text:
\section{Introduction}
Recent surveys consider linguistic typology as a potential source of knowledge to support multilingual natural language processing (NLP) tasks \cite{ohoran-etal-2016-survey,ponti-etal-2019-modeling}.
Linguistic typology studies language variation in terms of their functional processes
\cite{comrie1989language}. Several typological knowledge bases (KB) have been crafted, from where we can extract categorical language features \cite{littell-etal-2017-uriel}. Nevertheless, their sparsity and reduced coverage
present a challenge for an end-to-end integration into NLP algorithms.
For example, the World Atlas of Language Structure~\cite[WALS;][]{wals} encodes 143 features for 2,679 languages, but their mean coverage per language is barely around 14\%.

Dense and data-driven language representations have emerged in response. They are computed from multilingual settings of language modelling \cite{ostling-tiedemann-2017-continuous} and neural machine translation (NMT) \cite{malaviya-etal-2017-learning}.
However, the language diversity in the corpus-based representations is limited. The language coverage could be broadened with other knowledge, such as that encoded in WALS, to distinguish even more language properties.
Therefore, to obtain the best of both views (KB and task-learned) with minimal information loss,
we project a shared space of discrete and continuous features using a variant of canonical correlation analysis \cite{svcca-NIPS2017_7188}.

For our study, we fuse language-level embeddings from multilingual machine translation with syntactic features of WALS. We inspect how much typological knowledge is present by predicting features for new languages. Then, we infer language phylogenies and inspect whether specific relationships are induced from the task-learned vectors.

Furthermore, to demonstrate that our approach has practical benefits in NLP, we apply our language vectors in multilingual NMT with language clustering \cite{tan-etal-2019-multilingual} and adapt the ranking of related languages for multilingual transfer \cite{lin-etal-2019-choosing}.
As a side outcome, we identify that there is an ideal setting to encode language relationships in language embeddings from NMT. Finally, we present a simple tool to allow everyone to fuse, extend and compare their own representations\footnote{Once the paper is published, we will release the code  at: \url{https://github.com/aoncevay/multiview-langrep}}.

\section{Multi-view language representations}
\label{sec:multi-langrep}
Our primary goal is to fuse parallel representations of the same language in one shared space, and \textbf{canonical correlation analysis} (CCA)
allows us to find a projection of two views for a given set of data.
With CCA, we look for linear combinations that maximise the correlation of the two sources in each coordinate iteratively \cite{hardoon2004canonical}.
\begin{comment}
CCA solves a sequence of optimisation problems for $j \in\{1..m\}$ where $a_{j} \in \mathbb{R}^{1 \times d}$ and $b_{j} \in \mathbb{R}^{1 \times d^{\prime}}$:
\begin{equation*}
\begin{array}
{cl}{\underset{a_{j}, b_{j}}{\operatorname{argmax}}} &
{\qquad \operatorname{corr}\left(a_{j} X^{\top}, b_{j} Y^{\top}\right)} \\
{\text { such that }} &
{\operatorname{corr}\left(a_{j} X^{\top}, a_{k} X^{\top}\right)=0, \quad k<j} \\
{ } &
{\operatorname{corr}\left(b_{j} Y^{\top}\:, b_{k} Y^{\top}\:\right)=0, \quad k<j}
\end{array}
\end{equation*}
where the $\operatorname{corr}$ function returns the Pearson correlation between two vectors (pairwise element)\footnote{We only retain $j$-th dimension if $\operatorname{corr}(a_j,b_j) \geq 0.5$, as we look for moderate to strong uphill correlation scores between the transformed views.
}.
\end{comment}
After training, we can apply the transformation learned on a new sample from any view to obtain a CCA-based language representation\footnote{With language representations, we refer to an annotated or unsupervised characterisation of a language itself (e.g. Spanish or English), and not to word or sentence-level representations, as it is used in the recent NLP literature.}.

CCA considers all dimensions of the two views as equally important. However, our sources are potentially redundant: KB features are mostly one-hot-encoded, whereas task-learned ones inherit the high dimensionality of the embedding layer. Moreover, few samples and sparsity could make the convergence harder.
For the redundancy issue, \textbf{singular value decomposition} (SVD) is an appealing alternative.
 With SVD, we factorise the source data matrix to compute the principal components and singular values. Furthermore, to deal with sparsity, we adopt a truncated SVD approximation, which is also known as latent semantic analysis in the context of linear dimensionality reduction for term-count matrices \cite{dumais2004latent}.

The two-step transformation of SVD followed by CCA is called \textbf{singular vector canonical correlation analysis}~\cite[SVCCA;][]{svcca-NIPS2017_7188} in the context of understanding the representation learning throughout neural network layers. That being said, we use SVCCA to get language representations and not to inspect a neural architecture\footnote{As the SVD step performs a dimensionality reduction while preserving the most explained variance as possible, we can consider two additional parameters: a threshold value in the [0.5,1.0] range with 0.05 incremental steps, for the explained variance ratio of each view. With a value equal to 1, we bypass SVD and compute CCA only. We then tuned all our following experiments (see Appendix \ref{app:parameter-search} for details).}.

\section{Methodology and research questions}

To embed linguistic typology knowledge in dense representations for a broad set of languages, we employ SVCCA (\S\ref{sec:multi-langrep}) with the following sources:

\paragraph{KB view. }
We employ the language vectors from the URIEL and $\operatorname{lang2vec}$ database \cite{littell-etal-2017-uriel}. Precisely, we work with the $k$-NN vectors of the Syntax feature class ($U_S$; 103 feats.),
that are composed of binary features encoded from WALS~\cite{wals}.

\paragraph{(NMT) Learned view. }
\label{par:nmt-learned}

Firstly, we exploit the NMT-learned embeddings from the Bible ($L_B$; 512 dim.) \cite{malaviya-etal-2017-learning}. Up to 731 entries are available in $\operatorname{lang2vec}$ that intersects with $U_S$. They were trained in a many-to-English NMT model with a pseudo-token identifying the source language at the beginning of every input sentence.

Secondly, we take the many-to-English language embeddings learned for the language clustering task on multilingual NMT ($L_W$; 256 dim.) \cite{tan-etal-2019-multilingual
, where they use 23 languages of the WIT$^3$ corpus~\cite{cettoloEtAl:EAMT2012}.

One main difference for the latter is the use of factors in the architecture, meaning that the embedding of every input token was concatenated with the embedded pseudo-token that identifies the source language. The second difference is the neural architecture used to extract the embeddings: the former use a recurrent neural network, whereas the latter a small transformer model \cite{NIPS2017_7181}.

Finally, we train a new set of embeddings ($L_T$) that we extracted from the 53 languages of the TED corpus (many-to-English) processed by \citet{qi-etal-2018-pre}, using the approach of \citet{tan-etal-2019-multilingual}\footnote{We prefer to use factored embeddings over initial pseudo-tokens as we identified that there is a difference for encoding information about language similarity (see \S\ref{sec:factors2}).}.

\paragraph{What knowledge do we represent?} Each source embeds specialised knowledge to assess language relatedness. The KB vectors can measure typological similarity, whereas task-learned embeddings correlates with other kinds of language relationships (e.g. genetic) \cite{Bjerva2019-bl}. To analyse whether each kind of knowledge is induced with SVCCA, we assess the tasks of typological feature prediction (\S\ref{sec:typ-prediction}) and reconstruction of a language phylogeny (\S\ref{sec:tree-inference}).

\paragraph{What is the benefit for multilingual NMT (and NLP)?} Language-level representations can evaluate the distance between languages in a vector space. We then can assess their applicability on multilingual NMT tasks that require guidance from language relationships. Therefore, language clustering and ranking related partner languages for (multilingual) transfer are our study cases (\S\ref{sec:broadening-clustering}).

\section{Prediction of typological features}
\label{sec:typ-prediction}

An example of a typological feature is a word order specification, like whether the adjective is predominately placed before or after the noun (features \#24 and \#25 of $U_S$). Our task consists in predicting syntactic features ($U_S$) leaving one-language and one-language-family out to control phylogenetic relationships \cite{bjerva-etal-2019-probabilistic}. Previous work has shown that task-learned embeddings are potential candidates to predict features of a linguistic typology KB \cite{malaviya-etal-2017-learning}, and our goal is to evaluate whether SVCCA can enhance the NMT-learned language embeddings with typological knowledge from their KB parallel view.

\paragraph{Experimental setup.} We use a Logistic Regression classifier per $U_S$ feature, which is trained with the NMT-learned or SVCCA representations in both one-language-out and one-language-family-out settings. For prediction, we use the original embedding or its SVCCA projection as inputs.

\paragraph{Results.} In Table \ref{tab:typ-prediction}, we observe that SVCCA outperformed their NMT-learned counterparts for $L_W$ and $L_T$, where the performance is significantly better for the one-language-out setting. In the case of $L_B$ (with 731 entries), we notice that the overall performance drops, and the SVCCA transformation cannot improve it. We argue that a potential reason for the accuracy dropping is the method used to extract the NMT-learned embeddings (initial pseudo-token instead of factors: \S\ref{sec:factors2}), which could diminishes the information embedded about each language, and consequently, impacts the SVCCA projection. In conclusion, we notice that specific typological knowledge is usually hard to learn in an unsupervised way, and fusing them with KB vectors using SVCCA is feasible for inducing information of linguistics typology in some scenarios.

\begin{table}[t!]
\centering
\setlength\tabcolsep{4pt}
\resizebox{\linewidth}{!}{%
\begin{tabular}{lcc|ccc}
                     & \multicolumn{2}{c}{one-language-out} & \multicolumn{3}{c}{one-family-out} \\
                     & Single & SVCCA  & \#F. & Single & SVCCA  \\ \hline
$L_B$ (Bible)   & \textbf{72.77} & 71.68 & 134 & \textbf{72.15} & 70.62 \\
$L_W$ (WIT-23)  & 81.27 & \textbf{84.83} &  12 & 79.49 & \textbf{79.68} \\
$L_T$ (TED-53)  & 77.96 & \textbf{85.37} &  18 & 76.36 & \textbf{81.06} \\ \hline
\end{tabular}
}
\caption{Avg. accuracy ($\uparrow$) of typological feature prediction per NMT-learned and SVCCA($U_S$,$L_*$) setting.}

\label{tab:typ-prediction}
\end{table}

\section{Language phylogeny analysis}
\label{sec:tree-inference}

According to \citet{Bjerva2019-bl}, there is a positive correlation between the language distances in a phylogenetic tree and a pairwise distance-matrix of task-learned representations. Our goal therefore is to investigate whether fusing linguistic typology with SVCCA can preserve or enhance the embedded relationship information. For that reason, we first examine how well a language phylogeny can be reconstructed from language representations (\S\ref{subsec:infer-tree}), and study the correlation afterwards (\S\ref{sec:lexical-sim}).

\begin{comment}
\begin{figure}[b!]
\begin{center}
\centering
\includegraphics[width=.9\linewidth,trim={0.2cm 0.35cm 0.2cm 0.35cm},clip]{figs/_rand_trees.pdf}
\caption{APTED scores ($\downarrow$) between the original GS and randomly pruned (Delete) or shuffle (Modify) versions. The 18-th entry represents an entirely new tree computed from a random matrix. The confidence interval is obtained with 100 iterations. We also include a reference for the best value obtained by SVCCA.}
\label{fig:rand_trees}
\end{center}
\end{figure}

\end{comment}

\subsection{Inference of a phylogenetic tree}
\label{subsec:infer-tree}

\paragraph{Experimental design.} Based on previous work \cite{rabinovich-etal-2017-found}, we take a tree of 17 Indo-European languages \cite{Serva2008-fb} as a Gold Standard (GS), which is shown in Figure \ref{fig:GS-trees}a. We also use agglomerative clustering with variance minimisation \cite{ward1963hierarchical} as linkage, but we employ cosine similarity as \citet{Bjerva2019-bl}.
We also consider a concatenation ($\oplus$) of the KB and NMT-learned views as a baseline.

It is essential to highlight that none of the NMT-learned and $\oplus$ vectors have all the 17 language entries of the GS. Therefore, we can quickly preview one of the significant advantages of the SVCCA vectors, as we are able to represent ``unknown'' languages using one of the views. The NMT-learned views lack English, since they were extracted from the source side of a many-to-English system, but we were able to project the KB English vectors into the shared space. In addition, we project other four languages (Swedish, Danish, Latvian, Lithuanian) to complete the $L_W$ embeddings of \citet{tan-etal-2019-multilingual} and Latvian to complete our own $L_T$ set.

\paragraph{Evaluation metric.} We differ from previous studies and use a tree edit distance metric, which is defined as the minimum cost of transforming one tree into another by inserting, deleting or modifying (the label of) a node. Specifically, we used the All Path Tree Edit Distance algorithm \cite[APTED;][]{pawlik-augsten-2015-efficient,pawlik-augsten-2016-tree}, a novel one for the task. We chose an edit-distance method as it is more transparent for assessing what is the degree of impact for a single change of linkage in the GS.

As we need to compare inferred pruned trees with different number of nodes, we propose a normalised version given by: $\operatorname{nAPTED} = \operatorname{APTED} / (|\operatorname{GS}| + |\tau|)$, where $\tau$ is the inferred tree, and $|.|$ indicates the number of nodes. The denominator then is the maximum cost possible of deleting all nodes of $\tau$ and inserting each GS node.

\begin{table}[t!]
\centering
\setlength\tabcolsep{4pt}
\resizebox{\linewidth}{!}{%
\begin{tabular}{l|c|cc}
                     & Single & $U_S\oplus L_*$  & SVCCA($U_S$,$L_*$)  \\ \hline
$U_S$ (Syntax)  & 30 / 0.45 \\
$L_B$ (Bible)   & 35 / 0.54 & 27 / 0.42 & 23 / 0.34  \\
$L_W$ (WIT-23)  & 35 / 0.62 & 23 / 0.41 & 27 / 0.48  \\
$L_T$ (TED-53)  & 15 / 0.26 & 18 / 0.29 & \textbf{10} / \textbf{0.15} \\ \hline
\end{tabular}
}
\caption{APTED and nAPTED scores ($\downarrow$) between the GS and inferred trees from all scenarios. NMT-learned and concatenation ($\oplus$) can only reconstruct pruned trees of 16 ($L_B$), 12 ($L_W$) and 15 ($L_T$) languages.}
\label{tab:tree-TED-multi-views}
\end{table}

\paragraph{Results.} Table \ref{tab:tree-TED-multi-views} shows the results for all settings,
where the single-view scores are meagre in most of the cases. For instance, the $U_S$ inferred tree (Fig.\ref{fig:GS-trees}c) requires 30 editions to resemble the GS. The exception is $L_T$ (Fig.\ref{fig:GS-trees}d), which requires half the editions, although it is incomplete.

 We observe that the best absolute and normalised scores are obtained by fusing $U_S$ and $L_T$ with SVCCA (Fig.\ref{fig:GS-trees}b). English is projected in the Germanic branch, although Latvian is separated from the Balto-Slavic group. The latter case is similar for Bulgarian, which is misplaced in the original $L_T$ tree as well. Nevertheless, we only require ten editions to equate the GS (where 66 is the maximum cost possible), confirming that our approach is a robust alternative for completing language entries and inferring a language phylogeny. We then proceed to discuss what kind of relationship we are representing.

\begin{figure}[t!]
\begin{center}
\begin{subfigure}[t]{0.2\textwidth}
\caption{Gold Standard}
\label{fig:tree-gs}
\includegraphics[width=\linewidth,trim={0 0cm 0 0.375cm},clip]{figs/GoldStandard.pdf}
\end{subfigure}
\begin{subfigure}[t]{0.2\textwidth}
\caption{SVCCA($U_S$,$L_T$)
\label{fig:tree-svcca}
\includegraphics[width=\linewidth,trim={0 0cm 0 0.375cm},clip]{figs/GS-SVCCA-S-ted53-100-055.pdf}
\end{subfigure}

\begin{subfigure}[t]{0.2\textwidth}
\caption{$U_S$: Syntax
\label{fig:tree-syntax}
\includegraphics[width=\linewidth,trim={0 0.3cm 0 0.375cm},clip]{figs/GS-Syntax-050.pdf}
\end{subfigure}
\begin{subfigure}[t]{0.2\textwidth}
\caption{$L_T$: TED-53
\label{fig:tree-ted53}
\includegraphics[width=\linewidth,trim={0 0.3cm 0 0.375cm},clip]{figs/GS-Lted53-060.pdf}
\end{subfigure}

\caption{Gold Standard phylogeny (a) and reconstructed trees (b-d). $L_T$ is smaller.}
\label{fig:GS-trees}
\end{center}
\end{figure}

\subsection{Correlation with lexical similarity}
\label{sec:lexical-sim}
\citet{Bjerva2019-bl} argued that raw language embeddings from language modelling correlates with \emph{genetic} and structural similarity\footnote{We note that \citet{Bjerva2019-bl} used monolingual texts translated from different languages to investigate what kind of genetic information is preserved. Concerning structural similarity, they computed a distance matrix using syntax-dependency-tags counts per language from annotated treebanks. We leave this analysis for further work.}. For the former, they correlated a distance matrix with pairwise-leaf-distances of the GS. However, \citet{Serva2008-fb} originally inferred the phylogeny by comparing the translated Swadesh list of 200-words \cite{dyen1992indoeuropean} with Levenshtein (edit) distance. The list is a crafted set of concepts for comparative linguistics (e.g. I, eye, sleep), and it is usually processed by lexicostatistics methods to study language relationship through time. Therefore,
we prefer to argue that corpus-based embeddings could partially encode \textbf{lexical} similarity of languages.

We perform an Spearman correlation between the cophenetic matrix\footnote{ Pairwise-distances of the hierarchy's leaves (languages).} of the GS and the pairwise cosine-distance matrices of $U_S$, $L_T$ and SVCCA($U_S$,$L_T$), where we obtain correlation coefficients of 0.48, 0.68 and 0.80, respectively (p-values$<$0.001). Our conclusion is that typological knowledge strengthen the representation of lexical similarity within NMT-learned embeddings.

\section{Application in multilingual NMT}
\label{sec:broadening-clustering}

With multilingual NMT, we can translate several language-pairs using a single model. Low-resource languages are usually benefited through multilingual transfer, which resembles a simultaneous training of the parent(s) and child models. Therefore,
we want to take advantage of a language-level vector space for relating similar languages and enhancing multilingual transfer within multilingual NMT. For that reason, we first address the language clustering task proposed by \citet{tan-etal-2019-multilingual}, and afterwards, the language ranking model of \citet{lin-etal-2019-choosing}.

\paragraph{Language clustering.} The main idea is to obtain smaller multilingual NMT models as an intermediate point between maintaining many pairwise systems and a single massive multilingual model. With limited resources, it is challenging to support the first scenario, whereas the advantages for the massive setting are also very appealing (e.g. simplified training process, translation improvement for low-resource languages or zero-shot translation \cite{johnson-etal-2017-googles}). Therefore, to address the task, \citet{tan-etal-2019-multilingual} trained a factored multilingual NMT model of 23 languages from \citet{cettoloEtAl:EAMT2012}, where the language embedding is concatenated in every input token.
Then, they performed hierarchical clustering with the representations, and selected a number of clusters guided by the Elbow method. Finally, they compared the systems against individual, massive and language family-based cluster models.

Rather than only using our multi-view representations to compute a set of clusters, we also address the question: do we need to train the massive model again if we want to add one or more new languages to our setting? If one of the goals of working with clustered NMT models is to avoid training and maintaining massive systems, it is quite a significant problem for the new language scenario.

\paragraph{Language ranking.} The original goal of \textsc{LangRank} is to choose a parent language to perform transfer learning in different tasks, NMT included. To achieve this, \citet{lin-etal-2019-choosing} trained a model based on the performance of several hundred pairwise MT systems using the dataset of \citet{qi-etal-2018-pre}. For the input features, they considered linguistically-informed vectors from $\operatorname{lang2vec}$ \cite{littell-etal-2017-uriel} and corpus-based statistics, such as word/sub-word overlapping and the ratio of the token-types or the data size between the target child and potential candidates, where the latter features were one of the most relevant.

Considering the transfer capabilities within multilingual NMT and the possibility to obtain a ranked list of candidates from \textsc{LangRank}, we propose an adapted task of choosing $k$-related languages for multilingual transfer. We then use our multi-view representations to rank related languages from the vector space, as they embed information about typological and lexical relationships. This is similar to the features that \citet{lin-etal-2019-choosing} considers, but without training a ranking model fed with scores from pairwise MT systems.

\subsection{Experimental setup}
\label{sec:clust-setup}

We focus on the many-to-one (English) multilingual NMT setting to simplify the findings in both tasks. However, similar experiments could be performed in a one-to-many direction. Details about models, training and inference are described in Appendix \ref{app:model}.

\paragraph{Dataset. }
We use the dataset processed and tokenised by \citet{qi-etal-2018-pre} of 53 languages (TED-53), from where we learned our $L_T$ embeddings. We opted for TED-53 to better evaluate the extensibility of clusters and because it is also used to train the \textsc{LangRank} model. The list of languages, set sizes and other details are included in Appendix \ref{app:languages}. Before preprocessing the text, we drop any sentences from the training sets which overlap with any of the test sets. Since we are building many-to-English multilingual systems, this is important, as any such overlap will bias the results.

\begin{figure*}[t!]
\begin{center}
\centering
\includegraphics[width=\linewidth,trim={0 0.25cm .25cm 0},clip]{figs_ddg/elb+sil+ddg-SVCCA-S-Tan23-065-060.pdf}
\caption{Clustering of TED-53 using the SVCCA-53 representations. At the left, we include the Elbow and Silhouette criteria to define the number of clusters. For the former, it is not clear what is the value to choose, whereas for the later we automatically select the highest peak at ten clusters. }
\label{fig:dendrograms}
\end{center}
\end{figure*}

\paragraph{Clustering settings. } We first list the baselines and our approaches, with the number of clusters/models between brackets
\begin{enumerate}
    \item Individual [53]: Pairwise model per language.
    \item Massive [1]: A single model for all languages.
    \item Language families [20]: Based on historical linguistics. We divide the 33 Indo-European languages into 7 branches. Moreover, 11 groups only have one language.
    \item KB [3]: $U_S$ (Syntax) tends to agglomerate large clusters (with 4-13-33 languages) and behaves similar to the massive model.
    \item Learned [11]: We train a set of 53 factored embeddings ($L_T$) similar to \citet{tan-etal-2019-multilingual}.
    \item Concatenation [18]: $U_S \oplus L_T$
    \item SVCCA-53 [10]: Multi-view representation with SVCCA composing both $U_S$ and $L_T$ vectors. Figure \ref{fig:dendrograms} shows the inferred hierarchy.
    \item SVCCA-23 [10]: Similar to the previous setting, but we use the set of 23 language embeddings $L_W$ instead \cite{tan-etal-2019-multilingual}, and project the 30 complementary languages with SVCCA($U_S$,$L_W$).
\end{enumerate}
With the last setting, we are interrogating whether SVCCA is a useful method for rapidly increasing the number of languages without retraining massive models given new entries that require their NMT-learned embeddings for clustering.

Similar to \citet{tan-etal-2019-multilingual}, we use hierarchical agglomeration with average linkage and cosine similarity. However, we choose a different criterion for choosing the optimal number of clusters.

\paragraph{Selection of number of clusters. }
The Elbow criteria has been suggested for this purpose~\cite{tan-etal-2019-multilingual}; however, as we can see in Figure \ref{fig:dendrograms}, it might be ambiguous. Thus, we propose using a heuristic called Silhouette \cite{rousseeuw1987silhouettes}, which returns a score in the [-1,1] range. A sample cluster with a silhouette close to 1 indicates that it is cohesive and well-separated. With the average silhouette of all samples, we vary the number of cluster partitions, and look for the peak value.

\paragraph{Ranking settings.} We focus on five low-resource languages from TED-53: Bosnian (bos, Indo-European/Balto-Slavic), Galician (glg, Indo-European/Italic), Malay (zlm, Austronesian), Estonian (est, Uralic) and Georgian (kat, Kartvelian). They have between 5k and 13k translated sentences with English, and we chose them as they achieved the most significant improvement from the individual to the massive setting. We then identified the top-3 related languages using \textsc{LangRank}, which give us a multilingual training set of around 500 thousand sentences for each case.
Given that \textsc{LangRank} usually prefers to choose candidates with larger data size \cite{lin-etal-2019-choosing}, for a fair comparison, we use SVCCA and cosine similarity to choose the $k$ closest languages that can agglomerate a similar amount of parallel sentences.

\subsection{Language clustering results}
\label{sec:clust-res}

We first briefly discuss the composition of clusters obtained by SVCCA. Then, we analyse the results grouped by training size bins. We complement the analysis by family groups in Appendix \ref{app:families}.

\paragraph{Cluster composition: } In Figure \ref{fig:dendrograms}, we observe that SVCCA-53 has adopted ten clusters with a proportionally distributed number of languages (the smallest one is Greek-Arabic-Hebrew, and the largest one has seven entries). Moreover, the languages are usually grouped by phylogenetic or geographical criteria.

From a more detailed inspection, there are entries that do not correspond to their respective family branches, although the single-view sources might induce the bias. For instance, the $L_T$ tree (Fig\ref{fig:GS-trees}d) ``misplaced'' Bulgarian within Italic languages. Nevertheless, the unexpected agglomerations rely on the features encoded in the KB or the NMT learning process, and we expect they can uncover surprising clusters to avoid isolating languages without close relatives (e.g. Basque, or even Japanese as the only Japonic member in the set).

\paragraph{Training size bins: }
We manually define the upper bounds of the bins as [10,75,175,215]  thousands of training sentences, which results in groups composed by [14,14,13,12] languages. Figure \ref{fig:binsizes} shows the box plots of BLEU from where we can analyse each distribution (mean, variance).

Throughout all the bins, we observe that both SVCCA-53 and SVCCA-23 accomplish a comparable accuracy with the best setting in each group. In other words, their clusters provide stable performance for both low or high-resource languages.

In the first bin of the smallest corpora, the Massive baseline and the large clusters of $U_S$ barely surpass the SVCCA schemes. Nevertheless, SVCCA contributes a notable advantage if we want to train a multilingual NMT model for a specific low-resource language, and we do not have the resources for training a massive system. We further analyse this scenario in \S\ref{sec:ranking-res}.

In the rightmost bin, for the highest resource languages, the Massive and $U_S$ performed worse than  SVCCA. Furthermore, we show a competitive accuracy for the Individual and Family approaches. The former's clusters have steady performance across most of the bins as well. Nevertheless, they double the number of clusters that we have in both SVCCA settings, and with more than half of the ``clusters'' having only one language.

\begin{figure}[t!]
\centering

\includegraphics[width=\linewidth,trim={0.15cm 1cm 0.15cm 0.25cm},clip]{figs/ted53bins-catplot.pdf}

\caption{Box plots of BLEU scores per training-size bins. Each bin is represented by the range of minimum and maximum training size. Outliers are shown as diamonds.}
\label{fig:binsizes}
\end{figure}

Other approaches, like using the NMT-learned embeddings ($L_T$) as \citet{tan-etal-2019-multilingual} or the concatenation baseline, obtain similar translation results in the last three bins. However, we need to obtain the NMT-learned embeddings first in order to fulfil those methods (from a 53-languages massive model). Using SVCCA and a pre-trained smaller set of language embeddings is enough for projecting new representations, as we present with our SVCCA-23 approach.

\subsection{Language ranking results}
\label{sec:ranking-res}

After discussing overall translation accuracy for all the languages, we now focus on five specific low-resource cases and how multilingual transfer enhance their performance. Table \ref{tab:ranking-res} shows the BLEU scores of the translation into English for the smaller multilingual models that group each child language with their candidates ranked by \textsc{LangRank} and our SVCCA-53 representations.

We also include the results of the individual and massive MT systems. Even when the latter baseline provides a significant improvement over the former, we observe that many of the smaller multilingual models outperform the translation accuracy of the massive system. The result suggests that the amount of data is not the most important confound for supporting multilingual transfer in a low-resource language.

Comparing the two ranking approaches, we observe that SVCCA achieves a comparable performance in most of the cases. We note that \textsc{LangRank} prefers related languages with large datasets, as it only requires three candidates to group around half a million training samples, whereas SVCCA suggests to include from three to ten languages to reach a similar amount of parallel sentences. However, increasing the number of languages could impact the multilingual transfer negatively (see the case of Georgian or \emph{kat}), and it is analogous to adding different ``out-of-domain'' samples. To alleviate this, we could bypass candidate languages that do not possess a specific amount of training samples.

We argue that our method provides a robust alternative to determine which languages are suitable for multilingual transfer learning.
A notable advantage is that we do not need to pre-train MT systems from a specific dataset, and we can easily extend the coverage of languages without re-training the ranking model to consider new entries\footnote{However, we do not answer what multilingual NMT really transfers to the low-resource languages. We left that question for further research, together with optimising the $k$ number of languages or the amount of data per each language.}.

\begin{table}[h!]
\centering
\setlength\tabcolsep{4pt}
\begin{tabular}{c|cc|cc}
L                     & Ind. & Mas. & \textsc{LangRank} & SVCCA-53 \\  \hline

bos  & 4.2 & 26.6 & 28.8 \textsubscript{(434)} &  28.2 \textsubscript{(L=5)} \\
glg  & 8.4 & 24.9 & 27.7 \textsubscript{(443)} & 28.4 \textsubscript{(L=3)}  \\
zlm  & 4.1 & 20.1 & 21.2 \textsubscript{(463)} & 21.0 \textsubscript{(L=4)} \\
est  & 5.8 & 13.5 & 13.5 \textsubscript{(533)} & 12.1 \textsubscript{(L=6)} \\
kat  & 5.8 & 14.3 & 13.3 \textsubscript{(499)} & 10.5 \textsubscript{(L=10)} \\ \hline

\end{tabular}
\caption{BLEU scores (L$\rightarrow$English) for Individual, Massive and ranking approaches. \textsc{LangRank} shows the accumulated training size (in thousands) for the top-3 candidates, whereas with SVCCA we approximate the amount of data and include the number of languages.}
\label{tab:ranking-res}
\end{table}

\begin{comment}
\begin{table}[h!]
\centering
\setlength\tabcolsep{4pt}
\resizebox{\linewidth}{!}{%
\begin{tabular}{c|cc|cc|c}
L                     & Ind. & Mas. & \textsc{LangRank} & SVCCA & SVCCA$+$  \\  \hline

bos  & 4.2 & 26.6 & \textbf{28.8} \textsubscript{(434)} & 26.3 \textsubscript{(279)} & 28.2 \textsubscript{(418,L=7)} \\
glg  & 8.4 & 24.9 & 27.7 \textsubscript{(443)} & \textbf{28.4} \textsubscript{(455)} & - \\
zlm  & 4.1 & 20.1 & \textbf{21.2} \textsubscript{(463)} & 19.3 \textsubscript{(357)} & 21.0 \textsubscript{(568,L=4)} \\
est  & 5.8 & 13.5 & \textbf{13.5} \textsubscript{(533)} & 10.2 \textsubscript{(170)} & 12.1 \textsubscript{(514,L=6)} \\
kat  & 5.8 & 14.3 & 13.3 \textsubscript{(499)} & 5.6 \textsubscript{(33)} & 10.5 \textsubscript{(489,L=10)} \\
eus  & 2.2 & 11.1 & 10.6 \textsubscript{(491)} & 4.8 \textsubscript{(32)} & 7.7 \textsubscript{(413,L=10)} \\ \hline

\end{tabular}
}
\caption{BLEU scores (L$\rightarrow$English) for Individual, Massive and ranking approaches for top-3 languages. The training size (in thousands) is between brackets. With SVCCA$+$, we approximate the amount of data of \textsc{LangRank} and include the number of languages.}
\label{tab:ranking-res}
\end{table}
\end{comment}

\section{Factors over initial pseudo-tokens}
\label{sec:factors2}

We additionally argue that the configuration used to compute the language embeddings impacts what relationship they can learn. For the analysis, we extract an alternative set of 53 language embeddings ($L_T*$) but using the initial pseudo-token setting instead of factors. Then, we perform a silhouette analysis to identify whether we can build cohesive and well-separated clusters of languages.

Figure \ref{fig:pseudo-token-sil} shows the silhouette analysis for the aforementioned embeddings ($L_T*$) together with the Bible embeddings ($L_B$) that were trained with the same configuration. We observe that the silhouette score never exceeds 0.2, and the curve keeps degrading when we examine a higher number of clusters, which contrast the trend shown in Figure \ref{fig:dendrograms}. The pattern proves that the vectors are not suitable for clustering (the hierarchies are shown in Figure \ref{fig:dendrograms-full} in the Appendix), and they might only encode enough information to perform a classification task in the multilingual NMT training and inference. For that reason, we consider it essential to use language embeddings from factors for extracting language relationships.

\begin{figure}[t!]
\begin{center}
\centering

\begin{subfigure}[t]{0.475\linewidth}
\label{fig:sil-ted53pseudo}
\includegraphics[width=\linewidth,trim={5.5cm 0.75cm 20cm 0.675cm},clip]{figs/ddg+sil-ted53marian.pdf}
\end{subfigure}
\begin{subfigure}[t]{0.475\linewidth}
\label{fig:sil-bible}
\includegraphics[width=\linewidth,trim={5.5cm 1cm 20cm 0.675cm},clip]{figs/ddg+sil-Bible43-l2v.pdf}
\end{subfigure}

\caption{Silhouette analysis for the $L_T*$ embeddings trained using an initial pseudo-token (left) and the $L_B$ Bible vectors (right). Both cases present a downtrend curve with scores below 0.2. The hierarchies are shown in Figures \ref{fig:ddg-ted53-pseudotoken} and \ref{fig:ddg-bible}.}
\label{fig:pseudo-token-sil}
\end{center}
\end{figure}

\section{Related work}

For language-level representations, URIEL and $\operatorname{lang2vec}$ \cite{littell-etal-2017-uriel} allow a straightforward extraction of typological binary features from different KBs.
\citet{murawaki-2015-continuous, murawaki-2017-diachrony, murawaki-2018-analyzing} exploits them to build latent language representations with independent binary variables.
Language features are encoded from data-driven tasks as well, such as
NMT \cite{malaviya-etal-2017-learning} or language modelling \cite{tsvetkov-etal-2016-polyglot,ostling-tiedemann-2017-continuous,bjerva-augenstein-2018-tracking} with complementary linguistic-related target tasks \cite{bjerva-augenstein-2018-phonology}.

Our approach is most similar to \citet{bjerva-etal-2019-probabilistic}, as they build a generative model from typological features and use language embeddings, extracted from factored language modelling at character-level, as a prior of the model to extend the language coverage. However, our method primarily differs as it is mainly based in linear algebra, encodes information from both sources since the beginning, and can deal with a small number of shared entries (e.g. 23 from $L_W$) to compute robust representations.

There has been very little work on adopting typology knowledge for NMT. There is not a deep integration of the topics \cite{ponti-etal-2019-modeling}, but one shallow and prominent case is the ranking method  \cite{lin-etal-2019-choosing} that we analysed in \S\ref{sec:broadening-clustering}.

Finally, CCA and its variants have been previously used to derive embeddings at word-level \cite{faruqui-dyer-2014-improving,dhillon2015eigenwords,osborne-etal-2016-encoding}. \citet{kudugunta-etal-2019-investigating} also used SVCCA but to inspect sentence-level representations, where they uncover relevant insights about language similarity that are aligned with our results in \S\ref{sec:tree-inference}. However, as far as we know, this is the first time a CCA-based method has been used to compute language-level representations.

\section{Takeaways and practical tool}

We summarise our key findings as follows:
\begin{itemize}
    \item SVCCA can fuse linguistic typology KB entries with NMT-learned embeddings without diminishing the originally encoded typological and lexical similarity of languages.
    \item Our method is a robust alternative to identify clusters and choose related languages for an small-scale multilingual transfer in NMT. The advantage is notable when it is not feasible to pre-train a ranking model or learn embeddings from a massive multilingual system.
    \item Factored language embeddings encodes more information to agglomerate related languages than the initial pseudo-token setting.

\end{itemize}

Furthermore, we release a tool to compute language representations using SVCCA, together with our $L_T$ vectors. It is possible to use language vectors from KBs (e.g. $\operatorname{lang2vec}$ contains features from Phonology or Phonetic Inventory) or task-learned embeddings from different settings, such as one-to-many or many-to-many NMT and multilingual language modelling. Besides, we could rapidly project new language representations to assess tasks like clustering or ranking candidates for multilingual NMT (and NLP) that involves massive datasets of hundreds of languages.

\section{Conclusion}
We compute multi-view language representations with SVCCA using two sources: KB and NMT-learned vectors. We investigated that the knowledge contained in each source (typological and lexical similarity) is preserved in the combined representation. Moreover, our approach offers important advantages because we can evaluate projected languages with entries in only one of the views. The benefits are noticeable in multilingual NMT tasks, like language clustering and ranking related languages for multilingual transfer. We plan to study how to deeply incorporate our typologically-enriched embeddings in multilingual NMT, where there are promising avenues in parameter selection \cite{sachan-neubig-2018-parameter} and generation \cite{platanios-etal-2018-contextual}.

\section*{Acknowledgments}
\lettrine[image=true, lines=2, findent=1ex, nindent=0ex, loversize=.15]{figs/eu-logo.png}%
{T}his work was supported by funding from the European Union's Horizon 2020 research and innovation programme under grant agreements No 825299 (GoURMET) and the EPSRC fellowship grant EP/S001271/1 (MTStretch). Also, it was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (http://www.csd3.cam.ac.uk/), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/P020259/1), and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk). We express our thanks to Kenneth Heafield, who provided us with access to the computing resources.


text:
in: Blog posts, Compilation album
Lutres • 16 April 2016	• User blog:Lutres
Love Songs is a compilation album that comprises love songs recorded by The Rutles between 1962 and 1970. It was released by Capatal Records in the United States on 21 October 1977 (catalogue number SKBL-11711) and on Parlourphone in the United Kingdom on 19 November 1977 (PCSP 721). The compilation peaked at #24 in Billboard's Top LPs & Tape chart during a 31-week stay that began on 12 November 1977. The RIAA certified the album with sales of three million units in 2000 even though the compilation was deleted in the late 1980s. The New Zealand release followed the US release with cat. no. and pressing plates, and was released on 2 different EMI labels.
1 Track Listing
1.1 Side One
1.2 Side Two
1.3 Side Three
1.4 Side Four
2 Cancelled Single
I'll Borrow Your Son
I Need It (O'Hara)
In My Lunch
Words of Lunch
Out of Nowhere
Somewhat? (O'Hara)
With a Girl Like You
No,It Isn't
Side Three
Michelle (Go to Hell)
It's Only Lunch
You're Going to Squeeze That Girl
Now She's Left You
For Four Guys
She's Leaving Rome
Side Four
Let's Be Natural
Norwegian Weed
You've Got to Hide Your Lunch Away
My Pill
Cancelled Single
Capatal Records originally intended to release a single in promotion of the compilation. The song "Female" was going to be backed with "You're Going to Squeeze That Girl" as Capatal 4506. Picture sleeves for the single were printed, but the record was cancelled in early October 1977.
The Rutles Wiki is a FANDOM Music Community.

redpajama_set_name: RedPajamaCommonCrawl


Incredible athletes—and beautiful people—to watch for at the 2016 Olympics.
On August 5, the 2016 Olympic Games open in Rio de Janiero, Brazil. These 14 athletes, in the months preceding the lighting of the torch, are either hoping and training or already qualified to compete in their respective events. They are also the most attractive—and insanely athletic—competitors with their eyes set on Rio. We hope to see them there.
Stokke broke school pole vaulting records while studying at University of California, Berkeley, and her personal best is vaulting 4.36 meters—over 14 feet. Her partnership with GoPro resulted in this video, which has over 6 million views on YouTube.
Oliver competes in the 110 meter hurdles, and his personal best of 12.89 seconds puts him at fourth all-time fastest. He won bronze at the 2008 Beijing Olympics.
Raised in Green Bay, Wisconsin, Sesselmann gained Canadian citizenship in 2010 and played for the Canadian national team in the 2015 FIFA Women's World Cup—Canada lost to England in the quarterfinals. She is a defender.
Whittenburg was U.S. national champion on vault in 2014 and U.S. national champion on rings in 2015. He will be an alternate on the U.S. men's gymnastics team in Rio.
An MMA fighter and UFC competitor, Waterson is trained in WuShu, Muay Thai, Brazilian jiu-jitsu, boxing, wrestling, and karate. She is said to be training for Rio.
Dotto holds the record for the 100 meter freestyle in Italy with a time of 47.96 seconds. He has also modeled underwear and eyewear for Emporio Armani.
A two-time Olympic gold medalist for indoor volleyball, Carvalho is an outside hitter and winner of the Best Spiker award. She is 6 feet and 1 inch tall.
Neymar is the fifth-highest scorer for the Brazilian national team, with 46 goals in 70 games, and he is often compared to fellow Brazilian Pelé. Neymar plays for Barcelona during the regular season.
Hastings runs the 400 meter sprint and is an Olympic gold medalist. Her personal best for the 400 is 49.84 seconds, which she ran in 2007.
Thanks to @expressoshow and @ewan_strydom for the opportunity this morning. Was a great experience.
Manning centre or wing position, De Allende has played for rugby squads in Japan and South Africa. This year is the Olympic debut for Rugby Sevens, or rugby with seven players per team.
Blanchard is a Hawaii native, surfer, and model. Recent updates say surfing is likely to be officially included in the 2020 Olympic games in Tokyo.
Santana broke the junior world record twice for the 400 meter free in 2014. This will be his first Olympic Games.
A three-time USA boxing champion, Mayer will compete in the lightweight class. She did not pick up boxing until she was 17.
Hard work pays off!! Thank you @usagym for the new Pommel Horse to train on. Fun fact, it's the same Pommel Horse that is going to be used in the 2016 Rio Olympics. I can't wait to get working on this bad boy, definitely going to help my training!!
Naddour has been a member of the U.S. men's national team for three straight years, and he was an alternate at the 2012 Olympics. His best event is the pommel horse.


What would you do if someone offered you a full-length comedy special? Panic, obviously, if you aren't a performer, but even those with a big comedic vision sometimes pull the cameras away from the stage.
Take Paul F Taylor's new special on the excellent NextUp platform: it mostly takes place on the pleasingly brick-backed stage at the Kings Head in London's Crouch End, but also features a bunch of unexpected extra stuff.
Pick of the Litter features self-deprecatingly droll cutaways to a behind-the-scenes stalwart of that venue, plus the recognisable TV face of Toby Williams - you'll know him when you see him - playing a policeman who takes an interrogative interest in the aftermath of Taylor's gig. And it's quite an ending.
Those added elements are no surprise, really, if you know Taylor's comedy history. He's made and appeared in numerous acclaimed, sometimes award-winning shorts. There's the How to Live series with Rebecca Shorrocks, Battlecocks with Shorrocks and the aforementioned Toby Williams, and a brilliant sci-fi concept co-created with Williams: Timeholes. More on the latter below.
As for Taylor's onstage style, it's an eclectic mix of proper gags, gleeful flights of fancy and enjoyable awkwardness. But how did he get there? Let's find out.
Was there a particular moment that inspired you to get into comedy?
For years, comedy was a dream for me. I would listen to American albums like Steve Martin's Comedy Is Not Pretty and Steven Wright's I Have A Pony relentlessly, trying to work out why something was funny.
I loved stand-up, but I was a cripplingly shy and anxious guy with a strong instinct to conform. My parents would drill into me that I should get a qualification that you can earn a solid living from. No one I knew did anything even remotely like performing for a living. Comedy just wasn't an option.
Somewhere in my early 20s I was working as an IT consultant and I'd basically been living away from home in a hotel for a year doing a job that made me desperately sad. I despised the tie-obsessed corporate world, and I hated myself in it.
My girlfriend at the time, who I adored, quite rightly broke up with me because I just wasn't happy. I was devastated. However, looking back she did me a huge favour because it kickstarted the process of me shedding this misplaced identity of who I thought I was. Within six months I was travelling around Australia doing open spots.
What did you want to be originally - stand-up, actor, something else?
A stand-up. Secretly I would have loved to be an actor, but I had no acting training, or the guts to even admit to myself that I might be interested in acting. I knew that stand-up open mic gigs existed, and I didn't want to waste any more time, so comedy writing for stand-up seemed like something I could get going with straight away.
Did you hope to emulate anyone's career?
Starting out I was all about Mitch Hedberg, that guy was incredible. I wanted to write jokes how he did. Jokes that weren't just funny, they were inspired thinking, from a unique point of view. I also had a lot of Eddie Izzard work his way into my style. I saw him live for the first time in ages the other day and I love how much Eddie discovers on stage.
Also, whenever you bring up Eddie to bookers, they all have stories about how much he struggled when he first started out in comedy. Those kinds of stories make you feel better when you've just bombed because you did an impression of a sloth to silence for five minutes.
Has your style evolved over the years - how different were your early shows to the current ones?
When I started out, like many of my era, I was heavily influenced by Mitch Hedberg and Demitri Martin. My sets were short and punchy, with clearly defined punchlines. I cared very little for segues or themes and concentrated on the writing. Which is great to a point.
With that style you tend to get locked into the exact wording of the joke and unless the jokes are aimed inwards you reveal very little about yourself. After a few years I became hugely frustrated by the shortcomings of that style and started forcing myself to change.
Nowadays I would describe my current style as one-man sketch performed by a fool. I've pushed myself to be far more anti-perfectionist, I allow myself to play within my own material and find things on stage.
I like the way you almost commentate on your show while doing it - do you find the actual art of stand-up interesting?
I think my talking about my act on stage is linked to me pushing to be in the moment, but also, at its core I think it's a nervous thing.
Sometimes the reality of doing stand-up seems so absurd to me, I just want to laugh at it all because it's such a contrived scenario, having a person holding a mic trying to be funny, and I feel I have to break the façade by saying what I think the audience must be thinking.
If I'm honest, where I am right now, I wish I could be more like those comedians who just talk and style the whole 'stand-up' situation out. Was it Lenny Bruce who said "I'm not a comedian. I'm Lenny Bruce"? I'd love to be like that. I'd love to just walk on stage and say "Ok, this is me and this is what I have to say".
Did you have a goal in mind when putting together the special - were you always planning to do something different with it?
Yes, I feel when you're committing something to film like this there are no rules. It's released online so there's no required length for the piece to be, and it's not a live gig when people watch it so why not go more filmic and see what other things you can do with the format?
It's a bugbear of mine that so many comedians don't even think about what is possible. They seem happy to just capture a gig where they were funny in front of an audience, and that's it!
When I started thinking about what I wanted for Pick of the Litter, I knew I wanted it shot as much like [Zach Galifianakis' special] Live from the Purple Onion as I could get it. I wanted to capture that intimate gig feel and create something where the viewer felt like they were at the actual gig.
But I also wanted to make the stand-up itself fit into a larger filmic storyline. Which I think myself and the brilliant Frankie Lowe's filming team have achieved nicely.
Can you remember how those particular concepts came about - the police interview, the bored backstage guy, the end bit...?
It has mostly come together in stages, there was no grand plan. The ending was lifted from my Edinburgh show in 2016. It was a totally absurd and surprising way to end some stand-up, and it proved really successful with audiences.
The whole police interview idea was something we ended up filming last so that we had something to cut away from the gig to. I'm really pleased we did it because it helped to feed the end payoff really well and also enabled me to comment on my act from an entirely different person's point of view. I'm really thankful to the fantastic [filmmaker] Ben Mallaby, and Toby Williams, who helped to pull the idea together.
The backstage guy is Peter Grahame, he's nothing short of a legend in comedy and he's been running Downstairs at the Kings Head for more years than most people have been alive. Every time we see each other he has some new thinly-veiled insult about my act, which over time I've learnt is his way of showing affection.
I desperately wanted to have him in the film in some capacity. What he does in Pick of the Litter is exactly what he tells me he does every time I go onstage. I'm so pleased we captured it.
Did any ideas not make the cut?
Yes, there was a lot of material we had to cut out because I'd either messed it up in some way on the day of the performance, or we needed to remove it to keep the thing from being too long. We also had some ideas that just didn't work.
For example, the amazing Laura Lexx who dutifully plays my support act originally met a grizzly end - but what we shot didn't quite work when we got the footage into the edit. But she's gone on to brilliantly smash appearing on Live At The Apollo and pleasing the whole nation - so maybe it's a good thing we didn't kill her off after all?
I loved Timeholes - were there ever plans to do more with that concept?
Toby, Ben and I talked about making Timeholes a larger idea where we fully explored every angle of the concept. We did start the process, but quickly we got distracted by other projects. Time travel honestly scrambles your brain to write. Even with the most straightforward idea it all gets very complicated very quickly. Maybe we'll go back to it in the future? If I had a time hole I could go check if we do and come back to you?
You've a great line in the special - 'weird men doing weird stuff that has to stop' - ever been heckled right there...?
I've never knowingly been heckled at that point. But I have noticed that audiences sometimes shift awkwardly in their seats a little when I start to address the subject. Not that I really explore it in any detail, but the whole #MeToo movement has rightfully been a big deal over the past couple of years.
I suspect that if you are going to drag an audience into something you want to say about it, you better be saying something that they agree with, or it better be really funny.
But yes, I've had many weird audience reactions over the years. As a rule I find heckles are more useful than abject silence, at least with a heckle you have something to use. I try to view heckles as gifts and then incorporate them somehow into the show.
For example, last month some guy heckled me, asking me to sing a song, I assume because he was bored of my talking. So, I just sang the rest of my set, which pleased the people who wanted to keep hearing my act without the heckles, but also made the heckler suddenly start listening.
You're doing a new show, Odd Paul, at the Leicester Comedy Festival - is that another step in a new direction?
For Odd Paul my aim is to steer my comedic point of view over subjects that I really care about. Up until now, despite my best efforts, I've always been about just finding the funny. I'd like to keep that but reveal more about Paul somehow and give this thing I do more meaning.
I mean, I say that now; as to whether I'll manage it or not is another thing entirely. I'll probably just turn it into some big utterly absurd punchline.


To make cream of mushroom soup, simply sautee the contents of one bag with an additional 2 tbsp. butter. Mix 1/2 cup flour with 1 1/2 cups milk until smooth. Slowly add to mushrooms, whisking constantly. Then slowly add another 2 1/2 cups of milk. Whisk until smooth and bubbly.
Add 1 tsp. salt, 1/2 tsp. pepper, and 1 tsp. parsley flakes. Remove from heat. Soup will thicken as it cools. Feel free to add more milk to reach desired consistency.
Makes the equivalent of 3 cans cream of mushroom soup.


Meet our team! Our tutors, music instructors, test prep instructors, specialists and writers come from diverse backgrounds, have a multitude of skills, and are experts in the areas they tutor or their instrument. All of our instructors are full-time professionals and many of them are current teachers and hold at least a Bachelor's Degree. They are required to go through an intense screening process that involves a current background check. For the tutoring sessions, our instructors will either travel to your home or meet at a local library. All music lessons do take place in the comfort of your own home. Take a look at our amazing teachers…!


11 bit Studios revealed that Frostpunk is making its way to consoles! Frostpunk: Console Edition is a finely tuned adaptation of the PC hit that will be making its way to Xbox One and PlayStation 4 later this year.
Frostpunk is a society survival game where heat means life and every decision comes at a price. Presenting complex strategic gameplay, demanding challenges and rich story, Frostpunk extends its unmatched survival experience to consoles. Adapted for consoles with revised controls and adjusted mechanics, Frostpunk: Console Edition allows you to fully test your tactical prowess on the frozen wastelands.


text:
RFT 089: Fear of Flying Counselor Tom Bunn 0
After graduating from Wake Forest University in psychology, Captain Tom entered the U.S. Air Force. Number one in his class when he got his wings in 1960, he was given his choice of assignments, and chose to fly the Air Force's first supersonic jet fighter, the F-100.
He served from 1961 until 1965 with the 9th Tactical Fighter Squadron at Spangdahlem Air Base in Germany flying the F-100 and F-105. In addition to flying, he did accident investigation and developed a safety device for the F-100.
While in Germany, Captain Tom raced a Lola Mk5 Formula 3 at the Nurburgring, Zolder, Zandvordt, and Rouen. When returning to the U.S., he converted the car to SCCA Formula C specifications, and won a U.S. National Championship in 1965.
From 1965 until 1986, he flew DC-8s, 707s, and 747s internationally with Pan Am.
From 1986 until 1996, he flew 747s, 757s and 767s at United Airlines.
The first fear of flying program was started at Pan Am by Captain Truman "Slim" Cummings. Captain Tom worked with him on that program until founding SOAR in 1982 to develop more effective methods for dealing with flight problems. This led to graduate school at Fordham University where he earned a Masters Degree with top honors, and several years of postgraduate study at the Gestalt Center Of Long Island, the New York Training Institute For Neurolinguistic Programming, and The Masterson Institute. He was licensed as a therapist in 1990.
Tom's website is http://www.fearofflying.com/ . He has authored an outstanding book to help travelers overcome their fear of flying.


text:
Physicians Total Care, Inc. 12 January 2012
PLAVIX — clopidogrel bisulfate tablet, film coated
Physicians Total Care, Inc.
WARNING: DIMINISHED EFFECTIVENESS IN POOR METABOLIZERS
The effectiveness of Plavix is dependent on its activation to an active metabolite by the cytochrome P450 (CYP) system, principally CYP2C19 [see Warnings and Precautions (5.1)]. Plavix at recommended doses forms less of that metabolite and has a smaller effect on platelet function in patients who are CYP2C19 poor metabolizers. Poor metabolizers with acute coronary syndrome or undergoing percutaneous coronary intervention treated with Plavix at recommended doses exhibit higher cardiovascular event rates than do patients with normal CYP2C19 function. Tests are available to identify a patient's CYP2C19 genotype; these tests can be used as an aid in determining therapeutic strategy [see Clinical Pharmacology (12.5)]. Consider alternative treatment or treatment strategies in patients identified as CYP2C19 poor metabolizers [see Dosage and Administration (2.3)].
1 INDICATIONS AND USAGE
1.1 Acute Coronary Syndrome (ACS)
For patients with non-ST-segment elevation ACS [unstable angina (UA)/non-ST-elevation myocardial infarction (NSTEMI)], including patients who are to be managed medically and those who are to be managed with coronary revascularization, Plavix has been shown to decrease the rate of a combined endpoint of cardiovascular death, myocardial infarction (MI), or stroke as well as the rate of a combined endpoint of cardiovascular death, MI, stroke, or refractory ischemia.
For patients with ST-elevation myocardial infarction (STEMI), Plavix has been shown to reduce the rate of death from any cause and the rate of a combined endpoint of death, re-infarction, or stroke. The benefit for patients who undergo primary percutaneous coronary intervention is unknown.
The optimal duration of Plavix therapy in ACS is unknown.
1.2 Recent MI, Recent Stroke, or Established Peripheral Arterial Disease
For patients with a history of recent myocardial infarction (MI), recent stroke, or established peripheral arterial disease, Plavix has been shown to reduce the rate of a combined endpoint of new ischemic stroke (fatal or not), new MI (fatal or not), and other vascular death.
2 DOSAGE AND ADMINISTRATION
2.1 Acute Coronary Syndrome
Plavix can be administered with or without food [see Clinical Pharmacology (12.3)].
For patients with non-ST-elevation ACS (UA/NSTEMI), initiate Plavix with a single 300 mg oral loading dose and then continue at 75 mg once daily. Initiate aspirin (75–325 mg once daily) and continue in combination with Plavix [see Clinical Studies (14.1)].
For patients with STEMI, the recommended dose of Plavix is 75 mg once daily orally, administered in combination with aspirin (75–325 mg once daily), with or without thrombolytics. Plavix may be initiated with or without a loading dose [see Clinical Studies (14.1)].
The recommended daily dose of Plavix is 75 mg once daily orally, with or without food [see Clinical Pharmacology (12.3)].
2.3 CYP2C19 Poor Metabolizers
CYP2C19 poor metabolizer status is associated with diminished antiplatelet response to clopidogrel. Although a higher dose regimen in poor metabolizers increases antiplatelet response [see Clinical Pharmacology (12.5)] , an appropriate dose regimen for this patient population has not been established.
2.4 Use with Proton Pump Inhibitors (PPI)
 Avoid using omeprazole or esomeprazole with Plavix. Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Plavix. When concomitant administration of a PPI is required, consider using another acid-reducing agent with minimal or no CYP2C19 inhibitory effect on the formation of clopidogrel active metabolite [see Warnings and Precautions (5.1), Drug Interactions (7.1) and Clinical Pharmacology (12.3)].
3 DOSAGE FORMS AND STRENGTHS
75 mg tablets: Pink, round, biconvex, film-coated tablets debossed with "75″ on one side and "1171″ on the other
300 mg tablets: Pink, oblong, film-coated tablets debossed with "300″ on one side and "1332″ on the other
4 CONTRAINDICATIONS
4.1 Active Bleeding
Plavix is contraindicated in patients with active pathological bleeding such as peptic ulcer or intracranial hemorrhage.
4.2 Hypersensitivity
Plavix is contraindicated in patients with hypersensitivity (e.g., anaphylaxis) to clopidogrel or any component of the product [see Adverse Reactions (6.2)].
5 WARNINGS AND PRECAUTIONS
5.1 Diminished Antiplatelet Activity Due to Impaired CYP2C19 Function
 Clopidogrel is a prodrug. Inhibition of platelet aggregation by clopidogrel is achieved through an active metabolite. The metabolism of clopidogrel to its active metabolite can be impaired by genetic variations in CYP2C19 [see Boxed Warning] and by concomitant medications that interfere with CYP2C19.
 Proton Pump Inhibitors
 Avoid concomitant use of Plavix with omeprazole or esomeprazole because both significantly reduce the antiplatelet activity of Plavix [see Drug Interactions (7.1) and Dosage and Administration (2.4)].
5.2 General Risk of Bleeding
Thienopyridines, including Plavix, increase the risk of bleeding. If a patient is to undergo surgery and an antiplatelet effect is not desired, discontinue Plavix five days prior to surgery. In patients who stopped therapy more than five days prior to CABG the rates of major bleeding were similar (event rate 4.4% Plavix + aspirin; 5.3% placebo + aspirin). In patients who remained on therapy within five days of CABG, the major bleeding rate was 9.6% for Plavix + aspirin, and 6.3% for placebo + aspirin.
Thienopyridines inhibit platelet aggregation for the lifetime of the platelet (7–10 days), so withholding a dose will not be useful in managing a bleeding event or the risk of bleeding associated with an invasive procedure. Because the half-life of clopidogrel's active metabolite is short, it may be possible to restore hemostasis by administering exogenous platelets; however, platelet transfusions within 4 hours of the loading dose or 2 hours of the maintenance dose may be less effective.
5.3 Discontinuation of Plavix
Avoid lapses in therapy, and if Plavix must be temporarily discontinued, restart as soon as possible. Premature discontinuation of Plavix may increase the risk of cardiovascular events.
5.4 Patients with Recent Transient Ischemic Attack (TIA) or Stroke
In patients with recent TIA or stroke who are at high risk for recurrent ischemic events, the combination of aspirin and Plavix has not been shown to be more effective than Plavix alone, but the combination has been shown to increase major bleeding.
5.5 Thrombotic Thrombocytopenic Purpura (TTP)
TTP, sometimes fatal, has been reported following use of Plavix, sometimes after a short exposure (<2 weeks). TTP is a serious condition that requires urgent treatment including plasmapheresis (plasma exchange). It is characterized by thrombocytopenia, microangiopathic hemolytic anemia (schistocytes [fragmented RBCs] seen on peripheral smear), neurological findings, renal dysfunction, and fever [see Adverse Reactions (6.2)].
6 ADVERSE REACTIONS
The following serious adverse reactions are discussed below and elsewhere in the labeling:
Bleeding [see Warnings and Precautions (5.2)]
Thrombotic thrombocytopenic purpura [see Warnings and Precautions (5.5)]
6.1 Clinical Studies Experience
Because clinical trials are conducted under widely varying conditions and durations of follow up, adverse reaction rates observed in the clinical trials of a drug cannot be directly compared to rates in the clinical trials of another drug and may not reflect the rates observed in practice.
Plavix has been evaluated for safety in more than 54,000 patients, including over 21,000 patients treated for 1 year or more. The clinically important adverse reactions observed in trials comparing Plavix plus aspirin to placebo plus aspirin and trials comparing Plavix alone to aspirin alone are discussed below.
In CURE, Plavix use with aspirin was associated with an increase in major bleeding (primarily gastrointestinal and at puncture sites) compared to placebo with aspirin (see Table 1). The incidence of intracranial hemorrhage (0.1%) and fatal bleeding (0.2%) were the same in both groups. Other bleeding events that were reported more frequently in the clopidogrel group were epistaxis, hematuria, and bruise.
The overall incidence of bleeding is described in Table 1.
Table 1: CURE Incidence of Bleeding Complications (% patients)
Plavix (+ aspirin)*
Placebo (+ aspirin)*
(n=6259)
Other standard therapies were used as appropriate.
Life-threatening and other major bleeding.
Major bleeding event rate for Plavix + aspirin was dose-dependent on aspirin: <100 mg = 2.6%; 100–200 mg = 3.5%; >200 mg = 4.9%Major bleeding event rates for Plavix + aspirin by age were: <65 years = 2.5%, ≥65 to <75 years = 4.1%, ≥75 years = 5.9%
Major bleeding event rate for placebo + aspirin was dose-dependent on aspirin: <100 mg = 2.0%; 100–200 mg = 2.3%; >200 mg = 4.0%Major bleeding event rates for placebo + aspirin by age were: <65 years = 2.1%, ≥65 to <75 years = 3.1%, ≥75 years = 3.6%
Led to interruption of study medication.
Major bleeding † 3.7 ‡ 2.7 §
Life-threatening bleeding 2.2 1.8
Fatal 0.2 0.2
5 g/dL hemoglobin drop 0.9 0.9
Requiring surgical intervention 0.7 0.7
Hemorrhagic strokes 0.1 0.1
Requiring inotropes 0.5 0.5
Requiring transfusion (≥4 units) 1.2 1.0
Other major bleeding 1.6 1.0
Significantly disabling 0.4 0.3
Intraocular bleeding with significant loss of vision 0.05 0.03
Requiring 2–3 units of blood 1.3 0.9
Minor bleeding ¶ 5.1 2.4
Ninety-two percent (92%) of the patients in the CURE study received heparin or low molecular weight heparin (LMWH), and the rate of bleeding in these patients was similar to the overall results.
In COMMIT, similar rates of major bleeding were observed in the Plavix and placebo groups, both of which also received aspirin (see Table 2).
Table 2: Incidence of Bleeding Events in COMMIT (% patients)
Type of bleeding
Plavix(+ aspirin)(n=22961)
Placebo(+ aspirin)(n=22891)
Major bleeds were cerebral bleeds or non-cerebral bleeds thought to have caused death or that required transfusion.
The relative rate of major noncerebral or cerebral bleeding was independent of age. Event rates for Plavix + aspirin by age were: <60 years = 0.3%, ≥60 to <70 years = 0.7%, ≥70 years = 0.8%. Event rates for placebo + aspirin by age were: <60 years = 0.4%, ≥60 to <70 years = 0.6%, ≥70 years = 0.7%.
Major * noncerebral or cerebral bleeding † 0.6 0.5 0.59
Major noncerebral 0.4 0.3 0.48
Fatal 0.2 0.2 0.90
Hemorrhagic stroke 0.2 0.2 0.91
Other noncerebral bleeding (non-major) 3.6 3.1 0.005
Any noncerebral bleeding 3.9 3.4 0.004
CAPRIE (Plavix vs. Aspirin)
In CAPRIE, gastrointestinal hemorrhage occurred at a rate of 2.0% in those taking Plavix vs. 2.7% in those taking aspirin; bleeding requiring hospitalization occurred in 0.7% and 1.1%, respectively. The incidence of intracranial hemorrhage was 0.4% for Plavix compared to 0.5% for aspirin.
Other bleeding events that were reported more frequently in the Plavix group were epistaxis and hematoma.
Other Adverse Events
In CURE and CHARISMA, which compared Plavix plus aspirin to aspirin alone, there was no difference in the rate of adverse events (other than bleeding) between Plavix and placebo.
In CAPRIE, which compared Plavix to aspirin, pruritus was more frequently reported in those taking Plavix. No other difference in the rate of adverse events (other than bleeding) was reported.
Page 1 of 6 1 2 3 4 5 … » Last Page »
Also by this Manufacturer
Additional medications in the 'Prescription Medications' section by Physicians Total Care, Inc.
Or get Physicians Total Care, Inc. product information by RSS.
Within the 'Prescription Medications' section:
Brand names beginning with the letter 'P' (Plavix).
Medications with generic name beginning with the letter 'C' (Clopidogrel Bisulfate).
Medication information from January 2012, newest first or January 2012, earliest first.
Use Trial Bulletin to search for clinical trials involving this product:
All MedLibrary.org resources are included in as near-original form as possible, meaning that the information from the original provider has been rendered here with only typographical or stylistic modifications and not with any substantive alterations of content, meaning or intent.
https://medlibrary.org/lib/rx/meds/plavix-9/
Trustworthy Health Information
Medications Updates via RSS
MedLibrary.org
Medication Sections
Prescription Medications Feed
This site is provided for educational and informational purposes only, in accordance with our Terms of Use, and is not intended as a substitute for the advice of a medical doctor, nurse, nurse practitioner or other qualified health professional.
Privacy Policy | Copyright © 2020. All Rights Reserved.


text:
<?php

namespace Embera\Http;

/**
 * This class is in charge of doing http requests. Its a very minimal
 * wrapper for curl or file_get_contents
 */
interface HttpClientInterface
{
    /**
     * Sets the configuration array for this object
     *
     * @param array $config
     * @return void
     */
    public function setConfig(array $config = []);

    /**
     * Executes http requests
     *
     * @param string $url
     * @param array $params Additional parameters for the respective part
     * @return string|bool
     *
     * @throws \Exception when an error ocurred or if no way to do a request exists
     */
    public function fetch($url, array $params = []);

}

redpajama_set_name: RedPajamaGithub


text:
WRI 1100: Purcell: MLA
Acknowledging, Paraphrasing, and Quoting Sources
Excerpt from a document created by the University of Wisconsin - Madison which takes you through the process of creating an acceptable paraphrase.
"Summarizing, Paraphrasing, and Quoting" (1:56)
"Quoting vs Paraphrasing - APA Style" (3:07)
The Everyday Writer with Exercises with 2016 MLA Update by Andrea A. Lunsford
Call Number: PE1408 .L865 2016
MLA Handbook by The Modern Language Association of America
Call Number: LB2369 .G53 2016
The 8th edition of MLA style approaches the construction of citations in a new way. Here is the description of the new process from the Purdue OWL site (https://owl.english.purdue.edu/owl/resource/747/1/)
When deciding how to cite your source, start by consulting the list of core elements. These are the general pieces of information that MLA suggests including in each Works Cited entry. In your citation, the elements should be listed in the following order:
Author.
Title of source.
Title of container,
Other contributors,
Number,
Publisher,
Publication date,
Each element should be followed by the punctuation mark shown here. Earlier editions of the handbook included the place of publication, and required punctuation such as journal editions in parentheses, and colons after issue numbers. In the current version, punctuation is simpler (just commas and periods separate the elements), and information about the source is kept to the basics.
Basic MLA Citation Style
Basic Rules for In-Text Citations from Purdue OWL
Format for Works Cited List
Basic Format:
Last Name, First Name. Title of Book. Publisher, Publication Date.
One author:
McEvoy, Sean. Theatrical Unrest : Nine Riots in the History of the Stage 1601-2004. Routledge, 2016.
Two authors:
Frost, Anthony, and Ralph Yarrow. Improvisation in Drama, Theatre, and Performance. Palgrave, 2016.
Three or more authors:
Kaye, Deena, et al.Sound and Music for the Theatre. Focal P, 2016.
Book with one editor:
Eldridge, Richard, editor. The Oxford Handbook of Philosophy and Literature. Oxford UP, 2009.
Book with two editors:
Bull, Michael, and Les Back, editors. The Auditory Culture Reader. 2nd ed. Bloomsbury Academic, 2016.
Book with three or more editors:
Newburger, Harriet, et al., editors.. Neighborhood and Life Chances: How
Place Matters in Modern America. U of Pennsylvania P, 2011.
Articles (see bottom of page for various newspaper examples)
Basic Format: Author(s). "Title of Article." Title of Periodical, Day Month Year, pages.
Shook, John. "With Liberty & Justice for All." Humanist, Jan/Feb 2013, 21-24.
Scholarly Journal
Basic Format: Author(s). "Title of Article." Title of Journal, Volume, Issue, Year, pages.
Todd, Nathan R., et al. "Preliminary Validation of The Sanctification of Social Justice Scale." Psychology of Religion and Spirituality, vol. 6, no. 3, 2014, 245-256.
From an Online Database such as Academic Search Premier
Richardson, Jayson W, and Nicholas J Sauers. "Social Justice in India: Perspectives from School Leaders in Diverse Contexts." Management in Education, vol. 28, no. 3, 2014, pp. 106-109. Academic Search Premier, doi: http://dx.doi.org/10.1177/0892020614535799.
Basic Format: Editor, author, or compiler name (if available). Name of Site. Version number, Name of institution/organization affiliated with the site (sponsor or publisher), date of resource creation (if available), URL, DOI or permalink. Date of access (if applicable).
The Purdue OWL Family of Sites. The Writing Lab and OWL at Purdue and Purdue U, 2008, owl.english.purdue.edu/owl. Accessed 23 Apr. 2008.
A Page on a Website
McCarthy, Gina. "Climate Week - It's Time for Action." EPA Connect. Environmental Protection Agency, 22 Sept. 2014. https://blog.epa.gov/blog/2014/09/climate-week-its-time-for-action/. Accessed 26 Sept. 2014.
For more examples, see the PurdueOWL MLA Formatting site
From The Indian River State College Library in Florida
<< Previous: Evaluating Sources


There are some brand names which have become bywords for industry-leading quality, heaps of fun, technology innovation, supreme reliability, and… well, you get the picture… all the good stuff.
Shimano is, without a doubt, one of those brand names.
If you are anything like us, the Japanese bike accessory pioneers have been with you through it all. From the beginning, starting out as kids and falling in love with biking, to the present day, still building on that passion, seeing the Shimano brand logo on a piece of kit meant, quite simply, that it was among the best in the market.
At Racers Edge, we are very proud to be able to uphold that tradition and to give our customers the chance to buy great quality Shimano accessories in New Zealand. At our bike rental store in Wanaka, and also online, we provide a wide range of different pieces, each designed to augment and enhance the rider's experience on their bike.
This may include Shimano brake callipers and gear mechanisms, through to lightweight but robust pedals, shoes, headwear and apparel, drive chains and chain rings, or any other accessory which Shimano make available to buy in New Zealand. Browse our Shimano range online, and get in touch to enquire about the products we have currently available, or those we have inbound. It might be that we have the perfect piece of Shimano gear waiting for you just around the corner.
Shimano, as a brand, is interested in the science of cycling – the forces of motion and traction that make the sport such a thrill for so many people. Each new accessory they create – each new piece of technology they design and craft – focuses on this core aim; to create harmony between the bike, the rider, and the very concept of cycling itself, by harnessing science.
As anyone who has ever come into contact with Shimano before, this does not translate to a sterile, calculated, and traditionally scientific form of riding. Instead, the effect is a joyous one, with each and every thing that makes cycling and riding so enjoyable to so many people, supported by Shimano's proudly academic approach.
If you are looking to buy Shimano bike accessories in New Zealand, you came to the right place. To connect with our range of accessory pieces, or to buy another mountain or road bike for exploring New Zealand's roads and trails, take a look at our range at Racers Edge. Or get in touch. with any product or Wanaka queries, or to buy or hire bikes in Wanaka.


There aren't many things capable of sparking such summer nostalgia than a Slush Puppy machine.
Just the sight of that dog licking its chops, while the luminously bright slush whirs round and round could give me a summery feeling in the depths of January.
Now, I've just learnt it's possible to buy one of your own, to put in your kitchen, and payday can't come soon enough.
You can grab an iconic Slush Puppy Machine from ASOS for the surprisingly reasonable price of £59.99. Perfect for when you want your summer party to be a little bit on the quirky side.
The machine can whip up a whole litre worth of slush, so there'll be plenty to go around. Oh, and it comes with ten cups too, which is nice and handy.
In all seriousness, it never occurred to me you could actually own one of these absolute beauties and the blue-tongued seven-year-old in me is extremely excited.
All you have to do is purchase some official syrup – sadly not included but available at Menkind quite cheapish – and voila.
Rainbow Drops? Bubble tape? Space Invaders? Maybe even a sophisticated platter of cheese-strings and Dairylea Dippers?
Fizz Creations gets the mood just right with its innovative range of home accessories.
Now, ASOS has saved many of us over the years; providing simple last minute solutions to many a night out outfit. However, they've really earned our love this time.
I can promise you. I WILL HAVE MY OWN SLUSH PUPPIE MACHINE IN MY HOME!!!
Finding out that ASOS is selling a Slush Puppie machine has made my life.
Can I really justify buying a £60 Slush Puppie machine from ASOS?
It seems we're all about the slushies this summer. Coca Cola is set to launch its first Coke slushie and it sounds so fabulous, I'm genuinely surprised it's only just been invented.
Hitting Japan from April 16 onwards in picnic friendly pouches, this icy delight will have all the, frankly underrated, flavourings of lemon-flavoured Coke.
Oh and if you happen to live in Japan then they'll be sold at the recommended retail price of 130 yen ($1.21), which isn't bad at all.
Finding out that ASOS is selling a Slush Puppie machine has made my life ??
As if ASOS are selling slush puppie machines!!!
Somewhat frustratingly, this fabulous fizz fest is currently out of stock. However, believe me when I say I'll be checking every hour on the hour until said stock is replenished.
The question remains: blue or red or just a deliciously frosty mish-mash of both?


On the occasion of Lex Mex 2017, The MexCC is pleased to invite you to attend a private dinner where UK lawyers will meet with leading colleagues from Mexico to discuss recent developments, challenges and opportunities for collaboration amongst British and Mexican law firms. Our guests of honour is Judge Mariana Rodriguez Mier y Terán from the Supreme Court of Justice from the State of Tamaulipas., Mexico.
Note: Justice Luis Maria Aguilar Morales - President of the Mexican Supreme Court of Justice and Justice Jose Fernando Franco González Salas.
Time: 19:00 - 22:00 hrs.
Special thanks to the sponsorship of The Law Society.
Dinner Reservations SOLD OUT! Thank you.
Time: 8:15 am - 12:30 hrs Morning Session - Panel discussions.
Keynote Speaker: Judge Maria Rodíguez Mier y Terán - Supreme Court of the State of Tamaulipas.
11:00 - 11:20 The new National Anti-corruption System, how to comply?
Speaker: Judge Mariana Rodríguez Mier y Terán, Judge of the State of Tamaulipas.
Speaker: Yves Hayaux du Tilly, chairman, MexCC.
Breakout sessions - Please choose one of the following sessions on the booking page.
 Moderator: Frederico Singarajah, Hardwicke.
Tax - Hosted by: Ernst & Young - Canary Wharf, 25 Churchill Pl, London E14 5EY.
Tax Senior Manager at EY LABC Tax Desk .
Offer valid for bookings made before the 24th September 2017.


text:
class ClientsOffer < ActiveRecord::Base
  has_one :client
  has_one :offer

  attr_accessible :client_id, :offer_id, :participated, :position
end


A handy checklist to help you develop donor personas.
Tell your story in a way that inspires and engages your donors.
Learn the basics and best practices every online fundraiser should know!
Get ready to run the best-ever text giving campaign with these tips and strategies.
Make your nonprofit's website better than ever!
Tips to help you maximize your text giving program!


We signed off from May in style tonight, with some gorgeous spring sunshine, although there was still a slight chill in the air and the wind was still there!! Duncan had come dressed for summer and was shivering at the start, so Dave kindly nipped back to get him some armwarmers. By the time we were ready to depart, there was 14 of us, which I think might be a record for a night ride.
A route out into Lower Nidderdale was agreed, so we set off up the Otley Road and along Penny Pot Lane where we turned right at the end and headed past Menwith Hill, before dropping down into Darley. We then followed the road alongside the river into Birstwith before heading up Clint Bank and then dropping down into Hampsthwaite. By now the attention was being drawn to a post ride recovery pint, so we made our way out of Hampsthwaite up and over the A59 and back to Penny Pot. Instead of the direct route back, we turned right and went up the short and steep climb of Pot Bank, turning left in Beckwithshaw, before then making for the New Inn on the Otley Road for a recovery Guinness (coffee in Jamie's case).
This entry was posted in Ride Reports and tagged 25, cycle, cycling, harrogate, nidderdale, yorkshire. Bookmark the permalink.


kids beach towels minstys pirtes rective bech deutsche interior design interiorfox legal dublin.
kids beach towels s interior design deutsch deutschland point method matlab.
kids beach towels interior design blog point method explained interiorfox legal.
kids beach towels interior deutsch design software point method vs simplex.
kids beach towels interior point method example minister germany austria italy monologue joyce.
kids beach towels interior monologue schreiben design studium interiorfox limited.
kids beach towels interior design software free download styles for living room list.
kids beach towels interior design styles for living room fox review hamburg.
kids beach towels deutsche interior blog point method example interiorfox legal.
kids beach towels interior design course berlin monologue structure point method linear programming.
kids beach towels interior design styles monologue example point method linear programming.
kids beach towels interior monologue schreiben design studium hamburg course berlin.
kids beach towels interior design studium osterreich hamburg styles bedroom.
kids beach towels interior monologue in a sentence design software free download interiorfox gutschein.
kids beach towels interior monologue joyce design studium munchen point method matlab.
kids beach towels german interior minister thomas de maiziere interiorfox rabatt design styles pdf.
kids beach towels s interior minister germany design blog studium berlin.
kids beach towels interior design app monologue joyce point method vs simplex.
kids beach towels s olaf deutschland interior design point method current minister of germany.
kids beach towels deutsche interior design blogs blogger interiorfox legal.
kids beach towels interior monologue joyce definition literature interiorfox gutschein.
kids beach towels interior point method vs simplex design styles for small spaces monologue in a sentence.
kids beach towels del bech interior design deutsch ubersetzung studium koln.
kids beach towels s deutsche interior design blogs styles pdf blog.
kids beach towels moe s deutsche interior blogger blog interiorfox mobel.
kids beach towels interior deutsch englisch design studium wien point method complexity.
kids beach towels interiorfox rabatt interior design studium munchen styles for living room.
kids beach towels interior ministry deutschland design software mac free online.


Breed: I don't have a cat..
I'm allergic to cats but I would love to have one. I've recently heard that there is a medication you can give YOUR CAT to make you less allergic to them. Is this true and if so what is the name of this medication?
I wish there was a medication that you could give to cats so that people would not be allergic!!!
I doubt that the medication you have seen advertised is effective but you can respond and give me a link or the name and I can research it for you.
When people are allergic to cats they are usually allergic to the dandruff or dying skin cells from a cat. There are a few things that you can do for your cat that may help to reduce the amount of dander that is produced.
One thing would be to use a high quality food. With most cat food you tend to get what you pay for.
Another thing would be to add some omega fatty acids to the cat's food. This will help with the health of the coat and reduce the shedding and dander.
You will find products advertised that are wipes that you wipe down the back of the cat to reduce the dander. Really, you can get the same effect if you use a moist paper towel to do this once a day.


text:
C Clayton
FAMOUS QUOTES FROM THE BEATLES
One of the reasons The Beatles (John Lennon, Paul McCartney, George Harrison, and Ringo Starr) music has made such a lasting impression on the world is the wisdom found in the lyrics. They created catchy fun music, but the lyrics were inspiring and like poetry to the heart and soul. Beatles music transcends age, class, gender and race. They always had a way with words and could easily convey the innermost feelings through songs.
Paul McCartney and John Lennon shared a sad connection of losing their mothers as teenagers. This could possibly have been a driving force behind the pair. McCartney told The Late Show with Stephen Colbert in September 2019 "We had a kind of bond that we both knew about that, we knew that feeling,". "I never thought that it affected my music until years later. I certainly didn't mean it to be. But it could be, you know those things can happen."
Quotes from John Lennon
John Lennon will always be remembered as an enduring cultural icon. He was obviously a talented singer song writer, but he was also an activist for love. The man became an emblem of peace.
Here are some of his most inspirational quotes taken from his song lyrics:
"You may say I'm a dreamer, but I'm not the only one. I hope someday you'll join us. And the world will live as one."
"Love is the answer and you know that for sure. Love is a flower You got to let it, you gotta let it grow"
"Life is what happens while you're busy making other plans"
"I am he as you are he as you are me and we are all together"
"You may say I am a dreamer but I'm not the only one"
Peace Quotes John Lennon
After his massive success in the Beatles in 1971 he released his solo album "Imagine". Famously him and his wife, Yoko Ono invented a form of peace protest by staying in bed while being filmed and interviewed:
"Peace is not something you wish for; It's something you make, something you do, something you are, and something you give away."
"All we are saying is give peace a chance."
"If everyone demanded peace instead of another television set, then there'd be peace."
John Lennon Quotes about Life
Some of John Lennon's famous quotes about Life:
"I don't believe in yesterday, by the way."
"You don't need anybody to tell you who you are or what you are. You are what you are!"
"Life is what happens to you while you're busy making other plans."
"God is a concept by which we measure our pain"
"We've got this gift of love, but love is like a precious plant…. You've got to keep watering it. You've got to really look after it and nurture it."
"I can't wake you up. You can wake you up. I can't cure you. You can cure you."
"What we've got to do is keep hope alive. Because without it we'll sink."
"Reality leaves a lot to the imagination."
"It's better to fade away like an old soldier than to burn out."
"Art is only a way of expressing pain."
"I believe in everything until it's disproved. So I believe in fairies, the myths, dragons. It all exists, even if it's in your mind. Who's to say that dreams and nightmares aren't as real as the here and now?"
John Lennon Famous Love Quotes
"We live in a world where we have to hide to make love, while violence is practiced in broad daylight."
"If someone thinks that love and peace is a cliche that must have been left behind in the Sixties, that's his problem. Love and peace are eternal."
"We've got this gift of love, but love is like a precious plant. You can't just accept it and leave it in the cupboard or just think it's going to get on by itself. You've got to keep watering it. You've got to really look after it and nurture it."
"It matters not who you love, where you love, why you love, when you love or how you love, it matters only that you love".
"We need to learn to love ourselves first, in all our glory and our imperfections. If we cannot love ourselves, we cannot fully open to our ability to love others or our potential to create. Evolution and all hopes for a better world rest in the fearlessness and open-hearted vision of people who embrace life."
"Love is a promise, love is a souvenir, once given never forgotten, never let it disappear."
Quotes from Paul McCartney
Paul McCartney was born James Paul McCartney but today is known as Sir James Paul McCartney CH MBE. After the Beatles split in 1970, Paul started his solo career and formed the band Wings with his first wife, Linda. Today McCartney is one of the most successful composers and performers of all time.
"In the end, the love you take is equal to the love you make."
"When you were young, and your heart, was an open book. You used to say, live and let live."
"Suddenly, I'm not half the man I used to be"
"But if this ever-changing world in which we're living makes you give in and cry… Say live and let die."
"Bright are the stars that shine, dark is the sky… I know this love of mine will never die. And I love her"
"But as for me, I still remember how it was before… and I am holding back the tears no more"
"And don't you know that it's just you, hey Jude, you'll do / The movement you need is on your shoulder"
"You'd think that people would have had enough of silly love songs / I look around me and I see it isn't so."
"All the lonely people… Where do they all belong?"
"It was written that I would love you, from the moment I opened my eyes"
"Tomorrow may rain, so I'll follow the sun"
"Changing my life with a wave of her hand"
"Take these broken wings and learn to fly"
"With life and all I've been through, I do have a belief in goodness, a good spirit. I think what people have done with religion is personified good and evil, so good's become God with 'o' out, and evil's become Devil with a 'd' added. That's my theory of religion"
Funny Beatles Quotes
Over the years the boys got plenty of stick with reporters around the world and they became well known for their one-liners. The boys were very witty and sarcastic and gave clever answers to stupid questions. Over the years they became masters at answering interview questions. Some of the Beatles famous lines include:
Reporter: "You've got fame and fortune, if this were all to disappear overnight, and you were left with one thing you'd like to hang on to, what would that be?"
The Beatles in sync said: "The money,"
Reporter: "[The Americans] think your haircuts are un-American,"
Lennon: "Well, that's very observant of them, cause we aren't American, actually,"
Reporter: "How did you find America?"
Lennon: "Turned left at Greenland."
Reporter: "Which one are you?"
Lennon: "I'm Eric."
Reporter: "Has success changed your life?"
Harrison: "Yes."
Reporter: "What the inspiration for "Eleanor Rigby"?
Lennon: "two queers."
Reporter: "John, whats your favourite type of girl?"
Lennon: "My wife".
Reporter: "George, whats your favourite type of girl?"
George: "John's wife".
Ringo Starr Quotes
Ringo Starr as we know him was actually born Richard Starkey but today he is a Sir with an MBE. He was the drummer for the Beatles and a co-writer and singer. Some Ringo Starr quotes are as follows:
"I feel the older I get, the more I'm learning to handle life. Being on this quest for a long time, it's all about finding yourself."
"I want to deal with what's in front of me now to the best of my abilities, and sometimes that's not very good. But a lot of the days it is really great."
"I never studied anything, really. I didn't study the drums. I joined bands and made all the mistakes onstage."
"I don't collect any memorabilia. I wish I'd have kept everything I had. But who knew you had to keep it. Just gave it away. And we lost so much and we didn't look after a lot of it"
"I couldn't put my finger on one reason why we broke up. It was time, and we were spreading out. They were spreading out more than I was. I would've stayed with the band."
"People only look at me as a Beatle, but my friends look at me as a whole person. That's how life works, but it's not bugging me anymore."
"The Beatles were just four guys that loved each other. That's all they'll ever be."
"Everything government touches turns to crap."
"Of course I'm ambitious. What's wrong with that? Otherwise you sleep all day."
"America: It's like Britain, only with button"
"I was the new boy. It was like joining a new class at school where everybody knows everybody else but me"
"Do you remember when everyone began analyzing Beatles songs, I don't think I ever understood what some of them were supposed to be about."
Quotes from George Harrison
Some of the best George Harrison Quotes are below:
"If you don't know where you're going, any road will take you there."
"With our love, we could save the world. "
"All the world is birthday cake, so take a piece, but not too much."
"The Beatles saved the world from boredom. "
"I wanted to be successful, not famous. "
"I'll give up this sort of touring madness certainly, but music-everything is based on music. No, I'll never stop my music. "
As long as you hate, there will be people to hate. "
"Life goes on within you and without you. "
"As far as I'm concerned, there won't be a Beatles reunion as long as John Lennon remains dead. "
"Try to realize it's all within yourself no one else can make you change, and to see you're only very small and life flows on within you and without you"
"I'm really quite simple. I plant flowers and watch them grow… I stay at home and watch the river flow."
The Beatles Locations
The Beatles Locations for fans wanting to walk in the footsteps of the fab four. Locations in London and Liverpool.
Survivors Guide to Liverpool Scouse Terms
Britain is filled with an array of accents. Liverpool certainly has one of the most distinctive accents in the country: Our list Liverpool Scouse Terms should help you through your day in the city.
Liverpool sites – Places to visit in Liverpool
Liverpool sites: TOP Places to visit in Liverpool When people
The Beatles Best Albums
The Beatles Best Albums by Record Sales The Beatles music
In Memory John Lennon: A Fab4 Legend
IN MEMORY OF JOHN LENNON: A FAB4 LEGEND There are
Beatles Tours
COVID-19 Response & Guidelines
Privacy Policy, Cookies & Data Security
Latest News / Blog
Our Pick-Up Locations & Getting to Liverpool
SIGN UP TO LATEST NEWS & EXCLUSIVE OFFERS
Subscribe to our free newsletter & look out for exclusive offers on all things Beatles and music tours across the UK!
Facebook-f Instagram Youtube Linkedin Tripadvisor
Sign up to our latest news, exclusive tour offers & company updates.