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Meet England's backheel queen Alessia Russo who is the most prolific super sub in history
backheel queen of England Alessia Russo
Khushbu Kumari
Alessia Russo wowed the nation with her superb backheel over Sweden as England's Lionesses made it the final of Euro 2022 - but the Manchester United ace is much more than a flash-in-the-pan name
Alessia Russo is now England's best-ever substitute in terms of goals-to-game ratio - but the star is much more than just being a "super sub".
England made it through the finals of the Euros after an impressive 4-0 victory against Sweden on Tuesday night.
And it was the Manchester United ace who was once again on hand to help secure the win, this time with a delightful backheel goal - her fourth of the campaign.
But she has a rich past, even for a 23-year-old. Born in Maidstone in Kent, the attacker is of Italian descent, with her Sicilian grandfather moving all the way to England in the 1950s.
Meanwhile, she comes from a bit of a footballing family, with her father playing for the Met Police - and brother Giorgio playing non-league for Ramsgate in Kent.
Russo started her career playing Bearsted Girls under 10's, before linking up at Charlton Athletic's centre of excellence.
She found her break when she joined Chelsea's development squad and then moved south to sunny Brighton in 2017, where she found the back of the net three times in seven games.
After which she linked up with the North Carolina Tar Heels in America.
There she scored 28 goals in total. It was only a month after leaving the club that she signed a two-year contract with her dream club, United.
In the northwest last season, she notched 11 goals in 30 appearances in all competitions, earning herself the achievement of being the winner of the team's Players' Player of the Year award.
Russo keeps most of her personal life behind closed doors - but she does enjoy posting some glamorous snaps of herself online, as well as showing her 170k followers fresh pictures of her training for her side at club and national level.
And indeed, Russo, who is Euro 2022's joint top scorer, has hit national attention for being the star of the show as the Lionesses thrashed Sweden 4-0.
Last night, pundit Ian Wright likened Russo to Lionel Messi, saying: "'Lessi', not Messi, for what she [Russo] has done when she comes on."
"I don't know what she has to do to start a game. The improvisation is fantastic."
"When she came on she instantly made an impact. She links play. I'm not sure what more she needs to do to start."
"When you look at Ellen White's contribution over the years you can't question it."
"But she hasn't done enough in this tournament. This girl [Russo] is doing it. I don't think Wiegman will change it."
Meanwhile, AceOdds has revealed Russo is in fact the country's most prolific scorer from the bench and is officially England's best ever "super-sub."
England's top-scoring substitutes
Alessia Russo - Goals per substitute appearance, 0.70
Jermain Defoe - Goals per substitute appearance, 0.20
Peter Crouch - Goals per substitute appearance, 0.22
Michael Owen - Goals per substitute appearance, 0.40
Joe Cole - Goals per substitute appearance, 0.16
Harry Kane - Goals per substitute appearance, 0.24
Robbie Fowler - Goals per substitute appearance, 0.20
Shaun Wright-Phillips - Goals per substitute appearance, 0.14
Darius Vassell - Goals per substitute appearance, 0.19
Jamie Vardy - Goals per substitute appearance, 0.23
As well as this, Russo blows the rest of the subs out of the water in terms of how long it takes her to score a goal.
Coming off the bench, she finds the net every 39 minutes.
Edin Dzeko, in second place, does so every 89 mins, while Ole Gunnar Solskjaer managed every 93 minutes, joint with Javier Hernandez.
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\section{Introduction}

\label{sec:intro}
Fine-grained Classification aims to identify different subcategories of the same category, e.g., different kinds of birds, cars or planes. Early work mainly used the method of strong supervision in which people manually marked specific regions \cite{DBLP:conf/iccv/XieTHYZ13, DBLP:conf/eccv/ZhangDGD14, DBLP:journals/corr/BransonHBP14,DBLP:conf/cvpr/HuangXTZ16, DBLP:journals/corr/WeiXW16, DBLP:conf/cvpr/BergB13}. These methods required a lot of manpower and were prone to errors, which led to performance degradation \cite{DBLP:conf/iccvw/Krause0DF13}. Therefore, this field of research has gradually shifted to weak supervision approaches that do not require explicit labeling of regions \cite{DBLP:conf/iccv/ZhengFML17, DBLP:conf/iccvw/Krause0DF13, DBLP:conf/eccv/YangLWHGW18, DBLP:conf/cvpr/ChenBZM19, DBLP:conf/aaai/ZhuangW020,DBLP:conf/eccv/DuCBXMSG20, DBLP:journals/corr/abs-2103-07976}. These efforts incorporate features from different regions or pair of interactive comparative learning to make features more discriminative, resulting in significant performance improvements.

However, the existing approaches mentioned above mainly focus on using large-scale CNN \cite{DBLP:journals/corr/SimonyanZ14a, DBLP:conf/cvpr/HeZRS16, DBLP:conf/cvpr/XieGDTH17, DBLP:conf/cvpr/HuangLMW17} or visual transformer \cite{DBLP:conf/iclr/DosovitskiyB0WZ21} as feature extractor, which is well-known to be computationally expensive. In fact, fine-grained classification  scenarios often require a more lightweight mobile network that can be applied offline. For example, ornithologists conducting field research need to be able to use mobile devices to quickly identify birds they find. In the field of intelligent traffic, traffic police also rely on mobile devices to quickly identify vehicle models when performing tasks. However, the large-scale CNNs has a hefty price tag and makes it difficult to recognize objects in real-time. Therefore, we need new mobile networks to meet the requirements for lightweight models and fast offline recognition in the scenarios mentioned above.

\begin{figure}
  \centering
   \includegraphics[width=1\linewidth]{fig1.png}
   \caption{An overview of performance comparison of baseline and RMG-PMSI in CNN backbones on three datasets.}
   \label{fig:view}
\end{figure}

At present, because the feature extraction capability of lightweight mobile network \cite{DBLP:conf/cvpr/SandlerHZZC18,DBLP:conf/eccv/ZhouHCFY20} is weaker than the large-scale model, there is no research on a mobile network specifically for fine-grained recognition. However, if we can improve the utilization of the features based on the limited feature extraction capability, the performance of the model will be enhanced. Intuitively, the shallow layer of an end-to-end CNN will extract fine-grained local features of the image. As the network depth increases, the receptive field also expands. CNN will gradually extract the global features and use them for classification. During the process, much of the local information at the shallow layer that is helpful for classification is not taken into account at the end. So, if we can fully combine all of the local and deep global features that are extracted from different stages of CNN, the features of different stages can jointly boost the classification accuracy. As a result, the feature extraction ability of the model can be fully utilized to improve the performance of the lightweight model.

Inspired by the idea above, and based on the lightweight MobilenetV2, we propose a Progressive Multi-Stage Interactive training method for lightweight mobile networks using a Recursive Mosaic Generator (RMG-PMSI). Firstly, we introduce a Recursive Mosaic Generator (RMG) that generates images containing different granularities at different stages. Then, the features of different stages go through a Multi-Stage Interactive module (MSI) to reinforce and supplement the corresponding features of different stages. Finally, using the progressive training (P), the model focuses on learning stable local fine-grained features in the shallower layer and more abstract and large-grained global features in the deeper layer in the next phase. In addition, during the training process of each phase, different stages produce an interactive supplement instead of being disassociated, as it ensures the consistency in the whole network training process. By using RMG-PMSI as the training mode, the features extracted from the model at different stages can interact and be complementary with each other, which significantly improves the model performance.

The proposed method has been extensively evaluated on three prestigious fine-grained visual classification benchmarks (CUB-200-2011\cite{WahCUB_200_2011}, Stanford Cars \cite{DBLP:conf/iccvw/Krause0DF13}, Aircraft\cite{DBLP:journals/corr/MajiRKBV13}). An overview of the performance comparison can be seen in \cref{fig:view}. Compared to the baselines, the RMG-PMSI method provides a significant performance improvement. At the same time, we conducted two experiments testing robustness. The results show that the RMG-PMSI has good transferability to models of different scales and can equip them with significantly more anti-interference power. In summary, the main contributions of this paper are as follows:
\begin{itemize}

\item  A new data augmentation method for FGVC named Recursive Mosaic Generator (RMG) is introduced that can help models focus on features of different granularities at different training phases.

\item  A novel Progressive Multi-Stage Interactive training method (PMSI) for lightweight mobile networks is proposed for Fine-grained Visual Classification (FGVC), which can make better utilization of features and strengthen the anti-interference ability substantially.

\item The proposed RMG-PMSI approach has been implemented and evaluated on standard benchmarks, with performance being significantly improved, and moreover with good robustness and transferability, leading to the potential application on mobile devices.

\end{itemize}

\section{Related Work}
\label{sec:related work}
In this section, we mainly discuss methods related to FGVC, data argumentation, multi-stage feature fusion and progressive training.
\subsection{Fine-grained visual classification}
As visual models continue to evolve, the research on FGVC has shifted from strongly supervised methods with additional bounding boxes \cite{DBLP:conf/cvpr/BergB13,DBLP:conf/iccv/XieTHYZ13,DBLP:conf/eccv/ZhangDGD14,DBLP:conf/cvpr/HuangXTZ16} to weakly supervised ones with category labels only \cite{DBLP:conf/cvpr/FuZM17,DBLP:conf/iccv/ZhengFML17, DBLP:conf/eccv/YangLWHGW18, DBLP:conf/iccv/WeiY0DL19, DBLP:conf/cvpr/GeLY19, DBLP:conf/icmcs/ZhengC0M20, DBLP:journals/tvt/LiYCMC19}. Most of the weakly supervised methods aim at locating the most discriminative regions in the image. For example, Fu et al. \cite{DBLP:conf/cvpr/FuZM17} found that region detection and fine-grained feature learning could reinforce each other and constructed a series of networks during prediction to locate more differentiated regions for the following networks. Lin et al.\cite{DBLP:conf/iccv/LinRM15} presented bilinear pooling on the representation of two local patches in the image to learn more representative features. However, these approaches used a large-scale framework to extract high-level abstraction features at the end of an end-to-end network without utilizing shallow local fine-grained features. Due to the mobile network's limited feature extraction capability, the full utilization of features extracted at different stages remains to be explored.

\begin{figure*}
  \centering
   \includegraphics[width=1\linewidth]{PMSI_1.pdf}
   \caption{The structure of PMSI is illustrated. There are $StageNum+1$ phases at each iteration. Here, we set $StageNum=3$ for explanation. We set the increase in the number of feature map channels as the division between this stage and the next stage. There are a total of $N$ stages in the backbone. The Smooth Conv Block consists of two convolutional layers which are used to convert feature maps with different channel numbers generated by different stages into same number of channels. The MLP (multi-layer perceptron) has two fully connected layers. The classifier contains two fully connected layers with a softmax layer. The input image of each phase is different (corresponding to input 1 to input 4). Each phase produces different classification results and the common parts of each phase are marked in gray in the figure.}
   \label{fig:PMSI_overview}
\end{figure*}

\subsection{Data argumentation for FGVC}
In the field of computer vision, there are many popular data augmentation methods. For example, for conventional image recognition, data augmentation based on fusion of different images is widely used \cite{DBLP:conf/iccv/YunHCOYC19, DBLP:conf/iclr/ZhangCDL18}. Based on these tricks, a trainable image fusion augmentation is adopted to achieve great performance improvement in end-to-end training for FGVC \cite{DBLP:conf/aaai/HuangWT21}. In a weak supervision network, Jigsaw Puzzle is often used as an initialization method to achieve better conversion performance \cite{DBLP:conf/cvpr/WeiXRXS00Y19}. In a recent study, PMG \cite{DBLP:conf/eccv/DuCBXMSG20} encouraged the generation of different granular inputs through the Jigsaw Puzzle at different stages. However, none of these methods explicitly generated fine-grained local features and large-grained global features in a single image, which are very substantial  for FGVC. In this paper, we address the deficit, and propose a Recursive Mosaic Generator (RMG) accordingly by combining the mosaic augmentation and recursion.

\subsection{Multi-stage feature fusion and progressive training}

Multi-stage feature fusion is often applied in the field of object detection \cite{DBLP:conf/cvpr/LinDGHHB17,DBLP:journals/corr/abs-1712-00960,DBLP:conf/cvpr/RedmonF17}. By combining information from different stages, we can better distinguish the background and objects. In FGVC, API \cite{DBLP:conf/aaai/ZhuangW020} can well solve the problem of image confusion due to the nuances between fine-grained image classes by fusing two similar fine-grained image features to find distinguishing clues.

Progressive training is widely used in generation tasks  \cite{DBLP:conf/cvpr/KarrasLA19,DBLP:conf/iccv/ShahamDM19,DBLP:conf/cvpr/AhnKS18}, which starts with low resolution images and gradually improves the resolution by adding new layers to the network. Rather than learning features from all scales, the strategy allows the network to discover the large-scale structures of the image distribution and shift its attention to finer-scale details. In FGVC, \cite{DBLP:conf/eccv/DuCBXMSG20} adopted a progressive training method for image classification, which guided the training of the next stage by the training of the previous stage, gradually shifting the focus from local features to global features.

In this paper, by combining multi-stage feature fusion and progressive training, we propose a progressive multi-stage interactive training approach, which can make features of different stages jointly cooperate in the training and ensure the conformity of training goals. Together with the proposed RMG trick, the feature extraction capability of the model is fully utilized, and the model performance can thus be significantly improved.

\section{The Proposed Approach}
\label{sec:method}
In this section, we will present our proposed approach of RMG-PMSI, integrating Progressive Multi-Stage Interactive training (PMSI) and Recursive Mosaic Generator (RMG). The main idea is to enable interactive learning of features at different stages in the training phase, which can make better use of features of different granularities extracted from the network to recognize fine-grained images and improve the robustness of the model.

\subsection{Overview}
PMSI consists of two key components: (i) Recursive Mosaic Generator (RMG) for generating multi-granularities inputs; (ii) Progressive Multi-Stage Interaction (PMSI), as shown in \cref{fig:PMSI_overview}.

To be more specific, firstly, a batch of fine-grained images is fed into the RMG to generate inputs of different phases. The granularities of the input images in different phases are different so that each phase focuses on learning features from fine-grained granularities to larger-grained ones. After the input goes through the backbone, the features generated by multiple stages are extracted respectively. Then, the input of different stages generates interaction vectors $x_{m}$, and through a gate mechanism, the corresponding features of different stages are strengthened and supplemented. Finally, in each training phase, the network focuses on training the information of the corresponding granularity extracted by one of the stages. Through the above process, the model can be guided to learn stable fine-grained information in the shallow layer. With the progress of training, the model focuses on learning more abstract and larger-grained global information in the deeper layer in the next phase. In addition, in the training process of each phase, different stages are supplemented interactively rather than being separated and disassociated, which ensures the consistency of the goal in the whole network training phase, filtering out the disturbance of other stage features caused by the independent training of different stages.

\subsection{Recursive Mosaic Generator}
Mosaic data augmentation~\cite{DBLP:journals/corr/abs-2004-10934} is an effective mean of boosting performance in object detection. Meanwhile, Jigsaw Puzzle solving~\cite{DBLP:conf/cvpr/WeiXRXS00Y19,DBLP:conf/eccv/DuCBXMSG20} was found to be effective in self-supervised learning and progressive training tasks. We get inspiration from the above two techniques and propose accordingly a Recursive Mosaic Generator (RMG) for a picture to adapt to our progressive multi-stage interactive training.
The goal of the RMG is to design regions of different granularities and force the network to learn information specific to the corresponding level of granularity in different training phases. The recursive idea is that the generator will randomly select one of the patches based on the mosaic image generated in the previous step and perform the mosaic operation again. Then, the image contains a large granularity of global information that is difficult to learn, and furthermore, the stable and fine-grained local information that is easy to learn.

Expressly, assume that the input image $p\in W\times H\times C$ is randomly divided into $2\times2$ patches in the first recursion, and the size of each patch is $\frac{1}{2}W\times\frac{1}{2}H\times C$. In the second recursion, we will randomly divide one of the four patches generated in the first recursion into $2\times2$ patches, while the remaining three patches are unchanged. Similarly, one of the patches generated based on the previous recursion will be randomly selected to enter the following recursion in each recursion. Finally, we combine all the generated patches from the recursion into a new graph $G\left(p,r\right)$. Here, the number of recursions - the granularity contained in the image - is controlled by the hyperparameters $r$.

For the selection of $r$ in each stage, it is necessary to meet the following requirements. The size of the smallest patch in the images generated by RMG should be smaller than the receptive field of the corresponding stage; otherwise, it will increase the difficulty of learning in the corresponding stage of the network and affect the model's performance. Therefore, the size of the patch should increase proportionally with the size of the receptive field in each stage. For the input of the last phase, we assume $r=0$ (i.e., the initial image). So for each previous stage, the recursive hyperparameter $r$ should be increased by one. Assuming that phase \_num$=4$, the input image for phase 1 is $G\left(p,r=3\right)$.

As shown in~\cref{fig:RMG}, we use the RMG to process the initial image and obtain new images as the input to different phases of our network, and the new image carries the same label $y$ as the initial image. It is worth emphasizing that the RMG does not always guarantee that all patch parts contain meaningful information when $r$ increases. Therefore, the number of recursions should be controlled within a reasonable range (i.e., $r\le3$). Otherwise, if the patch is too small, too much noise will be introduced, making it difficult to learn.
\begin{figure}
  \centering
   \includegraphics[width=1\linewidth]{RMG.pdf}
   \caption{Recursive Mosaic Generator (RMG). In each phase, $2\times2$ mosic operation is performed again based on a random patch from the previous phase.}
   \label{fig:RMG}
\end{figure}

\subsection{Progressive multi-stage interaction}
In this section, we will introduce the Progressive Multi-stage Interaction (PMSI) module, which is the essential part of our proposed model. PMSI can be divided into two parts of multi-stage interaction and progressive training.
\subsubsection{Multi-stage interaction}
As shown in~\cref{fig:PMSI_overview}, the backbone generates different output feature maps in different stages. Then, the feature map passes through Smooth Conv and global max pooling to generate feature vectors $x_n\in\mathbb{R}^c$ , $n\in\left\{N-StageNum+1,\cdots,N-1,N\right\}$ at different stages, where $c$ is the number of channels of feature vectors. Then, we learn a mutual vector $x_m$ from $x_{N-StageNum+1}$ to $x_N$ as follows:

\begin{equation}
x_m=f_m\left(f_c(x_{N-StageNum+1},\cdots,x_{N-1},x_N\right)).
\end{equation}

where $f_c$ is a concatenation operation and $f_m$ is a mapping funtion of $\left[x_{N-StageNum+1},\cdots,x_{N-1},x_N\right].$ Specifically, we use the MLP as the mapping function. Since $x_m\in\mathbb{R}^c$ is adaptively summarized from features of different stages, it often contains information of different granularities.

After learning the mutual vector $x_m$, we proceed to expand it to $x_{N-StageNum+1},\cdots,x_{N-1},x_N$. The main reason is that, by interacting the mutual vector with the feature vectors of different stages, the features generated from each stage can be supplemented with different granularities from other stages, so as to make better advantage of features of different levels. In particular, we perform channel-wise product between $x_m$ and $x_n$. Then, we add a sigmoid function $\sigma$ to generate supplemental gate vectors $g_n\in\mathbb{R}^c$ corresponding to different stages as follows:

\begin{equation}
g_n=\sigma\left(x_m\odot x_n\right).
\end{equation}

where $n\in\left\{N-StageNum+1,\cdots,N-1,N\right\}$. Finally, we introduce an interactive supplement mechanism for the features extracted from each stage via residual connections as follows:

\begin{equation}
m_n=x_n+g_n\odot x_n.
\end{equation}

where $n\in\left\{N-StageNum+1,\cdots,N-1,N\right\}$, and $m_n\in\mathbb{R}^c$ is the output of multi-stage interactive module.

\subsubsection{Progressive training strategy}
The proposed RMG-PMSI model adopts the progressive training strategy to effectively capture discriminative information of different granularities. It could be implemented on any popular CNN feature extractor, such as the MobilenetV2 \cite{DBLP:conf/cvpr/SandlerHZZC18} and ResNet \cite{DBLP:conf/cvpr/HeZRS16}.

The model gets the feature vector $m_n$ after multi-stage interactions. Thereafter, a classification module $F_{classifier}^n$ consisting of two fully connected layers with batch  normalization \cite{DBLP:conf/icml/IoffeS15} and the activation function ELU \cite{DBLP:journals/corr/ClevertUH15} corresponding to the $n$-th stage, predicts the probability distribution over the classes as

\begin{equation}
{\hat{y}}_n=F_{classifier}^n\left(m_n\right).
\end{equation}

Here, we consider the last $StageNum$ stages with $n\in\left\{N-StageNum+1,\cdots,N-1,N\right\}$. Finally, we concatenate the outputs from the last $StageNum$ stages as
\begin{equation}
m_{concat}=\mathrm{concat}\left[m_{N-StageNum+1},\cdots,m_{N-1},m_N\right].
\end{equation}

This is followed by an additional classification module
\begin{equation}
{\hat{y}}_{concat}=F_{classifier}^{concat}\left(m_{concat}\right).
\end{equation}

During training, each iteration contains $StageNum+1$ phases. For training, we compute the cross entropy loss $L_{CE}$ between the ground truth label $y$ and the predicted output from each phase. At each iteration, a batch of data $d$ is used for $StageNum+1$ phases, and we only focus on training one stage's output $y_n$ at each phase. It needs to be emphasized that, except for the classifier, all parameters that are used in the current phase prediction will be optimized, even they might have been updated in previous phases, and the strategy helps all stages in the model collaborate together. The detailed implementation of the training algorithm of our RMG-PMSI model is shown in~\cref{alg:2}.

\begin{algorithm}
    \caption{RMG-PMSI}
    \label{alg:2}
    \begin{algorithmic}
        \STATE {Training data set $D$, \\ Training data for a batch $x$,\\ Training label for a batch $y$. }

        \FOR{$epoch$ $ \in [0,epochs)$}
            \FOR{$b$ $ \in [0,D/batchSize)$}
                \STATE {$x,y \Leftarrow$ batch \ $b$ \ of\ $D$}

                \FOR{$n  \in$ $[N - StageNum + 1, N]$}
                \STATE {$r \Leftarrow$ N - $n+1$}
                \STATE {$X \Leftarrow$ $G(x,r)$}
                \STATE {$m_{n} \Leftarrow F_{n}^{e}(X)$ }
                \STATE{$\hat{y}_{n}\Leftarrow F_{classifier}^{n}(m_{n})$}
                \STATE {Backprop$(L_{CE}(\hat{y}_n,y))$}
                \ENDFOR
            \STATE{$m_{concat}\Leftarrow$ $concat[m_{N-StageNum+1},...,m_{N-1},m_{N}]$}
            {$\hat{y}\Leftarrow F_{classifier}^{concat}(m_{concat})$}
            \STATE{Backprop $(L_{CE}(\hat{y_{concat},y})$}
            \ENDFOR
        \ENDFOR

    \end{algorithmic}
\end{algorithm}

In the testing phase, if we only use $\hat{y}_{concat}$  as the prediction, the final result of our module can be expressed as

\begin{equation}
P_{concat}=argmax(\hat{y}_{concat}).
\end{equation}

Nevertheless, the predictions of each phase are complementary. Hence, we combine all outputs to get the final result which can be calculated as

\begin{equation}
P_{mix}=argmax \left ((\sum_{n=N-StageNum+1}^{N} \hat{y}_{n})+\hat{y}_{concat} \right).
\end{equation}

\section{Experiments}
\label{sec:experiments}
In this section, we will introduce the datasets and implementation details of the experimental studies. Firstly, a series of ablation studies has been conducted to demonstrate the contributions of each module to the performance of our model. Next, we compare our model's performance to other state-of-the-art fine-grained classification counterparts. Moreover, we have carried out two experiments to test the robustness of RMG-PMSI. Finally, we visualize and qualitatively analyze the interior of the model.
\begin{table}[H]
\caption{Statistics of benchmark datasets
\label{table:1}
\centerin
\begin{tabular}{cccc}
\toprule
Dataset        & \#Classes & \#Train & \#Test \\

\toprule
CUB-200-2011   & 200       & 5994    & 5794   \\
Standford Cars & 196       & 8144    & 8041   \\
FGVC Aircraft  & 100       & 6667    & 3333   \\
\toprule
\end{tabular}
\end{table}
\subsection{Dataset and Implementation Details}
We have evaluated the performance of the proposed method on three prestigious fine-grained benchmarks: CUB-200-2011 (CUB) \cite{WahCUB_200_2011}, Stanford Cars (Car) \cite{DBLP:conf/iccvw/Krause0DF13} and FGVC-Aircraft (Air) \cite{DBLP:journals/corr/MajiRKBV13}, with details shown in~\cref{table:1}. It is worth emphasizing that in all experiments, the category labels of the images are the only annotations used for training.

We run all the experiments on the GTX 2080Ti GPU cluster using PyTorch with a version higher than 1.8.0. The method we proposed was evaluated on the widely used mobile network of MobilenetV2. During the training phase, we adjusted the input image to 512 × 512 and randomly cropped it to 448 × 448 after a random horizontal flip. During the test, we resized the image to 512 × 512 and cropped it from the center to 448 × 448 as input. We used Stochastic Gradient Descent (SGD) to optimize our network and we applied the pre-trained model on ImageNet. For pre-trained convolution layers, the initial learning rate was 0.0001 and reduced by the cosine annealing schedule. For newly added layers, the initial learning rate was 0.001. For all the aforementioned models, we trained them for up to 150 epochs with batch size as 32 and used a weight decay as 0.0005 and a momentum of 0.9. Besides, during training phase, we froze the backbone and only trained the newly-added layers in the first 5 epochs.

\subsection{Ablation studies}
\textbf{The impact of StageNum.} To demonstrate the effecacy of progressive interaction training, we conducted experiments without a Recursive Mosaic Generator (RMG) in CUB dataset.

We set the increase in the number of feature map channels as the division between this stage and the next stage. In order to obtain the best performance and prevent excessive noise being introduced by too shallow layer features, we use the last 5 stages as our experimental setup. The StageNum increases from 1 to 5. We use Top-1 Accuracy (Acc) as the evaluation criterion. The results of the experiment are shown in \cref{table:2}, where Concat is the result of the $P_{concat}$ (it is worth noting that when S={5}, Concat is equivalent to the concatenation of the output of stage 5 and its output after MLP) and Mix represents a mixed classification result $P_{mix}$. As the results show, when the number of stages (S) involved in interactive training is less than 4, the increase in StageNum improves the model's performance. Both Concat Accuracy and Mix Acc Accuracy began to decline when the StageNum=4. This might be caused by the low-stage layer focusing on class-independent features. However, the additional supervision forces the low-stage layer (S={1,2}) to focus prematurely on the features associated with classification, thus introducing too much low-stage noise to the high-stage classification through progressive interaction, resulting in a decrease in accuracy. In addition, using multi-stages of progressive interaction can lead to increased training costs. To sum up, we use the last three phases (S={5,4,3}, StageNum=3) as the optimal choice for progressive interactive training.
\begin{table*}[]
\caption{The performance of the proposed model when interacting at different stages.
\label{table:2}
\centerin
\begin{tabular}{lccccccc}
\toprule
\multicolumn{1}{c}{}  & \multicolumn{7}{c}{Acc(\%)}                                                                                \\ \toprule
Stage(S) / StageNum   & \multicolumn{1}{l}{S1} & \multicolumn{1}{l}{S2} & \multicolumn{1}{l}{S3} & \multicolumn{1}{l}{S4} & \multicolumn{1}{l}{S5} & \multicolumn{1}{l}{Concat} & Mix         \\
\{5\} / 1             & -                      & -                      & -                      & -                      & 84.2                   & 84.2                       & 84.3        \\
\{5, 4\} / 2          & -                      & -                      & -                      & 84.7                   & 84.5                   & 84.9                       & 85.2        \\
\{5, 4, 3\} / 3       & -                      & -                      & 82.9                   & 84.8                   & 84.3                   & \textbf{85.5}              & \textbf{85.9} \\
\{5, 4, 3, 2\} / 4    & -                      & 80.5                   & 83.3                   & 84.7                   & 83.9                   & 85.1                       & 85.7        \\
\{5, 4, 3, 2, 1\} / 5 & 74.2                   & 81.7                   & 83.0                     & 84.5                   & 84.3                   & 84.9                       & 85.1   \\
\toprule
\end{tabular}
\end{table*}

\textbf{The contribution of different components.} Specifically, RMG-PMSI can be split into three sub-modules, recursive mosaic generator (R), progressive training (P), and multi-stage interaction (M). To justify their contributions and joint effort, we validate all of their possible combinations in the CUB dataset\footnote{It should be noted beforehand that R must work with P.}. The results of the experiment are shown in~\cref{table:3}.

\begin{table}[]
\caption{The contribution of each component in RMG-PMSI.
\centerin
\label{table:3}
\begin{tabular}{lcc}
\toprule
\multicolumn{1}{c}{Stage} & Concat Acc (\%)  & Mix Acc (\%) \\ \toprule
Baseline                  & 81.5          & -                 \\
+ M                       & 85.1          & -                 \\
+ P                       & 85.2          & 85.5              \\
+ P \& M                  & 85.5          & 85.9              \\
+ P \& R                  & 86.4          & 86.8              \\
+ P \& M \& R             & \textbf{86.6} & \textbf{87.0}       \\ \toprule
\end{tabular}
\end{table}

We can see from the results that both multi-stage interaction (M) and progressive training (P) can significantly improve the model. Combining them further drives accuracy forward. This shows that progressive training (P) and multi-stage interaction (M) can effectively benefit rather than offset each other. Recursive mosaic generator (R) works very well with progressive training (P), helping the model pay more attention to the fine-grained local information in shallow and global features in deep stages. The above experiments demonstrate the power of the components of our RMG-PMSI model and their good collaboration.
\begin{table}[]
\caption{Accuracy of different data augmentations.
\centerin
\label{table:4}
\begin{tabular}{lcc}
\toprule
\multicolumn{1}{c}{data argumentation} & Concat Acc (\%)  & Mix Acc (\%) \\ \toprule
baseline                                 & 85.5          & 85.9                   \\
cutmix                                   & 85.1          & 85.3                   \\
mixup                                    & 85.6          & 86.0                   \\
snapmix                                  & 85.1          & 85.1                   \\
jigsaw                                   & 86.3          & 86.6                   \\
RMG (ours)      & \textbf{86.6} & \textbf{87.0}            \\ \toprule
\end{tabular}
\end{table}
\subsection{Comparative studies}
\textbf{Methods for augmenting fine-grained data.} We compared our RMG with other data augmentation methods that are widely used in fine-grained classifications on CUB200, including cutmix \cite{DBLP:conf/iccv/YunHCOYC19}, mixup \cite{DBLP:conf/iclr/ZhangCDL18}, snapmix \cite{DBLP:conf/aaai/HuangWT21}, and jigsaw \cite{DBLP:conf/eccv/DuCBXMSG20}. As can be seen in~\cref{table:4}, stitching different pictures does not bring much improvement or even decrease the model performance. We believe that it happens because progressive training and multi-stage interaction encourage the fusion of different features of an image from the local level to the global level. The stitching-based image augmentation greatly disturbs the process of learning features of different stages, which makes it difficult to boost the model performance.
On the other hand, the puzzle method jigsaw performs well. Compared to jigsaw, our method can make the image contain both fine-grained local features and large-grained global features, which promote better collaborative learning at different stages and thus achieve optimal performance.

\textbf{Models for Fine-grained recognition.} To further illustrate the advantages of our RMG-PMSI, we compare it with the widely used state-of-the-art fine-grained approaches on MobilenetV2 in CUB dataset. The results of the experiment are shown in~\cref{table:5}. From the table, we can see that because B-CNN, HBP, PC, and API rely more on the abstract features extracted by the model in deeper stages, their performances are limited by the feature extraction capability of the MobilenetV2. For PMG and RMG-PMSI, the performance of the model can be greatly improved. The key to improving performance on a lightweight mobile network is to take full advantage of the features that the model extracts at different stages. Finally, the RMG-PMSI offers better performance improvement than PMG because RMG and multi-stage interaction are better equipped to help different stages work together during the progressive training.

\begin{table}[]
\caption{Comparison with other state-of-the-art fine-grained recognition models
\centerin
\label{table:5}
\begin{tabular}{ccc}
\toprule
Models         & (Concat) Acc (\%)  & Mix Acc (\%) \\ \toprule
B-CNN \cite{DBLP:conf/iccv/LinRM15}    & 83.2          & -                      \\
HBP \cite{DBLP:conf/eccv/YuZZZY18}      & 85.1          & -                      \\
PC \cite{DBLP:conf/eccv/DubeyGGRFN18}      & 84.5          & -                      \\
API \cite{DBLP:conf/aaai/ZhuangW020}     & 85.7          & -                      \\
PMG \cite{DBLP:conf/eccv/DuCBXMSG20}    & 85.9          & 86.5                   \\
\multicolumn{1}{c}{\begin{tabular}[c]{@{}c@{}}RMG-PMSI\\ (ours)\end{tabular}} & \textbf{86.6} & \textbf{87.0}            \\ \toprule
\end{tabular}
\end{table}

\begin{table}[]
\caption{The results on other stronger backbones
\resizebox{1.05\linewidth}{!}{
\centerin
\label{table:6}
\begin{tabular}{c|ccc}
\hline \hline
Backbone & CUB & Car  & Air      \\ \hline
MobileNet-v2   & 81.5       &  90.9     & 88.9\\
+RMG-PMSI (ours) & \textbf{87.0 (+5.5)} &\textbf{94.0 (+3.1)}&\textbf{91.6 (+2.7)} \\\hline
ResNet-18   & 82.0 & 90.5  & 87.5  \\
+RMG-PMSI (ours) & \textbf{86.8 (+4.8)} & \textbf{93.4 (+2.9)} &\textbf{90.2 (+2.7)} \\ \hline

ResNet-34  &   84.1      &     91.8      & 89.7  \\
+RMG-PMSI (ours)  &      \textbf{88.0 (+3.9)}       & \textbf{94.2 (+2.4)}   & \textbf{91.7 (+2.0)}               \\ \hline \hline
\end{tabular}}
\end{table}

\begin{table*}[]
\caption{Comparison of anti-interference ability of baseline and RMG-PMSI.
\centerin
\label{table:7}
\begin{tabular}{ccc|cc|cc}
\hline
                 & \multicolumn{2}{c}{CUB}              & \multicolumn{2}{c}{Car}             & \multicolumn{2}{c}{Air}               \\ \hline
                 & Baseline     & +RMG-PMSI             & Baseline     & +RMG-PMSI             & Baseline     & +RMG-PMSI              \\
Origin           & 81.5         & \textbf{87.0}           & 90.9         & \textbf{94.0}           & 88.9         & \textbf{91.6}          \\  \hline
+ Color-Jitter   & 14.1 (-77.4) & \textbf{59.9 (-27.1)} & 69.7 (-21.2) & \textbf{79.3 (-14.7)} & 64.7 (-24.2) & \textbf{73.9 ( -17.7)} \\
+ Gaussian-Noise & 22.2 (-59.3) & \textbf{78.6 (-18.4)} & 75.0 (-15.9) & \textbf{86.5 (-7.5)}  & 82.6 (-6.3)  & \textbf{86.6 (-5.0)}   \\ \hline
\end{tabular}
\end{table*}

\subsection{Robustness analysis}

\textbf{Stronger backbones.} In order to demonstrate the robustness and transferability of our proposed approach on different scales, in addition to MobilenetV2, we applied RMG-PMSI on ResNet-18 and ResNet-34 as well. For RMG-PMSI, we used Mix Acc (\%) as the evaluation metrics. The experimental results are shown in~\cref{table:6}.

On the three backbones, RMG-PMSI has a significant performance improvement compared to the baseline, which shows that RMG-PMSI is extremely effective on backbones of different scales. At the same time, compared to ResNet-18 and ResNet-34, RMG-PMSI brings more significant scores on MobilenetV2, implying that RMG-PMSI is more suitable for lightweight mobile networks.

\textbf{Anti-interference analysis.} After justifying the robustness of the RMG-PMSI on models of different scales. We further test the anti-interference capabilities of RMG-PMSI. We used Color-Jitter to generate interference data on the testset of the three datasets. We set the Jitter coefficient to be 1, i.e., the image's brightness, contrast, and saturation will be randomly adjusted to 0\% to 200\% of the original image. In addition, we also generated interference images with Gaussian-Noise (mean=0, variance=0, amplitude=5) on the testset of the three datasets. And then we test these two interference data respectively on the standard MobilenetV2 (baseline) and MobilenetV2 with RMG-PMSI. For RMG-PMSI, we used Mix Acc (\%) as the evaluation metrics and the experimental results are shown in~\cref{table:7}. It can be seen that the accuracy degradation of RMG-PMSI on the two types of interference data on the three testsets is less than the baseline. Especially on CUB200, the anti-interference ability of RMG-PMSI is far better than baseline. This may be because the distinction of birds mainly comes from some important local parts, such as eyes, feathers, and beaks. Adding interference will have a greater impact on global information, and the introduction of local information through RMG-PMSI can effectively alleviate this global interference.

\subsection{Visualization}
In order to demonstrate more insights into our approach, we applied
GradCAM \cite{DBLP:conf/iccv/SelvarajuCDVPB17} to visualize the convolutional layers of the last three stages of our method. The visualization results in~\cref{fig:heatmap} show that RMG-PMSI can help the model gradually shift from a fine-grained local feature to a large-grained global feature. For example, in bird pictures(a), the focus is on multiple local features on feathers in the shallow layer (Stage N-2) of the model. In the deeper layer (Stage N), besides focusing on the eye, which is the most distinguishing part, the model also pays attention to feathers, beaks, and other features that play a supporting role in classification. The visualization demonstrates that RMG-PMSI helps the model capture more distinguishing features from both the local and global perspectives, enhancing the performance and anti-interference of the model.

\begin{figure}
  \centering
   \includegraphics[width=1\linewidth]{heatmap.pdf}
   \caption{Activation map based on the MobilenetV2 with RMG-PMSI. Columns (a)-(c) are CUB, Air, and Car respectively.}
   \label{fig:heatmap}
\end{figure}

\section{Conclusion}
In this paper, we propose a Progressive Multi-Stage Interactive training method together with a Recursive Mosaic Generator (RMG-PMSI) for mobile networks of Fine-grained Visual Classification (FGVC) . By progressively capturing local to global features, RMG-PMSI effectively helps the model identify and integrate more distinguishing features and makes better use of the features extracted by mobile networks. Experiments on three prestigious fine-grained benchmarks prove that our method can significantly improve the performance compared to state-of-the-art lightweight models, with good robustness and transferability. For the future work, RMG-PMSI can incorporate more tricks such as attention mechanism.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
package liquibase.executor.jvm;

import java.sql.ResultSet;
import java.sql.SQLException;
import java.util.ArrayList;
import java.util.List;

/**
 * Adapter implementation of the ResultSetExtractor interface that delegates
 * to a RowMapper which is supposed to create an object for each row.
 * Each object is added to the results List of this ResultSetExtractor.
 *

 *
Useful for the typical case of one object per row in the database table.
 * The number of entries in the results list will match the number of rows.
 *

 *
Note that a RowMapper object is typically stateless and thus reusable;
 * just the RowMapperResultSetExtractor adapter is stateful.
 *

 *
A usage example with JdbcTemplate:
 *

 *
JdbcTemplate jdbcTemplate = new JdbcTemplate(dataSource);  // reusable object
 * RowMapper rowMapper = new UserRowMapper();  // reusable object
 *

 * List allUsers = (List) jdbcTemplate.query(
 * "select * from user",
 * new RowMapperResultSetExtractor(rowMapper, 10));
 *

 * User user = (User) jdbcTemplate.queryForObject(
 * "select * from user where id=?", new Object[] {id},
 * new RowMapperResultSetExtractor(rowMapper, 1));

 *

 *
 * @author Spring Framework
 * @see RowMapper
 * @see liquibase.executor.Executor
 */
@SuppressWarnings({"unchecked"})
public class RowMapperResultSetExtractor implements ResultSetExtractor {

    protected final RowMapper rowMapper;

    protected final int rowsExpected;

    /**
     * Create a new RowMapperResultSetExtractor.
     *
     * @param rowMapper the RowMapper which creates an object for each row
     */
    public RowMapperResultSetExtractor(RowMapper rowMapper) {
        this(rowMapper, 0);
    }

    /**
     * Create a new RowMapperResultSetExtractor.
     *
     * @param rowMapper    the RowMapper which creates an object for each row
     * @param rowsExpected the number of expected rows
     *                     (just used for optimized collection handling)
     */
    public RowMapperResultSetExtractor(RowMapper rowMapper, int rowsExpected) {
        this.rowMapper = rowMapper;
        this.rowsExpected = rowsExpected;
    }

    @Override
    public Object extractData(ResultSet rs) throws SQLException {
        List results = ((this.rowsExpected > 0) ? new ArrayList(this.rowsExpected) : new ArrayList());
        int rowNum = 0;
        while (rs.next()) {
            results.add(this.rowMapper.mapRow(rs, rowNum++));
        }
        return results;
    }

}

redpajama_set_name: RedPajamaGithub


text:
Review: Phantom Lives: Collier by Elizabeth Loraine
Phantom Lives: Collier
by Elizabeth Loraine
2011 Arrais, Inc.
E-book Edition; 336 Pages
ISBN: 978-1466248229 (Softcover, September 2, 2011)
ASIN: B005I57M6C
Genre: Fiction / Paranormal
Source: Review Copy from Mystical Book Blog Tours
Abigail Black, an heiress from Memphis is on the run from her abusive boyfriend, Dallas. In the process she finds out that there she is being pulled towards something, and someone, that she thought only existed in her dreams.
Another page turning adventure from Elizabeth Loraine. Phantom Lives intertwines the modern world with the post Civil War world of Collier, a plantation Abigail had dreamt about her entire life. Now she is about to find out why. Another fantasy world of spirits and immortals is built in a way which fans of Ms Loraine's will again thoroughly enjoy. Find out who Abigail was in the past and how it changes everything.
Phantom Lives: Collier was an interesting paranormal tale, one with a bit of a gothic feel to it as the main character Abigail dealt with recurring dreams, secrets, paranormal visitations, and a burgeoning ability that left her wondering what was really going on in her world. I really enjoy novels that have their primary setting in old plantation homes as well as castles, manors, and anything along those lines, so this novel caught my attention pretty much due to its setting. With ghosts, secrets to be uncovered, magical gateways, other worlds, and burgeoning magical abilities in the main characters, there was a lot in this novel that was intriguing and fun to read.
As a main character, Abigail defininitely had a lot to offer. Being chased by an abusive boyfriend, Abbi heads south and to her surprise, discovers a dilapidated plantation right from her dreams, and decides to buy it on the spot. Using money from an inheritance, Abbi sets about restoring the mansion, and along the way meets a slew of characters who help her along her restoration path. But Abbi is in for an even greater surprise as she discovers the mansion has a mind of its own and sets about discovering its secrets and its past, realizing there is a connection to her own past. I found these scenes of self-discovery really interesting as the jump from past to present were done seamlessly and I had no trouble figuring out what century I was reading about. Time travel is something I enjoy, but this was something a little different as the house was showing Abbi what it looked like in the past in order to help her restore it to its glory. I am wondering as I write this if the house will play a great role in events in future novels as there was so much emphasis on it. I was actually quite impressed with the jumps back in time, as well as to other worlds, and enjoyed how they were done. I actually would like to learn more about some of the other worlds that were visited as they sound quite fascinating.
While Abbi was a fun and engaging character, there were times when her constant optimism did wear on my nerves and I would have liked to see broader emotions from her. I don't really feel like she developed as a main character, but basically stayed the throughout the novel, even as she developed her powers and grew more confident. I prefer my characters to be less two-dimensional and somewhat more flawed in nature. Bill had a bit more fight in him, as some of the arguments with the 'ghosts' attest to, but it makes the events seems more real in light of events. While I can see general acceptance occurring, there must have been some skepticism and doubt in some of the characters and not always this blind obedience and trust. Arguments are bound to happen to people who are dealing with new events and circumstances; it's just a normal thing. While there was some discord, I don't think there was enough to really get across the big changes that were happening in these characters' lives.
I did enjoy the plot quite a bit, and thought the novel was well-written, with a lot of interesting dialogue, but I was a little disappointed in the ending as I felt it was rushed after all the build-up and didn't really live up to the expectations. While I do understand this is only the first novel in a planned series, there were also a lot of unanswered questions, and while this can be a good thing as it makes the reader want to come back for home, there are some things that should have been resolved more succintly. The final events surrounding Dallas and his minions was a bit of a let-down in my opinion, and I don't really feel like I got enough information about his role in everything. Perhaps more will be explained in the second book.
Phantom Lives: Collier definitely had a lot to offer and I found many parts to be intriguing and fun to read. There were a couple twists and turns that I wasn't expecting, and with secrets to be revealed, this is always an attention-getting writing device that intrigues me. While I would have liked to have seen an ending that was more consice and with more of a climax, the creativity and imagination in this novel, as well as a host of interesting characters, make this one worth taking a look.
Labels: Review - Fiction / Paranormal
William Kendall September 23, 2011 at 5:08 PM
I haven't heard of this one yet, though it's not really my genre.
Good review!
LoriStrongin September 25, 2011 at 5:59 PM
I haven't come across this book yet either, but I'm a fan of stories where inanimate objects have a consciousness (not like straight-up haunted houses, like like Stephen King's Rose Red). Bummer to hear about the rushed ending, but I still think I'm gonna check this one out. Thanks for the rec!
Smiles!
Afterlight: End of the Word by J. Corsentino
Guest Post: Rhiannon Paille
Guest Post: Joseph Devon on Writing Horror
Review: Awaking by Madeline Freeman
Guest Post: Madeline Freeman and Giveaway
Guest Post with Scott Nicholson and Giveaway
Interview with Elizabeth Loraine
Review: The Sixes by Kate White
Guest Post: Elizabeth Loraine
Invitation for an E-ventful Book Party
Review: The Legacy by Katherine Webb
Review: The Last Seal by Richard Denning
WOW: The Night Circus

redpajama_set_name: RedPajamaCommonCrawl


Help Pirlock to recover his earldom!
If you are a "Point and Click" graphic adventure fan, this is your game!
Developed specially for tablets and smartphones, but with an old SCUMM game soul!
Enjoy this funny "Point and Click" style medieval graphic adventure, with an acid humor sense that will not leave you cold.
If you are a "Point and Click" graphic adventure fan and you remember with fondness "Monkey Island", "Day of the tentacle" and "Sam & Max", This is your game!
A tribute to the games developed by the sadly disappeared Lucas Arts! Enjoy this funny "Point and Click" style medieval graphic adventure, with an acid humor sense that will not let you cold.


text:
Aïshti Home Solutions Opens Flagship Store
By fradmin on June 29, 2018 in Spotlight
There's a brand-new concept store in Lebanon, where ultimate home appliance designs, combined with the trendiest furniture and home accessories, allow customers to have an exceptional touch-and-feel experience: Aïshti Home Solutions. Aïshti Home Solutions, which just opened its flagship showroom near Aïshti by the Sea in Antelias, covers four floors. The store, which carries exclusive items that cannot be found anywhere else in Lebanon, offers a broad collection of accessories: upscale TV models, appliances and state-of-the-art home automation solutions from Samsung, Sony, Philips and others; drones, 3D printers, gaming solutions, phones, tablets and their related accessories from Apple, Beats, B&O; a diverse range of kitchens and appliances from Barazza, Vikings, KitchenAid, and more catering to all tastes; and a wide range of furniture from the collections of Sawaya & Moroni, Flexform and Zaha Hadid. ...
Maison dinh van: A Niche Brand in Jewelry Celebrated in Beirut
By fradmin on June 29, 2018 in Fashion
The French brand Maison dinh van, which has carved its own niche in the world of jewelry, recently launched its latest designs in Beirut. The event took place at ABC Verdun, in the presence of the brand's International Director, Steve Jacquin along side the "Romance" team from Fattal Group, the exclusive agents of Maison dinh van in Lebanon. Inspired by design, especially Bauhaus movement, each piece is elegantly displayed under small glass cases for everyone to see. The new collection from dinh van on display included, Cube Diamant, Seventies, Serrure, as well as the brand's famous Menottes....
Step Into the Future: The Mycelium Shoe
The Mycelium Shoe is the premiere design in the launch of Ica & Kostika, a revolutionary 3D design studio transforming complex computational systems into luxe fashion footwear. The Mycelium Shoe is pioneering the exploration of computational design and mass customization using the latest 3D printing and data capturing technologies. How is this possible? The wearer downloads the app and takes a series of photos of their feet, then the algorithm creates a 3D representation with allowances for improved fit. Each shoe is 3D printed and finished with automotive grade technologies to form the final structure. Thus, the result is a structured yet highly intricate shoe that forms an elegant silhouette, creating both visual aesthetic and design functionality. "Humans have been creators since the dawn of time — it is our very nature — and we want our art to embody our evolutionary story from our...
Rising Talents: Maison&Objet Celebrates Lebanese Creatives
By fradmin on June 29, 2018 in Design & Architecture
Maison&Objet is famed for celebrating local design expertise and this time it's Lebanon's turn. Their motto this coming season of Maison&Objet is Bye Bye Routine, Hello Surprise and what a surprise it is to say that Rising Talents Awards will honor the very best Lebanese designers this September at an exclusive event at the Parc des Expositions – Paris Nord Villepinte, from 7 to 11 September, 2018. Before heading to Paris, a small celebration highlighting our local talents took place at Sursock Museum. Surrounded with talented people, the event was one that put us all in the mood to celebrate our local creatives. Bridging the gap between East and West, the choice to honor a country with a booming creative scene confirms Maison&Objet's efforts to keep pushing the boundaries of creation. The event is placed under the patronage of Rabih Kayrouz. The celebrated designers where...
John Paul Fauves Questions Identity Through Art
By fradmin on June 28, 2018 in Art & Culture
"I found myself, and quickly started searching again," says artist John Paul Fauves. Recently spotted at W22 GALERíA in his hometown, San José in Costa Rica, John Paul's eclectic style is part of a group show featuring three acclaimed artists from around the world, breaking the boundaries of contemporary art in our modern day" Emerging Escazu Project. FR digs more… John Paul Fauves is a visual artist who lives and works in San José, Costa Rica. In his paintings he engages questions of identity as they relate to art history as well as our everyday interactions with mainstream culture and social media. Greatly inspired by modernist masters as well as pop-artists, Fauves mixes fragments of different iconic images in vivid and colorful compositions. Of his experimental and high eclectic style, he says, "art is an expression from the soul, and the soul is something limitless....
Babu: Feel Close to Earth While Relaxing
Babu, designed by Jordi Ribaudí for Toru, is a nomadic chair. You can fly on a carpet or chill on the sand while sitting in it. Its design is inspired by the great journeys of the desert tribes, during which men feel close to the earth, rest on it and merge with the landscape. Babu is a spot for relaxation and meditation; a place to rest, a shelter. Babu is born straight from Jordi Ribaudí's soul, in Igualada, Barcelona. The town has strong roots in the local leather industry, and the company's headquarters is to be found in a restored nineteenth century tannery. His work is inspired by his passion for leather works. He looks for new purposes of the material in order to create innovative -and exciting- shapes and feels....
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Later this month, we'll celebrate Hope for New York Sunday, which is our annual event to show Redeemerites how they can partner with Hope for New York to love and serve the poor and marginalized in our city.
On October 26 (and October 25 for those of you who attend the Downtown evening service), we'll highlight volunteer opportunities across HFNY's 40+ affiliates and show how you can invest in Hope for New York's work to continue our movement of mercy and justice.
In preparation, here are four ways you can "do justice and love mercy" (Micah 6:8) with Hope for New York this month.
1. Check out the Learn, Watch, Read and Listen sections on our resources page for inspiration. In Luke 24:19, we're told that Jesus was mighty in word and deed — preaching the Gospel of salvation and restoration and showing himself powerful to heal the sick and feed the hungry. As Christians, we are called to live a life of service and to care about justice for our poor and marginalized neighbors.
2. Use HFNY's snazzy new volunteer search filter to find volunteer opportunities. We're really excited for this new feature on HFNY's website, because it's easier than ever to find volunteer opportunities and sign up on the spot. Need a Sunday night activity for 6 people? No problem. Want to serve specifically in your neighborhood? We've got you covered. Looking for a way to serve with your kids? Yep, we have those too.
3. Prayerfully consider giving financially to God's work of renewal. Last year, through the giving of individuals like you, HFNY distributed over $1M in grants to 40+ organizations serving New York's poor and marginalized. Begin thinking about how you can join HFNY in helping provide mentoring for children, training programs for the homeless, medical care for those in need, shelter for trafficking victims, and more, through your sacrificial giving.
4. Exercise your prayer muscles with HFNY. As Redeemer focuses on prayer in the '14 - '15 ministry season, we'd love to share the many ways you can join us in prayer. Offer prayer to clients who are homeless at The Relief Bus or join Avail's prayer committee to pray for clients who are facing unexpected pregnancies. You can also sign up to receive our prayer newsletter by emailing [email protected] We'll send you specific ways you can pray for HFNY affiliates and the work they're doing to serve the needs of our city.


well it is not done or anything, But the sculpt has started so might as well show everyone the progress.
I am basing this off the same style and same artist.
Since it is simular I will be able to use some of allen's pieces as stepping stones for this one.
Keep in mind this will go back and forth in design, so a lot of this will change by the time it is done. But I wanted to show everyone the process.
Promised a face update today. But forgot I did the eyes in acrylic so going to wait till morning before I spray lacquer over it.
Cast is nice, No defect or bubbles on the front side.
But on the side, that is a different story.
Doh! Didn't have the mold positioned right so got a large gap. No worries Putty to the rescue!
Then sprayed white primer over it.
To ensure that the whites of the eyes will be WHITE I masked them off before I lay down the skintone.
First I placed tape over the face and traced the eyes with a pencil.
Then got a FRESH blade for my knife and cut along the pencil marks.
Then the masking tape is removed.
I then layed down the first layer of skin tone. This is my own custom mix. Which does not say a lot because I just made it one night when I felt I needed to make a jar or skin tone.
Then a lighter layer B is sprayed all over. This portion is part clear so layer A becomes darker and the piece more defined. though with it being gloss it is hard to tell.
Allen's scar is then placed. This is tricky since it is a whole piece of decal. Place the decal in water for 20-30 seconds then take it out and place the paper on the face, then slide the decal off while you pull the paper down. Position it. Then place decal set on it so It can conform to the shape of the face.
Next I mix up the mouth color with white enamel and red and yellow oil paint.
Next I do the eyebrow. Be careful with this as putting tape over the decal can rip it off if the decal was not set flat.
Then the tape is removed.
Touched them up a little but came out good.
Ok part 2 will be tomorrow.


This salwaar kameez is crafted with crepe material and pure chiffon dupatta, printed exclusively with embroidered laces. It is a designer piece with invouge patterns and prints. To make you look more stunning in regular days.


The 2018 Sundance Film Festival generated a total economic impact of $191.6 million — up 26% from the 2017 event, a study released Tuesday showed.
The Sundance Institute said that the increase from the 11-day festival was due partly to the addition of the 500-seat Ray Theatre, a custom-built two-story venue used for the New Frontier VR section, and an expanded section dedicated to episodic work.
The economic impact study, conducted by Y2 Analytics, determined that the 2018 festival generated over $19 million in state and local tax revenue; supported 3,323 jobs; and attracted more than 124,900 attendees from 49 states and 26 foreign countries. Attendance was up 74% from 71,600 in 2017.
Robert Redford launched the festival in 1978 in Salt Lake City, Utah. It moved to Park City in 1981, when Redford founded the non-profit Sundance Institute to support independent filmmakers.
This year's economic impact brought the five-year cumulative total since 2014 to $585.4 million, with more than $55 million in state and local tax revenue generated and more than 10,300 jobs supported.
Of the 124,900 attendees, the festival brought in 45,000 out-of-state visitors (an approximate increase of 7,500 people from 2017) and over 1,000 international visitors. The report found that 20% of out-of-state attendees indicated this was their first visit to Utah and 89% said they would likely visit Utah again within the next year.
Per-person spending for out-of-town visitors averaged $3,518 with a median stay of five days.
Lodging generated a total of $62.6 million in attendee spending. The second-largest expense was in recreation and entertainment with $49.9 million total attendee spending generated. Efforts to mitigate traffic and congestion contributed to $20.1 million spent on transportation methods.


Blackcircles.com tyre fitting centres offer tyre fittings across Castleford. If you are looking for new tyres Blackcircles.com has a fitting centre near you - we cover Pontefract, Ackworth, Sherburn-In-Elmet, Wakefield and Tadcaster.
Blackcircles.com works with 2 garages in the Castleford area.
Great guys, did a free tracking check. Quite speedy.
Extremely good service!! I arrived 15 minutes early of my appointment and my car was on the ramp straight away. Nothing was too much trouble for the technicianss.
Fitted at my agreed appointment time (even though I was a bit late!), fast and friendly service, answered all of my questions and sorted the tracking. No problems, would use again.
"I was recommended to order my tyres at Black Circle by the garage that fitted them and i'm glad I did. All went as planned and I was able to choose the tyres I wanted and at a good price. I always take my car to PCS at castleford, they are fab!"
"lovely all round experience , easy to select through the internet , emails answered very quickly ,and first class getting the tyres done lovely friendly place (and nice cuppas),thank you will definitely use again if needed . "
Fitted at my regular garage in Castleford. PCS. Good price on budget tyres on Black circles.com.
Appointment time was adhered to and tyre was fitted as I waited in next to no time. Excellent service throughout.


Ross Leckie was born in Lachine, Quebec, and has lived in Montreal, Toronto and Prince George. He is currently Director of Creative Writing at the University of New Brunswick in Fredericton. Leckie's work has appeared in numerous journals and anthologies including Landmarks (2001) and Why I Sing the Blues (2001). He is the author of three collections of poetry: Gravity's Plumb Line (GP, 2005), The Authority of Roses (1997) and A Slow Light (1983).
Gravity's Plumb Line is Ross Leckie's third collection of poetry. It begins on the Saint John River, then moves out into neighbouring fields, woods and landscapes beyond. These poems bring the lushness of natural abundance in contact with the process of comprehending its intricacies.
The collection's opening series leaves from Little St. John Lake, travelling through Edmundston and Grand Falls, down through the heart of New Brunswick, into the Mactaquac Dam, through the city of Fredericton and out into the Atlantic at Saint John Harbour. With inventive language, interest and affection, Leckie marvels at the river's shifting attitude through towns and cities, bridges, surrounding vegetation, cultural flavours, and the memories that surround his experience. In Grand Falls the poet imagines the town's dreamlife, while in Hartland he recalls an afternoon spent on the covered bridge in the company of his father, and in Mactaquac he considers the process of writing in the presence of the dam that churns river into electricity.
From the river Leckie moves outward, delighting in avian intellect, the shapes of trees, the density of ground. To each he brings sharp perceptions and an intriguing level of engagement with the process of conveying these perceptions. Readers are privy to missteps and moments of satisfaction as the poet hones his senses ever closer. In the forest Leckie draws out the elusive ice bird, as well as pine trees, sunlight, winter afternoons, iris and lady's slipper, among others. Indoors, his eye trains on imported oranges and the journey of water from source to thirst.
The title segment broadens into a distinctly musical cadence that hinges on wit, stimulating vocabulary, the occasional blues influence and solid rhythms. In "That Dirt Road," Leckie replays the lilting repetition of childhood reluctance along a country road, while "Woke up This Morning" and "Bending the Blue Notes" draw from a more formal appreciation of the blues genre.
In the final group of poems, Leckie re-envisions tenets of classical theatre—mimesis, hamartia, unity, character, spectacle, catharsis and six others. These concepts are transported out of doors and tested against woods, sky, garden, field, wind and pond. Seasons in the woods mimic the trials of human experience, and stars enact the downfall of a hero reaching beyond his means. In 12 brief poems Leckie locates the mainstays of human drama in the tumult of our environs—a resounding conclusion in the poet's stirring translations of life into art.
This 5 x 8-inch book is a smyth-sewn paperback bound in card stock with a letterpress-printed jacket. The text is printed offset on laid paper.


text:
Colorado Basin Roundtable OKs grant to study Crystal River backup water supply
Local | November 28, 2019
Heather Sackett
Aspen Journalism
The Colorado River Water Conservation District and the West Divide Conservancy District gave up their conditional water rights in 2011 that could have allowed for a reservoir on the Crystal River at Placita. A proposed back-up water supply study has some groups worried that the idea of dams on the Crystal could be resurrected.
Brent Gardner-Smith/Aspen Journalism
The fight over damming the Crystal River has been resurrected, this time before there are even any dam projects to fight over.
The Colorado Basin Roundtable voted Monday to recommend the state give $25,000 toward a water study in the Crystal River basin, despite calls from some to deny the Water Supply Reserve Fund request because of concerns that a study might conclude there is a need for water storage.
The Colorado River Water Conservation District and the West Divide Water Conservancy District brought the grant request to the roundtable in Glenwood Springs in an effort to solve a long-acknowledged problem on the Crystal: In dry years, there may not be enough water for both irrigators and some residential subdivisions.
On Nov. 18, the Gunnison Basin Roundtable gave its unanimous support to the grant application, even though its support was not necessary. Although the Crystal is in the Colorado River basin, its headwaters are in Gunnison County, and so the Gunnison roundtable decided to voice its support.
The feasibility study would look at water demands and options for creating a basinwide backup water supply plan, known as an augmentation plan. The study will look at small storage alternatives, probably off the main stem of the Crystal. Until the study is completed, it's unclear how much water is needed for a basinwide backup supply.
But some fear that the plan could include dams and reservoirs on the free-flowing Crystal, and they opposed the grant unless storage was off the table.
Pitkin County Commissioner Kelly McNicholas Kury requested two amendments to the grant application: that any reservoir would be off the main stem of the river and would only be located downstream of the Sweet Jessup Canal diversion (about 2 miles downstream of Avalanche Creek) to preserve the possibility of designating 39 miles of the Crystal River as Wild and Scenic.
"We are not going to support this application as it's currently written," McNicholas Kury told roundtable members Monday. "The county continues to support Wild and Scenic designation on the Crystal."
McNicholas Kury and two other roundtable members voted against the funding: recreation representative Ken Ransford and Eagle County representative Chuck Ogliby, who owns the Avalanche Ranch Cabins & Hot Springs in the Crystal River Valley.
The Crystal River Caucus, which doesn't have a seat on the roundtable, also objected to the grant application and passed a resolution at its Nov. 14 meeting to that effect. In a letter to the roundtable, the caucus said it does not support the grant and urged voting roundtable members to deny the request. The caucus would, however, support a study and augmentation plan that evaluates options other than storage.
But others downplayed the threat of dams, insisting they won't happen.
"You're not going to see a dam on the main stem of the Crystal," said Colorado River District President Dave Merritt. "It's not going to happen. The river district is not predisposed to dams. There is a need for a small amount of augmentation water up there. We are talking tens of acre-feet, probably."
No backup water supply
During the historic drought of late summer of 2018, the Ella Ditch, which irrigates agricultural land south of Carbondale, placed a call on the river for the first time ever. This means, in theory, that junior-rights holders upstream have to stop taking water so that the Ella Ditch, which has water rights dating to 1885, can receive its full decreed amount.
Most junior-rights holders have what's known as an augmentation plan, which lets them continue using water during a call by replacing the called-for water with water from another source, such as a reservoir or exchange. The problem on the Crystal is that several residential subdivisions don't have augmentation plans.
Without an augmentation plan, these entities — which are the town of Carbondale, the Marble Water Company, Chair Mountain Ranch, Crystal River Resort, Crystal View Heights and Seven Oaks Commons — could be fined for every day they are out of priority and could potentially have their water shut off, if there is a call on the river.
Colorado Division of Water Resources Division 5 engineer Alan Martellaro said instead of each subdivision coming up with its own augmentation plan, a basinwide approach makes more sense.
"We think it would save everyone money if we had a reasonable regional solution," he said. "It looks a lot to us that a call from the Ella Ditch is going to be more common in the future."
Contentious history
To understand why some groups are opposed to even just a study whether storage is an option, it helps to review the contentious history of water development in the Crystal River Valley.
In 2011, the West Divide district and the Colorado River District abandoned their conditional water rights for nearly 200,000 acre-feet of water storage on the Crystal River after local groups — Crystal River Caucus, Pitkin County and Crystal Valley Environmental Protection Association — opposed the reservoirs tied to the conditional rights. Known as the West Divide project, the now-defunct conditional water rights were tied to a dam on the Crystal just downstream from Redstone, which would have created Osgood Reservoir, and a dam on the Crystal at Placita, which is at the bottom of McClure Pass.
To try to prevent the specter of dams coming back to haunt the Crystal in the future, Pitkin County and other local groups have pushed for a federal designation under the Wild and Scenic River Act of 1968, which requires rivers to be free-flowing. The Colorado River District opposes the designation.
"With our challenging history with both the river district and West Divide … this is why we are very nervous whenever we hear discussion of any dams on the Crystal River," said Bill Jochems, Redstone resident and member of the Pitkin County Healthy Rivers board.
In the end, the roundtable approved the grant request. A motion to amend the request with a no-storage requirement failed.
"Obviously, storage is not the first choice," said Ken Neubecker, the roundtable's environmental representative and Colorado project director for environmental organization American Rivers. "But you have to look at all the options, including storage, or you're just not being responsible."
The two conservation districts plan to ask for a $50,000 grant from the Colorado Water Plan grant fund in early 2020 to fund the roughly $100,000 project. West Divide plans to contribute $15,000 and the Colorado River District $10,000.
Aspen Journalism collaborates with The Aspen Times and other Swift Communications newspapers on coverage of water and rivers. For more, go to aspenjournalism.org.
Rifle Police investigating crash that killed pedestrian
Rifle police are investigating a one-vehicle crash that killed a pedestrian Wednesday morning.
RFSD kindergarten registration April 17
Motorist incident south of Glenwood ends in arrest
Roaring Fork Schools sued over handling of GSHS student harassment claims following sex assault incident
Four-legged neighbor becomes a hero in Rifle


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If there is one stand you need to check out at this years Ploughing Championships its the one all about mediation which has come into law since Jan 1st of this year with regards to all disputes. The Mediators Institute of Ireland will be at Block 2, Row 11, Stand 243.
With over 300,000 people expected to attend this year Ploughing Championships to check out 1,700 exhibitors all of which connected in some way to agriculture is a great opportunity to promote all the benefits of mediation rather than litigation to the farming communities in Ireland. Mediation can settle disputes in private without cross examination and create an agreement that both parties' author rather than a stranger.
Many more people need to know that The Mediation Act 2017 has come into power since Jan 1st 2018 and all legal proceedings now require that both parties consider mediation before going further. Litigation is very costly, adversarial and takes place in public with the press being able to report on proceedings.
Mediation on the other hand is confidential, collaborative through the facilitation of a qualified and registered mediator and you both get to author the final written agreement. Mediation is a process in which an independent, neutral Mediator assists two or more disputing parties in resolving the dispute in a collaborative, consensual manner.
"These benefits are now available to those living on family farms and working in agriculture. The Act makes mediation even more robust and reliable. As a result, parties involved in a dispute are more likely to opt for mediation before heading into court particularly as solicitors will now advise their clients to consider mediation," stated President of the MII Sabine Walsh.
In fact, the entire legal system in Ireland is changing culture, in that, mediation is going to be used as a first course of action in disputes more and more from now on. This is fantastic news as a dispute that goes through mediation can be done and dusted in a day.
Mediators specialising in separations and divorce can support both parties to get to a written legal agreement in a day and costing on average 2,000 Euros as compared to 20,000 Euros which has become roughly the norm. Therefore one can easily see the savings to be made for the family and also the reduction in potential heartbreak and stress of a long drawn out adversarial process.
The exact same process can be successfully applied to land disputes.
"This year for the first time the MII will have a stand at the Ploughing. We will be making a number of presentations on how mediation can deliver for farm families and those working in agriculture," said Pat Finn, MII's Agriculture Sector Liaison officer.
Mediation can help to resolve family farm disputes and more info and qualified mediators will be available on the MII stand Block 2, Row 11, Stand 243.
In addition, MII will make a number of mediation information presentations on the stand at intervals over the 3 days. Make sure you catch one of these especially if you are a community leader in farming communities that could tremendously benefit from mediation rather than litigation to resolve all manner of disputes.
Mediation can also be used in disputes with banks.
The courts will be asking you to consider mediation first anyways so why not get the 'heads up' when you are at the Ploughing Championships this year.
Pass this info on as you never know who would need it and it would be great if people left with the conviction to try and resolve their disputes through mediation.
This could save some people a lot of hardship and grief anything that can do that is always worth checking out.
The last few years have been an amazing journey from starting mindfulness training in Ireland with 20 people in Foxford 2013 to expanding to empowering over 2000 people as of this year. Add in 10 HSE hospitals to rave reviews and scoring 10/10 consistently. Mindful Beauty Days for HSE nurses.
Earlier this year, I am glad to say, mediation legislation came into law in Ireland. This means a mediator needs to be registered and can be asked by the courts or called by the courts in cases. Many judges welcome the initiative especially when you think of the long process traditional legal routes such as litigation and arbitration can take.
Some commentators say the average legal costs of a divorce now in Dublin can be on average 20,000 Euros. This can take lots of time, stress and distress waiting for court dates. Whereas mediation by comparison can cost on average 2000 Euros a saving of 18,000 Euros and could be done and dusted with a legal agreement in a day. The process is also far less adversarial.
Mediators who do this work need extra training for divorce and separation in addition to their foundation training.
So disputes can be resolved in a fast and efficient manner. They say that 4 out of 5 mediation's succeed. Judges may send people to mediation now also prior to litigation to see if the dispute can be worked out through official mediation. The benefits of mediation are seen to also in the future reduce backlog in the courts.
For those who know me I am a bit of a fan of Dr Phil and I just love it when he asks arguing couples, 'Which do you want? To be right or happy?' But this can be applied a lot in disputes. Before they enter mediation each side can believe they are in the right and the courts is where you will get justice. To be frank people should not go to court for justice as you go to court for the law, for legal chess, and justice 'seen to be done'. Going for anything else can leave a person hurt, demoralised and possibly brokenhearted.
You go to mediation for a mutual agreement that both disputants design with the help of a qualified mediator.
This is the great thing about the Irish judiciary as they are the ones who are advocating mediation more than any other body in Irish society currently. They know the benefits and have seen the effects of financial ruin from litigation.
The mediation is a four part process which a qualified mediator guides both parties through.
This is a consequential stage by stage process the registered and qualified mediator has training in safely guiding you through.
Personally I have been attracted to mediation as something that empowers people rather than dis-empowers them. People get to speak and get stuff off their chest without a clever barrister putting words in their mouth and cornering them through cross examination.
As a mediator you have to be centered, so my martial arts training gives me that, and silent at times so the disputants can negotiate an agreement or appeal to each others humanity. As many people who know me from my meditation training I can stay silent for hours if I have too. Presence and being present is another aspect. Mostly its the deadlocks that have been destroying peoples wellbeing with stress diffusing. Mostly transformation alchemy.
You can witness the most profound transformations in mediations and put an end to a lot of suffering.


I'm seemingly the only one who does this, but I call the tours of industrial parks that are put on by our local Chamber "fam tours." The real purpose of the tours is a greater familiarization and, thus, the term "fam" for short.
Familiarization was the name of the game yesterday on a tour geared toward Mayors and elected officials with all three County Commissioners in attendance and mayors of five municipalities on board too. There were township trustees and council members among the 30+ on the bus.
ArmorSource, a company with Licking County ties that go way back, chose to locate its combat helmet operation in a building that became available when a corporate downsizing in the automotive industry emerged.
ArmorSource's advanced materials product is a composite of materials that protects soliders around the globe. Their test lab puts their product to the test at speeds up to 6,000 feet per second.
State Industrial Products, a company with a near 100-year history in Ohio, opened a new industrial chemicals production facility in Hebron. Similar to ArmorSource, the company found its way here upon the loss of a company that had a similar business in the building before.
SIP loves our workforce. Boy do they. The company reports that they are able to do in Hebron with 40 people what took them 80 people to do in their previous location.
Production floor efficiences probably account for some of that amazing productivity improvement, but the sentiment is that the bulk of the credit goes to a quality workforce.
So, once again, we find that the Chamber Fam Tour fams.


Hélianthus is a not for profit animal charity whose aim is to help stray, abandoned and distressed animals, and to assist elderly people on low income look after their little companions.
The association was founded in 2008 and its Charity Shop opened in Pré en Pail in December 2010.
Rehoming abandoned cats and helping with the spaying of feral colonies, assisting abused dogs find a better life or hand-rearing orphaned hedgehogs are only a few examples of what we do. We are also developing a network of foster homes to care for an elderly person's pet in case of short-term hospitalisation.


text:
Q: Java EE container internals As a Java EE newbie having a background in Java SE, I don't quite understand what the Java EE, especially those buzzwords like web container,Jave EE container mean.
What I know that to run a java program, you need to call java with classpath set to dependencies (normally .jar) and an also an entry class with static main method.
I think to start Java EE programs, there is no difference than calling java, right? So are containers most of the time just .jar files that contains implementation of Java EE standard annotations?
Are containers like JBOSS just a bunch of libraries that are used to support the so-called "enterprise applications" involving transactions, security and so on?

A:
Java SE = Standard Edition. This is the core Java programming
  platform. It contains all of the libraries and APIs that any Java
  programmer should learn (java.lang, java.io, java.math, java.net,
  java.util, etc...).
Java EE = Enterprise Edition. From Wikipedia:
The Java platform (Enterprise Edition) differs from the Java Standard
  Edition Platform (Java SE) in that it adds libraries which provide
  functionality to deploy fault-tolerant, distributed, multi-tier Java
  software, based largely on modular components running on an
  application server. In other words, if your application demands a very
  large scale, distributed system, then you should consider using Java
  EE. Built on top of Java SE, it provides libraries for database access
  (JDBC, JPA), remote method invocation (RMI), messaging (JMS), web
  services, XML processing, and defines standard APIs for Enterprise
  JavaBeans, servlets, portlets, Java Server Pages, etc...

Source: Difference between Java SE/EE/ME?

redpajama_set_name: RedPajamaStackExchange


text:
Q: Wicket - maven dependency - cannot find symbol [ERROR] symbol: class PageParameters While working with the "Apache Wicket User Guide" I first used the maven dependency:
<dependency>
    <groupId>org.apache.wicket</groupId>
    <artifactId>wicket</artifactId>
    <version>1.4.17</version>
</dependency>

This worked. I see that there are newer versions. So I tried to upgrade to 7.4.0
cannot find symbol [ERROR] symbol: class PageParameters

I tried the following dependencies without success:
<dependency>
    <groupId>org.apache.wicket</groupId>
    <artifactId>wicket-core</artifactId>
    <version>7.4.0</version>
</dependency>
<dependency>
    <groupId>org.apache.wicket</groupId>
    <artifactId>wicket-extensions</artifactId>
    <version>7.4.0</version>
</dependency>
<dependency>
    <groupId>org.apache.wicket</groupId>
    <artifactId>wicket-request</artifactId>
    <version>7.4.0</version>
</dependency>

And is this functionality gone?
add( new FeedbackPanel("feedbackMessage",
    new ExactErrorLevelFilter(FeedbackMessage.ERROR)));

Can you help me upgrade and still use PageParameters?

A: Remove the import for PageParameters and use your IDE to import it again. PageParameters class has been moved from one package to another in Wicket 1.5.0

A: wicket-core depends on wicket-request, following dependency should bring PageParameters class you need
<dependency>
    <groupId>org.apache.wicket</groupId>
    <artifactId>wicket-core</artifactId>
    <version>7.4.0</version>
</dependency>


A couple of weeks ago I gave a workshop on Docker and Service Fabric at KPN. In the next couple of blogs I will walk you through all the labs from this workshop so you can do them at home. This first one, out of a series of six, will be about the preparations enabling you to follow along. If you would like to follow this workshop in real-live, please contact me through LinkedIn or Twitter.
Docker for windows currently only runs on Windows 10 professional or Enterprise. This is mainly because of the availability of Hyper-V on these versions of Windows. During the upcoming labs I'm assuming you are using one of these two versions to run Docker on. You can create a virtual machine to do these labs one. Make sure you enable 'nested virtualization'. Using a virtual machine on Azure is an option as well. Make sure you select a vm from the D2-64 v3 range to make sure Hyper-V will run correctly.
Go to this URL and download the Docker installer. Follow the instructions. Docker will ask you to logout. Off course you will do this and log back in again. Docker will now start for the first time and ask you to enable Hyper-V. Click OK.
In this workshop we will use VSTS (Visual Studio Team Services). If you don't already have an account please go to this URL to create one by clicking on 'Get started for free'.
After creating an account or logging in with your existing Microsoft account you will see the following screen. Enter a nice name to claim your own subdomain on visualstudio.com. Leave 'Manage code using' to Git and click Continue.
Congratulations! You're ready to follow along!


Liz O'Donnell T.D., Minister of State at the Department of Foreign Affairs (with Special Responsibility for Overseas Development Assistance and Human Rights), has announced an unprecedented package of humanitarian and rehabilitation assistance to benefit 21 of the world's poorest and crisis-stricken countries.
The World Food Programme wishes to thank donors for their continued generosity. This year donors have contributed over US $ 1.3 billion, and WFP has used these resources to mobilise over 3.1 million tons of food aid distributed to needy people.
FAO's June Food Outlook report signals a one percent increase in cereal output in 2000, compared to the previous year. However, according to current forecasts, total cereal production will not be enough to cover utilization requirements in 2000/2001 and global cereal reserves will have to be drawn down. If current forecasts materialize, global stocks could fall slightly below minimum safe levels.
Latest indications continue to point to a larger cereal output in 2000. However, based on the current forecasts, total cereal production would not be sufficient to meet expected utilization requirements in 2000/01 and global cereal reserves would be drawn down again next season.


text:
Publisher: Avon (Harper Collins)
Genre: Historical Romance [1558, Scottish Highlands]
Rachel A Hyde
Reviewer Notes: Sexual Content
The Sweetest Sin
By Mary Reed McCall
The MacDonell clan has a secret treasure that protects them; a magical amulet. It is Aileana's task to guard it, but there is somebody who wants to snatch it away and for good reason. Duncan MacRae has suffered greatly at the hands of the MacDonells and only wants back what used to belong to his own clan, namely the amulet. However, he hasn't reckoned with Aileana who will do everything in her power to keep the treasure, or his own fate to fall in love with her.
There are several praiseworthy points about this book that set it above most of the other books in the sub-genre of Scottish historical romances. Firstly, it is not set during any of the most predictable periods - no Bonnie Prince Charlie hiding in the heather and Butcher Cumberland nowhere in sight. Secondly, it is not replete with dialect words and strange speech, which tends to make the dialogue in some books like this hard to read. There is a villainess instead of a villain, who is unusual indeed, and the light touch of fantasy adds to the charm. There is nothing new about the scenes between the two lovers as Aileana chafes against Duncan's initial cruelty, although basically he is a good man who has suffered just too much. Nevertheless, the author handles this well enough, and the result is an enjoyably readable novel. I don't usually care for this sub-genre for the reasons given above, but this is better than the average Highland romantic fling.


text:
Rediff.com » Cricket » SEE: Wanted to be with my wife for birth of our first child
SEE: Wanted to be with my wife for birth of our first child
November 26, 2020 22:49 IST
IMAGE: India captain Virat Kohli and his actress wife Anushka Sharma are expecting their first child in early January. Photograph: Twitter
India captain Virat Kohli, who will be on paternity leave after the first Test on December 17, said that he had communicated his decision to return to India to the selection committee in advance.
"The decision was made before the selection meeting that I will be flying back before the first Test. It was purely based on the fact that there was a quarantine period both ways.
"I wanted to be back in time to be with my wife for the birth of our first child. It is very, very special and a beautiful moment in our lives and that was the reason for my decision that was communicated to the selectors," he said.
Kohli and Anushka are expecting their first child in early January.
Related News: Virat Kohli, Anushka Sharma, India, IMAGE, SEE
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