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

In this paper, we applied \ac{CNN}-based underwater object detection from sonar
images. The main objective was to overcome data limitations in the underwater
environment by synthesizing sonar images obtained from a simulator and testing
over sonar images captured in a real underwater environment. Our results
validate that the proposed image synthesizing mimics real underwater images
without actual performing dives. The proposed training solution is applicable
for various target detection by using a 3D model of the target from the
simulator.

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

In many underwater operations \cite{cho-2015, purcell-2011, reed-2003,
belcher-2000, lee-2013, williams-2011, galceran-2012}, perceptual object
detection and classification are required, such as search and rescue, evidence
search, and defense missions for military purposes. Bodies of water often
present a critical decrease in visibility due to the high density of fine floats
or aquatic microorganisms \cite{lee-2017}. Due to this limitation of using
optical images, imaging sonar has been a widely accepted solution providing
reliable measurements regardless of the water's turbidity
\cite{yshin-2015-oceans, johnnsson-2010}. Although sonars extend the perceptual
range, the resulting images follow a different projection model, resulting in
less intuitive and low-resolution images and cannot be easily understood by
human operators. In addition, due to the sensor's physical characteristics, a
considerable level of noise is generated in the water image, so it is difficult
to ensure the reliability of sonar image analysis and identification
\cite{navigator}.

\begin{figure}[!t]
    \centering
    \includegraphics[width=0.95\columnwidth]{figs/overview_sm}

    \caption{Overview of the proposed method. We propose a sonar image
    synthesizing for the images generated by a simulator. We trained using
    images captured and synthesized from simulator, and tested over real
    underwater target detection scenario in water tank and real sea.}

    \label{fig:overview}
\end{figure}

Early work on sonar image-based classification was aimed at  \ac{ATR} or
sediment classification. Low resolution and image ambiguity due to the shadowing
effect has always been an issue in defining handcrafted features for sonar
images. \citeauthor{enric-2012} used multibeam, forward-looking sonar to detect
man-made objects \cite{enric-2012}. In their work, they applied a series of
estimation modules to detect objects. In \cite{zhou-2004}, the authors employed
power spectral analysis methods for seafloor sediment classification.  In
\cite{williams-2015-tgrs}, the author reported a useful measure for sonar
imaging called \textit{lacunarity} to classify seafloor characteristics.

Recently, to overcome these challenges, deep-learning-based approaches have been
introduced. Researchers focused on a partial application. They exploited feature
learning by learning features from the \ac{CNN} and then piped the learned
feature into another machine learning algorithm like a \ac{SVM}
\cite{pzhu-2017-cdc}. Other researchers used deep learning in a more end-to-end
fashion.  In \cite{williams-2016-icpr}, target classification using \ac{SAS}
images was introduced. Two target objects were considered in the study, and the
performance of the CNN-based approach was compared to that of the typical
feature-based classifier. \citeauthor{jkim-syu-2016-oceans} also focused on
applying deep learning for vehicle detection using forward-looking
sonar~\cite{jkim-syu-2016-oceans}.  In the aforementioned approaches, however,
the authors collected real sonar images and divided them into training and test
image sets.

When applying deep learning based approaches in underwater environment, training
with real sonar images from the target environment would be optimal but is
highly challenging in several aspects. First, the underwater imaging
specifically for classification results in a biased dataset. Second, obtaining
underwater images demands time and effort. Applying deep learning underwater
addresses the major challenge of scanty data. Many efforts have been made to
alleviate the training data shortage. For example, \cite{jmckay-2017-oceans}
exploited existing pre-trained weights from in-air images. They applied fine
tuning using sonar images.

A similar strategy to ours recently found in the literature synthetically
generates photo-realistic images. Authors in \cite{kdenos-2017-oceans} examined
the synthetic training set generation by applying a proper background of white
noise to the simulated images. This synthetic training image generation was also
thoroughly handled in \cite{jchen-2017-jasa}. The authors evaluated the \ac{GAN}
to learn underlying features in an unsupervised manner. They also examined the
effect of style transfer on background and shadow generation ability.

Differing from those early studies who focused on generation of images, this
paper proposes an end-to-end solution to prepare a training dataset for
underwater object detection and validating with real underwater sonar images. In
\figref{fig:overview}, we present a simulator-based training data generation
tool specifically for underwater sonar images. Our contributions are as follows:

\begin{itemize}

\item We propose a solution to the problem of scanty data in underwater sonar
applications by proposing synthetic training image generation via style
transfer. The proposed method takes one channel depth image from a simulator to
provide various aspects (e.g., scale, orientation and translation) of the
captured data.

\item We performed a thorough evaluation using real sonar data from pool and sea
trial experiments to validate the proposed method. Specifically, we present that
the proposed simulation trained network performs equally well as the real sea
data trained network. By doing so, the proposed training scheme alleviates the
training data issue in underwater sonar imaging studies.

\item We also verified the trained network with sample images from various sonar
sensors. The test sonar images are sampled from video provided by sonar
companies. This validation proves the proposed scheme could be widely applicable
for sonar images captured from various underwater environment. Note that the
sonar data used in testing was never been used in training phase. Therefore, we
suggest elimination of the real-data acquisition phase in deep learning for
underwater application.

\end{itemize}

\section{Experimental Results}
\label{sec:results}

In this section, we provides a series of experiments to evaluate style transfer
performance and its application to object detection.

\begin{figure}[!t]
  \centering
  \subfigure[Water tank test (\texttt{POOL})]{%
    \includegraphics[width = 0.45\columnwidth]{figs/CKI_picture_annot.jpg}
    \label{POOL_picture}
  }
  \subfigure[Sea trial (\texttt{SEA})]{%
    \includegraphics[width = 0.43\columnwidth]{figs/TDI_picture_annot.jpg}
    \label{SEA_picture}
  }
	\subfigure[Our own validation datasets]{
	  \label{tab:datalist}
	  \begin{minipage}[t]{0.9\columnwidth}
			\centering
		\vspace{10px}
	  \scriptsize
	  \begin{tabular}{c|ccc}
		  Name                 & Environment  & Description              & \# of Images\\
		  \hline\hline
		  \texttt{SIM}	       & UWSim        & Simulated depth camera   & 370\\
      \texttt{SIM-POOL}    & UWSim        & Water tank styled images & 370\\
      \texttt{SIM-SEA2017} & UWSim        & Sea styled images        & 370\\
      \hline
		  \texttt{POOL}	   	& Water tank   & Multibeam sonar images   & 735\\
		  \texttt{SEA2017}	& Sea          & Multibeam sonar images   & 1045\\
		  \hline
		  \texttt{SEA2018}	& Sea          & Multibeam sonar images   & 1935
      \vspace{5px}
	\end{tabular}
	\normalsize
\end{minipage}
	}

  \caption{Experiment set up for clean water tank (\texttt{POOL}) and real sea
  data (\texttt{SEA}). Sonar was mounted either on \ac{USV} (for \texttt{POOL})
  or kayak (for \texttt{SEA}).}

  \label{fig:exp_setup}
\end{figure}

\subsection{Datasets}

For training, images (\texttt{SIM}) are prepared using the algorithm described
in \secref{sec:sim}. The \texttt{SIM} images are styled targetting either water
tank (\texttt{SIM-POOL}) and sea (\texttt{SIM-SEA2017}) respectively. Details are
summarized in the table in \figref{fig:exp_setup}.

For validation, we used our own dataset listed in the table in
\figref{tab:datalist} together with the publicly available sample images from
sonar companies. When collecting our own validation dataset, images were
captured by imaging a human-sized dummy using a Teledyne BlueView M900-90, a
multibeam imaging sonar with a 90$^{\circ}$ field of view, 20$^{\circ}$ beam
width and \unit{100}{m} maximum range. Data were collected from a water tank and
from the sea as shown in \figref{fig:exp_setup}.

\begin{figure}[!t]
	\centering
	\includegraphics[width = 0.9\columnwidth]{figs/samples}

  \caption{Sample raw images from each environment without applying style
  transfer. All four sample images contain the target object marked as a green
  circle.}

	\label{fig:samples}
\end{figure}

\begin{figure}[!b]
	\centering
	\includegraphics[width = 0.95\columnwidth]{figs/style.jpg}

  \caption{Style transferred image samples. Given the depth images captured from
  the simulator, we generate color-map changed images. On the third and fourth
  column, style transferred images are shown. When style transferred to the
  water tank, the images showed a darker background well representing the actual
  images captured in the water tank.}

  \label{fig:style}
\end{figure}

The first dataset, called \texttt{POOL}, was captured in the very clean water
testbed of the \ac{KIRO}. The maximum depth of this water testbed was
approximately \unit{10}{m}. The dummy was positioned in a water depth of about
\unit{4}{m} to simulate a submerged body, as shown in \figref{POOL_picture}. The
imaging sonar was mounted on a \ac{USV} platform that was capable of rotating
the sonar sensor at an angular interval of 5$^{\circ}$ and enabled collection of
underwater sonar images from various angles. The second dataset (\texttt{SEA})
was captured in severely turbid water from Daecheon Beach in Korea. The BlueView
M900-90 was fixed to the lower part of the kayak, as shown in
\figref{SEA_picture}, and the heading direction was mounted at about
30$^{\circ}$ downward from the water's surface. In this experiment, the distance
between the sensor and the dummy was about 2 to 4 meters. The \texttt{SEA}
dataset was collected twice, and the datasets were named \texttt{SEA2017} and
\texttt{SEA2018}. The dataset of \texttt{POOL}, \texttt{SEA2017} and
\texttt{SEA2018} has 735, 1045, and 1935 images containing a submerged body
respectively.

\figref{fig:samples} illustrates the sample images captured from each
environment. Images from water tank reveals relatively darker images than sea
trial. The appearance of the target object change drastically even when captured
in the same environment depending on the viewpoint and nearby sediment
condition. As can be seen \texttt{SEA2018} presents a brighter image than
\texttt{SEA2017} capturing the target object farther than previous data.

\subsection{Experimental Setup and Evaluation criteria}

\begin{figure}[!t]
	\centering
	\includegraphics[width = 0.95\columnwidth]{figs/style_epoch.jpg}
  \caption{As the epoch evolves, the target object appears more clearly. At
  around 100 epoch, the shape of the body clearly shows and background reveals
  similar characteristics to the real images.}
  \label{fig:style_epoch}
\end{figure}

\begin{figure}[!b]
	\centering
  \subfigure[\texttt{SIM-POOL} to \texttt{POOL}]{
    \includegraphics[width = 0.45\columnwidth]{figs/SIM2CKI_CKI_025}
    \label{fig:pr_POOL_style}
  }
  \subfigure[\texttt{SIM-SEA2017} to \texttt{SEA2017}]{
    \includegraphics[width = 0.45\columnwidth]{figs/SIM2TDI2017_TDI2017_025.pdf}
    \label{fig:pr_SEA_style}
  }

  \caption{Object detection performance when target environment changes.
  \subref{fig:pr_POOL_style} the network is trained from simulator-generated
  images applied with water tank style, and is tested with real sonar images
  collected from water tank, \subref{fig:pr_SEA_style} the network is trained
  from simulator-generated images applied with sea style and is tested with real sea sonar images.}

	\label{fig:pr_style}
\end{figure}

Style transfer and object detection training was performed running on one NVIDIA
GTX 1080. Adam optimizer was used. The learning rates were set to $10^{-3}$ with
an exponential decay. Weight decay, $\beta_{1}$ and $\beta_{2}$ were set to
$10^{-5}$, $0.9$ and $0.999$, respectively.

We considered detection \ac{IOU} larger than 0.25 as the correct detection. For
terrestrial images, IOU $=0.5$ is often used.  Considering sonar images
resolution and underwater navigation accuracy, we alleviated criteria of the
detection IOU. We think, however, if the targeting sonar images are high
resolution such as \ac{SAS} different \ac{IOU} can be used as the detection
criterion.

\subsection{Style Transfer Performance}

We first validated the effect of the style transfer on performance. By using a
style bank, multiple aspects of the images can be synthesized. Using the base
input image from the simulator, synthetic images were generated for
\texttt{POOL} and \texttt{SEA2017}. The style transferred images are as given in
\figref{fig:style}. As can be seen in the figure, the original color map images
are style transferred to water tank and sea styles.  The style transfer results
by epoch are also given in \figref{fig:style_epoch}.  The chosen target object
evolves to be a cleaner, stronger object as the epoch increases.

We also validates the performance of the style transfer when generating and
testing for two different target environments. Using simulator-created images,
we style transferred to water tank style and sea style. Original 370
\texttt{SIM-POOL} and 370 \texttt{SIM-SEA2017} were trained with their augmented
images and tested over 735 \texttt{POOL} and 1045 \texttt{SEA2017} images.
These style transferred images from each environment were then trained and
tested with real data from each case, as in \figref{fig:pr_style}.  Both test
cases present training from styled images resulting in meaningful object
detection performance. Average precision of $0.77$ for \texttt{POOL} and $0.63$
for \texttt{SEA2017} are achieved. The accuracy when testing in a water tank is
higher than when testing at sea. This is because the noise induced from the
background sediment is lower when testing in a water tank, as can be seen in
sample images in \figref{fig:samples}.

\subsection{Simulation Training Evaluation}

\begin{figure}[!t]
	\centering
	\includegraphics[width = 0.8\columnwidth]{figs/pr}

  \caption{PR curves comparison when the network is trained by images from the
  water tank (\texttt{POOL}), style transferred images from the water tank
  (\texttt{styled-POOL}), style transferred images from the simulator
  (\texttt{styled-SIM}). Baseline result is obtained by training from real sea
  images captured in 2017 (\texttt{SEA2017}). All four cases are tested by using
  real sea sonar images captured in 2018 (\texttt{SEA2018}).}

	\label{fig:pr_curve}
\end{figure}

\begin{figure*}[!t]
  \centering
	\subfigure[Sample images]{%
    \includegraphics[width = 0.95\textwidth]{figs/video_samples_horz.pdf}
    \label{fig:co_samples_img}
  }\\
	\begin{minipage}[]{0.9\textwidth}
		\centering
	  \subfigure[Dataset lists and video sample image description]{
	  \label{tab:co_samples}
	  \footnotesize
			\begin{tabular}{c|ccccc}
	      Name  & manufacturer					  & Image \# & Target object                 & Range [m] & Sonar type\\
				\hline\hline
				T1	  & Teledyne (P900-45) 		  & 5        & Diver standing sea floor      & 5	       & Multibeam imaging sonar\\
	      T2	  & Teledyne (P900-130) 	  & 5        & Diver swimming near sea floor & 10        & Multibeam imaging sonar\\
	      T3		& Teledyne (P900-45) 		  & 5        & Diver swimming far    			   & 10        & Multibeam imaging sonar\\
				T4		& Teledyne (P900-45) 		  & 5 			 & Diver swimming near           & 2         & Multibeam imaging sonar\\
				T5		& SonarTech         	  	& 10			 & Diver approaching to sensor   & 1-25      & Multibeam imaging sonar\\
				T6		& SonarTech             	& 10			 & Diver swimming in-Water       & 10        & Multibeam imaging sonar\\
				T7    & SonarTech       			  & 5				 & Diver standing sea floor      & 3         & Multibeam imaging sonar \vspace{5px}\\
			\end{tabular}
		\normalsize
		}
  \end{minipage}

  \caption{Test sonar images captured from company provided sample videos. T1-T4
  were sampled from videos available from Teledyne and T5-T7 were captured from
  video provided by SonarTech. \subref{fig:co_samples_img} Sample images
  from each dataset. \subref{tab:co_samples} Summary of the dataset.}

  \label{fig:co_samples}
\end{figure*}

\begin{figure}[!h]
  \centering
  \includegraphics[width = 0.95\columnwidth]{figs/video_samples_results.pdf}

  \caption{Test results from sample images captured from video.}

  \label{fig:video_results}
\end{figure}

If possible, training from the real sea and testing with real sea images would
be ideal. Hence, we use the object detection results trained from
\texttt{SEA2017} and tested them on \texttt{SEA2018} as the baseline,
considering that this would be the optimal training method. As can be seen in
\figref{fig:pr_curve}, the baseline provides around 0.65 accuracy when detecting
the object.

In comparison to this baseline, we performed object detection from three cases:
when trained from a water tank (735 images from \texttt{POOL}), when trained
from stylized images from a water tank  (370 images from \texttt{SIM-POOL}) and
when trained from simulator using style transfer (370 images from
\texttt{SIM-SEA2017}). The precision-recall curve comparison is provided in
\figref{fig:pr_curve}. The accuracy and detection performance are slightly
degraded compared to training from real sea data. On the other hand, the
proposed method elaborated the simulation-generated images to include
characteristics of the real sea images via style transfer. The resulting object
detection performance is comparable to that of the detection result when trained
with real sea images.

\subsection{Validation to Public Data}
\label{sec:company}

Lastly, we verified that the proposed method is applicable to other types of
sonar from two different manufacturers by testing in the various environments.
Again, we trained the network using the simulator-generated images and by
applying style transfer. As described in \figref{fig:co_samples}, we collected
sample images from various sample videos. These images contain either a standing
or swimming diver at various ranges. The sample data were collected using
different sensors and from different sediment conditions. An object's relative
size within an image varies when captured at close (T4) vs far range (T5).
Depending on the viewing angle and diver's posture, a strong shadow occurred
when the diver was standing on the sea floor (T1 and T7). When the target is
swimming in water, the ground appears separately, as in T3 and T5.

Sample test results are shown in \figref{fig:video_results}. Despite the variety
of sample cases, the target object (i.e., diver in the sea) was successfully
detected. One notable case was found in T5 when a diver approached the sonar
starting from \unit{25}{m} away from the sensor. As can be seen in the sample
and result cases, only a couple of pixels indicate the object. The learned
network suffered from this subtle information and detected the object only when
the range became closer (less than \unit{5}{m}). Also, when the target object
was found in multiple pixels within a short range, the object was found multiple
times when diver motion was greater. The motion could be highly diverse when the
diver was swimming and this level of ambiguity was well secured by the training.
Furthermore, the trained network was not fooled by other objects such as rocks
or the ground, which also appear as bright objects in the scene.

\section*{ACKNOWLEDGMENT}

\bibliographystyle{IEEEtranN}

\section{Training Set Generation}
\label{sec:sim}

In this section, we introduce simulation-created training data generation for
underwater object detection.

\subsection{Base Images Preparation from Simulator}

Obtaining real images from the ocean would be ideal, as reported in
\cite{williams-2016-icpr}, where the author collected eight years of data from
marine missions to prepare and test classifications.  However, as reported, data
collection in underwater missions is demanding. To overcome this limitation, we
captured a base image for the synthetic training dataset from a simulated depth
camera in the UWSim \cite{uwsim}. Using a simulator allowed us to train with
various objects by loading a 3D CAD model of the target objects. By diversifying
pose and capturing altitude, multiple scenes of objects were collected, as shown
in the sample scene in \figref{fig:overview}.

The UWSim provides a diverse choice of underwater sensor modalities, and users
may be able to implement their own sensor module within the simulator
\cite{dgwon-2017-urai}. Developing a detailed sensor module for a specific
perceptual sensor would require careful design of the modules in the simulator
based on the detailed understanding of the sensor and the environment. However,
we found that generating a photo-realistic image from a rather simple depth
image may provide a feasible solution. Specifically, we proposed using the style
transfer to generate realistic-enough synthetic sonar images for training, and
the simulator only needed to provide a basic representation of the scene for
style transfer to be applied.

As depicted in \figref{fig:overview}, using the captured depth image from the
simulator, we applied a colormap in \cite{dgwon-2017-urai} added by white noise.
Then the images were normalized and prepared as a base image before entering the
style transfer phase.

\subsection{Image Synthesizing}

Given this base image, we adopted the StyleBankNet \cite{stylebank} to
synthesize the noise characteristics of sonar images acquired in various
underwater environments, such as water tank and sea. This network simultaneously
learns multiple target styles using an encoder ($\mathcal{E}$), decoder
($\mathcal{D}$), and StyleBank ($\mathcal{K}$), which consists of multiple sets
of style filters (\figref{fig:stylebank_architecture}). Each set of style
filters represents the style of one underwater environment.

\begin{figure}[!h]
  \centering
  \subfigure[StyleBankNet architecture]{%
    \includegraphics[width = 0.95\columnwidth]{figs/stylebank_architecture}
    \label{fig:stylebanknet_architecture_overview}
  }
	\subfigure[Detailed architecture of encoder, decoder, and style filters in StyleBank]{
	  \label{tab:stylebank_architecture_detailed}
			\centering
	    \scriptsize
      \vspace{10px}
      \begin{tabular}{c | c}
      Name & Architecture  \\
      \hline\hline
      Encoder & $c9s2-32, IN, C64, IN, C128, IN, C256, IN$ \\
      Decoder & $TC128, IN, TC64, IN, C32, IN, tc9s2-3$ \\
      $i^{\text{th}}$ style filters in SB & $C256, IN, C256, IN$
      \vspace{5px}
      \end{tabular}
	    \normalsize
	}

\caption{Network architecture of StyleBankNet. It consists of three modules: encoder, decoder and StyleBank (SB).  $c9s2-32$: 9$\times$9 convolutional block with 32 filters and stride 2, $IN$: Instance Normalization, $Cn$: 3$\times$3 convolutional blocks with $n$ filters and stride 1, $TCn$: 3$\times$3 transposed convolutional blocks with $n$ filters with stride 1, and $tc9s2-3$: 9$\times$9 transposed convolutional block with 3 filters and stride 2.}
\label{fig:stylebank_architecture}
\end{figure}

\subsubsection{Losses}

There are two different branches in the StyleBankNet: auto-encoder branch
($\mathcal{E} \rightarrow \mathcal{D}$) and stylizing branch ($\mathcal{E}
\rightarrow \mathcal{K} \rightarrow \mathcal{D}$). The StyleBankNet uses these
branches to decouple styles and contents of sonar images. The auto-encoder
branch uses a reconstruction loss to train the encoder and decoder for
generating an output image that is as close as possible to an input image.
\begin{align}
\mathcal{L}_{\mathcal{R} } (\it{C}, \it{O}  ) = || \it{O} - \it{C} ||^{2},
\label{eqn:reconstruction_loss}
\end{align}
where $C$ and $O$ is input and output images, respectively.  The stylizing
branch uses a {\it{perceptual loss}}  to jointly train the the encoder, decoder,
and StyleBank \cite{jjohnson-2016-eccv}:
\begin{align}
\nonumber
\mathcal{L}_{\mathcal{P} } (\it{C}, \it{S}_{i}, \it{O}_{i} ) &= \alpha \cdot \mathcal{L}_{c} (\it{O}_{i}, \it{C_i} )
\nonumber
\\ & ~ + \beta \cdot \mathcal{L}_{s} (\it{O}_{i} , \it{S}_{i})
\nonumber
\\ & ~ + \gamma \cdot \mathcal{L}_{reg} (\it{O}_{i} )
\nonumber
\\ & ~ + \delta \cdot \mathcal{L}_{atki} (\it{O}_{i} , \it{S}_{i}),
\label{eqn:perceptual_loss}
\end{align}
where $\it{S}_{i}$ is one of images with $i^{\text{th}}$ style. $\mathcal{L}_{c}
(\it{O}_{i}, \it{C}_{i} )$, $\mathcal{L}_{s} (\it{O}_{i} , \it{S}_{i})$,
$\mathcal{L}_{reg} (\it{O}_{i} )$ and $\mathcal{L}_{atki} (\it{O}_{i} ,
\it{S}_{i})$ are feature reconstruction loss, style reconstruction loss,
regularization loss, and \ac{ATKI} loss, respectively \cite{jjohnson-2016-eccv}.
In this equation, the style reconstruction loss measures the difference between
output and style images in style such as colors, textures, patterns, etc:
\begin{align}
\mathcal{L}_{s} (\it{O}_{i}, \it{S}_{i} )= \sum_{l \in \{l_{s} \} } || G(F^{l}(\it{O}_{i}) ) - G(F^{l}(\it{S}_{i} ) ) ||^{2},
\label{eqn:style_loss}
\end{align}
where $F^{l}$ and G are feature map and Gram matrix computed from $l^{\text{th}}$ layer
of VGG-16 layers $l_{s}$, respectively.

The last term $\mathcal{L}_{atki} (\it{O}_{i} , \it{S}_{i})$ is a \ac{ATKI}
loss. We added it to the original perceptual loss \cite{jjohnson-2016-eccv}, as
the StyleBankNet is able to learn the unique intensity distribution
characteristics of sonar images. In a sonar image, some parts appear much
brighter than other parts. These brighter parts may contain objects of interest
because sonar signals are reflected by objects and floors.  Although the
intensity distribution of the brighter parts is much different from the global
intensity distribution, it is likely to be overlooked when computing the style
reconstruction loss to train the StyleBankNet because the brighter parts are
usually much smaller than other parts. As a result, the characteristics of the
brighter parts are not learned appropriately. Motivated by the \ac{ATKI}
\cite{yfan-2017-nips}, the \ac{ATKI} loss is used to measure the intensity
distributions of brighter parts in output and style images.
\begin{align}
\mathcal{L}_{atki} (\it{O}_{i}, \it{S}_{i} )= \frac{1}{k} \sum^{k}_{j=1} || \it{O_{G} }_{i}^{[j]}  - \it{S_{G} }_{i}^{[j]} ||^{2},
\label{eqn:atki_loss}
\end{align}
where $\it{O_{G} }_{i}^{[j]}$ and $\it{S_{G} }_{i}^{[j]}$ are the $j^{\text{th}}$ largest intensity values in grayscale output and style images, respectively. By applying the \ac{ATKI} loss, the unique intensity distributions can be synthesized by the StyleBankNet.

\begin{figure}[!t]
	\centering
	\includegraphics[width = 0.95\columnwidth]{figs/atkiloss}

  \caption{Effect of \ac{ATKI}. By considering additional loss from \ac{ATKI}, the target object is styled more strongly.}

	\label{fig:atkiloss}
\end{figure}

The effect of \ac{ATKI} loss is depicted in \figref{fig:atkiloss}.  When
additionally using the \ac{ATKI} loss, the target object appears more clearly
and brightly than when not using the loss.

\subsubsection{Training}

The dataset for training the StyleBankNet consists of a content set, which is
composed of base images and multiple style sets (e.g., pool and sea).  Each set
contains object-centered 300 images. A single mini-batch consists of randomly
sampled content images and style images with style indices. To better examine
generalized characteristics of sonar images, a pair between base and style
images is not fixed in each iteration.  A (${T+1}$)-step alternative training
strategy is employed to ensure a balanced learning of the encoder, decoder, and
StyleBank using two branches \cite{igoodfellow-2014-nips}. Parameters of the
StyleBankNet is updated in every ${T+1}^{\text{th}}$ iteration using the
auto-encoder branch; otherwise, the styling branch is used.

\section{Application to Object Detection}

\subsection{\ac{CNN} Architecture}

We used the deep learning toolbox including the Faster \ac{R-CNN}
\cite{ren2017faster} model released in Matlab for underwater object detection.
Although the region proposal algorithms such as EdgeBoxes \cite{zitnick2014edge}
or Selective Search \cite{uijlings2013selective} are typically applied, the use
of these techniques becomes the processing bottleneck in the older model
\cite{girshick2015fast}.  Faster \ac{R-CNN} addresses this issue by implementing
the region proposal mechanism using the CNN and thereby making region proposal
in the CNN training and prediction steps. For this \ac{RPN} training, the layers
were basically set up as follows; Input layer ($32\times32\times3$), 1st
Convolution layer ($5\times5\times32$), Relu, MaxPooling ($3\times3$), 2nd
Convolution layer ($3\times3\times64$), Relu, MaxPooling($3\times3$), 3rd
Convolution layer($3\times3\times32$), Relu, MaxPooling($3\times3$),
Fully-connected Layer(200), Relu, Fully-connected Layer(2), Softmax Layer,
Classification layer. In some training cases, the hyper-parameters were slightly
reduced compared to this base model.

\subsection{Training Image Augmentation}

The style transferred image can be directly used for training, and the
synthesized images themselves could be sourced for many sonar imaging
applications. In this application, we propose a synthesizing scheme generally
applicable to various sonar images. Thus, in this augmentation phase, we
converted the images to grayscale and their inverted image in the form of
general one-channel sonar images. Including inverted images is critical for
sonar images because when an object is imaged by sonar, the intensity of the
object may be brighter or darker than the background depending on the relative
material property of the object and environment. To remedy this situation, we
generated two types of images from a single channel synthesized image, as shown
in \figref{fig:overview}.

For deep learning application, ensuring sufficient diversity in training
datasets is meaningful. When capturing data from the simulator, physical
diversity was considered to include various rotation, translation, and scaling.
Additionally, we randomly flipped the captured base images. We applied
variations in scale, rotation, and translation for the training dataset.

redpajama_set_name: RedPajamaArXiv


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Rosacea is a chronic inflammatory skin condition which primarily affects the face. Initially, it presents as facial redness. Then gradually red pimples and pus-filled bumps will appear. Rosacea may appear on the nose and cheeks where small blood vessels become visible and the condition is called rhinophyma. Rosacea may also affect the eyes and cause conjunctivitis and/or blepharitis.
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Rosacea is more common in adults, women and fair skinned people.
The most common symptom is frequent blushing or flushing (redness) of the skin that affects the central part of the face –chin, cheeks, nose and central forehead. Small red pimples and pus-filled bumps known as papules or pustules may appear. Small blood vessels across the nose and cheeks are dilated. The area of the skin may be swollen warm and red. Other symptoms may include burning sensation, fluid under the skin (oedema), and dry appearance of the skin.
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At One Skin Clinic, you will benefit from the vast experience and expertise of Dr. Najia Shaikh who will perform a complete evaluation, identify possible triggers and prescribe treatment for the rosacea to bring back the health and vitality to your skin.


I am a huge fan of (seed), the super affordable, grapeseed-enriched line of natural skin care. I've told you before about their awesome Fragrance Free Healthy Hand Cream and now I have to tell you about this fabulous new oil.
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Dogs are welcome in all of Birmingham's parks and open spaces, but there are some simple requirements and restrictions as outlined below.
Please clear up immediately if your dog fouls. This is a legal requirement under the Fouling of Land by Dogs Order 2017.
You can use any litter bin or special dog bin to deposit the waste.
Ensure your dog wears a collar with your name and address on. This is a legal requirement, and can help return lost dogs.
Keep your dog under control. If your dog is causing a nuisance or a threat to other park users, including dogs or any wildlife on site, you will be asked to put your dog on a lead. Failure to comply with this request is an offence.
Keep your dog in sight at all times.
Dogs by law are not permitted in clearly demarcated children's play areas. Restrictions might also apply to other designated areas such as tennis courts, ball courts, bowling greens etc. Please observe any signage.
No more than four dogs may be walked by any one person.
If your dog causes damage to park equipment, you will be reported to the police.
Any dog owner found not complying with the above may be issued with a Fixed Penalty Notice.


text:
using namespace arua;
using namespace std;

Context::Context(Ptr<Context> parent)
		: Value(ValueType::CONTEXT)
		, parent(parent) {
}

Ptr<Value> Context::resolve(string name, Ptr<Value> requester) throw() {
	// determine if the request is 'privileged' - if it's privileged, it means
	// that it is able to access privatized members in this context and above contexts.
	bool privileged = false;
	Ptr<Context> cur;
	if (requester) {
		cur = requester->getValueType() == ValueType::CONTEXT
			? requester.as<Context>()
			: requester->getContext();

		if (!cur) {
			cerr << "aruab: warning: resolve was given a value with no context. Is the parser feeling okay?" << endl;
		}
	}

	for (; cur; cur = cur->parent) {
		if (cur.get() == this) {
			privileged = true;
			break;
		}
	}

	if (!privileged && !this->publicNames.count(name)) {
		// it doesn't even matter if the thing really exists as a private member
		// or not; if it's not privileged and not publicized, then return nullptr.
		//
		// in the future, this will be able to return some sort of reason as to
		// why it was denied, in order to get better error messages.
		return nullptr;
	}

	{
		auto itr = this->types.find(name);
		if (itr != this->types.end()) {
			return itr->second.as<Value>();
		}
	}

	return parent ? parent->resolve(name) : nullptr;
}

void Context::addType(Ptr<Type> type, bool pub) throw() {
	if (type->getName().empty()) {
		cerr << "warning: Context::addType() was passed a type with no name" << endl;
		return;
	}

	this->types[type->getName()] = type;
	if (pub) {
		this->publicNames.insert(type->getName());
	}
}

redpajama_set_name: RedPajamaGithub


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text:
Despite Challenges, Nigerian Bitcoin Miners Are Poised To Profit Today
By Antoni Aug 11, 2022
This is an opinion editorial by Emeka Ugbah, host of the "Bitcoin This Blockchain That" podcast based in Rwanda.
Imagine that you are nursing a cup of caffé mocha in a coffee shop in Lower Manhattan. Then you overhear this African-looking fellow — who, from his countenance, confident demeanor and high-energy voice, strikes you as a Nigerian — saying that he is a Bitcoin miner. You would probably try to get a better glimpse of the fellow, because you immediately think that he is one of them — those scammers who slide into DMs, promising mouth-watering ROIs if you invest in their Bitcoin mining schemes. He can't possibly be legit.
Yes, I would probably think the same if I were in the booth with you, even though I am Nigerian.
We all know that Nigeria is considered the African nation with the highest adoption rate of bitcoin and cryptocurrencies — a story that I love to tell. However, having Bitcoin's proof-of-work consensus mechanism in mind and how dependent on power it is, as well as the fact that Nigeria isn't a dependable choice when it comes to cheap or stable electricity, it is hard to imagine that any significant hash power could emanate from the country.
So, in truth, you would probably be correct in thinking the fellow isn't legit. But should you?
Photo by Gustavo Fring
Obstacles For Mining Bitcoin In Nigeria
Lack Of Reliable Power
Nigeria is the 14th largest African country by landmass and its most populous, with a population of more than 200 million people, of which approximately 52% live in urban areas. It is disconcerting to know that these people have to depend on a depressing 4,000 megawatts that the nation's power grid manages to generate.
I used the adjective "depressing" because that is the actual state of the power situation in the country. Homes and businesses — from small scale to larger establishments — are often forced to secure alternative power sources or risk staying in the dark and ceasing operations until power returns. These alternative power sources, from petrol- and diesel-powered generators to solar, are supposed to serve auxiliary power in typical scenarios. But in the case of Nigeria, they have now become the main sources of power for many in the country.
So, it's almost inconceivable that Bitcoin mining can be successful in the country. But it is — Nigeria does indeed contribute to Bitcoin's global hash rate, and I'll tell you how as we progress with this conversation.
Photo by Kindel Media
Unclear, Borderline-Hostile, Regulatory Landscape
As with nation states, Nigeria's stance on Bitcoin and cryptocurrencies has been somewhat combative.
We vividly recall that fateful Friday, February 5, 2021, when the apex bank, the Central Bank of Nigeria (CBN) released a statement warning all financial services providers to discontinue any bitcoin- or cryptocurrency-related transactions. And the warning didn't end there: banks were also directed to freeze the accounts belonging to any individual or entity that had previously utilized the bank's infrastructure as an on- or off-ramp into and out of cryptocurrency exchanges.
Manyaccounts were subsequently frozen as per the directive, which forced Nigerians to increasingly adopt the peer-to-peer system of value exchange which was, after all, one of the reasons behind the creation of Bitcoin in the first place: the transfer of value from person to person, without the need of a trusted third party.
Despite the fact that Nigeria's Securities and Exchange Commission (SEC) recently issued some regulations that seem to be a little less hawkish toward digital assets, things remain largely unclear on what stance regulators will take toward mining, especially because miners (for lack of a better term) "print money."
Central Bank Of Nigeria. Source.
Resilience Amid Adversaries
There are a plethora of things — great things — that Nigerians are known for, although some of the first things that come to mind when many people think of Nigerians can be less than confidence inspiring.
Nevertheless, it is also known by many that a Nigerian can survive, and even thrive, in the harshest conditions. This is evident in the fact that one out of every five people of African origin — found anywhere in the world, no matter how inhospitable the place is — is Nigerian.
As a Nigerian, I can attest to this resilience. We have this innate ability to adjust and adapt to almost any condition, almost anywhere that we find ourselves. At this point in the conversation, the fact that there are Nigerians who have ventured into Bitcoin mining, damning the debilitating odds, shouldn't be too much of a surprise, right?
It is challenging to determine when exactly the first ASIC based in Nigeria connected to the Bitcoin network, because miners there are putting a great deal of effort into staying as off-the-radar as possible. And yes, I know that the hash rate generated from the country might not amount to as much as a blip or a pulse in the network. As a matter of fact, a fairly-debated data source has it that Africa as a whole contributes less than 0.2% of the global Bitcoin hash rate. However, the innovative effort the Nigerian miners are putting into securing the world's most decentralized, censorship-resistant monetary system shouldn't go unnoticed — their stories shouldn't stay untold.
Stories From Nigerian Bitcoin Miners
When I started researching this topic, I was privileged to get in touch with a few individuals who mine Bitcoin in Nigeria. We had long conversations about how they got into it and how they are managing to navigate through the challenges they are constantly faced with. For security reasons, which border around the hostile regulatory landscape I mentioned earlier, they chose to stay anonymous, so we will be referring to them by pseudonyms.
The first individual, let's call him Kola. Relatively new to Bitcoin mining, Kola began his journey in September 2021 with just one unit of the Antminer S9, which he purchased for about $450 to $500 at the time. According to him, he decided to take a stab at mining with that one ASIC but has since then expanded and acquired seven others, all of which are Innosilicon T2THs, all purchased as used miners from China and shipped to Nigeria. Then, so as not to give room for too many questions from the authorities, especially considering the fact that he is importing "money printing" machines, the ASICs were cleared at customs as computer graphics processing units (GPUs)
Kola chose not to divulge his location, however, stated that his operation is situated somewhere in the north-central part of the country, close to one of the nation's hydroelectric power sources. As a result of being close to this major source of power which makes it relatively cheap, Kola spends as little as $15 to $20 monthly on electricity to run his operation. However, seeing as the "Nigerian factor" still comes into play, he said that he gets between 20 to 22 hours of electricity supply per day. Being solely dependent on that one power source means that he is subjected to shutting down operations for between two and four hours each day.
Source: Rebcenter Moscow
Now, here's another interesting bit: Due to the unclear regulatory state of things and to the local power authorities, Kola's mining operation is written off on the books as a facility that houses servers for cloud storage renting services. Interesting, right?
I had a long conversation with Kola and I can go on and on detailing all that we talked about, but this article isn't only about my conversation with him, so I'll wind it up here.
Now, the second individual is a more seasoned Bitcoin pleb, who has been in the space considerably longer than Kola. Let's call him Gbenga. He requested that I keep a lot of what he shared with me about his setup off the record, so I'll be more discreet with specifics here.
With several Antminer S9 ASICs, all purchased as "used," and imported from China, again as computer GPUs, Gbenga's operation is situated in the outskirts of the nation's largest business hub, Lagos. Much like Kola, Gbenga started his mining venture with a much lower number of ASICs than he operates with now, and he has exponentially expanded, seeing as he is a bigger player in the space.
Gbenga's setup is one of the many businesses in the country that depend on alternate power sources for electricity, In his case, a 200 KVA diesel-powered generator. Now, this would, in an ideal situation, not be an advisable venture, considering the fact that the price per liter of diesel in the country goes for a little more than $1 at the time of writing, and his operation runs through 300 liters per day on the average or, on days where it gets power supply from the grid, for about half of the day. But Gbenga has affiliations with a few big-time individuals in the oil and gas industry, so he gets diesel at a much cheaper rate than is obtainable in the open market.
According to Gbenga, his major challenge has been the constant need to maintain and repair the ASICs as they often develop power-related issues due to the unstable supply of electricity. He, however, has a team of skilled technicians and electricians, recruited from the pool of local talents, that didn't initially have experience working on ASICs, but as a result of working in Bitcoin mining and with Gbenga, have gained the requisite skills.
Bitcoin Mining's Future In Nigeria
There are so many other Bitcoin miners in Nigeria. A lot of them weren't enthusiastic to talk to me because they are worried about their safety — for good reason, too. However, that doesn't mean that there aren't big players already in or researching ways to get into the mining space.
We may yet be a long way away from Africa contributing to a large portion of the global Bitcoin hash rate, but with individuals like Gbenga and Kola, as well as all the others within Nigeria and Africa as a whole, along with the ever growing adoption of Bitcoin in the continent, it may not be too far fetched that the future may not be so distant.
The greatest challenge facing it all is funding. We all know how difficult it is to get loans in the traditional financial space, particularly in Nigeria — you have to know someone who knows someone. But the playground will definitely look different the minute that corporate entities and big businesses — from the telecom space, to banking and restaurant chains, which have tons of stranded electricity because they have to keep their facilities running round the clock — realize what Bitcoin is. It incentivises the use of stranded electricity, converts that with the proof-of-work protocol into a store-of-value asset with a deflationary supply, and yields asymmetric returns.
This is a guest post by Emeka Ugbah. Opinions expressed are entirely their own and do not necessarily reflect those of BTC Inc or Bitcoin Magazine.
All Eyes Are On The Consumer Price Index Today
LFG = Landfill Gas: Bitcoin Mining Can Turn Gas From Garbage Into Money Today

redpajama_set_name: RedPajamaCommonCrawl


Heartbit is a decentralized blockchain based electrocardiography technology which strives to use Artificial intelligence in its ECG analyzing devices. A secure Heartbit platform will be offered with the use of distributed ledger technology which will help to search for the most suitable agreement for the health insurance scheme. The health related important information can be recorded and monitor easily with the system and products offered by Heartbit. With the aim to improve the health condition globally, Heartbit is going to start its worldwide health care program. Sometime an unmentioned irregularity can lead to severe heart problem. So, the consumers are able to track the heart beat in all states which will save them from the error of irregularities in their ECG.
What is the problem in the market?
Due to the increase in stressful office work, the people get less time to get involve in physical activities. According to a recent report, more than 17 million people die from CVD each year all over the world. Lots of dollars are spend to treat the heart disease but it is creating a burden in life. Although with the evolution of technology better medicines and gadgets are available in the market but still they are not efficient enough to deal with the whole heart condition perfectly. The expensive treatment consumes both sources and time of a person. The wearable heart analyzing devices are released in the market but the geographical and payments restrictions causing the main barrier for the regular use of these products.
What is the solution provided by Heartbit?
Record and analysis: The recorded data is analyzed for the selection of relevant monitoring and the results are appeared on the dashboard of the consumer with the advice and treatment strategies.
Daily alerts: To maintain and develop daily physical activity routine, alerts and warning are generated regularly. The training routine is set after the regular monitoring of the consumer daily life activities via machine learning activity of the algorithm. It will decrease the tension to visit the doctor for a proper medical checkup or ECG.
Access to premium level: The premium level of services will allow the users to get benefits from the consultation with medical experts, physical trainers and nutritionists. The heartbit token will be used to initiate the reward program.
What are the main features of this platform?
24 hours recording: The ECG data is recorded and analyze continuously all the time from sleep to morning walk and at work.
Machine learning Technology and AI: To monitor the physical activity of the Heartbit device user, the IBM Watson based machine learning technology is used with AI.
Data security: The user heart state information remains secured with the use of blockchain technology and Heartbit is giving the data sharing ownership to the users. They can share their ECG information and reports directly to any person anytime.
Sensors: The environmental sensors are used in the Heartbit gadgets to detect the harmful gases. It is also help to determine the suitable environment condition and place for physical activity.
Personal plans: On the basis of your selected goal and current heart status, your special training plan is decided.
Monitoring: Just feel free and participate in your sports without any health care tension. To detect, monitor and notify the heart beat problem, a special customized algorithm is being developed. The electrical impulses between body and heart are continuously measured to reduce the risk of stroke or heart problem.
Precise and accurate: To get a clear algorithm signal, the extra noises are avoided and filtered with the combination of software with hardware.
Heartbit app: To keep a track of your previous reports and to get the best training tools, use the Heartbit app and create your own social network. The user friendly interface and mobile app will decrease the cost and social burden.
Which token is used by this platform?
Heartbit is using a utility token HBIT which is based upon ERC20 standard. This token is best to buy the services and products on the platform. The users can get the HBIT token after participating in the Heartbit rewards scheme where the users are rewarded for their consistent physical activity and better performance. The HBIT token is also used to get access to medical information by the research institutes.
Heartbit strives to revolutionized the traditional heart monitoring ECG technique with the daily fitness checker. With the combination of special heart device, power bank, app and member zone, Heartbit is going to offer special training advice with real time analysis. In near future this platform will become the high standard ecosystem with best doctors and advance heart monitoring devices. If you want to know any further things about Heartbit or its ICO then visit the main Heartbit site for more details.


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PubMed:Prediction of the key binding site of odorant-binding protein of Holotrichia oblita Faldermann (Coleoptera: Scarabaeida).
PubMed:Effects of the novel poly(methyl methacrylate) (PMMA)-encapsulated organic ultraviolet (UV) filters on the UV absorbance and in vitro sun protection factor (SPF).
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text:
alter table stream_definitions add column description varchar(255);

alter table stream_definitions add column original_definition text;

alter table task_definitions add column description varchar(255);

CREATE TABLE task_execution_metadata (
  id int8 NOT NULL,
  task_execution_id int8 NOT NULL,
  task_execution_manifest TEXT,
  primary key (id),
  CONSTRAINT TASK_METADATA_FK FOREIGN KEY (task_execution_id)
  REFERENCES TASK_EXECUTION(TASK_EXECUTION_ID)
);

CREATE SEQUENCE task_execution_metadata_seq MAXVALUE 9223372036854775807 NO CYCLE;


20 years ago today, after some time hosting a personal running page on the UW-Stout student pages, I registered the hillrunner.com domain and HillRunner.com was born. It's hard to believe it's been 20 years.
I just wanted to take a moment today, on HillRunner.com's 20th birthday to thank all of you. Whether your first visit was 20 years ago today or your first visit is today, I appreciate every one of you and am thrilled that you have found your way here and can be a part of this great community.
HillRunner.com has given me so much over the past 20 years and seen so many changes as what I aim to accomplish through it has evolved. Two things have always stayed the same, though.
First, the underlying goal of what I aim to accomplish through HillRunner.com always has been and always will be to give back to the running community that has given me so much.
Second, the great people who have become a part of the HillRunner.com community humble me every day. I don't even know what to say beyond that.
The earliest archive: October 9, 1999.
In September 2002, a college teammate of mine tragically passed away while running a half marathon. I quickly added a tribute that is still on the HillRunner.com homepage today.
HillRunner.com at 5 years old (after covering the Olympic Trials the day before turning 5).
HillRunner.com at 10 years old.
HillRunner.com at 15 years old.
And, for those looking back at this post some time in the future, the most recent capture.
As for what I've been doing to celebrate 20 years, to be honest, I don't have any grand reveal.
What I have been working on is a lot of what people in the IT world call technical debt. The biggest issue I wanted to resolve for some time is a clean up of links.
Before I started the cleanup, HillRunner.com had over 10,000 links, most in some state of disrepair after many years of the Internet changing. I've done my best to fix links if possible, either by redirecting them to where pages have moved or by making heavy use of the Wayback Machine where pages have been removed and redirects couldn't be found. When neither of those options was available, I had no choice but to remove the links. If you see something that looks like a link crossed out, this is a link that has been removed. You can hover over it to see where the link once pointed to but the link no longer worked and there was no replacement available so I removed the link.
After these changes, there are still almost 9,000 links on HillRunner.com and the good news now is that every single link will be checked weekly. I can happily say that the large volume of broken links that formerly existed has become a thing of the past.
I've also been working on making the forums more user friendly. I would love to see discussions return there. I'll be doing what I can to start some topics and I hope some of you will also. The forums were once a very active and fun part of the site, I hope we can get some of that going again.
Once again, thank you all for making the first 20 years of HillRunner.com such a great experience. Hopefully the next 20 years will be even better!


Upton Gardens, an 842 unit mixed-tenure development of private sale and affordable housing has been launched on the site of West Ham's former Upton Park stadium in East London. Continuing our work with the London Borough of Newham, we will be providing consultancy on pricing, allocation, sales and marketing for the first 113 affordable units to come onto the market.
The development, which is part of wider regeneration of the Newham area, includes 66 properties which have been allocated for shared ownership, with the remaining 47 available under affordable rent. Affordable rent units will be prioritised for key workers who live or work in the Newham Borough. The unique architecture of the buildings mirrors the layout of the former football ground which was West Ham's stadium for over 110 years.
Every home is specified to a high quality standard with a modern and stylish decor and a private outdoor space, as well as a range of onsite amenities such as landscaped gardens, a concierge service and a residents' fitness studio.
Prices begin at £325,000 for the one-bedroom properties, rising to £399,000 for the two-bedroom properties and £490,000 for the three-bedroom properties.
The development is situated in an area teeming with local history and culture. Just a quick stroll away is the vibrant Upton Park High Street which offers an array of varied cuisine options, as well as colourful textile and fresh fruit markets. Also within walking distance are the beautiful green spaces of West Ham Park and Beckton Park and a range of outstanding primary schools, with every shopping need catered for at the nearby Westfield Shopping Centre in Stratford.
The development has excellent public transport connections, just six minutes' walk from Upton Park Underground station and 15 minutes on the tube to central London.
Visit the website for more information on this stunning development.


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Clean This! An Office Cleaners' Strike is Just What Occupy Wall Street Needs
Ari Paul Dec 21, 2011
In the post-champagne haze of the first business days of 2012 the white collar workforce of this great city might just find itself in a messy situation.
The 22,000 office cleaners and commercial building workers represented by Local 32BJ of the Service Employees International Union in New York have authorized a strike if a new deal with the Realty Advisory Board (RAB) isn't struck by midnight Dec. 31. A strike could directly affect 1,500 commercial office buildings, including Rockefeller Center, the Met Life Building and the Time Warner Center. While the union has a long history of issuing strike threats to the RAB and then reaching a deal at the 11th hour, this time is not an idle threat. The givebacks the landlords have demanded from the workers who clean up after them are simply too egregious for the membership to ignore, according to sources within the union.
To begin with, the RAB, which represents the city's large building owners, demanded that a lower wage tier be created for new workers, a move that would create hostility between rookies and old-timers, as well as sow the seeds for a new workforce that would be inclined to decertify the union a few years down the road — not to mention the fact that it would make it impossible to live in the city on a building cleaner's wage.
The top pay rate for this majority immigrant workforce is $47,000 a year, according to the union. Enma Mehmedovic, who cleans the Sony Building in Midtown Manhattan, said in a statement, "$47,000 a year is not a lot of money in New York City … I'm raising my two teenage children by myself. It takes two paychecks just to be able to buy my kids' clothes and shoes."
Union President Mike Fishman said in a statement, "These workers live in the city with the highest cost of living in the country, and the real estate market here is the most profitable in the country. Their wages afford them only a lower-middle-class standard of living."
The second issue is that the RAB refused to allow members' payments to the union's political action committee to be deducted automatically from their paychecks, asserting that employers should not have to aid an organization that is actively working against their interests.
As Occupy Wall Street, now homeless after the violent eviction by police in November, searches for a way to move forward, the imagery and timing of this possible work stoppage is just too perfect. For starters, the union said in a statement, "the $20 billion Manhattan commercial real estate industry has just experienced its busiest third quarter in three years — with sales activity reaching $6.3 billion, according to Crain's." On top of that, many of the occupants of these buildings are financial houses, corporate law firms and insurance companies. They are buzzing with people in the 1 percent and their apparatchiks.
While the rank and file of Local 32BJ would be marching for their contract demands, they would be emboldened if OWS were on the streets not just picketing, but helping in the union's disruption of the 1 percent's work life.
After all, one tactical goal of OWS has been to disrupt the system to the point that it makes the power brokers pay attention. The strike, which would leave toilets and floors sullied for the stock brokers and associate attorneys, would certainly have that effect. So would having hordes of unemployed and under-employed people blocking building entrances and occupying lobbies.
It would also bring the issue of good jobs to the forefront of the national debate about inequality. Far too often we've heard taunts such as "Shut up and get a job." The office cleaners want jobs, ones with good pay that employers can afford but are resisting. The onus will be on the RAB and its supporters to explain why they so steadfastly oppose their workers being allowed to earn an honest living.
Informal talks between OWS supporters and SEIU 32BJ organizers are already under way. More needs to happen, and while OWS supporters might wish for a general strike to accompany this movement, that is far off in the distance, and something like this is far more real — and just as disruptive.


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Home News Briefs Orhan Holding Names Larry Denton As President of Nobel Automotive Group
Orhan Holding Names Larry Denton As President of Nobel Automotive Group
Orhan Holding AS of Turkey names Larry A. Denton president of its wholly owned Nobel Automotive Group subsidiary. Denton will direct all strategic and growth initiatives. Before joining Nobel Automotive, he founded Denton Consulting, a firm that provides operational and strategic advisory services to private equity funds and transportation industry businesses.
Orhan Holding, A.S. announced today it has appointed Lawrence (Larry) A. Denton as president of Nobel Automotive Group. In his role as president, Larry will direct all strategic and growth initiatives for the global organization. His career spans more than 30 years in the automotive industry with executive leadership roles at OEM and supplier companies.
Orhan Holding Chairman and CEO, Murat Orhan said the addition of Larry to the organization will bolster the company's focus on innovation, growth and lean manufacturing efforts. "We are extremely pleased to announce the appointment of Larry to head our Nobel Automotive Group," M. Orhan said. "His extensive automotive leadership experience with car manufacturers and suppliers will be a tremendous asset as we grow our market position as a leading supplier of fluid transfer products to the global automotive industry."
Prior to joining Nobel, Larry founded Denton Consulting, a firm providing operational and strategic advisory services to private equity funds and transportation industry businesses. Before that, he served as chairman, chief executive officer and president of DURA Automotive Systems, Inc. from 2003-2008. Before joining DURA, Larry was president of Dow Automotive. Prior to joining Dow in 1996, Larry spent 24 years with Ford Motor Company, where he held a variety of senior management positions.
A Sloan Fellow, Larry holds a Master degree in Science from the Massachusetts Institute of Technology (MIT), a Master of Business Administration from the University of Michigan, and a Bachelor degree in Mechanical Engineering from Michigan State University.
About Nobel Automotive
Nobel Automotive is a leading design and production company specializing in fluid transfer products. The company has partnered with major automakers for more than 30 years and has expertise in combining plastic, metal and rubber technologies to create high value solutions. Nobel has eight global manufacturing facilities located in France, Korea, Mexico, Romania, Slovakia, Spain, Turkey and the United States. World-class engineering of its products is provided in regional technical centers in France, Turkey, the United Kingdom and the United States. World headquarters for Nobel is located in Poissy, France and the North American commercial and administrative team is based in Auburn Hills, Michigan. Nobel Automotive is wholly owned by Orhan Holding, A.S. based in Bursa, Turkey.
For more information, visit www.nobelautomotive.com.
SOURCE Orhan Holding, A.S.