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Which of the following city has the highest historical monuments?
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Delhi
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null | false
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What are things I can do to help me score better on a test?
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Here are things you can do better to score better on a test:
1. Pay attention to the instructor during classes
2. Attend office hours to ask questions you cannot figure out
3. Study with a friend group
4. Take practice tests or do practice questions
5. Read the text book or watch online tutorials
6. Teach the material to others to hone your knowledge on different topics
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1810.08732
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In the unsupervised stage, we used two types of unlabeled data to obtain Turkish word embeddings. The first one is a Turkish news-web corpus containing 423M words and 491M tokens, namely the BOUN Web Corpus BIBREF9 , BIBREF10 . The second one is composed of 21M Turkish tweets with 241M words and 293M tokens, where we combined 1M tweets from TS TweetS by Sezer-2013 and 20M Turkish Tweets by Bolat and Amasyalı.
The first one is a Turkish news-web corpus containing 423M words and 491M tokens, namely the BOUN Web Corpus BIBREF9 , BIBREF10 . The second one is composed of 21M Turkish tweets with 241M words and 293M tokens, where we combined 1M tweets from TS TweetS by Sezer-2013 and 20M Turkish Tweets by Bolat and Amasyalı.
|
What data was used to build the word embeddings?
|
The answers are shown as follows:
* Turkish news-web corpus
* TS TweetS by Sezer-2013 and 20M Turkish Tweets by Bolat and Amasyalı
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1909.06434
| false
| null |
We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets. To create a larger discrepancy between the tasks, so that there is a clear dataset size imbalance, the En-De data is artificially restricted to only 1 million parallel sentences, while the full En-Fr dataset, comprising almost 40 million parallel sentences, is used entirely. Words are split into subwords units with a joint vocabulary of 32K tokens. BLEU scores are computed on the tokenized output with multi-bleu.perl from Moses BIBREF10.
We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets. To create a larger discrepancy between the tasks, so that there is a clear dataset size imbalance, the En-De data is artificially restricted to only 1 million parallel sentences, while the full En-Fr dataset, comprising almost 40 million parallel sentences, is used entirely.
|
What datasets are used for experiments?
|
The answers are shown as follows:
* the WMT'14 English-French (En-Fr) and English-German (En-De) datasets.
|
1909.02265
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| null |
We use the publicly available dataset KVRET BIBREF5 in our experiments. This dataset is created by the Wizard-of-Oz method BIBREF10 on Amazon Mechanical Turk platform. This dataset includes dialogues in 3 domains: calendar, weather, navigation (POI) which is suitable for our mix-domain dialogue experiments. There are 2,425 dialogues for training, 302 for validation and 302 for testing, as shown in the upper half of Table TABREF12.
We use the publicly available dataset KVRET BIBREF5 in our experiments.
This dataset includes dialogues in 3 domains: calendar, weather, navigation (POI) which is suitable for our mix-domain dialogue experiments.
|
What multi-domain dataset is used?
|
The answers are shown as follows:
* KVRET
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null | false
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Give me a list of the ways you can be 'out' in cricket.
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- Bowled
- Caught
- Stumped
- Run-out
- Leg Before Wicket (LBW)
- Handling the ball
- Obstructing the field
- Double Hit (hitting the ball twice)
- Hit Wicket
- Timed Out
- Retired Out
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null | false
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Why are windows not attached directly to the studs of a house?
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When installing windows, it is important to remember that houses move over time. Whether from foundation settling, or swaying in the wind, houses are not totally stationary. As a consequence, if you were to attach a new window rigidly to wooden framing, the glass in the window, which is rigid and brittle, might experience cracking or warping. Additionally, the components of the window, such as the seals and hardware that allows the window to open and close, might break down. Window installers will use a combination of shims and flexible sealing materials such as caulk, along with proper flashing, vapor barriers, and siding to make sure windows are water and air tight, and will operate properly for many years into the future.
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An abundance of wildlife occurs in Arches National Park, including spadefoot toads, antelope squirrels, scrub jays, peregrine falcons, many kinds of sparrows, red foxes, desert bighorn sheep, kangaroo rats, mule deers, cougars, midget faded rattlesnakes, yucca moths, western rattlesnakes, and collared lizards.
A number of plant species are common in the park, including prickly pear cactus, Indian ricegrass, bunch grasses, cheatgrass, moss, liverworts, Utah juniper, Mormon tea, blackbrush, cliffrose, four-winged saltbrush, pinyon pine, evening primrose, sand verbena, yucca, and sacred datura.
Biological soil crust consisting of cyanobacteria, lichen, mosses, green algae, and microfungi is found throughout southeastern Utah. The fibrous growths help keep soil particles together, creating a layer that is more resistant to erosion. The living soil layer readily absorbs and stores water, allowing more complex forms of plant life to grow in places with low precipitation levels.
|
What plants and animals are native to Arches National Park
|
Spadefoot toads, antelope squirrels, scrub jays, peregrine falcons, many kinds of sparrows, red foxes, desert bighorn sheep, kangaroo rats, mule deers, cougars, midget faded rattlesnakes, yucca moths, western rattlesnakes, and collared lizards are native animals to Arches National Park.
Prickly pear cactus, Indian ricegrass, bunch grasses, cheatgrass, moss, liverworts, Utah juniper, Mormon tea, blackbrush, cliffrose, four-winged saltbrush, pinyon pine, evening primrose, sand verbena, yucca, and sacred datura are common plant species in Arches National Park
|
null | false
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What is the best city in Ohio?
|
Columbus
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null | false
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The New Deal was a series of programs, public work projects, financial reforms, and regulations enacted by President Franklin D. Roosevelt in the United States between 1933 and 1939. Major federal programs and agencies included the Civilian Conservation Corps (CCC), the Works Progress Administration (WPA), the Civil Works Administration (CWA), the Farm Security Administration (FSA), the National Industrial Recovery Act of 1933 (NIRA) and the Social Security Administration (SSA). They provided support for farmers, the unemployed, youth, and the elderly. The New Deal included new constraints and safeguards on the banking industry and efforts to re-inflate the economy after prices had fallen sharply. New Deal programs included both laws passed by Congress as well as presidential executive orders during the first term of the presidency of Franklin D. Roosevelt.
The programs focused on what historians refer to as the "3 R's": relief for the unemployed and for the poor, recovery of the economy back to normal levels, and reform of the financial system to prevent a repeat depression. The New Deal produced a political realignment, making the Democratic Party the majority (as well as the party that held the White House for seven out of the nine presidential terms from 1933 to 1969) with its base in progressive ideas, the South, big city machines and the newly empowered labor unions, and various ethnic groups. The Republicans were split, with progressive Republicans in support but conservatives opposing the entire New Deal as hostile to business and economic growth. The realignment crystallized into the New Deal coalition that dominated presidential elections into the 1960s while the opposing conservative coalition largely controlled Congress in domestic affairs from 1937 to 1964.
|
Given the reference text about "The New Deal", what are the "3 R's" historians refer to about the program.
|
The "3 R's" historians refer to are the following:
- Relief for the unemployed and for the poor
- Recovery of the economy back to normal levels
- Reform of the financial system to prevent a repeat depression
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Classify the following numbers as 'prime' or 'composite' - 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16.
|
'prime', 'prime', 'composite', 'prime', 'composite', 'prime', 'composite', 'composite', 'composite', 'prime', 'composite', 'prime', 'composite', 'composite', 'composite'.
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The Amazon rainforest, also called Amazon jungle or Amazonia, is a moist broadleaf tropical rainforest in the Amazon biome that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 km2 (2,700,000 sq mi), of which 5,500,000 km2 (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations and 3,344 formally acknowledged indigenous territories.
The majority of the forest, 60%, is in Brazil, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Bolivia, Ecuador, French Guiana, Guyana, Suriname, and Venezuela. Four nations have "Amazonas" as the name of one of their first-level administrative regions, and France uses the name "Guiana Amazonian Park" for French Guiana's protected rainforest area. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees in about 16,000 species.
More than 30 million people of 350 different ethnic groups live in the Amazon, which are subdivided into 9 different national political systems and 3,344 formally acknowledged indigenous territories. Indigenous peoples make up 9% of the total population, and 60 of the groups remain largely isolated
|
Extract the different ways to refer to the Amazon rainforest present in the passage. Separate them by a comma.
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Amazon rainforest, Amazon jungle, Amazonia, Amazonas, Amazon
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What country has the 2nd highest bungie jump in the world?
|
The Nevis bungie jump in Queestown, New Zealand is the seconds highest bungie jump in the world. It is considered world famous because travelers, including celebrities have taken the plunge. You freefall 134 meters in a canyon which offers about 5 - 7 seconds of freefall. The entire Lord of Rings cast has also done the Nevis bungie jump when they were filming the movie series in New Zealand. The Nevis bungie jump has an amazing safety track record and 0 deaths from the extreme sport they offer.
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|
1707.03764
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N-GrAM ranked first in all cases except for the language variety task. In this case, the baseline was the top-ranked system, and ours was second by a small margin. Our system significantly out-performed the baseline on the joint task, as the baseline scored significantly lower for the gender task than for the variety task.
N-GrAM ranked first in all cases except for the language variety task. In this case, the baseline was the top-ranked system, and ours was second by a small margin. Our system significantly out-performed the baseline on the joint task, as the baseline scored significantly lower for the gender task than for the variety task.
|
On which task does do model do worst?
|
Gender prediction task
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null | false
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Can you tell me about different types of hot water kettles?
|
I'd be happy to tell you about hot water kettles. Hot water kettles are used to heat up water for beverages like tea or coffee. There are a few different kinds:
- Stove-top kettles, which are placed on a stove and make a sound when the water is boiling.
- Electric water kettles, which heat up using electricity rather than a stove. These can have many controls and contain thermostats to control the target water heat.
- Gooseneck kettles, which can be electric or stovetop but feature a thin spout which is useful for making pour-over coffee.
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What is the ranking of poker hands from strongest to weakest that you can make with a regular deck of 52 cards and five cards total?
|
The ranking of poker hands with 52 regular playing cards is:
1) A Royal Flush - this hand consists of ten, jack, queen, king and ace of a single suit (e.g. ten, jack, queen, king, ace, all of spades)
2) A Straight Flush - this hand consists of five consecutive cards in a single suit. The ace counts as either the highest or lowest, but not both at the same time. (e.g. five, six, seven, eight, nine, all of hearts)
3) Four-of-a-kind - this hand contains four of the same card (e.g. four tens)
4) Full house - Three-of-a-kind of the same card, and a pair of a different card. (e.g. three sevens and two jacks)
5) Flush - Five cards of the same suits (e.g. five spades)
6) Straight - Five consecutive cards, where the ace can count as either the highest or lowest card, but not both at the same time (e.g. ace, two, three, four, five).
7) Three-of-a-kind - this hand contains three of the same card (e.g. three sevens)
8) Two pair - this hand contains two sets of two cards that are the same (e.g. two fives and two queens)
9) One pair - this hand contains one set of two cards that are the same (e.g. two tens)
10) High card - this hand contains none of the other options, and just has a high card.
For all poker hands, if they are of the same ranking, then one has to look at the height of the cards themselves to determine the strongest hand. A full house of three aces and two tens is stronger than a full house of three kings and two tens. At the same time, three kings and two tens beats three tens and two aces, because the kings are stronger.
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Which of the following are things you would commonly pack for a camping trip: freeze dried meals, sleeping bags, curling iron, lantern, axe, tent, high heels, hiking boots, mirrors, vintage trunk, antique lamp, swiss army knife
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freeze dried meals, sleeping bags, lantern, axe, tent, hiking boots, swiss army knife
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null | false
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The cello (/ˈtʃɛloʊ/ CHEL-oh), properly violoncello (/ˌvaɪələnˈtʃɛloʊ/ VY-ə-lən-CHEL-oh, Italian pronunciation: [vjolonˈtʃɛllo]), is a bowed (sometimes plucked and occasionally hit) string instrument of the violin family. Its four strings are usually tuned in perfect fifths: from low to high, C2, G2, D3 and A3. The viola's four strings are each an octave higher. Music for the cello is generally written in the bass clef, with tenor clef, and treble clef used for higher-range passages.
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How many strings does a cello have?
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A cello has four strings
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What are some ways to help someone fall asleep?
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Sleepiness is affected by light, so it is considered good sleep hygiene to dim the lights and stop using screens before bedtime. Additionally making sure that the bedroom is quiet and cool can greatly assist with sleep.
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null | false
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Which is the odd one out? Silver, Gold, Mercury, Phosphorous, Copper, Zinc
|
Phosphorus is the odd one out. The rest are metals.
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null | false
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How long is a marathon?
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A marathon is 26.2 miles and 42.195 kilometers
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null | false
| 32
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We have described the NLP4IF@EMNLP-IJCNLP 2019 shared task on fine-grained propaganda identification. We received 25 and 12 submissions on the test set for the sentence-level classification and the fragment-level classification tasks, respectively. Overall, the sentence-level task was easier and most submitted systems managed to outperform the baseline. The fragment-level task proved to be much more challenging, with lower absolute scores, but most teams still managed to outperform the baseline.
We plan to make the schema and the dataset publicly available to be used beyond NLP4IF. We hope that the corpus would raise interest outside of the community of researchers studying propaganda: the techniques related to fallacies and the ones relying on emotions might provide a novel setting for researchers interested in Argumentation and Sentiment Analysis.
As a kind of advertisement, Task 11 at SemEval 2020 is a follow up of this shared task. It features two complimentary tasks:
Given a free-text article, identify the propagandist text spans.
Given a text span already flagged as propagandist and its context, identify the specific propaganda technique it contains.
This setting would allow participants to focus their efforts on binary sequence labeling for Task 1 and on multi-class classification for Task 2.
Overall, the sentence-level task was easier and most submitted systems managed to outperform the baseline.
|
Was the sentence-level task easier?
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Yes.
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null | false
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Why do Jews eat matza on Passover?
|
The book of Exodus commands the Jews to eat matza. The bible says that at the time of the exodus, the Jews left Egypt in such a hurry that their dough did not have time to rise. To commemorate this, they eat unleavened bread, or matza, every Passover.
Many Jews eat matza today, even if they do not believe that they obeying a divine command. They may eat matza as a means of connecting with their Jewish heritage, or because they are participating in a seder, the traditional Passover meal, or just because they like the taste.
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null | false
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Identify which instrument is string or woodwind: Cello, Danso
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Cello is string, Danso is woodwind.
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null | false
| 393
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One of the significant challenges in contemporary information processing is the sheer volume of available data. BIBREF0 , for example, claim that the amount of digital data in the world doubles every two years. This trend underpins efforts to develop algorithms that can efficiently search for relevant information in huge datasets. One class of such algorithms, represented by, e.g., Locality Sensitive Hashing BIBREF1 , relies on hashing data into short, locality-preserving binary codes BIBREF2 . The codes can then be used to group the data into buckets, thereby enabling sublinear search for relevant information, or for fast comparison of data items. Most of the algorithms from this family are data-oblivious, i.e. can generate hashes for any type of data. Nevertheless, some methods target specific kind of input data, like text or image.
In this work we focus on learning binary codes for text documents. An important work in this direction has been presented by BIBREF3 . Their semantic hashing leverages autoencoders with sigmoid bottleneck layer to learn binary codes from a word-count bag-of-words (BOW) representation. Salakhutdinov & Hinton report that binary codes allow for up to 20-fold improvement in document ranking speed, compared to real-valued representation of the same dimensionality. Moreover, they demonstrate that semantic hashing codes used as an initial document filter can improve precision of TF-IDF-based retrieval. Learning binary representation from BOW, however, has its disadvantages. First, word-count representation, and in turn the learned codes, are not in itself stronger than TF-IDF. Second, BOW is an inefficient representation: even for moderate-size vocabularies BOW vectors can have thousands of dimensions. Learning fully-connected autoencoders for such high-dimensional vectors is impractical. Salakhutdinov & Hinton restricted the BOW vocabulary in their experiments to 2000 most frequent words.
Binary codes have also been applied to cross-modal retrieval where text is one of the modalities. Specifically, BIBREF4 incorporated tag information that often accompany text documents, while BIBREF5 employed siamese neural networks to learn single binary representation for text and image data.
Recently several works explored simple neural models for unsupervised learning of distributed representations of words, sentences and documents. BIBREF6 proposed log-linear models that learn distributed representations of words by predicting a central word from its context (CBOW model) or by predicting context words given the central word (Skip-gram model). The CBOW model was then extended by BIBREF7 to learn distributed representations of documents. Specifically, they proposed Paragraph Vector Distributed Memory (PV-DM) model, in which the central word is predicted given the context words and the document vector. During training, PV-DM learns the word embeddings and the parameters of the softmax that models the conditional probability distribution for the central words. During inference, word embeddings and softmax weights are fixed, but the gradients are backpropagated to the inferred document vector. In addition to PV-DM, Le & Mikolov studied also a simpler model, namely Paragraph Vector Distributed Bag of Words (PV-DBOW). This model predicts words in the document given only the document vector. It therefore disregards context surrounding the predicted word and does not learn word embeddings. Le & Mikolov demonstrated that paragraph vectors outperform BOW and bag-of-bigrams in information retrieval task, while using only few hundreds of dimensions. These models are also amendable to learning and inference over large vocabularies. Original CBOW network used hierarchical softmax to model the probability distribution for the central word. One can also use noise-contrastive estimation BIBREF8 or importance sampling BIBREF9 to approximate the gradients with respect to the softmax logits.
An alternative approach to learning representation of pieces of text has been recently described by BIBREF10 . Networks proposed therein, inspired by the Skip-gram model, learn to predict surrounding sentences given the center sentence. To this end, the center sentence is encoded by an encoder network and the surrounding sentences are predicted by a decoder network conditioned on the center sentence code. Once trained, these models can encode sentences without resorting to backpropagation inference. However, they learn representations at the sentence level but not at the document level.
In this work we present Binary Paragraph Vector models, an extensions to PV-DBOW and PV-DM that learn short binary codes for text documents. One inspiration for binary paragraph vectors comes from a recent work by BIBREF11 on learning binary codes for images. Specifically, we introduce a sigmoid layer to the paragraph vector models, and train it in a way that encourages binary activations. We demonstrate that the resultant binary paragraph vectors significantly outperform semantic hashing codes. We also evaluate binary paragraph vectors in transfer learning settings, where training and inference are carried out on unrelated text corpora. Finally, we study models that simultaneously learn short binary codes for document filtering and longer, real-valued representations for ranking. While BIBREF11 employed a supervised criterion to learn image codes, binary paragraph vectors remain unsupervised models: they learn to predict words in documents.
First, word-count representation, and in turn the learned codes, are not in itself stronger than TFIDF. Second, BOW is an inefficient representation: even for moderate-size vocabularies BOW vectors can have thousands of dimensions. Learning fully-connected autoencoders for such highdimensional vectors is impractical.
|
What are the disadvantages of learning binary representation from BOW?
|
First, word-count representation, and in turn the learned codes, are not in itself stronger than TFIDF. Second, BOW is an inefficient representation: even for moderate-size vocabularies BOW vectors can have thousands of dimensions. Learning fully-connected autoencoders for such highdimensional vectors is impractical.
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null | false
| null |
Open Doors is a non-denominational mission supporting persecuted Christians in the world. They work with local partners to distribute Bibles and Christian literature, give discipleship training and provide practical support, such as emergency relief aid. Open Doors' stated aims are to raise awareness of global persecution, mobilising prayer, support and action among Christians from around the world. It is based in Ermelo, The Netherlands. Open Doors is also a member of the Forum of Bible Agencies International.
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Please provided me some context in form of bullet points on Open Doors organisation
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1. Open Doors is a nondenominational organisation that helps Christians who are persecuted throughout the world.
2. They collaborate with local partners to spread Bibles and Christian literature, provide discipleship training, and offer practical assistance like immediate aid in times of need.
3. The stated goals of Open Doors are to increase awareness of international persecution and to encourage Christians all across the world to pray, support, and take action.
4. Its headquarters are in Ermelo, Netherlands.
5. Open Doors is a part of the International Forum of Bible Agencies.
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1705.09665
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We find that dynamic communities, such as Seahawks or starcraft, have substantially higher rates of monthly user retention than more stable communities (Spearman's INLINEFORM0 = 0.70, INLINEFORM1 0.001, computed with community points averaged over months; Figure FIGREF11 .A, left). Similarly, more distinctive communities, like Cooking and Naruto, exhibit moderately higher monthly retention rates than more generic communities (Spearman's INLINEFORM2 = 0.33, INLINEFORM3 0.001; Figure FIGREF11 .A, right).
As with monthly retention, we find a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community (Spearman's INLINEFORM0 = 0.41, INLINEFORM1 0.001, computed over all community points; Figure FIGREF11 .B, left). This verifies that the short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content. Interestingly, there is no significant relationship between distinctiveness and long-term engagement (Spearman's INLINEFORM2 = 0.03, INLINEFORM3 0.77; Figure FIGREF11 .B, right). Thus, while highly distinctive communities like RandomActsOfMakeup may generate focused commitment from users over a short period of time, such communities are unlikely to retain long-term users unless they also have sufficiently dynamic content.
We find that dynamic communities, such as Seahawks or starcraft, have substantially higher rates of monthly user retention than more stable communities (Spearman's INLINEFORM0 = 0.70, INLINEFORM1 0.001, computed with community points averaged over months; Figure FIGREF11 .A, left). Similarly, more distinctive communities, like Cooking and Naruto, exhibit moderately higher monthly retention rates than more generic communities (Spearman's INLINEFORM2 = 0.33, INLINEFORM3 0.001; Figure FIGREF11 .A, right).
As with monthly retention, we find a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community (Spearman's INLINEFORM0 = 0.41, INLINEFORM1 0.001, computed over all community points; Figure FIGREF11 .B, left). This verifies that the short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content. Interestingly, there is no significant relationship between distinctiveness and long-term engagement (Spearman's INLINEFORM2 = 0.03, INLINEFORM3 0.77; Figure FIGREF11 .B, right). Thus, while highly distinctive communities like RandomActsOfMakeup may generate focused commitment from users over a short period of time, such communities are unlikely to retain long-term users unless they also have sufficiently dynamic content.
|
How do the various social phenomena examined manifest in different types of communities?
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Dynamic communities have substantially higher rates of monthly user retention than more stable communities. More distinctive communities exhibit moderately higher monthly retention rates than more generic communities. There is also a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community - a short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content.
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null | false
| 491
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In this section, Figure 6 demonstrate that the discriminating performance of MANDERA when hierarchical clustering and Gaussian mixture models are used in-place of K-means for FASHIONMNIST data set remain robust.****Proof. According to Theorem 1, 2 and Corollary 1, when both N∗ and p are large enough, with probability 1 there exist (eb, vb), (em, vm) and δ > 0 such that ||(eb, vb) − (em, vm)||2 > δ, and ||(ei , vi) − (eb, vb)||2 ≤ δ 2 for ∀ i ∈ Ib and ||(ei , vi) − (em, vm)||2 ≤ δ 2 for ∀ i ∈ Im. Therefore, with a reasonable clustering algorithm such as K-mean with K = 2, we would expect Iˆb = Ib with probability 1. Because we can always find a ∆ > 0 such that ||Mi,: − Mj,: ||2 ≤ ∆ for any node pair (i, j) in a fixed dataset with a finite number of nodes, and mˆ b,: = mb,: when Iˆ b = Ib, we have E||mˆ b,: − mb,: ||2 ≤ ∆ · P(Iˆ b ≠ Ib), and thus lim N∗→∞ limp→∞ E||mˆ b,: − mb,: ||2 = 0. It completes the proof.
|
What about the use of a better clustering technique?
|
To provide an answer to your question “What about the use of a better clustering technique?”, we have performed an evaluation to replace the K-means algorithm in the current setting with other clustering methods, including hierarchical clustering and Gaussian mixture models. We found that performance of MANDERA is quite robust to different choices of the clustering methods. We have included these additional comparisons in Appendix E.
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The 1977 Tournament Players Championship was a golf tournament in Florida on the PGA Tour, held March 17–20 at Sawgrass Country Club in Ponte Vedra Beach, southeast of Jacksonville. The fourth Tournament Players Championship, it was the first of five consecutive at Sawgrass, which had agreed a multi-year with the PGA Tour in 1976.
Mark Hayes was the champion in windy conditions at 289 (+1), two strokes ahead of runner-up Mike McCullough. McCullough and Tom Watson were the co-leaders after the third round, with Hayes a stroke back. Defending champion Jack Nicklaus finished four strokes back, in a tie for fifth place.
Bob Menne set the tour's 72-hole record for fewest putts with 99, but tied for 47th at 303 (+15). The record had been 102 putts, by Bert Yancey in 1966 at his victory in the final Portland Open Invitational.
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Given this information about the players championship tournament, what were the weather conditions on March 20th 1977?
|
It was windy
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What is an interception in American football? Write your response as if you are American explaining it to a coworker from another country.
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The player that is under center is called the quarterback. His job is to throw or handoff the football to an eligible member of this own team. Usually, the ball is given to the running back, wide receiver, or tide end. An interception occurs when the quarterback throws the football to his intended team mate and a member of the opposing team catches the football instead. When this occurs, the opposing team gains possession of the football and their offense goes out into the field and they have an opportunity to drive the football down the field with the goal of scoring a touchdown or field goal.
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Rick was asked, Suppose your mother has four sons, named East, West, and South. What's the fourth son's name?
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Well, I hope you didn't expect me to say North? Because the question was asked to Rick, who is also the son of his mom, the answer would be his own name, Rick.
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| 163
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Errors committed by our system arise from multiple factors, starting from limitations of the eye-tracker hardware to errors committed by linguistic tools and resources. Also, aggregating various eye-tracking parameters to extract the cognitive features may have caused information loss in the regular classification setting.
Errors committed by our system arise from multiple factors, starting from limitations of the eye-tracker hardware to errors committed by linguistic tools and resources. Also, aggregating various eye-tracking parameters to extract the cognitive features may have caused information loss in the regular classification setting.
|
What could be the reasons for errors committed by the team's system?
|
Limitations of the eye-tracker hardware, errors committed by linguistic tools and resources, and aggregating various eye-tracking parameters to extract the cognitive features.
|
2002.05104
| false
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In this study we observed the actual impact of several components within VQA models. We have shown that transformer-based encoders together with GRU models provide the best performance for question representation. Notably, we demonstrated that using pre-trained text representations provide consistent performance improvements across several hyper-parameter configurations. We have also shown that using an object detector fine-tuned with external data provides large improvements in accuracy. Our experiments have demonstrated that even simple fusion strategies can achieve performance on par with the state-of-the-art. Moreover, we have shown that attention mechanisms are paramount for learning top performing networks, once they allow producing question-aware image representations that are capable of encoding spatial relations. It became clear that Top-Down is the preferred attention method, given its results with ReLU activation. It is is now clear that some configurations used in some architectures (e.g., additional RNN layers) are actually irrelevant and can be removed altogether without harming accuracy. For future work, we expect to expand this study in two main ways: (i) cover additional datasets, such as Visual Genome BIBREF37; and (ii) study in an exhaustive fashion how distinct components interact with each other, instead of observing their impact alone on the classification performance.
We have shown that transformer-based encoders together with GRU models provide the best performance for question representation. Notably, we demonstrated that using pre-trained text representations provide consistent performance improvements across several hyper-parameter configurations. We have also shown that using an object detector fine-tuned with external data provides large improvements in accuracy.
Moreover, we have shown that attention mechanisms are paramount for learning top performing networks, once they allow producing question-aware image representations that are capable of encoding spatial relations. It became clear that Top-Down is the preferred attention method, given its results with ReLU activation.
|
What components are identified as core components for training VQA models?
|
The answers are shown as follows:
* pre-trained text representations
* transformer-based encoders together with GRU models
* attention mechanisms are paramount for learning top performing networks
* Top-Down is the preferred attention method
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null | false
| null |
What is the best seasoning to use on steak?
|
The best seasoning combination for any steak is the classic Garlic, Pepper, and Salt combination. Salt will draw the water out of the meat, the water will dissolve the salt, and the moisture will then get reabsorbed back, breaking down protein, and ultimately tenderizing the meat. Garlic and Pepper pair very well with Salt and meat, crafting the best possible seasoning experience.
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null | false
| 217
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Extractive multi-document summarization (MDS) aims to summarize a collection of documents by selecting a small number of sentences that represent the original content appropriately. Typical objectives for assembling a summary include information coverage and non-redundancy. A wide variety of methods have been introduced to approach MDS.
Many approaches are based on sentence ranking, i.e. assigning each sentence a score that indicates how well the sentence summarizes the input BIBREF0 , BIBREF1 , BIBREF2 . A summary is created by selecting the top entries of the ranked list of sentences. Since the sentences are often treated separately, these models might allow redundancy in the summary. Therefore, they are often extended by an anti-redundancy filter while de-queuing ranked sentence lists.
Other approaches work at summary-level rather than sentence-level and aim to optimize functions of sets of sentences to find good summaries, such as KL-divergence between probability distributions BIBREF3 or submodular functions that represent coverage, diversity, etc. BIBREF4
The centroid-based model belongs to the former group: it represents sentences as bag-of-word (BOW) vectors with TF-IDF weighting and uses a centroid of these vectors to represent the whole document collection BIBREF5 . The sentences are ranked by their cosine similarity to the centroid vector. This method is often found as a baseline in evaluations where it usually is outperformed BIBREF0 , BIBREF6 .
This baseline can easily be adapted to work at the summary-level instead the sentence level. This is done by representing a summary as the centroid of its sentence vectors and maximizing the similarity between the summary centroid and the centroid of the document collection. A simple greedy algorithm is used to find the best summary under a length constraint.
In order to keep the method efficient, we outline different methods to select a small number of candidate sentences from each document in the input collection before constructing the summary.
We test these modifications on the DUC2004 dataset for multi-document summarization. The results show an improvement of Rouge scores over the original centroid method. The performance is on par with state-of-the-art methods which shows that the similarity between a summary centroid and the input centroid is a well-suited function for global summary optimization.
The summarization approach presented in this paper is fast, unsupervised and simple to implement. Nevertheless, it performs as well as more complex state-of-the-art approaches in terms of Rouge scores on the DUC2004 dataset. It can be used as a strong baseline for future research or as a fast and easy-to-deploy summarization tool.
We test these modifications on the DUC2004 dataset for multi-document summarization.
|
How to test the modifications for multi-document summarization?
|
The authors test these modifications on the DUC2004 dataset for multi-document summarization.
|
1805.09959
| true
| null |
Our study focused primarily on English tweets, since this was the language of our diagnostic training sample. Future studies could incorporate other languages using our proposed framework. It would be important to also expand the API queries with translations of `breast' and `cancer'. This could allow for a cross cultural comparison of how social media influences patients and what patients express on social media.
Our study focused primarily on English tweets, since this was the language of our diagnostic training sample.
|
Do the authors report results only on English datasets?
|
Yes.
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null | false
| null |
Write me a thank you note for my high-school teacher
|
Thank you for being a supportive teacher and encouraging me to keep learning. I will always remember your encouragement as I move forward in my studies.
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What are the names of popular Alternative music bands from the 1980s and 1990s.
|
Bands that were considered Alternative rock music in the 1980s were R.E.M., Depeche Mode, Nirvana, O.M.D., the Smiths, the Psychadelic Furs, the Housemartins, Green Day, the Violent Femmes, the B-52s, the Cure, UB40, and U2, just to name a few. Keep in mind that some of the bands that had been considered Alternative in the 1980s became more mainstream in the 1990s as they gained popularity (R.E.M., Depeche Mode, Green Day, U2, The Cure). The 1990s Alternative rock music bands that I remember are the Counting Crows, the Red Hot Chili Peppers, Third Eye Blind, the Stone Temple Pilots, Live, Toad the Wet Sprocket, Blues Traveler, the Spin Doctors, and No Doubt. In the 2000's, the popular Alternative rock music bands were Maroon 5, Coldplay, Train, the White Stripes, Linkin Park, Gorillaz, Kings of Leon, Weezer, MGMT, Nickelback, Radiohead, Goo Goo Dolls, Blink-182.
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null | false
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Earnst & Young Entrepreneur of the year awards 2021 was given to whom
|
Falguni Nayar(founder and CEO of Nykaa)
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null | false
| null |
What colors are on the United States of America flag?
|
Red, white, blue
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null | false
| null |
What is the biggest fish in the ocean?
|
Whale Sharks are the biggest fish in the ocean coming in at 23 feet. Several species of whales are much larger, but they are technically mammals.
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Thanjavur painting is a classical South Indian painting style, which was inaugurated from the town of Thanjavur (anglicized as Tanjore) in Tamil Nadu. The art form draws its immediate resources and inspiration from way back about 1600 AD, a period when the Nayakas of Thanjavur under the suzerainty of the Vijayanagara Rayas encouraged art—chiefly, classical dance and music—as well as literature, both in Telugu and Tamil and painting of chiefly Hindu religious subjects in temples. It is distinguished by its famous gold coating. However, it can safely be surmised that Thanjavur painting, as we know it now, originated in the Maratha court of Thanjavur (1676–1855).It has been recognized as a Geographical indication by the Government of India in 2007–08. Thanjavur paintings are characterized by rich and vivid colors, simple icon composition, glittering gold foils overlaid on delicate but extensive gesso work and inlay of glass beads and pieces or precious and semi-precious gems. In Thanjavur paintings one can see the influence of Deccani, Vijayanagar, Maratha and even European or Company styles of painting. Essentially serving as devotional icons, the subjects of most paintings are Hindu gods, goddesses, and saints. Episodes from Hindu Puranas, Sthala-puranas and other religious texts were visualized, sketched or traced and painted with the main figure or figures placed in the central section of the picture (mostly within an architecturally delineated space such as a mantapa or prabhavali) surrounded by several subsidiary figures, themes and subjects. There are also many instances when Jain, Sikh, Muslim, other religious and even secular subjects were depicted in Tanjore paintings. Thanjavur paintings are panel paintings done on wooden planks, and hence referred to as Palagai padam in local parlance. In modern times, these paintings have become souvenirs for festive occasions in South India.
|
Given this paragraph about Thanjavur paintings and its origins, what are the typical features of Thanjavur paintings?
|
Thanjavur paintings are easily discernible from the gold coating on wooden panels which form the base. They are characterized by vivid colors, simple composition of icons along with usage of glass beads and at times precious stones. The subjects of paintings have traditionally been Hindus gods though they have evolved to depict secular subjects from other religions.
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How do I cook basmati rice?
|
To cook basmati rice perfectly, first rinse the desired amount of rice until the water runs clear. Then, in a saucepan with a tight fitting lid and over medium heat, add rice and water in a 1C rice to 1.5C water or other liquid (like broth) ratio. Bring the pot to a simmer, reduce heat to low, cover and let cook for 15 minutes. Let the rice sit for another 10 minutes off of the heat with the lid on and the fluff with a fork and enjoy! To make the rice more flavorful, you can add cumin or other spices and salt.
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null | false
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Wat id DNA annotation?
|
DNA annotation or genome annotation is the process of identifying the locations of genes and all of the coding regions in a genome and determining what those genes do. An annotation (irrespective of the context) is a note added by way of explanation or commentary. Once a genome is sequenced, it needs to be annotated to make sense of it. Genes in a eukaryotic genome can be annotated using various annotation tools such as FINDER. A modern annotation pipeline can support a user-friendly web interface and software containerization such as MOSGA. Modern annotation pipelines for prokaryotic genomes are Bakta, Prokka and PGAP.
For DNA annotation, a previously unknown sequence representation of genetic material is enriched with information relating genomic position to intron-exon boundaries, regulatory sequences, repeats, gene names and protein products. This annotation is stored in genomic databases such as Mouse Genome Informatics, FlyBase, and WormBase. Educational materials on some aspects of biological annotation from the 2006 Gene Ontology annotation camp and similar events are available at the Gene Ontology website.
The National Center for Biomedical Ontology develops tools for automated annotation of database records based on the textual descriptions of those records.
As a general method, dcGO has an automated procedure for statistically inferring associations between ontology terms and protein domains or combinations of domains from the existing gene/protein-level annotations.
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null | false
| null |
King Field (alternately, Kingfield) is a neighborhood in the Southwest community in Minneapolis, Minnesota. Its boundaries are 36th Street to the north, Interstate 35W to the east, 46th Street to the south, and Lyndale Avenue to the west. King Field, within the King Field neighborhood is a park named after Martin Luther King Jr.
This was farm country in the 2nd half of the 19th century. Transportation was by horse and buggy. Fewer than 20 farms had been established by 1874. C.C. Garvey owned a dairy farm near 44th Street and Grand Ave. George Bichnell farmed 18 acres (73,000 m2) of land from Lyndale to Pleasant, 42nd to 43rd Streets. Hiram Van Nest farmed 28 acres (110,000 m2) of land from 40th to 42nd, Pleasant to Lyndale. The Farmsworth farm occupied 57 acres (230,000 m2) south of 47th Street and East of Nicollet.
|
Were cars used for transportation in the 19th century in King Field?
|
No, transportation was by horse and buggy
|
1805.03122
| false
| null |
When testing across languages, we report accuracy for two setups: average accuracy over each single-language model (Avg), and accuracy obtained when training on the concatenation of all languages but the target one (All). The latter setting is also used for the embeddings model. We report accuracy for all experiments.
When testing across languages, we report accuracy for two setups: average accuracy over each single-language model (Avg), and accuracy obtained when training on the concatenation of all languages but the target one (All). The latter setting is also used for the embeddings model. We report accuracy for all experiments.
|
What are the evaluation metrics used?
|
The answers are shown as follows:
* average accuracy over each single-language model (Avg), and accuracy obtained when training on the concatenation of all languages but the target one (All)
|
null | false
| null |
What is the greenhouse effect?
|
The greenhouse effect is the process by which the sun's energy gets trapped in a planet's atmosphere and heat's the planet's surface. The energy gets trapped due to the planet's atmosphere having a higher transparency to visible radiation from the sun than it does to infrared radiation that gets emitted after bouncing off of the planet's surface.
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Charlestown is the oldest neighborhood in Boston, Massachusetts, in the United States. Originally called Mishawum by the Massachusett tribe, it is located on a peninsula north of the Charles River, across from downtown Boston, and also adjoins the Mystic River and Boston Harbor waterways. Charlestown was laid out in 1629 by engineer Thomas Graves, one of its earliest settlers, during the reign of Charles I of England. It was originally a separate town and the first capital of the Massachusetts Bay Colony.
Charlestown became a city in 1848 and was annexed by Boston on January 5, 1874. With that, it also switched from Middlesex County, to which it had belonged since 1643, to Suffolk County. It has had a substantial Irish-American population since the migration of Irish people during the Great Irish Famine of the 1840s. Since the late 1980s, the neighborhood has changed dramatically because of its proximity to downtown and its colonial architecture. A mix of yuppie and upper-middle-class gentrification has influenced much of the area, as it has in many of Boston's neighborhoods, but Charlestown still maintains a strong Irish-American population.
In the 21st century, Charlestown's diversity has expanded dramatically, along with growing rates of the very poor and very wealthy. Today Charlestown is a largely residential neighborhood, with much housing near the waterfront, overlooking the Boston skyline. Charlestown is home to many historic sites, hospitals and organizations, with access from the Orange Line Sullivan Square or Community College stops or the I-93 expressway.
|
What is the oldest part of Boston to live in, based on the historic neighborhood?
|
Charlestown, Massachusetts
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null | false
| null |
The LIVA Miss Diva Supranational 2021 was given to whom?
|
Ritika Khatnani (Pune)
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|
2003.05377
| false
| null |
In this work, we use the Python Word2Vec implementation provided by the Gensim library. The Portuguese pre-trained word embeddings created by BIBREF10 and available for download was used to represent words as vectors. We only used models of dimension 300 and, for Word2Vec, Wang2Vec, and FastText, skip-gram architectured models.
We only used models of dimension 300 and, for Word2Vec, Wang2Vec, and FastText, skip-gram architectured models.
|
what word embedding techniques did they experiment with?
|
The answers are shown as follows:
* Word2Vec, Wang2Vec, and FastText
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null | false
| null |
Oganesson is a synthetic chemical element with the symbol Og and atomic number 118. It was first synthesized in 2002 at the Joint Institute for Nuclear Research (JINR) in Dubna, near Moscow, Russia, by a joint team of Russian and American scientists. In December 2015, it was recognized as one of four new elements by the Joint Working Party of the international scientific bodies IUPAC and IUPAP. It was formally named on 28 November 2016. The name honors the nuclear physicist Yuri Oganessian, who played a leading role in the discovery of the heaviest elements in the periodic table. It is one of only two elements named after a person who was alive at the time of naming, the other being seaborgium, and the only element whose eponym is alive as of 2023.
|
Based on the passage provided, what are the only two elements in the periodic table that were names after a person who was alive at the time of naming?
|
Organesson and Seaborgium are the only two elements in the periodic table that were names after a person who was alive at the time of naming.
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null | false
| null |
Give me a bulleted list of the 7 wonders of the ancient world:
|
Here are the 7 wonders of the ancient world:
- Great Pyramid of Giza, in El Giza, Egypt, the earliest of the wonders to be completed, as well as the only one that still exists in the present day.
- Colossus of Rhodes, in the harbor of the city of Rhodes, on the Greek island of the same name.
- Hanging Gardens of Babylon, in Babylon, near present-day Hillah, Babil province, Iraq; or Nineveh, Mosul, Nineveh Governorate, Iraq.
- Lighthouse of Alexandria, in Alexandria, Egypt.
- Mausoleum at Halicarnassus, in Halicarnassus, a city of the Achaemenid Empire in present-day Turkey.
- Statue of Zeus at Olympia, in Olympia, Greece.
- Temple of Artemis at Ephesus, in the city of Ephesus, near present-day Selçuk, Turkey.
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null | false
| null |
Which of these colors are primary colors?
|
Blue, Green, Violet, Orange, Indigo, Turquoise, Red
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null | false
| null |
Imagine you are the last person on Earth. Write a diary entry describing your thoughts and feelings.
|
I finally witness the day when humanity was brought to an end. It wasn't due to a technological explosion, depletion of resources, or uncontrolled wars. It was arrogance, the innate arrogance of humanity, that brought everything to an end. And now, this is the end of my life but will also mark the beginning of all cycles.
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|
1911.01188
| false
| null |
We train three systems (S1, S2 and S3) with the corpora summarised in Table TABREF5. The first two systems are transformer models trained on different amounts of data (6M vs. 18M parallel sentences as seen in the Table). The third system includes a modification to consider the information of full coreference chains throughout a document augmenting the sentence to be translated with this information and it is trained with the same amount of sentence pairs as S1. A variant of the S3 system participated in the news machine translation of the shared task held at WMT 2019 BIBREF43.
We train three systems (S1, S2 and S3) with the corpora summarised in Table TABREF5. The first two systems are transformer models trained on different amounts of data (6M vs. 18M parallel sentences as seen in the Table). The third system includes a modification to consider the information of full coreference chains throughout a document augmenting the sentence to be translated with this information and it is trained with the same amount of sentence pairs as S1.
|
Which three neural machine translation systems are analyzed?
|
The answers are shown as follows:
* first two systems are transformer models trained on different amounts of data
* The third system includes a modification to consider the information of full coreference chains
|
2001.02380
| false
| null |
We reason that, if a token is important for predicting the correct label, masking it will degrade the model's classification accuracy, or at least reduce its reported classification certainty. In SECREF36, it seems reasonable to assume that masking the word `To' has a greater impact on predicting the label purpose than masking the word `provide', and even less so, the following noun `information'. We therefore use reduction in softmax probability of the correct relation as our signaling strength metric for the model. We call this metric ${\Delta }_s$ (for delta-softmax), which can be written as:
where $rel$ is the true relation of the EDU pair, $t_i$ represents the token at index $i$ of $N$ tokens, and $X_{mask=i}$ represents the input sequence with the masked position $i$ (for $i \in 1 \ldots N$ ignoring separators, or $\phi $, the empty set).
We therefore use reduction in softmax probability of the correct relation as our signaling strength metric for the model. We call this metric ${\Delta }_s$ (for delta-softmax), which can be written as:
where $rel$ is the true relation of the EDU pair, $t_i$ represents the token at index $i$ of $N$ tokens, and $X_{mask=i}$ represents the input sequence with the masked position $i$ (for $i \in 1 \ldots N$ ignoring separators, or $\phi $, the empty set).
|
How is the delta-softmax calculated?
|
Answer with content missing: (Formula) Formula is the answer.
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null | false
| null |
What are equity securities?
|
Equity securities are financial assets that represent ownership of a corporation. The most prevalent type of equity security is common stock. And the characteristic that most defines an equity security—differentiating it from most other types of securities—is ownership.
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null | false
| 310
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After we obtain the representation of the source-target word pair by the convolution layer, we follow a similar architecture as BIBREF6 to refine the representation of the word pairs using feed-forward and recurrent networks.
Two feed-forward layers of size 400 with rectified linear units (ReLU; BIBREF9 );
One bi-directional gated recurrent unit (BiGRU; BIBREF10 ) layer with hidden size 200, where the forward and backward hidden states are concatenated and further normalized by layer normalization BIBREF11 .
Two feed-forward layers of hidden size 200 with rectified linear units;
One BiGRU layer with hidden size 100 using the same configuration of the previous BiGRU layer;
Two feed-forward layers of size 100 and 50 respectively with ReLU activation.
We concatenate the 31 baseline features extracted by the Marmot toolkit with the last 50 feed-forward hidden features. The baseline features are listed in Table TABREF13 . We then apply a softmax layer on the combined features to predict the binary labels.
We concatenate the 31 baseline features extracted by the Marmot2 toolkit with the last 50 feedforward hidden features. The baseline features are listed in Table 2.
|
What toolkit is used to extract baseline features?
|
The Marmot2 toolkit.
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null | false
| null |
If you are stranded in the African bush, what’s the best way to stay alive ?
|
The best advice is to stay calm, panicking will increase your stress levels and could lead to dehydration faster.
The next is to assess your situation, look for high trees , this can help you with both safety from animals on the ground and can help with getting a better assessment of your surroundings and see if you can get a connection on your phone.
If it’s safe to travel, do so only at dusk and dawn when the sun is lowest. Don’t travel at night. If you need to spend the night, don’t start a fire, this could spread uncontrollable and create an additional challenge for you. If you find a tree that is free of animals and snakes, try and use a piece of clothing to tie you to that tree so you can sleep without falling.
Finding water is essential, don’t drink from any standing water, it most likely contains dangerous pathogens. Try and find roots, bulbs or trees that have water. If you can see a river , try to follow its path downstream, it will most likely lead to more human populated areas but also be careful as animals often migrate to and from rivers.
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|
1912.03234
| false
| null |
A two-step validation was conducted for English-speaking customers. An initial A/B testing for the LR model in a production setting was performed to compare the labelling strategies. A second offline comparison of the models was conducted on historical data and a selected labelling strategy. One month of data and a subset of the customers was used (approx. eighty thousand). The sampled dataset presents a fraction of positive labels of approximately 0.5 for reuse and 0.2 for one-day return. Importantly, since this evaluation is done on a subset of users, the dataset characteristic's do not necessarily represent real production traffic. The joke corpus in this dataset contains thousands of unique jokes of different categories (sci-fi, sports, etc) and types (puns, limerick, etc). The dataset was split timewise into training/validation/test sets, and hyperparameters were optimized to maximize the AUC-ROC on the validation set. As a benchmark, we also consider two additional methods: a non-personalized popularity model and one that follows BIBREF16, replacing the transformer joke encoder with a CNN network (the specialized loss and other characteristics of the DL model are kept).
The joke corpus in this dataset contains thousands of unique jokes of different categories (sci-fi, sports, etc) and types (puns, limerick, etc).
|
Where did the real production data come from?
|
The answers are shown as follows:
* jokes of different categories (sci-fi, sports, etc) and types (puns, limerick, etc)
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null | false
| 134
|
We propose a method of stacking multiple long short-term memory (LSTM) layers for modeling sentences. In contrast to the conventional stacked LSTMs where only hidden states are fed as input to the next layer, our architecture accepts both hidden and memory cell states of the preceding layer and fuses information from the left and the lower context using the soft gating mechanism of LSTMs. Thus the proposed stacked LSTM architecture modulates the amount of information to be delivered not only in horizontal recurrence but also in vertical connections, from which useful features extracted from lower layers are effectively conveyed to upper layers. We dub this architecture Cell-aware Stacked LSTM (CAS-LSTM) and show from experiments that our models achieve state-of-the-art results on benchmark datasets for natural language inference, paraphrase detection, and sentiment classification.
For the evaluation of performance of the proposed method on the NLI task, SNLI BIBREF22 and MultiNLI BIBREF23 datasets are used. The objective of both datasets is to predict the relationship between a premise and a hypothesis sentence: entailment, contradiction, and neutral. SNLI and MultiNLI datasets are composed of about 570k and 430k premise-hypothesis pairs respectively.
GloVe pretrained word embeddings BIBREF49 are used and remain fixed during training. The dimension of encoder states ( INLINEFORM0 ) is set to 300 and a 1024D MLP with one or two hidden layers is used. We apply dropout BIBREF50 to the word embeddings and the MLP layers. The features used as input to the MLP classifier are extracted following Eq. EQREF28 .
Table TABREF32 and TABREF33 contain results of the models on SNLI and MultiNLI datasets. In SNLI, our best model achieves the new state-of-the-art accuracy of 87.0% with relatively fewer parameters. Similarly in MultiNLI, our models match the accuracy of state-of-the-art models in both in-domain (matched) and cross-domain (mismatched) test sets. Note that only the GloVe word vectors are used as word representations, as opposed to some models that introduce character-level features. It is also notable that our proposed architecture does not restrict the selection of pooling method; the performance could further be improved by replacing max-pooling with other advanced algorithms e.g. intra-sentence attention BIBREF39 and generalized pooling BIBREF19 .
We use Quora Question Pairs dataset BIBREF24 in evaluating the performance of our method on the PI task. The dataset consists of over 400k question pairs, and each pair is annotated with whether the two sentences are paraphrase of each other or not.
Similar to the NLI experiments, GloVe pretrained vectors, 300D encoders, and 1024D MLP are used. The number of CAS-LSTM layers is fixed to 2 in PI experiments. Two sentence vectors are aggregated using Eq. EQREF29 and fed as input to the MLP. The results on the Quora Question Pairs dataset are summarized in Table TABREF34 . Again we can see that our models outperform other models by large margin, achieving the new state of the art.
In evaluating sentiment classification performance, the Stanford Sentiment Treebank (SST) BIBREF25 is used. It consists of about 12,000 binary-parsed sentences where constituents (phrases) of each parse tree are annotated with a sentiment label (very positive, positive, neutral, negative, very negative). Following the convention of prior work, all phrases and their labels are used in training but only the sentence-level data are used in evaluation.
In evaluation we consider two settings, namely SST-2 and SST-5, the two differing only in their level of granularity with regard to labels. In SST-2, data samples annotated with `neutral' are ignored from training and evaluation. The two positive labels (very positive, positive) are considered as the same label, and similarly for the two negative labels. As a result 98,794/872/1,821 data samples are used in training/validation/test, and the task is considered as a binary classification problem. In SST-5, data are used as-is and thus the task is a 5-class classification problem. All 318,582/1,101/2,210 data samples for training/validation/test are used in the SST-5 setting.
We use 300D GloVe vectors, 2-layer 150D or 300D encoders, and a 300D MLP classifier for the models, however unlike previous experiments we tune the word embeddings during training. The results on SST are listed in Table TABREF35 . Our models achieve the new state-of-the-art accuracy on SST-2 and competitive accuracy on SST-5, without utilizing parse tree information.
We dub this architecture Cellaware Stacked LSTM (CAS-LSTM) and show from experiments that our models bring significant performance gain over the standard LSTMs on benchmark datasets for natural language inference, paraphrase detection, sentiment classification, and machine translation.****For the evaluation of performance of the proposed method on the NLI task, SNLI (Bowman et al., 2015) and MultiNLI (Williams et al., 2018b) datasets are used.****We use Quora Question Pairs dataset (Wang et al., 2017) in evaluating the performance of our method on the PI task.****In evaluating sentiment classification performance, the Stanford Sentiment Treebank (SST) (Socher et al., 2013) is used.
|
On what benchmark datasets do they evaluate the method?
|
SNLI, MultiNLI, Quora Question Pairs, and SST.
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null | false
| null |
The 5' 11" (1.80 m), 165 lb (75 kg) point guard, began his professional career in 1989 when he was selected in the first round of the NBA draft by the Seattle SuperSonics with the 16th pick. Most of his time spent in Seattle was as a backup to point guard Gary Payton, who was considered to be a much more competent ball handler and defender, as Barros was deemed more usable at shooting guard. Prior to the 1993–94 season, the Sonics traded Barros to the Charlotte Hornets in exchange for Kendall Gill. Two days later, Charlotte traded Barros, Sidney Green, and their 1993 draft choice, Greg Graham, to the Philadelphia 76ers for Hersey Hawkins. Barros became a free agent at the end of the 1994-95 NBA season. He signed with his hometown Boston Celtics, and remained for five seasons as a role player alongside David Wesley and Dee Brown and mentor to younger players such as Antoine Walker and Paul Pierce. In the offseason of 2000, Barros was traded to the Dallas Mavericks as part of a four-team deal that also involved the Utah Jazz and Golden State Warriors. By the time the 2000–01 season had commenced, the Mavs had traded Barros to the Detroit Pistons, where he played 89 games over two seasons, averaging 7.5 points per game, until waiving him in 2002.
|
Based on this paragraph, how many times was Barros traded during his professional basketball career?
|
Based on this paragraph, Barros was traded four times while in the NBA (from Seattle to Charlotte; from Charlotte to Philadelphia; from Boston to Dallas; from Dallas to Detroit).
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1607.00424
| false
| null |
To address our next question, we assessed our pipeline when learning relations independently (i.e., individually) versus learning relations jointly within the RDN, displayed in Table TABREF22 . Recall and F1 are omitted for conciseness – the conclusions are the same across all metrics. Joint learning appears to help in about half of the relations (8/14). Particularly, in person category, joint learning with gold standard outperforms their individual learning counterparts. This is due to the fact that some relations such as parents, spouse, siblings etc. are inter-related and learning them jointly indeed improves performance. Hence Q2 can be answered affirmatively for half the relations.
To address our next question, we assessed our pipeline when learning relations independently (i.e., individually) versus learning relations jointly within the RDN, displayed in Table TABREF22 .
|
What do they learn jointly?
|
The answers are shown as follows:
* relations
|
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| null |
How old was Mozart when he first performed?
|
He was six years old when he first performed in front of royalty
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| null |
The domestication of cattle in Africa preceded agriculture and seems to have existed alongside hunter-gatherer cultures. It is speculated that by 6000 BCE, cattle were domesticated in North Africa. In the Sahara-Nile complex, people domesticated many animals, including the donkey and a small screw-horned goat which was common from Algeria to Nubia.
Between 10,000 and 9,000 BCE, pottery was independently invented in the region of Mali in the savannah of West Africa.
In the steppes and savannahs of the Sahara and Sahel in Northern West Africa, people possibly ancestral to modern Nilo-Saharan and Mandé cultures started to collect wild millet, around 8000 to 6000 BCE. Later, gourds, watermelons, castor beans, and cotton were also collected. Sorghum was first domesticated in Eastern Sudan around 4000 BCE, in one of the earliest instances of agriculture in human history. Its cultivation would gradually spread across Africa, before spreading to India around 2000 BCE.
|
In Africa, were cattle domesticated before or after agriculture?
|
In Africa, cattle were domesticated before agriculture.
|
null | false
| 129
|
Relation classification is the task of assigning sentences with two marked entities to a predefined set of relations. The sentence “We poured the <e1>milk</e1> into the <e2>pumpkin mixture</e2>.”, for example, expresses the relation Entity-Destination(e1,e2). While early research mostly focused on support vector machines or maximum entropy classifiers BIBREF0 , BIBREF1 , recent research showed performance improvements by applying neural networks (NNs) BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 on the benchmark data from SemEval 2010 shared task 8 BIBREF8 .
This study investigates two different types of NNs: recurrent neural networks (RNNs) and convolutional neural networks (CNNs) as well as their combination. We make the following contributions:
(1) We propose extended middle context, a new context representation for CNNs for relation classification. The extended middle context uses all parts of the sentence (the relation arguments, left of the relation arguments, between the arguments, right of the arguments) and pays special attention to the middle part.
(2) We present connectionist bi-directional RNN models which are especially suited for sentence classification tasks since they combine all intermediate hidden layers for their final decision. Furthermore, the ranking loss function is introduced for the RNN model optimization which has not been investigated in the literature for relation classification before.
(3) Finally, we combine CNNs and RNNs using a simple voting scheme and achieve new state-of-the-art results on the SemEval 2010 benchmark dataset.
The extended middle context uses all parts of the sentence (the relation arguments, left of the relation arguments, between the arguments, right of the arguments) and pays special attention to the middle part.
|
Which part does the extended middle context use?
|
All parts of the sentence (the relation arguments, left of the relation arguments, between the arguments, right of the arguments) and pays special attention to the middle part.
|
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Is there The Disabled Students Allowance in the UK
|
The Disabled Students Allowance (DSA) is a Government grant in the United Kingdom (UK) available to students in Higher Education, originally established by the Department for Education and Skills (DfES).
|
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YouTube has been led by a CEO since its founding in 2005, and has retained this leadership model even after its acquisition by Google. The current CEO is Neal Mohan, who was appointed on February 16, 2023.
|
What is the first letter of every sentence, separated by commas?
|
Y,T
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null | false
| null |
What medicinal plants should I grow in my Mid-Atlantic garden?
|
Echinacea
Elder
Bee balm
Lemon balm
Mullein
Burdock
Yarrow
Wood Betony
|
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| null |
West High School is a public high school located on the west side of Columbus, Ohio, in the Westgate neighborhood in the Hilltop area. It is a part of Columbus City Schools.
History
Due to overcrowding at Central High School in 1890s, a section of students split and were moved to another location, setting the foundation for what would become three new schools, including West High School.
Designed by the first architect of Columbus schools, David Riebel, the Neo-Classical Revival style building was constructed in 1908 and opened in 1909 on South Central Avenue. The building became Starling Middle School when the new West High School opened. The new West High School opened in 1929. It was designed by prominent Ohio architect Howard Dwight Smith who also designed Ohio Stadium.
West High School continues today at its current location at 179 South Powell Avenue. In August 2016, the cupola was removed because it was deemed unstable and a new cupola was installed and celebrated with a lighting ceremony in 2018.
The school colors are buff and brown. The school nickname is the Cowboys.
|
What's West High School located?
|
The West High School is located on the west side of Columbus, Ohio, more specifically on 179 South Powell Avenue.
|
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Tell me whether these beverages are acidic: Orange Juice, Grapefruit Juice, Coca-Cola, Soy Milk, Coconut Water, Carrot Juice
|
Orange juice is a highly acidic beverage.
Grapefruit juice is a highly acidic beverage.
Coca-Cola is a highly acidic beverage.
Soy milk is low in acidity.
Coconut water is low in acidity.
Carrot juice is low in acidity.
|
|
null | false
| 4
|
We use the SimpleQuestions (Bordes et al., 2015) and WebQSP (Yih et al., 2016) datasets.
|
What dataset is used in the experiment?
|
They use the SimpleQuestions (Bordes et al., 2015) and WebQSP (Yih et al., 2016) datasets.
|
null | false
| null |
list 7 favourite flavours
|
Vanilla
Chocolate
Orange
Strawberry
pineapple
Mango
chocochip
|
|
1911.00523
| true
| null |
Although our approach strongly outperforms random baselines, the relatively low F1 scores indicate that predicting which word is echoed in explanations is a very challenging task. It follows that we are only able to derive a limited understanding of how people choose to echo words in explanations. The extent to which explanation construction is fundamentally random BIBREF47, or whether there exist other unidentified patterns, is of course an open question. We hope that our study and the resources that we release encourage further work in understanding the pragmatics of explanations.
Although our approach strongly outperforms random baselines, the relatively low F1 scores indicate that predicting which word is echoed in explanations is a very challenging task. It follows that we are only able to derive a limited understanding of how people choose to echo words in explanations. The extent to which explanation construction is fundamentally random BIBREF47, or whether there exist other unidentified patterns, is of course an open question. We hope that our study and the resources that we release encourage further work in understanding the pragmatics of explanations.
|
Do authors provide any explanation for intriguing patterns of word being echoed?
|
No.
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| null |
Various definitions of compactness may apply, depending on the level of generality. A subset of Euclidean space in particular is called compact if it is closed and bounded. This implies, by the Bolzano–Weierstrass theorem, that any infinite sequence from the set has a subsequence that converges to a point in the set. Various equivalent notions of compactness, such as sequential compactness and limit point compactness, can be developed in general metric spaces.
In contrast, the different notions of compactness are not equivalent in general topological spaces, and the most useful notion of compactness — originally called bicompactness — is defined using covers consisting of open sets (see Open cover definition below). That this form of compactness holds for closed and bounded subsets of Euclidean space is known as the Heine–Borel theorem. Compactness, when defined in this manner, often allows one to take information that is known locally — in a neighbourhood of each point of the space — and to extend it to information that holds globally throughout the space. An example of this phenomenon is Dirichlet's theorem, to which it was originally applied by Heine, that a continuous function on a compact interval is uniformly continuous; here, continuity is a local property of the function, and uniform continuity the corresponding global property.
|
Extract the two properties that are sufficient to show that a subset of Euclidean space is compact. Present these properties in a bullet list.
|
According to the Heine-Borel theorem the following properties are sufficient to show that a subset of Euclidean space is compact:
- The set is closed
- The set is bounded
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| null |
Baker attended the University of Southern California, where he played for coach Pete Carroll's USC Trojans football team from 2003 to 2007. He was a first-team All-American as a redshirt sophomore in 2005.
He was on the official 2006 watch list for the Lombardi Award and the Outland Trophy for the best lineman. In his collegiate career Baker blocked for Reggie Bush and Matt Leinart. He was named a first-team All-Pacific-10 Conference selection by the league's coaches in 2005 and 2006. He was a recognized as a consensus first-team All-American, having received first-team honors from the Football Writers Association of America, Sporting News and CBSSports.com.
Going into his fourth year as a starter, Baker was on the Lombardi Award and Outland Trophy watch lists. He was ranked as one of the "Top 20 Players Heading Into 2007" by Sports Illustrated. Before his final season in 2007, Baker was already regarded by most NFL scouts as a certain top-five player in the 2008 NFL Draft.
|
Given this paragraph about Sam Baker, what is the Outland Trophy?
|
The Outland Trophy is given to the best offensive lineman in college football.
|
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| null |
Give me a bulleted list of first person shooter games on PS4
|
* Killzone: Shadow Fall
* Titanfall 2
* Battlefield V
* DOOM Eternal
* Call of Duty franchise
* Metro Exodus
* Destiny 2
* Resident Evil Village
* Uncharted 4
* Far Cry 6
* Hunt: Showdown
|
|
null | false
| 185
|
We evaluate our model on the zero-shot cross-lingual answer-aware question generation task. The goal of question generation (QG) is to generate a question that asks towards the answer with the given passage and the expected answer. In the following experiments, we extend the QG task to the cross-lingual setting. By only using English QG training data, our goal is to generate questions in English or Chinese with the given passage-answer pair in English or Chinese.
We use SQuAD 1.1 BIBREF30 as the English QG dataset. It is a popular English question answering dataset containing over 100,000 questions and their corresponding annotated passages. Following BIBREF31, we regard the original development set as the test set, and sample 5000 examples from the training data of two datasets as the development sets. For Chinese QG, we follow the default data splits of WebQA BIBREF32. We regard the provided annotated evidence sentences as the input passages instead of entire documents. To construct the input sequence, we view the whole input passage as a single sentence, and concatenate the passage and the answer into one sequence with a special token S between them. During decoding Chinese, we utilize a subset of vocabulary, which is obtained from the passage sentences of the WebQA dataset.
We first conduct experiments on the supervised English-English QG setting. We compare our model to the following baselines:
CorefNqg BIBREF33 A sequence-to-sequence model with attention mechanism and a feature-rich encoder.
Mp-Gsn BIBREF31 A sequence-to-sequence model with gated self-attention and maxout pointer mechanism.
Xlm BIBREF5 The current state-of-the-art cross-lingual pre-training model. We initialize the Transformer-based sequence-to-sequence model with pre-trained XLM.
We evaluate models with BLEU-4 (BL-4), ROUGE (RG) and METEOR (MTR) metrics. As shown in Table TABREF16, our model outperforms the baselines, which demonstrates that our pre-trained model provides a good initialization for NLG.
We conduct experiments on the zero-shot Chinese-Chinese QG task to evaluate the cross-lingual transfer ability. In this task, models are trained with English QG data but evaluated with Chinese QG examples. We include the following models as our baselines:
Xlm Fine-tuning XLM with the English QG data.
Pipeline (Xlm) The pipeline of translating input Chinese sentences into English first, then performing En-En-QG with the XLM model, and finally translating back to the Chinese. We use the Transformer as the translator, which is also trained on the MultiUN dataset.
Pipeline (Xlm) with Google Translator Same to Pipeline (Xlm) but using Google Translator to translate the texts.
We evaluate models by both automatic evaluation metrics and human experts. The automatic metrics scores are computed by regarding each Chinese character as a token. For human evaluation, we consider three metrics for the generated questions: relatedness, fluency, and correctness, which are represented as integers ranged from 1 to 3. We randomly select 100 passage-answer pairs from the English QG test set, and use the models to generate questions. Then we present these examples to three experts to ask for the above scores. In Table TABREF17 and Table TABREF18, we present the results for the zero-shot Zh-Zh-QG. The results of monolingual supervised models are also reported in Table TABREF16 as reference. In the automatic evaluation, our model consistently performs better than baselines in both zero-shot and monolingual supervised setting. In the human evaluation, our model also obtains significant improvements in terms of relatedness and correctness.
In the zero-shot English-Chinese question generation experiments, we use Xlm and Pipeline (Xlm) as our baselines. Pipeline (Xlm) is a pipeline method that uses En-En-QG with Xlm to generate questions, and then translates the results to Chinese. Because there is no annotations for En-Zh-QG, we perform human evaluation studies for this setting. Table TABREF19 shows the human evaluation results, where our model surpasses all the baselines especially in terms of relatedness and correctness.
We also conduct experiments for zero-shot Chinese-English question generation, and adopt the same evaluation procedure to En-Zh-QG. Pipeline (Xlm) first translates Chinese input to English, and then conduct En-En-QG with Xlm. As shown in Table TABREF20, human evaluation results indicate that Xnlg achieves significant improvements on the three metrics.
We evaluate models with BLEU-4 (BL-4), ROUGE (RG) and METEOR (MTR) metrics.
|
What are the metrics in the English QG task?
|
BLEU-4 (BL-4), ROUGE (RG) and METEOR (MTR).
|
null | false
| null |
Cases of Anthrax were found in Kerala in which animals?
|
Wild Boars
|
|
null | false
| null |
The genre has existed since the early years of silent cinema, when Georges Melies' A Trip to the Moon (1902) employed trick photography effects. The next major example (first in feature length in the genre) was the film Metropolis (1927). From the 1930s to the 1950s, the genre consisted mainly of low-budget B movies. After Stanley Kubrick's landmark 2001: A Space Odyssey (1968), the science fiction film genre was taken more seriously. In the late 1970s, big-budget science fiction films filled with special effects became popular with audiences after the success of Star Wars (1977) and paved the way for the blockbuster hits of subsequent decades.
|
Extract all the movies from this passage and the year they were released out. Write each movie as a separate sentence
|
A Trip to the Moon was released in 1902. Metropolis came out in 1927. 2001: A Space Odyssey was released in 1968. Star Wars came out in 1977.
|
null | false
| 172
|
We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx.
The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person , judge , lawyer , country , city , street , landscape , organization , company , institution , court , brand , law , ordinance , European legal norm , regulation , contract , court decision, and legal literature.
|
What are the 19 fine-grained semantic classes in the dataset?
|
Person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature.
|
null | false
| null |
Queen Elizabeth II, who reigned UK for 70 years, passed away the age of 96 at
|
Balmoral
|
|
null | false
| null |
How can I travel backwards in time?
|
Due to some solutions in general relativity, there are methods that could allow this, most agree the true answer can be found in quantum mechanics or wormholes. Our current ability to understand quantum mechanics or wormholes is insufficient for proper traversal backward in time.
|
|
null | false
| null |
Provide me a list of some ways to get exercise that are fun
|
Here are a few of the ways you can exercise that are fun: dancing, playing frisbee, playing sports, playing with a pet or a child, skiing, or swimming in a lake.
|
|
null | false
| null |
what is the speed limit on the UK M1 Road?
|
In the UK, the M1 is considered as a motorway, the speed limits on motorways is 70 miles per hour.
|
|
null | false
| 2
|
There has been significant progress on Named Entity Recognition (NER) in recent years using models based on machine learning algorithms BIBREF0 , BIBREF1 , BIBREF2 . As with other Natural Language Processing (NLP) tasks, building NER systems typically requires a massive amount of labeled training data which are annotated by experts. In real applications, we often need to consider new types of entities in new domains where we do not have existing annotated data. For such new types of entities, however, it is very hard to find experts to annotate the data within short time limits and hiring experts is costly and non-scalable, both in terms of time and money.
In order to quickly obtain new training data, we can use crowdsourcing as one alternative way at lower cost in a short time. But as an exchange, crowd annotations from non-experts may be of lower quality than those from experts. It is one biggest challenge to build a powerful NER system on such a low quality annotated data. Although we can obtain high quality annotations for each input sentence by majority voting, it can be a waste of human labors to achieve such a goal, especially for some ambiguous sentences which may require a number of annotations to reach an agreement. Thus majority work directly build models on crowd annotations, trying to model the differences among annotators, for example, some of the annotators may be more trustful BIBREF3 , BIBREF4 .
Here we focus mainly on the Chinese NER, which is more difficult than NER for other languages such as English for the lack of morphological variations such as capitalization and in particular the uncertainty in word segmentation. The Chinese NE taggers trained on news domain often perform poor in other domains. Although we can alleviate the problem by using character-level tagging to resolve the problem of poor word segmentation performances BIBREF5 , still there exists a large gap when the target domain changes, especially for the texts of social media. Thus, in order to get a good tagger for new domains and also for the conditions of new entity types, we require large amounts of labeled data. Therefore, crowdsourcing is a reasonable solution for these situations.
In this paper, we propose an approach to training a Chinese NER system on the crowd-annotated data. Our goal is to extract additional annotator independent features by adversarial training, alleviating the annotation noises of non-experts. The idea of adversarial training in neural networks has been used successfully in several NLP tasks, such as cross-lingual POS tagging BIBREF6 and cross-domain POS tagging BIBREF7 . They use it to reduce the negative influences of the input divergences among different domains or languages, while we use adversarial training to reduce the negative influences brought by different crowd annotators. To our best knowledge, we are the first to apply adversarial training for crowd annotation learning.
In the learning framework, we perform adversarial training between the basic NER and an additional worker discriminator. We have a common Bi-LSTM for representing annotator-generic information and a private Bi-LSTM for representing annotator-specific information. We build another label Bi-LSTM by the crowd-annotated NE label sequence which reflects the mind of the crowd annotators who learn entity definitions by reading the annotation guidebook. The common and private Bi-LSTMs are used for NER, while the common and label Bi-LSTMs are used as inputs for the worker discriminator. The parameters of the common Bi-LSTM are learned by adversarial training, maximizing the worker discriminator loss and meanwhile minimizing the NER loss. Thus the resulting features of the common Bi-LSTM are worker invariant and NER sensitive.
For evaluation, we create two Chinese NER datasets in two domains: dialog and e-commerce. We require the crowd annotators to label the types of entities, including person, song, brand, product, and so on. Identifying these entities is useful for chatbot and e-commerce platforms BIBREF8 . Then we conduct experiments on the newly created datasets to verify the effectiveness of the proposed adversarial neural network model. The results show that our system outperforms very strong baseline systems. In summary, we make the following contributions:
In this paper, we propose an approach to training a Chinese NER system on the crowd-annotated data.
|
What is the method proposed by the author used for?
|
An approach to training a Chinese Named Entity Recognition (NER) system on the crowd-annotated data.
|
null | false
| null |
Tesrt
|
Answer
|
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| null |
What options does the Federal Reserve have to fight inflation?
|
The Federal Reserve is the central bank of the United States, and its remit is to provide the nation a more stable and durable monetary system. The Federal Reserve, or the Fed, is specifically tasked with maintaining maximum employment while ensuring price stability. The United States is currently experiencing persistent inflation. Typically, the Federal Reserve will raise interest rates when initially trying to combat inflation. The increased rates make lending more expensive, and subsequently discourages capital investment and borrowing. The added cost in turn reduces demand for products and services. Lower demand normally leads to dropping prices to try and motivate potential buyers. The other primary method involves open-market operations. When inflation is running higher than expected, the Fed can reduce the money supply by selling securities and taking currency out of circulation. Since there are fewer dollars in circulation, each remaining dollar is correspondingly worth more than it was before those transactions.
|
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| null |
Rahul Sharad Dravid (/ˌrɑːhʊl drəvɪd/ (listen); born 11 January 1973) is an Indian cricket coach and former captain of the Indian national team, currently serving as its head coach. Prior to his appointment to the senior men's national team, Dravid was the Head of Cricket at the National Cricket Academy (NCA), and the head coach of the India Under-19 and India A teams. Under his tutelage, the under-19 team finished runners up at the 2016 U-19 Cricket World Cup and won the 2018 U-19 Cricket World Cup. Known for his sound batting technique, Dravid scored 24,177 runs in international cricket and is widely regarded as one of the greatest batsmen in the history of cricket. He is colloquially known as Mr. Dependable and often referred to as The Wall. With India, he helped the team to be one of the joint-winners of the 2002 ICC Champions Trophy, where the title was shared with Sri Lanka.
Born in a Marathi family and raised in Bangalore, he started playing cricket at the age of 12 and later represented Karnataka at the under-15, under-17 and under-19 levels. Dravid was named one of the best five cricketers of the year by Wisden Cricketers' Almanack in 2000 and received the Player of the Year and the Test Player of the Year awards at the inaugural ICC awards ceremony in 2004. In December 2011, he became the first non-Australian cricketer to deliver the Bradman Oration in Canberra.
As of January 2022, Dravid is the fourth-highest run scorer in Test cricket, after Sachin Tendulkar, Ricky Ponting and Jacques Kallis. In 2004, after completing his century against Bangladesh in Chittagong, he became the first player to score a century in all the ten Test-playing countries (now 12). As of October 2012, he holds the record for the most catches taken by a player (non-wicket-keeper) in Test cricket, with 210. Dravid holds a unique record of never getting out for a Golden duck in the 286 Test innings which he has played. He has faced 31258 balls, which is the highest number of balls faced by any player in test cricket. He has also spent 44152 minutes at the crease, which is the highest time spent on crease by any player in test cricket. Dravid and Sachin Tendulkar are currently the highest scoring partnership in Test cricket history having scored 6920 runs combined when batting together for India.
In August 2011, after receiving a surprise recall in the ODI series against England, Dravid declared his retirement from ODIs as well as Twenty20 International (T20I), and in March 2012, he announced his retirement from international and first-class cricket. He appeared in the 2012 Indian Premier League as captain of the Rajasthan Royals.
Rahul Dravid, along with Glenn McGrath were honoured during the seventh annual Bradman Awards function in Sydney on 1 November 2012. Dravid has also been honoured with the Padma Shri and the Padma Bhushan award, India's fourth and third highest civilian awards respectively.
In 2014, Rahul Dravid joined the GoSports Foundation, Bangalore as a member of their board of advisors. In collaboration with GoSports Foundation he is mentoring India's future Olympians and Paralympians as part of the Rahul Dravid Athlete Mentorship Programme. Indian badminton player Prannoy Kumar, Para-swimmer Sharath Gayakwad and young Golfer S. Chikkarangappa were part of the initial group of athletes to be mentored by Rahul Dravid. In July 2018, Dravid became the fifth Indian cricketer to be inducted into ICC Hall of Fame.
|
describe the important event of Dravid's life in chronological order
|
Rahul Dravid
11 Jan 1973 - Born
1985 - Started playing cricket
2000 - named one of the best 5 cricketers by wisden cricketers almanack
2004 - received player of the year and test player of the year award at ICC awards ceremony
August 2011 - Dravid declared his retirement from ODIs as well as Twenty20 International (T20I)
Dec 2011 - first non-Australian cricketer to deliver the Bradman Oration in Canberra
March 2012 - announced his retirement from international and first-class cricket
2012 - appeared in Indian Premier League as captain of the Rajasthan Royal
1 November 2012 - honoured during the seventh annual Bradman Awards function in Sydney
2014 - joined the GoSports Foundation, Bangalore as a member of their board of advisors
July 2018 - became the fifth Indian cricketer to be inducted into ICC Hall of Fame
|
null | false
| null |
What are the best day trips near San Francisco?
|
The San Francisco Bay Area has a number of amazing day trips offering a variety of options. The wine country is less than an hour away. Santa Cruz, Monterey & Big Sur are all great escapes to the coastline. If you are interested in a spa experience Calistoga is full of options.
|
|
null | false
| null |
A low-flow fixture is a water saving plumbing fixture designed to achieve water savings by having a lower flow rate of water or a smaller quantity per flush. Some of these low-flow fixtures are faucets, showerheads, and toilets. In the United States a maximum water usage of conventional plumbing fixtures was federally mandated by the Energy policy act of 1992. Low-flow fixtures are designed to save water over conventional fixtures by having a lower flow rate while still maintaining satisfactory performance. The Environmental protection agency (EPA) WaterSense program has requirements for plumbing fixtures to achieve their definition for water saving low-flow.
|
What are some examples of low-flow fixtures?
|
faucets, showerheads, and toilets
|
1901.02534
| false
| null |
Our approach to FEVER is to fix the most obvious shortcomings of the baseline approaches to retrieval and entailment, and to train a sharp entailment classifier that can be used to filter a broad set of retrieved potential evidence. For the entailment classifier we compare Decomposable Attention BIBREF2 , BIBREF3 as implemented in the official baseline, ESIM BIBREF4 , and a transformer network with pre-trained weights BIBREF5 . The transformer network naturally supports out-of-vocabulary words and gives substantially higher performance than the other methods.
Our approach to FEVER is to fix the most obvious shortcomings of the baseline approaches to retrieval and entailment, and to train a sharp entailment classifier that can be used to filter a broad set of retrieved potential evidence. For the entailment classifier we compare Decomposable Attention BIBREF2 , BIBREF3 as implemented in the official baseline, ESIM BIBREF4 , and a transformer network with pre-trained weights BIBREF5 . The transformer network naturally supports out-of-vocabulary words and gives substantially higher performance than the other methods.
|
What baseline do they compare to?
|
The answers are shown as follows:
* For the entailment classifier we compare Decomposable Attention BIBREF2 , BIBREF3 as implemented in the official baseline, ESIM BIBREF4 , and a transformer network with pre-trained weights BIBREF5 .
|
null | false
| null |
Metals are typically malleable and ductile, deforming under stress without cleaving.The nondirectional nature of metallic bonding is thought to contribute significantly to the ductility of most metallic solids. In contrast, in an ionic compound like table salt, when the planes of an ionic bond slide past one another, the resultant change in location shifts ions of the same charge closer, resulting in the cleavage of the crystal. Such a shift is not observed in a covalently bonded crystal, such as a diamond, where fracture and crystal fragmentation occurs. Reversible elastic deformation in metals can be described by Hooke's Law for restoring forces, where the stress is linearly proportional to the strain.
|
Give a bulleted list of the main characteristic of metals listed in the passage
|
- Malleable
- Ductile
- Deforming under stress without cleaving
|
null | false
| null |
Give me a list of the last 7 FIFA world cup winning teams
|
2022 Argentina
2018 France
2014 Germany
2010 Spain
2006 Italy
2002 Brazil
1998 France
|
|
null | false
| null |
What are some best practices to prepare biryani
|
Preparing biryani is more of an art. And results continue to improve as you start taking cues from various aspects of the ingredients being used. Most important steps that could lead to quality issues are around managing moisture while protein cooks. Especially when you use chicken. Chicken tends to release a lot of moisture and that can spoil the dish. A recommendation would be to cook all of the chicken 50% through without any seasoning in a separate pot. It'll release significant moisture in this process. Once done, you can then separate out chicken from the stock and use the chicken for Biryani. One of the other gotcha while cooking Biryani is while preparing rice. Rice should never be fully cooked before it's mixed with chicken. Remember that rice and seasoned chicken has to be cooked together for several minutes. To get best results make sure you cook rice in boiling water only 70% through. It should be bit uncooked. Then use that rice to mix with seasoned chicken and continue following the Biryani recipe from that point onwards.
|
|
1908.10449
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The key idea behind our proposed interactive MRC (iMRC) is to restrict the document context that a model observes at one time. Concretely, we split a supporting document into its component sentences and withhold these sentences from the model. Given a question, the model must issue commands to observe sentences in the withheld set; we equip models with actions such as Ctrl+F (search for token) and stop for searching through partially observed documents. A model searches iteratively, conditioning each command on the input question and the sentences it has observed previously. Thus, our task requires models to `feed themselves' rather than spoon-feeding them with information. This casts MRC as a sequential decision-making problem amenable to reinforcement learning (RL).
The key idea behind our proposed interactive MRC (iMRC) is to restrict the document context that a model observes at one time. Concretely, we split a supporting document into its component sentences and withhold these sentences from the model. Given a question, the model must issue commands to observe sentences in the withheld set; we equip models with actions such as Ctrl+F (search for token) and stop for searching through partially observed documents. A model searches iteratively, conditioning each command on the input question and the sentences it has observed previously. Thus, our task requires models to `feed themselves' rather than spoon-feeding them with information. This casts MRC as a sequential decision-making problem amenable to reinforcement learning (RL).
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Do they provide decision sequences as supervision while training models?
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No.
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The Barkley course was the brain child of Gary "Lazarus Lake" Cantrell and Karl Henn (Raw Dog). The idea for the race was inspired upon hearing about the 1977 escape of James Earl Ray, the assassin of Martin Luther King Jr., from nearby Brushy Mountain State Penitentiary. Ray covered only about 12 miles (19 km) after running 54.5 hours in the woods hiding from air searches during the day. Cantrell said to himself, "I could do at least 100 miles," mocking Ray's low mileage. Thus, the Barkley Marathons was born. Cantrell named the race for his longtime neighbor and running companion, Barry Barkley. It was first run in 1986. Barkley died in 2019 at age 70.
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What is the Barkley Marathons?
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The Barkley Marathons is a foot race created by Gary Cantrell and Karl Henn, and named after a friend of Gary's, Barry Barkley (who died in 2019). This race was inspired from James Earl Ray's escape from the Brushy Mountain State Penitentiary in 1977 (he was in-prisoned there for the murder of Martin Luther King Jr). During his 54.5 hour escape, Earl Ray could only cover 12 miles. Cantrell thought he could have done 100 miles in this time, which gave him the idea to organise a foot race in this area. The first edition took place in 1986.
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What is the appeal of Flathead Lake as a Summer vacation destination?
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Flathead Lake is the largest natural freshwater lake West of the Mississippi in lower 48 contiguous states, with over 200 square miles of water and 185 miles of coastline. It is a beautiful lake, surrounded by the tall and impressive Mission Mountains on the east and the smaller Salish Mountains to the west. It borders several Indian reservations on the Southern end of the lake.
The lake offers an abundance of Summer activities including: sailing, boating, swimming, and camping. The lake offers excellent fishing and the fish species in the lake consist primarily of lake trout, pike, yellow perch and whitefish, with some rainbow trout, bass, and salmon. The lake trout can reach monstrous sizes, frequently exceeding twenty-pounds.
Overall, Flathead Lake is one of the premiere recreational and fishing lakes in Montana. It is popular for both fishing and pleasure boating and the sheer beauty of the lake makes it a memorable one regardless of which activity is pursued.
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What is the difference between benefits and compensation?
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Benefits are typically the medical benefits and coverage you have. For example, when you go to the doctor, insurance covers some of the cost. This is a benefit offered to you by your employer. However, benefits can also include things like a wellness reimbursement. Some companies offer benefits for employees to submit reimbursement for things like running shoes, a massage or the use of a mental health counselor. Compensation is the money you are paid for doing your job. This includes your base salary and any additional compensation you may receive such as commissions or a bonus.
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What is the last step before a star collapses into a black hole?
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Stars that have a higher mass than about 2.2 solar masses (the mass of our Sun) will typically collapse into black holes at the end of their life cycle when fusion has stopped and no longer pushes out against the inward pull of gravity. For stars of this mass, their inward pull of gravity is too strong to be resisted by other forces, thus their fate is to become black holes. However, stars under this limit and above the Chandrasekhar limit of 1.4 solar masses will end their lifecycles as neutron stars. Neutron stars are also the remnants of dead stars but since they have a smaller mass, their gravity is not enough for them to cross into becoming black holes. As the dead star compresses and becomes more and more dense, regular matter is turned into neutrons and the last force to resist the pull of gravity is called degenerate neutron pressure, which is a quantum effect that dictates that no two neutrons may be in the same quantum state. This creates an outward pressure that resists the pull of gravity and settles the star into a neutron star. If more mass is added later, from a star companion or other sources, once the star's mass crosses the limit of around 2.2 solar masses even degenerate neutron pressure isn't enough to balance gravity and the star will collapse into a black hole.
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