appropriate.
Market Green, Midleton, County Cork, IRELAND
midletonet.com
cause registration
Revenue (Charities Unit) (CHY): School
Charities Regulatory Authority (CRA): 20114633
View Midleton Educate Together National School on Benefacts.ie


text:
How to break bad habits and keep that new year resolve
William Sokolic
(South Jersey) Courier-Post
Instead of a New Year's resolution, Sandy Tulino needed a wake-up call. And in late 2013, she realized, at 180 pounds, enough is enough.
"I had no control over my life," said Tulino.
"Once I realized that, losing weight and exercise was part of putting me in charge again. Having control of myself was the key. I never dieted. I changed my lifestyle."
The 60-year-old Atlantic County, New Jersey, resident shed 75 pounds. This is what determination will do for breaking bad habits.
Not everyone has that kind of motivation. Still, a new year often brings resolve to turn over a new leaf.
According to Sean Duffy, associate professor of psychology and director of the graduate psychology program at Rutgers–Camden, research shows roughly a third of Americans make New Year's resolutions.
"By far the most common is to exercise more, followed by eating better, then reducing the use of alcohol or tobacco," he said.
New Year's Day is a good marker to set a resolution, Duffy added. Drag the last cigarette or scarf down the final burger before the ball drops on New Year's Eve.
But how long does resolve last?
"The gym I go to is always packed in January and February; in the spring, not so much," said Dr. Jay Schmulowitz, who runs the Life Counseling Center in Stratford, New Jersey.
"Most individuals do not follow through. There's no such thing as a quick fix. It takes time to break habits."
Only a quarter succeed in achieving their goal, whatever that is, Duffy noted.
Efforts to break bad habits are unsuccessful because they involve a large commitment to changing behavior problems, said Tulino's physician, Dr. Greg Taylor, a family medicine doctor and medical director of the Kennedy Health Alliance in Turnersville.
Despite the high failure rate, the act of making a resolution makes people feel better about their lives, explained Charlotte Markey, professor of psychology at Rutgers–Camden.
The thing about breaking habits is they are not just a New Year's thing.
"I see them throughout the year," said Marcy Rabinowitz, of South Jersey Nutrition, in Cherry Hill.
Lots of reasons lead to failure, Duffy remarked, including setting the bar too high, underestimating the difficulty of self-control, underestimating the influence of friends with bad habits, setting too many goals, goals too far into the future or ambiguous goals.
So, what's the recipe for success?
"People with unhealthy habits must unlearn many years of bad behaviors. Engaging others in change helps because others may keep you accountable," Duffy said.
And that's where groups like Weight Watchers, Alcoholics Anonymous or Smokers Anonymous come in.
"Exercise programs work if you set a realistic regimen," Taylor remarked. "If you say you will go to the gym every day for six weeks, you will fail. But if you say you will do 30 minutes a few times a week, you can see success."
Folks can make relatively easy dietary changes, Markey stated.
"For example, people can stop drinking their calories — giving up regular soda, drinking alcohol in moderation," noted Markey, author of "Smart People Don't Diet" which is about doing what you can live with and what will work for you.
Success in anything depends on taking small steps and small intervals. "Do not just set a goal of 90 pounds in a year or you'll get discouraged. Set intervals like one to two pounds a week in the next two weeks," Taylor said.
"And don't get depressed if you slip up, as it is easy to just give up," added Duffy.
Be realistic or you set yourself up for failure, exclaimed nutritionist Sean Inselberg, of the Nutritional Wellness Center of South Jersey in Palmyra. Write down your goals, he explained. "Subconsciously, if it's on paper it's a good starting point."
Schmulowitz uses three steps in dealing with weight loss:
First, come up with a competing response. When you crave a certain type of food, play a computer game till the craving passes.
Second, reward yourself for not indulging for an entire week, like treating yourself to a movie.
And third, have a consequence for doing a bad habit. For example, give money to a friend to donate to a cause you oppose.
"It's very hard to break a habit that you get satisfaction from. There is a release of tension when you do this habit," Schmulowitz said.
Mark Anderson got so exasperated by his inability to lose weight, he went to see Rabinowitz.
"Weight was always a problem in my family. I used to be a school teacher and very active. But I am genetically predisposed to turn everything into weight," explained Anderson, of Merchantville.
When he did the Atkins diet, he shed 35 pounds only to see it come right back.
Going to the nutritionist last May made a huge difference in understanding his body. When his fat turned to muscle and the scale said he lost very little weight, Anderson was disheartened until he understood that it's not just about what the scale says.
Anderson, 63, now weighs 237 pounds. His weight has been as high as 260 — but his goal is to crack 200 and move closer to an ideal weight of 185.
Rabinowitz does a body composition reading.
"You may be losing the wrong kind of weight. Losing fat pounds is important. Sometimes you can see in clothing how they are doing even if the scale does not show it."
Saying you are going to exercise or eat less is not enough, she noted.
"You need to find a reason why people became heavy in the first place. There can be all kinds of reasons. Is it hormones or medications? Sometime there are multiple causes."
Don't stop till you get results, Rabinowitz added.
"If someone is not moving forward, we tackle anything getting in the way. It's more a problem that the patient gets frustrated and stops because they do not see what they hope to see."
It may require a change in what is eaten. "Certain foods lower metabolism, weakening the digestive system," Inselberg said.
The answer may lie in finding foods with specific vitamins and minerals to strengthen digestion. "Remember, any goal means putting some effort and effort equals rewards," he added.
Amen to that, Tulino said. She went from a size 18 to size 0.
"Most people do not recognize me. My weight loss took 20 years off my life. When you are thin, you feel good about yourself inside and out."
Reach William Sokolic at (856) 486-2437 or wsokolic@courierpost
online.com or Twitter @WilliamSokolic
How to break bad habits
• Take small steps
• Set specific and realistic goals
• Write down the goals
• Seek counseling
• Consider a trainer
• Don't stop till you get results


Toulemonde Bochart combines the traditional skills and work on beautiful materials. In creative design carpets, Toulemonde Bochart brings contemporary interiors, classic or avant-garde, a style of modernity beyond the influences of fashion and a high comfort of sustainable range.
Toulemonde Bochart There are 28 products.
More understated in structure and tone than the Baya, Raja rug, in the traditional colours of Rajasthani fabrics, nevertheless brings the finesse of its motifs to the fore. Its effect is one of getting away from it all, at the same time harking back to tradition.
This hand knotted rug reveals shades and reflections of the subtly dyed jute, a natural fiber. The depth of the colors in all shades is enhanced by the contrasted pattern inspired by weavings of the Atlas.


text:
class StasherError: public std::runtime_error {
public:
    StasherError(const std::string& what):
        std::runtime_error(what) {}
};

class InvalidValueError:
    public StasherError {
public:
    InvalidValueError(const std::string& what):
        StasherError(what) {}
};

class EntityNotFoundError:
    public StasherError {
public:
    EntityNotFoundError(const std::string& what):
        StasherError(what) {}
};

redpajama_set_name: RedPajamaGithub


The Plant Management Network (PMN) is a nonprofit online resource of The American l Society for researchers, consultants, growers, pesticide applicators, and other professionals involved in the management of large and small crops.
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To subscribe, click the "Log In" link in the top banner above to sign in, or to create an account (if you are a new customer).


text:
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Richard Jenkins
Richard Jenkins Movies, TV Shows, & Awards
DeKalb, Illinois, USA
Academy Award, Actor in a Supporting Role for The Shape of Water
Screen Actors Guild Awards, Outstanding Performance by a Male Actor in a Supporting Role for The Shape of Water
Golden Globe, Best Performance By an Actor in a Supporting Role in a Motion Picture for The Shape of Water
Screen Actors Guild Awards, Outstanding Performance by a Male Actor in a Television Movie or Miniseries for Olive Kitteridge
Emmy (Primetime), Outstanding Lead Actor in a Limited Series or Movie for Olive Kitteridge
Academy Award, Actor in a Leading Role for The Visitor
Screen Actors Guild Awards, Outstanding Performance by a Male Actor in a Leading Role for The Visitor
Screen Actors Guild Awards, Outstanding Performance by an Ensemble in a Drama Series for Six Feet Under
154 programs featuring Richard Jenkins


The model is a size EU:44 and is 6'2" tall with a, 33" chest, 30" waist and 36" hips.
Summery flip-flops by HUGO, made with a textured footbed for comfortable grip. Crafted in Italy, these statement flip-flops feature the HUGO logo in both its original and reversed form. A signature branding patch seals off the iconic attitude of this distinctive pair.


SCP (J1850) Invalid or Missing Data for Function Read Engine Torque is the generic description for the U1308 code, but the manufacturer may have a different description. Currently we have no further information abouth the U1308 OBDII code. Please post your question on the Q&A section for more information: AutoCodes.com Q&A.


(a) Maintain appropriate records relating to its receipt, storage, use, transfer, generation, treatment, or disposal and maintain such other records as the department may require, subject to such exemptions as may be provided by rule, and to use existing data reporting systems when possible.
(b) Maintain appropriate records showing the radiation exposure of all individuals for whom personnel monitoring may be required by the department, subject to such exemptions as may be provided by rule.
Copies of all records required to be kept by this subsection shall be submitted to the department or its duly authorized agents upon request.
(2) The department is authorized to require that any person possessing or using a source of ionizing radiation, at the request of any employee for whom personnel monitoring is required, furnish to such employee a copy of such employee's personnel exposure record annually, upon termination of employment, and at any time such employee has received excessive exposure.
History.—s. 1, ch. 78-373; s. 2, ch. 81-318; ss. 8, 22, ch. 82-186; ss. 17, 18, ch. 84-190; s. 4, ch. 91-429.


text:
About Timothy Judd
Beethoven's Fifth Symphony: An Exhilarating Motivic Journey
October 6, 2021 by Timothy Judd
"Short, short, short, long…"
The four notes which open Beethoven's Fifth Symphony outline what is perhaps music history's most iconic motif. It's a motif which has been subjected to pop culture cliches and dubious superimposed poetic associations, such as "fate knocking at the door." This motivic kernel, perhaps derived from Luigi Cherubini's 1794 French Revolution anthem Hymne au Panthéon, is the seed out of which the entire Fifth Symphony develops. Preceded by a silent, suspenseful downbeat, it explodes with exhilarating ferocity, launching the Symphony's heroic journey from dark, turbulent C minor to the transcendence of C major.
The Fifth Symphony was premiered in Vienna on December 22, 1808 at a time when Beethoven was facing increasing deafness and the armies of Napoleon were marching across Europe. The under-rehearsed performance was part of a marathon, five-hour-long concert of Beethoven's music presented at an unheated Theater an der Wien. (The Sixth Symphony, the Fourth Piano Concerto, and the Choral Fantasy were also on the program). We can only imagine the effect this bold, revolutionary music had on the first audiences. The author and critic E.T.A. Hoffmann called the Fifth Symphony "one of the most important works of the master whose stature as a first-rate instrumental composer probably no one will now dispute…the instrumental music of Beethoven opens the realm of the colossal and the immeasurable for us." In an 1813 essay, Hoffmann wrote,
How this wonderful composition, in a climax that climbs on and on, leads the listener imperiously forward into the spirit world of the infinite!… No doubt the whole rushes like an ingenious rhapsody past many a man, but the soul of each thoughtful listener is assuredly stirred, deeply and intimately, by a feeling that is none other than that unutterable portentous longing, and until the final chord—indeed, even in the moments that follow it—he will be powerless to step out of that wondrous spirit realm where grief and joy embrace him in the form of sound…
Beethoven's Third Symphony, the "Eroica," was monumental and groundbreaking in scale and length. In contrast, the Fifth Symphony is a whirlwind of taut, compressed energy. The first movement (Allegro con brio) develops in short, unrelenting motivic cells. The fateful opening motif lurks around every corner. As the strings and woodwinds trade the second theme, the motif emerges sneakily in the low strings. It consumes the fiery and tense development section, at one moment breaking down into disjointed rhythmic strands before regrouping with renewed power. The mysterious oboe cadenza in the recapitulation remains one of the movement's inexplicable surprises. The brash, militant sounds of the French Revolution abound. The coda surges to a furious conclusion. The hushed anxiety of the final bars is interrupted by the brusk, forceful hammer blows of the cadence.
The second movement (Andante con moto), set in the unexpected key of A-flat major, is an example of double variation form. Here, variations are developed in two alternating themes. Tender introspection meets the sounds of a Revolutionary military march. The courtly, aristocratic elegance of Mozart and Haydn is replaced by grandiose fanfares, punctuated by trumpets and drums. Listen carefully, and you will hear the "short-short-short-long" rhythmic motif return in new guises. It concludes the first theme and rises to prominence in the second theme's spirited march.
Beethoven abandons the classical minuet and instead gives us a Scherzo third movement. The movement opens with hushed mystery and an arpeggiated figure which pays subtle homage to the final movement of Mozart's Symphony No. 40. Suddenly, the "short-short-short-long" rhythm returns, this time as a heroic statement in the horns. The trio section erupts with a vigorous fugue which moves from the depths of the orchestra upward. When the Scherzo returns, it comes in the form of quietly tiptoeing pizzicati and woodwind staccatos. The first audiences would have expected a repeat, verbatim, of the original music. They must have held their breath and listened with rapt attention to this sudden extended decrescendo. What follows would have been even more unprecedented. Rather than a conventional ending, the movement dissolves into a bridge filled with intense mystery and expectation. A cosmic crescendo leads to the triumphant opening of the fourth movement.
It's a heroic proclamation which begins with the outlining of a C major triad. The powerful and transformative voices of the three trombones change the character of the sound. This was one of the earliest symphonies to use the trombones. As the movement progresses, there are two additional newcomers, the piccolo and the deep contrabassoon. Again, we hear music infused with the "short-short-short-long" rhythmic motif. (You'll hear it in the basses in this passage). Notice the way a passing inner voice underlying the second theme becomes an exalted statement at the end of the development section. A brief recollection of the Scherzo precedes the thunderous recapitulation. The gradually accelerating coda brings joyful and celebratory musical fireworks. The commentator Charles Rosen believed that the repeating C major cadential chords in the final bars are necessary "to ground the extreme tension of this immense work." Indeed, Beethoven's Fifth Symphony unleashes a new kind of intensity and delivers the ultimate dramatic journey from struggle to transcendence.
Here is Carlos Kleiber's legendary 1974 studio recording with the Vienna Philharmonic:
Here is John Eliot Gardiner's period performance with the Orchestre Révolutionnaire et Romantique. Interestingly, Gardiner asks the trombones to stand during their initial entrance in the final movement:
Five Great Recordings
Beethoven: Symphony No. 5 in C minor, Op. 67, Carlos Kleiber, Vienna Philharmonic Amazon
Beethoven: Symphony No. 5 in C minor, Op. 67, John Eliot Gardiner, Orchestre Révolutionnaire et Romantique Amazon
Arturo Toscanini and the NBC Symphony Orchestra (1952 recording)
Wilhelm Furtwängler and the Berlin Philharmonic (1954 recording)
Andrés Orozco-Estrada and the Frankfurt Radio Symphony (2016 concert performance)
Featured Image: A manuscript copy of the Fifth Symphony from 1809
A native of Upstate New York, Timothy Judd has been a member of the Richmond Symphony violin section since 2001. He is a graduate of the Eastman School of Music where he earned the degrees Bachelor of Music and Master of Music, studying with world renowned Ukrainian-American violinist Oleh Krysa.
The son of public school music educators, Timothy Judd began violin lessons at the age of four through Eastman's Community Education Division. He was a student of Anastasia Jempelis, one of the earliest champions of the Suzuki method in the United States.
A passionate teacher, Mr. Judd has maintained a private violin studio in the Richmond area since 2002 and has been active coaching chamber music and numerous youth orchestra sectionals.
In his free time, Timothy Judd enjoys working out with Richmond's popular SEAL Team Physical Training program.
View all posts by Timothy Judd | Website
Categories Classical Period, Symphony Tags Beethoven, Carlos Kleiber, John Eliot Gardiner, Symphony No. 5 in C minor
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3 thoughts on "Beethoven's Fifth Symphony: An Exhilarating Motivic Journey"
Lee Steels
A 2015 recording of the 5th and 7th by the Pittsburg SO under Manfred Honeck is a good one. To my ears it has the best sound of all. His phrasing may bother some but I'm certain that L B would love it.
Timothy Judd
Thank you for this recommendation, Lee. Here is the first movement: https://youtu.be/icKeTON0a60
Beethoven `s fabulous famous 5th Symphony.
Kleibers rendering is very good,-BUT he is piiped at the post I think by Gardiner who shows "fire in his belly" with an all out blast from the start, keeping the tempo going, and we can see the determination and influence this has on the orchestra. I can`t help feeling that this could be more in the liking of Beethoven, who also had a firey temprement.
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A native of Upstate New York, Timothy Judd has been a member of the Richmond Symphony violin section since 2001. In addition to performing and teaching, Timothy Judd is the author of the popular classical music appreciation blog, The Listeners' Club…
Tchaikovsky's First Piano Concerto: From Rejection to Triumph
December 6, 2022 October 26, 2022
Mozart's Sinfonia Concertante for Violin, Viola, and Orchestra: A Sublime Hybrid
Beethoven's "Razumovsky" Cycle: String Quartet No. 7 in F Major, Op. 59, No. 1
Mozart's Symphony No. 40 in G Minor: Opening the Door to the Romantic World
Bach's Concerto for Two Violins, The Netherlands Bach Society
Mahler's Third Symphony: A Progression to the Divine
Mickey Fuson on Mahler's Fourth Symphony: Heaven Through a Child's Eyes
Ralph West on Sibelius' Seventh Symphony: "Turning Space Upside Down"
Satid S. on Five Great Perlman Recordings
Christopher Long on Mendelssohn's "Scottish" Third Symphony: Landscapes and Ruins
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Increasing regulatory and consumer demands have intensified the pressure on the food industry to implement reliable methods of food inspection to ensure product safety and quality. X-ray technology can be used in food inspection to detect physical contaminants and to study the internal structure of food products for quality purposes.
The operation of food safety management systems incorporates the principles of Hazard Analysis and Critical Control Points (HACCP); inspection forms a key part of procedures designed to control potential hazards. The role of technology for inspection purposes has become increasingly important due to the ever-increasing emphasis by consumers and regulatory authorities on food safety and quality.1 Emerging issues such as fraud and the intentional contamination of food have also highlighted the importance of food inspection technology.
Many different methods and technologies are available for the inspection of food, including metal detectors, optical camera systems, magnetic resonance imaging, ultrasound, and X-rays.2 The application of a specific inspection technology is related to the nature of the food and the specific purpose of that inspection.
Depending on the type of X-ray inspection equipment and the nature of the food product, X-ray inspection can identify a variety of physical contaminants including metal, glass, rubber, stone and some plastics.3 Because X-ray inspection provides non-destructive imaging, its use has become more widespread for packaged, processed foods, particularly those in bottles, cans, jars and pouches.2 Increasingly, as the technology advances X-ray inspection is being used for in-line production control and verification.
Concern has been expressed that operators may be exposed to harmful levels of radiation from X-ray inspection systems. However, under normal circumstances the level of radiation that an operator in direct contact with an X-ray system will receive is less than that received in a year from natural background radiation.
Haff RP & Toyofuku N. (2008). X-ray detection of defects and contaminants in the food industry. Sens Instrumen Food Qual 2:262–73.
Graves M et al. (1998). Approaches to foreign body detection in foods. Trends Food Sci Technol 9(1):21–7.
Mery D et al. (2011). Automated fish bone detection using X-ray imaging. J Food Eng 105(3):485–92.