text:
\section{Introduction}
Few-shot learning is increasingly popular because it can handle machine learning tasks with just a few learning examples. It is also more biologically plausible and closer to what we observe in nature. While learning a new task, one normally does not start from a randomly initialised neural network presenting hundreds of thousands of examples in several thousands epochs.

When you are told to remember a person from a picture, you are able to distinguish this person from others even when you see her in different positions or environments. In machine learning, this is called one shot learning. The task of one shot learning is to learn new classes given only one instance available for each class. Three-way five-shot learning means learning three classes given five training instances each. You do not learn classifiers from scratch, but you typically use neural networks trained on similar tasks using much more data. This also reflects the natural situation when the visual perception is already well trained on similar tasks when trying to remember a new person from the picture.
This process can be also called meta learning or transfer learning as one uses a pretrained neural network called a backbone network.
Also, in a few-shot learning scenario, you can often utilise unlabelled instances apart of those few labelled samples that are available for the task.

MetaDl challenge 2020\footnote{\url{https://competitions.codalab.org/competitions/26638}} focused on few shot learning of image classification tasks. Participants trained a meta-learner on a meta-train set and produced a learner which was subsequently used to train on classification tasks generated from the meta-test set and evaluated. The goal was to discover learners with ability to quickly adapt to new unseen image classification tasks.

Our submissions scored second in the final leaderboard. This paper describes methods we have experimented with and the architecture of the meta-learning pipeline responsible for second best result in the competition.
The architecture of our solution mainly follows \cite{hu2020leveraging} with important improvements in the preprocessing of latent space output of the backbone model $B$. The main improvement is in the different normalization of the transformed feature vectors which resembles the Gaussian distribution assumption better.
Since this is the key assumption for the proper functionality of the Sinkhorn mapping algorithm, it leads to more accurate results.

\section{Related Work}
There are several different approaches to few shot learning. The survey \cite{wang2020generalizing} is a good resource to learn about general overview and taxonomy of few shot learning methods.
Prototypical networks \cite{snell2017prototypical} and the Siamese networks \cite{koch2015siamese} focus on learning embeddings transforming the data in a way that it can be recognised with a simple classifier. This approach is further enhanced by relation networks \cite{sung2018learning} which is able to classify images of new classes by predicting distances between query images and the few examples of each new class.

Another interesting direction aims at the learning process itself. In \cite{DBLP:conf/iclr/RaviL17} a recurrent network based meta-learner model learns the exact optimization algorithm used to train
another learner neural network classifier in the few-shot setup. Meta-transfer learning \cite{sun2019meta} adapts a deep neural network for few shot learning tasks. Transfer is achieved by learning scaling and shifting functions of DNN weights for each task.

\begin{figure*}[h]
  \includegraphics[width=\linewidth]{fewshot-finals.pdf}
  \caption{In order to predict the class label of a test example, we transform the image using a backbone CNN to the latent space and preprocess vectors by the Latent Space Transform algorithm that helps to transform distribution of individual classes to Gaussian like. Then a test example is processed and compared to the class centres that have been iteratively adjusted using a Sinkhorn mapping with unlabeled data projected to the latent space in the same way. The closest class is assigned to the test example as prediction.}
  \label{fig:diagram}
\end{figure*}

We further extend the direction of few-shot learning research that is leveraging classification capabilities in robust backbone models (neural networks) pretrained on similar tasks. These {\bf transfer learning based methods} need to find mapping of few-shot classes to similar classes used to train the backbone model.

In \cite{rohrbach2013transfer} the Propagated Semantic Transfer has been applied to employ semantic knowledge transfer to original classes, combine the transferred predictions with labels for the novel classes, exploit the manifold structure of novel classes by graph based learning and improve the local neighborhood in such graph structures by replacing the raw feature-based representation with an attribute-based representation.

When transferring the knowledge, deep embeddings are far superior, compared to weight transfer, as a starting point for novel tasks as investigated in \cite{scott2018adapted}. Another similar approach is TransMatch \cite{yu2020transmatch}, where a feature extractor is pre-trained on original classes and subsequently used to initialize few-shot classifier weights for the novel classes, the classifier is also updated with a semisupervised learning method.

Our research proceeds from \cite{hu2020leveraging}, where the latent space produced by a backbone deep network is preprocessed by a power transform and optimal-transport algorithm maps original classes to novel classes while centres on new classes are iteratively adjusted. This approach has shown significant improvement in accuracy in our experiments. The importance of feature transformation for few-shot learning is confirmed by \cite{wang2019simpleshot}.

\section{Model description}
Formally, in a few-shot learning task one has a dataset $D$ containing a part $D_S$ with a few labelled samples from $w$ classes and
a part $D_Q$ with some unlabelled samples.
The goal is to predict the classes for samples in $D_Q$. We will assume that $D_S$ contains exactly $s$ labelled samples for each class and $D_Q$ contains
exactly $q$ unlabelled samples for each class. Hence, there are $w s$ samples in $D_S$ and $w q$ samples in $D_Q$. The $i$-th sample from $D$ will be denoted by
$x_i$ and if it is from $D_S$ we will denote its label by $y_i$.

Moreover, let us assume that there is another dataset $D_B$ corresponding to some related task, such as image classification to some novel classes.
This dataset can be used to train the backbone model $b$ which maps the initial space into some latent feature space $\mathcal L = \mathbb R^d$.
In order to train such a model one might train the neural network for classification and then remove the last classification layers as we did in the experiments.
Or an encoder part of an autoencoder might be used.

The next step is to preprocess the points in the latent space to be prepared for the final prediction algorithm that estimates the labels.
As was recently researched this step is crucial and may lead to significant improvements of the result, see \cite{wang2019simpleshot}.
To proceed we will further assume that the features obtained from the backbone model $B$ are non-negative, i.e. $\mathcal L = \mathbb R_+^d$.
This is often the case when one extracts $b$ as a part of some neural network with the ReLU activation function on inner layers.
Let us denote by $B$ the dataset $D$ transformed by $b$ and by $B_S$ and $B_Q$ its parts corresponding to $D_S$ and $D_Q$, respectively.

In the preprocessing we transform the dataset $D$ of points in the latent space $\mathcal L$ to a final dataset $F$ of points in the final
feature space $\mathcal F = \mathbb R^r$, where the dimension
$r = \min\{d, w (s + q)\}$ is the minimum of the dimension $d$ of $\mathcal L$ and the number of points in the dataset $D$.
The preprocessing is a composition of three steps and we will call it the Latent Space Transform algorithm (LST). The first is the power transform combined with the semi-normalization of each point given by
\[
    f_1(u) = \frac{(u + \varepsilon)^{\beta}}{\|(u + \varepsilon)^\beta\|_{2}^{\delta}}\quad \mathrm{for\ all}\ u \in \mathcal L,
\]
where the power is taken component-wise, $\varepsilon = 10^{-6}$ is the normalization parameter, and $\| \cdot \|$ is the Euclidean norm.
The hyperparameter $\beta$ controls the strength of the power transform and the hyperparameter $\delta$ controls the strength of
the normalization, where $\delta = 1$ means the full normalization and $\delta = 0$ yields no normalization at all.
The power transform is known to help stabilising the variance and making the data more Gaussian distribution-like by reducing its skewness, see \cite{boxcox1964}.
The normalization on the other hand leads to the projection on the unit sphere which is not compatible with the assumption used later in the optimal-transport that the components of points in the same class are independent with Gaussian distribution of the same variance.
Hence, the semi-normalization controlled by the hyperparameter $\delta$ enables for having some variance in the perpendicular
direction to the unit sphere surface and thus does not a priori break the compatibility of the resulting distribution with the Gaussian assumption.
Let us denote the dataset with all points in $B$ transformed using $f_1$ by $F_1$ and $F_{1,S}, F_{1,Q}$ analogously.

The second step is the removal of unnecessary dimensions using the QR decomposition of the transposition of the already preprocessed data matrix
$\mathbf{F}_{1} \in \mathbb R^{w(s+q), d}$ corresponding to dataset $F_1$,
\[
    \mathbf F_1^T = \mathbf Q \mathbf R
\]
and thus we define
\[
    \mathbf F_2 = \mathbf F_1 \mathbf Q
\]
so that $\mathbf F_2 \in \mathbb R^{w(s+q), r}$, where $r = \min\{d, w (s + q)\}$, and the corresponding dataset is denoted by $F_2$.
We again denote by $F_{2,S}$ and $F_{2,Q}$ the parts of $F_2$ that corresponds to samples originally in $D_S$ and $D_Q$, respectively.
It corresponds to the change of the orthonormal basis in the $\mathbb R^d$ and throwing away the dimensions that are zero for the data points.

The last preprocessing step is the centering and further semi-normalization given by
\[
    f_3(u) = \frac{u - \bar u}{\|u\|_{2}^{\gamma}},
\]
where
\[
    \bar u = \frac{1}{w(s+q)}\sum_{i = 1}^{w(s+q)} u_i
\]
is the centroid (component-wise average) of the dataset $F_2$.
Again, the hyperparameter $\gamma$ allows to control the strength of the normalization. For $\gamma < 1$ the resulting points are only partially
normalized and one may expect to better resemble the Gaussian distribution assumed in the next step. The typical result for the final Euclidean norms of transformed points is shown in Figure \ref{fig:normsLST}.
\begin{figure}[h]
  \includegraphics[width=\linewidth]{LST_norms_CUB.pdf}
  \caption{Latent Space Transform algorithm produces Gaussian like distribution also for the norms of the transformed samples.
  The figure was produced for one batch from the CUB dataset with $s = 5, q = 15, \beta = 0.5, \delta = 0.3$, and $\gamma = 0.9$.}
  \label{fig:normsLST}
\end{figure}

Let us denote the final preprocessed dataset by $F$ and its respective parts corresponding to original parts $D_S$ and $D_Q$ by $F_S$ and $F_Q$, respectively.

Once the preprocessing of the dataset is finished, the actual optimal-transport can begin. In this part we directly follow \cite{hu2020leveraging}.
The preliminary assumption of the method is the independent Gaussian distributions of all components of points in individual classes with class centres $c_1, \dots, c_w$ as parameters. Moreover, it is assumed that all the Gaussian distributions have the same variance $\lambda/2$, where $\lambda$ is the hyperparameter.
Under this assumption the maximum a posteriori estimate (MAP) $\hat y_1, \dots, \hat y_{wq}$ of
the labels of unlabelled samples $f_1, \dots, f_{wq}$ from $F_Q$ corresponds to
\begin{multline*}
    \{\hat y_j\}_{j=1}^{wq}, \{\hat c_k\}_{k=1}^w =  \arg\max_{\{y_j\}, \{c_k\}} \prod_i P(y_i | f_i) \\
    = \arg\max_{\{y_j\}, \{c_k\}} \prod_i P(f_i | y_i) P(y_i) \\
    = \arg\max_{\{y_j\}, \{c_k\}} \prod_i e^{-\lambda^{-1} \|f_i - c_{y_i}\|_2^2} P(y_i).
\end{multline*}
This is directly related to the Optimal Transport theory, see \cite{hu2020leveraging,marco2013Sinkhorn,berman2020,villani2003},
and one may use the iterative expectation-maximization like approach incorporating the Sinkhorn algorithm to get the MAP estimate.
It consists of repeating of two steps, where the first is the construction of the mapping matrix $\mathbf M^*$ with elements $\mathbf M^*_{ij} = P(y_i = j)$ which is maximizing the previous term for a given centres $c_1, \dots, c_w$ and the second step is the estimation of class centres
that is for the fixed
mapping matrix again optimizing the previous term.
For the Sinkhorn algorithm, see  \cite{marco2013Sinkhorn} the mapping matrix is defined as
\begin{multline*}
    \mathbf M^* = \mathrm{Sinkhorn}(\mathbf L,a,b,\lambda) \\
    = \arg\min_{\mathbf M\in U(a,b)}\sum_{i, j}\mathbf M_{ij} \mathbf L_{ij} + \lambda H(\mathbf M),
\end{multline*}
where $U(a,b)$ is a set of positive matrices in $\mathbb R^{wq\times w}$ for which the rows sums to a vector $a$ and columns sums to a vector $b$,
$\mathbf L\in R^{wq\times w}$ is the cost function consisting of Euclidean distances between unlabelled instances and class centres, that is
$\mathbf L_{ij} = \|f_i - c_{j}\|_2^2$, the hyperparameter $\lambda$ is a regularisation coefficient forcing the entropy
$H(\mathbf M)=-\sum_{ij} \mathbf M_{ij}\log \mathbf M_{ij}$ to become smaller,
$a$ denotes the distribution of the amount that each unlabelled example uses for class allocation, i.e. $a$ is the vector of ones with $wq$ elements,
and $b$ denotes the distribution of the amount of unlabelled examples allocated to each class, i.e. $b$ is the vector with $w$ elements that equals to $q$.

The iterative approach starts with initialising the class centres from the labelled samples in $F_S$.
Then the mapping matrix $\mathbf M^*$ is calculated using the Sinkhorn algorithm. It is then used to re-estimate the class centres via the update using
\[
    \mu_j=\frac{\sum_{f_i \in F_Q}\mathbf M^*_{ij}f_i+\sum_{f_k\in F_S, y_k = j}f_k}{s + \sum_{i=1}^{wq}\mathbf M^*_{ij}}.
\]
To avoid unnecessarily big steps in centre estimations, the new centre is set to be $c_j = c_j + \alpha(\mu_j-c_j)$, where the $\alpha$ is the learning rate. The number of iterations is fixed to $n_{\mathrm{steps}}$. Once the iteration process finishes, the labels of the samples from $F_Q$ might be estimated
from the last mapping matrix as
\[
    \hat y_i = \arg\max_j \mathbf M^*_{ij}.
\]
The overview of the algorithm is given in Algorithm \ref{alg:sink}. The overall process of our approach is depicted in Figure \ref{fig:diagram}. The code is available at https://github.com/ctom2/latent-space-transform.

\SetKwFor{RepTimes}{repeat}{times:}{end}
\begin{algorithm}
\SetAlgoLined
\textbf{Parameters:} $w,s,q,\lambda,\alpha,n_{\mathrm{steps}}$\\
\textbf{Initialisation:} $c_j=\frac{1}{s}\sum_{f_k\in F_S, y_k = j}f_k$\\
 \RepTimes{$n_{\mathrm{steps}}$}{
  $\mathbf L_{ij}=||f_i-c_j||^2, \forall i,j$\;
  $\mathbf M^*=\mathrm{Sinkhorn}(\mathbf L,p=1_{wq},q=q1_w,\lambda)$\;
  Calculate $\mu_j$\;
  $c_j=c_j+\alpha(\mu_j-c_j)$\;
 }
 \Return{$\hat y_i = \arg\max_j \mathbf M^*_{ij}$}
 \caption{Optimal map algorithm}
 \label{alg:sink}
\end{algorithm}

\section{Experiments}
The performance of the stated methods was measured based on standardised few-shot classification datasets CIFAR-FS \cite{bertinetto2019metalearning} and CUB \cite{WahCUB_200_2011}. CIFAR-FS dataset consists of images with size of $32\times 32$ distributed into 100 classes, each containing 600 images. The dataset is split into 64 base classes, 16 validation classes and 20 novel classes. CUB dataset contains 11,788 images of birds, each with size $84\times 84$, distributed over 200 classes. The dataset is split into 100 base classes, 50 validation classes and 50 novel classes.

In each testing run, $w$ classes are randomly and uniformly drawn from novel classes, where each class consists of $s$ instances with label and $q$ instances without label.

\begin{table}[]
\caption{Hyperparameters used in the final evaluation of the LST+MAP model.}
\vspace{5mm}
\label{tab:hyperparams}
\centering
\begin{tabular}{c|cc|cc}
\hline
         & \multicolumn{2}{c|}{\textbf{1-shot}} & \multicolumn{2}{c}{\textbf{5-shot}} \\ \cline{2-5}
Parameter & CIFAR-FS            & CUB            & CIFAR-FS            & CUB            \\ \hline
$\beta$ & 0.5                 & 0.5            & 0.5                 & 0.5            \\ \hline
$\lambda$ & 10                 & 10            & 10                 & 10            \\ \hline
$\alpha$ & 0.3                 & 0.4            & 0.2                 & 0.2            \\ \hline
$n_{\textit{steps}}$ & 20                 & 30            & 20                 & 20            \\ \hline
$\delta$ & 0.3                 & 0.7            & 0.4                 & 0.3            \\ \hline
$\gamma$ & 0.98                & 0.95           & 0.95                & 0.9  \\ \hline
\end{tabular}
\end{table}

Because of the high performance of WideResNet \cite{zagoruyko2017wide} augmented with the S2M2 method \cite{mangla2020charting} in the few-shot setting, we chose it as the backbone architecture for our model. The latent representation of images produced by the backbone is a vector with dimension of 640. The QR decomposition reduces the said dimension to 80 in 1-shot setting, and to 100 in 5-shot setting.

All experiments are based on $w=5,q=15$ and $s=1$ or $5$. To evaluate the performance of the models we run 10,000 random draws to obtain mean accuracy with $95\%$ confidence scores.

\begin{table}[h!]
\caption{1-shot accuracy of models based on Power Transform (PT), our proposed Latent Space Transform (LST) and WideResNet backbone.}
\vspace{5mm}
\label{tab:accuracies_1shot}
\centering
\resizebox{.99\columnwidth}{!}{%
\begin{tabular}{l|c|cc}
\hline
               &          & \multicolumn{2}{c}{\textbf{1-shot}}                                                             \\
Method         & Backbone & \multicolumn{1}{c}{CIFAR}                              & CUB                                   \\ \hline
PT+MAP         & WRN      & \multicolumn{1}{c}{$87.69 \pm 0.23\%$}                  & $91.55\pm 0.19\%$                     \\ \hline
PT+GMM          & WRN      & \multicolumn{1}{l}{$86.96\pm 0.22\%$} & \multicolumn{1}{l}{$90.06\pm 0.18\%$} \\ \hline
PT+KNN         & WRN      & \multicolumn{1}{c}{$86.17 \pm 0.19\%$}                  & $89.07\pm 0.17\%$ \\ \hline\hline
LST+MAP         & WRN      & \multicolumn{1}{c}{$\mathbf{87.79 \pm 0.23\%}$}                  & $\mathbf{91.68\pm 0.19\%}$                     \\ \hline
LST+GMM         & WRN      & \multicolumn{1}{c}{$87.01\pm 0.21\%$}                  & $89.9 \pm 0.18\%$                     \\ \hline
LST+KNN         & WRN      & \multicolumn{1}{c}{$85.76\pm 0.19\%$}                  & $89.26 \pm 0.17\%$                     \\ \hline

\end{tabular}
}
\end{table}

By tuning the hyperparameters of the model we observed evolution in accuracy in both 1-shot and 5-shot setting with dependency on tested dataset. The overview with the hyperparameters can be found in Table \ref{tab:hyperparams}. The final accuracy can be seen in Table \ref{tab:accuracies_1shot} and Table \ref{tab:accuracies_5shot} for 1-shot and 5-shot setting, respectively. Moreover, the tables include results obtained by substituting MAP with different clustering algorithms, Gaussian Mixture model and $k$-means model, that take the transformed features as their input. The $k$-means model is initiated with centres corresponding to the labeled instances in a testing run. The centres are then iteratively refined to produce better representations of the class centres. Similarly, Gaussian Mixture model is provided with initial means corresponding to the labeled examples at the beginning of each run. To compare our proposed transform method with the Power Transform (PT) \cite{hu2020leveraging}, we performed the same substitutions for the PT+MAP model.

The scores show that even by omitting the MAP part from the architecture and replacing it with simpler classification approaches while keeping the transformation intact produces competitive results. Moreover, to compare the statistical significance of the superiority of the LST+MAP model against the PT+MAP model we performed the paired t-test with $p$-values presented in Table \ref{tab:ttest}. We can see that except for the CUB dataset in 5-shot scenario the LST+MAP model is significantly better than the PT+MAP model.

In terms of execution time, we measured an average of $0.0026s$ per run in 1-shot setting and $0.003s$ per run in 5-shot setting with the GPU backend.

\begin{table}[h!]
\caption{5-shot accuracy of models based on Power Transform (PT), our proposed Latent Space Transform (LST) and WideResNet backbone. The authors of the PT+MAP model presented accuracy $93.99\pm 0.10\%$ in 5-shot setting for CUB dataset, however we were able to obtain higher accuracy with their described model configuration.}
\vspace{5mm}
\label{tab:accuracies_5shot}
\centering
\resizebox{.99\columnwidth}{!}{%
\begin{tabular}{l|c|cc}
\hline
               &          & \multicolumn{2}{c}{\textbf{5-shot}}                                                             \\
Method         & Backbone & \multicolumn{1}{c}{CIFAR}                              & CUB                                   \\ \hline
PT+MAP         & WRN      & \multicolumn{1}{c}{$90.68\pm 0.15\%$}                  & $94.09\pm 0.09\%$                     \\ \hline
PT+GMM          & WRN      & \multicolumn{1}{l}{$87.16\pm 0.21\%$} & \multicolumn{1}{l}{$90.04\pm 0.20\%$} \\ \hline
PT+KNN         & WRN      & \multicolumn{1}{c}{$86.70\pm 0.19\%$}                  & $89.72\pm 0.18\%$ \\ \hline\hline
LST+MAP         & WRN      & \multicolumn{1}{c}{$\mathbf{90.73\pm 0.15\%}$}                  & $\mathbf{94.09\pm 0.09\%}$                  \\ \hline
LST+GMM         & WRN      & \multicolumn{1}{c}{$87.33\pm 0.20\%$}                  & $90.06\pm 0.18\%$                     \\ \hline
LST+KNN         & WRN      & \multicolumn{1}{c}{$86.56\pm 0.18\%$}                  & $89.64\pm 0.18\%$                     \\ \hline

\end{tabular}
}
\end{table}

\begin{table}[]
\caption{$p$-values of the paired t-test with the null hypothesis that the accuracy of the PT+MAP model is greater or equal than the accuracy of the LST+MAP model against the alternative that the accuracy of the PT+MAP model is smaller than the accuracy of the LST+MAP model.}
\vspace{5mm}
\label{tab:ttest}
\centering
\resizebox{.99\columnwidth}{!}{%
\begin{tabular}{c|cc|cc}
\hline
            & \multicolumn{2}{c|}{\textbf{1-shot}} & \multicolumn{2}{c}{\textbf{5-shot}} \\ \cline{2-5}
            & CIFAR-FS          & CUB              & CIFAR-FS          & CUB              \\ \hline
$p$-value   & $9.09\mathrm{e}{-5}$           & $1.99\mathrm{e}{-9}$          & $1.68\mathrm{e}{-7}$           & $0.78$    \\ \hline
\end{tabular}
}
\end{table}

\section{Challenge submission}
In this section, we describe modification to our method we have elaborated for the MetaDl challenge 2020. The main limitation of the challenge was the submission runtime which had to include backbone training time and was limited to two hours. Therefore we were not able to utilise the WRN backbone as we suggest above.

Our best performing solution was relying on a lighter backbone network based on the ResNet architecture. During the backbone training, the fed images could either be left as they were, or their saturation or brightness could be changed with the probability set to $1/3$ for each alteration. Moreover, the training batches also included the same images rotated by 90, 180 and 270 degrees to further improve the backbone capabilities and augment the training overall.

\section{Conclusion}
Extracted features from backbones often do not resemble Gaussian-like distributions, even though multiple algorithms are built on that assumption. In this paper we show how to transform feature vectors into better Gaussian-like distributions. By applying an iterative optimal-transport algorithm to estimate class centres empirically, the subsequent clustering method gains significant improvement over other few-shot classification methods.

Our experiments confirmed that the Latent Space Transform algorithm introduced above outperforms other forms of feature preprocessing including the Power Transform. We have also compared our approach based on optimal transport mapping to other classification methods based on Gaussian mixtures and nearest neighbours. For both CIFAR and CUB datasets, our approach proved to be superior in both 1-shot and 5-shot learning scenarios.

We have adjusted our method for the MetaDl challenge 2020 competition and scored second in the final leaderboard.

\section{Acknowledgment}
This work was supported by the Student Summer Research Program 2020 of FIT CTU in Prague. Moreover, the research was supported by the Grant Agency of the Czech Technical University in Prague (SGS20/213/OHK3/3T/18) and the Czech Science Foundation (GA\v{C}R 18-18080S).

\bibliographystyle{aaai}

redpajama_set_name: RedPajamaArXiv


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redpajama_set_name: RedPajamaCommonCrawl


Why wait to get the bathroom you've always dreamed of? With Bath Planet of Chicagoland's Woosung bathroom remodeling services, you can get rid of the old and start enjoying the new—at a fraction of the cost of traditional bathroom remodeling methods. Our products are top-quality and can be completely customized to fit your needs and home. With highly trained and certified installers, you can count on always getting the best and most friendly service available.
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Whether you're interested in getting a Woosung walk-in tub or a quality shower system installed in your home, we only offer industry-leading products made from durable acrylic that won't chip, fade, or crack. Our acrylic material is nonporous and mold- and mildew-resistant, so it's also easy to maintain! With a wide variety of styles, colors, and accessories to choose from, you can bet that you'll find the perfect Woosung showers and tubs for your home and family.
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This past weekend was perfectly delightful. Five guys from the Sam Houston State University BSM stayed with us and volunteered. Friday night they showed up around six in the evening and put together our new trampoline. My girls were in bed when they finished putting it together, so I tested it out for them. Of course, they all were on there over the next day. They did this for the children, I'm sure.
They did a ton of work around the property by cleaning up brush, putting up more fence, trimming our giant trees, and putting foam on the ceiling in the living room. They are all around nice guys that bless us. We are so grateful for their coming. Thank you, gentleman, for your hard work and gracious hearts. Come again anytime.
What I made for supper: Chicken breast topped with cream cheese, jalapeño, bacon, and a slice of aged swiss.
In other news, our beloved bassist and drummer are going on tour with the O Hellos and opening for Need To Breathe. We have been interviewing fill in players. I'm really thankful for the journey that God has our boys (who are truly grown men) on. Bless the Lord.
It's a month from the start of summer camp season, y'all. I'm trying to prepare myself mentally for this. It's pretty hard to leave home for 3.5 months. It is a wonderful place to be ad the staff is amazing. It's just hard because my husband is busy all day and night. I really miss him. Everyone is so busy that I don't have much fellowship. It's kind of lonely. I am happy to go a serve and counsel and love people though. It is rich and rewarding. Pray for all of the staff there. You can only imagine the warfare that hits these people. SO much kingdom work is happening and the enemy is on an all out war against it.
I'll be home some this summer as well hosting groups and guests. I am grateful to do it. Please think about volunteering here for an hour or a day or a week. Bring your husband, bestie, small group, or church.


text:
Sustainability Series: How can airports reduce their plastic waste?
24 May 2019 | By Mike Winter
As plastic continues to plague the planet, large organisations are trying to reduce single-use plastics. But are airports doing their bit?
Studio ORD selected to design new Chicago O'Hare terminal
29 March 2019 | By International Airport Review
The expansion project at Chicago O'Hare is expected to cost $8.5 billion and is hoped to increase the ease with which passengers travel through the terminal, increasing the passenger experience.
Chicago O'Hare Airport becomes America's busiest airport again
6 February 2019 | By International Airport Review
For the first time since 2014, Chicago O'Hare International Airport has overtaken Atlanta Hartsfield-Jackson as America's busiest airport.
Real-time updates now available at Port Authority Airports
Real-time information is now available on airport websites and terminal monitors for passengers travelling through JFK, LaGuardia, Newark Liberty and New York Stewart.
Another route added to Denver International's nonstop routes
30 November 2018 | By International Airport Review
Denver International Airport announces new nonstop service to Frankfurt on United Airlines, adding another route to its growing portfolio.
One-step biometric boarding at Washington Reagan National Airport
The Metropolitan Washington Airports Authority, in partnership with JetBlue, launched a one-step biometric boarding process at Reagan National Airport using its newly developed veriScanTM system.
Dublin Airport passenger numbers up 60 per cent since Terminal 2 opened
Dublin Airport's Terminal 2 has welcomed more than 80 million passengers since it opened eight years ago, on the 19 November 2010.
Václav Havel Airport opened a new stand and gate for long-haul aircraft
18 September 2018 | By International Airport Review
The new reconfigured stand can service either one long-haul aircraft with 65 meters wing-span, or two smaller ones with a wing-span of up to 36 meters.
Self-service check in now available at Indianapolis International Airport
American Airline and Southwest Airlines now offer new self-serve kiosks for passengers to check-in their own luggage.
Route development: Ambition to connect the world
20 August 2018 | By Gordon Dewar
Being at the location where Scotland meets the world, Edinburgh Airport wants to extend its welcome as far as possible. Gordon Dewar, Chief Executive of Edinburgh Airport, reveals the long and exciting journey the airport has taken to secure a direct route to China.
Top ten most lucrative air travel routes in the world revealed
25 July 2018 | By International Airport Review
A list of the top 10, highest grossing routes has been published and a British Airways route saw over US$1 billion in revenue between April 2017 and March 2018
"Watershed moment" as Chicago Mayor and airlines unify on O'Hare upgrade
The Mayor of Chicago and two major airlines have announced their support for a multi-billion proposal to modernise Chicago O'Hare International Airport after a disagreement over gate numbers threatened to upset the project.
Chicago Mayor unveils plans for $8.5 billion expansion at O'Hare
5 March 2018 | By International Airport Review
The first major capital investment in 25 years at Chicago O'Hare International Airport will see Terminal 5 to undergo an upgrade and expansion, Terminal 2 to be largely demolished and rebuilt to include a brand new international arrivals facility, and renovations made to Terminals 1 and 3.
Houston to Sydney service to bring Australia $47 million annual visitor spending
22 January 2018 | By International Airport Review
An Australian tourism minister and the CEO of Sydney Airport have welcomed the news of a new United Airlines service between Houston and Australia's biggest city.
United Airlines bridges the capitals from Scotland to USA
It has been announced that a direct route between Edinburgh and Washington DC will begin operation by United Airlines as of next summer.
Heathrow risks closure amidst strike action over pay dispute
HS2 reveals design for people mover with direct route to airport
Airport Guardian software is selected by Los Angeles World Airports
Baggage handling: Reducing congestion with innovative solutions
CAA publishes annual report on airport accessibility


The Mandarin Oriental in Munich offers the same enticing mix of oriental luxury and local charm that can be found in any hotel under the Mandarin Oriental banner. Set in the heart of this beautiful city, the hotel is nestled in a quiet street by only minutes from many of Munich's famous attractions. Providing guests with luxurious comfort, the hotel offers fantastic service, beautiful décor and oriental touches.
When we entered the hotel we were immediately greeted by immaculately turned-out staff. Their service with a smile was the perfect start to our visit to the hotel. We checked in whilst standing in the hotel's extravagant lobby. Immaculately presented and elegantly designed, our first impression of the hotel was one of fascination and awe.
During our stay we were in the deluxe junior suite. Individually designed, our suite was incredibly spacious and beautifully in-keeping with the hotel's traditional but stylish décor. In fact, the rooms and suites in the Mandarin Oriental are some of the largest in Munich.
The suite had an open-plan layout, with space for guests as well as ample space for the bedroom area. With French doors opening out onto a terrace, we had a perfect view of Hofbräuhaus. Combining traditional oriental style with modern German touches, the suite commanded attention from guests with beautiful artwork and impressive furnishings.
Our suite was fully stocked with many essentials, as well as a few extra comforts. A Nespresso machine and a large desk were welcome additions to the room, making it a real home from home. The bathroom was also very impressive, with Molton Brown toiletries and heated floors and towel rails. The suite also came complete with a Bang & Olufsen television and audio system, as well as fast and high-quality internet services.
Outside of our room, we took advantage of some of the facilities offered at the Mandarin Oriental, Munich. The hotel's fully-equipped fitness centres provides all the fitness facilities you could possibly need on your visit. Accompanying the fitness centre is a relaxing Turkish steam bath and Finnish sauna, the height of luxury.
However, the hotel's masterpiece is probably its rooftop swimming pool. Complete with 360° views, the pool lets you explore this beautiful city from the comfort of the hotel. Heated and of a good size, the pool is the perfect way to relax during the summer months.
The hotel also offers the use of Mercedes Benz bicycles. Available without a charge, these bicycles provide the perfect mode of transport for exploring Munich, and even come with three suggested bicycle tours of the city.
The Mandarin Oriental Hotel, Munich, offers a variety of dining experiences. On the rooftop of the hotel, you can enjoy traditional Bavarian dishes or light lunches at the China Moon Roof terrace. The Mandarin Bar, adjacent to the hotel lobby, also provides a variety of food along with a large selection of both alcoholic and non-alcoholic beverages.
For a slightly bigger meal you can visit BistroMO, which serves a range of meals in a relaxing atmosphere. The hotel also offers Michelin-starred French cuisine with an Asian influence at Restaurant Mark's. Wherever we chose to dine, the service was always impeccable and friendly; the perfect way to start your day or end your evening.
Our stay at the Mandarin Orientel was wonderful. The service was consistently above expectations and the facilities available to us never failed to delight. We would be glad to stay here again.


text:
Q: Elasticsearch - find IPs from which only anonymous requests came I have network logs in my Elasticsearch. Each log has an username and an IP field. Something like this:
{"username":"user1", "ip": "1.2.3.4"}
{"username":"anonymous", "ip": "1.2.3.4"}
{"username":"anonymous", "ip": "2.3.4.5"}
{"username":"user2", "ip": "3.4.5.6"}

I have a seemingly simple task: list all IP-s from which only anonymous requests came. The problem is, I can not simply filter for anonymous, because then I'll list false IP-s which appear with anonymous, but not exclusively. Manually I can do this with a 3 step process:

*

*List all unique IP-s

*List unique IP-s that appear with something other than anonymous

*Exclude items of 2nd list from the first.

But is there a way to do this with a single ES query? My first instinct was to use bool query. My current approach is this:
GET /sample1/_search
{
  "query": {
    "bool": {
      "must": {
        "wildcard": {
          "ip": "*"
        }
      },
        "must_not": {
          "term": {
            "username": "-anonymous"
          }
        }
      }
  },
  "size": 0,
  "aggs": {
    "ips": {
      "terms": {
        "field": "ip.keyword"
      }
    }
  }
}

I expect "2.3.4.5", but it returns all 3 unique IPs. I searched the web and tried different query types for hours. Any ideas?

A: Please find the below mapping, sample docs, the respective query for your scenario and the response:
Mapping:
PUT my_ip_index
{
  "mappings": {
    "properties": {
      "user":{
        "type": "keyword"
      },
      "ip":{
        "type": "ip"
      }
    }
  }
}

Documents:
POST my_ip_index/_doc/1
{
  "user": "user1",
  "ip": "1.2.3.4"
}

POST my_ip_index/_doc/2
{
  "user": "anonymous",
  "ip": "1.2.3.4"
}

POST my_ip_index/_doc/3
{
  "user": "anonymous",
  "ip": "2.3.4.5"
}

POST my_ip_index/_doc/4
{
  "user": "user2",
  "ip": "3.4.5.6"
}

Aggregation Query:
POST my_ip_index/_search
{
  "size": 0,
  "aggs": {
    "my_valid_ips": {
      "terms": {
        "field": "ip",
        "size": 10
      },
      "aggs": {
        "valid_users": {
          "terms": {
            "field": "user",
            "size": 10,
            "include": "anonymous"
          }
        },
        "min_bucket_selector": {
          "bucket_selector": {
            "buckets_path": {
              "valid_users_count": "valid_users._bucket_count",
              "my_valid_ips_count": "_count"
            },
            "script": {
              "source": "params.valid_users_count == 1 && params.my_valid_ips_count == 1"
            }
          }
        }
      }
    }
  }
}

Note how I've made use of Terms Aggregation and Bucket Selector Aggregation in the above query.
I've added include part in Terms Agg so as to consider only anonymous users and the logic inside bucket aggregation is to filter out only if it is a single doc count in the top level terms aggregation for e.g. 2.3.4.5 followed by single bucket count in the second level terms aggregation.
Response:
{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 4,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "my_valid_ips" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "2.3.4.5",                 <---- Expected IP/Answer
          "doc_count" : 1,
          "valid_users" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "anonymous",
                "doc_count" : 1
              }
            ]
          }
        }
      ]
    }
  }
}

Hope it helps!

redpajama_set_name: RedPajamaStackExchange


text:
Guide to Scripts Used in English Writings up to 1500. Roberts, Jane (9781781382660). Paperback.
The resurgence of interest in the history of the English language has prompted this indispensable introductory guide to the scripts used in Old and Middle English writing. The best way to gain a sense of changes in scripts across time is through visual examples. The reader is introduced gradually to vocabulary suitable for the description of script through a range of plates, for example, Caedmon's Hymn (the earliest extant English poem); the opening of an Exeter Book poem; the Lindisfarne Gospels; the opening page of King Alfred's first translation; an illustrated version of the story of Abraham and Isaac; passages from the Anglo-Saxon Chronicle; early (Laȝamon) and late tellings of the story of Arthur (Malory); contrasting manuscripts of Chaucer's Troilus and Criseyde; Langland; York plays. Each plate is reproduced full size where possible, accompanied by a full transcript, commentary and notes.


some other java programms (at least Eclipse) do not start too.
and follow on screen instructions. Import settings and launch the netbeans from app drawer.
For Installation of Netbeans 9.0 see here.
I was able to get it working by installing and setting JDK8 as default.
I was able to solve this by running sudo update-alternatives --config java and then selecting the java-8 option rather than the default java-11. You will need to have the java-8 openjdk package still installed for this to work though.
Same problem with me as well after upgrading to Ubuntu 18.04. Netbeans stopped working. I tried many ways changing the configuration. The primary reason for this problem is java dependency. Netbeans is directly dependent on Oracle Java. Ubuntu 18.04 comes with default open jdk11 installed and set as default java. When Netbeans starts it checks for the dependencies and fails due to this. Though in few cases it opens but not all the features working. Sometime only few windows only working. Specially project explorer doesn't work.
Finally what worked for me is.
Restart the Netbeans and it will work all well.
NOTE: Changing the above in system /etc/netbeans.conf directory will not work.
When it asks about Java Location select /usr/lib/jvm/java-8-openjdk-amd64.
if you chose default location.
I have the same problem ,though i resolved it! Actual problem which jdk version is used by Netbeans.As in the bionic beaver comes with new jdk version ,while old cofiguration of the Netbeans use the old one ,first uninstall existing netbeans and then download netbeans from the website the during installation ,seletct jdk8 as option . It worked for me , may work for you !
I am learner, your suggestion are always welcome!
Not the answer you're looking for? Browse other questions tagged java crash netbeans jvm or ask your own question.
How do I install Netbeans 9.0+?


All 3 FVH teams won medals at this years finals day with the U13G winning 2 Golds.
The U13 Girls took a clean sweep of medals for the indoors on Sunday. After winning the Superteams earlier in the year, the same 4 girls won the relay in some style. Earlier in the day the U13 Girls team won the sail final by a 1/2 point from Giffnock. The Girls won 3 of the events during the day and showed a great team spirit throughout, which was displayed in the relays at the end of a long long day.
With several girls at the schools training day in Pitreavie, the U17W battled to a bronze medal and only 7 pts behind the winners.
The 3rd team from FVH the U20 men were not to be out done. Although not in the original top 5 for the final (Dunbar dropped out) the guys put in a really strong spirited fight, coming out in 3rd place only 8 pts behind the winners.
So a good day by all. I would also like to thank the parents for their support, the coaches for producing good athletes and for the athletes for working so hard and taking away many PBs and medals during the Indoor League.


text:
HomeOpinionEditorialThe Concept of Evil
The Concept of Evil
November 10, 2017 Melissa Rosman Editorial, Opinion 0
Alexandria LaFlair, Staff Writer |
We've all watched Stars Wars, in which the antagonist is Darth Vader. We have all heard of Cruella de Vil and her plan for the Dalmatians. We see the stories on news channels of mass shooters and family members stabbing one another over trivial things, such as money. But do we really know what evil is? Where does evil stem from? Is it an objective standard or a character trait?
Since the dawn of time, "good vs. evil" has been a prevalent theme in the human condition and narrative. Characteristics of evil were largely defined by religion, and the study of evil grew during the Enlightenment. In Randolph Trumbach's novel "Sex and the Gender Revolution," he focuses on how promiscuous women were viewed as evil, as it was believed they that were solely created to give birth and raise children rather than desire sex for personal pleasure. As feminism grew as a study and a major movement, the targets of Americans' perception of evil shifted to African-Americans and homosexual people.
Jennifer Dumin's article in Wisconsin's Women's Law Journal hones in on people's past belief that black women were witches and that homosexuals were dividing the country with "their HIV/AIDS and disintegration of the traditional family." Even today, we can see targets of hate who are scapegoated as the root of "evil."
But what is evil? What gives someone the authority to define evil?
Merriam-Webster describes evil as "morally reprehensible; causing discomfort or repulsion; causing harm."
In those terms, pickles are evil. An ex can be considered an evil person.
Scholars argue that the concept of evil is often associated with what causes people deep mental, emotional, or physical pain.
J.L. Mackie related this topic back to religion, stating "God is wholly good; yet evil exists."
Religions such as Christianity personify these concepts in a good God and a Devil, who is the producer of evil acts.
N. T. Wright's novel "Evil and the Justice of God" reflects on major natural disasters and terrorist attacks done for religious purposes, asking why these events continue to occur when people consistently pray and give themselves over to God.
This complex discussion may never have a proven answer or complete theory. My take on the topic is that an individual's perception of evil comes from their religious point of view and their emotions, which are very much based on childhood experiences. People want to believe in and be a part of something bigger than themselves; religion can be a convenient source of comfort in that it gives a standard of good and evil, which people can use and interpret for better or worse.
"Intellect, the Emotions, and the Moral Nature" stresses the importance of emotions in regards to one's perception of evil, evil acts, and evil thoughts. Those can be related to one's childhood, whether it was a so-called "fulfilling," "easy" childhood or a troublesome childhood.
Through these vague theories, we cannot escape the fact that evil has always existed and always will. There will always be someone in opposition of what people define as "good," and there will always be people willing to go to any lengths to perpetuate their beliefs. Hopefully, society can continue to combat evil with moral and logical education.
Houston Astros Capture Their First World Series
NHL Has a Wild Card in Vegas


Roman Torres looked to have done it again, and done it against the odds. Then, he had it taken from him.
The 29-year-old Millionarios defender had scored the only goal of Wednesday's Gold Cup semifinal at the Georgia Dome in Atlanta to give Panama a 1-0 lead over Mexico. Torres' goal put El Tri on the verge of elimination, after scoring the match-winner to eliminate Mexico in 2013.
But American referee Mark Geiger awarded Mexico a controversial 89th minute penalty kick, and then a much less controversial PK in the 104th. Andres Guardado converted both attempts, and Mexico went on to beat Panama 2-1 and advance to the Gold Cup final.
Mexico will face Jamaica on Sunday, while Panama meets the U.S. in the tournament's third place match on Saturday.
A soft red card to Panama came early in the match, and seemed a surefire sign that Mexico was on its way to another Gold Cup final. Torres and his stingy teammates had other plans.
Luis Tejada picked up a red from American referee Mark Geiger when he went arm-first into Francisco Rodriguez's face during a 50/50 ball. It was a foul and, in our eyes, a yellow card.
Torres put Panama up 1-0 with a second-half header, and Los Canaleros were about a half-hour away from advancing to a second-straight final. And they made it into the final minute, when Geiger made this now infamous decision.
Mexican fans reigned down debris on Panama players, and the match was held up for 11 minutes before Guardado stepped up to nail his first penalty kick and send the match to extra time.
After the final whistle, Panama's entire bench charged the referee crew. They were led down the tunnel by a host of yellow-shirted security guards.


Port Burwell Ontario Businesses. Port Burwell Ontario Business Directory. Businesses in Port Burwell Ontario, Businesses Port Burwell Ontario.
Details India is home to a rich diversity of wildlife supplemented by an equally rich variety of flora and fauna. The sight and sounds of a majestic elephant, a peacock's dance, the stride of a camel, the roar of a tiger are unparalleled experiences in themselves. The country offers immense opportunities for wildlife tourism. So Book your Holiday Now.


text:
XO Korea/business
< XO Korea
english | 한국어 HowTo [ID# 38220] +/-
환영합니다 | Portal | XO Korea | Deployment | Content | Hardware | Software | Mesh Network | Ethics | LOS | XO City | Accreditation | Consortium
XO Korea hopes to distribute Larger XO and Mesh Phone to business sectors of Korea.
In particular, Mesh Phone is expected to subsititue current wire or wireless phones (and voice communication systems) in Korea with those based on National Wireless Mesh Networks.
Briefly speaking, XO Korea hopes to restructure the entire IT infrastructure of Korea (not only edcuation fields, but government and business sectors also) into Linux, Mesh Network, XO, Sugar, and its Activities.
This page is for Business Sectors in Korea, classified based on United Nation Standard Product and Service Calssification, and Korean Standard Industrial Classification.
XO Budget = Num of (Employees) * $220 (per Person cost)
per Person Cost $220 =
$200 -- Unit Price of Larger XO +
$10 -- 1/20 of Unit Price of School Server +
$10 -- Logistics + Extra Machines cost + Other Expenses
See Financial Plans for details.
Mesh Phone Budget = Num of (Students + Teachers + Officers) * $220 (per Person cost)
per Person Cost $50 =
$30 -- Unit Price of MEsh Phone +
$10 -- 1/3 of Unit Price for Mesh Network +
$10 -- Logistics + Extra Expenses
XO Deployment in business sectors of Korea will be managed by 16 XO Service offices geographically distributed.
See XO Service Naitonal about the schedule and parties in charge of XO Deployment in Korea.
Employees Budget for Larger XO Budget for Mesh Phone SUM
19,277,370 US$4,241,021,400 US$963,868,500 US$5,204,889,900
A. AGRICULTURE AND FORESTRY 1,997,830 US$439,522,600 US$99,891,500 US$539,414,100
Agriculture 1,991,877 US$438,212,940 US$99,593,850 US$537,806,790
Growing of Crops, Market Gardening, Horticulture 1,860,556 US$409,322,320 US$93,027,800 US$502,350,120
Farming of Animals 102,962 US$22,651,640 US$5,148,100 US$27,799,740
Growing of Crops Combined With Farming of Animals : Mixed Farming 2,273 US$500,060 US$113,650 US$613,710
Planting for Landscape, Agricultural and Animal Husbandry Service Activities, Except Veterinary Activities 21,797 US$4,795,340 US$1,089,850 US$5,885,190
Hunting, Trapping and Game Propagation Including Related Service Activities 26 US$5,720 US$1,300 US$7,020
Agriculture Unknown 4,263 US$937,860 US$213,150 US$1,151,010
Forestry 5,953 US$1,309,660 US$297,650 US$1,607,310
Unknown 26 US$5,720 US$1,300 US$7,020
B. FISHING 102,467 US$22,542,740 US$5,123,350 US$27,666,090
Fishing 102,467 US$22,542,740 US$5,123,350 US$27,666,090
Fishing and Gathering of Marine Materials 72,081 US$15,857,820 US$3,604,050 US$19,461,870
Operation of Fish Hatcheries and Fish Farms; Services Incidental to Fishing 29,959 US$6,590,980 US$1,497,950 US$8,088,930
Unknown 427 US$93,940 US$21,350 US$115,290
C. MINING AND QUARRYING 20,402 US$4,488,440 US$1,020,100 US$5,508,540
Mining of Coal, Crude Petroleum and Natural Gas, Uranium and Thorium Ores 6,742 US$1,483,240 US$337,100 US$1,820,340
Mining and Agglomeration of Coal and Lignite , Extraction of Peat 6,479 US$1,425,380 US$323,950 US$1,749,330
Extraction of Crude Petroleum and Natural Gas, and Related Services 212 US$46,640 US$10,600 US$57,240
Mining of Metal Ores 51 US$11,220 US$2,550 US$13,770
Mining of Metal Ores 449 US$98,780 US$22,450 US$121,230
Mining of Iron Ores 245 US$53,900 US$12,250 US$66,150
Mining of Non-ferrous Metal Ores, Except Uranium and Thorium Ores 182 US$40,040 US$9,100 US$49,140
Mining of Non-metallic Minerals, Except Fuel 13,178 US$2,899,160 US$658,900 US$3,558,060
Quarrying of Stone, Sand and Gravel 9,846 US$2,166,120 US$492,300 US$2,658,420
Mining of Other Industrial Non-Metal Ores, Except Fuel 3,258 US$716,760 US$162,900 US$879,660
Unknown 74 US$16,280 US$3,700 US$19,980
D. MANUFACTURING 3,804,029 US$836,886,380 US$190,201,450 US$1,027,087,830
Manufacture of Food Products and Beverages 303,684 US$66,810,480 US$15,184,200 US$81,994,680
Production, Processing and Preserving of Meat, Fishes, Fruit, Vegetables, Oils and Fats 81,929 US$18,024,380 US$4,096,450 US$22,120,830
Manufacture of Dairy Products and Icecream 17,399 US$3,827,780 US$869,950 US$4,697,730
Manufacture of Grain Mill Products 32,464 US$7,142,080 US$1,623,200 US$8,765,280
Manufacture of Other Food Products 143,320 US$31,530,400 US$7,166,000 US$38,696,400
Manufacture of Beverages 25,894 US$5,696,680 US$1,294,700 US$6,991,380
Unknown 2,678 US$589,160 US$133,900 US$723,060
Manufacture of Tobacco Products 4,643 US$1,021,460 US$232,150 US$1,253,610
Manufacture of Textiles, Except Sewn Wearing apparel 194,184 US$42,720,480 US$9,709,200 US$52,429,680
Preparation and Spinning of Textile Fibers 28,234 US$6,211,480 US$1,411,700 US$7,623,180
Weaving of Textiles 51,071 US$11,235,620 US$2,553,550 US$13,789,170
Manufacture of knitted and corcheted fabrics and articles 20,154 US$4,433,880 US$1,007,700 US$5,441,580
Dyeing and Finishing Textiles 36,496 US$8,029,120 US$1,824,800 US$9,853,920
Other Dyeing and Finishing Textiles 56,931 US$12,524,820 US$2,846,550 US$15,371,370
Unknown 1,298 US$285,560 US$64,900 US$350,460
Manufacture of Sewn Wearing Apparel and Fur Articles 226,921 US$49,922,620 US$11,346,050 US$61,268,670
Manufacture of Sewn Wearing Apparel, Except Fur Apparel 222,410 US$48,930,200 US$11,120,500 US$60,050,700
Dressing and Dyeing of Fur, Manufacture of Articles of Fur 2,008 US$441,760 US$100,400 US$542,160
Tanning and Dressing of Leather , Manufacture of Luggage and Footwear 62,231 US$13,690,820 US$3,111,550 US$16,802,370
Tanning and Dressing of Leather 7,476 US$1,644,720 US$373,800 US$2,018,520
Manufacture of Luggage, Handbags and the Like, Saddlery and Harness 17,117 US$3,765,740 US$855,850 US$4,621,590
Manufacture of Footwear 37,100 US$8,162,000 US$1,855,000 US$10,017,000
Unknown 538 US$118,360 US$26,900 US$145,260
Manufacture of Wood and of Products of Wood and Cork, Except Furniture; Manufacture of Articles of Straw and Plaiting Materials 36,203 US$7,964,660 US$1,810,150 US$9,774,810
Sawmilling and Planing of Wood 10,073 US$2,216,060 US$503,650 US$2,719,710
Manufacture of Products of Wood, Cork, Straw and Plaiting Materials 25,897 US$5,697,340 US$1,294,850 US$6,992,190
Unknown 233 US$51,260 US$11,650 US$62,910
Manufacture of Pulp, Paper and Paper Products 67,098 US$14,761,560 US$3,354,900 US$18,116,460
Manufacture of Pulp, Paper and Paperboard 20,308 US$4,467,760 US$1,015,400 US$5,483,160
Manufacture of Articles of Paper and Paperboard 46,344 US$10,195,680 US$2,317,200 US$12,512,880
Publishing, Printing and Reproduction of Recorded Media 167,737 US$36,902,140 US$8,386,850 US$45,288,990
Publishing 83,911 US$18,460,420 US$4,195,550 US$22,655,970
Printing and Service Activities Related to Printing 81,859 US$18,008,980 US$4,092,950 US$22,101,930
Reproduction of Recorded Media 387 US$85,140 US$19,350 US$104,490
Manufacture of Coke, Refined Petroleum Products and Nuclear Fuel 13,717 US$3,017,740 US$685,850 US$3,703,590
Manufacture of Coke and Related Products 113 US$24,860 US$5,650 US$30,510
Manufacture of Refined Petroleum Products 13,209 US$2,905,980 US$660,450 US$3,566,430
Processing of Nuclear Fuel 351 US$77,220 US$17,550 US$94,770
Unknown 44 US$9,680 US$2,200 US$11,880
Manufacture of Chemicals and Chemical Products 183,216 US$40,307,520 US$9,160,800 US$49,468,320
Manufacture of Basic Chemicals 51,513 US$11,332,860 US$2,575,650 US$13,908,510
Manufacture of Pharmaceuticals, Medicinal Chemicals and Botanical Products 48,770 US$10,729,400 US$2,438,500 US$13,167,900
Manufacture of Other Chemical Products 78,041 US$17,169,020 US$3,902,050 US$21,071,070
Manufacture of Man-Made Fibers 3,316 US$729,520 US$165,800 US$895,320
Manufacture of Rubber and Plastic Products 144,887 US$31,875,140 US$7,244,350 US$39,119,490
Manufacture of Rubber Products 38,606 US$8,493,320 US$1,930,300 US$10,423,620
Manufacture of Plastic Products 105,306 US$23,167,320 US$5,265,300 US$28,432,620
Manufacture of Other Non-metallic Mineral Products 123,280 US$27,121,600 US$6,164,000 US$33,285,600
Manufacture of Glass and Glass Products 30,077 US$6,616,940 US$1,503,850 US$8,120,790
Manufacture of Ceramic Ware 22,090 US$4,859,800 US$1,104,500 US$5,964,300
Manufacture of Cement, Lime and Plaster and Its Products 49,634 US$10,919,480 US$2,481,700 US$13,401,180
Manufacture of Other Non-metallic Mineral Products 20,705 US$4,555,100 US$1,035,250 US$5,590,350
Manufacture of Basic Metals 131,985 US$29,036,700 US$6,599,250 US$35,635,950
Manufacture of Basic Iron and Steel 92,279 US$20,301,380 US$4,613,950 US$24,915,330
Manufacture of Basic Precious and Non-ferrous Metals 25,393 US$5,586,460 US$1,269,650 US$6,856,110
Cast of Metals 13,557 US$2,982,540 US$677,850 US$3,660,390
Manufacture of Fabricated Metal Products, Except Machinery and Furniture 292,370 US$64,321,400 US$14,618,500 US$78,939,900
Manufacture of Structural Metal Products, Tanks, Reservoirs and Steam Generators 89,859 US$19,768,980 US$4,492,950 US$24,261,930
Manufacture of Other Fabricated Metal Products and Metal Treating services 199,899 US$43,977,780 US$9,994,950 US$53,972,730
Manufacture of Other Machinery and Equipment 354,713 US$78,036,860 US$17,735,650 US$95,772,510
Manufacture of General Purpose Machinery 111,937 US$24,626,140 US$5,596,850 US$30,222,990
Manufacture of Machine-tools 48,050 US$10,571,000 US$2,402,500 US$12,973,500
Manufacture of Other Special Purpose Machinery 118,589 US$26,089,580 US$5,929,450 US$32,019,030
Manufacture of Weapons and Ammunition 7,721 US$1,698,620 US$386,050 US$2,084,670
Manufacture of Other Domestic Appliances 59,767 US$13,148,740 US$2,988,350 US$16,137,090
Unknown 8,649 US$1,902,780 US$432,450 US$2,335,230
Manufacture of Computers and Office Machinery 64,626 US$14,217,720 US$3,231,300 US$17,449,020
Manufacture of Electrical Machinery and Apparatuseses n.e.c. 118,195 US$26,002,900 US$5,909,750 US$31,912,650
Manufacture of Electric Motors, Generators and Transformers 24,751 US$5,445,220 US$1,237,550 US$6,682,770
Manufacture of Electricity Distribution and Control Apparatuseses 26,767 US$5,888,740 US$1,338,350 US$7,227,090
Manufacture of Insulated Wires and Cables, Including Insulated Code Sets 21,104 US$4,642,880 US$1,055,200 US$5,698,080
Manufacture of Accumulators, Primary Cells and Primary Batteries 10,333 US$2,273,260 US$516,650 US$2,789,910
Manufacture of Electric Lamps and Lighting Equipment 17,575 US$3,866,500 US$878,750 US$4,745,250
Manufacture of Other Electrical Equipment 16,556 US$3,642,320 US$827,800 US$4,470,120
Manufacture of Electronic Components, Radio, Television and Communication Equipment and Apparatuses 530,034 US$116,607,480 US$26,501,700 US$143,109,180
Manufacture of Semiconductor and Other Electronic Components 304,189 US$66,921,580 US$15,209,450 US$82,131,030
Manufacture of Television and Radio Transmitters and Apparatuseses for Line Telephony and Line Telegraphy 146,414 US$32,211,080 US$7,320,700 US$39,531,780
Manufacture of Television and Radio Receivers, Sound or Video Recording or Reproducing Apparatuses, and Related Goods 74,489 US$16,387,580 US$3,724,450 US$20,112,030
Manufacture of Medical, Precision and Optical Instruments, Watches and Clocks 76,681 US$16,869,820 US$3,834,050 US$20,703,870
Manufacture of Medical Appliances and Instruments 28,243 US$6,213,460 US$1,412,150 US$7,625,610
Manufacture of Instruments and Appliances for Measuring, Checking, Testing, Navigation and Other Purposes, Except Optical Instruments 26,641 US$5,861,020 US$1,332,050 US$7,193,070
Manufacture of Other Optical Instruments and Spectacle, Photographic Equipment 18,062 US$3,973,640 US$903,100 US$4,876,740
Manufacture of Watches, Clocks and its Parts 2,916 US$641,520 US$145,800 US$787,320
Manufacture of Motor Vehicles, Trailers and Semitrailers 366,449 US$80,618,780 US$18,322,450 US$98,941,230
Manufacture of Motor Vehicles and Engines for Motor Vehicles 102,734 US$22,601,480 US$5,136,700 US$27,738,180
Manufacture of Bodies for Motor Vehicles ; Manufacture of Trailers and Semitrailers 7,270 US$1,599,400 US$363,500 US$1,962,900
Manufacture of Parts and Accessories for Motor Vehicles and Engines 254,366 US$55,960,520 US$12,718,300 US$68,678,820
Manufacture of Other Transport Equipment 142,798 US$31,415,560 US$7,139,900 US$38,555,460
Building of Ships and Boats 126,580 US$27,847,600 US$6,329,000 US$34,176,600
Manufacture of Railway and Tramway Locomotives and Rolling Stock 5,757 US$1,266,540 US$287,850 US$1,554,390
Manufacture of Aircraft, Spacecraft and its Parts 6,625 US$1,457,500 US$331,250 US$1,788,750
Manufacture of Other Transport Equipment 3,362 US$739,640 US$168,100 US$907,740
Manufacture of Furniture; Manufacturing of Articles n.e.c. 186,015 US$40,923,300 US$9,300,750 US$50,224,050
Manufacture of Furniture 81,433 US$17,915,260 US$4,071,650 US$21,986,910
Other Manufacturing 102,545 US$22,559,900 US$5,127,250 US$27,687,150
Recycling 4,401 US$968,220 US$220,050 US$1,188,270
Recycling of Metal Waste and Scrap 1,209 US$265,980 US$60,450 US$326,430
Recycling of Non-metal Waste and Scrap 3,119 US$686,180 US$155,950 US$842,130
E. ELECTRICITY, GAS AND WATER SUPPLY 86,539 US$19,038,580 US$4,326,950 US$23,365,530
Electricity, Gas, Steam and Hot Water Supply 71,605 US$15,753,100 US$3,580,250 US$19,333,350
Production, Collection and Distribution of Electricity 51,456 US$11,320,320 US$2,572,800 US$13,893,120
Manufacture of Gas, Distribution of Gaseous Fuel Through Mains 17,634 US$3,879,480 US$881,700 US$4,761,180
Steam and Hot Water Supply 2,019 US$444,180 US$100,950 US$545,130
Collection, Purification and Distribution of Water 14,934 US$3,285,480 US$746,700 US$4,032,180
Unknown 111 US$24,420 US$5,550 US$29,970
F. CONSTRUCTION 1,442,733 US$317,401,260 US$72,136,650 US$389,537,910
General Construction 639,770 US$140,749,400 US$31,988,500 US$172,737,900
Heavy Construction 155,125 US$34,127,500 US$7,756,250 US$41,883,750
Building of Complete or Partial Constructions 478,853 US$105,347,660 US$23,942,650 US$129,290,310
Special Trade Construction 802,222 US$176,488,840 US$40,111,100 US$216,599,940
Special Trade Construction for Civil Engineering and Buildings 103,912 US$22,860,640 US$5,195,600 US$28,056,240
Building Installation 120,661 US$26,545,420 US$6,033,050 US$32,578,470
Electrical and Communication Works 145,701 US$32,054,220 US$7,285,050 US$39,339,270
Building Completion 312,093 US$68,660,460 US$15,604,650 US$84,265,110
Renting of Construction or Demolition Equipment with Operator 113,485 US$24,966,700 US$5,674,250 US$30,640,950
G.WHOLESALE AND RETAIL TRADE 2,896,767 US$637,288,740 US$144,838,350 US$782,127,090
Sale of Motor Vehicles and Motorcycles ; Retail Sale of Automotive Fuel 184,503 US$40,590,660 US$9,225,150 US$49,815,810
Sale of Motor Vehicles 75,906 US$16,699,320 US$3,795,300 US$20,494,620
Sale of Motor Vehicle Parts and Accessories 43,820 US$9,640,400 US$2,191,000 US$11,831,400
Sale of Motorcycles and Related Parts and Accessories 6,352 US$1,397,440 US$317,600 US$1,715,040
Retail Sale of Automotive Fuel 57,021 US$12,544,620 US$2,851,050 US$15,395,670
Wholesale Trade and Commission Trade, Except of Motor Vehicles and Motorcycles 806,721 US$177,478,620 US$40,336,050 US$217,814,670
Wholesale on a Fee or Contract Basis 33,223 US$7,309,060 US$1,661,150 US$8,970,210
Brokerage of Industrial Agricultural Raw Materials and Living Animals 21,883 US$4,814,260 US$1,094,150 US$5,908,410
Brokerage of Food, Beverages and Tobaccos 204,543 US$44,999,460 US$10,227,150 US$55,226,610
Wholesale of Household Goods 191,498 US$42,129,560 US$9,574,900 US$51,704,460
Wholesale of Construction Materials and Hardware 72,629 US$15,978,380 US$3,631,450 US$19,609,830
Wholesale of Metal Ores and Basic Metals 23,764 US$5,228,080 US$1,188,200 US$6,416,280
Wholesale of Other Intermediate Products, Waste and Scrap 103,460 US$22,761,200 US$5,173,000 US$27,934,200
Wholesale of Machinery, Equipment and Supplies 118,906 US$26,159,320 US$5,945,300 US$32,104,620
Wholesale of Other Goods 22,206 US$4,885,320 US$1,110,300 US$5,995,620
Unknown 14,609 US$3,213,980 US$730,450 US$3,944,430
Retail Trade, Except Motor Vehicles and Motorcycles 1,904,694 US$419,032,680 US$95,234,700 US$514,267,380
Retail Sale in Non-Specialized Stores 508,879 US$111,953,380 US$25,443,950 US$137,397,330
Retail Sale in Non-Specialized Stores with Food or Beverages Predominating 244,138 US$53,710,360 US$12,206,900 US$65,917,260
Retail Sale of Pharmaceuticals and Medical Equipments, Cosmetics and Toilet Articles 118,812 US$26,138,640 US$5,940,600 US$32,079,240
Retail Sale of Textiles, Clothing, Footwear and Leather Goods 278,494 US$61,268,680 US$13,924,700 US$75,193,380
Retail Sale of Electrical Household Appliances, Furniture and Household Appliances 172,923 US$38,043,060 US$8,646,150 US$46,689,210
Retail Sale in Other Specialized Stores 298,032 US$65,567,040 US$14,901,600 US$80,468,640
Retail Sale of Used Goods in Stores 8,300 US$1,826,000 US$415,000 US$2,241,000
Retail Sale not in Stores 258,057 US$56,772,540 US$12,902,850 US$69,675,390
H. HOTELS AND RESTAURANTS 1,492,923 US$328,443,060 US$74,646,150 US$403,089,210
Hotels and Restaurants 1,492,923 US$328,443,060 US$74,646,150 US$403,089,210
Accommodation 121,951 US$26,829,220 US$6,097,550 US$32,926,770
Restaurants, Bars and Canteens 1,358,589 US$298,889,580 US$67,929,450 US$366,819,030
I. TRANSPORT 891,883 US$196,214,260 US$44,594,150 US$240,808,410
Land Transport ; Transport Via Pipelines 661,613 US$145,554,860 US$33,080,650 US$178,635,510
Interurban Rail Transportation 31,104 US$6,842,880 US$1,555,200 US$8,398,080
Transit and Ground Passenger Transportation 408,164 US$89,796,080 US$20,408,200 US$110,204,280
Road Freight Transport 216,718 US$47,677,960 US$10,835,900 US$58,513,860
Transport Via Pipelines 453 US$99,660 US$22,650 US$122,310
Water Transport 20,594 US$4,530,680 US$1,029,700 US$5,560,380
Sea and Coastal Water Transport 18,439 US$4,056,580 US$921,950 US$4,978,530
Inland Water Transport 2,059 US$452,980 US$102,950 US$555,930
Air Transport 28,113 US$6,184,860 US$1,405,650 US$7,590,510
Scheduled Air Transport 27,249 US$5,994,780 US$1,362,450 US$7,357,230
Non-Scheduled Air Transport and Airplane Rental with Operator 591 US$130,020 US$29,550 US$159,570
Supporting and Auxiliary Transport Activities ; Activities of Travel Agencies 181,542 US$39,939,240 US$9,077,100 US$49,016,340
Cargo Handling 21,334 US$4,693,480 US$1,066,700 US$5,760,180
Warehousing 35,410 US$7,790,200 US$1,770,500 US$9,560,700
Activities of Travel Agencies and Tour Operators ; Tourist Assistance Activities 42,117 US$9,265,740 US$2,105,850 US$11,371,590
Other Services Allied to Transport Agency 81,076 US$17,836,720 US$4,053,800 US$21,890,520
J. POST AND TELECOMMUNICATIONS 224,897 US$49,477,340 US$11,244,850 US$60,722,190
Post and Telecommunications 224,897 US$49,477,340 US$11,244,850 US$60,722,190
Postal Services and Couriers 110,162 US$24,235,640 US$5,508,100 US$29,743,740
Telecommunications 113,059 US$24,872,980 US$5,652,950 US$30,525,930
K. FINANCIAL INSTITUTIONS AND INSURANCE 633,237 US$139,312,140 US$31,661,850 US$170,973,990
Financial Institutions, Except Insurance and Pension Funding 303,757 US$66,826,540 US$15,187,850 US$82,014,390
Money-Creating Institutions 158,605 US$34,893,100 US$7,930,250 US$42,823,350
Non-Money-Creating Institutions 142,884 US$31,434,480 US$7,144,200 US$38,578,680
Insurance and Pension Funding, Except Compulsory Social Security 263,337 US$57,934,140 US$13,166,850 US$71,100,990
Activities Auxiliary to Financial Intermediation 66,116 US$14,545,520 US$3,305,800 US$17,851,320
Activities Auxiliary to Financial Intermediation, Except Insurance and Pension Funding 40,119 US$8,826,180 US$2,005,950 US$10,832,130
Activities Auxiliary to Insurance and Pension Funding 25,461 US$5,601,420 US$1,273,050 US$6,874,470
L. REAL ESTATE AND RENTING AND LEASIN 461,279 US$101,481,380 US$23,063,950 US$124,545,330
Real Estate Activities 421,240 US$92,672,800 US$21,062,000 US$113,734,800
Real Estate Activities with Own or Leased Property 63,428 US$13,954,160 US$3,171,400 US$17,125,560
Activities Related to Real Estate 354,211 US$77,926,420 US$17,710,550 US$95,636,970
Renting of Machinery and Equipment without Operator and of Personal and Household Goods 40,017 US$8,803,740 US$2,000,850 US$10,804,590
Renting of Transport Equipment 7,564 US$1,664,080 US$378,200 US$2,042,280
Renting of Machinery and Equipment 10,923 US$2,403,060 US$546,150 US$2,949,210
Renting of Personal and Household Goods 21,140 US$4,650,800 US$1,057,000 US$5,707,800
M. BUSINESS ACTIVITIES 1,122,011 US$246,842,420 US$56,100,550 US$302,942,970
Computer and Related Activities 205,314 US$45,169,080 US$10,265,700 US$55,434,780
Computer System Design and Consultancy 21,393 US$4,706,460 US$1,069,650 US$5,776,110
Software Consultancy and Supply 155,328 US$34,172,160 US$7,766,400 US$41,938,560
Data Processing and Computer Facilities Management Services 7,826 US$1,721,720 US$391,300 US$2,113,020
Database Activities and On-line Information Provision Services 15,889 US$3,495,580 US$794,450 US$4,290,030
Other Computer Activities 2,340 US$514,800 US$117,000 US$631,800
Research and Development 103,015 US$22,663,300 US$5,150,750 US$27,814,050
Research and Experimental Development On Natural Sciences and Engineering 91,740 US$20,182,800 US$4,587,000 US$24,769,800
Research and Experimental Development On Social Sciences and Humanities 10,306 US$2,267,320 US$515,300 US$2,782,620
Professional, Scientific and Technical Services 471,731 US$103,780,820 US$23,586,550 US$127,367,370
Legal, Accounting and Tax Preparation Services 113,941 US$25,067,020 US$5,697,050 US$30,764,070
Market Research and Management Consulting Services 33,412 US$7,350,640 US$1,670,600 US$9,021,240
Architectural, Engineering Services 134,134 US$29,509,480 US$6,706,700 US$36,216,180
Scientific and Technical Services 43,792 US$9,634,240 US$2,189,600 US$11,823,840
Advertising 43,397 US$9,547,340 US$2,169,850 US$11,717,190
Specialized Design Services 62,136 US$13,669,920 US$3,106,800 US$16,776,720
Other Professional, Scientific and Technical Services 35,620 US$7,836,400 US$1,781,000 US$9,617,400
Business Support Services 341,951 US$75,229,220 US$17,097,550 US$92,326,770
Facilities Support and Employment Services 140,452 US$30,899,440 US$7,022,600 US$37,922,040
Other Business Support Services 198,516 US$43,673,520 US$9,925,800 US$53,599,320
N. PUBLIC ADMINISTRATION AND DEFENCE ; COMPULSORY SOCIAL SECURITY 741,448 US$163,118,560 US$37,072,400 US$200,190,960
Public Administration and Defence ; Compulsory Social Security 741,448 US$163,118,560 US$37,072,400 US$200,190,960
Executive, Legislative and General Government Support 379,277 US$83,440,940 US$18,963,850 US$102,404,790
Administration of Industrial and Social Policy of Community 72,043 US$15,849,460 US$3,602,150 US$19,451,610
Foreign Affairs and Defence Activities 117,732 US$25,901,040 US$5,886,600 US$31,787,640
Justice, Public Order and Safety Activities 166,335 US$36,593,700 US$8,316,750 US$44,910,450
Compulsory Social Security Activities 687 US$151,140 US$34,350 US$185,490
O. EDUCATION 1,305,479 US$287,205,380 US$65,273,950 US$352,479,330
Education 1,305,479 US$287,205,380 US$65,273,950 US$352,479,330
Primary Education 296,161 US$65,155,420 US$14,808,050 US$79,963,470
Secondary Education 285,995 US$62,918,900 US$14,299,750 US$77,218,650
Higher Education 193,337 US$42,534,140 US$9,666,850 US$52,200,990
Schools for the Handicapped and Foreigners 8,813 US$1,938,860 US$440,650 US$2,379,510
Adult and Other Education 511,022 US$112,424,840 US$25,551,100 US$137,975,940
P. HEALTH AND SOCIAL WOR 693,545 US$152,579,900 US$34,677,250 US$187,257,150
Human Health and Veterinary Activities 513,885 US$113,054,700 US$25,694,250 US$138,748,950
Human Health Activities 503,328 US$110,732,160 US$25,166,400 US$135,898,560
Veterinary Activities 6,321 US$1,390,620 US$316,050 US$1,706,670
Unknown 4,236 US$931,920 US$211,800 US$1,143,720
Social Work Activities 179,660 US$39,525,200 US$8,983,000 US$48,508,200
Social Work Activities with Accommodation 35,028 US$7,706,160 US$1,751,400 US$9,457,560
Social Works Without Accommodation 143,490 US$31,567,800 US$7,174,500 US$38,742,300
Q. RECREATIONAL, CULTURAL AND SPORTING ACTIVITIES 392,793 US$86,414,460 US$19,639,650 US$106,054,110
Motion Picture, Broadcasting and Performing Arts Industries 111,294 US$24,484,680 US$5,564,700 US$30,049,380
Motion Picture Industries 27,049 US$5,950,780 US$1,352,450 US$7,303,230
Broadcasting 47,133 US$10,369,260 US$2,356,650 US$12,725,910
Performing Arts Industries 35,985 US$7,916,700 US$1,799,250 US$9,715,950
Other Recreational, Cultural and Sporting Activities 281,499 US$61,929,780 US$14,074,950 US$76,004,730
News Agency Activities 1,643 US$361,460 US$82,150 US$443,610
Library, Archives, Museums and Other Cultural Activities 24,853 US$5,467,660 US$1,242,650 US$6,710,310
Sports and Other Recreational Sports Services 120,898 US$26,597,560 US$6,044,900 US$32,642,460
Other Recreational Activities 131,649 US$28,962,780 US$6,582,450 US$35,545,230
R. OTHER COMMUNITY, REPAIR AND PERSONAL SERVICE ACTIVITIES 846,367 US$186,200,740 US$42,318,350 US$228,519,090
Sewage and Refuse Disposal, Sanitation and Similar Activities 43,244 US$9,513,680 US$2,162,200 US$11,675,880
Sewage, Human and Animal Waste Treatment Services 7,865 US$1,730,300 US$393,250 US$2,123,550
Waste Collection and Disposal 30,239 US$6,652,580 US$1,511,950 US$8,164,530
Sanitation and Similar Activities 4,859 US$1,068,980 US$242,950 US$1,311,930
Membership Organizations n.e.c. 182,226 US$40,089,720 US$9,111,300 US$49,201,020
Business and Professional Organizations 19,917 US$4,381,740 US$995,850 US$5,377,590
Labour Organizations 4,121 US$906,620 US$206,050 US$1,112,670
Other Membership Organizations 156,497 US$34,429,340 US$7,824,850 US$42,254,190
Maintenance and Repair Services 273,801 US$60,236,220 US$13,690,050 US$73,926,270
Maintenance and Repair Services of Machinery and Equipment 72,174 US$15,878,280 US$3,608,700 US$19,486,980
Maintenance and Repair Services of Motor Vehicles, and Motorcycles 143,661 US$31,605,420 US$7,183,050 US$38,788,470
Repair Services of Personal and Household Goods 55,740 US$12,262,800 US$2,787,000 US$15,049,800
Other Services Activities 347,085 US$76,358,700 US$17,354,250 US$93,712,950
Personal Care Services 213,644 US$47,001,680 US$10,682,200 US$57,683,880
Other Physical Well-being Services 130,544 US$28,719,680 US$6,527,200 US$35,246,880
Unknown 11 US$2,420 US$550 US$2,970
S. PRIVATE HOUSEHOLDS WITH EMPLOYED PERSONS 51,732 US$11,381,040 US$2,586,600 US$13,967,640
Private Households with Employed Persons 51,732 US$11,381,040 US$2,586,600 US$13,967,640
T. EXTRA-TERRITORIAL ORGANIZATIONS AND BODIES 9,011 US$1,982,420 US$450,550 US$2,432,970
Extra-Territorial Organizations and Bodies 9,011 US$1,982,420 US$450,550 US$2,432,970
Industry Unknown 59,998 US$13,199,560 US$2,999,900 US$16,199,460
Retrieved from "http://wiki.laptop.org/mediawiki/index.php?title=XO_Korea/business&oldid=38220"
XO Korea
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Last edited on 00:53, 17 May 2007.


Which version of jfreechart and jcommon jar files should I use? I need full clarification.
Use the latest version, JFreeChart is still compiling and running with Java 6. If you want JavaFX support, then you'll need Java 8.


text:
Pittsburgh Panthers (0-1)
Atlantic Coast ( Coastal Division )
Virginia Tech Hokies 1 0 1.000 45 42 0-0 1-0 0-0 0-0 W1
#7 Miami (FL) Hurricanes 1 0 1.000 44 28 1-0 0-0 0-0 0-0 W1
Virginia Cavaliers 1 0 1.000 44 20 1-0 0-0 0-0 0-0 W1
#18 Georgia Tech Yellow Jackets 1 0 1.000 24 21 0-0 1-0 0-0 0-0 W1
Pittsburgh Panthers 0 1 .000 19 22 0-1 0-0 0-0 0-0 L1
Duke Blue Devils 0 1 .000 17 45 0-0 0-1 0-0 0-0 L1
North Carolina Tar Heels 0 1 .000 7 40 0-0 0-1 0-0 0-0 L1
Week 1, 2042: The players have elected their team captains for the season. The following players will serve as captains: William Carter - LB Tomas Baines - CB Eddie Loy - FS
Hennessey, O. CB Pulled Hamstring Doubtful
Week 1, 2042: Mario Duran - FB ( 2 Star / Rank: 2991 ) committed to Pittsburgh Panthers
Week 1, 2042: Walter Henderson - LB ( 3 Star / Rank: 2193 ) committed to Pittsburgh Panthers
Offense 19.0 (11/88) 398.0 (9/51) 70.0 (10/69) 328.0 (5/27)
Defense 22.0 (8/55) 217.0 (2/24) -19.0 (2/4) 236.0 (8/51)
Grant, T. 43 23 328 2
Lake, R. 21 65 3.1 0
Lockwood, R. 6 120 20 1
1 Iowa Hawkeyes L 22-19
2 Purdue Boilermakers
3 @Troy Trojans
4 North Carolina Tar Heels
6 Georgia Tech Yellow Jackets
7 @Boston College Eagles (0-0)
8 @Virginia Tech Hokies
9 @Duke Blue Devils
10 Syracuse Orange
11 Virginia Cavaliers
12 Notre Dame Fighting Irish
14 @Miami (FL) Hurricanes
Carroll, G. QB Fr 81 46 71 84 57 90 5 60 1 2 1 1 1 66
Grant, T. QB Sr 85 47 61 82 82 84 15 58 22 1 1 2 1 90
Graves, E. QB Fr (RS) 68 54 43 68 81 60 2 42 6 1 1 1 1 85
Williams, J. QB Jr 80 39 76 88 78 61 15 85 1 1 1 1 1 67
Boucher, J. RB Jr 84 80 72 20 55 5 40 86 58 20 15 1 1 72
Knight, M. RB Fr 84 69 73 21 48 19 42 85 70 27 31 1 3 85
Lake, R. RB So 84 74 88 12 50 27 31 83 51 23 26 1 1 71
Shifflett, J. RB Fr 84 60 71 28 39 13 42 95 73 20 29 1 1 69
Stepp, J. RB Jr 81 64 91 30 55 9 23 74 52 31 21 1 1 78
Turk, C. RB So (RS) 76 73 68 26 49 3 28 75 59 16 33 3 1 71
Allen, B. FB Sr 82 75 58 1 65 1 42 60 51 52 58 1 1 89
Duran, M. FB Fr 65 64 71 1 53 1 32 67 51 30 51 1 1 65
Bower, M. G Fr 80 85 36 1 65 1 24 42 4 83 76 1 1 78
Centeno, J. G So (RS) 72 91 40 1 57 1 49 45 27 62 69 1 1 71
Compton, K. G So 81 75 38 1 62 1 49 55 10 70 88 1 1 68
Lindsey, W. G So (RS) 74 89 61 1 54 1 42 38 21 78 66 1 1 81
Mckinney, H. G So 76 80 49 1 60 1 48 56 28 85 65 1 1 75
Thrash, S. G So 81 76 64 1 66 1 30 34 3 86 80 1 1 75
Gordon, C. T So 82 87 54 1 59 1 25 43 9 86 66 1 1 76
Gresham, T. T So 84 79 57 1 62 1 25 33 22 85 87 1 1 63
Hillman, J. T Fr (RS) 75 76 51 1 61 1 33 41 24 67 89 1 1 60
Matson, J. T Sr 83 79 40 1 62 1 51 40 14 86 80 1 1 63
Redden, G. T So 80 85 62 1 62 1 32 62 11 74 73 1 1 65
Summers, W. T So 78 83 52 1 75 1 39 54 27 76 58 1 1 68
Burnside, J. C Sr (RS) 81 77 45 1 57 1 34 58 20 76 80 1 1 60
Carney, J. C Fr 82 92 34 1 54 1 47 63 13 85 71 1 1 74
Davidson, C. C Jr (RS) 82 94 33 1 52 1 31 49 29 90 73 1 1 61
Mcnabb, C. C Sr 82 88 61 1 75 1 26 44 16 74 75 1 1 55
Daniel, S. TE Sr 88 74 63 1 63 1 41 49 84 59 61 1 1 81
Hamilton, R. TE Sr 84 74 56 1 55 1 47 75 64 50 51 1 1 81
Sheehan, G. TE Sr 81 73 53 1 40 1 42 49 86 26 29 1 1 61
Tan, C. TE Fr 82 66 68 1 66 1 49 74 57 46 44 1 1 69
Allman, P. WR So 80 42 75 1 61 1 23 76 89 21 12 1 1 84
Battles, R. WR So 80 48 88 1 68 1 41 69 87 22 13 1 1 79
Christie, D. WR So 86 60 76 1 61 1 30 83 90 8 36 1 1 87
Dorman, L. WR Jr 84 39 87 1 61 1 28 85 79 15 31 1 1 69
Fonseca, S. WR Jr 82 63 91 1 47 1 41 79 74 21 22 1 3 69
Lockwood, R. WR Sr 89 60 95 1 71 1 25 90 67 15 16 1 3 91
Mears, D. WR Jr 83 40 87 1 58 1 32 85 76 21 26 1 1 67
Sinclair, L. WR Fr 84 43 92 1 49 1 17 86 74 18 30 1 1 66
Baines, T. CB Sr 88 41 88 1 45 3 39 89 60 1 1 1 1 80
Driscoll, G. CB Fr 80 37 76 1 60 1 47 69 65 1 1 1 1 86
Heckman, T. CB Fr (RS) 82 42 87 1 43 1 44 75 55 1 1 1 1 88
Hennessey, O. CB Sr 91 45 87 1 66 1 50 90 56 2 1 1 1 89
Payne, D. CB Fr 85 39 72 1 40 1 46 86 67 1 1 1 1 90
Pyle, D. CB Fr 83 44 66 1 41 1 37 90 66 1 1 1 1 81
Bohn, D. LB So 84 56 64 1 61 1 89 72 37 1 1 3 1 68
Bruce, W. LB Sr 82 56 73 1 62 1 85 62 33 1 1 1 1 87
Carter, W. LB Sr 85 63 72 1 64 1 73 84 41 1 1 1 1 86
Cox, G. LB So (RS) 80 55 58 1 63 1 80 78 22 1 1 1 1 58
Griffin, G. LB Jr 83 47 81 1 61 1 86 70 38 3 1 1 1 87
Henderson, W. LB Jr 78 54 66 1 56 1 81 60 34 1 1 1 1 91
Keller, W. LB So 82 46 85 1 38 1 96 56 53 1 1 1 1 99
Thompson, M. LB Jr 82 50 79 1 45 1 80 81 35 1 1 1 1 87
Whiteside, T. LB So 81 71 67 1 62 1 63 83 46 1 1 1 1 54
Bertrand, R. DT Fr 76 76 38 1 50 1 53 52 23 1 1 1 1 70
Gainey, K. DT Sr 87 79 48 1 64 1 72 54 49 1 1 2 1 53
Givens, H. DT Jr 82 71 59 1 56 1 70 51 47 1 1 1 1 58
Lee, G. DT So (RS) 77 80 51 1 28 2 53 55 12 1 1 1 1 70
Martz, J. DT So 83 82 44 1 49 1 59 59 28 1 3 1 1 76
Mauro, B. DT So (RS) 74 58 61 1 32 3 71 63 37 1 1 1 1 45
Ogle, J. DT Fr 83 84 44 1 63 1 61 36 26 1 3 1 1 69
Trujillo, L. DT Jr 80 90 26 1 37 1 50 52 20 1 1 1 1 52
Bennett, F. DE So 84 77 39 1 47 1 56 78 11 1 2 1 1 75
Betts, E. DE So (RS) 78 77 48 1 45 1 42 71 19 1 1 1 1 71
Blanco, J. DE Jr 70 82 58 1 61 1 41 46 19 1 1 1 3 78
Cabral, P. DE Fr 77 76 50 1 41 1 66 52 47 1 1 1 1 70
Hart, J. DE Fr 76 61 43 1 37 1 56 67 48 1 1 1 1 70
Lind, I. DE So 83 77 59 1 58 3 62 63 36 1 1 2 1 77
Milner, F. DE So 86 72 61 1 66 1 57 77 27 1 1 1 1 60
Snow, M. DE Jr 79 61 65 1 51 1 36 79 16 1 1 1 1 73
Capps, T. FS Fr 82 52 69 1 50 1 72 86 47 1 1 1 1 82
Harmon, L. FS Fr 78 58 64 1 65 1 58 82 47 1 1 1 1 75
Loy, E. FS Sr 88 38 90 1 47 1 77 86 49 1 1 1 1 62
Pearson, M. FS So 68 43 56 1 47 1 61 65 52 2 1 1 1 66
Johnson, L. SS So (RS) 77 43 86 1 43 1 66 68 46 1 1 1 1 67
Lawson, J. SS So 81 53 64 1 60 1 72 84 45 1 1 1 1 79
Shank, D. SS Jr 78 35 87 1 58 1 75 73 32 1 1 1 1 70
Velazquez, J. SS Fr 85 66 63 1 39 1 65 86 68 1 1 3 1 70
Antoine, H. K Sr 89 28 42 23 78 1 21 47 32 1 1 89 85 96
Roberts, T. K Sr (RS) 81 29 43 24 55 1 21 42 31 1 1 88 84 93
Wayne, T. K Fr 83 36 44 16 72 1 29 51 29 1 1 75 82 100
Perez, S. P Sr (RS) 75 19 37 23 52 1 31 65 16 1 1 71 82 91
Sacco, A. P Jr 82 15 33 1 63 1 22 39 1 1 3 89 87 80
Weaver, C. P Fr 79 35 51 7 63 1 25 56 22 1 1 73 85 88
Passing Attempts 2032 11 Pittsburgh Holden, J. Miami (FL) 70
Passing Completions 2032 12 Pittsburgh Holden, J. Notre Dame 46
Pass Completion Pct 2017 2 Pittsburgh Johnson, G. Middle Tennessee 100
Passing Interceptions 2032 10 Pittsburgh Holden, J. North Carolina State 7
Passing Yards 2018 4 Pittsburgh Johnson, G. Virginia 532
Passing TDs 2018 4 Pittsburgh Johnson, G. Virginia 6
Passing Rating 2026 2 Pittsburgh Totten, B. Eastern Michigan 1102
Longest Pass 2039 5 Pittsburgh Grant, T. Syracuse 87
Rush Attempts 2021 3 Pittsburgh Fennell, B. Marshall 30
Rushing Yards 2039 12 Pittsburgh Shaffer, K. Notre Dame 204
Rushing TDs 2023 8 Pittsburgh Byrd, C. Duke 4
Longest Run 2023 1 Pittsburgh Kuntz, D. Mississippi State 99
Catches 2032 2 Pittsburgh Pyle, D. Cincinnati 10
Receiving Yards 2018 4 Pittsburgh Chambliss, D. Virginia 223
Receiving TDs 2033 11 Pittsburgh Allen, R. North Carolina 4
Longest Catch 2039 5 Pittsburgh Watts, J. Syracuse 87
Fumbles 2039 5 Pittsburgh Shaffer, K. Syracuse 3
Fumbles Lost 2039 5 Pittsburgh Shaffer, K. Syracuse 3
Interceptions 2036 13 Pittsburgh Schreiner, M. Temple 4
Interception Return Yards 2036 10 Pittsburgh May, L. North Carolina 100
Tackles 2035 8 Pittsburgh Stoddard, J. Miami (FL) 17
Sacks 2023 3 Pittsburgh Reiter, J. Iowa 3
Forced Fumbles 2016 2 Pittsburgh Frazier, D. Miami (OH) 2
Fumbles Recovered 2021 13 Pittsburgh Fennell, B. Georgia Tech 2
Fumble Return Yards 2030 3 Pittsburgh Wiles, J. New Mexico 33
Defensive TDs 2016 2 Pittsburgh Woods, J. Miami (OH) 1
Punt Return Yards 2035 5 Pittsburgh Ashe, M. Louisville 114
Punt Return TDs 2026 4 Pittsburgh Camacho, M. Clemson 1
Longest Punt Return 2016 12 Pittsburgh Jeffries, W. Notre Dame 75
Kickoff Return Yards 2022 10 Pittsburgh Hilliard, E. Virginia 227
Kickoff Return TDs 2017 4 Pittsburgh Lemke, R. Virginia 1
Longest Kickoff Return 2026 6 Pittsburgh Magee, T. Duke 108
FG Attempted 2018 9 Pittsburgh Chavez, L. Virginia Tech 7
FG Made 2018 9 Pittsburgh Chavez, L. Virginia Tech 7
Longest FG 2034 5 Pittsburgh Cagle, H. North Carolina 57
XP Attempted 2026 12 Pittsburgh Burden, F. Notre Dame 9
XP Made 2026 12 Pittsburgh Burden, F. Notre Dame 9
FG Pct 2016 1 Pittsburgh Barber, J. Texas Christian 100
XP Pct 2016 1 Pittsburgh Barber, J. Texas Christian 100
Punt Average 2036 13 Pittsburgh Rogers, P. Temple 60
Punt Yards 2036 8 Pittsburgh Garcia, L. Virginia Tech 630
Longest Punt 2033 11 Pittsburgh Hartman, L. North Carolina 76
Tackles For Loss 2035 0 Pittsburgh Linton, B. Iowa 6
Pancakes 2020 13 Pittsburgh Polk, T. Florida State 10
Sacks Allowed 2028 7 Pittsburgh Halcomb, W. Virginia 3
Passing Attempts 2032 Pittsburgh Holden, J. 680
Passing Completions 2032 Pittsburgh Holden, J. 427
Pass Completion Pct 2031 Pittsburgh Holden, J. 66.5
Passing Interceptions 2032 Pittsburgh Holden, J. 25
Passing Yards 2032 Pittsburgh Holden, J. 4763
Passing TDs 2026 Pittsburgh Malone, L. 44
Passing Rating 2026 Pittsburgh Malone, L. 191.2
Longest Pass 2039 Pittsburgh Grant, T. 87
Rush Attempts 2021 Pittsburgh Fennell, B. 302
Rushing Yards 2039 Pittsburgh Shaffer, K. 1622
Rushing TDs 2039 Pittsburgh Shaffer, K. 19
Longest Run 2023 Pittsburgh Kuntz, D. 99
Catches 2032 Pittsburgh Forte, R. 81
Receiving Yards 2026 Pittsburgh Merrell, R. 1479
Receiving TDs 2040 Pittsburgh Lockwood, R. 17
Longest Catch 2039 Pittsburgh Watts, J. 87
Fumbles 2040 Pittsburgh Boucher, J. 10
Fumbles Lost 2039 Pittsburgh Shaffer, K. 6
Interceptions 2036 Pittsburgh Schreiner, M. 10
Interception Return Yards 2036 Pittsburgh May, L. 264
Tackles 2028 Pittsburgh Lehman, G. 96
Sacks 2033 Pittsburgh Rodgers, C. 12
Forced Fumbles 2040 Pittsburgh Baines, T. 6
Fumbles Recovered 2026 Pittsburgh Eller, R. 5
Fumble Return Yards 2030 Pittsburgh Wiles, J. 34
Defensive TDs 2036 Pittsburgh May, L. 3
Punt Return Yards 2035 Pittsburgh Ashe, M. 392
Punt Return TDs 2038 Pittsburgh Gomez, W. 2
Longest Punt Return 2016 Pittsburgh Jeffries, W. 75
Kickoff Return Yards 2022 Pittsburgh Fennell, B. 1406
Kickoff Return TDs 2017 Pittsburgh Lemke, R. 1
Longest Kickoff Return 2026 Pittsburgh Magee, T. 108
FG Attempted 2034 Pittsburgh Nakamura, A. 34
FG Made 2018 Pittsburgh Chavez, L. 29
Longest FG 2034 Pittsburgh Cagle, H. 57
XP Attempted 2026 Pittsburgh Burden, F. 67
XP Made 2026 Pittsburgh Burden, F. 67
FG Pct 2018 Pittsburgh Chavez, L. 96.7
XP Pct 2026 Pittsburgh Burden, F. 100
Punt Average 2032 Pittsburgh Hess, R. 52
Punt Yards 2036 Pittsburgh Garcia, L. 4805
Longest Punt 2033 Pittsburgh Hartman, L. 76
Tackles For Loss 2020 Pittsburgh Baker, P. 18
Pancakes 2039 Pittsburgh Williams, E. 76
Sacks Allowed 2028 Pittsburgh Halcomb, W. 9
Passing Attempts 2036 Pittsburgh Spearman, C. 61
Passing Completions 2036 Pittsburgh Spearman, C. 32
Pass Completion Pct 2040 Pittsburgh Grant, T. 80
Passing Interceptions 2036 Pittsburgh Spearman, C. 5
Passing Yards 2026 Pittsburgh Malone, L. 421
Passing TDs 2040 Pittsburgh Grant, T. 5
Passing Rating 2040 Pittsburgh Grant, T. 245.1
Longest Pass 2028 Pittsburgh Montgomery, L. 56
Rush Attempts 2034 Pittsburgh Fernandes, E. 24
Rushing Yards 2039 Pittsburgh Shaffer, K. 182
Rushing TDs 2025 Pittsburgh Ramsey, R. 2
Longest Run 2026 Pittsburgh Magee, T. 61
Catches 2025 Pittsburgh Hearn, A. 9
Receiving Yards 2036 Pittsburgh Ragan, W. 153
Receiving TDs 2028 Pittsburgh Freitas, J. 3
Longest Catch 2028 Pittsburgh Freitas, J. 56
Fumbles 2018 Pittsburgh Kelley, R. 1
Fumbles Lost 2025 Pittsburgh Baker, D. 1
Interceptions 2018 Pittsburgh Robinson, R. 1
Interception Return Yards 2028 Pittsburgh Martin, E. 61
Tackles 2030 Pittsburgh Hair, H. 11
Sacks 2032 Pittsburgh Asher, J. 2
Forced Fumbles 2028 Pittsburgh Hair, H. 1
Fumbles Recovered 2031 Pittsburgh Porter, D. 2
Fumble Return Yards 2031 Pittsburgh Porter, D. 14
Defensive TDs 2028 Pittsburgh Martin, E. 1
Punt Return Yards 2034 Pittsburgh Ashe, M. 57
Longest Punt Return 2028 Pittsburgh Martin, E. 56
Kickoff Return Yards 2033 Pittsburgh Stewart, J. 166
Kickoff Return TDs 0 N/A N/A 0
Longest Kickoff Return 2033 Pittsburgh Stewart, J. 60
FG Attempted 2018 Pittsburgh Chavez, L. 5
FG Made 2018 Pittsburgh Chavez, L. 5
Longest FG 2039 Pittsburgh Levine, C. 49
XP Attempted 2028 Pittsburgh Brown, E. 6
XP Made 2028 Pittsburgh Brown, E. 6
FG Pct 2018 Pittsburgh Chavez, L. 100
XP Pct 2018 Pittsburgh Chavez, L. 100
Punt Average 2032 Pittsburgh Hartman, L. 51.2
Punt Yards 2031 Pittsburgh Kang, T. 459
Longest Punt 2018 Pittsburgh Krauss, M. 67
Tackles For Loss 2035 Pittsburgh Linton, B. 6
Sacks Allowed 2018 Pittsburgh Larkin, R. 1
WR 8 5 1 3 3 1
LB 9 6 0 4 3 2


©MikeOrazzi All Rights Reserved Evangeline photographed at Black Point in Rhode Island.
Another pattern, this one was inspired when I was looking at some music notes.
At the 2010 Tattoo Jam, Doncaster, South Yorkshire.
I've learned a few things about the person who lost the memory card. I know that this was the last recorded trip the owner of the lost memory card made before he lost the card. The Stampede Trail is state land that is sandwiched between Federal land on three sides in the northeast corner of Denali Park . It seems like they took a smoke break to consult the map. Maybe they didn't expect to have to cross any substantial rivers. **While surveying out in Denali Park, miles from the nearest road, I found a 1GB memory card that still works. It seems to have wintered somewhere around the Stampede Trail along the Teklanika River. Here is a link to the story of how I found the card, and a link to the conclusion of how I found the memory card's owner.


Dell N5050 Part Number FP8FN Motherboard without graphics available in ITSERVICE at reasonable price.
Refurbished Dell N5050 Part Number FP8FN Motherboard without graphics available in ITSERVICE at best Price with more models. Dell N5050 has 100% well tested model with good condition Use for more details Our live chat or feel free to call us 9094583777 to purchase the product through online. We are offering a bulk order to save your money.


Preparing to bring your new bundle of joy (puppy) home can be stressful. Is the house puppy proof? Will the kids bond well with the four legged friend? There are a lot of questions.
This requires a little more work if you're getting a puppy, since they can be champion chewers and have a knack for getting into things they shouldn't. But no matter what your dog's age, you'll want to do some organising ahead of time.