text:
\section{Introduction}
Given a document in one source language, cross-lingual summarization aims to produce a summary in a different target language, which can help people efficiently acquire the gist of an article in a foreign language. Traditional approaches to CLS are based on the pipeline paradigm, which either first translates the original document into target language and then summarizes the translated document~\cite{leuski2003cross} or first summarizes the original document and then translates the summary into target language~\cite{lim2004multi, orasan2008evaluation, wan2010cross}. However, the current machine translation (MT) is not perfect, which results in the error propagation problem. Although end-to-end deep learning has made great progress in natural language processing, no one has yet applied it to CLS due to the lack of large-scale supervised dataset.

The input and output of CLS are in two different languages, which makes the data acquisition much more difficult than monolingual summarization (MS). To the best of our knowledge, no one has studied how to automatically build a high-quality large-scale CLS dataset. Therefore, in this work, we introduce a novel approach to directly address the lack of data. Specifically, we propose a simple yet effective round-trip translation strategy to obtain cross-lingual document-summary pairs from existing monolingual summarization datasets~\cite{hermann2015, Zhu2018MSMO, lcsts}. More details can be found in Section~\ref{dataconstruction} below.

Based on the dataset that we have constructed, we propose end-to-end models on cross-lingual summarization, which we refer to as Neural Cross-Lingual Summarization (NCLS). Furthermore, we consider improving CLS with two related tasks: MS and MT. We incorporate the training process of MS and MT into that of CLS under the multi-task learning framework~\cite{caruana1997multitask}. Experimental results demonstrate that NCLS achieves remarkable improvement over traditional pipeline paradigm. In addition, both MS and MT can significantly help to produce better summaries.
\begin{figure*}[t]
\setlength{\abovecaptionskip}{0.12cm}
\setlength{\belowcaptionskip}{-0.3cm}
  \centering
  \includegraphics[width=1\linewidth]{datanew.pdf}\\
  \caption{Overview of CLS corpora construction. Our method can be extended to many other language pairs and we focus on En2Zh and Zh2En in this paper. During RTT, we filter the sample in which ROUGE F1 score between the original reference and the round-trip translated reference is below a preset threshold $T$.}
  \label{data}
\end{figure*}

Our main contributions are as follows:
\begin{itemize}
	\item We propose a novel round-trip translation strategy to acquire large-scale CLS datasets from existing large-scale MS datasets. We have constructed a 370K English-to-Chinese (En2Zh) CLS corpus and a 1.69M Chinese-to-English (Zh2En) CLS corpus.
	\item To train the CLS systems in an end-to-end manner, we present neural cross-lingual summarization.  Furthermore, we propose to improve NCLS by incorporating MT and MS into CLS training process under multi-task learning. To the best of our knowledge, this is the first work to present an end-to-end CLS framework that trained on parallel corpora.
	\item Experimental results demonstrate that NCLS can achieve +4.87 ROUGE-2 on En2Zh and +5.07 ROUGE-2 on Zh2En over traditional pipeline paradigm. In addition, NCLS with multi-task learning can further achieve +3.60 ROUGE-2 on En2Zh and +0.72 ROUGE-2 on Zh2En. Our methods can be regarded as a benchmark for further NCLS studying.
\end{itemize}

\begin{table*}[t]
\centering
\setlength{\abovecaptionskip}{0.1cm}
\setlength{\belowcaptionskip}{-0.3cm}
\begin{tabular}{@{}lrrrlrrr@{}}
\toprule
En2ZhSum         & \multicolumn{1}{c}{train} & \multicolumn{1}{c}{valid} & \multicolumn{1}{c}{test} & Zh2EnSum         & \multicolumn{1}{c}{train} & \multicolumn{1}{c}{valid} & \multicolumn{1}{c}{test} \\ \midrule
\#Documents      & 364,687                   & 3,000                     & 3,000                    & \#Documents      & 1,693,713                 & 3,000                     & 3,000                    \\
\#AvgWords (S)   & 755.09                    & 759.55                    & 744.84                   & \#AvgChars (S)   & 103.59                    & 103.56                    & 140.06                   \\
\#AvgEnWords (R) & 55.21                     & 55.28                     & 54.76                    & \#AvgZhChars (R) & 17.94                     & 18.00                     & 18.08                    \\
\#AvgZhChars (R) & 95.96                     & 96.05                     & 95.33                    & \#AvgEnWords (R) & 13.70                     & 13.74                     & 13.84                    \\
\#AvgSentsWords  & 19.62                     & 19.63                     & 19.61                    & \#AvgSentsChars  & 52.73                     & 52.41                     & 53.38                    \\
\#AvgSents       & 40.62                     & 41.08                     & 40.25                    & \#AvgSents       & 2.32                      & 2.33                      & 2.30                     \\ \bottomrule
\end{tabular}
\caption{Corpus statistics. \textbf{\#AvgWords (S)} is the average number of English words in the source document. Each reference has a bilingual version since each reference in CLS corpus is translated from the corresponding reference in the MS corpus. \textbf{\#AvgEnWords (R)} means the average number of words in English reference and \textbf{\#AvgZhChars (R)} denotes the average number of characters in Chinese reference. \textbf{\#AvgSentsWords} (\textbf{\#AvgSentsChars}) indicates the average number of words (characters) in a sentence in the source document. \textbf{\#AvgSents} refers to the average number of sentences in the source document.   \label{statistics}}
\end{table*}

\section{Dataset Construction} \label{dataconstruction}
Existing large-scale monolingual summarization datasets are automatically collected from the internet. CNN/Dailymail~\cite{hermann2015} dataset has been collected from \textit{CNN} and \textit{DailyMail} websites, where the article and news highlights are treated as the input and output respectively.  Similar to \citet{hermann2015}, \citet{Zhu2018MSMO} have constructed a multimodal summarization dataset MSMO where the text input and output are similar to that in CNN/Dailymail. We refer to the union set of CNN/Dailymail and MSMO as ENSUM\footnote{It contains 626,634 English summarization pairs.}. \citet{lcsts} introduce a large-scale corpus of Chinese short text summarization (LCSTS\footnote{It contains 2,400,591 Chinese summarization pairs.}) dataset constructed from the Chinese microblogging website \textit{Sina Weibo}. In this section, we introduce how to construct the En2Zh and Zh2En CLS datasets based on ENSUM and LCSTS respectively.

\textbf{Round-trip translation strategy.} Round-trip translation\footnote{\url{https://en.wikipedia.org/wiki/Round-trip_translation}} (RTT) is the process of translating a text into another language (forward translation), then translating the result back into the original language (back translation), using MT service\footnote{\url{http://www.anylangtech.com}}. Inspired by \citet{lample2018unsupervised}, we propose to adopt the round-trip translation to acquire CLS dataset from MS dataset. The process of constructing our corpora is shown in Figure~\ref{data}.

Taking the construction of En2Zh corpus as an example, given a document-summary pair $(D_{\textrm{en}},S_{\textrm{en}})$, we first translate the summary $S_{\textrm{en}}$ into Chinese $S_{\textrm{zh}}$ and then back into English $S'_{\textrm{en}}$.
The En2Zh document-summary pair $(D_{\textrm{en}},S_{\textrm{zh}})$, which satisfies $\textrm{ROUGE-1}(S_{\textrm{en}}, S'_{\textrm{en}}) \geqslant T_1$ and $\textrm{ROUGE-2}(S_{\textrm{en}}, S'_{\textrm{en}}) \geqslant T_2$ ($T_1$ is set to 0.45 for English and 0.6 for Chinese respectively, and $T_2$ is set to 0.2 here\footnote{The values are obtained by conducting a manual estimation on some samples randomly selected from two corpora.}), will be regarded as a positive pair. Otherwise, the pair will be filtered. Note that there are multiple sentences in $S_{\textrm{en}}$ in ENSUM, we apply the RTT to filter low-quality translated reference sentence by sentence. Once more than two-thirds of the sentences in the summary in a sample are retained, we will keep the sample. This process helps to ensure that the final compression ratio in our task does not differ too much from the actual compression ratio. Similar process is used on constructing Zh2En corpus. The ROUGE scores between Chinese sentences are calculated using Chinese characters as segmentation units.

\textbf{Corpus Statistics.} After conducting the round-trip translation strategy, we have obtained 370,759 En2Zh CLS pairs from ENSUM and 1,699,713 Zh2En CLS pairs from LCSTS. The statistics of En2Zh corpus (En2ZhSum) and Zh2En corpus (Zh2EnSum) are presented in Table~\ref{statistics}. In order to evaluate various CLS methods more reliably, we recruit 10 volunteers to correct the reference in the test sets in two constructed corpora.

\section{Approach}
The traditional approaches (Section~\ref{baseline}) intuitively treat CLS as a pipeline process which leads to error propagation. To handle that, we present the neural cross-lingual summarization methods (Section~\ref{single}), which train CLS in an end-to-end manner for the first time. Due to the strong relationship between CLS, MS, and MT tasks, we propose to incorporate MS and MT into CLS training under multi-task learning (Section~\ref{multi}).

\subsection{Baseline Pipeline Methods} \label{baseline}
In general, traditional CLS is composed of summarization step and translation step. The different order of these two steps leads to the following two strategies. Take En2Zh CLS as an example.

\textbf{Early Translation (ETran).} This strategy first translates the English document to Chinese document with machine translation. Then a Chinese summary is generated by a summarization model.

\textbf{Late Translation (LTran).} This strategy first summarizes the English document to a short English summary and then translates it into Chinese.

\begin{figure}[t]
\setlength{\abovecaptionskip}{0.11cm}
\setlength{\belowcaptionskip}{-0.4cm}
  \centering
  \includegraphics[width=6cm]{transformer.pdf}\\
  \caption{Transformer-based NCLS models (TNCLS).}
  \label{trans-ncls}
\end{figure}

\subsection{Neural Cross-Lingual Summarization} \label{single}
Considering the excellent text generation performance of Transformer encoder-decoder network~\cite{transformer}, we implement our NCLS models entirely based on this framework in this work. As shown in Figure~\ref{trans-ncls}, given a set of CLS data $D={(X^{(i)},Y^{(i)})}$ where both $X$ and $Y$ are a sequence of tokens, the encoder maps the input document $X=(x_1,x_2,\cdots,x_n)$ into a sequence of continuous representations $z=(z_1,z_2,\cdots,z_n)$ whose size varies with respect to the source sequence length. The decoder generates a summary $Y=(y_1,y_2,\cdots,y_m)$, which is in a different language, from the continuous representations. The encoder and decoder are trained jointly to maximize the conditional probability of target sequence given a source sequence:
\begin{equation}
\small
\setlength\abovedisplayskip{0.1cm}
\setlength\belowdisplayskip{0.1cm}
	L_{\theta} = \sum_{t=1}^{N}\rm{log}P(y_t|y_{<t},x;\theta)
\end{equation}

Transformer is composed of stacked encoder and decoder layers. Consisting of two blocks, the encoder layer is a self-attention block followed by a position-wise feed-forward block. Despite the same architecture as the encoder layer, the decoder layer has an extra encoder-decoder attention block. Residual connection and layer normalization are used around each block. In addition, the self-attention block in the decoder is modified with masking to prevent present positions from attending to future positions during training.

For self-attention and encoder-decoder attention, a multi-head attention block is used to obtain information from different representation subspaces at different positions. Each head corresponds to a scaled dot-product attention, which operates on the query $Q$, key $K$, and value $V$:
\begin{equation}
\small
\setlength\abovedisplayskip{0.15cm}
\setlength\belowdisplayskip{0.15cm}
	\textrm{Attention}(Q,K,V) = \textrm{softmax}(\frac{QK^T}{\sqrt{d_k}})V
\end{equation}
where $d_k$ is the dimension of the key.

Finally, the output values are concatenated and projected by a feed-forward layer to get final values:
\begin{equation}
\small
\setlength\abovedisplayskip{0.15cm}
\setlength\belowdisplayskip{0.15cm}
\begin{split}
\textrm{MultiHead}(Q,K,V) &= \textrm{Concat}(\textrm{head}_1,\ldots,\textrm{head}_h) W^O  \\
\textrm{where}\ \textrm{head}_i &= \textrm{Attention}(QW_i^Q, KW_i^K, VW_i^V)
\end{split}
\end{equation}
where $W^O$, $QW_i^Q$, $KW_i^K$, and $VW_i^V$ are learnable matrices, $h$ is the number of heads.

\subsection{Improving NCLS with MS and MT} \label{multi}
Considering there is a strong relationship between CLS task and MS task, as well as between CLS task and MT task: (1) CLS shares the same goal with MS, i.e., to grasp the core idea of the original document, but the final results are presented in different languages. (2) From the perspective of information compression, machine translation can be regarded as a special kind of cross-lingual summarization with a compression ratio of 1:1. Therefore, we consider using MS and MT datasets to further improve the performance of CLS task under multi-task learning.

Inspired by \citet{luong2016}, we employ the one-to-many scheme to incorporate the training process of MS and MT into that of CLS. As shown in Figure~\ref{mncls}, this scheme involves one encoder and multiple decoders for tasks in which the encoder can be shared. We study two different task combinations here: CLS+MS and CLS+MT.

\textbf{CLS+MS.} Note that the reference in each of CLS datasets has a bilingual version. For instance, En2ZhSum dataset contains a total of 370,687 documents with corresponding summaries in both Chinese and English. Thus, we consider jointly training CLS and MS as follows. Given a source document, the encoder encodes it into continuous representations, and then the two decoders simultaneously generate the output of their respective tasks. The loss can be calculated as follows:
\begin{equation}
\small
\setlength\abovedisplayskip{0.15cm}
\setlength\belowdisplayskip{0.15cm}
	L_{\theta} = \sum_{t=1}^{N^{(1)}}\rm{log}P(y^{(1)}_t|y^{(1)}_{<t},x;\theta) + \sum_{t=1}^{N^{(2)}}\rm{log}P(y^{(2)}_t|y^{(2)}_{<t},x;\theta)
\end{equation}
where $y^{(1)}$ and $y^{(2)}$ are the outputs of two tasks.

\textbf{CLS+MT.} Since CLS input-output pairs are different from MT input-output pairs, we consider adopting the alternating training strategy~\cite{dong2015multi}, which optimizes each task for a fixed number of mini-batches before switching to the next task, to jointly train CLS and MT. For MT task, we employ 2.08M\footnote{LDC2000T50, LDC2002L27, LDC2002T01, LDC2002E18, LDC2003E07, LDC2003E14, LDC2003T17, LDC2004T07} sentence pairs from LDC corpora with CLS dataset to train CLS+MT.

\begin{figure}[t]
\setlength{\abovecaptionskip}{0.11cm}
\setlength{\belowcaptionskip}{-0.4cm}
  \centering
  \includegraphics[width=7.5cm]{multitask.pdf}\\
  \caption{Overview of multi-task NCLS. The lower half is CLS+MT using alternating training strategy. Different colors represent different languages.}
  \label{mncls}
\end{figure}

\section{Experiments}
\subsection{Experimental Settings}
 For English, we apply two different granularities of segmentation, i.e., words and subwords~\cite{bpe}. We lowercase all English characters. We truncate the input to 200 words and the output to 120 words (150 characters for Chinese output) . For Chinese, we employ three different granularities of segmentation: characters, words, and subwords. It is worth noting that we only apply subword-based segmentation in Zh2En model since subword-based segmentation will make the English article much longer in En2Zh (especially at the Chinese target-side output), which makes the Transformer performs extremely poor. For our baseline pipeline models, the vocabulary size of Chinese characters is 10,000, and that of Chinese words, Chinese subwords, and English words are all 100,000. In our En2Zh NCLS models, the vocabulary size of source-side English words is 100,000, and that of target-side Chinese characters and words are 18,000, and 50,000 respectively. In our Zh2En models, the vocabulary size of source-side Chinese characters, words, and subwords are 10,000, 100,000, and 100,000 respectively, and that of target-side English words and subwords are all 40,000. We initialize all the parameters via Xavier initialization methods~\cite{Glorot10understandingthe}. We train our models using configuration \textit{transformer\_base}~\cite{transformer}, which contains a 6-layer encoder and a 6-layer decoder with 512-dimensional hidden representations.

 During training, in En2Zh models, each mini-batch contains a set of document-summary pairs with roughly 2,048 source and 2,048 target tokens; in Zh2En models, each mini-batch contains a set of document-summary pairs with roughly 4,096 source and 4,096 target tokens. We use Adam optimizer~\cite{kingma2014adam} with $\beta_1=0.9$, $\beta_2=0.998$, and $\epsilon=10^{-9}$. We use a single NVIDIA TITAN X to train our models. Convergence is reached within 1,000,000 iterations in both TNCLS models and baseline models. We train each task for about 800,000 iterations in multi-task NCLS models (reaching convergence). At test time, our summaries are produced using beam search with beam size 4.

\begin{table}[t]
\small
\setlength{\abovecaptionskip}{0.1cm}
\setlength{\belowcaptionskip}{-1cm}
\begin{tabular}{@{}lccc@{}}
\toprule
Model & ROUGE-1 & ROUGE-2 & ROUGE-L \\ \midrule
\citet{gu2016copy} & 35.00 & 22.30 & 32.00 \\
\citet{piji2017-deep} & 36.99 & 24.15 & 34.21 \\
Transformer & 39.71 & 27.45 & 37.13 \\ \bottomrule
\end{tabular}
\caption{Performance of our implemented transformer-based monolingual summarization model on LCSTS. \label{base1}}
\end{table}

\subsection{Baselines and Model Variants}
We compare our NCLS models with the following two traditional methods:

\textbf{TETran:} We first translate the source document via a Transformer-based machine translation model trained on LDC corpora. Then we employ LexRank~\cite{erkan2004lexrank}, a strong and widely used unsupervised summarization method, to summarize the translated document. The reason why we choose to apply an unsupervised method is that we lack the version of MS dataset in the target language to train a supervised model to summarize the translated document.

\textbf{TLTran:} We first build a Transformer-based MS model which is trained on the original MS dataset. Then the MS model aims to summarize the source document into a summary. Finally, we translate the summary into target language by using the Transformer-based machine translation model trained on LDC corpora. The performance of our transformer-based MS models is given in Table~\ref{base1} and Table~\ref{base2}.

To make our experiments more comprehensive, during the process of TETran and TLTran, we replace the Transformer-based machine translation model with Google Translator\footnote{\url{https://translate.google.com/}}, which is one of the state-of-the-art machine translation systems. We refer to these two methods as \textbf{GETran} and \textbf{GLTran} respectively.

There are three variants of our NCLS models:

\textbf{TNCLS:} Transformer-based NCLS models where the input and output are different granularities combinations of units.

\textbf{CLS+MS:} It refers to the multi-task NCLS model which accepts an input text and simultaneously performs text generation for both CLS and MS tasks and calculates the total losses.

\textbf{CLS+MT:} It trains CLS and MT tasks via alternating training strategy. Specifically, we optimize the CLS task in a mini-batch, and we optimize the MT task in the next mini-batch.

\begin{table}[t]
\small
\setlength{\abovecaptionskip}{0.1cm}
\setlength{\belowcaptionskip}{-0.6cm}
\begin{tabular}{@{}lccc@{}}
\toprule
Model & ROUGE-1 & ROUGE-2 & ROUGE-L \\ \midrule
\citet{see2017} & 39.53 & 17.28 & 36.38 \\
Transformer & 39.24 & 16.67 & 36.42 \\ \bottomrule
\end{tabular}
\caption{Performance of our implemented transformer-based MS model on CNN/Dailymail. \label{base2}}
\end{table}

\subsection{Experimental Results and Analysis}

\begin{table*}[]
\centering
\setlength{\abovecaptionskip}{0.1cm}
\setlength{\belowcaptionskip}{-0.1cm}
\begin{tabular}{@{}lccccc@{}}
\toprule
\multirow{2}{*}{Model}  & \multirow{2}{*}{Unit} & En2ZhSum                   & En2ZhSum*                  & Zh2EnSum                   & Zh2EnSum*                  \\ \cmidrule(l){3-6}
                        &                       & RG1-RG2-RGL($\uparrow$)    & RG1-RG2-RGL($\uparrow$)    & RG1-RG2-RGL($\uparrow$)    & RG1-RG2-RGL($\uparrow$)    \\ \midrule
TETran                  & --                    & \textbf{26.12-10.59-23.21} & \textbf{26.15-10.60-23.24} & \textbf{22.81-\, 7.17-18.55} & \textbf{23.09-\, 7.33-18.74} \\ \midrule
GETran                  & --                    & \textbf{28.17-11.38-25.75} & \textbf{28.19-11.40-25.77} & \textbf{24.03-\, 8.91-19.92} & \textbf{24.34-\, 9.14-20.13} \\ \midrule
\multirow{3}{*}{TLTran} & c-c                   & --                         & --                         & 32.85-15.34-29.21          & 33.01-15.43-29.32          \\
                        & w-w                   & \textbf{30.20-12.20-27.02} & \textbf{30.22-12.20-27.04} & 31.11-13.23-27.55          & 31.38-13.42-27.69          \\
                        & sw-sw                 & --                         & --                         & \textbf{33.64-15.58-29.74} & \textbf{33.92-15.81-29.86} \\ \midrule
\multirow{3}{*}{GLTran} & c-c                   & --                         & --                         & 34.44-15.71-30.13          & 34.58-16.01-30.25          \\
                        & w-w                   & \textbf{32.15-13.84-29.42} & \textbf{32.17-13.85-29.43} & 32.42-15.19-28.75          & 32.52-15.39-28.88          \\
                        & sw-sw                 & --                         & --                         & \textbf{35.28-16.59-31.08} & \textbf{35.45-16.86-31.28} \\ \midrule
\multirow{4}{*}{TNCLS}  & c-w                   & --                         & --                         & 36.36-19.74-32.66          & 35.82-19.04-32.06          \\
                        & w-c                   & \textbf{36.83-18.76-33.22} & \textbf{36.82-18.72-33.20} & --                         & --                         \\
                        & w-w                   & 33.09-14.85-29.82          & 33.10-14.83-29.82          & 38.54-22.34-35.05          & 37.70-21.15-34.05          \\
                        & sw-sw                 & --                         & --                         & \textbf{39.80-23.15-36.11} & \textbf{38.85-21.93-35.05} \\ \bottomrule
\end{tabular}
\caption{ROUGE F1 scores (\%) on En2ZhSum and Zh2EnSum test sets. En2ZhSum* and Zh2EnSum* are the corresponding human-corrected test sets. \textit{Unit} denotes the granularity combination of text units, where \textit{c} means character, \textit{w} means word, and \textit{sw} means subword. RG refers to ROUGE for short. $\uparrow$ indicates that the larger values, the better the results are. Our NCLS models perform significantly better than baseline models by the 95\% confidence interval measured by the official ROUGE script\protect\footnotemark. \label{result1}}
\end{table*}
\footnotetext{The parameter for ROUGE script here is ``-c 95 -r 1000 -n 2 -a".}

\textbf{Comparison between NCLS with baselines.} We evaluate different models with the standard ROUGE metric~\cite{lin2004rouge}, reporting the F1 scores for ROUGE-1, ROUGE-2, and ROUGE-L. The results are presented in Table~\ref{result1}.

We can find that GLTran outperforms TLTran and GETran outperforms TETran, which indicates that pipeline-based methods perform better when using a stronger machine translation system. Compared with GLTran or GETran, our TNCLS models both achieve significant improvements, which can verify our motivation and demonstrate the efficacy of our constructed corpora.

\begin{table*}[t]
\small
\setlength{\abovecaptionskip}{0.1cm}
\centering
\begin{tabular}{@{}lccccc@{}}
\toprule
\multirow{2}{*}{DataVersion} & \multicolumn{1}{l}{\multirow{2}{*}{BT?}} & En2ZhSum & En2ZhSum* & Zh2EnSum & Zh2EnSum* \\ \cmidrule(l){3-6}
 & \multicolumn{1}{l}{} & RG1-RG2-RGL($\uparrow$) & RG1-RG2-RGL($\uparrow$) & RG1-RG2-RGL($\uparrow$) & RG1-RG2-RGL($\uparrow$) \\ \midrule
Filter & YES & 36.83-18.76-33.22 & 36.82-18.72-33.20 & 39.80-23.15-36.11 & 38.85-21.93-35.05 \\
Pseudo-Filter & NO & 36.04-17.80-32.49 & 36.03-17.78-32.48 & 35.58-17.93-31.71 & 35.00-17.37-31.10 \\ \midrule
Non-Filter & NO & 37.62-19.88-33.99 & 37.62-19.85-33.99 & 36.51-19.23-32.77 & 36.03-18.63-32.19 \\ \bottomrule
\end{tabular}
\caption{Experimental results on different versions of datasets. \textit{Filter} refers to the version of dataset for which we employ RTT strategy to filter. \textit{Non-Filter} denotes the version of the dataset obtained by simply forward translation without filtering process including back translation. \textit{Pseudo-Filter} is the dataset randomly sampled from \textit{Non-Filter} version and is of the same size as \textit{Filter} version. BT refers to back translation in RTT. For En2Zh task, we train the TNCLS (\textit{w}-\textit{c}). For Zh2En task, we train the TNCLS (\textit{sw}-\textit{sw}).
\label{result2}}
\end{table*}

In En2Zh CLS task, the results of each model on En2ZhSum are similar to those on En2ZhSum*. This is because the original ENSUM dataset comes from the news reports. Existing MT for news reports has excellent performance. Besides, we have pre-filtered samples with low translation quality during dataset construction. Therefore, the quality of the automatic test set is high. TNCLS (\textit{w}-\textit{c}) performs significantly better than TNCLS (\textit{w}-\textit{w}). This is because the character-based segmentation can greatly reduce the vocabulary size at the Chinese target-side, which leads to generating nearly no UNK token during the decoding process.

In Zh2En CLS task, the subword-based models outperform others since subword-based segmentation can greatly reduce the vocabulary size and the generation of UNK. Compared with baselines, TNCLS can achieve maximum improvement up to \textbf{+4.52 ROUGE-1}, \textbf{+6.56 ROUGE-2}, \textbf{+5.03 ROUGE-L} on Zh2EnSum and \textbf{+3.40 ROUGE-1}, \textbf{+5.07 ROUGE-2}, \textbf{+3.77 ROUGE-L} on Zh2EnSum*. The results of TNCLS drops obviously on the human-corrected test set, showing that the quality of the translated reference is not as perfect as expected. The reason is straightforward that the original LCSTS dataset comes from social media so that the proportion of abbreviations and omitting punctuation in its text is much higher than in news, resulting in lower translation quality.

In conclusion, TNCLS models significantly outperform the traditional pipeline methods on both En2Zh and Zh2En CLS tasks.

\begin{table*}[h]
\small
\setlength{\abovecaptionskip}{0.1cm}
\centering
\begin{tabular}{@{}lcccc@{}}
\toprule
\multirow{2}{*}{Model} & En2ZhSum & En2ZhSum* & Zh2EnSum & Zh2EnSum* \\ \cmidrule(l){2-5}
 & RG1-RG2-RGL($\uparrow$) & RG1-RG2-RGL($\uparrow$) & RG1-RG2-RGL($\uparrow$) & RG1-RG2-RGL($\uparrow$) \\ \midrule
TNCLS & \textbf{36.83-18.76-33.22} & \textbf{36.82-18.72-33.20} & \textbf{39.80-23.15-36.11} & \textbf{38.85-21.93-35.05} \\ \midrule
CLS+MS & 38.23-20.21-34.76 & 38.25-20.20-34.76 & 41.08-23.67-37.19 & \textbf{40.34-22.65-36.39} \\
CLS+MT & \textbf{40.24-22.36-36.61} & \textbf{40.23-22.32-36.59} & \textbf{41.09-23.70-37.17} & 40.25-22.58-36.21 \\ \bottomrule
\end{tabular}
\caption{Results of multi-task NCLS. The granularity combination of input and output in En2Zh task is ``word to character" (\textit{w}-\textit{c}), and that in Zh2En task is ``subword to subword" (\textit{sw}-\textit{sw}).
\label{result3}}
\end{table*}

\textbf{Why Back Translation?} To show the influence of filtering the corpus by back translation during the RTT process, we use three kinds of datasets to train our TNCLS models and compare their performance. They are: (a) the CLS dataset obtained by simply employing forward translation on MS dataset (\textit{Non-Filter}); (b) the CLS dataset obtained by a complete RTT process (\textit{Filter}); (c) the dataset obtained by sampled from \textit{Non-Filter} dataset to keep the same size as the \textit{Filter} dataset (\textit{Pseudo-Filter}). The results are given in Table~\ref{result2}. The models trained on \textit{Filter} dataset significantly outperform the models trained on \textit{Pseudo-Filter} dataset on both En2Zh and Zh2En tasks, which indicates that the back translation can effectively filter dirty samples and improve the overall quality of corpora, thus boosting the performance of NCLS. In En2Zh task, the model trained on \textit{Non-Filter} dataset performs best. The reasons are two-fold: (1) the quality of machine translation for English news is reliable; (2) the scale of \textit{Non-Filter} dataset is almost twice that of the two others so that after the amount of data reaches a certain level, it can make up for the noises caused by the translation error in the corpus. In Zh2En task, the performance of the model trained on \textit{Non-Filter} dataset is not as good as that on \textit{Filter}. It can be attributed to the fact that current MT is not very ideal in the translation of texts on social media so that the dataset constructed by only using forward translation contains too many noises. Therefore, when the quality of machine translation is not that ideal, backward translation is especially important during the process of constructing corpus.

\textbf{Results of Multi-task NCLS.} To explore whether MS and MT can further improve NCLS, we compare the multi-task NCLS with NCLS using one same granularity combination of units. The results are given in Table~\ref{result3}. As shown in Table~\ref{result3}, both CLS+MS and CLS+MT can improve the performance of NCLS, which can be attributed to that the encoder is enhanced by incorporating MS and MT data into the training process. CLS+MT significantly outperforms CLS+MS in En2Zh task while CLS+MS performs comparably with CLS+MT in Zh2En task. The reasons are two-fold: (1) In En2Zh task, MT dataset is much larger than both MS and CLS datasets, which makes it more necessary for enhancing the robustness of encoder. (2) We use the LDC MT dataset, which belongs to the news domain similar to our En2ZhSum, during the training of CLS+MT. However, Zh2EnSum belongs to social media domain, thus resulting in the greater improvement of CLS+MT in En2Zh than in Zh2En. In general, NCLS with multi-task learning achieves more significant improvement in En2Zh task than in Zh2En task, which illustrates that extra dataset in other related tasks is essentially important for boosting the performance when CLS dataset is not very large.

\textbf{Human Evaluation.} We conduct the human evaluation on 25 random samples from each of the En2ZhSum and Zh2EnSum test set. We compare the summaries generated by our methods (including TNCLS, CLS+MS, and CLS+MT) with the summaries generated by GLTran. Three graduate students are asked to compare the generated summaries with human-corrected references, and assess each summary from three independent perspectives: (1) How informative the summary is? (2) How concise the summary is? (3) How fluent, grammatical the summary is? Each property is assessed with a score from 1 (worst) to 5 (best). The average results are presented in Table~\ref{human}.

\begin{table}[]
\small
\setlength{\abovecaptionskip}{0.1cm}
\setlength{\belowcaptionskip}{-0.5cm}
\centering
\begin{tabular}{@{}lcccccc@{}}
\toprule
\multirow{2}{*}{Model} & \multicolumn{3}{c}{En2Zh} & \multicolumn{3}{c}{Zh2En} \\ \cmidrule(l){2-7}
 & IF & CC & FL & IF & CC & FL \\ \midrule
GLTran & 3.06 & 3.37 & 3.13 & 3.53 & 4.21 & 4.25 \\
TNCLS & 3.25 & 3.33 & 3.17 & 3.67 & 4.25 & 4.24 \\
CLS+MS & 3.53 & 3.58 & 3.53 & 3.72 & 4.31 & 4.28 \\
CLS+MT & \textbf{3.58} & \textbf{3.76} & \textbf{3.63} & \textbf{3.78} & \textbf{4.43} & \textbf{4.35} \\ \bottomrule
\end{tabular}
\caption{Human evaluation results. IF, CC and FL denote informative, concise, and fluent respectively.}
\label{human}
\end{table}

\begin{figure*}[t]
\setlength{\abovecaptionskip}{0.11cm}
\setlength{\belowcaptionskip}{-0.5cm}
  \centering
  \includegraphics[width=1\linewidth]{casestudy.pdf}\\
  \caption{Examples of generated summaries.}
  \label{example}
\end{figure*}

As shown in Table~\ref{human}, TNCLS can generate more informative summaries compared with GLTran, which shows the advantage of end-to-end models. The conciseness score and fluency score of TNCLS are comparable to those of GLTran. This is because both GLTtrans and TNCLS employ a single encoder-decoder model, which easily leads to under-generation and repetition. Our CLS+MS and CLS+MT can significantly improve the conciseness and fluency of generated summaries, which shows that these methods can generate shorter summaries and reduce grammatical errors. In conclusion, TNCLS can generate more informative summaries, but it is difficult to improve the conciseness and fluency. However, with the help of MT and MS tasks, conciseness and fluency scores can be significantly improved.

\subsection{Case Study}
We show the case study of a sample from the Zh2EnSum human-corrected test set in Figure~\ref{example}. As shown in Figure~\ref{example}, the summary generated by \textbf{GETran} obviously suffers from errors of machine translation (``distribution companies" should be corrected as ``circulation enterprises"). Since \textbf{GETran} first translates all the source text, it is easier to bring the errors from machine translation. The \textbf{GLTran}-generated summary contracts the fact that the year in it should be 2012 instead of 2011. The translation quality of the sentence is relatively reliable, thus the errors are probably produced during the summarization step. Compared with the first two generated summaries, although the summary produced by \textbf{TNCLS} does not emphasize the time and place of occurrence, there is no mistake in the logic of its expression. The summaries generated by \textbf{CLS+MS} and \textbf{CLS+MT} are generally consistent with the facts, but their emphases are different. The \textbf{CLS+MS} summary matches the gold summary better. The flaws of both of them are that they do not reflect the ``scale" in the original text. In conclusion, our methods can produce more accurate summaries than baselines.

\section{Related Work}
Cross-lingual summarization has been proposed to present the most salient information of a source document in a different language, which is very important in the field of multilingual information processing. Most of the existing methods handle the task of CLS via simply applying two typical translation schemes, i.e., early translation~\cite{leuski2003cross, ouyang2019robust} and late translation~\cite{orasan2008evaluation, wan2010cross}. The early translation scheme first translates the original document into target language and then generates the summary of the translated document. The late translation scheme first summarizes the original document into a summary in the source language and then translates it into target language.

\citet{leuski2003cross} translate the Hindi document to English and then generate the English headline for it. \citet{ouyang2019robust} present a robust abstractive summarization system for low resource languages where no summarization corpora are currently available. They train a neural abstractive summarization model on noisy English documents and clean English reference summaries. Then the model can learn to produce fluent summaries from disfluent inputs, which allows generating summaries for translated documents.
\citet{orasan2008evaluation} summarize the Romanian news with the maximal marginal relevance method~\cite{goldstein2000multi} and produce the English summaries for English speakers. \citet{wan2010cross} adopt the late translation scheme for the task of English-to-Chinese CLS. They extract English sentences considering both the informativeness and translation quality of sentences and automatically translate the English summary into the final Chinese summary. The above researches only make use of the information from only one language side.

Some methods have been proposed to improve CLS with bilingual information. \citet{wan2011using} proposes two graph-based summarization methods to leverage both the English-side and Chinese-side information in the task of English-to-Chinese CLS. Inspired by the phrase-based translation models, \citet{yao2015phrase} introduce a compressive CLS, which simultaneously performs sentence selection and compression. They calculate the sentence scores based on the aligned bilingual phrases obtained by MT service and perform compression via deleting redundant or poorly translated phrases. \citet{zhang2016abstractive} propose an abstractive CLS which constructs a pool of bilingual concepts represented by the bilingual elements of the source-side predicate-argument structures (PAS) and the target-side counterparts. The final summary is generated by maximizing both the salience and translation quality of the PAS elements.

However, all these researches belong to the pipeline paradigm which not only relies heavily on hand-crafted features but also causes error propagation. End-to-end deep learning has proven to be able to alleviate these two problems, while it has been absent due to the lack of large-scale training data.
Recently, \citet{liu2018zero} present zero-shot cross-lingual headline generation based on existing parallel corpora of translation and monolingual headline generation. Similarly, \citet{duan-etal-2019-zero} propose to use monolingual abstractive sentence summarization system to teach zero-shot cross-lingual abstractive sentence summarization on both summary word generation and attention. Although great efforts have been made in cross-lingual summarization, how to automatically build a high-quality large-scale cross-lingual summarization dataset remains unexplored.

In this paper, we focus on English-to-Chinese and Chinese-to-English CLS and try to automatically construct two large-scale corpora respectively. In addition, based on the two corpora, we perform several end-to-end training methods noted as Neural Cross-Lingual Summarization.

\section{Conclusion and Future Work}
In this paper, we present neural cross-lingual summarization for the first time. To achieve that goal, we propose to acquire large-scale supervised data from existing monolingual summarization datasets via round-trip translation strategy. Then we apply end-to-end methods on our constructed datasets and find our NCLS models significantly outperform the traditional pipeline paradigm. Furthermore, we consider utilizing machine translation and monolingual summarization to further improve NCLS. Experimental results have shown that both machine translation and monolingual summarization can significantly help NCLS generate better summaries.

In our future work, we will adopt our RTT strategy to obtain CLS datasets of other language pairs, such as English-to-Japanese, English-to-German, Chinese-to-Japanese, and Chinese-to-German, etc.

\section{Acknowledgments}
The research work described in this paper has been supported by the National Key Research and Development Program of China under Grant No. 2016QY02D0303. We thank the three anonymous reviewers for their careful reading of our paper and their many insightful comments and suggestions. We would like to thank He Bai, Yuchen Liu, Haitao Lin, Yang Zhao, Cong Ma, Lu Xiang, Weikang Wang, Zhen Wang, and Jiaqi Liang for their invaluable contributions in shaping the early stage of this work. We thank Xina Fu, Jinliang Lu, and Sikai Liu for conducting human evaluation.

redpajama_set_name: RedPajamaArXiv


text:
Text Text Text
I'm well aware I haven't blogged for a while. Things have been extremely busy. After I checked in code to implement the new textrun abstraction on Mac and Windows, there were some performance and correctness regressions. Bad me! I've fixed the Mac performance regressions and we have a fix in hand for the Windows regressions ... thanks to some heroic wrestling of VTune by Chris Double. Another thing I broke was the display of "missing-glyph boxes" when the user has no font with glyphs for a given character. To fix that I've done a cross-platform implementation of Pango-style "hexboxes" --- when glyphs are missing, we draw a box and inscribe inside it tiny hex characters with the character index we're missing glyphs for. It should be very useful for debugging complex scripts (and for users who've memorized the Unicode character table).
With those fires waning I've spent time working on the new textframe code. The gfx side is working pretty well for now and apart from the outstanding regression fixes, what's on trunk is adequate to support the new textframe. First I polished off a number of bugs triggered by our pageload performance test suite, so I could get some performance data. Then today I fixed a bunch of bugs triggered by our "reftest" layout test suite. All these tests work correctly now as far as I know. All these new-textframe fixes have been landed on trunk so it is now possible to build and test the new-textframe code and expect to be able to actually browse. This just requires you to set MOZ_NEW_TEXTFRAME=1 in layout/generic/Makefile.in. There are still lots of bugs, I'm sure; I know there are serious bugs in caret movement. There are also areas I've barely tested yet such as complex script handling and preformatted tabs.
Performance of new-textframe is interesting. Right now it's a slight slowdown in my Mac tests. However almost all the text-related performance cost is in textrun construction, and I've looked carefully at the textruns that are being created in our test suite. The current trunk code usually creates one textrun per word and caches them; the new textframe creates one textrun per run of text with a uniform font, but doesn't use a cache. The current code creates 28K textruns while the new code creates 21K textruns, but the textruns in the new code are much longer on average. The good news is that 8K of those 21K consist of a single space! Optimizing that will be easy. Furthermore if we cache textruns for use with the new text frame (which can be done quite efficiently), then we should get a 30% hit rate which will further reduce the textrun construction cost. So I think we can quite easily turn new-textframe into a significant net performance win.
Tim Rowley is working on integrating textruns into SVG text. He already has prototype code that works reasonably well. Transitioning SVG from the cairo toy text API to gfxTextRun has immediate benefits --- in particular gfxTextRuns implement font substitution, so non-complex international scripts (e.g., Chinese) will be displayed properly as we fall back to Chinese fonts when the default font does not contain glyphs for Chinese characters. It will take more work to support complex scripts with clusters, though.
One problem we've noticed is that SVG text requires bidi reordering. This means that when you have mixed-direction text (e.g. Hebrew and English in the same line) it's possible for SVG <tspan> and other elements to require "splitting" because a single <tspan> may be rendered as multiple discontiguous pieces. This is really tricky stuff. We've already implemented it for HTML+CSS and I'm not looking forward to implementing it again. According to SVG people it's "OK" for SVG to duplicate this functionality because it's done in a "compatible way" so in theory we can share code. But in practice we already have our bidi code tied into the HTML/XML/CSS code and extracting it for reuse with SVG would not be at all easy, especially given that we must avoid slowing down the existing code paths. There is another way that might work, though: have SVG create hidden HTML-style layouts of its text content, applying the HTML text layout engine including bidi processing. Then SVG could take apart the resulting HTML layout and massage it for display SVG-fashion, even including drawing text along a path. Not easy, but perhaps the best way forward. We'll see.
There are other exciting things going on. Offline apps are moving ahead; we've landed significant offline app support on the trunk so a number of interesting demos are possible with Firefox nightly builds. I think other people are blogging about that. There's good discussion going on regarding a first-class <video> element for HTML, and we are preparing to implement it. I spent quite a bit of time reviewing some cool new drag-and-drop code --- download a Firefox nightly build and try selecting content and then dragging it. And a new contractor started work in our Auckland office on Monday.
Looking forward, OOPSLA submissions closed on Monday so I'll have to spend 2-3 of the next six weeks doing OOPSLA reviews, which will be challenging but fun. There are a lot of really interesting-looking submissions. I'm also working on preparing the Amber debugger code for release. Unfortunately I have to rename everything because there's some BIOS debugging tool already called Amber ... I've chosen the name "Continuum" instead. And in just over two weeks I'll be in Mountain View for nearly a week, for a Mozilla Corporation all-hands meeting. When I get the chance I'll be driving the new textframe forward and fixing textrun-related regressions. It's all on!
Update I'm thinking maybe "Chronicle" for the debugger instead. If anyone knows of a project that's using that name, let me know! There's is some blog-related project with that name, but it looks pretty dead (and pretty far away in subject, anyway).
jens.b 25 March 2007 at 17:43
"Continuum" is also the name of a continuous integration server by the Apache project. I don't know whether that will cause problems, but I guess it's popular enough that you want to avoid using the same name.
I'm aware of it, but there are so many projects out there, I'm not confident I can find a good name that doesn't clash with *some* project. As long as it's not in the same area...
jmdesp 26 March 2007 at 09:50
Nice info, roc.
On the trunk nightlies, I don't see MOZ_NEW_TEXTFRAME on in about:buildconfig, but I see bug 372629 ("missing-glyph boxes").
Dao 26 March 2007 at 10:08
"download a Firefox nightly build and try selecting content and then dragging it" -- doesn't make a difference on Windows, does it?
are there any nightlies with MOZ_NEW_TEXTFRAME=1?
Nightlies are not being built with MOZ_NEW_TEXTFRAME. You'll need to build your own.
Dao: not sure what you mean. The new drag code should be working on Windows.
Martijn 27 March 2007 at 02:14
Well, according to the bug ( https://bugzilla.mozilla.org/show_bug.cgi?id=178513 ) it was fixed for Mac. But you probably would know better, since you reviewed the patch in question.
The bug that handles this for Windows, https://bugzilla.mozilla.org/show_bug.cgi?id=374593 , isn't fixed yet.
Oh, you're absolutely right of course. Sorry, it's fixed on Mac and Linux but not yet on Windows.
Manoj 1 April 2007 at 22:33
Pardon my naivete but what was the core motivation behind the TextFrame changes?
Eyal 20 April 2007 at 17:30
Any news about continuum/chronicle? It sounds like a handy tool...
Elizabeth J. Neal 22 May 2015 at 04:07
It's not necessarily too late to rekindle the love with your ex, but don't get rash ... "Try sending a sweet text, text your ex back
Questions That Must Be Answered
Festival Mania

redpajama_set_name: RedPajamaCommonCrawl


Before we look at the various plugins and ways to extend Docker, we should look at what a typical life cycle of a container looks like.
Using the example from the previous section, let's launch the official PHP 5.6 container and then replace it with the official PHP 7.0 one.
Before we can launch our containers, we need to get Docker up and running; luckily, this is a simple process.
In the following chapter, we will be getting into bootstrapping our Docker environments using Docker Machine; however, for now, let's perform a quick installation of Docker on a cloud server.


A commision piece that was done for a couple that went through a trauma of flood and voience. This is a starting over and transformation from long cherished family treasures. It is about making beauty from broken dreams and pain.


text:
       {{'VULNERABILITY.STATE.STOPPED' | translate}}

        {{'VULNERABILITY.STATE.QUEUED' | translate}}

            <clr-icon shape="error" class="is-error" size="24"></clr-icon>
            {{'VULNERABILITY.STATE.ERROR' | translate}}

{{'VULNERABILITY.STATE.SCANNING' | translate}}

<progress></progress>

        <hbr-vulnerability-summary-chart [summary]="summary"></hbr-vulnerability-summary-chart>

        <clr-icon shape="warning" class="is-warning" size="24"></clr-icon>
        {{'VULNERABILITY.STATE.UNKNOWN' | translate}}

redpajama_set_name: RedPajamaGithub


text:
15 Market Pl, Inverurie
The retail listing at 15 Market Pl, Inverurie AB51 3PU is no longer advertised on Realla
Retail storefront for rent in Inverurie
8 months ago. Similar availability from HSA Retail
Property facts for 15 Market Pl, Inverurie
Inverurie is located approximately 16 miles to the north west of Aberdeen on the A96 Aberdeen to Inverness trunk road. The town has a population of approximately 14,000 people and is well served via rail and road transport links. The subjects are situated within the prime section of Market Place, Inverurie's main shopping thoroughfare. There is ample car parking provided opposite the unit and generally within the town centre. Nearby occupiers include; the Royal Bank of Scotland, Boots and Greggs. A trader's plan is attached highlighting the exact location of the unit.
31-33 West High St, Inverurie
STAFF UNAWARE The available space comprises 1,881 sq ft of ground floor retaiil accommodation.
31-33 West High St, Inverurie AB51 3SA
12 days ago. J & E Shepherd
61 West High St, Inverurie
The subjects are located on the south side of West High Street within Inverurie town centre close to the junction of North Street and Blackhall Road. There are a mixture of commercial and residential uses immediately adjacent to the subjects.
61 West High St, Inverurie AB51 3QQ
5 days ago. FG Burnett
1-5 North St, Inverurie
Takeaway Consent Granted. Prominent Roadside Location. Good Customer Parking. Large Retail Display Windows
1-5 North St, Inverurie AB51 4RJ
Elphinstone Rd, Inverurie
The property comprises a stone building with a slate covered roof. The external walls are harled in places and an extension is located to the north in blockwork, harled externally with a flat felt covered roof. To the rear of the premises is located external storage which is accessed via a tarmacadam car park which is shared with the restaurant...
Elphinstone Rd, Inverurie AB51 3UR
12 days ago. AB Robb
Disclaimer. Property descriptions, images and related information displayed on this page are based on marketing materials found on the website registered to HSA Retail. Realla does not warrant or accept any responsibility for the accuracy or completeness of the property descriptions or related information provided here and they do not constitute property particulars. Please contact HSA Retail for full details and further information. 8737435


text:
Cecilia de Grecia y Dinamarca (Tatoi, 22 de junio de 1911 -  Ostende, 16 de noviembre de 1937) fue hija del príncipe Andrés de Grecia y de la princesa Alicia de Battenberg y hermana del duque Felipe de Edimburgo.

Matrimonio e hijos

Durante su estancia en Inglaterra, a finales de los años veinte, su tía, la princesa heredera Luisa de Suecia, intentó concertar un matrimonio entre ella y el príncipe heredero Federico de Dinamarca, quien poco antes había intentado casarse con su prima, la princesa Olga de Grecia.

A los dieciocho años, Cecilia se comprometió con el gran duque heredero Jorge Donato de Hesse y del Rin, el cual era primo hermano de su madre .

La boda de Cecilia y Jorge Donato, celebrada por los ritos ortodoxo y luterano, tuvo lugar en el Palacio Nuevo de Darmstadt el 2 de febrero de 1931. La pareja se instaló en su residencia delcastillo de Wolfsgarten.

Tuvieron varios hijos:
Luis Ernesto, nacido en 1931 y muerto en 1937.
Alejandro Jorge, nacido en 1933 y muerto en 1937.
Juana, nacida en 1936 y muerta en 1939.

El 9 de octubre de 1937 falleció el gran duque Ernesto Luis, sucediéndole su hijo Jorge Donato como jefe de la casa gran ducal de Hesse y del Rin.

Fallecimiento

El 20 de noviembre de ese mismo año, se celebraría en Londres la boda del único hermano del Gran Duque, Luis, con la inglesa Margarita Geddes. La familia gran ducal, para asistir, abordó un avión de la compañía Sabena, en el cual iban Jorge Donato, Cecilia (embarazada del que hubiera sido su cuarto hijo), los pequeños príncipes Luis y Alejandro y la gran duquesa viuda Leonor, junto con algunos acompañantes. El avión tenía que hacer escala en Ostende para recoger dos pasajeros más, pero al acercarse a la ciudad una espesa niebla provocó el choque del aparato contra la chimenea de una fábrica lo que ocasionó que se precipitase a tierra. En el accidente, perecieron todos los pasajeros. Solamente la pequeña Juana, quien había permanecido en Darmstadt, se salvó. Después de su boda, Luis de Hesse-Darmstadt y su esposa Margarita adoptaron a Juana, quien falleció poco después, en junio de 1939, a causa de una meningitis.

Títulos, tratamientos y distinciones honoríficas

Títulos nobiliarios
 22 de junio de 1911 - 2 de febrero de 1931: Su Alteza Real la princesa Cecilia de Grecia y Dinamarca
 2 de febrero de 1931 - 16 de noviembre de 1937: Su Alteza Real la gran duquesa heredera de Hesse y del Rin

Distinciones honoríficas
Reino de Grecia
 Dama gran gruz de la Orden de las Santas Olga y Sofía.
Extranjeras
 Dama gran gruz de la Orden del León Dorado. ( Casa de Hesse).
 Medalla de la Coronación de Jorge VI (, 12/05/1937).

Ancestros

Referencias

Duquesas consortes de la casa de Glücksburg
Princesas de Grecia de Grecia del siglo XX de la casa de Glücksburg
Familia Real Danesa
Casa de Hesse
Damas grandes cruces de la Orden de las Santas Olga y Sofía
Nobles alemanas del siglo XX
Nacidos en Atenas

Fallecidos por accidentes aéreos

redpajama_set_name: RedPajamaWikipedia


text:
stockmarket
cryptocurrency market
info@businesstelegraph.co.uk
ebackpage
ibackpage
Business Telegraph
Business Telegraph > stockmarket > 'Wake up': markets warn central banks to get a grip on inflation
'Wake up': markets warn central banks to get a grip on inflation
November 26, 2021No comment
Financial markets fear the world's leading central banks are risking "economic disaster" by misjudging the threat of rising inflation and not turning off the stimulus taps that have flooded the global economy with money.
From the Federal Reserve to the European Central Bank, policymakers are grappling with a surge in prices not seen for decades while trying to keep wobbly economies on course to recovery from the ravages of the coronavirus pandemic.
While central banks stick largely to the mantra that inflation is "transitory" and price pressures on everything from timber to turkeys will ease in the coming months, economists, business leaders and investors are ringing alarm bells.
They fear that without swift action, such as a rise in interest rates, runaway inflation – which has not been seen in developed economies since the early 1980s – will become so embedded by next year that a policy switch will be too late to have any effect. At the very least, they see this as critical moment to end the massive money-printing schemes that were ramped up to counter a pandemic recession.
Julian Jessop, an independent economist who has worked at the UK Treasury and City firms, said most central banks were "well behind the curve" and that rising costs throughout the supply chain, such as in shipping, would continue to put upward pressure on prices well into next year.
"Central banks need to respond to changing economic conditions," he said. "The recession that justified the additional quantitative easing and keeping interest rates at emergency lows is over."
Given that interest rates were at record lows, modest increases "would not be an economic disaster, but should help to prevent one", Jessop added.
"Interest rates and borrowing costs are still likely to remain near historical lows – especially real rates, after allowing for inflation. In reality, central banks would simply be taking the foot off the accelerator, rather than slamming on the brakes."
Poland, where inflation hit 6.8% in October, its highest for 20 years, has decided to go on the attack immediately. The Polish prime minister, Mateusz Morawiecki, said on Thursday that the government would cut tax on fuels and energy from December, and would offer bonuses to the hardest hit households.
Describing the move as an"anti-inflation shield", he said it would cost the government about 10bn zlotys (£1.8bn) and that additional funds would come from spending cuts.
Morawiecki blamed inflation, which hit 6.8% in October, its highest since 2001, on greater energy costs, saying they stem from Russia's gas policy, the European Union's climate policy and CO2 emission certificate prices, as well as on bonuses that were paid out to help businesses survive the Covid-19 pandemic.
Prices have risen on foods, fuels and energy. "We are offering a large reduction of tax, in order to cushion the effects of the inflation," Morawiecki said, adding that inflation may still rise in the winter months of December to March.
Inflation has been stalking the global economy for months but has burst into the open in recent weeks. The 6.2% jump in US inflation in the year to October stunned markets and highlighted huge increases in the cost of some consumer basics, such as a 46% rise in petrol prices and 11% for meat, fish and eggs. In the UK, inflation is running hot at 4.2%, pumped up by record natural gas prices.
With pandemic-induced supply constraints set to continue for months and a wave of pent-up Covid consumer cash chasing a limited flow of goods, claims by the chair of the Federal Reserve, Jerome Powell, that inflation is transitory look increasingly hollow.
Chris Watling, the chief executive and founder of the advisory firm Longview Economics, agrees that central banks risk being caught out.
After the financial crisis of 2008, they pursued loose monetary policy and tight fiscal policy in the form of quantitative easing and spending cuts. Now they have "loose monetary and loose fiscal", with too much money chasing too few goods.
"They will wake up one day in catch-up phase," he said. "Perhaps late next year, or 2023, and then they'll end up tightening quite quickly when prices are rising. And if you tighten into that situation, a bubble, it will burst. So it's a real challenge for them."
Mohamed El-Erian, global economist at insurance group Allianz, said that if the Fed were to leave it too late to increase rates, the US – and perhaps the world – could be pushed into recession. "Such a tightening would potentially coincide with three other contractionary forces in the US: a tightening of market financial conditions, the absence of any additional fiscal stimulus, and the erosion of household savings."
It is a precarious tightrope for policymakers. Inflation can swiftly undermine business and consumer confidence, but going too hard could jeopardise recovery and could also seriously spook booming property markets in countries such as the US, UK and Australia.
El-Erian said policymakers should also consider broader changes to boost productivity, and improvements to the oversight of financial stability risk, particularly in the non-bank sector.
Some central banks are already preparing to jump off the tightrope, most notably the Bank of England, which came close to raising interest rates earlier this month. The ominous US inflation number means policymakers seem certain to take the plunge and increase rates by 0.25 percentage points to 0.35% when they meet again in the first week of December.
Rising prices have exposed central bankers' "King Canute" theory of inflation, the former governor of the Bank Mervyn King said this week in a strong attack on how policymakers around the world have reacted to the Covid-19 crisis.
New Zealand does not often grab markets' attention, but this week the country's Reserve Bank announced the second rate rise in as many months in an effort to cool inflation that hit 4.9% last month. Across the Tasman Sea, the Reserve Bank of Australia reiterated its belief that rates would not go up from their record low of 0.1% until 2023 at the earliest, but the markets are betting on them being 1% this time next year. Lenders are voting with their feet though, with the largest bank, the Commonwealth, on Friday hiking fixed rates for the third time in six weeks.
South Korea's central bank followed New Zealand's example, announcing a rise to 1% – its second increase of the year – amid concern over higher living costs. The country's inflation rate jreached 3.2% in October, a near-10-year high.
Alex Joiner, the chief economist at IFM Investors in Melbourne, said central banks were trying to wait it out and "hope against hope" that pressures from the pandemic would continue to ease, with supply issues resolving themselves.
"They are trying to temper market expectations but the problem is that markets are not believing them," he said. "Market pricing is aggressive, with investors showing that they think rates will go up."
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The cautiously optimistic Fed view prevails for now, and both Joiner and Watling point to signs of easing in supply chains. The benchmark for world shipping costs, known as the Baltic index, has been falling, and China is beginning to overcome the power shortages that hurt its giant manufacturing sector in September.
However, there is also the possibility that everyone has underestimated the extent of structural changes in the global economy that started in recent years and have been accelerated by Covid. These could mean there is never a return to the Goldilocks era when inflation and growth were both "just right".
John Studzinski, the managing director and vice-chair of Pimco, the world's biggest bond trader, told a recent Bloomberg forum that higher inflation could persist for three to five years. Supply chains need to be re-established as the world emerges from the pandemic crisis, he said, and with some deglobalisation of trade, inflation "could be very volatile".
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businesstelegraph November 26, 2021
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Electrical Wiring Residential (W/6 Prints)(2017 Nec) - A.) How high above the completed ground inside the living room are receptacles placed? 12in, (pg. 49) outdoor receptacle shops in this living are placed 18 in. Above grade. (Pg. Forty nine) in the spaces furnished, draw the suitable symbol for each of the descriptions listed in (a) thru (r).??no longer covered in the studying project the the front fringe of a container mounted in a flamable wall ought to be flush with the finished surface. (Pg. 35) listing the maximum variety of 12 awg conductors authorized in a: (pg. 44—table 314.16(a) a.) Four in. X 1½ in. Octagon box. ----------------6 b.) 4-eleven/16 in. X 1½ in. Square container.-----------Thirteen c.) 3 in. X 2 in. X three½ in. Tool container. ----------8.
12/2 and two 14/2 nonmetallic-sheathed awg cables enter a box. Every cable has an device grounding conductor. The 12 awg conductors are linked to a receptacle. Of the 14 awg conductors are linked to a toggle switch. The other two 14 awg conductors are spliced together considering they function a switch loop. The field carries cable clamps. Calculate the minimum cubic-inch extent required for the container. Solution: ground cord----#12 (2.25cu. In.)??1 x 2.25 ----------2.25 cu. In. Conductors: 2—#12/2 ---- 2 x 2 x 2.25 ------------------------------9.00 cu. In. 2—#14/2 ---- 2 x 2 x 2.00 ------------------------------8.00 cu. In. Devices: 1—toggle transfer –1 x 2 x 2.25 ------------------------four.50 cu. In. 1—receptacle –1 x 2 x 2.25 ----------------------------4.50 cu. In. Fittings (clamps) 2—cable clamps—1 x 2.25 ----------------------------2.25 cu. In. ------------------------------------------------------------- 31.50 cu. In. The usage of the identical variety and size of conductors as in questions 28, but the use of electric metallic tubing, calculate the minimum cubic-inch volume required for the box. There could be no separate equipment grounding conductors, nor will there be any clamps inside the box. Solution: ground cord—ought to figure on one ground wire due to the nearby code requirement of all conduit structures requiring a ground cord. The solutions will be the identical because the electric steel tubing will require the usage of metal connectors that must enter the container, you'll rely them as fittings, because of this that regardless of how many fittings are entering the container the you'll discern it at ( 1-instances the cubic inch of the most important conductor that enters the field. To allow for ok conductor duration at electric outlet and device boxed to make up connections, 300.14 requires that no longer much less than [ 3 in.(75 mm). 6 in. (150 mm), 9 in. (225 mm)] of conductor duration be provided. This length is measured from wherein the conductor emerges from the cable or raceway to the give up of the conductor. For container openings having any measurement much less than 8in. (200mm), the minimal duration of conductor measured shape the field starting within the wall to the stop of the conductor is [ three in. (75 mm), 6 in. (One hundred fifty mm), nine in. (225 mm). Circle the right answer. Solution page 21 discern 2-2 whilst wiring a residence, what must be taken into consideration while putting in wall packing containers on both facets of a common partition that separates the storage and a habitable room? The fire rating of the walls have to be maintained which means that that containers won't be established "again to back" or inside the equal air void. A separation of 24 inches horizontal ought to be maintained except the packing containers are listed as being fireplace resistant. The boxes which might be at the hearth rated wall need to be well protected or encased with a fireplace puddy. Does the nec® allow steel raceways for use with nonmetallic containers? Sure _____x______ no__________ nec®______nec® 314.Three (exception) –previded that each one steel raceways or cables coming into the container are bonded collectively to maintanin the integrity of the grounding route to other gadget within the set up. P. 33.


GA :). Gillian :D. Wallpaper and background images in the Gillian Anderson club tagged: gillian gillian anderson.
This Gillian Anderson photo contains mahusay bihis tao, suit, pantalon suit, and pantsuit. There might also be business suit, hip boot, thigh boot, and kahon amerikana.


I once again begged my hubby to drive me up to the Lisa's sale. This time he didn't humour me by waiting in line, instead he waited in the car.
After a 35 minute wait, I no longer was in a line and staring at empty Tim Horton cups, I was inside and shopping with rude people.
-The lines are long, be prepared to freeze outside, so dress warm and bring a charged phone to play games on.
-Once inside people are rude, I mean they don't move even when you say excuse me, they stand in the worst spots and just stand there until you shove them hard.
-You get a red mesh bag to put stuff in, well people drag them on the floor and you will trip and fall on them. I am not sure what these peoples issues are but they are complete d**ks and I want to slap them all with something.
-People will shove you hard and make no apologies for it. Now I realize I just said you have to shove people, but at least I say excuse me several times before resorting to shoving.
-There are no children allowed! Period. Which think of how much worse all these rude people would be with children...ick.
-The staff are quite nice and polite, makes up for the idiot shoppers.
That all being said, I went for xmas gifts and not myself so my haul was small.
All the smashbox sets I got in the past with the exception of the Wish set, which I have zero interest in.
Cargo has been added this year, $9.99 for sets makes them a great deal.
Skin care sets are also available at great deals.
Sally Hansen Tweezers - $1.49 - mine have gone dull.
ListerinetoGo -$2.99- This might help kill germs when I can't brush...let us see.
Annabelle mini Pigment set -$1.99- I wanted this last time and never saw it, for $1.99 I couldn't pass it up.
Quality Street -$10.00- I got this to snack on..you can see I have already done that.
I need a memory card, so no pics taken yet.


Now is the time to contemplate how you will keep these nutritional habits, mindful living, rest periods, exercise routines, yoga practice and spontaneous movement a regular part of your life forever. You can go back to the Your De-Stress Journal – Reflective Practice to contemplate how you take your changes into the future and even write your own version to fit with your needs.
Write a letter to yourself saying why it is the better choice to pick up again, however simply that needs to be to start.
Make a clear list of anything practical you know will help; shopping list for a particular store, times of favourite yoga or dance classes, activities you love.
Write any affirmations that will help at this time – positive statements that you can use to reinforce that only better quality of life, health and enjoyment come from looking after yourself. Phrases like 'I feel clear-headed and in control when I don't eat sugar' or 'I feel happy about my body when I exercise regularly' can have a profound effect and this is the time to write them; when you absolutely know that they are true. This can help enormously when you feel less connected to these feelings.
There is always help at my Facebook page to keep you feeling connected to people with the same needs and concerns as you. I offer daily help to keep up those eating, lifestyle, movement and fitness habits in our busy world and it's a great place to ask me questions that might also benefit others.
Feeling supported is how you will continue these feelings through your life choices today and into the future. You've made these choices and feelings a reality in the past six weeks so it is logical that you can do this moving forward, especially when you can remember how good it made you feel.


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Watch Incoherent John Kerry Get Destroyed Over His Bogus Climate Change Claims
'It's Frustrating': Texas Law Enforcement Braces for Another Busy Year of the Border Crisis
Posted: Apr 09, 2019 2:20 PM
Former Secretary of State John Kerry was on Capitol Hill Tuesday morning testifying in front of the House Oversight Committee about the "threat" of climate change.
He presented the typical fear mongering argument about the world ending if we don't do "something" to stop it and advocated for massive government intervention to "fix" the problem.
But during an exchange with Republican Congressman Thomas Massie, Kerry got tripped up on the facts about how much CO2 is really detrimental to the environment.
.@RepThomasMassie grills John Kerry on historic atmospheric CO2 levels at climate change hearing.
Kerry complains that it's "just not a serious conversation."
Massie: "Your testimony is not serious. When you can't answer the question, that's the best answer you got." pic.twitter.com/Eyx5t59mOa
— Nate Madden (@NateOnTheHill) April 9, 2019
Notice how Kerry attempted to end the conversation by saying wasn't "serious." Massie simply pointed out CO2 levels were much higher before humans made their way onto the planet. A serious, relevant, historical scientific fact.
Then, Massie went after Kerry's credentails on the issue.
Massie also questioned Kerry's qualifications as a witness on climate science, nothing that he holds a BA in political science:
"So I think it is somewhat appropriate that someone with a pseudo science degree is here pushing pseudo science to this committee." pic.twitter.com/1NrV5qK6W0
The Green New Deal, which bans air travel, cows and requires the uprooting and remodeling of every building in America, has broad support on the left in Congress and on the 2020 presidential campaign trail. Massie warned about the consequences of rapidly changing the economy through government force, which is what the Green New Deal would require.
Massie also responds to worst case "if the climate alarmists are wrong."
Notes problems like energy cost hikes and "losing focus on real pollution and real problems that we have to solve" like storing nuclear waste.
Also adds he's been living on 100% solar for over a decade. pic.twitter.com/27UVzFi88M
climate alarmism
CARTOONS | AF Branco


Time to get crafty! Instead of turning the natural shape of your brows into waves (or photoshopping them to look like you did), try adding some bling instead! Grab some small rhinestones and lash glue and add them around your eyebrows in various patterns for a fun, bold look. This bling will catch all the light and have your brows blinding any haters.
For this look, we recommend using our MorpheMe PK45 Angle Brow brush from our LiveGlam Brush Club to fill in your brows. It's a very dense and firm brush that allows you to precisely carve out the perfect brow to dress with bling. If you're already a MorpheMe member, you got this brush in your October collection!
If you're more on the artistic side, try painting some small flowers on your brows for a beautiful visual. MUA @bronya_h gives us some serious brow inspo and reminds us that makeup truly is an art.
For the flower power, try using face paint from the party store, cream eyeshadows, or the Make Up For Ever Aqua XL Color Paint Shadow with a M250-2 Detail Liner brush from your July MorpheMe collection.
For a simple way to change it up, add some color to your brows using eyeshadow, or our KissMe lippies like this! For a strong, pigmented look, mix any colored eyeshadow with lotion or vaseline to create a liquid consistency. You can also use a product like Mac Prep + Prime Fix+ or rubbing alcohol + vitamin E oil to achieve the same effect. Then, apply it to your brows using your MorpheMe PK45 Brow brush.
These brows serve as the perfect way to spice up a simple outfit, or make anyone take a double look.
To glam up your colored brows even more, you can easily add some glitter on top! To do so, take your favorite glitter pigment(s) and a glitter adhesive, eyebrow gel, or eyelash glue (for an extra strong hold) and pat the glitter along your brows. We all know how messy and annoying glitter can be, so take your time and be careful when applying!
Warning: by the end of the night, you can definitely expect to find glitter in places you wouldn't of even imagined.
Whether it's squiggle brows, bling, flowers, or glitter, remember to have fun with it and go out and slay. The LiveGlam Fam is all about trying new things, so show (or tell) us how you get your eyebrows on fleek in the comments below! Also, make sure to check out our LiveGlam Brush Club, MorpheMe, to get 3-8 new brushes delivered straight to your door for just $19.99 a month!


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7, rue Métreaux, Bourdon Port-au-Prince, Haïti
info@papyrushaiti.com
Why Papyrus
Papyrus is a majority woman-owned management firm that connects local and international aspirations. We help local and international organizations to do business more effectively in Haiti by designing and efficiently managing funds and other project resources. Papyrus is the bridge that connects the resources of global companies, the private sector and donor organizations to local resources and people who share similar values.
We conduct business at a global standard, integrating expertise with strong professional networks and strategic partnerships with extensive, specialized knowledge of the Haitian market and the Caribbean region.
People are the soul of who we are and what we do at Papyrus.
It's the reason why our diverse, talented team works diligently on private and public-sector projects that benefit the community and create change for the greater good.
Find out more about our services and how we can help with your next project.
A Vision for the Future
Our vision is to be a pioneer in the development of the region, to participate in changing the perception of Haiti by exporting our competencies, and to offer a progressive and modern working environment. Together with our dedicated partners and team we are moving closer to this vision of a brighter future.
In 2007, Papyrus S.A. Founder Alice Nkunzimana realized that international organizations doing business in Haiti could achieve better results with local and uniquely Haitian solutions.
The entrepreneurial and resilient spirit on which Papyrus was founded is an important part of our culture. We successfully navigate the Haitian market both because of our adherence to international-standard operating practices and our agility, proactive problem solving and creativity.
Over the past decade, Papyrus S.A. has evolved from a translation company to a respected management company with a team that is well connected to the private sector. This is why we are a trusted partner, designing and managing projects for renowned public and private organizations such as Heineken, USAID, the Inter-American Development Bank, Global Affairs Canada, and the University of Florida, to name a few.
Each member of our executive leadership team at Papyrus has international experience and deep roots in Haiti combined with unparalleled industry knowledge.
Alice Nkunzimana
Ms. Nkunzimana was born in Canada but was raised in Haiti from a very early age. After attending graduate school in the United States and working in Israel for a few years, she returned to Haiti where she established Papyrus S.A., offering essential management, implementation and logistics services. She is a human performance improvement specialist with 20 years of working experience and has overseen the rapid growth of Papyrus from a language services provider to a dynamic and successful private management company. Papyrus is currently managing several high-value, high-visibility projects for the private and donor sectors.
Alice has an excellent understanding of Haitian society, culture and reality and is at ease working with a wide variety of people from local villagers to high-ranking officials. She has an excellent work ethic and has distinguished herself as a highly competent, hard working and effective manager.
John Currelly
An agronomist by training, Mr. Currelly draws on 40 years of professional experience in agriculture, agribusiness, microfinance, donor project management, field project development and management, and NGO development and management, bringing together the business and development sectors in Haiti to create new durable solutions to thorny development issues.
During his career John as served as the executive director of Haiti's largest private sector foundation, the resident representative of one of Haiti's most highly-regarded NGOs and as a project manager with USAID. He has ten years of private consulting experience, and gained deep knowledge of the Haitian private sector as co-founder of Haiti's first fertilizer bulk-blending plant.
John's experience has been used to create a series of innovative and highly successful projects combining the impetus of the private sector with the social considerations of the poor.
Mélissa Dunwell Padberg
Ms. Dunwell Padberg, was born and raised in Haiti. She has a degree in cultural anthropology from Haverford College in the United States and has worked in a number of sectors including advertising, hospitality and business development. Ms. Padberg oversaw the leadership of the historic Hotel Villa Créole in Pétion-Ville, with 140 staff and strong management, compliance and financial management systems. The Villa Creole was a landmark hotel that operated successfully over three generations and remains the symbol of quality in Haitian service provision due to the business ethics and quality standards it upheld until its closing post-earthquake.
Mélissa is a seasoned manager with excellent communication skills and extensive knowledge of the Haitian business sector. She is passionate about creating spaces where Haitians can be part of building sustainable pathways for the country's economic development.
7, rue Métreaux, Bourdon
Port-au-Prince, Haïti
papyrushaiti.com
2018 © Papyrus S.A.


Press conference by the head coach of Fenerbahce.
- There are many reasons to be happy today. On the field, I saw a lot of things that I liked. We played against a very strong team, operated boldly and boasted our trump cards. Both opponents have tried hard, and I'm glad that my players performed well. On the other hand, I was pleased with the support of fans. I am sure that thanks to this support, some great success awaits us in the future.
- In the absence of Gokhan Gonul, what do you make of Kaldirim's performance today?
- I want to thank him for the match. He showcased his fitness. We might say that he sacrificed himself for the team. Once again many thanks to him for his strong performance on the wing.
- Have you brought Diego to the level where he has become the most important player of Fenerbahce?
- I'll tell you a story. When I worked in Saudi Arabia, there was a moment when I had to change tactics. To do that, I called up the Brazilian footballer. Why is that? Because we speak the same language ... I say this to confirm the fact that, if necessary, you can call up one clever player who is able to respond quickly to what is required of him. In our situation, Diego is such a player. I picked him. And this is not a new method in international practice.
- Was it necessary to make a transfer to secure a midfield position?
- First of all, we need time. I would like to see the team more organised. It is also important that my players get faster. Thank goodness, I had time to decide on the team I would be working with, and I chose the optimum one for myself. As regards transfers, the work is still going on. Once again I want to emphasize that I am very happy to have the opportunity to work at such a club, Fenerbahce are a strong team. And one more aspect: I am delighted that the fans were always with us, we just felt them. Therefore, I have confidence that in the future we will get even stronger.
- Just now, Mircea Lucescu said that 0-0 is a dangerously even outcome. What are the chances of Fenerbahce in the return leg?
- We had a very short period of time to get prepared - just three or four weeks. The team gathered players from many countries who speak different languages. But I still think we came out of this game with dignity, as we were challenged by very strong opponents. Shakhtar are better prepared performance wise, but we also looked good. I'm proud of my players! As for the victory in the second leg, I will not say anything. Of course, we will continue to work to improve the game organization. We are not afraid of anything, although the return match will certainly be difficult. Fenerbahce are just at the beginning of their path, but today I've seen that the team are ready to go head to head with Shakhtar.


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Home > News Details
Town of Huntington, NY - News Details
11/11/2018 - Town Board Urges Governor, State Legislature to Pass LIPA Ratepayers Protection Act
At its November 8 meeting, the Huntington Town Board urged the New York State Legislature and Governor Andrew Cuomo to enact the Long Island Power Authority Ratepayers Protection Act, waived parking meter fees in Huntington Village starting November 23 for the holiday season and scheduled various public hearings for the November 20 and December 4 Town Board Meetings.
The Town Board approved a resolution urging the New York State Legislature and Governor Andrew Cuomo to enact the Long Island Power Authority Ratepayers Protection Act, which would make the LIPA trustees responsible to Long Islanders, not Albany, and provide oversight and transparency needed to restore confidence by residents of the service area and ensure that the best interests of Long Island ratepayers are served.
Currently the LIPA Board of Trustees consist of nine unelected appointees of the governor, temporary president of the senate and speaker of the assembly. The LIPA Ratepayers Protection Act would require eight of the nine LIPA Board of Trustees be elected from districts of equal population established by the State Legislature and one trustee, serving as chairperson, be appointed by the governor subject to confirmation by the senate.
The Town waived parking meter fees in the downtown shopping area known as Huntington Village from November 23, 2018 through January 1, 2019. The Board also authorized the installation of holiday type lights and decorations during the holiday season by the Huntington Township Chamber of Commerce, Huntington Village Business Improvement District, Huntington Station Business Improvement District and the East Northport Chamber of Commerce in Huntington Village, Huntington Station and East Northport.
Based on input gathered from residents at the public hearing held October 23, the Town also approved the enactment and installation of No Parking restrictions during school days from 8:00 AM to 1:00 PM on Lucille Lane in Dix Hills, between Vanderbilt Parkway and Landview Drive.
The Town Board scheduled public hearings for the Tuesday, November 20, 2018 Town Board Meeting at 7:00 PM:
· To consider authorizing various actions be taken upon certain properties designated as blighted in accordance with Chapter 156, Article VII, §156-60 (Blighted Property).
· To consider amending the Code of the Town of Huntington, Chapter 198 Zoning, Article XVI Zoning Board of Appeals §198-112 Rules as to Procedure; Hearings; Notice, specifically, to consider streamlining the notice requirement procedure for the Zoning Board of Appeals: in the event a ZBA meeting is cancelled, hearings scheduled for that date will automatically be rescheduled for the next scheduled ZBA hearing date with no re-advertising required, saving the Town from duplicative costs of advertising, and so that applicants do not need to wait an additional six to eight weeks to be heard.
· To consider adding No Parking restrictions on the east and west side of West Gate Drive in Cold Spring Harbor, from 250 feet north of Promenade Drive extending 200 feet north in order to prevent interferences with parked vehicles and golfers at the recommendation of the Town Department of Transportation and Traffic Safety.
The Town Board also scheduled public hearings for the Tuesday, December 4, 2018 Town Board Meeting at 2:00 PM:
· To consider designating as an historic landmark the building and property known as the "The Burr House" or Old Burr Farm, 423 Town Line Road, Commack.
· To consider the granting of a variance and the issuance of a special use permit and to consider the execution of a license agreement pursuant to the Marine Conservation Law, Town Code Chapter 137 for the construction of a residential fixed pier and floating dock assembly. Applicant: Douglas J. Whitcomb, Location: 50 South Harbor Road, Northport, N.Y.
In other action, the Town Board:
· Authorized the Supervisor to apply for and receive grant funding under the State and Municipal Facilities Capital Program: $7.35 million for a parking deck at Cold Spring Harbor LIRR train station; $4 million for Huntington LIRR train station parking improvements; $38,608.50 for lighting at Greenlawn Memorial Park.
· Authorized the Comptroller to appropriate $16,300 from the Environmental Open Space and Park Improvements Fund to purchase and install engineered woodchips, which will be cleaner, easier to maintain, safer and more handicap-accessible, to replace sand bases in playgrounds at Arboretum Park and John J. Walsh Park.
· Reappointed existing members, appointed new members and designated a Chair of the Town of Huntington Conservation Board: reappointing Joy S. Squires, Paul J. Ponturo, Scott Driver, Richard Meyer, Andrew Wilson, Julie Sullivan; appointing Kamazima Lwiza, Mary Feigman, Dr. Matthew J. Zisel; and designating Joy S. Squires as Chairperson.
· Reappointed existing members, appointed new members and designated a Chair of the Town of Huntington Citizens' Advisory Committee for Persons with Disabilities: reappointing Maureen Donohue, Trudy Exelbert, Alfred Huberman, Marianne Iannacone, Pearl Levy, Martin Mandelker, Thomas Mangan, Tamar Sherman; appointing Jennifer Forte; and designating Marianne Iannacone as Chairperson.
· Authorized the adoption of revised Town of Huntington Policy Against Discrimination and Harassment and Complaint Procedure.
· Authorized the adoption of the Town of Huntington Concussion Management Protocol.
· As members of the Huntington Local Development Corporation, appointed Leah Michelle Jefferson as Executive Director for the Local Development Corporation.


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Hughes India and Airtel Announce New Satellite Venture to Serve Enterprise and Government Customers in India
Combination of India's leading VSAT operators brings needed scale, efficiencies and reach to enable the networks that power Digital India
Washington, DC, May 7, 2019 – SATELLITE 2019, Booth 1100 – Hughes Communications India Ltd, (HCIL), a majority-owned subsidiary of Hughes Network Systems, LLC (HUGHES), the world's leading provider of broadband satellite networks and services, and Bharti Airtel Limited ("Airtel"), India's leading telecommunications services provider, today announced an agreement to combine their Very Small Aperture Terminal (VSAT) operations in India.
The combined entity will bring greater scale, operational efficiencies and market reach to serve India with secure and reliable broadband satellite and hybrid solutions for enterprise and government networks. Hughes and its subsidiaries will maintain majority ownership in HCIL, the surviving entity, and Airtel will have a significant shareholding.
"In India today, satellite is an important layer in the enterprise networks across all kinds of industries – from retail to petroleum to banking to military," said Partho Banerjee, president and managing director, HCIL. "Together with Airtel, HCIL will expand our VSAT and terrestrial operations to serve the growing demand for always on, always available network connectivity across the country."
Ajay Chitkara, director and chief executive officer, Airtel Business, said: "We are pleased to combine our VSAT operations with Hughes to serve the connectivity needs of Digital India. Combining the proven capabilities of both the companies, the partnership will bring synergies to the forefront for the benefit of customers across the length and breadth of India."
The largest satellite service operator in India, HCIL provides broadband networking technologies, solutions, and services for businesses, governments. Airtel is a significant VSAT service provider in India, offering satellite connectivity to unreachable terrains and helping businesses supplement their terrestrial networks with satellite for primary and backup connectivity.
The combined entity will be positioned to introduce new VSAT and related technologies to deliver a wide range of quaility products and services. HCIL will continue to serve existing HCIL and Airtel customers while focusing on serving the growing networking needs of enterprise and government customers in India.
Completion of the transaction is subject to customary regulatory approvals and closing conditions.
Hughes at SATELLITE 2019: Connecting People, Enterprises and Things
Hughes will exhibit at SATELLITE 2019 on May 6-9 at the Walter E. Washington Convention Center in Washington, DC, showcasing how consumers, businesses, governments and communities around the world benefit from the connected experiences enabled by Hughes technologies and services, today…and tomorrow. See Hughes executives at SATELLITE 2019 for unique perspective on connecting people, enterprises and things–including GEO HTS and LEO systems; community Wi-Fi and cellular backhaul solutions; M2M, AI and IoT networks; aero and maritime connectivity; MilSatCom and more.
About Bharti Airtel
Bharti Airtel Limited is a leading global telecommunications company with operations in 18 countries across Asia and Africa. Headquartered in New Delhi, India, the company ranks amongst the top 3 mobile service providers globally in terms of subscribers. In India, the company's product offerings include 2G, 3G and 4G wireless services, mobile commerce, fixed line services, high speed home broadband, DTH, enterprise services including national & international long distance services to carriers. In the rest of the geographies, it offers 2G, 3G, 4G wireless services and mobile commerce. Bharti Airtel had over 403 million customers across its operations at the end of December 2018. To know more please visit, www.airtel.com.