text:
Match Winning Indian Bowler declared fit to Play
By Rohit Sarkar
Indian Premium Fast Bowler Bhuvneshwar Kumar has been suffering with his back issues for over a month, due to which he missed out on some matchs in the Limited Overs Series as well.Finally,He saw himself getting sidelined from the much awaited Test Series against England. He missed out on the Nidhas Trophy and the Test against Afghanistan due to the Same Reason. Finally comes some good news for Bhuvneshwar Kumar and Team India Fans as he is declared fit to play in India A Quadrangular Series.
BCCI declared on their Twitter Handle, that he has been declared fit and will play against South Africa A on Wednesday in order to prove his match fitness. If he proves his match fitness there,then it will be very interesting to see if Bhuvi is called up to play the Fifth and Final Test against England at the Kia Oval in case India manages to square the series in Southampton.
UPDATE: Bhuvneshwar Kumar has been declared fit & will be a part of the India A squad in the ongoing Quadrangular series. He will feature in the side's next fixture against South Africa A. pic.twitter.com/4TXF7jBU9i
— BCCI Domestic (@BCCIdomestic) August 27, 2018
India A and South Africa A will play against each other for the Third place face off in the Quadrangular Series in Alur. If Bhuvneshwar Kumar manages to Prove his fitness in the match against South Africa A,then he might get a chance to play in the upcoming Asia Cup where India will face their Arch Rivals Pakistan on September 18.
England VS India 2018
India A
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text:
/Manchester City and the story of the game described as "The Spinning Snake Game"
Manchester City and the story of the game described as "The Spinning Snake Game"
In a very exciting match that I consider some of the most exciting this season, the League Stadium in Manchester City, between Manchester City and Leicester City in the English Super League, has seen it.
The match saw "strange" swings during its run, prompting the Manchester City coach to call it a "roller coaster."
Manchester City started the game spectacularly, scoring 4 goals in just 25 minutes, prompting the crowd to expect a broad result for Leicester City, who seemed to succumb to their fate.
But the Foxes came back strong in the second inning, scoring three touchdowns, in 10 minutes, between 55 and 65 minutes.
All of a sudden, Manchester City found themselves facing a complex confrontation, in which the opponent returned to the one-goal difference.
But Guardiola's men rebuffed, scoring two goals, the fifth and sixth, to end the dramatic saga that has become the talk of social media for its excitement and volatility.
During the meeting, Algerian international Riad Mehers, who scored one goal and made another, sparkled in an exciting match that contributed to the league lead remaining in Manchester City's grip.

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}

	Neural architecture search (NAS) have attracted great attention with remarkable performance in various deep learning tasks. Impressive results have been shown for reinforcement learning (RL) based methods \cite{zoph2018learning,zoph2016neural}, for example, which train and evaluate more than $20,000$ neural networks across $500$ GPUs over $4$ days. Recent methods like differentiable architecture search (DARTs) reduce the search time by formulating the task in a differentiable manner \cite{liu2018darts}. DARTS relaxes the search space to be continuous, so that the architecture can be optimized with respect to its validation set performance by gradient descent, which provides a fast solution for effective network architecture search. To reduce the redundancy in the network space,  partially-connected DARTs (PC-DARTs) was recently  introduced to perform a more efficient search without compromising the performance of DARTS \cite{xu2019pcdarts}.

	Although the network optimized by DARTS or its variants has a smaller model size than  traditional light models,  the searched network   still suffers from an inefficient  inference process due to the complicated  architectures generated by multiple stacked  full-precision convolution operations. Consequently, the adaptation of the searched network to an embedded device is still computationally expensive and inefficient.  Clearly the problem requires further exploration to overcome these challenges.

	One way to address these challenges is to  transfer the NAS to a binarized neural architecture search (BNAS), by exploring the advantages of binarized neural networks (BNNs) on memory saving and computational cost reduction \cite{shen2019searching}. Binarized filters have been used in traditional convolutional neural networks (CNNs) to compress deep models \cite{paper09,paper10,paper14,paper15}, showing up to 58-time speedup and 32-time memory saving. In \cite{paper15}, the XNOR network is presented where both the weights and inputs attached to the convolution are approximated with binary values.  This results in an efficient implementation of convolutional operations by reconstructing the unbinarized filters with a single scaling factor. In \cite{gu2019projection}, a projection  convolutional neural network (PCNN) is proposed to realize BNNs based on a simple back propagation algorithm. In our BNAS framework, we re-implement XNOR and PCNN for the effectiveness  validation. We show that the BNNs obtained by BNAS can outperform conventional models by a large margin. It is a significant contribution in the field of BNNs, considering that the performance of conventional BNNs are not yet comparable with their corresponding full-precision models in terms of accuracy.

	\begin{figure*}[htbp!]
		\centering
		\includegraphics[scale=.46]{1690-performance-based}
		\caption{The main steps of our BNAS: (1) Search an architecture based on $\mathcal{O}^{(i,j)}$ using PC-DARTS. (2) Select half the operations with less potential from $\mathcal{O}^{(i,j)}$ for each edge, resulting in $\mathcal{O}^{(i,j)}_{smaller}$. (3) Select an architecture by sampling (without replacement) one operation from $\mathcal{O}^{(i,j)}_{smaller}$ for every edge, and then train the selected architecture. (4) Update the operation selection likelihood $s(o^{(i,j)}_k)$ based on the accuracy obtained from the selected architecture on the validation data. (5) Abandon the operation with the minimal selection likelihood from the search space $\{\mathcal{O}^{(i,j)}\}$ for every edge.}
		\label{fig:performance-based}
	\end{figure*}

	The search process of our BNAS consists of two steps. One is the operation potential ordering based on partially-connected DARTs (PC-DARTs) \cite{xu2019pcdarts} which serves as a baseline for our BNAS. It is further sped up with a second operation reduction step guided by a performance-based strategy. In the operation reduction step, we prune one operation at each iteration from one-half of the operations with less potential as calculated by PC-DARTS. As such, the optimization of the two steps becomes faster and faster because the search space is reduced due to the operation pruning. We can take advantage of the differential framework of DARTS where the search and performance evaluation are in the same setting. We also enrich the search strategy of DARTS. Not only is the gradient used to determine which operation is better, but the proposed performance evaluation is included for further reduction of the search space. In this way BNAS is fast and well built. The contributions of our paper include:

	\begin{itemize}
		\item
		BNAS is developed based on a new search algorithm which solves the BNNs optimization and architecture search in a unified framework.
		\item
		The search space is greatly reduced through a performance-based strategy used to abandon  operations with less potential, which improves the search efficiency by $40\%$.

		\item
		Extensive experiments demonstrate that the proposed algorithm achieves much better performance than other light models on  CIFAR-10 and ImageNet.
	\end{itemize}

	\section{Related Work}
	Thanks to the rapid development of deep learning, significant gains in performance have been realized in a wide range of computer vision tasks, most of which are manually designed network architectures \cite{krizhevsky2012imagenet,simonyan2014very,he2016deep,huang2017densely}. Recently, the new approach called neural architecture search (NAS) has been attracting increased attention. The goal is to find automatic ways of designing neural architectures to replace conventional hand-crafted ones. Existing NAS approaches need to explore a very large search space and can be roughly divided into three type of approaches: evolution-based, reinforcement-learning-based and one-shot-based.

	In order to implement the architecture search within a short period of time, researchers try to reduce the cost of evaluating each searched candidate. Early efforts include sharing weights between searched and newly generated networks \cite{cai2018efficient}.  Later, this method was generalized into a more elegant framework named one-shot architecture search \cite{brock2017smash,cai2018proxylessnas,liu2018darts,pham2018efficient,xie2018snas,zheng2019multinomial,zheng2019dynamic}. In these approaches, an over-parameterized network or super network covering all candidate operations is trained only once, and the final architecture is obtained by sampling from this super network. For example, Brock et al. \cite{brock2017smash} trained the over-parameterized network using a HyperNet \cite{2016Hypernetworks}, and Pham et al. \cite{pham2018efficient} proposed to share parameters among child models to avoid retraining each candidate from scratch. The paper \cite{liu2017hierarchical} is based on DARTS, which introduces a differentiable framework and thus combines the search and evaluation stages into one. Despite its simplicity, researchers have found some of its drawbacks and proposed a few improved approaches over DARTS \cite{cai2018proxylessnas,xie2018snas,Chen2019SMASH}.

	Unlike previous methods, we study BNAS based on efficient operation reduction. We prune one operation at each iteration from one-half of the operations with smaller weights calculated by PC-DARTS, and the search becomes faster and faster in the optimization.

	\section{Binarized Neural Architecture Search}

	In this section, we first describe the search space in a general form, where the computation procedure for an architecture (or a cell in it) is represented as a directed acyclic graph. We then review the baseline PC-DARTS \cite{xu2019pcdarts}, which improves the memory efficiency, but is   not enough for BNAS. Finally, an operation sampling and a performance-based search strategy are proposed to effectively reduce the search space. Our BNAS framework is shown in Fig. \ref{fig:performance-based} and additional details of which are described in the rest of this section.

	\subsection{Search Space}
	Following Zoph et al. (2018); Real et al. (2018); Liu et al. (2018a;b), we search for a computation cell as the building block of the final architecture. A network consists of a pre-defined number of cells \cite{zoph2016neural}, which can be either normal cells or reduction cells. Each cell takes the outputs of the two previous cells as input. A cell is a fully-connected directed acyclic graph (DAG) of $M$ nodes, \emph{i.e.}, $\{B_1, B_2, ..., B_M\}$, as illustrated in Fig. \ref{fig:cell}. Each node $B_i$ takes its dependent nodes as input, and generates an output through a sum operation $B_j = \sum_{i<j} o^{(i,j)}(B_i).$ Here each node is a specific tensor (\emph{e.g.,} a feature map in convolutional neural networks) and each directed edge $(i,j)$ between $B_i$ and $B_j$ denotes an operation $o^{(i,j)} (.)$, which is sampled from $\mathcal{O}^{(i,j)}=\{o^{(i,j)}_1, ..., o^{(i,j)}_K\}$. Note that the constraint $i<j$ ensures there are no cycles in a cell. Each cell takes the outputs of two dependent cells as input, and we define the two input nodes of a cell as $B_{-1}$ and $B_{0}$ for simplicity. Following \cite{liu2018darts}, the set of the operations $\mathcal{O}$ consists of $K = 8$ operations.  They include $3\times3$ max pooling, no connection (zero), $3\times3$ average pooling, skip connection (identity), $3\times3$ dilated convolution with rate $2$, $5\times5$ dilated convolution with rate $2$, $3\times3$ depth-wise separable convolution, and $5\times5$ depth-wise separable convolution, as illustrated in Fig. \ref{fig:operationset}. The search space of a cell is constructed by the operations of all the edges, denoted as $\{\mathcal{O}^{(i,j)}\}$.

	Unlike conventional convolutions, our BNAS is achieved by transforming all the convolutions in $\mathcal{O}$ to binarized convolutions. We denote the  full-precision and binarized kernels as $X$ and $\hat X$ respectively. A convolution operation in $\mathcal{O}$ is represented as  $B_j = B_i \otimes \hat X $ as shown in Fig. \ref{fig:operationset}, where $\otimes$ denotes convolution. To build BNAS, one key step is how to binarize the kernels from  $X$ to $\hat X$, which can be implemented based on state-of-the-art BNNs, such as XNOR or PCNN. As we know, the optimization of BNNs is more challenging than that of conventional CNNs \cite{gu2019projection,Rastegari2016XNOR}, which adds an additional burden to NAS. To solve it, we introduce channel sampling and  operation space reduction into differentiable NAS to significantly reduce the cost of GPU hours, leading to an efficient BNAS.

	\begin{figure}[htbp]
		\centering
		\subfigure[Cell]{
			\includegraphics[scale=.36]{1690-cell}
			\label{fig:cell}
		}
		\quad

		\subfigure[Operation Set]{
			\includegraphics[scale=.4]{1690-operationset}
			\label{fig:operationset}
		}
		\caption{ (a) A cell contains 7 nodes, two input nodes $B_{-1}$ and $B_0$, four intermediate nodes $B_1$, $B_2$, $B_3$, $B_4$ that apply sampled operations on the input nodes and upper nodes, and an output node that concatenates the outputs of the four intermediate nodes. (b) The set of operations $\mathcal{O}^{(i,j)}$ between $B_i$ and $B_j$, including binarized convolutions.}
	\end{figure}

	\subsection{PC-DARTS}

	The core idea of PC-DARTS is to take advantage of partial channel connections to improve memory efficiency. Taking the connection from $B_i$ to $B_j$ for example, this involves defining a channel sampling mask $S^{(i,j)}$, which assigns $1$ to selected channels and $0$ to masked ones. The selected channels are sent to a mixed computation of $|\mathcal{O}^{(i,j)}|$ operations, while the masked ones bypass these operations. They are directly copied to the output, which is formulated as:

	\begin{equation}
	\label{eq:pc}
	\small
	\begin{aligned}
	&f^{(i,j)}(B_i,S^{(i,j)}) \\
	&= \sum_{o^{i,j}_k\in \mathcal{O}^{(i,j)}} \frac{exp\{\alpha_{o^{(i,j)}_k}\}}{\sum_{o^{(i,j)}_{k^{'}} \in \mathcal{O}^{(i,j)}} exp\{\alpha_{o^{(i,j)}_{k^{'}}}\} } \cdot o^{(i,j)}_k(S^{(i,j)} * B_i) \\
	&+ (1 - S^{(i,j)}) * B_i,
	\end{aligned}
	\end{equation}
	where $S^{(i,j)} * B_i$ and $(1 - S^{(i,j)}) * B_i$ denote the selected and masked channels, respectively, and $\alpha_{o^{(i,j)}_k}$ is the parameter of operation $o^{(i,j)}_k$ between $B_i$ and $B_j$.

	PC-DARTS sets the proportion of selected channels to $1/C$ by regarding $C$ as a hyper-parameter. In this case, the computation cost can also be reduced by $C$ times. However, the size of the whole search space is $2 \times K^{|\mathcal{E_M}|}$, where $\mathcal{E_M}$ is the set of possible edges with $M$ intermediate nodes in the fully-connected DAG, and the "$2$" comes from the two types of cells. In our case with $M=4$, together with the two input nodes, the total number of cell structures in the search space is $2 \times 8^{2+3+4+5} = 2 \times 8^{14}$.  This is an extremely large space to search for a binarized neural architectures which need more time than a full-precision NAS. Therefore, efficient optimization strategies for BNAS are required.

	\subsection{Sampling for BNAS}
	For BNAS, PC-DARTS is still time and memory consuming because of the large search space, although it is already faster than most of existing NAS methods. We introduce another approach to increasing memory efficiency by reducing the search space $\{\mathcal{O}^{(i,j)}\}$. According to $\{\alpha_{o^{(i,j)}_k}\}$, we can select half the operations with less potential from $\mathcal{O}^{(i,j)}$ for each edge, resulting in $\mathcal{O}^{(i,j)}_{smaller}$. We then sample an operation from $\mathcal{O}^{(i,j)}_{smaller}$ for each edge guided by a performance-based strategy proposed in the next section in order to reduce the search space. We follow the rule of sampling without replacement $K/2$ times. Here sampling without replacement means that after one operation is sampled randomly from $\mathcal{O}^{(i,j)}_{smaller}$, this operation is removed from $\mathcal{O}^{(i,j)}_{smaller}$. For convenience of description, the $K/2$ operations in each edge are transformed to a one-hot indicator vector. In other words we sample only one operation according to the performance-based strategy, which effectively reduces the memory cost compared with PC-DARTS \cite{xu2019pcdarts}.

	\subsection{Performance-based Strategy for BNAS} \label{sec:PBS}
	Reinforcement learning is inefficient in the architecture search due to the delayed rewards in network training, i.e., the evaluation of a structure is usually done after the network training converges. On the other hand, we can perform the evaluation of a cell when training the network. Inspired by \cite{ying2019bench}, we use a performance-based strategy to boost the search efficiency by a large margin. Ying \emph{et al.} \cite{ying2019bench} did a series of experiments showing that in the early stage of training, the validation accuracy ranking of different network architectures is not a reliable indicator of the final architecture quality. However, we observe that the experiment results actually suggest a nice property that if an architecture performs badly in the beginning of training, there is little hope that it can be part of the final optimal model. As the training progresses, this observation shows less uncertainty. Based on this observation, we derive a simple yet effective operation abandoning process. During training, along with the increasing epochs, we progressively abandon the worst performing operation in each edge.

	To this end, we randomly sample one operation from the $K/2$ operations in $\mathcal{O}^{(i,j)}_{smaller}$ for every edge, then obtain the validation accuracy by training the sampled network for one epoch, and finally assign this accuracy to all the sampled operations. These three steps are performed $K/2$ times by sampling without replacement, leading to each operation having exactly one accuracy for every edge.

	We repeat it $T$ times. Thus each operation for every edge has $T$ accuracies $\{y_{k,1}^{(i,j)}, y_{k,2}^{(i,j)}, ..., y_{k,T}^{(i,j)}\}$. Then we define the selection likelihood of the $k$th operation in $\mathcal{O}^{(i,j)}_{smaller}$  for each edge as:

	\begin{equation}\label{eq:performance_prob_smaller}
	\small
	s_{smaller}(o^{(i,j)}_k) = \frac{exp\{\bar{y}_k^{(i,j)}\}}{\sum_m exp\{\bar{y}_m^{(i,j)}\}},
	\end{equation}
	where $\bar{y}_k^{(i,j)} = \frac{1}{T}  \sum_t y_{k,t}^{(i,j)}$. And the selection likelihoods of the other operations not in $\mathcal{O}^{(i,j)}_{smaller}$ are defined as:

	\begin{equation}\label{eq:performance_prob_larger}
	\small
	\begin{aligned}
	&s_{larger}(o^{(i,j)}_k) \\
	=& \frac{1}{2} (\mathop{\max}\limits_{o^{(i,j)}_k}{\{s_{smaller}(o^{(i,j)}_k)\}} + \frac{1}{\lceil K/2 \rceil} \sum_{o^{(i,j)}_k}{s_{smaller}(o^{(i,j)}_k)}),
	\end{aligned}
	\end{equation}
	where $\lceil K/2 \rceil$ denotes the smallest integer $\geq K/2$. The reason to use it is because $K$ can be an odd integer during iteration  in the proposed Algorithm \ref{alg:MDL}. Eq. \ref{eq:performance_prob_larger} is an estimation for the rest operations using a  value balanced between the maximum and average of $s_{smaller}(o^{(i,j)}_k)$. Then, $s(o^{(i,j)}_k)$ is updated by:

	\begin{equation}\label{eq:performance_prob}
	\begin{aligned}
	\smaller
	s(o^{(i,j)}_k) \leftarrow & \frac{1}{2} s(o^{(i,j)}_k) + q_k^{(i,j)}s_{smaller}(o^{(i,j)}_k) + \\
	&  (1 - q_k^{(i,j)})s_{larger}(o^{(i,j)}_k),
	\end{aligned}
	\end{equation}
	where $q_k^{(i,j)}$ is a mask, which is $1$ for the operations in $\mathcal{O}_{smaller}^{(i,j)}$ and $0$ for the others.

	Finally, we abandon the operation with the minimal selection likelihood for each edge. Such that the search space size is significantly reduced from $2 \times |\mathcal{O}^{(i,j)}|^{14}$ to $2 \times (|\mathcal{O}^{(i,j)}|-1)^{14}$.  We have:

	\begin{equation}\label{eq:min}
		\small
		\mathcal{O}^{(i,j)} \leftarrow \mathcal{O}^{(i,j)} - \{ \mathop{\arg\min}\limits_{o^{(i,j)}_k}{s(o^{(i,j)}_k)} \}.
	\end{equation}

	The optimal structure is obtained when there is only one operation left in each edge. Our performance-based search algorithm is presented in Algorithm \ref{alg:MDL}. Note that in line 1, PC-DARTS is performed for $L$ epochs as the warm-up to find an initial architecture, and line 14 is used to update the architecture parameters $\alpha_{o^{(i,j)}_k}$ for all the edges due to the reduction of the search space $\{\mathcal{O}^{(i,j)}\}$.

	\begin{algorithm}[h]
		\small
		\caption{Performance-Based Search \label{alg:MDL}}
		\LinesNumbered
		\KwIn{Training data, Validation data, Searching hyper-graph: $\mathcal{G}$, $K=8$, $s(o^{(i,j)}_k)=0$ for all edges;}
		\KwOut{Optimal structure $\alpha$;}
		Search an architecture for $L$ epochs based on $\mathcal{O}^{(i,j)}$ using PC-DARTS; \\
		\While{$(K>1)$}{
			Select $\mathcal{O}^{(i,j)}_{smaller}$ consisting of $\lceil K/2 \rceil$ operations with smallest $\alpha_{o^{(i,j)}_k}$ from $\mathcal{O}^{(i,j)}$ for every edge; \\
			\For{$t= 1,...,T$ \rm epoch}{
				$\mathcal{O}^{'(i,j)}_{smaller} \leftarrow \mathcal{O}^{(i,j)}_{smaller}$; \\
				\For{ $ e=1,..., \lceil K/2 \rceil $ \rm epoch}{
					Select an architecture by sampling (without replacement) one operation from $\mathcal{O}^{'(i,j)}_{smaller}$ for every edge; \\
					Train the selected architecture and get the accuracy on the validation data; \\
					Assign this accuracy to all the sampled operations; \\
				}
			}
			Update $s(o^{(i,j)}_k)$ using Eq. \ref{eq:performance_prob}; \\
			Update the search space \{$\mathcal{O}^{(i,j)}$\} using Eq. \ref{eq:min};\\

			Search the architecture for $V$ epochs based on $\mathcal{O}^{(i,j)}$ using PC-DARTS; \\
			$K = K -1$; \\
		}
	\end{algorithm}

	\subsection{Optimization for BNAS}
	%
	In this paper, the binarized kernel weights are computed based on XNOR \cite{Rastegari2016XNOR} or PCNN \cite{gu2019projection}. Both methods are easily implemented in our BNAS framework, and the source code will be publicly available soon.

	Binarizing CNNs, to the best of our knowledge, shares the same implementation framework. Without loss of generality, at layer $l$, let $D_i^l$ be the direction of a full-precision kernel $X_i^l$, and $A^l$ be the shared amplitude. For the binarized kernel $\hat X_i^l$ corresponding to $X_i^l$, we have $\hat{X}_i^l = A^l \odot D_i^l$, where $\odot$ denotes the element-wise multiplication between two matrices. We then employ an amplitude loss function to reconstruct the full-precision kernels as:

	\begin{equation}
	\small
	L_{A} = \frac{\theta}{2}\sum_{i, l}\|X^l_i-\hat{X}^l_i\|^2 = \frac{\theta}{2}\sum_{i, l}\|X^l_i-  A^l \odot D^l_i\|^2,
	\label{loss_A}
	\end{equation}
	where $D_i^l=sign(X_i^l)$. The element-wise multiplication combines the binarized kernels and the amplitude matrices to approximate the full-precision kernels. The amplitudes $A^l$ are solved in different BNNs, such as \cite{gu2019projection} and \cite{Rastegari2016XNOR}. The complete loss function $L$ for BNAS is defined as:
	\begin{equation}
	L = L_S + L_{A},
	\label{loss_L}
	\end{equation}
	where $L_S$ is the conventional loss function, e.g., cross-entropy.

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

	In this section, we compare our BNAS with state-of-the-art NAS methods, and also compare the BNNs obtained by our BNAS based on XNOR \cite{Rastegari2016XNOR} and PCNN \cite{gu2019projection}.

	\subsection{Experiment Protocol}

	In these experiments, we first search neural architectures on an over-parameterized network on CIFAR-10, and then evaluate the best architecture with a stacked deeper network on the same data set. Then we further perform experiments to search architectures directly on ImageNet. We run the experiment multiple times and find that the resulting architectures only show slight variation in performance, which demonstrates the stability of the proposed method.

	\begin{table*}[htbp]
		\small
		\begin{center}
				\begin{tabular}{lcccc}
					\toprule[1pt]
					\multirow{2}{*}{\textbf{Architecture}} & \textbf{Test Error} & \textbf{\# Params} & \textbf{Search Cost} & \textbf{Search} \\
					& \textbf{(\%)} & \textbf{(M)}    & \textbf{(GPU days)} & \textbf{Method} \\
					\hline
					ResNet-18 \cite{he2016deep}  & 3.53  & 11.1 (32 bits) & - & Manual \\
					WRN-22 \cite{zagoruyko2016wide}  & 4.25 & 4.33 (32 bits) & - & Manual  \\
					DenseNet \cite{huang2017densely} & 4.77 & 1.0 (32 bits) & - & Manual \\
					SENet \cite{hu2018squeeze} & 4.05 & 11.2 (32 bits) & - & Manual \\
					\hline
					ResNet-18 (XNOR) & 6.69 & 11.17 (1 bit)  & - & Manual  \\
					ResNet-18 (PCNN) & 5.63 & 11.17 (1 bit)  & - & Manual  \\
					WRN22 (PCNN) \cite{gu2019projection} & 5.69 & 4.29 (1 bit)  & - & Manual \\
					Network in {\cite{mcdonnell2018training}} & 6.13 & 4.30 (1 bit) & - & Manual\\
					\hline
					NASNet-A \cite{zoph2018learning} & 2.65 & 3.3 (32 bits) & 1800 & RL \\
					AmoebaNet-A \cite{real2018regularized} & 3.34 & 3.2 (32 bits) & 3150 & Evolution \\
					PNAS \cite{liu2018progressive} & 3.41 & 3.2 (32 bits) & 225 & SMBO \\
					ENAS \cite{pham2018efficient}  & 2.89 & 4.6 (32 bits) & 0.5 & RL \\
					Path-level NAS \cite{cai2018path} & 3.64 & 3.2 (32 bits) & 8.3 & RL \\
					DARTS(first order) \cite{liu2018darts}  & 2.94 & 3.1 (32 bits) & 1.5 & Gradient-based \\
					DARTS(second order) \cite{liu2018darts} & 2.83 & 3.4 (32 bits) & 4 & Gradient-based \\
					PC-DARTS & 2.78 & 3.5 (32 bits) & 0.15 & Gradient-based \\
					\hline
					\textbf{BNAS} (full-precision) & 2.84  & 3.3 (32 bits) & 0.08 & Performance-based \\
					\textbf{BNAS} (XNOR) & 5.71 & 2.3 (1 bit) & 0.104 & Performance-based \\
					\textbf{BNAS} (XNOR, larger) & 4.88 & 3.5 (1 bit) & 0.104 & Performance-based \\
					\textbf{BNAS} (PCNN) & 3.94 & 2.6 (1 bit) & 0.09375 & Performance-based \\
					\textbf{BNAS} (PCNN, larger) & 3.47 & 4.6 (1 bit) & 0.09375  & Performance-based \\
					\bottomrule[1pt]
				\end{tabular
			\end{center}
			\caption{Test error rates for human-designed full-precision networks, human-designed binarized networks, full-precision networks obtained by NAS, and networks obtained by our BNAS on CIFAR-10. Note that the parameters are $1$ bit in binarized networks, and are 32 bits in full-precision networks. For fair comparison, we select the architectures by NAS with similar parameters ($<$ $5$M). In addition, we also train an optimal architecture in a larger setting, \emph{i.e.,} with more initial channels ($44$ in XNOR or $48$ in PCNN).}
			\label{tab:cifar_results}
		\end{table*}

		We use the same datasets and evaluation metrics as existing NAS works \cite{liu2018darts,cai2018path,zoph2018learning,liu2018progressive}. First, most experiments are conducted on CIFAR-10 \cite{krizhevsky2009learning}, which has $50$K training images and $10$K testing images with resolution $32 \times 32$ and from $10$ classes. The color intensities of all images are normalized to $[-1, +1]$. During architecture search, the $50$K training samples of CIFAR-10 is divided into two subsets of equal size, one for training the network weights and the other for finding the architecture hyper-parameters. When reducing the search space, we randomly select $5$K images from the training set as a validation set (used in line 8 of Algorithm \ref{alg:MDL}). To further evaluate the generalization capability, we stack the discovered optimal cells on CIFAR-10 into a deeper network, and then evaluate the classification accuracy on ILSVRC 2012 ImageNet \cite{russakovsky2015imagenet}, which consists of $1,000$ classes with $1.28$M training images and $50$K validation images.

		In the search process, we consider a total of $6$ cells in the network, where the reduction cell is inserted in the second and the fourth layers, and the others are normal cells. There are $M=4$ intermediate nodes in each cell. Our experiments follow PC-DARTS. We set the hyper-parameter $C$ in PC-DARTS to $2$ for CIFAR-10 so only $1/2$ features are sampled for each edge. The batch size is set to $128$ during the search of an architecture for $L=5$ epochs based on $\mathcal{O}^{(i,j)}$ (line 1 in Algorithm \ref{alg:MDL}). Note for $5 \le L \le 10$, the larger $L$ has little effect on the final performance, but will cost more search time. We freeze the network hyper-parameters such as $\alpha$, and only allow the network parameters such as filter weights to be tuned in the first $3$ epochs.  Then in the next 2 epochs, we train both the network hyper-parameters and the network parameters. This is to provide an initialization for the network parameters and thus alleviates the drawback of parameterized operations compared with free parameter operations. We also set $T = 3$ (line 4 in Algorithm \ref{alg:MDL}) and $V = 1$ (line 14), so the network is trained less than $60$ epochs, with a larger batch size of $400$ (due to few operation samplings) during reducing the search space. The initial number of channels is $16$. We use SGD with momentum to optimize the network weights, with an initial learning rate of $0.025$ (annealed down to zero following a cosine schedule), a momentum of 0.9, and a weight decay of $5 \times 10^{-4}$. The learning rate for finding the hyper-parameters is set to $0.01$.

		After search, in the architecture evaluation step, our experimental setting is similar to \cite{liu2018darts,zoph2018learning,pham2018efficient}. A larger network of $20$ cells ($18$ normal cells and $2$ reduction cells) is trained on CIFAR-10 for $600$ epochs with a batch size of $96$ and an additional regularization cutout \cite{devries2017improved}. The initial number of channels is $36$. We use the SGD optimizer with an initial learning rate of $0.025$ (annealed down to zero following a cosine schedule without restart), a momentum of $0.9$, a weight decay of $3 \times 10^{-4}$ and a gradient clipping at $5$. When stacking the cells to evaluate on ImageNet, the evaluation stage follows that of DARTS, which starts with three convolution layers of stride $2$ to reduce the input image resolution from $224 \times 224$ to $28 \times 28$. $14$ cells ($12$ normal cells and $2$ reduction cells) are stacked after these three layers, with the initial channel number being $64$. The network is trained from scratch for $250$ epochs using a batch size of $512$. We use the SGD optimizer with a momentum of $0.9$, an initial learning rate of $0.05$ (decayed down to zero following a cosine schedule), and a weight decay of $3 \times 10^{-5}$. Additional enhancements are adopted including label smoothing and an auxiliary loss tower during training. All the experiments and models are implemented in PyTorch \cite{paszke2017automatic}.

		\subsection{Results on CIFAR-10}
		We compare our method with both manually designed networks and networks searched by NAS. The manually designed networks include ResNet \cite{he2016deep}, Wide ResNet (WRN) \cite{zagoruyko2016wide}, DenseNet \cite{huang2017densely} and SENet \cite{hu2018squeeze}. For the networks obtained by NAS, we classify them according to different search methods, such as RL (NASNet \cite{zoph2018learning}, ENAS \cite{pham2018efficient}, and Path-level NAS \cite{cai2018path}), evolutional algorithms (AmoebaNet \cite{real2018regularized}), Sequential Model Based Optimization (SMBO) (PNAS \cite{liu2018progressive}), and gradient-based methods (DARTS \cite{liu2018darts} and PC-DARTS \cite{xu2019pcdarts}).

		The results for different architectures on CIFAR-10 are summarized in Tab. \ref{tab:cifar_results}. Using BNAS, we search for two binarized networks based on XNOR \cite{Rastegari2016XNOR} and PCNN \cite{gu2019projection}. In addition, we also train a larger XNOR variant with $44$ initial channels and a larger PCNN variant with $48$ initial channels. We can see that the test errors of the binarized networks obtained by our BNAS are comparable to or smaller than those of the full-precision human designed networks, and are significantly smaller than those of the other binarized networks.

		\begin{table*}[htbp]
			\small
			\begin{center}
				\setlength{\tabcolsep}{3mm}{
					\begin{tabular}{lccccc}
						\toprule
						\multirow{2}{*}{\textbf{Architecture}} & \multicolumn{2}{c}{\textbf{Accuracy (\%)}} & \textbf{Params} & \textbf{Search Cost} & \textbf{Search} \\ \cline{2-3}
						& \textbf{Top1} & \textbf{Top5} & \textbf{(M)} & \textbf{(GPU days)} & \textbf{Method} \\
						\hline
						ResNet-18 \cite{gu2019projection} & 69.3 & 89.2 & 11.17 (32 bits) & - & Manual \\
						MobileNetV1 \cite{howard2017mobilenets} & 70.6 & 89.5 & 4.2 (32 bits) & - & Manual \\
						\hline
						ResNet-18 (PCNN) \cite{gu2019projection} & 63.5 & 85.1 & 11.17 (1 bit) & - & Manual \\
						\hline
						NASNet-A \cite{zoph2018learning} & 74.0  & 91.6 & 5.3 (32 bits) & 1800 & RL \\
						AmoebaNet-A \cite{real2018regularized} & 74.5 & 92.0 & 5.1 (32 bits) & 3150 & Evolution \\
						AmoebaNet-C \cite{real2018regularized} & 75.7 & 92.4 & 6.4 (32 bits) & 3150 & Evolution \\
						PNAS \cite{liu2018progressive} & 74.2 & 91.9 & 5.1 (32 bits) & 225 & SMBO \\
						DARTS \cite{liu2018darts} & 73.1 & 91.0 & 4.9 (32 bits) & 4 & Gradient-based \\
						PC-DARTS \cite{xu2019pcdarts} & 75.8 & 92.7 & 5.3 (32 bits) & 3.8 & Gradient-based \\
						\hline
						BNAS (PCNN) & 71.3 & 90.3 & 6.2 (1 bit)  & \textbf{2.6} & Performance-based \\
						\bottomrule
					\end{tabular}}
				\end{center}
				\caption{Comparison with the state-of-the-art image classification methods on ImageNet. BNAS and PC-DARTS are obtained directly by NAS and BNAS on ImageNet, others are searched on CIFAR-10 and then directly transferred to ImageNet.}
				\label{tab:imagenet_results}
			\end{table*}

			Compared with the full-precision networks obtained by other NAS methods, the binarized networks by our BNAS have comparable test errors but with much more compressed models. Note that the numbers of parameters of all these searched networks are less than 5M, but the binarized networks only need $1$ bit to save one parameter, while the full-precision networks need $32$ bits. In terms of search efficiency, compared with the previous fastest PC-DARTS, our BNAS is $40\%$ faster (tested on our platform (NVIDIA GTX TITAN Xp). We attribute our superior results to the proposed way of solving the problem with the novel scheme of search space reduction.

			Our BNAS method can also be used to search full-precision networks. In Tab. \ref{tab:cifar_results}, BNAS (full-precision) and PC-DARTS perform equally well, but BNAS is $47\%$ faster. Both the binarized methods XNOR and PCNN in our BNAS perform well, which shows the generalization of BNAS. Fig. \ref{fig:xnor} and Fig. \ref{fig:mcn} show the best cells searched by BNAS based on XNOR and PCNN, respectively.

			We also use PC-DARTS to perform a binarized architecture search based on PCNN on CIFAR10, resulting in a network denoted as PC-DARTS (PCNN). Compared with PC-DARTS (PCNN), BNAS (PCNN) achieves a better performance ($95.12$\% vs. $96.06$\% in test accuracy) with less search time ($0.18$ vs. $0.09375$ GPU days). The reason for this may be because the performance based strategy can help find bet

				\begin{figure}[h]
					\centering
					\subfigure[Normal Cell]{
						\includegraphics[scale=.27]{1690-normal_x}
					}
					\quad
					\subfigure[Reduction Cell]{
						\includegraphics[scale=.18]{1690-reduction_x}
					}
					\caption{Detailed structures of the best cells discovered on CIFAR-10 using BNAS based on XNOR. In the normal cell, the stride of the operations on $2$ input nodes is 1, and in the reduction cell, the stride is 2.}
					\label{fig:xnor}
				\end{figure}
			\noindent ter operations for recognition.

			\subsection{Results on ImageNet}
			We further compare the state-of-the-art image classification methods on ImageNet. All the searched networks are obtained directly by NAS and BNAS on ImageNet by stacking the cells. Our binarized network is based on PCNNs. From the results in Tab. \ref{tab:imagenet_results}, we have the following observations: (1) BNAS (PCNN) performs better than human-designed binarized networks { (71.3\% vs. 63.5\%)} and has far fewer parameters { (6.1M vs. 11.17M)}. (2) BNAS (PCNN) has a performance similar to the human-designed full-precision networks { (71.3\% vs. 70.6\%)},  with a much more highly compressed model. (3) Compared with the full-precision networks obtained by other NAS methods, BNAS (PCNN) has little performance drop, but is fastest in terms of search efficiency { (0.09375 vs. 0.15 GPU days)} and is a much more highly compressed model due to the binarization of the network. The above results show the excellent transferability of our BNAS method.

			\begin{figure}[h]
				\centering
				\subfigure[Normal Cell]{
					\includegraphics[scale=.27]{1690-normal_m}
				}
				\quad
				\subfigure[Reduction Cell]{
					\includegraphics[scale=.27]{1690-reduction_m}
				}
				\caption{Detailed structures of the best cells discovered on CIFAR-10 using BNAS based on PCNN. In the normal cell, the stride of the operations on $2$ input nodes is 1, and in the reduction cell, the stride is 2.}
				\label{fig:mcn}
			\end{figure}

			\section{Conclusion}

			In this paper, we have proposed BNAS, the first binarized neural architecture search algorithm, which effectively reduces the search time by pruning the search space in early training stages. It is faster than the previous most efficient search method PC-DARTS. The binarized networks searched by BNAS can achieve excellent accuracies on CIFAR-10 and ImageNet. They perform comparable to the full-precision networks obtained by other NAS methods, but with much compressed models.

	\section{Acknowledgements}
	The work was supported in part by National Natural Science Foundation of China under Grants 61672079, 61473086, 61773117, 614730867. This work is supported by Shenzhen Science and Technology Program KQTD2016112515134654. Baochang Zhang is also with Shenzhen Academy of Aerospace Technology, Shenzhen 100083, China.

		\bibliographystyle{aaai}

redpajama_set_name: RedPajamaArXiv


text:
La Regionalliga West fue la segunda liga de fútbol más importante de Alemania desde la creación de la Bundesliga hasta la creación de la 2. Bundesliga en 1974.

Historia
Fue creada en el año 1963 tras de la creación de la Bundesliga como la nueva primera división de fútbol de Alemania. Comprendía la región de Renana del Norte-Westfalia, la región más poblada de Alemania y era la liga sucesora de la 2. Oberliga West como la nueva segunda división alemana con 20 clubes en su temporada inaugural junto a otras cuatro Regionalligen divididas geográficamente.

El primer y segundo lugar de cada temporada tenían el derecho de disputar la promoción por el ascenso a la Bundesliga, aunque no de manera directa hasta que la liga desapareció en 1974 luego de que se creara la Zweite Bundesliga como la nueva segunda división de Alemania.

Renacimiento de la Liga
En 1994 se reestableceron las Regionalligen pero como ligas de tercera división, y en el 2008 la liga fue refundada con el mismo nombre de Regionalliga West, pero como liga de cuarta categoría.

Ediciones Anteriores

 En Negrita los equipos que lograron el ascenso a la Bundesliga.

Equipos por Temporada

Fuente:Das deutsche Fußball-Archiv  Tablas Históricas del Fútbol Alemán

Simbología

Notas
Duisburger SV y Duisburg 48/99 se fusionaron en 1964 para formar al Eintracht Duisburg.
En 1973, Eintracht Gelsenkirchen se fusionó con el STV Horst-Emscher, llamándose STV Eintracht Gelsenkirchen hasta 1978, cuando regresaron al nombre STV Horst-Emscher.

Referencias

Enlaces externos
   Das deutsche Fussball Archiv
 Regionalligas en Fussballdaten.de

Campeonatos de fútbol desaparecidos entre clubes
Regionalliga

redpajama_set_name: RedPajamaWikipedia


text:
Q: Ignoring SQLPLUS commands in JDBC I have a .sql file that contains below statements:
SET LINESIZE 2000
WHENEVER SQLERROR EXIT 1 ROLLBACK
WHENEVER OSERROR EXIT 1 ROLLBACK
SET PAGESIZE 0
SET HEADING ON
SET FEEDBACK OFF
SET VERIFY OFF

INSERT INTO TABLE_A
--get some value from TABLE B that will be added in Table A....

COMMIT;

EXIT;

When I run this SQL in my SQL Editor (TOAD/SQL Navigator etc.) , it works fine. I see some messages though when SQLNavigator execute this command:
SQL*Plus command ignored.
Processing ...
WHENEVER SQLERROR EXIT 1 ROLLBACK

SQL*Plus command ignored.
Processing ...
WHENEVER OSERROR EXIT 1 ROLLBACK

SQL*Plus command ignored.
Processing ...
SET PAGESIZE 0

When I run this SQL through JDBC, i get an exception:
Caused by: java.sql.SQLException: ORA-00922: missing or invalid option

    at oracle.jdbc.driver.DatabaseError.throwSqlException(DatabaseError.java:112)
    at oracle.jdbc.driver.T4CTTIoer.processError(T4CTTIoer.java:331)
    at oracle.jdbc.driver.T4CTTIoer.processError(T4CTTIoer.java:288)
    at oracle.jdbc.driver.T4C8Oall.receive(T4C8Oall.java:743)
    at oracle.jdbc.driver.T4CPreparedStatement.doOall8(T4CPreparedStatement.java:216)
    at oracle.jdbc.driver.T4CPreparedStatement.executeForRows(T4CPreparedStatement.java:955)
    at oracle.jdbc.driver.OracleStatement.doExecuteWithTimeout(OracleStatement.java:1168)
    at oracle.jdbc.driver.OraclePreparedStatement.executeInternal(OraclePreparedStatement.java:3285)
    at oracle.jdbc.driver.OraclePreparedStatement.executeUpdate(OraclePreparedStatement.java:3368)

I'm assuming this error is because JDBC is not able to understand SQLPlus statements and failing. Is there a way I can tell JDBC to ignore those statements and just run the main SQL? Or to resolve this I just need to modify the SQL file by removing the SQLPlus statements?

A: You'll need to remove the SQL*Plus commands from the file.
JDBC is intended to be a database-agnostic interface so it is designed to work the same with a variety of different database engines.  SQL*Plus commands are designed to work just with SQL*Plus connecting to an Oracle database (though other tools that support Oracle databases will often support a subset of SQL*Plus commands as well).  It wouldn't make sense for JDBC to know about what constitutes a SQL*Plus command so it has no way to figure out what is a SQL*Plus command or to filter them out.
Beyond that, simply removing the SQL*Plus commands will change the semantics of the script.  The WHENEVER SQLERROR and WHENEVER OSEROR commands instruct SQL*Plus to issue a rollback in the event of an error (which it sounds like your script generates).  You'd need to code that logic into your JDBC application if you want to match the behavior.

A: When you run these statements through TOAD, etc. they usually parse the text either by a delimiter (e.g. semi-column) or by line feed and run the statements separately. However, when you send the whole text to JDBC, it's probably trying to run it all at once, hence the error. You may have to parse the statements and separate them properly before sending to JDBC.

redpajama_set_name: RedPajamaStackExchange


text:
Montreal All Destinations
Explore Montreal
Montreal Tours
All Montreal Tours
Old Montreal (Vieux-Montréal)
Bonsecours Market (Marché Bonsecours)
Montreal Champ de Mars
Plateau-Mont-Royal (the Plateau)
Montreal Little Italy
Jean-Talon Market (Marché Jean-Talon)
Two Days in Montreal
Wine Tasting in Montreal
Montreal Walking and Tram Tours
Haunted Montreal
Wine and Beer Tasting in Montreal
Quebec City Day Trips From Montreal
Things to do in Quebec
Top Montreal Cruises & Sailing
You are viewing Virtual Experiences in Montreal. See more Virtual Experiences around the globe. 🌎
14 Montreal Cruises & Sailing | All Montreal Tours
Montreal History Guided Sightseeing Cruise
Set sail for fun with a guided cruise near Montreal on the scenic St. Lawrence River, enjoying skyline views and learning about city history as you ride. Step on board your ship, the AML Cavalier Maxim, complete with sunny terraces and a glass-enclosed dining area, passing area landmarks including the Jacques-Cartier Bridge, the islands of Boucherville and the scenic shoreline. During your Montreal cruise, hear tales about the city's founding and role of a legendary French explorer in its settlement.
Montreal Gourmet Dinner Cruise with music
Enjoy delicious cuisine and live entertainment with a view of Montreal and its surrounding islands on this dinner cruise along the Saint Lawrence River. On board the elegant Cavalier Maxim cruise ship, choose from one of three glass-enclosed dining rooms or head to one of the open-air outdoor terraces to admire the view. Dine on a delicious 5-course meal, then dance the night away or simply take in the view of the largest city in Quebec under a moonlit sky.
Montreal Shore Excursion: Pre- or Post-Cruise Guided Sightseeing Tour of Montreal
Before or after your cruise, get a comprehensive guided sightseeing tour of Montreal aboard a deluxe coach. Your informative and friendly guide will offer live commentary as you pass by several key attractions in the city and more than 200 points of interest, such as Old Montreal, Notre-Dame Basilica and Mt Royal Park.
Small-Group Sightseeing Tour of Montreal
A fulfilling tour of Montreal in less than 4 hours! You will get to see Mount Royal, Westmount, St. Joseph's Oratory, The Olympic Park as well as Saint Helen's Island, Old Montreal and the Old Port and waterfront. Your tour will begin with a drive through Downtown and Mount Royal via the upscale neighborhoods of Westmount and the famous St. Joseph's Oratory, where you will get to see scenic views. Then, we shall head to the eastern side of the city where you could take closer pictures of Montreal's landmark; the Olympic Park. Going through what is still consider as more French neighborhoods of the city will then lead you across the Jacques Cartier bridge to the site of the unforgettable expo 67 site on St. Helen's Island from where you would appreciate a breathtaking view of the Montreal Skyline. The visit of Old Montreal and Old Port area will conclude the half day tour that includes a visit of the largest church in Canada and most prestigious of Montreal; the Notre-Dame Basilica.
Half day private city tour
Complete tour of the City, taking in over 200 points of interest. Visit historic Old Montreal, City Hall, Place Jacques Cartier, Notre Dame Basilica (admission extra), Olympia Park built for the 1976 summer Olympic games with an optional visit to the tallest inclined tower(Observatory is close on Monday), the Montreal Observatory Tower (admission extra), Mount Royal Park where you will have a panoramic view of the downtown area and the river. Here you will visit St. Joseph Oratory which is one of the world?s most visited shrines with a dome that reaches 97 meters. Discover Montreal through hundreds of places of interest with a friendly, knowledgeable & bilingual driver guide.
Montreal evening cruise
Enjoy the largest floating terrace in Montreal aboard the Cavalier Maxim. Savor a unique point of view on the metropolis. Also enjoy an artistic circus circus performance by artists of international caliber. On the terrace, enjoy the bars and the musical entertainment with our live dj. An atmosphere conducive to partying and dancing!
City tour; afternoon in Montreal
Have a fulfilling tour of Montreal that fits your schedule ! In this afternoon tour you get to see Westmount, visit St. Joseph's Oratory and Mont Royal park. You will also visit Le Plateau Mont Royal, one of the most attractive neighborhoods in the city. We shall also visit Downtown, Old Montreal and the Old Port and waterfront. Your tour will take you in a drive through the upscale neighborhoods of Westmount and then you shall visit the largest Basilica in Canada, the famous St. Joseph's Oratory. Next will be a drive to Mont Royal where you will get to walk in the city's most well known park and breathtaking views. The tour will take you then to Le Plateau Mont Royal, where you can appreciate it's architecture and lifestyle. Downtown will come next before concluding the tour by visiting Old Montreal and the Old Port area.
Montreal Intercultural City Tour
Discover the amazing cultural mosaic of the city of Montreal beyond its traditional bi-cultural image. Learn about the city and how immigration shaped its development in the 20th century. On top of that, have a taste of Montreal specialties. The tour will start with a short walk that takes you through the oldest streets of Montreal. Your guide will take you for a drive through Chinatown and the Quartier des Spectacles where most Montreal cultural events and performances take place. You will then visit downtown and go for a short discovery visit of the city's famous Underground City. The Multicultural Mile-End neighborhood will be next to visit before heading towards Little Italy, the traditional center of Italian culture and gastronomy in town where you will visit le marché Jean-Talon, North America's largest farmers' market. The Tour will then be concluded with a visit of the famous Le Plateau Mount-Royal neighborhood where you will discover its hidden charms.
Montreal Shore Excursion: Walking Tour of Old Montreal
While in port in Montreal, discover top attractions in Old Montreal on this 1.5-hour Montreal walking tour. Follow your guide from Place d'Armes into Old Montreal's winding streets. Learn of the city's rich history and heritage, and gain insight into the vibrant culture that makes Montreal tick. Discover top local landmarks including the Champs de Mars, Place Jacques-Cartier, Place d'Armes, the Royal Bank, Bonsecours Market and City Hall. Along the way, enjoy soaking up the traditional French atmosphere for which Montreal's charming Old Town is famous.
Day trip on Chemin du Roy (King's Road) from Montreal to Trois-Rivières
Discover the treasures of the oldest road in Quebec, Chemin du Roy (King's road) is the olde road that was built between Quebec and Montreal Cities during the new France era. Admire the Architectural and industrial heritage of the small towns along the road. See one of the most beautiful small towns downtown in the province of Quebec. Visit the 2nd oldest city in Canada and see both its old parts and popular downtown. Visit Canada's National Shrine to Our Blessed Mother.
Day trip to Eastern Townships
Take a relaxing day trip to Eastern Townships region of Quebec province and discover its amazing natural beauty. Experience the dual character of the region; a country-side and high end retreat. Learn about local specialties and have the opportunity to purchase local products. See small villages and towns like Austin, Ayer's Cliff, Hatley and many more as well as the cities of Magog and Sherbrooke. Marvel at the wonderful scenery and enjoy strolls by different lakes. See animal farms and rural and urban living-style.
Half day private city tour - SUV
Complete tour of the City, taking in over 200 points of interest. Visit historic Old Montreal, City Hall, Place Jacques Cartier, Notre Dame Basilica (admission extra), Olympia Park built for the 1976 summer Olympic games with an optional visit to the tallest inclined tower, the Montreal Observatory Tower (admission extra), Mount Royal Park where you will have a panoramic view of the downtown area and the river. Here you will visit St. Joseph Oratory which is one of the world?s most visited shrines with a dome that reaches 97 meters. Discover Montreal through hundreds of places of interest with an uninterrupted commentary of a friendly, knowledgeable & bilingual driver guide.
Full-Day City Tour Montreal with Pickup and Food Tasting
A comprehensive tour of Montreal to see all key attractions in one day. Enjoy local food specialties tasting. Start with a drive to upscale neighborhoods of Westmount and take a picture of the famous St. Joseph's Oratory. Visit Mount Royal and enjoy scenic views. Next, get closer to the Olympic Park. See what is still consider as more French neighborhoods of the city and then cross the Jacques Cartier bridge to the site of the unforgettable expo 67 site on St. Helen's Island. Heading to Old Montreal, marvel at a view of the city Skyline. In Old Montreal, visit the largest church in Canada and most prestigious of Montreal; the Notre-Dame Basilica. After Lunch/Shopping break we start with a short walking tour of Old Montreal to see its historical buildings then we head to down town and explore the Underground city. Next, we explore the city characteristic neighborhoods Mile-End, Little Italy, Le plateau and Visit Jean-Talon Market, one of the largest farmers markets in North America.
rent a jeep Rubicon and drive through Quebec's adrenaline rush muddy trails
when going on a guided trail we will bring you where no one else will our guided tours are based on how extreme you want to go!!!
What are the best Cruises & Sailing in Montreal?
The best Cruises & Sailing in Montreal according to Viator travelers are:
See all Cruises & Sailing in Montreal on Viator.
What Cruises & Sailing in Montreal are taking additional safety precautions?
These Montreal experiences are providing additional COVID-19 safety measures:
What Cruises & Sailing in Montreal are good for avoiding crowds?
These hidden gems in Montreal may have limited interaction with crowds:
More Tours in Montreal
Attractions in Montreal
Atwater Market (Marché Atwater)
Escalade Clip 'n Climb Laval
Lachine Canal (Canal de Lachine)
Pointe-à-Callière
Grevin Museum (Musée Grévin Montréal)
Montreal Insectarium (Insectarium de Montréal)
Montreal Biodome (Biodome de Montreal)
St. Joseph's Oratory of Mount Royal (L'Oratoire Saint-Joseph du Mont-Royal)
Saguenay-St. Lawrence Marine Park (Parc Marin du Saguenay-Saint-Laurent)
Les Cours Mont-Royal
Old Port of Montreal (Vieux Port de Montréal)
Montreal Museum of Fine Arts (Musée des Beaux-Arts)
Montreal City Hall (Hotel de Ville de Montréal)
Jacques-Cartier Square (Place Jacques-Cartier)
Notre-Dame Street (Rue Notre-Dame)
Notre-Dame Basilica of Montreal (Basilique Notre-Dame de Montréal)
Crescent Street (Rue Crescent)
Things to do near Montreal
Things to do in Quebec City
Things to do in Salem
Things to do in Niagara Falls
Things to do in Buffalo
Things to do in Massachusetts
Recommended for Montreal


There is an unmistakable B-movie exuberance to the guilty pleasure flick Olympus has Fallen that can't be denied. It may be escapism schlock but its done with energy, style and most importantly a sense of humor (not the self-deprecating kind that marred the last Die Hard installment). Sporting a top-notch cast with familiar faces and Oscar-winning actors in supporting roles, this Die Hard wannabe is actually the best of its kind since Under Siege.
North Korean extremists have taken control of the white house and the president along with it. Lapses in security protocol and trained personal have resulted in the first hostile take over of the most protected building in the world since the British burnt down the place in 1812. It is now up to formally disgraced secret service agent Mike Banning (Butler) to rescue the president and his young son, who is hiding within the covert corridors of the White House.
Gerard Butler is back in action-hero mode and frankly, it's where he belongs. After ill-advised detours into romantic comedies that didn't score and dramas that veered into melodrama, Butler is once again an engaging screen presence and a worthy successor to the everyman-hero archetype. Arron Eckhart who has played numerous politicians in pervious other films, is finally given the role of the Commander-in-Chief. It is appropriate casting and Eckhart makes for the most rugged and square-jawed leader since Harrison Ford took down terrorists in 1997s Air Force One. Classing up the joint is the nearly majestic presence of screen royalty Morgan Freeman who is excellent even in projects that seem beneath his talent level. I must admit after having been burnt out on her continually worsening film career, Ashley Judd makes a welcomed, albeit cameo return to the screen as a modern level-headed first lady. Then there is Dylan McDermott in the Alan Rickman/Tommy Lee Jones/Dennis Hopper/ Powers Booth role as the nefarious turncoat aiding the foreign military mastermind. McDermott isn't the actor those perviously mentioned names are, he is a bit too lightweight to be taken seriously. But this villainous role coupled with an offbeat appearance in last years' comedy The Campaign have made for two questionable career choices in a row.
There are moments when Olympus has Fallen lapses into a Team America parody territory, but director Antoine Fuqua is always quick to deflect your attention to another bit of nasty business taking place elsewhere in the story. It's smart populist filmmaking, the kind that propels word of mouth and box-office grosses but is ultimately overlooked in the critical community.
It was a nice movie that didn't require a lot of thinking. Classic popcorn flick!


text:
How much do you love your practitioner?
I know I've seen an Emilio Gonzalez tattoo in the past, and now a tattoo of Steve Truitt's visage (whose work has been heavily featured on ModBlog!) just got tattooed on _Stigmata_, a regular customer and friend. Tattoo by Alex Pinto at Stay Gold Tattoo in Albuquerque, NM.
This entry was posted in ModBlog and tagged BMEBoys, Body Modification, Funny, Tattoos by Shannon Larratt. Bookmark the permalink.
15 thoughts on "How much do you love your practitioner?"
B on April 25, 2007 at 4:17 pm said:
I like his shirt.
MistahG on April 25, 2007 at 4:22 pm said:
Now this is a quality tattoo. Strong imagery and quality artwork.
Bruce Potts on April 25, 2007 at 4:43 pm said:
Wow! Jon a living memorial to Steve!!! Beautifully Done!
stainless on April 25, 2007 at 6:13 pm said:
Haha, awesome, Jon's tattoo is hilarious. I love it.
nickythegreek on April 25, 2007 at 6:57 pm said:
absurd tattoo! nice work jon
cas on April 25, 2007 at 7:01 pm said:
dedication. nice.
Bastian on April 25, 2007 at 8:01 pm said:
I KNEW this would end up here! Congratulations!
Rachael on April 25, 2007 at 11:13 pm said:
I love his shirt.
The tattoo is pretty wicked too.
_Stigmata_ on April 26, 2007 at 12:24 am said:
hahaha now when will someone be gettin my face tattooed on them.
Kat on April 26, 2007 at 12:47 am said:
Damn is he in love with steve or what?
hekatesgal on April 26, 2007 at 1:01 am said:
I think its interesting that he had his face tattooed before having his calf done.
_Stigmata_ on April 26, 2007 at 1:18 am said:
I had my face tattooed long before most my other ink. Just kinda worked out that way.When i started most of my ink was visible
Ben on April 26, 2007 at 5:23 am said:
thats one awesome portrait,great work,i like his facial work also… especially the forehead area
he needs to ink that scalp up next
Empress on April 26, 2007 at 6:32 pm said:
Nice ink.
*waves ello stiggy :)


Quality has always underpinned the philosophy of Eight Pedals since the very beginning. We offer a fleet of Cannondale road bikes for rent as well as a tailored service in order to make sure our clients enjoy their pedalling experience on Majorcan roads.
Many of the cycle tourists visiting Majorca are torn between taking their own bicycle with them on the plane and renting one on the island. When pondering the alternatives, all they worry about is finding a quality bicycle with a similar performance to that of their own. At Eight Pedals we help this customer profile in particular, as we rent out high-quality bicycles that are as similar as possible to the ones they ride in their home country. We offer models such as Cannondale Supersix, Cannondale Synapse and Cannondale Synapse Carbon Ultegra.
When visitors rent a bicycle on Majorca, they are actually avoiding a significant number of issues and mishaps that are more common than one might think. In order to protect the bike from impact and damage, a special bike bag is required. These bags are very expensive and also don't fully ensure that the bike will arrive in good condition.
First of all, if visitors end up taking their bikes on the plane, they must arrive at the airport well in advance of their flight, since they must check in early to avoid the much-feared overbooking… the consequences of which are simply tremendous. It is also advisable to hand them in at the "special luggage" counter to prevent them from being badly treated by baggage handlers, as it happens in many airports, where luggage is tossed and dropped.
If you choose to take your own bike with you, it is likely that it will land with bumps and faults that are sometimes hard to repair and may even put your holidays at risk. Some examples include: the edges of the axle have damaged the frame, the derailleur hanger has bent, some of the gear teeth have bent, the frame has been scratched by the derailleur or the chain… All this sort of problems may stop you from fully enjoying your time on Majorca due to the unexpected change of plans.
At Eight Pedals we share our clients' passion for cycling and take care of our bikes like nobody else does. Our main goal is to help our customers enjoy, pedal and ride on Majorcan roads. All our bikes are in perfect condition and we make sure that they are delivered and picked up at the agreed address. In addition, we solve any technical issue you may have and even help you plan a cycling route in the event you don't know the island that well and you need some advice.


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The action research report published here is one of a set of reports that present new initiatives tested by school boards as part of the renewal of reception, referral, counselling and support services for adults. The Ministère is delighted with this unusual form of collaboration with the school boards, and with the ensuing contribution to the development of renewed services for adults. In particular, it appreciates the fact that these school boards were willing to involve practitioners in a research activity with which they were not necessarily familiar, and admires the level of commitment and professionalism shown by the practitioners during the research process.


Now you can keep your thumb on it. Larger sizes and thumb-cuts provide quicker access to inside files. Reinforced tabs and extra sturdy stock reduce wear and tear. These folders are also closed on 3 sides to secure documents. Many practices give this one the thumbs-up.


A dad-of-one from London is about to undertake the ride of his life after losing one of his best friends to suicide this year.
Adam Mitchinson, from London is cycling a 400-mile bike ride to Hull and back in aid of Calm, a male suicide prevention charity who provide support to men in the UK, of any age who are down or in a crisis.
It comes after one of his friends, Simon Berger, 34, killed himself in January this year. Originally from Brisbane, Simon met his Beverley-born wife, Helen, in East Yorkshire when the they worked together and with Adam.
Dad-of-one Adam, a graphic designer, said he is now cycling from London to Beverley Minster - where he last saw his friend alive.
"I worked more with Helen," said Adam.
Adam is completing the bike ride to Hull as it's the last place he saw his friend.
He said: "We went to Beverley Minister and that's where we last saw each other.
Suicide is the single biggest killer of men under the age of 45 in the UK. In 2015, 75 per cent of all UK suicides were male.
Mr Mitchinson spoke of how different a death from suicide can affect you. He said: "It's hard to rationalise it. When someone dies from natural causes you think it must have been their time.
As well as raising money to get people talking about male suicide, Adam wanted to find a way to help look after Simon's two young sons.
Simon's wife Helen said: "Simon really did think his illness was obvious to everyone around him, but it wasn't. He was amazing at hiding it, without him even realising he was doing so.
"Men don't tend to talk about their feelings – and that's something that, as the mother of two young boys, I'm desperate to change.
"Raising awareness of male suicide in this way is amazing and vital, and the boys and I are so grateful for, and amazed by, what Adam's doing."
Adam is cycling with two others, along with Helen's mum and friends, who will ride the last few miles with the group into Hull.
At the last count the To Hull and Back cause raised £3,000.
The group will complete their 400 mile ride on September 1 at London's Olympic Village and returning to the capital four days later.


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"Odisha's pace of transformation since 2000 has been exceptional." Governor Ganeshi Lal
February 6, 2019 utkalreporter Odisha 0
Bhubaneswar: On the economy of Odisha, the Government claims that it is on the path of steady growth with a rate consistently higher than that of the national average, and the state's "transformational journey since 2000 has been remarkable"
Governor of Odisha Prof. Ganeshi Lal, in his address in the Odisha assembly on February 4, said, "Odisha's transformational journey since 2000 has been remarkable." and presented the whole lot of schemes that his government has instituted during the time to hold the claim.
He, in about 50-minutes speech, stated his government's spectacular performance in arresting poverty and positioning Odisha as a progressive state.
"My government is untiringly striving to eradicate poverty, accelerate the pace of economic growth, reduce regional disparity and gender differences through various socio-economic measures and pro-people initiatives." said the Governor.
He said that Odisha that was known for its acute poverty has brought about the highest poverty reduction in the country to 24.61pecent in less than a decade. Odisha once in a state of financial bankruptcy is now known for its excellent financial administration, stated Prof. Lal.
He also claimed that Odisha is the only state in the country which has doubled its farmer's income in real term. Odisha was a food grain deficit state depending on the center for food, now it is among the largest contributors to the PDS and playing a major role in the food security of the country, he said. And added, "Odisha is now an agriculturally progressive state."
The Governor also highlighted two of its ambitious schemes- the farmers' assistance scheme KALIA, and the near-universal free health cover scheme Biju Swasthya Kalyan Yojana.
He said that his government has introduced KALIA, a "progressive and inclusive" scheme to further accelerate the agricultural progress and reduce poverty. Under the scheme, Prof.Lal said, 12.45 lakh small and marginal farmers and 58,000 share -croppers have received Rs 5000 each in their account amounting to Rs 652 crore.
In Biju Swasthya Kalyan Yojana 70 lakh families with about 3.5 crore people of Odisha get access to free health care service up to Rs 5lakh per year and additional Rs 2 lakh for the women.
Ganeshi lal
governor of Odisha
governor's address
BUDGET 2019-20: THE THREE BIG ANNOUNCEMENTS BY THE MODI-GOVERNMENT-TAX REBATE, ASSURED INCOME AND PENSION
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text:
Tag: Frida Kahlo
Handicapping Your Career
Yesterday I visited the Museo de Filatelia en Oaxaca. If you take it by name only, it is a postage stamp museum. But that's only part of it. It is an amazing facility, housing stamps and much more, in an 18th century building that has been remade contemporary on the inside. Remarkable!
What captivated me most were the actual letters Frida Kahlo wrote to her doctor. They were much more than doctor/patient: they had an intimate relationship, and often these are love letters. One 13 page+ missive presents Frida's anguished questioning the news that her leg needed to be amputated. It was.
There are also a number of photos of the artist: using a device that enabled her to paint in bed as she lay in traction, the body cast she wore for awhile and painted in her iconic style, the mesmerizing portrait with those eyebrows and that little touch of a moustache . . . and make no mistake: she was a beautiful woman.
While she only produced about 200 paintings, they represent some of the most powerful and influential images in 20th Century art. In Mexico she is almost a Saint. In the Mercados, whole tiendas are set up to sell items with just her image or those of her paintings on them. It is rare that a tourist escapes without buying something Frida.
Here is a woman who suffered a horrendous accident as a girl, whose body was nearly destroyed. Her life was filled with pain and long spells where she was confined to bed, in traction or a cast of some kind. She lived only to age 47. But despite the pain, she produced.
There are similar examples in almost every field, every location: people overcoming remarkable circumstances to attain their full potential. I am remembering the Will Smith movie, 'The Pursuit of Happyness.' And Oscar Pistorius, the South African Double amputee runner. Autistic Temple Grandin, who has a Phd in animal science and is one of the foremost animal behaviorists on the planet. Stephen Hawking.
Almost all of us have handicaps to overcome in almost every aspect of life. And maybe that's what life's about: overcoming the handicaps that keep us from our full potential.
My question today is: What are your real estate handicaps and what are you doing to overcome them?
Are you stuck in a cycle of bad thinking?
Do you believe that 2005-2006 (the good old days) were normal and, when they return, you will once again be successful?
Do you believe that the problem is the market and not you?
Do you believe that your 75% split somehow makes up for the fact that you did only 8 transactions last year?
Do you think that it's just too hard today and like Scarlett O'Hara, you'll think about it tomorrow?
Are you mired in a self-created universe of scarcity?
Are you unwilling to expend the resources it would take to get the help you'd need to get to the next level?
Are you NOT marketing because you don't have the cash?
Are you doggedly pursuing D level clients because they are the only clients you have?
Are you trying to pound your old skills into a new Universe (rather than pound new skills into you)?
Are you intimidated by new technology and unwilling to invest the time and effort to achieve a comfort level?
Are you still using the schtick you learned in that seminar 7 years ago, the script that sounded so good at the time but that hasn't really produced for you?
Are you not (calling FSBOS, door knocking, doing a thorough market analysis, cold calling, etc.) because they just don't work?
I could go on and on, but you get the picture. What's holding you back? And what are you going to do about it? Frida was challenged by the fact that she had to be in traction or a cast for a long period of time. Her attitude was not: Ï am incapacitated, therefore I cannot paint. It was: I must paint, therefore I will design a tool that enables me to paint while in bed.
The question really boils down to this:
What do you want your life to be about? Your handicap or how you overcame it? Stricken with Parkinson's Disease, Michael J. Fox could have stepped out of the spotlight and quietly waited for the inevitable, but he didn't. Bill Clinton could have let the Monica Lewinsky scandal drive him into isolation and end his career as a Statesman, but he didn't. (It really doesn't matter what you think of Clinton or the scandal; the point is he didn't let that become the story of his life. Neither did Richard Nixon after his famous, 'You won't have Nixon to kick around any more,'statement.)
If you're not finding a way around the obstacles that are holding you back, you are making a decision. It's a decision to make the story of your life a Greek Tragedy about a flaw you could never overcome. Why not make it about how you became successful despite the challenges you encountered along the way.
Postscript: 2/14/2013
Imagine my surprise, two days after I wrote this piece, when I opened my news channel and saw this: Pistorius Accused of Murder So much for heroes, I guess. I wonder how he'll rise above this . . .
Author James DingmanPosted on February 12, 2013 Categories AttitudeTags Frida Kahlo, Michael J. Fox, Museo de Filatelia, Oaxaca, oscar pistorius, overcoming handicaps, Stephen Hawking, Temple GrandenLeave a comment on Handicapping Your Career


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Dead Space: How to Get Stasis Module
Analysis1 year ago
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The Call of Duty Vanguard and Call of Duty Warzone close-up can now be unlocked. Here we tell you how to do it and what requirements...
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'I only did what I had to do' | Bicyclist helps lead Austin police to woman's body in the woods
Detectives discovered the body of 23-year-old Nicole Coleman in the woods near Ed Bluestein Boulevard and Manor Road on Monday afternoon.
Author: Pattrik Perez
Published: 8:53 PM CST January 3, 2019
Updated: 10:17 PM CST January 3, 2019
AUSTIN — Nicole Coleman was a daughter and a niece.
Now, she's the victim of a murder.
According to Austin police, someone killed 23-year-old Coleman and left her body in the woods near Ed Bluestein Boulevard and Manor Road. Detectives discovered her body on Monday afternoon.
A bicyclist, who asked not to be identified because he fears for his safety, helped lead police to Coleman's body with the help of his friend.
"There's a body over there," the man recalled his friend telling him.
Confused and afraid, the man asked his friend to repeat himself.
"Are you sure that it's a body? Because I'm going to call the police," the man told his friend.
"Wait for me. Maybe I saw wrong, and it's bags of sand," his friend told him.
That's when the man said his friend walked down the hill near the sidewalk and into the wooded area.
"He went down, a lot farther down, and came out running, saying, 'Yes, it's a body.'"
RELATED: Seeking more information, Austin police identify woman found dead on Ed Bluestein Blvd.
It was Coleman's body, naked and with obvious signs of trauma, according to police.
At first, the man hesitated calling police because he said he and his friend are homeless, and they didn't want to get in trouble.
But, he knew it was the right thing to do.
"I only did what I had to do, like any person would," the man said.
With the help of two patrons at a gas station down the road, the man called 9-1-1.
Now, he hopes police find whoever is responsible for Coleman's murder.
Detectives would not reveal how Coleman died in order to protect the integrity of the investigation.
Online records show Coleman lived in Katy, near the Houston area, but was originally from Austin.
If you know anything about what led up to Coleman's death or if you had spoken with her recently, call Crime Stoppers at (512) 472-TIPS.


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Women's Rugby banned from Government Schools
Tuesday, March 20, 2018 - 18:16. Updated on Thursday, March 22, 2018 - 17:34.
A member of Tonga High School's girls tag rugby team told Matangi Tonga today that her team was pulled out of a competition last week and told to stop playing rugby because they were girls.
Tonga Rubgy
Read more about Women's Rugby banned from Government Schools
Interesting logic. I believe
Submitted by Boston Rob on Tuesday 20 March 2018 10:33pm
Interesting logic. I believe that we should ensure that we preserve Tongan women dignity by not allowing them any jobs but to stay at home.
It seems that Ministers of
Submitted by Stephen Vete on Wednesday 21 March 2018 1:57pm
It seems that Ministers of the Tonga Government and their officials are all following their own logic. Whether it's the Minister, the Acting CEO or whoever is responsible for signing the letter to stop girls from playing rugby and boxing, one thing is clear. They are all ignoring the recent plea from His Majesty Tupou 6 to work together. Working together means to talk to each other, discuss the issues/problems so that you arrive at a solution that those you have talked with are going to help to implement. In this case, if it was to preserve Tonga culture and tradition. Isn't there a recently revived Traditions Committe which includes some church leaders? Isn't there a School Principals Association - were they consulted? I just read that the Hon PM of NZ is going to direct aid to sports directly to the villages rather than giving the aid money to the present Tonga Government. No wonder our beloved country is in such a mess - it seems that those who have been appointed to high positions all want to be "king". Doesn't our country have more important priorities to attend to than stopping girls/young women from playing the sports of their choice? Looking forward to the return of one and only King to the last remaining Polynesian Kingdom.
RE: "The reason is because it
Submitted by Mata'utu on Wednesday 21 March 2018 2:17pm
RE: "The reason is because it is against our culture to maintain dignity of women so it still upholds the tradition of which its basis had been set out since the olden days making Tonga exceptional in the Pacific and the world."
Two observations of Captain James Cook, who visited Tonga in the 18th century
1. This entertainment was sometimes suspended for
a short space, and the intervals of time were filled
up with wrestling and boxing matches. The first
were performed in the manner practised at Tahiti,
and the second differed very little from the English
manner. A couple of stout wenches next stepped
forth, and without ceremony began boxing with as
much dexterity as the men. This contest, however,
was but of short duration, for in the space of half a
minute one of them gave it up. The victorious heroine
was applauded by the spectators in the same
manner as the successful combatants of the other
sex. Though we expressed some disapprobation at
this part of the entertainment, it did not hinder two
other females from entering the lists, who seemed to
be spirited girls, and if two old women had not interposed
to part them, would probably have given
each other a good drubbing. At least three thousand
spectators were present when these combats
were exhibited, and every thing was conducted with
the most perfect good humour on all sides; though
Some of the champions of both sexes received blows
which they must have felt the effects of for some
time after.
2. While the natives were in expectation of this
evening exhibition, they engaged, for the greatest
part of the afternoon, in wrestling and boxing. They
preserve great temper in these exercises, and leave
the spot without the least displeasure in their countenances.
Not only boys engage in both these exercises;
but it not infrequently happens, that little girls box
with great obstinacy.
Malo Lahi for this
Submitted by Stephen Vete on Friday 23 March 2018 1:27pm
Malo Lahi for this information. Mata'utu. I hope that the media people and everybody draws this to the attention of the Minister, the Education Ministry's staff to enlighten those responsible for this decision. The story has generated a lot of adverse publicity for our present Government and our country throughout the world. It seems that the supporters of sport do not support our Minister's decision. I have just read that Tonga's flag bearer to the Commonwealth Games is a young woman boxer Megan Maka. Congratulations Ms Maka. Let's all hope and pray that the return of His Majesty and the coming Easter break will help to enlighten our Prime Minister, his appointed Ministers and supporters. Blessed Easter to y'all.


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Emergency, fuel system, Germany, Lufthansa
Lufthansa Cityline Flight Returns to Munich due to Fuel Pressure Problem
February 15, 2019 February 15, 2019 emergency landing, fuel pressure, Germany, Lufthansa Cityline, Luxembourg, Munich
Lufthansa Cityline flight LH-2316 had to return and make an emergency landing in Munich, Germany, on February 13th.
The plane took off for Luxembourg but had to turn back due to a fuel pressure problem.
British Airways, Emergency, fumes, UK
British Airways Flight Makes Emergency Landing at Heathrow Airport
February 12, 2019 February 12, 2019 Berlin Tegel Airport, British Airways, emergency landing, England, Fumes, Germany, Heathrow Airport
British Airways flight BA-993 made an emergency landing at Heathrow Airport, England, on February 10th.
The plane was flying from Berlin Tegel Airport, Germany, when the crew reported fumes in the flight deck and cabin.
One crew member became sick due to the fumes.
The plane continued for a safe landing.
Airbus, Condor, Emergency, Ireland, smoke
Condor Flight Makes Emergency Landing at Shannon Airport
February 8, 2019 February 8, 2019 Cancun, Condor, emergency landing, Frankfurt, Germany, Ireland, Mexico, Shannon Airport, smoke
Condor flight DE-2116 made an emergency landing at Shannon Airport, Ireland, on February 6th.
The Airbus A330-200 plane heading from Frankfurt, Germany, to Cancun, Mexico, was diverted after the crew noticed smoke in the cockpit.
The plane landed safely. Four crew members and a passenger were taken to a hospital.
Airbus, Emergency, Germania, Germany, smoke
Germania Flight Makes Emergency Landing in Nuremberg
February 6, 2019 February 6, 2019 emergency landing, Germania, Germany, Las Palmas, Nuremberg, smoke, Spain
Germania flight ST-3712 had to return and make an emergency landing in Nuremberg, Germany, on February 3rd.
The Airbus A321-200 plane took off for Las Palmas, Spain, but had to turn back due to smoke in the cockpit.
Boeing, Emergency, Germany, Landing Gear, RyanAir
Ryanair Flight Returns to Frankfurt-Hahn Airport due to Gear Issue
January 31, 2019 January 31, 2019 Bari, Frankfurt-Hahn Airport, gear problem, Germany, Italy, Ryanair
Ryanair flight FR-5214 returned to Frankfurt-Hahn Airport, Germany, on January 29th.
The Boeing 737-800 plane took off for Bari, Italy, but had to turn back due to a gear problem.
The plane landed back safely. Everyone aboard remained safe.
Emergency, Landing Gear, Lufthansa, Switzerland
Lufthansa Cityline Flight Makes Emergency Landing in Zurich
January 27, 2019 January 27, 2019 emergency landing, Germany, landing gear, Lufthansa Cityline, Munich, Switzerland, Zurich
Lufthansa Cityline flight LH-2373 had to return and make an emergency landing in Zurich, Switzerland, on January 26th.
The plane took off for Munich, Germany, but had to turn back after the crew failed to retract the landing gear.
Airbus, Emergency, Germany, Lufthansa, odor
January 25, 2019 January 25, 2019 emergency landing, Germany, Lufthansa, Muenster, Munich, odor
Lufthansa flight LH-2135 made an emergency landing in Munich, Germany, on January 21st.
The Airbus A320-200 plane flying from Muenster, Germany, was on final approach to Munich when the crew reported a strong acid odor in the cockpit.
The plane continued for a safe landing. All passengers and crew members remained safe.
Boeing, Germany, smoke, Sun Express
SunExpress Deutschland Flight Diverts to Munich due to Smoke in Cockpit
January 9, 2019 January 9, 2019 Egypt, emergency landing, Germany, Hurghada, Munich, Nuremberg, smoke, SunExpress Deutschland
SunExpress Deutschland flight XG-4665 made an emergency landing in Munich, Germany, on January 6th.
The Boeing 737-800 plane heading from Nuremberg, Germany, to Hurghada, Egypt, was diverted due to smoke in the cockpit.
Emergency, Germany, Lufthansa, odor, smell of burning
Lufthansa Cityline Flight Makes Emergency Landing due to Burning Odor
December 25, 2018 December 25, 2018 Billund, burning odor, Denmark, emergency landing, Frankfurt, Germany, Hamburg, Lufthansa Cityline
Lufthansa Cityline flight LH-843 made an emergency landing in Hamburg, Germany, on December 23rd.
The plane heading from Billund, Denmark, to Frankfurt, Germany, was diverted due to a burning odor on-board.
The plane landed safely. All ninety people aboard remained unharmed.
Emergency, engine trouble, Germany, Lufthansa
Lufthansa Flight Makes Emergency Landing in Cologne
December 19, 2018 December 19, 2018 Cologne, emergency landing, engine issue, Frankfurt, Germany, John F. Kennedy International Airport, Lufthansa, New York
Lufthansa flight GEC-8332 had to make an emergency landing in Cologne, Germany, on December 17th.
The plane heading from Frankfurt, Germany, to John F. Kennedy International Airport, New York, was diverted after the crew noticed an engine issue.
The plane landed safely. Everyone aboard remained unharmed.
Emergency, flap problems, KLM Royal Dutch Airlines, Netherlands
KLM Cityhopper Flight Makes Emergency Landing in Maastricht
December 18, 2018 December 18, 2018 Amsterdam, emergency landing, flaps, Frankfurt, Germany, KLM Cityhopper, Maastricht, Netherlands
KLM Cityhopper flight KL-1767 made an emergency landing in Maastricht, Netherlands, on December 16th.
The Embraer ERJ-190 plane heading from Amsterdam, Netherlands, to Frankfurt, Germany, was diverted due to an issue with the flaps.
airspeed, bird strike, Boeing, Emergency, Germany, RyanAir
Ryanair Flight Makes Emergency Landing in Cologne
December 6, 2018 December 5, 2018 Bari, bird strike, Cologne, disagreeing airspeed, emergency landing, Germany, Italy, Weeze Airport
Ryanair flight FR-8723 made an emergency landing in Cologne, Germany, on December 3rd.
The Boeing 737-800 plane heading from Weeze Airport, Germany, to Bari, Italy, was diverted after a bird strike caused damage to a pitot tube, resulting in the issue of disagreeing airspeed.
Airbus, Austria, Austrian Airlines, Emergency, odor, smoke
Austrian Airlines Flight Makes Emergency Landing in Vienna
November 20, 2018 November 20, 2018 Austria, Austrian Airlines, emergency landing, Frankfurt, Germany, odor, smoke, Vienna
Austrian Airlines flight OS-125 had to return and make an emergency landing in Vienna, Austria, on November 18th.
The Airbus A321-200 plane took off for Frankfurt, Germany, but had to turn back after a strong odor and smoke was noticed on-board.
The plane landed safely. The crew were taken to a hospital for precautionary examination.
Eurowings, lightning, Spain
Eurowings Plane Suffers Lightning Strike in Spain
November 20, 2018 November 20, 2018 Dusseldorf, Eurowings, Germany, Jerez Airport, Lightning Strike, Spain
Eurowings flight EW-9571 suffered a lightning strike while climbing out of Jerez Airport, Spain, on November 18th.
The incident happened when the plane was heading to Dusseldorf, Germany.
The plane continued to Dusseldorf for a safe landing. All one hundred and sixteen passengers and six crew members remained unharmed.
Boeing, Emergency, engine shut down, Ireland, UPS
UPS Flight Makes Emergency Landing at Shannon Airport
November 19, 2018 November 19, 2018 Cologne, emergency landing, engine shut down, Germany, Ireland, Pennsylvania, Philadelphia, Shannon Airport, UPS
UPS flight 5X-209 made an emergency landing at Shannon Airport, Ireland, on November 18th.
The Boeing 767-300 plane heading from Philadelphia, Pennsylvania, to Cologne, Germany, was diverted after the crew needed to shut down one of the engines.
The plane landed safely. Everyone aboard remained safe.
Airbus, British Airways, cabin pressure, Emergency, Germany
British Airways Flight Makes Emergency Landing at Berlin Tegel Airport
November 19, 2018 November 19, 2018 Berlin Tegel Airport, British Airways, Cabin pressure, emergency landing, England, Germany, Heathrow Airport, Moscow Domodedovo Airport, Russia
British Airways flight BA-232 made an emergency landing at Berlin Tegel Airport, Germany, on November 16th.
The Airbus A321-200 plane heading from Moscow Domodedovo Airport, Russia, to Heathrow Airport, England, was diverted due to the loss of cabin pressure.
Airbus, bird strike, Emergency, Germany
Laudamotion Flight Makes Emergency Landing in Dusseldorf
November 11, 2018 November 10, 2018 bird strike, Dusseldorf, emergency landing, Faro, Germany, Laudamotion, Portugal
Laudamotion flight OE-3160 had to return and make an emergency landing in Dusseldorf, Germany, on November 9th.
The Airbus A320-200 plane took off for Faro, Portugal, but had to turn back due to a suspected bird strike.
Delta, Emergency, mechanical failure, USA
Delta Airlines Flight Diverts to Boston due to Mechanical Issue
November 4, 2018 November 4, 2018 Boston, delta, emergency landing, Frankfurt, Germany, John F. Kennedy International Airport, Massachusetts, mechanical issue, New York
Delta Airlines flight 106 made an emergency landing in Boston, Massachusetts, on November 2nd.
The plane heading from John F. Kennedy International Airport, New York, to Frankfurt, Germany, was diverted due to a mechanical issue.
Airbus, cracked windshield, Emergency, Germany, Lufthansa
Lufthansa Flight Returns to Munich due to Cracked Windshield
October 28, 2018 October 28, 2018 cracked windshield, emergency landing, Germany, India, Lufthansa, Mumbai, Munich
Lufthansa flight LH-764 had to return and make an emergency landing in Munich, Germany, on October 27th.
The Airbus A330-300 plane took off for Mumbai, India, but had to turn back due to a cracked windshield.
The plane landed back safely. All two hundred and forty-eight people aboard remained unharmed.
Airbus, Emergency, fumes, oil leak, USA
Lufthansa Flight Makes Emergency Landing in Boston
October 28, 2018 October 28, 2018 Boston, emergency landing, Frankfurt, Germany, John F. Kennedy International Airport, Lufthansa, Massachusetts, medical emergency, New York, oil fumes
Lufthansa flight LH-401 made an emergency landing in Boston, Massachusetts, on October 26th.
The Airbus A380-800 plane heading from John F. Kennedy International Airport, New York, to Frankfurt, Germany, was diverted due to oil fumes on board.
The plane landed safely. Several passengers and crew members needed medical attention after landing.
Airbus, Emergency, engine surge, Germany, indicator, oil leak, Oman Air
Oman Air Flight Makes Emergency Landing in Munich
October 21, 2018 October 21, 2018 emergency landing, engine oil, Frankfurt, Germany, indication, Munich, Muscat, Oman, Oman Air
Oman Air flight WY-116 had to make an emergency landing in Munich, Germany, on October 17th.
The Airbus A330-300 plane heading from Frankfurt, Germany, to Muscat, Oman, was diverted after the crew received a low engine oil quantity indication.