text:
\section{Introduction}\label{sec:intro}
Learning cross-lingual word representations has been the focus of many recent works  \citep{klementiev12a,mikolov13a,faruqui14a,ammar16,artetxe16a,conneau18a}.
It aims at learning a shared embedding space for words across two (bilingual word embeddings or BWE) or more languages (multilingual word embeddings or MWE), by mapping similar words (or concepts) across different languages close to each other in the common embedding space. Such a representation is useful in various applications such as cross-lingual text classification \citep{klementiev12a,ammar16}, building bilingual lexicons \citep{mikolov13a}, cross-lingual information retrieval \citep{vulic15a}, sentiment analysis \citep{zhou15}, and machine translation \citep{gu2018,lample18a,artetxe18c}, to name a few.

\citet{mikolov13a} observe that word embeddings exhibit similar structure across different languages. In particular, they show that the geometric arrangement of word embeddings can be (approximately) preserved by linearly transforming the word embeddings from one language space to another.
Thereafter, several works have explored learning BWEs in supervised \citep{xing15a,artetxe16a,artetxe18a,smith17b,mjaw19a}  as well as unsupervised \citep{zhang17a,zhang17b,conneau18a,artetxe18b,alvares18a,hoshen18,grave18} settings.
Supervision is usually in the form of a bilingual lexicon. Unsupervised BWE approaches enjoy the advantage of not requiring a bilingual lexicon during the training stage.

Generalization from bilingual  to multilingual setting is desirable in various multilingual applications such as document classification, dependency parsing \citep{ammar16}, machine translation, etc. Representing the word embeddings of several languages in a common shared space can allow knowledge transfer across languages wherein a single classifier may be learned on multilingual datasets \citep{heyman19a}.

In this work, we propose a two-stage framework for learning a shared multilingual space in the unsupervised setting. The two stages aim at solving the following  sub-problems: a) learning unsupervised word alignment between a few pairs of languages, and \textit{subsequently} b) learning how to map the monolingual embeddings of each language to a shared multilingual space.
The sub-problems are separately solved using existing techniques.
In contrast, existing state-of-the-art unsupervised MWE approaches \citep{chen18a,heyman19a,alaux19a} aim at solving the above sub-problems jointly.
Though it appears like a simple baseline approach, the proposed framework provides the robustness and versatility often desirable while learning an effective shared MWE space for distant languages. Unsupervised alignment often fail for distant languages \citep{sogaard18,glavas19a,vulic19a}.
Our approach can seamlessly work in ``hybrid'' settings, where supervision is available for some languages (but not for others).
Integrating such hybrid setups in the joint optimization framework of existing unsupervised MWE approaches \citep{chen18a,heyman19a,alaux19a} may not\footnote{\citet{heyman19a}, for example, state that their approach is impractical in the supervised setting as it requires pairwise dictionaries for all pair of languages.} always be feasible.

We evaluate our approach on different tasks such as bilingual lexicon induction (BLI), cross-lingual word similarity, and two downstream multilingual tasks: document classification and dependency parsing. We summarize our findings below.
\begin{itemize}
\item For a group consisting of similar languages, we observe that the proposed approach achieves BLI score similar to the existing unsupervised MWE approaches.
We also observe that all multilingual approaches, including ours, benefit from transfer learning  across languages in such a setting.
\item In a group comprising of distant languages, we observe sub-optimal BLI performance of existing unsupervised MWE approaches. The presence of distant languages sometimes adversely affect the alignment of even similar language pairs in such methods. The proposed approach, however, is robust and outperforms other multilingual approaches in such settings.
\item The proposed approach performs better than other MWE baselines on cross-lingual word similarity, document classification, and dependency parsing tasks.
\end{itemize}

The rest of the paper is organized as follows.
Section~\ref{sec:proposed} discusses the proposed unsupervised MWE framework. Section~\ref{sec:experimentalsetup} describes our experimental setup and Section~\ref{sec:results} presents and analyzes the empirical results. Section~\ref{sec:conclusion} concludes the paper. We begin by discussing related works in the next section.

\section{Related Work}\label{sec:relatedworks}
\textbf{Bilingual setting:} The problem of learning bilingual mapping of word embeddings in the supervised setup is as follows.
Let ${\mathbf x}_i\in\mathbb{R}^d$ and ${\mathbf z}_j\in\mathbb{R}^d$ denote the $i$-th and $j$-th word embeddings of the source and the target languages, respectively.
A seed translation dictionary ${\mathbf Y}\in\{0,1\}^{n\times n}$ is available during the training phase such that ${\mathbf Y}(i,j)\coloneqq y_{ij}=1$ if ${\mathbf x}_i$ corresponds to ${\mathbf z}_j$ and $y_{ij}=0$ otherwise. The aim is to learn a mapping from the source language embedding space to the target language embedding space.
A popular approach to learn this mapping ${\mathbf W}\in\mathbb{R}^{d\times d}$ is by solving the following Orthogonal Procrustes problem:
\begin{equation}\label{eqn:procrustes}
\mathop{\rm min}\limits_{{\mathbf W}\in\mathbb{O}^d} \sum_{i,j} y_{ij}\norm{{\mathbf W}{\mathbf x}_i-{\mathbf z}_j}^2,
\end{equation}
where $\mathbb{O}^d$ is the set of $d$-dimensional orthogonal matrices.
Problem (1) admits a closed-form solution \citep{schonemann1966a}. Several improvements of above have been explored in this direction including pre/post-processing of embeddings \citep{artetxe18a}, additional transformations \citep{doval18a}, different loss functions \citep{joulin18b,mjaw19a}, to name a few.

In the unsupervised setting, the seed dictionary (matrix ${\mathbf Y}$) is unknown. Popular unsupervised BWE frameworks include self-learning \citep{artetxe18b}, adversarial \citep{zhang17a,conneau18a}, and optimal transport \citep{zhang17b,alvares18a} based approaches.
Most unsupervised algorithms aim at learning both ${\mathbf Y}$ and ${\mathbf W}$ simultaneously, in a joint optimization framework.
A few such the Gromov-Wasserstein approach \citep{peyre16a,alvares18a} explicitly aim at learning only the bilingual word alignment, i.e., the ${\mathbf Y}$ matrix,  and they suggest using (\ref{eqn:procrustes}) to learn the mapping operator (${\mathbf W}$). A few recent works have analyzed the effectiveness of supervised and unsupervised BWE approaches \citep{sogaard18,glavas19a,vulic19a}.

\noindent\textbf{Multilingual setting:} In the supervised setting, a popular multilingual approach is to bilingually map the embeddings of all other languages to a chosen pivot language \citep{ammar16,smith17b}. \citet{kementchedjhieva18a} propose to employ the generalized Procrustes analysis method \citep{gower75} to learn a MWE space. This, however, requires $n$-way dictionary for $n$ languages, which is a stringent constraint in real-world applications.
Recently, \citet{mjaw19a} have proposed a geometric approach for learning a latent shared MWE space using only a few bilingual dictionaries ($n-1$ bilingual dictionaries for $n$ languages suffice).

\citet{chen18a} are among the first to propose learning MWE in the unsupervised setting. They extend the unsupervised BWE approach of \citep{conneau18a} -- adversarial training and iterative refinement procedure -- to the multilingual setting.
However, the GAN-based procedure of \citet{conneau18a} has known concerns related to optimization stability with distant language pairs \citep{sogaard18}.
\citet{alaux19a} propose a joint optimization framework for learning word alignment (${\mathbf Y}$) and mapping (${\mathbf W}$) between several pair of languages. It aims to learn the shared MWE space by optimizing direct mappings between pairs of languages as well as indirect mappings (via a pivot language).
The bilingual alignments are learned as doubly-stochastic matrices and are modeled using the Gromov-Wasserstein loss function. The mapping operators are modeled using the non-smooth RCSLS loss function \citep{joulin18b}. For efficient optimization, they employ alternate minimization in a stochastic setting \citep{grave18}. \citet{heyman19a} propose to learn the shared MWE space by incrementally adding languages to it, one in each iteration. Their approach is based on a reformulation of the unsupervised BWE approach of \citet{artetxe18b}.

\section{Unsupervised Multilingual Multi-stage Framework}\label{sec:proposed}
We develop a two-stage algorithm for unsupervised learning of multilingual word embeddings (MWEs). The key idea is as follows:
\begin{itemize}
\item learn unsupervised word alignment between a few pairs of languages, and then
\item use the above (learned) knowledge to learn the shared MWE space.
\end{itemize}
We propose to solve the above two stages sequentially, using existing techniques. This is in contrast to the existing unsupervised MWE works  \citep{alaux19a,chen18a,heyman19a} that aim at learning the unsupervised word alignments and cross-lingual word embedding mappings jointly.
Though the proposed approach appears simple, we empirically observe that it has better generalization ability and robustness.
We summarize the proposed approach, termed as \textbf{U}nsupervised \textbf{M}ultilingual \textbf{M}ulti-stage \textbf{L}earning (UMML), in Algorithm~\ref{alg:geommgraph} and discuss the details below.

\begin{algorithm}[t]
    \centering
    \caption{Unsupervised Multilingual Multi-stage Learning (UMML)}\label{alg:geommgraph}

   \begin{tabular}{p{1\textwidth}}
    {\bfseries Input:} Monolingual embeddings ${\mathbf X}_i$ for each language $L_i$ and
    an undirected, connected graph $G(V,E)$ with $V=\{L_1,\ldots,L_n\}$\\\\
    /*{\bfseries Stage 1:} Generating unsupervised bilingual lexicons ${\mathbf Y}_{ij}$*/ \\
     {\bfseries for }{each unordered pair  $(L_i,L_j)\in E$ }{\bfseries do}\\
     \ \ ${\mathbf Y}_{ij}\leftarrow$ \texttt{UnsupWordAlign}$({\mathbf X}_i,{\mathbf X}_j)$ \\
     {\bfseries end for}\\\\
    /*{\bfseries Stage 2:} Learning multilingual word embeddings in a shared latent space*/\\
    Run \texttt{GeoMM} on $G(V,E)$ with monolingual embeddings ${\mathbf X}_i$ for all languages $L_i$ and bilingual lexicons ${\mathbf Y}_{ij}$ for all language pairs $(L_i,L_j)\in E$ \\\\
    Output of \texttt{GeoMM}:\\
    a) metric $\mathbf B$ (a positive definite matrix), and \\
    b) orthogonal matrices ${\mathbf U}_i\ \forall i=1,\ldots,n$\\\\
    /*Representing word embedding $x$ of language $L_i$ in the common multilingual space*/\\
    $x\rightarrow \mathbf B^\frac{1}{2}{\mathbf U}_i^\top x $\\
   \end{tabular}
   }
\end{algorithm}

\subsection{Stage 1: Generating Bilingual Lexicons}
The first stage of our framework is to generate bilingual lexicons for a few pairs of languages. These lexicons are used in learning a shared MWE space in the second stage.
We employ existing unsupervised bilingual word alignment algorithms \citep{artetxe18b,alvares18a} to generate the bilingual lexicons.
It should be noted that these lexicons are learned in the bilingual setting independent of each other. Additionally, they can be learned in parallel.
Our framework allows usage of different unsupervised bilingual word alignment algorithms \citep{artetxe18b,alvares18a,conneau18a} for different pairs of languages.
More generally, bilingual lexicon for different pairs of languages may even be obtained using different class of algorithms/resources: unsupervised, weakly-supervised with bootstrapping \citep{artetxe17a}, human supervision, etc.
This is because the second stage of our framework is agnostic of how the lexicons are obtained. Such flexibility in obtaining bilingual lexicons is desirable for learning a good quality shared MWE space for real-world applications since it has been observed that existing unsupervised bilingual word embedding algorithms may fail when languages are from distant families \citep{sogaard18,mjaw19a,glavas19a,vulic19a}.
To the best of our knowledge, existing unsupervised MWE approaches do not discuss extensibility to such hybrid settings.

We use two unsupervised bilingual word alignment algorithms \citep{alvares18a,artetxe18b} for generating bilingual lexicons, described in Section~\ref{subsec:implementation}. It should be emphasized that these bilingual lexicons are learned only for a few pairs of languages. Such pairs may be randomly chosen but should satisfy a very simple graph-connectivity criterion mentioned in the following section. In our experiments, $n-1$ bilingual lexicons are generated for $n$ languages.

\subsection{Stage 2: Learning Multilingual Word Embeddings}\label{subsec:geomm}
As stated earlier, we learn the MWEs using the bilingual lexicons obtained from the first stage. To achieve our objective, we propose to employ the Geometry-aware Multilingual Mapping (GeoMM) algorithm \citep{mjaw19a}.

The setting of GeoMM may be formalized as an undirected, connected graph $G$, whose nodes represent languages and edges between nodes imply availability of bilingual dictionaries (for the corresponding pair of languages). GeoMM represents multiple languages in a common latent space by learning language-specific rotations for each language ($d\times d$ orthogonal matrix ${\mathbf U}_i$ for each language $L_i$) and a similarity metric common across languages (a $d\times  d$ symmetric positive-definite matrix $\mathbf B$), where $d$ is the dimensionality of the monolingual word embeddings.
The rotation matrices align the language embeddings to a common latent space, while the (common) metric $\mathbf B$ governs how distances are measured in this latent space.
Both the language-specific parameters (${\mathbf U}_i\ \forall L_i$) and the shared parameter ($\mathbf B$) are learned via a joint optimization problem \citep[refer][Equation $3$]{mjaw19a}.
The function that maps a word embedding $x$ from language $L_i$'s space to the common latent space is given by: $x\rightarrow \mathbf B^\frac{1}{2}{\mathbf U}_i^\top x$.

\subsection{Implementation Details}\label{subsec:implementation}
We develop two variants of the proposed approach which differ only in the unsupervised bilingual word alignment algorithm employed in the first stage.

\textbf{UMML-SL:} In this method, the \texttt{UnsupWordAlign} subroutine in Algorithm~\ref{alg:geommgraph} employs the unsupervised self-learning  algorithm developed by \citet{artetxe18b}.
It should be noted that for UMML-SL, we do not employ various pre-processing and post-processing steps (such as whitening, de-whitening, symmetric re-weighting, etc.) that are included in the pipeline proposed by \citet{artetxe18b}.
Hence, the simplified version of the self-learning algorithm used in our first stage only involves unsupervised initialization followed by stochastic dictionary induction \citep{artetxe18b}.
This is done to ensure that all the compared approaches (for experiments discussed in Sections~\ref{sec:experimentalsetup} \& \ref{sec:results}) have the same input monolingual embeddings for their main algorithm.

\textbf{UMML-GW:}
We employ the Gromov-Wasserstein (GW) algorithm \citep{alvares18a} in the first stage of this method.
The GW approach formulates the bilingual word alignment problem within the optimal transport framework \citep{memoli11a,peyre16a,peyre19a}.
It learns a doubly stochastic matrix
We then run a refinement procedure to obtain a bilingual word alignment \citep{conneau18a}.
The key idea in the refinement procedure is that given the (probabilistic) word alignment obtained from the GW algorithm, a Procrustes cross-lingual mapping operator ${\mathbf W}$ is learned by solving (\ref{eqn:procrustes}). The mapping operator ${\mathbf W}$, in turn, is used to induce a (refined) bilingual lexicon ${\mathbf Y}$, using cross-domain similarity local scaling (CSLS) similarity measure \citep{conneau18a}. The vocabularies used for the refinement procedure is the same as those provided to the GW algorithm (i.e., top $20\,000$ most frequent words).

\section{Experimental Setup}\label{sec:experimentalsetup}
The experiments are aimed the following key questions on learning multilingual embeddings via unsupervised word alignment:
\begin{enumerate}
\item How does the proposed approach, learning bilingual word alignments and shared multilingual space sequentially, fare against existing methods that learn them jointly?
\item How robust are the existing multilingual methods when distant languages are involved? Does the presence of a distant language affect the learning between two close-by languages?
\end{enumerate}

We perform rigorous evaluations on a number of tasks to answer the above questions.

\noindent\textbf{Bilingual lexicon induction (BLI):} A popular task to evaluate the learned cross-lingual mappings \citep{artetxe18b,chen18a,alaux19a,heyman19a}. We perform evaluations on the MUSE \citep{conneau18a} and the VecMap \citep{dinu15a,artetxe17a,artetxe18a} test datasets. Both the datasets contain pre-trained monolingual embeddings (but trained on different corpora). Both also provide test bilingual dictionaries for various language pairs involving English (en). MUSE also has dictionaries between a few non-English European languages: Spanish (es), French (fr), German (de), Italian (it), and Portuguese (pt). VecMap contains English to other language dictionaries for four languages: de, it, es, and Finnish (fi). MUSE contains more number of languages, many of which are included in our experiments: Arabic (ar), Bulgarian (bg), Czech (cs), Danish (da), Dutch (nl), Finnish(fi), Greek (el), Hindi (hi), Hungarian (hu), Polish (po), Russian (ru), and Swedish (sv). In a given dataset, if the test bilingual dictionary of a language pair is missing, (e.g., po-cs in MUSE), we follow \citep{alaux19a} and use the intersection of their full dictionaries with English (e.g., po-en and en-cs in MUSE) to construct a test set. Please refer \citep{dinu15a,artetxe18a} and \citep{conneau18a} for more details on VecMap and MUSE, respectively.
Following existing works \citep{chen18a,heyman19a,alaux19a}, we report Precision$@1$ in the BLI experiments.
For inference, we employ the cross-domain similarity local scaling (CSLS) score \citep{conneau18a} in the nearest neighbor search.
The BLI results on the VecMap dataset are discussed in Appendix \ref{app:additional_results}.

\noindent\textbf{Cross-lingual word similarity (CLWS):} We also evaluate the quality of multilingual word embeddings on the CLWS task using the SemEval 2017 dataset \citep{collados17}.

One of the main goals for learning multilingual word embeddings is to enable transfer learning across languages for various downstream natural language applications. Hence, we also evaluate the methods on two other tasks:  multilingual document classification (MLDC) and multilingual dependency parsing (MLDP) \citep{ammar16,duong17a,heyman19a}. \citet{ammar16} provide a platform to evaluate MWEs on the two tasks.

\noindent\textbf{MLDP:} We evaluate the quality of learned multilingual embeddings on MLDP dataset, MLParsing, sampled from the Universal Dependencies 1.1 corpus \citep{agic15a}. The dataset has twelve languages: Bulgarian, Czech, Danish, German, Greek, English, Spanish, Finnish, French, Hungarian, Italian, and Swedish. It has training and test sets, containing $6748$ and $1200$ sentences, respectively. While the test set for each language contains $100$ sentences, the sentences in the training set of each language vary from $98$ to $6694$. The stack-LSTM parser \citep{dyer15a} used in this setup is configured to not use any part-of-speech/morphology attributes and to keep the input word embeddings fixed \citep{ammar16}.
\newline
\noindent\textbf{MLDC:} This task is evaluated on the ReutersMLDC document classification dataset, which has documents in seven languages: Danish, German, English, Spanish, French, Italian, and Swedish. The training and the test sets contains $7000$ and $13\,058$ documents, respective, well balanced across languages \citep{ammar16,heyman19a}. The document classifier is based on the average perceptron \citep{klementiev12a}.

\textbf{Compared methods:} In addition to the proposed methods \textbf{UMML-GW} and \textbf{UMML-SL}, discussed in Section~\ref{subsec:implementation}, we consider other unsupervised multilingual word embeddings baselines: \textbf{UMWE} \citep{chen18a} and \textbf{UMH}  \citep{alaux19a}.
For BLI experiments, we also evaluate state-of-the-art unsupervised bilingual word embeddings approach of \citet{artetxe18b}, \textbf{BilingUnsup}, to observe the effect of transfer learning in multilingual approaches.
As in existing works, all the unsupervised methods use the top $20\,000$ most frequent words of each language to learn the shared embedding space.
We use the same hyper-parameters for UMWE, UMH, and BilingUnsup as suggested by their authors.

\begin{table*}[t]\centering
\setlength{\tabcolsep}{5pt}
{\footnotesize
\centering
\begin{tabular}{lrrrrrrrrrrrrr}
\toprule
 & \thead{de-xx} & \thead{en-xx}  & \thead{es-xx} & \thead{fr-xx}  & \thead{it-xx}  & \thead{pt-xx} & \thead{xx-de} & \thead{xx-en}  & \thead{xx-es} & \thead{xx-fr}  & \thead{xx-it} & \thead{xx-pt} & \thead{avg.} \\
\midrule
BilingUnsup & $60.9$ & $76.9$ & $75.6$ & $72.7$ & $75.2$ & $75.3$ & $61.6$ & $76.1$ & $77.2$ & $75.9$ & $73.6$ & $72.2$ & $72.8$ \\
\midrule
UMML-GW    & $69.3$ & $80.2$ & $81.2$ & $78.9$ & $80.3$ & $79.9$ & $69.0$ & $\mathbf{81.7}$ & $81.7$ & $82.0$ & $78.7$ & $76.7$ & $78.3$\\
UMML-SL  & $\mathbf{70.5}$ & $80.0$ & $81.7$ & $79.7$ & $\mathbf{80.9}$ & $\mathbf{80.9}$ & $\mathbf{69.9}$ & $80.6$ & $82.3$ & $83.1$ & $79.6$ & $78.2$ & $\mathbf{79.0}$ \\
UMWE         & $70.4$ & $\mathbf{80.6}$ & $\mathbf{82.0}$ & $\mathbf{79.8}$ & $80.6$ & $80.6$ & $69.5$ & $77.4$ & $\mathbf{83.5}$ & $\mathbf{84.1}$ & $\mathbf{80.4}$ & $\mathbf{79.0}$ & $\mathbf{79.0}$\\
UMH            & $69.2$ & $79.9$ & $81.8$ & $79.4$ & $80.6$ & $80.6$ & $69.0$ & $80.7$ & $82.3$ & $82.8$ & $79.0$ & $77.6$ & $78.6$ \\
\bottomrule
\end{tabular}
}
\caption{
Average Precision$@1$ for BLI obtained by multilingual algorithms on six European languages (German, English, Spanish, French, Italian, and Portuguese) from the MUSE dataset. The results are obtained for every combination of source-target pair. The column de-xx (xx-de) denotes average performance when German is considered the source (target) language. It can be observed that all the approaches obtain similar generalization on the test set. The final average of each method is computed on thirty results.
}\label{table:multilingual-six-euro}
\end{table*}

\begin{table*}\centering
\setlength{\tabcolsep}{5pt}
{\footnotesize
\centering
\begin{tabular}{lrrrrrrrrrrrr}
\toprule
 & \thead{cs-xx}  & \thead{da-xx} & \thead{de-xx}  & \thead{en-xx} & \thead{es-xx}  & \thead{fr-xx} & \thead{it-xx}  & \thead{nl-xx} & \thead{pl-xx}  & \thead{pt-xx} & \thead{ru-xx} \\%& \thead{avg.} \\
\midrule
BilingUnsup & $61.8$ & $58.7$ & $58.4$ & $64.9$ & $65.0$ & $63.3$ & $64.5$ & $63.7$ & $62.0$ & $64.4$ & $59.3$ \\
\midrule
UMML-GW    & $64.6$ & $\mathbf{61.7}$ & $\mathbf{64.1}$ & $70.0$ & $\mathbf{69.3}$ & $68.0$ & $\mathbf{68.7}$ & $67.1$ & $65.6$ & $68.3$ & $62.4$ \\%& $66.3$\\
UMML-SL      & $\mathbf{65.1}$ & $61.3$ & $\mathbf{64.1}$ & $\mathbf{70.2}$ & $\mathbf{69.3}$ & $\mathbf{68.1}$ & $\mathbf{68.7}$ & $\mathbf{67.4}$ & $\mathbf{66.0}$ & $\mathbf{68.7}$ & $\mathbf{63.3}$ \\%& $\mathbf{66.6}$\\
UMWE         & $57.6$ & $54.1$ & $56.8$ & $63.1$ & $62.9$ & $61.5$ & $61.9$ & $0.0$ & $58.6$ & $61.6$ & $56.3$ \\%& $54.0$\\
UMH            & $63.7$ & $60.8$ & $62.8$ & $68.8$ & $68.9$ & $67.5$ & $68.0$ & $66.1$ & $64.2$ & $67.8$ & $61.9$ \\%& $65.5$\\
\midrule
& \thead{xx-cs}  & \thead{xx-da} & \thead{xx-de}  & \thead{xx-en} & \thead{xx-es}  & \thead{xx-fr} & \thead{xx-it}  & \thead{xx-nl} & \thead{xx-pl}  & \thead{xx-pt} & \thead{xx-ru} & \thead{avg.} \\
\midrule
BilingUnsup & $51.0$ & $56.6$ & $64.5$ & $69.6$ & $71.7$ & $70.1$ & $67.7$ & $66.1$ & $53.6$ & $68.8$ & $46.3$ & $62.4$ \\
\midrule
UMML-GW  & $53.1$ & $\mathbf{62.1}$ & $69.3$ & $74.6$ & $\mathbf{76.3}$ & $75.6$ & $72.3$ & $70.0$ & $55.1$ & $73.8$ & $47.6$ & $66.3$\\
UMML-SL  & $\mathbf{53.6}$ & $61.9$ & $\mathbf{69.5}$ & $\mathbf{75.0}$ & $\mathbf{76.3}$ & $\mathbf{75.7}$ & $\mathbf{72.4}$ & $\mathbf{70.1}$ & $\mathbf{55.2}$ & $\mathbf{74.0}$ & $\mathbf{48.4}$ & $\mathbf{66.6}$\\
UMWE         & $49.5$ & $57.6$ & $60.3$ & $63.1$ & $68.4$ & $67.9$ & $65.2$ & $0.0$ & $51.0$ & $66.3$ & $45.0$ & $54.0$\\
UMH            & $52.9$ & $60.4$ & $68.3$ & $74.1$ & $75.6$ & $74.6$ & $71.4$ & $68.6$ & $54.8$ & $72.6$ & $47.4$ & $65.5$\\
\bottomrule
\end{tabular}
}
\caption{Average Precision$@1$ for BLI on eleven European languages from the MUSE dataset. The languages are from different families such as Latin (Spanish, French, Italian, Portuguese), Germanic (Danish, German, English, Dutch), and Slavic (Czech, Polish, Russian). The results are obtained for every combination of source-target pair.
The proposed methods, UMML-SL and UMML-GW, obtain better performance than UMH and UMWE in each column. This illustrates the robustness of the proposed  framework with respect to diversity in languages.
The final average of each method is computed on $110$ results.}\label{table:multilingual-eleven-euro}
\end{table*}

\section{Results and discussion}\label{sec:results}
We now discuss and analyze the results obtained on the experimental setup described in Section \ref{sec:experimentalsetup}.

\subsubsection*{Bilingual Lexicon Induction (BLI) Results}
\noindent\textbf{Experiment 1:} We begin with BLI task on a group of six relatively close European languages -- German, English, Spanish, French, Italian, and Portuguese -- from the MUSE dataset. We experiment on every pair of languages and in both directions, leading to thirty results for each method. Table~\ref{table:multilingual-six-euro} provides summarized results of this experiment. We observe that the proposed two-stage methods, UMML-GW and UMML-SL, obtain scores on par with state-of-the-art methods, UMWE and UMH. This shows that in case of similar/close-by languages, all the methods are able to learn a shared multilingual space with similar generalization performance. We also observe that all the multilingual methods outperform BilingUnsup, highlighting the benefit of transfer learning in this scenario.

\noindent\textbf{Experiment 2:} We next expand the language set in the first experiment to include five other European languages (Czech, Danish, Dutch, Polish, Russian) from diverse language families (all from the MUSE dataset). This group of eleven languages has also been employed by \citet{alaux19a} in their BLI experiments.
Table~\ref{table:multilingual-eleven-euro} reports the summarized results. We observe that the proposed two-stage methods, UMML-GW and UMML-SL, perform better than UMH and outperforms UMWE. In fact both the proposed methods obtain better results than UMH and UMWE in the every column of Table~\ref{table:multilingual-eleven-euro}.
In this multilingual setting, UMWE fails to satisfactorily map the Dutch language word embeddings in the shared multilingual space, though Dutch is similar to English. It should be noted that in the bilingual setup UMWE learns an effective English-Dutch cross-lingual space (obtaining average en-nl and nl-en score of $75.2$). It, therefore, appears that UMWE has limitations in such multilingual settings which lead to its poor performance.

\begin{table*}[t]\centering
\setlength{\tabcolsep}{3.7pt}
{\footnotesize
\centering
\begin{tabular}{lrrrrrrrrrrrrrrrr}
\toprule
 & \thead{ar-de} & \thead{ar-en}  & \thead{ar-fr} & \thead{ar-hi}  & \thead{ar-ru}  & \thead{de-en} & \thead{de-fr} & \thead{de-hi}  & \thead{de-ru} & \thead{en-fr}  & \thead{en-hi} & \thead{en-ru} & \thead{fr-hi}  & \thead{fr-ru} & \thead{hi-ru} \\
\midrule
BilingUnsup & $\mathbf{46.5}$ & $46.4$ & $55.0$ & $36.9$ & $\mathbf{35.2}$ & $70.8$ & $61.9$ & $31.8$ & $43.6$ & $79.8$ & $31.3$ & $44.1$ & $36.1$ & $44.9$ & $24.9$ \\
\midrule
UMML-GW & $\mathbf{46.5}$ & $\mathbf{50.5}$ & $58.1$ & $0.0$ & $33.6$ & $74.0$ & $\mathbf{75.5}$ & $0.0$ & $44.4$ & $\mathbf{82.5}$ & $0.0$ & $47.7$ & $0.0$ & $46.2$ & $0.0$ \\
UMML-SL   & $46.2$ & $49.5$ & $56.5$ & $39.4$ & $34.1$ & $74.6$ & $75.2$ & $\mathbf{38.4}$ & $\mathbf{45.2}$ & $\mathbf{82.5}$ & $\mathbf{39.0}$ & $\mathbf{49.7}$ & $\mathbf{42.7}$ & $\mathbf{47.1}$ & $\mathbf{29.7}$ \\
UMWE         & $0.0$ & $45.0$ & $\mathbf{58.4}$ & $\mathbf{41.4}$ & $0.0$ & $0.0$ & $0.0$ & $0.0$ & $40.2$ & $81.9$ & $36.4$ & $0.0$ & $42.6$ & $0.0$ & $0.0$\\
UMH            & $0.1$ & $0.1$ & $0.3$ & $0.0$ & $0.1$ & $\mathbf{74.7}$ & $72.5$ & $0.0$ & $44.7$ & $82.0$ & $0.0$ & $46.5$ & $0.0$ & $44.5$ & $0.0$\\
\midrule
& \thead{de-ar} & \thead{en-ar}  & \thead{fr-ar} & \thead{hi-ar}  & \thead{ru-ar}  & \thead{en-de} & \thead{fr-de} & \thead{hi-de}  & \thead{ru-de} & \thead{fr-en}  & \thead{hi-en} & \thead{ru-en} & \thead{hi-fr}  & \thead{ru-fr} & \thead{ru-hi} & \thead{avg.}  \\
\midrule
BilingUnsup & $30.8$ & $29.4$ & $37.7$ & $28.7$ & $\mathbf{35.0}$ & $72.0$ & $61.3$ & $42.0$ & $59.6$ & $78.7$ & $37.6$ & $59.2$ & $45.4$ & $62.6$ & $32.3$ & $46.7$ \\
\midrule
UMML-GW & $\mathbf{31.5}$ & $35.6$ & $37.5$ & $0.0$ & $32.8$ & $74.6$ & $ 70.5$ & $0.0$ & $61.3$ & $83.1$ & $0.0$ & $62.9$ & $0.0$ & $ 65.8$ & $0.0$ & $37.2$\\
UMML-SL   & $31.1$ & $35.5$ & $37.4$ & $\mathbf{29.7}$ & $33.9$ & $\mathbf{75.1}$ & $\mathbf{70.7}$ & $\mathbf{45.5}$ & $\mathbf{61.7}$ & $82.9$ & $\mathbf{47.6}$ & $\mathbf{65.6}$ & $\mathbf{51.9}$ & $\mathbf{66.6}$ & $\mathbf{39.9}$ & $\mathbf{50.8}$\\
UMWE         & $0.1$ & $\mathbf{37.6}$ & $\mathbf{39.8}$ & $23.7$ & $0.1$ & $0.0$ & $0.0$ & $0.0$ & $55.7$ & $ 79.5$ & $34.1$ & $0.0$ & $48.4$ & $0.0$ & $0.0$ & $22.2$\\
UMH            & $0.2$ & $0.1$ & $0.2$ & $0.0$ & $0.1$ & $ 74.3$ & $ 69.9$ & $ 0.0$ & $60.8$ & $\mathbf{83.2}$ & $0.0$ & $62.5$ & $0.0$ & $ 65.1$ & $0.0$ & $26.1$\\
\bottomrule
\end{tabular}
}
\caption{
Precision$@1$ for BLI obtained by multilingual algorithms on a highly diverse group of six languages (Arabic, German, English, French, Hindi, and Russian) from the MUSE dataset.
The `avg' column reports the average performance of each method.
Both the proposed methods, UMML-GW and UMML-SL, obtain better results than UMH and UMWE in this challenging BLI setup, with  UMML-SL outperforming every method.
}\label{table:multilingual-diverse}
\end{table*}

\noindent\textbf{Experiment 3: } We next aim to learn the multilingual space for a more diverse group of languages from the MUSE dataset: Arabic, German, English, French, Hindi, and Russian. Table~\ref{table:multilingual-diverse} reports the BLI performance for each pair of languages. We observe that, except the proposed UMML-SL, all other multilingual methods fail to learn a reasonably good shared multilingual space for all languages.
The proposed UMML-GW fails to obtain a reasonable BLI score ($<1$ Precision$@1$) in $10$ out of $30$ pairs.
UMWE and UMH suffer from such failure on $16$ and $18$ pairs, respectively.
On the other hand, the proposed UMML-SL obtains effective alignment on all pairs of languages, illustrating its robustness in such challenging setting.
It obtains better performance than BilingUnsup, benefiting from transfer learning in such a diverse group as well.
In the following, we analyze the results of the other three multilingual methods:\\
$\bullet$ The Gromov-Wasserstein (GW) alignment algorithm \citep{alvares18a}, which is used in the first stage of the proposed UMML-GW, does not learn an effective alignment of English and Hindi words. However, in its second stage, this ``misalignment" does not adversely affect the BLI performance of  language pairs not involving Hindi. This can be concluded from the observation that its BLI score for all language pairs not involving Hindi is similar to the corresponding scores obtained by UMML-SL.
Overall, UMML-GW is able to learn suitable multilingual embeddings for five languages (ar, de, en, fr, and ru). \\
$\bullet$ UMH also employs GW based formulation \citep{alvares18a} in its joint optimization framework and it, too, does not learn suitable Hindi embeddings in the shared multilingual space. However, UMH also fails to learn suitable Arabic embeddings in the shared multilingual space. This is surprising since the GW algorithm (employed in the first stage of UMML-GW) learns an effective alignment of English and Arabic words. Hence, it appears that jointly learning the unsupervised alignment and multilingual mapping can adversely affect distant languages (Arabic in this case). Overall, UMH learns suitable multilingual embeddings for four languages (de, en, fr, and ru). \\
$\bullet$ The GAN-based approach, UMWE, learns two groups of alignment in the shared multilingual space. The first group consists of Arabic, English, French, and Hindi language embeddings, which are suitably aligned with each other. However, these are ``misaligned'' with German and Russian language embeddings in the shared space.
On the other hand, the German and Russian language embeddings are suitably aligned with each other (but not with any other language).
Such grouping cannot be attributed to language similarity (since English and German are closer than, for e.g., English and Arabic) and may result from optimizing instability \citep{sogaard18}.

\begin{table*}[t]\centering
{\footnotesize
\centering
\begin{tabular}{lrrrrrrr}
\toprule
& \thead{en-de} & \thead{en-es}  & \thead{de-es} & \thead{en-it}  & \thead{de-it} & \thead{es-it} & \thead{avg.} \\
\midrule
Luminoso  \citep{speer17}   & $0.769$ & $0.772$ & $0.735$ & $0.787$ & $0.747$ & $0.767$ & $0.763$ \\
NASARI \citep{collados16}       & $0.594$ & $0.630$ & $0.548$ & $0.647$ & $0.557$ & $0.592$ & $0.595$ \\
\midrule
UMML-GW & $0.726$ & $0.724$ & $0.723$ & $0.728$ & $0.706$ & $0.742$ & $0.725$ \\
UMML-SL   & $0.725$ & $0.723$ & $0.721$ & $0.728$ & $0.705$ & $0.742$ & $0.724$ \\
UMWE         & $0.713$ & $0.706$ & $0.698$ & $0.708$ & $0.684$ & $0.727$ & $0.706$ \\
UMH            & $0.719$ & $0.721$ & $0.710$ & $0.727$ & $0.696$ & $0.732$ & $0.718$ \\
\bottomrule
\end{tabular}
}
\caption{Spearman correlation $(\rho)$ on the SemEval 2017 cross-lingual word similarity task.
Among unsupervised methods, the proposed UMML-GW and UMML-SL perform better than UMWE and UMH.
}\label{table:clws}
\end{table*}

\begin{table}\centering
{\footnotesize
\centering
\begin{tabular}{lrr}
\toprule
& \thead{MLDC} & \thead{MLDP}  \\
\midrule
UMML-GW & $89.7$ & $69.9$\\
UMML-SL   & $\mathbf{90.3}$ & $\mathbf{71.0}$ \\
UMWE         & $88.3$ & $\mathbf{71.0}$ \\
UMH            & $90.0$ & $70.6$ \\
\bottomrule
\end{tabular}
}
\caption{
Average accuracy and unlabeled attachment score (UAS) across languages on ReutersMLDC (MLDC) and MLParsing (MLDP) datasets, respectively. The proposed UMML-SL obtain the best results in both downstream applications.
}\label{table:downstream}
\end{table}

\subsubsection*{Cross-lingual Word Similarity (CLWS) Results}
\noindent\textbf{Experiment 4: } Table~\ref{table:clws} reports performance on the SemEval 2017 CLWS task for four languages: English, German, Spanish, and Italian. For evaluating the unsupervised MWE approaches, we consider the multilingual word embeddings of the four languages learned in BLI Experiment 2 (corresponding to Table~\ref{table:multilingual-eleven-euro}).
Among the unsupervised MWE approaches, we observe that UMML-GW and UMML-SL obtain the best results.
For reference, we also include the results of the SemEval 2017 baseline and the best reported system, NASARI \citep{collados16}  and Luminoso \citep{speer17}, respectively, in Table~\ref{table:clws}. However, it should be noted that both NASARI and Luminoso use additional knowledge sources like the Europarl and the OpenSubtitles2016 parallel corpora.

\subsubsection*{Results on Downstream Applications}
\noindent\textbf{Experiment 5: } For each of the four methods, we learn the shared multilingual space for twelve languages in the multilingual dependency parsing (MLDP) dataset: Bulgarian, Czech, Danish, German, Greek, English, Spanish, Finnish, French, Hungarian, Italian, and Swedish.
We employ the $300$ dimensional pre-trained monolingual embeddings from the MUSE dataset, vocabulary list being the top $200\,000$ words in each language, as in the BLI experiments.
The learned multilingual embeddings are also employed in the multilingual document classification (MLDC) evaluation, which has seven languages: Danish, German, English, Spanish, French, Italian, and Swedish.
It should be noted that for this MLDP and MLDP tasks, related works \citep{ammar16,duong17a,heyman19a} trained $512$ dimensional monolingual embeddings on the datasets used by \citet{ammar16} and \citet{duong17a}. Hence, the presented results are not comparable with previously reported results.

Table~\ref{table:downstream} reports the performance on the MLDP and MLDC tasks. We observe that the proposed two-stage approaches perform well on the downstream tasks with UMML-SL obtaining the best results.

\section{Discussion and Conclusion}\label{sec:conclusion}
In this work, we propose a two-stage framework for learning unsupervised multilingual word embeddings (MWE). The two stages correspond to first learning unsupervised word alignment between a few pairs of languages and subsequently learning a latent shared multilingual space. The two problem are solved using existing techniques: the first stage is solved using the self-learning \citep{artetxe18b} or the Gromov-Wasserstein alignment \citep{alvares18a} algorithms and the second stage is solved using the GeoMM algorithm \citep{mjaw19a}.

Though the two-stage framework seems a simple approach compared to the existing joint optimization methods \citep{chen18a,alaux19a}, our main contribution has been to show that it is a strong performer. We observe that the proposed approach (UMML-SL) outperforms existing approaches in various tasks such as bilingual lexicon induction, cross-lingual word similarity, multilingual document classification, and multilingual document parsing. The proposed approach also exhibit robustness while learning the MWE space for highly diverse groups of languages, a challenging setting for existing approaches.
The experiments were focused on the unsupervised setting, but the proposed two-stage framework has the necessary flexibility to be easily employed in fully supervised setting or in hybrid setups where supervision is available for some languages but may be unavailable for others. Our results encourage development of multi-stage models for learning multilingual word embeddings.


source command for an alias (see below).
const char *slaveCmd (in)                         Name of source command for alias.
arguments  to pass to the alias command.
This storage is owned by the caller.
pass to the aliased command.
command is defined for an alias.
arguments  to  pass to an alias command.
is owned by the called function.
choice, since it creates interpreters in a known-safe state.
identified by slaveName.  If no such slave interpreter exists, NULL is returned.
master (it is a top-level interpreter) then NULL is returned.
and the result field in askingInterp contains the error message.
used, without any restrictions on how they are related.
to pass as additional arguments instead of a vector of strings.
Tcl_Obj structures instead of a vector of strings.
operation also returns TCL_ERROR and the result field in interp contains an error message.
use cmdName in a call to Tcl_Eval or with the Tcl eval command will fail.
For a description of the Tcl interface to multiple interpreters, see interp(3tcl).


text:
Stephanie Irene Bice (* 11. November 1973 in Oklahoma City, Oklahoma) ist eine US-amerikanische Politikerin. Seit 2021 vertritt sie den 5. Kongresswahlbezirk des Bundesstaates Oklahoma.

Werdegang
Stephanie Bice stammt in vierter Generation aus Oklahoma. Sie hat an der Oklahoma State University – Oklahoma City Marketing studiert. Von 2014 bis 2020 saß sie im Senat von Oklahoma Bei der Wahl 2020 konnte sie die Demokratin Kendra Horn mit 52,1 % besiegen. Ihre Legislaturperiode im Repräsentantenhaus des 117. Kongresses läuft noch bis zum 3. Januar 2023.

Sie ist verheiratet und hat zwei Kinder.

Weblinks

Einzelnachweise

Mitglied des Repräsentantenhauses der Vereinigten Staaten für Oklahoma
Mitglied der Republikanischen Partei
Politiker (21. Jahrhundert)
US-Amerikaner
Geboren 1973
Frau

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text:
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Moa Wallin is a freelance artist and illustrator based in Stockholm, Sweden. Her detailed and imaginative paintings walk the line between the alien and the adorable, occasionally tipping over to either side. This can easily be seen in her recent work illustrating the cover for Jayde Ver Elst's sci-fi comedy adventure novella, Usu. Her rich landscapes and creatures are equal parts adorable and mysterious, evoking a uniquely surreal fantasy world. She was kind enough to share some time for an interview for the recent release of Usu, so read on below, and also explore a small gallery of her amazing work.
What made you decide to pursue art as a profession or hobby?
It sort of just happened. I've been told I'm good at drawing my whole life. I feel like my surroundings have always pushed me in that direction. I used to find it annoying, and didn't think that was what I'd end up doing at all.
When I was around 17 I dropped out of school for various reasons. Once my head was no longer occupied with studies and awkward teenage social situations, my interest in drawing and painting grew stronger than ever. I looked at a lot of art on the internet, and became intensely focused on improving my skills. Somewhere along the line people started offering me money to paint for them. Now, seven years later, this is apparently my job. Weird.
It didn't feel much like a choice, but I'm very happy this is where I ended up.
Self taught. I tend to lose my motivation when I don't have full control over my own learning process, so I've always been afraid that taking art classes might take the joy out of it for me.
My process is always evolving, but usually I start by getting out a basic composition and color theme for the painting. I avoid adding too many details before I feel like there's a good balance between the various elements in the picture. Errors in these areas can be a real pain to correct at a later stage.
Once I start detailing it all gets pretty chaotic, and things tend to change a lot before I'm finished.
You generally work digitally; simple convenience or are there more reasons for choosing it over traditional mediums?
Mostly convenience. It's great when doing work for clients, since it's easier to make changes along the way. I like that it doesn't require as much preparation, it's easier to be spontaneous. It also allows for more experimentation without the risk of ruining the whole painting, so I feel like I learn faster.
Doing more traditional painting is always on my mental to-do list, though.
Most of your pieces depict complete scenes in fantasy settings; is there a story in mind before you begin work, or does that come after you have finished a painting?
I rarely start out with a story in mind. Usually several different ones pop up in my head while I work on a painting. I don't really decide what it's about until it's completely finished.
Sometimes I show the finished painting to other people and ask them what they think is going on in it, and sometimes their interpretation is way more interesting than what I originally had in mind. That's always fun!
Is there an author/band/franchise/company you would like to create art for?
Not really anything in particular. Any project that sparks my imagination and pushes it in an interesting direction.
I'm not much of a long term planner, so basically to keep doing what I'm doing and seeing where I'll end up. Improving my skills and expanding out of my comfort zones. I really enjoy working with books though, so I'll probably try to steer in that direction.
Lastly, and most importantly, why snails?
Snails are just the most amazing animals. Just look at them! They are like cute and friendly little aliens. They're thoroughly good natured. They literally don't have a single bad bone in their bodies.
I actually have a few giant African snails as pets. Looking at them just soothes me. They make the weirdest facial expressions, and they give really good advice.
It's my sincere and unwavering belief that no home is complete without a snail or two.
All images copyright Moa Wallin.


Here's a big shout-out to author Jazmin Steele on sharing a compelling story of abandonment, love, loss, betrayal, forgiveness and resilience, in her debut novel Love Incorruptible (Lulu, $15.95 – soft cover and $26.95- hard cover).
The protagonist, Brooklen Sapphire, is based on real life occurrences in Steele's life who resonates instantly with readers from beginning to end. The writer bares all in the telling of a complicated story of a young woman's journey to freedom from years of heartbreaking emotional, and physical abuse.
The author takes the reader on a journey into how child abandonment and dysfunction can turn into a thirst for looking for love and acceptance from all of the wrong people, in all the wrong places.
Steele's ability to go deep on a range of relationships will have you gasping for air.
Love Incorruptible is a beautifully written book from a talented writer, that offers a voice to young girls and women who have struggled with insecurities and self-worth, produced by a cycle of broken and unstable relationships.
Some might argue the reading as strictly for adults. I say, please share with your young teenaged girls.


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USA attitude during denuclearization talks 'regrettable', says North Korea But after two days of theatrical amity in Pyongyang it appeared to mark a return to the North's traditional hardline position. The statement was widely seen as a negotiating tactic to put the United States on the back foot as talks get underway.
President Erdogan heralds Turkey's 'fresh start' Presidential aide Ibrahim Kalin said Erdogan would visit Ataturk's mausoleum following the swearing-in ceremony. The most intense attention will focus on who will be responsible for foreign policy and the economy.
Millwall fans fundraise to fix ambulance damaged during World Cup celebrations The auto was left with a completely smashed windscreen and heavy denting to the bonnet, roof and bumper. It said if the insurance company pays for repairs, the money will be donated to the ambulance service.
Sir Geoff Hurst thinks it's coming home The team also features several players of Afro-Caribbean heritage, including Raheem Sterling who was born in Jamaica. But now we know, it's more than just a waistcoat (also available at Marks and Spencer); it's a lucky waistcoat.
Croatia move into semifinals with penalty shootout win over Russian Federation England trio Harry Kane , Kyle Walker and Harry Maguire are looking to guide their side to only their second World Cup final. We made ourselves happy but also everybody back home in Croatia happy. "I would like to become a coach, a good coach".
Trump narrows high court choice down to '3 or 2' Susan Collins of ME affirmed to reporters Wednesday she will not affirm a nominee with "demonstrated hostility" to Roe v. Conversely, in Nevada, Democratic Senate challenger Jacky Rosen is accusing Republican Sen.
Jubilation as 4 more boys rescued from flooded Thai cave Thai officials have been tight-lipped about the rescue operation, and would not comment on how many people were removed Monday. Rescuers are moving ahead quickly because they are afraid monsoon rains will completely flood the caves.
Timehop's database breached compromising data of 21 million users Doing so may end up leading to a bunch of content being inaccessible for a while whilst a new set of keys establishes itself. There is no such thing as flawless when it comes to cyber security but we are committed to protecting user data.
Brock Lesnar Storms Stage After Cormier Knocks Out Miocic at UFC 226 The guy is the most successful heavyweight champion of all-time, having defended the belt more times than any of his predecessors. If you've been waiting for Daniel Cormier to take on Brock Lesnar then today's your lucky day.
Kane urges England team-mates to 'write our own history' The figure is easily more than the portion of goals from these situations in 2014 (27%), 2010 (24%), 2002 (29%), and 1994 (33%). Vida later said he was joking and "the victory is for Croatia, no politics".
Maisie Williams bids a bloody farewell to 'Game Of Thrones' But still, any hint is worth exploring in the long wait until 2019, when the fantasy show is set to premiere Season 8. After the caption, Maisie added, "#lastwomanstanding #barely #immasleepforthenextfouryears #justkiddingidontsleep ".
Brexit Secretary David Davis resigns He is said to have warned colleagues it could be a "serious inhibitor to free trade" and striking deals with other countries. As well as potentially hard talks ahead with Brussels, Mrs May also faces the task of winning over Eurosceptic backbenchers.
Samsung goes for smartphone expansion in India as competition heats up The South Korean President is on a three-day visit to India, having arrived in New Delhi on Sunday evening. South Korean President Moon Jae-in will also grace the event alongside the Prime Minister.
China Paper Denounces US Navy Ships' Taiwan Strait Passage The last time a USA aircraft carrier transited the Taiwan Strait was in 2007, during the administration of George W. China regularly says Taiwan is the most sensitive issue in its ties with the United States.
Captain Marvel is flying off set and into the wild blue yonder, as the next Marvel movie has just wrapped shooting.
"You already know as much as you need to", Feige told us of the Larson-led Captain Marvel, the studio's first production to feature a woman in the leading role. The 6th was marked as the 'last day and the 7th 'home'.
Of course, true to Marvel Cinematic Universe form, no secrets or hints were given away from this photo.
As Deadline explains, Marvel Studios won't have a presence at San Diego Comic-Con this month, so it could be some time before we learn more about Captain Marvel.
The story follows Carol Danvers as she becomes one of the universe's most powerful heroes when Earth is caught in the middle of a galactic war between two alien races. Marvel has yet to release a trailer or share any further details on Captain Marvel, though the character was referenced during Infinity War.
Captain Marvel herself took to Instagram and shared a picture of the film's clapperboard, signed by cast and crew.
"Captain Marvel" will hit theaters March 8, 2019.
Filming was directed by Anna Boden and Ryan Fleck of Ryan Gosling schoolteacher drama "Half Nelson" and the Ryan Reynolds and Ben Mendelsohn gambling movie "Mississippi Grind".
Franchise veterans Samuel L. Jackson and Clark Gregg return as S.H.I.E.L.D. agents Nick Fury and Phil Coulson, respectively, alongside returning players Lee Pace (Ronan the Accuser) and Djimon Hounsou (Korath the Accuser), who both made their first appearances in Guardians of the Galaxy.


An aviation salvage team is being brought in to pull the wreckage of a Gadsden-based single-engine plane from a reservoir in southwestern Colorado.
Five Gadsden residents died Saturday when the Socata TBM700 crashed into Ridgway Reservoir in Ridgway State Park, near Montrose, Colo., about 180 miles southwest of Denver.
Marti Whitmore, county attorney and public information officer for Ouray County, Colo., on Tuesday said contractors were expected at the site later in the day. They will continue arriving Wednesday and begin planning recovery operations for Thursday.
Divers on Monday confirmed that the bodies of all five victims remain inside the plane's wreckage. Authorities said the wreckage must be pulled to shore before the bodies can be removed.
Officials in Colorado haven't officially identified the victims. However, multiple social media reports have identified the passengers as Gadsden businessman Jimmy Hill, president of Gadsden Tool; Katrina Vinzant Barksdale and her two sons, Xander and Kobe, students at Mitchell Elementary School; and Barksdale's nephew Seth McDuffie, a student at Westbrook Christian School.
They took off Saturday morning from the Northeast Alabama Regional Airport in Gadsden, stopped in Bartlesville, Okla., then continued westward toward Montrose, its destination.
Witnesses say they heard the plane's engine struggling before the crash.
The wreckage was found Sunday by using sonar and a rover with a camera.
Kirsten Copeland of the Colorado Parks Department said Monday that the plane is upside down in 2 to 3 feet of silt, under about 60 feet of water. It has extensive damage to the tail section and to one of the wings, which is nearly detached but still in a position like it was intact.
Ridgway State Park remains closed while recovery operations continue.


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How Is Hepatitis Transmitted Sexually
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Hepatitis C And Safe Sex
As with hepatitis B, the time to tell any sexual partner about your diagnosis is before you have sex or if you have already had sex, before you have sex again. Again, if you are having sex you need to use condoms and avoid risky sexual activity.
In patients with hepatitis C in stable monogamous relationships, the rate of transmission appears to be in the range of 2 to 4 percent per year, says Fallon.
Even though the chance of transmitting hepatitis C is lower if you are having sex with only one partner, you still need to be responsible and cautious. Since there is no effective vaccine prevention is of particular importance in this group, warns Fallon. Using condoms will decrease the risk of transmitting your hepatitis C infection to your partner.
How Is Hepatitis A Treated
Unlike other types of viral hepatitis, hepatitis A rarely causes long-term liver damage and doesnt become a long-term illness. There isnt a specific treatment for hepatitis A, and most people will recover fully within one to two months. Usually, symptoms are managed at home with plenty of rest and painkillers and/or medication to help with itchiness, nausea or vomiting may be prescribed.
Occasionally hepatitis A can last longer and, in rare cases, it can be life-threatening if it causes the liver to stop working properly .
Whether youve got symptoms or not, dont prepare food for others or have sex until a healthcare professional tells you that youre no longer infectious.
Once youve recovered from hepatitis A youre immune this means you cant get it again. But you can get other types of hepatitis.
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Is Hepatitis A Sexually Transmitted Disease
Robert Burakoff, MD, MPH, is board-certified in gastroentrology. He is the vice chair for ambulatory services for the department of medicine at Weill Cornell Medical College in New York, where he is also a professor. He was the founding editor and co-editor in chief of Inflammatory Bowel Diseases.
Some types of viral hepatitis can be transmitted through sexual contact, in addition to being transmitted via other routes. Each type of hepatitis virus has a different risk of being transmitted through sex.
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Willowbrook State School Experiments
A New York University researcher named Saul Krugman continued this research into the 1950s and 1960s, most infamously with his experiments on mentally disabled children at the Willowbrook State School in New York, a crowded urban facility where hepatitis infections were highly endemic to the student body. Krugman injected students with gamma globulin, a type of antibody. After observing the temporary protection against infection this antibody provided, he then tried injected live hepatitis virus into students. Krugman also controversially took feces from infected students, blended it into milkshakes, and fed it to newly admitted children.
Sharing Personal Care Items
The chances of spreading hepatitis C within your household are low but possible. To be safe, donât share personal care items that could be contaminated with blood, Lee says. These include razors, toothbrushes, cuticle scissors, and nail clippers.
In addition, be mindful when you go to nail salons or barbershops, where the same tools are used on all customers. A study published in the November-December 2014 issue of the Journal of Public Health Management & Practicefound that while regulations to safeguard the public exist in most states, itâs unknown how many businesses comply with them. Ask about tool-sterilization procedures before you frequent these establishments. You can also bring your own nail care supplies.
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What Is Hepatitis A
The term hepatitis means inflammation of the liver. Hepatitis can be caused by many things, including heavy alcohol use, certain medications or health problems, toxins, or viruses, including hepatitis A, hepatitis B, hepatitis C and the far less common hepatitis D and E. While hepatitis B and C can cause chronic infections, hepatitis A typically causes a short-term infection and symptoms usually subside within 8 weeks . Symptoms can include general discomfort, fever, diarrhea, nausea, dark urine, and/or jaundice , although not everyone has symptoms. Fortunately there is a vaccine, and doctors in the United States recommend that all children receive the vaccine at their first birthday .
How Is Hepatitis A Transmitted
Hepatitis A is spread when a person accidentally ingests traces of fecal matter from an infected individual. This can happen by eating food prepared by an infected person who hasnt washed their hands properly, by drinking water from a contaminated source, or by eating undercooked food. The best way to spread the infection is to wash your hands before preparing or eating food and after going to the bathroom or changing diapers.
Like hepatitis B and hepatitis C, hepatitis A can be passed through sexual activity and needle sharing, although this isnt common in the United States.
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Health Services For Screening And Treatment Of Stis Remain Weak
People seeking screening and treatment for STIs face numerous problems. These include limited resources, stigmatization, poor quality of services and often out-of-pocket expenses.
In many settings, STI services in low- and middle-income countries are often neglected and underfunded. These problems lead to difficulties in providing screening for asymptomatic infections, insufficient number of trained personnel, limited laboratory capacity and inadequate supplies of appropriate medicines.
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How Is Hepatitis Transmitted?
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More Ways To Reduce The Risk Of Infection
If you arent sure whether you have hepatitis C, get tested. Testing is especially important if you are having sex with more than one person or if you have other risk factors for hepatitis C, including having had a blood transfusion prior to 1992 or injecting drugs .
Talk to your partner about getting tested as well, for hepatitis C and other STIs, so you know the risks before having sex. People who are at risk for hepatitis C are also at risk for HIV and other STIs, notes Talal.
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Hiv And Hepatitis C Coinfection
HCV infection is common among people with HIV who also inject drugs. Nearly 75% of people living with HIV who report a history of injection drug use are co-infected with HCV. All people who are diagnosed with HIV are recommended to be tested for HCV at least once. People living with HIV are at greater risk for complications and death from HCV infection. Fortunately, direct acting antivirals that are used to treat HCV work equally well in people with and without HIV infection. For more information about HIV and HCV coinfection, visit the HIV.govs pages about hepatitis C and HIV coinfection.
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Why Are We Talking About Sexual Transmission Of Hepatitis C Among Gbmsm
Drug use, and injection drug use in particular, is a well-understood pathway for hepatitis C transmission. Sexual transmission of hepatitis C isnt as clear. The evidence suggests that sexual transmission of hepatitis C among heterosexual couples is rare.1 However, as we will discuss below, sexual transmission of hepatitis C can occur among gbMSM. Further, changing sexual practices among gbMSM2 using new HIV prevention approaches may be having an impact on the rates of sexual transmission of hepatitis C.
The Blueprint to inform hepatitis C elimination efforts in Canada indicates that gbMSM are an emerging priority population for efforts to eliminate hepatitis C infection. The Blueprint states that sexual transmission and/or transmission through drug use are the main risk factors for hepatitis C among gbMSM.3 However, hepatitis C infection may not be widely considered a priority for sexual health services and education for gbMSM.
It is challenging to understand the risk of sexual transmission among gbMSM, including separating the risks of transmitting hepatitis C through sex and through drug use. In the studies discussed in this article, where information on drug use is included in the research it is often based on self-reported data, which may be an underestimate because of the stigma that can be associated with drug use.
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How Is Hepatitis B Virus Transmitted Between Sexual Partners
Sexual transmission
HBV is transmitted more easily than human immunodeficiency virus or HCV. Infection is associated with vaginal intercourse, genital-rectal intercourse, and oral-genital intercourse. An estimated 30% of sexual partners of patients infected with HBV also contract HBV infection. However, HBV cannot be transmitted through kissing, hugging, or household contact . Sexual activity is estimated to account for as many as 50% of HBV cases in the US.
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What Is A Sexually Transmitted Disease
The definition of a sexually transmitted disease is A bacterial or viral infection transmitted from an infected person to an uninfected person through sexual contact. Examples of sexually transmitted viruses and bacteria are chlamydia, gonorrhoea, herpes, human papillomavirus infection, HIV/AIDS, and syphilis.
Important: Hepatitis Cases In Children
The number of cases of hepatitis in children has increased recently. Public health doctors and scientists are looking into what could be causing this.
See a GP if your child has symptoms of hepatitis, including yellowing of the eyes and skin .
Good hygiene, including supervising hand washing in young children, can help to prevent infections that can cause hepatitis.
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Parenteral Routes: Transmission Of Hepatitis B Hepatitis D And Hepatitis C
Hepatitis B, C, and D viruses are all transmitted by what is known as the parenteral route. Parenteral simply means that these viruses can be introduced by all routes except through the intestinal tract, which leaves the door wide open in terms of possible exposure. Lets look at the possible transmission routes for each of these types of hepatitis virus more closely.
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Description Of The Outbreak
What is Hepatitis B? Signs, Symptoms, #Hepatitis Transmission and How to get #Tested
Case definition
A case of hepatitis A was defined as any person aged 18years or more, with a positive detection of anti-hepatitis A virus IgM antibodies from December 2016 to December 2017, in Seine-Maritime department. Cases were subsequently classified according to isolation or not of European outbreak strains as 1) confirmed outbreak case if genotype IA was isolated whether or not the genetic sequence was available , 2) possible outbreak case if no further information on genotyping was available, 3) unrelated case if another genotype than genotype IA was identified.
Enhanced surveillance
Since 2005, HAV surveillance relies on a mandatory notification by clinicians and laboratories . At case notification, the public health nurse from the regional health directorate collects details on possible source of contamination and closed contacts using a standard questionnaire. Information on sexual exposure is not collected in routine. Cases receive counselling on good personal hygiene. Vaccination of contact is usually recommended.
Virological surveillance
The National Hepatitis A Reference laboratory performed genotyping as previously described . Analysis were performed each month on a subset of sera due to the flow of request received by the reference laboratory.
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Contaminated Food And Water
Hepatitis A is most commonly passed on by eating food prepared by someone with the virus whose hands have not been washed properly. You can also get it by drinking dirty water and by eating raw or undercooked shellfish from dirty water.
You can protect yourself by:
Washing your hands each time you go to the toilet, before you prepare or eat food, after coughing or sneezing, or handling rubbish or other dirty items.
Peeling and washing all your fresh fruit and vegetables avoiding raw or undercooked meat and fish avoiding all drinks if youre not sure if theyre safe with or without ice.
If tap water isnt safe and bottled water isnt available, boil tap water before drinking it.
People living in places with poor sanitation and hygiene are at a greater risk of hepatitis A infection. You may also be exposed to hepatitis A through your work, for example, sewage workers, staff in institutions where levels of personal hygiene may be poor , people working with animals that may be infected with hepatitis A and daycare centres.
Could I Give It To Other People
Yes. As long as you carry the virus, you can infect others. You may pass it on to your sex partner, to those who live in close contact with you, and to those who share your needles for injecting drugs. All of these contacts should be examined by a doctor. If they are not yet infected, they should be vaccinated.
Pregnant women who are carriers may pass hepatitis B on to their babies around the time of birth. Most infected infants become carriers. A pregnant woman should have a test for hepatitis B at her first visit to a doctor. If she is a carrier, the infant can be vaccinated at birth to protect against infection.
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Treatment For Hepatitis A
Most cases are diagnosed by GPs rather than sexual health clinics and no special treatment is needed.
A blood test will confirm whether you have picked up the virus.
The usual treatment for hepatitis A is simply to rest. You may need some time off work while you recover from the flu-like symptoms.
You should also:
avoid recreational drugs to allow your liver to get better
avoid alcohol until your liver recovers.
Once you have had hepatitis A youre immune and cannot get it again, but you can still get other types of hepatitis.
Achievement Of Expected Outcomes
PHACs activities have contributed to the creation and uptake of STBBI-related knowledge products. PHAC has developed and shared a variety of products, such as surveillance information, peer-reviewed journal articles, and guidance on STI.
While there has been considerable work done to generate, share and support the application of knowledge, there are opportunities to further the application of STBBI knowledge products, such as guidelines, through enhanced outreach efforts. While PHAC knowledge products are well known and used by professionals in the field of sexual health, awareness of these products is low among general and family practitioners.
PHACs activities have contributed to increasing the capacity and skills of audiences and populations to prevent and control viral hepatitis and STI. For example, NML has provided reliable testing services to enhance the capacity of regional, provincial, and territorial public health services through genotyping or serological testing for diseases such as gonorrhea, chlamydia, HPV, and herpes. Frontline health care workers are using information produced through PHAC-funded activities in their work. More precisely, they used information to educate or inform clients, colleagues, and members of the public, and to improve their own work practices.
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Prevention And The Treatment Potential
Currently, there is no vaccine to prevent HCV infection. However, the recent availability of DAA for the treatment of chronic HCV with cures rates over 95% has created optimism towards HCV elimination. In many countries treatment is now available for all individuals with a chronic HCV infection, irrespective of fibrosis stage . Modelling studies were the first to demonstrate that rapid scaleup of DAA might limit onward transmission and chronic HCV prevalence and incidence among MSM could decline , , . However, for substantial reductions a decline in risk behaviour is needed as the scaleup of DAA is counterbalanced by ongoing risk behaviour, resulting in initial and reinfections , , . In addition, early treatment, including treatment of acute infection, might further reduce HCV incidence , . As treatment is costly and treatment uptake varies considerably across countries , effective behavioural interventions for MSM at risk of infection are urgently needed. Qualitative research among HIVpositive MSM with a cured HCV infection in the preDAA era showed that the strongest motive to implement risk reduction strategies was the reward of avoiding HCV retreatment and its side effects , but this may have changed with the less burdensome DAA treatment. Also sexual risk norms within the MSM population, HCV stigma and nondisclosure of HCV status forms barriers to safer sex, and drug use directly impedes the selfefficacy of MSM to take risk reduction measures .
Can Vaccines Prevent The Sexual Transmission Of Hepatitis
Hepatitis A: Measures used to prevent the spread of STDs, like the use of condoms, do not prevent hepatitis A transmission. Fortunately, an effective vaccine for preventing Hepatitis A transmission is available and is the most important measure to protect people at risk of infection.
Hepatitis B: A hepatitis B vaccine safely and effectively protects against infection of the virus. The Centers for Disease Control and Prevention and the Advisory Committee on Immunization Practices have recommended the hepatitis B vaccination for:
Sexually active people who are not in long-term, mutually monogamous relationships .
People seeking evaluation or treatment for a sexually transmitted disease.
CDC also recommends hepatitis B testing and hepatitis B vaccination for
Sexual partners of people with hepatitis B.
Hepatitis C: There is no vaccine available for hepatitis C. The most effective protection against hepatitis C is avoiding behaviors that can spread the disease, like sharing needles or other equipment to inject drugs and avoiding multiple sexual partners.
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What Are The Side Effects Of Hepatitis B


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Articles Europe Germany History White News
5 Pics: 40,000 Years ago in Germany: The oldest known, uncontested example of figurative art: The Lion-Man!
30th May 2018 31st May 2018 jlamprecht 1654 Views 1 Comment
[There is a lot of artwork that is old. But as it gets older, its also harder to prove that its art. However, this figurine comes from Germany and is dated as being 35-40,000 Years old. Its also an astounding piece of art. It was broken into pieces and they've assembled it and reassembled it. Its 31cm high. This is a truly incredible piece of quality art despite its ancient past.
A tremendous amount of effort has gone into trying to determine what it is and putting it together. Scientists estimate that it took 400 hours of work to construct. Various discoveries have led some scientists to think that this piece of art had a religious significance.
NB: It was sculpted from mammoth ivory! Yes, whites used to hunt the massive woolly mammoths! Jan]
The Löwenmensch figurine or Lion-man of the Hohlenstein-Stadel is a prehistoric ivory sculpture that was discovered in the Hohlenstein-Stadel, a German cave in 1939. The German name, Löwenmensch meaning "lion-human", is used most frequently because it was discovered and is exhibited in Germany.
The lion-headed figurine is the oldest-known zoomorphic (animal-shaped) sculpture in the world, and the oldest-known uncontested example of figurative art. It has been determined to be between 35,000 and 40,000 years old by carbon dating of material from the layer in which it was found, and thus, is associated with the archaeological Aurignacian culture.[1] It was carved out of woolly mammoth ivory using a flint stone knife. Seven parallel, transverse, carved gouges are on the left arm.
After several reconstructions that have incorporated newly found fragments, the figurine stands 31.1 cm (12.2 in) tall, 5.6 cm (2.2 in) wide, and 5.9 cm (2.3 in) thick. It currently is displayed in the Ulm Museum, Germany.
2 Interpretation
Systematic excavations at Hohlenstein-Stadel cave began in 1937 under the direction of prehistoric historian Robert Wetzel.[2] The discovery of a fragmented mammoth–ivory figurine was made on 25 August 1939 by geologist Otto Völzing.[3] The start of World War II just one week later, meant that the fieldwork was left incomplete and analysis of the finds was not undertaken. The excavation trenches were backfilled with the same soil in which the ivory had been found.[4]
For approximately thirty years, the fragments lay forgotten at the nearby Museum of Ulm. It was not until archaeologist Joachim Hahn started an inventory and assembly of more than 200 fragments that a figurine with animal and human features began to emerge.[4]
Wetzel continued to spend summers digging at the site until 1961,[5] and further finds of ivory were made on the cave floor in the 1970s. In 1982, paleontologist Elisabeth Schmid combined the new fragments with Hahn's reconstruction, correcting some errors and adding pieces of the nose and mouth that emphasized the figurine's feline characteristics.[4][a]
In 1987, a comprehensive restoration began in the workshops of the Landesmuseum Württemberg by Ute Wolf in cooperation with Schmid. During the work, which took more than six months, it was realized that the figurine was only about two-thirds complete. The back is severely damaged and the legs are missing some ivory lamellae. The ears, eye-holes, two-thirds of the mouth and nose, and the back of the head are preserved. To fill gaps in the head and body a reversible substance consisting of a mixture of beeswax, artificial wax, and chalk was used.[6]
From 2008, further excavations were carried out in the cave. All layers were sifted systematically, which led to many minute fragments being discovered. The first new adjustments were simulated virtually so that fragments could be added without having to disassemble the original recreation.[7][b]
In 2012, a second restoration was begun in the workshops of the State Office for the Preservation of Historical Monuments in Esslingen under the leadership of Nicole Ebinger-Rist. The figurine was disassembled into its individual parts and newly discovered fragments were added along with the old ones, allowing further completion of areas of the head, back, and right side of the body, and artificial additions used during the first restoration were discarded.[8] The Löwenmensch figurine grew in height from 296 to 311 millimetres.[9] Work was completed in late 2013.[8]
The Löwenmensch figurine after restoration in 2013
Assignment of gender became an objective of certain researchers. Initially, the figurine was classified as male by Hahn who suggested a plate on the abdomen could be a flaccid penis. Schmid later classified this feature as a pubic triangle,[3] however, and from examination of new parts of the sculpture, determined that the figurine was that of a woman with the head of a "Höhlenlöwin" (female European cave lion).[10] Male European cave lions often lacked distinctive manes, so the absence of a mane could not determine categorically that the figurine was that of a lioness, and a debate about its gender ensued among some involved in the research and the popular press. Kurt Wehrberger of the Ulm Museum stated that the statue had become an "icon of the feminist movement".[3]
After the 2012–13 restoration it was realized that the triangular platelet in the genital area was processed all around separating it from the figurine. A fracture point suggests that originally it may have been square in shape. Although an objective determination of the gender of the Löwenmensch figurine is impossible, debate continues, with the most common interpretation of the fragment being a stylized male sex organ.[11]
The Löwenmensch figurine lay in a chamber almost 30 metres from the entrance of the Stadel cave and was accompanied by many other remarkable objects. Bone tools and worked antlers were found, along with jewellery consisting of pendants, beads, and perforated animal teeth. The chamber was probably a special place, possibly used as a storehouse or hiding-place, or maybe as an area for cultic rituals.[12]
A similar but smaller lion-headed human sculpture was found along with other animal figurines and several flutes in the nearby Vogelherd Cave. This leads to the possibility that the Löwenmensch figurines were important in the mythology of humans of the early Upper Paleolithic. Archaeologist Nicholas Conard has suggested that the second lion-figurine "lends support to the hypothesis that Aurignacian people may have practised shamanism … and that it should be considered strong evidence for fully symbolic communication and cultural modernity".[13]
The figurine shares certain similarities with later French cave paintings, which also show hybrid creatures with human-like lower bodies and animal heads such as the "Sorcerer" from the Trois Frères in the Pyrennes or the "Bison-man" from the Grotte de Gabillou in the Dordogne.[14]
The carving of the figurine from hard mammoth tusk would have been a complex and time-consuming task.[c] A similarly-sized tusk found in the same cave has marks that "indicate that the skin and thin bone around the tooth cavity of the upper jaw were cut through to the surface of the tooth, which was then exposed for detachment with a hammer. The tip was harder and had to be removed by wedging and splitting."[15]
Wulf Hein and Kurt Wehrberger conducted an experimental replication with the kinds of stone tool available at the time. Removing the base of the tusk took ten hours. The body was carved with steep-fronted scraper; the burins requiring regular resharpening. Several different tools were needed to separate the torso from the insides of the arms while shaping the head and shoulders, which involved difficult cutting across the grain of the ivory, often required two hands on the tool. The basic shaping took around 200 hours, and in total the recreation took more than 370 hours.[d] Jill Cook, Curator of Paleolithic collections at the British Museum, suggests that "unless the sculpture was created slowly at odd moments over several months, someone as skilled as an artist may have been excused from other subsistence tasks to work specially on this piece."[15]
In his October 2017 BBC Radio 4 series Living with the Gods, Neil MacGregor asked Cook "[…] so why would a community living on the edge of subsistence, whose primary concerns were finding food, keeping that fire going, protecting children from predators, allow someone to spend so much time away from those tasks?", to which she replied that it was about "[…] a relationship to things unseen, to the vital forces of nature, that you need to perhaps propitiate, perhaps connect to, in order to ensure your successful life".[16]
Source: https://en.wikipedia.org/wiki/Lion-man
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One thought on "5 Pics: 40,000 Years ago in Germany: The oldest known, uncontested example of figurative art: The Lion-Man!"
SidVic
1st June 2018 at 12:01 am
That lion appear to have tremendously large balls! Was gout prevalent at the time?
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