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You are here: Home / Guides / Top Ten London: Top 10 Things to See and Do in Bromley
Top Ten London: Top 10 Things to See and Do in Bromley
Dec 18, 2018 By John Rabon
The London Borough of Bromley is the largest in greater London, covering fifty-nine square miles. Like many of London's boroughs, it was formed in 1965 by the London Government Act of 1963 and was created out of the Metropolitan Boroughs of Bromley and Beckenham as well as the Urban Districts of Penge, Orpington, and Chislehurst. Being the biggest of London's boroughs, there is certainly plenty to do here, and we've found our top ten places and things you need to see here. Let us know your own favorite Bromley hot spots in the comments.
The Glades Shopping Center
One of the borough's best shopping destinations can be found right in the middle of the town center. With 135 shops, The Glades has everything from Marks & Spencer to the Disney Store as well as plenty of uniquely British shops. The shopping center recently added a restaurant terrace with five new food offerings in addition to the many cafes and kiosks.
Biggin Hill Airport Open House Air Day
Biggin Hill's airport used to host an RAF themed air show, but that ceased several years ago due to financial problems. Beginning in 2011, however, the regional airport began a series of Open House days and air shows of their own that celebrate the airport's history in the Battle of Britain as well as showing attendees how an airport runs.
Bromley Charter Market
London's markets offer a unique opportunity to get a real taste of the boroughs and Bromley Charter Market is no exception. The Bromley Market has existed in the area for over 800 years and today offers a range of products ranging from antiques to fresh produce. Perusing the stalls, you might find some pretty rare items, so it's always worth the trip.
Churchill Theater
The center of culture in Bromley, the Churchill Theater offers any number of live performances from concerts to plays. The theater's bar and dining options offer attendees the chance to dine before (and sometimes during) the show as well as enjoy a nice drink as they're entertained. Be sure to check the schedule before you go and find out what upcoming show is to your satisfaction.
As the name might suggest, Christmas Tree Farm does sell Christmas trees, but it is a working farm that you can visit throughout the year. During warmer months, the farm's animals are out and about for visitors to interact with, feed, and pet. Interestingly, the farm is located near the former home of Charles Darwin and where he wrote The Origin of the Species.
Down House
Nearby is Darwin's real home, Down House. Living there from 1842 until his death in 1882, Down House has been preserved as it was when it was the Darwin family home, down to his and his wife's recreated room. You can also wander the gardens where Darwin formulated many of his evolutionary ideas and walk in the footsteps of one of history's most famous scientists.
Crofton Roman Villa
The foundations of modern London come from the Roman settlement here during the height of the empire, and there are still many ruins left from this time. Crofton Roman Villa was inhabited from 140 to 400 AD and was the center of a farm that stretched 500 acres. The site was uncovered in 1926 and the museum built over it to preserve this important piece of London's history.
Of course, if you want to know more about the borough's history and its contribution to World War II, one of the best places to visit is the Biggin Hill Memorial Museum. Dedicated to The Few who defended London in the Battle of Britain, BHMM not only has exhibits dedicated to the battle and those stationed at the nearby airfield (now Biggin Hill Airport) but it has actual Spitfires in the hanger for you to marvel over.
Crystal Palace Park
London's public parks are wonderful green spaces in the middle of a bustling city, and Crystal Palace Park is no exception. In addition to the lovely greenery, the park offers such attractions as a hedge maze, skatepark, the Crystal Palace Museum, and the Dinosaur Court which is full of statues of history's great lizards.
Chislehurst Caves
More mines than caves, the Chislehurst Caves are perhaps the most distinguishing feature and a landmark in Bromley. They were carved out as mines for chalk and lime and have served multiple roles since mining stopped in the 1830s including as a munitions storage site in World War I, an air raid shelter in World War II, a filming location, and even a concert venue. Entry into the Caves is by tour only, so be sure to book your visit in advance.
Filed Under: Featured, Guides, Tips, Top 10 London
About John Rabon
John is a regular writer for Anglotopia and its sister websites. He is currently engaged in finding a way to move books slightly to the left without the embarrassment of being walked in on by Eddie Izzard. For any comments, questions, or complaints, please contact the Lord Mayor of London, Boris Johnson's haircut.
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Bowles, William Leslie (1885–1954)
by Nancy D. H. Underhill
William Leslie Bowles (1885-1954), by unknown photographer
Australian War Memorial, P04333.001
William Leslie Bowles (1885-1954), sculptor, was born on 26 February 1885 at Leichhardt, New South Wales, son of William Hixson Bowles, an Irish-born compositor, and his wife Rachel, née Mark. He attended the Kangaroo Point State School, Brisbane, then studied carving and modelling at the Brisbane Technical College under Lewis J. Harvey, a careful and dedicated teacher who stressed drawing and the proper use of materials. Harvey's fine art nouveau furniture and pottery introduced Bowles to the style and to current academic techniques before he went to England on a scholarship from the college in 1910. He worked there with several sculptors, including (Sir) E. Bertram Mackennal, and attended night classes at South London School of Sculpture and at the Royal Academy. In Mackennal's studio work was then concentrated mainly on large public monuments, such as the London Memorial and Tomb for King Edward VII, and equestrian statues of the king for Melbourne, London and Calcutta. It is not surprising that Bowles's later independent work reflects that of Mackennal, and almost never stems directly from other contemporaries.
During World War I Bowles enlisted in the 2nd/25th London Regiment, and then joined the Royal Tank Corps at its inception. After the war he exhibited regularly at the Royal Academy and was employed on the Wembley British Empire Exhibition. He married Mary Lees of Kelso, Scotland, on 24 February 1924; late that year he returned to Australia and settled in Prahran, Melbourne. He was employed with other artists in the Melbourne Exhibition Building on the projected Australian War Memorial, for which he executed several sculptures, the models for figures in the dioramas, and two plans for the Hall of Memory. Though forced to sell hire purchase radios during the Depression, his War Memorial work provided security which evaded other sculptors.
Bowles's work depends heavily on narrative or moral content with little exploration of materials or their surfaces: it conformed with the almost exclusive use of sculpture in the 1930s and 1940s to expand civic and national pride and myth-making. His most interesting artistic quality is his subjection of sculptural elements of large monuments to an overall 'architectural design'. The 1939 proposal for the Hall of Memory was a draped female figure on a sarcophagus surmounted by soldiers' arms and equipment; his excellent 1949 version was a pale yellow marble shaft representing the four freedoms, carefully relating its central location to the Hall's other features. Unfortunately, political interference prevented its execution. The King George V Memorial in Melbourne, designed in 1937 and cast after World War II, acknowledges the site's importance by minimizing both sculpture and variations of stone colouring. This attention to site and simplicity seems stronger than in the case of his contemporaries George Lambert, C. D. Richardson, or even Rayner Hoff. In 1937 he won the competition for a memorial to Sir John Monash and his equestrian statue was erected in the Melbourne Domain in 1951.
Bowles had strong, if predictable, views on art criticism, art ethics and the art clique that ruled Melbourne taste in the 1940s. He especially disliked George Bell who criticized Ivor Hele's work for the War Memorial. The only critic Bowles approved of was his friend James S. MacDonald, who also maintained that symmetry and beauty were the proper goals of art. In 1926 Bowles had been made a member of the Royal Society of British Sculptors. He sometimes exhibited with the Victorian Artists' Society between 1925 and 1932, and with the Australian Art Association in the mid-1930s and 1940s. He was a foundation member and secretary of the Sculptors' Society of Australia, founded in 1932 at a meeting in Ola Cohn's studio, and held this office until the war when the society ceased to function. He also adjudicated the Jubilee Medal award, and was a foundation member of the Australian Academy of Art.
When poor health stopped his work for the War Memorial, Bowles asked that it be completed by his former assistant Ray Ewers. In 1938 he had established a studio and home at Frankston; he died there of coronary vascular disease on 21 February 1954 and was cremated. His wife survived him.
A. McCulloch, Encyclopedia of Australian Art (Lond, 1968)
J. Johnson and A. Greutzner (eds), Dictionary of British Artists 1880-1940 (Suffolk, 1976)
Sydney Morning Herald, 25 Nov 1937, 22 Nov 1951
records (Australian War Memorial).
Ewers, Raymond Boultwood (mentored)
Nancy D. H. Underhill, 'Bowles, William Leslie (1885–1954)', Australian Dictionary of Biography, National Centre of Biography, Australian National University, https://adb.anu.edu.au/biography/bowles-william-leslie-5313/text8971, published first in hardcopy 1979, accessed online 2 February 2023.
Leichhardt, Sydney, New South Wales, Australia
21 February, 1954 (aged 68)
Frankston, Melbourne, Victoria, Australia

redpajama_set_name: RedPajamaCommonCrawl


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Q: Running plugins without opening Eclipse IDE Using Eclipse IDE we have created plugin which is used to open the Directory Dialog on selection of the option openDialog in menu bar which performs the operation of reading the file names inside the directory and then creates a new file and pops up the Save Dialog in which we can specify the desired location to save the created file. So to run this plugin we need Eclipse IDE to be opened.
Question 1: Can we run that plugin without opening the Eclipse IDE.
Question 2: I want to export this plugin in the form of .exe or jar such that when I run that .exe in the windows we want to start executing that plugin.
Finally we want to run the plugins as Standalone application without opening the eclipse IDE
Thanks in Advance

A: No you can't run a plug-in on its own, it will depend on many other plug-ins and the Eclipse/OSGi framework.
You may be able to write an Eclipse Rich Client Program (RCP) standalone application containing the plug-in and the other required plug-ins.

redpajama_set_name: RedPajamaStackExchange


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Undergraduate - Course4410 - Bachelor of Commerce (Accounting)
This course entry applies to students commencing this course in 2014 and should be read in conjunction with information provided in the 'Faculty information' section of this Handbook by the Faculty of Business and Economics
BCom(Acc)
On-campus (Malaysia)
Visit http://www.buseco.monash.edu.au/contact.html
Dr Ross Booth
This course provides professional education for those wishing to become accounting professionals. It has a strong emphasis on developing analytical skills and offers a solid grounding and professional competence in all aspects of commerce required for careers in the business or public sector.
These course outcomes are aligned with the Australian Qualifications Framework level 7, the Bologna Cycle 1 and Monash Graduate AttributesAustralian Qualifications Framework level 7, the Bologna Cycle 1 and Monash Graduate Attributes (http://opvclt.monash.edu.au/curriculum-by-design/aligning-course-outcomes-with-aqf-bologna.html).
(c.) have comprehensive understanding of their discipline and be able to provide discipline based solutions relevant to the business, professional and public policy communities that we serve
(d.) be able to construct conceptual frameworks and use these to analyse complex issues in the corporate sector, government and the professions
Refer to the faculty's Professional recognition of coursesProfessional recognition of courses (http://www.buseco.monash.edu.au/professional-recognition/) page in this Handbook.
The course has three main components:
a set of core units which provide an introduction to the key areas of commerce
a major in accounting
eight open electives which may be taken from any Faculty of Business and Economics programs and campuses, or from disciplines offered by another faculty. These open electives can, in some cases, form a second major.
Students must complete 24 units over a maximum of eight years as follows:
(a.) 16 units (96 points) from the Faculty of Business and Economics in the Bachelor of Commerce (Accounting) at Monash University Malaysia as follows:
(i.) six compulsory common core units (36 points):
ACC1000 Principles of accounting and finance*
BTC1110 Business law
ECC1000 Principles of microeconomics
ETC1000 Business and economic statistics
MGC1010 Introduction to management
MKC1200 Principles of marketing
(ii.) the following two units (12 points):
ACC1030 Introduction to financial accounting*
ECC1100 Principles of macroeconomics
(iii.) a major of eight units in accounting (48 points):
at least two units (12 points) must be completed at each of second and third-year levels
* Note: Two of the units in the major will have already been studied as part of the group of common core units - students must complete additional units as detailed below.
(iv.) the following additional units from the faculty in the Bachelor of Commerce at Monash University Malaysia to reach the required minimum of 16 units:
BFC2000 Financial institutions and markets
MGX3100 Management ethics and corporate governance
(b.) eight open elective units (48 points) as follows:
may be taken from disciplines offered by another faculty as individual units
may be taken from the Faculty of Business and Economics to form a second major (for example Banking and Finance, etc.), or taken as individual units.
Additional Bachelor of Commerce (Accounting) degree requirements:
a maximum of ten first-year level units (60 points)
a minimum of four third-year level units (24 points) from those offered by the faculty at the student's campus of enrolment
in addition to specific unit prerequisites, third-year level units require the successful completion of two second-year level units and second-year level units require the successful completion of four first-year level units.
Bachelor of Commerce (Accounting)


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redpajama_set_name: RedPajamaGithub


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\section{Appendices}
\label{sec:appendix}

\subsection{Training Details for Reproducibility} \label{sec:details}
Although English is not the best source language for some target languages \cite{Lin2019transfer_languages}, this zero-shot cross-lingual transfer setting is still practical useful as many NLP tasks only have English annotations. In the following paragraphs, we show details for reproducing our results on zero-shot cross-lingual transfer setting.

\paragraph{\textbf{Model:}} We use the same architecture as mBERT for {\scshape amber}\xspace, and we build our {\scshape amber}\xspace trained with the alignment objectives on top of the original mBERT implementation at \url{https://github.com/google-research/bert}, and are released at \url{http://github.com/junjiehu/amber}.

\paragraph{\textbf{Pre-training:}} We first train the model on the Wikipedia data for 1M steps using the default hyper-parameters in the original repository except that we use a larger batch of 8,192 sentence pairs. The max number of subwords in the concatenation of each sentence pair is set to 256. To continue training {\scshape amber}\xspace with additional objectives on parallel data, we use 10K warmup steps with the peak learning rate of 1e-4, and use a linear decay of the learning rate. All models are pre-trained with our proposed objectives on TPU v3, and we use the same hyper-parameter setting for our {\scshape amber}\xspace variants in the experiments. We follow the practice of mBERT at \url{https://github.com/google-research/bert/blob/master/multilingual.md#data-source-and-sampling} to sample from mutlilingual data for training. We select the checkpoint of all models at the 1M step for a fair comparison. It takes about 1 week to finish the pre-training.

\paragraph{\textbf{Fine-tuning:}} For fine-tuning the models on the downstream applications, we use the constant learning rate of 2e-5 as suggested in the original paper~\cite{devlin2019bert}. We fine-tune all the models for 10 epochs on the cross-lingual POS tag prediction task, and 5 epochs on the sentence classification task. We use the batch size of 32 for all the models. All models are fine-tuned on 2080Ti GPUs, and the training can be finished within 1 day.

\paragraph{\textbf{Datasets:}} We use the same parallel data that is used to train XLM-15. The parallel data can be processed by this script: \url{https://github.com/facebookresearch/XLM/blob/master/get-data-para.sh}. All the datasets in the downstream applications can be downloaded by the script at \url{https://github.com/google-research/xtreme/blob/master/scripts/download_data.sh}. Table~\ref{tab:languages} lists all the data statistic of parallel data by languages.

\begin{table*}[]
\centering
\resizebox{\textwidth}{!}{%
\begin{tabular}{l c c c l l ccccc}
\toprule
Language & \begin{tabular}[c]{@{}l@{}}ISO\\ 639-1\\ code\end{tabular} &
\begin{tabular}[c]{@{}l@{}}\# Parallel\\ sentences (in\\ millions)\end{tabular} &
\begin{tabular}[c]{@{}l@{}}\# Wikipedia\\ articles (in\\ millions)\end{tabular} & Script & \begin{tabular}[c]{@{}l@{}}Language\\ family\end{tabular} & \begin{tabular}[c]{@{}l@{}}Diacritics /\\ special\\ characters\end{tabular} & \begin{tabular}[c]{@{}l@{}}Extensive\\ compound-\\ ing\end{tabular} & \begin{tabular}[c]{@{}l@{}}Bound\\ words /\\ clitics\end{tabular} & \begin{tabular}[c]{@{}l@{}}Inflec-\\ tion\end{tabular} & \begin{tabular}[c]{@{}l@{}}Deriva-\\ tion\end{tabular} \\ \midrule
Arabic & ar & 9.8 & 1.02 & Arabic & Afro-Asiatic & X &  & X & X &   \\
Bulgarian & bg & 0.6 & 0.26 & Cyrillic & IE: Slavic & X &  & X & X &   \\
English & en & 40.2 & 5.98 & Latin & IE: Germanic &  &  &  &  &   \\
French & fr & 13.2 & 2.16 & Latin & IE: Romance & X &  & X &  &   \\
German & de & 9.3 & 2.37 & Latin & IE: Germanic &  & X &  & X &   \\
Greek & el & 4.0 & 0.17 & Greek & IE: Greek & X & X &  & X &   \\
Hindi & hi & 1.6 &  0.13 & Devanagari & IE: Indo-Aryan & X & X & X & X & X  \\
Mandarin & zh & 9.6 & 1.09 & Chinese ideograms & Sino-Tibetan &  & X &  &  &  \\
Russian & ru & 11.7 & 1.58 & Cyrillic & IE: Slavic &  &  &  & X &   \\
Spanish & es & 11.4 & 1.56 & Latin & IE: Romance & X &  & X &  &   \\
Swahili & sw & 0.2 & 0.05 & Latin & Niger-Congo &  &  & X & X & X  \\
Thai & th & 3.3 & 0.13 & Brahmic & Kra-Dai & X &  &  &  &   \\
Turkish & tr & 0.5 &  0.34 & Latin & Turkic & X & X &  & X & X  \\
Urdu & ur & 0.7 & 0.15 & Perso-Arabic & IE: Indo-Aryan & X & X & X & X & X \\
Vietnamese & vi & 3.5 &  1.24 & Latin & Austro-Asiatic & X &  &  &  &   \\
\bottomrule
\end{tabular}%
}
\caption{Statistics about languages used for pre-training with our alignment objectives. Languages belong to 7 language families, with Indo-European (IE) having the most members. Diacritics / special characters: Language adds diacritics (additional symbols to letters). Compounding: Language makes extensive use of word compounds. Bound words / clitics: Function words attach to other words. Inflection: Words are inflected to represent grammatical meaning (e.g.~case marking). Derivation: A single token can represent entire phrases or sentences.}
\label{tab:languages}
\end{table*}

\subsection{Source-to-target attention matrix} \label{sec:s2t_attention}
We derive the source-to-target attention matrix as follow:

\begin{resizealign} \label{eq:s2t_attn}
    & \gb^l_{x_j} = \text{Attn}(\Qb=\gb^{l-1}_{x_j}, \Kb\Vb=\gb^{l-1}_{\textcolor{red}{[x_{<j}; y]}}; W^{l}) , \\
    & \gb^l_{y_j} = \text{Attn}(\Qb=\gb_{y_i}^{l-1}, \Kb\Vb=\gb^{l-1}_{\textcolor{red}{y}}; W^{l}) , \\
    & \text{Attn}(\Qb\Kb\Vb; W) = \text{softmax}(\Qb\Wb^q (\Kb \Wb^k)^T) \Vb \Wb^v \\
    & A_{x\rightarrow y}[j,i] = \gb_{x_j}^L \cdot \gb_{y_i}^L .
\end{resizealign}

\subsection{Detailed Results} \label{sec:detailed_results}
We show the detailed results over all languages on the cross-lingual POS task in Table~\ref{tab:conll17-align}, on the PAWS-X task in Table~\ref{tab:pawsx-align}, on the XNLI task in Table~\ref{tab:xnli-result}, and on the Tatoeba retrieval task in Table~\ref{tab:tatoeba-align}.

\begin{table*}[]
\centering
\resizebox{0.6\textwidth}{!}{%
\begin{tabular}{l|lllll|l}
\toprule
Model           & de            & en            & es            & fr            & zh            & Avg           \\ \midrule
mBERT (public)     & 85.7          & 94.0          & 87.4          & 87.0          & 77.0          & 86.2               \\
XLM-15             & 88.5          & \textbf{94.7} & 89.3          & 89.6          & 78.1          & 88.0               \\
XLM-100            & 85.9          & 94.0          & 88.3          & 87.4          & 76.5          & 86.4               \\
XLM-R-base          & 87.0          & 94.2          & 88.6          & 88.7          & 78.5          & 87.4               \\
XLM-R-large         & \textbf{89.7} & \textbf{94.7} & \textbf{90.1} & \textbf{90.4} & \textbf{82.3} & \textbf{89.4}      \\ \midrule
{\scshape amber}\xspace (MLM, our mBERT)    & 87.3          & 93.9          & 87.5          & 87.8          & 78.8          & 87.1               \\
{\scshape amber}\xspace (MLM+TLM)                & 87.6          & \textbf{95.8} & 87.4          & 88.9          & 78.7          & 87.7               \\
{\scshape amber}\xspace(MLM+TLM+WA) & 88.9 & 95.5          & 88.9 & \textbf{90.7} & \textbf{81.1} & 89.0      \\
{\scshape amber}\xspace(MLM+TLM+WA+SA)   & \textbf{89.4}          & 95.6          & \textbf{89.2}          & \textbf{90.7}          & 80.9          & \textbf{89.2} \\\bottomrule
\end{tabular}%
}
\caption{Accuracy of zero-shot cross-lingual classification on PAWS-X. Bold numbers highlight the highest scores across languages on the existing models (upper part) and {\scshape amber}\xspace variants (bottom part).}
\label{tab:pawsx-align}
\end{table*}

\begin{table*}[!t]
\centering
\resizebox{\textwidth}{!}{%
\begin{tabular}{l|lllllllllllll|l}
\toprule
models       & ar            & bg            & de            & el            & en            & es            & fr            & hi            & ru            & tr            & ur            & vi            & zh            & Avg  \\ \midrule
mBERT (public)         & 14.9          & 85.2          & 89.3          & 82.8          & 95.3          & 85.7          & 84.1          & 65.1          & 86.0          & 67.5          & 57.4          & 18.5          & 58.9          & 68.5 \\
XLM-15       & 17.5          & 86.1          & 89.3          & 85.4          & 95.7          & 85.9          & 84.9          & 63.9          & 86.8          & 69.3          & 55.1          & 18.0          & 57.2          & 68.8 \\
XLM-100      & 17.1          & 85.8          & 89.3          & 85.7          & 95.4          & 85.3          & 84.3          & 67.0          & 87.1          & 65.0          & 62.0          & 19.2          & \textbf{60.2}          & 69.5 \\
XLM-R-base    & 17.6          & \textbf{88.5} & 91.1          & \textbf{88.2} & 95.8          & 87.2          & 85.7          & 70.1          & 88.9          & 72.7          & 61.6          & 19.2          & 27.9          & 68.8 \\
XLM-R-large   & \textbf{18.1} & 87.4          & \textbf{91.9} & 87.9          & \textbf{96.3} & \textbf{87.8} & \textbf{87.3} & \textbf{76.1} & \textbf{89.9} & \textbf{74.3} & \textbf{67.6} & \textbf{19.5} & 26.5          & \textbf{70.0} \\ \midrule
{\scshape amber}\xspace (MLM, our mBERT) & 15.4          & 86.6          & 90.1          & 84.3          & 95.5          & 86.5          & 84.6          & 68.2          & 86.8          & 69.0          & 59.2          & 18.7          & 62.1          & 69.8 \\
{\scshape amber}\xspace (MLM+TLM)          & \textbf{16.0} & \textbf{87.2} & \textbf{91.5} & \textbf{86.4} & \textbf{95.7} & 86.9          & 85.2          & 67.7          & \textbf{87.9} & 72.9          & 57.9          & 19.1          & \textbf{62.7} & 70.5 \\
{\scshape amber}\xspace (MLM+TLM+WA)       & 14.8          & 86.9          & 90.4          & 84.9          & 95.6          & 86.7          & 84.8          & \textbf{72.5} & 87.8          & \textbf{73.9} & \textbf{63.8} & \textbf{19.5} & 62.3          & \textbf{71.1} \\
{\scshape amber}\xspace (MLM+TLM+WA+SA)    & 14.6          & 87.1          & 90.6          & 85.9          & 95.5          & \textbf{87.0} & \textbf{86.0} & 68.6          & 87.4          & 73.4          & 60.2          & 18.8          & 61.8          & 70.5 \\ \bottomrule
\end{tabular}}
\caption{F1 scores of part-of-speech tag predictions from the Universal Dependency v2.3. Bold numbers highlight the highest scores across languages on the existing models (upper part) and {\scshape amber}\xspace variants (bottom part). }
\label{tab:conll17-align}
\end{table*}

\begin{table*}[h]
\centering
\resizebox{\textwidth}{!}{%
\begin{tabular}{l|lllllllllllllll|l}
\toprule
Models                                  & en            & zh            & es            & de            & ar            & ur            & ru            & bg            & el            & fr            & hi            & sw            & th                        & tr            & vi            & avg           \\ \midrule
mBERT (public)                          & 80.8          & 67.8          & 73.5          & 70.0          & 64.3          & 57.2          & 67.8          & 68.0          & 65.3          & 73.4          & 58.9          & 49.7          & 54.1                      & 60.9          & 69.3          & 65.4          \\
XLM-15                                  & 84.1          & 68.8          & 77.8          & 75.7          & 70.4          & 62.2          & 75.0          & 75.7          & 73.3          & 78.0          & 67.3          & 67.5          & 70.5 & 70.0          & 73.0          & 72.6          \\
XLM-100                                 & 82.8          & 70.2          & 75.5          & 72.7          & 66.0          & 59.8          & 69.9          & 71.9          & 70.4          & 74.3          & 62.5          & 58.1          & 65.5 & 66.4          & 70.7          & 69.1          \\
XLM-R-base                               & 83.9          & 73.6          & 78.3          & 75.2          & 71.9          & 65.4          & 75.1          & 76.7          & 75.4          & 77.4          & 69.1          & 62.2          & 72.0                      & 70.9          & 74.0          & 73.4          \\
XLM-R-large                              & \textbf{88.7} & \textbf{78.2} & \textbf{83.7} & \textbf{82.5} & \textbf{77.2} & \textbf{71.7} & \textbf{79.1} & \textbf{83.0} & \textbf{80.8} & \textbf{82.2} & \textbf{75.6} & \textbf{71.2} & \textbf{77.4}             & \textbf{78.0} & \textbf{79.3} & \textbf{79.2} \\ \midrule
{\scshape amber}\xspace (MLM, our mBERT) & 82.1          & 71.0          & 75.3          & 72.7          & 66.2          & 60.1          & 70.4          & 71.3          & 67.9          & 74.4          & 63.6          & 50.1          & 55.0                      & 64.2          & 71.6          & 67.7          \\
{\scshape amber}\xspace (MLM+TLM)        & 84.3          & 71.6          & \textbf{77.2} & 73.9          & 69.1          & 59.6          & 72.5          & 73.6          & 70.9          & \textbf{78.0} & 64.7          & 57.4          & 65.0                      & 72.2          & 73.1          & 70.9          \\
{\scshape amber}\xspace (MLM+TLM+WA)     & 84.1          & \textbf{72.1} & 76.6          & \textbf{74.7} & 69.3          & \textbf{61.5} & 72.9          & 73.9          & 71.6          & 77.7          & 65.7          & 58.6          & 65.3                      & 72.7          & \textbf{73.4} & 71.3          \\
{\scshape amber}\xspace (MLM+TLM+WA+SA)  & \textbf{84.7} & 71.6          & 76.9          & 74.2          & \textbf{70.2} & 61.0          & \textbf{73.3} & \textbf{74.3} & \textbf{72.5} & 76.6          & \textbf{66.2} & \textbf{59.9} & \textbf{65.7}             & \textbf{73.2} & \textbf{73.4} & \textbf{71.6} \\ \bottomrule
\end{tabular} }
\caption{Accuracy of zero-shot crosslingual classification on the XNLI dataset. Bold numbers highlight the highest scores across languages on the existing models (upper part) and {\scshape amber}\xspace variants (bottom part).}
\label{tab:xnli-result}
\end{table*}

\begin{table*}[!htbp]
\centering
\resizebox{\textwidth}{!}{%
\begin{tabular}{l|llllllllllllll|l}
\toprule
Method                             & ar            & bg            & de            & el            & es            & fr            & hi            & ru            & sw            & th            & tr            & ur            & vi            & zh            & Avg           \\ \midrule
mBERT (public)                               & 25.8          & 49.3          & 77.2          & 29.8          & 68.7          & 66.3          & 34.8          & 61.2          & 11.5          & 13.7          & 34.8          & 31.6          & 62.0          & 71.6          & 45.6          \\
XLM-15 & \textbf{63.5}	& 71.5	& \textbf{92.6}	& \textbf{73.1}	& \textbf{85.5}	& \textbf{82.5}	& \textbf{81.0} & \textbf{82.0} & 	\textbf{47.9} &	\textbf{90.3} &	\textbf{67.6}	 & \textbf{68.4} &	\textbf{91.1} & 	\textbf{84.1}	& \textbf{77.2} \\
XLM-100                            & 18.2          & 40.0          & 66.2          & 25.6          & 58.4          & 54.5          & 26.5          & 44.8          & 12.6          & 31.8          & 26.2          & 18.1          & 47.1          & 42.2          & 36.6          \\
XLM-R-base                          & 36.8          & 67.6          & 89.9 & 53.7          & 74.0          & 74.1 & 54.2          & 72.5          & 19.0          & 38.3 & 61.1          & 36.6 & 68.4          & 60.8          & 57.6          \\
XLM-R-large                         & 47.5 & \textbf{71.6} & 88.8          & 61.8 & 75.7 & 73.7          & 72.2 & 74.1 & 20.3 & 29.4          & 65.7 & 24.3          & 74.7 & 68.3 & 60.6 \\ \midrule
{\scshape amber}\xspace (MLM, our mBERT)                               & 30.7          & 54.9          & 81.4          & 37.7          & 72.7          & 72.7          & 47.5          & 67.5          & 15.1          & 25.7          & 48.3          & 42.6          & 64.6          & 75.1          & 52.6          \\
{\scshape amber}\xspace (MLM+TLM)       & 47.1          & 61.8          & 89.0          & 53.8          & 76.3          & 77.9          & 72.3          & 69.8          & 20.5          & 83.4          & 88.1          & 50.0          & 86.9          & 78.0          & 68.2          \\
{\scshape amber}\xspace (MLM+TLM+WA)    & 46.8          & 63.3          & 88.8          & 52.2          & 78.3          & 79.5          & 66.9          & 71.6          & 27.4          & 77.2          & 86.9          & 56.5          & 86.5          & 81.6          & 68.8          \\
{\scshape amber}\xspace (MLM+TLM+WA+SA) & \textbf{78.5} & \textbf{87.1} & \textbf{95.5} & \textbf{75.3} & \textbf{93.3} & \textbf{92.2} & \textbf{95.0} & \textbf{91.5} & \textbf{52.8} & \textbf{94.5} & \textbf{98.4} & \textbf{84.5} & \textbf{97.4} & \textbf{94.3} & \textbf{87.9} \\ \bottomrule
\end{tabular}%
}
\caption{Sentence retrieval accuracy on the Tatoeba dataset. Bold numbers highlight the highest scores across languages on the existing models (upper part) and {\scshape amber}\xspace variants (bottom part).}
\label{tab:tatoeba-align}
\end{table*}

\subsection{Detailed Results on Performance Difference by Languages}
Figure~\ref{fig:pos_all} and Figure~\ref{fig:xnli_all} show the performance deference between {\scshape amber}\xspace trained with alignment objectives and {\scshape amber}\xspace trained with only MLM objective on the POS and XNLI tasks over all languages.

\begin{figure*}
    \centering
    \includegraphics[width=0.8\linewidth]{xnli_all.pdf}
    \caption{Performance difference between {\scshape amber}\xspace trained with alignments on parallel data and {\scshape amber}\xspace (MLM) on XNLI task. Languages are sorted by no. of parallel data used for training {\scshape amber}\xspace with alignments.}
    \label{fig:xnli_all}
\end{figure*}

\begin{figure*}
    \centering
    \includegraphics[width=0.8\linewidth]{pos_all.pdf}
    \caption{Performance difference between {\scshape amber}\xspace trained with alignments on parallel data and {\scshape amber}\xspace (MLM) on POS task. Languages are sorted by no. of parallel data used for training {\scshape amber}\xspace with alignments.}
    \label{fig:pos_all}
\end{figure*}

\section{Introduction}
\label{sec:intro}

Cross-lingual embeddings, both traditional non-contextualized word embeddings~\cite{faruqui2014improving} and the more recent contextualized word embeddings \cite{devlin2019bert}, are an essential tool for cross-lingual transfer in downstream applications. In particular, multilingual contextualized word representations have proven effective in reducing the amount of supervision needed in a variety of cross-lingual NLP tasks such as sequence labeling~\cite{pires2019multilingual}, question answering~\cite{artetxe2020cross}, parsing~\cite{wang-etal-2019-cross}, sentence classification~\cite{wu2019beto} and retrieval~\cite{yang2019improving}.

Some attempts at training multilingual representations \citep{devlin2019bert,conneau2019unsupervised} simply train a (masked) language model on monolingual data from many languages. These methods can only \emph{implicitly} learn which words and structures correspond to each-other across languages in an entirely unsupervised fashion, but are nonetheless quite effective empirically \cite{wu2019emerging,K2020Cross-Lingual}.
On the other hand, some methods directly leverage multilingual parallel corpora~\cite{mccann2017learned,eriguchi2018zero,conneau2019cross,huang2019unicoder,siddhant2019evaluating}, which gives some degree of supervision implicitly aligning the words in the two languages.
However, the pressure on the model to learn clear correspondences between the contextualized representations in the two languages is still implicit and somewhat weak.
Because of this, several follow-up works \citep{schuster-etal-2019-cross,wang2020crosslingual,cao2020multilingual} have proposed methods that use word alignments from parallel corpora as the supervision signals to align multilingual contextualized representations, albeit in a \emph{post-hoc} fashion.

In this work, we propose a training regimen for learning contextualized word representations that encourages symmetry at both the word and sentence levels \emph{at training time}.
Our word-level alignment objective is inspired by work in machine translation that defines objectives encouraging consistency between the source-to-target and target-to-source attention matrices~\cite{cohn2016incorporating}. Our sentence-level alignment objective encourages prediction of the correct translations within a mini-batch for a given source sentence, which is inspired by work on learning multilingual sentence representations~\cite{yang2019improving,wieting-etal-2019-simple}. In experiments, we evaluate the zero-shot cross-lingual transfer performance of {\scshape amber}\xspace on four different NLP tasks in the XTREME benchmark~\cite{hu2020xtreme} including part-of-speech (POS) tagging, paraphrase classification, and sentence retrieval. We show that {\scshape amber}\xspace obtains gains of up to 1.1 average F1 score on cross-lingual POS tagging, up to 27.3 average accuracy score on sentence retrieval, and achieves competitive accuracy in paraphrase classification when compared with the XLM-R-large model. This is despite the fact that XLM-R-large is trained on data 23.8x as large\footnote{{\scshape amber}\xspace is trained on 26GB parallel data and 80GB monolingual Wikipedia data, while XLM-R-large is trained on 2.5TB monolingual CommonCrawl data.} and has 3.2x parameters of {\scshape amber}\xspace. This shows that compared to large amounts of monolingual data, even a small amount of parallel data leads to significantly better cross-lingual transfer learning.

\section{Cross-lingual Alignment}
\label{sec:method}
This section describes three objectives for training contextualized embeddings. We denote the monolingual and parallel data as $\Mcal$ and $\Pcal$ respectively.

\vspace{0.3cm}
\noindent \textbf{Masked Language Modeling (MLM)}
A masked language modeling objective takes a pair of sentences $x, y$, and optimizes the prediction of randomly masked tokens in the concatenation of the sentence pair as follows:
\begin{align}
    \ell_\text{MLM}(x,y) = - \EE_{s\sim [1,|z|]} \log P(z_{s} | z_{\setminus s}),
\end{align}
where $z$ is the concatenation of the sentence pair $z=[x; y]$, $z_s$ are the masked tokens randomly sampled from $z$, and $z_{\setminus s}$ indicates all the other tokens except the masked ones.

In the standard monolingual setting, $x,y$ are two contiguous sentences in a monolingual corpus. In \citet{conneau2019cross}, $x,y$ are two sentences in different languages from a parallel corpus, an objective we will refer to as Translation Language Modeling (TLM).

\noindent \textbf{Sentence Alignment}
Our first proposed objective encourages cross-lingual alignment of sentence representations. For a source-target sentence pair $(x,y)$ in the parallel corpus, we separately calculate sentence embeddings denoted as $\cbb_x, \cbb_y$ by averaging the embeddings in the final layer as the sentence embeddings.%
\footnote{In comparison, mBERT encodes a sentence pair jointly, then uses the CLS token embedding to perform its next sentence prediction task.} We then encourage the model to predict the correct translation $y$ given a source sentence $x$. To do so, we model the conditional probability of a candidate sentence $y$ being the correct translation of a source sentence $x$ as:
\begin{align}\label{eq:rank}
    P(y|x) &= {e^{\cbb_x^T\cbb_y} \over \sum_{y'\in \Mcal\cup \Pcal} e^{\cbb_x^T\cbb_{y'}}} .

\end{align}
where $y'$ can be any sentence in any language. Since the normalization term in Eq.~(\ref{eq:rank}) is intractable, we approximate $P(y|x)$ by sampling $y'$ within a mini-batch $\Bcal$ rather than $\Mcal \cup \Pcal$.
We then define the sentence alignment loss as the average negative log-likelihood of the above probability:
\begin{align}
    \ell_\text{SA}(x,y) = - \log P(y|x).
\end{align}

\noindent \textbf{Bidirectional Word Alignment}
Our second proposed objective encourages alignment of word embeddings by leveraging the attention mechanism in the Transformer model.
Motivated by the work on encouraging the consistency between the source-to-target and target-to-source translations \cite{cohn2016incorporating,he2016dual}, we create two different attention masks as the inputs to the Transformer model, and obtain two attention matrices in the top layer of the Transformer model. We compute the target-to-source attention matrix $\Ab_{y\rightarrow x}$ as follows:

\begin{resizealign} \label{eq:t2s_attn}
    & \gb^l_{y_i} = \text{Attn}(\Qb=\gb^{l-1}_{y_i}, \Kb\Vb=\gb^{l-1}_{\textcolor{red}{[y_{<i}; x]}}; W^{l}) , \\
    & \gb^l_{x_j} = \text{Attn}(\Qb=\gb_{x_j}^{l-1}, \Kb\Vb=\gb^{l-1}_{\textcolor{red}{x}}; W^{l}) , \\
    & \text{Attn}(\Qb\Kb\Vb; W) = \text{softmax}(\Qb\Wb^q (\Kb \Wb^k)^T) \Vb \Wb^v \\
    & A_{y\rightarrow x}[i,j] = \gb_{y_i}^L \cdot \gb_{x_j}^L .
\end{resizealign}

\noindent where $\gb_{y_t}^{l}$ is the embedding of the $t$-th word in $y$ on the $l$-th layer, $A_{y\rightarrow x}[i,j]$ is the $(i,j)$-th value in the attention matrix from $y$ to $x$, and $W=\{\Wb^q, \Wb^k, \Wb^v\}$ are the linear projection weights for $Q, K, V$ respectively. We compute the source-to-target matrix $\Ab_{x\rightarrow y}$ by switching $x$ and $y$.

To encourage the model to align source and target words in both directions, we aim to minimize the distance between the forward and backward attention matrices. Similarly to \citet{cohn2016incorporating}, we aim to maximize the trace of two attention matrices, i.e., $\tr{({{\Ab_{y\rightarrow x}}}^T \Ab_{x\rightarrow y})}$. Since the attention scores are normalized in $[0,1]$, the trace of two attention matrices is upper bounded by $\min(|x|, |y|)$, and the maximum value is obtained when the two matrices are identical. Since the Transformer generates multiple attention heads, we average the trace of the bidirectional attention matrices generated by all the heads denoted by the superscript $h$
\begin{align} \label{eq:sent_align}
    \ell_\text{WA}(x,y) =  1 - {1\over H}\sum_{h=1}^H{\tr{({\Ab^h_{y\rightarrow x}}^T \Ab^h_{x\rightarrow y})} \over \min(|x|,|y|)}.
\end{align}

Notably, in the target-to-source attention in Eq~(\ref{eq:t2s_attn}), with attention masking we enforce a constraint that the $t$-th token in $y$ can only perform attention over its preceding tokens $y_{<t}$ and the source tokens in $x$. This is particularly useful to control the information access of the query token $y_t$, in a manner similar to that of the decoding stage of NMT. Without attention masking, the standard Transformer performs self-attention over all tokens, i.e., $Q=K=\gb^h_{
z}$, and minimizing the distance between the two attention matrices by Eq.~(\ref{eq:sent_align}) might lead to a trivial solution where $\Wb^q \approx \Wb^k$.

\noindent \textbf{Combined Objective}
Finally we combine the masked language modeling objective with the alignment objectives and obtain the total loss in Eq.~(\ref{eq:total_loss}). Notice that in each iteration, we sample a mini-batch of sentence pairs from $\Mcal\cup\Pcal$.
\begin{align} \label{eq:total_loss}
    \Lcal =& \EE_{(x,y)\in \Mcal\cup\Pcal}~\ell_\text{MLM}(x,y) \\ \nonumber
           &+\EE_{(x,y)\in \Pcal}~[\ell_\text{SA}(x,y) + \ell_\text{WA}(x,y)]
\end{align}

\begin{table}[]
\setlength{\tabcolsep}{2.0pt}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{l cccccc}
\toprule
Model       & Data &  Langs & Vocab & Layers & Parameters & Ratio\\ \midrule
{\scshape amber}\xspace     & Wiki \& MT   & 104   & 120K     & 12  & 172M & 1.0   \\
mBERT       & Wiki     & 104   & 120K     & 12 & 172M & 1.0     \\
XLM-15      & Wiki \& MT   & 15    & 95K      & 12  & 250M & 1.5x   \\
XLM-100     & Wiki   & 100   & 200K     & 12  & 570M & 3.3x   \\
XLM-R-base  & CommonCrawl & 100   & 250K     & 12  & 270M  & 1.6x   \\
XLM-R-large & CommonCrawl & 100   & 250K     & 24  & 550M  & 3.2x   \\
Unicoder  & CommonCrawl \& MT  & 100   & 250K     & 12  & 270M  & 1.6x   \\
\bottomrule
\end{tabular}
}
\vspace{-0.3cm}
\caption{Details of baseline and state-of-the-art models. }
\vspace{-0.4cm}
\label{tab:params}
\end{table}

\section{Experiments}
\subsection{Training setup}
Following the setting of \citet{hu2020xtreme}, we focus on \textit{zero-shot cross-lingual transfer} where we fine-tune models on English annotations and apply the models to predict on non-English data.

\noindent \textbf{Models}: Table~\ref{tab:params} shows details of models in comparison.  We adopt the same architecture as mBERT for {\scshape amber}\xspace. Notably, {\scshape amber}\xspace, XLM-15 and Unicoder are trained on the additional parallel data, while the others are trained only on monolingual data. Besides, XLM-R-base/large models have 2.6x/4.8x the parameters of {\scshape amber}\xspace and are trained on the larger CommonCrawl corpus. We use a simple setting for our {\scshape amber}\xspace variants in the ablation study to show the effectiveness of our proposed alignment objectives without other confounding factors such as model sizes, hyper-parameters and tokenizations in different existing studies.

\noindent \textbf{Pre-training}: We train {\scshape amber}\xspace on the Wikipedia data for 1M steps first using the default hyper-parameters as mBERT\footnote{\url{https://github.com/google-research/bert}} except that we use a larger batch of 8,192 sentence pairs, as this has proven effective in \citet{liu2019roberta}. We then continue training the model by our objectives for another 1M steps with a batch of 2,048 sentence pairs from Wikipedia corpus and parallel corpus which is used to train XLM-15 \cite{conneau2019cross}. We use the same monolingual data as mBERT and follow \citet{conneau2019cross} to prepare the parallel data with one change to maintain truecasing. We set the maximum number of subwords in the concatenation of each sentence pair to 256 and use 10k warmup steps with the peak learning rate of 1e-4 and a linear decay of the learning rate. We train {\scshape amber}\xspace on TPU v3 for about 1 week.

\subsection{Datasets}
\noindent \textbf{Cross-lingual Part-Of-Speech (POS)} contains data in 13 languages from the Universal Dependencies v2.3~\cite{nivre2018universal}.

\noindent \textbf{PAWS-X}~\cite{Yang2019paws-x} is a paraphrase detection dataset. We train on the English data \cite{Zhang2019paws}, and evaluate the prediction accuracy on the test set translated into 4 other languages.

\noindent \textbf{XNLI}~\cite{conneau2018xnli} is a natural language inference dataset in 15 languages. We train models on the English MultiNLI training data \cite{Williams2018multinli}, and evaluate on the other 14.

\noindent \textbf{Tatoeba}~\cite{Artetxe2019massively} is a testbed for parallel sentence identification. We select the 14 non-English languages covered by our parallel data, and follow the setup in \citet{hu2020xtreme} finding the English translation for a given a non-English sentence with maximum cosine similarity.

\subsection{Result Analysis}

In Table~\ref{tab:all-results}, we show the average results over all languages in all the tasks, and show detailed results for each language in Appendix~\ref{sec:detailed_results}. First, we find that our re-trained mBERT ({\scshape amber}\xspace with MLM) performs better than the publicly available mBERT on all the tasks, confirming the utility of pre-training BERT models with larger batches for more steps~\cite{liu2019roberta}. Second, {\scshape amber}\xspace trained by the word alignment objective obtains a comparable average F1 score with respect to the best performing model (Unicoder) in the POS tagging task, which shows the effectiveness of the word-level alignment in the syntactic structure prediction tasks at the token level. Besides, it is worth noting that Unicoder is initialized from the larger XLM-R-base model that is pre-trained on a larger corpus than {\scshape amber}\xspace, and Unicoder improves over XLM-R-base on all tasks. Third, for the sentence classification tasks, {\scshape amber}\xspace trained with our explicit alignment objectives obtain a larger gain (up to 2.1 average accuracy score in PAWS-X, and 3.9 average accuracy score in XNLI) than {\scshape amber}\xspace with only the MLM objective.  Although we find that {\scshape amber}\xspace trained with only the MLM objective falls behind existing XLM/XLM-R/Unicoder models with many more parameters,
{\scshape amber}\xspace trained with our alignment objectives significantly narrows the gap of classification accuracy with respect to XLM/XLM-R/Unicoder. Finally, for sentence retrieval tasks, we find that XLM-15 and Unicoder are both trained on additional parallel data, outperforming the other existing models trained only on monolingual data. Using additional parallel data, {\scshape amber}\xspace with MLM and TLM objectives also significantly improves over {\scshape amber}\xspace with the MLM objective by 15.6 average accuracy score, while combining our word-level alignment objective yields a marginal improvement over {\scshape amber}\xspace with MLM and TLM objectives. However, adding the sentence-level alignment objective, {\scshape amber}\xspace trained by the combined objective can further improve {\scshape amber}\xspace with the MLM and word-level alignment objectives by 19.1 average accuracy score. This confirms our intuition that the explicit sentence-level objective can effectively leverage the alignment supervision in the parallel corpus, and encourage contextualized sentence representations of aligned pairs to be close according to the cosine similarity metric.

\begin{table}[]
\centering
\setlength{\tabcolsep}{2.0pt}
\resizebox{0.48\textwidth}{!}{%
\begin{tabular}{l|cccc}
\toprule
Model               & POS           & PAWS-X        & XNLI          & Tatoeba       \\ \midrule
mBERT (public)      & 68.5          & 86.2          & 65.4          & 45.6          \\
XLM-15              & 68.8          & 88.0          & 72.6          & \textbf{77.2} \\
XLM-100             & 69.5          & 86.4          & 69.1          & 36.6          \\
XLM-R-base          & 68.8          & 87.4          & 73.4          & 57.6          \\
XLM-R-large         & 70.0 & \textbf{89.4} & \textbf{79.2} & 60.6          \\
Unicoder            & \textbf{71.7} & 88.1          & 74.8          & 72.2 \\ \midrule
{\scshape amber}\xspace(MLM)           & 69.8          & 87.1          & 67.7          & 52.6          \\
{\scshape amber}\xspace(MLM+TLM)       & 70.5          & 87.7          & 70.9          & 68.2          \\
{\scshape amber}\xspace(MLM+TLM+WA)    & \textbf{71.1} & 89.0          & 71.3          & 68.8          \\
{\scshape amber}\xspace(MLM+TLM+WA+SA) & 70.5          & \textbf{89.2} & \textbf{71.6} & \textbf{87.9} \\ \bottomrule
\end{tabular}%
\vspace{-3mm}
}
\caption{Overall results on POS, PAWS-X, XNLI, Tatoeba tasks. Bold numbers highlight the highest scores across languages on the existing models (upper part) and {\scshape amber}\xspace variants (bottom part).}
\vspace{-5mm}
\label{tab:all-results}
\end{table}

\subsection{How does alignment help by language?}
In Figure~\ref{fig:lang}, we investigate the improvement of the alignment objectives over the MLM objective on low-resourced and high-resourced languages, by computing the performance difference between {\scshape amber}\xspace trained with alignment objectives and {\scshape amber}\xspace (MLM). First, we find that {\scshape amber}\xspace trained with alignment objectives significantly improves the performance on languages with relatively small amounts of parallel data, such as Turkish, Urdu, Swahili, while the improvement on high-resourced languages is marginal. Through a further analysis (Appendix~\ref{sec:detailed_results}), we observe that {\scshape amber}\xspace (MLM) performs worse on these low-resourced and morphologically rich languages than on high-resourced Indo-European languages, while {\scshape amber}\xspace trained with alignment objectives can effectively bridge the gap. Moreover, {\scshape amber}\xspace trained with our word-level alignment objective yields the highest improvement on these low-resourced languages on the POS task, and {\scshape amber}\xspace trained with sentence-level alignment performs the best on XNLI.

\subsection{Alignment with Attention vs Dictionary }
Recent studies~\cite{cao2020multilingual,wang2020crosslingual} have proposed to use a bilingual dictionary to align cross-lingual word representations. Compared with these methods, our word-level alignment objective encourages the model to automatically discover word alignment patterns from the parallel corpus in an end-to-end training process, which avoids potential errors accumulated in separate steps of the pipeline. Furthermore, an existing dictionary may not have all the translations for source words, especially for words with multiple senses. Even if the dictionary is relatively complete, it also requires a heuristic way to find the corresponding substrings in the parallel sentences for alignment. If we use a word alignment tool to extract a bilingual dictionary in a pipeline, errors may accumulate, hurting the accuracy of the model. Besides, \citet{wang2020crosslingual} is limited in aligning only fixed contextual embeddings from the model's top layer. Finally, we also compare {\scshape amber}\xspace trained with all the objectives and \citet{cao2020multilingual} on a subset of languages on XNLI in Table~\ref{tab:cao_xnli}. We find that our full model obtains a gain of 1.8 average F1 score.

\begin{table}[]
\centering
\setlength{\tabcolsep}{2.0pt}
\resizebox{0.48\textwidth}{!}{%
    \begin{tabular}{l|cccccc|c} \toprule
         Methods & en & bg & de & el & es & fr & Avg. \\ \midrule
         \citet{cao2020multilingual} & 80.1 & 73.4 & 73.1 & 71.4 & 75.5 & 74.5 & 74.7  \\
         {\scshape amber}\xspace (full)       & 84.7 & 74.3 & 74.2 & 72.5 & 76.9 & 76.6 & 76.5 \\ \bottomrule
    \end{tabular}}
    \caption{F1 scores of {\scshape amber}\xspace trained with all objectives and \citet{cao2020multilingual} on 6 languages on XNLI.}
    \label{tab:cao_xnli}
\end{table}

\begin{figure}
    \centering
    \includegraphics[width=\linewidth]{legend.png}
    \begin{subfigure}{0.238\textwidth}
        \includegraphics[width=\linewidth]{pos.pdf}
          \caption{Part-of-Speech}
          \label{fig:pos}
      \end{subfigure}
    \begin{subfigure}{0.237\textwidth}
        \includegraphics[width=\linewidth]{xnli.pdf}
          \caption{XNLI}
          \label{fig:xnli}
      \end{subfigure}
      \vspace{-0.35cm}
    \caption{Performance difference between {\scshape amber}\xspace trained with alignments on parallel data and {\scshape amber}\xspace (MLM). Languages are sorted by no.~of parallel data (Million) used for training {\scshape amber}\xspace with alignments.}
    \label{fig:lang}
    \vspace{-0.4cm}
\end{figure}

\section{Related Work}
\label{sec:related}
While cross-lingual alignment is a long-standing challenge dating back to the early stage of research in word alignment~\cite{brown-etal-1993-mathematics}, cross-lingual embeddings~\cite{faruqui2014improving,xing2015normalized,devlin2019bert,conneau2019unsupervised} are highly promising in their easy integration into neural network models for a variety of cross-lingual applications. Analysis studies on recent cross-lingual contextualized representations~\cite{pires2019multilingual,wu2019beto,hu2020xtreme,siddhant2019evaluating} further demonstrates this advantage for zero-shot cross-lingual transfer in a representative set of languages and tasks. In particular to improve cross-lingual transfer, some attempts directly leverage multilingual parallel corpus to train contextualized representations~\cite{mccann2017learned,eriguchi2018zero,conneau2019cross,huang2019unicoder} with the hope of aligning words implicitly. The other line of work uses word alignments from parallel corpora as the alignment supervision in a post-hoc fashion~\cite{cao2020multilingual,wang2020crosslingual}. Notably, {\scshape amber}\xspace does not rely on any word alignment tools, and explicitly encourage the correspondence both on the word and sentence level.

\section{Discussion and Future Work}
In this paper, we demonstrate the effectiveness of our proposed explicit alignment objectives in learning better cross-lingual representations for downstream tasks. Nonetheless, several challenging and promising directions can be considered in the future. First, most existing multilingual models tokenize words into subword units, which makes the alignment less interpretable. How to align a span of subword units with meaningful semantics at the phrase level deserves further investigation. Second, several studies~\cite{ghader-monz-2017-attention,li-etal-2019-word} have shown that attention may fail to capture word alignment for some language pairs, and a few works~\cite{legrand-etal-2016-neural,alkhouli-etal-2018-alignment} proposed neural word alignment to improve the word alignment quality. Incorporating such recent advances into the alignment objective is one future direction. Third, how to fine-tune a well-aligned multilingual model on English annotations without catastrophic forgetting of the alignment information is a potential way to improve cross-lingual generalization on the downstream applications.

\section*{Acknowledgements}
We'd like to thank Yinfei Yang and Wei-Cheng Chang for answering our questions on the data and code. JH and GN were supported in part by a Google Faculty Award, and NSF Award \#1761548.

redpajama_set_name: RedPajamaArXiv


We found more records about Ethel G. Manning.
Grave Site of Ethel G.
Ethel G. Manning is buried in the Aultorest Memorial Park at the location displayed on the map below. This GPS information is ONLY available at BillionGraves. Our technology can help you find the gravesite and other family members buried nearby.
Ethel G. Manning was 7 years old when Electrical engineer Nikola Tesla gives the first public demonstration of radio in St. Louis, Missouri. Nikola Tesla was a Serbian American inventor, electrical engineer, mechanical engineer, physicist, and futurist who is best known for his contributions to the design of the modern alternating current (AC) electricity supply system.
Ethel G. Manning was 19 years old when Albert Einstein publishes his first paper on the special theory of relativity. Albert Einstein was a German-born theoretical physicist who developed the theory of relativity, one of the two pillars of modern physics. His work is also known for its influence on the philosophy of science. He is best known to the general public for his mass–energy equivalence formula E = mc2, which has been dubbed "the world's most famous equation". He received the 1921 Nobel Prize in Physics "for his services to theoretical physics, and especially for his discovery of the law of the photoelectric effect", a pivotal step in the development of quantum theory.
Ethel G. Manning was 31 years old when Tsar Nicholas II of Russia was forced to abdicate in the February Revolution, ending three centuries of Romanov rule. Nicholas II or Nikolai II, known as Saint Nicholas in the Russian Orthodox Church, was the last Emperor of Russia, ruling from 1 November 1894 until his forced abdication on 15 March 1917. His reign saw the fall of the Russian Empire from one of the foremost great powers of the world to economic and military collapse. He was given the nickname Nicholas the Bloody or Vile Nicholas by his political adversaries due to the Khodynka Tragedy, anti-Semitic pogroms, Bloody Sunday, the violent suppression of the 1905 Russian Revolution, the executions of political opponents, and his perceived responsibility for the Russo-Japanese War (1904-1905). Soviet historians portray Nicholas as a weak and incompetent leader whose decisions led to military defeats and the deaths of millions of his subjects.
Ethel G. Manning was 43 years old when Babe Ruth becomes the first baseball player to hit 500 home runs in his career with a home run at League Park in Cleveland, Ohio. George Herman "Babe" Ruth Jr. was an American professional baseball player whose career in Major League Baseball (MLB) spanned 22 seasons, from 1914 through 1935. Nicknamed "The Bambino" and "The Sultan of Swat", he began his MLB career as a stellar left-handed pitcher for the Boston Red Sox, but achieved his greatest fame as a slugging outfielder for the New York Yankees. Ruth established many MLB batting records, including career home runs (714), runs batted in (RBIs) (2,213), bases on balls (2,062), slugging percentage (.690), and on-base plus slugging (OPS) (1.164); the latter two still stand as of 2018. Ruth is regarded as one of the greatest sports heroes in American culture and is considered by many to be the greatest baseball player of all time. In 1936, Ruth was elected into the Baseball Hall of Fame as one of its "first five" inaugural members.
Ethel G. Manning was 53 years old when World War II: Nazi Germany and Slovakia invade Poland, beginning the European phase of World War II. World War II, also known as the Second World War, was a global war that lasted from 1939 to 1945, although conflicts reflecting the ideological clash between what would become the Allied and Axis blocs began earlier. The vast majority of the world's countries—including all of the great powers—eventually formed two opposing military alliances: the Allies and the Axis. It was the most global war in history; it directly involved more than 100 million people from over 30 countries. In a state of total war, the major participants threw their entire economic, industrial, and scientific capabilities behind the war effort, blurring the distinction between civilian and military resources. World War II was the deadliest conflict in human history, marked by 50 to 85 million fatalities, most of whom were civilians in the Soviet Union and China. It included massacres, the genocide of the Holocaust, strategic bombing, premeditated death from starvation and disease and the only use of nuclear weapons in war.
Ethel G. Manning was 59 years old when World War II: Hiroshima, Japan is devastated when the atomic bomb "Little Boy" is dropped by the United States B-29 Enola Gay. Around 70,000 people are killed instantly, and some tens of thousands die in subsequent years from burns and radiation poisoning. World War II, also known as the Second World War, was a global war that lasted from 1939 to 1945, although conflicts reflecting the ideological clash between what would become the Allied and Axis blocs began earlier. The vast majority of the world's countries—including all of the great powers—eventually formed two opposing military alliances: the Allies and the Axis. It was the most global war in history; it directly involved more than 100 million people from over 30 countries. In a state of total war, the major participants threw their entire economic, industrial, and scientific capabilities behind the war effort, blurring the distinction between civilian and military resources. World War II was the deadliest conflict in human history, marked by 50 to 85 million fatalities, most of whom were civilians in the Soviet Union and China. It included massacres, the genocide of the Holocaust, strategic bombing, premeditated death from starvation and disease and the only use of nuclear weapons in war.
Ethel G. Manning was 71 years old when Space Race: Launch of Sputnik 1, the first artificial satellite to orbit the Earth. The Space Race refers to the 20th-century competition between two Cold War rivals, the Soviet Union (USSR) and the United States (US), for dominance in spaceflight capability. It had its origins in the missile-based nuclear arms race between the two nations that occurred following World War II, aided by captured German missile technology and personnel from the Aggregat program. The technological superiority required for such dominance was seen as necessary for national security, and symbolic of ideological superiority. The Space Race spawned pioneering efforts to launch artificial satellites, uncrewed space probes of the Moon, Venus, and Mars, and human spaceflight in low Earth orbit and to the Moon.


Dr. Catherine Hart Weber's latest release, Flourish: Discover the Daily Joy of Abundant, Vibrant Living offers women the tools necessary to move from a life of mere existence. She ushers us away from the fast-paced, no-time-for-me kind of life and implores us to take time to really examine our livelihood. Does it reflect the abundant life Christ came to give us as believers? In Flourish, Dr. Weber uses a Biblical centered psychological approach in teaching readers how to lay hold of more joy, peace, hope and fruitful relationships.
I found that the book was a slow read for me. Not because it was uninformative or disinteresting but rather the questions for reflection and other practical advice contained in the book made me want to take my time in digesting the material. Although the book is specifically stated to be for women, I think anyone could benefit from not just reading the book but applying it.
Did you know that you can actually spread and catch the emotions of the people around you, like you can catch a cold?
Couldn't we all think of someone, who has a way of letting the air out of our good day, when they come around coughing out complaints and criticisms?
Overindulging yourself in what may seem temporarily pleasurable can lead to unhappiness.
In current evangelical Christian life, often the opportunity to go deeper with God in the dark moments of our life is missed, mainly because people too quickly run away from dark times and try to grab hold of surface joys and comforts.
DISCLOSURE: I was provided an advance reading copy for review purposes by Bethany House Publishers. The views and opinions expressed are my own.
I'm getting a rare indulgence right now: Up early, so I'm reading Blogs with a coffee while the baby sleeps, and having a delightful time!
Complaints and criticism…like having a doctor repeatedly tell you what is wrong without ever offering a solution. Will make any day or diagnosis worse.
Sounds like a good one, Wanda. I like what Bluebutterfly wrote…so very true. I can so see how sometimes we are tempted to grab on to surface joys while experiencing dark times of trials. It's the "get me outta here" syndrome of human nature. But true joy runs way deeper. Thanks for sharing and for stopping by too.
Sounds lovely and encouraging, Wanda. Thanks!
Thanks for sharing – GOD BLESS!


Where Can You Find a Free Resume Format Online?
Several customizable resume templates can be found at LiveCareer and Resume Genius. Downloadable templates that can be edited in Microsoft Word or Office Online are available on Microsoft's website.
A resume should begin with a header that includes the applicant's name and contact information. This is followed by a brief summary of his education and work history as it pertains to the job he is applying to. An optional objective statement may also be included in which the applicant briefly discusses his work goals and what he is trying to accomplish. The resume may also contain sections for skills and abilities relevant to the position.
Where Can You Download a Free Template for a Basic Resume?
Are Free Resume Templates Available?
What Is the Correct Format for Writing a Resume?
Where Can You Find Free Resume Templates?
What Does a Stay-at-Home Mom Put on Her Resume?
What Is a 90-Day Business Plan for a Job Interview?
How Can You Apply for a Job at Lady Foot Locker?
What Is a Casting Call?
What Can You Learn From Free Basic Resume Samples?
Where Can You Find a Free Printable Resume Builder?
How Can You Get a Sample of a Resume for Free?


text:
Maurikios Maurikiou: In the show I will play I am paired with a man
Maurikios Maurikiou revealed that he had tried to participate in the past
At the "Eleftherios Venizelos" airport, Maurikios Maurikiou, a well-known music-theatre producer, was seen and proceeded to make statements.
He revealed that he was offered to participate in the theatrical performance "The cage with crazy people" and take the lead role.
"I don't know if he has accepted, but I know that an offer has been made to Kostas Fragolias to play my husband," added Maurikios Maurikiou.
Maurikios Maurikiou revealed that he had tried to participate in the past, but had not been accepted.
He also mentioned that he and Ilaira have sung songs given to them by the production, while he said that he is impressed that Fotini Darra and Panagiotis Triantafyllou always sing songs of the same category.
Source: news247.gr
Pique: Uploaded photo on Instagram with Clara Kia Marti
Survivor All Star Spoiler: Who leaves the game early
Nikos Xanthopoulos has "gone" at the age of 89
Christos Mastoras: She was dumped on his partner and he became furious – "Fool, aren't you a little ashamed?" (BINTEO)
Shakira: Record on YouTube made the song with the "shots" against Pique
The miracle of Saint Marina to the daughter of Christiana Aristotelous [VIDEO]
Giorgos Tsalikis: Touched a chandelier and ended up on a patron's head (VIDEO)
Dimitris Alexandrou: He showed us the first picture of his newborn son from the maternity ward


Q: Are there any new algorithms on the H9000?
The vast majority of the H8000's algorithms have been ported to the H9000 as well as all of the algorithms from our stompbox family (i.e. H9 Max) are included.
New FUTURE algorithms is the main reason we created the H9000. The decision to migrate from fixed point DSP to ARM was, in large part, driven by our conclusion that creating new ways to effect sound will be more efficient using the tools available for general purpose processors. The ARM platform also makes porting our plug-ins to the H9000 more efficient.
Q: Is "longdelay" supported in the H9000?
Q: Is there still a sampling facility?
Q: Will it support custom scales?
Q: Can user created sigfiles for the H8000 run on the H9000?
Yes, if you have the actual source sigfiles. If they are stored on the H8000, they are in a compiled format that we currently don't have a mechanism for extracting the source sigfile from.
Q: Does the H9000 Support Dolby Atmos?
Q: Does the H9000 support VSIG?
Q: Will emote support iOS?
Q: Will the VSIG version supported by the H9000 be backwards compatible with the H8000FW?
Q: Will VSIG be supported on Mac?
Q: Is there a "light" version of the H9000 planned with less processing power?
The H9000 is our flagship and is built on our platform for future variants.
Q: Will there be a dedicated hardware controller for the H9000 like Eve/Net?
Not planned. Our remote control program, emote, is a free download and is available as a stand alone app for Mac and (soon) Windows and as a plug-in.
Q: What is the product release schedule and pricing for the different H9000 models and the expansion cards?
H9000R priced at $4,999 and production is scheduled for early 2019.
Dante expansion card is available now for $999.
MADI expansion card is available now for $799.
Other expansion cards are planned.
Q: Will the H9000 have the potential to expand the number of outputs to 24?
Specifically, to accommodate surround sound formats up to 22.2? The expansion cards on the H9000 support up to 32 input and output channels, and the currently available H9000 expansion cards for Dante and MADI both support 32 channels. It is possible to install all 3 expansion cards and support 96 channels of audio. Each DSP module has access to 32 channels of audio.
Q: Will there be a ProTools expansion card?
We are considering offering a Pro Tools expansion card, but there are no current plans.
Q: Will there be a Ravenna expansion card?
We do plan to release a Ravenna expansion card.
Q: Will there be an AVB expansion card?
We do plan to support AES67.
Q: Does sending audio and control between DSP Engines work like it does on the Orville / H8000? Any restrictions on sending between Effects Chains?
You currently cannot route the output of one FX Chain into the input of another FX Chain. We are planning on adding this as a feature in a future update.
Q: Does the H9000 support mixing the output of various FX Chains to a stereo pair?
The current software release does not provide for mixing the outputs of FX Chains. As a result, two or more FX Chain outputs cannot be combined internally. You'd have to route the outputs to an external mixer. We are considering adding mixing functionality in a future release but that project is not scheduled.
Q: Does the H9000 have upgradeable DSP's?
The H9000's quad core ARM DSPs reside on modules allowing for possible upgrades as more powerful ARM processors become available.
Q: How do the H9000 ARM DSP processors compare to the H8000's DSP chips?
It's not exactly a one-to-one comparison because some of the H8000 algorithms are dual core; that is they require both of the DSP chips of the H8000 to run. On the H9000, a single ARM core can run nearly all of the algorithms of the H8000 except for ~20 of the dual core algorithms from the H8000.
Q: Does the H9000 support 192K (or higher) sample rates?
The H9000 currently supports 44.1K, 48K, 88.2K and 96K sample rates.
192K is currently not supported but the H9000's hardware supports it. We plan to add 192K support in a future software release.
The H9000s hardware doesn't support sample rates higher than 192K.
Q: How can I change the USB audio sample rate?
When switching to a new sample rate, you should first change the H9000's sample rate, then change the computer's sample rate. The audio will be muted if the sample rates don't match. If you switch the computer's sample rate first (e.g. by loading a session with a different sample rate), you will have to change the H9000's sample rate, then change the computer's sample rate again once the H9000's clock is locked to the new rate.
Q: Can the H9000 accommodate an instrument level signal?
No. The H8000 could accommodate a direct instrument connection via its pre-A/D boost/cut capability of +30 dB/-90 dB. The H9000 supports -10 dB / +4 dB. If you'd like to connect an instrument level signal directly to the H9000, you'll need a DI box.
A: 1. Download "USB Installer for H9000" and unzip the zip. 2. Copy the two files in the zip to the root folder of a USB drive formatted with the FAT32 file system. 3. Restart the H9000 with the check and x keys in the navigation key section pressed for 5 - 10 seconds. 4. The screen will be black for around a minute while the H9000 restarts in update mode. 5. Follow the instructions on the update screen to install the release.
Q: How is memory allocated between the four cores on an ARM chip? What are the user's responsibilities in managing this memory?
Each DSP module has its own memory which is shared between the 4 cores. Each algorithm allocates the memory it needs at load time, leaving the rest available to the others. The user doesn't have to do anything to manage the memory. However, you could potentially write a sigfile that would consume enough memory that it would not load under certain circumstances, or prevent other algorithms from loading. The H9000 has much more memory available to algorithms than previous products and we don't anticipate it being a significant limiting factor.
Q: What are the sonic consequences of migrating from fixed point DSP chips to floating point ARM processors?
Sound is everything and the arithmetic matters. The same algorithm running on a 24 bit fixed chip could, with some inputs and some parameter/variable values, give a different result when running on a 32 bit floating point chip. The H9000 development process took over 5 years in part because we made it a requirement that all of our effects, all of their presets ported from the H8000 to the H9000, would sound the same. The task of 'listening' to every algorithm with every potential parameter value was not humanly possible - at least not possible for a human listener. Our development team instead created an automated listener - a program that could run each algorithm, make detailed comparisons, and report the results. This automated listener program is ground-breaking and in fact, our developers published a paper and presented it at the audio DSP conference, DAFx 2017.
Q: What can we expect from the A/D converters in the H9000? How do they compare to the H8000's converters?
The H8000 analog specs are world-class, the H9000 is even a tad better. Click here to see the specs.
Q: How do I recover from a system error?
Restarting the H9000 while pressing and holding the SESSION and FXCHAINS switches will clear the H9000's current state. This will not affect your saved sessions or presets. Restarting the H9000 while pressing and holding the SESSION, FXCHAINS and ALGORITHMS switches will reset the H9000 to its factory state. This will delete any sessions or presets you might have saved to the H9000.
Q: Why is my USB audio muted?
It's likely there's a sample rate mismatch between your computer and the H9000.
When switching to a new sample rate, you should first change the H9000's sample rate, then change the computer's sample rate. The audio will be muted if the sample rates don't match.
If you switch the computer's sample rate first (e.g. by loading a session with a different sample rate), you will have to change the H9000's sample rate then change the computer's sample rate again once the H9000's clock is locked to the new rate.


1. C - an overview.
3. The disign and development of C programs.
6. More C statements and commands.
7. The C standard library.
Appendix A: The standard C library.
Appendix B: The MRH C library.
Appendix C: The Hisoft C library.
Appendix E: The Amstrad/Hisoft memory map.
Appendix F: The Spectrum/Hisoft memory map.
This page was last modified on 15 December 2010, at 15:34.


We had a blast at lawn bowling last weekend of the #NewinNOLA crew. We were on the neutral ground (New Orleans term for median) on Napoleon and Coliseum.
NewinNOLA's Chapman and Margaret were our fabulous hosts. We made new friends, learned the art of bocce and were introduced to the beer quiver. Good times.
We'll be back at a restaurant next time. Details to come soon. Hope you can join us.


To create partitions on a schema based XMLtype table, you have to define the partitions inside the XML schema by using the xdb:tableProps attribute.
Additionally the partition column name must be defined in capitals. If it is not in capital letters, a SQL name must be defined.
This example clearly demonstrates, IMHO, the advantages regarding maintenance, design and control for the created objects via a XML�Schema. Via such an XML�Schema the wanted DESIGN and PHYSICAL presentation of the wanted structures can be enforced and maintained in a more easy manner than via, most of the time, very complex manual SQL statements.
The mentioned example is based on an XMLType Object Relational storage table. This physical storage structure is very performant in situations where you XML is "Content Driven" and is a very XML structured in nature. Translated into "relational" definitions and context, these definitions could be explained as "the XML is almost relational in structure and its data access is always driven on parts of the info contained in the XML".
A disadvantage of such a XMLType table (object relationnal storage based) is that it is driven / depending on a XML�Schema (and therefore a trade off regarding CPU time etc for XML validation is in / can be in order) and the generated types, nested tables, varrays, etc need extra resources for maintenance. A lot of these "disadvantages" can be controled, managed via "xdb:annotations". These "xdb:annotations" give you the advantage to disable unwanted behavior, for instance, regarding, index creation, DOM validation, etc.
An XMLType Table based on Object Relational storage will deliver, if applied / designed correctly, you with great performance in the case of select or update XML requirements or XML Schema evolution, thanks to big improvements from 11gR1 and onwards.
Within a Oracle database you are able to model you XML storage and structures based on, not only Object Relational XML structures, but also CLOB (Basicfile / Securefile) based, Binary XML and or a "hybrid" form combining the advantages of Object Relational and CLOB based storage. So in other words you have a whole range of functionality to address your XML requirements from native XML up to shredded storage principles.


text:
Netflix Co-Founder Reed Hastings Stepping Down as CEO
Reed Hastings will transition from CEO of Netflix, the company he co-founded some 25 years ago, to executive chairman, according to a blog post from the company on Thursday.
Fairfax Media / Contributor
Reed Hastings in 2022.
"I'm so proud of our first 25 years, and so excited about our next quarter of a century. We can do so much more to better entertain the world and deliver more joy to our members," he wrote in the post.
Hastings was co-CEO along with Ted Sarandos. Former Chief Operating Officer Greg Peters will now be Sarandos' fellow co-CEO.
Hastings and Marc Randolph started brainstorming about their company in 1997, toying with the idea of competing with Blockbuster, but with DVDs, and delivering them by mail. The company went live in 1998.
Hastings was responsible for an array of strategic decisions that made the company the giant that it is today, per The Hollywood Reporter. Netflix went public in 2002 (at under $1 a share) and introduced streaming in 2007. Netflix reported some $7.85 billion in revenue in Q4, per CNBC.
Related: Why Netflix Co-founder Marc Randolph Doesn't Regret Stepping Down as CEO
Hastings wrote in the blog post that the transition has been happening behind the scenes for some time. Sarandos became his co-CEO in July 2020, and along with then COO Peters, the duo increasingly began to manage more of the company.
Hasting said Sarandos taking reins during the pandemic was a "baptism by fire," but that the company needs to continue to develop "a clear path to reaccelerate our revenue and earnings growth. So the board and I believe it's the right time to complete my succession," he wrote.
Related: Netflix Loses Subscribers For First Time In A Decade
Netflix enjoyed a boom at the beginning of the stay-at-home era but saw layoffs and drops in subscribers in 2022, as well as a much-depressed share price.
"I'll be serving as Executive Chairman, a role that founders often take (Jeff Bezos, Bill Gates, etc.) after they pass the CEO baton to others," Hastings wrote. "Even founders need to evolve."
Related Topics:business newsNetflixNews and TrendsOperations & Logistics
Former Disney CEO Has $20 Million 'Golden Parachute'
5 Ways to Tweak Your Business' Sales Process to Generate More Revenue


text:
Zsa Zsa Gabor Dies; Beloved Actress Was 99 Years Old
by Hilton Hater at December 18, 2016 9:24 pm .
Zsa Zsa Gabor, a Hungarian actress and socialite who some might consider to have been the original Kim Kardashian, died on Sunday afternoon of a heart attack.
She was 99 years old.
"They tried to save her," her husband, Frédéric Prinz von Anhalt, told NBC News of his wife's demise, adding:
"She was still breathing when they got here, but we ended up losing her. I am grateful that she died with no pain."
Gabor, who was set to turn 100 years old in February, had suffered through a number of physical ailments and health problems over the past few years.
This is understandable, of course, considering her advanced age.
In 2011, for example, Gabor had to have her right leg partially amputated due to an infection.
Her husband continued to hold birthday parties for Gabor over the past decade, but the long-time star was so sick that visitors were not permitted in her bedroom.
Zsa Zsa Gabor and her younger sister, Eva Gabor, were socialites in Beverly Hills for several years back in the day.
The late actress appeared in over 80 television shows and movies combined, often portraying herself toward the latter stages of her career.
She was married to Conrad Hilton, Paris Hilton's, grandfather, from 1942-1947.
Overall, Gabor was married an astonishing nine times.
As time progressed and the comparison to social media stars of this generation increased, those close to Gabor grew irate.
They pointed to her long and respected film career as evidence that she earned her fame and wealth via hard work and talent, which is more than many can say about Hilton, Kardashian and others.
"I hate it when there's been comparisons [of] when the Gabors were at their peak - quoted everywhere and in photos with kings and queens and international playboys - and then there's comparisons to the Kardashians," Zsa Zsa's publicist, Edward Lozzi, told NBC after his client's passing.
He added that these comparisons make him "sick."
Gabor was born in Budapest.. studied at a Swiss boarding school… was crowned Miss Hungary in 1936 … and sang and acted in Europe in the 1930s before arriving in Hollywood.
She portrayed singer Jane Avril, in 1952's Moulin Rouge, which was nominated for seven Academy Awards.
Over the final years of her life, the aforementioned health problems prompted Gabor to retreat from the spotlight; whether it was a severe car accident in 2002, a stroke in 2005 or a broken hip in 2010.
But she remained well known up until her final breath.
Celebrities Who Passed Away in 2016: Gone, But Not Forgotten
We send our condolences to the friends, family members and loved ones of Zsa Zsa Gabor.
Tags: R.I.P., Zsa Zsa Gabor
Harry Brant, Son of Stephanie Seymour, Dead at 24
Siegfried Fischbacher, Legendary Magician, Dead of Pancreatic Cancer
Jessica Campbell, Election and Freaks and Geeks Star, Dies at 38
R.I.P. Photos
R.I.P. Videos
Teen Passes Out in Shower, Drowns After Her Hair Clogs Drain
Kate Spade: Dead at 55
11 Celebrities Who Have Committed Suicide


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text:
The Amazing Lyrebird
Crazy:
Chinese Foot Binding
Oh, how crazy human culture can get...
Patriarchy in a nutshell. Stage Blue in a nutshell.
Does It Spark Joy?
The popular tidying guru, Marie Kondo, has a simple method for how to de-clutter your home. You hold an item in your hands — like an old sweater — and ask of it, "Does it spark joy?" If it does, you keep it. If it doesn't, you throw it away. In this way you minimize your material possessions to the most essential items.
But never mind de-cluttering your house — that's peanuts. The elephant in the room is asking this question of your life activities as a whole. Imagine designing a life in which most of the activities you do every day are ones that spark the greatest joy in you. Imagine what that would be for you. Now imagine a life where all the activities you do don't spark joy. Which life do you want?
Of course the problem is that most people have no clear idea of what activities spark the greatest joy for them. And the second problem is that they don't believe they can design a life which aligns with those. Well, you can figure both of those out. In fact you'll need to in order to have a good life. That's what my Life Purpose Course is designed to help you do.
But for now, just spend the next week asking yourself, "What sparks the greatest joy in my life?" Make a list. And then start to wonder how you can turn one of these activities into a career. That which sparks the greatest joy in you is a tiny kernel of the career you will one day build. For more specifics on how to go about this, see my Life Purpose Course.
The Evolution Of Samurai Castles
I like to study the evolution of things. You can study the evolution of virtually anything to gain deep insights about how reality works. It can be especially insightful to study the evolution of cultural human things, as it gives you insight into the nature of culture and human behavior.
Here's an excellent video about the evolution of Japanese castles. As you watch it, contemplate how and why the evolution is happening. What is it doing at the meta level? — so to speak. Notice the deep intelligence at the heart of evolution.
Beware Of The Toxic Life Purpose
Even though purpose is completely relative, arbitrary, and imaginary, the human creature needs purpose.
People who don't have a positive sense of life purpose will not just sit idly by enjoying life, they will get depressed and recruited and radicalized into a toxic life purpose.
A lot of the "evil" committed in the world is done by people who were not shown how to develop a positive and conscious sense of life purpose, so they adopted a toxic one. If you don't consciously develop a positive and loving life purpose, the default position will be that you get sucked into a selfish and unconscious one.
It happened with Hitler. It happens very often with white nationalists. It happens with Islamic and Christian fundamentalists. And it's now infected the American government:
Idle hands do the devil's work.
If you want to develop a positive and conscious life purpose, don't overlook my Life Purpose Course. It's over 25 hours of exclusive video content on how to do it. Don't become like the guy above.
On Solitude
"Ordinary men hate solitude. But the Master makes use of it, embracing his aloneness, realizing he is one with the whole universe."
Systemic Racism In Two Quotes
"The whole problem is really the blacks. The key is to devise a system that recognized this, while not appearing to."
-- Nixon's Chief of Staff, H.R. Haldeman
"You start out in 1954 by saying niger, niger, niger. By 1968 you can't say niger. That hurts you. Backfires. So you say stuff like 'forced busing', 'states rights', and all that stuff. You're getting so abstract now, you're talking about cutting taxes. And all these things you're talking about are totally economic things, and the byproduct of them is that blacks get hurt worse than whites."
-- Republican strategist, Lee Atwater
I would be interested to hear how conservatives mental-gymnastics their way out of this one.
The reason I love to study the conservative mind so much is that conservativism is a feat of such audacious denial, ignorance, selfishness, and self-deception that it rises to the level of performance art. It's like watching an exotic tropical bird performing an elaborate mating dance without a conscious clue of what he's actually doing and why. But he does it with such self-righteous confidence that it blinds him to how ridiculous he looks to onlookers.
The History Of Socialism In America
A good overview:
Note: I am not necessarily advocating for socialism. My purpose is to educate people and open their minds. Government is far too complex a topic to be boiled down to simplistic attitudes such as capitalism bad, socialism good — or vice versa. What's necessary is a deep, unbiased historical understanding of how government works.
Understanding The Alt-Right
Great documentary about Steve Bannon, architect of the alt-right and Trumpism:
Trumpism is a toxic mix of stage Red, Blue, and low Orange, with a massive Green shadow.
People who find the alt-right appealing are not merely Blue/Orange, the have a Red underbelly. Which is what makes the movement dangerous. It's fueled by anger, fear, vindictiveness, and greed. Which is why so many of Trump's lieutenants have been forced to step down due to allegations of domestic abuse, rape, bribery, tax evasion, corruption, misuse of public funds, sexism, racism, alcoholism, etc. That's because such people are precisely the sort who find strongmen and con-artists like Trump appealing. Because he's one of their kind, and so is basically any top Trump lieutenant. Because a conscious human who has transcended Red would find Trump's style abhorrent. But what Trump's top loyalists and fans find most appealing about him is that Red underbelly which cannot be satisfied by any normal career politician, left or right. Trump's scumminess is not a bug, it's a feature for people with a Red underbelly.
So that's how Trump manipulates his most loyal base: by tossing them Red meat. And that red meat is stage Green SJWs.
If you wanted to start a fascist movement in America, this is precisely how you'd do it.
Stage Green/Turquoise Video Game
You play as a potted flower in a city dreaming of the open flower fields.
The grassy gameplay is the flower's dream!
Beautiful game: