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null | false
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Are you going for a walk today?
|
I am definitely going for a walk today, the weather is finally good! These last raining days haven't been the best, but now with this shinning sun, it would be a shame to just stay at home!
|
|
null | false
| null |
Can you fish in the Kalamazoo river and lake in Allegan County?
|
Yes, the fishing in this waterway is very good. There are an abundance of species of fish available year round whether ice fishing, shore fishing, or using a boat. Large and small mouth bass, bluegill, perch, walleye, bowfin, steelhead, salmon, carp, muskie, northern pike, and catfish are all found swimming in these waters.
|
|
null | false
| null |
Provide a list of cities you drive through to get from San Diego to Phoenix
|
Alpine, El Centro, Yuma, Gila Bend
|
|
null | false
| 209
|
Language variety identification aims at labelling texts in a native language (e.g. Spanish, Portuguese, English) with their specific variation (e.g. Argentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). Although at first sight language variety identification may seem a classical text classification problem, cultural idiosyncrasies may influence the way users construct their discourse, the kind of sentences they build, the expressions they use or their particular choice of words. Due to that, we can consider language variety identification as a double problem of text classification and author profiling, where information about how language is shared by people may help to discriminate among classes of authors depending on their language variety.
This task is specially important in social media. Despite the vastness and accessibility of the Internet destroyed frontiers among regions or traits, companies are still very interested in author profiling segmentation. For example, when a new product is launched to the market, knowing the geographical distribution of opinions may help to improve marketing campaigns. Or given a security threat, knowing the possible cultural idiosyncrasies of the author may help to better understand who could have written the message.
Language variety identification is a popular research topic of natural language processing. In the last years, several tasks and workshops have been organized: the Workshop on Language Technology for Closely Related Languages and Language Variants @ EMNLP 2014; the VarDial Workshop @ COLING 2014 - Applying NLP Tools to Similar Languages, Varieties and Dialects; and the LT4VarDial - Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialect @ RANLP BIBREF0 BIBREF1 . We can find also several works focused on the task. In BIBREF2 the authors addressed the problem of identifying Arabic varieties in blogs and social fora. They used character $n$ -gram features to discriminate between six different varieties and obtained accuracies between 70%-80%. Similarly, BIBREF3 collected 1,000 news articles of two varieties of Portuguese. They applied different features such as word and character $n$ -grams and reported accuracies over 90%. With respect to the Spanish language, BIBREF4 focused on varieties from Argentina, Chile, Colombia, Mexico and Spain in Twitter. They used meta-learning and combined four types of features: i) character $n$ -gram frequency profiles, ii) character $n$ -gram language models, iii) Lempel-Ziv-Welch compression and iv) syllable-based language models. They obtained an interesting 60%-70% accuracy of classification.
We are interested in discovering which kind of features capture higher differences among varieties. Our hypothesis is that language varieties differ mainly in lexicographic clues. We show an example in Table 1 .
In this work we focus on the Spanish language variety identification. We differentiate from the previous works as follows: i) instead of $n$ -gram based representations, we propose a low dimensionality representation that is helpful when dealing with big data in social media; ii) in order to reduce the possible over-fitting, our training and test partitions do not share any author of instance between them; and iii) in contrast to the Twitter dataset of BIBREF4 , we will make available our dataset to the research community.
Due to that, we can consider language variety identification as a double problem of text classification and author profiling, where information about how language is shared by people may help to discriminate among classes of authors depending on their language variety.
|
What can we consider language variety identification as?
|
We can consider language variety identification as a double problem of text classification and author profiling.
|
1912.11602
| false
| null |
To compare with our model, we select a number of strong summarization models as baseline systems. $\textsc {Lead-X}$ uses the top $X$ sentences as a summary BIBREF19. The value of $X$ is 3 for NYT and CNN/DailyMail and 1 for XSum to accommodate the nature of summary length. $\textsc {PTGen}$ BIBREF4 is the pointer-generator network. $\textsc {DRM}$ BIBREF10 leverages deep reinforcement learning for summarization. $\textsc {TConvS2S}$ BIBREF2 is based on convolutional neural networks. $\textsc {BottomUp}$ BIBREF11 uses a bottom-up approach to generate summarization. ABS BIBREF26 uses neural attention for summary generation. DRGD BIBREF27 is based on a deep recurrent generative decoder. To compare with our pretrain-only model, we include several unsupervised abstractive baselines: SEQ$^3$ BIBREF28 employs the reconstruction loss and topic loss for summarization. BottleSum BIBREF23 leverages unsupervised extractive and self-supervised abstractive methods. GPT-2 BIBREF7 is a large-scaled pretrained language model which can be directly used to generate summaries.
To compare with our model, we select a number of strong summarization models as baseline systems. $\textsc {Lead-X}$ uses the top $X$ sentences as a summary BIBREF19.
$\textsc {DRM}$ BIBREF10 leverages deep reinforcement learning for summarization. $\textsc {TConvS2S}$ BIBREF2 is based on convolutional neural networks. $\textsc {BottomUp}$ BIBREF11 uses a bottom-up approach to generate summarization. ABS BIBREF26 uses neural attention for summary generation. DRGD BIBREF27 is based on a deep recurrent generative decoder. To compare with our pretrain-only model, we include several unsupervised abstractive baselines: SEQ$^3$ BIBREF28 employs the reconstruction loss and topic loss for summarization. BottleSum BIBREF23 leverages unsupervised extractive and self-supervised abstractive methods. GPT-2 BIBREF7 is a large-scaled pretrained language model which can be directly used to generate summaries.
|
What were the baselines?
|
The answers are shown as follows:
* $\textsc {Lead-X}$
* $\textsc {PTGen}$
* $\textsc {DRM}$
* $\textsc {TConvS2S}$
* $\textsc {BottomUp}$
* ABS
* DRGD
* SEQ$^3$
* BottleSum
* GPT-2
|
null | false
| null |
Stalemate is a situation in chess where the player whose turn it is to move is not in check and has no legal move. Stalemate results in a draw. During the endgame, stalemate is a resource that can enable the player with the inferior position to draw the game rather than lose. In more complex positions, stalemate is much rarer, usually taking the form of a swindle that succeeds only if the superior side is inattentive.[citation needed] Stalemate is also a common theme in endgame studies and other chess problems.
The outcome of a stalemate was standardized as a draw in the 19th century. Before this standardization, its treatment varied widely, including being deemed a win for the stalemating player, a half-win for that player, or a loss for that player; not being permitted; and resulting in the stalemated player missing a turn. Stalemate rules vary in other games of the chess family.
|
If I have more pieces at the time of stalemate, have I won?
|
No.
Stalemate is a drawn position. It doesn't matter who has captured more pieces or is in a winning position
|
null | false
| null |
Classify each of the following as a fast food or sit down restaurant: McDonalds, Subway, Chili's, Popeyes, Carrabbas, Domino's, Hooters
|
McDonalds: Fast Food Restaurant
Subway: Fast Food Restaurant
Chili's: Sit Down Restaurant
Popeyes: Fast Food Restaurant
Carrabbas: Sit Down Restaurant
Domino's: Fast Food Restaurant
Hooters: Sit Down Restaurant
|
|
null | false
| null |
When I ski my legs form an A-frame shape and it doesn't look very nice, how can I fix this?
|
A-frame can occur due to many reasons, this includes improper boot setup, for example, the cuff alignment is incorrect, or technique issues. It is best to consult an experienced boot fitter to ensure the boot is set up correctly. Once you've ensured the boot is set up correctly, it is likely a technique issue. A common cause of A-frame is a result of rotating the outside knee inside more than the inside knee, and this can happen due to wider skis or wanting to create a bigger edge angle. Ideally, we should avoid over-rotating the knee inside as this creates a weaker position and instead rely on moving the hip and upper body into the inside of the turn to create a bigger edge angle and try to keep the outside leg relatively straight.
A good exercise to practice this is the T-drill, where you try to make sure your outside leg is as straight as possible and create edge angle by leaning your whole body into the turn, you'll need to shorten your inside leg by bringing your need to your chest, which will then still allow you to balance mostly on your straight downhill leg.
|
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null | false
| null |
How will chatGPT and other large language models effect education and engineering disciplines?
|
It is too early to tell how technologies like chatGPT will change industries, but people think that some portion of jobs will be replaced in education and engineering. People also think that written tests and coding assignments for interviews will need to be refactored to reduce the chance that students or candidates can submit answers generated by large language models.
|
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null | false
| null |
Fourteen-year-old piano prodigy, Kōsei Arima, becomes famous after winning several music competitions. When his mother Saki dies, Kōsei has a mental breakdown while performing at a piano recital; this results in him becoming unable to hear the sound of his piano, even though his hearing is otherwise unaffected.
Two years later, Kōsei has not touched the piano and views the world in monochrome. He does not focus on excelling in any activities and often spends time with his friends Tsubaki Sawabe and Ryōta Watari. Kōsei meets Kaori Miyazono, an audacious, free-spirited, fourteen-year-old violinist whose playing style reflects her manic personality. Kaori helps Kōsei return to playing the piano, and shows him his playing style can be free and groundbreaking. As Kaori continues to uplift Kōsei's spirits, he quickly realizes he loves her, although she seems to only be interested in Ryōta.
During a performance, Kaori, who later explains that she is anemic and needs routine testing, collapses and is hospitalized. She is later diagnosed with Aplastic Anemia. She invites Kōsei to play with her at a gala but she does not arrive. Her health deteriorates and she becomes dejected. Kōsei plays a duet with a friend, which motivates Kaori to attempt a risky and potentially deadly surgery so she may possibly play with Kosei once more. While playing in the finals of the Eastern Japan Piano Competition, Kōsei sees Kaori's spirit accompanying him and realizes she has died during the surgery.
At her funeral, Kaori's parents give Kōsei a letter from Kaori that reveals she was aware of her impending death and became more free-spirited, both as a person and in her music, so she would not take her regrets to Heaven. She confesses she had been in love with Kōsei's piano playing since watching him perform at a concert when she was five. This inspired her to play the violin so she could play with him one day. Kaori fabricated her feelings towards Ryōta so she could get closer to Kōsei without hurting Tsubaki, who harbored affection for Kōsei. She then confesses her love for him. Tsubaki comforts Kōsei and tells him she will be by his side. Kaori also leaves behind a picture of herself as a child, coming back from the concert that inspired her, with Kōsei in the background. Kōsei later frames this picture.
|
What is Your Lie in April about?
|
Your Lie in April is a manga and an anime about friendship through music. The protagonist Kosei Arima is a very talented piano prodigy who goes through a mental breakdown from the pressure of performing and becomes deaf to the sound of his own piano. The world becomes monotonous and depressing until he meets Kaori Miyazono, a free-spirited violinist who reminds him of how to play music freely again. However, Kaori suffers from anemia and often has to go to the hospital. Despite this, the two often play together and Kosei falls in love. Kaori passes away soon after and leaves a letter for Kosei explaining how she loved him back and his piano playing. Kosei uses this as inspiration to get back into music and play freely once more.
|
null | false
| null |
What are the words of House Karstark?
|
"The Sun of Winter"
|
|
null | false
| null |
What type of weather is typically associated with the desert?
|
The desert is known for being very hot and very dry
|
|
null | false
| null |
What do players of American football wear helmets? Write the response as if your are an American explaining to a coworker from another country.
|
American football is a high impact sport where injuries such as concussions are very common. The helmet is meant to lesson the occurrence of traumatic brain injuries like concussions by providing padding to the skull. The helmet is hard shell on the outside with soft padding on the inside. Players are required to wear them anytime they are on the field of play. Helmets were originally made of leather but over the years improvements have been made to better protect players.
|
|
null | false
| null |
What is the California Alpine Club?
|
The California Alpine Club (CAC) is a membership club with overnight lodges on Mount Tamalpais in Mill Valley, CA and at Echo Lake, which is 20 minutes outside of South Lake Tahoe.
|
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null | false
| 339
|
For an ever increasing spectrum of applications (e.g., medical text analysis, opinion mining, sentiment analysis, social media text analysis, customer intelligence, fraud analytics etc.) mining and analysis of unstructured natural language text data is necessary BIBREF0, BIBREF1, BIBREF2.
One of key challenge while designing such text analytics (TA) applications is to identify right set of features. For example, for text classification problem, different sets of features have been considered in different works (spanning a history of more than twenty years) including `bag of words', `bag of phrases', `bag of n-grams', `WordNet based word generalizations', and `word embeddings' BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7. Even for recent end-to-end designs using deep neural networks, specification of core features remains manually driven BIBREF8, BIBREF9. During feature engineering, often data scientists manually determine which features to use based upon their experience and expertise with respect to the underlying application domain as well as state-of-the-art tools and techniques. Different tools (e.g., NLTK BIBREF10, Mallet BIBREF11, Stanford CoreNLP BIBREF12, Apache OpenNLP BIBREF13, Apache Lucene BIBREF14, etc.) available to a NLP data scientist for TA application design and development often differ in terms of support towards extraction of features, level of granularity at which feature extraction process is to be specified; and these tools often use different programing vocabularies to specify semantically equivalent features.
Currently, there is no generic method or approach, which can be applied during TA application's design process to define and extract features for any arbitrary application in an automated or semi-automated manner. Even there is no single way to express wide range of NLP features, resulting into increased efforts during feature engineering which has to start new for each data scientist and automated reuse of features across semantically similar or related applications designed by different data scientists is difficult. This also hinders foundational studies on NLP feature engineering including why certain features are more critical than others.
In this paper, we aim to present an approach towards automating NLP feature engineering. We start with an outline of a language for expressing NLP features abstracting over the feature extraction process, which often implicitly captures intent of the NLP data scientist to extract specific features from given input text. We next discuss a method to enable automated reuse of features across semantically related applications when a corpus of feature specifications for related applications is available. Proposed language and system would help achieving reduction in manual effort towards design and extraction of features, would ensure standardization in feature specification process, and could enable effective reuse of features across similar and/or related applications.
For an ever increasing spectrum of applications (e.g., medical text analysis, opinion mining, sentiment analysis, social media text analysis, customer intelligence, fraud analytics etc.) mining and analysis of unstructured natural language text data is necessary [1, 2, 3].
|
What is necessary for an ever-increasing spectrum of applications?
|
The mining and analysis of unstructured natural language text data is necessary.
|
1808.03430
| false
| null |
What datasets are used to evaluate the introduced method?
|
They used a dataset from Taobao which contained a collection of conversation records between customers and customer service staffs. It contains over five kinds of conversations,
including chit-chat, product and discount consultation, querying delivery progress and after-sales feedback.
|
|
1704.00253
| false
| null |
While the mixing strategy compensates for most of the gap between the Fr-De* and the Fr*-De (3.01 $\rightarrow $ 0.17) in the De $\rightarrow $ Fr case, the resulting PSEUDOmix still shows lower BLEU than the target-originated Fr-De* corpus. We thus enhance the quality of the synthetic examples of the source-originated Fr*-De data by further training its mother translation model (En $\rightarrow $ Fr). As illustrated in Figure 2 , with the target-originated Fr-De* corpus being fixed, the quality of the models trained with the source-originated Fr*-De data and PSEUDOmix increases in proportion to the quality of the mother model for the Fr*-De corpus. Eventually, PSEUDOmix shows the highest BLEU, outperforming both Fr*-De and Fr-De* data. The results indicate that the benefit of the proposed mixing approach becomes much more evident when the quality gap between the source- and target-originated synthetic data is within a certain range.
As presented in Table 6 , we observe that fine-tuning using ground truth parallel data brings substantial improvements in the translation qualities of all NMT models. Among all fine-tuned models, PSEUDOmix shows the best performance in all experiments. This is particularly encouraging for the case of De $\rightarrow $ Fr, where PSEUDOmix reported lower BLEU than the Fr-De* data before it was fine-tuned. Even in the case where PSEUDOmix shows comparable results with other synthetic corpora in the Pseudo Only scenario, it shows higher improvements in the translation quality when fine-tuned with the real parallel data. These results clearly demonstrate the strengths of the proposed PSEUDOmix, which indicate both competitive translation quality by itself and relatively higher potential improvement as a result of the refinement using ground truth parallel corpora.
While the mixing strategy compensates for most of the gap between the Fr-De* and the Fr*-De (3.01 $\rightarrow $ 0.17) in the De $\rightarrow $ Fr case, the resulting PSEUDOmix still shows lower BLEU than the target-originated Fr-De* corpus. We thus enhance the quality of the synthetic examples of the source-originated Fr*-De data by further training its mother translation model (En $\rightarrow $ Fr). As illustrated in Figure 2 , with the target-originated Fr-De* corpus being fixed, the quality of the models trained with the source-originated Fr*-De data and PSEUDOmix increases in proportion to the quality of the mother model for the Fr*-De corpus. Eventually, PSEUDOmix shows the highest BLEU, outperforming both Fr*-De and Fr-De* data.
As presented in Table 6 , we observe that fine-tuning using ground truth parallel data brings substantial improvements in the translation qualities of all NMT models. Among all fine-tuned models, PSEUDOmix shows the best performance in all experiments. This is particularly encouraging for the case of De $\rightarrow $ Fr, where PSEUDOmix reported lower BLEU than the Fr-De* data before it was fine-tuned. Even in the case where PSEUDOmix shows comparable results with other synthetic corpora in the Pseudo Only scenario, it shows higher improvements in the translation quality when fine-tuned with the real parallel data.
|
How many improvements on the French-German translation benchmark?
|
one
|
null | false
| null |
Governors/Chief Executive Officers
The following individuals have been appointed to serve as chief executive of the Commonwealth Bank of Australia, or precursor titles:
Order Name Title Term start Term end Time in office
1 Sir Denison Miller KCMG Governor June 1912 June 1923 11 years, 29 days
2 James Kell June 1923 October 1926 3 years, 122 days
3 Sir Ernest Riddle October 1926 February 1938 11 years, 121 days
4 Sir Harry Sheehan CBE March 1938 March 1941 3 years, 0 days
5 Hugh Traill Armitage CMG July 1941 December 1948 7 years, 183 days
6 Dr H. C. Coombs January 1949 January 1960 11 years, 0 days
7 Ernest Richardson CBE Managing Director January 1960 March 1965 5 years, 89 days
8 Sir Bede Callaghan CBE May 1965 August 1976 11 years, 92 days
9 Sir Ronald Elliott August 1976 August 1981 5 years, 0 days
10 Vern Christie AO August 1981 March 1987 5 years, 212 days
11 Donald Sanders AO CB March 1987 December 1990 3 years, 275 days
Chief Executive Officer January 1991 June 1992 1 year, 181 days
12 David Murray AO June 1992 September 2005 13 years, 92 days
13 Sir Ralph Norris KNZM September 2005 November 2011 6 years, 29 days
14 Ian Narev December 2011 April 2018 11 years, 119 days
15 Matt Comyn April 2018 incumbent 4 years, 355 days
|
Using the following text, extract the last 3 CEO's. Place them in a bullet list in order of current to least current.
|
* Matt Comyn - Current / Incumbent since April 2018
* Ian Narev - December 2011 to April 2018
* Sir Ralph Norris KNZM - September 2005 to November 2011
* David Murray AO - June 1992 to September 2005
|
null | false
| null |
Classify the following movies as either Comedy or Horror: Weekend at Bernie's, Scream, What Lies Beneath, Happy Gilmore, Cujo, Billy Madison, Kingpin, Carrie, Halloween, Encino Man, Misery
|
Weekend at Bernie's: Comedy
Scream: Horror
What Lies Beneath: Horror
Happy Gilmore: Comedy
Cujo: Horror
Billy Madison: Comedy
Kingpin: Comedy
Carrie: Horror
Halloween: Horror
Encino Man: Comedy
Misery: Horror
|
|
null | false
| null |
What is a Profit and loss account in finance?
|
A profit and loss account shows the money a business has earned from selling goods and
services, less the money spent on goods, services and overheads.
|
|
null | false
| null |
Why are people migrating in the US from the NE and Mid-West to the South and SE?
|
better weather year round
|
|
null | false
| null |
Compared to a human, categorize the following as fast or slow animals: sloth, cheetah, eagle, tortoise, hippo, slug, horse.
|
A sloth, tortoise and slug are all slower than a sprinting human. If traveling at top speed, a cheetah, eagle, hippo, and horse are all faster than a sprinting human.
|
|
null | false
| null |
Which of the following are weather events: rain, snow, sleet, sunshine, hail, tornadoes, hurricanes, trees, lightening, rocks?
|
Rain, snow, sleet, sunshine, hail, tornadoes, hurricanes, and lightening are weather events. Trees and rocks are not weather events.
|
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null | false
| null |
Field offices by region
The following list of all UNESCO Field Offices is organized geographically by UNESCO Region and identifies the members states and associate members of UNESCO which are served by each office.
Africa
Abidjan – National Office to Côte d'Ivoire
Abuja – National Office to Nigeria
Accra – Cluster Office for Benin, Côte d'Ivoire, Ghana, Liberia, Nigeria, Sierra Leone and Togo
Addis Ababa – Liaison Office with the African Union and with the Economic Commission for Africa
Bamako – Cluster Office for Burkina Faso, Guinea, Mali and Niger
Brazzaville – National Office to the Republic of the Congo
Bujumbura – National Office to Burundi
Dakar – Regional Bureau for Education in Africa and Cluster Office for Cape Verde, Gambia, Guinea-Bissau, and Senegal
Dar es Salaam – Cluster Office for Comoros, Madagascar, Mauritius, Seychelles and Tanzania
Harare – Cluster Office for Botswana, Malawi, Mozambique, Zambia and Zimbabwe
Juba – National Office to South Sudan
Kinshasa – National Office to the Democratic Republic of the Congo
Libreville – Cluster Office for the Republic of the Congo, Democratic Republic of the Congo, Equatorial Guinea, Gabon and São Tomé and Príncipe
Maputo – National Office to Mozambique
Nairobi – Regional Bureau for Sciences in Africa and Cluster Office for Burundi, Djibouti, Eritrea, Kenya, Rwanda, Somalia, South Sudan and Uganda
Windhoek – National Office to Namibia
Yaoundé – Cluster Office to Cameroon, Central African Republic and Chad
Arab States
Amman – National Office to Jordan
Beirut – Regional Bureau for Education in the Arab States and Cluster Office to Lebanon, Syria, Jordan, Iraq and Palestine
Cairo – Regional Bureau for Sciences in the Arab States and Cluster Office for Egypt and Sudan
Doha – Cluster Office to Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, United Arab Emirates and Yemen
Iraq – National Office for Iraq (currently located in Amman, Jordan)
Khartoum – National Office to Sudan
Manama – Arab Regional Centre for World Heritage
Rabat – Cluster Office to Algeria, Libya, Mauritania, Morocco and Tunisia
Ramallah – National Office to the Palestinian Territories
Asia and Pacific
See also: UNESCO Asia Pacific Heritage Awards
Almaty – Cluster Office to Kazakhstan, Kyrgyzstan, Tajikistan and Uzbekistan
Apia – Cluster Office to Australia, Cook Islands, Fiji, Kiribati, Marshall Islands, Federated States of Micronesia, Nauru, New Zealand, Niue, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu and Tokelau (Associate Member)
Bangkok – Regional Bureau for Education in Asia and the Pacific and Cluster Office to Thailand, Burma, Laos, Singapore and Vietnam
Beijing – Cluster Office to North Korea, Japan, Mongolia, the People's Republic of China and South Korea
Dhaka – National Office to Bangladesh
Hanoi – National Office to Vietnam
Islamabad – National Office to Pakistan
Jakarta – Regional Bureau for Sciences in Asia and the Pacific and Cluster Office to the Philippines, Brunei, Indonesia, Malaysia, and East Timor
Manila – National Office to the Philippines
Kabul – National Office to Afghanistan
Kathmandu – National Office to Nepal
New Delhi – Cluster Office to Bangladesh, Bhutan, India, Maldives and Sri Lanka
Phnom Penh – National Office to Cambodia
Tashkent – National Office to Uzbekistan
Tehran – Cluster Office to Afghanistan, Iran, Pakistan and Turkmenistan
Europe and North America
Brussels – Liaison Office to the European Union and its subsidiary bodies in Brussels
Geneva – Liaison Office to the United Nations in Geneva
New York City – Liaison Office to the United Nations in New York
Venice – Regional Bureau for Sciences and Culture in Europe
Latin America and the Caribbean
Carondelet Palace, Presidential Palace – with changing of the guards. The Historic Center of Quito, Ecuador, is one of the largest, least-altered and best-preserved historic centres in the Americas. This centre was, together with the historic centre of Kraków in Poland, the first to be declared World Heritage Site by UNESCO on 18 September 1978.
Brasília – National Office to Brazil
Guatemala City – National Office to Guatemala
Havana – Regional Bureau for Culture in Latin America and the Caribbean and Cluster Office to Cuba, Dominican Republic, Haiti and Aruba
Kingston – Cluster Office to Antigua and Barbuda, Bahamas, Barbados, Belize, Dominica, Grenada, Guyana, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Suriname and Trinidad and Tobago as well as the associate member states of British Virgin Islands, Cayman Islands, Curaçao and Sint Maarten
Lima – National Office to Peru
Mexico City – National Office to Mexico
Montevideo – Regional Bureau for Sciences in Latin America and the Caribbean and Cluster Office to Argentina, Brazil, Chile, Paraguay and Uruguay
Port-au-Prince – National Office to Haiti
Quito – Cluster Office to Bolivia, Colombia, Ecuador and Venezuela
San José – Cluster Office to Costa Rica, El Salvador, Guatemala, Honduras, Mexico, Nicaragua and Panama
Santiago de Chile – Regional Bureau for Education in Latin America and the Caribbean and National Office to Chile
|
Extract the names of the cities where UNESCO has field offices in Asia and Pacific. Separate them with a semi-colon
|
Almaty; Apia; Bangkok; Beijing; Dhaka; Hanoi; Islamabad; Jakarta; Manila; Kabul; Kathmandu; New Delhi; Phnom Penh; Tashkent; Tehran
|
2001.05467
| false
| null |
We employ several complementary metrics to capture different aspects of the model. The F1 results are shown in Table TABREF24. Among all single models, LFT performs the best, followed by MinAvgOut. RL is also comparable with previous state-of-the-art models VHRED (attn) and Reranking-RL. We think that this is because LFT exerts no force in pulling the model predictions away from the ground-truth tokens, but rather just makes itself aware of how dull each response is. Consequently, its responses appear more relevant than the other two approaches. Moreover, the hybrid model (last row) outperforms all other models by a large margin. One might expect that minimizing AVGOUT causes the models to move further away from the ground-truth tokens, so that it will hurt relevance. However, our F1 results show that as the responses become more diverse, they are more likely to include information more related and specific to the input contexts, which actually makes the model gain on both diversity and relevance. This will be further confirmed by the output examples in Table TABREF29.
Among all single models, LFT performs the best, followed by MinAvgOut. RL is also comparable with previous state-of-the-art models VHRED (attn) and Reranking-RL. We think that this is because LFT exerts no force in pulling the model predictions away from the ground-truth tokens, but rather just makes itself aware of how dull each response is. Consequently, its responses appear more relevant than the other two approaches. Moreover, the hybrid model (last row) outperforms all other models by a large margin.
|
Which one of the four proposed models performed best?
|
the hybrid model MinAvgOut + RL
|
null | false
| null |
Who is Santa Clause?
|
Santa Clause is known as Father Christmas or Saint Nicholas in Christian culture. According to the myth Santa Clause rides a slay and brings presents to all the children all over the world during Christmas every year. Santa Clause lives at the North Pole with the elves. When he is not busy delivering presents he and the elves are hard work at the North Pole making presents for the following year. Christian families let their kids believe this myth until they are at a reasoning age and can figure out for themselves that Santa Clause is not real.
|
|
null | false
| null |
Classify each of the following as either a natural hair color or a grocery store bakery brand: Hostess, blonde, brunette, Betty Crocker, Pilsbury, red, black, Entenmann's
|
Natural hair color: blonde, brunette, red, black
Grocery store bakery brands: Hostess, Betty Crocker, Pilsbury, Entenmann's
|
|
null | false
| null |
Name the largest cities in Canada
|
The largest cities in Canada by population are Toronto, Montreal and Vancouver. Toronto has a population of 5.6 million, Montreal has a population of 3.6 million and Vancouver has a population of 2.4 million.
|
|
null | false
| 203
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By means of cross-entropy, we can also visualize the relation between languages in the multilingual space. Figure FIGREF12 plots the interpolation results for two relatively dissimilar languages, English and German. As expected, once the language vector moves too close to the German one, model performance drops drastically.
More interesting results can be obtained if we interpolate between two language variants and compute cross-entropy of a text that represents an intermediate form. fig:eng-enm shows the cross-entropy of the King James Version of the Bible (published 1611), when interpolating between Modern English (1500–) and Middle English (1050–1500). The optimal point turns out to be close to the midway point between them.
By means of cross-entropy, we can also visualize the relation between languages in the multilingual space.
|
They can visualize the relationship between languages in the multilingual space by what?
|
Cross-entropy.
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Describe the perfect weekend trip to Boston, Massachusetts.
|
The best way to spend a weekend in Boston is walking the Freedom Trail in Downtown Boston. Seeing a Red Sox game at Fenway Park is practically required, and if visiting in the off-season, taking a tour of the stadium will suffice. You need to have a lobster roll at the Union Oyster House for Lunch. You should have a cannoli from Mike's Pastry in the North End for dessert as they are the best. Take the T from Government Center to the MFA stop to visit the Museum of Fine Arts. You should stay at the XV Beacon Hotel by the Boston Common. The Boston Commons is a lovely place to have a picnic as well.
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What are the names of the three largest United States Virgin Islands?
|
St. Croix, St. John, and St. Thomas.
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|
1909.01638
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Taking the idea of reducing cross-lingual supervision to the extreme, the latest CLWE developments almost exclusively focus on fully unsupervised approaches BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 , BIBREF27 , BIBREF28 , BIBREF29 , BIBREF30 : they fully abandon any source of (even weak) supervision and extract the initial seed dictionary by exploiting topological similarities between pre-trained monolingual embedding spaces. Their modus operandi can roughly be described by three main components: C1) unsupervised extraction of a seed dictionary; C2) a self-learning procedure that iteratively refines the dictionary to learn projections of increasingly higher quality; and C3) a set of preprocessing and postprocessing steps (e.g., unit length normalization, mean centering, (de)whitening) BIBREF31 that make the entire learning process more robust.
FLOAT SELECTED: Table 1: Configurations obtained by varying components C1, C2, and C3 used in our empirical comparison in §4.
FLOAT SELECTED: Figure 1: General unsupervised CLWE approach.
Model Configurations. Note that C2 and C3 can be equally used on top of any (provided) seed lexicon (i.e., $D^{(1)}$ := $D_0$ ) to enable weakly supervised learning, as we propose here. In fact, the variations of the three key components, C1) seed lexicon, C2) self-learning, and C3) preprocessing and postprocessing, construct various model configurations which can be analyzed to probe the importance of each component in the CLWE induction process. A selection of representative configurations evaluated later in § "Results and Discussion" is summarized in Table 1 .
three main components: C1) unsupervised extraction of a seed dictionary; C2) a self-learning procedure that iteratively refines the dictionary to learn projections of increasingly higher quality; and C3) a set of preprocessing and postprocessing steps (e.g., unit length normalization, mean centering, (de)whitening)
FLOAT SELECTED: Table 1: Configurations obtained by varying components C1, C2, and C3 used in our empirical comparison in §4.
FLOAT SELECTED: Figure 1: General unsupervised CLWE approach.
representative configurations evaluated later in § "Results and Discussion" is summarized in Table 1 .
|
What methods were used for unsupervised CLWE?
|
The answers are shown as follows:
* Unsupervised CLWEs. These methods first induce a seed dictionary $D^{(1)}$ leveraging only two unaligned monolingual spaces (C1). While the algorithms for unsupervised seed dictionary induction differ, they all strongly rely on the assumption of similar topological structure between the two pretrained monolingual spaces. Once the seed dictionary is obtained, the two-step iterative self-learning procedure (C2) takes place: 1) a dictionary $D^{(k)}$ is first used to learn the joint space $\mathbf {Y}^{(k)} = \mathbf {X{W}}^{(k)}_x \cup \mathbf {Z{W}}^{(k)}_z$ ; 2) the nearest neighbours in $\mathbf {Y}^{(k)}$ then form the new dictionary $D^{(k+1)}$ . We illustrate the general structure in Figure 1 .
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Who is the prime minister of Japan?
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The current prime minister of Japan is Fumio Kishida, who assumed office on the 29th of September 2021. He is also the president of the Liberal Democratic Party (LDP) of Japan. He was born on the 29th of July 1957.
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What is the smallest state in the USA?
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Rhode Island
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The 1998–99 NBA season was the Hawks' 50th season in the National Basketball Association, and 31st season in Atlanta. On March 23, 1998, the owners of all 29 NBA teams voted 27–2 to reopen the league's collective bargaining agreement, seeking changes to the league's salary cap system, and a ceiling on individual player salaries. The National Basketball Players Association (NBPA) opposed to the owners' plan, and wanted raises for players who earned the league's minimum salary. After both sides failed to reach an agreement, the owners called for a lockout, which began on July 1, 1998, putting a hold on all team trades, free agent signings and training camp workouts, and cancelling many NBA regular season and preseason games. Due to the lockout, the NBA All-Star Game, which was scheduled to be played in Philadelphia on February 14, 1999, was also cancelled. However, on January 6, 1999, NBA commissioner David Stern, and NBPA director Billy Hunter finally reached an agreement to end the lockout. The deal was approved by both the players and owners, and was signed on January 20, ending the lockout after 204 days. The regular season began on February 5, and was cut short to just 50 games instead of the regular 82-game schedule
|
Why was there a lockout during the 1998-1999 NBA season?
|
The players and owners failed to reach an agreement on salaries.
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Why do people like to travel?
|
There are many reasons that people like to travel and the motivation to travel is always unique to each person. The most common reasons are to explore globally famous landmarks, new cultures, different authentic food and to meet new people, which will expand the perspective of different cultures and their value systems.
Also, to unwind and relax from their day to busy schedules. Apart from that with the wide use of social media platforms people have more information and knowledge to get influence. People also travel for research and educational purposes to places such as museums, and historical landmarks.
Overall, traveling can be an enriching, relaxing, and fulfilling experience that will gather beautiful lifelong memories, experiences, and lessons.
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Following the summer break, teams begin training camps in late September. Training camps allow the coaching staff to evaluate players (especially rookies), scout the team's strengths and weaknesses, prepare the players for the rigorous regular season and determine the 12-man active roster (and a 3-man inactive list) with which they will begin the regular season. Teams have the ability to assign players with less than two years of experience to the NBA G League. After training camp, a series of preseason exhibition games are held. Preseason matches are sometimes held in non-NBA cities, both in the United States and overseas. The NBA regular season begins in the last week of October.
During the regular season, each team plays 82 games, 41 each home and away. A team faces opponents in its own division four times a year (16 games). Each team plays six of the teams from the other two divisions in its conference four times (24 games), and the remaining four teams three times (12 games). Finally, each team plays all the teams in the other conference twice apiece (30 games). This asymmetrical structure means the strength of schedule will vary between teams (but not as significantly as the NFL or MLB). Over five seasons, each team will have played 80 games against their division (20 games against each opponent, 10 at home, 10 on the road), 180 games against the rest of their conference (18 games against each opponent, 9 at home, 9 on the road), and 150 games against the other conference (10 games against each team, 5 at home, 5 on the road).
The NBA is also the only league that regularly schedules games on Christmas Day. The league has been playing games regularly on the holiday since 1947, though the first Christmas Day games were not televised until 1983–84. Games played on this day have featured some of the best teams and players. Christmas is also notable for NBA on television, as the holiday is when the first NBA games air on network television each season. Games played on this day have been some of the highest-rated games during a particular season.
In February, the regular season pauses to celebrate the annual NBA All-Star Game. Fans vote throughout the United States, Canada, and on the Internet, and the top vote-getters in each conference are named captains. Fan votes determine the rest of the allstar starters. Coaches vote to choose the remaining 14 All-Stars. Then, the top vote-getters in each conference draft their own team from a player pool of allstars. The top vote-getter in the league earns first pick and so forth. The player with the best performance during the game is rewarded with a Game MVP award. Other attractions of the All-Star break include the Rising Stars Challenge (originally Rookie Challenge), where the top rookies and second-year players in the NBA play in a 5-on-5 basketball game, with the current format pitting U.S. players against those from the rest of the world; the Skills Challenge, where players compete to finish an obstacle course consisting of shooting, passing, and dribbling in the fastest time; the Three Point Contest, where players compete to score the highest number of three-point field goals in a given time; and the NBA Slam Dunk Contest, where players compete to dunk the ball in the most entertaining way according to the judges. These other attractions have varying names which include the names of the various sponsors who have paid for naming rights.
Shortly after the All-Star break is the trade deadline, which is set to fall on the 16th Thursday of the season (usually in February) at 3 pm Eastern Time. After this date, teams are not allowed to exchange players with each other for the remainder of the season, although they may still sign and release players. Major trades are often completed right before the trading deadline, making that day a hectic time for general managers.
Around the middle of April, the regular season ends. It is during this time that voting begins for individual awards, as well as the selection of the honorary, league-wide, postseason teams. The Sixth Man of the Year Award is given to the best player coming off the bench (must have more games coming off the bench than actual games started). The Rookie of the Year Award is awarded to the most outstanding first-year player. The Most Improved Player Award is awarded to the player who is deemed to have shown the most improvement from the previous season. The Defensive Player of the Year Award is awarded to the league's best defender. The Coach of the Year Award is awarded to the coach that has made the most positive difference to a team. The Most Valuable Player Award is given to the player deemed the most valuable for (his team) that season. Additionally, Sporting News awards an unofficial (but widely recognized) Executive of the Year Award to the general manager who is adjudged to have performed the best job for the benefit of his franchise.
The postseason teams are the All-NBA Team, the All-Defensive Team, and the All-Rookie Team; each consists of five players. There are three All-NBA teams, consisting of the top players at each position, with first-team status being the most desirable. There are two All-Defensive teams, consisting of the top defenders at each position. There are also two All-Rookie teams, consisting of the top first-year players regardless of position.
|
Summarize the main milestones of an NBA season
|
An NBA season starts with training camp in September, which is followed by preseason during which teams play several exhibition games. In the last week of October, the regular season starts. Individual teams play a total of 82 games over the course of the roughly six months the regular season lasts. In February is the All-Star break in which a set of players, voted for by fans and coaches, compete. The All-Star break is followed by the trade deadline after which trades between teams are not possible anymore. The regular season ends around mid April.
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Clustering multi-lingual documents has received much attention in natural language processing. Among approaches not using some form of machine translation, one can mention BIBREF1 , who essentially employ a bilingual dictionary to bring some words in the documents to a language-independent form and then to perform clustering. In the section "Experiment Design and Evaluation" we show that our approach based on neural embeddings significantly outperforms their reported results.
BIBREF2 proposed training joint multi-lingual neural embedding models. Theoretically, this can be used to achieve our aim of language-independent semantic representations for documents. Unfortunately, it demands a large word-aligned parallel corpus. This is not the case with the more recent Trans-gram approach introduced in BIBREF3 , also able to learn multi-lingual models. However, it still needs sentence-aligned corpora to train on (in the size of millions of paired sentences). Large parallel corpora (whether word- or sentence-aligned) are often a scarce resource, especially in the case of under-represented languages.
The approach described in this paper takes as an input only comparable monolingual corpora and bilingual dictionaries in the size of several thousand word pairs. Such resources are much easier to find and evaluate. We employ the idea of learning a linear transformation matrix to map or project word embeddings from the semantic space of one language to that of another. This idea was first proposed in BIBREF4 , who applied it to lexical translation between English, Spanish, Czech and Vietnamese. We extend it from continuous representations of single words or collocations to `semantic fingerprints' of documents as a whole.
We employ the idea of learning a linear transformation matrix to map or project word embeddings from the semantic space of one language to that of another. This idea was first proposed in (Mikolov et al., 2013a), who applied it to lexical transla tion between English, Spanish, Czech and Vietnamese.
|
Is the idea of learning a linear transformation matrix to map or project word embeddings from the semantic space of one language to that of another first proposed by the authors?
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No.
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List five titles by Margaret Atwood.
|
The Handmaid's Tale, The Testaments, The Edible Woman, Oryx and Crake, The Year of the Flood
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Is wolf a domestic or a wild animal?
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Generally speaking, wolves are regarded as wild animals. While there are cases where wolves have been domesticated, they're not the same as dogs as pets.
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| 62
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koehn-knowles:2017:NMT showed that NMT is unable to handle low-resource language pairs as opposed to PBSMT. Transfer learning approaches BIBREF5 , BIBREF6 , BIBREF7 work well when a large helping parallel corpus is available. This restricts one of the source or the target languages to be English which, in our case, is not possible. Approaches involving bi-directional NMT modeling is shown to drastically improve low-resource translation BIBREF11 . However, like most other, this work focuses on translation from and into English.
Remaining options include (a) unsupervised MT BIBREF12 , BIBREF13 , BIBREF14 , (b) parallel sentence mining from non-parallel or comparable corpora BIBREF15 , BIBREF16 , (c) generating pseudo-parallel data BIBREF17 , and (d) MT based on pivot languages BIBREF10 . The linguistic distance between Japanese and Russian makes it extremely difficult to learn bilingual knowledge, such as bilingual lexicons and bilingual word embeddings. Unsupervised MT is thus not promising yet, due to its heavy reliance on accurate bilingual word embeddings. Neither does parallel sentence mining, due to the difficulty of obtaining accurate bilingual lexicons. Pseudo-parallel data can be used to augment existing parallel corpora for training, and previous work has reported that such data generated by so-called back-translation can substantially improve the quality of NMT. However, this approach requires base MT systems that can generate somewhat accurate translations. It is thus infeasible in our scenario, because we can obtain only a weak system which is the consequence of an extremely low-resource situation. MT based on pivot languages requires large in-domain parallel corpora involving the pivot languages. This technique is thus infeasible, because the in-domain parallel corpora for Ja INLINEFORM0 En and Ru INLINEFORM1 En pairs are also extremely limited, whereas there are large parallel corpora in other domains. Section SECREF4 empirically confirms the limit of these existing approaches.
Fortunately, there are two useful transfer learning solutions using NMT: (e) multilingual modeling to incorporate multiple language pairs into a single model BIBREF8 and (f) domain adaptation to incorporate out-of-domain data BIBREF9 . In this paper, we explore a novel method involving step-wise fine-tuning to combine these two methods. By improving the translation quality in this way, we can also increase the likelihood of pseudo-parallel data being useful to further improve translation quality.
Fortunately, there are two useful transfer learning solutions using NMT: (e) multilingual modeling to incorporate multiple language pairs into a single model (Johnson et al., 2017) and (f) domain adaptation to incorporate out-of-domain data (Chu et al., 2017).
|
What are two useful migration models for using NMT?
|
(e) multilingual modeling to incorporate multiple language pairs into a single model and (f) domain adaptation to incorporate out-of-domain data.
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Classify each of the following as characters who appear in books by Kevin Kwan or Veronica Roth: Astrid Leong-Teo, Kitty Pong, Colin Khoo, Peik Lin Goh, Charlie Wu, Caleb Prior, Beatrice Prior, Tori, Jeanine Matthews, Tobias Eaton
|
Kevin Kwan characters: Astrid Leong-Teo, Kitty Pong, Colin Khoo, Peik Lin Goh, Charlie Wu
Veronica Roth characters: Caleb Prior, Beatrice Prior, Tori, Jeanine Matthews, Tobias Eaton
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Historians who address the origins of the American Civil War today agree that the preservation of slavery in the United States was the principal aim of the 11 Southern states (seven states before the onset of the war and four states after the onset) that declared their secession from the United States (the Union) and united to form the Confederate States of America (known as the "Confederacy"). However, while historians in the 21st century agree on the centrality of the conflict over slavery—it was not just "a cause" of the war but "the cause"—they disagree sharply on which aspects of this conflict (ideological, economic, political, or social) were most important, and on the North’s reasons for refusing to allow the Southern states to secede. Proponents of the pseudo-historical Lost Cause ideology have denied that slavery was the principal cause of the secession, a view that has been disproven by the overwhelming historical evidence against it, notably the seceding states' own secession documents.
|
What were the origins of the American Civil War?
|
Many historians agree that the issue of slavery was the main cause of the American Civil War. A total of eleven southern states wanted to preserve slavery and as a result voted to secede from the United States. These states subsequently declared themselves the Confederate States of America, also known as the Confederacy. Also most historians agree that slavery was the cause of the war, they have differing views regarding which aspects (idealogical, economic, political, or social) were most important. Their opinions also differ regarding the Northern States reasons for not allowing the Southern states to succeed from the Union. Followers of an ideology known as the Lost Cause Idealogy have denied that slavery was the root cause of the war however this view has been disproven by overwhelming evidence, including the Southern states own secession documents.
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If 2 carpenters can build 4 chairs in 3 hours, then how many chairs can 6 carpenters build in 6 hours?
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If 2 carpenters can build 4 chairs in 3 hours, then a carpenter can build 1 chair in 1.5 hours. 1 carpenter can build 4 chairs in 6 hours, and 6 carpenters can build 24 chairs in 6 hours.
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Identify which instrument is string or percussion: Yuka, Molo
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Molo is string, Yuka is percussion.
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| 214
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The growing popularity of online interactions through social media has been shown to have both positive and negative impacts. While social media improves information sharing, it also facilitates the propagation of online harassment, including hate speech. These negative experiences can have a measurable negative impact on users. Recently, the Pew Research Center BIBREF0 reported that “roughly four-in-ten Americans have personally experienced online harassment, and 63% consider it a major problem.”
To address the growing problem of online hate, an extensive body of work has focused on developing automatic hate speech detection models and datasets BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8. However, simply detecting and blocking hate speech or suspicious users often has limited ability to prevent these users from simply turning to other social media platforms to continue to engage in hate speech as can be seen in the large move of individuals blocked from Twitter to Gab BIBREF9. What's more, such a strategy is often at odds with the concept of free speech. As reported by the Pew Research Center BIBREF0, “Despite this broad concern over online harassment, 45% of Americans say it is more important to let people speak their minds freely online; a slightly larger share (53%) feels that it is more important for people to feel welcome and safe online.” The special rapporteurs representing the Office of the United Nations High Commissioner for Human Rights (OHCHR) have recommended that “The strategic response to hate speech is more speech.” BIBREF10 They encourage to change what people think instead of merely changing what they do, so they advocate more speech that educates about cultural differences, diversity, and minorities as a better strategy to counter hate speech.
Therefore, in order to encourage strategies of countering online hate speech, we propose a novel task of generative hate speech intervention and introduce two new datasets for this task. Figure FIGREF5 illustrates the task. Our datasets consist of 5K conversations retrieved from Reddit and 12k conversations retrieved from Gab. Distinct from existing hate speech datasets, our datasets retain their conversational context and introduce human-written intervention responses. The conversational context and intervention responses are critical in order to build generative models to automatically mitigate the spread of these types of conversations.
To summarize, our contributions are three-fold:
We introduce the generative hate speech intervention task and provide two fully-labeled hate speech datasets with human-written intervention responses.
Our data is collected in the form of conversations, providing better context.
The two data sources, Gab and Reddit, are not well studied for hate speech. Our datasets fill this gap.
Due to our data collecting strategy, all the posts in our datasets are manually labeled as hate or non-hate speech by Mechanical Turk workers, so they can also be used for the hate speech detection task. The performance of commonly-used classifiers on our datasets is shown in Section SECREF6.
Therefore, in order to encourage strategies of countering online hate speech, we propose a novel task of generative hate speech intervention and introduce two new datasets for this task.
|
How many datasets did the authors introduce in this paper?
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Two.
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What are some good body weight exercises for core and abs?
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Good body weight exercises that strengthen the core and abs are crunches, bicycle crunches, side v-ups, sit ups, planks, dead bugs, and wheel pose.
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The PGA Championship (often referred to as the US PGA Championship or USPGA outside the United States) is an annual golf tournament conducted by the Professional Golfers' Association of America. It is one of the four men's major championships in professional golf.
|
What is the PGA Championship?
|
The PGA Championship (often referred to as the US PGA Championship or USPGA outside the United States) is an annual golf tournament conducted by the Professional Golfers' Association of America. It is one of the four men's major championships in professional golf.
It was formerly played in mid-August on the third weekend before Labor Day weekend, serving as the fourth and final men's major of the golf season. Beginning in 2019, the tournament is played in May on the weekend before Memorial Day, as the season's second major following the Masters Tournament in April. It is an official money event on the PGA Tour, European Tour, and Japan Golf Tour, with a purse of $11 million for the 100th edition in 2018.
In line with the other majors, winning the PGA gains privileges that improve career security. PGA champions are automatically invited to play in the other three majors (Masters Tournament, U.S. Open, and The Open Championship) and The Players Championship for the next five years, and are eligible for the PGA Championship for life. They receive membership on the PGA Tour for the following five seasons and on the European Tour for the following seven seasons. The PGA Championship is the only one of the four majors that is exclusively for professional players.
The PGA Championship has been held at various venues. Some of the early sites are now quite obscure, but in recent years, the event has generally been played at a small group of celebrated courses.
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The September 11 attacks, commonly known as 9/11, were four coordinated suicide terrorist attacks carried out by the militant Islamist extremist network al-Qaeda against the United States on September 11, 2001. That morning, nineteen terrorists hijacked four commercial airliners scheduled to travel from the East Coast to California. The hijackers crashed the first two planes into the Twin Towers of the World Trade Center in New York City and the third into the Pentagon (the headquarters of the United States military) in Arlington County, Virginia, near Washington, D.C. The fourth plane was similarly intended to hit a federal government building in D.C., but crashed in a field following a passenger revolt. The attacks killed nearly 3,000 people and instigated the global war on terror.
|
What happened on September 11, 2001?
|
The September 11 attacks, commonly known as 9/11, were four coordinated suicide terrorist attacks carried out by the militant Islamist extremist network al-Qaeda against the United States on September 11, 2001. That morning, nineteen terrorists hijacked four commercial airliners scheduled to travel from the East Coast to California. The hijackers crashed the first two planes into the Twin Towers of the World Trade Center in New York City and the third into the Pentagon (the headquarters of the United States military) in Arlington County, Virginia, near Washington, D.C. The fourth plane was similarly intended to hit a federal government building in D.C., but crashed in a field following a passenger revolt. The attacks killed nearly 3,000 people and instigated the global war on terror.
The first impact was that of American Airlines Flight 11. It was crashed into the North Tower of the World Trade Center complex in Lower Manhattan at 8:46 a.m. Seventeen minutes later, at 9:03, the World Trade Center's South Tower was hit by United Airlines Flight 175. Both 110-story skyscrapers collapsed within an hour and forty-one minutes, bringing about the destruction of the remaining five structures in the WTC complex, as well as damaging or destroying various other buildings surrounding the towers. A third flight, American Airlines Flight 77, crashed into the Pentagon at 9:37 a.m., causing a partial collapse. The fourth and final flight, United Airlines Flight 93, flew in the direction of Washington, D.C. Alerted to the previous attacks, the plane's passengers attempted to gain control of the aircraft, but the hijackers ultimately crashed the plane in a field in Stonycreek Township, Pennsylvania, near Shanksville at 10:03 a.m. Investigators determined that Flight 93 target was either the United States Capitol or the White House.
Within hours of the attacks, the Central Intelligence Agency determined that al-Qaeda was responsible. The United States formally responded by launching the war on terror and invading Afghanistan to depose the Taliban, which had not complied with U.S. demands to expel al-Qaeda from Afghanistan and extradite its leader, Osama bin Laden. The U.S.'s invocation of Article 5 of the North Atlantic Treaty—its only usage to date—called upon allies to fight al-Qaeda. As U.S. and NATO ground forces swept through Afghanistan, bin Laden fled to the White Mountains, where he narrowly avoided capture by U.S.-led forces. Although bin Laden initially denied any involvement, in 2004 he formally claimed responsibility for the attacks. Al-Qaeda's cited motivations included U.S. support of Israel, the presence of U.S. troops in Saudi Arabia, and sanctions against Iraq. After evading capture for almost a decade, bin Laden was killed by the U.S. military on May 2, 2011. U.S. and NATO troops remained in Afghanistan until 2021.
The attacks resulted in 2,977 non-hijacker fatalities, an indeterminate number of injuries, and substantial long-term health consequences, in addition to at least $10 billion in infrastructure and property damage. It remains the deadliest terrorist attack in human history as well as the single deadliest incident for firefighters and law enforcement officers in U.S. history, with 340 and 72 killed, respectively. The destruction of the World Trade Center and its environs seriously harmed the New York City economy and induced global market shocks. Many other countries strengthened anti-terrorism legislation and expanded their powers of law enforcement and intelligence agencies. Cleanup of the World Trade Center site (colloquially "Ground Zero") took eight months and was completed in May 2002, while the Pentagon was repaired within a year. After delays in the design of a replacement complex, the One World Trade Center began construction in November 2006 and opened in November 2014. Memorials to the attacks include the National September 11 Memorial & Museum in New York City, the Pentagon Memorial in Arlington County, Virginia, and the Flight 93 National Memorial at the Pennsylvania crash site.
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What does the acronym MEDDPICC stand for?
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MEDDPICC is a sales acronym that stands for Metric, Economic Buyer, Decision Criteria, Decision Process, Paper Process, Identified Pain, Champion and Competition. MEDDPICC is leveraged as a methodology and can be applied to any complex sales process. Sales Managers will use MEDDPICC to inspect deals to determine if the deal is committable for a particular quarter.
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What makes the world go around?
|
Some say money makes the world go around, others say it is love. It has even been suggested that music is the fuel that makes the world go around. These explanations are however based on human spirituality and needs. As far as other living creatures are concerned, the drive to survive is what makes the world go around. In reality the world physically started going around itself, rotating around its axis, because of the way it was formed.
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Vettel started his Formula One career as a test driver for BMW Sauber in 2006, making a one-off racing appearance in 2007. As part of the Red Bull Junior Team, Vettel appeared for Toro Rosso later that year and was kept as a full-time driver for 2008, where he scored a victory in the Italian Grand Prix to become the then-youngest race winner. Vettel was promoted to Red Bull in 2009. With Red Bull, Vettel won four consecutive titles from 2010 to 2013, the first of which made him the sport's youngest World Champion. In 2013, he set the record for the most consecutive race wins with nine. Vettel signed for Ferrari for 2015 replacing Fernando Alonso and became Mercedes' and Lewis Hamilton's closest challenger in two title fights in 2017 and 2018, although he finished both years as runner-up. He parted ways with Ferrari at the end of the 2020 season to race with Aston Martin for the 2021 and 2022 seasons, before retiring from Formula One at the end of the 2022 season.
|
Extract the names of the Formula One teams Vettel was a part of from the text. Separate them with a comma.
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BMW Sauber, Toro Rosso, Red Bull, Ferrari, Aston Martin
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Are electric vehicles better for the environment than cars powered by fossil fuels?
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The rise of electric vehicles (or EVs) has been fueled by many factors -- including innovation, acceleration, design and tax breaks -- but the underlying assumption that electric vehicles are less harmful to the environment and may even mitigate global warming has been a primary motivator for hundreds of thousands of buyers.
Despite the virtuous aura of owning an electric vehicle, the jury is still out on whether EVs have a net positive impact on the environment or will do anything to curtail climate change. That's because, while the experience of driving an EV may feel like a zero-compromise embrace of green energy, the vast industrial network needed to build and power EVs diminishes the halo effect. On the other hand, the fossil fuels that power traditional vehicles are definitely contributing to global warming and leave the world vulnerable to oil shocks and pressure from oil-producing countries.
Whether EVs are better or worse for the environment is still to be determined. But a more insidious trend is the growing size and power of electric vehicles. Smaller vehicles, traveling fewer miles, represents a proven formula for reducing emissions and reversing climate change. But already, we're seeing the advent of electric trucks and SUVs, which are heavier, loaded with bells and whistles so they require more and more power to move people around. Coupled with the problem of drivers feeling like their EV gives them the right to drive as much as they want and you can see a reality emerging where the problem of Americans driving giant gas guzzlers is being replaced with American driving giant, power-sucking EVs. And that's leading us even faster down the road to environmental disaster.
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is customer support a great career
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yes
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| 407
|
In this section, we discuss the properties of the proposed method, specifically the computational complexity and the stabilization.
In the proposed method, we use neural networks to represent models and estimate them by optimizing their parameters using stochastic gradient descent methods. Since each neural network represents a simple scalar function, it is unnecessary to be a complex design. Therefore, the train-ing time is shorter than the general deep models such as image recognition and language processing.
On the other hand, the number of estimations grows exponentially as the number of variables increases. Given n variables, the identification of causal order requires n − 1 iterations. In each iteration, we compare k models for the remaining k variables. Therefore, a total of n + (n − 1) + • • • + 2 = n(n − 1)/2 − 1 model estimations are required, which is the same as RESIT and DirectLiNGAM. Additionally, the proposed method trains one model several times to stabilize results. Consequently, the total number of training becomes t(n(n−1)/2−1), where t is the number of training trials per one model. Similar to the identification of causal order, the pruning procedure also requires an exponential number of estimations. For the worst case, where all the edges will be pruned, a total of t(k(k − 1)/2 − 1) estimations are required for each multi-cause substructure with k cause variables 4 . The number of estimations increases as the ground truth DAG becomes sparse.
One effective approach to alleviate the computational time is parallelization. Since all the estimations in one iteration of the identification of causal order can be performed separately, we can reduce the time by parallelizing them. If enough computational resources are available, we can identify the causal order with n − 1 serial iterations. Similarly, the time of the pruning can be reduced by the same manner. In the experiment in Section 4, we used this parallelization. We additionally applied the proposed method to synthetic problems with n = 15 and confirmed that it requires roughly one day for one problem on a server capable of (maximum) 40-threading 5 .
Estimating one model multiple times in the proposed method has a high impact on the total computational time. Therefore, we think improving the stability is an important issue that should be addressed not only for improving the performance of causal discovery but for reducing the computational time. One of the typical approaches is to change the optimizing algorithm, while there is a trade-off between stability and speed. Another possible option is to add a regularization term to the loss. To use this idea, we need to consider carefully the impact of changing the loss on using its value when comparing models in the sink identification.
In this section, we discuss the properties of the proposed method, specifically the computational complexity and the stabilization.
In the proposed method, we use neural networks to represent models and estimate them by optimizing their parameters using stochastic gradient descent methods. Since each neural network
represents a simple scalar function, it is unnecessary to be a complex design. Therefore, the training time is much shorter than the general deep models such as image recognition and language
processing.
On the other hand, the number of estimations grows exponentially as the number of variables increases. Given n variables, the identification of causal order requires n − 1 iterations. In each
iteration, we compare k models for the remaining k variables. Therefore, a total of n + (n − 1) + · · · + 2 = n(n − 1)/2 − 1 model estimations are required, which is the same as RESIT and DirectLiNGAM. Additionally, the proposed method trains one model several times to stabilize results. Consequently, the total number of training becomes t(n(n−1)/2−1), where t is the number of training trials per one model. Similar to the identification of causal order, the pruning procedure also requires an exponential number of estimations. For the worst case, where all the edges will be pruned, a total of t(k(k − 1)/2 − 1) estimations are required for each multi-cause substructure with k cause variables4 . The number of estimations increases as the ground truth DAG becomes sparse.
One effective approach to alleviate the computational time is parallelization. Since all the estimations in one iteration of the identification of causal order can be performed separately, we can reduce the time by parallelizing them. If enough computational resources are available, we can identify the causal order with n − 1 serial iterations. Similarly, the time of the pruning can be reduced by the same manner. In the experiment in Section 4, we used this parallelization. We additionally applied the proposed method to synthetic problems with n = 15 and confirmed that it requires roughly one day for one problem on a server capable of (maximum) 40-threading5 .
Estimating one model multiple times in the proposed method has a high impact on the total computational time. Therefore, we think improving the stability is an important issue that should be addressed not only for improving the performance of causal discovery but also for reducing the computational time. One of the typical approaches is to change the optimizing algorithm, while there is a trade-off between stability and speed. Another possible option is to add a regularization term to the loss. To use this idea, we need to consider carefully the impact of changing the loss on using its value when comparing models in the sink identification
|
Could the issue of stabilization be elaborated in the Discussion?
|
We will set up the Discussion section and add more detailed discussions.
As stated in the paper, we estimate one PNL model by training the corresponding neural networks several times to stabilize results. Therefore, the stabilization of the training is important for not only improving the performance but also reducing the computational cost. One of the typical approaches is to change the optimizer, while there is a trade-off between stability and speed. Another possible option is to add a regularization term to the loss. To use this idea, we need to consider carefully the impact of changing the loss on using its value when comparing models in the sink identification.
My main concern is about the numerical comparison. In fact only one single DAG with a few SCMs is considered and the proposed approach compared to the RESIT estimator of ANM and DirectLinGAM. As mentioned above, there seems to be another relevant neural network estimator that could be considered here.
In the experiment in section 4.1, we randomly generated 100 linear and nonlinear problems, respectively, as stated in the caption of Table 1. In this procedure, not only causal equations but also DAG structures are generated randomly. Two examples in Fig. 2 are (coincidentally) have the same DAG structure.
We apologize for insufficient and misleading expressions. To make it clear, we will add the descriptions in the main text and change the examples in Fig. 2.
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Why do teenagers disappoint their parents?
|
Teenagers by definition are going through a process of maturation and individuation. They are finding their true self, distinct from their parents who have been their role model since infancy. Part of this process requires pushing boundaries set by parents. Parents often interpret this as disobedience and are disappointed. In fact teenagers who seem to be disappointing their parents are merely going through the healthy process of self identification.
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| null |
How do you play basketball
|
Basketball is a game played on a court with five players on each team. There are two baskets that are 10 feet tall on each end of the court and the goal of the game is to get the ball into the basket at the opposing end of the court. Each basket is worth two points, unless the ball is shot from outside of an arc that is painted on the floor. If the shot it made outside of the arc, then it's worth three points. The games are 48 minutes long with 4 even quarters at 12 minutes each. The clock also stops every time a basket is made, the ball goes out of bounds, or a foul is committed. When running with the ball, a player must continuously bounce it off the ground or else it's considered traveling which is a not allowed. Additionally, aggressive contact is also considered a foul. Basketball is more about finesse than it is about physicality. Whichever team scores the most points by the end of the game wins
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1709.05413
| false
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We first expand upon previous work and generic dialogue act taxonomies, developing a fine-grained set of dialogue acts for customer service, and conducting a systematic user study to identify these acts in a dataset of 800 conversations from four Twitter customer service accounts (i.e. four different companies in the telecommunication, electronics, and insurance industries). We then aim to understand the conversation flow between customers and agents using our taxonomy, so we develop a real-time sequential SVM-HMM model to predict our fine-grained dialogue acts while a conversation is in progress, using a novel multi-label scheme to classify each turn. Finally, using our dialogue act predictions, we classify conversations based on the outcomes of customer satisfaction, frustration, and overall problem resolution, then provide actionable guidelines for the development of automated customer service systems and intelligent agents aimed at desired customer outcomes BIBREF3 , BIBREF4 .
We first expand upon previous work and generic dialogue act taxonomies, developing a fine-grained set of dialogue acts for customer service, and conducting a systematic user study to identify these acts in a dataset of 800 conversations from four Twitter customer service accounts (i.e. four different companies in the telecommunication, electronics, and insurance industries).
|
Which Twitter customer service industries are investigated?
|
The answers are shown as follows:
* four different companies in the telecommunication, electronics, and insurance industries
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| null |
Which is a species of fish? Shaver or Razorfish
|
Razorfish
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null | false
| null |
Where is Babylon remain located?
|
Irag
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null | false
| null |
When was the big bang?
|
13,8 billion years ago
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null | false
| null |
What is the capital of Norway and when was it founded?
|
Oslo is the capital of Norway. It was founded as a city at the end of the Viking Age in 1040 and was given the name Ánslo in 1048 by Harald Hardrada. Haakon V of Norway declared Oslo the capital around 1300. The city was destroyed by a fire in 1624 and then rebuilt.
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| 91
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To make the conclusion convincing, we first choose two large-scale datasets that are publicly available, i.e., Chinese-English and English-French. Since English, French, and Chinese all belong to the subject-verb-object (SVO) family, we choose another very different subject-object-verb (SOV) language, Japanese, which might bring some interesting linguistic behaviors in English-Japanese translation.
For Chinese-English task, we use WMT17 Chinese-English dataset that consists of $20.6$M sentence pairs. For English-French task, we use WMT14 English-French dataset that comprises $35.5$M sentence pairs. For English-Japanese task, we follow BIBREF17 to use the first two sections of WAT17 English-Japanese dataset that consists of $1.9$M sentence pairs. Following the standard NMT procedure, we adopt the standard byte pair encoding (BPE) BIBREF18 with 32K merge operations for all language pairs. We believe that these datasets are large enough to confirm the rationality and validity of our experimental analyses.
We choose the state-of-the-art Transformer BIBREF1 model and the conventional RNN-Search model BIBREF0 as our test bed. We implement the Attribution method based on the Fairseq-py BIBREF19 framework for the above models. All models are trained on the training corpus for 100k steps under the standard settings, which achieve comparable translation results. All the following experiments are conducted on the test dataset, and we estimate the input word importance using the model generated hypotheses.
In the following experiments, we compare IG (Attribution) with several black-box methods (i.e., Content, Frequency, Attention) as introduced in Section SECREF8. In Section SECREF21, to ensure that the translation performance decrease attributes to the selected words instead of the perturbation operations, we randomly select the same number of words to perturb (Random), which serves as a baseline. Since there is no ranking for content words, we randomly select a set of content words as important words. To avoid the potential bias introduced by randomness (i.e., Random and Content), we repeat the experiments for 10 times and report the averaged results. We calculate the Attention importance in a similar manner as the Attribution, except that the attention scores use a max operation due to the better performance.
We evaluate the effectiveness of estimating word importance by the translation performance decrease. More specifically, unlike the usual way, we measure the decrease of translation performance when perturbing a set of important words that are of top-most word importance in a sentence. The more translation performance degrades, the more important the word is.
We use the standard BLEU score as the evaluation metric for translation performance. To make the conclusion more convincing, we conduct experiments on different types of synthetic perturbations (Section SECREF21), as well as different NMT architectures and language pairs (Section SECREF27). In addition, we compare with a supervised erasure method, which requires ground-truth translations for scoring word importance (Section SECREF30).
In this experiment, we investigate the effectiveness of word importance estimation methods under different synthetic perturbations. Since the perturbation on text is notoriously hard BIBREF20 due to the semantic shifting problem, in this experiment, we investigate three types of perturbations to avoid the potential bias :
Deletion perturbation removes the selected words from the input sentence, and it can be regarded as a specific instantiation of sentence compression BIBREF21.
Mask perturbation replaces embedding vectors of the selected words with all-zero vectors BIBREF22, which is similar to Deletion perturbation except that it retains the placeholder.
Grammatical Replacement perturbation replaces a word by another word of the same linguistic role (i.e., POS tags), yielding a sentence that is grammatically correct but semantically nonsensical BIBREF23, BIBREF24, such as “colorless green ideas sleep furiously”.
Figure FIGREF19 illustrates the experimental results on Chinese$\Rightarrow $English translation with Transformer. It shows that Attribution method consistently outperforms other methods against different perturbations on a various number of operations. Here the operation number denotes the number of perturbed words in a sentence. Specifically, we can make the following observations.
Under three different perturbations, perturbing words of top-most importance leads to lower BLEU scores than Random selected words. It confirms the existence of important words, which have greater impacts on translation performance. Furthermore, perturbing important words identified by Attribution outperforms the Random method by a large margin (more than 4.0 BLEU under 5 operations).
Figure FIGREF19 shows that two black-box methods (i.e., Content, Frequency) perform only slightly better than the Random method. Specifically, the Frequency method demonstrates even worse performances under the Mask perturbation. Therefore, linguistic properties (such as POS tags) and the word frequency can only partially help identify the important words, but it is not as accurate as we thought. In the meanwhile, it is intriguing to explore what exact linguistic characteristics these important words reveal, which will be introduced in Section SECREF5.
We also evaluate the Attention method, which bases on the encoder-decoder attention scores at the last layer of Transformer. Note that the Attention method is also used to simulate the best black-box method SOCRAT, and the results show that it is more effective than black-box methods and the Random baseline. Given the powerful Attention method, Attribution method still achieves best performances under all three perturbations. Furthermore, we find that the gap between Attribution and Attention is notably large (around $1.0+$ BLEU difference). Attention method does not provide as accurate word importance as the Attribution, which exhibits the superiority of gradient-based methods and consists with the conclusion reported in the previous study BIBREF8.
In addition, as shown in Figure FIGREF19, the perturbation effectiveness of Deletion, Mask, and Grammatical Replacement varies from strong to weak. In the following experiments, we choose Mask as the representative perturbation operation for its moderate perturbation performance, based on which we compare two most effective methods Attribution and Attention.
We validate the effectiveness of the proposed approach using a different NMT architecture RNN-Search on the Chinese$\Rightarrow $English translation task. The results are shown in Figure FIGREF20(a). We observe that the Attribution method still outperforms both Attention method and Random method by a decent margin. By comparing to Transformer, the results also reveal that the RNN-Search model is less robust to these perturbations. To be specific, under the setting of five operations and Attribution method, Transformer shows a relative decrease of $55\%$ on BLEU scores while the decline of RNN-Search model is $64\%$.
We further conduct experiments on another two language pairs (i.e., English$\Rightarrow $French, English$\Rightarrow $Japanese in Figures FIGREF20(b, c)) as well as the reverse directions (Figures FIGREF20(d, e, f)) using Transformer under the Mask perturbation. In all the cases, Attribution shows the best performance while Random achieves the worst result. More specifically, Attribution method shows similar translation quality degradation on all three language-pairs, which declines to around the half of the original BLEU score with five operations.
There exists another straightforward method, Erasure BIBREF7, BIBREF22, BIBREF25, which directly evaluates the word importance by measuring the translation performance degradation of each word. Specifically, it erases (i.e., Mask) one word from the input sentence each time and uses the BLEU score changes to denote the word importance (after normalization).
In Figure FIGREF31, we compare Erasure method with Attribution method under the Mask perturbation. The results show that Attribution method is less effective than Erasure method when only one word is perturbed. But it outperforms the Erasure method when perturbing 2 or more words. The results reveal that the importance calculated by erasing only one word cannot be generalized to multiple-words scenarios very well. Besides, the Erasure method is a supervised method which requires ground-truth references, and finding a better words combination is computation infeasible when erasing multiple words.
We close this section by pointing out that our gradient-based method consistently outperforms its black-box counterparts in various settings, demonstrating the effectiveness and universality of exploiting gradients for estimating word importance. In addition, our approach is on par with or even outperforms the supervised erasure method (on multiple-word perturbations). This is encouraging since our approach does not require any external resource and is fully unsupervised.
To make the conclusion convincing, we first choose two large-scale datasets that are publicly available, i.e., Chinese-English and EnglishFrench.
|
Which two data sets did the author use?
|
Chinese-English and EnglishFrench.
|
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| null |
Write a paragraph on the difficulties of seizure detection among epilepsy patients.
|
Seizure detection in epilepsy usually faces the problem of subjectivity where one reviewer's response may differ from another. This makes the seizure classification very difficult, wasting a lot of time. The gold standard, video-EEG, is also time-consuming and resource-intensive. An epilepsy patient can also have multiple types of seizures, making the detection even more difficult. This becomes more challenging when seizure types like hyperkinetic seizures have similar traits to non-epilepsy functional seizures.
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| null |
Identify which instrument is string or percussion: Slenthem, Scheiholt
|
Scheiholt is string, Slenthem is percussion.
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| 22
|
Chatbots such as dialog and question-answering systems have a long history in AI and natural language processing. Early such systems were mostly built using markup languages such as AIML, handcrafted conversation generation rules, and/or information retrieval techniques BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Recent neural conversation models BIBREF4 , BIBREF5 , BIBREF6 are even able to perform open-ended conversations. However, since they do not use explicit knowledge bases and do not perform inference, they often suffer from generic and dull responses BIBREF5 , BIBREF7 . More recently, BIBREF8 and BIBREF9 proposed to use knowledge bases (KBs) to help generate responses for knowledge-grounded conversation. However, one major weakness of all existing chat systems is that they do not explicitly or implicitly learn new knowledge in the conversation process. This seriously limits the scope of their applications. In contrast, we humans constantly learn new knowledge in our conversations. Even if some existing systems can use very large knowledge bases either harvested from a large data source such as the Web or built manually, these KBs still miss a large number of facts (knowledge) BIBREF10 . It is thus important for a chatbot to continuously learn new knowledge in the conversation process to expand its KB and to improve its conversation ability.
In recent years, researchers have studied the problem of KB completion, i.e., inferring new facts (knowledge) automatically from existing facts in a KB. KB completion (KBC) is defined as a binary classification problem: Given a query triple, ( INLINEFORM0 , INLINEFORM1 , INLINEFORM2 ), we want to predict whether the source entity INLINEFORM3 and target entity INLINEFORM4 can be linked by the relation INLINEFORM5 . However, existing approaches BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 solve this problem under the closed-world assumption, i.e., INLINEFORM6 , INLINEFORM7 and INLINEFORM8 are all known to exist in the KB. This is a major weakness because it means that no new knowledge or facts may contain unknown entities or relations. Due to this limitation, KBC is clearly not sufficient for knowledge learning in conversations because in a conversation, the user can say anything, which may contain entities and relations that are not already in the KB.
In this paper, we remove this assumption of KBC, and allow all INLINEFORM0 , INLINEFORM1 and INLINEFORM2 to be unknown. We call the new problem open-world knowledge base completion (OKBC). OKBC generalizes KBC. Below, we show that solving OKBC naturally provides the ground for knowledge learning and inference in conversations. In essence, we formulate an abstract problem of knowledge learning and inference in conversations as a well-defined OKBC problem in the interactive setting.
From the perspective of knowledge learning in conversations, essentially we can extract two key types of information, true facts and queries, from the user utterances. Queries are facts whose truth values need to be determined. Note that we do not study fact or relation extraction in this paper as there is an extensive work on the topic. (1) For a true fact, we will incorporate it into the KB. Here we need to make sure that it is not already in the KB, which involves relation resolution and entity linking. After a fact is added to the KB, we may predict that some related facts involving some existing relations in the KB may also be true (not logical implications as they can be automatically inferred). For example, if the user says “Obama was born in USA,” the system may guess that (Obama, CitizenOf, USA) (meaning that Obama is a citizen of USA) could also be true based on the current KB. To verify this fact, it needs to solve a KBC problem by treating (Obama, CitizenOf, USA) as a query. This is a KBC problem because the fact (Obama, BornIn, USA) extracted from the original sentence has been added to the KB. Then Obama and USA are in the KB. If the KBC problem is solved, it learns a new fact (Obama, CitizenOf, USA) in addition to the extracted fact (Obama, BornIn, USA). (2) For a query fact, e.g., (Obama, BornIn, USA) extracted from the user question “Was Obama born in USA?” we need to solve the OKBC problem if any of “Obama, “BornIn”, or “USA" is not already in the KB.
We can see that OKBC is the core of a knowledge learning engine for conversation. Thus, in this paper, we focus on solving it. We assume that other tasks such as fact/relation extraction and resolution and guessing of related facts of an extracted fact are solved by other sub-systems.
We solve the OKBC problem by mimicking how humans acquire knowledge and perform reasoning in an interactive conversation. Whenever we encounter an unknown concept or relation while answering a query, we perform inference using our existing knowledge. If our knowledge does not allow us to draw a conclusion, we typically ask questions to others to acquire related knowledge and use it in inference. The process typically involves an inference strategy (a sequence of actions), which interleaves a sequence of processing and interactive actions. A processing action can be the selection of related facts, deriving inference chain, etc., that advances the inference process. An interactive action can be deciding what to ask, formulating a suitable question, etc., that enable us to interact. The process helps grow the knowledge over time and the gained knowledge enables us to communicate better in the future. We call this lifelong interactive learning and inference (LiLi). Lifelong learning is reflected by the facts that the newly acquired facts are retained in the KB and used in inference for future queries, and that the accumulated knowledge in addition to the updated KB including past inference performances are leveraged to guide future interaction and learning. LiLi should have the following capabilities:
This setting is ideal for many NLP applications like dialog and question-answering systems that naturally provide the scope for human interaction and demand real-time inference.
LiLi starts with the closed-world KBC approach path-ranking (PR) BIBREF11 , BIBREF17 and extends KBC in a major way to open-world knowledge base completion (OKBC). For a relation INLINEFORM0 , PR works by enumerating paths (except single-link path INLINEFORM1 ) between entity-pairs linked by INLINEFORM2 in the KB and use them as features to train a binary classifier to predict whether a query INLINEFORM3 should be in the KB. Here, a path between two entities is a sequence of relations linking them. In our work, we adopt the latest PR method, C-PR BIBREF16 and extend it to make it work in the open-world setting. C-PR enumerates paths by performing bidirectional random walks over the KB graph while leveraging the context of the source-target entity-pair. We also adopt and extend the compositional vector space model BIBREF20 , BIBREF21 with continual learning capability for prediction.
Given an OKBC query ( INLINEFORM0 , INLINEFORM1 , INLINEFORM2 ) (e.g., “(Obama, CitizenOf, USA), which means whether Obama a citizen of USA), LiLi interacts with the user (if needed) by dynamically formulating questions (see the interaction example in Figure 1, which will be further explained in §3) and leverages the interactively acquired knowledge (supporting facts (SFs) in the figure) for continued inference. To do so, LiLi formulates a query-specific inference strategy and executes it. We design LiLi in a Reinforcement Learning (RL) setting that performs sub-tasks like formulating and executing strategy, training a prediction model for inference, and knowledge retention for future use. To the best of our knowledge, our work is the first to address the OKBC problem and to propose an interactive learning mechanism to solve it in a continuous or lifelong manner. We empirically verify the effectiveness of LiLi on two standard real-world KBs: Freebase and WordNet. Experimental results show that LiLi is highly effective in terms of its predictive performance and strategy formulation ability.
Chatbots such as dialog and question-answering systems have a long history in AI and natural language processing. Early such systems were mostly built using markup languages such as AIML, handcrafted conversation generation rules, and/or information retrieval techniques (Banchs and Li, 2012; Ameixa et al. , 2014; Lowe et al. , 2015; Serban et al. , 2015).
|
What were the early chatbot systems mostly built with?
|
Markup languages such as AIML, handcrafted conversation generation rules, and/or information retrieval techniques.
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| 164
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In this phase, we use our models to explore or answer our research questions. For example, given a topic model we can look at the connection between topics and metadata elements. Tags such as “hate speech" or metadata information imply a certain way of organizing the collection. Computational models provide another organization, which may differ in ways that provide more insight into how these categories manifest themselves, or fail to do so.
Moreover, when using a supervised approach, the “errors”, i.e. disagreement between the system output and human-provided labels, can point towards interesting cases for closer analysis and help us reflect on our conceptualizations. In the words of BIBREF2 , they can be “opportunities for interpretation”. Other types of “failures” can be insightful as well. Sometimes there is a “dog that didn't bark” BIBREF49 –i.e., something that everyone thinks we should have found, but we did not. Or, sometimes the failures are telling us about the existence of something in the data that nobody noticed, or thought important, until then (e.g., the large number of travel journals in Darwin's reading lists).
Computational text analysis is not a replacement for but rather an addition to the approaches one can take to analyze social and cultural phenomena using textual data. By moving back and forth between large-scale computational analyses and small-scale qualitative analyses, we can combine their strengths so that we can identify large-scale and long-term trends, but also tell individual stories. For example, the Reddit study on hate speech BIBREF0 raised various follow-up questions: Can we distinguish hate speech from people talking about hate speech? Did people find new ways to express hate speech? If so, did the total amount of online hate speech decrease after all? As possible next steps, a qualitative discourse analyst might examine a smaller corpus to investigate whether commenters were indeed expressing hate speech in new ways; a specialist in interview methodologies might reach out to commenters to better understand the role of online hate speech in their lives. Computational text analysis represents a step towards better understanding social and cultural phenomena, and it is in many cases better suited towards opening questions rather than closing them.
Computational text analysis represents a step towards better understanding social and cultural phenomena, and it is in many cases better suited towards opening questions rather than closing them.
|
Does computational text analysis represent a step towards better understanding social and cultural phenomena?
|
Yes.
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Is society ready for flying cars ?
|
No, significant work needs to done in terms of :
Infrastructure to support cars that can traverse the airspace
New FAA regulations to manage airspace safety
New driver safety training and licenses
New charging stations
New insurance policies
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| null |
What are the solar system planets?
|
Solar system planets:
- Mercury
- Venus
- Earth
- Mars
- Jupiter
- Saturn
- Uranus
- Neptune
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| null |
Which of the following famous basketball players have won an NBA championship: Lebron James, Steve Nash, Michael Jordan, Charles Barkley, Dwight Howard, Larry Bird, and Chris Paul
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Chris Paul, Charles Barkley, Dwight Howard, and Steve Nash have never won an NBA championship. Lebron James, Michael Jordan, and Larry Bird have all won an NBA championship. In fact, all three of those players have won multiple championships.
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| null |
give me a bulleted list of ways to get to the other side of a lake.
|
- walk across the suspension bridge hovering over the lake
- swim across the lake
- if you are a scorpion, hitch a ride on a frogs back and behave
- canoe across the lake
- drive around the lake and reach the other side
- zip line across the lake
- skate across the lake in the winter when the lake is frozen
- water ski across the lake
- lay on a float and hope you end up on the other side of the lake
- drain the lake and walk across on the lake bed
- take a ferry across the lake
- scuba dive across the lake
|
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null | false
| null |
How object-oriented programming is different from functional programming?
|
Functional programming focuses on functions, whereas object-oriented programming focuses on classes and respective objects.
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| 107
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We re-implemented five keyphrase extraction models : the first two are commonly used as baselines, the third is a resource-lean unsupervised graph-based ranking approach, and the last two were among the top performing systems in the SemEval-2010 keyphrase extraction task BIBREF0 . We note that two of the systems are supervised and rely on the training set to build their classification models. Document frequency counts are also computed on the training set. Stemming is applied to allow more robust matching. The different keyphrase extraction models are briefly described below:
Each model uses a distinct keyphrase candidate selection method that provides a trade-off between the highest attainable recall and the size of set of candidates. Table summarizes these numbers for each model. Syntax-based selection heuristics, as used by TopicRank and WINGNUS, are better suited to prune candidates that are unlikely to be keyphrases. As for KP-miner, removing infrequent candidates may seem rather blunt, but it turns out to be a simple yet effective pruning method when dealing with long documents. For details on how candidate selection methods affect keyphrase extraction, please refer to BIBREF16 .
Apart from TopicRank that groups similar candidates into topics, the other models do not have any redundancy control mechanism. Yet, recent work has shown that up to 12% of the overall error made by state-of-the-art keyphrase extraction systems were due to redundancy BIBREF6 , BIBREF17 . Therefore as a post-ranking step, we remove redundant keyphrases from the ranked lists generated by all models. A keyphrase is considered redundant if it is included in another keyphrase that is ranked higher in the list.
We re-implemented five keyphrase extraction models : the first two are commonly used as baselines, the third is a resource-lean unsupervised graph-based ranking approach, and the last two were among the top performing systems in the SemEval-2010 keyphrase extraction task (Kim et al., 2010).
|
What are the baseline models?
|
There are no baseline models but five models to be studied.
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| null |
Think of a number of different ways you can cook eggs
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Eggs are versatile and can be cooked using a number of methods including boiling, scrambling, poaching, frying and baking. They can also be beaten and augmented with other ingredients to make an Omelette or Frittata
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1811.01734
| false
| null |
Baselines. We compare our approach with several methods BIBREF1 , BIBREF31 , BIBREF11 , BIBREF8 , BIBREF10 , BIBREF39 in two cross-domain settings. Using string kernels, Giménez-Pérez et al. BIBREF10 reported better performance than SST BIBREF31 and KE-Meta BIBREF11 in the multi-source domain setting. In addition, we compare our approach with SFA BIBREF1 , CORAL BIBREF8 and TR-TrAdaBoost BIBREF39 in the single-source setting.
Transductive string kernels. We present a simple and straightforward approach to produce a transductive similarity measure suitable for strings. We take the following steps to derive transductive string kernels. For a given kernel (similarity) function INLINEFORM0 , we first build the full kernel matrix INLINEFORM1 , by including the pairwise similarities of samples from both the train and the test sets. For a training set INLINEFORM2 of INLINEFORM3 samples and a test set INLINEFORM4 of INLINEFORM5 samples, such that INLINEFORM6 , each component in the full kernel matrix is defined as follows: DISPLAYFORM0
We next present a simple yet effective approach for adapting a one-versus-all kernel classifier trained on a source domain to a different target domain. Our transductive kernel classifier (TKC) approach is composed of two learning iterations. Our entire framework is formally described in Algorithm SECREF3 .
We compare our approach with several methods BIBREF1 , BIBREF31 , BIBREF11 , BIBREF8 , BIBREF10 , BIBREF39 in two cross-domain settings. Using string kernels, Giménez-Pérez et al. BIBREF10 reported better performance than SST BIBREF31 and KE-Meta BIBREF11 in the multi-source domain setting. In addition, we compare our approach with SFA BIBREF1 , CORAL BIBREF8 and TR-TrAdaBoost BIBREF39 in the single-source setting.
Transductive string kernels. We present a simple and straightforward approach to produce a transductive similarity measure suitable for strings.
Our transductive kernel classifier (TKC) approach is composed of two learning iterations.
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What machine learning algorithms are used?
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The answers are shown as follows:
* string kernels
* SST
* KE-Meta
* SFA
* CORAL
* TR-TrAdaBoost
* Transductive string kernels
* transductive kernel classifier
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What is the best place for tacos in San Francisco?
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This answer depends on whether you want to optimize for ambiance or the quality of the food. If the latter, the Mission District of San Francisco has countless taquerias and taco trucks with tasty and authentic street tacos. If you are looking for a night out with friends or a date night, I would suggest going to a Mexican restaurant - and my favorite is Tacko in the Marina neighborhood of San Francisco. Tacko has amazing guacamole and cheap pitchers of margaritas.
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Name a few popular racquet sports
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Tennis, Ping Pong, Pickleball, Squash, Badminton
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Why do people like playing games?
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There are many reasons why people like playing games. 1. Games are different from real world and hence players can usually do/achieve a lot more than what they can do in the real world. 2. Games give quick results 3. People/players can feel less responsibility (e.g. players can just quit the games if the games do not go well)
However, because of these very reasons, games also have side-effects such as making people be less responsible, want more quick responsive answers, and not be able to discern between real world and game world, etc.
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The United States version is produced by Mark Burnett and hosted by Jeff Probst, who also serves as an executive producer. Each competition is called a season, has a unique name, and lasts from 13 to 16 episodes. The first season, Survivor: Borneo, was broadcast as a summer replacement show in 2000. Starting with the third season, Survivor: Africa, there have been two seasons aired during each U.S. television season. Starting with the forty-first season, no subtitle has been used in promotion of the season. Instead, the show began following a number format similar to Big Brother and The Amazing Race.
In the first season, there was a 75-person crew. By season 22, the crew had grown to 325 people.
A total of 662 contestants have competed on Survivor's 44 seasons.
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How many seasons of Survivor has there been?
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There have been 44 seasons of Survivor.
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Write a poem about lost friendship
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Thought we were sandstorms,
Getting rid of everything on our way.
Thought we were revolutionaries,
Changing the usual ways of society.
Thought we were inseparable,
Holding our hands firmly.
Thought we were makers of our future,
Closing our eyes at future.
Thought of the new ideas we would implement,
Being ignorant of life's ways.
But, now I know,
We were never destined to take the same path,
Never will we sit and chat as we once used to.
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what are the best tips for parenting?
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Be role models to your children. Behave the way you want your kids to behave. Spend plenty of time with them as they grow up. Be a patient, non-judgemental, supportive companion when they needed. Allow them to pursue their own dreams.
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1904.07342
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There are several caveats in our work: first, tweet sentiment is rarely binary (this work could be extended to a multinomial or continuous model). Second, our results are constrained to Twitter users, who are known to be more negative than the general U.S. population BIBREF9 . Third, we do not take into account the aggregate effects of continued natural disasters over time. Going forward, there is clear demand in discovering whether social networks can indicate environmental metrics in a “nowcasting" fashion. As climate change becomes more extreme, it remains to be seen what degree of predictive power exists in our current model regarding climate change sentiments with regards to natural disasters.
There are several caveats in our work: first, tweet sentiment is rarely binary (this work could be extended to a multinomial or continuous model). Second, our results are constrained to Twitter users, who are known to be more negative than the general U.S. population BIBREF9 . Third, we do not take into account the aggregate effects of continued natural disasters over time. Going forward, there is clear demand in discovering whether social networks can indicate environmental metrics in a “nowcasting" fashion. As climate change becomes more extreme, it remains to be seen what degree of predictive power exists in our current model regarding climate change sentiments with regards to natural disasters.
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Do the authors mention any confounds to their study?
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No.
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Which one of these is a music band, and which one is a Football team : Manchester United, Linkin Park ?
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Linkin Park is a music band. Manchester United is a Football team.
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1910.09295
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We work with a dataset composed of 3,206 news articles, each labeled real or fake, with a perfect 50/50 split between 1,603 real and fake articles, respectively. Fake articles were sourced from online sites that were tagged as fake news sites by the non-profit independent media fact-checking organization Verafiles and the National Union of Journalists in the Philippines (NUJP). Real articles were sourced from mainstream news websites in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera.
We work with a dataset composed of 3,206 news articles, each labeled real or fake, with a perfect 50/50 split between 1,603 real and fake articles, respectively. Fake articles were sourced from online sites that were tagged as fake news sites by the non-profit independent media fact-checking organization Verafiles and the National Union of Journalists in the Philippines (NUJP). Real articles were sourced from mainstream news websites in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera.
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What is the source of the dataset?
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Online sites tagged as fake news site by Verafiles and NUJP and news website in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera
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Abel Janszoon Tasman (Dutch: [ˈɑbəl ˈjɑnsoːn ˈtɑsmɑn]; 1603 – 10 October 1659) was a Dutch seafarer and explorer, best known for his voyages of 1642 and 1644 in the service of the Dutch East India Company (VOC).
Born in 1603 in Lutjegast, Netherlands, Tasman started his career as a merchant seaman and became a skilled navigator. In 1633, he joined the VOC and sailed to Batavia, now Jakarta, Indonesia. He participated in several voyages, including one to Japan. In 1642, Tasman was appointed by the VOC to lead an expedition to explore the uncharted regions of the Southern Pacific Ocean. His mission was to discover new trade routes and to establish trade relations with the native inhabitants. After leaving Batavia, Tasman sailed eastward and reached the coast of Tasmania, which he named Van Diemen's Land after his patron. He then sailed north and discovered the west coast of New Zealand, which he named Staten Landt, but later renamed Nieuw Zeeland after the Dutch province of Zeeland.
Despite his achievements, Tasman's expedition was not entirely successful. The encounter with the Māori people on the South Island of New Zealand resulted in a violent confrontation, which left four of Tasman's men dead. He returned to Batavia without having made any significant contact with the native inhabitants or establishing any trade relations. Nonetheless, Tasman's expedition paved the way for further exploration and colonization of Australia and New Zealand by the Europeans. Tasman continued to serve the Dutch East India Company until his death in 1659, leaving behind a legacy as one of the greatest explorers of his time.
Origins and early life
Portrait of Abel Tasman, his wife and daughter. Attributed to Jacob Gerritsz Cuyp, 1637 (not authenticated).
Abel Tasman was born around 1603 in Lutjegast, a small village in the province of Groningen, in the north of the Netherlands. The oldest available source mentioning him is dated 27 December 1631 when, as a seafarer living in Amsterdam, the 28-year-old became engaged to marry 21-year-old Jannetje Tjaers, of Palmstraat in the Jordaan district of the city.
Relocation to the Dutch East Indies
Employed by the Dutch East India Company (VOC), Tasman sailed from Texel (Netherlands) to Batavia, now Jakarta, in 1633 taking the southern Brouwer Route. While based in Batavia, Tasman took part in a voyage to Seram Island (in what is now the Maluku Province in Indonesia) because the locals had sold spices to other European nationalities than the Dutch. He had a narrow escape from death when in an incautious landing several of his companions were killed by the inhabitants of the island.
By August 1637, Tasman was back in Amsterdam, and the following year he signed on for another ten years and took his wife with him to Batavia. On 25 March 1638 he tried to sell his property in the Jordaan, but the purchase was cancelled.
He was second-in-command of a 1639 expedition of exploration into the north Pacific under Matthijs Quast. The fleet included the ships Engel and Gracht and reached Fort Zeelandia (Dutch Formosa) and Deshima (an artificial island off Nagasaki, Japan).
First major voyage
In August 1642, the Council of the Indies, consisting of Antonie van Diemen, Cornelis van der Lijn, Joan Maetsuycker, Justus Schouten, Salomon Sweers, Cornelis Witsen, and Pieter Boreel in Batavia dispatched Tasman and Franchoijs Jacobszoon Visscher on a voyage of exploration to little-charted areas east of the Cape of Good Hope, west of Staten Land (near the Cape Horn of South America) and south of the Solomon Islands.
One of the objectives was to obtain knowledge of "all the totally unknown" Provinces of Beach. This was a purported yet non-existent landmass said to have plentiful gold, which had appeared on European maps since the 15th century, as a result of an error in some editions of Marco Polo's works.
The expedition was to use two small ships, Heemskerck and Zeehaen.
Mauritius
In accordance with Visscher's directions, Tasman sailed from Batavia on 14 August 1642 and arrived at Mauritius on 5 September 1642, according to the captain's journal. The reason for this was the crew could be fed well on the island; there was plenty of fresh water and timber to repair the ships. Tasman got the assistance of the governor Adriaan van der Stel.
Because of the prevailing winds, Mauritius was chosen as a turning point. After a four-week stay on the island, both ships left on 8 October using the Roaring Forties to sail east as fast as possible. (No one had gone as far as Pieter Nuyts in 1626/27.) On 7 November, snow and hail influenced the ship's council to alter course to a more north-easterly direction, with the intention of having the Solomon Islands as their destination.
Tasmania
Coastal cliffs of Tasman Peninsula
On 24 November 1642, Tasman reached and sighted the west coast of Tasmania, north of Macquarie Harbour. He named his discovery Van Diemen's Land, after Antonio van Diemen, Governor-General of the Dutch East Indies.
Proceeding south, Tasman skirted the southern end of Tasmania and turned north-east. He then tried to work his two ships into Adventure Bay on the east coast of South Bruny Island, but he was blown out to sea by a storm. This area he named Storm Bay. Two days later, on 1 December, Tasman anchored to the north of Cape Frederick Hendrick just north of the Forestier Peninsula. On 2 December, two ship's boats under the command of the Pilot, Major Visscher, rowed through the Marion Narrows into Blackman Bay, and then west to the outflow of Boomer Creek where they gathered some edible "greens". Tasman named the bay, Frederick Hendrik Bay, which included the present North Bay, Marion Bay and what is now Blackman Bay. (Tasman's original naming, Frederick Henrick Bay, was mistakenly transferred to its present location by Marion Dufresne in 1772). The next day, an attempt was made to land in North Bay. However, because the sea was too rough, a ship's carpenter swam through the surf and planted the Dutch flag. Tasman then claimed formal possession of the land on 3 December 1642.
For two more days, he continued to follow the east coast northward to see how far it went. When the land veered to the north-west at Eddystone Point, he tried to follow the coast line but his ships were suddenly hit by the Roaring Forties howling through Bass Strait. Tasman was on a mission to find the Southern Continent not more islands, so he abruptly turned away to the east and continued his continent-hunting.
New Zealand
Murderers' Bay, drawing by Isaack Gilsemans
Māori haka
Tasman had intended to proceed in a northerly direction but as the wind was unfavourable he steered east. The expedition endured a rough voyage and in one of his diary entries Tasman claimed that his compass was the only thing that had kept him alive.
On 13 December 1642 they sighted land on the north-west coast of the South Island of New Zealand, becoming the first Europeans to sight New Zealand. Tasman named it Staten Landt "in honour of the States General" (Dutch parliament). He wrote, "it is possible that this land joins to the Staten Landt but it is uncertain", referring to Isla de los Estados, a landmass of the same name at the southern tip of South America, encountered by the Dutch navigator Jacob Le Maire in 1616. However, in 1643 Brouwer's expedition to Valdivia found out that Staaten Landt was separated by sea from the hypothetical Southern Land. Tasman continued: "We believe that this is the mainland coast of the unknown Southland." Tasman thought he had found the western side of the long-imagined Terra Australis that stretched across the Pacific to near the southern tip of South America.
After sailing north then east for five days, the expedition anchored about 7 kilometres (4.3 mi) from the coast off what is now Golden Bay. A group of Māori paddled out in a waka (canoe) and attacked some sailors who were rowing between the two Dutch vessels. Four sailors were clubbed to death with patu.
In the evening about one hour after sunset we saw many lights on land and four vessels near the shore, two of which betook themselves towards us. When our two boats returned to the ships reporting that they had found not less than thirteen fathoms of water, and with the sinking of the sun (which sank behind the high land) they had been still about half a mile from the shore. After our people had been on board about one glass, people in the two canoes began to call out to us in gruff, hollow voices. We could not in the least understand any of it; however, when they called out again several times we called back to them as a token answer. But they did not come nearer than a stone's shot. They also blew many times on an instrument, which produced a sound like the moors' trumpets. We had one of our sailors (who could play somewhat on the trumpet) play some tunes to them in answer."
As Tasman sailed out of the bay he observed 22 waka near the shore, of which "eleven swarming with people came off towards us." The waka approached the Zeehaen which fired and hit a man in the largest waka holding a small white flag. Canister shot also hit the side of a waka. It has been speculated that the local Maori had thought the Dutch were trying to land at a major agricultural area, which the Māori were trying to protect. Tasman named the area "Murderers' Bay".
The expedition then sailed north, sighting Cook Strait, which it mistook for a bight and named "Zeehaen's Bight". Two names that the expedition gave to landmarks in the far north of New Zealand still endure: Cape Maria van Diemen and Three Kings Islands. (Kaap Pieter Boreels was renamed Cape Egmont by Captain James Cook 125 years later.)
Return voyage
Tongatapu, the main island of Tonga; drawing by Isaack Gilsemans
The bay of Tongatapu with the two ships; drawing by Isaack Gilsemans
En route back to Batavia, Tasman came across the Tongan archipelago on 20 January 1643. While passing the Fiji Islands Tasman's ships came close to being wrecked on the dangerous reefs of the north-eastern part of the Fiji group. He charted the eastern tip of Vanua Levu and Cikobia-i-Lau before making his way back into the open sea.
The expedition turned north-west towards New Guinea and arrived back in Batavia on 15 June 1643.
Second major voyage
Tasman left Batavia on 30 January 1644 on his second voyage with three ships (Limmen, Zeemeeuw and the tender Braek). He followed the south coast of New Guinea eastwards in an attempt to find a passage to the eastern side of New Holland. However, he missed the Torres Strait between New Guinea and Australia, probably due to the numerous reefs and islands obscuring potential routes, and continued his voyage by following the shore of the Gulf of Carpentaria westwards along the north Australian coast. He mapped the north coast of Australia, making observations on New Holland and its people. He arrived back in Batavia in August 1644.
From the point of view of the Dutch East India Company, Tasman's explorations were a disappointment: he had neither found a promising area for trade nor a useful new shipping route. Although Tasman was received courteously on his return, the company was upset that Tasman had not fully explored the lands he found, and decided that a more "persistent explorer" should be chosen for any future expeditions. For over a century, until the era of James Cook, Tasmania and New Zealand were not visited by Europeans; mainland Australia was visited, but usually only by accident.
Later life
On 2 November 1644, Abel Tasman was appointed a member of the Council of Justice in Batavia. He went to Sumatra in 1646, and in August 1647 to Siam (now Thailand) with letters from the company to the King. In May 1648, he was in charge of an expedition sent to Manila to try to intercept and loot the Spanish silver ships coming from America, but he had no success and returned to Batavia in January 1649. In November 1649, he was charged and found guilty of having in the previous year hanged one of his men without trial, was suspended from his office of commander, fined, and made to pay compensation to the relatives of the sailor. On 5 January 1651, he was formally reinstated in his rank and spent his remaining years at Batavia. He was in good circumstances, being one of the larger landowners in the town. He died at Batavia on 10 October 1659 and was survived by his second wife and a daughter by his first wife. His property was divided between his wife and his daughter. In his will (dating from 1657), he left 25 guilders to the poor of his village, Lutjegast.
Although Tasman's pilot, Frans Visscher, published Memoir concerning the discovery of the South land in 1642, Tasman's detailed journal was not published until 1898. Nevertheless, some of his charts and maps were in general circulation and used by subsequent explorers. The journal signed by Abel Tasman of the 1642 voyage is held in the Dutch National Archives at The Hague.
Legacy
Tasman's ten-month voyage in 1642–43 had significant consequences. By circumnavigating Australia (albeit at a distance) Tasman proved that the small fifth continent was not joined to any larger sixth continent, such as the long-imagined Southern Continent. Further, Tasman's suggestion that New Zealand was the western side of that Southern Continent was seized upon by many European cartographers who, for the next century, depicted New Zealand as the west coast of a Terra Australis rising gradually from the waters around Tierra del Fuego. This theory was eventually disproved when Captain Cook circumnavigated New Zealand in 1769.
Multiple places have been named after Tasman, including:
the Australian island and state of Tasmania, renamed after him, formerly Van Diemen's land. It includes features such as:
the Tasman Peninsula.
the Tasman Bridge.
the Tasman Highway.
the Tasman Sea.
in New Zealand:
the Tasman Glacier.
Tasman Lake.
the Tasman River.
Mount Tasman.
the Abel Tasman National Park.
Tasman Bay / Te Tai-o-Aorere.
the Tasman District.
Abel Tasman Monument.
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Based on this information that I have shared about Abel Janszoon Tasman, can you summarize it for me?
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Sure, here is summary about Abel Janszoon Tasman:
Abel Janszoon Tasman (Dutch: [ˈɑbəl ˈjɑnsoːn ˈtɑsmɑn]; 1603 – 10 October 1659) was a Dutch seafarer and explorer best known for his voyages of 1642 and 1644 in the service of the Dutch East India Company (VOC). He was appointed by the VOC to explore the uncharted regions of the Southern Pacific Ocean and discovered Van Diemen's Land, Staten Landt, and Nieuw Zeeland. Despite his achievements, his expedition was not entirely successful due to a violent confrontation with the Māori people on the South Island of New Zealand. Nonetheless, his expedition paved the way for further exploration and colonization of Australia and New Zealand by the Europeans. Abel Tasman was born around 1603 in Lutjegast, a small village in the province of Groningen, in the north of the Netherlands.
He served the Dutch East India Company until his death in 1659, leaving behind a legacy as one of the greatest explorers of his time. He was engaged to marry 21-year-old Jannetje Tjaers, of Palmstraat in the Jordaan district of the city, and took part in a voyage to Seram Island. He was second-in-command of a 1639 expedition of exploration into the north Pacific under Matthijs Quast, which reached Fort Zeelandia and Deshima.
The Council of the Indies dispatched Tasman and Franchoijs Jacobszoon Visscher on a voyage to explore the Provinces of Beach, which had appeared on European maps since the 15th century. Tasman attempted to land in North Bay, but the sea was too rough, so he turned away and continued his continent-hunting.
Tasman and his crew sighted land on the north-west coast of New Zealand, naming it Staten Landt in honour of the States General. Tasman's expedition encountered a group of Māori who attacked four sailors and named the area "Murderers' Bay".
Tasman's second voyage was a disappointment for the Dutch East India Company, who decided that a more persistent explorer should be chosen for future expeditions. Tasman's voyage in 1642-43 proved that the small fifth continent was not joined to any larger sixth continent, and his suggestion that New Zealand was the western side of that Southern Continent was seized upon by European cartographers.
Tasmanians have named places after him, including the Tasman Peninsula, Tasman Highway, Tasman Glacier, Tasman Lake, Mount Tasman, and Abel Tasman Monument.
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What are common titles for the executives of large companies?
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Common titles for executives of large companies include:
- Chief Executive Officer (CEO)
- Chief Technology Officer (CTO)
- Chief Financial Officer (CFO)
- Chief Legal Officer (CLO)
- Chief Product Officer (CPO)
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What are five totally distinct, creative ways to describe the usefulness of a broken mind.
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1. A broken mind is like a rusty hinge, capable of opening up new possibilities.
2. A broken mind is like a cracked mirror, reflecting a distorted view of reality.
3. A broken mind is like a sieve, allowing ideas to pass through and filter out the important ones.
4. A broken mind is like a pebble in a stream, providing a gentle nudge to go in a new direction.
5. A broken mind is like a silent voice, conveying a message that is not always heard.
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What is the genomics of disease?
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With the advent of next-generation sequencing we are obtaining enough sequence data to map the genes of complex diseases including infertility, breast cancer or Alzheimer's disease. Genome-wide association studies are a useful approach to pinpoint the mutations responsible for such complex diseases. Through these studies, thousands of DNA variants have been identified that are associated with similar diseases and traits. Furthermore, the possibility for genes to be used at prognosis, diagnosis or treatment is one of the most essential applications. Many studies are discussing both the promising ways to choose the genes to be used and the problems and pitfalls of using genes to predict disease presence or prognosis.
Genome-wide association studies have successfully identified thousands of common genetic variants for complex diseases and traits; however, these common variants only explain a small fraction of heritability. Rare variants may account for some of the missing heritability. Large-scale whole genome sequencing studies have rapidly sequenced millions of whole genomes, and such studies have identified hundreds of millions of rare variants. Functional annotations predict the effect or function of a genetic variant and help to prioritize rare functional variants, and incorporating these annotations can effectively boost the power of genetic association of rare variants analysis of whole genome sequencing studies. Some tools have been developed to provide all-in-one rare variant association analysis for whole-genome sequencing data, including integration of genotype data and their functional annotations, association analysis, result summary and visualization. Meta-analysis of whole genome sequencing studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes.
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What is family time?
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Family during is time where each person in the family is actively engaged in the same activity together. Family activities like watching a movie together, eating a meal together, going to the circus together, exercising together, or going for a walk is all good family time. During family time you can share what has been going on in your own life and learn about the other members of your family. Family time is important to strengthen the bonds you all have with one another.
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Text classification has become an indispensable task due to the rapid growth in the number of texts in digital form available online. It aims to classify different texts, also called documents, into a fixed number of predefined categories, helping to organize data, and making easier for users to find the desired information. Over the past three decades, many methods based on machine learning and statistical models have been applied to perform this task, such as latent semantic analysis (LSA), support vector machines (SVM), and multinomial naive Bayes (MNB).
The first step in utilizing such methods to categorize textual data is to convert the texts into a vector representation. One of the most popular text representation models is the bag-of-words model BIBREF0 , which represents each document in a collection as a vector in a vector space. Each dimension of the vectors represents a term (e.g., a word, a sequence of words), and its value encodes a weight, which can be how many times the term occurs in the document.
Despite showing positive results in tasks such as language modeling and classification BIBREF1 , BIBREF2 , BIBREF3 , the BOW representation has limitations: first, feature vectors are commonly very high-dimensional, resulting in sparse document representations, which are hard to model due to space and time complexity. Second, BOW does not consider the proximity of words and their position in the text and consequently cannot encode the words semantic meanings.
To solve these problems, neural networks have been employed to learn vector representations of words BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . In particular, the word2vec representation BIBREF8 has gained attention. Given a training corpus, word2vec can generate a vector for each word in the corpus that encodes its semantic information. These word vectors are distributed in such a way that words from similar contexts are represented by word vectors with high correlation, while words from different contexts are represented by word vectors with low correlation.
One crucial aspect of the word2vec representation is that arithmetic and distance calculation between two word vectors can be performed, giving information about their semantic relationship. However, rather than looking at pairs of word vectors, we are interested in studying the relationship between sets of vectors as a whole and, therefore, it is desirable to have a text representation based on a set of these word vectors.
To tackle this problem, we introduce the novel concept of word subspace. It is mathematically defined as a low dimensional linear subspace in a word vector space with high dimensionality. Given that words from texts of the same class belong to the same context, it is possible to model word vectors of each class as word subspaces and efficiently compare them in terms of similarity by using canonical angles between the word subspaces. Through this representation, most of the variability of the class is retained. Consequently, a word subspace can effectively and compactly represent the context of the corresponding text. We achieve this framework through the mutual subspace method (MSM) BIBREF9 .
The word subspace of each text class is modeled by applying PCA without data centering to the set of word vectors of the class. When modeling the word subspaces, we assume only one occurrence of each word inside the class.
However, as seen in the BOW approach, the frequency of words inside a text is an informative feature that should be considered. In order to introduce this feature in the word subspace modeling and enhance its performance, we further extend the concept of word subspace to the term-frequency (TF) weighted word subspace.
In this extension, we consider a set of weights, which encodes the words frequencies, when performing the PCA. Text classification with TF weighted word subspace can also be performed under the framework of MSM. We show the validity of our modeling through experiments on the Reuters database, an established database for natural language processing tasks. We demonstrate the effectiveness of the word subspace formulation and its extension, comparing our methods' performance to various state-of-art methods.
The main contributions of our work are:
The remainder of this paper is organized as follows. In Section "Related Work" , we describe the main works related to text classification. In Section "Word subspace" , we present the formulation of our proposed word subspace. In Section "Conventional text classification methods" , we explain how text classification with word subspaces is performed under the MSM framework. Then, we present the TF weighted word subspace extension in Section "TF weighted word subspace" . Evaluation experiments and their results are described in Section "Experimental Evaluation" . Further discussion is then presented in Section "Discussion" , and our conclusions are described in Section "Conclusions and Future Work" .
It is mathematically defined as a low dimensional linear subspace in a word vector space with high dimensionality.
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What is the novel concept of word subspace in this paper?
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It is mathematically defined as a low dimensional linear subspace in a word vector space with high dimensionality.
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My coffee tastes too acidic, what can I do to balance it out?
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Here are some of the steps you can take to tweak your coffee brewing process:
* you might be under extracting, try grinding you coffee beans finer
* another way to increase your extraction rate is to use less coffee grinds or more water
* if you’re doing the pour-over method, blooming the coffee first might help removing the co2, improving the extraction
* you might want to try a darker roast, these have more nutty and chocolaty flavors that might better fit your taste
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Hot pot or hotpot (simplified Chinese: 火锅; traditional Chinese: 火鍋; pinyin: huǒguō; lit. 'fire pot'), also known as soup-food or steamboat, is a dish whereby a heat source placed on the dining table keeps a pot of soup stock simmering, and a variety of Chinese foodstuffs and ingredients are served beside the pot for the diners to put into the hot stock.
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Given a reference text that describes about hotpot, from which country does this dish originate?
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Hot pot or Hotpot is originated from China.
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The Muscovy duck has been domesticated for centuries, and is widely traded as "Barbary duck". Muscovy breeds are popular because they have stronger-tasting meat — sometimes compared to roasted beef — than that of the domestic duck. The meat is lean when compared to the fatty meat of mallard-derived ducks, its leanness and tenderness being often compared to veal. Muscovy ducks are also less noisy, and sometimes marketed as a "quackless" duck; even though they are not completely silent, they do not actually quack (except in cases of extreme stress). The carcass of a Muscovy duck is also much heavier than most other domestic ducks, which makes it ideal for the dinner table.
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Extract the ways to spell "Muscovy duck" using the sample as a reference, and place them in a list.
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- Muscovy duck
- Barbary duck
- Quackless duck
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I'm considering buying a pocket knife for my child. How do I know which one I should buy?
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On the market there are many different versions of pocket knives for different purposes and qualities. There are even pocket knives designed specifically for younger children who have no or very little experience with pocket knives, where the blades are not as sharp as usual. Color, size, functions and cost are other factors to consider.
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null | false
| 62
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Simply using NMT systems trained on out-of-domain data for in-domain translation is known to perform badly. In order to effectively use large-scale out-of-domain data for our extremely low-resource task, we propose to perform domain adaptation through either (a) conventional fine-tuning, where an NMT system trained on out-of-domain data is fine-tuned only on in-domain data, or (b) mixed fine-tuning BIBREF9 , where pre-trained out-of-domain NMT system is fine-tuned using a mixture of in-domain and out-of-domain data. The same options are available for transferring from Ja INLINEFORM0 En and Ru INLINEFORM1 En to Ja INLINEFORM2 Ru.
We inevitably involve two types of transfer learning, i.e., domain adaptation for Ja INLINEFORM0 En and Ru INLINEFORM1 En and multilingual transfer for Ja INLINEFORM2 Ru pair. Among several conceivable options for managing these two problems, we examined the following multistage fine-tuning.
Pre-train a multilingual model only on the Ja INLINEFORM0 En and Ru INLINEFORM1 En out-of-domain parallel data (I), where the vocabulary of the model is determined on the basis of the in-domain parallel data in the same manner as the M2M NMT models examined in Section SECREF4 .
Fine-tune the pre-trained model (I) on the in-domain Ja INLINEFORM0 En and Ru INLINEFORM1 En parallel data (fine-tuning, II) or on the mixture of in-domain and out-of-domain Ja INLINEFORM2 En and Ru INLINEFORM3 En parallel data (mixed fine-tuning, III).
Further fine-tune the models (each of II and III) for Ja INLINEFORM0 Ru on in-domain parallel data for this language pair only (fine-tuning, IV and VI) or on all the in-domain parallel data (mixed fine-tuning, V and VII).
We chose this way due to the following two reasons. First, we need to take a balance between several different parallel corpora sizes. The other reason is division of labor; we assume that solving each sub-problem one by one should enable gradual shift of parameters.
We chose this way due to the following two reasons. First, we need to take a balance between several different parallel corpora sizes. The other reason is division of labor; we assume that solving each sub-problem one by one should enable gradual shift of parameters.
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What are the reasons why the authors use both migration models?
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First, they need to take a balance between several different parallel corpora sizes. The other reason is division of labor; they assume that solving each sub-problem one by one should enable gradual shift of parameters.
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null | false
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What is the best data warehouse
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The lakehouse
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null | false
| 171
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How should conversational agents respond to verbal abuse through the user? To answer this question, we conduct a large-scale crowd-sourced evaluation of abuse response strategies employed by current state-of-the-art systems. Our results show that some strategies, such as "polite refusal" score highly across the board, while for other strategies demographic factors, such as age, as well as the severity of the preceding abuse influence the user's perception of which response is appropriate. In addition, we find that most data-driven models lag behind rule-based or commercial systems in terms of their perceived appropriateness.
We describe a dataset developed for Named Entity Recognition in German federal court decisions.
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What dataset do the authors describe?
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It is a dataset developed for Named Entity Recognition in German federal court decisions.
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how to use coffee mug other than drinking
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Use mug as a golf put
Use mug to grow a plant
Use mug for shaving vessel
Use mug as pencil holder
Use mug as a mouse trap
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What is the difference between Columbia and Colombia.
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Colombia is a country located in South America. Columbia can refer to a university or a clothing company. Columbia University is an Ivy League University in New York City. Columbia Sportsware is an American clothing company.
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Who invented the music notation that we know today?
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The greeks
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