text:
\section{Introduction}
	\label{sec:intro}
	Current mainstream object detectors are two-stage detectors represented by the R-CNN series (\emph{e.g}, R-CNN~\cite{Girshick2014RichFH}, Fast R-CNN~\cite{girshick2015fast}, Faster R-CNN~\cite{Ren2015FasterRT} and R-FCN~\cite{pang2019libra}) and one-stage detectors such as RetinaNet~\cite{lin2017focal}, YOLO~\cite{Redmon2016YouOL,Redmon2017YOLO9000BF} and SSD~\cite{Liu2016SSDSS,fu2017dssd}. A two-stage detector applies the convolutional network to classify the candidate object proposals generated in the first stage, while a one-stage detector classifies a dense set of object locations directly. However, whether it is a two-stage detector or a one-stage detector, they are facing easy-hard samples imbalance problem. The number of easy negatives far exceeds the number of hard negatives. This within-class imbalance problem has an adverse effect on detection performance. As shown in Figure \ref{fig:imb}, hard samples (overlapping areas of two categories) have more contributions to build the decision surface in training. For a network with insufficient hard samples to train (Figure \ref{fig:imb} (a)), if the number of easy samples reduces, the probability of hard samples to be selected will be raised under the sampling strategy. The learned decision surface will be more close to the real decision surface (Figure \ref{fig:imb} (b)).

	To some extent, the foreground-background class imbalance problem is addressed in the R-CNN series detectors as the majority background proposals are filtered out through sampling heuristics.
	In two-stage detectors, region proposals which are more likely to be background samples generated in proposal stage are rapidly decreased. The second classification stage uses the sampling strategy to fix the ratio (1:3) of foreground-background candidate proposals in a mini-batch for training, which aims to guarantee a manageable balance between foreground and background samples. However, the random sampling in two-stage detectors cannot solve easy-hard samples imbalance problem, because the distribution of samples in IoU (Intersection over Union) is uneven~\cite{pang2019libra}. On the contrary, a one-stage detector has to handle a dense set of candidate object locations, even applying sampling strategy to it, it is still dominated by plenty of easy background samples during the training procedure. Commonly used and effective methods to balance the easy-hard samples are OHEM (online hard sample mining~\cite{Shi2015EarlyBD,li2017s}), Focal loss~\cite{lin2017focal} and so forth~\cite{Wang2017AFastRCNNHP}. They either create hard samples or perform hard samples mining in the training stage.

	\begin{figure}
		\begin{center}
			\includegraphics[width=0.75\textwidth]{./distribution.pdf}
		\end{center}
		\caption{Illustration of easy-hard imbalance problem after the random sampling strategy in detectors. It is assumed that the positive (blue) and negative (orange) samples satisfy the Gaussian distribution. The hollow circles, solid circles, hollow diamonds and solid diamonds denote the easy positive samples, hard positive samples, easy negative samples and hard negative samples, respectively. $\pi$ is the real decision surface, $\pi ^ 1$ and $\pi ^ 2$ are the learned decision surface.}
		\label{fig:imb}
	\end{figure}

	In this paper, we propose a Proposal-balanced Network (ProbaNet) collaborated with RPN (Region Proposal Network) to address the easy-hard samples imbalance problem in current two-stage object detectors. ProbaNet assigns lower weights to majority background, which include many easy samples, and higher weights to object areas, which include many meaningful proposals and hard samples. Then we utilize a threshold to filter out easy samples, thus the distribution of training data will be more balanced than its original form. Another advantage of ProbaNet is that it tends to assign higher focus to candidate proposals that are more meaningful (\emph{e.g}, the proposals containing objects), thus more meaningful proposals will be selected in the test stage. In addition, we apply a statistical constraint in ProbaNet which guarantees a larger difference among proposals, thus enabling ProbaNet to distinguish the hard samples. ProbaNet has good compatibility and is easy to be embedded in other deep network models. Experimental results confirm that the proposed ProbaNet alleviates the easy-hard samples imbalance problem in object detectors and improves the object detection strength while offering very small extra computational cost.

	\section{Related work}
	\label{sec:work}
    \textbf{Object detectors:}
	Deep learning based detectors dominate modern object detection. R-CNN~\cite{Girshick2014RichFH} generates candidate object locations that contain all objects theoretically in the first stage, uses CNN to extract features for every region proposal and classifies them as foreground or background classes through SVM (Support Vector Machine) algorithm in the second stage. Fast R-CNN~\cite{girshick2015fast} achieves near real-time rates using the convolutional network for extracting features and classifying, regardless of the time spent on region proposals. Faster R-CNN~\cite{Ren2015FasterRT} using RPN to integrate the region proposal process and the second stage into a single and unified network. The other mainstream modern object detectors are one-stage detectors (\emph{e.g}, SSD~\cite{Liu2016SSDSS,fu2017dssd} and YOLO~\cite{Redmon2016YouOL,Redmon2017YOLO9000BF}). These detectors have been adjusted for speed, but their accuracy usually lags behind the two-stage approach.
	Multiple improvements in terms of speed and accuracy have been proposed for object detectors or backbones. For sample, for object location, G-CNN~\cite{Najibi2016GCNNAI} removes the object proposal phase in the CNN-based object detection framework and models the detection problem as an iterative regression problem which further improves accuracy and speed. Instead of correcting the object positions by regression in Faster R-CNN, Grid R-CNN~\cite{Lu2018GridR} drops the fully connected layers in the original model, instead, it uses a fully convolutional network (FCN~\cite{long2015fully}) to get more precise object positions.
	For the capability of detection, Wang \emph{et al}~\cite{Wang2017AFastRCNNHP} trained a model with samples having occlusions and deformations generated by the adversarial network to make detectors invariant to occlusions and deformations, while Li \emph{et al}~\cite{Li2017PerceptualGA} improved small object detection through a generative adversarial network.
	The popular method attention mechanisms~\cite{Chorowski2015AttentionBasedMF} have utility across many tasks including object detections, such as~\cite{Ren2015FasterRT,Hu2018SqueezeandExcitationN}. In terms of the base network structure, Hu \emph{et al}~\cite{Hu2018SqueezeandExcitationN} proposed the Squeeze-and-Excitation (SE) block that adaptively recalibrates channel-wise feature responses which brought outstanding improvements for CNNs. Deformable convolution and deformable RoI pooling proposed by Dai \emph{et al}~\cite{dai2017deformable} enhance the geometric transformation modeling capability of CNNs.

	\noindent
	\textbf{Class imbalance:}

	Class imbalance problem has been thoroughly studied in classical machine learning and data mining. In the field of object detection, R-CNN-like detectors address foreground-background class imbalance through sampling strategy and a cascade classifier~\cite{Ren2015FasterRT}, but they are still facing with easy-hard samples imbalance problem. Both two-stage detectors and one-stage detectors address classification loss and box regression loss imbalance by applying the certain penalty on the corresponding loss~\cite{girshick2015fast,Ren2015FasterRT,Redmon2016YouOL,Redmon2017YOLO9000BF}. OHEM~\cite{Shi2015EarlyBD,li2017s}, Focal Loss~\cite{lin2017focal} and GHM (Gradient Harmonizing Mechanism)~\cite{li2018gradient} unearth hard samples according to their loss. OHEM is sensitive to noise samples and Focal loss needs extra hyper-parameters for data having a different distribution, while GHM performs worse than Focal loss in small object detection. Wang \emph{et al}~\cite{Wang2017AFastRCNNHP} created hard positive samples through an adversarial network. Libra R-CNN~\cite{pang2019libra} propose three components named as IoU-balanced sampling, balanced feature pyramid and balanced $L1$ loss to solve imbalance problem from sample level, feature level, and objective level, respectively.

	\section{ProbaNet: Approach Details}
	\label{sec:ProbaNet}

	Two-stage object detectors address the foreground-background class imbalance problem through fixing the ratio of foreground to background samples (1:3). Because the number of easy background proposals far exceeds than the number of hard negatives, random sampling strategy is likely to choose more easy negatives under uneven distribution in IoU (Intersection over Union). These easy negatives have less contribution to the update of the gradient. So far, most scholars have focused on creating or mining hard samples through the loss function and balancing the loss between classification and regression based on cost-sensitive methods~\cite{zhou2006training}. There have been some attempts to assign weights to the datasets themselves. Can we assign weights to different candidate proposals and raise the probability of hard negatives to be selected? In this paper, we attempt to meet this objective by proposing ProbaNet. The procedure of the proposed ProbaNet is discussed in the following subsections.

	\begin{figure}
		\begin{center}
			\includegraphics[width=.92\textwidth]{./Proba_faster.pdf}
		\end{center}
		\caption{Faster R-CNN with ProbaNet. It is still a single and unified network. The red dotted frame is the detail of the black solid frame. (Point 1,2 and 3 represent convolutional transformation.)}
		\label{fig:teaser}
	\end{figure}

	\subsection{ProbaNet Design}

	An overview of Faster R-CNN with ProbaNet framework is shown in Figure \ref{fig:teaser}. We embed ProbaNet in RPN, so it is still a single and unified network. The shaded part is RPN with ProbaNet, the upper and left part (the red arrow flow) in the dotted box represents details of ProbaNet, the rest is just the original components in Faster R-CNN defined in~\cite{Ren2015FasterRT}. As discussed above, in the field of object detection, usually, the proportion of background is larger than objects, even overwhelming foreground. Just looking at the image shown in left-bottom of Figure \ref{fig:teaser}, the black dog, the person with a blue coat and the white horse have less proportion than the grassland background. Taking proposals generated in RPN as a sample, for the training stage, the majority proposals containing no objects (easy samples) are easy to be classified and have limited contribution to the detector in training. For the test stage, the number of proposals containing no objects far exceeds the proposals containing objects which potentially harm the overall performance of the model.

	It is a well-known fact that the objective of RPN in Faster R-CNN is to generate proposals. So can we further improve the network capability to reasonably weight different proposals to address the imbalance problem? We propose ProbaNet aimed at suppressing samples having a small contribution for training or deserving high focus for test. The architecture and formulation of ProbaNet are discussed as follows:

	As is shown in the dotted box of Figure \ref{fig:teaser}, following the convolutional layers in RPN, we stack two convolutional layers and a sigmoid activation to generate the corresponding weights with the proposals. Suppose that convolutional feature maps generated from the backbone are represented as $\mathbf{X} \in \mathbb{R}^{H \times W \times C}$, where $H \times W$ is the spatial dimension and $C$ is the channel of $\mathbf{X}$ . $\mathbf{A} \in \mathbb{R}^{H\times W \times {C^{'}}}$ represents the candidate proposals generated during RPN and $C^{'}$ is the channel of $\mathbf{A}$. For simplicity, we ignore the ReLU function following convolution layers here. Then the process of ProbaNet can be seen as

	\begin{align}
	\label{ProbaNet_function}
	\begin{split}
	\mathbf{A}^{'}=\mathbf{A} \odot \mathbf{T}_2
	& = F^1_{conv}(\mathbf{X}) \odot \sigma(F^3_{conv}(\mathbf{T}_1)) \\
	& = F^1_{conv}(\mathbf{X}) \odot \sigma(F^3_{conv}(F^2_{conv}(\mathbf{X}))).
	\end{split}
	\end{align}

	Here, ${\mathbf{T}_1} \in {\mathbb{R}^{H \times W \times {C / r}}}$ represents temp feature maps during ProbaNet, $r$ is the reduction parameter that needs to be mannually set, and ${\mathbf{T}_2} \in {\mathbb{R}^{H \times W \times C}}^{'}$ stands for the final weights learned for $\mathbf{A}$. ${\mathbf{A}^{'}} \in {\mathbb{R}^{H \times W \times {C^{'}}}}$ represents the final candidate object locations with weights. ${F^k_{conv}}$ ($k=1,2,3$) is taken to be convolutional transformation, $ \sigma $ is Sigmoid function where $ \sigma (x) = {1 / (1 + {e^{ - x}})}$. $\odot$ is Hadamard product. We use $1 \times 1$ sliding window in both two convolution layers because ProbaNet focuses on assignment of appropriate weights to candidate proposals. This enables ProbaNet to establish associations among candidate proposals.

	\subsection{Proposal Selection}

	\textbf{For training stage:} Due to the effect of minimizing the loss in ProbaNet based RPN, proposals generated by RPN working together with ProbaNet will get more proper focus than those only generated by RPN. However, the easy negatives with slight weights but vast numbers still have higher chances to be selected under the random sampling strategy. They have less contribution to models in the training stage. To choose more valuable samples (hard samples) in the training stage, we further dismiss proposals with weights less than $th$ $(0 \le th < 1)$ . Therefore the final proposals $B$ reserved are

	\begin{equation}
	\label{final_proposals}
	\mathbf{B}(i,j) =
	\begin{cases}
	\mathbf{A}^{'}(i,j),  & \mbox{if } \mathbf{T}_2(i,j) \succ th \\
	0, & \mbox{otherwise.}
	\end{cases}
	\end{equation}

	Thus more easy samples will be screened out in training. In equation \ref{final_proposals}, ${i,j} \in {\mathbb{N}}$ and $i,j \leq H$, $i,j \leq W$. The value of parameter $th$ should be considered according to the distribution of datasets. If the easy-hard samples imbalance is heavy, $th$ should be set small enough because the ProbaNet will give little focus on proposals which are likely to be easy negatives samples. On the contrary, $th$ should be set higher if the class imbalance is light. The rest network components are the same as the original Faster R-CNN.

	Furthermore, For better assisting the ProbaNet, we impose a variance constraint on proposals to strengthen the discrimination capacity of ProbaNet before its focus assignment,
	\begin{equation}
	\label{variance}
	V =  \mathrm{E}\left[(\mathbf{X}-\mathrm{E}\left[{\mathbf{X}}\right])^T (\mathbf{X}-\mathrm{E}\left[{\mathbf{X}}\right])\right],
	\end{equation}
	where $\mathrm{E}\left[{\mathbf{X}}\right] $ is the average value of candidate proposals' weights $\mathbf{X}$, which are the input of the ProbaNet. Equation \ref{variance} can enhance the discrimination between weights of foreground and background. In our experiments, we utilize this constrain as loss function as follows,
	\begin{equation}
	\label{variance_loss}
	\mathcal{L}_{ProbaNet} = \beta e^{1/V},
	\end{equation}
	Where $\mathcal{L}_{ProbaNet}$ is the loss function of the proposed ProbaNet. The $\beta$ guarantees $\mathcal{L}_{ProbaNet} < \mathcal{L}_{cls}$, where $\mathcal{L}_{cls}$ is the classification loss of RPN and $\beta$ is automatically adjusted in training.

	\noindent
	\textbf{For test stage:} The process of ProbaNet in the test is the same with its training process except the $th$ in equation \ref{final_proposals} is set to 0. Our experiments show that the proposed ProbaNet assign higher weights to the proposals containing objects and give them higher probabilities of being selected.

	In general, we propose RPN with ProbaNet to address within-class imbalance problem. The black solid frame in Figure \ref{fig:teaser} shows how ProbaNet works. ProbaNet gives a certain focus on proposals generated by RPN. Through the threshold truncation and the variance constraint, the number of easy negatives will be limited such that to alleviate the adverse effects brought by easy-hard samples imbalance problem in training. In addition, the proposals containing objects with higher weights are more likely to be selected.

	\subsection{Analysis of Model Complexity}

	\begin{table}
		\begin{center}
			\footnotesize
			\begin{tabular}{|l|cc|cc|}
				\hline
				&Re-FastR  &Re-FastR+ProbaNet  &Re-R-FCN  &Re-R-FCN+ProbaNet\\
				\hline  \hline
				Params(MB)      &522.91   &522.98   &216.33  &216.40\\
				FLOPs(G)        &151.30   &151.33   &75.75   &75.78\\
				\hline
			\end{tabular}
		\end{center}
		\caption{Compare the re-implementation model with the model with ProbaNet. Re-FastR is short for re-implemented Faster R-CNN. The parameter $r$ is 16 and $th$ is 0.5. Size of the input is $600\times800\times3$.}
		\label{tab:paflop}
	\end{table}

	For practical use, object detectors with the proposed ProbaNet should offer a good trade-off between improved performance and increased model complexity. We consider a comparison between the re-implementation model and the ProbaNet based model. Both experiments are conducted on a server with one NVIDIA GTX-1080Ti GPU.
	We calculate the additional parameters introduced by ProbaNet. The additional parameters are generated solely by the two convolutional layers, making up a small portion of the total network capacity. Concretely, the total number of extra parameters is given by

	\begin{equation}
	\label{extra_cost}
	C \times (\frac{C}{16}+1) + \frac{C}{16} \times(C^{'}+1),
	\end{equation}
	where $C$ refers to the dimension of the input channels and $C^{'}$ denotes the dimension of the output channels. As shown in Table \ref{tab:paflop}, ProbaNet introduces only extra 0.07MB parameters and 0.03G FLOPs. Faster R-CNN takes 44.9ms per image, compared to 45.2ms for Faster R-CNN with ProbaNet, while the latter get 1.1$\%$ higher mAP than the former. Hence, it is evident that ProbaNet improves the object detector capabilities while offering a very little amount of extra computational cost.

	\section{Experiments}
	\label{sec:exp}

	In this section, we conduct experiments on the PASCAL VOC 2007 and PASCAL VOC 2012 detection benchmarks to investigate the effectiveness of the proposed ProbaNet based method. We perform the most ablative study on PASCAL VOC 2007 and PASCAL VOC 2012 with Faster R-CNN. In addition, we compare ProbaNet with OHEM, Focal loss and compare R-FCN with R-FCN+ProbaNet. Furthermore, we visualize some weights learned by ProbaNet and show proposals with top5$\%$ weights in the original image. Finally, we validate that ProbaNet indeed alleviates the easy-hard samples imbalance problem.

	\subsection{Implementation Details}

	We use identical training schemes for the re-implemented models and models with ProbaNet. We normalize the input images by averaging channel subtraction and re-scale the input so that the shorter side has 600 pixels. In RPN, we sample 256 proposals from 2 images within a mini-batch. The whole network is trained with stochastic gradient descent (SGD). All models are trained with an initial learning rate of 0.001, weight decay of 0.005 after every 5 epoch and momentum of 0.9. We apply ProbaNet to the whole image during the test stage.

	\subsection{Experimental settings}
	Experiments are performed using VGG-16 for Faster R-CNN and ResNet-101 for R-FCN. The VGG-16 and ResNet-101 models are pre-trained on ImageNet. For the PASCAL VOC datasets, we use the 'trainval' set for training and 'test' set for testing. PASCAL VOC 2007 dataset consists of 5k trainval images composed of 13K objects and 5k test images over 20 object categories, while about 12k trainval images composed of 27K objects and 11k test images over 20 object categories in PASCAL VOC 2012 dataset, which has no public labels and requires the use of the evaluation server. Furthermore, we train models with the 'trainval' set in both VOC datasets. Among these experiments, the only difference between the re-implementation method and our proposed method is that the proposed method uses ProbaNet.

	\subsection{Experiments on PASCAL VOC}
	In this subsection, we validate the effectiveness of the proposed ProbaNet based method by taking into account different baseline architectures. In the first experiment, we develop Faster R-CNN and compare its performance with ProbaNet based Faster R-CNN, the results are reported in Table \ref{tab:07test}. In the second experiment, we develop Faster R-CNN using OHEM or Focal loss and compare its performance with ProbaNet based Faster R-CNN, the results are reported in Table \ref{tab:focal}.  Additionally, we also developed another baseline architecture i.e. R-FCN and compared its performance with ProbaNet based R-FCN, the results are shown in Table \ref{tab:07test}. In the third experiment, we evaluate the sensitivity of the hyper-parameter $th$ and $r$ inside the proposed ProbaNet, the results can be seen in Table \ref{tab:th_r}. Details about these experiments are discussed as follows:

	\newcommand{\tabincell}[2]{\begin{tabular}{@{}#1@{}}#2\end{tabular}}

	\begin{table}
		\begin{center}
			\footnotesize
			\begin{tabular}{|l|c|ccc|cc|}
				\hline
				\multirow{2}{*}{\tabincell{c}{Test\\ data}}  &\multirow{2}{*}{\tabincell{c}{Training\\ data}}   &\multicolumn{3}{c}{FastR}   &\multicolumn{2}{|c|}{R-FCN}\\
				\cline{3-5} \cline{6-7}  &  &FastR   &Re-FastR  &Re-FastR+ProbaNet  &Re-R-FCN  &Re-R-FCN+ProbaNet\\
				\hline \hline
				\multirow{3}{*}{07}   &07     &69.9  &69.9  &\textbf{71.1}   &74.1   &\textbf{74.9}\\
				&12     &-     &73.2  &\textbf{73.5}   &76.2   &\textbf{76.3}\\
				&07+12  &73.2  &75.9  &\textbf{76.4}   &79.1   &\textbf{79.5}\\
				\hline
				\multirow{3}{*}{12}   &07     &-     &63.5  &\textbf{64.4}   &69.0     &\textbf{69.1}\\
				&12     &67.0  &69.9  &\textbf{70.2}   &73.2     &\textbf{73.6}\\
				&07+12  &70.4     &72.0  &\textbf{72.3}   &75.1     &\textbf{75.5}\\
				\hline
			\end{tabular}
		\end{center}
		\caption{Detection results (mAP($\%$)) on PASCAL VOC test set. The backbone is VGG-16. FastR is short for the official Faster R-CNN, Re-FastR represents for re-implementated Faster R-CNN. Training data: ``07'': VOC 2007 trainval, ``07+12'': union set of VOC 2007 trainval and VOC 2012 trainval. ``12'': VOC 2012 trainval. The data having underline means that it is trained with the union set of VOC 2007 trainval and VOC 2012 trainval.}
		\label{tab:07test}
	\end{table}

	\noindent
	\textbf{Experiments with Faster R-CNN and R-FCN.} As is shown in Table \ref{tab:07test}, Faster R-CNN combined with ProbaNet gives $0.3\% \sim 1.2\%$ boost upon the baseline, while R-FCN combined with ProbaNet gives $0.8\%$ improvement on the baseline.

	\begin{table}
		\begin{center}
			\footnotesize
			\begin{tabular}{|l|cccc|}
				\hline
				Training data   &Re-FastR    &Re-FastR+OHEM   &Re-FastR+Focal loss  &Re-FastR+ProbaNet\\
				\hline  \hline
				07        &69.9   &70.2   &70.4   &\textbf{71.1}   \\
				12        &73.2   &73.0   &73.4   &\textbf{73.5}   \\
				07+12     &75.9   &75.4   &76.2   &\textbf{76.4}   \\
				\hline
			\end{tabular}
		\end{center}
		\caption{Detectors' detection results(mAP($\%$)) on PASCAL VOC 2007 test set when combined with different methods. Re-FastR is short for re-implementated Faster R-CNN. The backbone for Faster R-CNN is VGG-16 and for R-FCN is ResNet-101. Training data: ``07'': VOC 2007 trainval, ``07+12'': union set of VOC 2007 trainval and VOC 2012 trainval. ``12'': VOC 2012 trainval.}
		\label{tab:focal}
	\end{table}

	\noindent
	\textbf{Experiments with methods for easy-hard samples imbalance.} Table \ref{tab:focal} shows results comparing ProbaNet with other methods for addressing easy-hard samples imbalance in object detection. For Faster R-CNN trained with VOC 2007 trainval set, using OHEM and Focal loss achieve 70.2$\%$ and 70.4$\%$ mAP, respectively. When combined with ProbaNet, mAP of the model has been improved up to 71.1$\%$. When trained with VOC 2012 and the union set of VOC 2007 and VOC 2012, all these methods have similar boost upon the baseline.

	\begin{table}
		\begin{center}
			\footnotesize
			\begin{tabular}{|l|c|ccccc|}
				\hline
				\multirow{2}{*}{$th=0.5$} &$r$    &16            &8     &4     &2     &1\\
				\cline{2-7}         &mAP($\%$)    &\textbf{71.1} &70.1  &70.6  &70.5  &70.5\\
				\hline \hline
				\multirow{2}{*}{$r=16$}  &$th$    &0.3  &0.4   &0.5            &0.6     &0.7\\
				\cline{2-7}         &mAP($\%$)    &70.5 &70.2  &\textbf{71.1}  &70.6    &70.1\\
				\hline
			\end{tabular}
		\end{center}
		\caption{Detection results on VOC 2007 test set using Faster R-CNN with ProbaNet. Training with VOC 2007 trainval set and the backbone is VGG-16.}
		\label{tab:th_r}
	\end{table}

	\noindent
	\textbf{Evaluating the sensitivity of the hyper-parameters.} Table \ref{tab:th_r} shows how the hyper-parameters $th$ and $r$ in ProbaNet effect the models' performance. $r$ is decided to control the ability to represent interdependencies among proposals, $th$ is meant to select which proposals need to be filtered out. To evaluate the sensitivity of this hyper-parameters in ProbaNet, we conduct experiments with ProbaNet based Faster R-CNN using VOC 2007 dataset. We observe that with enough focus, ProbaNet is able to overfit to the interdependencies among proposals. For VOC 2007 dataset, the threshold $th = 0.5$ and $r = 16$ achieves the best performance.

	\subsection{Experiments to further analyze effectiveness of ProbaNet}

	Our approach is designed to assign appropriate weights to candidate proposals for helping counterweight easy-hard samples imbalance in object detection networks. Although we have shown that our method is effective from many aspects, to investigate the effectiveness of our method more deeply, we further do some experiments. To demonstrate the effectiveness of ProbaNet, in Figure \ref{fig:w} we visualize weights learned for candidate proposals and proposals with top 5$\%$ and top 1$\%$ weights generated by ProbaNet based RPN. In Figure \ref{fig:rpn_out} (a) we show the ratio of hard samples to all samples in a mini-batch during the training process. To validate the effectiveness of the statistical constraint in ProbaNet, we show the output after RPN with ProbaNet in Figure \ref{fig:rpn_out} (b).

	\begin{figure}
		\begin{center}
			\includegraphics[width=0.8\textwidth]{./visualize_weights.pdf}
		\end{center}
		\caption{Visualization of weights generated by ProbaNet in testing. The framework we utilize is Faster R-CNN. (a) Original images of the test dataset. (b) and (d): Visualization of weights in one feature map learned in ProbaNet corresponding to one kind of proposals. (c) and (e): Proposals mapping to original images with corresponding weights showed in (b) and (d). The blue proposals are assigned as top5$\%$ weights by ProbaNet and the red ones are assigned as top1$\%$ weights. (d) and (e) just as the same as (b) and (c) respectively.}
		\label{fig:w}
	\end{figure}

	\noindent
	\textbf{Experiments for Visualizing the Focus of ProbaNet.} Figure \ref{fig:w} shows some samples for visualization purposes. We randomly select five test images which are displayed in column (a). We normalize weights corresponding to 2 kinds of proposals (total 9) generated by ProbaNet as shown in column (b) and (d). In column (b) and (d), the value of weights are larger when the color in positions approaching white, and minimum if that color is black. Every weight is related to a part of the area near the point where the weight exist. For display deeply, we further map the proposals to the original images and draw proposals with top 5$\%$ weights in blue and top 1$\%$ weights in red. These proposals in column (c) and (e) are corresponding to weights in column (b) and (d) respectively. It is obvious that the minority objects in the whole images have more focus than the majority background due to the higher weight. Thus from Figure \ref{fig:w}, It shows that ProbaNet gives the foreground proposals more focus but less for background proposals. Otherwise, better candidate proposals generally have higher weights. This fact is clearly observed by the airplane of picture 1, the person on the beach of picture 3 and horses near the lake of picture 4.

	\noindent
	\textbf{Experiments for Validating the effectiveness of ProbaNet.} As shown in Figure \ref{fig:rpn_out} (a), when combined with ProbaNet, the detector indeed select more hard proposals in a mini-batch. As is shown in Figure \ref{fig:rpn_out} (b), the results of our method have more discrimination capability. ProbaNet indeed alleviates the imbalance problem, the statistical constraint in ProbaNet indeed increase the degree of distinction among samples.

	\begin{figure}
		\begin{center}
			\includegraphics[width=0.95\textwidth]{./ha_rpnout.pdf}
		\end{center}
		\caption{(a) Comparison of hard proposals' ratio in a mini-batch between Faster R-CNN and our method in training, the horizontal axis refers the iteration in training. (b) Output after the softmax layer in RPN, the vertical axis and horizontal axis refer the output of softmax layer and the 256 samples in the mini-batch. The left one in (b) is results of Faster R-CNN, the right one is results of our method. The red fork in (b) is weight to be foreground and the blue circle is weight to be background.}
		\label{fig:rpn_out}
	\end{figure}

	\section{Conclusion}
	\label{sec:con}
	Object detectors based on CNNs face easy-hard samples imbalance problem, this issue has an adverse effect on performance. In this work, we proposed ProbaNet to combine with object detectors. ProbaNet not only alleviates the imbalance issue by suppressing the vast easy negatives in training stages but also emphasizes the minority but meaningful samples in testing. Experimental results confirmed that ProbaNet alleviates the imbalance problem, and improves the detection accuracy while offering less computational cost.

	In the future, we will design adaptive adjustment parameters to replace the reduction parameter $r$ and the threshold $th$. Furthermore, we will apply the proposed ProbaNet to one-stage detectors for evaluating its performance and study imbalance problem more deeply and systematically.

redpajama_set_name: RedPajamaArXiv


In a world where the international threats of terrorism focus all eyes abroad, the risk of domestic insurgency grows every day. While the world watches in horror, the reality of homegrown discontent boils over and the rocks the free world. A devastating assignation sends the United States into a tailspin, exposing the harsh realities of a divided and unsettled population. Racial strife and political discord reach its breaking point and insurrection threaten to tear the fabric of democracy apart. As the front lines of this civil war stand three men, sworn to protect and defend this great nation from all threats – foreign and domestic. Together they must risk the jobs, freedom, and life as they know it and face off against well-organized, and well trained homegrown terrorists. Will it be enough to pull the free world back from the brink? Or will the U.S. crumble under the weight of The Second Civil War?


The Division of Tagum City is a medium-sized division in Region XI. Presently, it has a total of One Thousand Eight Hundred Six (1,806) teaching and non-teaching personnel holding plantilla positions. It continues to improve its services which aims to establish globally competitive employees and learners as well as collaborative stakeholders.
With the implementation of K to 12 curriculum, school enrolment has increased tremendously. Schools had been established in different barangays as well as school extensions for secondary level. Consequently, teaching and non-teaching items were issued by the Department of Budget and Management (DBM) as requested by DepEd.
Memorandum Circular No. 28 of the Civil Service Commission (CSC) also supports that " Publication and posting requirements must be compiled within a period of sixty (60) calendar days.
Publication of vacancy maybe through the Bulletin of Vacancies in the CSC or other modes (e.g. newspapers, website, etc). The vacancy shall also be posted in three (3) conspicuous places for at least fifteen (15) calendar days for local government agencies or ten (10) calendar days for other government agencies.
After compliance with the publication/ posting requirements and had undertaken the selection and ranking processes, the appointing authority may issue an appointment immediately.
However, the Schools Division of Tagum City faces new challenges in the recruitment and selection process which is an internal issue. This is evident in the current situation of the Division. Based on the records of the Personnel Office, there were vacant positions that took months or even years to fill in.
This project will therefore seek to understand the factors which cause delays in the filling in of natural vacancies and in doing so will provide insight both from an academic perspective to improve the process. It hopes that through this project, not only our Division but also other divisions will gain from it.
If the vacant items be filled-in earlier within the prescribed period, the vacuum of work vacated by the personnel involved will be given attention which will result to the quick and effective delivery of services to the clients. Hence, the Division Office and schools will have sufficient personnel to cater to the needs of the learners.


text:
Application of Big Data and Artificial Intelligence technologies to dementia prevention research: an opportunity for low-and-middle-income countries
Samuel O Danso, Graciela Muniz-Terrera, Saturnino Luz, Craig Ritchie, and on behalf of the Global Dementia Prevention Program (GloDePP)
It is estimated that about 66% of the number of people with dementia globally live in regions within the low-and-middle-income countries (LMIC). The estimates further suggest that about 1 million additional cases are recorded annually in these LMICs, with approximately 60-70% of the dementia cases being due to Alzheimer disease, AD [1]. Despite this high incidence of AD, treatment options are currently limited. This is notwithstanding the fact that numerous clinical trials have taken place, are still on going or planned [2]. However, the rate of failure of clinical trials and predictions based on available results from randomised control trials suggest that an immediate breakthrough to obtaining treatment drugs for AD is unlikely [3].
While the symptoms of dementia appear to be well known, including decline in activities of daily living and social functioning, the exact cause remains unknown [4]. However, there is strong evidence based on several epidemiological studies to suggest that complex interactions exist between exposures such as adverse environmental factors and lifestyle choices and how these contribute to the risk and timing of dementia onset [5,6]. A comprehensive account of the various risk factors that interact to influence the risk profile and the timing of dementia onset is provided in the recently published Lancet Commission report [4]. The report furthermore suggests that the effect of these risk factors in terms of their contribution to dementia are beginning to be understood through complex modelling. These risk factors can be categorised into modifiable (such as lifestyle) and non-modifiable (such as genetics). The ability to alter the modifiable risk factors has been demonstrated theoretically to have influence on the onset of dementia. For example, it may be possible to reduce dementia prevalence by 50% if the onset was to be delayed by 5 years based on population-attributable risk estimation [7].
In the absence of strong epidemiological data from LMIC, evidence from numerous epidemiological studies carried out in high-income-countries (HIC) point to the fact that prevention is vitally important to reducing the dementia burden. These findings may be extended to LMIC and this article discusses these opportunities and how they can be applied.
. Photo: LMICs leapfrogging with collaborative support and lessons leaned from HICs to tackle dementia (photo taken from wpclipart collections: used under the terms and conditions).
MODIFIABLE RISK FACTORS
It has now been established that the onset of dementia begins much earlier in life than had previously been assumed [5]. This is further supported by evidence from clinicopathologic studies, which suggest that AD lesions develop much earlier in specific regions of the brain which meet neuropathological AD diagnosis criteria even in the absence of dementia [8]. Meanwhile, numerous studies have also reported the effect of modifiable risk factors on the structural integrity of the brain. Examples include a 20-year longitudinal study, which found that cardiovascular risk factors such as hypertension, BMI from midlife to late life also increased the risk of severe white matter lesion in the brain [9]. A recent study also found a correlation between hypertension and reduction in brain reserve[10], and therefore adopting a healthy lifestyle may reduce the chances of hypertension which in turn also delay the onset of AD . Similar trends have been observed in nutrition studies [11]. Lifestyle factors such as physical activity through longitudinal observational studies have similarly been demonstrated to have improved cognitive reserves and reduction in dementia risk[12]. Furthermore, studies on lifestyle have also found smoking and excessive alcohol intake to have significant association with cognition and dementia[13], with findings from education also showing some mixed effect on cognition and cognitive preservations [14]. Establishing norms for LMIC populations for these risk factors and developing monitoring mechanisms capable of providing early warning signs is an avenue to the development of strategies that can focus on early interventions.
DEMENTIA EARLY INTERVENTION THROUGH BIG DATA AND AI IN LMIC
HIC continue to explore advanced techniques in neuroimaging as wells as neuropsychological and other data sources to effectively monitor brain health to detect early onset of dementia [15,16]. Recent advances in Big Data and AI technologies coupled that with the ever-increasing speed in data generation has seen exponential growth in research and development of these technologies within the context of dementia prevention. Researchers in HIC have already taken advantage of Big Data technologies and have developed data driven approaches to dementia prevention initiatives across various HICs and are able to process and manage these data with high throughput [17]. Current examples of such initiative include the ongoing European Prevention of Alzheimer Disease, EPAD [18] and PREVENT research programme [5]. These projects aim to generate not only large and high quality, but also phenotypically deep, data sets and are employing state-of-the-art Big Data technologies to process and make these data available through secured analytics environment for hypothesis testing.
Similar trends have also been observed in the analytics space where AI including sophisticated Machine Learning (ML) algorithms and frameworks such as deep learning continue to be developed and improved. A systematic review by Pellegrini et al.[19] found over 110 publications on various initiatives where ML approaches have been employed to develop prediction models for cognitive impairment and AD using neuroimaging data. These research efforts are being extended to other data sources and domains such as linguistic analysis of text messages; speech analysis [20,21]; and also through the human eyes using retinal imaging [22] with promising results.
While ML models based on neuroimaging data may not be practical in LMIC due to the huge cost associated with them, having alternative low cost approaches to acquiring modifiable risk factors data and analysing these data sources to help monitor populations brain health for the purposes of screening for dementia risks would be a useful epidemiological tool. For example, wearable devices and smartphones are now able to acquire real time data on daily activities, conversations through text messages and speech. The proliferation of the internet and smartphone usage in LMIC presents great opportunities for collecting these types of data using these devices. Big Data technologies allow efficient integration of data that come from a variety of sources and in different types and formats scale [23]. Harmonising these sources of data provides an excellent opportunity to develop ML methods and prediction models. AI and machine learning technologies are also revolutionising the approaches to the analysis of large volumes of data in real time, with potential benefits to dementia research, including the ability to learn patterns from large data sets where use of traditional statistical methods may not be possible [24]. Additionally, the efficiencies and consistency in the use of AI–based methods may result in reduced cost and less human errors in decision-making.
It is an undeniable fact that Big Data and AI have potential ethical challenges such as data privacy and respect for human rights. This has led to a shift in focus and a new branch of ethics known as data ethics, which involves the study of moral issues around data, algorithms and practices [25]. Regardless of the place of application, these ethical issues remain universal and applicable in LMIC. Nevertheless, regulatory frameworks such as the recently launched General Data Protection Regulations in May 2018 to regulate the use of personal data across Europe [26] and practises employed in other HIC such as the consent approach where study participants consent to researchers keeping their personal data centrally for the purposed of data linkage but these are not transferred to the data users. Another approach is the de-identification method where personal identifiable data are removed at the point of data collection before further processing and transmission as described in [23]. These approaches can serve as good examples to follow in LMIC.
This article discussed opportunities for development of early intervention strategies to address the burden of dementia in LMIC through the application of Big Data and AI. The potential efficiency and cost-effectiveness of this approach suggests that window of opportunity now exists for researchers in LMIC to collaborate with colleagues in HIC in this area. LMIC can employ a leapfrog approach to adapt in order to benefit from these Big Data and AI technologies, which have already been tried and tested in HIC. With this approach, the emerging success stories in HIC can also be replicated in LMIC.
[1] Funding: None.
[2] Authorship contributions: SD drafted the manuscript with inputs and review from co-authors.
[3] Conflict interest: The authors completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available upon request from the corresponding author), and declare no conflicts of interest.
[1] M Prince, D Acosta, E Albanese, R Arizaga, CP Ferri, and M Guerra. Ageing and dementia in low and middle income countries-Using research to engage with public and policy makers. Int Rev Psychiatry.. 2008;;20::332--43. . DOI: 10.1080/09540260802094712. [18925482]
[2] S Andrieu, N Coley, S Lovestone, PS Aisen, and B Vellas. Prevention of sporadic Alzheimer's disease: lessons learned from clinical trials and future directions. Lancet Neurol.. 2015;;14::926--44. . DOI: 10.1016/S1474-4422(15)00153-2. [26213339]
[3] LS Schneider, F Mangialasche, N Andreasen, H Feldman, E Giacobini, and R Jones. Clinical trials and late-stage drug development for Alzheimer's disease: an appraisal from 1984 to 2014. J Intern Med.. 2014;;275::251--83. . DOI: 10.1111/joim.12191. [24605808]
[4] G Livingston, A Sommerlad, V Orgeta, SG Costafreda, J Huntley, and D Ames. Dementia prevention, intervention, and care. Lancet.. 2017;;390::2673--4. . DOI: 10.1016/S0140-6736(17)31363-6. [28735855]
[5] CW Ritchie and K Ritchie. The PREVENT study: a prospective cohort study to identify mid-life biomarkers of late-onset Alzheimer's disease. BMJ Open.. 2012;;2::e001893. DOI: 10.1136/bmjopen-2012-001893. [23166135]
[6] B Imtiaz, AM Tolppanen, M Kivipelto, and H Soininen. Future directions in Alzheimer's disease from risk factors to prevention. Biochem Pharmacol.. 2014;;88::661--70. . DOI: 10.1016/j.bcp.2014.01.003. [24418410]
[7] S Norton, FE Matthews, DE Barnes, K Yaffe, and C Brayne. Potential for primary prevention of Alzheimer's disease: an analysis of population-based data 2014. Lancet Neurol.. 2014;;13::788 . DOI: 10.1016/S1474-4422(14)70136-X. [25030513]
[8] M Vuorinen, A Solomon, S Rovio, L Nieminen, I Kåreholt, and J Tuomilehto. Changes in vascular risk factors from midlife to late life and white matter lesions: a 20-year follow-up study. Dement Geriatr Cogn Disord.. 2011;;31::119--25. . DOI: 10.1159/000323810. [21273771]
[9] RJ Killiany, BT Hyman, T Gomez-Isla, MB Moss, R Kikinis, and F Jolesz. MRI measures of entorhinal cortex vs hippocampus in preclinical AD. Neurology.. 2002;;58::1188--96. . DOI: 10.1212/WNL.58.8.1188. [11971085]
[10] SY Jeon, MS Byun, D Yi, JH Lee, YM Choe, and K Ko. Influence of hypertension on brain amyloid deposition and Alzheimer's disease signature neurodegeneration. Neurobiol Aging.. 2019;;75::62--70. . DOI: 10.1016/j.neurobiolaging.2018.11.001. [30553154]
[11] R Power, A Prado-Cabrero, A Mulcahy, A Howard, and JM Nolan. The role of nutrition for the aging population: Implications for cognition and Alzheimer's Disease. Annu Rev Food Sci Technol.. 2019;;10::619--39. . DOI: 10.1146/annurev-food-030216-030125. [30908950]
[12] SJ Blondell, R Hammersley-Mather, and JL Veerman. Does physical activity prevent cognitive decline and dementia?: A systematic review and meta-analysis of longitudinal studies. BMC Public Health.. 2014;;14::510 . DOI: 10.1186/1471-2458-14-510. [24885250]
[13] T Ngandu, EL Helkala, H Soininen, B Winblad, J Tuomilehto, and A Nissinen. Alcohol drinking and cognitive functions: Findings from the Cardiovascular Risk Factors Aging and Dementia (CAIDE) study. Dement Geriatr Cogn Disord.. 2007;;23::140--9. . DOI: 10.1159/000097995. [17170526]
[14] RS Wilson, L Yu, M Lamar, JA Schneider, PA Boyle, and DA Bennett. Education and cognitive reserve in old age. Neurology.. 2019;;92::e1041--50. . DOI: 10.1212/WNL.0000000000007036. [30728309]
[15] K Ritchie, I Carrière, D Howett, L Su, M Hornberger, and JT O'Brien. Allocentric and egocentric spatial processing in middle-aged adults at high risk of late-onset Alzheimer's Disease: The PREVENT Dementia Study. J Alzheimers Dis.. 2018;;65::885--96. . DOI: 10.3233/JAD-180432. [30103333]
[16] M Mortamais, JA Ash, J Harrison, J Kaye, J Kramer, and C Randolph. Detecting cognitive changes in preclinical Alzheimer's disease: A review of its feasibility. Alzheimers Dement.. 2017;;13::468--92. . DOI: 10.1016/j.jalz.2016.06.2365. [27702618]
[17] FN Doubal, M Ali, DG Batty, A Charidimou, M Eriksdotter, and M Hofmann-Apitius. Big data and data repurposing - using existing data to answer new questions in vascular dementia research. BMC Neurol.. 2017;;17::72 . DOI: 10.1186/s12883-017-0841-2. [28412946]
[18] A Solomon, M Kivipelto, JL Molinuevo, B Tom, and CW Ritchie. European prevention of Alzheimer's Dementia Longitudinal Cohort Study (EPAD LCS): study protocol. BMJ Open.. 2019;;8::e021017. DOI: 10.1136/bmjopen-2017-021017. [30782589]
[19] E Pellegrini, L Ballerini, MDCV Hernandez, FM Chappell, V González-Castro, and DS Anblagan. Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review. Alzheimers Dement (Amst).. 2018;;10::519--35. . DOI: 10.1016/j.dadm.2018.07.004. [30364671]
[20] SO Orimaye, JSM Wong, KJ Golden, CP Wong, and NI Soyiri. Predicting probable Alzheimer's disease using linguistic deficits and biomarkers. BMC Bioinformatics.. 2017;;18::34 . DOI: 10.1186/s12859-016-1456-0. [28088191]
[21] S de la Fuente Garcia, CW Ritchie, and S Luz. Protocol for a conversation-based analysis study: PREVENT-ED investigates dialogue features that may help predict dementia onset in later life. BMJ Open.. 2019;;9::e026254. DOI: 10.1136/bmjopen-2018-026254. [30918035]
[22] L Csincsik, TJ MacGillivray, E Flynn, E Pellegrini, G Papanastasiou, and N Barzegar-Befroei. Peripheral retinal imaging biomarkers for Alzheimer's Disease: A pilot study. Ophthalmic Res.. 2018;;59::182--92. . DOI: 10.1159/000487053. [29621759]
[23] Deetjen U, Meyer ET, Schroeder R. Big data for Advancing Dementia research: an evaluation of data sharing practices in research on age-related neurodegenerative diseases, 2015. OECD Digital Economy Papers, No. 246, OECD Publishing. Available: https://www.oecd-ilibrary.org/docserver/5js4sbddf7jk-en.pdf?expires=1575056588&id=id&accname=guest&checksum=91214D7FDBC5665BA4B8152516600565. Accessed: 29 November 2019.
[24] L Breiman. Statistical modeling: The two cultures. Stat Sci.. 2001;;16::199--231. . DOI: 10.1214/ss/1009213726
[25] L Floridi and M Taddeo. What is data ethics? Phil. Trans. R. Soc. A.. 2016;;374::20160360. DOI: 10.1098/rsta.2016.0360. [28336805]
[26] H Li, L Yu, and W He. The impact of GDPR on global technology development. J Global Inform Tech Manag.. 2019;;22::1--6. . DOI: 10.1080/1097198X.2019.1569186

redpajama_set_name: RedPajamaCommonCrawl


Set up a campus visit here and tell the Admissions office that you would like to visit the Division of Biology. Let them know if you have a specific interest area, such as pre-health, microbiology, fisheries & wildlife, or conservation. Alternatively, you can contact our Office of Undergraduate Studies at 785-532-5718 and ask to talk to an advisor, or email the main Division of Biology office. We'll be happy to answer your questions in person.


Vladislav Delay, Uusitalo, Luomo, Conoco, Sistol. Under a raft of different names this Berlin-based Finnish artist (Sasu Ripatti *1976) has experimented with ambient, broken beat, glitch as well as grooves and house music with soulful vocals. In addition he has collaborated with and worked for a wide range of artists ranging from Scissor Sisters and Craig Armstrong to AGF, Black Dice, Massive Attack, Towa Tei and Ryuichi Sakamoto. While his side-projects and remixes are there to flirt with more stable rhythms and dancefloor institutions, his main love as well as his background is in experimenting with different kind of sounds and rhythms; a quest to find some music not heard yet.
CO producciones and KBB made possible the realization of this concert programmed in the context of the project Fokus Finland.


« How to get sponsoring for a bicycle trip?
After some testing, we have added the community features to BikeTravellers.com. What does this mean for you and your blog?
Your blog is still the same (yourname.biketravellers.com), so nothing changes there. You might only notice a new adminstration bar on top, with links to your new profile, your blogs and more. Check it out!
On the new homepage a lot has changed: you can now see the latest posts of all bikebloggers, the latest comments and much more.
There are now links to lists of blogs, users, forums and more, check out the menu above.
We have added forums and groups, so you can discuss with other users about specific subjects. You can add as many groups and forums as you want!
You can even add a groupblog to your group, where all members can post.
You can add Avatars/logos to your profile, groups, forums etc, all with just a few clicks.
More features will be added and I have not even described the current ones here, so check out the new Biketravellers.com, your profile and all possibilities Let your friends know about the new site, if there are more members it will be beneficial for all.
ps: a lot of questions are answered on the new FAQ which I just posted. It will explain some of the new possibilities in more detail.
This entry was posted by Harry on 14 October, 2009 at 20:43, and is filed under FAQ, Manual. Follow any responses to this post through RSS 2.0. You can leave a response or trackback from your own site.
Does any body kwnos if I can rent a bike in Marrakech to travel arround Morroco.


While planning a family trip it gets difficult to choose a destination let alone book a perfect hotel that provides with not just basic luxuries but also fun activities. So here is a quick guide for all the travelers on Phuket Family Hotels who are planning there next trip in Phuket. Phuket is one of the southern provinces of Thailand. It is a rain forested, mountainous island with many popular beaches. The island consists of many resorts, hotels and restaurants. It was even voted as one of the top 5 retirement destinations by Fortune Magazine.
With beautiful mountains and mesmerising sea views, Phuket is one of the most popular destinations for families. The hotels even offer separate children's pools. Novotel Phuket Vintage Park even provides with a special kids club. The hotels even serve a fine variety of dining options for tourists of all age groups. The sea-view rooms sure makes tourists forget about the chaos of the city and help spend a wonderful quality time with loved ones. There are various popular tourist destinations including Patong, Karon, Kata etc which help tourists unwind in their beautiful resorts and luxury at its best.
For travelers what is more important destination is what experience they can get which they will cherish for the rest of their lives. Grand Sunset Boutique Hotel is famous for its rooftop concept. It includes rooftop bar and dining and even various events like sunset party, Christmas party, New Year's party etc. Amari Phuket accounts for the best family experience with so many entertainments like games, pool activities souvenirs, treats and much more for the little ones. They even have special crafting activities for families to enjoy together. The Front Village is yet another choice for enjoying with the family. The hotel offers a wide variety of experiences like cooking classes, village spa and more. The spacious deluxe room sure makes it one of the best Phuket family hotels. Andaman Seaview Hotel is yet another hotel with a variety of experiences for the ladies of the family like Samunprai spa, beauty salon and fitness room. Children's pool and kid's playground are also included in this hotel's experience for the little ones.
Most Phuket family hotels provide with all the basic facilities including spa, friendly servants, and comfortable rooms for a cosy experience. With a variety of pools, sea view rooms, doctor on call, 24 hour strict security, fine dining and fitness facilities, these hotels are the best choice for travelers. Baan Sailom Hotel Karon beach is one of the hotels which provides with all the basic facilities including beautifully decorated restaurants, pool, room service and Wi-Fi. Another hotel is The Marina Phuket hotel which provides with swimming pools, rooftop bars, ocean spa, fitness room, foot massage, Thai massage and even VIP spa treatments. Novotel hotel even provides with a special kid's club with various activities like painting, games and movies for the little ones.
A successful trip is the one with great food and wine facilities. Here at Phuket Family Hotels tourists don't need to worry when it comes to food as it provides not only best traditional Thai food but also offer a variety of other cuisines. Patong Beach Hotel is one of the hotels providing the best dining experience with various themed restaurants like Andaman Garden, Banana Pub and Grill, The Deck, The Andaman Coffee Shop etc. With great variety in food and spacious seating areas, it accounts for one of the best hotels. Centara Karon Phuket is yet another choice when it comes to fine dining. The restaurants include Lotus, Azure, The Mix, Tropics Pool Bar, lagoon Pool Bar, terrace Pool Bar and even in room dining service. Grand Mercure Hotel also has various restaurant options like Bubbles Restaurant, Chou Chou Deli Shop, Craft Beer Lounge and Bounce Pool Bar. Many of the hotels even provide with Thai cooking classes for all the cooking lovers.
What is the best season to visit Phuket?
The best season or the best time to visit Phuket is from November to March.
What are the best family hotels in Phuket?
Hilton Phuket Arcadia, Centara Karon Phuket, Patong Beach Hotel are the best family hotels in Phuket.
Which hotels provide with best kid's experiences?
Best kid's experiences will be experienced at Novotel Phuket Vintage Park, Andaman Seaview Hotel, Amari Phuket.
Do these hotels provide with all basic amenities as well as experiences?
Almost all hotels offer basic amenities and even special experiences including sport activities, underwater dining and much more.
The various Phuket Family Hotels provide with best family experience with luxurious rooms, fine dining options, special Thai classes, fitness rooms, spa, pool facilities and special facilities for kids like kid's pools, clubs, gaming zones, painting and crafting activities. The experiences travelers will get here will be embedded in their memories for a lifetime. Other options in hotels are Ramada by Wyndham Phuket Southsea, Batic House by Sharaya Hotel, Avista Grand Phuket Karon, Hilton Phuket Arcadia etc. All these hotels with wonderful facilities and mesmerising bedroom views will ensure leave travelers relaxed and refreshed and wanting for more.


Location: 4 Days (3 Nights); Warrior Adventure; This safari itinerary covers; Tsavo East National Park the famous for the Red Elephants , Amboseli National Park famous for its big game and its great scenic beauty – and the landscape is dominated by the towering Mount Kilimanjaro and Voyager Ziwani for night game drives and walking safari.
Day 2: Amboseli National Park -Voyager Ziwani.
Day 3: Voyager Ziwani – Tsavo West National Park.
Early morning game drives before breakfast then depart to Tsavo West National Park. See the volcanic formations (Shetani Lava Flow) on the way to Mzima Springs where Hippos and Crocodiles are seen from the underwater hide. Check in at the lodge as well for lunch, relax until 16:00hrs, time to visit the Rhino Sanctuary. Tsavo West National Park is covering a large area consisting of diverse terrains; the park stretches 9,065 square kilometers at a distance of about 240 km from Nairobi and about 255km from Mombasa. It provides diverse habitats for its animals as altitude ranges from 500 ft to 6,000 ft above sea level. There are rivers, forests, lakes, wooded grasslands and mountains, each harboring a distinct set of animals. The park became famous through the notorious "Man Eaters of Tsavo" incident at the turn of the century, when man-eating lions were preying on the workers building the great Kenya-Uganda Railway. This incident was also depicted in the popular movie "Ghost and the Darkness. Explore the Chaimu and Shetani Lava Flow. There is a quite a variety of wildlife and birdlife in the park. Over 600 species of birds have been recorded. Moreover, game viewing is fairly easy. Animals include the leopard, cheetah, buffalo, rhino, elephant, giraffe, zebra, lion, crocodile, mongoose, hyrax, dik dik and porcupine. Finally, the park offers excellent views of Mount Kilimanjaro. Dinner and Overnight at Ngulia Safari Lodge or Rhino Valley Lodge, ( Full Board).
Early morning game drives before breakfast. See the beautiful sceneries of Tsavo West National Park. Return to the lodge for breakfast then proceed for the last game drive en route to Zomeni Lion Hill Lodge for Lunch, later proceed to Malindi, arriving in the late afternoon at your respective hotel, (Breakfast & Lunch).
4 Days Warrior Adventure: Meal Plans as per above package itinerary.


text:
Q: Trying to limit my shapemap to state boundary lines in R I have a feeling I'm either trying to do this incorrectly, or I'm just not searching for the correct terms, but I cannot figure out how to do what I want to do.
I am trying to plot the ranges of fish species (shapefile #1) in Maryland/Delaware/Virginia superimposed over the state maps, then add major rivers (shapefile #2) in R.
I have no trouble getting everything plotted, but since both shapefiles are for the entire United States, they plot polygons/lines on my maps outside the MD/DE/VA region that I'm interested in.
Here's what I've done so far in R:
> library(maps)
> library(maptools)
> library(scales)
> mudsunfish=readShapePoly("~/Desktop/Maryland/Acantharchus_pomotis.shp")
> map('state', regions=c('maryland', 'virginia', 'delaware'), lwd=3)
> plot(mudsunfish, add=TRUE, col=alpha("skyblue", 0.3), border=FALSE)

This gives me the below image (not worried about the rivers in this instance):

Notice the blue shading in the upper right, where New Jersey and Pennsylvania would be. I don't want that. I can remove the shading in Photoshop, but with >120 species and rivers to add, I'd like to not have to do that.
How can I limit my shapefiles to the borders of the states? Should I be going about this with different packages? I'm sure someone else has run into this before, but I'm at a loss for what phrase(s) to Google to find my answer.

A: Convert your map to a SpatialPolygons object and then use gIntersection from the rgeos package. This clips your blue polygons exactly to the state boundaries. Inspired by this answer.
library(rgeos)
library(maps)
library(maptools)

mmap <- map('state', regions=c('maryland', 'virginia', 'delaware'), fill=TRUE)
IDs <- sapply(strsplit(mmap$names, ":"), function(x) x[1])
mmap.sp <- map2SpatialPolygons(mmap, IDs=IDs, proj4string=CRS("+proj=longlat +datum=WGS84"))

# make up some polygons to clip
mpoly <- readWKT("MULTIPOLYGON(((-80 36, -80 38, -75 38, -75 36, -80 36)), ((-77 39, -77 41, -74 41, -74 39, -77 39)))")
proj4string(mpoly) <- CRS("+proj=longlat +datum=WGS84")

plot(mmap.sp)
plot(mpoly, add=T, col="blue")

# clip
mpoly.clipped <- gIntersection(mmap.sp, mpoly, byid=TRUE)

plot(mmap.sp)
plot(mpoly.clipped, add=T, col="blue")

redpajama_set_name: RedPajamaStackExchange


As an added convenience, Elite Coaches now offers generator rentals to be used with or without our fleet of luxury restroom trailers.
Our power supply generator rentals offer a simple one stop shop to meet your project or event needs. As always, you can expect superior, reliable service at a great value.
View Johnny on the Spot's Power Supply Generator features and measurements, below!
Full tank (diesel) = approx. 75 hrs run time (normal load) before needing re-fueling.


text:
Amanita velosa  ("amanita sin anillo naranja agridulce", en inglés "bittersweet orange ringless amanita") es una especie de hongos en la familia  Amanitaceae. Es un hongo comestible propio de  California, Oregón y Baja California.

Descripción y clasificación
Es parte de la sección Vaginatae de  Amanita y, al igual que otras especies de este grupo, se caracteriza por la falta de un anillo, margen de pileo estriado, restos gruesos de velo universal que comprenden el parche de velo, volva y partes del pileo, esporas inamiloides y la falta de toxinas  características de Amanita como amatoxinas y ácido iboténico. Se distingue de otras especies en la sección Vaginatae por su falta de cualquier tipo de umbo en su sombrero, su corto pileo estriado y su coloración naranja claro a salmón claro cuando es joven. Su coloración puede volverse más marrón con la edad y ocasionalmente también se ven especímenes completamente blancos. Al igual que muchas otras Amanita, las laminillas son blancas, pero ocasionalmente tienen un tinte rosado o anaranjado característico. En especímenes más viejos, el olor puede volverse picante y sospechoso.

Comestibilidad e identificación

Se le considera una especie comestible excepcional con un sabor distintivamente dulce o a nuez. Sin embargo, se debe tener mucho cuidado para identificarlo adecuadamente, ya que la mortal Amanita ocreata (y ocasionalmente Amanita phalloides) también se encuentra en el mismo hábitat en la misma época del año, y a menudo se pueden encontrar muy cerca unas de otras. A. ocreata y A. phalloides tienen restos de velo universales delgados, una volva similar a un saco, un anillo, un margen de sombrero no estriado y un sombrero que es de un color diferente al de A. velosa.  Estas diferencias pueden desvanecerse a medida que el cuerpo fructífero envejece, por lo que es importante recolectar solo especímenes que tengan todas sus características de identificación intactas.

Referencias

Enlaces externos
Amanita velosa
Mushroom Observer: Amanita velosa

velosa

redpajama_set_name: RedPajamaWikipedia


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No Fairy Tale: CVC's Slowing Growth Prompts Concern
By webdesign | August 9, 2007
The overall Cablevision narrative continues to be told by a pair of key storylines: the financials from its dominant cable segment and the Dolan family's privatization bid. Yet while both remained positive as the MSO reported 2Q results, ebbing growth emerged as a troubling 3rd thread. Cable TV net rev rose 13% (highlighted by 168K new digital, HSD and phone adds; flat basic growth; and a 10% rev increase at Rainbow), but diminished operating guidance gave many analysts pause. Sanford Bernstein's Craig Moffett said CVC's slowing growth was expected, "but not quite so quickly." Oppenheimer went further, calling the MSO's results "arguably the worst in the company's recent history, and the worst among major cable operators." COO Tom Rutledge said the lowered guidance can be attributed, in part, to "very high penetration" in video (68% overall, 81% digital) and growth slowdown in all- digital NYC, although he admitted having no explanation for lower sequential broadband growth. It's clear, however, that management had noticed some disturbing trends during 2Q, as a "strategy change" was enacted in early July when CVC began marketing to existing customers a triple-play bundle that was previously available to only new subs. The MSO also downplayed a material effect on results from Verizon, even as Rutledge disclosed an increase in marketing spending "so that our position in the marketplace is not drowned out" by the telco's estimated $80-$100mln annual in-market broadcast ad spend. Rutledge said Verizon owns 750K active passings in CVC's footprint and has built out 1mln. Some good news, according to Moffett, is that "Cablevision is holding its own" against Verizon, which should hearten the cable industry "inasmuch as Cablevision's overlap… is already larger than will be the case at Comcast and Time Warner Cable, even by 2012." Also a boon, many agree, is the Dolan's offer of $36.36/share, which CEO Jim Dolan said is still on the table despite widespread financing concerns stemming from a weak US credit market. A special shareholder's meeting to discuss the deal will occur this fall. CVC shares closed at $34, up 1%.


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A modest proposal, Commentary, Kondiaronk - My Montréal Experience, Let's make this an election issue, Montréal Architecture, Montréal Landmarks, Perspectives on the City, Political commentary, Social commentary, Urban Redevelopment, Worth reconsidering
Premier Marois to Stifle Opposition with Métro Extension Plan
September 21, 2013 Taylor C. Noakes	2 Comments
AMT Métro Extension Plan
The mayoral contest officially kicked off today with Projet Montréal taking a strong lead, winning the idiotically-named 'poster war' thanks primarily to the fact the party is fielding 103 candidates and an immense volunteer effort. Aside from Projet's street sign ubiquity, Mélanie Joly may have come in second place (who's counting?) with her unconventional Super Woman pose and dark background posters.
And in response to a week of outright hostility from nearly all quarters of our city, the Premiere, Benevolent Queen Pauline Marois, announced a Métro extension. As the CBC puts it, "Montreal's Metro system is about to get its biggest and most expensive upgrade since the Laval extension."
In fact, the PQ has announced that they've set aside $38.8 million for a planning office with a two year mandate. Whether the PQ lasts that long is another issue.
So don't get your hopes up – this isn't a 'shovel-in-hand' announcement of the immediate construction of Métro tunnels. It's more an announcement of intent to eventually do something.
When it comes to the Métro, that's pretty much all we've gotten for years anyways. The Charest government made a similar announcement back in 2009 though nothing came of it, and the idea to extend the Blue Line further east dates back to the mid-1980s when the line was first developed. Of note, Charest's 2009 plan called for closing the Orange Line loop, as well as extensions in both directions of the Yellow Line, in addition to the Blue Line extension, as you can see in the above image. Today's announcement mentioned that a Yellow Line extension would be contemplated once the Blue Line project is completed.
Why not do both?
Why not do the 2009 plan?
Wouldn't we save money in the long run if we streamline one big Métro expansion, rather than small, piecemeal extensions? It would certainly streamline bidding processes and purchasing, no?
The Blue Line's proposed eastern extension to Anjou (specifically, to an intermodal terminus at the Galleries d'Anjou suburban shopping complex) will undoubtedly alleviate congestion on the Metropolitan Expressway and extend a convenient and efficient mass transit system into a broad medium density residential area. There's no question about whether the extension is the right way to go, but we need to be vigilant regarding the estimated cost.
The PQ is projecting a $250 to $300 million cost per kilometre and a total extended length of six kilometres (about the distance from University to the Olympic Stadium along Sherbrooke) with five stations. On the outside that's a $1.8 billion extension to serve a combined population of about 120,000 Montrealers living in the boroughs of Saint-Leonard and Anjou, one hell of an investment in a relatively small number of citizens.
The cost to extend the Orange Line to Laval by three stations cost about half that amount per kilometre, and that project was announced in the late 1990s but only completed in 2007. As you might expect, post-industrial Québec takes a lot longer to get anything done.
So don't expect this Blue Line extension any time soon; those making the announcement today were indicating 'the beginning of the 2020s' for 'full operations'.
Christ; I'll be old by then.
I'll say it one more time – we built 26 stations between 1962 and 1967 across three lines and it cost just under $1.5 billion (or 213.7 million in 1966 dollars).
Granted I'm obviously not an economist, but I would like to know why the cost of construction has increased so much in the past decade in particular. You'd figure we'd be getting some kind of rebate in Post Charb Commish Quebec, but this is as expensive as ever.
And we're not exactly reinventing the wheel either – so how the hell did it suddenly become so expensive to build basic mass transit systems in our city?
Original design of Edouard-Montpetit station's connection with Mount Royal Tunnel
There's another issue we should consider when thinking about the Blue Line and any potential future extensions. It has the lowest ridership of all four lines and the trains are shorter by three cars (you'll notice that the platforms at Blue Line stations have barricades at either end as the stations were designed to operate 'full' nine-car trains). I think this is as a consequence of the line not directly connecting with the city centre.
As long as we're re-hashing old ideas, why not take a closer look at the original design of Edouard-Montpetit station, which was intended to act as a transfer point between the Blue Line and the commuter rail line passing fifty meters under the Métro in the Mount Royal Tunnel (as you can see in the station's original design plan above). The tunnel is now owned and operated by the Agence Métropolitain de Transport and is in need of upgrading to support new dual-power locomotives inasmuch as some kind of emergency exit at some point in between the tunnel entrances. I would argue strongly in favour of developing a connection between the Métro and the Mount Royal Tunnel as a means to transfer passengers on the Blue Line to Gare Centrale. This would not only require high-speed, high-capacity elevators (as they have at some Parisian Métro stations), but the potential construction of a short 'by-pass' tunnel deep underground. A difficult job no doubt, but far from impossible.
The benefit is that the Blue Line becomes a lot more useful with this upgrade. I'd even argue prioritizing this element of the original design before any eastern extension. If this connection were made, transferring at Edouard-Montpetit would give Blue Line passengers access to the Orange and Green lines via the Gare Centrale and Place Ville-Marie portion of the Underground City. For the hundreds of thousands of people living along the line's route, Downtown Montreal suddenly becomes much, much closer – about five minutes from Université de Montréal to the heart of the financial district.
Such a development could lead to increased land values of properties within proximity of the Blue Line, not to mention give the Blue Line's extension a more practical raison-d'etre. Call me a cynic, but I smell subtle vote-buying.
Don't get me wrong – expanding eastwards is a good if very costly idea, and I'd like to know why this is taking so long and costing so much.
But if we're going to extend the Blue Line's reach, why not also expand its capacity and increase its utility as well?
I have a feeling realizing the original plan would have the effect of increasing ridership on the Blue Line to such an extent that the STM upgrades to nine-car trains on the line, thus giving the line the ability to truly operate at full capacity.
In any event, I should close with a thought.
There was once a time in which elected officials had to deliver on promises made, otherwise they'd lose the public's confidence and the right to govern.
This is not the case today. The people are so incredibly disengaged and cynical we don't expect anything from our supposed leaders at all. We carry on despite them. Sometimes they do something good, most of the time they're an annoyance, occasionally they're discovered to be outright criminals.
I don't know what was so different about life in this city back in the 1960s and 1970s that made the people here demand action and quick results for their political support. I don't know what lit a fire under people's asses to get shit done. I know many people suggest Expo and Olympics being the sole motivating factors, but surely this can't be the case. The people wanted action and their will was respected. We elected, and kept electing, a visionary mayor, who paid us back by giving us a truly global city to live, love and play in.
Today we get flashy press conferences that ultimately only promise more study and preparation for some interminable project whose only purpose seems to be to sap whatever confidence the people have in their elected officials.
I suppose my question is why the PQ isn't coming to us with a plan to actually begin development?
I wish government had the self-respect and restraint to only bother the people with announcements of actual accomplishments.
Blue LineMétro de MontréalMétro ExpansionMétro Expansion PlansMontréal MétroMontréal public-transitPQSTM
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2 thoughts on "Premier Marois to Stifle Opposition with Métro Extension Plan"
Taylor C. Noakes says:
Hi Justin –
The reasons, as far as I can figure them, are as follows:
1. The WI communities are all separate. Thus, they lack the ability to lobby together, and I would hazard to guess they pay less than the city of Montreal to the STM, which is in charge of public transit for much of the city.
2. The West Island has access to two high-capacity commuter rail lines as is. Eastern Montreal, by contrast, does not (or at least won't until the Train de l'Est is completed). That train, incidentally, is supposed to pass far further to the North than the Blue Line extension.
3. The total West Island population is only about a quarter million people. The combined population of Saint-Michel, Saint-Leonard, Montreal-Nord and Anjou is easily more than that, but far more concentrated.
4. Extending the Métro to the West Island (like Fairview as an example) wouldn't do much to ease congestion on Boul. St-Jean. It may even make it far worse. Further, I doubt Fairview's management wants to see their parking spaces eaten up by commuters.
I definitely want better public transit options for the West Island, but we have to look at improving connectivity in smaller steps. For instance, extending the Orange Line through VSL to connect with the AMT's Deux-Montagnes Line at Bois-Franc. This would help alleviate congestion on the most high-use AMT line, which would be good for commuters and potentially allow more people to use the DM line.
We could also look at extending hours of operation on the Vaudreil-Hudson line as well, and dedicating more reserved bus lanes throughout the West Island to quickly and efficiently move people from the residential subdivisions to major transit points, like commuter rail stations.
Personally, I'm of the opinion improving the stations and termini in the West Island would do a lot to encourage more West Island residents to take public transit. The biggest challenge we face in securing new riders is the often abysmal conditions of our public transit infrastructure. Have you been to the Dorval Terminus lately? It looks awful and there's no services whatsoever. It's pathetic.
Final note, before exploring a West Island Métro, we should look to see if the AMT could use the Hymus Spur – a rail line that goes nearly as far as Boul. St-Jean at highway 40.
What about the West Island? We have a huge population, the traffic coming in from the west island is terrible along Des Sources, St-Jean and St-Charles every single morning and all the calls going east into VSL and downtown. Why not add a line where actual masses of people could take advantage of it, rather than trying to increase ridership and property values on the least used line in the network?!


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Q: No Visual property of RowDetalsTemplate in the data grid I want to add row details to each row, however I don't find RowDetailsTemplate property in the datagrid to add row details through UI. Do I need to manually change xaml?
I am using recent version of WPF Toolkit which requires 3.5 Sp1. Also using Visual Studio 2008 Sp1.

A: The property grid of the XAML Designer in VS 2008 generally doesn't show any properties of type FrameworkTemplate (and derived types, like DataTemplate). Only the VS2010 designer added these properties, and lets you select an existing template resource.
So, short reply: Yes, in VS2008, you have to manually edit the XAML in order to define a RowDetailsTemplate.


Noted historian and best-selling author James Swanson and his wife, Andrea, invited guests into their Capitol Hill home over the weekend to celebrate the release of a new book about Abraham Lincoln by former senator and 1972 presidential candidate George McGovern.
"Well, I voted for George McGovern, and the country did go to hell," Mr. McGovern, in later remarks to the crowd, quoted the guest as saying - referring, obviously, to the disgraced Richard Nixon and Watergate.
The former Democratic senator turned serious when cautioning President Obama not to go through with his plans to increase U.S. military strength in Afghanistan by 30,000 troops.
Former Clinton White House chief of staff and California congressman Leon E. Panetta was confirmed by the Senate to become CIA director and has now been privately sworn-in before a small group on the seventh floor of CIA headquarters.
The oath was administered to Mr. Panetta by CIA deputy director Steve Kappes (there will be a formal swearing-in ceremony at the CIA this week).
The other dog was "Stump," the first Sussex spaniel to win the top pooch award. The previous oldest winner, crowned in 1999, was an 8-year-old Papillon.
Mr. Panetta's predecessor, retired four-star Gen. Michael V. Hayden, was 61 when he became CIA director in 2006.
So quipped (actually, he was dead serious) best-selling author and NBC "Today Show" travel editor Peter Greenberg while dining at DC Coast on Sunday night with a few fellow frequent fliers, including Keith Bellows, National Geographic Traveler's editor-in-chief, and former Deputy Assistant Secretary of Education Christina Erland Culver of Culver Strategies and C/H Global Strategies.
Actually, Mr. Greenberg, called the nation's pre-eminent expert on the travel experience - packing to unpacking - doesn't lug his suitcases to the airline counter and check them. He ships them ahead of time to his destination, hotel room or home, and he was doing so long before airlines began charging costly luggage fees.
"As I always say, there are only two kinds of luggage: carry-on and lost," he pointed out, naming several dedicated luggage-shipping companies, as well as UPS, FedEx and DHL.
(Note: On Sunday, Mr. Greenberg will be correspondent for a one-hour NBC "Dateline" special surrounding the 2000 crash of an Air France Concorde. After an intensive eight-year investigation, the French government in July charged Continental Airlines with involuntary manslaughter in connection with the crash. "For the first time, we're going to tell people what really happened when the Concorde crashed in Paris," Mr. Greenberg said.).
The new chairman of the Republican National Committee, former Maryland Lt. Gov. Michael S. Steele, recently headlined the Republican Women's Federal Forum at the Capitol Hill Club, and suffice to say he stole the show.
Eventually, the group's chairman, Judy Black, grabbed the microphone and requested that the crowd of women allow Mr. Steele to leave the room because he had other appointments to keep.
• John McCaslin can be reached at 202/636-3284 or e-mail John McCaslin.


Comparisons have been drawn between this fire and the 1990 nightclub fire in New York City that claimed 87 lives. That fire also involved controversial operations of the structure, and suffered from similar conditions including lack of emergency exits and poor maintenance.
A minimum of 10 complaints had been filed regarding the Ghost Ship since 1998, most recently in November of this year. City building inspectors visited the warehouse on November 17, but left when no one answered the door. A spokesperson for the Planning and Building Department noted that an event like the December 2 concert required a special permit, but none was issued. The City of Oakland's planning director revealed that the building had not been inspected for 30 years.
It is unknown what caused the fire. An initial report indicated the fire was started by a refrigerator, but this theory was rejected by agents from the federal Bureau of Alcohol, Firearms, and Explosives (ATF) (although the ATF left open the possibility that the fire was started by another electrical appliance). Max Ohr, creative director of the artist collective, stated that he had reported electrical problems to the building's owner prior to the fire. The building's operator and master tenant, Derick Ion Almena, apologized for the incident in an emotionally wrought interview.
At press conference this month, the Alameda District Attorney said that the ruins of the warehouse are "a potential crime scene." Criminal charges could theoretically range from misdemeanor code violations to manslaughter or even murder. (Manslaughter does not require malice or intent, while second-degree can be found where the defendant understands an act could result in loss of life and does it anyway.) Civil suits require victims to prove their case by a preponderance of the evidence, a substantially lower standard than that in a criminal case: beyond a reasonable doubt.
A San Francisco Attorney whose firm represented the family of a 1997 San Francisco fire said the Oakland fire is "clearly  the result of people not paying attention to safety." Some Bay Area lawyers indicate they have already been retained represent victims of the fire. Wrongful-death lawsuits would list the property owner and Almena. Other potential defendants include any contractors, engineers and architects; the city of Oakland; promoters of the concert held the night of the incident (the Ghost Ship had a long history of hosting parties); and manufacturers of items inside the building that may have contributed to the fire.
Given the large number of victims in Oakland, civil lawyers will likely search for "deep pocket" defendants who can pay large amounts of damages. If the warehouse owner were found liable, lawyers would try to recover damages from her insurance company and by going after her other assets, such as other properties.
Many have blamed the Bay Area's housing crisis for forcing lower-income individuals into illegal and potentially dangerous living spaces like the Ghost Ship. Oakland Mayor Libby Schaaf has ordered an independent analysis of the city's handling of the Ghost Ship and other similar buildings.
California Appeals Court Affirms Grant of Summary Judgment to Defendant Restaurant Following Slip and Fall, Neumann Law Group, December 8, 2016.


In October 2011, the North American Division appointed the following members to a biblical research committee to study the theology of ordination.
Gordon Bietz has been the president of Southern Adventist University in Collegedale, Tennessee, since 1997. Prior to that he pastored the Collegedale Church for 13 years and served as Georgia-Cumberland Conference president. Bietz received doctoral and master's degrees from Andrews University, and a baccalaureate degree from LaSierra University.
Kyoshin Ahn is the Associate Secretary for the North American Division. He holds a doctorate in New Testament and Early Christianity from Vanderbilt University. Prior to that he graduated with a master of divinity degree from Andrews University, and a baccelaureate degree from Sahm Yook University in Korea.
Dwight Nelson is senior pastor at Pioneer Memorial Church, on the campus of Andrews University in Berrien Springs, Michigan. He holds a doctor of ministry from the Seventh-day Adventist Theological Seminary and is the author of several books, including The Chosen, Creation and Evolution, The Eleventh Commandment, and Outrageous Grace.
Leslie N. Pollard, Ph.D., D.Min.
Edwin Reynolds is a professor of New Testament and biblical languages and is the School of Religion graduate program coordinator at Southern Adventist University. He holds a doctoral degree in New Testament from the Seventh-day Adventist Theological Seminary. Reynolds was the editor of the Guide for Research Writing: AIIAS Theological Seminary, 2d ed. (Silang, Cavite, Philippines: AIIAS Publications, 2002) and Asia Adventist Seminary Studies, and is a former editor of and regular contributor to the Journal of the Adventist Theological Society.
Stephen A. L. Richardson, M.A., M.Div.
Stephen Richardson is the ministerial director for Allegheny East Conference. He is a PhD candidate in Hebrew Bible from Vanderbilt University. He holds a master's degree in religion from Vanderbilt and a master of divinity degree from the Seventh-day Adventist Theological Seminary. He earned a baccalaureate degree in theology and business administration from Oakwood University.
Kendra Haloviak Valentine is an associate professor of New Testament studies and chair of the Department of Biblical Studies at the H. M. S. Richards Divinity School at La Sierra University. Haloviak Valentine holds a doctorate in New Testament studies and ethics from The Graduate Theological Union at Berkeley, a master's in New Testament studies from Andrews University, and a baccalaureate degree in theology and English from Columbia Union College. Her recent publications include "The Book of Revelation," in The Dictionary of Scripture and Ethics, Joel Green, ed. (Baker Academic, 2011) and Signs to Life: Reading and Responding to John's Gospel (Signs Publishing, 2013).
Clinton Wahlen is an associate director of the Biblical Research Institute. He holds a Ph.D. degree in New Testament from Cambridge University, U.K., and has authored Jesus and the Impurity of Spirits in the Synoptic Gospels (Mohr Siebeck, 2004) and James (Adult Bible Study Guide, forthcoming). He has also published academic articles in journals such as New Testament Studies and Biblical Interpretation, and written for biblical dictionaries published by InterVarsity Press.


Cedric Jones has been working with precious metals, fine gemstones and cultured pearls since he originally founded his Company in 1976. The Company began as a family business working from home until rapid success and expansion obliged Cedric to find bigger premises. It was at this stage that he decided to try some retailing as well as manufacturing his home-grown designs. The Company had changed from being solely an importer of high quality Continental jewellery to becoming a manufacturer of individually designed British made products. These designs were marketed not only in the United Kingdom but exported to many countries around the world.
So, still employing modern techniques, such as CAD design and laser welding, Cedric maintains his professional interest and dedication to design of high quality manufacture and continues to produce innovative designs.


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Sports Reach 2019
Staines Congregational Church
Espresso Church
Home → About Us → Our Story
Staines Congregational Church was founded as an independent chapel in 1789 and was for many years a member of the Congregational Union of England and Wales.
In the early 1960s this body changed their constitution, which eventually led to the amalgamation of the Congregational Union and the Presbyterian Church of England to form the United Reform Church in 1972. Staines Congregational Church chose, on doctrinal issues, not to join in this process and became an independent Congregational Church in 1966.
The church became one of the founder members of the Evangelical Fellowship of Congregational Churches (EFCC) in 1976.
The present church was built in 1956 when its previous site in the centre of Staines was required for redevelopment.
Over the past 60 years the church has had four pastors. Rev Nick Gray was ordained as its present pastor in August 2010.
For further information, we encourage you to read our church's history from 1789 to today.
© Staines Congregational Church, Kingston Rd, Staines TW18 1AY - 01784 453256 - Cookie Policy - Good News - iKnow


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Music » Music Stories & Interviews
The overwhelming success of the Acoustic Medicine Music Series has organizers switch their footing
by Nicolette Good
Courtesy photo Noëlle Hampton (right) and André Moran will perform at the Bharmacy on December 3.
Courtesy photos Coming soon to Acoustic Medicine: Matt Harlan (above) and Art & Lisa (below) on December 17.
The future of the Acoustic Medicine Music Series is uncertain, but not for lack of success. When the Current previewed the four-month program in August, its hostess and creator, Melissa Ludwig (from the Melissa Ludwig Band), had a simple goal: elevate a venue to the level of better-known out-of-town music halls while pairing quality regional acoustic acts with local talent. She wanted to forge a creative corridor from San Antonio to Austin in bi-weekly installments. And she did just that.
"The series has opened up a new clientele for us," says Carlos Padilla, the Olmos Bharmacy's owner. "One of the goals was to add a new crowd to the good crowd we already have."
Many of the shows at the Olmos Bharmacy were standing room only and gained plenty of media attention. I played one of my best home turf shows when I opened for Lincoln Durham in October, selling and signing more CDs than ever to my most attentive, energized audience in months.
Despite its success, however, Acoustic Medicine is difficult to produce for Ludwig and her husband, Mitch Connell, who runs sound. They had planned to execute the event in their free time, unpaid, and that's the way they wanted it from the get-go. "I'm doing this to highlight and to showcase other artists that I think are great," Ludwig told the Current in August. "We will continue to perform as a band as we always do, but we're just not going to perform [or get paid] at this series."
While Connell is a veteran at running sound for the Melissa Ludwig Band, doing so for Acoustic Medicine meant accommodating two unique bands each night in addition to pumping the audio to a live broadcast — previously uncharted territory for Connell — hosted by Dave Maisano's Radio Free Texas.
"It was all a challenge," says Ludwig. "Getting sponsors was something I had never tried to do before. We did offer a lot of exposure for the money we were asking, but you still have to go out there and ask people for money."
Monetary sponsorships meant hiring a graphic designer to brand the series, building a professional website, and investing in advertising, all of which helped lure in warm bodies. It was an ambitious project, but the numbers didn't always add up. In spite of this, another strong turnout is expected on December 3, when Austin roots-rocker Noëlle Hampton revisits San Antonio as a duo with Andre Moran on electric guitar.
Hampton is a compelling singer-songwriter with a voice that's at once tender, raw, and road-tested. "I've always been influenced by people like James Taylor, Shawn Colvin, Rickie Lee Jones," says Hampton, whose résumé includes opening spots for Bob Dylan and Wilco, and whose Americana anthems have been featured on ABC's Men in Trees and Lifetime's Army Wives. "Those songwriters are built into my system somewhere."
Hampton plans a new album to follow up Thin Line, her 2008 release. "I have most of the songs [ready]," Hampton said. "Until then, I really want to play in San Antonio, because it's so close to home. I appreciate Melissa for creating a scenario where we can do it."
If the series keeps up, it could transform Americana and folk music in San Antonio significantly. Bharmacy owner Padilla wants to help keep the event afloat, and has already discussed with Ludwig about how to scale it down to a more sustainable level. "Melissa and I definitely want to keep up the series," Padilla said. "We've spent a lot of time and energy already, so we might shoot for once a month starting in January. We also have a better perspective now on how to use the money more wisely."
That means future shows won't be live-streamed on Radio Free Texas, for instance. And Padilla will start to hunt those sponsorships as Ludwig moves toward a new job and plans for a new album. "Once a month sounded more doable to everyone," she said, "but we may go to twice a month if the transition goes smoothly."
Regardless of its scheduling in the coming year, Acoustic Medicine has sent a clear message: both for top-notch out-of-town and local artists, San Antonio is a viable place to play. •
Acoustic Medicine Music Series feat. Noëlle Hampton and Andre Moran with Chris Taylor
8pm Sat, Dec 3
Olmos Bharmacy
3902 McCullough
medicineseries.com
Additional Music Slideshows
Everything we saw during Khruangbin's second mind-bending night at San Antonio's Tobin Center
Everything we saw as The Toadies and Rev. Horton Heat blew the roof off of San Antonio's Aztec Theatre
All the San Antonio Current's best music photography from 2021


Hi all! I'm going to share with you all the new books I've adopted over this month. Yes it looks like a lot but I will say all but one is secondhand AND my Mama and my boyfriend, Nathan, brought most of them. Being secondhand means that most of these books were $3 or $4.
Like always this post is going to be mostly pretty pictures so sit back and enjoy the books.
The Godfather was a Girl … And Blanche Dubois was a Guy by Eamon Evans; this was the only brand new purchase of the month.
So that's it for this book haul. Comment down below and let me know was your first book purchase of the year.