text:
\section{Conclusion}

We have proposed a new deep message learning framework for structured CRF prediction.
Learning deep message estimators for the message passing inference reveals a new direction for learning deep structured model.
Learning CNN message estimators is efficient,
which does not involve expensive inference steps for gradient calculation.
The network output dimension for message estimation is the same as the number of classes,
which does not increase with the order of the potentials,
and thus CNN message learning has less network parameters and is more scalable in
the number of classes compared to conventional potential function learning.
Our impressive performance for semantic segmentation demonstrates the effectiveness and usefulness of
the proposed deep message learning.
Our framework is general and can be readily applied to other structured prediction applications.

{
\bibliographystyle{IEEEtran}

\section{Experiments}

We evaluate the proposed CNN message learning method for semantic image segmentation.
We use the publicly available PASCAL VOC 2012 dataset \cite{everingham2010pascal}.
There are 20 object categories and one background category in the dataset.
Its contains $1464$ images in the training set, $1449$ images in the ``val" set and $1456$ images in the test set.
Following the common setting in \cite{BharathECCV2014,ChenPKMY14},
the training set is augmented to $10582$ images by including the extra annotations provided in \cite{HariharanABMM11} for the VOC images.
We use intersection-over-union (IoU) score \cite{everingham2010pascal} to evaluate the segmentation performance.
For the learning and prediction of our method, we only use one message passing iteration.

The recent work in \cite{linpiece} (referred to as ContextDCRF) learns multi-scale fully convolutional CNNs (FCNNs)
for unary and pairwise potential functions to capture contextual information.
We follow this CRF learning method and replace the potential functions by the proposed message estimators.
We consider $2$ types of spatial relations for constructing the pairwise connections of variable nodes.
One is the ``surrounding" spatial relation, for which one node is connected to its surround nodes.
The other one is the ``above/below" spatial relation, for which one node is connected to the nodes that lie above.
For the pairwise connections, the neighborhood size is defined by a range box.
We learn one type of unary message estimator and $3$ types of pairwise message estimators in total.
One type of pairwise message estimator is for the ``surrounding" spatial relations,
and the other two are for the ``above/below" spatial relations.
We formulate one network for one type of message estimator.

We formulate our message estimators as multi-scale FCNNs, for which
we apply a similar network configuration as in \cite{linpiece}.
The network $C^{(1)}$ (see Sec.\ref{sec:message_network} for details) has $6$ convolution blocks and $C^{(2)}$ has 2 fully connected layers (with $K$ output units).
Our networks are initialized using the VGG-16 model \cite{simonyan2014very}.
We train all layers using back-propagation.

We first evaluate our method on the VOC 2012 ``val" set.
We compare with several recent CNN based methods with available results on the ``val" set.
Results are shown in Table \ref{tab:voc12_val}.
Our method achieves the best performance.
As mentioned, ContextDCRF learns CNN based potential functions in CRFs to capture contextual information.
ContextDCRF follows a conventional CRF learning and prediction scheme:
They first learn potentials and then perform inference based on the learned potentials to output final predictions.
The result shows that learning the CNN message estimators is able to achieve similar performance
compared to learning CNN potential functions in CRFs.
Note that here we only use one message passing iteration for the training and prediction,
the inference time cost is almost negligible. Hence our method enables much more efficient inference.

To further improve the performance, we perform simple data augmentation in training.
We generate extra $4$ scales ($[0.8, 0.9, 1.1, 1.2]$) of the training images and their flipped images for training. This result is denoted by ``ours+" in the result table.

We further evaluate our method on the VOC 2012 test set.
We compare with  recent   state-of-the-art CNN methods with competitive performance.
The results are described in Table \ref{tab:voc12_test}.
Since the ground truth labels are not available for the test set,
we evaluate our method through the VOC evaluation server.
We achieve impressive performance on the test set: $73.4$ IoU score\footnote{ \scriptsize
The result link provided by VOC evaluation server:
\url{http://host.robots.ox.ac.uk:8080/anonymous/DBD0SI.html}},
which is the so far best performance compared to the methods that use the same augmented VOC dataset \cite{HariharanABMM11}
(marked as ``VOC extra" in the table). These results validate the effectiveness of direct message learning with CNNs.

We also include the comparison with the methods
which are trained on the much larger COCO dataset (around $133$K training images).
Our performance is comparable with these methods, while our method uses much less number of training images.

The results for each category is shown in Table \ref{tab:voc12_test_details}.
We compare with several recent methods which transfer layers from the same VGG-16 model and use the same training data.
Our method performs the best for most categories.

\begin{table*}[t]
\caption{Category results on the PASCAL VOC 2012 test set. Our method performs the best.}
\centering
\resizebox{1\linewidth}{!}
  {
  \begin{tabular}{ r | c | c c c c c c c c c c c c c c c c c c c c }

method & mean & \rot{aero}  &\rot{bike} &\rot{bird} &\rot{boat} &\rot{bottle}   &\rot{bus}  &\rot{car}  &\rot{cat}  &\rot{chair}    &\rot{cow}  &\rot{table}    &\rot{dog}  &\rot{horse}    &\rot{mbike}    &\rot{person}   &\rot{potted}   &\rot{sheep}    &\rot{sofa} &\rot{train}    &\rot{tv}\\ \hline \hline
DeepLab-CRF \cite{ChenPKMY14} & 66.4 &78.4   &33.1   &  78.2  &55.6   &65.3   &81.3   &75.5   &78.6   &25.3   &69.2   &52.7   &75.2   &69.0   &79.1   &77.6   &54.7   &78.3   &45.1   &73.3   &56.2 \\
DeepLab-MCL \cite{ChenPKMY14} & 71.6  & 84.4 & \bf 54.5 & \bf 81.5 &  63.6 &65.9 &85.1 &79.1 & 83.4 &30.7 & 74.1 & 59.8 &79.0 &76.1 & \bf 83.2 &80.8 & \bf 59.7 & 82.2 & 50.4 &73.1 &63.7 \\
FCN-8s \cite{LongSD14} & 62.2   &76.8   &34.2   &68.9   &49.4   &60.3   &75.3   &74.7   & 77.6   &21.4   &62.5   &46.8   &71.8   &63.9   &76.5   &73.9   &45.2   &72.4   &37.4   &70.9   &55.1 \\
CRF-RNN \cite{zheng2015conditional} & 72.0 & 87.5	&39.0	& 79.7	&\bf 64.2	&68.3	&87.6	&80.8	&\bf 84.4	&30.4	&78.2	&\bf 60.4	&80.5	&77.8	&83.1	&80.6	&59.5	&82.8	&47.8	&78.3	&67.1 \\
ours &\bf 73.4 &\bf 90.1	&38.6	&77.8	&61.3	&\bf 74.3	&\bf 89.0	&\bf 83.4	&83.3	&\bf 36.2	&\bf 80.2	&56.4	&\bf 81.2	&\bf 81.4	& 83.1	&\bf 82.9	& 59.2	&\bf 83.4	&\bf 54.3	&\bf 80.6	&\bf 70.8 \\
\end{tabular}
  }
\label{tab:voc12_test_details}
\end{table*}

\begin{table}[t]
\vspace{3pt}
\caption{Segmentation results on the PASCAL VOC 2012 test set.
Compared to methods that use the same augmented VOC dataset,
our method has the best performance.
}
\centering
\resizebox{.6\linewidth}{!}
  {
  \begin{tabular}{ r | c c c c }

method	&training set	&\# train (approx.)	&IoU test set\\ \hline \hline
ContextDCRF \cite{linpiece}	&VOC extra	&10k	&70.7\\
Zoom-out \cite{MostajabiYS14}	&VOC extra	&10k	&64.4\\
FCN-8s \cite{LongSD14}	&VOC extra	&10k	&62.2\\
SDS \cite{BharathECCV2014}	&VOC extra	&10k	&51.6\\
DeconvNet-CRF \cite{Hyeonwoo15} &VOC extra	&10k	&72.5\\
DeepLab-CRF \cite{ChenPKMY14}	&VOC extra	&10k	&66.4\\
DeepLab-MCL \cite{ChenPKMY14} &VOC extra  &10k  &71.6\\
CRF-RNN \cite{zheng2015conditional}	&VOC extra	&10k	&72.0\\
\hline
DeepLab-CRF \cite{papandreou2015weakly}	&VOC extra + COCO	&133k	&70.4\\
DeepLab-MCL \cite{papandreou2015weakly} &VOC extra + COCO &133k &72.7\\
BoxSup (semi) \cite{Dai2015arXiv}	&VOC extra + COCO	&133k	&71.0\\
CRF-RNN \cite{zheng2015conditional}	&VOC extra + COCO	&133k	&74.7\\ \hline
ours &VOC extra	&10k	& 73.4\\

 \end{tabular}
  }
\label{tab:voc12_test}
\end{table}

\section{Introduction}

Learning deep structured models has attracted considerable research attention
recently. One popular approach to deep structured model is formulating
conditional random fields (CRFs) using deep Convolutional Neural Networks
(CNNs) for the potential functions. This combines the power of CNNs for feature
representation learning and of the ability for CRFs to model complex relations.
The typical approach for the joint learning of CRFs and CNNs
\cite{zheng2015conditional,schwing2015fully,linpiece,liu2014deep,chen2014learning},
is to learn the CNN potential functions by optimizing the CRF objective, e.g.,
maximizing the log-likelihood. The CNN and CRF joint learning has shown
impressive performance for semantic image segmentation.

For the joint learning of CNNs and CRFs,
stochastic gradient descent (SGD) is typically applied for optimizing the conditional likelihood.
This approach requires the marginal inference for calculating the gradient.
For loopy graphs, marginal inference is generally expensive even when using approximate solutions.
Given that learning the CNN potential functions typically requires a large number of gradient iterations,
repeated marginal inference would make the training intractably slow.
Applying an approximate training objective is a solution to avoid repeat inference;
pseudo-likelihood learning \cite{besag1977efficiency} and piecewise learning \cite{SuttonM05,linpiece}
are examples of this kind of approach. In this work,
we advocate a new direction for efficient deep structured model learning.

In conventional CRF approaches, the final prediction is the result of inference based on the learned potentials.
However, our ultimate goal is the final prediction (not the potentials themselves), so we propose to directly optimize the inference procedure for the final prediction.
Our focus here is on the extensively studied message passing based inference algorithms.
As discussed in \cite{ross2011learning},
we can directly learn message estimators to output the required messages in the inference procedure,
rather than learning the potential functions as in conventional CRF learning approaches.
With the learned message estimators, we then obtain the final prediction by performing message passing inference.

Our main contributions are as follows.

\begin{itemize}

  \item

We explore a new direction for efficient deep structured learning.
We propose to {\em
directly learn the messages in message passing inference as training deep CNNs in an end-to-end learning fashion.
}
Message learning does not require any inference step for the gradient calculation,
which allows efficient training.
It can be cast into traditional classification problems.

The network output dimension for message estimation is the same as the number of classes ($K$),
while the network output for general CNN potential functions in CRFs is $K^a$,
which is exponential in the order ($a$) of the potentials
(for example, $a=2$ for pairwise potentials, $a=3$ for triple-cliques, etc).
Hence CNN based message learning has significantly fewer network parameters
and thus is  more scalable, especially in the cases of a large number of classes involved.

\item
  The  number of  iterations in message passing inference can be explicitly taken into consideration in
the message learning procedure.
In this paper, we are particularly interested in learning messages that are able to offer high-quality CRF prediction results
with  only one message passing iteration, making the message passing inference very fast.

\item

We apply our method to semantic image segmentation on the PASCAL VOC 2012 dataset and achieve impressive performance.

\end{itemize}

\subsection{Related work}

Combining the strengths of CNNs and CRFs for segmentation
has been explored in  several recent methods.
Some methods resort to a simple combination of CNN classifiers and CRFs without joint learning.
DeepLab-CRF in \cite{ChenPKMY14} first train fully CNN for pixel classification and applies a dense CRF \cite{krahenbuhl2012efficient} method as a post-processing step.
Later the method in \cite{schwing2015fully} extends DeepLab by jointly learning the dense CRFs and CNNs.
RNN-CRF in \cite{zheng2015conditional} also performs joint learning of CNNs and the dense CRFs.
They implement the mean-field inference as Recurrent Neural Networks which facilitates the end-to-end learning.
These methods usually use CNNs for modelling the unary potentials only.
The work in \cite{linpiece} trains CNNs to model both the unary and pairwise potentials in order
to capture contextual information.
Jointly learning CNNs and CRFs has also been explored for other applications like depth estimation \cite{liu2014deep,liu2015learning}.
The work in \cite{chen2014learning} explores joint training of Markov random fields and deep networks for
predicting words from noisy images and image classification.

All these above-mentioned methods that combine CNNs and CRFs are based upon conventional CRF approaches.
They aim to jointly learn or incorporate pre-trained CNN potential functions,
and then perform inference/prediction using the potentials.
In contrast, our method here directly learns CNN message estimators for the message passing inference,
rather than learning the potentials.

The inference machine proposed in \cite{ross2011learning}
is relevant to our work in that
it has discussed the idea of directly learning message estimators instead of learning potential functions for structured prediction.
They train traditional logistic regressors with hand-crafted features as message estimators.
Motivated by the tremendous  success of CNNs,
we propose to train deep CNNs based message estimators in an end-to-end learning style without using hand-crafted features.
Unlike the approach in \cite{ross2011learning}  which aims to learn {\em variable-to-factor} message estimators,
our proposed method aims to learn the {\em factor-to-variable} message estimators.
Thus we are able to naturally formulate the variable marginals,
which is the ultimate goal for CRF inference, as the training objective (see Sec. \ref{sec:message_train}).
The approach in \cite{Lecun_nips14} jointly learns CNNs and CRFs for pose estimation,
in which they learn the marginal likelihood of body parts but ignore the partition function in the likelihood.
 %
 Message learning is not discussed in this work, and the exact relation between this pose estimation approach and message learning remains unclear.

\section{Learning CRF with CNN potentials}

Before describing our message learning method,
we review the CRF-CNN joint learning approach and discuss limitations.
 %
An input image is denoted by ${\boldsymbol x} \in \mathcal{X}$ and the corresponding labeling mask is denoted by  ${\boldsymbol y} \in \mathcal{Y}$.
The energy function is denoted by $E({\boldsymbol y}, {\boldsymbol x})$, which measures the score of the prediction ${\boldsymbol y}$ given the input image ${\boldsymbol x}$.
We consider the following form of conditional likelihood:
\begin{equation}\label{eq:prob}
\begin{aligned}
{\displaystyle P}({\boldsymbol y}|{\boldsymbol x}) = \frac{1}{Z({\boldsymbol x})} \operatorname*{exp\,} [- E({\boldsymbol y}, {\boldsymbol x})] = \frac{\operatorname*{exp\,} [- E({\boldsymbol y}, {\boldsymbol x})] }{\sum_{{\boldsymbol y}'} \operatorname*{exp\,} [ -E({\boldsymbol y}', {\boldsymbol x}) ]}.
\end{aligned}
\end{equation}
Here $Z$ is the partition function.
The CRF model is decomposed by a factor graph over a set of factors $\mathcal{F}$.
Generally, the energy function is written as a sum of potential functions (factor functions):
\begin{align}
\textstyle
	E({\boldsymbol y}, {\boldsymbol x}) =   \sum_{F \in \mathcal{F}} E_{F}({\boldsymbol y}_F, {\boldsymbol x}_F).
\end{align}
Here $F$ indexes one factor in the factor graph; ${\boldsymbol y}_F$ denotes the variable nodes
 which are connected to the factor $F$;
$E_F$ is the (log-) potential function (factor function).
The potential function can be a unary, pairwise, or high-order potential function.
The recent method in \cite{linpiece} describes
examples of constructing general CNN based unary and pairwise potentials.

Take semantic image segmentation as an example.
To predict the pixel labels of a test image, we can find the mode of the joint label distribution
  by solving the maximum a posteriori (MAP) inference problem: ${\boldsymbol y}^{\star}=\operatorname*{argmax\,}_{{\boldsymbol y}} {\displaystyle P}({\boldsymbol y}|{\boldsymbol x}).$
  %
We can also obtain the final prediction by calculating the label marginal distribution of each variable, which requires to solve a marginal inference problem:
\begin{align} \label{eq:marginal}
\textstyle
\forall p \in \mathcal{N}: \;\; P(y_p | {\boldsymbol x} ) = \sum_{{\boldsymbol y} \backslash y_p} {\displaystyle P}({\boldsymbol y}|{\boldsymbol x}).
\end{align}
Here ${\boldsymbol y} \backslash y_p$ indicates the output variables ${\boldsymbol y}$ excluding $y_p$.
For a general CRF graph with cycles,
the above inference problems is known to be NP-hard,
thus approximate inference algorithms are applied.
Message passing is a type of widely applied algorithms for approximate inference:
loopy belief propagation (BP) \cite{Nowozinstruct}, tree-reweighted message passing \cite{kolmogorov2006convergent}
and mean-field approximation \cite{Nowozinstruct}
are examples of the message passing methods.

CRF-CNN joint learning is to learn CNN potential functions by optimizing the CRF objective,
typically, the negative conditional log-likelihood, which is:
\begin{align}
 - \operatorname*{log\,} {\displaystyle P}({\boldsymbol y} | {\boldsymbol x}; {\boldsymbol \theta}) = E({\boldsymbol y}, {\boldsymbol x}; {\boldsymbol \theta}) + \operatorname*{log\,} Z({\boldsymbol x}; {\boldsymbol \theta}).
\label{eq:Prr}
\end{align}
The energy function $E({\boldsymbol y}, {\boldsymbol x})$ is constructed by CNNs, for which all the network parameters are denoted by ${\boldsymbol \theta}$.
Adding regularization,
 %
 minimizing negative log-likelihood for CRF learning is: %
\begin{align}
\label{eq:crf_learning_org}
\textstyle
\min_{{\boldsymbol \theta}}
\frac{\lambda}{2} \fnorm {\boldsymbol \theta} +
\sum_{i=1}^N [ E({\boldsymbol y}^{(i)}, {\boldsymbol x}^{(i)}; {\boldsymbol \theta}) + \operatorname*{log\,} Z({\boldsymbol x}^{(i)}; {\boldsymbol \theta}) ].
\end{align}
Here ${\boldsymbol x}^{(i)}$, ${\boldsymbol y}^{(i)}$ denote the $i$-th training image and its segmentation mask; $N$ is the number of training images; $\lambda$ is the weight decay parameter.
We can apply stochastic gradient descent (SGD) to optimize the above problem for learning ${\boldsymbol \theta}$.
The energy function $E ({\boldsymbol y}, {\boldsymbol x}; {\boldsymbol \theta})$ is constructed from CNNs, and its
gradient ${\nabla}_{\boldsymbol \theta} E ({\boldsymbol y}, {\boldsymbol x}; {\boldsymbol \theta})$ can be easily computed
by applying the chain rule
as in conventional CNNs.
However, the partition function $Z$ brings difficulties for optimization.  Its gradient is:
\begin{align}
{\nabla}_{\boldsymbol \theta}  \operatorname*{log\,}  Z({\boldsymbol x} ; {\boldsymbol \theta})  %
	 %
	 = &  \sum_{\boldsymbol y} \frac{\operatorname*{exp\,}[-E({\boldsymbol y}, {\boldsymbol x}; {\boldsymbol \theta})]} {\sum_{{\boldsymbol y}'} \operatorname*{exp\,}[-E({\boldsymbol y}', {\boldsymbol x}; {\boldsymbol \theta})]} {\nabla}_{\boldsymbol \theta} [ - E ({\boldsymbol y}, {\boldsymbol x}; {\boldsymbol \theta})] \notag \\
	 %
   = &  - {\mathbb E}_{{\boldsymbol y} \sim {\displaystyle P}({\boldsymbol y} | {\boldsymbol x} ; {\boldsymbol \theta})} {\nabla}_{\boldsymbol \theta} E ({\boldsymbol y}, {\boldsymbol x}; {\boldsymbol \theta}).
\end{align}

Direct calculation of the above gradients is computationally infeasible for  general CRF graphs.
Usually it is necessary to perform approximate marginal inference to calculate the gradients at each SGD iteration
\cite{Nowozinstruct}.
However, repeated marginal inference can be extremely expensive, as discussed in \cite{linpiece}.
CNN training usually requires a huge number of SGD iterations (hundreds of thousands, or even millions),
hence this inference based learning approach is in general not scalable or even infeasible.

\section{Learning CNN message estimators}

In conventional CRF approaches,
the potential functions are first learned, and then inference is
performed based on the learned potential functions in order to generate the final prediction.
In contrast, our approach directly optimizes the inference procedure for final prediction.
We propose to learn CNN estimators to directly output the required intermediate values in an inference algorithm.

Here
we focus on the message passing based inference algorithm which has been extensively studied and widely applied.
In the CRF prediction procedure, the ``message" vectors are recursively calculated based on the learned potentials.
We propose to construct and learn CNNs to  directly estimate these messages in the message passing procedure,
rather than learning the potential functions.
In particular, we directly learn factor-to-variable message estimators.
Our message learning framework is general and can accommodate all message passing based algorithms
such as loopy belief propagation (BP) \cite{Nowozinstruct}, mean-field approximation \cite{Nowozinstruct} and their variants.
Here we discuss using loopy BP
for calculating variable marginals.
As shown by Yedidia et al. \cite{yedidia2000generalized}, loopy BP has a close relation with Bethe free energy approximation.

Typically, the message is a $K$-dimensional vector ($K$ is the number of classes) which encodes the information of the label distribution.
For each variable-factor connection,
we need to recursively compute the variable-to-factor message: ${\boldsymbol \beta}_{p \to F} \in \mathbb{R}^K$, and the factor-to-variable message: ${\boldsymbol \beta}_{F \to p} \in \mathbb{R}^K$.
For numerical reasons, the $\operatorname*{log\,}$ operation is applied to the marginals before deriving message passing algorithms.
The unnormalized variable-to-factor message is computed as:
\begin{align}
\textstyle
	\bar {\boldsymbol \beta}_{p \to F} (y_p)  = \sum_{F' \in \mathcal{F}_p \backslash F}  {\boldsymbol \beta}_{F' \to p} (y_p).
\end{align}
Here $\mathcal{F}_p$ is a set of factors connected to the variable $p$; $\mathcal{F}_p \backslash F$ is the set of factors $\mathcal{F}_p$ excluding the factor $F$.
For loopy graph, the variable-to-factor message is normalized in each iteration:
\begin{align}
	\label{eq:message_pf}
	{\boldsymbol \beta}_{p \to F} (y_p) & =  \operatorname*{log\,} \frac{ \operatorname*{exp\,} \bar {\boldsymbol \beta}_{p \to F} (y_p)}
	{\sum_{y'_p} \operatorname*{exp\,} \bar {\boldsymbol \beta}_{p \to F} (y'_p)}
	%
	%
\end{align}
The factor-to-variable message is computed as:
\begin{align}
	%
	%
	{\boldsymbol \beta}_{F \to p} (y_p) & = \operatorname*{log\,} \sum_{
	{\boldsymbol y}'_{F} \backslash y'_p, y'_p=y_p }
	\operatorname*{exp\,} \biggr[ - E_{F}({\boldsymbol y}'_F)
	+ \sum_{ q \in \mathcal{N}_{F} \backslash p }  {\boldsymbol \beta}_{q \to F} (y'_q) \biggr].
\end{align}
Here $\mathcal{N}_F$ is a set of variables connected to the factor $F$; $\mathcal{N}_F \backslash p$ is the set of variables $\mathcal{N}_F$ excluding the variable $p$.
Once we get all the factor-to-variable messages of one variable node,
we are able to calculate the marginal distribution (beliefs) of that variable:
\begin{align}
	{\displaystyle P}(y_p | {\boldsymbol x} ) = \sum_{{\boldsymbol y} \backslash y_p} {\displaystyle P}({\boldsymbol y}|{\boldsymbol x}) =
	\frac{1}{Z_p} \operatorname*{exp\,} \biggr[ \sum_{F \in \mathcal{F}_p} {\boldsymbol \beta}_{F \to p} (y_p) \biggr],
\end{align}
in which $Z_p$ is a normalizer: $Z_p = \sum_{y_p} \operatorname*{exp\,} [ \sum_{F \in \mathcal{F}_p} {\boldsymbol \beta}_{F \to p} (y_p) ].$

\subsection{CNN message estimators}

The calculation of factor-to-variable message ${\boldsymbol \beta}_{F \to p}$ depends on the variable-to-factor messages ${\boldsymbol \beta}_{p \to F}$.
Substituting the definition of ${\boldsymbol \beta}_{p \to F}$ in \eqref{eq:message_pf},
 ${\boldsymbol \beta}_{F \to p}$ can be re-written as:
\begin{align}
	\label{eq:message_fp_final}
	{\boldsymbol \beta}_{F \to p} (y_p)
	& = \operatorname*{log\,} \sum_{{\boldsymbol y}'_{F} \backslash y'_p, y'_p=y_p } \operatorname*{exp\,}
	\biggr \{
	- E_{F}({\boldsymbol y}'_F) +
	\sum_{q \in \mathcal{N}_{F} \backslash p }
	\biggr[
	\operatorname*{log\,} \frac{ \operatorname*{exp\,} \bar {\boldsymbol \beta}_{q \to F} (y'_q)}
	{\sum_{y''_q} \operatorname*{exp\,} \bar {\boldsymbol \beta}_{q \to F} ( y''_q)}
	\biggr]
	 \biggr \} \notag \\
	 & = \operatorname*{log\,} \sum_{{\boldsymbol y}'_{F} \backslash y'_q, y'_p=y_p } \operatorname*{exp\,} \biggr \{ - E_{F}({\boldsymbol y}'_F) +
	 \sum_{q \in \mathcal{N}_{F} \backslash p}
	\biggr[
	\operatorname*{log\,} \frac{ \operatorname*{exp\,} \sum_{F' \in \mathcal{F}_q \backslash F}  {\boldsymbol \beta}_{F' \to q} (y'_q) }
	{\sum_{y''_q} \operatorname*{exp\,} \sum_{F' \in \mathcal{F}_q \backslash F}  {\boldsymbol \beta}_{F' \to q} (y''_q) }
	\biggr]
	\biggr \}
\end{align}
Here $q$ denotes the variable node which is connected to the node $p$ by the factor $F$ in the factor graph.
We refer to the variable node $q$ as a neighboring node of $q$.
$\mathcal{N}_F \backslash p $ is a set of variables connected to the factor $F$ excluding the node $p$.
Clearly, for a pairwise factor which only connects to two variables, the set $\mathcal{N}_F \backslash p $ only contains one variable node.
The above equations show that the factor-to-variable message ${\boldsymbol \beta}_{F \to p}$ depends on the potential $E_F$ and ${\boldsymbol \beta}_{F' \to q}$.
Here ${\boldsymbol \beta}_{F' \to q}$ is the factor-to-variable message which is calculated from a neighboring node $q$ and a factor $F'\neq F$.

Conventional CRF learning approaches learn the potential function then follow
 the above equations to compute the messages for calculating marginals.
As discussed in \cite{ross2011learning}, given that  the goal is to estimate the marginals,
it is not necessary to exactly follow the above equations, which involve learning potential functions, to calculate messages.
We can directly learn message estimators, rather than indirectly learning the potential functions as in conventional methods.

Consider the calculation in \eqref{eq:message_fp_final}.
The message ${\boldsymbol \beta}_{F \to p}$ depends on the observation ${\boldsymbol x}_{pF}$ and the messages ${\boldsymbol \beta}_{F' \to q}$.
Here ${\boldsymbol x}_{pF}$ denotes the observations that correspond to the node $p$ and the factor $F$.
We are able to formulate a factor-to-variable message estimator which takes ${\boldsymbol x}_{pF}$
and ${\boldsymbol \beta}_{F' \to q}$ as inputs and outputs the message vector,
and we directly learn such estimators.
Since one message ${\boldsymbol \beta}_{F \to p}$ depends on a number of previous messages ${\boldsymbol \beta}_{F' \to q}$,
we can formulate a sequence of message estimators to model the dependence.
Thus the output from a previous message estimator will be the input of the following message estimator.

There are two message passing strategies for loopy BP: synchronous and asynchronous passing.
We here focus on the synchronous message passing,
for which all messages are computed before passing them to the neighbors.
The synchronous passing strategy results in much simpler message dependences than the asynchronous strategy,
which simplifies the training procedure.
We define one inference iteration as one pass of the graph with the synchronous passing strategy.

We propose to learn CNN based factor-to-variable message estimator.
The message estimator models the interaction between neighboring variable nodes.
We denote by $M$ a message estimator.
The factor-to-variable message is calculated as:
\begin{align}
\label{eq:M}
	%
	{\boldsymbol \beta}_{F \to p} (y_p) & =  M_F({\boldsymbol x}_{pF}, \mathbf{d}_{pF}, y_p).
	%
	%
\end{align}
We refer to
$\mathbf{d}_{pF}$ as the dependent message feature vector which encodes all dependent messages
from the neighboring nodes that are connected to the node $p$ by $F$.
Note that the dependent messages are the output of message estimators at the previous inference iteration.
In the case of running only one message passing iteration, there is no dependent messages for $M_F$,
 and thus we do not need to incorporate $\mathbf{d}_{pF}$.
To have a general exposition,
 %
we here describe the case of running arbitrarily many inference iterations.

We can choose any effective strategy to generate the feature vector $\mathbf{d}_{pF}$  from the dependent messages.
Here we discuss a simple example.
According to \eqref{eq:message_fp_final},
we define the feature vector $\mathbf{d}_{pF}$
as a $K$-dimensional vector which aggregates all dependent messages.
In this case, $\mathbf{d}_{pF}$ is computed as:
\begin{align}
\label{eq:d_pf}
\mathbf{d}_{pF} (y)
	 & =
	 \sum_{q \in \mathcal{N}_{F} \backslash p}
	\biggr[
	\operatorname*{log\,} \frac{ \operatorname*{exp\,} \sum_{F' \in \mathcal{F}_q \backslash F}  M_{F'}({\boldsymbol x}_{qF'}, \mathbf{d}_{qF'}, y)  }
	{\sum_{y'} \operatorname*{exp\,} \sum_{F' \in \mathcal{F}_q \backslash F} M_{F'}({\boldsymbol x}_{qF'}, \mathbf{d}_{qF'}, y') }
	\biggr].
\end{align}
With the definition of $\mathbf{d}_{pF}$ in \eqref{eq:d_pf} and  ${\boldsymbol \beta}_{F \to p}$ in \eqref{eq:M},
it clearly shows that the message estimation requires evaluating a sequence of message estimators.
Another example is to concatenate all dependent messages to construct the feature vector $\mathbf{d}_{pF}$.

There are different strategies to formulate the message estimators in different iterations.
The simple strategy is using the same message estimator across all inference iteration.
In this case the message estimator becomes a recursive function,
and thus the CNN based estimator becomes a recurrent neural network (RNN).
Another strategy is to formulate different estimator for each inference iteration.

\subsection{Details for message estimator networks}
\label{sec:message_network}

We formulate the estimator $M_F$ as a CNN, thus the estimation is the network outputs:
\begin{align}
\textstyle
	%
	{\boldsymbol \beta}_{F \to p} (y_p)  =  M_F({\boldsymbol x}_{pF}, \mathbf{d}_{pF}, y_p; {\boldsymbol \theta}_F)
	=  {\sum_{k=1}^{K}} \delta(k=y_p) z_{pF,k} ({\boldsymbol x}, \mathbf{d}_{pF}; {\boldsymbol \theta}_F).
	%
\end{align}
Here ${\boldsymbol \theta}_F$ denotes the network parameter which we need to learn.
$\delta(\cdot)$ is the indicator function, which equals $1$ if the input is true and $0$ otherwise.
We denote by ${\boldsymbol z}_{pF} \in \mathbb{R}^{K}$ as the $K$-dimensional output vector ($K$ is the number of classes) of the message estimator network for the node $p$ and the factor $F$;
$z_{pF,k}$ is the $k$-th value in the network output ${\boldsymbol z}_{pF}$ corresponding to the $k$-th class.

We can consider any possible strategies for implementing ${\boldsymbol z}_{pF}$ with CNNs.
For example, we here describe a strategy which is analogous to the network design in \cite{linpiece}.
We denote by $C^{(1)}$ as a fully convolutional network (FCNN) \cite{LongSD14} for convolutional feature generation,
 and $C^{(2)}$ as a traditional fully connected network for message estimation.

Given an input image ${\boldsymbol x}$, the network output $C^{(1)}({\boldsymbol x}) \in \mathbb{R}^{N_1 \times N_2 \times r}$ is a convolutional feature map,
in which $N_1 \times N_2 = N$ is the feature map size and $r$ is the dimension of one feature vector.
Each spatial position (each feature vector) in the feature map $C^{(1)}({\boldsymbol x})$ corresponds to one variable node in the CRF graph.
We denote by $C^{(1)}({\boldsymbol x}, p) \in \mathbb{R}^{r}$ as the feature vector corresponding to the variable node $p$.
Likewise, $C^{(1)}({\boldsymbol x}, {\mathcal{N}_F \backslash p}) \in \mathbb{R}^{r}$ is the averaged vector of the feature vectors that correspond to the set of nodes $\mathcal{N}_F \backslash p$.
Recall that ${\mathcal{N}_F \backslash p }$ is a set of nodes connected by the factor $F$ excluding the node $p$.
For pairwise factors, ${\mathcal{N}_F \backslash p }$ contains only one node.

We construct the feature vector ${\boldsymbol z}^{C^{(1)}}_{pF} \in \mathbb{R}^{2r}$ for the node-factor pair $(p, F)$
by concatenating $C^{(1)}({\boldsymbol x}, p)$ and $C^{(1)}({\boldsymbol x}, {\mathcal{N}_F \backslash p})$.
Finally, we concatenate the node-factor feature vector ${\boldsymbol z}^{C^{(1)}}_{pF}$ and
the dependent message feature vector $\mathbf{d}_{pF}$ as the input for the second network $C^{(2)}$.
Thus the input dimension for $C^{(2)}$ is $(2r+K)$.
For running only one inference iteration, the input for $C^{(2)}$ is ${\boldsymbol z}^{C^{(1)}}_{pF}$ alone.
The final output from the second network $C^{(2)}$ is the $K$-dimensional message vector ${\boldsymbol z}_{pF}$.
To sum up, we generate the final message vector ${\boldsymbol z}_{pF}$ as:
\begin{align}
	{\boldsymbol z}_{pF}= C^{(2)} \{ \; [ \; C^{(1)}({\boldsymbol x}, p)^{\!\top}; \;  C^{(1)}({\boldsymbol x}, {\mathcal{N}_F \backslash p} \; )^{\!\top}; \; \mathbf{d}_{pF}^{\!\top} \; ]^{\!\top} \; \}.
\end{align}

For a general CNN based potential function in conventional CRFs,
the potential network is usually required to have a large number of output units
(exponential in the order of the potentials). For example,
 it requires $K^2$ ($K$ is the number of classes)
outputs for the pairwise potentials \cite{linpiece}.
A large number of output units would significantly increase the number of network parameters.
It leads to expensive computations and tend to  over-fit the training data.
In contrast, for learning our CNN message estimator,
we only need to formulate $K$ output units for the network.
Clearly it is more scalable in the cases of a large number of classes.

\subsection{Training CNN message estimators}
\label{sec:message_train}

Our goal is to estimate the variable marginals in \eqref{eq:marginal},
which can be re-written with the estimators:
\begin{align}
	{\displaystyle P}(y_p | {\boldsymbol x} )  = \sum_{{\boldsymbol y} \backslash y_p} {\displaystyle P}({\boldsymbol y}|{\boldsymbol x}) & =
	\frac{1}{Z_p} \operatorname*{exp\,} \biggr[ \sum_{F \in \mathcal{F}_p} {\boldsymbol \beta}_{F \to p} (y_p) \biggr]
	 = \frac{1}{Z_p} \operatorname*{exp\,} \sum_{F \in \mathcal{F}_p} M_F({\boldsymbol x}_{pF}, \mathbf{d}_{pF}, y_p; {\boldsymbol \theta}_F). \notag
\end{align}
Here $Z_p$ is the normalizer.
The ideal variable marginal
 %
has the probability of $1$ for the ground truth class and $0$ for the remaining classes.
Here we consider the cross entropy loss between the ideal marginal and the estimated marginal.
\begin{align}
	%
	%
	J({\boldsymbol x}, \hat{{\boldsymbol y}}; {\boldsymbol \theta}) & = - \sum_{p \in \mathcal{N}} \sum_{y_p = 1}^K \delta(y_p=\hat{y}_p) \operatorname*{log\,} {\displaystyle P}(y_p | {\boldsymbol x}; {\boldsymbol \theta}) \notag \\
	& = - \sum_{p \in \mathcal{N}} \sum_{y_p = 1}^K  \delta(y_p=\hat{y}_p)
	\operatorname*{log\,} \frac{\operatorname*{exp\,} \sum_{F \in \mathcal{F}_p} M_F({\boldsymbol x}_{pF}, \mathbf{d}_{pF}, y_p; {\boldsymbol \theta}_F)}
	{\sum_{y'_p} \operatorname*{exp\,} \sum_{F \in \mathcal{F}_p} M_F({\boldsymbol x}_{pF}, \mathbf{d}_{pF}, y'_p; {\boldsymbol \theta}_F) },
\end{align}
in which $\hat{y}_p$ is the ground truth label for the variable node $p$.
Given a set of $N$ training images and label masks, the optimization problem for learning the message estimator network is:
\begin{align}
\label{eq:msg_learning_org}
\textstyle
\min_{{\boldsymbol \theta}} \frac{\lambda}{2} \fnorm {\boldsymbol \theta} + \sum_{i=1}^N J({\boldsymbol x}^{(i)}, \hat{{\boldsymbol y}}^{(i)}; {\boldsymbol \theta}).
\end{align}

The work in \cite{ross2011learning} propose to learn the variable-to-factor message (${\boldsymbol \beta}_{p \to F}$).
Unlike their approach, we aim to learn the factor-to-variable message (${\boldsymbol \beta}_{F \to p}$),
for which we are able to naturally formulate the variable marginals,
which is the ultimate goal for prediction, as the training objective.
Moreover, for learning ${\boldsymbol \beta}_{p \to F}$ in their approach,
the message estimator will depend on all neighboring nodes (connected by any factors).
Given that variable nodes will have different number of neighboring nodes,
they only consider a fixed number of neighboring nodes (e.g., $20$) and concatenate their features
to generate a fixed-length feature vector for classification.
In our case for learning ${\boldsymbol \beta}_{F \to p}$,
the message estimator only depends a fixed number of neighboring nodes (connected by one factor),
thus we do not have this problem.
Most importantly,
they learn message estimators by training traditional probabilistic classifiers (e.g., simple logistic regressors)
with hand-craft features,
and in contrast, we train deep CNNs in an end-to-end learning style without using hand-craft features.

\subsection{Message learning with inference-time budgets}

One advantage of message learning is that we are able to explicitly incorporate
the expected number of inference iteration into the learning procedure.
The number of inference iteration defines the learning sequence of message estimators.
This is particular useful if
we aim to learn the estimators which are able to have high-quality prediction
for only running a few number of inference iterations.
In contrast, the conventional potential function learning in CRFs
are not able to directly incorporate the expected number of inference iterations.

We are particularly interested in learning message estimators
for using only one message passing iteration,
for which the inference can be very fast.
In this case it might be preferable to have large-range neighborhood connections,
for which the large range interaction can be captured by running one inference pass.

redpajama_set_name: RedPajamaArXiv


Since Girl Scout Week is here, it's only fitting to celebrate the Girl Scouts who achieved Girl Scouting's greatest honor - our Young Women of Distinction. Head over to My Central Jersey and read about Sixteen year old Shannon McNamara, who has been named one of the Girl Scouts' 2009 National Young Women of Distinction for her efforts to empower African girls through education. The Young Women of Distinction honor was earned by ten Girl Scouts this year.
Not only have our Young Women of Distinction earned a very prestigious award and made the world a better place through exceptional Girl Scout Gold Award Projects - they have also been recognized by the Ford Motor Company Fund, which donated a $2000 scholarship to each of our Young Women of Distinction. Cheers to Ford and congrats to all the girls who won!


text:
Q: Having problems selecting values from a  to put into JavaScript This is my code. I am attempting to get values from the drop down menu and text, put them into the equation. What am I doing wrong so that the function is stopping half way through. I know it is probably really easy to solve but I cannot find a solution anywhere. Please help.
<!doctype html>
<html>
    <head>
        <meta charset="utf-8">
    </head>
    <body>
        <h3> Question 6 </h3>
        <h4> How long can you safely tan for? </h4>
        <script language="javascript">
            function calculate5() {
                var skin = document.getElementById("skinselect");
                var skinselect = Math.floor(skinselect.options[skinselect.selectedIndex].value);
                var cloud = document.getElementById("cloudselect");
                var cloudselect = Math.floor(cloudselect.options[cloudselect.selectedIndex].value);
                var temp = Math.floor(document.getElementById("temp").value);
                var time = ((skinselect + cloudselect) * temp) - 50;
                alert("Your safe sunbaking time is " + time + " minutes");
            }
        </script>
        <select id="skinselect">
            <option value="error">Skin type: 0 is light, 5 is dark </option>
            <option value="0"> 0 </option>
            <option value="1"> 1 </option>
            <option value="2"> 2 </option>
            <option value="3"> 3 </option>
            <option value="4"> 4 </option>
            <option value="5"> 5 </option>
        </select>
        <select id="cloudselect">
            <option value="error">Cloud Cover: 0 is clear, 5 is overcast </option>
            <option value="0"> 0 </option>
            <option value="1"> 1 </option>
            <option value="2"> 2 </option>
            <option value="3"> 3 </option>
            <option value="4"> 4 </option>
            <option value="5"> 5 </option>
        </select>
        <input type="text" value="Temperature (C)" id="temp" size="16">
        <input type="button" value="Calculate" onClick="calculate5()">
    </body>
</html>

A: In your function:
var skinselect = Math.floor(skin.options[skin.selectedIndex].value);

and
var cloudselect = Math.floor(cloud.options[cloud.selectedIndex].value);

A: document.getElementById("skinselect").value contains its selected option value.
Set a default value in case any option hasn't already been selected.
Eg.
      <select id="skinselect" value="error">
(because error is the default value due to the fact it's on the top of the options list)

A: inside your function you need to access option from select id
var skin = document.getElementById("skinselect");
var skinselect = Math.floor(skin.options[skin.selectedIndex].value);
var cloud = document.getElementById("cloudselect");
var cloudselect = Math.floor(cloud.options[cloud.selectedIndex].value);
var temp = Math.floor(document.getElementById("temp").value);
var time = ((skinselect + cloudselect) * temp) - 50;

you can refer
Get selected value in dropdown list using JavaScript?

redpajama_set_name: RedPajamaStackExchange


text:
This section will talk about some of the Little Ten teams that stood above all the rest.
1958-59 Shabbona Indians
The Indians were 8-0 entering the annual DeKalb Holiday Tournament (which featured schools much larger than tiny Shabbona). Shabbona rolled to the title with wins over Rochelle (66-41), Aurora West (69-67), Maine (59-46) and Rock Falls (78-64) in the championship contest. Following that tournament, Shabbona was given a fourth position ranking in the United Press International's ratings of the state's top teams.
Little Ten Conference Champs in both the regular season (9-0) {the closest game was a 70-52 win over Leland} and league tournament (defeated Earlville 97-38, Serena 81-35 in the semifinals and finally Leland 91-51 in the title game.) That 40 point win in the championship game is still the largest margin of victory in the tournament's 89 year history. (Marv Straw, Jerry Wisted and Ron Espe were all named first team All-LTC, while Bill Mullins and Denny Fleming were on the All-LTC second team)
They ended the regular season 27-0 and began post season play in their own district tournament. They opened with a dominate 102-35 over Hiawatha and captured the title with a 92-48 drubbing of Waterman, who fell to the Indians for the third time that season.
Next, the Indians moved to the DeKalb Regional, they started with a 63-38 victory over Genoa-Kingston. They moved into the title game with a 58-33 win over Sycamore to set up the third straight regional title match-up with DeKalb, who had beat them the previous two years. The Indians were able to get over the hump as they triumphed over the Barbs 48-37 to capture the regional title and move on to the HInsdale Sectional to play the host squad.
With a jam packed gym of over 5,000 fans the Indians were out-sized by the taller Red Devils and trailed 19-15 after one quarter. Hinsdale's Dave Brown nailed a 35-foot desperation at the halftime buzzer to give his club a 32-25 advantage at halftime. Shabbona found themselves down by ten (41-31) early in the third period, but mounted a come-back to trail just 45-43 entering the fourth quarter. The Indians took their only lead of the game with :19 left in regulation when guard Denny Fleming scored a lay up to make it 54-52, but Hinsdale was able to convert two free throws with :08 left to send the game to overtime.
Hinsdale opened the scoring in overtime with two free tosses, but Shabbona center Jerry Wisted put the teams even again (56-all) with a lay in. In the final seconds, with the game still tied, Bill Mullins stole the ball from Hinsdale and made a quick pass to Wisted, who nailed a jump shot that sent the Indians joyfully running into their locker room. However, after a lengthy discussion between officials at the scorers table, it was ruled Wisted's shot came a fraction of a second after the official timer's gun. In the second "sudden death" overtime, Hinsdale won the jump ball and seventeen seconds later Brown was fouled and calmly sank both ends of a one-and-bonus sent end Shabbona's magical season.
Notes: Every seat in the gym was filled and hour and fifteen minutes before tip-off.
Hinsdale's front court went 6-foot-8, 6-5 and 6-3, while Marv Straw was Shabbona's tallest player at 6-4, Wisted 6-2, Mullins 6-1. The Indians out-rebounded Hisdale 30-17 in the game.
1985-1986 Newark Norsemen
Ended season 29-1. More on this team in near future.

redpajama_set_name: RedPajamaCommonCrawl


text:
1951 Nancy 2019
Nancy Schuh
May 14, 1951 — February 22, 2019
Nancy Jean Schuh, 67 of Chippewa Falls, died surrounded by love of family and friends, February 22, 2019.
She was born on May 14, 1951, in Eau Claire, Wisconsin to Bruce Arthur and Lois Arlene Hopkins. In 1969 Nancy graduated from North High School in Eau Claire. For 31 years she worked in Eau Claire in the same office in supervisory positions for the following four companies: AT&T, Wisconsin Bell, Ameritech, SBC and back to AT&T. She retired in 2000.
On August 28, 1992, Nancy married James Paul "Jim" Schuh at the historic Octagon House in Hudson, Wisconsin. In 1985 they made their home for more than 34 years on small Lake Wissota.
Nancy generously volunteered her service to community in several organizations. In the 1990s she was a member of the Downtown Noon Optimist Club and also served as the Vice President. She was a member of the Eau Claire City Park and Recreation Board in the 1990s. From 1992 to 1997 Nancy volunteered for the Eau Claire Main Street Program where she was Volunteer of the Year in 1996. From 1997 to 2007 she volunteered for the Chippewa Falls Main Street Program where she was Volunteer of the Year in 2004. From 2002 to 2011 Nancy mentored at Stillson Elementary School, Chippewa Falls Middle School, and Chippewa Falls High School. In 2002 Nancy co-authored a pictorial history of Chippewa Falls: Images of America, Chippewa Falls, Wisconsin. In 2005 she co-authored a second pictorial history of Chippewa Falls: Images of America, Chippewa Falls Main Street. From 2002 to 2017 Nancy was a member of the Chippewa Valley Cultural Association Board of Directors, where she served as the Board Secretary from 2004 to 2016. Starting in 2002 Nancy volunteered for the Chippewa County Historical Society and served as archivist until 2018. From 2010 to 2019 she served on the Board of Directors for the Chippewa County Historical Society and in 2013 was inducted into their Hall of Fame.
In Nancy's spare time she enjoyed reading, writing, researching family history, baking amazing cakes for family and friends, cooking, growing beautiful flower gardens, photography, boating and traveling. In her retirement, Nancy was selflessly devoted to caring for her parents. She felt great love for her family, knew her duty to family, and serenely carried those duties out. She was a quiet beauty, an animal lover, and a thoughtful, sincere, altruistic woman who gave much to her community, friends, family, and neighbors.
Nancy is survived by her husband, Jim Schuh; sister, Diane Hopkins Whelan; daughter, Joelle (Stuart) Johnson; nieces, Joy (Jack) Camp, Jenni Beals, and Serena Whelan; nephew, John (Bridget) Black; grandnephew, Auggie Hopkins Camp; cousin, Janet Hampton; sisters-in-law, Donna (David) Gibson, Valerie (Mitch) Black, Marlene (Ronald) Wipfli, Joan Randall; and many other relatives and beloved friends.
She was preceded in death by her father, Bruce Arthur Hopkins; mother, Lois Arlene Hopkins; brother, Kent Arthur Hopkins; grandparents, Clarence and Clara Peterson, and Arthur and Dorothy Hopkins; aunts and uncles, Dorothy and Kenny Christenson, Bill and Sandy Hopkins, Donald Hopkins, Frank Peterson, George McManus, Bernard Bucholtz High, Yvonne Hall, Clarabelle Velie, Beulah Boudreau and Tamar Borgenson; mother-in-law, Doris Schuh and father-in-law, Charles Schuh; brothers-in-law, Peter Whelan, Chuck Schuh, Donald Anderson, and Lester "Buck" Noble; cousins, David Christenson and John Hampton.
The family thanks the staff of Marshfield Clinic Comfort and Recovery Suites in Eau Claire for their care and kindness.
A celebration of Nancy's life will be held, the day before Easter, on Saturday, April 20, 2019, from 3 to 6 p.m. at the Heyde Center for the Arts, 3 South High Street, Chippewa Falls WI. To honor Nancy's wishes, memorial contributions may be directed to the Chippewa County Historical Society, 123 Allen Street, Chippewa Falls, WI 54729.
Pederson-Volker Funeral Chapel & Cremation Services, Chippewa Falls is serving the family. Please share your memories and condolences online at pedersonvolker.com
To plant a beautiful memorial tree in memory of Nancy J. Schuh, please visit our Tree Store.
To order memorial trees or send flowers to the family in memory of Nancy Schuh, please visit our flower store.
Starts at 3:00pm (Central time)
Heyde Center for the Arts
3 S High St, Chippewa Falls, WI 54729


Frank Pallone talks about the need for more mental health services and more to practice psychiatry and psychology.
Congressman Donald Payne, Jr. discusses the Safer Neighborhoods Gun Buyback Act and how the NRA's stronghold in Washington as lobbyists makes passing gun legislation difficult.
Senator Robert Menendez spoke about guns, terrorism and immigration.
Deadline looming for Congress to pass a long-term federal transit bill.
Congressman Frank LoBiondo says the United States needs to be a leader.
Lance says that no matter who becomes the Speaker, he wants Republicans to make sure that government continues to stay open and fiscally responsible.


text:
Home › Supermarkets › Boxer Superstores
Boxer Superstores information
Opening hours, contact and more
www.boxer.co.za
Discount card Boxer Superstores See more
Job offers in Boxer Superstores See more
3.1 Rating: 3.1 / 5
from 80 votes
Add opinion for Boxer Superstores
All shops Boxer Superstores South Africa
Choose your city and see all Boxer Superstores offers next to you
Boxer Superstores
Do you want to receive by email the offers of Boxer Superstores and other specials from allcatalogues.co.za?
In April 1977, Boxer Superstores was founded in ZwaZulu- Natal province. The company originated under the name KwaZulu Cash & Carry. Several years the company was run by its original founders while in the year of 1988 was taken from independent investors. The company changed its name into Boxer Cash & Carry' in the year of 1991. In 1995, the branch opened two stores outside KwaZulu – Natal province in Eastern Cape region. A year later, another store opened doors in Mpumalanga. The company faced new changes of its name as well as new corporate identity logo during 1997. The new name was selected to be Boxer Superstores, which is also the current name of the company. During the next years the Boxer Superstores expanded itself and opened more stores in Limpopo and North- West provinces. Pick 'n Pay bought Boxer Superstores in the year of 2002. In 2004, was created new brand division called Boxer Build, which was specific for builder hardware. The first Boxer Build store was opened in Eastern Cape. Nowadays, there are four different Boxer divisions: Boxer Superstores, Boxer Build, Boxer Superliquors and Punch Stores, which are serving the best services.
Boxer Superstores has a variety of catalogues offered to consumers. There are separated into regions, with the best deals for a certain period of time. The brochures offered an assortment of fruit and vegetables, variety of food products and all the needed items for the household.
Boxer Superstores is headquartered in Westville, KwaZulu- Natal. It is operating throughout South Africa as well as in Swaziland. For the company is really essential the consumers' trust and love. It is one of the leading supermarket retailer around South Africa, preferred by the consumers for the quality of the offered products and the competitive prices. In the stores the customers can find a variety of sections with different branded items. In the offered goods can be found groceries, items for the household, quality food products as well as in- store bakery and butchery with always fresh baked bread and frozen or fresh top – quality meat for every taste. To the consumers are offered section with fresh cooked meals as well as snacks.
Popular shops in category Supermarkets
Shown 10 of 39 shops
Pick n Pay Hyper
Oxford freshmarket
Checkers Hyper
OK Minimark


text:
<?php

namespace Bella;

class API{

	private $baseURL = '';
	private $token = '';
	private $me = null;
	private $version = '';

	function __construct($uri){
		if(substr($uri, strlen($uri)-1, 1)){
			$uri = substr($uri, 0, strlen($uri)-1);
		}
		$this->baseURL = $uri;
	}

	/**
     *  Post a collection of values to remote service
     *  Method : push(String $action [, Array $parameters])
     *  Return : a JSON object, same structure as returned by Web Service
    **/
    public function post($action, $parameters){
        $url = $this->baseURL.'/'.(!!$this->version?$this->version.'/':'').$action;
        $data = $this->merge($parameters);
        $response = Remote::push($url, $data);
        if(!!$response){
            $ob = json_decode($response);
            if(!!$ob && is_object($ob)){
                return $ob;
            }
        }
        return false;
    }

    /**
     *  Get data from remote service
     *  Method : pull(String $action [, Array $parameters])
     *  Return : a JSON object, same structure as returned by Web Service
    **/
    public function get($action, $parameters=false){
        $url = $this->baseURL.'/'.(!!$this->version?$this->version.'/':'').$action.'?';
        $url = $this->attach($url, $parameters);
        $response = Remote::pull($url);
        if(!!$response){
            $ob = json_decode($response);
            if(!!$ob && is_object($ob)){
				if(!isset($ob->error) || !$ob->error){
					return $ob;
				}
            }
        }
        return false;
    }

    /**
     *  Some small helpers
    **/

	public function getBaseURL(){
		return $this->baseURL;
	}

	public function me(){
		return $me;
	}

	/**
     *  Make a query string by adding action and parameters to base URI
     *  Method : merge(String $action [, Array $data])
     *  Return : a string URL
    **/
    private function buildQuery($action, $data=false){
        $url = $this->baseURL;
        if(!!$action){
            $url.='/'.$action.'?';
        }
        if(!!$data){
            if(is_string($data)){
                $url.=$data;
            }
            else{
                $url = $this->attach($url, $data);
            }
        }
        return $url;
    }

    /**
     *  Merge the stored and pre-defined values with new parameters
     *  Method : merge([Array $data])
     *  Return : an array contains the parameters will be send to web service
    **/
    private function merge($data=false){
        $output = array();
        $arr = array(
            'token'  => $this->token,
        );
        if(!!$data && is_array($data)){
            foreach($data as $key=>$val){
                $output[$key] = $val;
            }
        }
        foreach($arr as $key=>$val){
            if(!isset($output[$key])){
                $output[$key] = $val;
            }
        }
        return $output;
    }

    /**
     *  Add more parameters to query string
     *  Method : attach(String $url [, Array $data])
     *  Return : an URL string with parameters
    **/
    private function attach($url, $data=false){
        $queries = $this->merge($data);
        $parameters = array();
        foreach($queries as $key=>$val){
            array_push($parameters, $key.'='.urlencode($val));
        }
        if(count($parameters)>0){
            $url.=implode('&', $parameters);
        }
        return $url;
    }
}

redpajama_set_name: RedPajamaGithub


text:
{-# LANGUAGE BangPatterns #-}
{-# LANGUAGE CPP          #-}

-- | This module contains a 'HashTable' typeclass for the hash table
-- implementations in this package. This allows you to provide functions which
-- will work for any hash table implementation in this collection.
--
-- It is recommended to create a concrete type alias in your code when using this
-- package, i.e.:
--
-- > import qualified Data.HashTable.IO as H
-- >
-- > type HashTable k v = H.BasicHashTable k v
-- >
-- > foo :: IO (HashTable Int Int)
-- > foo = do
-- >     ht <- H.new
-- >     H.insert ht 1 1
-- >     return ht
--
-- or
--
-- > import qualified Data.HashTable.ST.Cuckoo as C
-- > import qualified Data.HashTable.Class as H
-- >
-- > type HashTable s k v = C.HashTable s k v
-- >
-- > foo :: ST s (HashTable s k v)
-- > foo = do
-- >     ht <- H.new
-- >     H.insert ht 1 1
-- >     return ht
--
-- Firstly, this makes it easy to switch to a different hash table
-- implementation, and secondly, using a concrete type rather than leaving your
-- functions abstract in the 'HashTable' class should allow GHC to optimize
-- away the typeclass dictionaries.
--
-- Note that the functions in this typeclass are in the 'ST' monad; if you want
-- hash tables in 'IO', use the convenience wrappers in "Data.HashTable.IO".
--
module Data.HashTable.Class
  ( HashTable(..)
  , fromList
  , fromListWithSizeHint
  , toList
  ) where

#if !MIN_VERSION_base(4,8,0)
import           Control.Applicative
import           Data.Word           (Word)
#endif
import           Control.Monad.ST
import           Data.Hashable
import           Prelude             hiding (mapM_)

-- | A typeclass for hash tables in the 'ST' monad. The operations on these
-- hash tables are typically both key- and value-strict.
class HashTable h where
    -- | Creates a new, default-sized hash table. /O(1)/.
    new      :: ST s (h s k v)

    -- | Creates a new hash table sized to hold @n@ elements. /O(n)/.
    newSized :: Int -> ST s (h s k v)

    -- | Generalized update. Given a key /k/, and a user function /f/, calls:
    --
    --   - `f Nothing` if the key did not exist in the hash table
    --   - `f (Just v)` otherwise
    --
    -- If the user function returns @(Nothing, _)@, then the value is deleted
    -- from the hash table. Otherwise the mapping for /k/ is inserted or
    -- replaced with the provided value.
    --
    -- Returns the second part of the tuple returned by /f/.
    mutate :: (Eq k, Hashable k) =>
              h s k v -> k -> (Maybe v -> (Maybe v, a)) -> ST s a
    mutate tbl k f = mutateST tbl k (pure . f)

    -- | As 'mutate', except that the action can perform additional side
    -- effects.
    mutateST :: (Eq k, Hashable k) =>
                h s k v -> k -> (Maybe v -> ST s (Maybe v, a)) -> ST s a

    -- | Inserts a key/value mapping into a hash table, replacing any existing
    -- mapping for that key.
    --
    -- /O(n)/ worst case, /O(1)/ amortized.
    insert   :: (Eq k, Hashable k) => h s k v -> k -> v -> ST s ()

    -- | Deletes a key-value mapping from a hash table. /O(n)/ worst case,
    -- /O(1)/ amortized.
    delete   :: (Eq k, Hashable k) => h s k v -> k -> ST s ()

    -- | Looks up a key-value mapping in a hash table. /O(n)/ worst case,
    -- (/O(1)/ for cuckoo hash), /O(1)/ amortized.
    lookup   :: (Eq k, Hashable k) => h s k v -> k -> ST s (Maybe v)

    -- | A strict fold over the key-value records of a hash table in the 'ST'
    -- monad. /O(n)/.
    foldM    :: (a -> (k,v) -> ST s a) -> a -> h s k v -> ST s a

    -- | A side-effecting map over the key-value records of a hash
    -- table. /O(n)/.
    mapM_    :: ((k,v) -> ST s b) -> h s k v -> ST s ()

    -- | Looks up the index of a key-value mapping in a hash table suitable
    -- for passing to 'nextByIndex'.
    lookupIndex :: (Eq k, Hashable k) => h s k v -> k -> ST s (Maybe Word)

    -- | Returns the next key-value mapping stored at the given index or at
    -- a greater index. The index, key, and value of the next record are
    -- returned.
    nextByIndex :: h s k v -> Word -> ST s (Maybe (Word,k,v))

    -- | Computes the overhead (in words) per key-value mapping. Used for
    -- debugging, etc; time complexity depends on the underlying hash table
    -- implementation. /O(n)/.
    computeOverhead :: h s k v -> ST s Double

------------------------------------------------------------------------------
-- | Create a hash table from a list of key-value pairs. /O(n)/.
fromList :: (HashTable h, Eq k, Hashable k) => [(k,v)] -> ST s (h s k v)
fromList l = do
    ht <- new
    go ht l

  where
    go ht = go'
      where
        go' [] = return ht
        go' ((!k,!v):xs) = do
            insert ht k v
            go' xs
{-# INLINE fromList #-}

------------------------------------------------------------------------------
-- | Create a hash table from a list of key-value pairs, with a size hint. /O(n)/.
fromListWithSizeHint :: (HashTable h, Eq k, Hashable k) =>
                        Int
                     -> [(k,v)]
                     -> ST s (h s k v)
fromListWithSizeHint n l = do
    ht <- newSized n
    go ht l

  where
    go ht = go'
      where
        go' [] = return ht
        go' ((!k,!v):xs) = do
            insert ht k v
            go' xs
{-# INLINE fromListWithSizeHint #-}

------------------------------------------------------------------------------
-- | Given a hash table, produce a list of key-value pairs. /O(n)/.
toList :: (HashTable h) => h s k v -> ST s [(k,v)]
toList ht = do
    l <- foldM f [] ht
    return l

  where
    f !l !t = return (t:l)
{-# INLINE toList #-}


Attorney Mekisha Creal at Huntington High School, Shreveport, La.
Law Day 2014, the LCLCE organized 63 presentations statewide. Eighteen judges (18) and twenty-eight (28) attorneys made presentations at 39 schools reaching 2625 students. To view photos of the programs, click here.
Judge Randall L. Bethancourt, Judge Vincent J. Borne, Judge June Berry Darensburg, Judge W. Ross Foote, Judge Andrea P. Janzen, Judge Patricia E. Koch, Judge Lawrence L. Lagarde, Judge C. Wendell Manning, Judge Michele R. Morel, Judge Richard J. Putnam III, Judge J. Wilson Rambo, Judge Cameron B. Simmons, Judge Parris A. Taylor, Judge M'elise Trahan, Judge Zorraine M. Waguespack, Judge Trudy M. White, Judge H. Stephens Winters, E. Adrian Adams, Denise A. Allemand, Claire E. Bergeron, Benjamin J. Bleich, Danielle N. Brown, Michael J. Busada, Thomas R. Caruso, Mekisha S. Creal, Trina T. Chu, Albert D. Clary, John F. Dillon, J. Keith Gates, Gerald J. Hampton Jr., Fred D. Jones, Joseph P. Landreneau, J. Clay LeJeune, Gernine M. Mailhes, Sarah S. J. Midboe, Paula E. Miles, Joseph H. L. Perez-Montes, Mark A. Myers, Monique F. Rauls, Erica J. Rose, Thomas B. Thompson, Angel G. Varnado, Jason M. Verdigets, Amanda L. Westergard and Christie C. Wood.


These young members of the potato family are among the most adaptable. Try them roasted, mashed, or boiled, or in potato salads and soups.
In Season: New potatoes are in season in spring and early summer. Any variety of potato that is harvested early is considered a new potato. Since they are picked before their sugars have converted to starch, new potatoes are crisp and waxy and high in moisture. They also have thin skins, making them great for cooking and eating unpeeled.
What to Look For: New potatoes should be firm, smooth, and free of cracks or soft brown spots. Choose potatoes of similar size so they cook evenly.
How to Store: Potatoes should not be refrigerated; keep them in a cool, dark place and use within a week or so of buying.
How to Prep: If potatoes are to be cooked in their skins, rinse under cool water, scrub with a vegetable brush, and cut away any greenish parts (caused by overexposure to light) or any eyes.
Good butter and fresh chives are all you need to gussy up boiled baby Yukon Golds.
One of Martha's favorite potato recipes, this company-worthy soup pairs ice-cold buttermilk with tender red new potatoes, caramelized fennel and shallot, and shredded kale.
DIY preserved lemons cut the richness of the homemade aioli in this sophisticated potato side dish. Garnish with a generous sprinkling of poppy seeds and fresh chives.
Boiled new potatoes add just the right amount of heft to this summery take on salade Nicoise that calls for roasted salmon instead of the usual anchovies or tuna.
Get the brunt of the braising done for the lamb before you add the new potatoes -- the pan juices will infuse them with tons of flavor.
Quarter your new potatoes if they're on the larger side so that they'll cook in the same amount of time as your oyster mushrooms. Toss with fresh parsley, sherry vinegar, and capers and serve warm.
Boiled new potatoes are just one of the elements in this showstopping kitchen-sink salad. You can use any protein you please -- we went with leftover cedar-planked salmon, grilled herb shrimp, flame-kissed chorizo, and hard-boiled eggs.
No mayo in this potato salad! Just whole-grain mustard, white-wine vinegar, and fresh chives. Pair it with flame-kissed kielbasa to kick off your next cookout.
Have you tried sorrel yet? It has a tangy, pleasantly sour flavor that contrasts nicely with the sweet new potatoes and rich cream in this elegant side dish.
You can skip halving your new potatoes if they're really small. They're roasted in the same pan as the salmon and cabbage in this recipe. You know what that means -- easy cleanup!
Everyone will love this Spanish-inspired potato salad flavored with sherry vinegar, olive oil, and sweet smoked paprika. A garnish of fresh scallions gives it a subtle bite.
New potatoes not only make this classic New England dish more substantial, but they also soak up the delicious flavor from all the fresh seafood -- in this case, clams, mussels, and shrimp.
New potatoes are roasted along with summer squash, shallots, and oyster mushrooms, making a delicious vegetable base for flaky, succulent hailbut.
A couple slices of bacon up the ante in this smashed potato and sauteed green bean salad. Finish with fresh lemon juice and plenty of salt and pepper.
Roasting in a foil pouch cuts down on the cooking time for these mini spuds. Add a dollop of ricotta and a drizzle of olive oil before serving.
Tiny taters are tossed with chicken broth, balsamic vinegar, garlic, and thyme and baked until tender in this easy side dish.
Don't feel like turning on the oven? You can get the same irresistible texture on the stove top. The trick is to brown the potatoes first, then cook with water, covered.
We love a good three-ingredient side dish. Here, olive oil and lemon zest are all you need to jazz up lightly mashed potatoes.
Melted butter flavored with Tabasco and Old Bay gives these kebabs a delicious Cajun twist. Make sure you boil the new potatoes before you thread them on skewers with corn, andouille, and shrimp, as the other ingredients will cook way faster on the grill.
New potatoes are just as good for mashing as your standard russets. This version gets its richness from half-and-half and its intrigue from celery root.


text:
Q: Purging 7 day old data from SQL while also converting date format to INT I need to set up an automated data purging (delete 7 day old data) on my SQL box.
The date column which helps me decide on whether to/not to delete the row set is in YYYYMMDD format.
I understand that in order to use GETDATE() I need to convert it (to YYYYMMDD) and here is the conversion i found-
CONVERT(CHAR(8), GETDATE(), 112)

Below is my delete statement, how do I include the before-mentioned conversion into it to make it work?
DELETE FROM dbo.MyDBName
WHERE StreamDateId < DATEADD (DAY, -7, GETDATE())

A: Best solution for this problem would be to use proper datatype for this date column. Anyway you can do the following....
DELETE FROM dbo.MyDBName
WHERE CAST(StreamDateId AS DATETIME) < DATEADD(DAY, -7, GETDATE())

OR
DELETE FROM dbo.MyDBName
WHERE StreamDateId  < CONVERT(VARCHAR(8), DATEADD(DAY, -7, GETDATE()), 112)


text:
Home Commentaries 2015 Archive The Problem Is the State
The Problem Is the State
The litmus test of how governments and populations treat refugees and how Myanmar treats minority groups, is the pathetic plight of the Rohingya.
THE waves of hapless refugees lately washing ashore in Asia and Europe confront the world with two starkly contrasting realities.
First, that human hardships are universal yet avoidable. And second, that political expediencies are just as universal in creating those hardships in the first place.
Illegal migration has become a "non-traditional threat," meaning that as an offence it has become a new, socially destabilising and politically aggravating challenge from abroad.
Although the phenomenon itself is not new, particularly in border regions of neighbouring countries, the new development is its increased numbers and frequency.
Recent weeks and months have seen waves of illegal migrants at sea drifting towards Europe and South-East Asia. Some but not all of these are legitimate refugees fleeing lethal violence and abuse.
From parts of West Asia and North Africa, migrants have boarded boats illegally bound for Europe. Many fear for their lives as a result of internecine strife.
European governments and their peoples are generally unsympathetic to accepting refugees this time. It marks a significant departure from the immediate post-war (World War II) period, but then those refugees were European themselves.
This time, however, the refugees are different in race and religion. That Europe also happens to be in a time of economic decline has further helped close the doors to them.
Ironically, the links between Europe and such refugee source countries as Libya and Syria go back to colonial times, when the stamp of the European imperial powers was involuntarily forced on them.
Political upheavals continued in the post-colonial period until today. And even when some European powers still have a hand in the turmoil, the refugees produced as a result are made to feel unwelcome.
Europe's rejectionist attitude has been adopted by Australian Prime Minister Tony Abbott. He shuns the prospect of providing refuge to even genuine refugees, saying Australia would not do anything to encourage more asylum seekers hoping to go to "a European country."
Australia occasionally reveals its Western core, usually in times of stress. Abbott says countries in South-East Asia nearer to Myanmar should resettle the migrants but not Australia.
In the early months of this year, 38,000 refugees headed for Europe by sea. About 5% of them died on the way.
In the same period, 25,000 headed for South-East Asia, mostly hoping to arrive in Malaysia or Indonesia.
But after these countries turned the boats away upon repairing them and supplying them with food and water, Western governments criticised and pressured them to accept the migrants.
Countries have the right to deny entry to illegal migrants, but are obliged by international law to grant asylum to refugees. Much of the contention revolves around definitions.
Genuine refugees are people seeking sanctuary from persecution. They want the most basic human need, safety from harm, by fleeing from oppression in their home country.
They are different from economic migrants who seek better jobs or a higher standard of living. They are also not criminals fleeing prosecution by due process, whatever the legal system may be like at home.
Among the current boatloads of illegal migrants in South-East Asia, less than half are estimated to be refugees from Myanmar. Most of them are said to be economic migrants from Bangladesh.
The illegal migrants who reach shore may be deported forthwith. Their greater numbers compared to occasional trickles at other times does not make them more acceptable, instead rather less so.
The Rohingya refugees from Myanmar represent the crux of the problem for countries in the region and beyond. Despite the Myanmar government's repeated denials, there is no doubt that they are a persecuted minority in their country and forced to flee.
Neighbouring Bangladesh already has 200,000 Rohingya in camps, although the official number is a fraction of that. Thailand has another 100,000.
Malaysia has taken in some 150,000 refugees from around the world, nearly a third of whom are Rohingya. Indonesia, with nearly 10 times the population and six times the land area, has taken a total of 12,000 refugees, most of them Rohingya.
In one estimate, there was said to be 1.2 million Rohingya in Myanmar's Rakhine state. Some reports now say there are just 800,000, following forced evictions and murders by racist Myanmar groups.
All countries except Myanmar recognise the problem as Naypyidaw's own creation. The government rejects the Rohingya as Myanmar citizens, thus denying them civil, political and even human rights.
At the same time, Naypyidaw turns a blind eye to mass murders and rapes of Rohingya as extremist groups raze their villages. Human rights reports have cited the involvement of Myanmar troops in such crimes.
For generations, the Rohingya have been native to Rakhine state, previously known as Arakan. They have formed the Muslim community of Arakan for centuries, a territory which used to have its own kingdom.
Through its tumultuous history, Arakan grew into a regional power on its own terms between the 15th and 18th centuries. But it could not match the power of Burma, which in turn was contained by China.
In the late 18th century, imperial Burma invaded and sacked the Siamese capital of Ayutthaya. However, a resurgent Siam soon drove out the Burmese who then turned their attention to Arakan.
Burmese forces conquered Arakan in 1785 but lost it to Britain in the First Anglo-Burmese War four decades later. After three Anglo-Burmese Wars, all of Burma came under British colonial rule.
By the time Burma gained independence in 1948, Arakan had been made a state in the new Burma. In 1989 the ruling military junta renamed Burma as "Myanmar" and Arakan as "Rakhine".
Naypyidaw insists that the strife afflicting the Rohingya is not based on religious grounds. That is a technical detail that is in effect disingenuous.
There are certainly no grounds for anyone in Myanmar to discriminate against Muslims. However, in a country that denies the national integration of ethnic minority groups, Rohingya Muslims are evidently different from the majority Buddhist "Mongoloid Orientals."
That does not make the Rohingya any less Myanmar by identity, since their homeland is Rakhine or the former Arakan.
Myanmar's official "line" is that Rohingya are Bengalis. That would be going by skin complexion, facial features and historical links predating the formation of today's Myanmar – and engaging in racial discrimination.
Communities living in border regions tend to resemble the majority populations of neighbouring nations: Asiatics in the Asian regions of a Eurasian Russia, East Asians in the northern states of India near China, and Rohingya in today's Rakhine state next to Bangladesh.
There have been some limited migrant flows from Bangladesh into Rakhine as in other border regions elsewhere. But the Rohingya remain a distinct community in Myanmar worthy of all due rights and entitlements as citizens.
Opposition leader Aung San Suu Kyi has become a flawed icon by refusing to confront the persecution of Rohingya. Senior members of her party have even joined in attacks of the Rohingya, as government and opposition parties unite on that sordid front.
The cruel oppression of the Rohingya is the sharpest edge of Myanmar's racist treatment of its many minority groups. So long as that problem is not satisfactorily solved, none of the country's ethnic problems can conceivably be solved.
Article by Bunn Nagara which appeared in The Star, 31 May 2015.
BN_The_Star_31May2015.pdf


text:
Define: Chairman Of The Board
Definition: Chairman Of The Board
Quick Summary of Chairman Of The Board
The highest-ranking position in a corporation; the member of the board of directors who presides over board meetings. However, the chairman of the board may not always have the greatest actual authority; this is reserved for the Chief Executive Officer. Sometimes the title is honorary. In small companies, it is common for one person to hold several titles.
What is the dictionary definition of Chairman Of The Board?
The Chairman of the board is the head of a company's board of directors.
Full Definition of Chairman Of The Board
The Chairman of the Board is a senior corporate figure who presides over the board of directors. Much like other members of the board, the Chairman of the Board is elected by the company shareholders, and like the rest of the directorate, the Chairman of the Board is an active representative of the investors. In addition to being the highest officer in a corporation, the Chairman of the Board is responsible for overseeing the function of the board of directors, which includes presiding over the board meetings. The Chairman of the Board must also work closely with chief executive officers and other senior staff to ensure a smooth strategic operation of the company. Other duties of the Chairman of the Board may include devising business strategies and maintaining corporate integrity. A Chairman of the Board may or may not have executive authority. Typically, the Chairman of the Board is elected from the board of governors.
The Chairman (or Chair) of the board's function is to ensure that the board of directors' decision-making process proceeds in an orderly fashion and to help build consensus decisions within the board. As a result of their primary duty, the key asset a successful Chair must have is good people skills.
A chair is selected by his/her fellow board members for the position.
In modern times this term has been increasingly replaced by Chairperson and/or Chair to recognize the more inclusive nature of today's workplace although the term Chairman is still commonly used.
The Chairman of the board does not have to be a separate individual from the CEO of the same company although the Chair's authority in most cases supersedes that of the CEO. The Sarbanes-Oxley Act strongly encourages boards to have independent directors among their membership but does not formally forbid the practice of an executive dual serving as both chair of the board and CEO. Many investors consider a CEO who dually serves as both chair and CEO to be a sign that corporate governance at the company is not sufficiently strong for investment.
https://payrollheaven.com/define/chairman-of-the-board/
Chairman Of The Board. PayrollHeaven.com. Payroll & Accounting Heaven Ltd.
January 17, 2022 https://payrollheaven.com/define/chairman-of-the-board/.
https://payrollheaven.com/define/chairman-of-the-board/ (accessed: January 17, 2022).
Chairman Of The Board. PayrollHeaven.com. Retrieved January 17, 2022
, from PayrollHeaven.com website: https://payrollheaven.com/define/chairman-of-the-board/
Definitions for Chairman Of The Board are sourced/syndicated and enhanced from:
This glossary post was last updated: 4th August, 2021 | 5 Views.


These hand decorated (Large) glitter wine glasses are created by us for you.
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I was so impressed with these glasses! I was a bit nervous that for only £5 they might not be the best quality, but I couldn't have been more wrong. They are nice large glasses and not thin delicate glass. The glitter stem and silhouette are really good quality, such a professional finish. Will make the perfect gift for my friends. Would definitely recommend. Will be ordering more gifts form here.


Manitowoc, WI	Hi-Spy Coin Operated Binocular located in Manitowoc, WI.
Brighton, Newfoundland	Hi-Spy Telescope overlooking the ocean in Brighton, Newfoundland.
Montenegro	Enjoying a beautiful view in Montenegro.
Toronto Zoo	The Hi-Spy binocular at the Toronto Zoo brings the animals closer.
Presquile Beach, Ontario	Taking in the view on an early spring day at Presquile Beach, Ontario.
Birch Aquarium, San Diego Hi-Spy binocular overlooking the ocean at Birch Aquarium.
Pismo Beach CA - Eagle Scout Project	Dual Viewer just installed at Pismo Beach as part of Will Howell's Eagle Scout Project.
Pismo Beach CA - Eagle Scout Project	Pismo Beach CA, Dual Viewer installed as part of Will Howell's Eagle Scout Project.
Pismo Beach CA - Eagle Scout Project	Will Howell contacted Hi-Spy Viewing Machines regarding his Eagle Scout project and his desire to purchase and install a Hi-Spy Viewing Scope for the Pismo Beach community.
Hi-Spy Binoculars atop the Stratosphere Tower	Hi-Spy Binoculars located at the Stratosphere Tower, Las Vegas, Nevada.
Woodside California	A beautiful view at Woodside California.
Viareggio pier	Viareggio pier in Tuscany.


text:
Young Man Escaping From Kidnappers Killed By Soldiers He Ran To For Help In Edo
Homepage – Life News PRO › Forums › Coolval Entertainment news › Young Man Escaping From Kidnappers Killed By Soldiers He Ran To For Help In Edo
December 18, 2021 at 7:38 am #1496636 Reply
Boy Escaping From Kidnappers in Edo Ran to Soldiers for Help. 'But They Killed Him'
Ehima Mercy, a police officer in Agbo, Delta State, has accused soldiers attached to the railway checkpoint in Igbanke, Edo State, of killing Christian Konor Ehima, her eldest son.
Mercy told FIJ that she met her son in a terrible state after the soldiers brutalised and left him to die on a dusty road. He eventually died in a hospital.
On December 10, Konor left the house of one of his cousins, who was to get married the following day, for his mother's house, a 35-minute journey. But on getting to the bus stop at 5 pm, there was hardly any bus.
By 7:30 pm, Mercy called to ask him why he was yet to be home, but the voice she heard on the phone was not his.
"I asked them, 'Who are you?' And they claimed to be soldiers stationed at the railway checkpoint between Igbanke and Epon. I introduced myself as a police officer and I asked them where my son was," she told FIJ.
"The soldiers did not say they had beaten him, but claimed my son had dragged their guns with them. I began to plead with the soldiers not to touch him. Then they asked me to come and see them."
When Mercy got to the soldiers, she thanked them and collected Konor's phone from them. After delaying her for a while, they showed her where her son was.
"When I saw my son, dust was all over him. So one may not recognise him. He was only in his boxers. They did not give me his trousers; I knew it was because they wanted to tell a different story to the police," she said.
The soldiers made videos of her when she went to see her son and said he showed signs of lunacy. Konor strived to meet his mother on hearing her voice, but he couldn't stand upright. His legs had been broken and he had sustained heavy injuries to his spine due to the soldiers' beating.
"Seeing how my son was, I pleaded with them to use their van to take him to a nearby hospital, but they refused because I am a police officer. I immediately called my family doctor, who then took another hour before he got to Igbanke," she said.
"I looked at my son and every part of his body was covered with clay. He was half-conscious and his eyes were dim. He was heavy. I then pleaded again with the soldiers to lend us their van."
When they got to the hospital, Konor woke up after he had received two drips. He asked his mother for food but the doctor stopped her from giving him, saying he was bleeding internally.
KONOR'S EXPERIENCE
Konor told his mother how he escaped being killed by kidnappers. He had boarded their bus as he thought it was a commercial vehicle.
"Konor said when he found out he was in a kidnapper's bus, he jumped out of the moving vehicle and hid in a bush. He texted 'I love you mummy' at this point, but I didn't get it because I was cooking. When he saw a snake, he ran out of the bush and saw some soldiers," she said.
"He said he ran to the soldiers, but on seeing him, they cocked their guns. He attempted to explain, but he was asked to shut his mouth up and lie on the ground."
The soldiers ordered him to stand up. As he did, one of them kicked his legs and he fell. They did it continuously for some time. Later, they began to step on his laps until he could not walk again.
"After he said this, he died. The time was 2:47 am on Saturday," Mercy told FIJ.
She reported the incident to Igbanke Police Station and the soldiers were summoned. While they could not exactly exonerate themselves, they claimed Konor was a lunatic.
"One of them said I knew my son was crazy, and that was why I brought milk to the checkpoint. Another soldier said I knew he was a lunatic because I asked my family doctor if he came to the checkpoint with injections," Mercy said.
"I thought the injections would make him stronger and the milk would give him strength, but the soldiers used it against me because he was no more alive."
Konor's body is lying in a mortuary in Edo State, while his mother is trying to get justice for her son. "Worst thing is these soldiers are still at this checkpoint," said Mercy.
According to her, Philip Aliyu Ogbadu, the Edo State Commissioner of Police, has written to the army on the incident.
When FIJ contacted Onyema Nwachukwu, the army's spokesperson, he said Konor's mother should write to the military police in Edo State.
"Let her write to the military police in the state," he told FIJ. "The fact that she is a police officer has solved half of the issue. The army does not support bad behaviour; it is punished."
https://fij.ng/article/boy-escaping-from-kidnappers-in-edo-ran-to-soldiers-for-help-but-they-killed-him/
Peace Egwuatu
December 18, 2021 at 10:17 am #1496698 Reply
I have said it our security personnel are the real criminals
Even if the guy were to be a mad man, what right do you guys have to kill him. Nigeria army and their level of ignorance
Oh my goodness look at this beautiful guy my God 🙏🏿,
Those policemen will never die well o
Naija i no kn de time we go grow up walahi ☝.
Justice for this guy biko 😭.
He deserves justice
This is too much
Serious matter
Reply To: Young Man Escaping From Kidnappers Killed By Soldiers He Ran To For Help In Edo


I'm currently in the airport in San Francisco, waiting to get my flight back to Charlotte. I thought this would be a good time to post the past week's Twitter #weirdscifacts! Hopefully life will settle down somewhat soon and I'll be back to more regular science and pulp fiction postings.
313. Jan 20: The 1827-28 Burke & Hare murders; motive was cash for anatomy college specimens. In that era, the need for anatomy specimens was on the rise, but legal sources — namely executed criminals — were on the decline. Burke and Hare took it upon themselves to create their own source of fresh corpses, via murder.
314. Jan 21: The Great Thunderstorm of Widecombe-in-the-Moor, 1638: apparent ball lightning kills 4, injures 60. The ball lightning hit during a church service; it was "explained" at the time as the devil arriving to claim the soul of a man sleeping during church.
315. Jan 22: Scientists have used tombstones to track environmental changes. Tombstones are long-lasting and can serve as a record of pollution and other environmental factors.
316. Jan 23: Scientist & philosopher Francis Bacon died in 1626 from pneumonia, possibly due to studying the use of snow to preserve meat. The connection is not quite certain, but Bacon is often cited as a man who literally died in the pursuit of scientific knowledge.
317. Jan 24: The doctor who kept Einstein's brain in a jar for 43 years.
318. Jan 25: Flesh-eating plant doubles as bat-cave. Couldn't beat this post by @edyong209 for sheer weirdness on this day!
That seems a little out of the ordinary for protocol in a coroners office, whouldn't Einstein's brain, or any his organs have to be accounted for? Never understood how he got away with this.
It is sort of baffling to me, too. There does seem to have been some sort of legal action, but little apparently came of it.


text:
Glean Art Show: A Must See
Nancy Ranchel
It's the Glean Art Show at the PNCA Bison Building in NE Portland. Why do you need to see this show? Because it's one of those cool Portland events that you need to experience. You can tell your think-they're-so-hip midwestern cousins about it. They won't understand. They're still drinking from Styro. Here's how the show works: Every year five artists (for '16 the artists are Erinn Kathryn, Hilary Pfeifer, Dan Pillers, Amanda Triplett and Austin Turley) are selected to go to the Recology transfer station and pick out material to use in their work. They receive a stipend for materials, and work on their art as the summer progresses.
This year I was particularly interested in the work of Hilary Pfeifer. I've seen Hilary's work before, and follow her on Instagram (so I'm basically a groupie / stalker and the Glean show is a perfect excuse to finally buy one of her pieces!). All work from the Glean show is made from trash - that's a process we all need to get behind, folks! I'm paving the way. Hilary made freaky cool hanging sculptures for the show (Pic 1). By poring over the posts on the Glean blog, I made my selection (Pic 2). And promptly started fretting.
Here's the problem, because there's always a problem, right? Particularly for tightly wound accountant types. Last year I went to the show on its last weekend. It was at Disjecta, and I walked in and immediately fell in love with just about everything. Someone had made bowls out of nails. Nails from the trash! I wanted everything, and most of it was sold. Gone. Promised to and paid for by someone else. This year I was determined to not make the same mistake. I was going to get the piece I wanted, which required being there opening night, and being there early, because pre-purchasing is not allowed. (Here's another cool fact about the Glean show: the art is affordable.)
Have you ever been to a concert at Edgefield or the Zoo in the summer? Everyone queues up and when the gates are finally open people run in, knocking each other over and fighting for the best places on the grass? Well, it's not like that at the opening of the Glean show. It's civilized, plus there are crackers and cheese. And wine. The people are nice. There's no running. Opening night was heaps o' fun. The sculpture is mine. The good news is that the show runs through August 27. And there's lot more for you to pick up for yourself. Promise. You can find the time to get over to the show and have a look-see. Skip that GD company picnic. Go buy some art instead - art inspired by trash, made by really talented artists, art that's affordable and cool.


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Q: How to apply filter on pivot table? Model of Category
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Model of Listing
public function categories(){
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}

Controller: when I dd this query then I get all lists that is in listings table but I just want to get those lists whose have these categories.
if($request->categories){
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    }])->get();
    dd($lists);

A: This code is working perfect
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A: Give some id name to your table
<table id="userTable" style="display: table;" class="table table-responsive text-center table-striped table-hover">

and then use the following script:
<script type="text/javascript">

    $(document).ready(function() {
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        scrollY:        "400px",
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        order: [[ 4, 'dsc' ]],
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    table.on( 'order.dt search.dt', function () {
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    } ).draw();
} );

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now you must able to search, filter, reorder your table.


text:
Jude Law >
'Dom Hemingway' Could Be Jude Law's Tour-de-Force [Trailer +...
'Dom Hemingway' Could Be Jude Law's Tour-de-Force [Trailer + Pictures]
By Michael West in Movies / TV / Theatre on 25 September 2013
Follow Jude Law
Jude Law Richard E. Grant
Jude Law excels in Dom Hemingway, from Sexy Beast producer Peter Webster.
Jude Law. He's a bankable movie star. He's also a well-respected movie star, with two Academy Award nominations and a BAFTA. The Talented Mr Ripley, Cold Mountain, Road to Perdition and Sherlock Holmes form his finest moments, though all could be eclipsed by Dom Hemingway - a new dark comedy-cum-gangster flick from Peter Watson, the producer behind Sexy Beast, perhaps the last classic of its genre.
Jude Law [L] as Dom Hemingway and the superb Richard E. Grant [R] as Dickie
Law plays a washed up lothario safecracker who refuses to leave behind his life of crime after getting released from prison. He tries to gain revenge on his former boss (the Oscar nominee Demian Bichir) and teams up with his former partner (Richard E. Grant) to do it. He's also trying to reconnect with his daughter, played by Game of Thrones' Emilia Clarke, which appears to make for an interesting and comic subplot.
The movie premiered at the Toronto Film Festival two weeks ago and earned strong reviews, despite British gangster films rarely convincing foreign critics. Variety called the lead character "one of the the meatiest personalities in a genre known for larger-than-life types," while Film.com said, "A marvelous lead character, and Jude Law, a little puffier and hairier than usual, turns in a career best performance."
Dom Hemingway Shows His Behind
"He's got the most extraordinary poetic way of expressing himself, he loves the sound of his own voice," Law told reporters of his character in Toronto this month, "He has a power with words. He stills sees himself as a hero, a champion of the people, a rock and roll star, and of course he's not that at all.
"...and it was a challenge..it was a nice for a few months to hang out, eat what I wanted to, drink what I wanted to and smoke what I wanted to," he added.
Of bringing his movie to Toronto, Law explained, "Funnily enough, this year more than ever, bringing a film like this, a truly independent movie...it has great potential. So driving here past queues of people trying to get into cinemas is so exciting. I'm a film buff...so it's lovely coming to a city that embraces that."
Dom and Dickie Catch Up On Old Times in 'Dom Hemingway'
Several commentators have mused of a potential "comeback" for Law on the back of Dom Hemingway, though his recent credits list, Side Effects, Anna Karenina, Sherlock Holmes, Hugo and Wes Anderson's forthcoming The Grand Budapest Hotel suggest otherwise. However, something that's certain is that Richard Shepard's latest movie could give Hollywood movie directors something to think about when considering their next leading man.
A little older and with a little less hair (certainly in Dom Hemingway for which he gained 2 stone and sported a receding hairline), Jude Law has broken free of his pretty boy reputation in recent years - though will Hollywood listen?
Jude Law Says He Drank, Ate and Smoked What He Want For 'Dom Hemingway'
In a genre blighted by at best sheer mediocrity and at worst pure bilge, Dom Hemingway could easily be the best British gangster movie since Layer Cake. Rise of the Footsoldier, RocknRolla, The Business and Welcome to the Punch found their audiences, though a lack of humour just opens everything up to ridicule.
Dom Hemingway could, just could, be different.
It opens in cinemas in the UK on November 15, 2013.
Watch the Dom Hemingway trailer:
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